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What hurts more, the pain of hard work or the pain of regret?

JVM源码分析之G1垃圾收集器分析一

发表于 更新于 分类于 ， ， ， 阅读次数： 阅读次数： Disqus：

对 Java 的 gc 实现比较感兴趣，原先一般都是看周志明的书，但其实并没有讲具体的 gc 源码，而是把整个思路和流程讲解了一下
特别是 G1 的具体实现
一般对 G1 的理解其实就是把原先整块的新生代老年代分成了以 region 为单位的小块内存，简而言之，就是原先对新生代老年代的收集会涉及到整个代的堆内存空间，而G1 把它变成了更细致的小块内存
这带来了一个很明显的好处和一个很明显的坏处，好处是内存收集可以更灵活，耗时会变短，但整个收集的处理复杂度就变高了
目前看了一点点关于 G1 收集的预期时间相关的代码

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HeapWord* G1CollectedHeap::do_collection_pause(size_t word_size,
                                               uint gc_count_before,
                                               bool* succeeded,
                                               GCCause::Cause gc_cause) {
  assert_heap_not_locked_and_not_at_safepoint();
  VM_G1CollectForAllocation op(word_size,
                               gc_count_before,
                               gc_cause,
                               false, /* should_initiate_conc_mark */
                               g1_policy()->max_pause_time_ms());
  VMThread::execute(&op);

  HeapWord* result = op.result();
  bool ret_succeeded = op.prologue_succeeded() && op.pause_succeeded();
  assert(result == NULL || ret_succeeded,
         "the result should be NULL if the VM did not succeed");
  *succeeded = ret_succeeded;

  assert_heap_not_locked();
  return result;
}

这里就是收集时需要停顿的，其中VMThread::execute(&op);是具体执行的，真正执行的是VM_G1CollectForAllocation::doit方法

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void VM_G1CollectForAllocation::doit() {
  G1CollectedHeap* g1h = G1CollectedHeap::heap();
  assert(!_should_initiate_conc_mark || g1h->should_do_concurrent_full_gc(_gc_cause),
      "only a GC locker, a System.gc(), stats update, whitebox, or a hum allocation induced GC should start a cycle");

  if (_word_size > 0) {
    // An allocation has been requested. So, try to do that first.
    _result = g1h->attempt_allocation_at_safepoint(_word_size,
                                                   false /* expect_null_cur_alloc_region */);
    if (_result != NULL) {
      // If we can successfully allocate before we actually do the
      // pause then we will consider this pause successful.
      _pause_succeeded = true;
      return;
    }
  }

  GCCauseSetter x(g1h, _gc_cause);
  if (_should_initiate_conc_mark) {
    // It's safer to read old_marking_cycles_completed() here, given
    // that noone else will be updating it concurrently. Since we'll
    // only need it if we're initiating a marking cycle, no point in
    // setting it earlier.
    _old_marking_cycles_completed_before = g1h->old_marking_cycles_completed();

    // At this point we are supposed to start a concurrent cycle. We
    // will do so if one is not already in progress.
    bool res = g1h->g1_policy()->force_initial_mark_if_outside_cycle(_gc_cause);

    // The above routine returns true if we were able to force the
    // next GC pause to be an initial mark; it returns false if a
    // marking cycle is already in progress.
    //
    // If a marking cycle is already in progress just return and skip the
    // pause below - if the reason for requesting this initial mark pause
    // was due to a System.gc() then the requesting thread should block in
    // doit_epilogue() until the marking cycle is complete.
    //
    // If this initial mark pause was requested as part of a humongous
    // allocation then we know that the marking cycle must just have
    // been started by another thread (possibly also allocating a humongous
    // object) as there was no active marking cycle when the requesting
    // thread checked before calling collect() in
    // attempt_allocation_humongous(). Retrying the GC, in this case,
    // will cause the requesting thread to spin inside collect() until the
    // just started marking cycle is complete - which may be a while. So
    // we do NOT retry the GC.
    if (!res) {
      assert(_word_size == 0, "Concurrent Full GC/Humongous Object IM shouldn't be allocating");
      if (_gc_cause != GCCause::_g1_humongous_allocation) {
        _should_retry_gc = true;
      }
      return;
    }
  }

  // Try a partial collection of some kind.
  _pause_succeeded = g1h->do_collection_pause_at_safepoint(_target_pause_time_ms);

  if (_pause_succeeded) {
    if (_word_size > 0) {
      // An allocation had been requested. Do it, eventually trying a stronger
      // kind of GC.
      _result = g1h->satisfy_failed_allocation(_word_size, &_pause_succeeded);
    } else {
      bool should_upgrade_to_full = !g1h->should_do_concurrent_full_gc(_gc_cause) &&
                                    !g1h->has_regions_left_for_allocation();
      if (should_upgrade_to_full) {
        // There has been a request to perform a GC to free some space. We have no
        // information on how much memory has been asked for. In case there are
        // absolutely no regions left to allocate into, do a maximally compacting full GC.
        log_info(gc, ergo)("Attempting maximally compacting collection");
        _pause_succeeded = g1h->do_full_collection(false, /* explicit gc */
                                                   true   /* clear_all_soft_refs */);
      }
    }
    guarantee(_pause_succeeded, "Elevated collections during the safepoint must always succeed.");
  } else {
    assert(_result == NULL, "invariant");
    // The only reason for the pause to not be successful is that, the GC locker is
    // active (or has become active since the prologue was executed). In this case
    // we should retry the pause after waiting for the GC locker to become inactive.
    _should_retry_gc = true;
  }
}

这里可以看到核心的是G1CollectedHeap::do_collection_pause_at_safepoint这个方法，它带上了目标暂停时间的值

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G1CollectedHeap::do_collection_pause_at_safepoint(double target_pause_time_ms) {
  assert_at_safepoint_on_vm_thread();
  guarantee(!is_gc_active(), "collection is not reentrant");

  if (GCLocker::check_active_before_gc()) {
    return false;
  }

  _gc_timer_stw->register_gc_start();

  GCIdMark gc_id_mark;
  _gc_tracer_stw->report_gc_start(gc_cause(), _gc_timer_stw->gc_start());

  SvcGCMarker sgcm(SvcGCMarker::MINOR);
  ResourceMark rm;

  g1_policy()->note_gc_start();

  wait_for_root_region_scanning();

  print_heap_before_gc();
  print_heap_regions();
  trace_heap_before_gc(_gc_tracer_stw);

  _verifier->verify_region_sets_optional();
  _verifier->verify_dirty_young_regions();

  // We should not be doing initial mark unless the conc mark thread is running
  if (!_cm_thread->should_terminate()) {
    // This call will decide whether this pause is an initial-mark
    // pause. If it is, in_initial_mark_gc() will return true
    // for the duration of this pause.
    g1_policy()->decide_on_conc_mark_initiation();
  }

  // We do not allow initial-mark to be piggy-backed on a mixed GC.
  assert(!collector_state()->in_initial_mark_gc() ||
          collector_state()->in_young_only_phase(), "sanity");

  // We also do not allow mixed GCs during marking.
  assert(!collector_state()->mark_or_rebuild_in_progress() || collector_state()->in_young_only_phase(), "sanity");

  // Record whether this pause is an initial mark. When the current
  // thread has completed its logging output and it's safe to signal
  // the CM thread, the flag's value in the policy has been reset.
  bool should_start_conc_mark = collector_state()->in_initial_mark_gc();

  // Inner scope for scope based logging, timers, and stats collection
  {
    EvacuationInfo evacuation_info;

    if (collector_state()->in_initial_mark_gc()) {
      // We are about to start a marking cycle, so we increment the
      // full collection counter.
      increment_old_marking_cycles_started();
      _cm->gc_tracer_cm()->set_gc_cause(gc_cause());
    }

    _gc_tracer_stw->report_yc_type(collector_state()->yc_type());

    GCTraceCPUTime tcpu;

    G1HeapVerifier::G1VerifyType verify_type;
    FormatBuffer<> gc_string("Pause Young ");
    if (collector_state()->in_initial_mark_gc()) {
      gc_string.append("(Concurrent Start)");
      verify_type = G1HeapVerifier::G1VerifyConcurrentStart;
    } else if (collector_state()->in_young_only_phase()) {
      if (collector_state()->in_young_gc_before_mixed()) {
        gc_string.append("(Prepare Mixed)");
      } else {
        gc_string.append("(Normal)");
      }
      verify_type = G1HeapVerifier::G1VerifyYoungNormal;
    } else {
      gc_string.append("(Mixed)");
      verify_type = G1HeapVerifier::G1VerifyMixed;
    }
    GCTraceTime(Info, gc) tm(gc_string, NULL, gc_cause(), true);

    uint active_workers = AdaptiveSizePolicy::calc_active_workers(workers()->total_workers(),
                                                                  workers()->active_workers(),
                                                                  Threads::number_of_non_daemon_threads());
    active_workers = workers()->update_active_workers(active_workers);
    log_info(gc,task)("Using %u workers of %u for evacuation", active_workers, workers()->total_workers());

    TraceCollectorStats tcs(g1mm()->incremental_collection_counters());
    TraceMemoryManagerStats tms(&_memory_manager, gc_cause(),
                                collector_state()->yc_type() == Mixed /* allMemoryPoolsAffected */);

    G1HeapTransition heap_transition(this);
    size_t heap_used_bytes_before_gc = used();

    // Don't dynamically change the number of GC threads this early.  A value of
    // 0 is used to indicate serial work.  When parallel work is done,
    // it will be set.

    { // Call to jvmpi::post_class_unload_events must occur outside of active GC
      IsGCActiveMark x;

      gc_prologue(false);

      if (VerifyRememberedSets) {
        log_info(gc, verify)("[Verifying RemSets before GC]");
        VerifyRegionRemSetClosure v_cl;
        heap_region_iterate(&v_cl);
      }

      _verifier->verify_before_gc(verify_type);

      _verifier->check_bitmaps("GC Start");

#if COMPILER2_OR_JVMCI
      DerivedPointerTable::clear();
#endif

      // Please see comment in g1CollectedHeap.hpp and
      // G1CollectedHeap::ref_processing_init() to see how
      // reference processing currently works in G1.

      // Enable discovery in the STW reference processor
      _ref_processor_stw->enable_discovery();

      {
        // We want to temporarily turn off discovery by the
        // CM ref processor, if necessary, and turn it back on
        // on again later if we do. Using a scoped
        // NoRefDiscovery object will do this.
        NoRefDiscovery no_cm_discovery(_ref_processor_cm);

        // Forget the current alloc region (we might even choose it to be part
        // of the collection set!).
        _allocator->release_mutator_alloc_region();

        // This timing is only used by the ergonomics to handle our pause target.
        // It is unclear why this should not include the full pause. We will
        // investigate this in CR 7178365.
        //
        // Preserving the old comment here if that helps the investigation:
        //
        // The elapsed time induced by the start time below deliberately elides
        // the possible verification above.
        double sample_start_time_sec = os::elapsedTime();

        g1_policy()->record_collection_pause_start(sample_start_time_sec);

        if (collector_state()->in_initial_mark_gc()) {
          concurrent_mark()->pre_initial_mark();
        }

        g1_policy()->finalize_collection_set(target_pause_time_ms, &_survivor);

        evacuation_info.set_collectionset_regions(collection_set()->region_length());

        // Make sure the remembered sets are up to date. This needs to be
        // done before register_humongous_regions_with_cset(), because the
        // remembered sets are used there to choose eager reclaim candidates.
        // If the remembered sets are not up to date we might miss some
        // entries that need to be handled.
        g1_rem_set()->cleanupHRRS();

        register_humongous_regions_with_cset();

        assert(_verifier->check_cset_fast_test(), "Inconsistency in the InCSetState table.");

        // We call this after finalize_cset() to
        // ensure that the CSet has been finalized.
        _cm->verify_no_cset_oops();

        if (_hr_printer.is_active()) {
          G1PrintCollectionSetClosure cl(&_hr_printer);
          _collection_set.iterate(&cl);
        }

        // Initialize the GC alloc regions.
        _allocator->init_gc_alloc_regions(evacuation_info);

        G1ParScanThreadStateSet per_thread_states(this, workers()->active_workers(), collection_set()->young_region_length());
        pre_evacuate_collection_set();

        // Actually do the work...
        evacuate_collection_set(&per_thread_states);

        post_evacuate_collection_set(evacuation_info, &per_thread_states);

        const size_t* surviving_young_words = per_thread_states.surviving_young_words();
        free_collection_set(&_collection_set, evacuation_info, surviving_young_words);

        eagerly_reclaim_humongous_regions();

        record_obj_copy_mem_stats();
        _survivor_evac_stats.adjust_desired_plab_sz();
        _old_evac_stats.adjust_desired_plab_sz();

        double start = os::elapsedTime();
        start_new_collection_set();
        g1_policy()->phase_times()->record_start_new_cset_time_ms((os::elapsedTime() - start) * 1000.0);

        if (evacuation_failed()) {
          set_used(recalculate_used());
          if (_archive_allocator != NULL) {
            _archive_allocator->clear_used();
          }
          for (uint i = 0; i < ParallelGCThreads; i++) {
            if (_evacuation_failed_info_array[i].has_failed()) {
              _gc_tracer_stw->report_evacuation_failed(_evacuation_failed_info_array[i]);
            }
          }
        } else {
          // The "used" of the the collection set have already been subtracted
          // when they were freed.  Add in the bytes evacuated.
          increase_used(g1_policy()->bytes_copied_during_gc());
        }

        if (collector_state()->in_initial_mark_gc()) {
          // We have to do this before we notify the CM threads that
          // they can start working to make sure that all the
          // appropriate initialization is done on the CM object.
          concurrent_mark()->post_initial_mark();
          // Note that we don't actually trigger the CM thread at
          // this point. We do that later when we're sure that
          // the current thread has completed its logging output.
        }

        allocate_dummy_regions();

        _allocator->init_mutator_alloc_region();

        {
          size_t expand_bytes = _heap_sizing_policy->expansion_amount();
          if (expand_bytes > 0) {
            size_t bytes_before = capacity();
            // No need for an ergo logging here,
            // expansion_amount() does this when it returns a value > 0.
            double expand_ms;
            if (!expand(expand_bytes, _workers, &expand_ms)) {
              // We failed to expand the heap. Cannot do anything about it.
            }
            g1_policy()->phase_times()->record_expand_heap_time(expand_ms);
          }
        }

        // We redo the verification but now wrt to the new CSet which
        // has just got initialized after the previous CSet was freed.
        _cm->verify_no_cset_oops();

        // This timing is only used by the ergonomics to handle our pause target.
        // It is unclear why this should not include the full pause. We will
        // investigate this in CR 7178365.
        double sample_end_time_sec = os::elapsedTime();
        double pause_time_ms = (sample_end_time_sec - sample_start_time_sec) * MILLIUNITS;
        size_t total_cards_scanned = g1_policy()->phase_times()->sum_thread_work_items(G1GCPhaseTimes::ScanRS, G1GCPhaseTimes::ScanRSScannedCards);
        g1_policy()->record_collection_pause_end(pause_time_ms, total_cards_scanned, heap_used_bytes_before_gc);

        evacuation_info.set_collectionset_used_before(collection_set()->bytes_used_before());
        evacuation_info.set_bytes_copied(g1_policy()->bytes_copied_during_gc());

        if (VerifyRememberedSets) {
          log_info(gc, verify)("[Verifying RemSets after GC]");
          VerifyRegionRemSetClosure v_cl;
          heap_region_iterate(&v_cl);
        }

        _verifier->verify_after_gc(verify_type);
        _verifier->check_bitmaps("GC End");

        assert(!_ref_processor_stw->discovery_enabled(), "Postcondition");
        _ref_processor_stw->verify_no_references_recorded();

        // CM reference discovery will be re-enabled if necessary.
      }

#ifdef TRACESPINNING
      ParallelTaskTerminator::print_termination_counts();
#endif

      gc_epilogue(false);
    }

    // Print the remainder of the GC log output.
    if (evacuation_failed()) {
      log_info(gc)("To-space exhausted");
    }

    g1_policy()->print_phases();
    heap_transition.print();

    // It is not yet to safe to tell the concurrent mark to
    // start as we have some optional output below. We don't want the
    // output from the concurrent mark thread interfering with this
    // logging output either.

    _hrm.verify_optional();
    _verifier->verify_region_sets_optional();

    TASKQUEUE_STATS_ONLY(print_taskqueue_stats());
    TASKQUEUE_STATS_ONLY(reset_taskqueue_stats());

    print_heap_after_gc();
    print_heap_regions();
    trace_heap_after_gc(_gc_tracer_stw);

    // We must call G1MonitoringSupport::update_sizes() in the same scoping level
    // as an active TraceMemoryManagerStats object (i.e. before the destructor for the
    // TraceMemoryManagerStats is called) so that the G1 memory pools are updated
    // before any GC notifications are raised.
    g1mm()->update_sizes();

    _gc_tracer_stw->report_evacuation_info(&evacuation_info);
    _gc_tracer_stw->report_tenuring_threshold(_g1_policy->tenuring_threshold());
    _gc_timer_stw->register_gc_end();
    _gc_tracer_stw->report_gc_end(_gc_timer_stw->gc_end(), _gc_timer_stw->time_partitions());
  }
  // It should now be safe to tell the concurrent mark thread to start
  // without its logging output interfering with the logging output
  // that came from the pause.

  if (should_start_conc_mark) {
    // CAUTION: after the doConcurrentMark() call below,
    // the concurrent marking thread(s) could be running
    // concurrently with us. Make sure that anything after
    // this point does not assume that we are the only GC thread
    // running. Note: of course, the actual marking work will
    // not start until the safepoint itself is released in
    // SuspendibleThreadSet::desynchronize().
    do_concurrent_mark();
  }

  return true;
}

往下走就是这一步G1Policy::finalize_collection_set,去处理新生代和老年代

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void G1Policy::finalize_collection_set(double target_pause_time_ms, G1SurvivorRegions* survivor) {
  double time_remaining_ms = _collection_set->finalize_young_part(target_pause_time_ms, survivor);
  _collection_set->finalize_old_part(time_remaining_ms);
}

这里分别调用了两个方法，可以看到剩余时间是往下传的，来看一下具体的方法

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double G1CollectionSet::finalize_young_part(double target_pause_time_ms, G1SurvivorRegions* survivors) {
  double young_start_time_sec = os::elapsedTime();

  finalize_incremental_building();

  guarantee(target_pause_time_ms > 0.0,
            "target_pause_time_ms = %1.6lf should be positive", target_pause_time_ms);

  size_t pending_cards = _policy->pending_cards();
  double base_time_ms = _policy->predict_base_elapsed_time_ms(pending_cards);
  double time_remaining_ms = MAX2(target_pause_time_ms - base_time_ms, 0.0);

  log_trace(gc, ergo, cset)("Start choosing CSet. pending cards: " SIZE_FORMAT " predicted base time: %1.2fms remaining time: %1.2fms target pause time: %1.2fms",
                            pending_cards, base_time_ms, time_remaining_ms, target_pause_time_ms);

  // The young list is laid with the survivor regions from the previous
  // pause are appended to the RHS of the young list, i.e.
  //   [Newly Young Regions ++ Survivors from last pause].

  uint survivor_region_length = survivors->length();
  uint eden_region_length = _g1h->eden_regions_count();
  init_region_lengths(eden_region_length, survivor_region_length);

  verify_young_cset_indices();

  // Clear the fields that point to the survivor list - they are all young now.
  survivors->convert_to_eden();

  _bytes_used_before = _inc_bytes_used_before;
  time_remaining_ms = MAX2(time_remaining_ms - _inc_predicted_elapsed_time_ms, 0.0);

  log_trace(gc, ergo, cset)("Add young regions to CSet. eden: %u regions, survivors: %u regions, predicted young region time: %1.2fms, target pause time: %1.2fms",
                            eden_region_length, survivor_region_length, _inc_predicted_elapsed_time_ms, target_pause_time_ms);

  // The number of recorded young regions is the incremental
  // collection set's current size
  set_recorded_rs_lengths(_inc_recorded_rs_lengths);

  double young_end_time_sec = os::elapsedTime();
  phase_times()->record_young_cset_choice_time_ms((young_end_time_sec - young_start_time_sec) * 1000.0);

  return time_remaining_ms;
}

下面是老年代的部分

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void G1CollectionSet::finalize_old_part(double time_remaining_ms) {
  double non_young_start_time_sec = os::elapsedTime();
  double predicted_old_time_ms = 0.0;

  if (collector_state()->in_mixed_phase()) {
    cset_chooser()->verify();
    const uint min_old_cset_length = _policy->calc_min_old_cset_length();
    const uint max_old_cset_length = _policy->calc_max_old_cset_length();

    uint expensive_region_num = 0;
    bool check_time_remaining = _policy->adaptive_young_list_length();

    HeapRegion* hr = cset_chooser()->peek();
    while (hr != NULL) {
      if (old_region_length() >= max_old_cset_length) {
        // Added maximum number of old regions to the CSet.
        log_debug(gc, ergo, cset)("Finish adding old regions to CSet (old CSet region num reached max). old %u regions, max %u regions",
                                  old_region_length(), max_old_cset_length);
        break;
      }

      // Stop adding regions if the remaining reclaimable space is
      // not above G1HeapWastePercent.
      size_t reclaimable_bytes = cset_chooser()->remaining_reclaimable_bytes();
      double reclaimable_percent = _policy->reclaimable_bytes_percent(reclaimable_bytes);
      double threshold = (double) G1HeapWastePercent;
      if (reclaimable_percent <= threshold) {
        // We've added enough old regions that the amount of uncollected
        // reclaimable space is at or below the waste threshold. Stop
        // adding old regions to the CSet.
        log_debug(gc, ergo, cset)("Finish adding old regions to CSet (reclaimable percentage not over threshold). "
                                  "old %u regions, max %u regions, reclaimable: " SIZE_FORMAT "B (%1.2f%%) threshold: " UINTX_FORMAT "%%",
                                  old_region_length(), max_old_cset_length, reclaimable_bytes, reclaimable_percent, G1HeapWastePercent);
        break;
      }

      double predicted_time_ms = predict_region_elapsed_time_ms(hr);
      if (check_time_remaining) {
        if (predicted_time_ms > time_remaining_ms) {
          // Too expensive for the current CSet.

          if (old_region_length() >= min_old_cset_length) {
            // We have added the minimum number of old regions to the CSet,
            // we are done with this CSet.
            log_debug(gc, ergo, cset)("Finish adding old regions to CSet (predicted time is too high). "
                                      "predicted time: %1.2fms, remaining time: %1.2fms old %u regions, min %u regions",
                                      predicted_time_ms, time_remaining_ms, old_region_length(), min_old_cset_length);
            break;
          }

          // We'll add it anyway given that we haven't reached the
          // minimum number of old regions.
          expensive_region_num += 1;
        }
      } else {
        if (old_region_length() >= min_old_cset_length) {
          // In the non-auto-tuning case, we'll finish adding regions
          // to the CSet if we reach the minimum.

          log_debug(gc, ergo, cset)("Finish adding old regions to CSet (old CSet region num reached min). old %u regions, min %u regions",
                                    old_region_length(), min_old_cset_length);
          break;
        }
      }

      // We will add this region to the CSet.
      time_remaining_ms = MAX2(time_remaining_ms - predicted_time_ms, 0.0);
      predicted_old_time_ms += predicted_time_ms;
      cset_chooser()->pop(); // already have region via peek()
      _g1h->old_set_remove(hr);
      add_old_region(hr);

      hr = cset_chooser()->peek();
    }
    if (hr == NULL) {
      log_debug(gc, ergo, cset)("Finish adding old regions to CSet (candidate old regions not available)");
    }

    if (expensive_region_num > 0) {
      // We print the information once here at the end, predicated on
      // whether we added any apparently expensive regions or not, to
      // avoid generating output per region.
      log_debug(gc, ergo, cset)("Added expensive regions to CSet (old CSet region num not reached min)."
                                "old: %u regions, expensive: %u regions, min: %u regions, remaining time: %1.2fms",
                                old_region_length(), expensive_region_num, min_old_cset_length, time_remaining_ms);
    }

    cset_chooser()->verify();
  }

  stop_incremental_building();

  log_debug(gc, ergo, cset)("Finish choosing CSet. old: %u regions, predicted old region time: %1.2fms, time remaining: %1.2f",
                            old_region_length(), predicted_old_time_ms, time_remaining_ms);

  double non_young_end_time_sec = os::elapsedTime();
  phase_times()->record_non_young_cset_choice_time_ms((non_young_end_time_sec - non_young_start_time_sec) * 1000.0);

  QuickSort::sort(_collection_set_regions, _collection_set_cur_length, compare_region_idx, true);
}

上面第三行是个判断，当前是否是 mixed 回收阶段，如果不是的话其实是没有老年代什么事的，所以可以看到代码基本是从这个 if 判断
if (collector_state()->in_mixed_phase()) {开始往下走的
先写到这，偏向于做笔记用，有错轻拍

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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}JVM源码分析之G1垃圾收集器分析一 | Nicksxs's Blog

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JVM源码分析之G1垃圾收集器分析一

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对 Java 的 gc 实现比较感兴趣，原先一般都是看周志明的书，但其实并没有讲具体的 gc 源码，而是把整个思路和流程讲解了一下
特别是 G1 的具体实现
一般对 G1 的理解其实就是把原先整块的新生代老年代分成了以 region 为单位的小块内存，简而言之，就是原先对新生代老年代的收集会涉及到整个代的堆内存空间，而G1 把它变成了更细致的小块内存
这带来了一个很明显的好处和一个很明显的坏处，好处是内存收集可以更灵活，耗时会变短，但整个收集的处理复杂度就变高了
目前看了一点点关于 G1 收集的预期时间相关的代码

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HeapWord* G1CollectedHeap::do_collection_pause(size_t word_size,
                                               uint gc_count_before,
                                               bool* succeeded,
                                               GCCause::Cause gc_cause) {
  assert_heap_not_locked_and_not_at_safepoint();
  VM_G1CollectForAllocation op(word_size,
                               gc_count_before,
                               gc_cause,
                               false, /* should_initiate_conc_mark */
                               g1_policy()->max_pause_time_ms());
  VMThread::execute(&op);

  HeapWord* result = op.result();
  bool ret_succeeded = op.prologue_succeeded() && op.pause_succeeded();
  assert(result == NULL || ret_succeeded,
         "the result should be NULL if the VM did not succeed");
  *succeeded = ret_succeeded;

  assert_heap_not_locked();
  return result;
}

这里就是收集时需要停顿的，其中VMThread::execute(&op);是具体执行的，真正执行的是VM_G1CollectForAllocation::doit方法

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void VM_G1CollectForAllocation::doit() {
  G1CollectedHeap* g1h = G1CollectedHeap::heap();
  assert(!_should_initiate_conc_mark || g1h->should_do_concurrent_full_gc(_gc_cause),
      "only a GC locker, a System.gc(), stats update, whitebox, or a hum allocation induced GC should start a cycle");

  if (_word_size > 0) {
    // An allocation has been requested. So, try to do that first.
    _result = g1h->attempt_allocation_at_safepoint(_word_size,
                                                   false /* expect_null_cur_alloc_region */);
    if (_result != NULL) {
      // If we can successfully allocate before we actually do the
      // pause then we will consider this pause successful.
      _pause_succeeded = true;
      return;
    }
  }

  GCCauseSetter x(g1h, _gc_cause);
  if (_should_initiate_conc_mark) {
    // It's safer to read old_marking_cycles_completed() here, given
    // that noone else will be updating it concurrently. Since we'll
    // only need it if we're initiating a marking cycle, no point in
    // setting it earlier.
    _old_marking_cycles_completed_before = g1h->old_marking_cycles_completed();

    // At this point we are supposed to start a concurrent cycle. We
    // will do so if one is not already in progress.
    bool res = g1h->g1_policy()->force_initial_mark_if_outside_cycle(_gc_cause);

    // The above routine returns true if we were able to force the
    // next GC pause to be an initial mark; it returns false if a
    // marking cycle is already in progress.
    //
    // If a marking cycle is already in progress just return and skip the
    // pause below - if the reason for requesting this initial mark pause
    // was due to a System.gc() then the requesting thread should block in
    // doit_epilogue() until the marking cycle is complete.
    //
    // If this initial mark pause was requested as part of a humongous
    // allocation then we know that the marking cycle must just have
    // been started by another thread (possibly also allocating a humongous
    // object) as there was no active marking cycle when the requesting
    // thread checked before calling collect() in
    // attempt_allocation_humongous(). Retrying the GC, in this case,
    // will cause the requesting thread to spin inside collect() until the
    // just started marking cycle is complete - which may be a while. So
    // we do NOT retry the GC.
    if (!res) {
      assert(_word_size == 0, "Concurrent Full GC/Humongous Object IM shouldn't be allocating");
      if (_gc_cause != GCCause::_g1_humongous_allocation) {
        _should_retry_gc = true;
      }
      return;
    }
  }

  // Try a partial collection of some kind.
  _pause_succeeded = g1h->do_collection_pause_at_safepoint(_target_pause_time_ms);

  if (_pause_succeeded) {
    if (_word_size > 0) {
      // An allocation had been requested. Do it, eventually trying a stronger
      // kind of GC.
      _result = g1h->satisfy_failed_allocation(_word_size, &_pause_succeeded);
    } else {
      bool should_upgrade_to_full = !g1h->should_do_concurrent_full_gc(_gc_cause) &&
                                    !g1h->has_regions_left_for_allocation();
      if (should_upgrade_to_full) {
        // There has been a request to perform a GC to free some space. We have no
        // information on how much memory has been asked for. In case there are
        // absolutely no regions left to allocate into, do a maximally compacting full GC.
        log_info(gc, ergo)("Attempting maximally compacting collection");
        _pause_succeeded = g1h->do_full_collection(false, /* explicit gc */
                                                   true   /* clear_all_soft_refs */);
      }
    }
    guarantee(_pause_succeeded, "Elevated collections during the safepoint must always succeed.");
  } else {
    assert(_result == NULL, "invariant");
    // The only reason for the pause to not be successful is that, the GC locker is
    // active (or has become active since the prologue was executed). In this case
    // we should retry the pause after waiting for the GC locker to become inactive.
    _should_retry_gc = true;
  }
}

这里可以看到核心的是G1CollectedHeap::do_collection_pause_at_safepoint这个方法，它带上了目标暂停时间的值

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G1CollectedHeap::do_collection_pause_at_safepoint(double target_pause_time_ms) {
  assert_at_safepoint_on_vm_thread();
  guarantee(!is_gc_active(), "collection is not reentrant");

  if (GCLocker::check_active_before_gc()) {
    return false;
  }

  _gc_timer_stw->register_gc_start();

  GCIdMark gc_id_mark;
  _gc_tracer_stw->report_gc_start(gc_cause(), _gc_timer_stw->gc_start());

  SvcGCMarker sgcm(SvcGCMarker::MINOR);
  ResourceMark rm;

  g1_policy()->note_gc_start();

  wait_for_root_region_scanning();

  print_heap_before_gc();
  print_heap_regions();
  trace_heap_before_gc(_gc_tracer_stw);

  _verifier->verify_region_sets_optional();
  _verifier->verify_dirty_young_regions();

  // We should not be doing initial mark unless the conc mark thread is running
  if (!_cm_thread->should_terminate()) {
    // This call will decide whether this pause is an initial-mark
    // pause. If it is, in_initial_mark_gc() will return true
    // for the duration of this pause.
    g1_policy()->decide_on_conc_mark_initiation();
  }

  // We do not allow initial-mark to be piggy-backed on a mixed GC.
  assert(!collector_state()->in_initial_mark_gc() ||
          collector_state()->in_young_only_phase(), "sanity");

  // We also do not allow mixed GCs during marking.
  assert(!collector_state()->mark_or_rebuild_in_progress() || collector_state()->in_young_only_phase(), "sanity");

  // Record whether this pause is an initial mark. When the current
  // thread has completed its logging output and it's safe to signal
  // the CM thread, the flag's value in the policy has been reset.
  bool should_start_conc_mark = collector_state()->in_initial_mark_gc();

  // Inner scope for scope based logging, timers, and stats collection
  {
    EvacuationInfo evacuation_info;

    if (collector_state()->in_initial_mark_gc()) {
      // We are about to start a marking cycle, so we increment the
      // full collection counter.
      increment_old_marking_cycles_started();
      _cm->gc_tracer_cm()->set_gc_cause(gc_cause());
    }

    _gc_tracer_stw->report_yc_type(collector_state()->yc_type());

    GCTraceCPUTime tcpu;

    G1HeapVerifier::G1VerifyType verify_type;
    FormatBuffer<> gc_string("Pause Young ");
    if (collector_state()->in_initial_mark_gc()) {
      gc_string.append("(Concurrent Start)");
      verify_type = G1HeapVerifier::G1VerifyConcurrentStart;
    } else if (collector_state()->in_young_only_phase()) {
      if (collector_state()->in_young_gc_before_mixed()) {
        gc_string.append("(Prepare Mixed)");
      } else {
        gc_string.append("(Normal)");
      }
      verify_type = G1HeapVerifier::G1VerifyYoungNormal;
    } else {
      gc_string.append("(Mixed)");
      verify_type = G1HeapVerifier::G1VerifyMixed;
    }
    GCTraceTime(Info, gc) tm(gc_string, NULL, gc_cause(), true);

    uint active_workers = AdaptiveSizePolicy::calc_active_workers(workers()->total_workers(),
                                                                  workers()->active_workers(),
                                                                  Threads::number_of_non_daemon_threads());
    active_workers = workers()->update_active_workers(active_workers);
    log_info(gc,task)("Using %u workers of %u for evacuation", active_workers, workers()->total_workers());

    TraceCollectorStats tcs(g1mm()->incremental_collection_counters());
    TraceMemoryManagerStats tms(&_memory_manager, gc_cause(),
                                collector_state()->yc_type() == Mixed /* allMemoryPoolsAffected */);

    G1HeapTransition heap_transition(this);
    size_t heap_used_bytes_before_gc = used();

    // Don't dynamically change the number of GC threads this early.  A value of
    // 0 is used to indicate serial work.  When parallel work is done,
    // it will be set.

    { // Call to jvmpi::post_class_unload_events must occur outside of active GC
      IsGCActiveMark x;

      gc_prologue(false);

      if (VerifyRememberedSets) {
        log_info(gc, verify)("[Verifying RemSets before GC]");
        VerifyRegionRemSetClosure v_cl;
        heap_region_iterate(&v_cl);
      }

      _verifier->verify_before_gc(verify_type);

      _verifier->check_bitmaps("GC Start");

#if COMPILER2_OR_JVMCI
      DerivedPointerTable::clear();
#endif

      // Please see comment in g1CollectedHeap.hpp and
      // G1CollectedHeap::ref_processing_init() to see how
      // reference processing currently works in G1.

      // Enable discovery in the STW reference processor
      _ref_processor_stw->enable_discovery();

      {
        // We want to temporarily turn off discovery by the
        // CM ref processor, if necessary, and turn it back on
        // on again later if we do. Using a scoped
        // NoRefDiscovery object will do this.
        NoRefDiscovery no_cm_discovery(_ref_processor_cm);

        // Forget the current alloc region (we might even choose it to be part
        // of the collection set!).
        _allocator->release_mutator_alloc_region();

        // This timing is only used by the ergonomics to handle our pause target.
        // It is unclear why this should not include the full pause. We will
        // investigate this in CR 7178365.
        //
        // Preserving the old comment here if that helps the investigation:
        //
        // The elapsed time induced by the start time below deliberately elides
        // the possible verification above.
        double sample_start_time_sec = os::elapsedTime();

        g1_policy()->record_collection_pause_start(sample_start_time_sec);

        if (collector_state()->in_initial_mark_gc()) {
          concurrent_mark()->pre_initial_mark();
        }

        g1_policy()->finalize_collection_set(target_pause_time_ms, &_survivor);

        evacuation_info.set_collectionset_regions(collection_set()->region_length());

        // Make sure the remembered sets are up to date. This needs to be
        // done before register_humongous_regions_with_cset(), because the
        // remembered sets are used there to choose eager reclaim candidates.
        // If the remembered sets are not up to date we might miss some
        // entries that need to be handled.
        g1_rem_set()->cleanupHRRS();

        register_humongous_regions_with_cset();

        assert(_verifier->check_cset_fast_test(), "Inconsistency in the InCSetState table.");

        // We call this after finalize_cset() to
        // ensure that the CSet has been finalized.
        _cm->verify_no_cset_oops();

        if (_hr_printer.is_active()) {
          G1PrintCollectionSetClosure cl(&_hr_printer);
          _collection_set.iterate(&cl);
        }

        // Initialize the GC alloc regions.
        _allocator->init_gc_alloc_regions(evacuation_info);

        G1ParScanThreadStateSet per_thread_states(this, workers()->active_workers(), collection_set()->young_region_length());
        pre_evacuate_collection_set();

        // Actually do the work...
        evacuate_collection_set(&per_thread_states);

        post_evacuate_collection_set(evacuation_info, &per_thread_states);

        const size_t* surviving_young_words = per_thread_states.surviving_young_words();
        free_collection_set(&_collection_set, evacuation_info, surviving_young_words);

        eagerly_reclaim_humongous_regions();

        record_obj_copy_mem_stats();
        _survivor_evac_stats.adjust_desired_plab_sz();
        _old_evac_stats.adjust_desired_plab_sz();

        double start = os::elapsedTime();
        start_new_collection_set();
        g1_policy()->phase_times()->record_start_new_cset_time_ms((os::elapsedTime() - start) * 1000.0);

        if (evacuation_failed()) {
          set_used(recalculate_used());
          if (_archive_allocator != NULL) {
            _archive_allocator->clear_used();
          }
          for (uint i = 0; i < ParallelGCThreads; i++) {
            if (_evacuation_failed_info_array[i].has_failed()) {
              _gc_tracer_stw->report_evacuation_failed(_evacuation_failed_info_array[i]);
            }
          }
        } else {
          // The "used" of the the collection set have already been subtracted
          // when they were freed.  Add in the bytes evacuated.
          increase_used(g1_policy()->bytes_copied_during_gc());
        }

        if (collector_state()->in_initial_mark_gc()) {
          // We have to do this before we notify the CM threads that
          // they can start working to make sure that all the
          // appropriate initialization is done on the CM object.
          concurrent_mark()->post_initial_mark();
          // Note that we don't actually trigger the CM thread at
          // this point. We do that later when we're sure that
          // the current thread has completed its logging output.
        }

        allocate_dummy_regions();

        _allocator->init_mutator_alloc_region();

        {
          size_t expand_bytes = _heap_sizing_policy->expansion_amount();
          if (expand_bytes > 0) {
            size_t bytes_before = capacity();
            // No need for an ergo logging here,
            // expansion_amount() does this when it returns a value > 0.
            double expand_ms;
            if (!expand(expand_bytes, _workers, &expand_ms)) {
              // We failed to expand the heap. Cannot do anything about it.
            }
            g1_policy()->phase_times()->record_expand_heap_time(expand_ms);
          }
        }

        // We redo the verification but now wrt to the new CSet which
        // has just got initialized after the previous CSet was freed.
        _cm->verify_no_cset_oops();

        // This timing is only used by the ergonomics to handle our pause target.
        // It is unclear why this should not include the full pause. We will
        // investigate this in CR 7178365.
        double sample_end_time_sec = os::elapsedTime();
        double pause_time_ms = (sample_end_time_sec - sample_start_time_sec) * MILLIUNITS;
        size_t total_cards_scanned = g1_policy()->phase_times()->sum_thread_work_items(G1GCPhaseTimes::ScanRS, G1GCPhaseTimes::ScanRSScannedCards);
        g1_policy()->record_collection_pause_end(pause_time_ms, total_cards_scanned, heap_used_bytes_before_gc);

        evacuation_info.set_collectionset_used_before(collection_set()->bytes_used_before());
        evacuation_info.set_bytes_copied(g1_policy()->bytes_copied_during_gc());

        if (VerifyRememberedSets) {
          log_info(gc, verify)("[Verifying RemSets after GC]");
          VerifyRegionRemSetClosure v_cl;
          heap_region_iterate(&v_cl);
        }

        _verifier->verify_after_gc(verify_type);
        _verifier->check_bitmaps("GC End");

        assert(!_ref_processor_stw->discovery_enabled(), "Postcondition");
        _ref_processor_stw->verify_no_references_recorded();

        // CM reference discovery will be re-enabled if necessary.
      }

#ifdef TRACESPINNING
      ParallelTaskTerminator::print_termination_counts();
#endif

      gc_epilogue(false);
    }

    // Print the remainder of the GC log output.
    if (evacuation_failed()) {
      log_info(gc)("To-space exhausted");
    }

    g1_policy()->print_phases();
    heap_transition.print();

    // It is not yet to safe to tell the concurrent mark to
    // start as we have some optional output below. We don't want the
    // output from the concurrent mark thread interfering with this
    // logging output either.

    _hrm.verify_optional();
    _verifier->verify_region_sets_optional();

    TASKQUEUE_STATS_ONLY(print_taskqueue_stats());
    TASKQUEUE_STATS_ONLY(reset_taskqueue_stats());

    print_heap_after_gc();
    print_heap_regions();
    trace_heap_after_gc(_gc_tracer_stw);

    // We must call G1MonitoringSupport::update_sizes() in the same scoping level
    // as an active TraceMemoryManagerStats object (i.e. before the destructor for the
    // TraceMemoryManagerStats is called) so that the G1 memory pools are updated
    // before any GC notifications are raised.
    g1mm()->update_sizes();

    _gc_tracer_stw->report_evacuation_info(&evacuation_info);
    _gc_tracer_stw->report_tenuring_threshold(_g1_policy->tenuring_threshold());
    _gc_timer_stw->register_gc_end();
    _gc_tracer_stw->report_gc_end(_gc_timer_stw->gc_end(), _gc_timer_stw->time_partitions());
  }
  // It should now be safe to tell the concurrent mark thread to start
  // without its logging output interfering with the logging output
  // that came from the pause.

  if (should_start_conc_mark) {
    // CAUTION: after the doConcurrentMark() call below,
    // the concurrent marking thread(s) could be running
    // concurrently with us. Make sure that anything after
    // this point does not assume that we are the only GC thread
    // running. Note: of course, the actual marking work will
    // not start until the safepoint itself is released in
    // SuspendibleThreadSet::desynchronize().
    do_concurrent_mark();
  }

  return true;
}

往下走就是这一步G1Policy::finalize_collection_set,去处理新生代和老年代

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void G1Policy::finalize_collection_set(double target_pause_time_ms, G1SurvivorRegions* survivor) {
  double time_remaining_ms = _collection_set->finalize_young_part(target_pause_time_ms, survivor);
  _collection_set->finalize_old_part(time_remaining_ms);
}

这里分别调用了两个方法，可以看到剩余时间是往下传的，来看一下具体的方法

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double G1CollectionSet::finalize_young_part(double target_pause_time_ms, G1SurvivorRegions* survivors) {
  double young_start_time_sec = os::elapsedTime();

  finalize_incremental_building();

  guarantee(target_pause_time_ms > 0.0,
            "target_pause_time_ms = %1.6lf should be positive", target_pause_time_ms);

  size_t pending_cards = _policy->pending_cards();
  double base_time_ms = _policy->predict_base_elapsed_time_ms(pending_cards);
  double time_remaining_ms = MAX2(target_pause_time_ms - base_time_ms, 0.0);

  log_trace(gc, ergo, cset)("Start choosing CSet. pending cards: " SIZE_FORMAT " predicted base time: %1.2fms remaining time: %1.2fms target pause time: %1.2fms",
                            pending_cards, base_time_ms, time_remaining_ms, target_pause_time_ms);

  // The young list is laid with the survivor regions from the previous
  // pause are appended to the RHS of the young list, i.e.
  //   [Newly Young Regions ++ Survivors from last pause].

  uint survivor_region_length = survivors->length();
  uint eden_region_length = _g1h->eden_regions_count();
  init_region_lengths(eden_region_length, survivor_region_length);

  verify_young_cset_indices();

  // Clear the fields that point to the survivor list - they are all young now.
  survivors->convert_to_eden();

  _bytes_used_before = _inc_bytes_used_before;
  time_remaining_ms = MAX2(time_remaining_ms - _inc_predicted_elapsed_time_ms, 0.0);

  log_trace(gc, ergo, cset)("Add young regions to CSet. eden: %u regions, survivors: %u regions, predicted young region time: %1.2fms, target pause time: %1.2fms",
                            eden_region_length, survivor_region_length, _inc_predicted_elapsed_time_ms, target_pause_time_ms);

  // The number of recorded young regions is the incremental
  // collection set's current size
  set_recorded_rs_lengths(_inc_recorded_rs_lengths);

  double young_end_time_sec = os::elapsedTime();
  phase_times()->record_young_cset_choice_time_ms((young_end_time_sec - young_start_time_sec) * 1000.0);

  return time_remaining_ms;
}

下面是老年代的部分

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void G1CollectionSet::finalize_old_part(double time_remaining_ms) {
  double non_young_start_time_sec = os::elapsedTime();
  double predicted_old_time_ms = 0.0;

  if (collector_state()->in_mixed_phase()) {
    cset_chooser()->verify();
    const uint min_old_cset_length = _policy->calc_min_old_cset_length();
    const uint max_old_cset_length = _policy->calc_max_old_cset_length();

    uint expensive_region_num = 0;
    bool check_time_remaining = _policy->adaptive_young_list_length();

    HeapRegion* hr = cset_chooser()->peek();
    while (hr != NULL) {
      if (old_region_length() >= max_old_cset_length) {
        // Added maximum number of old regions to the CSet.
        log_debug(gc, ergo, cset)("Finish adding old regions to CSet (old CSet region num reached max). old %u regions, max %u regions",
                                  old_region_length(), max_old_cset_length);
        break;
      }

      // Stop adding regions if the remaining reclaimable space is
      // not above G1HeapWastePercent.
      size_t reclaimable_bytes = cset_chooser()->remaining_reclaimable_bytes();
      double reclaimable_percent = _policy->reclaimable_bytes_percent(reclaimable_bytes);
      double threshold = (double) G1HeapWastePercent;
      if (reclaimable_percent <= threshold) {
        // We've added enough old regions that the amount of uncollected
        // reclaimable space is at or below the waste threshold. Stop
        // adding old regions to the CSet.
        log_debug(gc, ergo, cset)("Finish adding old regions to CSet (reclaimable percentage not over threshold). "
                                  "old %u regions, max %u regions, reclaimable: " SIZE_FORMAT "B (%1.2f%%) threshold: " UINTX_FORMAT "%%",
                                  old_region_length(), max_old_cset_length, reclaimable_bytes, reclaimable_percent, G1HeapWastePercent);
        break;
      }

      double predicted_time_ms = predict_region_elapsed_time_ms(hr);
      if (check_time_remaining) {
        if (predicted_time_ms > time_remaining_ms) {
          // Too expensive for the current CSet.

          if (old_region_length() >= min_old_cset_length) {
            // We have added the minimum number of old regions to the CSet,
            // we are done with this CSet.
            log_debug(gc, ergo, cset)("Finish adding old regions to CSet (predicted time is too high). "
                                      "predicted time: %1.2fms, remaining time: %1.2fms old %u regions, min %u regions",
                                      predicted_time_ms, time_remaining_ms, old_region_length(), min_old_cset_length);
            break;
          }

          // We'll add it anyway given that we haven't reached the
          // minimum number of old regions.
          expensive_region_num += 1;
        }
      } else {
        if (old_region_length() >= min_old_cset_length) {
          // In the non-auto-tuning case, we'll finish adding regions
          // to the CSet if we reach the minimum.

          log_debug(gc, ergo, cset)("Finish adding old regions to CSet (old CSet region num reached min). old %u regions, min %u regions",
                                    old_region_length(), min_old_cset_length);
          break;
        }
      }

      // We will add this region to the CSet.
      time_remaining_ms = MAX2(time_remaining_ms - predicted_time_ms, 0.0);
      predicted_old_time_ms += predicted_time_ms;
      cset_chooser()->pop(); // already have region via peek()
      _g1h->old_set_remove(hr);
      add_old_region(hr);

      hr = cset_chooser()->peek();
    }
    if (hr == NULL) {
      log_debug(gc, ergo, cset)("Finish adding old regions to CSet (candidate old regions not available)");
    }

    if (expensive_region_num > 0) {
      // We print the information once here at the end, predicated on
      // whether we added any apparently expensive regions or not, to
      // avoid generating output per region.
      log_debug(gc, ergo, cset)("Added expensive regions to CSet (old CSet region num not reached min)."
                                "old: %u regions, expensive: %u regions, min: %u regions, remaining time: %1.2fms",
                                old_region_length(), expensive_region_num, min_old_cset_length, time_remaining_ms);
    }

    cset_chooser()->verify();
  }

  stop_incremental_building();

  log_debug(gc, ergo, cset)("Finish choosing CSet. old: %u regions, predicted old region time: %1.2fms, time remaining: %1.2f",
                            old_region_length(), predicted_old_time_ms, time_remaining_ms);

  double non_young_end_time_sec = os::elapsedTime();
  phase_times()->record_non_young_cset_choice_time_ms((non_young_end_time_sec - non_young_start_time_sec) * 1000.0);

  QuickSort::sort(_collection_set_regions, _collection_set_cur_length, compare_region_idx, true);
}

上面第三行是个判断，当前是否是 mixed 回收阶段，如果不是的话其实是没有老年代什么事的，所以可以看到代码基本是从这个 if 判断
if (collector_state()->in_mixed_phase()) {开始往下走的
先写到这，偏向于做笔记用，有错轻拍

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What hurts more, the pain of hard work or the pain of regret?

聊一下 RocketMQ 的 DefaultMQPushConsumer 源码

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： 阅读次数： Disqus：

首先看下官方的小 demo

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public static void main(String[] args) throws InterruptedException, MQClientException {

        /*
         * Instantiate with specified consumer group name.
         * 首先是new 一个对象出来，然后指定 Consumer 的 Group
         * 同一类Consumer的集合，这类Consumer通常消费同一类消息且消费逻辑一致。消费者组使得在消息消费方面，实现负载均衡和容错的目标变得非常容易。要注意的是，消费者组的消费者实例必须订阅完全相同的Topic。RocketMQ 支持两种消息模式：集群消费（Clustering）和广播消费（Broadcasting）。
         */
        DefaultMQPushConsumer consumer = new DefaultMQPushConsumer("please_rename_unique_group_name_4");

        /*
         * Specify name server addresses.
         * <p/>
         * 这里可以通知指定环境变量或者设置对象参数的形式指定名字空间服务的地址
         *
         * Alternatively, you may specify name server addresses via exporting environmental variable: NAMESRV_ADDR
         * <pre>
         * {@code
         * consumer.setNamesrvAddr("name-server1-ip:9876;name-server2-ip:9876");
         * }
         * </pre>
         */

        /*
         * Specify where to start in case the specified consumer group is a brand new one.
         * 指定消费起始点
         */
        consumer.setConsumeFromWhere(ConsumeFromWhere.CONSUME_FROM_FIRST_OFFSET);

        /*
         * Subscribe one more more topics to consume.
         * 指定订阅的 topic 跟 tag，注意后面的是个表达式，可以以 tag1 || tag2 || tag3 传入
         */
        consumer.subscribe("TopicTest", "*");

        /*
         *  Register callback to execute on arrival of messages fetched from brokers.
         *  注册具体获得消息后的处理方法
         */
        consumer.registerMessageListener(new MessageListenerConcurrently() {

            @Override
            public ConsumeConcurrentlyStatus consumeMessage(List<MessageExt> msgs,
                ConsumeConcurrentlyContext context) {
                System.out.printf("%s Receive New Messages: %s %n", Thread.currentThread().getName(), msgs);
                return ConsumeConcurrentlyStatus.CONSUME_SUCCESS;
            }
        });

        /*
         *  Launch the consumer instance.
         * 启动消费者
         */
        consumer.start();

        System.out.printf("Consumer Started.%n");
    }

然后就是看看 start 的过程了

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/**
     * This method gets internal infrastructure readily to serve. Instances must call this method after configuration.
     *
     * @throws MQClientException if there is any client error.
     */
    @Override
    public void start() throws MQClientException {
        setConsumerGroup(NamespaceUtil.wrapNamespace(this.getNamespace(), this.consumerGroup));
        this.defaultMQPushConsumerImpl.start();
        if (null != traceDispatcher) {
            try {
                traceDispatcher.start(this.getNamesrvAddr(), this.getAccessChannel());
            } catch (MQClientException e) {
                log.warn("trace dispatcher start failed ", e);
            }
        }
    }

具体的逻辑在this.defaultMQPushConsumerImpl.start()，这个 defaultMQPushConsumerImpl 就是

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/**
     * Internal implementation. Most of the functions herein are delegated to it.
     */
    protected final transient DefaultMQPushConsumerImpl defaultMQPushConsumerImpl;
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public synchronized void start() throws MQClientException {
        switch (this.serviceState) {
            case CREATE_JUST:
                log.info("the consumer [{}] start beginning. messageModel={}, isUnitMode={}", this.defaultMQPushConsumer.getConsumerGroup(),
                    this.defaultMQPushConsumer.getMessageModel(), this.defaultMQPushConsumer.isUnitMode());
                // 这里比较巧妙，相当于想设立了个屏障，防止并发启动，不过这里并不是悲观锁，也不算个严格的乐观锁
                this.serviceState = ServiceState.START_FAILED;

                this.checkConfig();

                this.copySubscription();

                if (this.defaultMQPushConsumer.getMessageModel() == MessageModel.CLUSTERING) {
                    this.defaultMQPushConsumer.changeInstanceNameToPID();
                }

                // 这个mQClientFactory，负责管理client（consumer、producer），并提供多中功能接口供各个Service（Rebalance、PullMessage等）调用；大部分逻辑均在这个类中完成
                this.mQClientFactory = MQClientManager.getInstance().getOrCreateMQClientInstance(this.defaultMQPushConsumer, this.rpcHook);

                // 这个 rebalanceImpl 主要负责决定，当前的consumer应该从哪些Queue中消费消息；
                this.rebalanceImpl.setConsumerGroup(this.defaultMQPushConsumer.getConsumerGroup());
                this.rebalanceImpl.setMessageModel(this.defaultMQPushConsumer.getMessageModel());
                this.rebalanceImpl.setAllocateMessageQueueStrategy(this.defaultMQPushConsumer.getAllocateMessageQueueStrategy());
                this.rebalanceImpl.setmQClientFactory(this.mQClientFactory);

                // 长连接，负责从broker处拉取消息，然后利用ConsumeMessageService回调用户的Listener执行消息消费逻辑
                this.pullAPIWrapper = new PullAPIWrapper(
                    mQClientFactory,
                    this.defaultMQPushConsumer.getConsumerGroup(), isUnitMode());
                this.pullAPIWrapper.registerFilterMessageHook(filterMessageHookList);

                if (this.defaultMQPushConsumer.getOffsetStore() != null) {
                    this.offsetStore = this.defaultMQPushConsumer.getOffsetStore();
                } else {
                    switch (this.defaultMQPushConsumer.getMessageModel()) {
                        case BROADCASTING:
                            this.offsetStore = new LocalFileOffsetStore(this.mQClientFactory, this.defaultMQPushConsumer.getConsumerGroup());
                            break;
                        case CLUSTERING:
                            this.offsetStore = new RemoteBrokerOffsetStore(this.mQClientFactory, this.defaultMQPushConsumer.getConsumerGroup());
                            break;
                        default:
                            break;
                    }
                    this.defaultMQPushConsumer.setOffsetStore(this.offsetStore);
                }
                // offsetStore 维护当前consumer的消费记录（offset）；有两种实现，Local和Rmote，Local存储在本地磁盘上，适用于BROADCASTING广播消费模式；而Remote则将消费进度存储在Broker上，适用于CLUSTERING集群消费模式；
                this.offsetStore.load();

                if (this.getMessageListenerInner() instanceof MessageListenerOrderly) {
                    this.consumeOrderly = true;
                    this.consumeMessageService =
                        new ConsumeMessageOrderlyService(this, (MessageListenerOrderly) this.getMessageListenerInner());
                } else if (this.getMessageListenerInner() instanceof MessageListenerConcurrently) {
                    this.consumeOrderly = false;
                    this.consumeMessageService =
                        new ConsumeMessageConcurrentlyService(this, (MessageListenerConcurrently) this.getMessageListenerInner());
                }

                // 实现所谓的"Push-被动"消费机制；从Broker拉取的消息后，封装成ConsumeRequest提交给ConsumeMessageSerivce，此service负责回调用户的Listener消费消息；
                this.consumeMessageService.start();

                boolean registerOK = mQClientFactory.registerConsumer(this.defaultMQPushConsumer.getConsumerGroup(), this);
                if (!registerOK) {
                    this.serviceState = ServiceState.CREATE_JUST;
                    this.consumeMessageService.shutdown();
                    throw new MQClientException("The consumer group[" + this.defaultMQPushConsumer.getConsumerGroup()
                        + "] has been created before, specify another name please." + FAQUrl.suggestTodo(FAQUrl.GROUP_NAME_DUPLICATE_URL),
                        null);
                }

                mQClientFactory.start();
                log.info("the consumer [{}] start OK.", this.defaultMQPushConsumer.getConsumerGroup());
                this.serviceState = ServiceState.RUNNING;
                break;
            case RUNNING:
            case START_FAILED:
            case SHUTDOWN_ALREADY:
                throw new MQClientException("The PushConsumer service state not OK, maybe started once, "
                    + this.serviceState
                    + FAQUrl.suggestTodo(FAQUrl.CLIENT_SERVICE_NOT_OK),
                    null);
            default:
                break;
        }

        this.updateTopicSubscribeInfoWhenSubscriptionChanged();
        this.mQClientFactory.checkClientInBroker();
        this.mQClientFactory.sendHeartbeatToAllBrokerWithLock();
        this.mQClientFactory.rebalanceImmediately();
    }

然后我们往下看主要的目光聚焦mQClientFactory.start()

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public void start() throws MQClientException {

        synchronized (this) {
            switch (this.serviceState) {
                case CREATE_JUST:
                    this.serviceState = ServiceState.START_FAILED;
                    // If not specified,looking address from name server
                    if (null == this.clientConfig.getNamesrvAddr()) {
                        this.mQClientAPIImpl.fetchNameServerAddr();
                    }
                    // Start request-response channel
                    // 这里主要是初始化了个网络客户端
                    this.mQClientAPIImpl.start();
                    // Start various schedule tasks
                    // 定时任务
                    this.startScheduledTask();
                    // Start pull service
                    // 这里重点说下
                    this.pullMessageService.start();
                    // Start rebalance service
                    this.rebalanceService.start();
                    // Start push service
                    this.defaultMQProducer.getDefaultMQProducerImpl().start(false);
                    log.info("the client factory [{}] start OK", this.clientId);
                    this.serviceState = ServiceState.RUNNING;
                    break;
                case START_FAILED:
                    throw new MQClientException("The Factory object[" + this.getClientId() + "] has been created before, and failed.", null);
                default:
                    break;
            }
        }
    }

我们来看下这个 pullMessageService，org.apache.rocketmq.client.impl.consumer.PullMessageService，

实现了 runnable 接口，
然后可以看到 run 方法

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public void run() {
        log.info(this.getServiceName() + " service started");

        while (!this.isStopped()) {
            try {
                PullRequest pullRequest = this.pullRequestQueue.take();
                this.pullMessage(pullRequest);
            } catch (InterruptedException ignored) {
            } catch (Exception e) {
                log.error("Pull Message Service Run Method exception", e);
            }
        }

        log.info(this.getServiceName() + " service end");
    }

接着在看 pullMessage 方法

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private void pullMessage(final PullRequest pullRequest) {
        final MQConsumerInner consumer = this.mQClientFactory.selectConsumer(pullRequest.getConsumerGroup());
        if (consumer != null) {
            DefaultMQPushConsumerImpl impl = (DefaultMQPushConsumerImpl) consumer;
            impl.pullMessage(pullRequest);
        } else {
            log.warn("No matched consumer for the PullRequest {}, drop it", pullRequest);
        }
    }

实际上调用了这个方法，这个方法很长，我在代码里注释下下每一段的功能

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public void pullMessage(final PullRequest pullRequest) {
        final ProcessQueue processQueue = pullRequest.getProcessQueue();
        // 这里开始就是检查状态，确定是否往下执行
        if (processQueue.isDropped()) {
            log.info("the pull request[{}] is dropped.", pullRequest.toString());
            return;
        }

        pullRequest.getProcessQueue().setLastPullTimestamp(System.currentTimeMillis());

        try {
            this.makeSureStateOK();
        } catch (MQClientException e) {
            log.warn("pullMessage exception, consumer state not ok", e);
            this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
            return;
        }

        if (this.isPause()) {
            log.warn("consumer was paused, execute pull request later. instanceName={}, group={}", this.defaultMQPushConsumer.getInstanceName(), this.defaultMQPushConsumer.getConsumerGroup());
            this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_SUSPEND);
            return;
        }

        // 这块其实是个类似于限流的功能块，对消息数量和消息大小做限制
        long cachedMessageCount = processQueue.getMsgCount().get();
        long cachedMessageSizeInMiB = processQueue.getMsgSize().get() / (1024 * 1024);

        if (cachedMessageCount > this.defaultMQPushConsumer.getPullThresholdForQueue()) {
            this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_FLOW_CONTROL);
            if ((queueFlowControlTimes++ % 1000) == 0) {
                log.warn(
                    "the cached message count exceeds the threshold {}, so do flow control, minOffset={}, maxOffset={}, count={}, size={} MiB, pullRequest={}, flowControlTimes={}",
                    this.defaultMQPushConsumer.getPullThresholdForQueue(), processQueue.getMsgTreeMap().firstKey(), processQueue.getMsgTreeMap().lastKey(), cachedMessageCount, cachedMessageSizeInMiB, pullRequest, queueFlowControlTimes);
            }
            return;
        }

        if (cachedMessageSizeInMiB > this.defaultMQPushConsumer.getPullThresholdSizeForQueue()) {
            this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_FLOW_CONTROL);
            if ((queueFlowControlTimes++ % 1000) == 0) {
                log.warn(
                    "the cached message size exceeds the threshold {} MiB, so do flow control, minOffset={}, maxOffset={}, count={}, size={} MiB, pullRequest={}, flowControlTimes={}",
                    this.defaultMQPushConsumer.getPullThresholdSizeForQueue(), processQueue.getMsgTreeMap().firstKey(), processQueue.getMsgTreeMap().lastKey(), cachedMessageCount, cachedMessageSizeInMiB, pullRequest, queueFlowControlTimes);
            }
            return;
        }

        // 若不是顺序消费（即DefaultMQPushConsumerImpl.consumeOrderly等于false），则检查ProcessQueue对象的msgTreeMap:TreeMap<Long,MessageExt>变量的第一个key值与最后一个key值之间的差额，该key值表示查询的队列偏移量queueoffset；若差额大于阈值（由DefaultMQPushConsumer. consumeConcurrentlyMaxSpan指定，默认是2000），则调用PullMessageService.executePullRequestLater方法，在50毫秒之后重新将该PullRequest请求放入PullMessageService.pullRequestQueue队列中；并跳出该方法；这里的意思主要就是消息有堆积了，等会再来拉取
        if (!this.consumeOrderly) {
            if (processQueue.getMaxSpan() > this.defaultMQPushConsumer.getConsumeConcurrentlyMaxSpan()) {
                this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_FLOW_CONTROL);
                if ((queueMaxSpanFlowControlTimes++ % 1000) == 0) {
                    log.warn(
                        "the queue's messages, span too long, so do flow control, minOffset={}, maxOffset={}, maxSpan={}, pullRequest={}, flowControlTimes={}",
                        processQueue.getMsgTreeMap().firstKey(), processQueue.getMsgTreeMap().lastKey(), processQueue.getMaxSpan(),
                        pullRequest, queueMaxSpanFlowControlTimes);
                }
                return;
            }
        } else {
            if (processQueue.isLocked()) {
                if (!pullRequest.isLockedFirst()) {
                    final long offset = this.rebalanceImpl.computePullFromWhere(pullRequest.getMessageQueue());
                    boolean brokerBusy = offset < pullRequest.getNextOffset();
                    log.info("the first time to pull message, so fix offset from broker. pullRequest: {} NewOffset: {} brokerBusy: {}",
                        pullRequest, offset, brokerBusy);
                    if (brokerBusy) {
                        log.info("[NOTIFYME]the first time to pull message, but pull request offset larger than broker consume offset. pullRequest: {} NewOffset: {}",
                            pullRequest, offset);
                    }

                    pullRequest.setLockedFirst(true);
                    pullRequest.setNextOffset(offset);
                }
            } else {
                this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
                log.info("pull message later because not locked in broker, {}", pullRequest);
                return;
            }
        }

        // 以PullRequest.messageQueue对象的topic值为参数从RebalanceImpl.subscriptionInner: ConcurrentHashMap, SubscriptionData>中获取对应的SubscriptionData对象，若该对象为null，考虑到并发的关系，调用executePullRequestLater方法，稍后重试；并跳出该方法；
        final SubscriptionData subscriptionData = this.rebalanceImpl.getSubscriptionInner().get(pullRequest.getMessageQueue().getTopic());
        if (null == subscriptionData) {
            this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
            log.warn("find the consumer's subscription failed, {}", pullRequest);
            return;
        }

        final long beginTimestamp = System.currentTimeMillis();

        // 异步拉取回调，先不讨论细节
        PullCallback pullCallback = new PullCallback() {
            @Override
            public void onSuccess(PullResult pullResult) {
                if (pullResult != null) {
                    pullResult = DefaultMQPushConsumerImpl.this.pullAPIWrapper.processPullResult(pullRequest.getMessageQueue(), pullResult,
                        subscriptionData);

                    switch (pullResult.getPullStatus()) {
                        case FOUND:
                            long prevRequestOffset = pullRequest.getNextOffset();
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());
                            long pullRT = System.currentTimeMillis() - beginTimestamp;
                            DefaultMQPushConsumerImpl.this.getConsumerStatsManager().incPullRT(pullRequest.getConsumerGroup(),
                                pullRequest.getMessageQueue().getTopic(), pullRT);

                            long firstMsgOffset = Long.MAX_VALUE;
                            if (pullResult.getMsgFoundList() == null || pullResult.getMsgFoundList().isEmpty()) {
                                DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            } else {
                                firstMsgOffset = pullResult.getMsgFoundList().get(0).getQueueOffset();

                                DefaultMQPushConsumerImpl.this.getConsumerStatsManager().incPullTPS(pullRequest.getConsumerGroup(),
                                    pullRequest.getMessageQueue().getTopic(), pullResult.getMsgFoundList().size());

                                boolean dispatchToConsume = processQueue.putMessage(pullResult.getMsgFoundList());
                                DefaultMQPushConsumerImpl.this.consumeMessageService.submitConsumeRequest(
                                    pullResult.getMsgFoundList(),
                                    processQueue,
                                    pullRequest.getMessageQueue(),
                                    dispatchToConsume);

                                if (DefaultMQPushConsumerImpl.this.defaultMQPushConsumer.getPullInterval() > 0) {
                                    DefaultMQPushConsumerImpl.this.executePullRequestLater(pullRequest,
                                        DefaultMQPushConsumerImpl.this.defaultMQPushConsumer.getPullInterval());
                                } else {
                                    DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                                }
                            }

                            if (pullResult.getNextBeginOffset() < prevRequestOffset
                                || firstMsgOffset < prevRequestOffset) {
                                log.warn(
                                    "[BUG] pull message result maybe data wrong, nextBeginOffset: {} firstMsgOffset: {} prevRequestOffset: {}",
                                    pullResult.getNextBeginOffset(),
                                    firstMsgOffset,
                                    prevRequestOffset);
                            }

                            break;
                        case NO_NEW_MSG:
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());

                            DefaultMQPushConsumerImpl.this.correctTagsOffset(pullRequest);

                            DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            break;
                        case NO_MATCHED_MSG:
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());

                            DefaultMQPushConsumerImpl.this.correctTagsOffset(pullRequest);

                            DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            break;
                        case OFFSET_ILLEGAL:
                            log.warn("the pull request offset illegal, {} {}",
                                pullRequest.toString(), pullResult.toString());
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());

                            pullRequest.getProcessQueue().setDropped(true);
                            DefaultMQPushConsumerImpl.this.executeTaskLater(new Runnable() {

                                @Override
                                public void run() {
                                    try {
                                        DefaultMQPushConsumerImpl.this.offsetStore.updateOffset(pullRequest.getMessageQueue(),
                                            pullRequest.getNextOffset(), false);

                                        DefaultMQPushConsumerImpl.this.offsetStore.persist(pullRequest.getMessageQueue());

                                        DefaultMQPushConsumerImpl.this.rebalanceImpl.removeProcessQueue(pullRequest.getMessageQueue());

                                        log.warn("fix the pull request offset, {}", pullRequest);
                                    } catch (Throwable e) {
                                        log.error("executeTaskLater Exception", e);
                                    }
                                }
                            }, 10000);
                            break;
                        default:
                            break;
                    }
                }
            }

            @Override
            public void onException(Throwable e) {
                if (!pullRequest.getMessageQueue().getTopic().startsWith(MixAll.RETRY_GROUP_TOPIC_PREFIX)) {
                    log.warn("execute the pull request exception", e);
                }

                DefaultMQPushConsumerImpl.this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
            }
        };
        // 如果为集群模式，即可置commitOffsetEnable为 true
        boolean commitOffsetEnable = false;
        long commitOffsetValue = 0L;
        if (MessageModel.CLUSTERING == this.defaultMQPushConsumer.getMessageModel()) {
            commitOffsetValue = this.offsetStore.readOffset(pullRequest.getMessageQueue(), ReadOffsetType.READ_FROM_MEMORY);
            if (commitOffsetValue > 0) {
                commitOffsetEnable = true;
            }
        }

        // 将上面获得的commitOffsetEnable更新到订阅关系里
        String subExpression = null;
        boolean classFilter = false;
        SubscriptionData sd = this.rebalanceImpl.getSubscriptionInner().get(pullRequest.getMessageQueue().getTopic());
        if (sd != null) {
            if (this.defaultMQPushConsumer.isPostSubscriptionWhenPull() && !sd.isClassFilterMode()) {
                subExpression = sd.getSubString();
            }

            classFilter = sd.isClassFilterMode();
        }

        // 组成 sysFlag
        int sysFlag = PullSysFlag.buildSysFlag(
            commitOffsetEnable, // commitOffset
            true, // suspend
            subExpression != null, // subscription
            classFilter // class filter
        );
        // 调用真正的拉取消息接口
        try {
            this.pullAPIWrapper.pullKernelImpl(
                pullRequest.getMessageQueue(),
                subExpression,
                subscriptionData.getExpressionType(),
                subscriptionData.getSubVersion(),
                pullRequest.getNextOffset(),
                this.defaultMQPushConsumer.getPullBatchSize(),
                sysFlag,
                commitOffsetValue,
                BROKER_SUSPEND_MAX_TIME_MILLIS,
                CONSUMER_TIMEOUT_MILLIS_WHEN_SUSPEND,
                CommunicationMode.ASYNC,
                pullCallback
            );
        } catch (Exception e) {
            log.error("pullKernelImpl exception", e);
            this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
        }
    }

以下就是拉取消息的底层 api，不够不是特别复杂，主要是在找 broker，和设置请求参数

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public PullResult pullKernelImpl(
    final MessageQueue mq,
    final String subExpression,
    final String expressionType,
    final long subVersion,
    final long offset,
    final int maxNums,
    final int sysFlag,
    final long commitOffset,
    final long brokerSuspendMaxTimeMillis,
    final long timeoutMillis,
    final CommunicationMode communicationMode,
    final PullCallback pullCallback
) throws MQClientException, RemotingException, MQBrokerException, InterruptedException {
    FindBrokerResult findBrokerResult =
        this.mQClientFactory.findBrokerAddressInSubscribe(mq.getBrokerName(),
            this.recalculatePullFromWhichNode(mq), false);
    if (null == findBrokerResult) {
        this.mQClientFactory.updateTopicRouteInfoFromNameServer(mq.getTopic());
        findBrokerResult =
            this.mQClientFactory.findBrokerAddressInSubscribe(mq.getBrokerName(),
                this.recalculatePullFromWhichNode(mq), false);
    }

    if (findBrokerResult != null) {
        {
            // check version
            if (!ExpressionType.isTagType(expressionType)
                && findBrokerResult.getBrokerVersion() < MQVersion.Version.V4_1_0_SNAPSHOT.ordinal()) {
                throw new MQClientException("The broker[" + mq.getBrokerName() + ", "
                    + findBrokerResult.getBrokerVersion() + "] does not upgrade to support for filter message by " + expressionType, null);
            }
        }
        int sysFlagInner = sysFlag;

        if (findBrokerResult.isSlave()) {
            sysFlagInner = PullSysFlag.clearCommitOffsetFlag(sysFlagInner);
        }

        PullMessageRequestHeader requestHeader = new PullMessageRequestHeader();
        requestHeader.setConsumerGroup(this.consumerGroup);
        requestHeader.setTopic(mq.getTopic());
        requestHeader.setQueueId(mq.getQueueId());
        requestHeader.setQueueOffset(offset);
        requestHeader.setMaxMsgNums(maxNums);
        requestHeader.setSysFlag(sysFlagInner);
        requestHeader.setCommitOffset(commitOffset);
        requestHeader.setSuspendTimeoutMillis(brokerSuspendMaxTimeMillis);
        requestHeader.setSubscription(subExpression);
        requestHeader.setSubVersion(subVersion);
        requestHeader.setExpressionType(expressionType);

        String brokerAddr = findBrokerResult.getBrokerAddr();
        if (PullSysFlag.hasClassFilterFlag(sysFlagInner)) {
            brokerAddr = computPullFromWhichFilterServer(mq.getTopic(), brokerAddr);
        }

        PullResult pullResult = this.mQClientFactory.getMQClientAPIImpl().pullMessage(
            brokerAddr,
            requestHeader,
            timeoutMillis,
            communicationMode,
            pullCallback);

        return pullResult;
    }

    throw new MQClientException("The broker[" + mq.getBrokerName() + "] not exist", null);
}

再看下一步的

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public PullResult pullMessage(
    final String addr,
    final PullMessageRequestHeader requestHeader,
    final long timeoutMillis,
    final CommunicationMode communicationMode,
    final PullCallback pullCallback
) throws RemotingException, MQBrokerException, InterruptedException {
    RemotingCommand request = RemotingCommand.createRequestCommand(RequestCode.PULL_MESSAGE, requestHeader);

    switch (communicationMode) {
        case ONEWAY:
            assert false;
            return null;
        case ASYNC:
            this.pullMessageAsync(addr, request, timeoutMillis, pullCallback);
            return null;
        case SYNC:
            return this.pullMessageSync(addr, request, timeoutMillis);
        default:
            assert false;
            break;
    }

    return null;
}

通过 communicationMode 判断是同步拉取还是异步拉取，异步就调用

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private void pullMessageAsync(
        final String addr,
        final RemotingCommand request,
        final long timeoutMillis,
        final PullCallback pullCallback
    ) throws RemotingException, InterruptedException {
        this.remotingClient.invokeAsync(addr, request, timeoutMillis, new InvokeCallback() {
            @Override
            public void operationComplete(ResponseFuture responseFuture) {
                异步
                RemotingCommand response = responseFuture.getResponseCommand();
                if (response != null) {
                    try {
                        PullResult pullResult = MQClientAPIImpl.this.processPullResponse(response);
                        assert pullResult != null;
                        pullCallback.onSuccess(pullResult);
                    } catch (Exception e) {
                        pullCallback.onException(e);
                    }
                } else {
                    if (!responseFuture.isSendRequestOK()) {
                        pullCallback.onException(new MQClientException("send request failed to " + addr + ". Request: " + request, responseFuture.getCause()));
                    } else if (responseFuture.isTimeout()) {
                        pullCallback.onException(new MQClientException("wait response from " + addr + " timeout :" + responseFuture.getTimeoutMillis() + "ms" + ". Request: " + request,
                            responseFuture.getCause()));
                    } else {
                        pullCallback.onException(new MQClientException("unknown reason. addr: " + addr + ", timeoutMillis: " + timeoutMillis + ". Request: " + request, responseFuture.getCause()));
                    }
                }
            }
        });
    }

并且会调用前面 pullCallback 的onSuccess和onException方法，同步的就是调用

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private PullResult pullMessageSync(
        final String addr,
        final RemotingCommand request,
        final long timeoutMillis
    ) throws RemotingException, InterruptedException, MQBrokerException {
        RemotingCommand response = this.remotingClient.invokeSync(addr, request, timeoutMillis);
        assert response != null;
        return this.processPullResponse(response);
    }

然后就是这个 remotingClient 的 invokeAsync 跟 invokeSync 方法

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@Override
    public void invokeAsync(String addr, RemotingCommand request, long timeoutMillis, InvokeCallback invokeCallback)
        throws InterruptedException, RemotingConnectException, RemotingTooMuchRequestException, RemotingTimeoutException,
        RemotingSendRequestException {
        long beginStartTime = System.currentTimeMillis();
        final Channel channel = this.getAndCreateChannel(addr);
        if (channel != null && channel.isActive()) {
            try {
                doBeforeRpcHooks(addr, request);
                long costTime = System.currentTimeMillis() - beginStartTime;
                if (timeoutMillis < costTime) {
                    throw new RemotingTooMuchRequestException("invokeAsync call timeout");
                }
                this.invokeAsyncImpl(channel, request, timeoutMillis - costTime, invokeCallback);
            } catch (RemotingSendRequestException e) {
                log.warn("invokeAsync: send request exception, so close the channel[{}]", addr);
                this.closeChannel(addr, channel);
                throw e;
            }
        } else {
            this.closeChannel(addr, channel);
            throw new RemotingConnectException(addr);
        }
    }
@Override
    public RemotingCommand invokeSync(String addr, final RemotingCommand request, long timeoutMillis)
        throws InterruptedException, RemotingConnectException, RemotingSendRequestException, RemotingTimeoutException {
        long beginStartTime = System.currentTimeMillis();
        final Channel channel = this.getAndCreateChannel(addr);
        if (channel != null && channel.isActive()) {
            try {
                doBeforeRpcHooks(addr, request);
                long costTime = System.currentTimeMillis() - beginStartTime;
                if (timeoutMillis < costTime) {
                    throw new RemotingTimeoutException("invokeSync call timeout");
                }
                RemotingCommand response = this.invokeSyncImpl(channel, request, timeoutMillis - costTime);
                doAfterRpcHooks(RemotingHelper.parseChannelRemoteAddr(channel), request, response);
                return response;
            } catch (RemotingSendRequestException e) {
                log.warn("invokeSync: send request exception, so close the channel[{}]", addr);
                this.closeChannel(addr, channel);
                throw e;
            } catch (RemotingTimeoutException e) {
                if (nettyClientConfig.isClientCloseSocketIfTimeout()) {
                    this.closeChannel(addr, channel);
                    log.warn("invokeSync: close socket because of timeout, {}ms, {}", timeoutMillis, addr);
                }
                log.warn("invokeSync: wait response timeout exception, the channel[{}]", addr);
                throw e;
            }
        } else {
            this.closeChannel(addr, channel);
            throw new RemotingConnectException(addr);
        }
    }

再往下看

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public RemotingCommand invokeSyncImpl(final Channel channel, final RemotingCommand request,
        final long timeoutMillis)
        throws InterruptedException, RemotingSendRequestException, RemotingTimeoutException {
        final int opaque = request.getOpaque();

        try {
            同步跟异步都是会把结果用ResponseFuture抱起来
            final ResponseFuture responseFuture = new ResponseFuture(channel, opaque, timeoutMillis, null, null);
            this.responseTable.put(opaque, responseFuture);
            final SocketAddress addr = channel.remoteAddress();
            channel.writeAndFlush(request).addListener(new ChannelFutureListener() {
                @Override
                public void operationComplete(ChannelFuture f) throws Exception {
                    if (f.isSuccess()) {
                        responseFuture.setSendRequestOK(true);
                        return;
                    } else {
                        responseFuture.setSendRequestOK(false);
                    }

                    responseTable.remove(opaque);
                    responseFuture.setCause(f.cause());
                    responseFuture.putResponse(null);
                    log.warn("send a request command to channel <" + addr + "> failed.");
                }
            });
            // 区别是同步的是在这等待
            RemotingCommand responseCommand = responseFuture.waitResponse(timeoutMillis);
            if (null == responseCommand) {
                if (responseFuture.isSendRequestOK()) {
                    throw new RemotingTimeoutException(RemotingHelper.parseSocketAddressAddr(addr), timeoutMillis,
                        responseFuture.getCause());
                } else {
                    throw new RemotingSendRequestException(RemotingHelper.parseSocketAddressAddr(addr), responseFuture.getCause());
                }
            }

            return responseCommand;
        } finally {
            this.responseTable.remove(opaque);
        }
    }

    public void invokeAsyncImpl(final Channel channel, final RemotingCommand request, final long timeoutMillis,
        final InvokeCallback invokeCallback)
        throws InterruptedException, RemotingTooMuchRequestException, RemotingTimeoutException, RemotingSendRequestException {
        long beginStartTime = System.currentTimeMillis();
        final int opaque = request.getOpaque();
        boolean acquired = this.semaphoreAsync.tryAcquire(timeoutMillis, TimeUnit.MILLISECONDS);
        if (acquired) {
            final SemaphoreReleaseOnlyOnce once = new SemaphoreReleaseOnlyOnce(this.semaphoreAsync);
            long costTime = System.currentTimeMillis() - beginStartTime;
            if (timeoutMillis < costTime) {
                once.release();
                throw new RemotingTimeoutException("invokeAsyncImpl call timeout");
            }

            final ResponseFuture responseFuture = new ResponseFuture(channel, opaque, timeoutMillis - costTime, invokeCallback, once);
            this.responseTable.put(opaque, responseFuture);
            try {
                channel.writeAndFlush(request).addListener(new ChannelFutureListener() {
                    @Override
                    public void operationComplete(ChannelFuture f) throws Exception {
                        if (f.isSuccess()) {
                            responseFuture.setSendRequestOK(true);
                            return;
                        }
                        requestFail(opaque);
                        log.warn("send a request command to channel <{}> failed.", RemotingHelper.parseChannelRemoteAddr(channel));
                    }
                });
            } catch (Exception e) {
                responseFuture.release();
                log.warn("send a request command to channel <" + RemotingHelper.parseChannelRemoteAddr(channel) + "> Exception", e);
                throw new RemotingSendRequestException(RemotingHelper.parseChannelRemoteAddr(channel), e);
            }
        } else {
            if (timeoutMillis <= 0) {
                throw new RemotingTooMuchRequestException("invokeAsyncImpl invoke too fast");
            } else {
                String info =
                    String.format("invokeAsyncImpl tryAcquire semaphore timeout, %dms, waiting thread nums: %d semaphoreAsyncValue: %d",
                        timeoutMillis,
                        this.semaphoreAsync.getQueueLength(),
                        this.semaphoreAsync.availablePermits()
                    );
                log.warn(info);
                throw new RemotingTimeoutException(info);
            }
        }
    }
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What hurts more, the pain of hard work or the pain of regret?

聊一下 RocketMQ 的 DefaultMQPushConsumer 源码

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： 阅读次数： Disqus：

首先看下官方的小 demo

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public static void main(String[] args) throws InterruptedException, MQClientException {

        /*
         * Instantiate with specified consumer group name.
         * 首先是new 一个对象出来，然后指定 Consumer 的 Group
         * 同一类Consumer的集合，这类Consumer通常消费同一类消息且消费逻辑一致。消费者组使得在消息消费方面，实现负载均衡和容错的目标变得非常容易。要注意的是，消费者组的消费者实例必须订阅完全相同的Topic。RocketMQ 支持两种消息模式：集群消费（Clustering）和广播消费（Broadcasting）。
         */
        DefaultMQPushConsumer consumer = new DefaultMQPushConsumer("please_rename_unique_group_name_4");

        /*
         * Specify name server addresses.
         * <p/>
         * 这里可以通知指定环境变量或者设置对象参数的形式指定名字空间服务的地址
         *
         * Alternatively, you may specify name server addresses via exporting environmental variable: NAMESRV_ADDR
         * <pre>
         * {@code
         * consumer.setNamesrvAddr("name-server1-ip:9876;name-server2-ip:9876");
         * }
         * </pre>
         */

        /*
         * Specify where to start in case the specified consumer group is a brand new one.
         * 指定消费起始点
         */
        consumer.setConsumeFromWhere(ConsumeFromWhere.CONSUME_FROM_FIRST_OFFSET);

        /*
         * Subscribe one more more topics to consume.
         * 指定订阅的 topic 跟 tag，注意后面的是个表达式，可以以 tag1 || tag2 || tag3 传入
         */
        consumer.subscribe("TopicTest", "*");

        /*
         *  Register callback to execute on arrival of messages fetched from brokers.
         *  注册具体获得消息后的处理方法
         */
        consumer.registerMessageListener(new MessageListenerConcurrently() {

            @Override
            public ConsumeConcurrentlyStatus consumeMessage(List<MessageExt> msgs,
                ConsumeConcurrentlyContext context) {
                System.out.printf("%s Receive New Messages: %s %n", Thread.currentThread().getName(), msgs);
                return ConsumeConcurrentlyStatus.CONSUME_SUCCESS;
            }
        });

        /*
         *  Launch the consumer instance.
         * 启动消费者
         */
        consumer.start();

        System.out.printf("Consumer Started.%n");
    }

然后就是看看 start 的过程了

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/**
     * This method gets internal infrastructure readily to serve. Instances must call this method after configuration.
     *
     * @throws MQClientException if there is any client error.
     */
    @Override
    public void start() throws MQClientException {
        setConsumerGroup(NamespaceUtil.wrapNamespace(this.getNamespace(), this.consumerGroup));
        this.defaultMQPushConsumerImpl.start();
        if (null != traceDispatcher) {
            try {
                traceDispatcher.start(this.getNamesrvAddr(), this.getAccessChannel());
            } catch (MQClientException e) {
                log.warn("trace dispatcher start failed ", e);
            }
        }
    }

具体的逻辑在this.defaultMQPushConsumerImpl.start()，这个 defaultMQPushConsumerImpl 就是

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/**
     * Internal implementation. Most of the functions herein are delegated to it.
     */
    protected final transient DefaultMQPushConsumerImpl defaultMQPushConsumerImpl;
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public synchronized void start() throws MQClientException {
        switch (this.serviceState) {
            case CREATE_JUST:
                log.info("the consumer [{}] start beginning. messageModel={}, isUnitMode={}", this.defaultMQPushConsumer.getConsumerGroup(),
                    this.defaultMQPushConsumer.getMessageModel(), this.defaultMQPushConsumer.isUnitMode());
                // 这里比较巧妙，相当于想设立了个屏障，防止并发启动，不过这里并不是悲观锁，也不算个严格的乐观锁
                this.serviceState = ServiceState.START_FAILED;

                this.checkConfig();

                this.copySubscription();

                if (this.defaultMQPushConsumer.getMessageModel() == MessageModel.CLUSTERING) {
                    this.defaultMQPushConsumer.changeInstanceNameToPID();
                }

                // 这个mQClientFactory，负责管理client（consumer、producer），并提供多中功能接口供各个Service（Rebalance、PullMessage等）调用；大部分逻辑均在这个类中完成
                this.mQClientFactory = MQClientManager.getInstance().getOrCreateMQClientInstance(this.defaultMQPushConsumer, this.rpcHook);

                // 这个 rebalanceImpl 主要负责决定，当前的consumer应该从哪些Queue中消费消息；
                this.rebalanceImpl.setConsumerGroup(this.defaultMQPushConsumer.getConsumerGroup());
                this.rebalanceImpl.setMessageModel(this.defaultMQPushConsumer.getMessageModel());
                this.rebalanceImpl.setAllocateMessageQueueStrategy(this.defaultMQPushConsumer.getAllocateMessageQueueStrategy());
                this.rebalanceImpl.setmQClientFactory(this.mQClientFactory);

                // 长连接，负责从broker处拉取消息，然后利用ConsumeMessageService回调用户的Listener执行消息消费逻辑
                this.pullAPIWrapper = new PullAPIWrapper(
                    mQClientFactory,
                    this.defaultMQPushConsumer.getConsumerGroup(), isUnitMode());
                this.pullAPIWrapper.registerFilterMessageHook(filterMessageHookList);

                if (this.defaultMQPushConsumer.getOffsetStore() != null) {
                    this.offsetStore = this.defaultMQPushConsumer.getOffsetStore();
                } else {
                    switch (this.defaultMQPushConsumer.getMessageModel()) {
                        case BROADCASTING:
                            this.offsetStore = new LocalFileOffsetStore(this.mQClientFactory, this.defaultMQPushConsumer.getConsumerGroup());
                            break;
                        case CLUSTERING:
                            this.offsetStore = new RemoteBrokerOffsetStore(this.mQClientFactory, this.defaultMQPushConsumer.getConsumerGroup());
                            break;
                        default:
                            break;
                    }
                    this.defaultMQPushConsumer.setOffsetStore(this.offsetStore);
                }
                // offsetStore 维护当前consumer的消费记录（offset）；有两种实现，Local和Rmote，Local存储在本地磁盘上，适用于BROADCASTING广播消费模式；而Remote则将消费进度存储在Broker上，适用于CLUSTERING集群消费模式；
                this.offsetStore.load();

                if (this.getMessageListenerInner() instanceof MessageListenerOrderly) {
                    this.consumeOrderly = true;
                    this.consumeMessageService =
                        new ConsumeMessageOrderlyService(this, (MessageListenerOrderly) this.getMessageListenerInner());
                } else if (this.getMessageListenerInner() instanceof MessageListenerConcurrently) {
                    this.consumeOrderly = false;
                    this.consumeMessageService =
                        new ConsumeMessageConcurrentlyService(this, (MessageListenerConcurrently) this.getMessageListenerInner());
                }

                // 实现所谓的"Push-被动"消费机制；从Broker拉取的消息后，封装成ConsumeRequest提交给ConsumeMessageSerivce，此service负责回调用户的Listener消费消息；
                this.consumeMessageService.start();

                boolean registerOK = mQClientFactory.registerConsumer(this.defaultMQPushConsumer.getConsumerGroup(), this);
                if (!registerOK) {
                    this.serviceState = ServiceState.CREATE_JUST;
                    this.consumeMessageService.shutdown();
                    throw new MQClientException("The consumer group[" + this.defaultMQPushConsumer.getConsumerGroup()
                        + "] has been created before, specify another name please." + FAQUrl.suggestTodo(FAQUrl.GROUP_NAME_DUPLICATE_URL),
                        null);
                }

                mQClientFactory.start();
                log.info("the consumer [{}] start OK.", this.defaultMQPushConsumer.getConsumerGroup());
                this.serviceState = ServiceState.RUNNING;
                break;
            case RUNNING:
            case START_FAILED:
            case SHUTDOWN_ALREADY:
                throw new MQClientException("The PushConsumer service state not OK, maybe started once, "
                    + this.serviceState
                    + FAQUrl.suggestTodo(FAQUrl.CLIENT_SERVICE_NOT_OK),
                    null);
            default:
                break;
        }

        this.updateTopicSubscribeInfoWhenSubscriptionChanged();
        this.mQClientFactory.checkClientInBroker();
        this.mQClientFactory.sendHeartbeatToAllBrokerWithLock();
        this.mQClientFactory.rebalanceImmediately();
    }

然后我们往下看主要的目光聚焦mQClientFactory.start()

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public void start() throws MQClientException {

        synchronized (this) {
            switch (this.serviceState) {
                case CREATE_JUST:
                    this.serviceState = ServiceState.START_FAILED;
                    // If not specified,looking address from name server
                    if (null == this.clientConfig.getNamesrvAddr()) {
                        this.mQClientAPIImpl.fetchNameServerAddr();
                    }
                    // Start request-response channel
                    // 这里主要是初始化了个网络客户端
                    this.mQClientAPIImpl.start();
                    // Start various schedule tasks
                    // 定时任务
                    this.startScheduledTask();
                    // Start pull service
                    // 这里重点说下
                    this.pullMessageService.start();
                    // Start rebalance service
                    this.rebalanceService.start();
                    // Start push service
                    this.defaultMQProducer.getDefaultMQProducerImpl().start(false);
                    log.info("the client factory [{}] start OK", this.clientId);
                    this.serviceState = ServiceState.RUNNING;
                    break;
                case START_FAILED:
                    throw new MQClientException("The Factory object[" + this.getClientId() + "] has been created before, and failed.", null);
                default:
                    break;
            }
        }
    }

我们来看下这个 pullMessageService，org.apache.rocketmq.client.impl.consumer.PullMessageService，

实现了 runnable 接口，
然后可以看到 run 方法

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public void run() {
        log.info(this.getServiceName() + " service started");

        while (!this.isStopped()) {
            try {
                PullRequest pullRequest = this.pullRequestQueue.take();
                this.pullMessage(pullRequest);
            } catch (InterruptedException ignored) {
            } catch (Exception e) {
                log.error("Pull Message Service Run Method exception", e);
            }
        }

        log.info(this.getServiceName() + " service end");
    }

接着在看 pullMessage 方法

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private void pullMessage(final PullRequest pullRequest) {
        final MQConsumerInner consumer = this.mQClientFactory.selectConsumer(pullRequest.getConsumerGroup());
        if (consumer != null) {
            DefaultMQPushConsumerImpl impl = (DefaultMQPushConsumerImpl) consumer;
            impl.pullMessage(pullRequest);
        } else {
            log.warn("No matched consumer for the PullRequest {}, drop it", pullRequest);
        }
    }

实际上调用了这个方法，这个方法很长，我在代码里注释下下每一段的功能

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public void pullMessage(final PullRequest pullRequest) {
        final ProcessQueue processQueue = pullRequest.getProcessQueue();
        // 这里开始就是检查状态，确定是否往下执行
        if (processQueue.isDropped()) {
            log.info("the pull request[{}] is dropped.", pullRequest.toString());
            return;
        }

        pullRequest.getProcessQueue().setLastPullTimestamp(System.currentTimeMillis());

        try {
            this.makeSureStateOK();
        } catch (MQClientException e) {
            log.warn("pullMessage exception, consumer state not ok", e);
            this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
            return;
        }

        if (this.isPause()) {
            log.warn("consumer was paused, execute pull request later. instanceName={}, group={}", this.defaultMQPushConsumer.getInstanceName(), this.defaultMQPushConsumer.getConsumerGroup());
            this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_SUSPEND);
            return;
        }

        // 这块其实是个类似于限流的功能块，对消息数量和消息大小做限制
        long cachedMessageCount = processQueue.getMsgCount().get();
        long cachedMessageSizeInMiB = processQueue.getMsgSize().get() / (1024 * 1024);

        if (cachedMessageCount > this.defaultMQPushConsumer.getPullThresholdForQueue()) {
            this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_FLOW_CONTROL);
            if ((queueFlowControlTimes++ % 1000) == 0) {
                log.warn(
                    "the cached message count exceeds the threshold {}, so do flow control, minOffset={}, maxOffset={}, count={}, size={} MiB, pullRequest={}, flowControlTimes={}",
                    this.defaultMQPushConsumer.getPullThresholdForQueue(), processQueue.getMsgTreeMap().firstKey(), processQueue.getMsgTreeMap().lastKey(), cachedMessageCount, cachedMessageSizeInMiB, pullRequest, queueFlowControlTimes);
            }
            return;
        }

        if (cachedMessageSizeInMiB > this.defaultMQPushConsumer.getPullThresholdSizeForQueue()) {
            this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_FLOW_CONTROL);
            if ((queueFlowControlTimes++ % 1000) == 0) {
                log.warn(
                    "the cached message size exceeds the threshold {} MiB, so do flow control, minOffset={}, maxOffset={}, count={}, size={} MiB, pullRequest={}, flowControlTimes={}",
                    this.defaultMQPushConsumer.getPullThresholdSizeForQueue(), processQueue.getMsgTreeMap().firstKey(), processQueue.getMsgTreeMap().lastKey(), cachedMessageCount, cachedMessageSizeInMiB, pullRequest, queueFlowControlTimes);
            }
            return;
        }

        // 若不是顺序消费（即DefaultMQPushConsumerImpl.consumeOrderly等于false），则检查ProcessQueue对象的msgTreeMap:TreeMap<Long,MessageExt>变量的第一个key值与最后一个key值之间的差额，该key值表示查询的队列偏移量queueoffset；若差额大于阈值（由DefaultMQPushConsumer. consumeConcurrentlyMaxSpan指定，默认是2000），则调用PullMessageService.executePullRequestLater方法，在50毫秒之后重新将该PullRequest请求放入PullMessageService.pullRequestQueue队列中；并跳出该方法；这里的意思主要就是消息有堆积了，等会再来拉取
        if (!this.consumeOrderly) {
            if (processQueue.getMaxSpan() > this.defaultMQPushConsumer.getConsumeConcurrentlyMaxSpan()) {
                this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_FLOW_CONTROL);
                if ((queueMaxSpanFlowControlTimes++ % 1000) == 0) {
                    log.warn(
                        "the queue's messages, span too long, so do flow control, minOffset={}, maxOffset={}, maxSpan={}, pullRequest={}, flowControlTimes={}",
                        processQueue.getMsgTreeMap().firstKey(), processQueue.getMsgTreeMap().lastKey(), processQueue.getMaxSpan(),
                        pullRequest, queueMaxSpanFlowControlTimes);
                }
                return;
            }
        } else {
            if (processQueue.isLocked()) {
                if (!pullRequest.isLockedFirst()) {
                    final long offset = this.rebalanceImpl.computePullFromWhere(pullRequest.getMessageQueue());
                    boolean brokerBusy = offset < pullRequest.getNextOffset();
                    log.info("the first time to pull message, so fix offset from broker. pullRequest: {} NewOffset: {} brokerBusy: {}",
                        pullRequest, offset, brokerBusy);
                    if (brokerBusy) {
                        log.info("[NOTIFYME]the first time to pull message, but pull request offset larger than broker consume offset. pullRequest: {} NewOffset: {}",
                            pullRequest, offset);
                    }

                    pullRequest.setLockedFirst(true);
                    pullRequest.setNextOffset(offset);
                }
            } else {
                this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
                log.info("pull message later because not locked in broker, {}", pullRequest);
                return;
            }
        }

        // 以PullRequest.messageQueue对象的topic值为参数从RebalanceImpl.subscriptionInner: ConcurrentHashMap, SubscriptionData>中获取对应的SubscriptionData对象，若该对象为null，考虑到并发的关系，调用executePullRequestLater方法，稍后重试；并跳出该方法；
        final SubscriptionData subscriptionData = this.rebalanceImpl.getSubscriptionInner().get(pullRequest.getMessageQueue().getTopic());
        if (null == subscriptionData) {
            this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
            log.warn("find the consumer's subscription failed, {}", pullRequest);
            return;
        }

        final long beginTimestamp = System.currentTimeMillis();

        // 异步拉取回调，先不讨论细节
        PullCallback pullCallback = new PullCallback() {
            @Override
            public void onSuccess(PullResult pullResult) {
                if (pullResult != null) {
                    pullResult = DefaultMQPushConsumerImpl.this.pullAPIWrapper.processPullResult(pullRequest.getMessageQueue(), pullResult,
                        subscriptionData);

                    switch (pullResult.getPullStatus()) {
                        case FOUND:
                            long prevRequestOffset = pullRequest.getNextOffset();
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());
                            long pullRT = System.currentTimeMillis() - beginTimestamp;
                            DefaultMQPushConsumerImpl.this.getConsumerStatsManager().incPullRT(pullRequest.getConsumerGroup(),
                                pullRequest.getMessageQueue().getTopic(), pullRT);

                            long firstMsgOffset = Long.MAX_VALUE;
                            if (pullResult.getMsgFoundList() == null || pullResult.getMsgFoundList().isEmpty()) {
                                DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            } else {
                                firstMsgOffset = pullResult.getMsgFoundList().get(0).getQueueOffset();

                                DefaultMQPushConsumerImpl.this.getConsumerStatsManager().incPullTPS(pullRequest.getConsumerGroup(),
                                    pullRequest.getMessageQueue().getTopic(), pullResult.getMsgFoundList().size());

                                boolean dispatchToConsume = processQueue.putMessage(pullResult.getMsgFoundList());
                                DefaultMQPushConsumerImpl.this.consumeMessageService.submitConsumeRequest(
                                    pullResult.getMsgFoundList(),
                                    processQueue,
                                    pullRequest.getMessageQueue(),
                                    dispatchToConsume);

                                if (DefaultMQPushConsumerImpl.this.defaultMQPushConsumer.getPullInterval() > 0) {
                                    DefaultMQPushConsumerImpl.this.executePullRequestLater(pullRequest,
                                        DefaultMQPushConsumerImpl.this.defaultMQPushConsumer.getPullInterval());
                                } else {
                                    DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                                }
                            }

                            if (pullResult.getNextBeginOffset() < prevRequestOffset
                                || firstMsgOffset < prevRequestOffset) {
                                log.warn(
                                    "[BUG] pull message result maybe data wrong, nextBeginOffset: {} firstMsgOffset: {} prevRequestOffset: {}",
                                    pullResult.getNextBeginOffset(),
                                    firstMsgOffset,
                                    prevRequestOffset);
                            }

                            break;
                        case NO_NEW_MSG:
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());

                            DefaultMQPushConsumerImpl.this.correctTagsOffset(pullRequest);

                            DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            break;
                        case NO_MATCHED_MSG:
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());

                            DefaultMQPushConsumerImpl.this.correctTagsOffset(pullRequest);

                            DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            break;
                        case OFFSET_ILLEGAL:
                            log.warn("the pull request offset illegal, {} {}",
                                pullRequest.toString(), pullResult.toString());
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());

                            pullRequest.getProcessQueue().setDropped(true);
                            DefaultMQPushConsumerImpl.this.executeTaskLater(new Runnable() {

                                @Override
                                public void run() {
                                    try {
                                        DefaultMQPushConsumerImpl.this.offsetStore.updateOffset(pullRequest.getMessageQueue(),
                                            pullRequest.getNextOffset(), false);

                                        DefaultMQPushConsumerImpl.this.offsetStore.persist(pullRequest.getMessageQueue());

                                        DefaultMQPushConsumerImpl.this.rebalanceImpl.removeProcessQueue(pullRequest.getMessageQueue());

                                        log.warn("fix the pull request offset, {}", pullRequest);
                                    } catch (Throwable e) {
                                        log.error("executeTaskLater Exception", e);
                                    }
                                }
                            }, 10000);
                            break;
                        default:
                            break;
                    }
                }
            }

            @Override
            public void onException(Throwable e) {
                if (!pullRequest.getMessageQueue().getTopic().startsWith(MixAll.RETRY_GROUP_TOPIC_PREFIX)) {
                    log.warn("execute the pull request exception", e);
                }

                DefaultMQPushConsumerImpl.this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
            }
        };
        // 如果为集群模式，即可置commitOffsetEnable为 true
        boolean commitOffsetEnable = false;
        long commitOffsetValue = 0L;
        if (MessageModel.CLUSTERING == this.defaultMQPushConsumer.getMessageModel()) {
            commitOffsetValue = this.offsetStore.readOffset(pullRequest.getMessageQueue(), ReadOffsetType.READ_FROM_MEMORY);
            if (commitOffsetValue > 0) {
                commitOffsetEnable = true;
            }
        }

        // 将上面获得的commitOffsetEnable更新到订阅关系里
        String subExpression = null;
        boolean classFilter = false;
        SubscriptionData sd = this.rebalanceImpl.getSubscriptionInner().get(pullRequest.getMessageQueue().getTopic());
        if (sd != null) {
            if (this.defaultMQPushConsumer.isPostSubscriptionWhenPull() && !sd.isClassFilterMode()) {
                subExpression = sd.getSubString();
            }

            classFilter = sd.isClassFilterMode();
        }

        // 组成 sysFlag
        int sysFlag = PullSysFlag.buildSysFlag(
            commitOffsetEnable, // commitOffset
            true, // suspend
            subExpression != null, // subscription
            classFilter // class filter
        );
        // 调用真正的拉取消息接口
        try {
            this.pullAPIWrapper.pullKernelImpl(
                pullRequest.getMessageQueue(),
                subExpression,
                subscriptionData.getExpressionType(),
                subscriptionData.getSubVersion(),
                pullRequest.getNextOffset(),
                this.defaultMQPushConsumer.getPullBatchSize(),
                sysFlag,
                commitOffsetValue,
                BROKER_SUSPEND_MAX_TIME_MILLIS,
                CONSUMER_TIMEOUT_MILLIS_WHEN_SUSPEND,
                CommunicationMode.ASYNC,
                pullCallback
            );
        } catch (Exception e) {
            log.error("pullKernelImpl exception", e);
            this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
        }
    }

以下就是拉取消息的底层 api，不够不是特别复杂，主要是在找 broker，和设置请求参数

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public PullResult pullKernelImpl(
    final MessageQueue mq,
    final String subExpression,
    final String expressionType,
    final long subVersion,
    final long offset,
    final int maxNums,
    final int sysFlag,
    final long commitOffset,
    final long brokerSuspendMaxTimeMillis,
    final long timeoutMillis,
    final CommunicationMode communicationMode,
    final PullCallback pullCallback
) throws MQClientException, RemotingException, MQBrokerException, InterruptedException {
    FindBrokerResult findBrokerResult =
        this.mQClientFactory.findBrokerAddressInSubscribe(mq.getBrokerName(),
            this.recalculatePullFromWhichNode(mq), false);
    if (null == findBrokerResult) {
        this.mQClientFactory.updateTopicRouteInfoFromNameServer(mq.getTopic());
        findBrokerResult =
            this.mQClientFactory.findBrokerAddressInSubscribe(mq.getBrokerName(),
                this.recalculatePullFromWhichNode(mq), false);
    }

    if (findBrokerResult != null) {
        {
            // check version
            if (!ExpressionType.isTagType(expressionType)
                && findBrokerResult.getBrokerVersion() < MQVersion.Version.V4_1_0_SNAPSHOT.ordinal()) {
                throw new MQClientException("The broker[" + mq.getBrokerName() + ", "
                    + findBrokerResult.getBrokerVersion() + "] does not upgrade to support for filter message by " + expressionType, null);
            }
        }
        int sysFlagInner = sysFlag;

        if (findBrokerResult.isSlave()) {
            sysFlagInner = PullSysFlag.clearCommitOffsetFlag(sysFlagInner);
        }

        PullMessageRequestHeader requestHeader = new PullMessageRequestHeader();
        requestHeader.setConsumerGroup(this.consumerGroup);
        requestHeader.setTopic(mq.getTopic());
        requestHeader.setQueueId(mq.getQueueId());
        requestHeader.setQueueOffset(offset);
        requestHeader.setMaxMsgNums(maxNums);
        requestHeader.setSysFlag(sysFlagInner);
        requestHeader.setCommitOffset(commitOffset);
        requestHeader.setSuspendTimeoutMillis(brokerSuspendMaxTimeMillis);
        requestHeader.setSubscription(subExpression);
        requestHeader.setSubVersion(subVersion);
        requestHeader.setExpressionType(expressionType);

        String brokerAddr = findBrokerResult.getBrokerAddr();
        if (PullSysFlag.hasClassFilterFlag(sysFlagInner)) {
            brokerAddr = computPullFromWhichFilterServer(mq.getTopic(), brokerAddr);
        }

        PullResult pullResult = this.mQClientFactory.getMQClientAPIImpl().pullMessage(
            brokerAddr,
            requestHeader,
            timeoutMillis,
            communicationMode,
            pullCallback);

        return pullResult;
    }

    throw new MQClientException("The broker[" + mq.getBrokerName() + "] not exist", null);
}

再看下一步的

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public PullResult pullMessage(
    final String addr,
    final PullMessageRequestHeader requestHeader,
    final long timeoutMillis,
    final CommunicationMode communicationMode,
    final PullCallback pullCallback
) throws RemotingException, MQBrokerException, InterruptedException {
    RemotingCommand request = RemotingCommand.createRequestCommand(RequestCode.PULL_MESSAGE, requestHeader);

    switch (communicationMode) {
        case ONEWAY:
            assert false;
            return null;
        case ASYNC:
            this.pullMessageAsync(addr, request, timeoutMillis, pullCallback);
            return null;
        case SYNC:
            return this.pullMessageSync(addr, request, timeoutMillis);
        default:
            assert false;
            break;
    }

    return null;
}

通过 communicationMode 判断是同步拉取还是异步拉取，异步就调用

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private void pullMessageAsync(
        final String addr,
        final RemotingCommand request,
        final long timeoutMillis,
        final PullCallback pullCallback
    ) throws RemotingException, InterruptedException {
        this.remotingClient.invokeAsync(addr, request, timeoutMillis, new InvokeCallback() {
            @Override
            public void operationComplete(ResponseFuture responseFuture) {
                异步
                RemotingCommand response = responseFuture.getResponseCommand();
                if (response != null) {
                    try {
                        PullResult pullResult = MQClientAPIImpl.this.processPullResponse(response);
                        assert pullResult != null;
                        pullCallback.onSuccess(pullResult);
                    } catch (Exception e) {
                        pullCallback.onException(e);
                    }
                } else {
                    if (!responseFuture.isSendRequestOK()) {
                        pullCallback.onException(new MQClientException("send request failed to " + addr + ". Request: " + request, responseFuture.getCause()));
                    } else if (responseFuture.isTimeout()) {
                        pullCallback.onException(new MQClientException("wait response from " + addr + " timeout :" + responseFuture.getTimeoutMillis() + "ms" + ". Request: " + request,
                            responseFuture.getCause()));
                    } else {
                        pullCallback.onException(new MQClientException("unknown reason. addr: " + addr + ", timeoutMillis: " + timeoutMillis + ". Request: " + request, responseFuture.getCause()));
                    }
                }
            }
        });
    }

并且会调用前面 pullCallback 的onSuccess和onException方法，同步的就是调用

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private PullResult pullMessageSync(
        final String addr,
        final RemotingCommand request,
        final long timeoutMillis
    ) throws RemotingException, InterruptedException, MQBrokerException {
        RemotingCommand response = this.remotingClient.invokeSync(addr, request, timeoutMillis);
        assert response != null;
        return this.processPullResponse(response);
    }

然后就是这个 remotingClient 的 invokeAsync 跟 invokeSync 方法

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@Override
    public void invokeAsync(String addr, RemotingCommand request, long timeoutMillis, InvokeCallback invokeCallback)
        throws InterruptedException, RemotingConnectException, RemotingTooMuchRequestException, RemotingTimeoutException,
        RemotingSendRequestException {
        long beginStartTime = System.currentTimeMillis();
        final Channel channel = this.getAndCreateChannel(addr);
        if (channel != null && channel.isActive()) {
            try {
                doBeforeRpcHooks(addr, request);
                long costTime = System.currentTimeMillis() - beginStartTime;
                if (timeoutMillis < costTime) {
                    throw new RemotingTooMuchRequestException("invokeAsync call timeout");
                }
                this.invokeAsyncImpl(channel, request, timeoutMillis - costTime, invokeCallback);
            } catch (RemotingSendRequestException e) {
                log.warn("invokeAsync: send request exception, so close the channel[{}]", addr);
                this.closeChannel(addr, channel);
                throw e;
            }
        } else {
            this.closeChannel(addr, channel);
            throw new RemotingConnectException(addr);
        }
    }
@Override
    public RemotingCommand invokeSync(String addr, final RemotingCommand request, long timeoutMillis)
        throws InterruptedException, RemotingConnectException, RemotingSendRequestException, RemotingTimeoutException {
        long beginStartTime = System.currentTimeMillis();
        final Channel channel = this.getAndCreateChannel(addr);
        if (channel != null && channel.isActive()) {
            try {
                doBeforeRpcHooks(addr, request);
                long costTime = System.currentTimeMillis() - beginStartTime;
                if (timeoutMillis < costTime) {
                    throw new RemotingTimeoutException("invokeSync call timeout");
                }
                RemotingCommand response = this.invokeSyncImpl(channel, request, timeoutMillis - costTime);
                doAfterRpcHooks(RemotingHelper.parseChannelRemoteAddr(channel), request, response);
                return response;
            } catch (RemotingSendRequestException e) {
                log.warn("invokeSync: send request exception, so close the channel[{}]", addr);
                this.closeChannel(addr, channel);
                throw e;
            } catch (RemotingTimeoutException e) {
                if (nettyClientConfig.isClientCloseSocketIfTimeout()) {
                    this.closeChannel(addr, channel);
                    log.warn("invokeSync: close socket because of timeout, {}ms, {}", timeoutMillis, addr);
                }
                log.warn("invokeSync: wait response timeout exception, the channel[{}]", addr);
                throw e;
            }
        } else {
            this.closeChannel(addr, channel);
            throw new RemotingConnectException(addr);
        }
    }

再往下看

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public RemotingCommand invokeSyncImpl(final Channel channel, final RemotingCommand request,
        final long timeoutMillis)
        throws InterruptedException, RemotingSendRequestException, RemotingTimeoutException {
        final int opaque = request.getOpaque();

        try {
            同步跟异步都是会把结果用ResponseFuture抱起来
            final ResponseFuture responseFuture = new ResponseFuture(channel, opaque, timeoutMillis, null, null);
            this.responseTable.put(opaque, responseFuture);
            final SocketAddress addr = channel.remoteAddress();
            channel.writeAndFlush(request).addListener(new ChannelFutureListener() {
                @Override
                public void operationComplete(ChannelFuture f) throws Exception {
                    if (f.isSuccess()) {
                        responseFuture.setSendRequestOK(true);
                        return;
                    } else {
                        responseFuture.setSendRequestOK(false);
                    }

                    responseTable.remove(opaque);
                    responseFuture.setCause(f.cause());
                    responseFuture.putResponse(null);
                    log.warn("send a request command to channel <" + addr + "> failed.");
                }
            });
            // 区别是同步的是在这等待
            RemotingCommand responseCommand = responseFuture.waitResponse(timeoutMillis);
            if (null == responseCommand) {
                if (responseFuture.isSendRequestOK()) {
                    throw new RemotingTimeoutException(RemotingHelper.parseSocketAddressAddr(addr), timeoutMillis,
                        responseFuture.getCause());
                } else {
                    throw new RemotingSendRequestException(RemotingHelper.parseSocketAddressAddr(addr), responseFuture.getCause());
                }
            }

            return responseCommand;
        } finally {
            this.responseTable.remove(opaque);
        }
    }

    public void invokeAsyncImpl(final Channel channel, final RemotingCommand request, final long timeoutMillis,
        final InvokeCallback invokeCallback)
        throws InterruptedException, RemotingTooMuchRequestException, RemotingTimeoutException, RemotingSendRequestException {
        long beginStartTime = System.currentTimeMillis();
        final int opaque = request.getOpaque();
        boolean acquired = this.semaphoreAsync.tryAcquire(timeoutMillis, TimeUnit.MILLISECONDS);
        if (acquired) {
            final SemaphoreReleaseOnlyOnce once = new SemaphoreReleaseOnlyOnce(this.semaphoreAsync);
            long costTime = System.currentTimeMillis() - beginStartTime;
            if (timeoutMillis < costTime) {
                once.release();
                throw new RemotingTimeoutException("invokeAsyncImpl call timeout");
            }

            final ResponseFuture responseFuture = new ResponseFuture(channel, opaque, timeoutMillis - costTime, invokeCallback, once);
            this.responseTable.put(opaque, responseFuture);
            try {
                channel.writeAndFlush(request).addListener(new ChannelFutureListener() {
                    @Override
                    public void operationComplete(ChannelFuture f) throws Exception {
                        if (f.isSuccess()) {
                            responseFuture.setSendRequestOK(true);
                            return;
                        }
                        requestFail(opaque);
                        log.warn("send a request command to channel <{}> failed.", RemotingHelper.parseChannelRemoteAddr(channel));
                    }
                });
            } catch (Exception e) {
                responseFuture.release();
                log.warn("send a request command to channel <" + RemotingHelper.parseChannelRemoteAddr(channel) + "> Exception", e);
                throw new RemotingSendRequestException(RemotingHelper.parseChannelRemoteAddr(channel), e);
            }
        } else {
            if (timeoutMillis <= 0) {
                throw new RemotingTooMuchRequestException("invokeAsyncImpl invoke too fast");
            } else {
                String info =
                    String.format("invokeAsyncImpl tryAcquire semaphore timeout, %dms, waiting thread nums: %d semaphoreAsyncValue: %d",
                        timeoutMillis,
                        this.semaphoreAsync.getQueueLength(),
                        this.semaphoreAsync.availablePermits()
                    );
                log.warn(info);
                throw new RemotingTimeoutException(info);
            }
        }
    }
0%
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What hurts more, the pain of hard work or the pain of regret?

聊一下 RocketMQ 的 NameServer 源码

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： 阅读次数： Disqus：

前面介绍了，nameserver相当于dubbo的注册中心，用与管理broker，broker会在启动的时候注册到nameserver，并且会发送心跳给namaserver，nameserver负责保存活跃的broker，包括master和slave，同时保存topic和topic下的队列，以及filter列表，然后为producer和consumer的请求提供服务。

启动过程

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public static void main(String[] args) {
        main0(args);
    }

    public static NamesrvController main0(String[] args) {

        try {
            NamesrvController controller = createNamesrvController(args);
            start(controller);
            String tip = "The Name Server boot success. serializeType=" + RemotingCommand.getSerializeTypeConfigInThisServer();
            log.info(tip);
            System.out.printf("%s%n", tip);
            return controller;
        } catch (Throwable e) {
            e.printStackTrace();
            System.exit(-1);
        }

        return null;
    }

入口的代码时这样子，其实主要的逻辑在createNamesrvController和start方法，来看下这两个的实现

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public static NamesrvController createNamesrvController(String[] args) throws IOException, JoranException {
        System.setProperty(RemotingCommand.REMOTING_VERSION_KEY, Integer.toString(MQVersion.CURRENT_VERSION));
        //PackageConflictDetect.detectFastjson();

        Options options = ServerUtil.buildCommandlineOptions(new Options());
        commandLine = ServerUtil.parseCmdLine("mqnamesrv", args, buildCommandlineOptions(options), new PosixParser());
        if (null == commandLine) {
            System.exit(-1);
            return null;
        }

        final NamesrvConfig namesrvConfig = new NamesrvConfig();
        final NettyServerConfig nettyServerConfig = new NettyServerConfig();
        nettyServerConfig.setListenPort(9876);
        if (commandLine.hasOption('c')) {
            String file = commandLine.getOptionValue('c');
            if (file != null) {
                InputStream in = new BufferedInputStream(new FileInputStream(file));
                properties = new Properties();
                properties.load(in);
                MixAll.properties2Object(properties, namesrvConfig);
                MixAll.properties2Object(properties, nettyServerConfig);

                namesrvConfig.setConfigStorePath(file);

                System.out.printf("load config properties file OK, %s%n", file);
                in.close();
            }
        }

        if (commandLine.hasOption('p')) {
            InternalLogger console = InternalLoggerFactory.getLogger(LoggerName.NAMESRV_CONSOLE_NAME);
            MixAll.printObjectProperties(console, namesrvConfig);
            MixAll.printObjectProperties(console, nettyServerConfig);
            System.exit(0);
        }

        MixAll.properties2Object(ServerUtil.commandLine2Properties(commandLine), namesrvConfig);

        if (null == namesrvConfig.getRocketmqHome()) {
            System.out.printf("Please set the %s variable in your environment to match the location of the RocketMQ installation%n", MixAll.ROCKETMQ_HOME_ENV);
            System.exit(-2);
        }

        LoggerContext lc = (LoggerContext) LoggerFactory.getILoggerFactory();
        JoranConfigurator configurator = new JoranConfigurator();
        configurator.setContext(lc);
        lc.reset();
        configurator.doConfigure(namesrvConfig.getRocketmqHome() + "/conf/logback_namesrv.xml");

        log = InternalLoggerFactory.getLogger(LoggerName.NAMESRV_LOGGER_NAME);

        MixAll.printObjectProperties(log, namesrvConfig);
        MixAll.printObjectProperties(log, nettyServerConfig);

        final NamesrvController controller = new NamesrvController(namesrvConfig, nettyServerConfig);

        // remember all configs to prevent discard
        controller.getConfiguration().registerConfig(properties);

        return controller;
    }

这个方法里其实主要是读取一些配置啥的，不是很复杂，

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public static NamesrvController start(final NamesrvController controller) throws Exception {

        if (null == controller) {
            throw new IllegalArgumentException("NamesrvController is null");
        }

        boolean initResult = controller.initialize();
        if (!initResult) {
            controller.shutdown();
            System.exit(-3);
        }

        Runtime.getRuntime().addShutdownHook(new ShutdownHookThread(log, new Callable<Void>() {
            @Override
            public Void call() throws Exception {
                controller.shutdown();
                return null;
            }
        }));

        controller.start();

        return controller;
    }

这个start里主要关注initialize方法，后面就是一个停机的hook，来看下initialize方法

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public boolean initialize() {

        this.kvConfigManager.load();

        this.remotingServer = new NettyRemotingServer(this.nettyServerConfig, this.brokerHousekeepingService);

        this.remotingExecutor =
            Executors.newFixedThreadPool(nettyServerConfig.getServerWorkerThreads(), new ThreadFactoryImpl("RemotingExecutorThread_"));

        this.registerProcessor();

        this.scheduledExecutorService.scheduleAtFixedRate(new Runnable() {

            @Override
            public void run() {
                NamesrvController.this.routeInfoManager.scanNotActiveBroker();
            }
        }, 5, 10, TimeUnit.SECONDS);

        this.scheduledExecutorService.scheduleAtFixedRate(new Runnable() {

            @Override
            public void run() {
                NamesrvController.this.kvConfigManager.printAllPeriodically();
            }
        }, 1, 10, TimeUnit.MINUTES);

        if (TlsSystemConfig.tlsMode != TlsMode.DISABLED) {
            // Register a listener to reload SslContext
            try {
                fileWatchService = new FileWatchService(
                    new String[] {
                        TlsSystemConfig.tlsServerCertPath,
                        TlsSystemConfig.tlsServerKeyPath,
                        TlsSystemConfig.tlsServerTrustCertPath
                    },
                    new FileWatchService.Listener() {
                        boolean certChanged, keyChanged = false;
                        @Override
                        public void onChanged(String path) {
                            if (path.equals(TlsSystemConfig.tlsServerTrustCertPath)) {
                                log.info("The trust certificate changed, reload the ssl context");
                                reloadServerSslContext();
                            }
                            if (path.equals(TlsSystemConfig.tlsServerCertPath)) {
                                certChanged = true;
                            }
                            if (path.equals(TlsSystemConfig.tlsServerKeyPath)) {
                                keyChanged = true;
                            }
                            if (certChanged && keyChanged) {
                                log.info("The certificate and private key changed, reload the ssl context");
                                certChanged = keyChanged = false;
                                reloadServerSslContext();
                            }
                        }
                        private void reloadServerSslContext() {
                            ((NettyRemotingServer) remotingServer).loadSslContext();
                        }
                    });
            } catch (Exception e) {
                log.warn("FileWatchService created error, can't load the certificate dynamically");
            }
        }

        return true;
    }

这里的kvConfigManager主要是来加载NameServer的配置参数，存到org.apache.rocketmq.namesrv.kvconfig.KVConfigManager#configTable中，然后是以BrokerHousekeepingService对象为参数初始化NettyRemotingServer对象，BrokerHousekeepingService对象作为该Netty连接中Socket链接的监听器（ChannelEventListener）；监听与Broker建立的渠道的状态（空闲、关闭、异常三个状态），并调用BrokerHousekeepingService的相应onChannel方法。其中渠道的空闲、关闭、异常状态均调用RouteInfoManager.onChannelDestory方法处理。这个BrokerHousekeepingService可以字面化地理解为broker的管家服务，这个类内部三个状态方法其实都是调用的org.apache.rocketmq.namesrv.NamesrvController#getRouteInfoManager方法，而这个RouteInfoManager里面的对象有这些

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public class RouteInfoManager {
    private static final InternalLogger log = InternalLoggerFactory.getLogger(LoggerName.NAMESRV_LOGGER_NAME);
    private final static long BROKER_CHANNEL_EXPIRED_TIME = 1000 * 60 * 2;
    private final ReadWriteLock lock = new ReentrantReadWriteLock();
  // topic与queue的对应关系
    private final HashMap<String/* topic */, List<QueueData>> topicQueueTable;
  // Broker名称与broker属性的map
    private final HashMap<String/* brokerName */, BrokerData> brokerAddrTable;
  // 集群与broker集合的对应关系
    private final HashMap<String/* clusterName */, Set<String/* brokerName */>> clusterAddrTable;
  // 活跃的broker信息
    private final HashMap<String/* brokerAddr */, BrokerLiveInfo> brokerLiveTable;
  // Broker地址与过滤器
    private final HashMap<String/* brokerAddr */, List<String>/* Filter Server */> filterServerTable;

然后接下去就是初始化了一个线程池，然后注册默认的处理类this.registerProcessor();默认都是这个处理器去处理请求 org.apache.rocketmq.namesrv.processor.DefaultRequestProcessor#DefaultRequestProcessor然后是初始化两个定时任务

第一是每10秒检查一遍Broker的状态的定时任务，调用scanNotActiveBroker方法；遍历brokerLiveTable集合，查看每个broker的最后更新时间（BrokerLiveInfo.lastUpdateTimestamp）是否超过2分钟，若超过则关闭该broker的渠道并调用RouteInfoManager.onChannelDestory方法清理RouteInfoManager类的topicQueueTable、brokerAddrTable、clusterAddrTable、filterServerTable成员变量。

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this.scheduledExecutorService.scheduleAtFixedRate(new Runnable() {

            @Override
            public void run() {
                NamesrvController.this.routeInfoManager.scanNotActiveBroker();
            }
        }, 5, 10, TimeUnit.SECONDS);
public void scanNotActiveBroker() {
        Iterator<Entry<String, BrokerLiveInfo>> it = this.brokerLiveTable.entrySet().iterator();
        while (it.hasNext()) {
            Entry<String, BrokerLiveInfo> next = it.next();
            long last = next.getValue().getLastUpdateTimestamp();
            if ((last + BROKER_CHANNEL_EXPIRED_TIME) < System.currentTimeMillis()) {
                RemotingUtil.closeChannel(next.getValue().getChannel());
                it.remove();
                log.warn("The broker channel expired, {} {}ms", next.getKey(), BROKER_CHANNEL_EXPIRED_TIME);
                this.onChannelDestroy(next.getKey(), next.getValue().getChannel());
            }
        }
    }

    public void onChannelDestroy(String remoteAddr, Channel channel) {
        String brokerAddrFound = null;
        if (channel != null) {
            try {
                try {
                    this.lock.readLock().lockInterruptibly();
                    Iterator<Entry<String, BrokerLiveInfo>> itBrokerLiveTable =
                        this.brokerLiveTable.entrySet().iterator();
                    while (itBrokerLiveTable.hasNext()) {
                        Entry<String, BrokerLiveInfo> entry = itBrokerLiveTable.next();
                        if (entry.getValue().getChannel() == channel) {
                            brokerAddrFound = entry.getKey();
                            break;
                        }
                    }
                } finally {
                    this.lock.readLock().unlock();
                }
            } catch (Exception e) {
                log.error("onChannelDestroy Exception", e);
            }
        }

        if (null == brokerAddrFound) {
            brokerAddrFound = remoteAddr;
        } else {
            log.info("the broker's channel destroyed, {}, clean it's data structure at once", brokerAddrFound);
        }

        if (brokerAddrFound != null && brokerAddrFound.length() > 0) {

            try {
                try {
                    this.lock.writeLock().lockInterruptibly();
                    this.brokerLiveTable.remove(brokerAddrFound);
                    this.filterServerTable.remove(brokerAddrFound);
                    String brokerNameFound = null;
                    boolean removeBrokerName = false;
                    Iterator<Entry<String, BrokerData>> itBrokerAddrTable =
                        this.brokerAddrTable.entrySet().iterator();
                    while (itBrokerAddrTable.hasNext() && (null == brokerNameFound)) {
                        BrokerData brokerData = itBrokerAddrTable.next().getValue();

                        Iterator<Entry<Long, String>> it = brokerData.getBrokerAddrs().entrySet().iterator();
                        while (it.hasNext()) {
                            Entry<Long, String> entry = it.next();
                            Long brokerId = entry.getKey();
                            String brokerAddr = entry.getValue();
                            if (brokerAddr.equals(brokerAddrFound)) {
                                brokerNameFound = brokerData.getBrokerName();
                                it.remove();
                                log.info("remove brokerAddr[{}, {}] from brokerAddrTable, because channel destroyed",
                                    brokerId, brokerAddr);
                                break;
                            }
                        }

                        if (brokerData.getBrokerAddrs().isEmpty()) {
                            removeBrokerName = true;
                            itBrokerAddrTable.remove();
                            log.info("remove brokerName[{}] from brokerAddrTable, because channel destroyed",
                                brokerData.getBrokerName());
                        }
                    }

                    if (brokerNameFound != null && removeBrokerName) {
                        Iterator<Entry<String, Set<String>>> it = this.clusterAddrTable.entrySet().iterator();
                        while (it.hasNext()) {
                            Entry<String, Set<String>> entry = it.next();
                            String clusterName = entry.getKey();
                            Set<String> brokerNames = entry.getValue();
                            boolean removed = brokerNames.remove(brokerNameFound);
                            if (removed) {
                                log.info("remove brokerName[{}], clusterName[{}] from clusterAddrTable, because channel destroyed",
                                    brokerNameFound, clusterName);

                                if (brokerNames.isEmpty()) {
                                    log.info("remove the clusterName[{}] from clusterAddrTable, because channel destroyed and no broker in this cluster",
                                        clusterName);
                                    it.remove();
                                }

                                break;
                            }
                        }
                    }

                    if (removeBrokerName) {
                        Iterator<Entry<String, List<QueueData>>> itTopicQueueTable =
                            this.topicQueueTable.entrySet().iterator();
                        while (itTopicQueueTable.hasNext()) {
                            Entry<String, List<QueueData>> entry = itTopicQueueTable.next();
                            String topic = entry.getKey();
                            List<QueueData> queueDataList = entry.getValue();

                            Iterator<QueueData> itQueueData = queueDataList.iterator();
                            while (itQueueData.hasNext()) {
                                QueueData queueData = itQueueData.next();
                                if (queueData.getBrokerName().equals(brokerNameFound)) {
                                    itQueueData.remove();
                                    log.info("remove topic[{} {}], from topicQueueTable, because channel destroyed",
                                        topic, queueData);
                                }
                            }

                            if (queueDataList.isEmpty()) {
                                itTopicQueueTable.remove();
                                log.info("remove topic[{}] all queue, from topicQueueTable, because channel destroyed",
                                    topic);
                            }
                        }
                    }
                } finally {
                    this.lock.writeLock().unlock();
                }
            } catch (Exception e) {
                log.error("onChannelDestroy Exception", e);
            }
        }
    }

第二个是每10分钟打印一次NameServer的配置参数。即KVConfigManager.configTable变量的内容。

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this.scheduledExecutorService.scheduleAtFixedRate(new Runnable() {

            @Override
            public void run() {
                NamesrvController.this.kvConfigManager.printAllPeriodically();
            }
        }, 1, 10, TimeUnit.MINUTES);

然后这个初始化就差不多完成了，后面只需要把remotingServer start一下就好了

处理请求

直接上代码，其实主体是swtich case去判断

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@Override
    public RemotingCommand processRequest(ChannelHandlerContext ctx,
        RemotingCommand request) throws RemotingCommandException {

        if (ctx != null) {
            log.debug("receive request, {} {} {}",
                request.getCode(),
                RemotingHelper.parseChannelRemoteAddr(ctx.channel()),
                request);
        }


        switch (request.getCode()) {
            case RequestCode.PUT_KV_CONFIG:
                return this.putKVConfig(ctx, request);
            case RequestCode.GET_KV_CONFIG:
                return this.getKVConfig(ctx, request);
            case RequestCode.DELETE_KV_CONFIG:
                return this.deleteKVConfig(ctx, request);
            case RequestCode.QUERY_DATA_VERSION:
                return queryBrokerTopicConfig(ctx, request);
            case RequestCode.REGISTER_BROKER:
                Version brokerVersion = MQVersion.value2Version(request.getVersion());
                if (brokerVersion.ordinal() >= MQVersion.Version.V3_0_11.ordinal()) {
                    return this.registerBrokerWithFilterServer(ctx, request);
                } else {
                    return this.registerBroker(ctx, request);
                }
            case RequestCode.UNREGISTER_BROKER:
                return this.unregisterBroker(ctx, request);
            case RequestCode.GET_ROUTEINTO_BY_TOPIC:
                return this.getRouteInfoByTopic(ctx, request);
            case RequestCode.GET_BROKER_CLUSTER_INFO:
                return this.getBrokerClusterInfo(ctx, request);
            case RequestCode.WIPE_WRITE_PERM_OF_BROKER:
                return this.wipeWritePermOfBroker(ctx, request);
            case RequestCode.GET_ALL_TOPIC_LIST_FROM_NAMESERVER:
                return getAllTopicListFromNameserver(ctx, request);
            case RequestCode.DELETE_TOPIC_IN_NAMESRV:
                return deleteTopicInNamesrv(ctx, request);
            case RequestCode.GET_KVLIST_BY_NAMESPACE:
                return this.getKVListByNamespace(ctx, request);
            case RequestCode.GET_TOPICS_BY_CLUSTER:
                return this.getTopicsByCluster(ctx, request);
            case RequestCode.GET_SYSTEM_TOPIC_LIST_FROM_NS:
                return this.getSystemTopicListFromNs(ctx, request);
            case RequestCode.GET_UNIT_TOPIC_LIST:
                return this.getUnitTopicList(ctx, request);
            case RequestCode.GET_HAS_UNIT_SUB_TOPIC_LIST:
                return this.getHasUnitSubTopicList(ctx, request);
            case RequestCode.GET_HAS_UNIT_SUB_UNUNIT_TOPIC_LIST:
                return this.getHasUnitSubUnUnitTopicList(ctx, request);
            case RequestCode.UPDATE_NAMESRV_CONFIG:
                return this.updateConfig(ctx, request);
            case RequestCode.GET_NAMESRV_CONFIG:
                return this.getConfig(ctx, request);
            default:
                break;
        }
        return null;
    }

以broker注册为例，

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case RequestCode.REGISTER_BROKER:
                Version brokerVersion = MQVersion.value2Version(request.getVersion());
                if (brokerVersion.ordinal() >= MQVersion.Version.V3_0_11.ordinal()) {
                    return this.registerBrokerWithFilterServer(ctx, request);
                } else {
                    return this.registerBroker(ctx, request);
                }

做了个简单的版本管理，我们看下前面一个的代码

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public RemotingCommand registerBrokerWithFilterServer(ChannelHandlerContext ctx, RemotingCommand request)
    throws RemotingCommandException {
    final RemotingCommand response = RemotingCommand.createResponseCommand(RegisterBrokerResponseHeader.class);
    final RegisterBrokerResponseHeader responseHeader = (RegisterBrokerResponseHeader) response.readCustomHeader();
    final RegisterBrokerRequestHeader requestHeader =
        (RegisterBrokerRequestHeader) request.decodeCommandCustomHeader(RegisterBrokerRequestHeader.class);

    if (!checksum(ctx, request, requestHeader)) {
        response.setCode(ResponseCode.SYSTEM_ERROR);
        response.setRemark("crc32 not match");
        return response;
    }

    RegisterBrokerBody registerBrokerBody = new RegisterBrokerBody();

    if (request.getBody() != null) {
        try {
            registerBrokerBody = RegisterBrokerBody.decode(request.getBody(), requestHeader.isCompressed());
        } catch (Exception e) {
            throw new RemotingCommandException("Failed to decode RegisterBrokerBody", e);
        }
    } else {
        registerBrokerBody.getTopicConfigSerializeWrapper().getDataVersion().setCounter(new AtomicLong(0));
        registerBrokerBody.getTopicConfigSerializeWrapper().getDataVersion().setTimestamp(0);
    }

    RegisterBrokerResult result = this.namesrvController.getRouteInfoManager().registerBroker(
        requestHeader.getClusterName(),
        requestHeader.getBrokerAddr(),
        requestHeader.getBrokerName(),
        requestHeader.getBrokerId(),
        requestHeader.getHaServerAddr(),
        registerBrokerBody.getTopicConfigSerializeWrapper(),
        registerBrokerBody.getFilterServerList(),
        ctx.channel());

    responseHeader.setHaServerAddr(result.getHaServerAddr());
    responseHeader.setMasterAddr(result.getMasterAddr());

    byte[] jsonValue = this.namesrvController.getKvConfigManager().getKVListByNamespace(NamesrvUtil.NAMESPACE_ORDER_TOPIC_CONFIG);
    response.setBody(jsonValue);

    response.setCode(ResponseCode.SUCCESS);
    response.setRemark(null);
    return response;
}

可以看到主要的逻辑还是在org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#registerBroker这个方法里

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public RegisterBrokerResult registerBroker(
        final String clusterName,
        final String brokerAddr,
        final String brokerName,
        final long brokerId,
        final String haServerAddr,
        final TopicConfigSerializeWrapper topicConfigWrapper,
        final List<String> filterServerList,
        final Channel channel) {
        RegisterBrokerResult result = new RegisterBrokerResult();
        try {
            try {
                this.lock.writeLock().lockInterruptibly();

              // 更新这个clusterAddrTable
                Set<String> brokerNames = this.clusterAddrTable.get(clusterName);
                if (null == brokerNames) {
                    brokerNames = new HashSet<String>();
                    this.clusterAddrTable.put(clusterName, brokerNames);
                }
                brokerNames.add(brokerName);

                boolean registerFirst = false;

              // 更新brokerAddrTable
                BrokerData brokerData = this.brokerAddrTable.get(brokerName);
                if (null == brokerData) {
                    registerFirst = true;
                    brokerData = new BrokerData(clusterName, brokerName, new HashMap<Long, String>());
                    this.brokerAddrTable.put(brokerName, brokerData);
                }
                Map<Long, String> brokerAddrsMap = brokerData.getBrokerAddrs();
                //Switch slave to master: first remove <1, IP:PORT> in namesrv, then add <0, IP:PORT>
                //The same IP:PORT must only have one record in brokerAddrTable
                Iterator<Entry<Long, String>> it = brokerAddrsMap.entrySet().iterator();
                while (it.hasNext()) {
                    Entry<Long, String> item = it.next();
                    if (null != brokerAddr && brokerAddr.equals(item.getValue()) && brokerId != item.getKey()) {
                        it.remove();
                    }
                }

                String oldAddr = brokerData.getBrokerAddrs().put(brokerId, brokerAddr);
                registerFirst = registerFirst || (null == oldAddr);

              // 更新了org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#topicQueueTable中的数据
                if (null != topicConfigWrapper
                    && MixAll.MASTER_ID == brokerId) {
                    if (this.isBrokerTopicConfigChanged(brokerAddr, topicConfigWrapper.getDataVersion())
                        || registerFirst) {
                        ConcurrentMap<String, TopicConfig> tcTable =
                            topicConfigWrapper.getTopicConfigTable();
                        if (tcTable != null) {
                            for (Map.Entry<String, TopicConfig> entry : tcTable.entrySet()) {
                                this.createAndUpdateQueueData(brokerName, entry.getValue());
                            }
                        }
                    }
                }

              // 更新活跃broker信息
                BrokerLiveInfo prevBrokerLiveInfo = this.brokerLiveTable.put(brokerAddr,
                    new BrokerLiveInfo(
                        System.currentTimeMillis(),
                        topicConfigWrapper.getDataVersion(),
                        channel,
                        haServerAddr));
                if (null == prevBrokerLiveInfo) {
                    log.info("new broker registered, {} HAServer: {}", brokerAddr, haServerAddr);
                }

              // 处理filter
                if (filterServerList != null) {
                    if (filterServerList.isEmpty()) {
                        this.filterServerTable.remove(brokerAddr);
                    } else {
                        this.filterServerTable.put(brokerAddr, filterServerList);
                    }
                }

              // 当当前broker非master时返回master信息
                if (MixAll.MASTER_ID != brokerId) {
                    String masterAddr = brokerData.getBrokerAddrs().get(MixAll.MASTER_ID);
                    if (masterAddr != null) {
                        BrokerLiveInfo brokerLiveInfo = this.brokerLiveTable.get(masterAddr);
                        if (brokerLiveInfo != null) {
                            result.setHaServerAddr(brokerLiveInfo.getHaServerAddr());
                            result.setMasterAddr(masterAddr);
                        }
                    }
                }
            } finally {
                this.lock.writeLock().unlock();
            }
        } catch (Exception e) {
            log.error("registerBroker Exception", e);
        }

        return result;
    }

这个是注册 broker 的逻辑，再看下根据 topic 获取 broker 信息和 topic 信息，org.apache.rocketmq.namesrv.processor.DefaultRequestProcessor#getRouteInfoByTopic 主要是这个方法的逻辑

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public RemotingCommand getRouteInfoByTopic(ChannelHandlerContext ctx,
        RemotingCommand request) throws RemotingCommandException {
        final RemotingCommand response = RemotingCommand.createResponseCommand(null);
        final GetRouteInfoRequestHeader requestHeader =
            (GetRouteInfoRequestHeader) request.decodeCommandCustomHeader(GetRouteInfoRequestHeader.class);

        TopicRouteData topicRouteData = this.namesrvController.getRouteInfoManager().pickupTopicRouteData(requestHeader.getTopic());

        if (topicRouteData != null) {
            if (this.namesrvController.getNamesrvConfig().isOrderMessageEnable()) {
                String orderTopicConf =
                    this.namesrvController.getKvConfigManager().getKVConfig(NamesrvUtil.NAMESPACE_ORDER_TOPIC_CONFIG,
                        requestHeader.getTopic());
                topicRouteData.setOrderTopicConf(orderTopicConf);
            }

            byte[] content = topicRouteData.encode();
            response.setBody(content);
            response.setCode(ResponseCode.SUCCESS);
            response.setRemark(null);
            return response;
        }

        response.setCode(ResponseCode.TOPIC_NOT_EXIST);
        response.setRemark("No topic route info in name server for the topic: " + requestHeader.getTopic()
            + FAQUrl.suggestTodo(FAQUrl.APPLY_TOPIC_URL));
        return response;
    }

首先调用org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#pickupTopicRouteData从org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#topicQueueTable获取到org.apache.rocketmq.common.protocol.route.QueueData这里面存了 brokerName，再通过org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#brokerAddrTable里获取到 broker 的地址信息等，然后再获取 orderMessage 的配置。

简要分析了下 RocketMQ 的 NameServer 的代码，比较粗粒度。

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What hurts more, the pain of hard work or the pain of regret?

聊一下 RocketMQ 的 NameServer 源码

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： 阅读次数： Disqus：

前面介绍了，nameserver相当于dubbo的注册中心，用与管理broker，broker会在启动的时候注册到nameserver，并且会发送心跳给namaserver，nameserver负责保存活跃的broker，包括master和slave，同时保存topic和topic下的队列，以及filter列表，然后为producer和consumer的请求提供服务。

启动过程

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public static void main(String[] args) {
        main0(args);
    }

    public static NamesrvController main0(String[] args) {

        try {
            NamesrvController controller = createNamesrvController(args);
            start(controller);
            String tip = "The Name Server boot success. serializeType=" + RemotingCommand.getSerializeTypeConfigInThisServer();
            log.info(tip);
            System.out.printf("%s%n", tip);
            return controller;
        } catch (Throwable e) {
            e.printStackTrace();
            System.exit(-1);
        }

        return null;
    }

入口的代码时这样子，其实主要的逻辑在createNamesrvController和start方法，来看下这两个的实现

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public static NamesrvController createNamesrvController(String[] args) throws IOException, JoranException {
        System.setProperty(RemotingCommand.REMOTING_VERSION_KEY, Integer.toString(MQVersion.CURRENT_VERSION));
        //PackageConflictDetect.detectFastjson();

        Options options = ServerUtil.buildCommandlineOptions(new Options());
        commandLine = ServerUtil.parseCmdLine("mqnamesrv", args, buildCommandlineOptions(options), new PosixParser());
        if (null == commandLine) {
            System.exit(-1);
            return null;
        }

        final NamesrvConfig namesrvConfig = new NamesrvConfig();
        final NettyServerConfig nettyServerConfig = new NettyServerConfig();
        nettyServerConfig.setListenPort(9876);
        if (commandLine.hasOption('c')) {
            String file = commandLine.getOptionValue('c');
            if (file != null) {
                InputStream in = new BufferedInputStream(new FileInputStream(file));
                properties = new Properties();
                properties.load(in);
                MixAll.properties2Object(properties, namesrvConfig);
                MixAll.properties2Object(properties, nettyServerConfig);

                namesrvConfig.setConfigStorePath(file);

                System.out.printf("load config properties file OK, %s%n", file);
                in.close();
            }
        }

        if (commandLine.hasOption('p')) {
            InternalLogger console = InternalLoggerFactory.getLogger(LoggerName.NAMESRV_CONSOLE_NAME);
            MixAll.printObjectProperties(console, namesrvConfig);
            MixAll.printObjectProperties(console, nettyServerConfig);
            System.exit(0);
        }

        MixAll.properties2Object(ServerUtil.commandLine2Properties(commandLine), namesrvConfig);

        if (null == namesrvConfig.getRocketmqHome()) {
            System.out.printf("Please set the %s variable in your environment to match the location of the RocketMQ installation%n", MixAll.ROCKETMQ_HOME_ENV);
            System.exit(-2);
        }

        LoggerContext lc = (LoggerContext) LoggerFactory.getILoggerFactory();
        JoranConfigurator configurator = new JoranConfigurator();
        configurator.setContext(lc);
        lc.reset();
        configurator.doConfigure(namesrvConfig.getRocketmqHome() + "/conf/logback_namesrv.xml");

        log = InternalLoggerFactory.getLogger(LoggerName.NAMESRV_LOGGER_NAME);

        MixAll.printObjectProperties(log, namesrvConfig);
        MixAll.printObjectProperties(log, nettyServerConfig);

        final NamesrvController controller = new NamesrvController(namesrvConfig, nettyServerConfig);

        // remember all configs to prevent discard
        controller.getConfiguration().registerConfig(properties);

        return controller;
    }

这个方法里其实主要是读取一些配置啥的，不是很复杂，

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public static NamesrvController start(final NamesrvController controller) throws Exception {

        if (null == controller) {
            throw new IllegalArgumentException("NamesrvController is null");
        }

        boolean initResult = controller.initialize();
        if (!initResult) {
            controller.shutdown();
            System.exit(-3);
        }

        Runtime.getRuntime().addShutdownHook(new ShutdownHookThread(log, new Callable<Void>() {
            @Override
            public Void call() throws Exception {
                controller.shutdown();
                return null;
            }
        }));

        controller.start();

        return controller;
    }

这个start里主要关注initialize方法，后面就是一个停机的hook，来看下initialize方法

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public boolean initialize() {

        this.kvConfigManager.load();

        this.remotingServer = new NettyRemotingServer(this.nettyServerConfig, this.brokerHousekeepingService);

        this.remotingExecutor =
            Executors.newFixedThreadPool(nettyServerConfig.getServerWorkerThreads(), new ThreadFactoryImpl("RemotingExecutorThread_"));

        this.registerProcessor();

        this.scheduledExecutorService.scheduleAtFixedRate(new Runnable() {

            @Override
            public void run() {
                NamesrvController.this.routeInfoManager.scanNotActiveBroker();
            }
        }, 5, 10, TimeUnit.SECONDS);

        this.scheduledExecutorService.scheduleAtFixedRate(new Runnable() {

            @Override
            public void run() {
                NamesrvController.this.kvConfigManager.printAllPeriodically();
            }
        }, 1, 10, TimeUnit.MINUTES);

        if (TlsSystemConfig.tlsMode != TlsMode.DISABLED) {
            // Register a listener to reload SslContext
            try {
                fileWatchService = new FileWatchService(
                    new String[] {
                        TlsSystemConfig.tlsServerCertPath,
                        TlsSystemConfig.tlsServerKeyPath,
                        TlsSystemConfig.tlsServerTrustCertPath
                    },
                    new FileWatchService.Listener() {
                        boolean certChanged, keyChanged = false;
                        @Override
                        public void onChanged(String path) {
                            if (path.equals(TlsSystemConfig.tlsServerTrustCertPath)) {
                                log.info("The trust certificate changed, reload the ssl context");
                                reloadServerSslContext();
                            }
                            if (path.equals(TlsSystemConfig.tlsServerCertPath)) {
                                certChanged = true;
                            }
                            if (path.equals(TlsSystemConfig.tlsServerKeyPath)) {
                                keyChanged = true;
                            }
                            if (certChanged && keyChanged) {
                                log.info("The certificate and private key changed, reload the ssl context");
                                certChanged = keyChanged = false;
                                reloadServerSslContext();
                            }
                        }
                        private void reloadServerSslContext() {
                            ((NettyRemotingServer) remotingServer).loadSslContext();
                        }
                    });
            } catch (Exception e) {
                log.warn("FileWatchService created error, can't load the certificate dynamically");
            }
        }

        return true;
    }

这里的kvConfigManager主要是来加载NameServer的配置参数，存到org.apache.rocketmq.namesrv.kvconfig.KVConfigManager#configTable中，然后是以BrokerHousekeepingService对象为参数初始化NettyRemotingServer对象，BrokerHousekeepingService对象作为该Netty连接中Socket链接的监听器（ChannelEventListener）；监听与Broker建立的渠道的状态（空闲、关闭、异常三个状态），并调用BrokerHousekeepingService的相应onChannel方法。其中渠道的空闲、关闭、异常状态均调用RouteInfoManager.onChannelDestory方法处理。这个BrokerHousekeepingService可以字面化地理解为broker的管家服务，这个类内部三个状态方法其实都是调用的org.apache.rocketmq.namesrv.NamesrvController#getRouteInfoManager方法，而这个RouteInfoManager里面的对象有这些

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public class RouteInfoManager {
    private static final InternalLogger log = InternalLoggerFactory.getLogger(LoggerName.NAMESRV_LOGGER_NAME);
    private final static long BROKER_CHANNEL_EXPIRED_TIME = 1000 * 60 * 2;
    private final ReadWriteLock lock = new ReentrantReadWriteLock();
  // topic与queue的对应关系
    private final HashMap<String/* topic */, List<QueueData>> topicQueueTable;
  // Broker名称与broker属性的map
    private final HashMap<String/* brokerName */, BrokerData> brokerAddrTable;
  // 集群与broker集合的对应关系
    private final HashMap<String/* clusterName */, Set<String/* brokerName */>> clusterAddrTable;
  // 活跃的broker信息
    private final HashMap<String/* brokerAddr */, BrokerLiveInfo> brokerLiveTable;
  // Broker地址与过滤器
    private final HashMap<String/* brokerAddr */, List<String>/* Filter Server */> filterServerTable;

然后接下去就是初始化了一个线程池，然后注册默认的处理类this.registerProcessor();默认都是这个处理器去处理请求 org.apache.rocketmq.namesrv.processor.DefaultRequestProcessor#DefaultRequestProcessor然后是初始化两个定时任务

第一是每10秒检查一遍Broker的状态的定时任务，调用scanNotActiveBroker方法；遍历brokerLiveTable集合，查看每个broker的最后更新时间（BrokerLiveInfo.lastUpdateTimestamp）是否超过2分钟，若超过则关闭该broker的渠道并调用RouteInfoManager.onChannelDestory方法清理RouteInfoManager类的topicQueueTable、brokerAddrTable、clusterAddrTable、filterServerTable成员变量。

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this.scheduledExecutorService.scheduleAtFixedRate(new Runnable() {

            @Override
            public void run() {
                NamesrvController.this.routeInfoManager.scanNotActiveBroker();
            }
        }, 5, 10, TimeUnit.SECONDS);
public void scanNotActiveBroker() {
        Iterator<Entry<String, BrokerLiveInfo>> it = this.brokerLiveTable.entrySet().iterator();
        while (it.hasNext()) {
            Entry<String, BrokerLiveInfo> next = it.next();
            long last = next.getValue().getLastUpdateTimestamp();
            if ((last + BROKER_CHANNEL_EXPIRED_TIME) < System.currentTimeMillis()) {
                RemotingUtil.closeChannel(next.getValue().getChannel());
                it.remove();
                log.warn("The broker channel expired, {} {}ms", next.getKey(), BROKER_CHANNEL_EXPIRED_TIME);
                this.onChannelDestroy(next.getKey(), next.getValue().getChannel());
            }
        }
    }

    public void onChannelDestroy(String remoteAddr, Channel channel) {
        String brokerAddrFound = null;
        if (channel != null) {
            try {
                try {
                    this.lock.readLock().lockInterruptibly();
                    Iterator<Entry<String, BrokerLiveInfo>> itBrokerLiveTable =
                        this.brokerLiveTable.entrySet().iterator();
                    while (itBrokerLiveTable.hasNext()) {
                        Entry<String, BrokerLiveInfo> entry = itBrokerLiveTable.next();
                        if (entry.getValue().getChannel() == channel) {
                            brokerAddrFound = entry.getKey();
                            break;
                        }
                    }
                } finally {
                    this.lock.readLock().unlock();
                }
            } catch (Exception e) {
                log.error("onChannelDestroy Exception", e);
            }
        }

        if (null == brokerAddrFound) {
            brokerAddrFound = remoteAddr;
        } else {
            log.info("the broker's channel destroyed, {}, clean it's data structure at once", brokerAddrFound);
        }

        if (brokerAddrFound != null && brokerAddrFound.length() > 0) {

            try {
                try {
                    this.lock.writeLock().lockInterruptibly();
                    this.brokerLiveTable.remove(brokerAddrFound);
                    this.filterServerTable.remove(brokerAddrFound);
                    String brokerNameFound = null;
                    boolean removeBrokerName = false;
                    Iterator<Entry<String, BrokerData>> itBrokerAddrTable =
                        this.brokerAddrTable.entrySet().iterator();
                    while (itBrokerAddrTable.hasNext() && (null == brokerNameFound)) {
                        BrokerData brokerData = itBrokerAddrTable.next().getValue();

                        Iterator<Entry<Long, String>> it = brokerData.getBrokerAddrs().entrySet().iterator();
                        while (it.hasNext()) {
                            Entry<Long, String> entry = it.next();
                            Long brokerId = entry.getKey();
                            String brokerAddr = entry.getValue();
                            if (brokerAddr.equals(brokerAddrFound)) {
                                brokerNameFound = brokerData.getBrokerName();
                                it.remove();
                                log.info("remove brokerAddr[{}, {}] from brokerAddrTable, because channel destroyed",
                                    brokerId, brokerAddr);
                                break;
                            }
                        }

                        if (brokerData.getBrokerAddrs().isEmpty()) {
                            removeBrokerName = true;
                            itBrokerAddrTable.remove();
                            log.info("remove brokerName[{}] from brokerAddrTable, because channel destroyed",
                                brokerData.getBrokerName());
                        }
                    }

                    if (brokerNameFound != null && removeBrokerName) {
                        Iterator<Entry<String, Set<String>>> it = this.clusterAddrTable.entrySet().iterator();
                        while (it.hasNext()) {
                            Entry<String, Set<String>> entry = it.next();
                            String clusterName = entry.getKey();
                            Set<String> brokerNames = entry.getValue();
                            boolean removed = brokerNames.remove(brokerNameFound);
                            if (removed) {
                                log.info("remove brokerName[{}], clusterName[{}] from clusterAddrTable, because channel destroyed",
                                    brokerNameFound, clusterName);

                                if (brokerNames.isEmpty()) {
                                    log.info("remove the clusterName[{}] from clusterAddrTable, because channel destroyed and no broker in this cluster",
                                        clusterName);
                                    it.remove();
                                }

                                break;
                            }
                        }
                    }

                    if (removeBrokerName) {
                        Iterator<Entry<String, List<QueueData>>> itTopicQueueTable =
                            this.topicQueueTable.entrySet().iterator();
                        while (itTopicQueueTable.hasNext()) {
                            Entry<String, List<QueueData>> entry = itTopicQueueTable.next();
                            String topic = entry.getKey();
                            List<QueueData> queueDataList = entry.getValue();

                            Iterator<QueueData> itQueueData = queueDataList.iterator();
                            while (itQueueData.hasNext()) {
                                QueueData queueData = itQueueData.next();
                                if (queueData.getBrokerName().equals(brokerNameFound)) {
                                    itQueueData.remove();
                                    log.info("remove topic[{} {}], from topicQueueTable, because channel destroyed",
                                        topic, queueData);
                                }
                            }

                            if (queueDataList.isEmpty()) {
                                itTopicQueueTable.remove();
                                log.info("remove topic[{}] all queue, from topicQueueTable, because channel destroyed",
                                    topic);
                            }
                        }
                    }
                } finally {
                    this.lock.writeLock().unlock();
                }
            } catch (Exception e) {
                log.error("onChannelDestroy Exception", e);
            }
        }
    }

第二个是每10分钟打印一次NameServer的配置参数。即KVConfigManager.configTable变量的内容。

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this.scheduledExecutorService.scheduleAtFixedRate(new Runnable() {

            @Override
            public void run() {
                NamesrvController.this.kvConfigManager.printAllPeriodically();
            }
        }, 1, 10, TimeUnit.MINUTES);

然后这个初始化就差不多完成了，后面只需要把remotingServer start一下就好了

处理请求

直接上代码，其实主体是swtich case去判断

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@Override
    public RemotingCommand processRequest(ChannelHandlerContext ctx,
        RemotingCommand request) throws RemotingCommandException {

        if (ctx != null) {
            log.debug("receive request, {} {} {}",
                request.getCode(),
                RemotingHelper.parseChannelRemoteAddr(ctx.channel()),
                request);
        }


        switch (request.getCode()) {
            case RequestCode.PUT_KV_CONFIG:
                return this.putKVConfig(ctx, request);
            case RequestCode.GET_KV_CONFIG:
                return this.getKVConfig(ctx, request);
            case RequestCode.DELETE_KV_CONFIG:
                return this.deleteKVConfig(ctx, request);
            case RequestCode.QUERY_DATA_VERSION:
                return queryBrokerTopicConfig(ctx, request);
            case RequestCode.REGISTER_BROKER:
                Version brokerVersion = MQVersion.value2Version(request.getVersion());
                if (brokerVersion.ordinal() >= MQVersion.Version.V3_0_11.ordinal()) {
                    return this.registerBrokerWithFilterServer(ctx, request);
                } else {
                    return this.registerBroker(ctx, request);
                }
            case RequestCode.UNREGISTER_BROKER:
                return this.unregisterBroker(ctx, request);
            case RequestCode.GET_ROUTEINTO_BY_TOPIC:
                return this.getRouteInfoByTopic(ctx, request);
            case RequestCode.GET_BROKER_CLUSTER_INFO:
                return this.getBrokerClusterInfo(ctx, request);
            case RequestCode.WIPE_WRITE_PERM_OF_BROKER:
                return this.wipeWritePermOfBroker(ctx, request);
            case RequestCode.GET_ALL_TOPIC_LIST_FROM_NAMESERVER:
                return getAllTopicListFromNameserver(ctx, request);
            case RequestCode.DELETE_TOPIC_IN_NAMESRV:
                return deleteTopicInNamesrv(ctx, request);
            case RequestCode.GET_KVLIST_BY_NAMESPACE:
                return this.getKVListByNamespace(ctx, request);
            case RequestCode.GET_TOPICS_BY_CLUSTER:
                return this.getTopicsByCluster(ctx, request);
            case RequestCode.GET_SYSTEM_TOPIC_LIST_FROM_NS:
                return this.getSystemTopicListFromNs(ctx, request);
            case RequestCode.GET_UNIT_TOPIC_LIST:
                return this.getUnitTopicList(ctx, request);
            case RequestCode.GET_HAS_UNIT_SUB_TOPIC_LIST:
                return this.getHasUnitSubTopicList(ctx, request);
            case RequestCode.GET_HAS_UNIT_SUB_UNUNIT_TOPIC_LIST:
                return this.getHasUnitSubUnUnitTopicList(ctx, request);
            case RequestCode.UPDATE_NAMESRV_CONFIG:
                return this.updateConfig(ctx, request);
            case RequestCode.GET_NAMESRV_CONFIG:
                return this.getConfig(ctx, request);
            default:
                break;
        }
        return null;
    }

以broker注册为例，

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case RequestCode.REGISTER_BROKER:
                Version brokerVersion = MQVersion.value2Version(request.getVersion());
                if (brokerVersion.ordinal() >= MQVersion.Version.V3_0_11.ordinal()) {
                    return this.registerBrokerWithFilterServer(ctx, request);
                } else {
                    return this.registerBroker(ctx, request);
                }

做了个简单的版本管理，我们看下前面一个的代码

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public RemotingCommand registerBrokerWithFilterServer(ChannelHandlerContext ctx, RemotingCommand request)
    throws RemotingCommandException {
    final RemotingCommand response = RemotingCommand.createResponseCommand(RegisterBrokerResponseHeader.class);
    final RegisterBrokerResponseHeader responseHeader = (RegisterBrokerResponseHeader) response.readCustomHeader();
    final RegisterBrokerRequestHeader requestHeader =
        (RegisterBrokerRequestHeader) request.decodeCommandCustomHeader(RegisterBrokerRequestHeader.class);

    if (!checksum(ctx, request, requestHeader)) {
        response.setCode(ResponseCode.SYSTEM_ERROR);
        response.setRemark("crc32 not match");
        return response;
    }

    RegisterBrokerBody registerBrokerBody = new RegisterBrokerBody();

    if (request.getBody() != null) {
        try {
            registerBrokerBody = RegisterBrokerBody.decode(request.getBody(), requestHeader.isCompressed());
        } catch (Exception e) {
            throw new RemotingCommandException("Failed to decode RegisterBrokerBody", e);
        }
    } else {
        registerBrokerBody.getTopicConfigSerializeWrapper().getDataVersion().setCounter(new AtomicLong(0));
        registerBrokerBody.getTopicConfigSerializeWrapper().getDataVersion().setTimestamp(0);
    }

    RegisterBrokerResult result = this.namesrvController.getRouteInfoManager().registerBroker(
        requestHeader.getClusterName(),
        requestHeader.getBrokerAddr(),
        requestHeader.getBrokerName(),
        requestHeader.getBrokerId(),
        requestHeader.getHaServerAddr(),
        registerBrokerBody.getTopicConfigSerializeWrapper(),
        registerBrokerBody.getFilterServerList(),
        ctx.channel());

    responseHeader.setHaServerAddr(result.getHaServerAddr());
    responseHeader.setMasterAddr(result.getMasterAddr());

    byte[] jsonValue = this.namesrvController.getKvConfigManager().getKVListByNamespace(NamesrvUtil.NAMESPACE_ORDER_TOPIC_CONFIG);
    response.setBody(jsonValue);

    response.setCode(ResponseCode.SUCCESS);
    response.setRemark(null);
    return response;
}

可以看到主要的逻辑还是在org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#registerBroker这个方法里

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public RegisterBrokerResult registerBroker(
        final String clusterName,
        final String brokerAddr,
        final String brokerName,
        final long brokerId,
        final String haServerAddr,
        final TopicConfigSerializeWrapper topicConfigWrapper,
        final List<String> filterServerList,
        final Channel channel) {
        RegisterBrokerResult result = new RegisterBrokerResult();
        try {
            try {
                this.lock.writeLock().lockInterruptibly();

              // 更新这个clusterAddrTable
                Set<String> brokerNames = this.clusterAddrTable.get(clusterName);
                if (null == brokerNames) {
                    brokerNames = new HashSet<String>();
                    this.clusterAddrTable.put(clusterName, brokerNames);
                }
                brokerNames.add(brokerName);

                boolean registerFirst = false;

              // 更新brokerAddrTable
                BrokerData brokerData = this.brokerAddrTable.get(brokerName);
                if (null == brokerData) {
                    registerFirst = true;
                    brokerData = new BrokerData(clusterName, brokerName, new HashMap<Long, String>());
                    this.brokerAddrTable.put(brokerName, brokerData);
                }
                Map<Long, String> brokerAddrsMap = brokerData.getBrokerAddrs();
                //Switch slave to master: first remove <1, IP:PORT> in namesrv, then add <0, IP:PORT>
                //The same IP:PORT must only have one record in brokerAddrTable
                Iterator<Entry<Long, String>> it = brokerAddrsMap.entrySet().iterator();
                while (it.hasNext()) {
                    Entry<Long, String> item = it.next();
                    if (null != brokerAddr && brokerAddr.equals(item.getValue()) && brokerId != item.getKey()) {
                        it.remove();
                    }
                }

                String oldAddr = brokerData.getBrokerAddrs().put(brokerId, brokerAddr);
                registerFirst = registerFirst || (null == oldAddr);

              // 更新了org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#topicQueueTable中的数据
                if (null != topicConfigWrapper
                    && MixAll.MASTER_ID == brokerId) {
                    if (this.isBrokerTopicConfigChanged(brokerAddr, topicConfigWrapper.getDataVersion())
                        || registerFirst) {
                        ConcurrentMap<String, TopicConfig> tcTable =
                            topicConfigWrapper.getTopicConfigTable();
                        if (tcTable != null) {
                            for (Map.Entry<String, TopicConfig> entry : tcTable.entrySet()) {
                                this.createAndUpdateQueueData(brokerName, entry.getValue());
                            }
                        }
                    }
                }

              // 更新活跃broker信息
                BrokerLiveInfo prevBrokerLiveInfo = this.brokerLiveTable.put(brokerAddr,
                    new BrokerLiveInfo(
                        System.currentTimeMillis(),
                        topicConfigWrapper.getDataVersion(),
                        channel,
                        haServerAddr));
                if (null == prevBrokerLiveInfo) {
                    log.info("new broker registered, {} HAServer: {}", brokerAddr, haServerAddr);
                }

              // 处理filter
                if (filterServerList != null) {
                    if (filterServerList.isEmpty()) {
                        this.filterServerTable.remove(brokerAddr);
                    } else {
                        this.filterServerTable.put(brokerAddr, filterServerList);
                    }
                }

              // 当当前broker非master时返回master信息
                if (MixAll.MASTER_ID != brokerId) {
                    String masterAddr = brokerData.getBrokerAddrs().get(MixAll.MASTER_ID);
                    if (masterAddr != null) {
                        BrokerLiveInfo brokerLiveInfo = this.brokerLiveTable.get(masterAddr);
                        if (brokerLiveInfo != null) {
                            result.setHaServerAddr(brokerLiveInfo.getHaServerAddr());
                            result.setMasterAddr(masterAddr);
                        }
                    }
                }
            } finally {
                this.lock.writeLock().unlock();
            }
        } catch (Exception e) {
            log.error("registerBroker Exception", e);
        }

        return result;
    }

这个是注册 broker 的逻辑，再看下根据 topic 获取 broker 信息和 topic 信息，org.apache.rocketmq.namesrv.processor.DefaultRequestProcessor#getRouteInfoByTopic 主要是这个方法的逻辑

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public RemotingCommand getRouteInfoByTopic(ChannelHandlerContext ctx,
        RemotingCommand request) throws RemotingCommandException {
        final RemotingCommand response = RemotingCommand.createResponseCommand(null);
        final GetRouteInfoRequestHeader requestHeader =
            (GetRouteInfoRequestHeader) request.decodeCommandCustomHeader(GetRouteInfoRequestHeader.class);

        TopicRouteData topicRouteData = this.namesrvController.getRouteInfoManager().pickupTopicRouteData(requestHeader.getTopic());

        if (topicRouteData != null) {
            if (this.namesrvController.getNamesrvConfig().isOrderMessageEnable()) {
                String orderTopicConf =
                    this.namesrvController.getKvConfigManager().getKVConfig(NamesrvUtil.NAMESPACE_ORDER_TOPIC_CONFIG,
                        requestHeader.getTopic());
                topicRouteData.setOrderTopicConf(orderTopicConf);
            }

            byte[] content = topicRouteData.encode();
            response.setBody(content);
            response.setCode(ResponseCode.SUCCESS);
            response.setRemark(null);
            return response;
        }

        response.setCode(ResponseCode.TOPIC_NOT_EXIST);
        response.setRemark("No topic route info in name server for the topic: " + requestHeader.getTopic()
            + FAQUrl.suggestTodo(FAQUrl.APPLY_TOPIC_URL));
        return response;
    }

首先调用org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#pickupTopicRouteData从org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#topicQueueTable获取到org.apache.rocketmq.common.protocol.route.QueueData这里面存了 brokerName，再通过org.apache.rocketmq.namesrv.routeinfo.RouteInfoManager#brokerAddrTable里获取到 broker 的地址信息等，然后再获取 orderMessage 的配置。

简要分析了下 RocketMQ 的 NameServer 的代码，比较粗粒度。

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

2020年中总结

发表于 分类于 ， ， 阅读次数： 阅读次数： Disqus：

很快2020 年就过了一半了，而且是今年这么特殊的一年，很多事情都发生的出乎意料，疫情这个绕不过去的话题，之前写了点比较愤青的文字，感觉不太适合发出来就烂在草稿箱里吧，这个目前一大影响估计是今年都没办法完全摘下口罩了，前面几个月来回杭州都开车，因为彭埠大桥不通行了，实在是非常不方便，每条路都灰常堵，心累，吐槽下杭州的交通规划和交警同志，工作实在做的不咋地。

另外一件是就是蜗壳，从前不知道黝黑蜗壳是啥意思，只是经常会在他的视频里看到，大学的时候在缘网下了一个集锦，炒鸡帅气，各种空接扣篮，越来越能明白那句“你永远不知道意外和明天不知道哪个会先来,且行且珍惜”的含义，只是听了很多道理，依然活不好这一生，知易行难，王阳明真的是这方面的大师，有空可以看看这方面的书，一直想写写我跟篮球跟蜗壳的这十几年，争取能早日写好吧，不过得找个静得下来的时候写。

正事方面上半年还是挺让人失望的，没有达成一些目标，应该还是能力不足吧，技术方面分析一下还是停留在看的表面层，有些实操的，或者结合业务场景的能力不太行，算是在坚持写写 blog，主要是被这个每周一篇的目标推着走，有时会比较焦虑，内容产出也还比较差，希望能在后面有些改善，可能会降低频率，只是觉得降低了也不一定能有比较好的提升，无法战胜自己的惰性，所以暂时还是坚持下这个目标吧，还有就是 coding 能力，有时候也应该刷刷题，提升思维敏捷度，大脑用太少可能生锈了，况且本来就不是很有优势，虽然失望也只能继续努力吧，日拱一卒，来日方长，加油吧~😔

还有就是跑步减肥了，截止今天，上半年跑了 136 公里了，因为疫情影响，农历年后是从 4 月 17 号开始跑的，去年跑到了 300 公里，奖励自己了一个手表（真的挺后悔的，还不如 200 块买个手表），今年希望可以能在这个基础上再进一步，一直跟领导说，跑步算是我坚持下来的唯一一个好习惯了，618 买了个跑步机，周末回家了可以不受天气影响的多跑跑，不过如果天气好可能还是会出去跑跑，跑步机跑道多少还是有点拘束，只是感觉可能是我还是吃得太多了🤦‍♂️，效果不是很明显，还在 80 这个坎徘徊，等于浪费了大半年，可能是年初的项目太费心力，压力比较大，吃得更多，是不是可以算工伤😄，这方面也需要好好调整，可以放得开一点，虽然不太可能一下子到位，但是总要去努力下，随着年龄成长总要承担更多，也要看得开一点，没法事事如愿，尽力就好了，减肥这个事情还在结合一些俯卧撑啥的，希望也能坚持下去，加油吧，不知道原话怎么说的，意思是人类最大的勇敢就是看透了人世间的苦难，仍然热爱生活。我当然没可能让内心变得这么强大，试着去努力吧，奥力给！

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

2020年中总结

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很快2020 年就过了一半了，而且是今年这么特殊的一年，很多事情都发生的出乎意料，疫情这个绕不过去的话题，之前写了点比较愤青的文字，感觉不太适合发出来就烂在草稿箱里吧，这个目前一大影响估计是今年都没办法完全摘下口罩了，前面几个月来回杭州都开车，因为彭埠大桥不通行了，实在是非常不方便，每条路都灰常堵，心累，吐槽下杭州的交通规划和交警同志，工作实在做的不咋地。

另外一件是就是蜗壳，从前不知道黝黑蜗壳是啥意思，只是经常会在他的视频里看到，大学的时候在缘网下了一个集锦，炒鸡帅气，各种空接扣篮，越来越能明白那句“你永远不知道意外和明天不知道哪个会先来,且行且珍惜”的含义，只是听了很多道理，依然活不好这一生，知易行难，王阳明真的是这方面的大师，有空可以看看这方面的书，一直想写写我跟篮球跟蜗壳的这十几年，争取能早日写好吧，不过得找个静得下来的时候写。

正事方面上半年还是挺让人失望的，没有达成一些目标，应该还是能力不足吧，技术方面分析一下还是停留在看的表面层，有些实操的，或者结合业务场景的能力不太行，算是在坚持写写 blog，主要是被这个每周一篇的目标推着走，有时会比较焦虑，内容产出也还比较差，希望能在后面有些改善，可能会降低频率，只是觉得降低了也不一定能有比较好的提升，无法战胜自己的惰性，所以暂时还是坚持下这个目标吧，还有就是 coding 能力，有时候也应该刷刷题，提升思维敏捷度，大脑用太少可能生锈了，况且本来就不是很有优势，虽然失望也只能继续努力吧，日拱一卒，来日方长，加油吧~😔

还有就是跑步减肥了，截止今天，上半年跑了 136 公里了，因为疫情影响，农历年后是从 4 月 17 号开始跑的，去年跑到了 300 公里，奖励自己了一个手表（真的挺后悔的，还不如 200 块买个手表），今年希望可以能在这个基础上再进一步，一直跟领导说，跑步算是我坚持下来的唯一一个好习惯了，618 买了个跑步机，周末回家了可以不受天气影响的多跑跑，不过如果天气好可能还是会出去跑跑，跑步机跑道多少还是有点拘束，只是感觉可能是我还是吃得太多了🤦‍♂️，效果不是很明显，还在 80 这个坎徘徊，等于浪费了大半年，可能是年初的项目太费心力，压力比较大，吃得更多，是不是可以算工伤😄，这方面也需要好好调整，可以放得开一点，虽然不太可能一下子到位，但是总要去努力下，随着年龄成长总要承担更多，也要看得开一点，没法事事如愿，尽力就好了，减肥这个事情还在结合一些俯卧撑啥的，希望也能坚持下去，加油吧，不知道原话怎么说的，意思是人类最大的勇敢就是看透了人世间的苦难，仍然热爱生活。我当然没可能让内心变得这么强大，试着去努力吧，奥力给！

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Leetcode 25 Reverse Nodes in k-Group 题解分析

发表于 更新于 分类于 ， 阅读次数： 阅读次数： Disqus：

Given a linked list, reverse the nodes of a linked list k at a time and return its modified list.

k is a positive integer and is less than or equal to the length of the linked list. If the number of nodes is not a multiple of k then left-out nodes in the end should remain as it is.

Example:

Given this linked list: 1->2->3->4->5

For k = 2, you should return: 2->1->4->3->5

For k = 3, you should return: 3->2->1->4->5

Note:

  • Only constant extra memory is allowed.
  • You may not alter the values in the list’s nodes, only nodes itself may be changed.

这个题也算是经典中的经典了，各种算法题中的保留曲目，可能不全一样，不过反正都是逆转，这个算是比较难度大的，普通的应该就是整个链表反转，或者只对一个链表中的 m 到 n 位做翻转，这里是 k 个一组做翻转，其实这道题比较难的点是两个，一个是想清楚怎么处理，一个是代码的处理
简单来看下

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public ListNode reverseKGroup(ListNode head, int k) {
    if (k <= 1) {
        return head;
    }
    if (head == null) {
        return null;
    }
    ListNode tempEnd = head;
    ListNode tempHead = head;
    ListNode lastHead = null;
    boolean isHeadSet = false;
    while (true) {
        // k 个一组获取头尾
        for (int i = 0; i < k - 1; i++) {
            System.out.println(tempEnd.val);
            if (tempEnd.next != null) {
                tempEnd  = tempEnd.next;
            } else {
                tempEnd = null;
                break;
            }
        }
        if (tempEnd != null) {
            if (!isHeadSet) {
                // 只有在第一组返回的时候需要赋值给 head
                head = reverse(null, tempHead, tempEnd);
                isHeadSet = !isHeadSet;
            } else {
                // 后面的需要将前一组的尾巴传进去，作为后一组头
                reverse(lastHead, tempHead, tempEnd);
            }
            if (tempHead.next != null) {
                // 如果后续节点还有
                lastHead = tempHead;
                tempEnd = tempHead.next;
                tempHead = tempHead.next;
            } else {
                break;
            }
        } else {
            break;
        }
    }

    return head;
}
public ListNode reverse(ListNode lastHead, ListNode tempHead, ListNode tempEnd) {
    // k 个一组的头尾传进来
    ListNode tail = null;
    while (tail != tempEnd) {
        // tail 表示是将头往后换时，把后面那个先拿着
        tail = tempHead.next;
        // 然后就是交换
        tempHead.next = tempEnd.next;
        tempEnd.next = tempHead;
        tempHead = tail;
    }
    if (lastHead !=null) {
        // 如果不是第一组，则需要接上前一组的尾巴
        lastHead.next = tail;
    }
    return tail;
}
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Leetcode 25 Reverse Nodes in k-Group 题解分析

发表于 更新于 分类于 ， 阅读次数： 阅读次数： Disqus：

Given a linked list, reverse the nodes of a linked list k at a time and return its modified list.

k is a positive integer and is less than or equal to the length of the linked list. If the number of nodes is not a multiple of k then left-out nodes in the end should remain as it is.

Example:

Given this linked list: 1->2->3->4->5

For k = 2, you should return: 2->1->4->3->5

For k = 3, you should return: 3->2->1->4->5

Note:

  • Only constant extra memory is allowed.
  • You may not alter the values in the list’s nodes, only nodes itself may be changed.

这个题也算是经典中的经典了，各种算法题中的保留曲目，可能不全一样，不过反正都是逆转，这个算是比较难度大的，普通的应该就是整个链表反转，或者只对一个链表中的 m 到 n 位做翻转，这里是 k 个一组做翻转，其实这道题比较难的点是两个，一个是想清楚怎么处理，一个是代码的处理
简单来看下

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public ListNode reverseKGroup(ListNode head, int k) {
    if (k <= 1) {
        return head;
    }
    if (head == null) {
        return null;
    }
    ListNode tempEnd = head;
    ListNode tempHead = head;
    ListNode lastHead = null;
    boolean isHeadSet = false;
    while (true) {
        // k 个一组获取头尾
        for (int i = 0; i < k - 1; i++) {
            System.out.println(tempEnd.val);
            if (tempEnd.next != null) {
                tempEnd  = tempEnd.next;
            } else {
                tempEnd = null;
                break;
            }
        }
        if (tempEnd != null) {
            if (!isHeadSet) {
                // 只有在第一组返回的时候需要赋值给 head
                head = reverse(null, tempHead, tempEnd);
                isHeadSet = !isHeadSet;
            } else {
                // 后面的需要将前一组的尾巴传进去，作为后一组头
                reverse(lastHead, tempHead, tempEnd);
            }
            if (tempHead.next != null) {
                // 如果后续节点还有
                lastHead = tempHead;
                tempEnd = tempHead.next;
                tempHead = tempHead.next;
            } else {
                break;
            }
        } else {
            break;
        }
    }

    return head;
}
public ListNode reverse(ListNode lastHead, ListNode tempHead, ListNode tempEnd) {
    // k 个一组的头尾传进来
    ListNode tail = null;
    while (tail != tempEnd) {
        // tail 表示是将头往后换时，把后面那个先拿着
        tail = tempHead.next;
        // 然后就是交换
        tempHead.next = tempEnd.next;
        tempEnd.next = tempHead;
        tempHead = tail;
    }
    if (lastHead !=null) {
        // 如果不是第一组，则需要接上前一组的尾巴
        lastHead.next = tail;
    }
    return tail;
}
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Leetcode 3 Longest Substring Without Repeating Characters 题解分析

发表于 分类于 ， ， ， ， 阅读次数： 阅读次数： Disqus：

又做了个题，看记录是以前用 C++写过的，现在捋一捋思路，用 Java 再写了一下，思路还是比较清晰的，但是边界细节处理得比较差

简要介绍

Given a string s, find the length of the longest substring without repeating characters.

样例

Example 1:

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Input: s = "abcabcbb"
Output: 3
Explanation: The answer is "abc", with the length of 3.

Example 2:

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Input: s = "bbbbb"
Output: 1
Explanation: The answer is "b", with the length of 1.

Example 3:

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Input: s = "pwwkew"
Output: 3
Explanation: The answer is "wke", with the length of 3.
Notice that the answer must be a substring, "pwke" is a subsequence and not a substring.

Example 4:

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Input: s = ""
Output: 0

就是一个最长不重复的字符串长度，因为也是中等难度的题，不太需要特别复杂的思考，最基本的就是O(N*N)两重循环，不过显然不太好，万一超时间，还有一种就是线性复杂度的了，这个就是需要搞定一个思路，比如字符串时 abcdefgaqwrty，比如遍历到第二个a的时候其实不用再从头去遍历了，只要把前面那个a给排除掉，继续往下算就好了

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class Solution {
    Map<String, Integer> counter = new HashMap<>();
    public int lengthOfLongestSubstring(String s) {
        int length = s.length();
        // 当前的长度
        int subStringLength = 0;
        // 最长的长度
        int maxSubStringLength = 0;
        // 考虑到重复的位置已经被跳过的情况，即已经在当前长度的字符串范围之前的重复字符不需要回溯
        int lastDuplicatePos = -1;
        for (int i = 0; i < length; i++) {
            // 使用 map 存储字符和上一次出现的位置，如果存在并且大于上一次重复位置
            if (counter.get(String.valueOf(s.charAt(i))) != null && counter.get(String.valueOf(s.charAt(i))) > lastDuplicatePos) {
                // 记录重复位置
                lastDuplicatePos = counter.get(String.valueOf(s.charAt(i)));
                // 重置不重复子串的长度，减去重复起点
                subStringLength = i - counter.get(String.valueOf(s.charAt(i))) - 1;
                // 替换当前位置
                counter.replace(String.valueOf(s.charAt(i)), i);
            } else {
                // 如果不存在就直接 put
                counter.put(String.valueOf(s.charAt(i)), i);
            }
            // 长度累加
            subStringLength++;
            if (subStringLength > maxSubStringLength) {
                // 简单替换
                maxSubStringLength = subStringLength;
            }
        }
        return maxSubStringLength;
    }
}

注释应该写的比较清楚了。

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Leetcode 3 Longest Substring Without Repeating Characters 题解分析

发表于 分类于 ， ， ， ， 阅读次数： 阅读次数： Disqus：

又做了个题，看记录是以前用 C++写过的，现在捋一捋思路，用 Java 再写了一下，思路还是比较清晰的，但是边界细节处理得比较差

简要介绍

Given a string s, find the length of the longest substring without repeating characters.

样例

Example 1:

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Input: s = "abcabcbb"
Output: 3
Explanation: The answer is "abc", with the length of 3.

Example 2:

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Input: s = "bbbbb"
Output: 1
Explanation: The answer is "b", with the length of 1.

Example 3:

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Input: s = "pwwkew"
Output: 3
Explanation: The answer is "wke", with the length of 3.
Notice that the answer must be a substring, "pwke" is a subsequence and not a substring.

Example 4:

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Input: s = ""
Output: 0

就是一个最长不重复的字符串长度，因为也是中等难度的题，不太需要特别复杂的思考，最基本的就是O(N*N)两重循环，不过显然不太好，万一超时间，还有一种就是线性复杂度的了，这个就是需要搞定一个思路，比如字符串时 abcdefgaqwrty，比如遍历到第二个a的时候其实不用再从头去遍历了，只要把前面那个a给排除掉，继续往下算就好了

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class Solution {
    Map<String, Integer> counter = new HashMap<>();
    public int lengthOfLongestSubstring(String s) {
        int length = s.length();
        // 当前的长度
        int subStringLength = 0;
        // 最长的长度
        int maxSubStringLength = 0;
        // 考虑到重复的位置已经被跳过的情况，即已经在当前长度的字符串范围之前的重复字符不需要回溯
        int lastDuplicatePos = -1;
        for (int i = 0; i < length; i++) {
            // 使用 map 存储字符和上一次出现的位置，如果存在并且大于上一次重复位置
            if (counter.get(String.valueOf(s.charAt(i))) != null && counter.get(String.valueOf(s.charAt(i))) > lastDuplicatePos) {
                // 记录重复位置
                lastDuplicatePos = counter.get(String.valueOf(s.charAt(i)));
                // 重置不重复子串的长度，减去重复起点
                subStringLength = i - counter.get(String.valueOf(s.charAt(i))) - 1;
                // 替换当前位置
                counter.replace(String.valueOf(s.charAt(i)), i);
            } else {
                // 如果不存在就直接 put
                counter.put(String.valueOf(s.charAt(i)), i);
            }
            // 长度累加
            subStringLength++;
            if (subStringLength > maxSubStringLength) {
                // 简单替换
                maxSubStringLength = subStringLength;
            }
        }
        return maxSubStringLength;
    }
}

注释应该写的比较清楚了。

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What hurts more, the pain of hard work or the pain of regret?

mybatis 的缓存是怎么回事

发表于 分类于 ， ， ， ， ， 阅读次数： 阅读次数： Disqus：

Java 真的是任何一个中间件，比较常用的那种，都有很多内容值得深挖，比如这个缓存，慢慢有过一些感悟，比如如何提升性能，缓存无疑是一大重要手段，最底层开始 CPU 就有缓存，而且又小又贵，再往上一点内存一般作为硬盘存储在运行时的存储，一般在代码里也会用内存作为一些本地缓存，譬如数据库，像 mysql 这种也是有innodb_buffer_pool来提升查询效率，本质上理解就是用更快的存储作为相对慢存储的缓存，减少查询直接访问较慢的存储，并且这个都是相对的，比起 cpu 的缓存，那内存也是渣，但是与普通机械硬盘相比，那也是两个次元的水平。

闲扯这么多来说说 mybatis 的缓存，mybatis 一般作为一个轻量级的 orm 使用，相对应的就是比较重量级的 hibernate，不过不在这次讨论范围，上一次是主要讲了 mybatis 在解析 sql 过程中，对于两种占位符的不同替换实现策略，这次主要聊下 mybatis 的缓存，前面其实得了解下前置的东西，比如 sqlsession，先当做我们知道 sqlsession 是个什么玩意，可能或多或少的知道 mybatis 是有两级缓存，

一级缓存

第一级的缓存是在 BaseExecutor 中的 PerpetualCache，它是个最基本的缓存实现类，使用了 HashMap 实现缓存功能，代码其实没几十行

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public class PerpetualCache implements Cache {

  private final String id;

  private final Map<Object, Object> cache = new HashMap<>();

  public PerpetualCache(String id) {
    this.id = id;
  }

  @Override
  public String getId() {
    return id;
  }

  @Override
  public int getSize() {
    return cache.size();
  }

  @Override
  public void putObject(Object key, Object value) {
    cache.put(key, value);
  }

  @Override
  public Object getObject(Object key) {
    return cache.get(key);
  }

  @Override
  public Object removeObject(Object key) {
    return cache.remove(key);
  }

  @Override
  public void clear() {
    cache.clear();
  }

  @Override
  public boolean equals(Object o) {
    if (getId() == null) {
      throw new CacheException("Cache instances require an ID.");
    }
    if (this == o) {
      return true;
    }
    if (!(o instanceof Cache)) {
      return false;
    }

    Cache otherCache = (Cache) o;
    return getId().equals(otherCache.getId());
  }

  @Override
  public int hashCode() {
    if (getId() == null) {
      throw new CacheException("Cache instances require an ID.");
    }
    return getId().hashCode();
  }

}

可以看一下BaseExecutor 的构造函数

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protected BaseExecutor(Configuration configuration, Transaction transaction) {
    this.transaction = transaction;
    this.deferredLoads = new ConcurrentLinkedQueue<>();
    this.localCache = new PerpetualCache("LocalCache");
    this.localOutputParameterCache = new PerpetualCache("LocalOutputParameterCache");
    this.closed = false;
    this.configuration = configuration;
    this.wrapper = this;
  }

就是把 PerpetualCache 作为 localCache，然后怎么使用我看简单看一下，BaseExecutor 的查询首先是调用这个函数

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler) throws SQLException {
    BoundSql boundSql = ms.getBoundSql(parameter);
    CacheKey key = createCacheKey(ms, parameter, rowBounds, boundSql);
    return query(ms, parameter, rowBounds, resultHandler, key, boundSql);
  }

可以看到首先是调用了 createCacheKey 方法，这个方法呢，先不看怎么写的，如果我们自己要实现这么个缓存，首先这个缓存 key 的设计也是个问题，如果是以表名加主键作为 key，那么分页查询，或者没有主键的时候就不行，来看看 mybatis 是怎么设计的

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@Override
  public CacheKey createCacheKey(MappedStatement ms, Object parameterObject, RowBounds rowBounds, BoundSql boundSql) {
    if (closed) {
      throw new ExecutorException("Executor was closed.");
    }
    CacheKey cacheKey = new CacheKey();
    cacheKey.update(ms.getId());
    cacheKey.update(rowBounds.getOffset());
    cacheKey.update(rowBounds.getLimit());
    cacheKey.update(boundSql.getSql());
    List<ParameterMapping> parameterMappings = boundSql.getParameterMappings();
    TypeHandlerRegistry typeHandlerRegistry = ms.getConfiguration().getTypeHandlerRegistry();
    // mimic DefaultParameterHandler logic
    for (ParameterMapping parameterMapping : parameterMappings) {
      if (parameterMapping.getMode() != ParameterMode.OUT) {
        Object value;
        String propertyName = parameterMapping.getProperty();
        if (boundSql.hasAdditionalParameter(propertyName)) {
          value = boundSql.getAdditionalParameter(propertyName);
        } else if (parameterObject == null) {
          value = null;
        } else if (typeHandlerRegistry.hasTypeHandler(parameterObject.getClass())) {
          value = parameterObject;
        } else {
          MetaObject metaObject = configuration.newMetaObject(parameterObject);
          value = metaObject.getValue(propertyName);
        }
        cacheKey.update(value);
      }
    }
    if (configuration.getEnvironment() != null) {
      // issue #176
      cacheKey.update(configuration.getEnvironment().getId());
    }
    return cacheKey;
  }

首先需要 id，这个 id 是 mapper 里方法的 id， 然后是偏移量跟返回行数，再就是 sql，然后是参数，基本上是会有影响的都加进去了，在这个 update 里面

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public void update(Object object) {
    int baseHashCode = object == null ? 1 : ArrayUtil.hashCode(object);

    count++;
    checksum += baseHashCode;
    baseHashCode *= count;

    hashcode = multiplier * hashcode + baseHashCode;

    updateList.add(object);
  }

其实是一个 hash 转换，具体不纠结，就是提高特异性，然后回来就是继续调用 query

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler, CacheKey key, BoundSql boundSql) throws SQLException {
    ErrorContext.instance().resource(ms.getResource()).activity("executing a query").object(ms.getId());
    if (closed) {
      throw new ExecutorException("Executor was closed.");
    }
    if (queryStack == 0 && ms.isFlushCacheRequired()) {
      clearLocalCache();
    }
    List<E> list;
    try {
      queryStack++;
      list = resultHandler == null ? (List<E>) localCache.getObject(key) : null;
      if (list != null) {
        handleLocallyCachedOutputParameters(ms, key, parameter, boundSql);
      } else {
        list = queryFromDatabase(ms, parameter, rowBounds, resultHandler, key, boundSql);
      }
    } finally {
      queryStack--;
    }
    if (queryStack == 0) {
      for (DeferredLoad deferredLoad : deferredLoads) {
        deferredLoad.load();
      }
      // issue #601
      deferredLoads.clear();
      if (configuration.getLocalCacheScope() == LocalCacheScope.STATEMENT) {
        // issue #482
        clearLocalCache();
      }
    }
    return list;
  }

可以看到是先从 localCache 里取，取不到再 queryFromDatabase，其实比较简单，这是一级缓存，考虑到 sqlsession 跟 BaseExecutor 的关系，其实是随着 sqlsession 来保证这个缓存不会出现脏数据幻读的情况，当然事务相关的后面可能再单独聊。

二级缓存

其实这个一级二级顺序有点反过来，其实查询的是先走的二级缓存，当然二级的需要配置开启，默认不开，
需要通过

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<setting name="cacheEnabled" value="true"/>

来开启，然后我们的查询就会走到

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public class CachingExecutor implements Executor {

  private final Executor delegate;
  private final TransactionalCacheManager tcm = new TransactionalCacheManager();

这个 Executor 中，这里我放了类里面的元素，发现没有一个 Cache 类，这就是一个特点了，往下看查询过程

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameterObject, RowBounds rowBounds, ResultHandler resultHandler) throws SQLException {
    BoundSql boundSql = ms.getBoundSql(parameterObject);
    CacheKey key = createCacheKey(ms, parameterObject, rowBounds, boundSql);
    return query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
  }

  @Override
  public <E> List<E> query(MappedStatement ms, Object parameterObject, RowBounds rowBounds, ResultHandler resultHandler, CacheKey key, BoundSql boundSql)
      throws SQLException {
    Cache cache = ms.getCache();
    if (cache != null) {
      flushCacheIfRequired(ms);
      if (ms.isUseCache() && resultHandler == null) {
        ensureNoOutParams(ms, boundSql);
        @SuppressWarnings("unchecked")
        List<E> list = (List<E>) tcm.getObject(cache, key);
        if (list == null) {
          list = delegate.query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
          tcm.putObject(cache, key, list); // issue #578 and #116
        }
        return list;
      }
    }
    return delegate.query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
  }

看到没，其实缓存是从 tcm 这个成员变量里取，而这个是什么呢，事务性缓存(直译下)，因为这个其实是用 MappedStatement 里的 Cache 作为key 从 tcm 的 map 取出来的

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public class TransactionalCacheManager {

  private final Map<Cache, TransactionalCache> transactionalCaches = new HashMap<>();

MappedStatement是被全局使用的，所以其实二级缓存是跟着 mapper 的 namespace 走的，可以被多个 CachingExecutor 获取到，就会出现线程安全问题，线程安全问题可以用SynchronizedCache来解决，就是加锁，但是对于事务中的脏读，使用了TransactionalCache来解决这个问题，

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public class TransactionalCache implements Cache {

  private static final Log log = LogFactory.getLog(TransactionalCache.class);

  private final Cache delegate;
  private boolean clearOnCommit;
  private final Map<Object, Object> entriesToAddOnCommit;
  private final Set<Object> entriesMissedInCache;

在事务还没提交的时候，会把中间状态的数据放在 entriesToAddOnCommit 中，只有在提交后会放进共享缓存中，

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public void commit() {
    if (clearOnCommit) {
      delegate.clear();
    }
    flushPendingEntries();
    reset();
  }
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mybatis 的缓存是怎么回事

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Java 真的是任何一个中间件，比较常用的那种，都有很多内容值得深挖，比如这个缓存，慢慢有过一些感悟，比如如何提升性能，缓存无疑是一大重要手段，最底层开始 CPU 就有缓存，而且又小又贵，再往上一点内存一般作为硬盘存储在运行时的存储，一般在代码里也会用内存作为一些本地缓存，譬如数据库，像 mysql 这种也是有innodb_buffer_pool来提升查询效率，本质上理解就是用更快的存储作为相对慢存储的缓存，减少查询直接访问较慢的存储，并且这个都是相对的，比起 cpu 的缓存，那内存也是渣，但是与普通机械硬盘相比，那也是两个次元的水平。

闲扯这么多来说说 mybatis 的缓存，mybatis 一般作为一个轻量级的 orm 使用，相对应的就是比较重量级的 hibernate，不过不在这次讨论范围，上一次是主要讲了 mybatis 在解析 sql 过程中，对于两种占位符的不同替换实现策略，这次主要聊下 mybatis 的缓存，前面其实得了解下前置的东西，比如 sqlsession，先当做我们知道 sqlsession 是个什么玩意，可能或多或少的知道 mybatis 是有两级缓存，

一级缓存

第一级的缓存是在 BaseExecutor 中的 PerpetualCache，它是个最基本的缓存实现类，使用了 HashMap 实现缓存功能，代码其实没几十行

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public class PerpetualCache implements Cache {

  private final String id;

  private final Map<Object, Object> cache = new HashMap<>();

  public PerpetualCache(String id) {
    this.id = id;
  }

  @Override
  public String getId() {
    return id;
  }

  @Override
  public int getSize() {
    return cache.size();
  }

  @Override
  public void putObject(Object key, Object value) {
    cache.put(key, value);
  }

  @Override
  public Object getObject(Object key) {
    return cache.get(key);
  }

  @Override
  public Object removeObject(Object key) {
    return cache.remove(key);
  }

  @Override
  public void clear() {
    cache.clear();
  }

  @Override
  public boolean equals(Object o) {
    if (getId() == null) {
      throw new CacheException("Cache instances require an ID.");
    }
    if (this == o) {
      return true;
    }
    if (!(o instanceof Cache)) {
      return false;
    }

    Cache otherCache = (Cache) o;
    return getId().equals(otherCache.getId());
  }

  @Override
  public int hashCode() {
    if (getId() == null) {
      throw new CacheException("Cache instances require an ID.");
    }
    return getId().hashCode();
  }

}

可以看一下BaseExecutor 的构造函数

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protected BaseExecutor(Configuration configuration, Transaction transaction) {
    this.transaction = transaction;
    this.deferredLoads = new ConcurrentLinkedQueue<>();
    this.localCache = new PerpetualCache("LocalCache");
    this.localOutputParameterCache = new PerpetualCache("LocalOutputParameterCache");
    this.closed = false;
    this.configuration = configuration;
    this.wrapper = this;
  }

就是把 PerpetualCache 作为 localCache，然后怎么使用我看简单看一下，BaseExecutor 的查询首先是调用这个函数

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler) throws SQLException {
    BoundSql boundSql = ms.getBoundSql(parameter);
    CacheKey key = createCacheKey(ms, parameter, rowBounds, boundSql);
    return query(ms, parameter, rowBounds, resultHandler, key, boundSql);
  }

可以看到首先是调用了 createCacheKey 方法，这个方法呢，先不看怎么写的，如果我们自己要实现这么个缓存，首先这个缓存 key 的设计也是个问题，如果是以表名加主键作为 key，那么分页查询，或者没有主键的时候就不行，来看看 mybatis 是怎么设计的

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@Override
  public CacheKey createCacheKey(MappedStatement ms, Object parameterObject, RowBounds rowBounds, BoundSql boundSql) {
    if (closed) {
      throw new ExecutorException("Executor was closed.");
    }
    CacheKey cacheKey = new CacheKey();
    cacheKey.update(ms.getId());
    cacheKey.update(rowBounds.getOffset());
    cacheKey.update(rowBounds.getLimit());
    cacheKey.update(boundSql.getSql());
    List<ParameterMapping> parameterMappings = boundSql.getParameterMappings();
    TypeHandlerRegistry typeHandlerRegistry = ms.getConfiguration().getTypeHandlerRegistry();
    // mimic DefaultParameterHandler logic
    for (ParameterMapping parameterMapping : parameterMappings) {
      if (parameterMapping.getMode() != ParameterMode.OUT) {
        Object value;
        String propertyName = parameterMapping.getProperty();
        if (boundSql.hasAdditionalParameter(propertyName)) {
          value = boundSql.getAdditionalParameter(propertyName);
        } else if (parameterObject == null) {
          value = null;
        } else if (typeHandlerRegistry.hasTypeHandler(parameterObject.getClass())) {
          value = parameterObject;
        } else {
          MetaObject metaObject = configuration.newMetaObject(parameterObject);
          value = metaObject.getValue(propertyName);
        }
        cacheKey.update(value);
      }
    }
    if (configuration.getEnvironment() != null) {
      // issue #176
      cacheKey.update(configuration.getEnvironment().getId());
    }
    return cacheKey;
  }

首先需要 id，这个 id 是 mapper 里方法的 id， 然后是偏移量跟返回行数，再就是 sql，然后是参数，基本上是会有影响的都加进去了，在这个 update 里面

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public void update(Object object) {
    int baseHashCode = object == null ? 1 : ArrayUtil.hashCode(object);

    count++;
    checksum += baseHashCode;
    baseHashCode *= count;

    hashcode = multiplier * hashcode + baseHashCode;

    updateList.add(object);
  }

其实是一个 hash 转换，具体不纠结，就是提高特异性，然后回来就是继续调用 query

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler, CacheKey key, BoundSql boundSql) throws SQLException {
    ErrorContext.instance().resource(ms.getResource()).activity("executing a query").object(ms.getId());
    if (closed) {
      throw new ExecutorException("Executor was closed.");
    }
    if (queryStack == 0 && ms.isFlushCacheRequired()) {
      clearLocalCache();
    }
    List<E> list;
    try {
      queryStack++;
      list = resultHandler == null ? (List<E>) localCache.getObject(key) : null;
      if (list != null) {
        handleLocallyCachedOutputParameters(ms, key, parameter, boundSql);
      } else {
        list = queryFromDatabase(ms, parameter, rowBounds, resultHandler, key, boundSql);
      }
    } finally {
      queryStack--;
    }
    if (queryStack == 0) {
      for (DeferredLoad deferredLoad : deferredLoads) {
        deferredLoad.load();
      }
      // issue #601
      deferredLoads.clear();
      if (configuration.getLocalCacheScope() == LocalCacheScope.STATEMENT) {
        // issue #482
        clearLocalCache();
      }
    }
    return list;
  }

可以看到是先从 localCache 里取，取不到再 queryFromDatabase，其实比较简单，这是一级缓存，考虑到 sqlsession 跟 BaseExecutor 的关系，其实是随着 sqlsession 来保证这个缓存不会出现脏数据幻读的情况，当然事务相关的后面可能再单独聊。

二级缓存

其实这个一级二级顺序有点反过来，其实查询的是先走的二级缓存，当然二级的需要配置开启，默认不开，
需要通过

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<setting name="cacheEnabled" value="true"/>

来开启，然后我们的查询就会走到

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public class CachingExecutor implements Executor {

  private final Executor delegate;
  private final TransactionalCacheManager tcm = new TransactionalCacheManager();

这个 Executor 中，这里我放了类里面的元素，发现没有一个 Cache 类，这就是一个特点了，往下看查询过程

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameterObject, RowBounds rowBounds, ResultHandler resultHandler) throws SQLException {
    BoundSql boundSql = ms.getBoundSql(parameterObject);
    CacheKey key = createCacheKey(ms, parameterObject, rowBounds, boundSql);
    return query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
  }

  @Override
  public <E> List<E> query(MappedStatement ms, Object parameterObject, RowBounds rowBounds, ResultHandler resultHandler, CacheKey key, BoundSql boundSql)
      throws SQLException {
    Cache cache = ms.getCache();
    if (cache != null) {
      flushCacheIfRequired(ms);
      if (ms.isUseCache() && resultHandler == null) {
        ensureNoOutParams(ms, boundSql);
        @SuppressWarnings("unchecked")
        List<E> list = (List<E>) tcm.getObject(cache, key);
        if (list == null) {
          list = delegate.query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
          tcm.putObject(cache, key, list); // issue #578 and #116
        }
        return list;
      }
    }
    return delegate.query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
  }

看到没，其实缓存是从 tcm 这个成员变量里取，而这个是什么呢，事务性缓存(直译下)，因为这个其实是用 MappedStatement 里的 Cache 作为key 从 tcm 的 map 取出来的

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public class TransactionalCacheManager {

  private final Map<Cache, TransactionalCache> transactionalCaches = new HashMap<>();

MappedStatement是被全局使用的，所以其实二级缓存是跟着 mapper 的 namespace 走的，可以被多个 CachingExecutor 获取到，就会出现线程安全问题，线程安全问题可以用SynchronizedCache来解决，就是加锁，但是对于事务中的脏读，使用了TransactionalCache来解决这个问题，

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public class TransactionalCache implements Cache {

  private static final Log log = LogFactory.getLog(TransactionalCache.class);

  private final Cache delegate;
  private boolean clearOnCommit;
  private final Map<Object, Object> entriesToAddOnCommit;
  private final Set<Object> entriesMissedInCache;

在事务还没提交的时候，会把中间状态的数据放在 entriesToAddOnCommit 中，只有在提交后会放进共享缓存中，

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public void commit() {
    if (clearOnCommit) {
      delegate.clear();
    }
    flushPendingEntries();
    reset();
  }
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Leetcode 104 二叉树的最大深度(Maximum Depth of Binary Tree) 题解分析

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题目介绍

给定一个二叉树，找出其最大深度。

二叉树的深度为根节点到最远叶子节点的最长路径上的节点数。

说明: 叶子节点是指没有子节点的节点。

示例：
给定二叉树 [3,9,20,null,null,15,7]，

1
2
3
4
5
  3
 / \
9  20
  /  \
 15   7

返回它的最大深度 3 。

代码

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// 主体是个递归的应用
public int maxDepth(TreeNode root) {
    // 节点的退出条件之一
    if (root == null) {
        return 0;
    }
    int left = 0;
    int right = 0;
    // 存在左子树，就递归左子树
    if (root.left != null) {
        left = maxDepth(root.left);
    }
    // 存在右子树，就递归右子树
    if (root.right != null) {
        right = maxDepth(root.right);
    }
    // 前面返回后，左右取大者
    return Math.max(left + 1, right + 1);
}

分析

其实对于树这类题，一般是以递归形式比较方便，只是要注意退出条件

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Leetcode 104 二叉树的最大深度(Maximum Depth of Binary Tree) 题解分析

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题目介绍

给定一个二叉树，找出其最大深度。

二叉树的深度为根节点到最远叶子节点的最长路径上的节点数。

说明: 叶子节点是指没有子节点的节点。

示例：
给定二叉树 [3,9,20,null,null,15,7]，

1
2
3
4
5
  3
 / \
9  20
  /  \
 15   7

返回它的最大深度 3 。

代码

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// 主体是个递归的应用
public int maxDepth(TreeNode root) {
    // 节点的退出条件之一
    if (root == null) {
        return 0;
    }
    int left = 0;
    int right = 0;
    // 存在左子树，就递归左子树
    if (root.left != null) {
        left = maxDepth(root.left);
    }
    // 存在右子树，就递归右子树
    if (root.right != null) {
        right = maxDepth(root.right);
    }
    // 前面返回后，左右取大者
    return Math.max(left + 1, right + 1);
}

分析

其实对于树这类题，一般是以递归形式比较方便，只是要注意退出条件

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Leetcode 234 回文链表(Palindrome Linked List) 题解分析

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题目介绍

Given a singly linked list, determine if it is a palindrome.
给定一个单向链表，判断是否是回文链表

例一 Example 1:

Input: 1->2
Output: false

例二 Example 2:

Input: 1->2->2->1
Output: true

挑战下自己

Follow up:
Could you do it in O(n) time and O(1) space?

简要分析

首先这是个单向链表，如果是双向的就可以一个从头到尾，一个从尾到头，显然那样就没啥意思了，然后想过要不找到中点，然后用一个栈，把前一半塞进栈里，但是这种其实也比较麻烦，比如长度是奇偶数，然后如何找到中点，这倒是可以借助于双指针，还是比较麻烦，再想一想，回文链表，就跟最开始的一样，链表只有单向的，我用个栈不就可以逆向了么，先把链表整个塞进栈里，然后在一个个 pop 出来跟链表从头开始比较，全对上了就是回文了

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/**
 * Definition for singly-linked list.
 * public class ListNode {
 *     int val;
 *     ListNode next;
 *     ListNode() {}
 *     ListNode(int val) { this.val = val; }
 *     ListNode(int val, ListNode next) { this.val = val; this.next = next; }
 * }
 */
class Solution {
    public boolean isPalindrome(ListNode head) {
        if (head == null) {
            return true;
        }
        ListNode tail = head;
        LinkedList<Integer> stack = new LinkedList<>();
        // 这里就是一个循环，将所有元素依次压入栈
        while (tail != null) {
            stack.push(tail.val);
            tail = tail.next;
        }
        // 在逐个 pop 出来，其实这个出来的顺序就等于链表从尾到头遍历，同时跟链表从头到尾遍历进行逐对对比
        while (!stack.isEmpty()) {
            if (stack.peekFirst() == head.val) {
                stack.pollFirst();
                head = head.next;
            } else {
                return false;
            }
        }
        return true;
    }
}
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Leetcode 234 回文链表(Palindrome Linked List) 题解分析

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题目介绍

Given a singly linked list, determine if it is a palindrome.
给定一个单向链表，判断是否是回文链表

例一 Example 1:

Input: 1->2
Output: false

例二 Example 2:

Input: 1->2->2->1
Output: true

挑战下自己

Follow up:
Could you do it in O(n) time and O(1) space?

简要分析

首先这是个单向链表，如果是双向的就可以一个从头到尾，一个从尾到头，显然那样就没啥意思了，然后想过要不找到中点，然后用一个栈，把前一半塞进栈里，但是这种其实也比较麻烦，比如长度是奇偶数，然后如何找到中点，这倒是可以借助于双指针，还是比较麻烦，再想一想，回文链表，就跟最开始的一样，链表只有单向的，我用个栈不就可以逆向了么，先把链表整个塞进栈里，然后在一个个 pop 出来跟链表从头开始比较，全对上了就是回文了

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/**
 * Definition for singly-linked list.
 * public class ListNode {
 *     int val;
 *     ListNode next;
 *     ListNode() {}
 *     ListNode(int val) { this.val = val; }
 *     ListNode(int val, ListNode next) { this.val = val; this.next = next; }
 * }
 */
class Solution {
    public boolean isPalindrome(ListNode head) {
        if (head == null) {
            return true;
        }
        ListNode tail = head;
        LinkedList<Integer> stack = new LinkedList<>();
        // 这里就是一个循环，将所有元素依次压入栈
        while (tail != null) {
            stack.push(tail.val);
            tail = tail.next;
        }
        // 在逐个 pop 出来，其实这个出来的顺序就等于链表从尾到头遍历，同时跟链表从头到尾遍历进行逐对对比
        while (!stack.isEmpty()) {
            if (stack.peekFirst() == head.val) {
                stack.pollFirst();
                head = head.next;
            } else {
                return false;
            }
        }
        return true;
    }
}
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Leetcode 105 从前序与中序遍历序列构造二叉树(Construct Binary Tree from Preorder and Inorder Traversal) 题解分析

发表于 分类于 ， ， ， ， ， 阅读次数： 阅读次数： Disqus：

题目介绍

Given preorder and inorder traversal of a tree, construct the binary tree.
给定一棵树的前序和中序遍历，构造出一棵二叉树

注意

You may assume that duplicates do not exist in the tree.
你可以假设树中没有重复的元素。(PS: 不然就没法做了呀)

例子:

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preorder = [3,9,20,15,7]
inorder = [9,3,15,20,7]

返回的二叉树

1
2
3
4
5
  3
 / \
9  20
  /  \
 15   7

简要分析

看到这个题可以想到一个比较常规的解法就是递归拆树，前序就是根左右，中序就是左根右，然后就是通过前序已经确定的根在中序中找到，然后去划分左右子树，这个例子里是 3，找到中序中的位置，那么就可以确定，9 是左子树，15,20,7是右子树，然后对应的可以根据左右子树的元素数量在前序中划分左右子树，再继续递归就行

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class Solution {
    public TreeNode buildTree(int[] preorder, int[] inorder) {
      // 获取下数组长度
        int n = preorder.length;
        // 排除一下异常和边界
        if (n != inorder.length) {
            return null;
        }
        if (n == 0) {
            return null;
        }
        if (n == 1) {
            return new TreeNode(preorder[0]);
        }
        // 获得根节点
        TreeNode node = new TreeNode(preorder[0]);
        int pos = 0;
        // 找到中序中的位置
        for (int i = 0; i < inorder.length; i++) {
            if (node.val == inorder[i]) {
                pos = i;
                break;
            }
        }
        // 划分左右再进行递归，注意下`Arrays.copyOfRange`的用法
        node.left = buildTree(Arrays.copyOfRange(preorder, 1, pos + 1), Arrays.copyOfRange(inorder, 0, pos));
        node.right = buildTree(Arrays.copyOfRange(preorder, pos + 1, n), Arrays.copyOfRange(inorder, pos + 1, n));
        return node;
    }
}
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Leetcode 105 从前序与中序遍历序列构造二叉树(Construct Binary Tree from Preorder and Inorder Traversal) 题解分析

发表于 分类于 ， ， ， ， ， 阅读次数： 阅读次数： Disqus：

题目介绍

Given preorder and inorder traversal of a tree, construct the binary tree.
给定一棵树的前序和中序遍历，构造出一棵二叉树

注意

You may assume that duplicates do not exist in the tree.
你可以假设树中没有重复的元素。(PS: 不然就没法做了呀)

例子:

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preorder = [3,9,20,15,7]
inorder = [9,3,15,20,7]

返回的二叉树

1
2
3
4
5
  3
 / \
9  20
  /  \
 15   7

简要分析

看到这个题可以想到一个比较常规的解法就是递归拆树，前序就是根左右，中序就是左根右，然后就是通过前序已经确定的根在中序中找到，然后去划分左右子树，这个例子里是 3，找到中序中的位置，那么就可以确定，9 是左子树，15,20,7是右子树，然后对应的可以根据左右子树的元素数量在前序中划分左右子树，再继续递归就行

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class Solution {
    public TreeNode buildTree(int[] preorder, int[] inorder) {
      // 获取下数组长度
        int n = preorder.length;
        // 排除一下异常和边界
        if (n != inorder.length) {
            return null;
        }
        if (n == 0) {
            return null;
        }
        if (n == 1) {
            return new TreeNode(preorder[0]);
        }
        // 获得根节点
        TreeNode node = new TreeNode(preorder[0]);
        int pos = 0;
        // 找到中序中的位置
        for (int i = 0; i < inorder.length; i++) {
            if (node.val == inorder[i]) {
                pos = i;
                break;
            }
        }
        // 划分左右再进行递归，注意下`Arrays.copyOfRange`的用法
        node.left = buildTree(Arrays.copyOfRange(preorder, 1, pos + 1), Arrays.copyOfRange(inorder, 0, pos));
        node.right = buildTree(Arrays.copyOfRange(preorder, pos + 1, n), Arrays.copyOfRange(inorder, pos + 1, n));
        return node;
    }
}
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Leetcode 160 相交链表(intersection-of-two-linked-lists) 题解分析

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题目介绍

写一个程序找出两个单向链表的交叉起始点，可能是我英语不好，图里画的其实还有一点是交叉以后所有节点都是相同的
Write a program to find the node at which the intersection of two singly linked lists begins.

For example, the following two linked lists:

begin to intersect at node c1.

Example 1:

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Input: intersectVal = 8, listA = [4,1,8,4,5], listB = [5,6,1,8,4,5], skipA = 2, skipB = 3
Output: Reference of the node with value = 8
Input Explanation: The intersected node's value is 8 (note that this must not be 0 if the two lists intersect). From the head of A, it reads as [4,1,8,4,5]. From the head of B, it reads as [5,6,1,8,4,5]. There are 2 nodes before the intersected node in A; There are 3 nodes before the intersected node in B.

分析题解

一开始没什么头绪，感觉只能最原始的遍历，后来看了一些文章，发现比较简单的方式就是先找两个链表的长度差，因为从相交点开始肯定是长度一致的，这是个很好的解题突破口，找到长度差以后就是先跳过长链表的较长部分，然后开始同步遍历比较 A，B 链表；

代码

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public ListNode getIntersectionNode(ListNode headA, ListNode headB) {
        if (headA == null || headB == null) {
            return null;
        }
        // 算 A 的长度
        int countA = 0;
        ListNode tailA = headA;
        while (tailA != null) {
            tailA = tailA.next;
            countA++;
        }
        // 算 B 的长度
        int countB = 0;
        ListNode tailB = headB;
        while (tailB != null) {
            tailB = tailB.next;
            countB++;
        }
        tailA = headA;
        tailB = headB;
        // 依据长度差，先让长的链表 tail 指针往后移
        if (countA > countB) {
            while (countA > countB) {
                tailA = tailA.next;
                countA--;
            }
        } else if (countA < countB) {
            while (countA < countB) {
                tailB = tailB.next;
                countB--;
            }
        }
        // 然后以相同速度遍历两个链表比较
        while (tailA != null) {
            if (tailA == tailB) {
                return tailA;
            } else {
                tailA = tailA.next;
                tailB = tailB.next;
            }
        }
        return null;
    }

总结

可能缺少这种思维，做的还是比较少，所以没法一下子反应过来，需要锻炼，我的第一反应是两重遍历，不过那样复杂度就高了，这里应该是只有 O(N) 的复杂度。

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Leetcode 160 相交链表(intersection-of-two-linked-lists) 题解分析

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题目介绍

写一个程序找出两个单向链表的交叉起始点，可能是我英语不好，图里画的其实还有一点是交叉以后所有节点都是相同的
Write a program to find the node at which the intersection of two singly linked lists begins.

For example, the following two linked lists:

begin to intersect at node c1.

Example 1:

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Input: intersectVal = 8, listA = [4,1,8,4,5], listB = [5,6,1,8,4,5], skipA = 2, skipB = 3
Output: Reference of the node with value = 8
Input Explanation: The intersected node's value is 8 (note that this must not be 0 if the two lists intersect). From the head of A, it reads as [4,1,8,4,5]. From the head of B, it reads as [5,6,1,8,4,5]. There are 2 nodes before the intersected node in A; There are 3 nodes before the intersected node in B.

分析题解

一开始没什么头绪，感觉只能最原始的遍历，后来看了一些文章，发现比较简单的方式就是先找两个链表的长度差，因为从相交点开始肯定是长度一致的，这是个很好的解题突破口，找到长度差以后就是先跳过长链表的较长部分，然后开始同步遍历比较 A，B 链表；

代码

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public ListNode getIntersectionNode(ListNode headA, ListNode headB) {
        if (headA == null || headB == null) {
            return null;
        }
        // 算 A 的长度
        int countA = 0;
        ListNode tailA = headA;
        while (tailA != null) {
            tailA = tailA.next;
            countA++;
        }
        // 算 B 的长度
        int countB = 0;
        ListNode tailB = headB;
        while (tailB != null) {
            tailB = tailB.next;
            countB++;
        }
        tailA = headA;
        tailB = headB;
        // 依据长度差，先让长的链表 tail 指针往后移
        if (countA > countB) {
            while (countA > countB) {
                tailA = tailA.next;
                countA--;
            }
        } else if (countA < countB) {
            while (countA < countB) {
                tailB = tailB.next;
                countB--;
            }
        }
        // 然后以相同速度遍历两个链表比较
        while (tailA != null) {
            if (tailA == tailB) {
                return tailA;
            } else {
                tailA = tailA.next;
                tailB = tailB.next;
            }
        }
        return null;
    }

总结

可能缺少这种思维，做的还是比较少，所以没法一下子反应过来，需要锻炼，我的第一反应是两重遍历，不过那样复杂度就高了，这里应该是只有 O(N) 的复杂度。

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Leetcode 124 二叉树中的最大路径和(Binary Tree Maximum Path Sum) 题解分析

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题目介绍

A path in a binary tree is a sequence of nodes where each pair of adjacent nodes in the sequence has an edge connecting them. A node can only appear in the sequence at most once. Note that the path does not need to pass through the root.

The path sum of a path is the sum of the node’s values in the path.

Given the root of a binary tree, return the maximum path sum of any path.

路径 被定义为一条从树中任意节点出发，沿父节点-子节点连接，达到任意节点的序列。该路径 至少包含一个 节点，且不一定经过根节点。

路径和 是路径中各节点值的总和。

给你一个二叉树的根节点 root ，返回其 最大路径和

简要分析

其实这个题目会被误解成比较简单，左子树最大的，或者右子树最大的，或者两边加一下，仔细想想都不对，其实有可能是产生于左子树中，或者右子树中，这两个都是指跟左子树根还有右子树根没关系的，这么说感觉不太容易理解，画个图

可以看到图里，其实最长路径和是左边这个子树组成的，跟根节点还有右子树完全没关系，然后再想一种情况，如果是整棵树就是图中的左子树，那么这个最长路径和就是左子树加右子树加根节点了，所以不是我一开始想得那么简单，在代码实现中也需要一些技巧

代码

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int ansNew = Integer.MIN_VALUE;
public int maxPathSum(TreeNode root) {
        maxSumNew(root);
        return ansNew;
    }
    
public int maxSumNew(TreeNode root) {
    if (root == null) {
        return 0;
    }
    // 这里是个简单的递归，就是去递归左右子树，但是这里其实有个概念，当这样处理时，其实相当于把子树的内部的最大路径和已经算出来了
    int left = maxSumNew(root.left);
    int right = maxSumNew(root.right);
    // 这里前面我有点没想明白，但是看到 ansNew 的比较，其实相当于，返回的是三种情况里的最大值，一个是左子树+根，一个是右子树+根，一个是单独根节点，
    // 这样这个递归的返回才会有意义，不然像原来的方法，它可能是跳着的，但是这种情况其实是借助于 ansNew 这个全局的最大值，因为原来我觉得要比较的是
    // left, right, left + root , right + root, root, left + right + root 这些的最大值，这里是分成了两个阶段，left 跟 right 的最大值已经在上面的
    // 调用过程中赋值给 ansNew 了    
    int currentSum = Math.max(Math.max(root.val + left , root.val + right), root.val);
    // 这边返回的是 currentSum，然后再用它跟 left + right + root 进行对比，然后再去更新 ans
    // PS: 有个小点也是这边的破局点，就是这个 ansNew
    int res = Math.max(left + right + root.val, currentSum);
    ans = Math.max(res, ans);
    return currentSum;
}

这里非常重要的就是 ansNew 是最后的一个结果，而对于 maxSumNew 这个函数的返回值其实是需要包含了一个连续结果，因为要返回继续去算路径和，所以返回的是 currentSum，最终结果是 ansNew

结果图

难得有个 100%，贴个图哈哈

0%
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Leetcode 124 二叉树中的最大路径和(Binary Tree Maximum Path Sum) 题解分析

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题目介绍

A path in a binary tree is a sequence of nodes where each pair of adjacent nodes in the sequence has an edge connecting them. A node can only appear in the sequence at most once. Note that the path does not need to pass through the root.

The path sum of a path is the sum of the node’s values in the path.

Given the root of a binary tree, return the maximum path sum of any path.

路径 被定义为一条从树中任意节点出发，沿父节点-子节点连接，达到任意节点的序列。该路径 至少包含一个 节点，且不一定经过根节点。

路径和 是路径中各节点值的总和。

给你一个二叉树的根节点 root ，返回其 最大路径和

简要分析

其实这个题目会被误解成比较简单，左子树最大的，或者右子树最大的，或者两边加一下，仔细想想都不对，其实有可能是产生于左子树中，或者右子树中，这两个都是指跟左子树根还有右子树根没关系的，这么说感觉不太容易理解，画个图

可以看到图里，其实最长路径和是左边这个子树组成的，跟根节点还有右子树完全没关系，然后再想一种情况，如果是整棵树就是图中的左子树，那么这个最长路径和就是左子树加右子树加根节点了，所以不是我一开始想得那么简单，在代码实现中也需要一些技巧

代码

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int ansNew = Integer.MIN_VALUE;
public int maxPathSum(TreeNode root) {
        maxSumNew(root);
        return ansNew;
    }
    
public int maxSumNew(TreeNode root) {
    if (root == null) {
        return 0;
    }
    // 这里是个简单的递归，就是去递归左右子树，但是这里其实有个概念，当这样处理时，其实相当于把子树的内部的最大路径和已经算出来了
    int left = maxSumNew(root.left);
    int right = maxSumNew(root.right);
    // 这里前面我有点没想明白，但是看到 ansNew 的比较，其实相当于，返回的是三种情况里的最大值，一个是左子树+根，一个是右子树+根，一个是单独根节点，
    // 这样这个递归的返回才会有意义，不然像原来的方法，它可能是跳着的，但是这种情况其实是借助于 ansNew 这个全局的最大值，因为原来我觉得要比较的是
    // left, right, left + root , right + root, root, left + right + root 这些的最大值，这里是分成了两个阶段，left 跟 right 的最大值已经在上面的
    // 调用过程中赋值给 ansNew 了    
    int currentSum = Math.max(Math.max(root.val + left , root.val + right), root.val);
    // 这边返回的是 currentSum，然后再用它跟 left + right + root 进行对比，然后再去更新 ans
    // PS: 有个小点也是这边的破局点，就是这个 ansNew
    int res = Math.max(left + right + root.val, currentSum);
    ans = Math.max(res, ans);
    return currentSum;
}

这里非常重要的就是 ansNew 是最后的一个结果，而对于 maxSumNew 这个函数的返回值其实是需要包含了一个连续结果，因为要返回继续去算路径和，所以返回的是 currentSum，最终结果是 ansNew

结果图

难得有个 100%，贴个图哈哈

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Leetcode 42 接雨水 (Trapping Rain Water) 题解分析

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题目介绍

给定 n 个非负整数表示每个宽度为 1 的柱子的高度图，计算按此排列的柱子，下雨之后能接多少雨水。

示例


输入：height = [0,1,0,2,1,0,1,3,2,1,2,1]
输出：6
解释：上面是由数组 [0,1,0,2,1,0,1,3,2,1,2,1] 表示的高度图，在这种情况下，可以接 6 个单位的雨水（蓝色部分表示雨水）。

简单分析

其实最开始的想法是从左到右扫区间，就是示例中的第一个水槽跟第二个水槽都可以用这个办法解决

前面这种是属于右侧比左侧高的情况，对于左侧高右侧低的就不行了，（写这篇的时候想起来可以再反着扫一遍可能可以）

所以这个方案不好，贴一下这个方案的代码

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public int trap(int[] height) {
    int lastLeft = -1;
    int sum = 0;
    int tempSum = 0;
    boolean startFlag = true;
    for (int j : height) {
        if (startFlag && j <= 0) {
            startFlag = false;
            continue;
        }
        if (j >= lastLeft) {
            sum += tempSum;
            tempSum = 0;
            lastLeft = j;
        } else {
            tempSum += lastLeft - j;
        }
    }
    return sum;
}

后面结合网上的解法，其实可以反过来，对于每个格子找左右侧的最大值，取小的那个和当前格子的差值就是这一个的储水量了

理解了这种想法，代码其实就不难了

代码

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int n = height.length;
if (n <= 2) {
    return 0;
}
// 思路转变下，其实可以对于每一格算储水量，算法就是找到这一格左边的最高点跟这一格右边的最高点，
// 比较两侧的最高点，取小的那个，然后再跟当前格子的高度对比，差值就是当前格的储水量
int maxL[] = new int[n];
int maxR[] = new int[n];
int max = height[0];
maxL[0] = 0;
// 计算左侧的最高点
for (int i = 1; i < n - 1; i++) {
    maxL[i] = max;
    if (max < height[i]) {
        max = height[i];
    }
}
max = height[n - 1];
maxR[n - 1] = 0;
int tempSum, sum = 0;
// 计算右侧的最高点，并且同步算出来储水量，节省一个循环
for (int i = n - 2; i > 0; i--) {
    maxR[i] = max;
    if (height[i] > max) {
        max = height[i];
    }
    tempSum = Math.min(maxL[i], maxR[i]) - height[i];
    if (tempSum > 0) {
        sum += tempSum;
    }
}
return sum;
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Leetcode 42 接雨水 (Trapping Rain Water) 题解分析

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题目介绍

给定 n 个非负整数表示每个宽度为 1 的柱子的高度图，计算按此排列的柱子，下雨之后能接多少雨水。

示例


输入：height = [0,1,0,2,1,0,1,3,2,1,2,1]
输出：6
解释：上面是由数组 [0,1,0,2,1,0,1,3,2,1,2,1] 表示的高度图，在这种情况下，可以接 6 个单位的雨水（蓝色部分表示雨水）。

简单分析

其实最开始的想法是从左到右扫区间，就是示例中的第一个水槽跟第二个水槽都可以用这个办法解决

前面这种是属于右侧比左侧高的情况，对于左侧高右侧低的就不行了，（写这篇的时候想起来可以再反着扫一遍可能可以）

所以这个方案不好，贴一下这个方案的代码

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public int trap(int[] height) {
    int lastLeft = -1;
    int sum = 0;
    int tempSum = 0;
    boolean startFlag = true;
    for (int j : height) {
        if (startFlag && j <= 0) {
            startFlag = false;
            continue;
        }
        if (j >= lastLeft) {
            sum += tempSum;
            tempSum = 0;
            lastLeft = j;
        } else {
            tempSum += lastLeft - j;
        }
    }
    return sum;
}

后面结合网上的解法，其实可以反过来，对于每个格子找左右侧的最大值，取小的那个和当前格子的差值就是这一个的储水量了

理解了这种想法，代码其实就不难了

代码

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int n = height.length;
if (n <= 2) {
    return 0;
}
// 思路转变下，其实可以对于每一格算储水量，算法就是找到这一格左边的最高点跟这一格右边的最高点，
// 比较两侧的最高点，取小的那个，然后再跟当前格子的高度对比，差值就是当前格的储水量
int maxL[] = new int[n];
int maxR[] = new int[n];
int max = height[0];
maxL[0] = 0;
// 计算左侧的最高点
for (int i = 1; i < n - 1; i++) {
    maxL[i] = max;
    if (max < height[i]) {
        max = height[i];
    }
}
max = height[n - 1];
maxR[n - 1] = 0;
int tempSum, sum = 0;
// 计算右侧的最高点，并且同步算出来储水量，节省一个循环
for (int i = n - 2; i > 0; i--) {
    maxR[i] = max;
    if (height[i] > max) {
        max = height[i];
    }
    tempSum = Math.min(maxL[i], maxR[i]) - height[i];
    if (tempSum > 0) {
        sum += tempSum;
    }
}
return sum;
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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

2021 年中总结

发表于 分类于 ， ， 阅读次数： 阅读次数： Disqus：

又到半年总结时，第一次写总结类型的文章感觉挺好写的，但是后面总觉得这过去的一段时间所做的事情，能力上的成长低于预期，但是是需要总结下，找找问题，顺便展望下未来。

这一年做的最让自己满意的应该就是看了一些书，由折腾群洋总发起的读书打卡活动，到目前为止已经读完了这几本书，《cUrl 必知必会》，《古董局中局 1》，《古董局中局 2》，《算法图解》，《每天 5 分钟玩转 Kubernetes》《幸福了吗？》《高可用可伸缩微服务架构：基于 Dubbo、Spring Cloud和 Service Mesh》《Rust 权威指南》后面可以写个专题说说看的这些书，虽然每天打卡如果时间安排不好，并且看的书像 rust 这样比较难的话还是会有点小焦虑，不过也是个调整过程，一方面可以在白天就抽空看一会，然后也不必要每次都看很大一章，注重吸收。

技术上的成长的话，有一些比较小的长进吧，对于一些之前忽视的 synchronized，ThreadLocal 和 AQS 等知识点做了下查漏补缺了，然后多了解了一些 Java 垃圾回收的内容，但是在实操上还是比较欠缺，成型的技术方案，架构上所谓的优化也比较少，一些想法也还有考虑不周全的地方，还需要多花时间和心思去学习加强，特别是在目前已经有的基础上如何做系统深层次的优化，既不要是鸡毛蒜皮的，也不能出现一些不可接受的问题和故障，这是个很重要的课题，需要好好学习，后面考虑定一些周期性目标，两个月左右能有一些成果和总结。

另外一部分是自己的服务，因为 ucloud 的机器太贵就没续费了，所以都迁移到腾讯云的小机器上了，顺便折腾了一点点 traefik，但是还很不熟练，不太习惯这一套，一方面是 docker 还不习惯，这也加重了对这套环境的不适应，还是习惯裸机部署，另一方面就是 k8s 了，家里的机器还没虚拟化，没有很好的条件可以做实验，这也是读书打卡的一个没做好的点，整体的学习效果受限于深度和实操，后面是看都是用 traefik，也找到了一篇文章可以 traefik 转发到裸机应用，因为主仓库用的是裸机的 gogs。

还有就是运动减肥上，唉，这又是很大的一个痛点，基本没效果，只是还算稳定，昨天看到一个视频说还需要力量训练来增肌，以此可以提升基础代谢，打算往这个方向尝试下，因为今天没有疫情限制了，在 6 月底完成了 200 公里的跑步小目标，只是有些膝盖跟大腿根外侧不适，抽空得去看下医生，后面打算每天也能做点卷腹跟俯卧撑。

下半年还希望能继续多看看书，比很多网上各种乱七八糟的文章会好很多，结合豆瓣评分，找一些评价高一些的文章，但也不是说分稍低点的就不行，有些也看人是不是适合，一般 6 分以上评价比较多的就可以试试。

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

2021 年中总结

发表于 分类于 ， ， 阅读次数： 阅读次数： Disqus：

又到半年总结时，第一次写总结类型的文章感觉挺好写的，但是后面总觉得这过去的一段时间所做的事情，能力上的成长低于预期，但是是需要总结下，找找问题，顺便展望下未来。

这一年做的最让自己满意的应该就是看了一些书，由折腾群洋总发起的读书打卡活动，到目前为止已经读完了这几本书，《cUrl 必知必会》，《古董局中局 1》，《古董局中局 2》，《算法图解》，《每天 5 分钟玩转 Kubernetes》《幸福了吗？》《高可用可伸缩微服务架构：基于 Dubbo、Spring Cloud和 Service Mesh》《Rust 权威指南》后面可以写个专题说说看的这些书，虽然每天打卡如果时间安排不好，并且看的书像 rust 这样比较难的话还是会有点小焦虑，不过也是个调整过程，一方面可以在白天就抽空看一会，然后也不必要每次都看很大一章，注重吸收。

技术上的成长的话，有一些比较小的长进吧，对于一些之前忽视的 synchronized，ThreadLocal 和 AQS 等知识点做了下查漏补缺了，然后多了解了一些 Java 垃圾回收的内容，但是在实操上还是比较欠缺，成型的技术方案，架构上所谓的优化也比较少，一些想法也还有考虑不周全的地方，还需要多花时间和心思去学习加强，特别是在目前已经有的基础上如何做系统深层次的优化，既不要是鸡毛蒜皮的，也不能出现一些不可接受的问题和故障，这是个很重要的课题，需要好好学习，后面考虑定一些周期性目标，两个月左右能有一些成果和总结。

另外一部分是自己的服务，因为 ucloud 的机器太贵就没续费了，所以都迁移到腾讯云的小机器上了，顺便折腾了一点点 traefik，但是还很不熟练，不太习惯这一套，一方面是 docker 还不习惯，这也加重了对这套环境的不适应，还是习惯裸机部署，另一方面就是 k8s 了，家里的机器还没虚拟化，没有很好的条件可以做实验，这也是读书打卡的一个没做好的点，整体的学习效果受限于深度和实操，后面是看都是用 traefik，也找到了一篇文章可以 traefik 转发到裸机应用，因为主仓库用的是裸机的 gogs。

还有就是运动减肥上，唉，这又是很大的一个痛点，基本没效果，只是还算稳定，昨天看到一个视频说还需要力量训练来增肌，以此可以提升基础代谢，打算往这个方向尝试下，因为今天没有疫情限制了，在 6 月底完成了 200 公里的跑步小目标，只是有些膝盖跟大腿根外侧不适，抽空得去看下医生，后面打算每天也能做点卷腹跟俯卧撑。

下半年还希望能继续多看看书，比很多网上各种乱七八糟的文章会好很多，结合豆瓣评分，找一些评价高一些的文章，但也不是说分稍低点的就不行，有些也看人是不是适合，一般 6 分以上评价比较多的就可以试试。

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Leetcode 028 实现 strStr() ( Implement strStr() ) 题解分析

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题目介绍

Implement strStr().

Return the index of the first occurrence of needle in haystack, or -1 if needle is not part of haystack.

Clarification:

What should we return when needle is an empty string? This is a great question to ask during an interview.

For the purpose of this problem, we will return 0 when needle is an empty string. This is consistent to C’s strstr() and Java’s indexOf().

示例

Example 1:

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Input: haystack = "hello", needle = "ll"
Output: 2

Example 2:

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Input: haystack = "aaaaa", needle = "bba"
Output: -1

Example 3:

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Input: haystack = "", needle = ""
Output: 0

题解

字符串比较其实是写代码里永恒的主题，底层的编译器等处理肯定需要字符串对比，像 kmp 算法也是很厉害

code

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public int strStr(String haystack, String needle) {
        // 如果两个字符串都为空，返回 -1
        if (haystack == null || needle == null) {
            return -1;
        }
        // 如果 haystack 长度小于 needle 长度，返回 -1
        if (haystack.length() < needle.length()) {
            return -1;
        }
        // 如果 needle 为空字符串，返回 0
        if (needle.equals("")) {
            return 0;
        }
        // 如果两者相等，返回 0
        if (haystack.equals(needle)) {
            return 0;
        }
        int needleLength = needle.length();
        int haystackLength = haystack.length();
        for (int i = needleLength - 1; i <= haystackLength - 1; i++) {
            // 比较 needle 最后一个字符，倒着比较稍微节省点时间
            if (needle.charAt(needleLength - 1) == haystack.charAt(i)) {
                // 如果needle 是 1 的话直接可以返回 i 作为位置了
                if (needle.length() == 1) {
                    return i;
                }
                boolean flag = true;
                // 原来比的是 needle 的最后一个位置，然后这边从倒数第二个位置开始
                int j = needle.length() - 2;
                for (; j >= 0; j--) {
                    // 这里的 i- (needleLength - j) + 1 ) 比较绕，其实是外循环的 i 表示当前 i 位置的字符跟 needle 最后一个字符
                    // 相同，j 在上面的循环中--，对应的 haystack 也要在 i 这个位置 -- ，对应的位置就是 i - (needleLength - j) + 1
                    if (needle.charAt(j) != haystack.charAt(i - (needleLength - j) + 1)) {
                        flag = false;
                        break;
                    }
                }
                // 循环完了之后，如果 flag 为 true 说明 从 i 开始倒着对比都相同，但是这里需要起始位置，就需要
                // i - needleLength + 1
                if (flag) {
                    return i - needleLength + 1;
                }
            }
        }
        // 这里表示未找到
        return  -1;
    }
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Leetcode 028 实现 strStr() ( Implement strStr() ) 题解分析

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题目介绍

Implement strStr().

Return the index of the first occurrence of needle in haystack, or -1 if needle is not part of haystack.

Clarification:

What should we return when needle is an empty string? This is a great question to ask during an interview.

For the purpose of this problem, we will return 0 when needle is an empty string. This is consistent to C’s strstr() and Java’s indexOf().

示例

Example 1:

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Input: haystack = "hello", needle = "ll"
Output: 2

Example 2:

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Input: haystack = "aaaaa", needle = "bba"
Output: -1

Example 3:

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Input: haystack = "", needle = ""
Output: 0

题解

字符串比较其实是写代码里永恒的主题，底层的编译器等处理肯定需要字符串对比，像 kmp 算法也是很厉害

code

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public int strStr(String haystack, String needle) {
        // 如果两个字符串都为空，返回 -1
        if (haystack == null || needle == null) {
            return -1;
        }
        // 如果 haystack 长度小于 needle 长度，返回 -1
        if (haystack.length() < needle.length()) {
            return -1;
        }
        // 如果 needle 为空字符串，返回 0
        if (needle.equals("")) {
            return 0;
        }
        // 如果两者相等，返回 0
        if (haystack.equals(needle)) {
            return 0;
        }
        int needleLength = needle.length();
        int haystackLength = haystack.length();
        for (int i = needleLength - 1; i <= haystackLength - 1; i++) {
            // 比较 needle 最后一个字符，倒着比较稍微节省点时间
            if (needle.charAt(needleLength - 1) == haystack.charAt(i)) {
                // 如果needle 是 1 的话直接可以返回 i 作为位置了
                if (needle.length() == 1) {
                    return i;
                }
                boolean flag = true;
                // 原来比的是 needle 的最后一个位置，然后这边从倒数第二个位置开始
                int j = needle.length() - 2;
                for (; j >= 0; j--) {
                    // 这里的 i- (needleLength - j) + 1 ) 比较绕，其实是外循环的 i 表示当前 i 位置的字符跟 needle 最后一个字符
                    // 相同，j 在上面的循环中--，对应的 haystack 也要在 i 这个位置 -- ，对应的位置就是 i - (needleLength - j) + 1
                    if (needle.charAt(j) != haystack.charAt(i - (needleLength - j) + 1)) {
                        flag = false;
                        break;
                    }
                }
                // 循环完了之后，如果 flag 为 true 说明 从 i 开始倒着对比都相同，但是这里需要起始位置，就需要
                // i - needleLength + 1
                if (flag) {
                    return i - needleLength + 1;
                }
            }
        }
        // 这里表示未找到
        return  -1;
    }
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Leetcode 053 最大子序和 ( Maximum Subarray ) 题解分析

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题目介绍

Given an integer array nums, find the contiguous subarray (containing at least one number) which has the largest sum and return its sum.

A subarray is a contiguous part of an array.

示例

Example 1:

Input: nums = [-2,1,-3,4,-1,2,1,-5,4]
Output: 6
Explanation: [4,-1,2,1] has the largest sum = 6.

Example 2:

Input: nums = [1]
Output: 1

Example 3:

Input: nums = [5,4,-1,7,8]
Output: 23

说起来这个题其实非常有渊源，大学数据结构的第一个题就是这个，而最佳的算法就是传说中的 online 算法，就是遍历一次就完了，最基本的做法就是记下来所有的连续子数组，然后求出最大的那个。

代码

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public int maxSubArray(int[] nums) {
        int max = nums[0];
        int sum = nums[0];
        for (int i = 1; i < nums.length; i++) {
            // 这里最重要的就是这一行了，其实就是如果前面的 sum 是小于 0 的，那么就不需要前面的 sum，反正加上了还不如不加大
            sum = Math.max(nums[i], sum + nums[i]);
            // max 是用来承载最大值的
            max = Math.max(max, sum);
        }
        return max;
    }
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Leetcode 053 最大子序和 ( Maximum Subarray ) 题解分析

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题目介绍

Given an integer array nums, find the contiguous subarray (containing at least one number) which has the largest sum and return its sum.

A subarray is a contiguous part of an array.

示例

Example 1:

Input: nums = [-2,1,-3,4,-1,2,1,-5,4]
Output: 6
Explanation: [4,-1,2,1] has the largest sum = 6.

Example 2:

Input: nums = [1]
Output: 1

Example 3:

Input: nums = [5,4,-1,7,8]
Output: 23

说起来这个题其实非常有渊源，大学数据结构的第一个题就是这个，而最佳的算法就是传说中的 online 算法，就是遍历一次就完了，最基本的做法就是记下来所有的连续子数组，然后求出最大的那个。

代码

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public int maxSubArray(int[] nums) {
        int max = nums[0];
        int sum = nums[0];
        for (int i = 1; i < nums.length; i++) {
            // 这里最重要的就是这一行了，其实就是如果前面的 sum 是小于 0 的，那么就不需要前面的 sum，反正加上了还不如不加大
            sum = Math.max(nums[i], sum + nums[i]);
            // max 是用来承载最大值的
            max = Math.max(max, sum);
        }
        return max;
    }
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Leetcode 83 删除排序链表中的重复元素 ( Remove Duplicates from Sorted List *Easy* ) 题解分析

发表于 更新于 分类于 ， 阅读次数： 阅读次数： Disqus：
给定一个已排序的链表的头 `head` ， 删除所有重复的元素，使每个元素只出现一次 。返回 已排序的链表 。

题目介绍

给定一个已排序的链表的头 head ， 删除所有重复的元素，使每个元素只出现一次 。返回 已排序的链表 。
PS：注意已排序，还有返回也要已排序

示例 1：

输入：head = [1,1,2]
输出：[1,2]

示例 2：

输入：head = [1,1,2,3,3]
输出：[1,2,3]

提示：

  • 链表中节点数目在范围 [0, 300] 内
  • -100 <= Node.val <= 100
  • 题目数据保证链表已经按 升序 排列

分析与题解

这题其实是比较正常的 easy 级别的题目，链表已经排好序了，如果还带一个排序就更复杂一点，
只需要前后项做个对比，如果一致则移除后项，因为可能存在多个重复项，所以只有在前后项不同
时才会更新被比较项

code

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public ListNode deleteDuplicates(ListNode head) {
    // 链表头是空的或者只有一个头结点，就不用处理了
    if (head == null || head.next == null) {
        return head;
    }
    ListNode tail = head;
    // 以处理节点还有后续节点作为循环边界条件
    while (tail.next != null) {
        ListNode temp = tail.next;
        // 如果前后相同，那么可以跳过这个节点，将 Tail  ---->   temp  ---> temp.next 
        // 更新成  Tail ---->  temp.next
        if (temp.val == tail.val) {
            tail.next = temp.next;
        } else {
            // 不相同，则更新 tail
            tail = tail.next;
        }
    }
    // 最后返回头结点
    return head;
}

链表应该是个需要反复的训练的数据结构，因为涉及到前后指针，然后更新操作，判空等，
我在这块也是掌握的不太好，需要多练习。

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Leetcode 83 删除排序链表中的重复元素 ( Remove Duplicates from Sorted List *Easy* ) 题解分析

发表于 更新于 分类于 ， 阅读次数： 阅读次数： Disqus：
给定一个已排序的链表的头 `head` ， 删除所有重复的元素，使每个元素只出现一次 。返回 已排序的链表 。

题目介绍

给定一个已排序的链表的头 head ， 删除所有重复的元素，使每个元素只出现一次 。返回 已排序的链表 。
PS：注意已排序，还有返回也要已排序

示例 1：

输入：head = [1,1,2]
输出：[1,2]

示例 2：

输入：head = [1,1,2,3,3]
输出：[1,2,3]

提示：

  • 链表中节点数目在范围 [0, 300] 内
  • -100 <= Node.val <= 100
  • 题目数据保证链表已经按 升序 排列

分析与题解

这题其实是比较正常的 easy 级别的题目，链表已经排好序了，如果还带一个排序就更复杂一点，
只需要前后项做个对比，如果一致则移除后项，因为可能存在多个重复项，所以只有在前后项不同
时才会更新被比较项

code

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public ListNode deleteDuplicates(ListNode head) {
    // 链表头是空的或者只有一个头结点，就不用处理了
    if (head == null || head.next == null) {
        return head;
    }
    ListNode tail = head;
    // 以处理节点还有后续节点作为循环边界条件
    while (tail.next != null) {
        ListNode temp = tail.next;
        // 如果前后相同，那么可以跳过这个节点，将 Tail  ---->   temp  ---> temp.next 
        // 更新成  Tail ---->  temp.next
        if (temp.val == tail.val) {
            tail.next = temp.next;
        } else {
            // 不相同，则更新 tail
            tail = tail.next;
        }
    }
    // 最后返回头结点
    return head;
}

链表应该是个需要反复的训练的数据结构，因为涉及到前后指针，然后更新操作，判空等，
我在这块也是掌握的不太好，需要多练习。

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Leetcode 1115 交替打印 FooBar ( Print FooBar Alternately *Medium* ) 题解分析

发表于 分类于 ， 阅读次数： 阅读次数： Disqus：
无聊随机了道并发题，还是比较明显的用信号量就可以解决的题，不过第一次做还是有点不适应，要加油多练练

无聊想去 roll 一题就看到了有并发题，就找到了这题，其实一眼看我的想法也是用信号量，但是用 condition 应该也是可以处理的，不过这类问题好像本地有点难调，因为它好像是抽取代码执行的，跟直观的逻辑比较不一样
Suppose you are given the following code:

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class FooBar {
  public void foo() {
    for (int i = 0; i < n; i++) {
      print("foo");
    }
  }

  public void bar() {
    for (int i = 0; i < n; i++) {
      print("bar");
    }
  }
}

The same instance of FooBar will be passed to two different threads:

  • thread A will call foo(), while
  • thread B will call bar().
    Modify the given program to output "foobar" n times.

示例

Example 1:

Input: n = 1
Output: “foobar”
Explanation: There are two threads being fired asynchronously. One of them calls foo(), while the other calls bar().
“foobar” is being output 1 time.

Example 2:

Input: n = 2
Output: “foobarfoobar”
Explanation: “foobar” is being output 2 times.

题解

简析

其实用信号量是很直观的，就是让打印 foo 的线程先拥有信号量，打印后就等待，给 bar 信号量 + 1，然后 bar 线程运行打印消耗 bar 信号量，再给 foo 信号量 + 1

code

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class FooBar {
    
    private final Semaphore foo = new Semaphore(1);
    private final Semaphore bar = new Semaphore(0);
    private int n;

    public FooBar(int n) {
        this.n = n;
    }

    public void foo(Runnable printFoo) throws InterruptedException {
        
        for (int i = 0; i < n; i++) {
            foo.acquire();
        	// printFoo.run() outputs "foo". Do not change or remove this line.
        	printFoo.run();
            bar.release();
        }
    }

    public void bar(Runnable printBar) throws InterruptedException {
        
        for (int i = 0; i < n; i++) {
            bar.acquire();
            // printBar.run() outputs "bar". Do not change or remove this line.
        	printBar.run();
            foo.release();
        }
    }
}
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Leetcode 1115 交替打印 FooBar ( Print FooBar Alternately *Medium* ) 题解分析

发表于 分类于 ， 阅读次数： 阅读次数： Disqus：
无聊随机了道并发题，还是比较明显的用信号量就可以解决的题，不过第一次做还是有点不适应，要加油多练练

无聊想去 roll 一题就看到了有并发题，就找到了这题，其实一眼看我的想法也是用信号量，但是用 condition 应该也是可以处理的，不过这类问题好像本地有点难调，因为它好像是抽取代码执行的，跟直观的逻辑比较不一样
Suppose you are given the following code:

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class FooBar {
  public void foo() {
    for (int i = 0; i < n; i++) {
      print("foo");
    }
  }

  public void bar() {
    for (int i = 0; i < n; i++) {
      print("bar");
    }
  }
}

The same instance of FooBar will be passed to two different threads:

  • thread A will call foo(), while
  • thread B will call bar().
    Modify the given program to output "foobar" n times.

示例

Example 1:

Input: n = 1
Output: “foobar”
Explanation: There are two threads being fired asynchronously. One of them calls foo(), while the other calls bar().
“foobar” is being output 1 time.

Example 2:

Input: n = 2
Output: “foobarfoobar”
Explanation: “foobar” is being output 2 times.

题解

简析

其实用信号量是很直观的，就是让打印 foo 的线程先拥有信号量，打印后就等待，给 bar 信号量 + 1，然后 bar 线程运行打印消耗 bar 信号量，再给 foo 信号量 + 1

code

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class FooBar {
    
    private final Semaphore foo = new Semaphore(1);
    private final Semaphore bar = new Semaphore(0);
    private int n;

    public FooBar(int n) {
        this.n = n;
    }

    public void foo(Runnable printFoo) throws InterruptedException {
        
        for (int i = 0; i < n; i++) {
            foo.acquire();
        	// printFoo.run() outputs "foo". Do not change or remove this line.
        	printFoo.run();
            bar.release();
        }
    }

    public void bar(Runnable printBar) throws InterruptedException {
        
        for (int i = 0; i < n; i++) {
            bar.acquire();
            // printBar.run() outputs "bar". Do not change or remove this line.
        	printBar.run();
            foo.release();
        }
    }
}
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Leetcode 20 有效的括号 ( Valid Parentheses *Easy* ) 题解分析

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题目介绍

Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.

An input string is valid if:

  1. Open brackets must be closed by the same type of brackets.
  2. Open brackets must be closed in the correct order.

示例

Example 1:

Input: s = “()”
Output: true

Example 2:

Input: s = “()[]{}”
Output: true

Example 3:

Input: s = “(]”
Output: false

Constraints:

  • 1 <= s.length <= 10^4
  • s consists of parentheses only '()[]{}'.

解析

easy题，并且看起来也是比较简单的，三种括号按对匹配，直接用栈来做，栈里面存的是括号的类型，如果是左括号，就放入栈中，如果是右括号，就把栈顶的元素弹出，如果弹出的元素不是左括号，就返回false，如果弹出的元素是左括号，就继续往下走，如果遍历完了，如果栈里面还有元素，就返回false，如果遍历完了，如果栈里面没有元素，就返回true

代码

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class Solution {
    public boolean isValid(String s) {

        if (s.length() % 2 != 0) {
            return false;
        }
        Stack<String> stk = new Stack<>();
        for (int i = 0; i < s.length(); i++) {
            if (s.charAt(i) == '{' || s.charAt(i) == '(' || s.charAt(i) == '[') {
                stk.push(String.valueOf(s.charAt(i)));
                continue;
            }
            if (s.charAt(i) == '}') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '{') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }
            if (s.charAt(i) == ']') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '[') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }
            if (s.charAt(i) == ')') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '(') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }

        }
        return stk.size() == 0;
    }
}
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Leetcode 20 有效的括号 ( Valid Parentheses *Easy* ) 题解分析

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题目介绍

Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.

An input string is valid if:

  1. Open brackets must be closed by the same type of brackets.
  2. Open brackets must be closed in the correct order.

示例

Example 1:

Input: s = “()”
Output: true

Example 2:

Input: s = “()[]{}”
Output: true

Example 3:

Input: s = “(]”
Output: false

Constraints:

  • 1 <= s.length <= 10^4
  • s consists of parentheses only '()[]{}'.

解析

easy题，并且看起来也是比较简单的，三种括号按对匹配，直接用栈来做，栈里面存的是括号的类型，如果是左括号，就放入栈中，如果是右括号，就把栈顶的元素弹出，如果弹出的元素不是左括号，就返回false，如果弹出的元素是左括号，就继续往下走，如果遍历完了，如果栈里面还有元素，就返回false，如果遍历完了，如果栈里面没有元素，就返回true

代码

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class Solution {
    public boolean isValid(String s) {

        if (s.length() % 2 != 0) {
            return false;
        }
        Stack<String> stk = new Stack<>();
        for (int i = 0; i < s.length(); i++) {
            if (s.charAt(i) == '{' || s.charAt(i) == '(' || s.charAt(i) == '[') {
                stk.push(String.valueOf(s.charAt(i)));
                continue;
            }
            if (s.charAt(i) == '}') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '{') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }
            if (s.charAt(i) == ']') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '[') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }
            if (s.charAt(i) == ')') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '(') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }

        }
        return stk.size() == 0;
    }
}
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Leetcode 1260 二维网格迁移 ( Shift 2D Grid *Easy* ) 题解分析

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题目介绍

Given a 2D grid of size m x n and an integer k. You need to shift the grid k times.

In one shift operation:

Element at grid[i][j] moves to grid[i][j + 1].
Element at grid[i][n - 1] moves to grid[i + 1][0].
Element at grid[m - 1][n - 1] moves to grid[0][0].
Return the 2D grid after applying shift operation k times.

示例

Example 1:

Input: grid = [[1,2,3],[4,5,6],[7,8,9]], k = 1
Output: [[9,1,2],[3,4,5],[6,7,8]]

Example 2:

Input: grid = [[3,8,1,9],[19,7,2,5],[4,6,11,10],[12,0,21,13]], k = 4
Output: [[12,0,21,13],[3,8,1,9],[19,7,2,5],[4,6,11,10]]

Example 3:

Input: grid = [[1,2,3],[4,5,6],[7,8,9]], k = 9
Output: [[1,2,3],[4,5,6],[7,8,9]]

提示

  • m == grid.length
  • n == grid[i].length
  • 1 <= m <= 50
  • 1 <= n <= 50
  • -1000 <= grid[i][j] <= 1000
  • 0 <= k <= 100

解析

这个题主要是矩阵或者说数组的操作，并且题目要返回的是个 List，所以也不用原地操作，只需要找对位置就可以了，k 是多少就相当于让这个二维数组头尾衔接移动 k 个元素

代码

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public List<List<Integer>> shiftGrid(int[][] grid, int k) {
        // 行数
        int m = grid.length;
        // 列数
        int n = grid[0].length;
        // 偏移值，取下模
        k = k % (m * n);
        // 反向取下数量，因为我打算直接从头填充新的矩阵
        /*
         *    比如
         *    1 2 3
         *    4 5 6
         *    7 8 9
         *    需要变成
         *    9 1 2
         *    3 4 5
         *    6 7 8
         *    就要从 9 开始填充
         */
        int reverseK = m * n - k;
        List<List<Integer>> matrix = new ArrayList<>();
        // 这类就是两层循环
        for (int i = 0; i < m; i++) {
            List<Integer> line = new ArrayList<>();
            for (int j = 0; j < n; j++) {
                // 数量会随着循环迭代增长, 确认是第几个
                int currentNum = reverseK + i * n +  (j + 1);
                // 这里处理下到达矩阵末尾后减掉 m * n
                if (currentNum > m * n) {
                    currentNum -= m * n;
                }
                // 根据矩阵列数 n 算出在原来矩阵的位置
                int last = (currentNum - 1) % n;
                int passLine = (currentNum - 1) / n;

                line.add(grid[passLine][last]);
            }
            matrix.add(line);
        }
        return matrix;
    }

结果数据


比较慢

0%
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Leetcode 1260 二维网格迁移 ( Shift 2D Grid *Easy* ) 题解分析

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题目介绍

Given a 2D grid of size m x n and an integer k. You need to shift the grid k times.

In one shift operation:

Element at grid[i][j] moves to grid[i][j + 1].
Element at grid[i][n - 1] moves to grid[i + 1][0].
Element at grid[m - 1][n - 1] moves to grid[0][0].
Return the 2D grid after applying shift operation k times.

示例

Example 1:

Input: grid = [[1,2,3],[4,5,6],[7,8,9]], k = 1
Output: [[9,1,2],[3,4,5],[6,7,8]]

Example 2:

Input: grid = [[3,8,1,9],[19,7,2,5],[4,6,11,10],[12,0,21,13]], k = 4
Output: [[12,0,21,13],[3,8,1,9],[19,7,2,5],[4,6,11,10]]

Example 3:

Input: grid = [[1,2,3],[4,5,6],[7,8,9]], k = 9
Output: [[1,2,3],[4,5,6],[7,8,9]]

提示

  • m == grid.length
  • n == grid[i].length
  • 1 <= m <= 50
  • 1 <= n <= 50
  • -1000 <= grid[i][j] <= 1000
  • 0 <= k <= 100

解析

这个题主要是矩阵或者说数组的操作，并且题目要返回的是个 List，所以也不用原地操作，只需要找对位置就可以了，k 是多少就相当于让这个二维数组头尾衔接移动 k 个元素

代码

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public List<List<Integer>> shiftGrid(int[][] grid, int k) {
        // 行数
        int m = grid.length;
        // 列数
        int n = grid[0].length;
        // 偏移值，取下模
        k = k % (m * n);
        // 反向取下数量，因为我打算直接从头填充新的矩阵
        /*
         *    比如
         *    1 2 3
         *    4 5 6
         *    7 8 9
         *    需要变成
         *    9 1 2
         *    3 4 5
         *    6 7 8
         *    就要从 9 开始填充
         */
        int reverseK = m * n - k;
        List<List<Integer>> matrix = new ArrayList<>();
        // 这类就是两层循环
        for (int i = 0; i < m; i++) {
            List<Integer> line = new ArrayList<>();
            for (int j = 0; j < n; j++) {
                // 数量会随着循环迭代增长, 确认是第几个
                int currentNum = reverseK + i * n +  (j + 1);
                // 这里处理下到达矩阵末尾后减掉 m * n
                if (currentNum > m * n) {
                    currentNum -= m * n;
                }
                // 根据矩阵列数 n 算出在原来矩阵的位置
                int last = (currentNum - 1) % n;
                int passLine = (currentNum - 1) / n;

                line.add(grid[passLine][last]);
            }
            matrix.add(line);
        }
        return matrix;
    }

结果数据


比较慢

0%
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Leetcode 16 最接近的三数之和 ( 3Sum Closest *Medium* ) 题解分析

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题目介绍

Given an integer array nums of length n and an integer target, find three integers in nums such that the sum is closest to target.

Return the sum of the three integers.

You may assume that each input would have exactly one solution.

简单解释下就是之前是要三数之和等于目标值，现在是找到最接近的三数之和。

示例

Example 1:

Input: nums = [-1,2,1,-4], target = 1
Output: 2
Explanation: The sum that is closest to the target is 2. (-1 + 2 + 1 = 2).

Example 2:

Input: nums = [0,0,0], target = 1
Output: 0

Constraints:

  • 3 <= nums.length <= 1000
  • -1000 <= nums[i] <= 1000
  • -10^4 <= target <= 10^4

简单解析

这个题思路上来讲不难，也是用原来三数之和的方式去做，利用”双指针法”或者其它描述法，但是需要简化逻辑

code

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public int threeSumClosest(int[] nums, int target) {
        Arrays.sort(nums);
        // 当前最近的和
        int closestSum = nums[0] + nums[1] + nums[nums.length - 1];
        for (int i = 0; i < nums.length - 2; i++) {
            if (i == 0 || nums[i] != nums[i - 1]) {
                // 左指针
                int left = i + 1;
                // 右指针
                int right = nums.length - 1;
                // 判断是否遍历完了
                while (left < right) {
                    // 当前的和
                    int sum = nums[i] + nums[left] + nums[right];
                    // 小优化，相等就略过了
                    while (left < right && nums[left] == nums[left + 1]) {
                        left++;
                    }
                    while (left < right && nums[right] == nums[right - 1]) {
                        right--;
                    }
                    // 这里判断，其实也还是希望趋近目标值
                    if (sum < target) {
                        left++;
                    } else {
                        right--;
                    }
                    // 判断是否需要替换
                    if (Math.abs(sum - target) < Math.abs(closestSum - target)) {
                        closestSum = sum;
                    }
                }
            }
        }
        return closestSum;
    }

结果

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Leetcode 16 最接近的三数之和 ( 3Sum Closest *Medium* ) 题解分析

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题目介绍

Given an integer array nums of length n and an integer target, find three integers in nums such that the sum is closest to target.

Return the sum of the three integers.

You may assume that each input would have exactly one solution.

简单解释下就是之前是要三数之和等于目标值，现在是找到最接近的三数之和。

示例

Example 1:

Input: nums = [-1,2,1,-4], target = 1
Output: 2
Explanation: The sum that is closest to the target is 2. (-1 + 2 + 1 = 2).

Example 2:

Input: nums = [0,0,0], target = 1
Output: 0

Constraints:

  • 3 <= nums.length <= 1000
  • -1000 <= nums[i] <= 1000
  • -10^4 <= target <= 10^4

简单解析

这个题思路上来讲不难，也是用原来三数之和的方式去做，利用”双指针法”或者其它描述法，但是需要简化逻辑

code

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public int threeSumClosest(int[] nums, int target) {
        Arrays.sort(nums);
        // 当前最近的和
        int closestSum = nums[0] + nums[1] + nums[nums.length - 1];
        for (int i = 0; i < nums.length - 2; i++) {
            if (i == 0 || nums[i] != nums[i - 1]) {
                // 左指针
                int left = i + 1;
                // 右指针
                int right = nums.length - 1;
                // 判断是否遍历完了
                while (left < right) {
                    // 当前的和
                    int sum = nums[i] + nums[left] + nums[right];
                    // 小优化，相等就略过了
                    while (left < right && nums[left] == nums[left + 1]) {
                        left++;
                    }
                    while (left < right && nums[right] == nums[right - 1]) {
                        right--;
                    }
                    // 这里判断，其实也还是希望趋近目标值
                    if (sum < target) {
                        left++;
                    } else {
                        right--;
                    }
                    // 判断是否需要替换
                    if (Math.abs(sum - target) < Math.abs(closestSum - target)) {
                        closestSum = sum;
                    }
                }
            }
        }
        return closestSum;
    }

结果

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What hurts more, the pain of hard work or the pain of regret?

Leetcode 278 第一个错误的版本 ( First Bad Version *Easy* ) 题解分析

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题目介绍

You are a product manager and currently leading a team to develop a new product. Unfortunately, the latest version of your product fails the quality check. Since each version is developed based on the previous version, all the versions after a bad version are also bad.

Suppose you have n versions [1, 2, ..., n] and you want to find out the first bad one, which causes all the following ones to be bad.

You are given an API bool isBadVersion(version) which returns whether version is bad. Implement a function to find the first bad version. You should minimize the number of calls to the API.

示例

Example 1:

Input: n = 5, bad = 4
Output: 4
Explanation:
call isBadVersion(3) -> false
call isBadVersion(5) -> true
call isBadVersion(4) -> true
Then 4 is the first bad version.

Example 2:

Input: n = 1, bad = 1
Output: 1

简析

简单来说就是一个二分查找，但是这个问题其实处理起来还是需要搞清楚一些边界问题

代码

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public int firstBadVersion(int n) {
    // 类似于双指针法
    int left = 1, right = n, mid;
    while (left < right) {
        // 取中点
        mid = left + (right - left) / 2;
        // 如果不是错误版本，就往右找
        if (!isBadVersion(mid)) {
            left = mid + 1;
        } else {
        // 如果是的话就往左查找
            right = mid;
        }
    }
    // 这里考虑交界情况是，在上面循环中如果 left 是好的，right 是坏的，那进入循环的时候 mid == left
    // 然后 left = mid + 1 就会等于 right，循环条件就跳出了，此时 left 就是那个起始的错误点了
    // 其实这两个是同一个值
    return left;
}

往右移动示例

往左移动示例

结果

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What hurts more, the pain of hard work or the pain of regret?

Leetcode 278 第一个错误的版本 ( First Bad Version *Easy* ) 题解分析

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题目介绍

You are a product manager and currently leading a team to develop a new product. Unfortunately, the latest version of your product fails the quality check. Since each version is developed based on the previous version, all the versions after a bad version are also bad.

Suppose you have n versions [1, 2, ..., n] and you want to find out the first bad one, which causes all the following ones to be bad.

You are given an API bool isBadVersion(version) which returns whether version is bad. Implement a function to find the first bad version. You should minimize the number of calls to the API.

示例

Example 1:

Input: n = 5, bad = 4
Output: 4
Explanation:
call isBadVersion(3) -> false
call isBadVersion(5) -> true
call isBadVersion(4) -> true
Then 4 is the first bad version.

Example 2:

Input: n = 1, bad = 1
Output: 1

简析

简单来说就是一个二分查找，但是这个问题其实处理起来还是需要搞清楚一些边界问题

代码

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public int firstBadVersion(int n) {
    // 类似于双指针法
    int left = 1, right = n, mid;
    while (left < right) {
        // 取中点
        mid = left + (right - left) / 2;
        // 如果不是错误版本，就往右找
        if (!isBadVersion(mid)) {
            left = mid + 1;
        } else {
        // 如果是的话就往左查找
            right = mid;
        }
    }
    // 这里考虑交界情况是，在上面循环中如果 left 是好的，right 是坏的，那进入循环的时候 mid == left
    // 然后 left = mid + 1 就会等于 right，循环条件就跳出了，此时 left 就是那个起始的错误点了
    // 其实这两个是同一个值
    return left;
}

往右移动示例

往左移动示例

结果

0%
\ No newline at end of file diff --git a/atom.xml b/atom.xml index ce220218be..feaf48343e 100644 --- a/atom.xml +++ b/atom.xml @@ -420,10 +420,10 @@ - - + + diff --git a/baidusitemap.xml b/baidusitemap.xml index 266de95585..7d2f7dcdb5 100644 --- a/baidusitemap.xml +++ b/baidusitemap.xml @@ -497,15 +497,15 @@ 2022-06-11 - https://nicksxs.me/2022/02/27/Disruptor-%E7%B3%BB%E5%88%97%E4%BA%8C/ + https://nicksxs.me/2022/02/13/Disruptor-%E7%B3%BB%E5%88%97%E4%B8%80/ 2022-06-11 - https://nicksxs.me/2020/08/22/Filter-Intercepter-Aop-%E5%95%A5-%E5%95%A5-%E5%95%A5-%E8%BF%99%E4%BA%9B%E9%83%BD%E6%98%AF%E5%95%A5/ + https://nicksxs.me/2022/02/27/Disruptor-%E7%B3%BB%E5%88%97%E4%BA%8C/ 2022-06-11 - https://nicksxs.me/2022/02/13/Disruptor-%E7%B3%BB%E5%88%97%E4%B8%80/ + https://nicksxs.me/2020/08/22/Filter-Intercepter-Aop-%E5%95%A5-%E5%95%A5-%E5%95%A5-%E8%BF%99%E4%BA%9B%E9%83%BD%E6%98%AF%E5%95%A5/ 2022-06-11 @@ -537,11 +537,11 @@ 2022-06-11 - https://nicksxs.me/2021/04/18/rust%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0-%E6%89%80%E6%9C%89%E6%9D%83%E4%BA%8C/ + https://nicksxs.me/2021/04/18/rust%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/ 2022-06-11 - https://nicksxs.me/2021/04/18/rust%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/ + https://nicksxs.me/2021/04/18/rust%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0-%E6%89%80%E6%9C%89%E6%9D%83%E4%BA%8C/ 2022-06-11 @@ -593,11 +593,11 @@ 2022-06-11 - https://nicksxs.me/2020/11/22/%E8%81%8A%E8%81%8A-Dubbo-%E7%9A%84%E5%AE%B9%E9%94%99%E6%9C%BA%E5%88%B6/ + https://nicksxs.me/2021/06/27/%E8%81%8A%E8%81%8A-Java-%E4%B8%AD%E7%BB%95%E4%B8%8D%E5%BC%80%E7%9A%84-Synchronized-%E5%85%B3%E9%94%AE%E5%AD%97-%E4%BA%8C/ 2022-06-11 - https://nicksxs.me/2021/06/27/%E8%81%8A%E8%81%8A-Java-%E4%B8%AD%E7%BB%95%E4%B8%8D%E5%BC%80%E7%9A%84-Synchronized-%E5%85%B3%E9%94%AE%E5%AD%97-%E4%BA%8C/ + https://nicksxs.me/2020/11/22/%E8%81%8A%E8%81%8A-Dubbo-%E7%9A%84%E5%AE%B9%E9%94%99%E6%9C%BA%E5%88%B6/ 2022-06-11 @@ -613,7 +613,7 @@ 2022-06-11 - https://nicksxs.me/2022/01/09/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E5%88%86%E5%BA%93%E5%88%86%E8%A1%A8%E4%B8%8B%E7%9A%84%E5%88%86%E9%A1%B5%E6%96%B9%E6%A1%88/ + https://nicksxs.me/2021/12/12/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E7%9A%84%E7%AE%80%E5%8D%95%E4%BD%BF%E7%94%A8/ 2022-06-11 @@ -621,11 +621,11 @@ 2022-06-11 - https://nicksxs.me/2021/12/12/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E7%9A%84%E7%AE%80%E5%8D%95%E4%BD%BF%E7%94%A8/ + https://nicksxs.me/2021/04/04/%E8%81%8A%E8%81%8A-dubbo-%E7%9A%84%E7%BA%BF%E7%A8%8B%E6%B1%A0/ 2022-06-11 - https://nicksxs.me/2021/04/04/%E8%81%8A%E8%81%8A-dubbo-%E7%9A%84%E7%BA%BF%E7%A8%8B%E6%B1%A0/ + https://nicksxs.me/2022/01/09/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E5%88%86%E5%BA%93%E5%88%86%E8%A1%A8%E4%B8%8B%E7%9A%84%E5%88%86%E9%A1%B5%E6%96%B9%E6%A1%88/ 2022-06-11 @@ -1013,27 +1013,23 @@ 2020-01-12 - https://nicksxs.me/2015/03/11/Reverse-Bits/ - 2020-01-12 - - - https://nicksxs.me/2015/03/13/Reverse-Integer/ + https://nicksxs.me/2019/12/10/Redis-Part-1/ 2020-01-12 - https://nicksxs.me/2019/12/10/Redis-Part-1/ + https://nicksxs.me/2015/03/11/Reverse-Bits/ 2020-01-12 - https://nicksxs.me/2015/01/14/Two-Sum/ + https://nicksxs.me/2015/03/13/Reverse-Integer/ 2020-01-12 - https://nicksxs.me/2017/05/09/ambari-summary/ + https://nicksxs.me/2016/09/29/binary-watch/ 2020-01-12 - https://nicksxs.me/2016/09/29/binary-watch/ + https://nicksxs.me/2015/01/14/Two-Sum/ 2020-01-12 @@ -1041,11 +1037,11 @@ 2020-01-12 - https://nicksxs.me/2016/10/11/minimum-size-subarray-sum-209/ + https://nicksxs.me/2017/05/09/ambari-summary/ 2020-01-12 - https://nicksxs.me/2019/06/18/openresty/ + https://nicksxs.me/2016/10/11/minimum-size-subarray-sum-209/ 2020-01-12 @@ -1053,7 +1049,7 @@ 2020-01-12 - https://nicksxs.me/2020/01/10/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%B8%89/ + https://nicksxs.me/2019/06/18/openresty/ 2020-01-12 @@ -1065,13 +1061,17 @@ 2020-01-12 - https://nicksxs.me/2014/12/30/Clone-Graph-Part-I/ + https://nicksxs.me/2020/01/10/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%B8%89/ 2020-01-12 https://nicksxs.me/2019/09/23/AbstractQueuedSynchronizer/ 2020-01-12 + + https://nicksxs.me/2014/12/30/Clone-Graph-Part-I/ + 2020-01-12 + https://nicksxs.me/2015/04/15/Leetcode-No-3/ 2020-01-12 @@ -1101,15 +1101,15 @@ 2020-01-12 - https://nicksxs.me/2017/03/28/spark-little-tips/ + https://nicksxs.me/2016/10/12/summary-ranges-228/ 2020-01-12 - https://nicksxs.me/2016/10/12/summary-ranges-228/ + https://nicksxs.me/2016/07/13/swoole-websocket-test/ 2020-01-12 - https://nicksxs.me/2016/07/13/swoole-websocket-test/ + https://nicksxs.me/2017/03/28/spark-little-tips/ 2020-01-12 diff --git a/categories/Java/GC/index.html b/categories/Java/GC/index.html index 11a3414461..a9449be862 100644 --- a/categories/Java/GC/index.html +++ b/categories/Java/GC/index.html @@ -6,4 +6,4 @@ Website: https://highlightjs.org/ License: see project LICENSE Touched: 2021 -*/pre 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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}分类: gc | Nicksxs's Blog

Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

gc 分类

2021
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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

GC 分类

2020
2019
0%
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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}分类: linked list | Nicksxs's Blog

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What hurts more, the pain of hard work or the pain of regret?

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： Disqus：

top 命令在日常的 Linux 使用中，特别是做一些服务器的简单状态查看，排查故障都起了比较大的作用，但是由于这个命令看到的东西比较多，一般只会看部分，或者说像我这样就会比较片面地看一些信息，比如默认是进程维度的，可以在启动命令的时候加-H进入线程模式

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-H  :Threads-mode operation
            Instructs top to display individual threads.  Without this command-line option a summation of all threads in each process  is  shown.   Later
            this can be changed with the `H' interactive command.

这样就能用在 Java 中去 jstack 中找到对应的线程
其实还有比较重要的两个操作，
一个是在 top 启动状态下，按c键，这样能把比如说是一个 Java 进程，具体的进程命令显示出来
像这样
执行前是这样

执行后是这样

第二个就是排序了

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SORTING of task window

          For  compatibility,  this top supports most of the former top sort keys.  Since this is primarily a service to former top users, these commands
          do not appear on any help screen.
                command   sorted-field                  supported
                A         start time (non-display)      No
                M         %MEM                          Yes
                N         PID                           Yes
                P         %CPU                          Yes
                T         TIME+                         Yes

          Before using any of the following sort provisions, top suggests that you temporarily turn on column highlighting using the `x' interactive com‐
          mand.  That will help ensure that the actual sort environment matches your intent.

          The following interactive commands will only be honored when the current sort field is visible.  The sort field might not be visible because:
                1) there is insufficient Screen Width
                2) the `f' interactive command turned it Off

             <  :Move-Sort-Field-Left
                 Moves the sort column to the left unless the current sort field is the first field being displayed.

             >  :Move-Sort-Field-Right
                 Moves the sort column to the right unless the current sort field is the last field being displayed.

查看 man page 可以找到这一段，其实一般 man page 都是最细致的，只不过因为太多了，有时候懒得看，这里可以通过大写 M 和大写 P 分别按内存和 CPU 排序，下面还有两个小技巧，通过按 x 可以将当前活跃的排序列用不同颜色标出来，然后可以通过<和>直接左右移动排序列

发表于 分类于 ， 阅读次数： Disqus：

事情源于周末来回家发生的两件事情，先是回去的时候从高铁下车要坐公交，现在算是有个比较好的临时候车点了，但是可能由于疫情好转，晚上都不用检查健康码就可以进候车点，但是上公交的时候还是需要看健康码，一般情况下从高铁下来的，各个地方的人都有，而且也不太清楚这边上公交车需要查验健康码，我的一个看法是候车的时候就应该放个横幅告示，或者再配合喇叭循环播放，请提前准备好健康码，上车前需要查验，因为像这周的情况，我乘坐的那辆车是间隔时间比较长，而且终点是工业开发区，可能是比较多外来务工人员的目的地，正好这部分人可能对于操作手机检验健康码这个事情不太熟悉，所以结果就是头上几个不知道怎么搞出来健康码，然后让几乎有满满一整车的人在后面堵着，司机又非常厌烦那些没有提前出示健康码的，有位乘客搞得久了点，还被误以为没刷卡买票，差点吵起来，其实公共交通或者高铁站负责的在公交指引路线上多写一句上车前需要查验健康码，可能就能改善比较多，还有就是那个积水的路，这个吐槽起来就一大坨了，整个绍兴像 dayuejin 一样到处都是破路。
第二个就是来杭州的时候，经过人行横道，远处车道的公交车停下来等我们了，为了少添麻烦总想快点穿过去，但是这时靠近我们的车道（晚上光线较暗，可见度不佳）有一辆从远处来的奥迪 A4 还是 A5 这种的车反而想加速冲过去，如果少看一下可能我已经残了，交规考的靠近人行道要减速好像基本都是个摆设了，杭州也只有公交车因为一些考核指标原因会主动礼让，人其实需要有同理心，虽然可能有些人是开车多于骑车走路的，但是总也不可能永远不穿人行道吧，甚至这些人可能还会在人行道红灯的时候走过去。这个事情不是吐槽公共交通的，只是也有些许关系，想起来还有一件事也是刚才来杭州的时候看到的，等公交的时候看到有辆路虎要加塞，而目标车道刚好是辆大货车，大货车看到按了喇叭，路虎犹豫了下还是挤进去了，可能是对路虎的扛撞性能非常自信吧，反正我是挺后怕的，这种级别的车，被撞了的话估计还是鸡蛋撞石头，吨位惯性在那，这里再延伸下，挺多开豪车的人好像都觉得这路上路权更大一些，谁谁都得让着他，可能实际吃亏的不多，所以越加巩固了这种思维，当真的碰到不管的可能就会明白了，路权这个事情在天朝也基本没啥人重视，也没想说个结论，就到这吧

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

You are given an array prices where prices[i] is the price of a given stock on the ith day.

You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock.

Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0.

给定一个数组 prices ，它的第 i 个元素 prices[i] 表示一支给定股票第 i 天的价格。

你只能选择 某一天 买入这只股票，并选择在 未来的某一个不同的日子 卖出该股票。设计一个算法来计算你所能获取的最大利润。

返回你可以从这笔交易中获取的最大利润。如果你不能获取任何利润，返回 0 。

简单分析

其实这个跟二叉树的最长路径和有点类似，需要找到整体的最大收益，但是在迭代过程中需要一个当前的值

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int maxSofar = 0;
public int maxProfit(int[] prices) {
    if (prices.length <= 1) {
        return 0;
    }
    int maxIn = prices[0];
    int maxOut = prices[0];
    for (int i = 1; i < prices.length; i++) {
        if (maxIn > prices[i]) {
            // 当循环当前值小于之前的买入值时就当成买入值，同时卖出也要更新
            maxIn = prices[i];
            maxOut = prices[i];
        }
        if (prices[i] > maxOut) {
            // 表示一个可卖出点，即比买入值高时
            maxOut = prices[i];
            // 需要设置一个历史值
            maxSofar = Math.max(maxSofar, maxOut - maxIn);
        }
    }
    return maxSofar;
}

总结下

一开始看到 easy 就觉得是很简单，就没有 maxSofar ，但是一提交就出现问题了
对于[2, 4, 1]这种就会变成 0，所以还是需要一个历史值来存放历史最大值，这题有点动态规划的意思

发表于 更新于 分类于 ， ， 阅读次数： Disqus：

最近看了下这本垃圾回收算法手册，看到了第三章的标记-整理回收算法，做个简单的读书笔记

双指针整理算法

对于一块待整理区域，通过两个指针，free 在区域的起始端，scan 指针在区域的末端，free 指针从前往后知道找到空闲区域，scan 从后往前一直找到存活对象，当 free 指针未与 scan 指针交叉时，会给 scan 位置的对象特定位置标记上 free 的地址，即将要转移的地址，不过这里有个限制，这种整理算法一般会用于对象大小统一的情况，否则 free 指针扫描时还需要匹配scan 指针扫描到的存活对象的大小。

Lisp 2 整理算法

需要三次完整遍历堆区域
第一遍是遍历后将计算出所有对象的最终地址（转发地址）
第二遍是使用转发地址更新赋值器线程根以及被标记对象中的引用，该操作将确保它们指向对象的新位置
第三次遍历是relocate最终将存活对象移动到其新的目标位置

引线整理算法

这个真的长见识了，

可以看到，原来是 A，B，C 对象引用了 N，这里会在第一次遍历的时候把这种引用反过来，让 N 的对象头部保存下 A 的地址，表示这类引用，然后在遍历到 B 的时候在链起来，到最后就会把所有引用了 N 对象的所有对象通过引线链起来，在第二次遍历的时候就把更新A，B，C 对象引用的 N 地址，并且移动 N 对象

单次遍历算法

这个一直提到过位图的实现方式，

可以看到在第一步会先通过位图标记，标记的方式是位图的每一位对应的堆内存的一个字（这里可能指的是 byte 吧），然后将一个存活对象的内存区域的第一个字跟最后一个字标记，这里如果在通过普通的方式就还需要一个地方在存转发地址，但是因为具体的位置可以通过位图算出来，也就不需要额外记录了

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新年开工开车来杭州，因为没有车位加限行今天来就没开车来了，从东站做公交回住的地方，这班神奇的车我之前也吐槽过了，有神奇的乘客和神奇的司机，因为基本上这班车是从我毕业就开始乘了，所以也算是比较熟悉了，以前总体感觉不太好的是乘坐时间太长了，不过这个也不能怪车，是我自己住得远（离东站），后来住到了现在的地方，也算是直达，并且 LD 比较喜欢直达的，不爱更快却要换乘的地铁，所以坐的频率比较高，也说过前面那些比较气人的乘客，自己不好好戴口罩，反而联合一起上车的乘客诽谤司机，说他要吃人了要打人了，也正是这个司机比较有意思，上车就让戴好口罩，还给大家讲，哪里哪里又有疫情了，我觉得其实这个司机还是不错的，特殊时期，对于这种公共交通，这样的确是比较负责任的做法，只是说话方式，语气这个因人而异，他也不是来伺候人的，而且这么一大车人，说了一遍不行，再说一遍，三遍以上了，嗓门大一点也属于正常的人的行为。
还是说回今天要说的，今天这位司机我看着跟前面说的那位有点像，因为上车的时候比较暗没看清脸，主要原因是这位司机开车比较猛，比较急，然后车上因为这个时间点，比较多大学开学来的学生，拎着个行李箱，一开始是前面已经都站满了人，后面还有很多空位，因为后面没地方放行李箱，就因为这样前面站着的有几个就在说司机开慢点，结果司机貌似也没听进去，还是我行我素，过了会又有人说司机开稳一点，就在这个人说完没一会，停在红绿灯路口的车里，就有人问有没有垃圾桶，接着又让司机开门，说晕车太严重了，要下车，司机开了门，我望出去两个妹子下了车，好像在路边草丛吐了，前面开门下车的时候就有人说她们第一次来杭州，可能有点责怪司机开的不稳，也影响了杭州交通给新来杭州的人的感受，说完了事情经过，其实我有蛮多感触，对于杭州公交司机，我大概是大一来了没多久，陪室友去文三路买电脑就晕车，下车的时候在公交车站吐了，可能是从大学开始缺乏锻炼，又饮食不规律，更加容易晕车，大部分晕车我觉得都是我自己的原因，有时候是上车前吃太多了，或者早上起太早，没睡好，没吃东西，反正自己也是挺多原因的，说到司机的原因的话，我觉得可能这班车还算好的，最让我难受的还是上下班高峰的时候，因为经过的那条路是比较重要的主干道，路比较老比较窄，并且还有很多人行道，所以经常一脚油门连带着一脚刹车，真的很难受，这种算是我觉得真的是公交体验比较差的一点，但是这一点呢也不能完全怪公交司机，杭州的路政规划是很垃圾，没看错，是垃圾，所以总体结论是公交还行，主要是路政规划就是垃圾，包括这条主干道这么多人行道，并且两边都是老小区，老年人在上班高峰可能要买菜送娃或者其他事情，在通畅的情况下可能只需要六分钟的路程，有时候因为各种原因，半小时都开不完，扯开去一点，杭州的路，核心的高速说封就封，本来是高架可以直接通到城西，结果没造，到了路本已经很拥挤的时候开始来造隧道，各种破坏，隧道接高架的地方，无尽的加塞，对于我这样的小白司机来说真的是太恶心了，所以我一直想说的就是杭州这个地方房价领先基础设施十年，地铁，高架，高速通通不行，地面道路就更不行了。
总结下，其实杭州的真正的公交体验差，应该还是路造成的，对于前面的那两位妹子来说，有可能是她们来自于公交司机都是开的特别稳，并且路况也很好的地方，也或者是我被虐习惯了🤦‍♂️

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： Disqus：

top 命令在日常的 Linux 使用中，特别是做一些服务器的简单状态查看，排查故障都起了比较大的作用，但是由于这个命令看到的东西比较多，一般只会看部分，或者说像我这样就会比较片面地看一些信息，比如默认是进程维度的，可以在启动命令的时候加-H进入线程模式

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-H  :Threads-mode operation
            Instructs top to display individual threads.  Without this command-line option a summation of all threads in each process  is  shown.   Later
            this can be changed with the `H' interactive command.

这样就能用在 Java 中去 jstack 中找到对应的线程
其实还有比较重要的两个操作，
一个是在 top 启动状态下，按c键，这样能把比如说是一个 Java 进程，具体的进程命令显示出来
像这样
执行前是这样

执行后是这样

第二个就是排序了

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SORTING of task window

          For  compatibility,  this top supports most of the former top sort keys.  Since this is primarily a service to former top users, these commands
          do not appear on any help screen.
                command   sorted-field                  supported
                A         start time (non-display)      No
                M         %MEM                          Yes
                N         PID                           Yes
                P         %CPU                          Yes
                T         TIME+                         Yes

          Before using any of the following sort provisions, top suggests that you temporarily turn on column highlighting using the `x' interactive com‐
          mand.  That will help ensure that the actual sort environment matches your intent.

          The following interactive commands will only be honored when the current sort field is visible.  The sort field might not be visible because:
                1) there is insufficient Screen Width
                2) the `f' interactive command turned it Off

             <  :Move-Sort-Field-Left
                 Moves the sort column to the left unless the current sort field is the first field being displayed.

             >  :Move-Sort-Field-Right
                 Moves the sort column to the right unless the current sort field is the last field being displayed.

查看 man page 可以找到这一段，其实一般 man page 都是最细致的，只不过因为太多了，有时候懒得看，这里可以通过大写 M 和大写 P 分别按内存和 CPU 排序，下面还有两个小技巧，通过按 x 可以将当前活跃的排序列用不同颜色标出来，然后可以通过<和>直接左右移动排序列

发表于 分类于 ， 阅读次数： Disqus：

事情源于周末来回家发生的两件事情，先是回去的时候从高铁下车要坐公交，现在算是有个比较好的临时候车点了，但是可能由于疫情好转，晚上都不用检查健康码就可以进候车点，但是上公交的时候还是需要看健康码，一般情况下从高铁下来的，各个地方的人都有，而且也不太清楚这边上公交车需要查验健康码，我的一个看法是候车的时候就应该放个横幅告示，或者再配合喇叭循环播放，请提前准备好健康码，上车前需要查验，因为像这周的情况，我乘坐的那辆车是间隔时间比较长，而且终点是工业开发区，可能是比较多外来务工人员的目的地，正好这部分人可能对于操作手机检验健康码这个事情不太熟悉，所以结果就是头上几个不知道怎么搞出来健康码，然后让几乎有满满一整车的人在后面堵着，司机又非常厌烦那些没有提前出示健康码的，有位乘客搞得久了点，还被误以为没刷卡买票，差点吵起来，其实公共交通或者高铁站负责的在公交指引路线上多写一句上车前需要查验健康码，可能就能改善比较多，还有就是那个积水的路，这个吐槽起来就一大坨了，整个绍兴像 dayuejin 一样到处都是破路。
第二个就是来杭州的时候，经过人行横道，远处车道的公交车停下来等我们了，为了少添麻烦总想快点穿过去，但是这时靠近我们的车道（晚上光线较暗，可见度不佳）有一辆从远处来的奥迪 A4 还是 A5 这种的车反而想加速冲过去，如果少看一下可能我已经残了，交规考的靠近人行道要减速好像基本都是个摆设了，杭州也只有公交车因为一些考核指标原因会主动礼让，人其实需要有同理心，虽然可能有些人是开车多于骑车走路的，但是总也不可能永远不穿人行道吧，甚至这些人可能还会在人行道红灯的时候走过去。这个事情不是吐槽公共交通的，只是也有些许关系，想起来还有一件事也是刚才来杭州的时候看到的，等公交的时候看到有辆路虎要加塞，而目标车道刚好是辆大货车，大货车看到按了喇叭，路虎犹豫了下还是挤进去了，可能是对路虎的扛撞性能非常自信吧，反正我是挺后怕的，这种级别的车，被撞了的话估计还是鸡蛋撞石头，吨位惯性在那，这里再延伸下，挺多开豪车的人好像都觉得这路上路权更大一些，谁谁都得让着他，可能实际吃亏的不多，所以越加巩固了这种思维，当真的碰到不管的可能就会明白了，路权这个事情在天朝也基本没啥人重视，也没想说个结论，就到这吧

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

You are given an array prices where prices[i] is the price of a given stock on the ith day.

You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock.

Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0.

给定一个数组 prices ，它的第 i 个元素 prices[i] 表示一支给定股票第 i 天的价格。

你只能选择 某一天 买入这只股票，并选择在 未来的某一个不同的日子 卖出该股票。设计一个算法来计算你所能获取的最大利润。

返回你可以从这笔交易中获取的最大利润。如果你不能获取任何利润，返回 0 。

简单分析

其实这个跟二叉树的最长路径和有点类似，需要找到整体的最大收益，但是在迭代过程中需要一个当前的值

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int maxSofar = 0;
public int maxProfit(int[] prices) {
    if (prices.length <= 1) {
        return 0;
    }
    int maxIn = prices[0];
    int maxOut = prices[0];
    for (int i = 1; i < prices.length; i++) {
        if (maxIn > prices[i]) {
            // 当循环当前值小于之前的买入值时就当成买入值，同时卖出也要更新
            maxIn = prices[i];
            maxOut = prices[i];
        }
        if (prices[i] > maxOut) {
            // 表示一个可卖出点，即比买入值高时
            maxOut = prices[i];
            // 需要设置一个历史值
            maxSofar = Math.max(maxSofar, maxOut - maxIn);
        }
    }
    return maxSofar;
}

总结下

一开始看到 easy 就觉得是很简单，就没有 maxSofar ，但是一提交就出现问题了
对于[2, 4, 1]这种就会变成 0，所以还是需要一个历史值来存放历史最大值，这题有点动态规划的意思

发表于 更新于 分类于 ， ， 阅读次数： Disqus：

最近看了下这本垃圾回收算法手册，看到了第三章的标记-整理回收算法，做个简单的读书笔记

双指针整理算法

对于一块待整理区域，通过两个指针，free 在区域的起始端，scan 指针在区域的末端，free 指针从前往后知道找到空闲区域，scan 从后往前一直找到存活对象，当 free 指针未与 scan 指针交叉时，会给 scan 位置的对象特定位置标记上 free 的地址，即将要转移的地址，不过这里有个限制，这种整理算法一般会用于对象大小统一的情况，否则 free 指针扫描时还需要匹配scan 指针扫描到的存活对象的大小。

Lisp 2 整理算法

需要三次完整遍历堆区域
第一遍是遍历后将计算出所有对象的最终地址（转发地址）
第二遍是使用转发地址更新赋值器线程根以及被标记对象中的引用，该操作将确保它们指向对象的新位置
第三次遍历是relocate最终将存活对象移动到其新的目标位置

引线整理算法

这个真的长见识了，

可以看到，原来是 A，B，C 对象引用了 N，这里会在第一次遍历的时候把这种引用反过来，让 N 的对象头部保存下 A 的地址，表示这类引用，然后在遍历到 B 的时候在链起来，到最后就会把所有引用了 N 对象的所有对象通过引线链起来，在第二次遍历的时候就把更新A，B，C 对象引用的 N 地址，并且移动 N 对象

单次遍历算法

这个一直提到过位图的实现方式，

可以看到在第一步会先通过位图标记，标记的方式是位图的每一位对应的堆内存的一个字（这里可能指的是 byte 吧），然后将一个存活对象的内存区域的第一个字跟最后一个字标记，这里如果在通过普通的方式就还需要一个地方在存转发地址，但是因为具体的位置可以通过位图算出来，也就不需要额外记录了

发表于 分类于 ， 阅读次数： Disqus：

新年开工开车来杭州，因为没有车位加限行今天来就没开车来了，从东站做公交回住的地方，这班神奇的车我之前也吐槽过了，有神奇的乘客和神奇的司机，因为基本上这班车是从我毕业就开始乘了，所以也算是比较熟悉了，以前总体感觉不太好的是乘坐时间太长了，不过这个也不能怪车，是我自己住得远（离东站），后来住到了现在的地方，也算是直达，并且 LD 比较喜欢直达的，不爱更快却要换乘的地铁，所以坐的频率比较高，也说过前面那些比较气人的乘客，自己不好好戴口罩，反而联合一起上车的乘客诽谤司机，说他要吃人了要打人了，也正是这个司机比较有意思，上车就让戴好口罩，还给大家讲，哪里哪里又有疫情了，我觉得其实这个司机还是不错的，特殊时期，对于这种公共交通，这样的确是比较负责任的做法，只是说话方式，语气这个因人而异，他也不是来伺候人的，而且这么一大车人，说了一遍不行，再说一遍，三遍以上了，嗓门大一点也属于正常的人的行为。
还是说回今天要说的，今天这位司机我看着跟前面说的那位有点像，因为上车的时候比较暗没看清脸，主要原因是这位司机开车比较猛，比较急，然后车上因为这个时间点，比较多大学开学来的学生，拎着个行李箱，一开始是前面已经都站满了人，后面还有很多空位，因为后面没地方放行李箱，就因为这样前面站着的有几个就在说司机开慢点，结果司机貌似也没听进去，还是我行我素，过了会又有人说司机开稳一点，就在这个人说完没一会，停在红绿灯路口的车里，就有人问有没有垃圾桶，接着又让司机开门，说晕车太严重了，要下车，司机开了门，我望出去两个妹子下了车，好像在路边草丛吐了，前面开门下车的时候就有人说她们第一次来杭州，可能有点责怪司机开的不稳，也影响了杭州交通给新来杭州的人的感受，说完了事情经过，其实我有蛮多感触，对于杭州公交司机，我大概是大一来了没多久，陪室友去文三路买电脑就晕车，下车的时候在公交车站吐了，可能是从大学开始缺乏锻炼，又饮食不规律，更加容易晕车，大部分晕车我觉得都是我自己的原因，有时候是上车前吃太多了，或者早上起太早，没睡好，没吃东西，反正自己也是挺多原因的，说到司机的原因的话，我觉得可能这班车还算好的，最让我难受的还是上下班高峰的时候，因为经过的那条路是比较重要的主干道，路比较老比较窄，并且还有很多人行道，所以经常一脚油门连带着一脚刹车，真的很难受，这种算是我觉得真的是公交体验比较差的一点，但是这一点呢也不能完全怪公交司机，杭州的路政规划是很垃圾，没看错，是垃圾，所以总体结论是公交还行，主要是路政规划就是垃圾，包括这条主干道这么多人行道，并且两边都是老小区，老年人在上班高峰可能要买菜送娃或者其他事情，在通畅的情况下可能只需要六分钟的路程，有时候因为各种原因，半小时都开不完，扯开去一点，杭州的路，核心的高速说封就封，本来是高架可以直接通到城西，结果没造，到了路本已经很拥挤的时候开始来造隧道，各种破坏，隧道接高架的地方，无尽的加塞，对于我这样的小白司机来说真的是太恶心了，所以我一直想说的就是杭州这个地方房价领先基础设施十年，地铁，高架，高速通通不行，地面道路就更不行了。
总结下，其实杭州的真正的公交体验差，应该还是路造成的，对于前面的那两位妹子来说，有可能是她们来自于公交司机都是开的特别稳，并且路况也很好的地方，也或者是我被虐习惯了🤦‍♂️

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What hurts more, the pain of hard work or the pain of regret?

发表于 更新于 分类于 ， 阅读次数： Disqus：

Condition也是 AQS 中很重要的一块内容，可以先看段示例代码，这段代码应该来自于Doug Lea大大，可以在 javadoc 中的 condition 部分找到，其实大大原来写过基于 synchronized 实现的，后面我也贴下代码

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import java.util.concurrent.locks.Condition;
import java.util.concurrent.locks.Lock;
import java.util.concurrent.locks.ReentrantLock;

class BoundedBuffer {
    final Lock lock = new ReentrantLock();
    // condition 依赖于 lock 来产生
    final Condition notFull = lock.newCondition();
    final Condition notEmpty = lock.newCondition();

    // 对象池子，put 跟 take 的就是这里的
    final Object[] items = new Object[100];
    int putptr, takeptr, count;

    // 生产
    public void put(Object x) throws InterruptedException {
        // 这里也说明了，需要先拥有锁
        lock.lock();
        try {
            while (count == items.length)
                notFull.await();  // 队列已满，等待，直到 not full 才能继续生产
            items[putptr] = x;
            if (++putptr == items.length) putptr = 0;
            ++count;
            notEmpty.signal(); // 生产成功，队列已经 not empty 了，发个通知出去
        } finally {
            lock.unlock();
        }
    }

    // 消费
    public Object take() throws InterruptedException {
        lock.lock();
        try {
            while (count == 0)
                notEmpty.await(); // 队列为空，等待，直到队列 not empty，才能继续消费
            Object x = items[takeptr];
            if (++takeptr == items.length) takeptr = 0;
            --count;
            notFull.signal(); // 被我消费掉一个，队列 not full 了，发个通知出去
            return x;
        } finally {
            lock.unlock();
        }
    }
}

介绍下 Condition 的结构

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public class ConditionObject implements Condition, java.io.Serializable {
        private static final long serialVersionUID = 1173984872572414699L;
        /** First node of condition queue. */
        private transient Node firstWaiter;
        /** Last node of condition queue. */
        private transient Node lastWaiter;

主要的就这么点，而且也复用了 AQS 阻塞队列或者大大叫 lock queue中同样的 Node 节点，只不过它没有使用其中的双向队列，也就是prev 和 next，而是在 Node 中的 nextWaiter，所以只是个单向的队列，没使用 next 其实还有个用处，后面会提到，看下结构的示意图

然后主要是看两个方法，await 和 signal,
先来看下 await

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/**
 * Implements interruptible condition wait.
 * <ol>
 * <li> If current thread is interrupted, throw InterruptedException.
 * <li> Save lock state returned by {@link #getState}.
 * <li> Invoke {@link #release} with saved state as argument,
 *      throwing IllegalMonitorStateException if it fails.
 * <li> Block until signalled or interrupted.
 * <li> Reacquire by invoking specialized version of
 *      {@link #acquire} with saved state as argument.
 * <li> If interrupted while blocked in step 4, throw InterruptedException.
 * </ol>
 */
public final void await() throws InterruptedException {
    if (Thread.interrupted())
        throw new InterruptedException();

    // 将当前节点包装成一个  condition waiter node 节点
    Node node = addConditionWaiter();

    // 完全释放占有的锁，这里需要是占有锁的线程
    int savedState = fullyRelease(node);
    int interruptMode = 0;

    // 判断下是否在阻塞队列中，因为有可能被其他节点从等待队列移动到阻塞队列
    while (!isOnSyncQueue(node)) {
        // park等待，等待被唤醒
        LockSupport.park(this);
        if ((interruptMode = checkInterruptWhileWaiting(node)) != 0)
            break;
    }

    // 被唤醒后进入阻塞队列，等待获取锁，这里继续用了fullyRelease返回的 state
    if (acquireQueued(node, savedState) && interruptMode != THROW_IE)
        interruptMode = REINTERRUPT;
    if (node.nextWaiter != null) // clean up if cancelled
        unlinkCancelledWaiters();
    if (interruptMode != 0)
        reportInterruptAfterWait(interruptMode);
}

添加条件队列节点

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/**
 * Adds a new waiter to wait queue.
 * @return its new wait node
 */
private Node addConditionWaiter() {
    Node t = lastWaiter;
    // If lastWaiter is cancelled, clean out.
    // 如果节点已经不是 CONDITION 状态了，表示已经取消了
    if (t != null && t.waitStatus != Node.CONDITION) {
        // 把等待队列中取消的节点清理出去
        unlinkCancelledWaiters();
        t = lastWaiter;
    }
    // 把当前线程包装成waitStatus=CONDITION 的节点
    Node node = new Node(Thread.currentThread(), Node.CONDITION);
    // 没有 lastWaiter 节点，直接是 firstWaiter
    if (t == null)
        firstWaiter = node;
    else
    // 不然就接在 lastWaiter 后面
        t.nextWaiter = node;
    // 当前节点就会变成新的 lastWaiter
    lastWaiter = node;
    return node;
}

清理取消的节点

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/**
 * Unlinks cancelled waiter nodes from condition queue.
 * Called only while holding lock. This is called when
 * cancellation occurred during condition wait, and upon
 * insertion of a new waiter when lastWaiter is seen to have
 * been cancelled. This method is needed to avoid garbage
 * retention in the absence of signals. So even though it may
 * require a full traversal, it comes into play only when
 * timeouts or cancellations occur in the absence of
 * signals. It traverses all nodes rather than stopping at a
 * particular target to unlink all pointers to garbage nodes
 * without requiring many re-traversals during cancellation
 * storms.
 */
private void unlinkCancelledWaiters() {
    Node t = firstWaiter;
    Node trail = null;
    // 循环遍历单向链表的节点，如果状态不是 CONDITION 就清出去
    while (t != null) {
        Node next = t.nextWaiter;
        // 循环链表操作，清掉取消的节点
        if (t.waitStatus != Node.CONDITION) {
            t.nextWaiter = null;
            if (trail == null)
                firstWaiter = next;
            else
                trail.nextWaiter = next;
            if (next == null)
                lastWaiter = trail;
        }
        else
            trail = t;
        t = next;
    }
}

完全释放锁

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/**
 * Invokes release with current state value; returns saved state.
 * Cancels node and throws exception on failure.
 * @param node the condition node for this wait
 * @return previous sync state
 */
final int fullyRelease(Node node) {
    boolean failed = true;
    try {
        // 获取下当前的 state 值，因为是可重入的，所以这个值要保存下来
        int savedState = getState();
        // 这里还包含比较多操作，不过跟前面分析 AQS 的释放比较类似，不深入了
        if (release(savedState)) {
            failed = false;
            // 返回这个值
            return savedState;
        } else {
            throw new IllegalMonitorStateException();
        }
    } finally {
        if (failed)
            node.waitStatus = Node.CANCELLED;
    }
}

判断是否在阻塞队列中

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/**
 * Returns true if a node, always one that was initially placed on
 * a condition queue, is now waiting to reacquire on sync queue.
 * @param node the node
 * @return true if is reacquiring
 */
final boolean isOnSyncQueue(Node node) {
    // 如果waitStatus 是 CONDITION 或者没有 prev 前置节点肯定就不在
    if (node.waitStatus == Node.CONDITION || node.prev == null)
        return false;
    // 这里就是我前面提到的 next 的作用
    if (node.next != null) // If has successor, it must be on queue
        return true;
    // 从 tail 开始找，是否在阻塞队列中
    /*
     * node.prev can be non-null, but not yet on queue because
     * the CAS to place it on queue can fail. So we have to
     * traverse from tail to make sure it actually made it.  It
     * will always be near the tail in calls to this method, and
     * unless the CAS failed (which is unlikely), it will be
     * there, so we hardly ever traverse much.
     */
    return findNodeFromTail(node);
}
/**
 * Returns true if node is on sync queue by searching backwards from tail.
 * Called only when needed by isOnSyncQueue.
 * @return true if present
 */
private boolean findNodeFromTail(Node node) {
    Node t = tail;
    // 从 tail 开始，从后往前找
    for (;;) {
        if (t == node)
            return true;
        if (t == null)
            return false;
        t = t.prev;
    }
}

await 的逻辑差不多就是这样子，主要的就是把自己包成一个 Node 节点，waitStatus 的状态是 CONDITION，挂在等待队列的最后，然后完全释放锁，park 等待

signal

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/**
 * Moves the longest-waiting thread, if one exists, from the
 * wait queue for this condition to the wait queue for the
 * owning lock.
 *
 * @throws IllegalMonitorStateException if {@link #isHeldExclusively}
 *         returns {@code false}
 */
public final void signal() {
    if (!isHeldExclusively())
        throw new IllegalMonitorStateException();
    // firstWaiter 肯定是最早开始等待的
    Node first = firstWaiter;
    // 如果不为空就唤醒
    if (first != null)
        doSignal(first);
}
/**
 * Removes and transfers nodes until hit non-cancelled one or
 * null. Split out from signal in part to encourage compilers
 * to inline the case of no waiters.
 * @param first (non-null) the first node on condition queue
 */
private void doSignal(Node first) {
    do {
        // 因为要去唤醒 first 节点了，firstWaiter 需要再从后面找一个
        // 并且判断是否为空，如果是空的话就直接可以把 lastWaiter 设置成空了
        if ( (firstWaiter = first.nextWaiter) == null)
            lastWaiter = null;
        //  first 不需要继续保存后面的 waiter 了，因为 firstWaiter 已经是 first 的后置节点了
        first.nextWaiter = null;
    // 如果 first 节点转移不成功，并且 firstWaiter 节点不为空，则继续进入循环
    } while (!transferForSignal(first) &&
             (first = firstWaiter) != null);
}
/**
 * Transfers a node from a condition queue onto sync queue.
 * Returns true if successful.
 * @param node the node
 * @return true if successfully transferred (else the node was
 * cancelled before signal)
 */
final boolean transferForSignal(Node node) {
    /*
     * If cannot change waitStatus, the node has been cancelled.
     */
    // 如果状态已经不是 CONDITION 就不会设置成功，返回 false
    if (!compareAndSetWaitStatus(node, Node.CONDITION, 0))
        return false;
    /*
     * Splice onto queue and try to set waitStatus of predecessor to
     * indicate that thread is (probably) waiting. If cancelled or
     * attempt to set waitStatus fails, wake up to resync (in which
     * case the waitStatus can be transiently and harmlessly wrong).
     */
    // 调用跟aqs 第一篇中一样的 enq 方法进入阻塞队列，返回入队后的前一节点
    Node p = enq(node);
    int ws = p.waitStatus;
    // 将前置节点状态设置成SIGNAL，表示后面有节点在等了
    if (ws > 0 || !compareAndSetWaitStatus(p, ws, Node.SIGNAL))
        LockSupport.unpark(node.thread);
    // 返回 true，上一个方法的循环就退出了
    return true;
}

这里其实就是把 condition 等待队列的第一个未取消的节点入队到阻塞队列去争锁

附录

synchronized 版的 BoundedBuffer

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/*
  File: BoundedBuffer.java

  Originally written by Doug Lea and released into the public domain.
  This may be used for any purposes whatsoever without acknowledgment.
  Thanks for the assistance and support of Sun Microsystems Labs,
  and everyone contributing, testing, and using this code.

  History:
  Date       Who                What
  11Jun1998  dl               Create public version
  17Jul1998  dl               Simplified by eliminating wait counts
  25aug1998  dl               added peek
   5May1999  dl               replace % with conditional (slightly faster)
*/

package EDU.oswego.cs.dl.util.concurrent;

/**
 * Efficient array-based bounded buffer class.
 * Adapted from CPJ, chapter 8, which describes design.
 * <p>[<a href="http://gee.cs.oswego.edu/dl/classes/EDU/oswego/cs/dl/util/concurrent/intro.html"> Introduction to this package. </a>] <p>
 **/

public class BoundedBuffer implements BoundedChannel {

  protected final Object[]  array_;      // the elements

  protected int takePtr_ = 0;            // circular indices
  protected int putPtr_ = 0;       

  protected int usedSlots_ = 0;          // length
  protected int emptySlots_;             // capacity - length

  /**
   * Helper monitor to handle puts. 
   **/
  protected final Object putMonitor_ = new Object();

  /**
   * Create a BoundedBuffer with the given capacity.
   * @exception IllegalArgumentException if capacity less or equal to zero
   **/
  public BoundedBuffer(int capacity) throws IllegalArgumentException {
    if (capacity <= 0) throw new IllegalArgumentException();
    array_ = new Object[capacity];
    emptySlots_ = capacity;
  }

  /**
   * Create a buffer with the current default capacity
   **/

  public BoundedBuffer() { 
    this(DefaultChannelCapacity.get()); 
  }

  /** 
   * Return the number of elements in the buffer.
   * This is only a snapshot value, that may change
   * immediately after returning.
   **/
  public synchronized int size() { return usedSlots_; }

  public int capacity() { return array_.length; }

  protected void incEmptySlots() {
    synchronized(putMonitor_) {
      ++emptySlots_;
      putMonitor_.notify();
    }
  }

  protected synchronized void incUsedSlots() {
    ++usedSlots_;
    notify();
  }

  protected final void insert(Object x) { // mechanics of put
    --emptySlots_;
    array_[putPtr_] = x;
    if (++putPtr_ >= array_.length) putPtr_ = 0;
  }

  protected final Object extract() { // mechanics of take
    --usedSlots_;
    Object old = array_[takePtr_];
    array_[takePtr_] = null;
    if (++takePtr_ >= array_.length) takePtr_ = 0;
    return old;
  }

  public Object peek() {
    synchronized(this) {
      if (usedSlots_ > 0)
        return array_[takePtr_];
      else
        return null;
    }
  }


  public void put(Object x) throws InterruptedException {
    if (x == null) throw new IllegalArgumentException();
    if (Thread.interrupted()) throw new InterruptedException();

    synchronized(putMonitor_) {
      while (emptySlots_ <= 0) {
	try { putMonitor_.wait(); }
        catch (InterruptedException ex) {
          putMonitor_.notify();
          throw ex;
        }
      }
      insert(x);
    }
    incUsedSlots();
  }

  public boolean offer(Object x, long msecs) throws InterruptedException {
    if (x == null) throw new IllegalArgumentException();
    if (Thread.interrupted()) throw new InterruptedException();

    synchronized(putMonitor_) {
      long start = (msecs <= 0)? 0 : System.currentTimeMillis();
      long waitTime = msecs;
      while (emptySlots_ <= 0) {
        if (waitTime <= 0) return false;
	try { putMonitor_.wait(waitTime); }
        catch (InterruptedException ex) {
          putMonitor_.notify();
          throw ex;
        }
        waitTime = msecs - (System.currentTimeMillis() - start);
      }
      insert(x);
    }
    incUsedSlots();
    return true;
  }



  public  Object take() throws InterruptedException { 
    if (Thread.interrupted()) throw new InterruptedException();
    Object old = null; 
    synchronized(this) { 
      while (usedSlots_ <= 0) {
        try { wait(); }
        catch (InterruptedException ex) {
          notify();
          throw ex; 
        }
      }
      old = extract();
    }
    incEmptySlots();
    return old;
  }

  public  Object poll(long msecs) throws InterruptedException { 
    if (Thread.interrupted()) throw new InterruptedException();
    Object old = null; 
    synchronized(this) { 
      long start = (msecs <= 0)? 0 : System.currentTimeMillis();
      long waitTime = msecs;
      
      while (usedSlots_ <= 0) {
        if (waitTime <= 0) return null;
        try { wait(waitTime); }
        catch (InterruptedException ex) {
          notify();
          throw ex; 
        }
        waitTime = msecs - (System.currentTimeMillis() - start);

      }
      old = extract();
    }
    incEmptySlots();
    return old;
  }

}

发表于 更新于 分类于 ， 阅读次数： Disqus：

很多东西都是时看时新，而且时间长了也会忘，所以再来复习下，也会有一些新的角度看法这次来聊下AQS的内容，主要是这几个点，

第一个线程

第一个线程抢到锁了，此时state跟阻塞队列是怎么样的，其实这里是之前没理解对的地方

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/**
         * Fair version of tryAcquire.  Don't grant access unless
         * recursive call or no waiters or is first.
         */
        protected final boolean tryAcquire(int acquires) {
            final Thread current = Thread.currentThread();
            int c = getState();
            // 这里如果state还是0说明锁还空着
            if (c == 0) {
                // 因为是公平锁版本的，先去看下是否阻塞队列里有排着队的
                if (!hasQueuedPredecessors() &&
                    compareAndSetState(0, acquires)) {
                    // 没有排队的，并且state使用cas设置成功的就标记当前占有锁的线程是我
                    setExclusiveOwnerThread(current);
                    // 然后其实就返回了，包括阻塞队列的head和tail节点和waitStatus都没有设置
                    return true;
                }
            }
            else if (current == getExclusiveOwnerThread()) {
                int nextc = c + acquires;
                if (nextc < 0)
                    throw new Error("Maximum lock count exceeded");
                setState(nextc);
                return true;
            }
            // 这里就是第二个线程会返回false
            return false;
        }
    }

第二个线程

当第二个线程进来的时候应该是怎么样，结合代码来看

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/**
     * Acquires in exclusive mode, ignoring interrupts.  Implemented
     * by invoking at least once {@link #tryAcquire},
     * returning on success.  Otherwise the thread is queued, possibly
     * repeatedly blocking and unblocking, invoking {@link
     * #tryAcquire} until success.  This method can be used
     * to implement method {@link Lock#lock}.
     *
     * @param arg the acquire argument.  This value is conveyed to
     *        {@link #tryAcquire} but is otherwise uninterpreted and
     *        can represent anything you like.
     */
    public final void acquire(int arg) {
        // 前面第一种情况是tryAcquire直接成功了，这个if判断第一个条件就是false，就不往下执行了
        // 如果是第二个线程，第一个条件获取锁不成功，条件判断!tryAcquire(arg) == true，就会走
        // acquireQueued(addWaiter(Node.EXCLUSIVE), arg)
        if (!tryAcquire(arg) &&
            acquireQueued(addWaiter(Node.EXCLUSIVE), arg))
            selfInterrupt();
    }

然后来看下addWaiter的逻辑

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/**
     * Creates and enqueues node for current thread and given mode.
     *
     * @param mode Node.EXCLUSIVE for exclusive, Node.SHARED for shared
     * @return the new node
     */
    private Node addWaiter(Node mode) {
        // 这里是包装成一个node
        Node node = new Node(Thread.currentThread(), mode);
        // Try the fast path of enq; backup to full enq on failure
        // 最快的方式就是把当前线程的节点放在阻塞队列的最后
        Node pred = tail;
        // 只有当tail，也就是pred不为空的时候可以直接接上
        if (pred != null) {
            node.prev = pred;
            // 如果这里cas成功了，就直接接上返回了
            if (compareAndSetTail(pred, node)) {
                pred.next = node;
                return node;
            }
        }
        // 不然就会继续走到这里
        enq(node);
        return node;
    }

然后就是enq的逻辑了

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/**
     * Inserts node into queue, initializing if necessary. See picture above.
     * @param node the node to insert
     * @return node's predecessor
     */
    private Node enq(final Node node) {
        for (;;) {
            // 如果状态没变化的话，tail这时还是null的
            Node t = tail;
            if (t == null) { // Must initialize
                // 这里就会初始化头结点，就是个空节点
                if (compareAndSetHead(new Node()))
                    // tail也赋值成head
                    tail = head;
            } else {
                // 这里就设置tail了
                node.prev = t;
                if (compareAndSetTail(t, node)) {
                    t.next = node;
                    return t;
                }
            }
        }
    }

所以从这里可以看出来，其实head头结点不是个真实的带有线程的节点，并且不是在第一个线程进来的时候设置的

解锁

通过代码来看下

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/**
     * Attempts to release this lock.
     *
     * <p>If the current thread is the holder of this lock then the hold
     * count is decremented.  If the hold count is now zero then the lock
     * is released.  If the current thread is not the holder of this
     * lock then {@link IllegalMonitorStateException} is thrown.
     *
     * @throws IllegalMonitorStateException if the current thread does not
     *         hold this lock
     */
    public void unlock() {
        // 释放锁
        sync.release(1);
    }
/**
     * Releases in exclusive mode.  Implemented by unblocking one or
     * more threads if {@link #tryRelease} returns true.
     * This method can be used to implement method {@link Lock#unlock}.
     *
     * @param arg the release argument.  This value is conveyed to
     *        {@link #tryRelease} but is otherwise uninterpreted and
     *        can represent anything you like.
     * @return the value returned from {@link #tryRelease}
     */
    public final boolean release(int arg) {
        // 尝试去释放
        if (tryRelease(arg)) {
            Node h = head;
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }
protected final boolean tryRelease(int releases) {
            int c = getState() - releases;
            if (Thread.currentThread() != getExclusiveOwnerThread())
                throw new IllegalMonitorStateException();
            boolean free = false;
    		// 判断是否完全释放锁，因为可重入
            if (c == 0) {
                free = true;
                setExclusiveOwnerThread(null);
            }
            setState(c);
            return free;
        }
// 这段代码和上面的一致，只是为了顺序性，又拷下来看下

public final boolean release(int arg) {
        // 尝试去释放，如果是完全释放，返回的就是true，否则是false
        if (tryRelease(arg)) {
            Node h = head;
            // 这里判断头结点是否为空以及waitStatus的状态，前面说了head节点其实是
            // 在第二个线程进来的时候初始化的，如果是空的话说明没后续节点，并且waitStatus
            // 也表示了后续的等待状态
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }

/**
     * Wakes up node's successor, if one exists.
     *
     * @param node the node
     */
// 唤醒后继节点
    private void unparkSuccessor(Node node) {
        /*
         * If status is negative (i.e., possibly needing signal) try
         * to clear in anticipation of signalling.  It is OK if this
         * fails or if status is changed by waiting thread.
         */
        int ws = node.waitStatus;
        if (ws < 0)
            compareAndSetWaitStatus(node, ws, 0);

        /*
         * Thread to unpark is held in successor, which is normally
         * just the next node.  But if cancelled or apparently null,
         * traverse backwards from tail to find the actual
         * non-cancelled successor.
         */
        Node s = node.next;
        // 如果后继节点是空或者当前节点取消等待了
        if (s == null || s.waitStatus > 0) {
            s = null;
            // 从后往前找，找到非取消的节点，注意这里不是找到就退出，而是一直找到头
            // 所以不必担心中间有取消的
            for (Node t = tail; t != null && t != node; t = t.prev)
                if (t.waitStatus <= 0)
                    s = t;
        }
        if (s != null)
            // 将其唤醒
            LockSupport.unpark(s.thread);
    }

发表于 分类于 ， ， ， 阅读次数： Disqus：

最近群里大佬发起了一个读书打卡活动，需要每天读一会书，在群里打卡分享感悟，争取一个月能读完一本书，说实话一天十分钟的读书时间倒是问题不大，不过每天都要打卡，而且一个月要读完一本书，其实难度还是有点大的，不过也想试试看。
之前某某老大给自己立了个 flag，说要读一百本书，这对我来说挺难实现的，一则我也不喜欢书只读一小半，二则感觉对于喜欢看的内容范围还是比较有限制，可能也算是比较矫情，不爱追热门的各类东西，因为往往会有一些跟大众不一致的观点看法，显得格格不入。所以还是这个打卡活动可能会比较适合我，书是人类进步的阶梯。
到现在是打卡了三天了，读的主要是白岩松的《幸福了吗》，对于白岩松，我们这一代人是比较熟悉，并且整体印象比较不错的一个央视主持人，从《焦点访谈》开始，到疫情期间的各类一线节目，可能对我来说是个三观比较正，敢于说一些真话的主持人，这中间其实是有个空档期，没怎么看电视，也不太关注了，只是在疫情期间的节目，还是一如既往地给人一种可靠的感觉，正好有一次偶然微信读书推荐了白岩松的这本书，就看了一部分，正好这次继续往下看，因为相对来讲不会很晦涩，并且从这位知名央视主持人的角度分享他的过往和想法看法，还是比较有意思的。
从对汶川地震，08 年奥运等往事的回忆和一些细节的呈现，也让我了解比较多当时所不知道的，特别是汶川地震，那时的我还在读高中，真的是看着电视，作为“猛男”都忍不住泪目了，共和国之殇，多难兴邦，但是这对于当事人来说，都是一场醒不过来的噩梦。
然后是对于足球的热爱，其实光这个就能掰扯很多，因为我不爱足球，只爱篮球，其中原因有的没的也挺多可以说的，但是看了他的书，才能比较深入的了解一个足球迷，对足球，对中国足球，对世界杯，对阿根廷的感情。
接下去还是想能继续坚持下去，加油！

发表于 分类于 ， ， ， ， ， ， 阅读次数： Disqus：

前面写过一系列的 redis 源码分析的，但是实际上很多的问题还是需要结合实际的使用，然后其实就避不开缓存使用的三个著名问题，穿透，击穿和雪崩，这三个概念也是有着千丝万缕的关系，

缓存穿透

缓存穿透是指当数据库中本身就不存在这个数据的时候，使用一般的缓存策略时访问不到缓存后就访问数据库，但是因为数据库也没数据，所以如果不做任何策略优化的话，这类数据就每次都会访问一次数据库，对数据库压力也会比较大。

缓存击穿

缓存击穿跟穿透比较类似的，都是访问缓存不在，然后去访问数据库，与穿透不一样的是击穿是在数据库中存在数据，但是可能由于第一次访问，或者缓存过期了，需要访问到数据库，这对于访问量小的情况其实算是个正常情况，但是随着请求量变高就会引发一些性能隐患。

缓存雪崩

缓存雪崩就是击穿的大规模集群效应，当大量的缓存过期失效的时候，这些请求都是直接访问到数据库了，会对数据库造成很大的压力。

对于以上三种场景也有一些比较常见的解决方案，但也不能说是万无一失的，需要随着业务去寻找合适的方案

解决缓存穿透

对于数据库中就没这个数据的时候，一种是可以对这个 key 设置下空值，即以一个特定的表示是数据库不存在的，这种情况需要合理地调整过期时间，当这个 key 在数据库中有数据了的话，也需要有策略去更新这个值，并且如果这类 key 非常多，这个方法就会不太合适，就可以使用第二种方法，就是布隆过滤器，bloom filter，前置一个布隆过滤器，当这个 key 在数据库不存在的话，先用布隆过滤器挡一道，如果不在的话就直接返回了，当然布隆过滤器不是绝对的准确的

解决缓存击穿

当一个 key 的缓存过期了，如果大量请求过来访问这个 key，请求都会落在数据库里，这个时候就可以使用一些类似于互斥锁的方式去让一个线程去访问数据库，更新缓存，但是这里其实也有个问题，就是如果是热点 key 其实这种方式也比较危险，万一更新失败，或者更新操作的时候耗时比较久，就会有一大堆请求卡在那，这种情况可能需要有一些异步提前刷新缓存，可以结合具体场景选择方式

解决缓存雪崩

雪崩的情况是指大批量的 key 都一起过期了，击穿的放大版，大批量的请求都打到数据库上了，一方面有可能直接缓存不可用了，就需要用集群化高可用的缓存服务，然后对于实际使用中也可以使用本地缓存结合 redis 缓存，去提高可用性，再配合一些限流措施，然后就是缓存使用过程总的过期时间最好能加一些随机值，防止在同一时间过期而导致雪崩，结合互斥锁防止大量请求打到数据库。

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

A path in a binary tree is a sequence of nodes where each pair of adjacent nodes in the sequence has an edge connecting them. A node can only appear in the sequence at most once. Note that the path does not need to pass through the root.

The path sum of a path is the sum of the node’s values in the path.

Given the root of a binary tree, return the maximum path sum of any path.

路径 被定义为一条从树中任意节点出发，沿父节点-子节点连接，达到任意节点的序列。该路径 至少包含一个 节点，且不一定经过根节点。

路径和 是路径中各节点值的总和。

给你一个二叉树的根节点 root ，返回其 最大路径和

简要分析

其实这个题目会被误解成比较简单，左子树最大的，或者右子树最大的，或者两边加一下，仔细想想都不对，其实有可能是产生于左子树中，或者右子树中，这两个都是指跟左子树根还有右子树根没关系的，这么说感觉不太容易理解，画个图

可以看到图里，其实最长路径和是左边这个子树组成的，跟根节点还有右子树完全没关系，然后再想一种情况，如果是整棵树就是图中的左子树，那么这个最长路径和就是左子树加右子树加根节点了，所以不是我一开始想得那么简单，在代码实现中也需要一些技巧

代码

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int ansNew = Integer.MIN_VALUE;
public int maxPathSum(TreeNode root) {
        maxSumNew(root);
        return ansNew;
    }
    
public int maxSumNew(TreeNode root) {
    if (root == null) {
        return 0;
    }
    // 这里是个简单的递归，就是去递归左右子树，但是这里其实有个概念，当这样处理时，其实相当于把子树的内部的最大路径和已经算出来了
    int left = maxSumNew(root.left);
    int right = maxSumNew(root.right);
    // 这里前面我有点没想明白，但是看到 ansNew 的比较，其实相当于，返回的是三种情况里的最大值，一个是左子树+根，一个是右子树+根，一个是单独根节点，
    // 这样这个递归的返回才会有意义，不然像原来的方法，它可能是跳着的，但是这种情况其实是借助于 ansNew 这个全局的最大值，因为原来我觉得要比较的是
    // left, right, left + root , right + root, root, left + right + root 这些的最大值，这里是分成了两个阶段，left 跟 right 的最大值已经在上面的
    // 调用过程中赋值给 ansNew 了    
    int currentSum = Math.max(Math.max(root.val + left , root.val + right), root.val);
    // 这边返回的是 currentSum，然后再用它跟 left + right + root 进行对比，然后再去更新 ans
    // PS: 有个小点也是这边的破局点，就是这个 ansNew
    int res = Math.max(left + right + root.val, currentSum);
    ans = Math.max(res, ans);
    return currentSum;
}

这里非常重要的就是 ansNew 是最后的一个结果，而对于 maxSumNew 这个函数的返回值其实是需要包含了一个连续结果，因为要返回继续去算路径和，所以返回的是 currentSum，最终结果是 ansNew

结果图

难得有个 100%，贴个图哈哈

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 更新于 分类于 ， 阅读次数： Disqus：

Condition也是 AQS 中很重要的一块内容，可以先看段示例代码，这段代码应该来自于Doug Lea大大，可以在 javadoc 中的 condition 部分找到，其实大大原来写过基于 synchronized 实现的，后面我也贴下代码

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import java.util.concurrent.locks.Condition;
import java.util.concurrent.locks.Lock;
import java.util.concurrent.locks.ReentrantLock;

class BoundedBuffer {
    final Lock lock = new ReentrantLock();
    // condition 依赖于 lock 来产生
    final Condition notFull = lock.newCondition();
    final Condition notEmpty = lock.newCondition();

    // 对象池子，put 跟 take 的就是这里的
    final Object[] items = new Object[100];
    int putptr, takeptr, count;

    // 生产
    public void put(Object x) throws InterruptedException {
        // 这里也说明了，需要先拥有锁
        lock.lock();
        try {
            while (count == items.length)
                notFull.await();  // 队列已满，等待，直到 not full 才能继续生产
            items[putptr] = x;
            if (++putptr == items.length) putptr = 0;
            ++count;
            notEmpty.signal(); // 生产成功，队列已经 not empty 了，发个通知出去
        } finally {
            lock.unlock();
        }
    }

    // 消费
    public Object take() throws InterruptedException {
        lock.lock();
        try {
            while (count == 0)
                notEmpty.await(); // 队列为空，等待，直到队列 not empty，才能继续消费
            Object x = items[takeptr];
            if (++takeptr == items.length) takeptr = 0;
            --count;
            notFull.signal(); // 被我消费掉一个，队列 not full 了，发个通知出去
            return x;
        } finally {
            lock.unlock();
        }
    }
}

介绍下 Condition 的结构

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public class ConditionObject implements Condition, java.io.Serializable {
        private static final long serialVersionUID = 1173984872572414699L;
        /** First node of condition queue. */
        private transient Node firstWaiter;
        /** Last node of condition queue. */
        private transient Node lastWaiter;

主要的就这么点，而且也复用了 AQS 阻塞队列或者大大叫 lock queue中同样的 Node 节点，只不过它没有使用其中的双向队列，也就是prev 和 next，而是在 Node 中的 nextWaiter，所以只是个单向的队列，没使用 next 其实还有个用处，后面会提到，看下结构的示意图

然后主要是看两个方法，await 和 signal,
先来看下 await

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/**
 * Implements interruptible condition wait.
 * <ol>
 * <li> If current thread is interrupted, throw InterruptedException.
 * <li> Save lock state returned by {@link #getState}.
 * <li> Invoke {@link #release} with saved state as argument,
 *      throwing IllegalMonitorStateException if it fails.
 * <li> Block until signalled or interrupted.
 * <li> Reacquire by invoking specialized version of
 *      {@link #acquire} with saved state as argument.
 * <li> If interrupted while blocked in step 4, throw InterruptedException.
 * </ol>
 */
public final void await() throws InterruptedException {
    if (Thread.interrupted())
        throw new InterruptedException();

    // 将当前节点包装成一个  condition waiter node 节点
    Node node = addConditionWaiter();

    // 完全释放占有的锁，这里需要是占有锁的线程
    int savedState = fullyRelease(node);
    int interruptMode = 0;

    // 判断下是否在阻塞队列中，因为有可能被其他节点从等待队列移动到阻塞队列
    while (!isOnSyncQueue(node)) {
        // park等待，等待被唤醒
        LockSupport.park(this);
        if ((interruptMode = checkInterruptWhileWaiting(node)) != 0)
            break;
    }

    // 被唤醒后进入阻塞队列，等待获取锁，这里继续用了fullyRelease返回的 state
    if (acquireQueued(node, savedState) && interruptMode != THROW_IE)
        interruptMode = REINTERRUPT;
    if (node.nextWaiter != null) // clean up if cancelled
        unlinkCancelledWaiters();
    if (interruptMode != 0)
        reportInterruptAfterWait(interruptMode);
}

添加条件队列节点

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/**
 * Adds a new waiter to wait queue.
 * @return its new wait node
 */
private Node addConditionWaiter() {
    Node t = lastWaiter;
    // If lastWaiter is cancelled, clean out.
    // 如果节点已经不是 CONDITION 状态了，表示已经取消了
    if (t != null && t.waitStatus != Node.CONDITION) {
        // 把等待队列中取消的节点清理出去
        unlinkCancelledWaiters();
        t = lastWaiter;
    }
    // 把当前线程包装成waitStatus=CONDITION 的节点
    Node node = new Node(Thread.currentThread(), Node.CONDITION);
    // 没有 lastWaiter 节点，直接是 firstWaiter
    if (t == null)
        firstWaiter = node;
    else
    // 不然就接在 lastWaiter 后面
        t.nextWaiter = node;
    // 当前节点就会变成新的 lastWaiter
    lastWaiter = node;
    return node;
}

清理取消的节点

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/**
 * Unlinks cancelled waiter nodes from condition queue.
 * Called only while holding lock. This is called when
 * cancellation occurred during condition wait, and upon
 * insertion of a new waiter when lastWaiter is seen to have
 * been cancelled. This method is needed to avoid garbage
 * retention in the absence of signals. So even though it may
 * require a full traversal, it comes into play only when
 * timeouts or cancellations occur in the absence of
 * signals. It traverses all nodes rather than stopping at a
 * particular target to unlink all pointers to garbage nodes
 * without requiring many re-traversals during cancellation
 * storms.
 */
private void unlinkCancelledWaiters() {
    Node t = firstWaiter;
    Node trail = null;
    // 循环遍历单向链表的节点，如果状态不是 CONDITION 就清出去
    while (t != null) {
        Node next = t.nextWaiter;
        // 循环链表操作，清掉取消的节点
        if (t.waitStatus != Node.CONDITION) {
            t.nextWaiter = null;
            if (trail == null)
                firstWaiter = next;
            else
                trail.nextWaiter = next;
            if (next == null)
                lastWaiter = trail;
        }
        else
            trail = t;
        t = next;
    }
}

完全释放锁

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/**
 * Invokes release with current state value; returns saved state.
 * Cancels node and throws exception on failure.
 * @param node the condition node for this wait
 * @return previous sync state
 */
final int fullyRelease(Node node) {
    boolean failed = true;
    try {
        // 获取下当前的 state 值，因为是可重入的，所以这个值要保存下来
        int savedState = getState();
        // 这里还包含比较多操作，不过跟前面分析 AQS 的释放比较类似，不深入了
        if (release(savedState)) {
            failed = false;
            // 返回这个值
            return savedState;
        } else {
            throw new IllegalMonitorStateException();
        }
    } finally {
        if (failed)
            node.waitStatus = Node.CANCELLED;
    }
}

判断是否在阻塞队列中

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/**
 * Returns true if a node, always one that was initially placed on
 * a condition queue, is now waiting to reacquire on sync queue.
 * @param node the node
 * @return true if is reacquiring
 */
final boolean isOnSyncQueue(Node node) {
    // 如果waitStatus 是 CONDITION 或者没有 prev 前置节点肯定就不在
    if (node.waitStatus == Node.CONDITION || node.prev == null)
        return false;
    // 这里就是我前面提到的 next 的作用
    if (node.next != null) // If has successor, it must be on queue
        return true;
    // 从 tail 开始找，是否在阻塞队列中
    /*
     * node.prev can be non-null, but not yet on queue because
     * the CAS to place it on queue can fail. So we have to
     * traverse from tail to make sure it actually made it.  It
     * will always be near the tail in calls to this method, and
     * unless the CAS failed (which is unlikely), it will be
     * there, so we hardly ever traverse much.
     */
    return findNodeFromTail(node);
}
/**
 * Returns true if node is on sync queue by searching backwards from tail.
 * Called only when needed by isOnSyncQueue.
 * @return true if present
 */
private boolean findNodeFromTail(Node node) {
    Node t = tail;
    // 从 tail 开始，从后往前找
    for (;;) {
        if (t == node)
            return true;
        if (t == null)
            return false;
        t = t.prev;
    }
}

await 的逻辑差不多就是这样子，主要的就是把自己包成一个 Node 节点，waitStatus 的状态是 CONDITION，挂在等待队列的最后，然后完全释放锁，park 等待

signal

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/**
 * Moves the longest-waiting thread, if one exists, from the
 * wait queue for this condition to the wait queue for the
 * owning lock.
 *
 * @throws IllegalMonitorStateException if {@link #isHeldExclusively}
 *         returns {@code false}
 */
public final void signal() {
    if (!isHeldExclusively())
        throw new IllegalMonitorStateException();
    // firstWaiter 肯定是最早开始等待的
    Node first = firstWaiter;
    // 如果不为空就唤醒
    if (first != null)
        doSignal(first);
}
/**
 * Removes and transfers nodes until hit non-cancelled one or
 * null. Split out from signal in part to encourage compilers
 * to inline the case of no waiters.
 * @param first (non-null) the first node on condition queue
 */
private void doSignal(Node first) {
    do {
        // 因为要去唤醒 first 节点了，firstWaiter 需要再从后面找一个
        // 并且判断是否为空，如果是空的话就直接可以把 lastWaiter 设置成空了
        if ( (firstWaiter = first.nextWaiter) == null)
            lastWaiter = null;
        //  first 不需要继续保存后面的 waiter 了，因为 firstWaiter 已经是 first 的后置节点了
        first.nextWaiter = null;
    // 如果 first 节点转移不成功，并且 firstWaiter 节点不为空，则继续进入循环
    } while (!transferForSignal(first) &&
             (first = firstWaiter) != null);
}
/**
 * Transfers a node from a condition queue onto sync queue.
 * Returns true if successful.
 * @param node the node
 * @return true if successfully transferred (else the node was
 * cancelled before signal)
 */
final boolean transferForSignal(Node node) {
    /*
     * If cannot change waitStatus, the node has been cancelled.
     */
    // 如果状态已经不是 CONDITION 就不会设置成功，返回 false
    if (!compareAndSetWaitStatus(node, Node.CONDITION, 0))
        return false;
    /*
     * Splice onto queue and try to set waitStatus of predecessor to
     * indicate that thread is (probably) waiting. If cancelled or
     * attempt to set waitStatus fails, wake up to resync (in which
     * case the waitStatus can be transiently and harmlessly wrong).
     */
    // 调用跟aqs 第一篇中一样的 enq 方法进入阻塞队列，返回入队后的前一节点
    Node p = enq(node);
    int ws = p.waitStatus;
    // 将前置节点状态设置成SIGNAL，表示后面有节点在等了
    if (ws > 0 || !compareAndSetWaitStatus(p, ws, Node.SIGNAL))
        LockSupport.unpark(node.thread);
    // 返回 true，上一个方法的循环就退出了
    return true;
}

这里其实就是把 condition 等待队列的第一个未取消的节点入队到阻塞队列去争锁

附录

synchronized 版的 BoundedBuffer

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/*
  File: BoundedBuffer.java

  Originally written by Doug Lea and released into the public domain.
  This may be used for any purposes whatsoever without acknowledgment.
  Thanks for the assistance and support of Sun Microsystems Labs,
  and everyone contributing, testing, and using this code.

  History:
  Date       Who                What
  11Jun1998  dl               Create public version
  17Jul1998  dl               Simplified by eliminating wait counts
  25aug1998  dl               added peek
   5May1999  dl               replace % with conditional (slightly faster)
*/

package EDU.oswego.cs.dl.util.concurrent;

/**
 * Efficient array-based bounded buffer class.
 * Adapted from CPJ, chapter 8, which describes design.
 * <p>[<a href="http://gee.cs.oswego.edu/dl/classes/EDU/oswego/cs/dl/util/concurrent/intro.html"> Introduction to this package. </a>] <p>
 **/

public class BoundedBuffer implements BoundedChannel {

  protected final Object[]  array_;      // the elements

  protected int takePtr_ = 0;            // circular indices
  protected int putPtr_ = 0;       

  protected int usedSlots_ = 0;          // length
  protected int emptySlots_;             // capacity - length

  /**
   * Helper monitor to handle puts. 
   **/
  protected final Object putMonitor_ = new Object();

  /**
   * Create a BoundedBuffer with the given capacity.
   * @exception IllegalArgumentException if capacity less or equal to zero
   **/
  public BoundedBuffer(int capacity) throws IllegalArgumentException {
    if (capacity <= 0) throw new IllegalArgumentException();
    array_ = new Object[capacity];
    emptySlots_ = capacity;
  }

  /**
   * Create a buffer with the current default capacity
   **/

  public BoundedBuffer() { 
    this(DefaultChannelCapacity.get()); 
  }

  /** 
   * Return the number of elements in the buffer.
   * This is only a snapshot value, that may change
   * immediately after returning.
   **/
  public synchronized int size() { return usedSlots_; }

  public int capacity() { return array_.length; }

  protected void incEmptySlots() {
    synchronized(putMonitor_) {
      ++emptySlots_;
      putMonitor_.notify();
    }
  }

  protected synchronized void incUsedSlots() {
    ++usedSlots_;
    notify();
  }

  protected final void insert(Object x) { // mechanics of put
    --emptySlots_;
    array_[putPtr_] = x;
    if (++putPtr_ >= array_.length) putPtr_ = 0;
  }

  protected final Object extract() { // mechanics of take
    --usedSlots_;
    Object old = array_[takePtr_];
    array_[takePtr_] = null;
    if (++takePtr_ >= array_.length) takePtr_ = 0;
    return old;
  }

  public Object peek() {
    synchronized(this) {
      if (usedSlots_ > 0)
        return array_[takePtr_];
      else
        return null;
    }
  }


  public void put(Object x) throws InterruptedException {
    if (x == null) throw new IllegalArgumentException();
    if (Thread.interrupted()) throw new InterruptedException();

    synchronized(putMonitor_) {
      while (emptySlots_ <= 0) {
	try { putMonitor_.wait(); }
        catch (InterruptedException ex) {
          putMonitor_.notify();
          throw ex;
        }
      }
      insert(x);
    }
    incUsedSlots();
  }

  public boolean offer(Object x, long msecs) throws InterruptedException {
    if (x == null) throw new IllegalArgumentException();
    if (Thread.interrupted()) throw new InterruptedException();

    synchronized(putMonitor_) {
      long start = (msecs <= 0)? 0 : System.currentTimeMillis();
      long waitTime = msecs;
      while (emptySlots_ <= 0) {
        if (waitTime <= 0) return false;
	try { putMonitor_.wait(waitTime); }
        catch (InterruptedException ex) {
          putMonitor_.notify();
          throw ex;
        }
        waitTime = msecs - (System.currentTimeMillis() - start);
      }
      insert(x);
    }
    incUsedSlots();
    return true;
  }



  public  Object take() throws InterruptedException { 
    if (Thread.interrupted()) throw new InterruptedException();
    Object old = null; 
    synchronized(this) { 
      while (usedSlots_ <= 0) {
        try { wait(); }
        catch (InterruptedException ex) {
          notify();
          throw ex; 
        }
      }
      old = extract();
    }
    incEmptySlots();
    return old;
  }

  public  Object poll(long msecs) throws InterruptedException { 
    if (Thread.interrupted()) throw new InterruptedException();
    Object old = null; 
    synchronized(this) { 
      long start = (msecs <= 0)? 0 : System.currentTimeMillis();
      long waitTime = msecs;
      
      while (usedSlots_ <= 0) {
        if (waitTime <= 0) return null;
        try { wait(waitTime); }
        catch (InterruptedException ex) {
          notify();
          throw ex; 
        }
        waitTime = msecs - (System.currentTimeMillis() - start);

      }
      old = extract();
    }
    incEmptySlots();
    return old;
  }

}

发表于 更新于 分类于 ， 阅读次数： Disqus：

很多东西都是时看时新，而且时间长了也会忘，所以再来复习下，也会有一些新的角度看法这次来聊下AQS的内容，主要是这几个点，

第一个线程

第一个线程抢到锁了，此时state跟阻塞队列是怎么样的，其实这里是之前没理解对的地方

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/**
         * Fair version of tryAcquire.  Don't grant access unless
         * recursive call or no waiters or is first.
         */
        protected final boolean tryAcquire(int acquires) {
            final Thread current = Thread.currentThread();
            int c = getState();
            // 这里如果state还是0说明锁还空着
            if (c == 0) {
                // 因为是公平锁版本的，先去看下是否阻塞队列里有排着队的
                if (!hasQueuedPredecessors() &&
                    compareAndSetState(0, acquires)) {
                    // 没有排队的，并且state使用cas设置成功的就标记当前占有锁的线程是我
                    setExclusiveOwnerThread(current);
                    // 然后其实就返回了，包括阻塞队列的head和tail节点和waitStatus都没有设置
                    return true;
                }
            }
            else if (current == getExclusiveOwnerThread()) {
                int nextc = c + acquires;
                if (nextc < 0)
                    throw new Error("Maximum lock count exceeded");
                setState(nextc);
                return true;
            }
            // 这里就是第二个线程会返回false
            return false;
        }
    }

第二个线程

当第二个线程进来的时候应该是怎么样，结合代码来看

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/**
     * Acquires in exclusive mode, ignoring interrupts.  Implemented
     * by invoking at least once {@link #tryAcquire},
     * returning on success.  Otherwise the thread is queued, possibly
     * repeatedly blocking and unblocking, invoking {@link
     * #tryAcquire} until success.  This method can be used
     * to implement method {@link Lock#lock}.
     *
     * @param arg the acquire argument.  This value is conveyed to
     *        {@link #tryAcquire} but is otherwise uninterpreted and
     *        can represent anything you like.
     */
    public final void acquire(int arg) {
        // 前面第一种情况是tryAcquire直接成功了，这个if判断第一个条件就是false，就不往下执行了
        // 如果是第二个线程，第一个条件获取锁不成功，条件判断!tryAcquire(arg) == true，就会走
        // acquireQueued(addWaiter(Node.EXCLUSIVE), arg)
        if (!tryAcquire(arg) &&
            acquireQueued(addWaiter(Node.EXCLUSIVE), arg))
            selfInterrupt();
    }

然后来看下addWaiter的逻辑

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/**
     * Creates and enqueues node for current thread and given mode.
     *
     * @param mode Node.EXCLUSIVE for exclusive, Node.SHARED for shared
     * @return the new node
     */
    private Node addWaiter(Node mode) {
        // 这里是包装成一个node
        Node node = new Node(Thread.currentThread(), mode);
        // Try the fast path of enq; backup to full enq on failure
        // 最快的方式就是把当前线程的节点放在阻塞队列的最后
        Node pred = tail;
        // 只有当tail，也就是pred不为空的时候可以直接接上
        if (pred != null) {
            node.prev = pred;
            // 如果这里cas成功了，就直接接上返回了
            if (compareAndSetTail(pred, node)) {
                pred.next = node;
                return node;
            }
        }
        // 不然就会继续走到这里
        enq(node);
        return node;
    }

然后就是enq的逻辑了

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/**
     * Inserts node into queue, initializing if necessary. See picture above.
     * @param node the node to insert
     * @return node's predecessor
     */
    private Node enq(final Node node) {
        for (;;) {
            // 如果状态没变化的话，tail这时还是null的
            Node t = tail;
            if (t == null) { // Must initialize
                // 这里就会初始化头结点，就是个空节点
                if (compareAndSetHead(new Node()))
                    // tail也赋值成head
                    tail = head;
            } else {
                // 这里就设置tail了
                node.prev = t;
                if (compareAndSetTail(t, node)) {
                    t.next = node;
                    return t;
                }
            }
        }
    }

所以从这里可以看出来，其实head头结点不是个真实的带有线程的节点，并且不是在第一个线程进来的时候设置的

解锁

通过代码来看下

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/**
     * Attempts to release this lock.
     *
     * <p>If the current thread is the holder of this lock then the hold
     * count is decremented.  If the hold count is now zero then the lock
     * is released.  If the current thread is not the holder of this
     * lock then {@link IllegalMonitorStateException} is thrown.
     *
     * @throws IllegalMonitorStateException if the current thread does not
     *         hold this lock
     */
    public void unlock() {
        // 释放锁
        sync.release(1);
    }
/**
     * Releases in exclusive mode.  Implemented by unblocking one or
     * more threads if {@link #tryRelease} returns true.
     * This method can be used to implement method {@link Lock#unlock}.
     *
     * @param arg the release argument.  This value is conveyed to
     *        {@link #tryRelease} but is otherwise uninterpreted and
     *        can represent anything you like.
     * @return the value returned from {@link #tryRelease}
     */
    public final boolean release(int arg) {
        // 尝试去释放
        if (tryRelease(arg)) {
            Node h = head;
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }
protected final boolean tryRelease(int releases) {
            int c = getState() - releases;
            if (Thread.currentThread() != getExclusiveOwnerThread())
                throw new IllegalMonitorStateException();
            boolean free = false;
    		// 判断是否完全释放锁，因为可重入
            if (c == 0) {
                free = true;
                setExclusiveOwnerThread(null);
            }
            setState(c);
            return free;
        }
// 这段代码和上面的一致，只是为了顺序性，又拷下来看下

public final boolean release(int arg) {
        // 尝试去释放，如果是完全释放，返回的就是true，否则是false
        if (tryRelease(arg)) {
            Node h = head;
            // 这里判断头结点是否为空以及waitStatus的状态，前面说了head节点其实是
            // 在第二个线程进来的时候初始化的，如果是空的话说明没后续节点，并且waitStatus
            // 也表示了后续的等待状态
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }

/**
     * Wakes up node's successor, if one exists.
     *
     * @param node the node
     */
// 唤醒后继节点
    private void unparkSuccessor(Node node) {
        /*
         * If status is negative (i.e., possibly needing signal) try
         * to clear in anticipation of signalling.  It is OK if this
         * fails or if status is changed by waiting thread.
         */
        int ws = node.waitStatus;
        if (ws < 0)
            compareAndSetWaitStatus(node, ws, 0);

        /*
         * Thread to unpark is held in successor, which is normally
         * just the next node.  But if cancelled or apparently null,
         * traverse backwards from tail to find the actual
         * non-cancelled successor.
         */
        Node s = node.next;
        // 如果后继节点是空或者当前节点取消等待了
        if (s == null || s.waitStatus > 0) {
            s = null;
            // 从后往前找，找到非取消的节点，注意这里不是找到就退出，而是一直找到头
            // 所以不必担心中间有取消的
            for (Node t = tail; t != null && t != node; t = t.prev)
                if (t.waitStatus <= 0)
                    s = t;
        }
        if (s != null)
            // 将其唤醒
            LockSupport.unpark(s.thread);
    }

发表于 分类于 ， ， ， 阅读次数： Disqus：

最近群里大佬发起了一个读书打卡活动，需要每天读一会书，在群里打卡分享感悟，争取一个月能读完一本书，说实话一天十分钟的读书时间倒是问题不大，不过每天都要打卡，而且一个月要读完一本书，其实难度还是有点大的，不过也想试试看。
之前某某老大给自己立了个 flag，说要读一百本书，这对我来说挺难实现的，一则我也不喜欢书只读一小半，二则感觉对于喜欢看的内容范围还是比较有限制，可能也算是比较矫情，不爱追热门的各类东西，因为往往会有一些跟大众不一致的观点看法，显得格格不入。所以还是这个打卡活动可能会比较适合我，书是人类进步的阶梯。
到现在是打卡了三天了，读的主要是白岩松的《幸福了吗》，对于白岩松，我们这一代人是比较熟悉，并且整体印象比较不错的一个央视主持人，从《焦点访谈》开始，到疫情期间的各类一线节目，可能对我来说是个三观比较正，敢于说一些真话的主持人，这中间其实是有个空档期，没怎么看电视，也不太关注了，只是在疫情期间的节目，还是一如既往地给人一种可靠的感觉，正好有一次偶然微信读书推荐了白岩松的这本书，就看了一部分，正好这次继续往下看，因为相对来讲不会很晦涩，并且从这位知名央视主持人的角度分享他的过往和想法看法，还是比较有意思的。
从对汶川地震，08 年奥运等往事的回忆和一些细节的呈现，也让我了解比较多当时所不知道的，特别是汶川地震，那时的我还在读高中，真的是看着电视，作为“猛男”都忍不住泪目了，共和国之殇，多难兴邦，但是这对于当事人来说，都是一场醒不过来的噩梦。
然后是对于足球的热爱，其实光这个就能掰扯很多，因为我不爱足球，只爱篮球，其中原因有的没的也挺多可以说的，但是看了他的书，才能比较深入的了解一个足球迷，对足球，对中国足球，对世界杯，对阿根廷的感情。
接下去还是想能继续坚持下去，加油！

发表于 分类于 ， ， ， ， ， ， 阅读次数： Disqus：

前面写过一系列的 redis 源码分析的，但是实际上很多的问题还是需要结合实际的使用，然后其实就避不开缓存使用的三个著名问题，穿透，击穿和雪崩，这三个概念也是有着千丝万缕的关系，

缓存穿透

缓存穿透是指当数据库中本身就不存在这个数据的时候，使用一般的缓存策略时访问不到缓存后就访问数据库，但是因为数据库也没数据，所以如果不做任何策略优化的话，这类数据就每次都会访问一次数据库，对数据库压力也会比较大。

缓存击穿

缓存击穿跟穿透比较类似的，都是访问缓存不在，然后去访问数据库，与穿透不一样的是击穿是在数据库中存在数据，但是可能由于第一次访问，或者缓存过期了，需要访问到数据库，这对于访问量小的情况其实算是个正常情况，但是随着请求量变高就会引发一些性能隐患。

缓存雪崩

缓存雪崩就是击穿的大规模集群效应，当大量的缓存过期失效的时候，这些请求都是直接访问到数据库了，会对数据库造成很大的压力。

对于以上三种场景也有一些比较常见的解决方案，但也不能说是万无一失的，需要随着业务去寻找合适的方案

解决缓存穿透

对于数据库中就没这个数据的时候，一种是可以对这个 key 设置下空值，即以一个特定的表示是数据库不存在的，这种情况需要合理地调整过期时间，当这个 key 在数据库中有数据了的话，也需要有策略去更新这个值，并且如果这类 key 非常多，这个方法就会不太合适，就可以使用第二种方法，就是布隆过滤器，bloom filter，前置一个布隆过滤器，当这个 key 在数据库不存在的话，先用布隆过滤器挡一道，如果不在的话就直接返回了，当然布隆过滤器不是绝对的准确的

解决缓存击穿

当一个 key 的缓存过期了，如果大量请求过来访问这个 key，请求都会落在数据库里，这个时候就可以使用一些类似于互斥锁的方式去让一个线程去访问数据库，更新缓存，但是这里其实也有个问题，就是如果是热点 key 其实这种方式也比较危险，万一更新失败，或者更新操作的时候耗时比较久，就会有一大堆请求卡在那，这种情况可能需要有一些异步提前刷新缓存，可以结合具体场景选择方式

解决缓存雪崩

雪崩的情况是指大批量的 key 都一起过期了，击穿的放大版，大批量的请求都打到数据库上了，一方面有可能直接缓存不可用了，就需要用集群化高可用的缓存服务，然后对于实际使用中也可以使用本地缓存结合 redis 缓存，去提高可用性，再配合一些限流措施，然后就是缓存使用过程总的过期时间最好能加一些随机值，防止在同一时间过期而导致雪崩，结合互斥锁防止大量请求打到数据库。

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题目介绍

A path in a binary tree is a sequence of nodes where each pair of adjacent nodes in the sequence has an edge connecting them. A node can only appear in the sequence at most once. Note that the path does not need to pass through the root.

The path sum of a path is the sum of the node’s values in the path.

Given the root of a binary tree, return the maximum path sum of any path.

路径 被定义为一条从树中任意节点出发，沿父节点-子节点连接，达到任意节点的序列。该路径 至少包含一个 节点，且不一定经过根节点。

路径和 是路径中各节点值的总和。

给你一个二叉树的根节点 root ，返回其 最大路径和

简要分析

其实这个题目会被误解成比较简单，左子树最大的，或者右子树最大的，或者两边加一下，仔细想想都不对，其实有可能是产生于左子树中，或者右子树中，这两个都是指跟左子树根还有右子树根没关系的，这么说感觉不太容易理解，画个图

可以看到图里，其实最长路径和是左边这个子树组成的，跟根节点还有右子树完全没关系，然后再想一种情况，如果是整棵树就是图中的左子树，那么这个最长路径和就是左子树加右子树加根节点了，所以不是我一开始想得那么简单，在代码实现中也需要一些技巧

代码

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int ansNew = Integer.MIN_VALUE;
public int maxPathSum(TreeNode root) {
        maxSumNew(root);
        return ansNew;
    }
    
public int maxSumNew(TreeNode root) {
    if (root == null) {
        return 0;
    }
    // 这里是个简单的递归，就是去递归左右子树，但是这里其实有个概念，当这样处理时，其实相当于把子树的内部的最大路径和已经算出来了
    int left = maxSumNew(root.left);
    int right = maxSumNew(root.right);
    // 这里前面我有点没想明白，但是看到 ansNew 的比较，其实相当于，返回的是三种情况里的最大值，一个是左子树+根，一个是右子树+根，一个是单独根节点，
    // 这样这个递归的返回才会有意义，不然像原来的方法，它可能是跳着的，但是这种情况其实是借助于 ansNew 这个全局的最大值，因为原来我觉得要比较的是
    // left, right, left + root , right + root, root, left + right + root 这些的最大值，这里是分成了两个阶段，left 跟 right 的最大值已经在上面的
    // 调用过程中赋值给 ansNew 了    
    int currentSum = Math.max(Math.max(root.val + left , root.val + right), root.val);
    // 这边返回的是 currentSum，然后再用它跟 left + right + root 进行对比，然后再去更新 ans
    // PS: 有个小点也是这边的破局点，就是这个 ansNew
    int res = Math.max(left + right + root.val, currentSum);
    ans = Math.max(res, ans);
    return currentSum;
}

这里非常重要的就是 ansNew 是最后的一个结果，而对于 maxSumNew 这个函数的返回值其实是需要包含了一个连续结果，因为要返回继续去算路径和，所以返回的是 currentSum，最终结果是 ansNew

结果图

难得有个 100%，贴个图哈哈

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， 阅读次数： Disqus：

今天真的是被气得不轻，情况是碰到一个有 70 多秒的直行红灯，然后直行就排了很长的队，但是左转车道没车，就有好几辆车占着左转车道，准备往直行车道插队加塞，一般这种加塞的，会挑个不太计较的，如果前面一辆不让的话就再等等，我因为赶着回家，就不想让，结果那辆车几次车头直接往里冲，当时怒气值基本已经蓄满了，我真的是分毫都不想让，如果路上都是让着这种人的，那么这种情况只会越来越严重，我理解的这种心态，就赌你怕麻烦，多一事不如少一事，结果就是每次都能顺利插队加塞，其实延伸到我们社会中的种种实质性的排队或者等同于排队的情况，都已经有这种惯有思维，一方面这种不符合规则，可能在严重程度上容易被很多人所忽视，基本上已经被很多人当成是“合理”行为，另一方面，对于这些“微小”的违规行为，本身管理层面也基本没有想要管的意思，就更多的成为了纵容这些行为的导火索，并且大多数人都是想着如果不让，发生点小剐小蹭的要浪费很多时间精力来处理，甚至会觉得会被别人觉得自己太小气等等，诸多内外成本结合起来，会真的去硬刚的可能少之又少了，这样也就让更多的人觉得这种行为是被默许的，再举个非常小的例子，以我们公司疫情期间的盒饭发放为例，有两个比较“有意思”的事情，第一个就是因为疫情，本来是让排队要间隔一米，但是可能除了我比较怕死会跟前面的人保持点距离基本没别人会不挨着前面的人，甚至我跟我前面的人保持点距离，后面的同学会推着我让我上去；第二个是关于拿饭，这么多人排着队拿饭，然后有部分同学，一个人拿好几份，帮组里其他人的都拿了，有些甚至一个人拿十份，假如这个盒饭发放是说明了可以按部门直接全领了那就没啥问题，但是当时的状况是个人排队领自己的那一份，如果一个同学直接帮着组里十几个人都拿了，后面排队的人是什么感受呢，甚至有些是看到队伍排长了，就找队伍里自己认识的比较靠前的人说你帮我也拿一份，其实作为我这个比较按规矩办事的“愣头青”来说，我是比较不能接受这两件小事里的行为的，再往下说可能就有点偏激了，先说到这~

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

写一个程序找出两个单向链表的交叉起始点，可能是我英语不好，图里画的其实还有一点是交叉以后所有节点都是相同的
Write a program to find the node at which the intersection of two singly linked lists begins.

For example, the following two linked lists:

begin to intersect at node c1.

Example 1:

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Input: intersectVal = 8, listA = [4,1,8,4,5], listB = [5,6,1,8,4,5], skipA = 2, skipB = 3
Output: Reference of the node with value = 8
Input Explanation: The intersected node's value is 8 (note that this must not be 0 if the two lists intersect). From the head of A, it reads as [4,1,8,4,5]. From the head of B, it reads as [5,6,1,8,4,5]. There are 2 nodes before the intersected node in A; There are 3 nodes before the intersected node in B.

分析题解

一开始没什么头绪，感觉只能最原始的遍历，后来看了一些文章，发现比较简单的方式就是先找两个链表的长度差，因为从相交点开始肯定是长度一致的，这是个很好的解题突破口，找到长度差以后就是先跳过长链表的较长部分，然后开始同步遍历比较 A，B 链表；

代码

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public ListNode getIntersectionNode(ListNode headA, ListNode headB) {
        if (headA == null || headB == null) {
            return null;
        }
        // 算 A 的长度
        int countA = 0;
        ListNode tailA = headA;
        while (tailA != null) {
            tailA = tailA.next;
            countA++;
        }
        // 算 B 的长度
        int countB = 0;
        ListNode tailB = headB;
        while (tailB != null) {
            tailB = tailB.next;
            countB++;
        }
        tailA = headA;
        tailB = headB;
        // 依据长度差，先让长的链表 tail 指针往后移
        if (countA > countB) {
            while (countA > countB) {
                tailA = tailA.next;
                countA--;
            }
        } else if (countA < countB) {
            while (countA < countB) {
                tailB = tailB.next;
                countB--;
            }
        }
        // 然后以相同速度遍历两个链表比较
        while (tailA != null) {
            if (tailA == tailB) {
                return tailA;
            } else {
                tailA = tailA.next;
                tailB = tailB.next;
            }
        }
        return null;
    }

总结

可能缺少这种思维，做的还是比较少，所以没法一下子反应过来，需要锻炼，我的第一反应是两重遍历，不过那样复杂度就高了，这里应该是只有 O(N) 的复杂度。

发表于 更新于 分类于 阅读次数： Disqus：

Java 中的这个话题也是比较常遇到的，关于这块原先也是比较忽略的，但是仔细想想又有点遗忘了就在这里记一下
简单看下代码
java.lang.Object#equals

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public boolean equals(Object obj) {
        return (this == obj);
    }

对于所有对象的父类，equals 方法其实对比的就是对象的地址，也就是是否是同一个对象，试想如果像 Integer 或者 String 这种，我们没有重写 equals，那其实就等于是在用==，可能就没法达到我们的目的，所以像 String 这种常用的 jdk 类都是默认重写了
java.lang.String#equals

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public boolean equals(Object anObject) {
        if (this == anObject) {
            return true;
        }
        if (anObject instanceof String) {
            String anotherString = (String)anObject;
            int n = value.length;
            if (n == anotherString.value.length) {
                char v1[] = value;
                char v2[] = anotherString.value;
                int i = 0;
                while (n-- != 0) {
                    if (v1[i] != v2[i])
                        return false;
                    i++;
                }
                return true;
            }
        }
        return false;
    }

然后呢就是为啥一些书或者 effective java 中写了 equals 跟 hashCode 要一起重写，这里涉及到当对象作为 HashMap 的 key 的时候
首先 HashMap 会使用 hashCode 去判断是否在同一个槽里，然后在通过 equals 去判断是否是同一个 key，是的话就替换，不是的话就链表接下去，如果不重写 hashCode 的话，默认的 object 的hashCode 是 native 方法，根据对象的地址生成的，这样其实对象的值相同的话，因为地址不同，HashMap 也会出现异常，所以需要重写，同时也需要重写 equals 方法，才能确认是同一个 key，而不是落在同一个槽的不同 key.

发表于 更新于 分类于 ， ， ， 阅读次数： Disqus：

前几天同事问了我个 mysql 索引的问题，虽然大概知道，但是还是想来实践下，就是 is null，is not null 这类查询是否能用索引，可能之前有些网上的文章说都是不能用索引，但是其实不是，我们来看个小试验

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CREATE TABLE `null_index_t` (
  `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
  `null_key` varchar(255) DEFAULT NULL,
  `null_key1` varchar(255) DEFAULT NULL,
  `null_key2` varchar(255) DEFAULT NULL,
  PRIMARY KEY (`id`),
  KEY `idx_1` (`null_key`) USING BTREE,
  KEY `idx_2` (`null_key1`) USING BTREE,
  KEY `idx_3` (`null_key2`) USING BTREE
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

用个存储过程来插入数据

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delimiter $	#以delimiter来标记用$表示存储过程结束
create procedure nullIndex1()
begin
declare i int;	
declare j int;	
set i=1;
set j=1;
while(i<=100) do	
	while(j<=100) do	
		IF (i % 3 = 0) THEN
	     INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (null , LEFT(MD5(RAND()), 8), LEFT(MD5(RAND()), 8));
    ELSEIF (i % 3 = 1) THEN
			 INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT(MD5(RAND()), 8), NULL, LEFT(MD5(RAND()), 8));
	  ELSE
			 INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT(MD5(RAND()), 8), LEFT(MD5(RAND()), 8), NULL);
    END IF;
		set j=j+1;
	end while;
	set i=i+1;
	set j=1;	
end while;
end 
$
call nullIndex1();

然后看下我们的 is null 查询

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EXPLAIN select * from null_index_t WHERE null_key is null;


再来看看另一个

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EXPLAIN select * from null_index_t WHERE null_key is not null;


从这里能看出来啥呢，可以思考下

从上面可以发现，is null应该是用上了索引了，所以至少不是一刀切不能用，但是看着is not null好像不太行额
我们在做一点小改动，把这个表里的数据改成 9100 条是 null，剩下 900 条是有值的，然后再执行下

然后再来看看执行结果

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EXPLAIN select * from null_index_t WHERE null_key is null;

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EXPLAIN select * from null_index_t WHERE null_key is not null;


是不是不一样了，这里再补充下我试验使用的 mysql 是 5.7 的，不保证在其他版本的一致性，
其实可以看出随着数据量的变化，mysql 会不会使用索引是会变化的，不是说 is not null 一定会使用，也不是一定不会使用，而是优化器会根据查询成本做个预判，这个预判尽可能会减小查询成本，主要包括回表啥的，但是也不一定完全准确。

发表于 分类于 ， ， ， 阅读次数： Disqus：

几年前看了柴静的《穹顶之下》觉得这个记者调查得很深入，挺有水平，然后再看到了她跟丁仲礼的采访，其实没看完整，也没试着去理解，就觉得环境问题挺严重的，为啥柴静这个对面的这位好像对这个很不屑的样子，最近因为丁仲礼上了美国制裁名单，B 站又有人把这个视频发了出来，就完整看了下，就觉得自己挺惭愧的，就抱着对柴静的好感而没来由的否定了丁老的看法和说法，所以人也需要不断地学习，改正之前错误的观点，当然不是说我现在说的就是百分百正确，只是个人的一些浅显的见解

先聊聊这个事情，整体看下来我的一些理解，IPCC给中国的方案其实是个很大的陷阱，它里面有几个隐藏的点是容易被我们外行忽略的，第一点是基数，首先发达国家目前（指2010年采访或者IPCC方案时间）的人均碳排放量已经是远高于发展中国家的了，这也就导致了所谓的发达国家承诺减排80%是个非常有诚意的承诺其实就是忽悠；第二点是碳排放是个累计过程，从1900年开始到2050年，或者说到2010年，发达国家已经排的量是远超过发展中国家的，这是非常不公平的；第三点其实是通过前两点推导出来的，也就是即使发达国家这么有诚意地说减排80%，扒开这层虚伪的外衣，其实是给他们11亿人分走了48%的碳排放量，留给发展中国家55亿人口的只剩下了52%；第四点，人是否因为国家的发达与否而应受到不平等待遇，如果按国家维度，丁老说的，摩纳哥要跟中国分同样的排放量么，中国人还算不算人；第五点，这点算是我自己想的，也可能是柴静屁股决定脑袋想不到的点，她作为一个物质生活条件已经足够好了，那么对于那些生活在物质条件平均线以下的，他们是否能像城里人那样有空调地暖，洗澡有热水器浴霸，上下班能开车，这些其实都直接或者间接地导致了碳排放；他们有没有改善物质生活条件地权利呢，并且再说回来，其实丁老也给了我们觉得合理地方案，我们保证不管发达国家不管减排多少，我们都不会超过他们的80%，我觉得这是真正的诚意，而不是接着减排80%的噱头来忽悠人，也是像丁老这样的专家才能看破这个陷阱，碳排放权其实就是发展权，就是人权，中国人就不是人了么，或者说站在贫困线以下的人民是否有改善物质条件的权利，而不是说像柴静这样，只是管她自己，可能觉得小孩因为空气污染导致身体不好，所以做了穹顶之下这个纪录片，想去改善这个事情，空气污染不是说对的，只是每个国家都有这个过程，如果不发展，哪里有资源去让人活得好，活得好了是前提，然后再去各方面都改善。

对于这个问题其实更想说的是人的认知偏差，之前总觉得美帝是更自由民主，公平啥的，或者说觉得美帝啥都好，有种无脑愤青的感觉，外国的月亮比较圆，但是经历了像川普当选美国总统以来的各种魔幻操作，还有对于疫情的种种不可思议的美国民众的反应，其实更让人明白第一是外国的月亮没比较圆，第二是事情总是没那么简单粗暴非黑即白，美国不像原先设想地那么领先优秀，但是的确有很多方面是全球领先的，天朝也有体制所带来的优势，不可妄自菲薄，也不能忙不自大，还是要多学习知识，提升认知水平。

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

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今天真的是被气得不轻，情况是碰到一个有 70 多秒的直行红灯，然后直行就排了很长的队，但是左转车道没车，就有好几辆车占着左转车道，准备往直行车道插队加塞，一般这种加塞的，会挑个不太计较的，如果前面一辆不让的话就再等等，我因为赶着回家，就不想让，结果那辆车几次车头直接往里冲，当时怒气值基本已经蓄满了，我真的是分毫都不想让，如果路上都是让着这种人的，那么这种情况只会越来越严重，我理解的这种心态，就赌你怕麻烦，多一事不如少一事，结果就是每次都能顺利插队加塞，其实延伸到我们社会中的种种实质性的排队或者等同于排队的情况，都已经有这种惯有思维，一方面这种不符合规则，可能在严重程度上容易被很多人所忽视，基本上已经被很多人当成是“合理”行为，另一方面，对于这些“微小”的违规行为，本身管理层面也基本没有想要管的意思，就更多的成为了纵容这些行为的导火索，并且大多数人都是想着如果不让，发生点小剐小蹭的要浪费很多时间精力来处理，甚至会觉得会被别人觉得自己太小气等等，诸多内外成本结合起来，会真的去硬刚的可能少之又少了，这样也就让更多的人觉得这种行为是被默许的，再举个非常小的例子，以我们公司疫情期间的盒饭发放为例，有两个比较“有意思”的事情，第一个就是因为疫情，本来是让排队要间隔一米，但是可能除了我比较怕死会跟前面的人保持点距离基本没别人会不挨着前面的人，甚至我跟我前面的人保持点距离，后面的同学会推着我让我上去；第二个是关于拿饭，这么多人排着队拿饭，然后有部分同学，一个人拿好几份，帮组里其他人的都拿了，有些甚至一个人拿十份，假如这个盒饭发放是说明了可以按部门直接全领了那就没啥问题，但是当时的状况是个人排队领自己的那一份，如果一个同学直接帮着组里十几个人都拿了，后面排队的人是什么感受呢，甚至有些是看到队伍排长了，就找队伍里自己认识的比较靠前的人说你帮我也拿一份，其实作为我这个比较按规矩办事的“愣头青”来说，我是比较不能接受这两件小事里的行为的，再往下说可能就有点偏激了，先说到这~

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

写一个程序找出两个单向链表的交叉起始点，可能是我英语不好，图里画的其实还有一点是交叉以后所有节点都是相同的
Write a program to find the node at which the intersection of two singly linked lists begins.

For example, the following two linked lists:

begin to intersect at node c1.

Example 1:

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Input: intersectVal = 8, listA = [4,1,8,4,5], listB = [5,6,1,8,4,5], skipA = 2, skipB = 3
Output: Reference of the node with value = 8
Input Explanation: The intersected node's value is 8 (note that this must not be 0 if the two lists intersect). From the head of A, it reads as [4,1,8,4,5]. From the head of B, it reads as [5,6,1,8,4,5]. There are 2 nodes before the intersected node in A; There are 3 nodes before the intersected node in B.

分析题解

一开始没什么头绪，感觉只能最原始的遍历，后来看了一些文章，发现比较简单的方式就是先找两个链表的长度差，因为从相交点开始肯定是长度一致的，这是个很好的解题突破口，找到长度差以后就是先跳过长链表的较长部分，然后开始同步遍历比较 A，B 链表；

代码

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public ListNode getIntersectionNode(ListNode headA, ListNode headB) {
        if (headA == null || headB == null) {
            return null;
        }
        // 算 A 的长度
        int countA = 0;
        ListNode tailA = headA;
        while (tailA != null) {
            tailA = tailA.next;
            countA++;
        }
        // 算 B 的长度
        int countB = 0;
        ListNode tailB = headB;
        while (tailB != null) {
            tailB = tailB.next;
            countB++;
        }
        tailA = headA;
        tailB = headB;
        // 依据长度差，先让长的链表 tail 指针往后移
        if (countA > countB) {
            while (countA > countB) {
                tailA = tailA.next;
                countA--;
            }
        } else if (countA < countB) {
            while (countA < countB) {
                tailB = tailB.next;
                countB--;
            }
        }
        // 然后以相同速度遍历两个链表比较
        while (tailA != null) {
            if (tailA == tailB) {
                return tailA;
            } else {
                tailA = tailA.next;
                tailB = tailB.next;
            }
        }
        return null;
    }

总结

可能缺少这种思维，做的还是比较少，所以没法一下子反应过来，需要锻炼，我的第一反应是两重遍历，不过那样复杂度就高了，这里应该是只有 O(N) 的复杂度。

发表于 更新于 分类于 阅读次数： Disqus：

Java 中的这个话题也是比较常遇到的，关于这块原先也是比较忽略的，但是仔细想想又有点遗忘了就在这里记一下
简单看下代码
java.lang.Object#equals

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public boolean equals(Object obj) {
        return (this == obj);
    }

对于所有对象的父类，equals 方法其实对比的就是对象的地址，也就是是否是同一个对象，试想如果像 Integer 或者 String 这种，我们没有重写 equals，那其实就等于是在用==，可能就没法达到我们的目的，所以像 String 这种常用的 jdk 类都是默认重写了
java.lang.String#equals

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public boolean equals(Object anObject) {
        if (this == anObject) {
            return true;
        }
        if (anObject instanceof String) {
            String anotherString = (String)anObject;
            int n = value.length;
            if (n == anotherString.value.length) {
                char v1[] = value;
                char v2[] = anotherString.value;
                int i = 0;
                while (n-- != 0) {
                    if (v1[i] != v2[i])
                        return false;
                    i++;
                }
                return true;
            }
        }
        return false;
    }

然后呢就是为啥一些书或者 effective java 中写了 equals 跟 hashCode 要一起重写，这里涉及到当对象作为 HashMap 的 key 的时候
首先 HashMap 会使用 hashCode 去判断是否在同一个槽里，然后在通过 equals 去判断是否是同一个 key，是的话就替换，不是的话就链表接下去，如果不重写 hashCode 的话，默认的 object 的hashCode 是 native 方法，根据对象的地址生成的，这样其实对象的值相同的话，因为地址不同，HashMap 也会出现异常，所以需要重写，同时也需要重写 equals 方法，才能确认是同一个 key，而不是落在同一个槽的不同 key.

发表于 更新于 分类于 ， ， ， 阅读次数： Disqus：

前几天同事问了我个 mysql 索引的问题，虽然大概知道，但是还是想来实践下，就是 is null，is not null 这类查询是否能用索引，可能之前有些网上的文章说都是不能用索引，但是其实不是，我们来看个小试验

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CREATE TABLE `null_index_t` (
  `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
  `null_key` varchar(255) DEFAULT NULL,
  `null_key1` varchar(255) DEFAULT NULL,
  `null_key2` varchar(255) DEFAULT NULL,
  PRIMARY KEY (`id`),
  KEY `idx_1` (`null_key`) USING BTREE,
  KEY `idx_2` (`null_key1`) USING BTREE,
  KEY `idx_3` (`null_key2`) USING BTREE
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

用个存储过程来插入数据

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delimiter $	#以delimiter来标记用$表示存储过程结束
create procedure nullIndex1()
begin
declare i int;	
declare j int;	
set i=1;
set j=1;
while(i<=100) do	
	while(j<=100) do	
		IF (i % 3 = 0) THEN
	     INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (null , LEFT(MD5(RAND()), 8), LEFT(MD5(RAND()), 8));
    ELSEIF (i % 3 = 1) THEN
			 INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT(MD5(RAND()), 8), NULL, LEFT(MD5(RAND()), 8));
	  ELSE
			 INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT(MD5(RAND()), 8), LEFT(MD5(RAND()), 8), NULL);
    END IF;
		set j=j+1;
	end while;
	set i=i+1;
	set j=1;	
end while;
end 
$
call nullIndex1();

然后看下我们的 is null 查询

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EXPLAIN select * from null_index_t WHERE null_key is null;


再来看看另一个

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EXPLAIN select * from null_index_t WHERE null_key is not null;


从这里能看出来啥呢，可以思考下

从上面可以发现，is null应该是用上了索引了，所以至少不是一刀切不能用，但是看着is not null好像不太行额
我们在做一点小改动，把这个表里的数据改成 9100 条是 null，剩下 900 条是有值的，然后再执行下

然后再来看看执行结果

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EXPLAIN select * from null_index_t WHERE null_key is null;

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EXPLAIN select * from null_index_t WHERE null_key is not null;


是不是不一样了，这里再补充下我试验使用的 mysql 是 5.7 的，不保证在其他版本的一致性，
其实可以看出随着数据量的变化，mysql 会不会使用索引是会变化的，不是说 is not null 一定会使用，也不是一定不会使用，而是优化器会根据查询成本做个预判，这个预判尽可能会减小查询成本，主要包括回表啥的，但是也不一定完全准确。

发表于 分类于 ， ， ， 阅读次数： Disqus：

几年前看了柴静的《穹顶之下》觉得这个记者调查得很深入，挺有水平，然后再看到了她跟丁仲礼的采访，其实没看完整，也没试着去理解，就觉得环境问题挺严重的，为啥柴静这个对面的这位好像对这个很不屑的样子，最近因为丁仲礼上了美国制裁名单，B 站又有人把这个视频发了出来，就完整看了下，就觉得自己挺惭愧的，就抱着对柴静的好感而没来由的否定了丁老的看法和说法，所以人也需要不断地学习，改正之前错误的观点，当然不是说我现在说的就是百分百正确，只是个人的一些浅显的见解

先聊聊这个事情，整体看下来我的一些理解，IPCC给中国的方案其实是个很大的陷阱，它里面有几个隐藏的点是容易被我们外行忽略的，第一点是基数，首先发达国家目前（指2010年采访或者IPCC方案时间）的人均碳排放量已经是远高于发展中国家的了，这也就导致了所谓的发达国家承诺减排80%是个非常有诚意的承诺其实就是忽悠；第二点是碳排放是个累计过程，从1900年开始到2050年，或者说到2010年，发达国家已经排的量是远超过发展中国家的，这是非常不公平的；第三点其实是通过前两点推导出来的，也就是即使发达国家这么有诚意地说减排80%，扒开这层虚伪的外衣，其实是给他们11亿人分走了48%的碳排放量，留给发展中国家55亿人口的只剩下了52%；第四点，人是否因为国家的发达与否而应受到不平等待遇，如果按国家维度，丁老说的，摩纳哥要跟中国分同样的排放量么，中国人还算不算人；第五点，这点算是我自己想的，也可能是柴静屁股决定脑袋想不到的点，她作为一个物质生活条件已经足够好了，那么对于那些生活在物质条件平均线以下的，他们是否能像城里人那样有空调地暖，洗澡有热水器浴霸，上下班能开车，这些其实都直接或者间接地导致了碳排放；他们有没有改善物质生活条件地权利呢，并且再说回来，其实丁老也给了我们觉得合理地方案，我们保证不管发达国家不管减排多少，我们都不会超过他们的80%，我觉得这是真正的诚意，而不是接着减排80%的噱头来忽悠人，也是像丁老这样的专家才能看破这个陷阱，碳排放权其实就是发展权，就是人权，中国人就不是人了么，或者说站在贫困线以下的人民是否有改善物质条件的权利，而不是说像柴静这样，只是管她自己，可能觉得小孩因为空气污染导致身体不好，所以做了穹顶之下这个纪录片，想去改善这个事情，空气污染不是说对的，只是每个国家都有这个过程，如果不发展，哪里有资源去让人活得好，活得好了是前提，然后再去各方面都改善。

对于这个问题其实更想说的是人的认知偏差，之前总觉得美帝是更自由民主，公平啥的，或者说觉得美帝啥都好，有种无脑愤青的感觉，外国的月亮比较圆，但是经历了像川普当选美国总统以来的各种魔幻操作，还有对于疫情的种种不可思议的美国民众的反应，其实更让人明白第一是外国的月亮没比较圆，第二是事情总是没那么简单粗暴非黑即白，美国不像原先设想地那么领先优秀，但是的确有很多方面是全球领先的，天朝也有体制所带来的优势，不可妄自菲薄，也不能忙不自大，还是要多学习知识，提升认知水平。

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， ， ， ， ， 阅读次数： Disqus：

题目介绍

Given preorder and inorder traversal of a tree, construct the binary tree.
给定一棵树的前序和中序遍历，构造出一棵二叉树

注意

You may assume that duplicates do not exist in the tree.
你可以假设树中没有重复的元素。(PS: 不然就没法做了呀)

例子:

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preorder = [3,9,20,15,7]
inorder = [9,3,15,20,7]

返回的二叉树

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3
4
5
  3
 / \
9  20
  /  \
 15   7

简要分析

看到这个题可以想到一个比较常规的解法就是递归拆树，前序就是根左右，中序就是左根右，然后就是通过前序已经确定的根在中序中找到，然后去划分左右子树，这个例子里是 3，找到中序中的位置，那么就可以确定，9 是左子树，15,20,7是右子树，然后对应的可以根据左右子树的元素数量在前序中划分左右子树，再继续递归就行

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class Solution {
    public TreeNode buildTree(int[] preorder, int[] inorder) {
      // 获取下数组长度
        int n = preorder.length;
        // 排除一下异常和边界
        if (n != inorder.length) {
            return null;
        }
        if (n == 0) {
            return null;
        }
        if (n == 1) {
            return new TreeNode(preorder[0]);
        }
        // 获得根节点
        TreeNode node = new TreeNode(preorder[0]);
        int pos = 0;
        // 找到中序中的位置
        for (int i = 0; i < inorder.length; i++) {
            if (node.val == inorder[i]) {
                pos = i;
                break;
            }
        }
        // 划分左右再进行递归，注意下`Arrays.copyOfRange`的用法
        node.left = buildTree(Arrays.copyOfRange(preorder, 1, pos + 1), Arrays.copyOfRange(inorder, 0, pos));
        node.right = buildTree(Arrays.copyOfRange(preorder, pos + 1, n), Arrays.copyOfRange(inorder, pos + 1, n));
        return node;
    }
}

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

Design a stack that supports push, pop, top, and retrieving the minimum element in constant time.
设计一个栈，支持压栈，出站，获取栈顶元素，通过常数级复杂度获取栈中的最小元素

  • push(x) – Push element x onto stack.
  • pop() – Removes the element on top of the stack.
  • top() – Get the top element.
  • getMin() – Retrieve the minimum element in the stack.

示例

Example 1:

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Input
["MinStack","push","push","push","getMin","pop","top","getMin"]
[[],[-2],[0],[-3],[],[],[],[]]

Output
[null,null,null,null,-3,null,0,-2]

Explanation
MinStack minStack = new MinStack();
minStack.push(-2);
minStack.push(0);
minStack.push(-3);
minStack.getMin(); // return -3
minStack.pop();
minStack.top();    // return 0
minStack.getMin(); // return -2

简要分析

其实现在大部分语言都自带类栈的数据结构，Java 也自带 stack 这个数据结构，所以这个题的主要难点的就是常数级的获取最小元素，最开始的想法是就一个栈外加一个记录最小值的变量就行了，但是仔细一想是不行的，因为随着元素被 pop 出去，这个最小值也可能需要梗着变化，就不太好判断了，所以后面是用了一个辅助栈。

代码

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class MinStack {
        // 这个作为主栈
        Stack<Integer> s1 = new Stack<>();
        // 这个作为辅助栈，放最小值的栈
        Stack<Integer> s2 = new Stack<>();
        /** initialize your data structure here. */
        public MinStack() {

        }

        public void push(int x) {
            // 放入主栈
            s1.push(x);
            // 当 s2 是空或者当前值是小于"等于" s2 栈顶时，压入辅助最小值的栈
            // 注意这里的"等于"非常必要，因为当最小值有多个的情况下，也需要压入栈，否则在 pop 的时候就会不对等
            if (s2.isEmpty() || x <= s2.peek()) {
                s2.push(x);
            }
        }

        public void pop() {
            // 首先就是主栈要 pop，然后就是第二个了，跟上面的"等于"很有关系，
            // 因为如果有两个最小值，如果前面等于的情况没有压栈，那这边相等的时候 pop 就会少一个了，可能就导致最小值不对了
            int x = s1.pop();
            if (x == s2.peek())  {
                s2.pop();
            }
        }

        public int top() {
            // 栈顶的元素
            return s1.peek();
        }

        public int getMin() {
            // 辅助最小栈的栈顶
            return s2.peek();
        }
    }

发表于 更新于 分类于 ， ， ， 阅读次数： Disqus：

前几天清华美院学姐的热点火了，然后仔细看了下，其实是个学姐诬陷以为其貌不扬的男同学摸她屁股

然后还在朋友圈发文想让他社死，我也是挺晚才知道这个词什么意思，然后后面我看到了这个图片，挺有意思的

本来其实也没什么想聊这个的，是在 B 站看了个吐槽这个的，然后刚好晚上乘公交的时候又碰到了有点类似的问题
故事描述下，我们从始发站做了公交，这辆公交司机上次碰到过一回，就是会比较关注乘客的佩戴情况，主要考虑到目前国内疫情，然后这次在差不多人都坐满的情况下，可能在提示了三次让车内乘客戴好口罩，但是他指的那个中年女性还是没有反应，司机就转头比较大声指着这个乘客（中年女性）让戴好口罩，然后这个乘客（中年女性）就大声的说“我口罩是滑下来了，你指着我干嘛，你态度这么差，要吃了我一样，我要投诉你”等等，然后可能跟她一块的一个中年女性也是这么帮腔指责司机，比较基本的理解，车子里这么多乘客，假如是处于这位乘客口罩滑下来了而不自知的情况下，司机在提示了三次以后回头指着她说，我想的是没什么问题的，但是这位却反而指责这位司机指着她，并且说是态度差，要吃了她，完全是不可理喻的，并且一直喋喋不休说她口罩滑掉了有什么错，要投诉这个司机，让他可以提前退休了，在其他乘客的劝说下司机准备继续开车时，又口吐芬芳“你个傻，你来打我呀”，真的是让我再次体会到了所谓的恶人先告状的又一完美呈现，后面还有个乘客还是表示要打死司机这个傻，让我有点不明所以，俗话说有人是得理不饶人，前提是得理，这种理亏不饶人真的是挺让人长见识的，试想下，司机在提示三次后，这位乘客还是没有把口罩戴好，如何在不指着这位乘客的情况下能准确的提示到她呢，并且觉得语气态度不好，司机要载着一车的人，因为你这一个乘客不戴好口罩而不能正常出发，有些着急应该很正常吧，可能是平时自己在家里耀武扬威的使唤别人习惯了吧，别人不敢这么大声跟她说话，其实想想这位中年女性应该年纪不会很大，还比较时髦的吧，像一些常见的中年杭州本地人可能是不会说傻*这个词的吧。
杭州的公交可能是在二月份疫情还比较严重的时候是要求上车出示健康码，后面比较缓和以后只要求佩戴好口罩，但是在我们小绍兴，目前还是一律要求检验健康码和佩戴口罩，对于疫情中，并且目前阶段国内也时有报出小范围的疫情的情况下，司机尽职要求佩戴好口罩其实也是为了乘客着想，另一种情况如果司机不严格要求，万一车上有个感染者，这位中年女性被传染了，如果能找到这个司机的话，是不是想“打死”这个司机，竟然让感染者上了车，反正她自己是不可能有错的，上来就是对方态度差，要投诉，自己不戴好口罩啥都没错，我就想知道如果因为自己没戴好口罩被感染了，是不是也是司机的错，毕竟没有像仆人那样点头哈腰求着她戴好口罩。
再说回来，整个车就她一个人没戴好口罩，并且还有个细节，其实这个乘客是上了车之后就没戴好了，本来上车的时候是戴好的，这种比较有可能是觉得上车的时候司机那看一眼就好了，如果好好戴着口罩，一点事情都没有，唉，纯粹是太气愤了，调理逻辑什么的就忽略吧

发表于 更新于 分类于 ， ， ， 阅读次数： Disqus：

之前看了 dubbo 的一些代码，在学习过程中，主要关注那些比较“高级”的内容，SPI，自适应扩展等，却忘了一些作为一个 rpc 框架最核心需要的部分，比如如何通信，序列化，网络，容错机制等等，因为其实这个最核心的就是远程调用，自适应扩展其实就是让代码可扩展性，可读性，更优雅等，写的搓一点其实也问题不大，但是一个合适的通信协议，序列化方法，如何容错等却是真正保证是一个 rpc 框架最重要的要素。
首先来看这张图
cluster
在集群调用失败时，Dubbo 提供了多种容错方案，缺省为 failover 重试。
各节点关系：

  • 这里的 Invoker 是 Provider 的一个可调用 Service 的抽象，Invoker 封装了 Provider 地址及 Service 接口信息
  • Directory 代表多个 Invoker，可以把它看成 List<Invoker> ，但与 List 不同的是，它的值可能是动态变化的，比如注册中心推送变更
  • Cluster 将 Directory 中的多个 Invoker 伪装成一个 Invoker，对上层透明，伪装过程包含了容错逻辑，调用失败后，重试另一个
  • Router 负责从多个 Invoker 中按路由规则选出子集，比如读写分离，应用隔离等
  • LoadBalance 负责从多个 Invoker 中选出具体的一个用于本次调用，选的过程包含了负载均衡算法，调用失败后，需要重选

集群容错模式

Failover Cluster

失败自动切换，当出现失败，重试其它服务器 1。通常用于读操作，但重试会带来更长延迟。可通过 retries=”2” 来设置重试次数(不含第一次)。

重试次数配置如下：

<dubbo:service retries=”2” />
这里重点看下 Failover Cluster集群模式的实现

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public class FailoverCluster implements Cluster {

    public final static String NAME = "failover";

    public <T> Invoker<T> join(Directory<T> directory) throws RpcException {
        return new FailoverClusterInvoker<T>(directory);
    }

}

这个代码就非常简单，重点需要看FailoverClusterInvoker里的代码，FailoverClusterInvoker继承了AbstractClusterInvoker类，其中invoke 方法是在抽象类里实现的

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@Override
public Result invoke(final Invocation invocation) throws RpcException {
    checkWhetherDestroyed();
    // binding attachments into invocation.
    // 绑定 attachments 到 invocation 中.
    Map<String, Object> contextAttachments = RpcContext.getContext().getObjectAttachments();
    if (contextAttachments != null && contextAttachments.size() != 0) {
        ((RpcInvocation) invocation).addObjectAttachments(contextAttachments);
    }
    // 列举 Invoker
    List<Invoker<T>> invokers = list(invocation);
    // 加载 LoadBalance 负载均衡器
    LoadBalance loadbalance = initLoadBalance(invokers, invocation);
    RpcUtils.attachInvocationIdIfAsync(getUrl(), invocation);
    // 调用 实际的 doInvoke 进行后续操作
    return doInvoke(invocation, invokers, loadbalance);
}
// 这是个抽象方法，实际是由子类实现的
 protected abstract Result doInvoke(Invocation invocation, List<Invoker<T>> invokers,
                                       LoadBalance loadbalance) throws RpcException;

然后重点就是FailoverClusterInvoker中的doInvoke方法了，其实它里面也就这么一个方法

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@Override
    @SuppressWarnings({"unchecked", "rawtypes"})
    public Result doInvoke(Invocation invocation, final List<Invoker<T>> invokers, LoadBalance loadbalance) throws RpcException {
        List<Invoker<T>> copyInvokers = invokers;
        checkInvokers(copyInvokers, invocation);
        String methodName = RpcUtils.getMethodName(invocation);
        // 获取重试次数，这里默认是 2 次，还有可以注意下后面的+1
        int len = getUrl().getMethodParameter(methodName, RETRIES_KEY, DEFAULT_RETRIES) + 1;
        if (len <= 0) {
            len = 1;
        }
        // retry loop.
        RpcException le = null; // last exception.
        List<Invoker<T>> invoked = new ArrayList<Invoker<T>>(copyInvokers.size()); // invoked invokers.
        Set<String> providers = new HashSet<String>(len);
        // 循环调用，失败重试
        for (int i = 0; i < len; i++) {
            //Reselect before retry to avoid a change of candidate `invokers`.
            //NOTE: if `invokers` changed, then `invoked` also lose accuracy.
            if (i > 0) {
                checkWhetherDestroyed();
                // 在进行重试前重新列举 Invoker，这样做的好处是，如果某个服务挂了，
                // 通过调用 list 可得到最新可用的 Invoker 列表
                copyInvokers = list(invocation);
                // check again
                // 对 copyinvokers 进行判空检查
                checkInvokers(copyInvokers, invocation);
            }
            // 通过负载均衡来选择 invoker
            Invoker<T> invoker = select(loadbalance, invocation, copyInvokers, invoked);
            // 将其添加到 invoker 到 invoked 列表中
            invoked.add(invoker);
            // 设置上下文
            RpcContext.getContext().setInvokers((List) invoked);
            try {
                // 正式调用
                Result result = invoker.invoke(invocation);
                if (le != null && logger.isWarnEnabled()) {
                    logger.warn("Although retry the method " + methodName
                            + " in the service " + getInterface().getName()
                            + " was successful by the provider " + invoker.getUrl().getAddress()
                            + ", but there have been failed providers " + providers
                            + " (" + providers.size() + "/" + copyInvokers.size()
                            + ") from the registry " + directory.getUrl().getAddress()
                            + " on the consumer " + NetUtils.getLocalHost()
                            + " using the dubbo version " + Version.getVersion() + ". Last error is: "
                            + le.getMessage(), le);
                }
                return result;
            } catch (RpcException e) {
                if (e.isBiz()) { // biz exception.
                    throw e;
                }
                le = e;
            } catch (Throwable e) {
                le = new RpcException(e.getMessage(), e);
            } finally {
                providers.add(invoker.getUrl().getAddress());
            }
        }
        throw new RpcException(le.getCode(), "Failed to invoke the method "
                + methodName + " in the service " + getInterface().getName()
                + ". Tried " + len + " times of the providers " + providers
                + " (" + providers.size() + "/" + copyInvokers.size()
                + ") from the registry " + directory.getUrl().getAddress()
                + " on the consumer " + NetUtils.getLocalHost() + " using the dubbo version "
                + Version.getVersion() + ". Last error is: "
                + le.getMessage(), le.getCause() != null ? le.getCause() : le);
    }

Failfast Cluster

快速失败，只发起一次调用，失败立即报错。通常用于非幂等性的写操作，比如新增记录。

Failsafe Cluster

失败安全，出现异常时，直接忽略。通常用于写入审计日志等操作。

Failback Cluster

失败自动恢复，后台记录失败请求，定时重发。通常用于消息通知操作。

Forking Cluster

并行调用多个服务器，只要一个成功即返回。通常用于实时性要求较高的读操作，但需要浪费更多服务资源。可通过 forks=”2” 来设置最大并行数。

Broadcast Cluster

广播调用所有提供者，逐个调用，任意一台报错则报错 2。通常用于通知所有提供者更新缓存或日志等本地资源信息。

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

Given a singly linked list, determine if it is a palindrome.
给定一个单向链表，判断是否是回文链表

例一 Example 1:

Input: 1->2
Output: false

例二 Example 2:

Input: 1->2->2->1
Output: true

挑战下自己

Follow up:
Could you do it in O(n) time and O(1) space?

简要分析

首先这是个单向链表，如果是双向的就可以一个从头到尾，一个从尾到头，显然那样就没啥意思了，然后想过要不找到中点，然后用一个栈，把前一半塞进栈里，但是这种其实也比较麻烦，比如长度是奇偶数，然后如何找到中点，这倒是可以借助于双指针，还是比较麻烦，再想一想，回文链表，就跟最开始的一样，链表只有单向的，我用个栈不就可以逆向了么，先把链表整个塞进栈里，然后在一个个 pop 出来跟链表从头开始比较，全对上了就是回文了

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/**
 * Definition for singly-linked list.
 * public class ListNode {
 *     int val;
 *     ListNode next;
 *     ListNode() {}
 *     ListNode(int val) { this.val = val; }
 *     ListNode(int val, ListNode next) { this.val = val; this.next = next; }
 * }
 */
class Solution {
    public boolean isPalindrome(ListNode head) {
        if (head == null) {
            return true;
        }
        ListNode tail = head;
        LinkedList<Integer> stack = new LinkedList<>();
        // 这里就是一个循环，将所有元素依次压入栈
        while (tail != null) {
            stack.push(tail.val);
            tail = tail.next;
        }
        // 在逐个 pop 出来，其实这个出来的顺序就等于链表从尾到头遍历，同时跟链表从头到尾遍历进行逐对对比
        while (!stack.isEmpty()) {
            if (stack.peekFirst() == head.val) {
                stack.pollFirst();
                head = head.next;
            } else {
                return false;
            }
        }
        return true;
    }
}
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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， ， ， ， ， 阅读次数： Disqus：

题目介绍

Given preorder and inorder traversal of a tree, construct the binary tree.
给定一棵树的前序和中序遍历，构造出一棵二叉树

注意

You may assume that duplicates do not exist in the tree.
你可以假设树中没有重复的元素。(PS: 不然就没法做了呀)

例子:

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preorder = [3,9,20,15,7]
inorder = [9,3,15,20,7]

返回的二叉树

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2
3
4
5
  3
 / \
9  20
  /  \
 15   7

简要分析

看到这个题可以想到一个比较常规的解法就是递归拆树，前序就是根左右，中序就是左根右，然后就是通过前序已经确定的根在中序中找到，然后去划分左右子树，这个例子里是 3，找到中序中的位置，那么就可以确定，9 是左子树，15,20,7是右子树，然后对应的可以根据左右子树的元素数量在前序中划分左右子树，再继续递归就行

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class Solution {
    public TreeNode buildTree(int[] preorder, int[] inorder) {
      // 获取下数组长度
        int n = preorder.length;
        // 排除一下异常和边界
        if (n != inorder.length) {
            return null;
        }
        if (n == 0) {
            return null;
        }
        if (n == 1) {
            return new TreeNode(preorder[0]);
        }
        // 获得根节点
        TreeNode node = new TreeNode(preorder[0]);
        int pos = 0;
        // 找到中序中的位置
        for (int i = 0; i < inorder.length; i++) {
            if (node.val == inorder[i]) {
                pos = i;
                break;
            }
        }
        // 划分左右再进行递归，注意下`Arrays.copyOfRange`的用法
        node.left = buildTree(Arrays.copyOfRange(preorder, 1, pos + 1), Arrays.copyOfRange(inorder, 0, pos));
        node.right = buildTree(Arrays.copyOfRange(preorder, pos + 1, n), Arrays.copyOfRange(inorder, pos + 1, n));
        return node;
    }
}

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

Design a stack that supports push, pop, top, and retrieving the minimum element in constant time.
设计一个栈，支持压栈，出站，获取栈顶元素，通过常数级复杂度获取栈中的最小元素

  • push(x) – Push element x onto stack.
  • pop() – Removes the element on top of the stack.
  • top() – Get the top element.
  • getMin() – Retrieve the minimum element in the stack.

示例

Example 1:

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Input
["MinStack","push","push","push","getMin","pop","top","getMin"]
[[],[-2],[0],[-3],[],[],[],[]]

Output
[null,null,null,null,-3,null,0,-2]

Explanation
MinStack minStack = new MinStack();
minStack.push(-2);
minStack.push(0);
minStack.push(-3);
minStack.getMin(); // return -3
minStack.pop();
minStack.top();    // return 0
minStack.getMin(); // return -2

简要分析

其实现在大部分语言都自带类栈的数据结构，Java 也自带 stack 这个数据结构，所以这个题的主要难点的就是常数级的获取最小元素，最开始的想法是就一个栈外加一个记录最小值的变量就行了，但是仔细一想是不行的，因为随着元素被 pop 出去，这个最小值也可能需要梗着变化，就不太好判断了，所以后面是用了一个辅助栈。

代码

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class MinStack {
        // 这个作为主栈
        Stack<Integer> s1 = new Stack<>();
        // 这个作为辅助栈，放最小值的栈
        Stack<Integer> s2 = new Stack<>();
        /** initialize your data structure here. */
        public MinStack() {

        }

        public void push(int x) {
            // 放入主栈
            s1.push(x);
            // 当 s2 是空或者当前值是小于"等于" s2 栈顶时，压入辅助最小值的栈
            // 注意这里的"等于"非常必要，因为当最小值有多个的情况下，也需要压入栈，否则在 pop 的时候就会不对等
            if (s2.isEmpty() || x <= s2.peek()) {
                s2.push(x);
            }
        }

        public void pop() {
            // 首先就是主栈要 pop，然后就是第二个了，跟上面的"等于"很有关系，
            // 因为如果有两个最小值，如果前面等于的情况没有压栈，那这边相等的时候 pop 就会少一个了，可能就导致最小值不对了
            int x = s1.pop();
            if (x == s2.peek())  {
                s2.pop();
            }
        }

        public int top() {
            // 栈顶的元素
            return s1.peek();
        }

        public int getMin() {
            // 辅助最小栈的栈顶
            return s2.peek();
        }
    }

发表于 更新于 分类于 ， ， ， 阅读次数： Disqus：

前几天清华美院学姐的热点火了，然后仔细看了下，其实是个学姐诬陷以为其貌不扬的男同学摸她屁股

然后还在朋友圈发文想让他社死，我也是挺晚才知道这个词什么意思，然后后面我看到了这个图片，挺有意思的

本来其实也没什么想聊这个的，是在 B 站看了个吐槽这个的，然后刚好晚上乘公交的时候又碰到了有点类似的问题
故事描述下，我们从始发站做了公交，这辆公交司机上次碰到过一回，就是会比较关注乘客的佩戴情况，主要考虑到目前国内疫情，然后这次在差不多人都坐满的情况下，可能在提示了三次让车内乘客戴好口罩，但是他指的那个中年女性还是没有反应，司机就转头比较大声指着这个乘客（中年女性）让戴好口罩，然后这个乘客（中年女性）就大声的说“我口罩是滑下来了，你指着我干嘛，你态度这么差，要吃了我一样，我要投诉你”等等，然后可能跟她一块的一个中年女性也是这么帮腔指责司机，比较基本的理解，车子里这么多乘客，假如是处于这位乘客口罩滑下来了而不自知的情况下，司机在提示了三次以后回头指着她说，我想的是没什么问题的，但是这位却反而指责这位司机指着她，并且说是态度差，要吃了她，完全是不可理喻的，并且一直喋喋不休说她口罩滑掉了有什么错，要投诉这个司机，让他可以提前退休了，在其他乘客的劝说下司机准备继续开车时，又口吐芬芳“你个傻，你来打我呀”，真的是让我再次体会到了所谓的恶人先告状的又一完美呈现，后面还有个乘客还是表示要打死司机这个傻，让我有点不明所以，俗话说有人是得理不饶人，前提是得理，这种理亏不饶人真的是挺让人长见识的，试想下，司机在提示三次后，这位乘客还是没有把口罩戴好，如何在不指着这位乘客的情况下能准确的提示到她呢，并且觉得语气态度不好，司机要载着一车的人，因为你这一个乘客不戴好口罩而不能正常出发，有些着急应该很正常吧，可能是平时自己在家里耀武扬威的使唤别人习惯了吧，别人不敢这么大声跟她说话，其实想想这位中年女性应该年纪不会很大，还比较时髦的吧，像一些常见的中年杭州本地人可能是不会说傻*这个词的吧。
杭州的公交可能是在二月份疫情还比较严重的时候是要求上车出示健康码，后面比较缓和以后只要求佩戴好口罩，但是在我们小绍兴，目前还是一律要求检验健康码和佩戴口罩，对于疫情中，并且目前阶段国内也时有报出小范围的疫情的情况下，司机尽职要求佩戴好口罩其实也是为了乘客着想，另一种情况如果司机不严格要求，万一车上有个感染者，这位中年女性被传染了，如果能找到这个司机的话，是不是想“打死”这个司机，竟然让感染者上了车，反正她自己是不可能有错的，上来就是对方态度差，要投诉，自己不戴好口罩啥都没错，我就想知道如果因为自己没戴好口罩被感染了，是不是也是司机的错，毕竟没有像仆人那样点头哈腰求着她戴好口罩。
再说回来，整个车就她一个人没戴好口罩，并且还有个细节，其实这个乘客是上了车之后就没戴好了，本来上车的时候是戴好的，这种比较有可能是觉得上车的时候司机那看一眼就好了，如果好好戴着口罩，一点事情都没有，唉，纯粹是太气愤了，调理逻辑什么的就忽略吧

发表于 更新于 分类于 ， ， ， 阅读次数： Disqus：

之前看了 dubbo 的一些代码，在学习过程中，主要关注那些比较“高级”的内容，SPI，自适应扩展等，却忘了一些作为一个 rpc 框架最核心需要的部分，比如如何通信，序列化，网络，容错机制等等，因为其实这个最核心的就是远程调用，自适应扩展其实就是让代码可扩展性，可读性，更优雅等，写的搓一点其实也问题不大，但是一个合适的通信协议，序列化方法，如何容错等却是真正保证是一个 rpc 框架最重要的要素。
首先来看这张图
cluster
在集群调用失败时，Dubbo 提供了多种容错方案，缺省为 failover 重试。
各节点关系：

  • 这里的 Invoker 是 Provider 的一个可调用 Service 的抽象，Invoker 封装了 Provider 地址及 Service 接口信息
  • Directory 代表多个 Invoker，可以把它看成 List<Invoker> ，但与 List 不同的是，它的值可能是动态变化的，比如注册中心推送变更
  • Cluster 将 Directory 中的多个 Invoker 伪装成一个 Invoker，对上层透明，伪装过程包含了容错逻辑，调用失败后，重试另一个
  • Router 负责从多个 Invoker 中按路由规则选出子集，比如读写分离，应用隔离等
  • LoadBalance 负责从多个 Invoker 中选出具体的一个用于本次调用，选的过程包含了负载均衡算法，调用失败后，需要重选

集群容错模式

Failover Cluster

失败自动切换，当出现失败，重试其它服务器 1。通常用于读操作，但重试会带来更长延迟。可通过 retries=”2” 来设置重试次数(不含第一次)。

重试次数配置如下：

<dubbo:service retries=”2” />
这里重点看下 Failover Cluster集群模式的实现

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public class FailoverCluster implements Cluster {

    public final static String NAME = "failover";

    public <T> Invoker<T> join(Directory<T> directory) throws RpcException {
        return new FailoverClusterInvoker<T>(directory);
    }

}

这个代码就非常简单，重点需要看FailoverClusterInvoker里的代码，FailoverClusterInvoker继承了AbstractClusterInvoker类，其中invoke 方法是在抽象类里实现的

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@Override
public Result invoke(final Invocation invocation) throws RpcException {
    checkWhetherDestroyed();
    // binding attachments into invocation.
    // 绑定 attachments 到 invocation 中.
    Map<String, Object> contextAttachments = RpcContext.getContext().getObjectAttachments();
    if (contextAttachments != null && contextAttachments.size() != 0) {
        ((RpcInvocation) invocation).addObjectAttachments(contextAttachments);
    }
    // 列举 Invoker
    List<Invoker<T>> invokers = list(invocation);
    // 加载 LoadBalance 负载均衡器
    LoadBalance loadbalance = initLoadBalance(invokers, invocation);
    RpcUtils.attachInvocationIdIfAsync(getUrl(), invocation);
    // 调用 实际的 doInvoke 进行后续操作
    return doInvoke(invocation, invokers, loadbalance);
}
// 这是个抽象方法，实际是由子类实现的
 protected abstract Result doInvoke(Invocation invocation, List<Invoker<T>> invokers,
                                       LoadBalance loadbalance) throws RpcException;

然后重点就是FailoverClusterInvoker中的doInvoke方法了，其实它里面也就这么一个方法

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@Override
    @SuppressWarnings({"unchecked", "rawtypes"})
    public Result doInvoke(Invocation invocation, final List<Invoker<T>> invokers, LoadBalance loadbalance) throws RpcException {
        List<Invoker<T>> copyInvokers = invokers;
        checkInvokers(copyInvokers, invocation);
        String methodName = RpcUtils.getMethodName(invocation);
        // 获取重试次数，这里默认是 2 次，还有可以注意下后面的+1
        int len = getUrl().getMethodParameter(methodName, RETRIES_KEY, DEFAULT_RETRIES) + 1;
        if (len <= 0) {
            len = 1;
        }
        // retry loop.
        RpcException le = null; // last exception.
        List<Invoker<T>> invoked = new ArrayList<Invoker<T>>(copyInvokers.size()); // invoked invokers.
        Set<String> providers = new HashSet<String>(len);
        // 循环调用，失败重试
        for (int i = 0; i < len; i++) {
            //Reselect before retry to avoid a change of candidate `invokers`.
            //NOTE: if `invokers` changed, then `invoked` also lose accuracy.
            if (i > 0) {
                checkWhetherDestroyed();
                // 在进行重试前重新列举 Invoker，这样做的好处是，如果某个服务挂了，
                // 通过调用 list 可得到最新可用的 Invoker 列表
                copyInvokers = list(invocation);
                // check again
                // 对 copyinvokers 进行判空检查
                checkInvokers(copyInvokers, invocation);
            }
            // 通过负载均衡来选择 invoker
            Invoker<T> invoker = select(loadbalance, invocation, copyInvokers, invoked);
            // 将其添加到 invoker 到 invoked 列表中
            invoked.add(invoker);
            // 设置上下文
            RpcContext.getContext().setInvokers((List) invoked);
            try {
                // 正式调用
                Result result = invoker.invoke(invocation);
                if (le != null && logger.isWarnEnabled()) {
                    logger.warn("Although retry the method " + methodName
                            + " in the service " + getInterface().getName()
                            + " was successful by the provider " + invoker.getUrl().getAddress()
                            + ", but there have been failed providers " + providers
                            + " (" + providers.size() + "/" + copyInvokers.size()
                            + ") from the registry " + directory.getUrl().getAddress()
                            + " on the consumer " + NetUtils.getLocalHost()
                            + " using the dubbo version " + Version.getVersion() + ". Last error is: "
                            + le.getMessage(), le);
                }
                return result;
            } catch (RpcException e) {
                if (e.isBiz()) { // biz exception.
                    throw e;
                }
                le = e;
            } catch (Throwable e) {
                le = new RpcException(e.getMessage(), e);
            } finally {
                providers.add(invoker.getUrl().getAddress());
            }
        }
        throw new RpcException(le.getCode(), "Failed to invoke the method "
                + methodName + " in the service " + getInterface().getName()
                + ". Tried " + len + " times of the providers " + providers
                + " (" + providers.size() + "/" + copyInvokers.size()
                + ") from the registry " + directory.getUrl().getAddress()
                + " on the consumer " + NetUtils.getLocalHost() + " using the dubbo version "
                + Version.getVersion() + ". Last error is: "
                + le.getMessage(), le.getCause() != null ? le.getCause() : le);
    }

Failfast Cluster

快速失败，只发起一次调用，失败立即报错。通常用于非幂等性的写操作，比如新增记录。

Failsafe Cluster

失败安全，出现异常时，直接忽略。通常用于写入审计日志等操作。

Failback Cluster

失败自动恢复，后台记录失败请求，定时重发。通常用于消息通知操作。

Forking Cluster

并行调用多个服务器，只要一个成功即返回。通常用于实时性要求较高的读操作，但需要浪费更多服务资源。可通过 forks=”2” 来设置最大并行数。

Broadcast Cluster

广播调用所有提供者，逐个调用，任意一台报错则报错 2。通常用于通知所有提供者更新缓存或日志等本地资源信息。

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

题目介绍

Given a singly linked list, determine if it is a palindrome.
给定一个单向链表，判断是否是回文链表

例一 Example 1:

Input: 1->2
Output: false

例二 Example 2:

Input: 1->2->2->1
Output: true

挑战下自己

Follow up:
Could you do it in O(n) time and O(1) space?

简要分析

首先这是个单向链表，如果是双向的就可以一个从头到尾，一个从尾到头，显然那样就没啥意思了，然后想过要不找到中点，然后用一个栈，把前一半塞进栈里，但是这种其实也比较麻烦，比如长度是奇偶数，然后如何找到中点，这倒是可以借助于双指针，还是比较麻烦，再想一想，回文链表，就跟最开始的一样，链表只有单向的，我用个栈不就可以逆向了么，先把链表整个塞进栈里，然后在一个个 pop 出来跟链表从头开始比较，全对上了就是回文了

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/**
 * Definition for singly-linked list.
 * public class ListNode {
 *     int val;
 *     ListNode next;
 *     ListNode() {}
 *     ListNode(int val) { this.val = val; }
 *     ListNode(int val, ListNode next) { this.val = val; this.next = next; }
 * }
 */
class Solution {
    public boolean isPalindrome(ListNode head) {
        if (head == null) {
            return true;
        }
        ListNode tail = head;
        LinkedList<Integer> stack = new LinkedList<>();
        // 这里就是一个循环，将所有元素依次压入栈
        while (tail != null) {
            stack.push(tail.val);
            tail = tail.next;
        }
        // 在逐个 pop 出来，其实这个出来的顺序就等于链表从尾到头遍历，同时跟链表从头到尾遍历进行逐对对比
        while (!stack.isEmpty()) {
            if (stack.peekFirst() == head.val) {
                stack.pollFirst();
                head = head.next;
            } else {
                return false;
            }
        }
        return true;
    }
}
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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， 阅读次数： Disqus：

一说到这个主题，想到的应该是双亲委派模型，不过讲的包括但不限于这个，主要内容是参考深入理解 Java 虚拟机书中的介绍，
一个类型的生命周期包含了七个阶段，加载，验证，准备，解析，初始化，使用，卸载。

  • 加载

  1. 通过一个类的全限定名来获取定义此类的二进制字节流
  2. 将这个字节流代表的静态存储结构转化为方法区的运行时数据结构
  3. 在内存中生成了一个代表这个类的 java.lang.Class 对象，作为方法区这个类的各种数据的访问入口

  • 验证

  1. 文件格式验证
  2. 元数据验证
  3. 字节码验证
  4. 符号引用验证

  • 准备

准备阶段是正式为类中定义的变量（即静态变量，被static修饰的变量）分配内存并设置类变量初始值的阶段

  • 解析

解析阶段是 Java 虚拟机将常量池内的符号引用替换为直接引用的过程

以上验证、准备、解析 三个阶段又合称为链接阶段，链接阶段要做的是将加载到JVM中的二进制字节流的类数据信息合并到JVM的运行时状态中。

  • 初始化

类的初始化阶段是类加载过程的最后一个步骤，也是除了自定义类加载器之外将主动权交给了应用程序，其实就是执行类构造器()方法的过程，()并不是我们在 Java 代码中直接编写的方法，它是 Javac编译器的自动生成物，()方法是由编译器自动收集类中的所有类变量的复制动作和静态句块(static{}块)中的语句合并产生的，编译器收集的顺序是由语句在原文件中出现的顺序决定的，静态语句块中只能访问定义在静态语句块之前的变量，定义在它之后的变量，在前面的静态语句块可以复制，但是不能访问，同时还要保证父类的执行先于子类，然后保证多线程下的并发问题

最终，方法区会存储当前类类信息，包括类的静态变量、类初始化代码（定义静态变量时的赋值语句 和 静态初始化代码块）、实例变量定义、实例初始化代码（定义实例变量时的赋值语句实例代码块和构造方法）和实例方法，还有父类的类信息引用。

发表于 分类于 ， ， 阅读次数： Disqus：

在前司和目前公司，用的配置中心都是使用的 Apollo，经过了业界验证，比较强大的配置管理系统，特别是在0.10 后开始支持对使用 value 注解的配置值进行自动更新，今天刚好有个同学问到我，就顺便写篇文章记录下，其实也是借助于 spring 强大的 bean 生命周期管理，可以实现BeanPostProcessor接口，使用postProcessBeforeInitialization方法，来对bean 内部的属性和方法进行判断，是否有 value 注解，如果有就是将它注册到一个 map 中，可以看到这个方法com.ctrip.framework.apollo.spring.annotation.SpringValueProcessor#processField

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@Override
  protected void processField(Object bean, String beanName, Field field) {
    // register @Value on field
    Value value = field.getAnnotation(Value.class);
    if (value == null) {
      return;
    }
    Set<String> keys = placeholderHelper.extractPlaceholderKeys(value.value());

    if (keys.isEmpty()) {
      return;
    }

    for (String key : keys) {
      SpringValue springValue = new SpringValue(key, value.value(), bean, beanName, field, false);
      springValueRegistry.register(beanFactory, key, springValue);
      logger.debug("Monitoring {}", springValue);
    }
  }

然后我们看下这个springValueRegistry是啥玩意

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public class SpringValueRegistry {
  private static final long CLEAN_INTERVAL_IN_SECONDS = 5;
  private final Map<BeanFactory, Multimap<String, SpringValue>> registry = Maps.newConcurrentMap();
  private final AtomicBoolean initialized = new AtomicBoolean(false);
  private final Object LOCK = new Object();

  public void register(BeanFactory beanFactory, String key, SpringValue springValue) {
    if (!registry.containsKey(beanFactory)) {
      synchronized (LOCK) {
        if (!registry.containsKey(beanFactory)) {
          registry.put(beanFactory, LinkedListMultimap.<String, SpringValue>create());
        }
      }
    }

    registry.get(beanFactory).put(key, springValue);

    // lazy initialize
    if (initialized.compareAndSet(false, true)) {
      initialize();
    }
  }

这类其实就是个 map 来存放 springvalue，然后有com.ctrip.framework.apollo.spring.property.AutoUpdateConfigChangeListener来监听更新操作，当有变更时

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@Override
 public void onChange(ConfigChangeEvent changeEvent) {
   Set<String> keys = changeEvent.changedKeys();
   if (CollectionUtils.isEmpty(keys)) {
     return;
   }
   for (String key : keys) {
     // 1. check whether the changed key is relevant
     Collection<SpringValue> targetValues = springValueRegistry.get(beanFactory, key);
     if (targetValues == null || targetValues.isEmpty()) {
       continue;
     }

     // 2. check whether the value is really changed or not (since spring property sources have hierarchies)
     // 这里其实有一点比较绕，是因为 Apollo 里的 namespace 划分，会出现 key 相同，但是 namespace 不同的情况，所以会有个优先级存在，所以需要去校验 environment 里面的是否已经更新，如果未更新则表示不需要更新
     if (!shouldTriggerAutoUpdate(changeEvent, key)) {
       continue;
     }

     // 3. update the value
     for (SpringValue val : targetValues) {
       updateSpringValue(val);
     }
   }
 }

其实原理很简单，就是得了解知道下

发表于 分类于 ， ， ， ， ， 阅读次数： Disqus：

题目介绍

给定一个二叉树，找出其最大深度。

二叉树的深度为根节点到最远叶子节点的最长路径上的节点数。

说明: 叶子节点是指没有子节点的节点。

示例：
给定二叉树 [3,9,20,null,null,15,7]，

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  3
 / \
9  20
  /  \
 15   7

返回它的最大深度 3 。

代码

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// 主体是个递归的应用
public int maxDepth(TreeNode root) {
    // 节点的退出条件之一
    if (root == null) {
        return 0;
    }
    int left = 0;
    int right = 0;
    // 存在左子树，就递归左子树
    if (root.left != null) {
        left = maxDepth(root.left);
    }
    // 存在右子树，就递归右子树
    if (root.right != null) {
        right = maxDepth(root.right);
    }
    // 前面返回后，左右取大者
    return Math.max(left + 1, right + 1);
}

分析

其实对于树这类题，一般是以递归形式比较方便，只是要注意退出条件

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终于回忆起来了，年纪大了写这种东西真的要立马写，不然很容易想不起来，那天应该是 9 月 12 日，也就是上周六，因为我爸也去了，而且娘亲（丈母娘，LD 这么叫，我也就随了她这么叫，当然是背后，当面就叫妈）也在那，早上一到那二爹就给我爸指挥了活，要挖一条院子的出水道，自己想出来的词，因为觉得下水道是竖的，在那稍稍帮了一会会忙，然后我还是比较惯例的跟着 LD 还有娘亲去住的家里，主要是老丈人可能也不太想让我干太累的活，因为上次已经差不多把三楼都整理干净了，然后就是二楼了，二楼说实话我也帮不上什么忙，主要是衣服被子什么的，正好是有张以前小孩子睡过的那种摇篮床，看上去虽然有一些破损，整体还是不错的，所以打算拿过去，我就负责把它拆掉了，比较简单的是只要拧螺丝就行了，但是其实是用了好多好多工具才搞定的，一开始只要螺丝刀就行了，但是因为年代久了，后面的螺帽也有点锈住或者本身就会串着会一起动，所以拿来了个扳手，大部分的其实都被这两个工具给搞定了，但是后期大概还剩下四分之一的时候，有一颗完全锈住，并且螺纹跟之前那些都不一样，但是这个已经是最大的螺丝刀了，也没办法换个大的了，所以又去找来个一字的，因为十字的不是也可以用一字的拧嘛，结果可能是我买的工具箱里的一字螺丝刀太新了，口子那很锋利，直接把螺丝花纹给划掉了，大的小的都划掉，然后真的变成凹进去一个圆柱体了，然后就想能不能隔一层布去拧，然而因为的确是已经变成圆柱体了，布也不太给力，不放弃的我又去找来了个老虎钳，妄图把划掉的螺丝用老虎钳钳住，另一端用扳手拧开螺帽，但是这个螺丝跟螺帽真的是生锈的太严重了，外加上钳不太牢，完全是两边一起转，实在是没办法了，在征得同意之后，直接掰断了，火死了，一颗螺丝折腾得比我拆一张床还久，那天因为早上去的也比较晚了，然后就快吃午饭了，正好想着带一点东西过去，就把一些脸盆，泡脚桶啥的拿过去了，先是去吃了饭，还是在那家快餐店，菜的口味还是依然不错，就是人比较多，我爸旁边都是素菜，都没怎么吃远一点的荤菜，下次要早点去，把荤菜放我爸旁边😄（PS：他们家饭还是依然尴尬，需要等），吃完就开到在修的房子那把东西拿了出来，我爸已经动作很快的打了一小半的地沟了，说实话那玩意真的是很重，我之前把它从三楼拿下来，就觉得这个太重了，这次还要用起来，感觉我的手会分分钟废掉，不过一开始我还是跟着LD去了住的家里，惯例睡了午觉，那天睡得比较踏实，竟然睡了一个小时，醒了想了下，其实LD她们收拾也用不上我（没啥力气活），我还是去帮我爸他们，跟LD说了下就去了在修的老房子那，两位老爹在一起钻地，看着就很累，我连忙上去想换一会他们，因为刚好是钻到混凝土地线，特别难，力道不够就会滑开，用蛮力就是钻进去拔不出来，原理是因为本身浇的时候就是很紧实的，需要边钻边动，那家伙实在是太重了，真的是汗如雨下，基本是三个人轮流来，我是个添乱的，经常卡住，然后把地线，其实就是一条混凝土横梁，里面还有14跟18的钢筋，需要割断，这个割断也是很有技巧，钢筋本身在里面是受到挤压的，直接用切割的，到快断掉的时候就会崩一下，非常危险，还是老丈人比较有经验，要留一点点，然后直接用榔头敲断就好了，本来以为这个是最难的了，结果下面是一块非常大的青基石，而且也是石头跟石头挤一块，边上一点点打钻有点杯水车薪，后来是用那种螺旋的钻，钻四个洞，相对位置大概是个长方形，这样子把中间这个长方形钻出来就比较容易地能拿出来了，后面的也容易搞出来了，后面的其实难度不是特别大了，主要是地沟打好之后得看看高低是不是符合要求的，不能本来是往外排水的反而外面高，这个怎么看就又很有技巧了，一般在地上的只要侧着看一下就好了，考究点就用下水平尺，但是在地下的，不用水平尺，其实可以借助于地沟里正要放进去的水管，放点水进去，看水往哪流就行了，铺好水管后，就剩填埋的活了，不是太麻烦了，那天真的是累到了，打那个混凝土的时候我真的是把我整个人压上去了，不过也挺爽的，有点把平时无处发泄的蛮力发泄出去了。

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又 roll 到了一个以前做过的题，不过现在用 Java 也来写一下，是 easy 级别的，所以就简单说下

简要介绍

You are given two non-empty linked lists representing two non-negative integers. The digits are stored in reverse order, and each of their nodes contains a single digit. Add the two numbers and return the sum as a linked list.

You may assume the two numbers do not contain any leading zero, except the number 0 itself.
就是给了两个链表，用来表示两个非负的整数，在链表中倒序放着，每个节点包含一位的数字，把他们加起来以后也按照原来的链表结构输出

样例

example 1

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Input: l1 = [2,4,3], l2 = [5,6,4]
Output: [7,0,8]
Explanation: 342 + 465 = 807.

example 2

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Input: l1 = [0], l2 = [0]
Output: [0]

example 3

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Input: l1 = [9,9,9,9,9,9,9], l2 = [9,9,9,9]
Output: [8,9,9,9,0,0,0,1]

题解

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public ListNode addTwoNumbers(ListNode l1, ListNode l2) {
        ListNode root = new ListNode();
        if (l1 == null && l2 == null) {
            return root;
        }
        ListNode tail = root;
        int entered = 0;
        // 这个条件加了 entered，就是还有进位的数
        while (l1 != null || l2 != null || entered != 0) {
            int temp = entered;
            if (l1 != null) {
                temp += l1.val;
                l1 = l1.next;
            }
            if (l2 != null) {
                temp += l2.val;
                l2 = l2.next;
            }
            entered = (temp - temp % 10) / 10;
            tail.val = temp % 10;
            // 循环内部的控制是为了排除最后的空节点
            if (l1 != null || l2 != null || entered != 0) {
                tail.next = new ListNode();
                tail = tail.next;
            }
        }
//        tail = null;
        return root;
    }

这里唯二需要注意的就是两个点，一个是循环条件需要包含进位值还存在的情况，还有一个是最后一个节点，如果是空的了，就不要在 new 一个出来了，写的比较挫

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， 阅读次数： Disqus：

一说到这个主题，想到的应该是双亲委派模型，不过讲的包括但不限于这个，主要内容是参考深入理解 Java 虚拟机书中的介绍，
一个类型的生命周期包含了七个阶段，加载，验证，准备，解析，初始化，使用，卸载。

  • 加载

  1. 通过一个类的全限定名来获取定义此类的二进制字节流
  2. 将这个字节流代表的静态存储结构转化为方法区的运行时数据结构
  3. 在内存中生成了一个代表这个类的 java.lang.Class 对象，作为方法区这个类的各种数据的访问入口

  • 验证

  1. 文件格式验证
  2. 元数据验证
  3. 字节码验证
  4. 符号引用验证

  • 准备

准备阶段是正式为类中定义的变量（即静态变量，被static修饰的变量）分配内存并设置类变量初始值的阶段

  • 解析

解析阶段是 Java 虚拟机将常量池内的符号引用替换为直接引用的过程

以上验证、准备、解析 三个阶段又合称为链接阶段，链接阶段要做的是将加载到JVM中的二进制字节流的类数据信息合并到JVM的运行时状态中。

  • 初始化

类的初始化阶段是类加载过程的最后一个步骤，也是除了自定义类加载器之外将主动权交给了应用程序，其实就是执行类构造器()方法的过程，()并不是我们在 Java 代码中直接编写的方法，它是 Javac编译器的自动生成物，()方法是由编译器自动收集类中的所有类变量的复制动作和静态句块(static{}块)中的语句合并产生的，编译器收集的顺序是由语句在原文件中出现的顺序决定的，静态语句块中只能访问定义在静态语句块之前的变量，定义在它之后的变量，在前面的静态语句块可以复制，但是不能访问，同时还要保证父类的执行先于子类，然后保证多线程下的并发问题

最终，方法区会存储当前类类信息，包括类的静态变量、类初始化代码（定义静态变量时的赋值语句 和 静态初始化代码块）、实例变量定义、实例初始化代码（定义实例变量时的赋值语句实例代码块和构造方法）和实例方法，还有父类的类信息引用。

发表于 分类于 ， ， 阅读次数： Disqus：

在前司和目前公司，用的配置中心都是使用的 Apollo，经过了业界验证，比较强大的配置管理系统，特别是在0.10 后开始支持对使用 value 注解的配置值进行自动更新，今天刚好有个同学问到我，就顺便写篇文章记录下，其实也是借助于 spring 强大的 bean 生命周期管理，可以实现BeanPostProcessor接口，使用postProcessBeforeInitialization方法，来对bean 内部的属性和方法进行判断，是否有 value 注解，如果有就是将它注册到一个 map 中，可以看到这个方法com.ctrip.framework.apollo.spring.annotation.SpringValueProcessor#processField

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@Override
  protected void processField(Object bean, String beanName, Field field) {
    // register @Value on field
    Value value = field.getAnnotation(Value.class);
    if (value == null) {
      return;
    }
    Set<String> keys = placeholderHelper.extractPlaceholderKeys(value.value());

    if (keys.isEmpty()) {
      return;
    }

    for (String key : keys) {
      SpringValue springValue = new SpringValue(key, value.value(), bean, beanName, field, false);
      springValueRegistry.register(beanFactory, key, springValue);
      logger.debug("Monitoring {}", springValue);
    }
  }

然后我们看下这个springValueRegistry是啥玩意

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public class SpringValueRegistry {
  private static final long CLEAN_INTERVAL_IN_SECONDS = 5;
  private final Map<BeanFactory, Multimap<String, SpringValue>> registry = Maps.newConcurrentMap();
  private final AtomicBoolean initialized = new AtomicBoolean(false);
  private final Object LOCK = new Object();

  public void register(BeanFactory beanFactory, String key, SpringValue springValue) {
    if (!registry.containsKey(beanFactory)) {
      synchronized (LOCK) {
        if (!registry.containsKey(beanFactory)) {
          registry.put(beanFactory, LinkedListMultimap.<String, SpringValue>create());
        }
      }
    }

    registry.get(beanFactory).put(key, springValue);

    // lazy initialize
    if (initialized.compareAndSet(false, true)) {
      initialize();
    }
  }

这类其实就是个 map 来存放 springvalue，然后有com.ctrip.framework.apollo.spring.property.AutoUpdateConfigChangeListener来监听更新操作，当有变更时

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@Override
 public void onChange(ConfigChangeEvent changeEvent) {
   Set<String> keys = changeEvent.changedKeys();
   if (CollectionUtils.isEmpty(keys)) {
     return;
   }
   for (String key : keys) {
     // 1. check whether the changed key is relevant
     Collection<SpringValue> targetValues = springValueRegistry.get(beanFactory, key);
     if (targetValues == null || targetValues.isEmpty()) {
       continue;
     }

     // 2. check whether the value is really changed or not (since spring property sources have hierarchies)
     // 这里其实有一点比较绕，是因为 Apollo 里的 namespace 划分，会出现 key 相同，但是 namespace 不同的情况，所以会有个优先级存在，所以需要去校验 environment 里面的是否已经更新，如果未更新则表示不需要更新
     if (!shouldTriggerAutoUpdate(changeEvent, key)) {
       continue;
     }

     // 3. update the value
     for (SpringValue val : targetValues) {
       updateSpringValue(val);
     }
   }
 }

其实原理很简单，就是得了解知道下

发表于 分类于 ， ， ， ， ， 阅读次数： Disqus：

题目介绍

给定一个二叉树，找出其最大深度。

二叉树的深度为根节点到最远叶子节点的最长路径上的节点数。

说明: 叶子节点是指没有子节点的节点。

示例：
给定二叉树 [3,9,20,null,null,15,7]，

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  3
 / \
9  20
  /  \
 15   7

返回它的最大深度 3 。

代码

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// 主体是个递归的应用
public int maxDepth(TreeNode root) {
    // 节点的退出条件之一
    if (root == null) {
        return 0;
    }
    int left = 0;
    int right = 0;
    // 存在左子树，就递归左子树
    if (root.left != null) {
        left = maxDepth(root.left);
    }
    // 存在右子树，就递归右子树
    if (root.right != null) {
        right = maxDepth(root.right);
    }
    // 前面返回后，左右取大者
    return Math.max(left + 1, right + 1);
}

分析

其实对于树这类题，一般是以递归形式比较方便，只是要注意退出条件

发表于 分类于 ， ， ， 阅读次数： Disqus：

终于回忆起来了，年纪大了写这种东西真的要立马写，不然很容易想不起来，那天应该是 9 月 12 日，也就是上周六，因为我爸也去了，而且娘亲（丈母娘，LD 这么叫，我也就随了她这么叫，当然是背后，当面就叫妈）也在那，早上一到那二爹就给我爸指挥了活，要挖一条院子的出水道，自己想出来的词，因为觉得下水道是竖的，在那稍稍帮了一会会忙，然后我还是比较惯例的跟着 LD 还有娘亲去住的家里，主要是老丈人可能也不太想让我干太累的活，因为上次已经差不多把三楼都整理干净了，然后就是二楼了，二楼说实话我也帮不上什么忙，主要是衣服被子什么的，正好是有张以前小孩子睡过的那种摇篮床，看上去虽然有一些破损，整体还是不错的，所以打算拿过去，我就负责把它拆掉了，比较简单的是只要拧螺丝就行了，但是其实是用了好多好多工具才搞定的，一开始只要螺丝刀就行了，但是因为年代久了，后面的螺帽也有点锈住或者本身就会串着会一起动，所以拿来了个扳手，大部分的其实都被这两个工具给搞定了，但是后期大概还剩下四分之一的时候，有一颗完全锈住，并且螺纹跟之前那些都不一样，但是这个已经是最大的螺丝刀了，也没办法换个大的了，所以又去找来个一字的，因为十字的不是也可以用一字的拧嘛，结果可能是我买的工具箱里的一字螺丝刀太新了，口子那很锋利，直接把螺丝花纹给划掉了，大的小的都划掉，然后真的变成凹进去一个圆柱体了，然后就想能不能隔一层布去拧，然而因为的确是已经变成圆柱体了，布也不太给力，不放弃的我又去找来了个老虎钳，妄图把划掉的螺丝用老虎钳钳住，另一端用扳手拧开螺帽，但是这个螺丝跟螺帽真的是生锈的太严重了，外加上钳不太牢，完全是两边一起转，实在是没办法了，在征得同意之后，直接掰断了，火死了，一颗螺丝折腾得比我拆一张床还久，那天因为早上去的也比较晚了，然后就快吃午饭了，正好想着带一点东西过去，就把一些脸盆，泡脚桶啥的拿过去了，先是去吃了饭，还是在那家快餐店，菜的口味还是依然不错，就是人比较多，我爸旁边都是素菜，都没怎么吃远一点的荤菜，下次要早点去，把荤菜放我爸旁边😄（PS：他们家饭还是依然尴尬，需要等），吃完就开到在修的房子那把东西拿了出来，我爸已经动作很快的打了一小半的地沟了，说实话那玩意真的是很重，我之前把它从三楼拿下来，就觉得这个太重了，这次还要用起来，感觉我的手会分分钟废掉，不过一开始我还是跟着LD去了住的家里，惯例睡了午觉，那天睡得比较踏实，竟然睡了一个小时，醒了想了下，其实LD她们收拾也用不上我（没啥力气活），我还是去帮我爸他们，跟LD说了下就去了在修的老房子那，两位老爹在一起钻地，看着就很累，我连忙上去想换一会他们，因为刚好是钻到混凝土地线，特别难，力道不够就会滑开，用蛮力就是钻进去拔不出来，原理是因为本身浇的时候就是很紧实的，需要边钻边动，那家伙实在是太重了，真的是汗如雨下，基本是三个人轮流来，我是个添乱的，经常卡住，然后把地线，其实就是一条混凝土横梁，里面还有14跟18的钢筋，需要割断，这个割断也是很有技巧，钢筋本身在里面是受到挤压的，直接用切割的，到快断掉的时候就会崩一下，非常危险，还是老丈人比较有经验，要留一点点，然后直接用榔头敲断就好了，本来以为这个是最难的了，结果下面是一块非常大的青基石，而且也是石头跟石头挤一块，边上一点点打钻有点杯水车薪，后来是用那种螺旋的钻，钻四个洞，相对位置大概是个长方形，这样子把中间这个长方形钻出来就比较容易地能拿出来了，后面的也容易搞出来了，后面的其实难度不是特别大了，主要是地沟打好之后得看看高低是不是符合要求的，不能本来是往外排水的反而外面高，这个怎么看就又很有技巧了，一般在地上的只要侧着看一下就好了，考究点就用下水平尺，但是在地下的，不用水平尺，其实可以借助于地沟里正要放进去的水管，放点水进去，看水往哪流就行了，铺好水管后，就剩填埋的活了，不是太麻烦了，那天真的是累到了，打那个混凝土的时候我真的是把我整个人压上去了，不过也挺爽的，有点把平时无处发泄的蛮力发泄出去了。

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

又 roll 到了一个以前做过的题，不过现在用 Java 也来写一下，是 easy 级别的，所以就简单说下

简要介绍

You are given two non-empty linked lists representing two non-negative integers. The digits are stored in reverse order, and each of their nodes contains a single digit. Add the two numbers and return the sum as a linked list.

You may assume the two numbers do not contain any leading zero, except the number 0 itself.
就是给了两个链表，用来表示两个非负的整数，在链表中倒序放着，每个节点包含一位的数字，把他们加起来以后也按照原来的链表结构输出

样例

example 1

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Input: l1 = [2,4,3], l2 = [5,6,4]
Output: [7,0,8]
Explanation: 342 + 465 = 807.

example 2

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Input: l1 = [0], l2 = [0]
Output: [0]

example 3

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Input: l1 = [9,9,9,9,9,9,9], l2 = [9,9,9,9]
Output: [8,9,9,9,0,0,0,1]

题解

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public ListNode addTwoNumbers(ListNode l1, ListNode l2) {
        ListNode root = new ListNode();
        if (l1 == null && l2 == null) {
            return root;
        }
        ListNode tail = root;
        int entered = 0;
        // 这个条件加了 entered，就是还有进位的数
        while (l1 != null || l2 != null || entered != 0) {
            int temp = entered;
            if (l1 != null) {
                temp += l1.val;
                l1 = l1.next;
            }
            if (l2 != null) {
                temp += l2.val;
                l2 = l2.next;
            }
            entered = (temp - temp % 10) / 10;
            tail.val = temp % 10;
            // 循环内部的控制是为了排除最后的空节点
            if (l1 != null || l2 != null || entered != 0) {
                tail.next = new ListNode();
                tail = tail.next;
            }
        }
//        tail = null;
        return root;
    }

这里唯二需要注意的就是两个点，一个是循环条件需要包含进位值还存在的情况，还有一个是最后一个节点，如果是空的了，就不要在 new 一个出来了，写的比较挫

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， ， ， ， ， 阅读次数： Disqus：

Java 真的是任何一个中间件，比较常用的那种，都有很多内容值得深挖，比如这个缓存，慢慢有过一些感悟，比如如何提升性能，缓存无疑是一大重要手段，最底层开始 CPU 就有缓存，而且又小又贵，再往上一点内存一般作为硬盘存储在运行时的存储，一般在代码里也会用内存作为一些本地缓存，譬如数据库，像 mysql 这种也是有innodb_buffer_pool来提升查询效率，本质上理解就是用更快的存储作为相对慢存储的缓存，减少查询直接访问较慢的存储，并且这个都是相对的，比起 cpu 的缓存，那内存也是渣，但是与普通机械硬盘相比，那也是两个次元的水平。

闲扯这么多来说说 mybatis 的缓存，mybatis 一般作为一个轻量级的 orm 使用，相对应的就是比较重量级的 hibernate，不过不在这次讨论范围，上一次是主要讲了 mybatis 在解析 sql 过程中，对于两种占位符的不同替换实现策略，这次主要聊下 mybatis 的缓存，前面其实得了解下前置的东西，比如 sqlsession，先当做我们知道 sqlsession 是个什么玩意，可能或多或少的知道 mybatis 是有两级缓存，

一级缓存

第一级的缓存是在 BaseExecutor 中的 PerpetualCache，它是个最基本的缓存实现类，使用了 HashMap 实现缓存功能，代码其实没几十行

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public class PerpetualCache implements Cache {

  private final String id;

  private final Map<Object, Object> cache = new HashMap<>();

  public PerpetualCache(String id) {
    this.id = id;
  }

  @Override
  public String getId() {
    return id;
  }

  @Override
  public int getSize() {
    return cache.size();
  }

  @Override
  public void putObject(Object key, Object value) {
    cache.put(key, value);
  }

  @Override
  public Object getObject(Object key) {
    return cache.get(key);
  }

  @Override
  public Object removeObject(Object key) {
    return cache.remove(key);
  }

  @Override
  public void clear() {
    cache.clear();
  }

  @Override
  public boolean equals(Object o) {
    if (getId() == null) {
      throw new CacheException("Cache instances require an ID.");
    }
    if (this == o) {
      return true;
    }
    if (!(o instanceof Cache)) {
      return false;
    }

    Cache otherCache = (Cache) o;
    return getId().equals(otherCache.getId());
  }

  @Override
  public int hashCode() {
    if (getId() == null) {
      throw new CacheException("Cache instances require an ID.");
    }
    return getId().hashCode();
  }

}

可以看一下BaseExecutor 的构造函数

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protected BaseExecutor(Configuration configuration, Transaction transaction) {
    this.transaction = transaction;
    this.deferredLoads = new ConcurrentLinkedQueue<>();
    this.localCache = new PerpetualCache("LocalCache");
    this.localOutputParameterCache = new PerpetualCache("LocalOutputParameterCache");
    this.closed = false;
    this.configuration = configuration;
    this.wrapper = this;
  }

就是把 PerpetualCache 作为 localCache，然后怎么使用我看简单看一下，BaseExecutor 的查询首先是调用这个函数

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler) throws SQLException {
    BoundSql boundSql = ms.getBoundSql(parameter);
    CacheKey key = createCacheKey(ms, parameter, rowBounds, boundSql);
    return query(ms, parameter, rowBounds, resultHandler, key, boundSql);
  }

可以看到首先是调用了 createCacheKey 方法，这个方法呢，先不看怎么写的，如果我们自己要实现这么个缓存，首先这个缓存 key 的设计也是个问题，如果是以表名加主键作为 key，那么分页查询，或者没有主键的时候就不行，来看看 mybatis 是怎么设计的

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@Override
  public CacheKey createCacheKey(MappedStatement ms, Object parameterObject, RowBounds rowBounds, BoundSql boundSql) {
    if (closed) {
      throw new ExecutorException("Executor was closed.");
    }
    CacheKey cacheKey = new CacheKey();
    cacheKey.update(ms.getId());
    cacheKey.update(rowBounds.getOffset());
    cacheKey.update(rowBounds.getLimit());
    cacheKey.update(boundSql.getSql());
    List<ParameterMapping> parameterMappings = boundSql.getParameterMappings();
    TypeHandlerRegistry typeHandlerRegistry = ms.getConfiguration().getTypeHandlerRegistry();
    // mimic DefaultParameterHandler logic
    for (ParameterMapping parameterMapping : parameterMappings) {
      if (parameterMapping.getMode() != ParameterMode.OUT) {
        Object value;
        String propertyName = parameterMapping.getProperty();
        if (boundSql.hasAdditionalParameter(propertyName)) {
          value = boundSql.getAdditionalParameter(propertyName);
        } else if (parameterObject == null) {
          value = null;
        } else if (typeHandlerRegistry.hasTypeHandler(parameterObject.getClass())) {
          value = parameterObject;
        } else {
          MetaObject metaObject = configuration.newMetaObject(parameterObject);
          value = metaObject.getValue(propertyName);
        }
        cacheKey.update(value);
      }
    }
    if (configuration.getEnvironment() != null) {
      // issue #176
      cacheKey.update(configuration.getEnvironment().getId());
    }
    return cacheKey;
  }

首先需要 id，这个 id 是 mapper 里方法的 id， 然后是偏移量跟返回行数，再就是 sql，然后是参数，基本上是会有影响的都加进去了，在这个 update 里面

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public void update(Object object) {
    int baseHashCode = object == null ? 1 : ArrayUtil.hashCode(object);

    count++;
    checksum += baseHashCode;
    baseHashCode *= count;

    hashcode = multiplier * hashcode + baseHashCode;

    updateList.add(object);
  }

其实是一个 hash 转换，具体不纠结，就是提高特异性，然后回来就是继续调用 query

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler, CacheKey key, BoundSql boundSql) throws SQLException {
    ErrorContext.instance().resource(ms.getResource()).activity("executing a query").object(ms.getId());
    if (closed) {
      throw new ExecutorException("Executor was closed.");
    }
    if (queryStack == 0 && ms.isFlushCacheRequired()) {
      clearLocalCache();
    }
    List<E> list;
    try {
      queryStack++;
      list = resultHandler == null ? (List<E>) localCache.getObject(key) : null;
      if (list != null) {
        handleLocallyCachedOutputParameters(ms, key, parameter, boundSql);
      } else {
        list = queryFromDatabase(ms, parameter, rowBounds, resultHandler, key, boundSql);
      }
    } finally {
      queryStack--;
    }
    if (queryStack == 0) {
      for (DeferredLoad deferredLoad : deferredLoads) {
        deferredLoad.load();
      }
      // issue #601
      deferredLoads.clear();
      if (configuration.getLocalCacheScope() == LocalCacheScope.STATEMENT) {
        // issue #482
        clearLocalCache();
      }
    }
    return list;
  }

可以看到是先从 localCache 里取，取不到再 queryFromDatabase，其实比较简单，这是一级缓存，考虑到 sqlsession 跟 BaseExecutor 的关系，其实是随着 sqlsession 来保证这个缓存不会出现脏数据幻读的情况，当然事务相关的后面可能再单独聊。

二级缓存

其实这个一级二级顺序有点反过来，其实查询的是先走的二级缓存，当然二级的需要配置开启，默认不开，
需要通过

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<setting name="cacheEnabled" value="true"/>

来开启，然后我们的查询就会走到

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public class CachingExecutor implements Executor {

  private final Executor delegate;
  private final TransactionalCacheManager tcm = new TransactionalCacheManager();

这个 Executor 中，这里我放了类里面的元素，发现没有一个 Cache 类，这就是一个特点了，往下看查询过程

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameterObject, RowBounds rowBounds, ResultHandler resultHandler) throws SQLException {
    BoundSql boundSql = ms.getBoundSql(parameterObject);
    CacheKey key = createCacheKey(ms, parameterObject, rowBounds, boundSql);
    return query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
  }

  @Override
  public <E> List<E> query(MappedStatement ms, Object parameterObject, RowBounds rowBounds, ResultHandler resultHandler, CacheKey key, BoundSql boundSql)
      throws SQLException {
    Cache cache = ms.getCache();
    if (cache != null) {
      flushCacheIfRequired(ms);
      if (ms.isUseCache() && resultHandler == null) {
        ensureNoOutParams(ms, boundSql);
        @SuppressWarnings("unchecked")
        List<E> list = (List<E>) tcm.getObject(cache, key);
        if (list == null) {
          list = delegate.query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
          tcm.putObject(cache, key, list); // issue #578 and #116
        }
        return list;
      }
    }
    return delegate.query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
  }

看到没，其实缓存是从 tcm 这个成员变量里取，而这个是什么呢，事务性缓存(直译下)，因为这个其实是用 MappedStatement 里的 Cache 作为key 从 tcm 的 map 取出来的

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public class TransactionalCacheManager {

  private final Map<Cache, TransactionalCache> transactionalCaches = new HashMap<>();

MappedStatement是被全局使用的，所以其实二级缓存是跟着 mapper 的 namespace 走的，可以被多个 CachingExecutor 获取到，就会出现线程安全问题，线程安全问题可以用SynchronizedCache来解决，就是加锁，但是对于事务中的脏读，使用了TransactionalCache来解决这个问题，

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public class TransactionalCache implements Cache {

  private static final Log log = LogFactory.getLog(TransactionalCache.class);

  private final Cache delegate;
  private boolean clearOnCommit;
  private final Map<Object, Object> entriesToAddOnCommit;
  private final Set<Object> entriesMissedInCache;

在事务还没提交的时候，会把中间状态的数据放在 entriesToAddOnCommit 中，只有在提交后会放进共享缓存中，

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public void commit() {
    if (clearOnCommit) {
      delegate.clear();
    }
    flushPendingEntries();
    reset();
  }

发表于 分类于 ， ， ， 阅读次数： Disqus：

小工记四

第四周去的时候让我们去了现在在住的房子里，去三楼整理东西了，蛮多的东西需要收拾整理，有些需要丢一下，以前往往是把不太要用的东西就放三楼了，但是后面就不会再去收拾整理，LD 跟丈母娘负责收拾，我不太知道哪些还要的，哪些不要了，而且本来也不擅长这种收拾🤦‍♂️，然后就变成她们收拾出来废纸箱，我负责拆掉，压平，这时候终于觉得体重还算是有点作用，总体来说这个事情我其实也不擅长，不擅长的主要是捆起来，可能我总是小题大做，因为纸箱大小不一，如果不做一下分类，然后把大的折小一些的话，直接绑起来，容易拎起来就散掉了，而且一些鞋盒子这种小件的纸盒会比较薄，冰箱这种大件的比较厚，厚的比较不容易变形，需要大力踩踏，而且扎的时候需要用体重压住捆实了之后那样子才是真的捆实的，不然待会又是松松垮垮容易滑出来散架，因为压住了捆好后，下来了之后箱子就会弹开了把绳子崩紧实，感觉又是掌握到生活小技巧了😃，我这里其实比较单调无聊，然后 LD 那可以说非常厉害了，一共理出来 11 把旧电扇，还有好多没用过全新的不锈钢脸盆大大小小的，感觉比店里在卖的还多，还有是有比较多小时候的东西，特别多小时候的衣服，其实这种对我来说最难了，可能需要读一下断舍离，蛮多东西都舍不得扔，但是其实是没啥用了，然后还占地方，这天应该算是比较轻松的一天了，上午主要是把收拾出来要的和不要的搬下楼，然后下午要去把纸板给卖掉。中午还是去小快餐店吃的，在住的家里理东西还有个好处就是中午吃完饭可以小憩一下，因为我个人是非常依赖午休的，不然下午完全没精神，而且心态也会比较烦躁，一方面是客观的的确比较疲惫，另一方面应该主观心理作用也有点影响，就像上班的时候也是觉得不午睡就会很难受，心理作用也有一点，不过总之能睡还是睡一会，真的没办法就心态好点，吃完午饭之后我们就推着小平板车去收废品的地方卖掉了上午我收拾捆起来的纸板，好像卖了一百多，都是直接过地磅了，不用一捆一捆地称，不过有个小插曲，那里另外一个大爷在倒他的三轮车的时候撞了我一下，还好车速慢，屁股上肉垫后，接下来就比较麻烦了，是LD 她们两姐妹从小到大的书，也要去卖掉，小平板车就载不下了，而且着实也不太好推，轮子不太平，导致推着很累，书有好多箱，本来是想去亲戚家借电动三轮车，因为不会开摩托的那种，摩托的那种 LD 邻居家就有，可是到了那发现那个也是很大，而且刹车是用脚踩的那种，俺爹不太放心，就说第二天周日他有空会帮忙去载了卖掉的，然后比较搞笑的来了，丈母娘看错了时间，以为已经快五点了，就让我们顺便在车里带点东西去在修的房子，放到那边三楼去，到了那还跟老丈人说已经这么迟了要赶紧去菜场买菜了，结果我们回来以后才发现看错了一个小时🤦‍♂️。
前面可以没提，前三周去的我们一般就周六去一天，然后周日因为要早点回杭州，而且可能想让我们周日能休息下，但是这周就因为周日的时候我爸要去帮忙载书，然后 LD 姐姐也会过来收拾东西，我们周日就又去整理收拾了，周日由于俺爹去的很早，我过去的时候书已经木有了，主要是去收拾东西了，把一些有用没用的继续整理，基本上三楼的就处理完毕了，舒了一大口气，毕竟让丈母娘一个人收拾实在是太累了，但是要扔掉的衣服比较棘手，附近知道的青蛙回收桶被推倒了，其他地方也不知道哪里有，我们就先载了一些东西去在修的房子那，然后去找青蛙桶，结果一个小区可以进，但是已经满了，另一个不让进，后来只能让 LD 姐姐带去她们小区扔了，塞了满满一车。因为要赶回杭州的车就没有等我爸一起回来，他还在那帮忙搞卫生间的墙缝。
虽然这两天不太热，活也不算很吃力，不过我这个体重和易出汗的体质，还是让短袖不知道湿透了多少次，灌了好多水和冰红茶（下午能提提神），回来周一早上称体重也比较喜人，差一点就达到阶段目标，可以想想去哪里吃之前想好的烤肉跟火锅了（估计吃完立马回到解放前）。

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

又做了个题，看记录是以前用 C++写过的，现在捋一捋思路，用 Java 再写了一下，思路还是比较清晰的，但是边界细节处理得比较差

简要介绍

Given a string s, find the length of the longest substring without repeating characters.

样例

Example 1:

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Input: s = "abcabcbb"
Output: 3
Explanation: The answer is "abc", with the length of 3.

Example 2:

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Input: s = "bbbbb"
Output: 1
Explanation: The answer is "b", with the length of 1.

Example 3:

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Input: s = "pwwkew"
Output: 3
Explanation: The answer is "wke", with the length of 3.
Notice that the answer must be a substring, "pwke" is a subsequence and not a substring.

Example 4:

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Input: s = ""
Output: 0

就是一个最长不重复的字符串长度，因为也是中等难度的题，不太需要特别复杂的思考，最基本的就是O(N*N)两重循环，不过显然不太好，万一超时间，还有一种就是线性复杂度的了，这个就是需要搞定一个思路，比如字符串时 abcdefgaqwrty，比如遍历到第二个a的时候其实不用再从头去遍历了，只要把前面那个a给排除掉，继续往下算就好了

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class Solution {
    Map<String, Integer> counter = new HashMap<>();
    public int lengthOfLongestSubstring(String s) {
        int length = s.length();
        // 当前的长度
        int subStringLength = 0;
        // 最长的长度
        int maxSubStringLength = 0;
        // 考虑到重复的位置已经被跳过的情况，即已经在当前长度的字符串范围之前的重复字符不需要回溯
        int lastDuplicatePos = -1;
        for (int i = 0; i < length; i++) {
            // 使用 map 存储字符和上一次出现的位置，如果存在并且大于上一次重复位置
            if (counter.get(String.valueOf(s.charAt(i))) != null && counter.get(String.valueOf(s.charAt(i))) > lastDuplicatePos) {
                // 记录重复位置
                lastDuplicatePos = counter.get(String.valueOf(s.charAt(i)));
                // 重置不重复子串的长度，减去重复起点
                subStringLength = i - counter.get(String.valueOf(s.charAt(i))) - 1;
                // 替换当前位置
                counter.replace(String.valueOf(s.charAt(i)), i);
            } else {
                // 如果不存在就直接 put
                counter.put(String.valueOf(s.charAt(i)), i);
            }
            // 长度累加
            subStringLength++;
            if (subStringLength > maxSubStringLength) {
                // 简单替换
                maxSubStringLength = subStringLength;
            }
        }
        return maxSubStringLength;
    }
}

注释应该写的比较清楚了。

发表于 分类于 ， ， ， 阅读次数： Disqus：

小工记三

前面这两周周末也都去老丈人家帮忙了，上上周周六先是去了那个在装修的旧房子那，把三楼收拾了下，因为要搬进来住，来不及等二楼装修好，就要把三楼里的东西都整理干净，这个活感觉是比较 easy，原来是就准备把三楼当放东西仓储的地方了，我们乡下大部分三层楼都是这么用的，这次也是没办法，之前搬进来的木头什么的都搬出去，主要是这上面灰尘太多，后面清理鼻孔的时候都是黑色的了，把东西都搬出去以后主要是地还是很脏，就扫了地拖了地，因为是水泥地，灰尘又太多了，拖起来都是会灰尘扬起来，整个脱完了的确干净很多，然而这会就出了个大乌龙，我们清理的是三楼的西边一间，结果老丈人上来说要住东边那间的🤦‍♂️，不过其实西边的也得清理，因为还是要放被子什么的，不算是白费功夫，接着清理东边那间，之前这个房子做过群租房，里面有个高低铺的床，当时觉得可以用在放被子什么的就没扔，只是拆掉了放旁边，我们就把它擦干净了又装好，发现螺丝🔩少了几个，亘古不变的真理，拆了以后装要不就多几个要不就少几个，不是很牢靠，不过用来放放被子省得放地上总还是可以的，对了前面还做了个事情就是铺地毯，其实也不是地毯，就是类似于墙布雨篷布那种，别人不用了送给我们的，三楼水泥地也不会铺瓷砖地板了就放一下，干净好看点，不过大小不合适要裁一下，那把剪刀是真的太难用了，我手都要抽筋了，它就是刀口只有一小个点是能剪下来的，其他都是钝的，后来还是用刀片直接裁，铺好以后，真的感觉也不太一样了，焕然一新的感觉
差不多中午了就去吃饭了，之前两次是去了一家小饭店，还是还比较干净，但是店里菜不好吃，还死贵，这次去了一家小快餐店，口味好，便宜，味道是真的不错，带鱼跟黄鱼都好吃，一点都不腥，我对这类比较腥的鱼真的是很挑剔的，基本上除了家里做的很少吃外面的，那天抱着试试的态度吃了下，真的还不错，后来丈母娘说好像这家老板是给别人结婚喜事酒席当厨师的，怪不得做的好吃，其实本来是有一点小抗拒，怕不干净什么的，后来发现菜很好吃，而且可能是老丈人跟干活的师傅去吃的比较多，老板很客气，我们吃完饭，还给我们买了葡萄吃，不过这家店有一个槽点，就是饭比较不好吃，有时候会夹生，不过后面聊起来其实是这种小菜馆饭点的通病，烧的太早太多容易多出来浪费，烧的迟了不够吃，而且大的电饭锅比较不容易烧好。
下午前面还是在处理三楼的，窗户上各种钉子，实在是太多了，我后面在走廊上排了一排🤦‍♂️，有些是直接断了，有些是就撬了出来，感觉我在杭州租房也没有这样子各种钉钉子，挂下衣服什么的也不用这么多吧，比较不能理解，搞得到处都是钉子。那天我爸也去帮忙了，主要是在卫生间里做白缝，其实也是个技术活，印象中好像我小时候自己家里也做过这个事情，但是比较模糊了，后面我们三楼搞完了就去帮我爸了，前面是我老婆二爹在那先刷上白缝，这里叫白缝，有些考究的也叫美缝，就是瓷砖铺完之后的缝，如果不去弄的话，里面水泥的颜色就露出来了，而且容易渗水，所以就要用白水泥加胶水搅拌之后糊在缝上，但是也不是直接糊，先要把缝抠一抠，因为铺瓷砖的还不会仔细到每个缝里的水泥都是一样满，而且也需要一些空间糊上去，不然就太表面的一层很容易被水直接冲掉了，然后这次其实也不是用的白水泥，而是直接现成买来就已经配好的用来填缝的，兑水搅拌均匀就好了，后面就主要是我跟我爸在搞，那个时候真的觉得我实在是太胖了，蹲下去真的没一会就受不了了，膝盖什么的太难受了，后面我跪着刷，然后膝盖又疼，也是比较不容易，不过我爸动作很快，我中间跪累了休息一会，我爸就能搞一大片，后面其实我也没做多少（谦虚一下），总体来讲这次不是很累，就是蹲着跪着腿有点受不了，是应该好好减肥了。

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： Disqus：

这个问题也是面试中常被问到的，就抽空来了解下这个，跳过一大段前面初始化的逻辑，
对于一条select * from t1 where id = #{id}这样的 sql，在初始化扫描 mapper 的xml文件的时候会根据是否是 dynamic 来判断生成 DynamicSqlSource 还是 RawSqlSource，这里它是一条 RawSqlSource，
在这里做了替换，将#{}替换成了?

前面说的是否 dynamic 就是在这里进行判断

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// org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseScriptNode
public SqlSource parseScriptNode() {
    MixedSqlNode rootSqlNode = parseDynamicTags(context);
    SqlSource sqlSource;
    if (isDynamic) {
      sqlSource = new DynamicSqlSource(configuration, rootSqlNode);
    } else {
      sqlSource = new RawSqlSource(configuration, rootSqlNode, parameterType);
    }
    return sqlSource;
  }
// org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseDynamicTags
protected MixedSqlNode parseDynamicTags(XNode node) {
    List<SqlNode> contents = new ArrayList<>();
    NodeList children = node.getNode().getChildNodes();
    for (int i = 0; i < children.getLength(); i++) {
      XNode child = node.newXNode(children.item(i));
      if (child.getNode().getNodeType() == Node.CDATA_SECTION_NODE || child.getNode().getNodeType() == Node.TEXT_NODE) {
        String data = child.getStringBody("");
        TextSqlNode textSqlNode = new TextSqlNode(data);
        if (textSqlNode.isDynamic()) {
          contents.add(textSqlNode);
          isDynamic = true;
        } else {
          contents.add(new StaticTextSqlNode(data));
        }
      } else if (child.getNode().getNodeType() == Node.ELEMENT_NODE) { // issue #628
        String nodeName = child.getNode().getNodeName();
        NodeHandler handler = nodeHandlerMap.get(nodeName);
        if (handler == null) {
          throw new BuilderException("Unknown element <" + nodeName + "> in SQL statement.");
        }
        handler.handleNode(child, contents);
        isDynamic = true;
      }
    }
    return new MixedSqlNode(contents);
  }
// org.apache.ibatis.scripting.xmltags.TextSqlNode#isDynamic
  public boolean isDynamic() {
    DynamicCheckerTokenParser checker = new DynamicCheckerTokenParser();
    GenericTokenParser parser = createParser(checker);
    parser.parse(text);
    return checker.isDynamic();
  }
    private GenericTokenParser createParser(TokenHandler handler) {
    return new GenericTokenParser("${", "}", handler);
  }

可以看到其中一个条件就是是否有${}这种占位符，假如说上面的 sql 换成 ${}，那么可以看到它会在这里创建一个 dynamicSqlSource,

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// org.apache.ibatis.scripting.xmltags.DynamicSqlSource
public class DynamicSqlSource implements SqlSource {

  private final Configuration configuration;
  private final SqlNode rootSqlNode;

  public DynamicSqlSource(Configuration configuration, SqlNode rootSqlNode) {
    this.configuration = configuration;
    this.rootSqlNode = rootSqlNode;
  }

  @Override
  public BoundSql getBoundSql(Object parameterObject) {
    DynamicContext context = new DynamicContext(configuration, parameterObject);
    rootSqlNode.apply(context);
    SqlSourceBuilder sqlSourceParser = new SqlSourceBuilder(configuration);
    Class<?> parameterType = parameterObject == null ? Object.class : parameterObject.getClass();
    SqlSource sqlSource = sqlSourceParser.parse(context.getSql(), parameterType, context.getBindings());
    BoundSql boundSql = sqlSource.getBoundSql(parameterObject);
    context.getBindings().forEach(boundSql::setAdditionalParameter);
    return boundSql;
  }

}

这里眼尖的同学可能就可以看出来了，RawSqlSource 在初始化的时候已经经过了 parse，把#{}替换成了?占位符，但是 DynamicSqlSource 并没有
再看这个图，我们发现在这的时候还没有进行替换
然后往里跟
好像是这里了

这里 rootSqlNode.apply 其实是一个对原来 sql 的解析结果的一个循环调用，不同类型的标签会构成不同的 node，像这里就是一个 textSqlNode

可以发现到这我们的 sql 已经被替换了，而且是直接作为 string 类型替换的，所以可以明白了这个问题所在，就是注入，不过细心的同学发现其实这里是有个

理论上还是可以做过滤的，不过好像现在没用起来。
我们前面可以发现对于#{}是在启动扫描 mapper的 xml 文件就替换成了 ?,然后是在什么时候变成实际的值的呢

发现到这的时候还是没有替换，其实说白了也就是 prepareStatement 那一套，

在这里进行替换，会拿到 org.apache.ibatis.mapping.ParameterMapping，然后进行替换，因为会带着类型信息，所以不用担心注入咯

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 分类于 ， ， ， ， ， 阅读次数： Disqus：

Java 真的是任何一个中间件，比较常用的那种，都有很多内容值得深挖，比如这个缓存，慢慢有过一些感悟，比如如何提升性能，缓存无疑是一大重要手段，最底层开始 CPU 就有缓存，而且又小又贵，再往上一点内存一般作为硬盘存储在运行时的存储，一般在代码里也会用内存作为一些本地缓存，譬如数据库，像 mysql 这种也是有innodb_buffer_pool来提升查询效率，本质上理解就是用更快的存储作为相对慢存储的缓存，减少查询直接访问较慢的存储，并且这个都是相对的，比起 cpu 的缓存，那内存也是渣，但是与普通机械硬盘相比，那也是两个次元的水平。

闲扯这么多来说说 mybatis 的缓存，mybatis 一般作为一个轻量级的 orm 使用，相对应的就是比较重量级的 hibernate，不过不在这次讨论范围，上一次是主要讲了 mybatis 在解析 sql 过程中，对于两种占位符的不同替换实现策略，这次主要聊下 mybatis 的缓存，前面其实得了解下前置的东西，比如 sqlsession，先当做我们知道 sqlsession 是个什么玩意，可能或多或少的知道 mybatis 是有两级缓存，

一级缓存

第一级的缓存是在 BaseExecutor 中的 PerpetualCache，它是个最基本的缓存实现类，使用了 HashMap 实现缓存功能，代码其实没几十行

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public class PerpetualCache implements Cache {

  private final String id;

  private final Map<Object, Object> cache = new HashMap<>();

  public PerpetualCache(String id) {
    this.id = id;
  }

  @Override
  public String getId() {
    return id;
  }

  @Override
  public int getSize() {
    return cache.size();
  }

  @Override
  public void putObject(Object key, Object value) {
    cache.put(key, value);
  }

  @Override
  public Object getObject(Object key) {
    return cache.get(key);
  }

  @Override
  public Object removeObject(Object key) {
    return cache.remove(key);
  }

  @Override
  public void clear() {
    cache.clear();
  }

  @Override
  public boolean equals(Object o) {
    if (getId() == null) {
      throw new CacheException("Cache instances require an ID.");
    }
    if (this == o) {
      return true;
    }
    if (!(o instanceof Cache)) {
      return false;
    }

    Cache otherCache = (Cache) o;
    return getId().equals(otherCache.getId());
  }

  @Override
  public int hashCode() {
    if (getId() == null) {
      throw new CacheException("Cache instances require an ID.");
    }
    return getId().hashCode();
  }

}

可以看一下BaseExecutor 的构造函数

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protected BaseExecutor(Configuration configuration, Transaction transaction) {
    this.transaction = transaction;
    this.deferredLoads = new ConcurrentLinkedQueue<>();
    this.localCache = new PerpetualCache("LocalCache");
    this.localOutputParameterCache = new PerpetualCache("LocalOutputParameterCache");
    this.closed = false;
    this.configuration = configuration;
    this.wrapper = this;
  }

就是把 PerpetualCache 作为 localCache，然后怎么使用我看简单看一下，BaseExecutor 的查询首先是调用这个函数

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler) throws SQLException {
    BoundSql boundSql = ms.getBoundSql(parameter);
    CacheKey key = createCacheKey(ms, parameter, rowBounds, boundSql);
    return query(ms, parameter, rowBounds, resultHandler, key, boundSql);
  }

可以看到首先是调用了 createCacheKey 方法，这个方法呢，先不看怎么写的，如果我们自己要实现这么个缓存，首先这个缓存 key 的设计也是个问题，如果是以表名加主键作为 key，那么分页查询，或者没有主键的时候就不行，来看看 mybatis 是怎么设计的

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@Override
  public CacheKey createCacheKey(MappedStatement ms, Object parameterObject, RowBounds rowBounds, BoundSql boundSql) {
    if (closed) {
      throw new ExecutorException("Executor was closed.");
    }
    CacheKey cacheKey = new CacheKey();
    cacheKey.update(ms.getId());
    cacheKey.update(rowBounds.getOffset());
    cacheKey.update(rowBounds.getLimit());
    cacheKey.update(boundSql.getSql());
    List<ParameterMapping> parameterMappings = boundSql.getParameterMappings();
    TypeHandlerRegistry typeHandlerRegistry = ms.getConfiguration().getTypeHandlerRegistry();
    // mimic DefaultParameterHandler logic
    for (ParameterMapping parameterMapping : parameterMappings) {
      if (parameterMapping.getMode() != ParameterMode.OUT) {
        Object value;
        String propertyName = parameterMapping.getProperty();
        if (boundSql.hasAdditionalParameter(propertyName)) {
          value = boundSql.getAdditionalParameter(propertyName);
        } else if (parameterObject == null) {
          value = null;
        } else if (typeHandlerRegistry.hasTypeHandler(parameterObject.getClass())) {
          value = parameterObject;
        } else {
          MetaObject metaObject = configuration.newMetaObject(parameterObject);
          value = metaObject.getValue(propertyName);
        }
        cacheKey.update(value);
      }
    }
    if (configuration.getEnvironment() != null) {
      // issue #176
      cacheKey.update(configuration.getEnvironment().getId());
    }
    return cacheKey;
  }

首先需要 id，这个 id 是 mapper 里方法的 id， 然后是偏移量跟返回行数，再就是 sql，然后是参数，基本上是会有影响的都加进去了，在这个 update 里面

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public void update(Object object) {
    int baseHashCode = object == null ? 1 : ArrayUtil.hashCode(object);

    count++;
    checksum += baseHashCode;
    baseHashCode *= count;

    hashcode = multiplier * hashcode + baseHashCode;

    updateList.add(object);
  }

其实是一个 hash 转换，具体不纠结，就是提高特异性，然后回来就是继续调用 query

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameter, RowBounds rowBounds, ResultHandler resultHandler, CacheKey key, BoundSql boundSql) throws SQLException {
    ErrorContext.instance().resource(ms.getResource()).activity("executing a query").object(ms.getId());
    if (closed) {
      throw new ExecutorException("Executor was closed.");
    }
    if (queryStack == 0 && ms.isFlushCacheRequired()) {
      clearLocalCache();
    }
    List<E> list;
    try {
      queryStack++;
      list = resultHandler == null ? (List<E>) localCache.getObject(key) : null;
      if (list != null) {
        handleLocallyCachedOutputParameters(ms, key, parameter, boundSql);
      } else {
        list = queryFromDatabase(ms, parameter, rowBounds, resultHandler, key, boundSql);
      }
    } finally {
      queryStack--;
    }
    if (queryStack == 0) {
      for (DeferredLoad deferredLoad : deferredLoads) {
        deferredLoad.load();
      }
      // issue #601
      deferredLoads.clear();
      if (configuration.getLocalCacheScope() == LocalCacheScope.STATEMENT) {
        // issue #482
        clearLocalCache();
      }
    }
    return list;
  }

可以看到是先从 localCache 里取，取不到再 queryFromDatabase，其实比较简单，这是一级缓存，考虑到 sqlsession 跟 BaseExecutor 的关系，其实是随着 sqlsession 来保证这个缓存不会出现脏数据幻读的情况，当然事务相关的后面可能再单独聊。

二级缓存

其实这个一级二级顺序有点反过来，其实查询的是先走的二级缓存，当然二级的需要配置开启，默认不开，
需要通过

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<setting name="cacheEnabled" value="true"/>

来开启，然后我们的查询就会走到

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public class CachingExecutor implements Executor {

  private final Executor delegate;
  private final TransactionalCacheManager tcm = new TransactionalCacheManager();

这个 Executor 中，这里我放了类里面的元素，发现没有一个 Cache 类，这就是一个特点了，往下看查询过程

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@Override
  public <E> List<E> query(MappedStatement ms, Object parameterObject, RowBounds rowBounds, ResultHandler resultHandler) throws SQLException {
    BoundSql boundSql = ms.getBoundSql(parameterObject);
    CacheKey key = createCacheKey(ms, parameterObject, rowBounds, boundSql);
    return query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
  }

  @Override
  public <E> List<E> query(MappedStatement ms, Object parameterObject, RowBounds rowBounds, ResultHandler resultHandler, CacheKey key, BoundSql boundSql)
      throws SQLException {
    Cache cache = ms.getCache();
    if (cache != null) {
      flushCacheIfRequired(ms);
      if (ms.isUseCache() && resultHandler == null) {
        ensureNoOutParams(ms, boundSql);
        @SuppressWarnings("unchecked")
        List<E> list = (List<E>) tcm.getObject(cache, key);
        if (list == null) {
          list = delegate.query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
          tcm.putObject(cache, key, list); // issue #578 and #116
        }
        return list;
      }
    }
    return delegate.query(ms, parameterObject, rowBounds, resultHandler, key, boundSql);
  }

看到没，其实缓存是从 tcm 这个成员变量里取，而这个是什么呢，事务性缓存(直译下)，因为这个其实是用 MappedStatement 里的 Cache 作为key 从 tcm 的 map 取出来的

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public class TransactionalCacheManager {

  private final Map<Cache, TransactionalCache> transactionalCaches = new HashMap<>();

MappedStatement是被全局使用的，所以其实二级缓存是跟着 mapper 的 namespace 走的，可以被多个 CachingExecutor 获取到，就会出现线程安全问题，线程安全问题可以用SynchronizedCache来解决，就是加锁，但是对于事务中的脏读，使用了TransactionalCache来解决这个问题，

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public class TransactionalCache implements Cache {

  private static final Log log = LogFactory.getLog(TransactionalCache.class);

  private final Cache delegate;
  private boolean clearOnCommit;
  private final Map<Object, Object> entriesToAddOnCommit;
  private final Set<Object> entriesMissedInCache;

在事务还没提交的时候，会把中间状态的数据放在 entriesToAddOnCommit 中，只有在提交后会放进共享缓存中，

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public void commit() {
    if (clearOnCommit) {
      delegate.clear();
    }
    flushPendingEntries();
    reset();
  }

发表于 分类于 ， ， ， 阅读次数： Disqus：

小工记四

第四周去的时候让我们去了现在在住的房子里，去三楼整理东西了，蛮多的东西需要收拾整理，有些需要丢一下，以前往往是把不太要用的东西就放三楼了，但是后面就不会再去收拾整理，LD 跟丈母娘负责收拾，我不太知道哪些还要的，哪些不要了，而且本来也不擅长这种收拾🤦‍♂️，然后就变成她们收拾出来废纸箱，我负责拆掉，压平，这时候终于觉得体重还算是有点作用，总体来说这个事情我其实也不擅长，不擅长的主要是捆起来，可能我总是小题大做，因为纸箱大小不一，如果不做一下分类，然后把大的折小一些的话，直接绑起来，容易拎起来就散掉了，而且一些鞋盒子这种小件的纸盒会比较薄，冰箱这种大件的比较厚，厚的比较不容易变形，需要大力踩踏，而且扎的时候需要用体重压住捆实了之后那样子才是真的捆实的，不然待会又是松松垮垮容易滑出来散架，因为压住了捆好后，下来了之后箱子就会弹开了把绳子崩紧实，感觉又是掌握到生活小技巧了😃，我这里其实比较单调无聊，然后 LD 那可以说非常厉害了，一共理出来 11 把旧电扇，还有好多没用过全新的不锈钢脸盆大大小小的，感觉比店里在卖的还多，还有是有比较多小时候的东西，特别多小时候的衣服，其实这种对我来说最难了，可能需要读一下断舍离，蛮多东西都舍不得扔，但是其实是没啥用了，然后还占地方，这天应该算是比较轻松的一天了，上午主要是把收拾出来要的和不要的搬下楼，然后下午要去把纸板给卖掉。中午还是去小快餐店吃的，在住的家里理东西还有个好处就是中午吃完饭可以小憩一下，因为我个人是非常依赖午休的，不然下午完全没精神，而且心态也会比较烦躁，一方面是客观的的确比较疲惫，另一方面应该主观心理作用也有点影响，就像上班的时候也是觉得不午睡就会很难受，心理作用也有一点，不过总之能睡还是睡一会，真的没办法就心态好点，吃完午饭之后我们就推着小平板车去收废品的地方卖掉了上午我收拾捆起来的纸板，好像卖了一百多，都是直接过地磅了，不用一捆一捆地称，不过有个小插曲，那里另外一个大爷在倒他的三轮车的时候撞了我一下，还好车速慢，屁股上肉垫后，接下来就比较麻烦了，是LD 她们两姐妹从小到大的书，也要去卖掉，小平板车就载不下了，而且着实也不太好推，轮子不太平，导致推着很累，书有好多箱，本来是想去亲戚家借电动三轮车，因为不会开摩托的那种，摩托的那种 LD 邻居家就有，可是到了那发现那个也是很大，而且刹车是用脚踩的那种，俺爹不太放心，就说第二天周日他有空会帮忙去载了卖掉的，然后比较搞笑的来了，丈母娘看错了时间，以为已经快五点了，就让我们顺便在车里带点东西去在修的房子，放到那边三楼去，到了那还跟老丈人说已经这么迟了要赶紧去菜场买菜了，结果我们回来以后才发现看错了一个小时🤦‍♂️。
前面可以没提，前三周去的我们一般就周六去一天，然后周日因为要早点回杭州，而且可能想让我们周日能休息下，但是这周就因为周日的时候我爸要去帮忙载书，然后 LD 姐姐也会过来收拾东西，我们周日就又去整理收拾了，周日由于俺爹去的很早，我过去的时候书已经木有了，主要是去收拾东西了，把一些有用没用的继续整理，基本上三楼的就处理完毕了，舒了一大口气，毕竟让丈母娘一个人收拾实在是太累了，但是要扔掉的衣服比较棘手，附近知道的青蛙回收桶被推倒了，其他地方也不知道哪里有，我们就先载了一些东西去在修的房子那，然后去找青蛙桶，结果一个小区可以进，但是已经满了，另一个不让进，后来只能让 LD 姐姐带去她们小区扔了，塞了满满一车。因为要赶回杭州的车就没有等我爸一起回来，他还在那帮忙搞卫生间的墙缝。
虽然这两天不太热，活也不算很吃力，不过我这个体重和易出汗的体质，还是让短袖不知道湿透了多少次，灌了好多水和冰红茶（下午能提提神），回来周一早上称体重也比较喜人，差一点就达到阶段目标，可以想想去哪里吃之前想好的烤肉跟火锅了（估计吃完立马回到解放前）。

发表于 分类于 ， ， ， ， 阅读次数： Disqus：

又做了个题，看记录是以前用 C++写过的，现在捋一捋思路，用 Java 再写了一下，思路还是比较清晰的，但是边界细节处理得比较差

简要介绍

Given a string s, find the length of the longest substring without repeating characters.

样例

Example 1:

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Input: s = "abcabcbb"
Output: 3
Explanation: The answer is "abc", with the length of 3.

Example 2:

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Input: s = "bbbbb"
Output: 1
Explanation: The answer is "b", with the length of 1.

Example 3:

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Input: s = "pwwkew"
Output: 3
Explanation: The answer is "wke", with the length of 3.
Notice that the answer must be a substring, "pwke" is a subsequence and not a substring.

Example 4:

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Input: s = ""
Output: 0

就是一个最长不重复的字符串长度，因为也是中等难度的题，不太需要特别复杂的思考，最基本的就是O(N*N)两重循环，不过显然不太好，万一超时间，还有一种就是线性复杂度的了，这个就是需要搞定一个思路，比如字符串时 abcdefgaqwrty，比如遍历到第二个a的时候其实不用再从头去遍历了，只要把前面那个a给排除掉，继续往下算就好了

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class Solution {
    Map<String, Integer> counter = new HashMap<>();
    public int lengthOfLongestSubstring(String s) {
        int length = s.length();
        // 当前的长度
        int subStringLength = 0;
        // 最长的长度
        int maxSubStringLength = 0;
        // 考虑到重复的位置已经被跳过的情况，即已经在当前长度的字符串范围之前的重复字符不需要回溯
        int lastDuplicatePos = -1;
        for (int i = 0; i < length; i++) {
            // 使用 map 存储字符和上一次出现的位置，如果存在并且大于上一次重复位置
            if (counter.get(String.valueOf(s.charAt(i))) != null && counter.get(String.valueOf(s.charAt(i))) > lastDuplicatePos) {
                // 记录重复位置
                lastDuplicatePos = counter.get(String.valueOf(s.charAt(i)));
                // 重置不重复子串的长度，减去重复起点
                subStringLength = i - counter.get(String.valueOf(s.charAt(i))) - 1;
                // 替换当前位置
                counter.replace(String.valueOf(s.charAt(i)), i);
            } else {
                // 如果不存在就直接 put
                counter.put(String.valueOf(s.charAt(i)), i);
            }
            // 长度累加
            subStringLength++;
            if (subStringLength > maxSubStringLength) {
                // 简单替换
                maxSubStringLength = subStringLength;
            }
        }
        return maxSubStringLength;
    }
}

注释应该写的比较清楚了。

发表于 分类于 ， ， ， 阅读次数： Disqus：

小工记三

前面这两周周末也都去老丈人家帮忙了，上上周周六先是去了那个在装修的旧房子那，把三楼收拾了下，因为要搬进来住，来不及等二楼装修好，就要把三楼里的东西都整理干净，这个活感觉是比较 easy，原来是就准备把三楼当放东西仓储的地方了，我们乡下大部分三层楼都是这么用的，这次也是没办法，之前搬进来的木头什么的都搬出去，主要是这上面灰尘太多，后面清理鼻孔的时候都是黑色的了，把东西都搬出去以后主要是地还是很脏，就扫了地拖了地，因为是水泥地，灰尘又太多了，拖起来都是会灰尘扬起来，整个脱完了的确干净很多，然而这会就出了个大乌龙，我们清理的是三楼的西边一间，结果老丈人上来说要住东边那间的🤦‍♂️，不过其实西边的也得清理，因为还是要放被子什么的，不算是白费功夫，接着清理东边那间，之前这个房子做过群租房，里面有个高低铺的床，当时觉得可以用在放被子什么的就没扔，只是拆掉了放旁边，我们就把它擦干净了又装好，发现螺丝🔩少了几个，亘古不变的真理，拆了以后装要不就多几个要不就少几个，不是很牢靠，不过用来放放被子省得放地上总还是可以的，对了前面还做了个事情就是铺地毯，其实也不是地毯，就是类似于墙布雨篷布那种，别人不用了送给我们的，三楼水泥地也不会铺瓷砖地板了就放一下，干净好看点，不过大小不合适要裁一下，那把剪刀是真的太难用了，我手都要抽筋了，它就是刀口只有一小个点是能剪下来的，其他都是钝的，后来还是用刀片直接裁，铺好以后，真的感觉也不太一样了，焕然一新的感觉
差不多中午了就去吃饭了，之前两次是去了一家小饭店，还是还比较干净，但是店里菜不好吃，还死贵，这次去了一家小快餐店，口味好，便宜，味道是真的不错，带鱼跟黄鱼都好吃，一点都不腥，我对这类比较腥的鱼真的是很挑剔的，基本上除了家里做的很少吃外面的，那天抱着试试的态度吃了下，真的还不错，后来丈母娘说好像这家老板是给别人结婚喜事酒席当厨师的，怪不得做的好吃，其实本来是有一点小抗拒，怕不干净什么的，后来发现菜很好吃，而且可能是老丈人跟干活的师傅去吃的比较多，老板很客气，我们吃完饭，还给我们买了葡萄吃，不过这家店有一个槽点，就是饭比较不好吃，有时候会夹生，不过后面聊起来其实是这种小菜馆饭点的通病，烧的太早太多容易多出来浪费，烧的迟了不够吃，而且大的电饭锅比较不容易烧好。
下午前面还是在处理三楼的，窗户上各种钉子，实在是太多了，我后面在走廊上排了一排🤦‍♂️，有些是直接断了，有些是就撬了出来，感觉我在杭州租房也没有这样子各种钉钉子，挂下衣服什么的也不用这么多吧，比较不能理解，搞得到处都是钉子。那天我爸也去帮忙了，主要是在卫生间里做白缝，其实也是个技术活，印象中好像我小时候自己家里也做过这个事情，但是比较模糊了，后面我们三楼搞完了就去帮我爸了，前面是我老婆二爹在那先刷上白缝，这里叫白缝，有些考究的也叫美缝，就是瓷砖铺完之后的缝，如果不去弄的话，里面水泥的颜色就露出来了，而且容易渗水，所以就要用白水泥加胶水搅拌之后糊在缝上，但是也不是直接糊，先要把缝抠一抠，因为铺瓷砖的还不会仔细到每个缝里的水泥都是一样满，而且也需要一些空间糊上去，不然就太表面的一层很容易被水直接冲掉了，然后这次其实也不是用的白水泥，而是直接现成买来就已经配好的用来填缝的，兑水搅拌均匀就好了，后面就主要是我跟我爸在搞，那个时候真的觉得我实在是太胖了，蹲下去真的没一会就受不了了，膝盖什么的太难受了，后面我跪着刷，然后膝盖又疼，也是比较不容易，不过我爸动作很快，我中间跪累了休息一会，我爸就能搞一大片，后面其实我也没做多少（谦虚一下），总体来讲这次不是很累，就是蹲着跪着腿有点受不了，是应该好好减肥了。

发表于 更新于 分类于 ， ， ， ， ， 阅读次数： Disqus：

这个问题也是面试中常被问到的，就抽空来了解下这个，跳过一大段前面初始化的逻辑，
对于一条select * from t1 where id = #{id}这样的 sql，在初始化扫描 mapper 的xml文件的时候会根据是否是 dynamic 来判断生成 DynamicSqlSource 还是 RawSqlSource，这里它是一条 RawSqlSource，
在这里做了替换，将#{}替换成了?

前面说的是否 dynamic 就是在这里进行判断

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// org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseScriptNode
public SqlSource parseScriptNode() {
    MixedSqlNode rootSqlNode = parseDynamicTags(context);
    SqlSource sqlSource;
    if (isDynamic) {
      sqlSource = new DynamicSqlSource(configuration, rootSqlNode);
    } else {
      sqlSource = new RawSqlSource(configuration, rootSqlNode, parameterType);
    }
    return sqlSource;
  }
// org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseDynamicTags
protected MixedSqlNode parseDynamicTags(XNode node) {
    List<SqlNode> contents = new ArrayList<>();
    NodeList children = node.getNode().getChildNodes();
    for (int i = 0; i < children.getLength(); i++) {
      XNode child = node.newXNode(children.item(i));
      if (child.getNode().getNodeType() == Node.CDATA_SECTION_NODE || child.getNode().getNodeType() == Node.TEXT_NODE) {
        String data = child.getStringBody("");
        TextSqlNode textSqlNode = new TextSqlNode(data);
        if (textSqlNode.isDynamic()) {
          contents.add(textSqlNode);
          isDynamic = true;
        } else {
          contents.add(new StaticTextSqlNode(data));
        }
      } else if (child.getNode().getNodeType() == Node.ELEMENT_NODE) { // issue #628
        String nodeName = child.getNode().getNodeName();
        NodeHandler handler = nodeHandlerMap.get(nodeName);
        if (handler == null) {
          throw new BuilderException("Unknown element <" + nodeName + "> in SQL statement.");
        }
        handler.handleNode(child, contents);
        isDynamic = true;
      }
    }
    return new MixedSqlNode(contents);
  }
// org.apache.ibatis.scripting.xmltags.TextSqlNode#isDynamic
  public boolean isDynamic() {
    DynamicCheckerTokenParser checker = new DynamicCheckerTokenParser();
    GenericTokenParser parser = createParser(checker);
    parser.parse(text);
    return checker.isDynamic();
  }
    private GenericTokenParser createParser(TokenHandler handler) {
    return new GenericTokenParser("${", "}", handler);
  }

可以看到其中一个条件就是是否有${}这种占位符，假如说上面的 sql 换成 ${}，那么可以看到它会在这里创建一个 dynamicSqlSource,

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// org.apache.ibatis.scripting.xmltags.DynamicSqlSource
public class DynamicSqlSource implements SqlSource {

  private final Configuration configuration;
  private final SqlNode rootSqlNode;

  public DynamicSqlSource(Configuration configuration, SqlNode rootSqlNode) {
    this.configuration = configuration;
    this.rootSqlNode = rootSqlNode;
  }

  @Override
  public BoundSql getBoundSql(Object parameterObject) {
    DynamicContext context = new DynamicContext(configuration, parameterObject);
    rootSqlNode.apply(context);
    SqlSourceBuilder sqlSourceParser = new SqlSourceBuilder(configuration);
    Class<?> parameterType = parameterObject == null ? Object.class : parameterObject.getClass();
    SqlSource sqlSource = sqlSourceParser.parse(context.getSql(), parameterType, context.getBindings());
    BoundSql boundSql = sqlSource.getBoundSql(parameterObject);
    context.getBindings().forEach(boundSql::setAdditionalParameter);
    return boundSql;
  }

}

这里眼尖的同学可能就可以看出来了，RawSqlSource 在初始化的时候已经经过了 parse，把#{}替换成了?占位符，但是 DynamicSqlSource 并没有
再看这个图，我们发现在这的时候还没有进行替换
然后往里跟
好像是这里了

这里 rootSqlNode.apply 其实是一个对原来 sql 的解析结果的一个循环调用，不同类型的标签会构成不同的 node，像这里就是一个 textSqlNode

可以发现到这我们的 sql 已经被替换了，而且是直接作为 string 类型替换的，所以可以明白了这个问题所在，就是注入，不过细心的同学发现其实这里是有个

理论上还是可以做过滤的，不过好像现在没用起来。
我们前面可以发现对于#{}是在启动扫描 mapper的 xml 文件就替换成了 ?,然后是在什么时候变成实际的值的呢

发现到这的时候还是没有替换，其实说白了也就是 prepareStatement 那一套，

在这里进行替换，会拿到 org.apache.ibatis.mapping.ParameterMapping，然后进行替换，因为会带着类型信息，所以不用担心注入咯

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What hurts more, the pain of hard work or the pain of regret?

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

redis中对于 set 其实有两种处理，对于元素均为整型，并且元素数目较少时，使用 intset 作为底层数据结构，否则使用 dict 作为底层数据结构，先看一下代码先

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typedef struct intset {
    // 编码方式
    uint32_t encoding;
    // 集合包含的元素数量
    uint32_t length;
    // 保存元素的数组
    int8_t contents[];
} intset;

/* Note that these encodings are ordered, so:
 * INTSET_ENC_INT16 < INTSET_ENC_INT32 < INTSET_ENC_INT64. */
#define INTSET_ENC_INT16 (sizeof(int16_t))
#define INTSET_ENC_INT32 (sizeof(int32_t))
#define INTSET_ENC_INT64 (sizeof(int64_t))

一眼看，为啥整型还需要编码，然后 int8_t 怎么能存下大整形呢，带着这些疑问，我们一步步分析下去，这里的编码其实指的是这个整型集合里存的究竟是多大的整型，16 位，还是 32 位，还是 64 位，结构体下面的宏定义就是表示了 encoding 的可能取值，INTSET_ENC_INT16 表示每个元素用2个字节存储，INTSET_ENC_INT32 表示每个元素用4个字节存储，INTSET_ENC_INT64 表示每个元素用8个字节存储。因此，intset中存储的整数最多只能占用64bit。length 就是正常的表示集合中元素的数量。最奇怪的应该就是这个 contents 了，是个 int8_t 的数组，那放毛线数据啊，最小的都有 16 位，这里我在看代码和《redis 设计与实现》的时候也有点懵逼，后来查了下发现这是个比较取巧的用法，这里我用自己的理解表述一下，先看看 8，16，32，64 的关系，一眼看就知道都是 2 的 N 次，并且呈两倍关系，而且 8 位刚好一个字节，所以呢其实这里的contents 不是个常规意义上的 int8_t 类型的数组，而是个柔性数组。看下 wiki 的定义

Flexible array members1 were introduced in the C99 standard of the C programming language (in particular, in section §6.7.2.1, item 16, page 103).2 It is a member of a struct, which is an array without a given dimension. It must be the last member of such a struct and it must be accompanied by at least one other member, as in the following example:

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struct vectord {
    size_t len;
    double arr[]; // the flexible array member must be last
};

在初始化这个 intset 的时候，这个contents数组是不占用空间的，后面的反正用到了申请，那么这里就有一个问题，给出了三种可能的 encoding 值，他们能随便换吗，显然不行，首先在 intset 中数据的存放是有序的，这个有部分原因是方便二分查找，然后存放数据其实随着数据的大小不同会有一个升级的过程，看下图

新创建的intset只有一个header，总共8个字节。其中encoding = 2, length = 0, 类型都是uint32_t，各占 4 字节，添加15, 5两个元素之后，因为它们是比较小的整数，都能使用2个字节表示，所以encoding不变，值还是2，也就是默认的 INTSET_ENC_INT16，当添加32768的时候，它不再能用2个字节来表示了（2个字节能表达的数据范围是-215~215-1，而32768等于215，超出范围了），因此encoding必须升级到INTSET_ENC_INT32（值为4），即用4个字节表示一个元素。在添加每个元素的过程中，intset始终保持从小到大有序。与ziplist类似，intset也是按小端（little endian）模式存储的（参见维基百科词条Endianness）。比如，在上图中intset添加完所有数据之后，表示encoding字段的4个字节应该解释成0x00000004，而第4个数据应该解释成0x00008000 = 32768

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跳表 skiplist

跳表是个在我们日常的代码中不太常用到的数据结构，相对来讲就没有像数组，链表，字典，散列，树等结构那么熟悉，所以就从头开始分析下，首先是链表，跳表跟链表都有个表字（太硬扯了我🤦‍♀️），注意这是个有序链表

如上图，在这个链表里如果我要找到 23，是不是我需要从3，5，9开始一直往后找直到找到 23，也就是说时间复杂度是 O(N),N 的一次幂复杂度，那么我们来看看第二个

这个结构跟原先有点不一样，它给链表中偶数位的节点又加了一个指针把它们链接起来，这样子当我们要寻找 23 的时候就可以从原来的一个个往下找变成跳着找，先找到 5，然后是 10，接着是 19，然后是 28，这时候发现 28 比 23 大了，那我在退回到 19，然后从下一层原来的链表往前找，

这里毛估估是不是前面的节点我就少找了一半,有那么点二分法的意思。
前面的其实是跳表的引子，真正的跳表其实不是这样，因为上面的其实有个比较大的问题，就是插入一个元素后需要调整每个元素的指针，在 redis 中的跳表其实是做了个随机层数的优化，因为沿着前面的例子，其实当数据量很大的时候，是不是层数越多，其查询效率越高，但是随着层数变多，要保持这种严格的层数规则其实也会增大处理复杂度，所以 redis 插入每个元素的时候都是使用随机的方式，看一眼代码

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/* ZSETs use a specialized version of Skiplists */
typedef struct zskiplistNode {
    sds ele;
    double score;
    struct zskiplistNode *backward;
    struct zskiplistLevel {
        struct zskiplistNode *forward;
        unsigned long span;
    } level[];
} zskiplistNode;

typedef struct zskiplist {
    struct zskiplistNode *header, *tail;
    unsigned long length;
    int level;
} zskiplist;

typedef struct zset {
    dict *dict;
    zskiplist *zsl;
} zset;

忘了说了，redis 是把 skiplist 跳表用在 zset 里，zset 是个有序的集合，可以看到 zskiplist 就是个跳表的结构，里面用 header 保存跳表的表头，tail 保存表尾，还有长度和最大层级，具体的跳表节点元素使用 zskiplistNode 表示，里面包含了 sds 类型的元素值，double 类型的分值，用来排序，一个 backward 后向指针和一个 zskiplistLevel 数组，每个 level 包含了一个前向指针，和一个 span，span 表示的是跳表前向指针的跨度，这里再补充一点，前面说了为了灵活这个跳表的新增修改，redis 使用了随机层高的方式插入新节点，但是如果所有节点都随机到很高的层级或者所有都很低的话，跳表的效率优势就会减小，所以 redis 使用了个小技巧，贴下代码

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#define ZSKIPLIST_P 0.25      /* Skiplist P = 1/4 */
int zslRandomLevel(void) {
    int level = 1;
    while ((random()&0xFFFF) < (ZSKIPLIST_P * 0xFFFF))
        level += 1;
    return (level<ZSKIPLIST_MAXLEVEL) ? level : ZSKIPLIST_MAXLEVEL;
}

当随机值跟0xFFFF进行与操作小于ZSKIPLIST_P * 0xFFFF时才会增大 level 的值，因此保持了一个相对递减的概率
可以简单分析下，当 random() 的值小于 0xFFFF 的 1/4,才会 level + 1，就意味着当有 1 - 1/4也就是3/4的概率是直接跳出，所以一层的概率是3/4,也就是 1-P，二层的概率是 P*(1-P),三层的概率是 P² * (1-P) 依次递推。

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redis是现在服务端很常用的缓存中间件，其实原来还有memcache之类的竞品，但是现在貌似 redis 快一统江湖，这里当然不是在吹，只是个人角度的一个感觉，不权威只是主观感觉。
redis 主要有五种数据结构，Strings，Lists，Sets，Hashes，Sorted Sets，这五种数据结构先简单介绍下，Strings类型的其实就是我们最常用的 key-value，实际开发中也会用的最多；Lists是列表，这个有些会用来做队列，因为 redis 目前常用的版本支持丰富的列表操作；还有是Sets集合，这个主要的特点就是集合中元素不重复，可以用在有这类需求的场景里；Hashes是叫散列，类似于 Python 中的字典结构；还有就是Sorted Sets这个是个有序集合；一眼看这些其实没啥特别的，除了最后这个有序集合，不过去了解背后的实现方式还是比较有意思的。

SDS 简单动态字符串

先从Strings开始说，了解过 C 语言的应该知道，C 语言中的字符串其实是个 char[] 字符数组，redis 也不例外，只是最开始的版本就对这个做了一丢丢的优化，而正是这一丢丢的优化，让这个 redis 的使用效率提升了数倍

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struct sdshdr {
    // 字符串长度
    int len;
    // 字符串空余字符数
    int free;
    // 字符串内容
    char buf[];
};

这里引用了 redis 在 github 上最早的 2.2 版本的代码，代码路径是https://github.com/antirez/redis/blob/2.2/src/sds.h,可以看到这个结构体里只有仨元素，两个 int 型和一个 char 型数组，两个 int 型其实就是我说的优化，因为 C 语言本身的字符串数组，有两个问题，一个是要知道它实际已被占用的长度，需要去遍历这个数组，第二个就是比较容易踩坑的是遍历的时候要注意它有个以\0作为结尾的特点；通过上面的两个 int 型参数，一个是知道字符串目前的长度，一个是知道字符串还剩余多少位空间，这样子坐着两个操作从 O(N)简化到了O(1)了，还有第二个 free 还有个比较重要的作用就是能防止 C 字符串的溢出问题，在存储之前可以先判断 free 长度，如果长度不够就先扩容了，先介绍到这，这个系列可以写蛮多的，慢慢介绍吧

链表

链表是比较常见的数据结构了，但是因为 redis 是用 C 写的，所以在不依赖第三方库的情况下只能自己写一个了，redis 的链表是个有头的链表，而且是无环的，具体的结构我也找了 github 上最早版本的代码

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typedef struct listNode {
    // 前置节点
    struct listNode *prev;
    // 后置节点
    struct listNode *next;
    // 值
    void *value;
} listNode;

typedef struct list {
    // 链表表头
    listNode *head;
    // 当前节点，也可以说是最后节点
    listNode *tail;
    // 节点复制函数
    void *(*dup)(void *ptr);
    // 节点值释放函数
    void (*free)(void *ptr);
    // 节点值比较函数
    int (*match)(void *ptr, void *key);
    // 链表包含的节点数量
    unsigned int len;
} list;

代码地址是这个https://github.com/antirez/redis/blob/2.2/src/adlist.h
可以看下节点是由listNode承载的，包括值和一个指向前节点跟一个指向后一节点的两个指针，然后值是 void 指针类型，所以可以承载不同类型的值
然后是 list结构用来承载一个链表，包含了表头，和表尾，复制函数，释放函数和比较函数，还有链表长度，因为包含了前两个节点，找到表尾节点跟表头都是 O(1)的时间复杂度，还有节点数量，其实这个跟 SDS 是同一个做法，就是空间换时间，这也是写代码里比较常见的做法，以此让一些高频的操作提速。

字典

字典也是个常用的数据结构，其实只是叫法不同，数据结构中叫 hash 散列，Java 中叫 Map，PHP 中是数组 array，Python 中也叫字典 dict，因为纯 C 语言本身不带这些数据结构，所以这也是个痛并快乐着的过程，享受 C 语言的高性能的同时也要接受它只提供了语言的基本功能的现实，各种轮子都需要自己造，redis 同样实现了自己的字典
下面来看看代码

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typedef struct dictEntry {
    void *key;
    void *val;
    struct dictEntry *next;
} dictEntry;

typedef struct dictType {
    unsigned int (*hashFunction)(const void *key);
    void *(*keyDup)(void *privdata, const void *key);
    void *(*valDup)(void *privdata, const void *obj);
    int (*keyCompare)(void *privdata, const void *key1, const void *key2);
    void (*keyDestructor)(void *privdata, void *key);
    void (*valDestructor)(void *privdata, void *obj);
} dictType;

/* This is our hash table structure. Every dictionary has two of this as we
 * implement incremental rehashing, for the old to the new table. */
typedef struct dictht {
    dictEntry **table;
    unsigned long size;
    unsigned long sizemask;
    unsigned long used;
} dictht;

typedef struct dict {
    dictType *type;
    void *privdata;
    dictht ht[2];
    int rehashidx; /* rehashing not in progress if rehashidx == -1 */
    int iterators; /* number of iterators currently running */
} dict;

看了下这个 2.2 版本的代码跟最新版的其实也差的不是很多，所以还是照旧用老代码，可以看到上面四个结构体中，其实只有三个是存储数据用的，dictType 是用来放操作函数的，那么三个存放数据的结构体分别是干嘛的，这时候感觉需要一个图来说明比较好，稍等，我去画个图~

这个图看着应该比较清楚这些都是用来干嘛的了，dict 是我们的主体结构，它有一个指向 dictType 的指针，这里面包含了字典的操作函数，然后是一个私有数据指针，接下来是一个 dictht 的数组，包含两个dictht，这个就是用来存数据的了，然后是 rehashidx 表示重哈希的状态，当是-1 的时候表示当前没有重哈希，iterators 表示正在遍历的迭代器的数量。
首先说说为啥需要有两个 dictht，这是因为字典 dict 这个数据结构随着数据量的增减，会需要在中途做扩容或者缩容操作，如果只有一个的话，对它进行扩容缩容时会影响正常的访问和修改操作，或者说保证正常查询，修改的正确性会比较复杂，并且因为需要高效利用空间，不能一下子申请一个非常大的空间来存很少的数据。当 dict 中 dictht 中的数据量超过 size 的时候负载就超过了 1，就需要进行扩容，这里的其实跟 Java 中的 HashMap 比较类似，超过一定的负载之后进行扩容。这里为啥 size 会超过 1 呢，可能有部分不了解这类结构的同学会比较奇怪，其实就是上图中画的，在数据结构中对于散列的冲突有几类解决方法，比如转换成链表，二次散列，找下个空槽等，这里就使用了链表法，或者说拉链法。当一个新元素通过 hashFunction 得出的 key 跟 sizemask 取模之后的值相同了，那就将其放在原来的节点之前，变成链表挂在数组 dictht.table下面，放在原有节点前是考虑到可能会优先访问。
忘了说明下 dictht 跟 dictEntry 的关系了，dictht 就是个哈希表，它里面是个dictEntry 的二维数组，而 dictEntry 是个包含了 key-value 结构之外还有一个 next 指针，因此可以将哈希冲突的以链表的形式保存下来。
在重点说下重哈希，可能同样写 Java 的同学对这个比较有感觉，跟 HashMap 一样，会以 2 的 N 次方进行扩容，那么扩容的方法就会比较简单，每个键重哈希要不就在原来这个槽，要不就在原来的槽加原 dictht.size 的位置；然后是重头戏，具体是怎么做扩容呢，其实这里就把第二个 ht 用上了，其实这两个hashtable 的具体作用有点类似于 jvm 中的两个 survival 区，但是又不全一样，因为 redis 在扩容的时候是采用的渐进式地重哈希，什么叫渐进式的呢，就是它不是像 jvm 那种标记复制的模式直接将一个 eden 区和原来的 survival 区存活的对象复制到另一个 survival 区，而是在每一次添加，删除，查找或者更新操作时，都会额外的帮忙搬运一部分的原 dictht 中的数据，这里会根据 rehashidx 的值来判断，如果是-1 表示并没有在重哈希中，如果是 0 表示开始重哈希了，然后rehashidx 还会随着每次的帮忙搬运往上加，但全部被搬运完成后 rehashidx 又变回了-1，又可以扯到Java 中的 Concurrent HashMap, 他在扩容的时候也使用了类似的操作。

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这是个 Java 面试的高频问题，我也遇到过，以往都是觉得这类题没意思，网上一搜一大堆，也不愿意记，其实说回来，主要还是没静下心来好好去理解，今天无意中看到一个课程，基本帮我把一些疑惑的点讲清楚了，首先单例是啥意思，这个其实是有范围一说，比如我起了个Spring Boot应用，在这个应用范围内，我的常规 bean 是单例的，意味着 getBean 的时候其实永远只会拿到那一个对象，那要怎么来写一个单例呢，首先就是传说中的饿汉模式，也是最简单的

饿汉模式

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public class Singleton1 {
    // 首先，将构造方法变成私有的
    private Singleton1() {};
    // 创建私有静态实例，这样第一次使用的时候就会进行创建
    private static Singleton instance = new Singleton1();

    // 使用这个对象都是通过这个 getInstance 来获取
    public static Singleton1 getInstance() {
        return instance;
    }
    // 瞎写一个静态方法。这里想说的是，如果我们只是要调用 Singleton.getDate(...)，
    // 本来是不想要生成 Singleton 实例的，不过没办法，已经生成了
    public static Date getDate(String mode) {return new Date();}
}

上面借鉴了一些代码，其实这是最基本，也不会错的方法，但是正如其中getDate方法里说的问题，有时候并没有想那这个对象，但是因为我调用了这个类的静态方法，导致对象已经生成了，可能这也是饿汉模式名字的来由，不管三七二十一给你生成个单例就完事了，不管有没有用，但是这种个人觉得也没啥大问题，如果是面试的话最好说出来它的缺点

饱汉模式

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public class Singleton2 {
    // 首先，也是先堵死 new Singleton() 这条路,将构造方法变成私有
    private Singleton2() {}
    // 和饿汉模式相比，这边不需要先实例化出来，注意这里的 volatile，它是必须的
    private static volatile Singleton2 instance = null;

    private int m = 9;

    public static Singleton getInstance() {
        if (instance == null) {
            // 加锁
            synchronized (Singleton2.class) {
                // 这一次判断也是必须的，不然会有并发问题
                if (instance == null) {
                    instance = new Singleton2();
                }
            }
        }
        return instance;
    }
}

这里容易错的有三点，理解了其实就比较好记了

第一点，为啥不在 getInstance 上整个代码块加 synchronized，这个其实比较容易理解，就是锁的力度太大，性能太差了，这点其实也要去理解，可以举个夸张的例子，比如我一个电商的服务，如果为了避免一个人的订单出现问题，是不是可以从请求入口就把他锁住，到请求结束释放，那么里面做的事情都有保障，然而这显然不可能，因为我们想要这种竞态条件抢占资源的时间尽量减少，防止其他线程等待。
第二点，为啥synchronized之已经检查了 instance == null,还要在里面再检查一次，这个有个术语，叫 double check lock,但是为啥要这么做呢，其实很简单，想象当有两个线程，都过了第一步为空判断，这个时候只有一个线程能拿到这个锁，另一个线程就等待了，如果不再判断一次，那么第一个线程新建完对象释放锁之后，第二个线程又能拿到锁，再去创建一个对象。
第三点，为啥要volatile关键字，原先对它的理解是它修饰的变量在 JMM 中能及时将变量值写到主存中，但是它还有个很重要的作用，就是防止指令重排序，instance = new Singleton();这行代码其实在底层是分成三条指令执行的，第一条是在堆上申请了一块内存放这个对象，但是对象的字段啥的都还是默认值，第二条是设置对象的值，比如上面的 m 是 9，然后第三条是将这个对象和虚拟机栈上的指针建立引用关联，那么如果我不用volatile关键字，这三条指令就有可能出现重排，比如变成了 1-3-2 这种顺序，当执行完第二步时，有个线程来访问这个对象了，先判断是不是空，发现不是空的，就拿去直接用了，是不是就出现问题了，所以这个volatile也是不可缺少的

嵌套类

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public class Singleton3 {

    private Singleton3() {}
    // 主要是使用了 嵌套类可以访问外部类的静态属性和静态方法 的特性
    private static class Holder {
        private static Singleton3 instance = new Singleton3();
    }
    public static Singleton3 getInstance() {
        return Holder.instance;
    }
}

这个我个人感觉是饿汉模式的升级版，可以在调用getInstance的时候去实例化对象，也是比较推荐的

枚举单例

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public enum Singleton {
    INSTANCE;
    
    public void doSomething(){
        //todo doSomething
    }
}

枚举很特殊，它在类加载的时候会初始化里面的所有的实例，而且 JVM 保证了它们不会再被实例化，所以它天生就是单例的。

发表于 更新于 分类于 ， ， 阅读次数： Disqus：

看完了村上春树的《1Q84》，这应该是第五本看的他的书了，继 跑步，挪威的森林，刺杀骑士团长，海边的卡夫卡之后，不是其中最长的，好像是海边的卡夫卡还是刺杀骑士团长比较长一点，都是在微信读书上看的，比较方便，最开始在上面看的是高晓松的《鱼羊野史》，不知道为啥取这个名字，但是还是满吸引我的，不过由于去年的种种，没有很多心思把它看完，而且本身的组织形式就是比较松散的，看到哪算哪，其实一些野史部分是我比较喜欢，有些谈到人物的就不太有兴趣，而且类似于大祥哥吃的东西，反正都是哇，怎么这么好吃，嗯，太爱（niu）你（bi）了，高晓松就是这个人是我最喜欢的 xxx 家，我也没去细究过他有没有说重复过，反正是不太爱，后来因为这书还一度对战争史有了浓厚的兴趣，然而事实告诉我，大部头的战争史，其实正史我是真的啃不下去，我可能只对其中 10%的内容感兴趣，不过终于也在今年把它看完了，好像高晓松的晓说也最终季了，貌似其中讲朝鲜战争的还被和谐了，看样子是说出了一些故事（truth）。

本来只是想把 《1Q84》的读后感写下，现在觉得还是把这篇当成我今年的读书总结吧，不过先从《1Q84》说起。

严格来讲，这不是很书面化的读后感，可能我想写的也只是像聊天一样的说下我读过的书，包括的技术博客其实也是类似的，以后或许会转变，但是目前水平如此吧，写多了可能会变好，也可能不会。

开始正文吧，这书有点类似于海边的卡夫卡，一开始是通过两条故事线，穿插着叙述，一条是青豆的，不算是个职业杀手的女杀手，要去解决一个经常家暴的斯文败类，穿着描述得比较性感吧，杀人方式是通过比较长的细针，从脖子后面一个精巧的位置插入，可以造成是未知原因死亡的假象，可能会推断成心梗之类的，这里有个前置的细节，就是青豆是乘坐一辆很高级的出租车，内饰什么的都非常有质感，有点不像一辆出租车，然后车里放了一首比较小众的歌，雅纳切克的《小交响曲》，但是青豆知道它，这跟后面的情节也有些许关系，这是女主人公青豆的出场；相应的男主的出场印象不是太深刻，男主叫天吾，是个不知名的作家，跟一个叫小松的编辑有比较好的关系，虽然天吾还没有拿到比较有分量的奖项，但是小松很看好他，也让他帮忙审校一个新作家奖的投稿文章，虽然天吾自身还没获得过这个奖，天吾还有个正式工作，是当数学老师，天吾在学生时代是个数学天才，但后面有对文学产生了兴趣，文学还不足以养活自己，靠着教课还是能保持温饱；

接下来是正式故事的起点了，就是小松收到了一部小说投稿，名叫《空气蛹》，是个叫深绘里的女孩子投的稿，小松对他赋予了很高的评价，这里好像记岔了，好像是天吾对这部小说很有好感，但是小松比较怀疑，然后小松看了之后也有了浓厚的兴趣，这里就是开端了，小松想让天吾来重写润色这部《空气蛹》，因为故事本身很有分量，但是描写手法叙事方式等都很拙劣，而天吾正好擅长这个，小松对天吾的评价是，描写技巧无可挑剔，就是故事主体的火花还没际遇迸发，需要一个导火索，这个就可以类比我们程序员，很多比较初中级的程序员主要擅长在原来的代码上修修改改或者给他分配个小功能，比较高级的程序员就需要能做一些项目的架构设计，核心的技术方案设计，以前我也觉得写文档这个比较无聊，但是当一个项目真的比较庞大，复杂的时候，整体和核心部分的架构设计和方案还是需要有文档沉淀的，不然别人不知道没法接受，自己过段时间也会忘记。

对于小松的这个建议，他的初衷是想搅一搅这个死气沉沉套路颇深的文坛，因为本身《空气蛹》这部小说的内容很吸引人，小松想通过天吾的润色补充让这部小说冲击新人奖，有种恶作剧的意图，天吾对此表示很多担心和顾虑，小松的这个建议其实也是一种文学作假，有两方面的担心，一方面是原作者深绘里是否同意如此操作，一方面是外界如果发现了这个事实会有什么样的后果，但是小松表示不用担心，前一步由小松牵线，让天吾跟原作者深绘里当面沟通这个代写是否被允许，结果当然是被允许了，这里有了对深绘里的初步描写，按我的理解是比较仙的感觉，然后语言沟通有些吃力，或者说有她自己的特色，当面沟通时貌似是让深绘里回去再考虑下，然后后面再由天吾去深绘里寄宿的戎野老师家沟通具体的细节。

2019年12月18日23:37:19 更新
去到戎野老师家之后，天吾知道了关于深绘里的一些事情，深绘里的父亲与戎野老师应该是老友，深绘里的父亲在当初成立了一个叫”先驱”的公社，一个独立运行的社会组织，以运营农场作为物资来源，追求更为松散的共同体，即不过分激进地公有制，进行松散的共同生活，承认私有财产，简而言之就是这样一个能稳定存活下来的独立社会组织，但是随着稳定运行，内部的激进派和稳健派开始出现分歧，不可磨合，后来两派就分裂了，深绘里的父亲，深田保留在了稳健派，但是此时其实深田保内心是矛盾的，以为一开始其实是他倡导的独立革命才组织起了这群人，然而现在他又认清了现实社会已经不太相信能通过革命来独立的可能性，后来激进派便开始越加封闭，而且进行军事训练和思想教育，而后这个先驱的激进派别便有了新的名字”黎明”，深绘里也是在此时从先驱逃离来投靠戎野老师
暂时先写到这，未完待续~

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

redis中对于 set 其实有两种处理，对于元素均为整型，并且元素数目较少时，使用 intset 作为底层数据结构，否则使用 dict 作为底层数据结构，先看一下代码先

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typedef struct intset {
    // 编码方式
    uint32_t encoding;
    // 集合包含的元素数量
    uint32_t length;
    // 保存元素的数组
    int8_t contents[];
} intset;

/* Note that these encodings are ordered, so:
 * INTSET_ENC_INT16 < INTSET_ENC_INT32 < INTSET_ENC_INT64. */
#define INTSET_ENC_INT16 (sizeof(int16_t))
#define INTSET_ENC_INT32 (sizeof(int32_t))
#define INTSET_ENC_INT64 (sizeof(int64_t))

一眼看，为啥整型还需要编码，然后 int8_t 怎么能存下大整形呢，带着这些疑问，我们一步步分析下去，这里的编码其实指的是这个整型集合里存的究竟是多大的整型，16 位，还是 32 位，还是 64 位，结构体下面的宏定义就是表示了 encoding 的可能取值，INTSET_ENC_INT16 表示每个元素用2个字节存储，INTSET_ENC_INT32 表示每个元素用4个字节存储，INTSET_ENC_INT64 表示每个元素用8个字节存储。因此，intset中存储的整数最多只能占用64bit。length 就是正常的表示集合中元素的数量。最奇怪的应该就是这个 contents 了，是个 int8_t 的数组，那放毛线数据啊，最小的都有 16 位，这里我在看代码和《redis 设计与实现》的时候也有点懵逼，后来查了下发现这是个比较取巧的用法，这里我用自己的理解表述一下，先看看 8，16，32，64 的关系，一眼看就知道都是 2 的 N 次，并且呈两倍关系，而且 8 位刚好一个字节，所以呢其实这里的contents 不是个常规意义上的 int8_t 类型的数组，而是个柔性数组。看下 wiki 的定义

Flexible array members1 were introduced in the C99 standard of the C programming language (in particular, in section §6.7.2.1, item 16, page 103).2 It is a member of a struct, which is an array without a given dimension. It must be the last member of such a struct and it must be accompanied by at least one other member, as in the following example:

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struct vectord {
    size_t len;
    double arr[]; // the flexible array member must be last
};

在初始化这个 intset 的时候，这个contents数组是不占用空间的，后面的反正用到了申请，那么这里就有一个问题，给出了三种可能的 encoding 值，他们能随便换吗，显然不行，首先在 intset 中数据的存放是有序的，这个有部分原因是方便二分查找，然后存放数据其实随着数据的大小不同会有一个升级的过程，看下图

新创建的intset只有一个header，总共8个字节。其中encoding = 2, length = 0, 类型都是uint32_t，各占 4 字节，添加15, 5两个元素之后，因为它们是比较小的整数，都能使用2个字节表示，所以encoding不变，值还是2，也就是默认的 INTSET_ENC_INT16，当添加32768的时候，它不再能用2个字节来表示了（2个字节能表达的数据范围是-215~215-1，而32768等于215，超出范围了），因此encoding必须升级到INTSET_ENC_INT32（值为4），即用4个字节表示一个元素。在添加每个元素的过程中，intset始终保持从小到大有序。与ziplist类似，intset也是按小端（little endian）模式存储的（参见维基百科词条Endianness）。比如，在上图中intset添加完所有数据之后，表示encoding字段的4个字节应该解释成0x00000004，而第4个数据应该解释成0x00008000 = 32768

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

跳表 skiplist

跳表是个在我们日常的代码中不太常用到的数据结构，相对来讲就没有像数组，链表，字典，散列，树等结构那么熟悉，所以就从头开始分析下，首先是链表，跳表跟链表都有个表字（太硬扯了我🤦‍♀️），注意这是个有序链表

如上图，在这个链表里如果我要找到 23，是不是我需要从3，5，9开始一直往后找直到找到 23，也就是说时间复杂度是 O(N),N 的一次幂复杂度，那么我们来看看第二个

这个结构跟原先有点不一样，它给链表中偶数位的节点又加了一个指针把它们链接起来，这样子当我们要寻找 23 的时候就可以从原来的一个个往下找变成跳着找，先找到 5，然后是 10，接着是 19，然后是 28，这时候发现 28 比 23 大了，那我在退回到 19，然后从下一层原来的链表往前找，

这里毛估估是不是前面的节点我就少找了一半,有那么点二分法的意思。
前面的其实是跳表的引子，真正的跳表其实不是这样，因为上面的其实有个比较大的问题，就是插入一个元素后需要调整每个元素的指针，在 redis 中的跳表其实是做了个随机层数的优化，因为沿着前面的例子，其实当数据量很大的时候，是不是层数越多，其查询效率越高，但是随着层数变多，要保持这种严格的层数规则其实也会增大处理复杂度，所以 redis 插入每个元素的时候都是使用随机的方式，看一眼代码

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/* ZSETs use a specialized version of Skiplists */
typedef struct zskiplistNode {
    sds ele;
    double score;
    struct zskiplistNode *backward;
    struct zskiplistLevel {
        struct zskiplistNode *forward;
        unsigned long span;
    } level[];
} zskiplistNode;

typedef struct zskiplist {
    struct zskiplistNode *header, *tail;
    unsigned long length;
    int level;
} zskiplist;

typedef struct zset {
    dict *dict;
    zskiplist *zsl;
} zset;

忘了说了，redis 是把 skiplist 跳表用在 zset 里，zset 是个有序的集合，可以看到 zskiplist 就是个跳表的结构，里面用 header 保存跳表的表头，tail 保存表尾，还有长度和最大层级，具体的跳表节点元素使用 zskiplistNode 表示，里面包含了 sds 类型的元素值，double 类型的分值，用来排序，一个 backward 后向指针和一个 zskiplistLevel 数组，每个 level 包含了一个前向指针，和一个 span，span 表示的是跳表前向指针的跨度，这里再补充一点，前面说了为了灵活这个跳表的新增修改，redis 使用了随机层高的方式插入新节点，但是如果所有节点都随机到很高的层级或者所有都很低的话，跳表的效率优势就会减小，所以 redis 使用了个小技巧，贴下代码

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#define ZSKIPLIST_P 0.25      /* Skiplist P = 1/4 */
int zslRandomLevel(void) {
    int level = 1;
    while ((random()&0xFFFF) < (ZSKIPLIST_P * 0xFFFF))
        level += 1;
    return (level<ZSKIPLIST_MAXLEVEL) ? level : ZSKIPLIST_MAXLEVEL;
}

当随机值跟0xFFFF进行与操作小于ZSKIPLIST_P * 0xFFFF时才会增大 level 的值，因此保持了一个相对递减的概率
可以简单分析下，当 random() 的值小于 0xFFFF 的 1/4,才会 level + 1，就意味着当有 1 - 1/4也就是3/4的概率是直接跳出，所以一层的概率是3/4,也就是 1-P，二层的概率是 P*(1-P),三层的概率是 P² * (1-P) 依次递推。

发表于 更新于 分类于 ， ， ， ， 阅读次数： Disqus：

redis是现在服务端很常用的缓存中间件，其实原来还有memcache之类的竞品，但是现在貌似 redis 快一统江湖，这里当然不是在吹，只是个人角度的一个感觉，不权威只是主观感觉。
redis 主要有五种数据结构，Strings，Lists，Sets，Hashes，Sorted Sets，这五种数据结构先简单介绍下，Strings类型的其实就是我们最常用的 key-value，实际开发中也会用的最多；Lists是列表，这个有些会用来做队列，因为 redis 目前常用的版本支持丰富的列表操作；还有是Sets集合，这个主要的特点就是集合中元素不重复，可以用在有这类需求的场景里；Hashes是叫散列，类似于 Python 中的字典结构；还有就是Sorted Sets这个是个有序集合；一眼看这些其实没啥特别的，除了最后这个有序集合，不过去了解背后的实现方式还是比较有意思的。

SDS 简单动态字符串

先从Strings开始说，了解过 C 语言的应该知道，C 语言中的字符串其实是个 char[] 字符数组，redis 也不例外，只是最开始的版本就对这个做了一丢丢的优化，而正是这一丢丢的优化，让这个 redis 的使用效率提升了数倍

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struct sdshdr {
    // 字符串长度
    int len;
    // 字符串空余字符数
    int free;
    // 字符串内容
    char buf[];
};

这里引用了 redis 在 github 上最早的 2.2 版本的代码，代码路径是https://github.com/antirez/redis/blob/2.2/src/sds.h,可以看到这个结构体里只有仨元素，两个 int 型和一个 char 型数组，两个 int 型其实就是我说的优化，因为 C 语言本身的字符串数组，有两个问题，一个是要知道它实际已被占用的长度，需要去遍历这个数组，第二个就是比较容易踩坑的是遍历的时候要注意它有个以\0作为结尾的特点；通过上面的两个 int 型参数，一个是知道字符串目前的长度，一个是知道字符串还剩余多少位空间，这样子坐着两个操作从 O(N)简化到了O(1)了，还有第二个 free 还有个比较重要的作用就是能防止 C 字符串的溢出问题，在存储之前可以先判断 free 长度，如果长度不够就先扩容了，先介绍到这，这个系列可以写蛮多的，慢慢介绍吧

链表

链表是比较常见的数据结构了，但是因为 redis 是用 C 写的，所以在不依赖第三方库的情况下只能自己写一个了，redis 的链表是个有头的链表，而且是无环的，具体的结构我也找了 github 上最早版本的代码

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typedef struct listNode {
    // 前置节点
    struct listNode *prev;
    // 后置节点
    struct listNode *next;
    // 值
    void *value;
} listNode;

typedef struct list {
    // 链表表头
    listNode *head;
    // 当前节点，也可以说是最后节点
    listNode *tail;
    // 节点复制函数
    void *(*dup)(void *ptr);
    // 节点值释放函数
    void (*free)(void *ptr);
    // 节点值比较函数
    int (*match)(void *ptr, void *key);
    // 链表包含的节点数量
    unsigned int len;
} list;

代码地址是这个https://github.com/antirez/redis/blob/2.2/src/adlist.h
可以看下节点是由listNode承载的，包括值和一个指向前节点跟一个指向后一节点的两个指针，然后值是 void 指针类型，所以可以承载不同类型的值
然后是 list结构用来承载一个链表，包含了表头，和表尾，复制函数，释放函数和比较函数，还有链表长度，因为包含了前两个节点，找到表尾节点跟表头都是 O(1)的时间复杂度，还有节点数量，其实这个跟 SDS 是同一个做法，就是空间换时间，这也是写代码里比较常见的做法，以此让一些高频的操作提速。

字典

字典也是个常用的数据结构，其实只是叫法不同，数据结构中叫 hash 散列，Java 中叫 Map，PHP 中是数组 array，Python 中也叫字典 dict，因为纯 C 语言本身不带这些数据结构，所以这也是个痛并快乐着的过程，享受 C 语言的高性能的同时也要接受它只提供了语言的基本功能的现实，各种轮子都需要自己造，redis 同样实现了自己的字典
下面来看看代码

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typedef struct dictEntry {
    void *key;
    void *val;
    struct dictEntry *next;
} dictEntry;

typedef struct dictType {
    unsigned int (*hashFunction)(const void *key);
    void *(*keyDup)(void *privdata, const void *key);
    void *(*valDup)(void *privdata, const void *obj);
    int (*keyCompare)(void *privdata, const void *key1, const void *key2);
    void (*keyDestructor)(void *privdata, void *key);
    void (*valDestructor)(void *privdata, void *obj);
} dictType;

/* This is our hash table structure. Every dictionary has two of this as we
 * implement incremental rehashing, for the old to the new table. */
typedef struct dictht {
    dictEntry **table;
    unsigned long size;
    unsigned long sizemask;
    unsigned long used;
} dictht;

typedef struct dict {
    dictType *type;
    void *privdata;
    dictht ht[2];
    int rehashidx; /* rehashing not in progress if rehashidx == -1 */
    int iterators; /* number of iterators currently running */
} dict;

看了下这个 2.2 版本的代码跟最新版的其实也差的不是很多，所以还是照旧用老代码，可以看到上面四个结构体中，其实只有三个是存储数据用的，dictType 是用来放操作函数的，那么三个存放数据的结构体分别是干嘛的，这时候感觉需要一个图来说明比较好，稍等，我去画个图~

这个图看着应该比较清楚这些都是用来干嘛的了，dict 是我们的主体结构，它有一个指向 dictType 的指针，这里面包含了字典的操作函数，然后是一个私有数据指针，接下来是一个 dictht 的数组，包含两个dictht，这个就是用来存数据的了，然后是 rehashidx 表示重哈希的状态，当是-1 的时候表示当前没有重哈希，iterators 表示正在遍历的迭代器的数量。
首先说说为啥需要有两个 dictht，这是因为字典 dict 这个数据结构随着数据量的增减，会需要在中途做扩容或者缩容操作，如果只有一个的话，对它进行扩容缩容时会影响正常的访问和修改操作，或者说保证正常查询，修改的正确性会比较复杂，并且因为需要高效利用空间，不能一下子申请一个非常大的空间来存很少的数据。当 dict 中 dictht 中的数据量超过 size 的时候负载就超过了 1，就需要进行扩容，这里的其实跟 Java 中的 HashMap 比较类似，超过一定的负载之后进行扩容。这里为啥 size 会超过 1 呢，可能有部分不了解这类结构的同学会比较奇怪，其实就是上图中画的，在数据结构中对于散列的冲突有几类解决方法，比如转换成链表，二次散列，找下个空槽等，这里就使用了链表法，或者说拉链法。当一个新元素通过 hashFunction 得出的 key 跟 sizemask 取模之后的值相同了，那就将其放在原来的节点之前，变成链表挂在数组 dictht.table下面，放在原有节点前是考虑到可能会优先访问。
忘了说明下 dictht 跟 dictEntry 的关系了，dictht 就是个哈希表，它里面是个dictEntry 的二维数组，而 dictEntry 是个包含了 key-value 结构之外还有一个 next 指针，因此可以将哈希冲突的以链表的形式保存下来。
在重点说下重哈希，可能同样写 Java 的同学对这个比较有感觉，跟 HashMap 一样，会以 2 的 N 次方进行扩容，那么扩容的方法就会比较简单，每个键重哈希要不就在原来这个槽，要不就在原来的槽加原 dictht.size 的位置；然后是重头戏，具体是怎么做扩容呢，其实这里就把第二个 ht 用上了，其实这两个hashtable 的具体作用有点类似于 jvm 中的两个 survival 区，但是又不全一样，因为 redis 在扩容的时候是采用的渐进式地重哈希，什么叫渐进式的呢，就是它不是像 jvm 那种标记复制的模式直接将一个 eden 区和原来的 survival 区存活的对象复制到另一个 survival 区，而是在每一次添加，删除，查找或者更新操作时，都会额外的帮忙搬运一部分的原 dictht 中的数据，这里会根据 rehashidx 的值来判断，如果是-1 表示并没有在重哈希中，如果是 0 表示开始重哈希了，然后rehashidx 还会随着每次的帮忙搬运往上加，但全部被搬运完成后 rehashidx 又变回了-1，又可以扯到Java 中的 Concurrent HashMap, 他在扩容的时候也使用了类似的操作。

发表于 更新于 分类于 ， ， 阅读次数： Disqus：

这是个 Java 面试的高频问题，我也遇到过，以往都是觉得这类题没意思，网上一搜一大堆，也不愿意记，其实说回来，主要还是没静下心来好好去理解，今天无意中看到一个课程，基本帮我把一些疑惑的点讲清楚了，首先单例是啥意思，这个其实是有范围一说，比如我起了个Spring Boot应用，在这个应用范围内，我的常规 bean 是单例的，意味着 getBean 的时候其实永远只会拿到那一个对象，那要怎么来写一个单例呢，首先就是传说中的饿汉模式，也是最简单的

饿汉模式

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public class Singleton1 {
    // 首先，将构造方法变成私有的
    private Singleton1() {};
    // 创建私有静态实例，这样第一次使用的时候就会进行创建
    private static Singleton instance = new Singleton1();

    // 使用这个对象都是通过这个 getInstance 来获取
    public static Singleton1 getInstance() {
        return instance;
    }
    // 瞎写一个静态方法。这里想说的是，如果我们只是要调用 Singleton.getDate(...)，
    // 本来是不想要生成 Singleton 实例的，不过没办法，已经生成了
    public static Date getDate(String mode) {return new Date();}
}

上面借鉴了一些代码，其实这是最基本，也不会错的方法，但是正如其中getDate方法里说的问题，有时候并没有想那这个对象，但是因为我调用了这个类的静态方法，导致对象已经生成了，可能这也是饿汉模式名字的来由，不管三七二十一给你生成个单例就完事了，不管有没有用，但是这种个人觉得也没啥大问题，如果是面试的话最好说出来它的缺点

饱汉模式

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public class Singleton2 {
    // 首先，也是先堵死 new Singleton() 这条路,将构造方法变成私有
    private Singleton2() {}
    // 和饿汉模式相比，这边不需要先实例化出来，注意这里的 volatile，它是必须的
    private static volatile Singleton2 instance = null;

    private int m = 9;

    public static Singleton getInstance() {
        if (instance == null) {
            // 加锁
            synchronized (Singleton2.class) {
                // 这一次判断也是必须的，不然会有并发问题
                if (instance == null) {
                    instance = new Singleton2();
                }
            }
        }
        return instance;
    }
}

这里容易错的有三点，理解了其实就比较好记了

第一点，为啥不在 getInstance 上整个代码块加 synchronized，这个其实比较容易理解，就是锁的力度太大，性能太差了，这点其实也要去理解，可以举个夸张的例子，比如我一个电商的服务，如果为了避免一个人的订单出现问题，是不是可以从请求入口就把他锁住，到请求结束释放，那么里面做的事情都有保障，然而这显然不可能，因为我们想要这种竞态条件抢占资源的时间尽量减少，防止其他线程等待。
第二点，为啥synchronized之已经检查了 instance == null,还要在里面再检查一次，这个有个术语，叫 double check lock,但是为啥要这么做呢，其实很简单，想象当有两个线程，都过了第一步为空判断，这个时候只有一个线程能拿到这个锁，另一个线程就等待了，如果不再判断一次，那么第一个线程新建完对象释放锁之后，第二个线程又能拿到锁，再去创建一个对象。
第三点，为啥要volatile关键字，原先对它的理解是它修饰的变量在 JMM 中能及时将变量值写到主存中，但是它还有个很重要的作用，就是防止指令重排序，instance = new Singleton();这行代码其实在底层是分成三条指令执行的，第一条是在堆上申请了一块内存放这个对象，但是对象的字段啥的都还是默认值，第二条是设置对象的值，比如上面的 m 是 9，然后第三条是将这个对象和虚拟机栈上的指针建立引用关联，那么如果我不用volatile关键字，这三条指令就有可能出现重排，比如变成了 1-3-2 这种顺序，当执行完第二步时，有个线程来访问这个对象了，先判断是不是空，发现不是空的，就拿去直接用了，是不是就出现问题了，所以这个volatile也是不可缺少的

嵌套类

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public class Singleton3 {

    private Singleton3() {}
    // 主要是使用了 嵌套类可以访问外部类的静态属性和静态方法 的特性
    private static class Holder {
        private static Singleton3 instance = new Singleton3();
    }
    public static Singleton3 getInstance() {
        return Holder.instance;
    }
}

这个我个人感觉是饿汉模式的升级版，可以在调用getInstance的时候去实例化对象，也是比较推荐的

枚举单例

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public enum Singleton {
    INSTANCE;
    
    public void doSomething(){
        //todo doSomething
    }
}

枚举很特殊，它在类加载的时候会初始化里面的所有的实例，而且 JVM 保证了它们不会再被实例化，所以它天生就是单例的。

发表于 更新于 分类于 ， ， 阅读次数： Disqus：

看完了村上春树的《1Q84》，这应该是第五本看的他的书了，继 跑步，挪威的森林，刺杀骑士团长，海边的卡夫卡之后，不是其中最长的，好像是海边的卡夫卡还是刺杀骑士团长比较长一点，都是在微信读书上看的，比较方便，最开始在上面看的是高晓松的《鱼羊野史》，不知道为啥取这个名字，但是还是满吸引我的，不过由于去年的种种，没有很多心思把它看完，而且本身的组织形式就是比较松散的，看到哪算哪，其实一些野史部分是我比较喜欢，有些谈到人物的就不太有兴趣，而且类似于大祥哥吃的东西，反正都是哇，怎么这么好吃，嗯，太爱（niu）你（bi）了，高晓松就是这个人是我最喜欢的 xxx 家，我也没去细究过他有没有说重复过，反正是不太爱，后来因为这书还一度对战争史有了浓厚的兴趣，然而事实告诉我，大部头的战争史，其实正史我是真的啃不下去，我可能只对其中 10%的内容感兴趣，不过终于也在今年把它看完了，好像高晓松的晓说也最终季了，貌似其中讲朝鲜战争的还被和谐了，看样子是说出了一些故事（truth）。

本来只是想把 《1Q84》的读后感写下，现在觉得还是把这篇当成我今年的读书总结吧，不过先从《1Q84》说起。

严格来讲，这不是很书面化的读后感，可能我想写的也只是像聊天一样的说下我读过的书，包括的技术博客其实也是类似的，以后或许会转变，但是目前水平如此吧，写多了可能会变好，也可能不会。

开始正文吧，这书有点类似于海边的卡夫卡，一开始是通过两条故事线，穿插着叙述，一条是青豆的，不算是个职业杀手的女杀手，要去解决一个经常家暴的斯文败类，穿着描述得比较性感吧，杀人方式是通过比较长的细针，从脖子后面一个精巧的位置插入，可以造成是未知原因死亡的假象，可能会推断成心梗之类的，这里有个前置的细节，就是青豆是乘坐一辆很高级的出租车，内饰什么的都非常有质感，有点不像一辆出租车，然后车里放了一首比较小众的歌，雅纳切克的《小交响曲》，但是青豆知道它，这跟后面的情节也有些许关系，这是女主人公青豆的出场；相应的男主的出场印象不是太深刻，男主叫天吾，是个不知名的作家，跟一个叫小松的编辑有比较好的关系，虽然天吾还没有拿到比较有分量的奖项，但是小松很看好他，也让他帮忙审校一个新作家奖的投稿文章，虽然天吾自身还没获得过这个奖，天吾还有个正式工作，是当数学老师，天吾在学生时代是个数学天才，但后面有对文学产生了兴趣，文学还不足以养活自己，靠着教课还是能保持温饱；

接下来是正式故事的起点了，就是小松收到了一部小说投稿，名叫《空气蛹》，是个叫深绘里的女孩子投的稿，小松对他赋予了很高的评价，这里好像记岔了，好像是天吾对这部小说很有好感，但是小松比较怀疑，然后小松看了之后也有了浓厚的兴趣，这里就是开端了，小松想让天吾来重写润色这部《空气蛹》，因为故事本身很有分量，但是描写手法叙事方式等都很拙劣，而天吾正好擅长这个，小松对天吾的评价是，描写技巧无可挑剔，就是故事主体的火花还没际遇迸发，需要一个导火索，这个就可以类比我们程序员，很多比较初中级的程序员主要擅长在原来的代码上修修改改或者给他分配个小功能，比较高级的程序员就需要能做一些项目的架构设计，核心的技术方案设计，以前我也觉得写文档这个比较无聊，但是当一个项目真的比较庞大，复杂的时候，整体和核心部分的架构设计和方案还是需要有文档沉淀的，不然别人不知道没法接受，自己过段时间也会忘记。

对于小松的这个建议，他的初衷是想搅一搅这个死气沉沉套路颇深的文坛，因为本身《空气蛹》这部小说的内容很吸引人，小松想通过天吾的润色补充让这部小说冲击新人奖，有种恶作剧的意图，天吾对此表示很多担心和顾虑，小松的这个建议其实也是一种文学作假，有两方面的担心，一方面是原作者深绘里是否同意如此操作，一方面是外界如果发现了这个事实会有什么样的后果，但是小松表示不用担心，前一步由小松牵线，让天吾跟原作者深绘里当面沟通这个代写是否被允许，结果当然是被允许了，这里有了对深绘里的初步描写，按我的理解是比较仙的感觉，然后语言沟通有些吃力，或者说有她自己的特色，当面沟通时貌似是让深绘里回去再考虑下，然后后面再由天吾去深绘里寄宿的戎野老师家沟通具体的细节。

2019年12月18日23:37:19 更新
去到戎野老师家之后，天吾知道了关于深绘里的一些事情，深绘里的父亲与戎野老师应该是老友，深绘里的父亲在当初成立了一个叫”先驱”的公社，一个独立运行的社会组织，以运营农场作为物资来源，追求更为松散的共同体，即不过分激进地公有制，进行松散的共同生活，承认私有财产，简而言之就是这样一个能稳定存活下来的独立社会组织，但是随着稳定运行，内部的激进派和稳健派开始出现分歧，不可磨合，后来两派就分裂了，深绘里的父亲，深田保留在了稳健派，但是此时其实深田保内心是矛盾的，以为一开始其实是他倡导的独立革命才组织起了这群人，然而现在他又认清了现实社会已经不太相信能通过革命来独立的可能性，后来激进派便开始越加封闭，而且进行军事训练和思想教育，而后这个先驱的激进派别便有了新的名字”黎明”，深绘里也是在此时从先驱逃离来投靠戎野老师
暂时先写到这，未完待续~

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\ No newline at end of file diff --git a/search.xml b/search.xml index 96dfcc8af1..629ef508d6 100644 --- a/search.xml +++ b/search.xml @@ -30,22 +30,23 @@ - 2020年中总结 - /2020/07/11/2020%E5%B9%B4%E4%B8%AD%E6%80%BB%E7%BB%93/ - 很快2020 年就过了一半了，而且是今年这么特殊的一年，很多事情都发生的出乎意料，疫情这个绕不过去的话题，之前写了点比较愤青的文字，感觉不太适合发出来就烂在草稿箱里吧，这个目前一大影响估计是今年都没办法完全摘下口罩了，前面几个月来回杭州都开车，因为彭埠大桥不通行了，实在是非常不方便，每条路都灰常堵，心累，吐槽下杭州的交通规划和交警同志，工作实在做的不咋地。

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另外一件是就是蜗壳，从前不知道黝黑蜗壳是啥意思，只是经常会在他的视频里看到，大学的时候在缘网下了一个集锦，炒鸡帅气，各种空接扣篮，越来越能明白那句“你永远不知道意外和明天不知道哪个会先来,且行且珍惜”的含义，只是听了很多道理，依然活不好这一生，知易行难，王阳明真的是这方面的大师，有空可以看看这方面的书，一直想写写我跟篮球跟蜗壳的这十几年，争取能早日写好吧，不过得找个静得下来的时候写。

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正事方面上半年还是挺让人失望的，没有达成一些目标，应该还是能力不足吧，技术方面分析一下还是停留在看的表面层，有些实操的，或者结合业务场景的能力不太行，算是在坚持写写 blog，主要是被这个每周一篇的目标推着走，有时会比较焦虑，内容产出也还比较差，希望能在后面有些改善，可能会降低频率，只是觉得降低了也不一定能有比较好的提升，无法战胜自己的惰性，所以暂时还是坚持下这个目标吧，还有就是 coding 能力，有时候也应该刷刷题，提升思维敏捷度，大脑用太少可能生锈了，况且本来就不是很有优势，虽然失望也只能继续努力吧，日拱一卒，来日方长，加油吧~😔

-

还有就是跑步减肥了，截止今天，上半年跑了 136 公里了，因为疫情影响，农历年后是从 4 月 17 号开始跑的，去年跑到了 300 公里，奖励自己了一个手表（真的挺后悔的，还不如 200 块买个手表），今年希望可以能在这个基础上再进一步，一直跟领导说，跑步算是我坚持下来的唯一一个好习惯了，618 买了个跑步机，周末回家了可以不受天气影响的多跑跑，不过如果天气好可能还是会出去跑跑，跑步机跑道多少还是有点拘束，只是感觉可能是我还是吃得太多了🤦‍♂️，效果不是很明显，还在 80 这个坎徘徊，等于浪费了大半年，可能是年初的项目太费心力，压力比较大，吃得更多，是不是可以算工伤😄，这方面也需要好好调整，可以放得开一点，虽然不太可能一下子到位，但是总要去努力下，随着年龄成长总要承担更多，也要看得开一点，没法事事如愿，尽力就好了，减肥这个事情还在结合一些俯卧撑啥的，希望也能坚持下去，加油吧，不知道原话怎么说的，意思是人类最大的勇敢就是看透了人世间的苦难，仍然热爱生活。我当然没可能让内心变得这么强大，试着去努力吧，奥力给！

+ 村上春树《1Q84》读后感 + /2019/12/18/1Q84%E8%AF%BB%E5%90%8E%E6%84%9F/ + 看完了村上春树的《1Q84》，这应该是第五本看的他的书了，继 跑步，挪威的森林，刺杀骑士团长，海边的卡夫卡之后，不是其中最长的，好像是海边的卡夫卡还是刺杀骑士团长比较长一点，都是在微信读书上看的，比较方便，最开始在上面看的是高晓松的《鱼羊野史》，不知道为啥取这个名字，但是还是满吸引我的，不过由于去年的种种，没有很多心思把它看完，而且本身的组织形式就是比较松散的，看到哪算哪，其实一些野史部分是我比较喜欢，有些谈到人物的就不太有兴趣，而且类似于大祥哥吃的东西，反正都是哇，怎么这么好吃，嗯，太爱（niu）你（bi）了，高晓松就是这个人是我最喜欢的 xxx 家，我也没去细究过他有没有说重复过，反正是不太爱，后来因为这书还一度对战争史有了浓厚的兴趣，然而事实告诉我，大部头的战争史，其实正史我是真的啃不下去，我可能只对其中 10%的内容感兴趣，不过终于也在今年把它看完了，好像高晓松的晓说也最终季了，貌似其中讲朝鲜战争的还被和谐了，看样子是说出了一些故事（truth）。

+

本来只是想把 《1Q84》的读后感写下，现在觉得还是把这篇当成我今年的读书总结吧，不过先从《1Q84》说起。

+

严格来讲，这不是很书面化的读后感，可能我想写的也只是像聊天一样的说下我读过的书，包括的技术博客其实也是类似的，以后或许会转变，但是目前水平如此吧，写多了可能会变好，也可能不会。

+

开始正文吧，这书有点类似于海边的卡夫卡，一开始是通过两条故事线，穿插着叙述，一条是青豆的，不算是个职业杀手的女杀手，要去解决一个经常家暴的斯文败类，穿着描述得比较性感吧，杀人方式是通过比较长的细针，从脖子后面一个精巧的位置插入，可以造成是未知原因死亡的假象，可能会推断成心梗之类的，这里有个前置的细节，就是青豆是乘坐一辆很高级的出租车，内饰什么的都非常有质感，有点不像一辆出租车，然后车里放了一首比较小众的歌，雅纳切克的《小交响曲》，但是青豆知道它，这跟后面的情节也有些许关系，这是女主人公青豆的出场；相应的男主的出场印象不是太深刻，男主叫天吾，是个不知名的作家，跟一个叫小松的编辑有比较好的关系，虽然天吾还没有拿到比较有分量的奖项，但是小松很看好他，也让他帮忙审校一个新作家奖的投稿文章，虽然天吾自身还没获得过这个奖，天吾还有个正式工作，是当数学老师，天吾在学生时代是个数学天才，但后面有对文学产生了兴趣，文学还不足以养活自己，靠着教课还是能保持温饱；

+

接下来是正式故事的起点了，就是小松收到了一部小说投稿，名叫《空气蛹》，是个叫深绘里的女孩子投的稿，小松对他赋予了很高的评价，这里好像记岔了，好像是天吾对这部小说很有好感，但是小松比较怀疑，然后小松看了之后也有了浓厚的兴趣，这里就是开端了，小松想让天吾来重写润色这部《空气蛹》，因为故事本身很有分量，但是描写手法叙事方式等都很拙劣，而天吾正好擅长这个，小松对天吾的评价是，描写技巧无可挑剔，就是故事主体的火花还没际遇迸发，需要一个导火索，这个就可以类比我们程序员，很多比较初中级的程序员主要擅长在原来的代码上修修改改或者给他分配个小功能，比较高级的程序员就需要能做一些项目的架构设计，核心的技术方案设计，以前我也觉得写文档这个比较无聊，但是当一个项目真的比较庞大，复杂的时候，整体和核心部分的架构设计和方案还是需要有文档沉淀的，不然别人不知道没法接受，自己过段时间也会忘记。

+

对于小松的这个建议，他的初衷是想搅一搅这个死气沉沉套路颇深的文坛，因为本身《空气蛹》这部小说的内容很吸引人，小松想通过天吾的润色补充让这部小说冲击新人奖，有种恶作剧的意图，天吾对此表示很多担心和顾虑，小松的这个建议其实也是一种文学作假，有两方面的担心，一方面是原作者深绘里是否同意如此操作，一方面是外界如果发现了这个事实会有什么样的后果，但是小松表示不用担心，前一步由小松牵线，让天吾跟原作者深绘里当面沟通这个代写是否被允许，结果当然是被允许了，这里有了对深绘里的初步描写，按我的理解是比较仙的感觉，然后语言沟通有些吃力，或者说有她自己的特色，当面沟通时貌似是让深绘里回去再考虑下，然后后面再由天吾去深绘里寄宿的戎野老师家沟通具体的细节。

+

2019年12月18日23:37:19 更新
去到戎野老师家之后，天吾知道了关于深绘里的一些事情，深绘里的父亲与戎野老师应该是老友，深绘里的父亲在当初成立了一个叫”先驱”的公社，一个独立运行的社会组织，以运营农场作为物资来源，追求更为松散的共同体，即不过分激进地公有制，进行松散的共同生活，承认私有财产，简而言之就是这样一个能稳定存活下来的独立社会组织，但是随着稳定运行，内部的激进派和稳健派开始出现分歧，不可磨合，后来两派就分裂了，深绘里的父亲，深田保留在了稳健派，但是此时其实深田保内心是矛盾的，以为一开始其实是他倡导的独立革命才组织起了这群人，然而现在他又认清了现实社会已经不太相信能通过革命来独立的可能性，后来激进派便开始越加封闭，而且进行军事训练和思想教育，而后这个先驱的激进派别便有了新的名字”黎明”，深绘里也是在此时从先驱逃离来投靠戎野老师
暂时先写到这，未完待续~

]]> 生活 - 年中总结 - 2020 + 读后感 + 村上春树 - 生活 - 年中总结 - 2020 + 读后感 @@ -74,41 +75,22 @@ - 村上春树《1Q84》读后感 - /2019/12/18/1Q84%E8%AF%BB%E5%90%8E%E6%84%9F/ - 看完了村上春树的《1Q84》，这应该是第五本看的他的书了，继 跑步，挪威的森林，刺杀骑士团长，海边的卡夫卡之后，不是其中最长的，好像是海边的卡夫卡还是刺杀骑士团长比较长一点，都是在微信读书上看的，比较方便，最开始在上面看的是高晓松的《鱼羊野史》，不知道为啥取这个名字，但是还是满吸引我的，不过由于去年的种种，没有很多心思把它看完，而且本身的组织形式就是比较松散的，看到哪算哪，其实一些野史部分是我比较喜欢，有些谈到人物的就不太有兴趣，而且类似于大祥哥吃的东西，反正都是哇，怎么这么好吃，嗯，太爱（niu）你（bi）了，高晓松就是这个人是我最喜欢的 xxx 家，我也没去细究过他有没有说重复过，反正是不太爱，后来因为这书还一度对战争史有了浓厚的兴趣，然而事实告诉我，大部头的战争史，其实正史我是真的啃不下去，我可能只对其中 10%的内容感兴趣，不过终于也在今年把它看完了，好像高晓松的晓说也最终季了，貌似其中讲朝鲜战争的还被和谐了，看样子是说出了一些故事（truth）。

-

本来只是想把 《1Q84》的读后感写下，现在觉得还是把这篇当成我今年的读书总结吧，不过先从《1Q84》说起。

-

严格来讲，这不是很书面化的读后感，可能我想写的也只是像聊天一样的说下我读过的书，包括的技术博客其实也是类似的，以后或许会转变，但是目前水平如此吧，写多了可能会变好，也可能不会。

-

开始正文吧，这书有点类似于海边的卡夫卡，一开始是通过两条故事线，穿插着叙述，一条是青豆的，不算是个职业杀手的女杀手，要去解决一个经常家暴的斯文败类，穿着描述得比较性感吧，杀人方式是通过比较长的细针，从脖子后面一个精巧的位置插入，可以造成是未知原因死亡的假象，可能会推断成心梗之类的，这里有个前置的细节，就是青豆是乘坐一辆很高级的出租车，内饰什么的都非常有质感，有点不像一辆出租车，然后车里放了一首比较小众的歌，雅纳切克的《小交响曲》，但是青豆知道它，这跟后面的情节也有些许关系，这是女主人公青豆的出场；相应的男主的出场印象不是太深刻，男主叫天吾，是个不知名的作家，跟一个叫小松的编辑有比较好的关系，虽然天吾还没有拿到比较有分量的奖项，但是小松很看好他，也让他帮忙审校一个新作家奖的投稿文章，虽然天吾自身还没获得过这个奖，天吾还有个正式工作，是当数学老师，天吾在学生时代是个数学天才，但后面有对文学产生了兴趣，文学还不足以养活自己，靠着教课还是能保持温饱；

-

接下来是正式故事的起点了，就是小松收到了一部小说投稿，名叫《空气蛹》，是个叫深绘里的女孩子投的稿，小松对他赋予了很高的评价，这里好像记岔了，好像是天吾对这部小说很有好感，但是小松比较怀疑，然后小松看了之后也有了浓厚的兴趣，这里就是开端了，小松想让天吾来重写润色这部《空气蛹》，因为故事本身很有分量，但是描写手法叙事方式等都很拙劣，而天吾正好擅长这个，小松对天吾的评价是，描写技巧无可挑剔，就是故事主体的火花还没际遇迸发，需要一个导火索，这个就可以类比我们程序员，很多比较初中级的程序员主要擅长在原来的代码上修修改改或者给他分配个小功能，比较高级的程序员就需要能做一些项目的架构设计，核心的技术方案设计，以前我也觉得写文档这个比较无聊，但是当一个项目真的比较庞大，复杂的时候，整体和核心部分的架构设计和方案还是需要有文档沉淀的，不然别人不知道没法接受，自己过段时间也会忘记。

-

对于小松的这个建议，他的初衷是想搅一搅这个死气沉沉套路颇深的文坛，因为本身《空气蛹》这部小说的内容很吸引人，小松想通过天吾的润色补充让这部小说冲击新人奖，有种恶作剧的意图，天吾对此表示很多担心和顾虑，小松的这个建议其实也是一种文学作假，有两方面的担心，一方面是原作者深绘里是否同意如此操作，一方面是外界如果发现了这个事实会有什么样的后果，但是小松表示不用担心，前一步由小松牵线，让天吾跟原作者深绘里当面沟通这个代写是否被允许，结果当然是被允许了，这里有了对深绘里的初步描写，按我的理解是比较仙的感觉，然后语言沟通有些吃力，或者说有她自己的特色，当面沟通时貌似是让深绘里回去再考虑下，然后后面再由天吾去深绘里寄宿的戎野老师家沟通具体的细节。

-

2019年12月18日23:37:19 更新
去到戎野老师家之后，天吾知道了关于深绘里的一些事情，深绘里的父亲与戎野老师应该是老友，深绘里的父亲在当初成立了一个叫”先驱”的公社，一个独立运行的社会组织，以运营农场作为物资来源，追求更为松散的共同体，即不过分激进地公有制，进行松散的共同生活，承认私有财产，简而言之就是这样一个能稳定存活下来的独立社会组织，但是随着稳定运行，内部的激进派和稳健派开始出现分歧，不可磨合，后来两派就分裂了，深绘里的父亲，深田保留在了稳健派，但是此时其实深田保内心是矛盾的，以为一开始其实是他倡导的独立革命才组织起了这群人，然而现在他又认清了现实社会已经不太相信能通过革命来独立的可能性，后来激进派便开始越加封闭，而且进行军事训练和思想教育，而后这个先驱的激进派别便有了新的名字”黎明”，深绘里也是在此时从先驱逃离来投靠戎野老师
暂时先写到这，未完待续~

+ 2020年中总结 + /2020/07/11/2020%E5%B9%B4%E4%B8%AD%E6%80%BB%E7%BB%93/ + 很快2020 年就过了一半了，而且是今年这么特殊的一年，很多事情都发生的出乎意料，疫情这个绕不过去的话题，之前写了点比较愤青的文字，感觉不太适合发出来就烂在草稿箱里吧，这个目前一大影响估计是今年都没办法完全摘下口罩了，前面几个月来回杭州都开车，因为彭埠大桥不通行了，实在是非常不方便，每条路都灰常堵，心累，吐槽下杭州的交通规划和交警同志，工作实在做的不咋地。

+

另外一件是就是蜗壳，从前不知道黝黑蜗壳是啥意思，只是经常会在他的视频里看到，大学的时候在缘网下了一个集锦，炒鸡帅气，各种空接扣篮，越来越能明白那句“你永远不知道意外和明天不知道哪个会先来,且行且珍惜”的含义，只是听了很多道理，依然活不好这一生，知易行难，王阳明真的是这方面的大师，有空可以看看这方面的书，一直想写写我跟篮球跟蜗壳的这十几年，争取能早日写好吧，不过得找个静得下来的时候写。

+

正事方面上半年还是挺让人失望的，没有达成一些目标，应该还是能力不足吧，技术方面分析一下还是停留在看的表面层，有些实操的，或者结合业务场景的能力不太行，算是在坚持写写 blog，主要是被这个每周一篇的目标推着走，有时会比较焦虑，内容产出也还比较差，希望能在后面有些改善，可能会降低频率，只是觉得降低了也不一定能有比较好的提升，无法战胜自己的惰性，所以暂时还是坚持下这个目标吧，还有就是 coding 能力，有时候也应该刷刷题，提升思维敏捷度，大脑用太少可能生锈了，况且本来就不是很有优势，虽然失望也只能继续努力吧，日拱一卒，来日方长，加油吧~😔

+

还有就是跑步减肥了，截止今天，上半年跑了 136 公里了，因为疫情影响，农历年后是从 4 月 17 号开始跑的，去年跑到了 300 公里，奖励自己了一个手表（真的挺后悔的，还不如 200 块买个手表），今年希望可以能在这个基础上再进一步，一直跟领导说，跑步算是我坚持下来的唯一一个好习惯了，618 买了个跑步机，周末回家了可以不受天气影响的多跑跑，不过如果天气好可能还是会出去跑跑，跑步机跑道多少还是有点拘束，只是感觉可能是我还是吃得太多了🤦‍♂️，效果不是很明显，还在 80 这个坎徘徊，等于浪费了大半年，可能是年初的项目太费心力，压力比较大，吃得更多，是不是可以算工伤😄，这方面也需要好好调整，可以放得开一点，虽然不太可能一下子到位，但是总要去努力下，随着年龄成长总要承担更多，也要看得开一点，没法事事如愿，尽力就好了，减肥这个事情还在结合一些俯卧撑啥的，希望也能坚持下去，加油吧，不知道原话怎么说的，意思是人类最大的勇敢就是看透了人世间的苦难，仍然热爱生活。我当然没可能让内心变得这么强大，试着去努力吧，奥力给！

]]> 生活 - 读后感 - 村上春树 - - - 读后感 - - - - 34_Search_for_a_Range - /2016/08/14/34-Search-for-a-Range/ - question

34. Search for a Range

Original Page

-

Given a sorted array of integers, find the starting and ending position of a given target value.

-

Your algorithm’s runtime complexity must be in the order of O(log n).

-

If the target is not found in the array, return [-1, -1].

-

For example,
Given [5, 7, 7, 8, 8, 10] and target value 8,
return [3, 4].

-

analysis

一开始就想到了二分查找，但是原来做二分查找的时候一般都是找到确定的那个数就完成了，
这里的情况比较特殊，需要找到整个区间，所以需要两遍查找，并且一个是找到小于target
的最大索引，一个是找到大于target的最大索引，代码参考leetcode discuss,这位仁
兄也做了详细的分析解释。

-

code

class Solution {
public:
    vector<int> searchRange(vector<int>& nums, int target) {
        vector<int> ret(2, -1);
        int i = 0, j = nums.size() - 1;
        int mid;
        while(i < j){
            mid = (i + j) / 2;
            if(nums[mid] < target) i = mid + 1;
            else j = mid;
        }
        if(nums[i] != target) return ret;
        else {
            ret[0] = i;
            if((i+1) < (nums.size() - 1) && nums[i+1] > target){
                ret[1] = i;
                return ret;
            }
        }   //一点小优化
        j = nums.size() - 1;
        while(i < j){
            mid = (i + j) / 2 + 1;
            if(nums[mid] > target) j = mid - 1;
            else i = mid;
        }
        ret[1] = j;
        return ret;
    }
};
]]> - - leetcode + 年中总结 + 2020 - leetcode - c++ + 生活 + 2020 + 年中总结 @@ -128,8 +110,8 @@ 生活 - 年中总结 2021 + 年中总结 技术 读书 @@ -209,23 +191,21 @@ - Apollo 如何获取当前环境 - /2022/09/04/Apollo-%E5%A6%82%E4%BD%95%E8%8E%B7%E5%8F%96%E5%BD%93%E5%89%8D%E7%8E%AF%E5%A2%83/ - 在用 Apollo 作为配置中心的过程中才到过几个坑，这边记录下，因为运行 java 服务的启动参数一般比较固定，所以我们在一个新环境里运行的时候没有特意去检查，然后突然发现业务上有一些数据异常，排查之后才发现java 服务连接了测试环境的 apollo，而原因是因为环境变量传了-Denv=fat，而在我们的环境配置中 fat 就是代表测试环境， 其实应该是-Denv=pro，而 apollo 总共有这些环境

-
public enum Env{
  LOCAL, DEV, FWS, FAT, UAT, LPT, PRO, TOOLS, UNKNOWN;

  public static Env fromString(String env) {
    Env environment = EnvUtils.transformEnv(env);
    Preconditions.checkArgument(environment != UNKNOWN, String.format("Env %s is invalid", env));
    return environment;
  }
}
-

而这些解释

-
/**
 * Here is the brief description for all the predefined environments:
 * <ul>
 *   <li>LOCAL: Local Development environment, assume you are working at the beach with no network access</li>
 *   <li>DEV: Development environment</li>
 *   <li>FWS: Feature Web Service Test environment</li>
 *   <li>FAT: Feature Acceptance Test environment</li>
 *   <li>UAT: User Acceptance Test environment</li>
 *   <li>LPT: Load and Performance Test environment</li>
 *   <li>PRO: Production environment</li>
 *   <li>TOOLS: Tooling environment, a special area in production environment which allows
 * access to test environment, e.g. Apollo Portal should be deployed in tools environment</li>
 * </ul>
 */
-

那如果要在运行时知道 apollo 当前使用的环境可以用这个

-
Env apolloEnv = ApolloInjector.getInstance(ConfigUtil.class).getApolloEnv();
-

简单记录下。

-]]> + 34_Search_for_a_Range + /2016/08/14/34-Search-for-a-Range/ + question

34. Search for a Range

Original Page

+

Given a sorted array of integers, find the starting and ending position of a given target value.

+

Your algorithm’s runtime complexity must be in the order of O(log n).

+

If the target is not found in the array, return [-1, -1].

+

For example,
Given [5, 7, 7, 8, 8, 10] and target value 8,
return [3, 4].

+

analysis

一开始就想到了二分查找，但是原来做二分查找的时候一般都是找到确定的那个数就完成了，
这里的情况比较特殊，需要找到整个区间，所以需要两遍查找，并且一个是找到小于target
的最大索引，一个是找到大于target的最大索引，代码参考leetcode discuss,这位仁
兄也做了详细的分析解释。

+

code

class Solution {
public:
    vector<int> searchRange(vector<int>& nums, int target) {
        vector<int> ret(2, -1);
        int i = 0, j = nums.size() - 1;
        int mid;
        while(i < j){
            mid = (i + j) / 2;
            if(nums[mid] < target) i = mid + 1;
            else j = mid;
        }
        if(nums[i] != target) return ret;
        else {
            ret[0] = i;
            if((i+1) < (nums.size() - 1) && nums[i+1] > target){
                ret[1] = i;
                return ret;
            }
        }   //一点小优化
        j = nums.size() - 1;
        while(i < j){
            mid = (i + j) / 2 + 1;
            if(nums[mid] > target) j = mid - 1;
            else i = mid;
        }
        ret[1] = j;
        return ret;
    }
};
]]> - Java + leetcode - Java - Apollo - environment + leetcode + c++ @@ -248,6 +228,26 @@ c++ + + Apollo 如何获取当前环境 + /2022/09/04/Apollo-%E5%A6%82%E4%BD%95%E8%8E%B7%E5%8F%96%E5%BD%93%E5%89%8D%E7%8E%AF%E5%A2%83/ + 在用 Apollo 作为配置中心的过程中才到过几个坑，这边记录下，因为运行 java 服务的启动参数一般比较固定，所以我们在一个新环境里运行的时候没有特意去检查，然后突然发现业务上有一些数据异常，排查之后才发现java 服务连接了测试环境的 apollo，而原因是因为环境变量传了-Denv=fat，而在我们的环境配置中 fat 就是代表测试环境， 其实应该是-Denv=pro，而 apollo 总共有这些环境

+
public enum Env{
  LOCAL, DEV, FWS, FAT, UAT, LPT, PRO, TOOLS, UNKNOWN;

  public static Env fromString(String env) {
    Env environment = EnvUtils.transformEnv(env);
    Preconditions.checkArgument(environment != UNKNOWN, String.format("Env %s is invalid", env));
    return environment;
  }
}
+

而这些解释

+
/**
 * Here is the brief description for all the predefined environments:
 * <ul>
 *   <li>LOCAL: Local Development environment, assume you are working at the beach with no network access</li>
 *   <li>DEV: Development environment</li>
 *   <li>FWS: Feature Web Service Test environment</li>
 *   <li>FAT: Feature Acceptance Test environment</li>
 *   <li>UAT: User Acceptance Test environment</li>
 *   <li>LPT: Load and Performance Test environment</li>
 *   <li>PRO: Production environment</li>
 *   <li>TOOLS: Tooling environment, a special area in production environment which allows
 * access to test environment, e.g. Apollo Portal should be deployed in tools environment</li>
 * </ul>
 */
+

那如果要在运行时知道 apollo 当前使用的环境可以用这个

+
Env apolloEnv = ApolloInjector.getInstance(ConfigUtil.class).getApolloEnv();
+

简单记录下。

+]]> + + Java + + + Java + Apollo + environment + + Apollo 客户端启动过程分析 /2022/09/18/Apollo-%E5%AE%A2%E6%88%B7%E7%AB%AF%E5%90%AF%E5%8A%A8%E8%BF%87%E7%A8%8B%E5%88%86%E6%9E%90/ @@ -321,23 +321,6 @@ 注解 - - Clone Graph Part I - /2014/12/30/Clone-Graph-Part-I/ - problem
Clone a graph. Input is a Node pointer. Return the Node pointer of the cloned graph.

A graph is defined below:
struct Node {
vector neighbors;
}
- - -

code

typedef unordered_map<Node *, Node *> Map;
 
Node *clone(Node *graph) {
    if (!graph) return NULL;
 
    Map map;
    queue<Node *> q;
    q.push(graph);
 
    Node *graphCopy = new Node();
    map[graph] = graphCopy;
 
    while (!q.empty()) {
        Node *node = q.front();
        q.pop();
        int n = node->neighbors.size();
        for (int i = 0; i < n; i++) {
            Node *neighbor = node->neighbors[i];
            // no copy exists
            if (map.find(neighbor) == map.end()) {
                Node *p = new Node();
                map[node]->neighbors.push_back(p);
                map[neighbor] = p;
                q.push(neighbor);
            } else {     // a copy already exists
                map[node]->neighbors.push_back(map[neighbor]);
            }
        }
    }
 
    return graphCopy;
}
-

anlysis

using the Breadth-first traversal
and use a map to save the neighbors not to be duplicated.

-]]> - - leetcode - - - leetcode - C++ - - AbstractQueuedSynchronizer /2019/09/23/AbstractQueuedSynchronizer/ @@ -356,37 +339,52 @@ - Disruptor 系列三 - /2022/09/25/Disruptor-%E7%B3%BB%E5%88%97%E4%B8%89/ - 原来一直有点被误导，
gatingSequences用来标识每个 processer 的操作位点，但是怎么记录更新有点搞不清楚
其实问题在于 gatingSequences 是个 Sequence 数组，首先要看下怎么加进去的，
可以看到是在 com.lmax.disruptor.RingBuffer#addGatingSequences 这个方法里添加
首先是 com.lmax.disruptor.dsl.Disruptor#handleEventsWith(com.lmax.disruptor.EventHandler<? super T>...)
然后执行 com.lmax.disruptor.dsl.Disruptor#createEventProcessors(com.lmax.disruptor.Sequence[], com.lmax.disruptor.EventHandler<? super T>[])

-
EventHandlerGroup<T> createEventProcessors(
        final Sequence[] barrierSequences,
        final EventHandler<? super T>[] eventHandlers)
    {
        checkNotStarted();

        final Sequence[] processorSequences = new Sequence[eventHandlers.length];
        final SequenceBarrier barrier = ringBuffer.newBarrier(barrierSequences);

        for (int i = 0, eventHandlersLength = eventHandlers.length; i < eventHandlersLength; i++)
        {
            final EventHandler<? super T> eventHandler = eventHandlers[i];

            // 这里将 handler 包装成一个 BatchEventProcessor
            final BatchEventProcessor<T> batchEventProcessor =
                new BatchEventProcessor<>(ringBuffer, barrier, eventHandler);

            if (exceptionHandler != null)
            {
                batchEventProcessor.setExceptionHandler(exceptionHandler);
            }

            consumerRepository.add(batchEventProcessor, eventHandler, barrier);
            processorSequences[i] = batchEventProcessor.getSequence();
        }

        updateGatingSequencesForNextInChain(barrierSequences, processorSequences);

        return new EventHandlerGroup<>(this, consumerRepository, processorSequences);
    }
+ AQS篇一 + /2021/02/14/AQS%E7%AF%87%E4%B8%80/ + 很多东西都是时看时新，而且时间长了也会忘，所以再来复习下，也会有一些新的角度看法这次来聊下AQS的内容，主要是这几个点，

+

第一个线程

第一个线程抢到锁了，此时state跟阻塞队列是怎么样的，其实这里是之前没理解对的地方

+
/**
         * Fair version of tryAcquire.  Don't grant access unless
         * recursive call or no waiters or is first.
         */
        protected final boolean tryAcquire(int acquires) {
            final Thread current = Thread.currentThread();
            int c = getState();
            // 这里如果state还是0说明锁还空着
            if (c == 0) {
                // 因为是公平锁版本的，先去看下是否阻塞队列里有排着队的
                if (!hasQueuedPredecessors() &&
                    compareAndSetState(0, acquires)) {
                    // 没有排队的，并且state使用cas设置成功的就标记当前占有锁的线程是我
                    setExclusiveOwnerThread(current);
                    // 然后其实就返回了，包括阻塞队列的head和tail节点和waitStatus都没有设置
                    return true;
                }
            }
            else if (current == getExclusiveOwnerThread()) {
                int nextc = c + acquires;
                if (nextc < 0)
                    throw new Error("Maximum lock count exceeded");
                setState(nextc);
                return true;
            }
            // 这里就是第二个线程会返回false
            return false;
        }
    }
+ +

第二个线程

当第二个线程进来的时候应该是怎么样，结合代码来看

+
/**
     * Acquires in exclusive mode, ignoring interrupts.  Implemented
     * by invoking at least once {@link #tryAcquire},
     * returning on success.  Otherwise the thread is queued, possibly
     * repeatedly blocking and unblocking, invoking {@link
     * #tryAcquire} until success.  This method can be used
     * to implement method {@link Lock#lock}.
     *
     * @param arg the acquire argument.  This value is conveyed to
     *        {@link #tryAcquire} but is otherwise uninterpreted and
     *        can represent anything you like.
     */
    public final void acquire(int arg) {
        // 前面第一种情况是tryAcquire直接成功了，这个if判断第一个条件就是false，就不往下执行了
        // 如果是第二个线程，第一个条件获取锁不成功，条件判断!tryAcquire(arg) == true，就会走
        // acquireQueued(addWaiter(Node.EXCLUSIVE), arg)
        if (!tryAcquire(arg) &&
            acquireQueued(addWaiter(Node.EXCLUSIVE), arg))
            selfInterrupt();
    }
+ +

然后来看下addWaiter的逻辑

+
/**
     * Creates and enqueues node for current thread and given mode.
     *
     * @param mode Node.EXCLUSIVE for exclusive, Node.SHARED for shared
     * @return the new node
     */
    private Node addWaiter(Node mode) {
        // 这里是包装成一个node
        Node node = new Node(Thread.currentThread(), mode);
        // Try the fast path of enq; backup to full enq on failure
        // 最快的方式就是把当前线程的节点放在阻塞队列的最后
        Node pred = tail;
        // 只有当tail，也就是pred不为空的时候可以直接接上
        if (pred != null) {
            node.prev = pred;
            // 如果这里cas成功了，就直接接上返回了
            if (compareAndSetTail(pred, node)) {
                pred.next = node;
                return node;
            }
        }
        // 不然就会继续走到这里
        enq(node);
        return node;
    }
+ +

然后就是enq的逻辑了

+
/**
     * Inserts node into queue, initializing if necessary. See picture above.
     * @param node the node to insert
     * @return node's predecessor
     */
    private Node enq(final Node node) {
        for (;;) {
            // 如果状态没变化的话，tail这时还是null的
            Node t = tail;
            if (t == null) { // Must initialize
                // 这里就会初始化头结点，就是个空节点
                if (compareAndSetHead(new Node()))
                    // tail也赋值成head
                    tail = head;
            } else {
                // 这里就设置tail了
                node.prev = t;
                if (compareAndSetTail(t, node)) {
                    t.next = node;
                    return t;
                }
            }
        }
    }
+ +

所以从这里可以看出来，其实head头结点不是个真实的带有线程的节点，并且不是在第一个线程进来的时候设置的

+

解锁

通过代码来看下

+
/**
     * Attempts to release this lock.
     *
     * <p>If the current thread is the holder of this lock then the hold
     * count is decremented.  If the hold count is now zero then the lock
     * is released.  If the current thread is not the holder of this
     * lock then {@link IllegalMonitorStateException} is thrown.
     *
     * @throws IllegalMonitorStateException if the current thread does not
     *         hold this lock
     */
    public void unlock() {
        // 释放锁
        sync.release(1);
    }
/**
     * Releases in exclusive mode.  Implemented by unblocking one or
     * more threads if {@link #tryRelease} returns true.
     * This method can be used to implement method {@link Lock#unlock}.
     *
     * @param arg the release argument.  This value is conveyed to
     *        {@link #tryRelease} but is otherwise uninterpreted and
     *        can represent anything you like.
     * @return the value returned from {@link #tryRelease}
     */
    public final boolean release(int arg) {
        // 尝试去释放
        if (tryRelease(arg)) {
            Node h = head;
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }
protected final boolean tryRelease(int releases) {
            int c = getState() - releases;
            if (Thread.currentThread() != getExclusiveOwnerThread())
                throw new IllegalMonitorStateException();
            boolean free = false;
    		// 判断是否完全释放锁，因为可重入
            if (c == 0) {
                free = true;
                setExclusiveOwnerThread(null);
            }
            setState(c);
            return free;
        }
// 这段代码和上面的一致，只是为了顺序性，又拷下来看下

public final boolean release(int arg) {
        // 尝试去释放，如果是完全释放，返回的就是true，否则是false
        if (tryRelease(arg)) {
            Node h = head;
            // 这里判断头结点是否为空以及waitStatus的状态，前面说了head节点其实是
            // 在第二个线程进来的时候初始化的，如果是空的话说明没后续节点，并且waitStatus
            // 也表示了后续的等待状态
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }

/**
     * Wakes up node's successor, if one exists.
     *
     * @param node the node
     */
// 唤醒后继节点
    private void unparkSuccessor(Node node) {
        /*
         * If status is negative (i.e., possibly needing signal) try
         * to clear in anticipation of signalling.  It is OK if this
         * fails or if status is changed by waiting thread.
         */
        int ws = node.waitStatus;
        if (ws < 0)
            compareAndSetWaitStatus(node, ws, 0);

        /*
         * Thread to unpark is held in successor, which is normally
         * just the next node.  But if cancelled or apparently null,
         * traverse backwards from tail to find the actual
         * non-cancelled successor.
         */
        Node s = node.next;
        // 如果后继节点是空或者当前节点取消等待了
        if (s == null || s.waitStatus > 0) {
            s = null;
            // 从后往前找，找到非取消的节点，注意这里不是找到就退出，而是一直找到头
            // 所以不必担心中间有取消的
            for (Node t = tail; t != null && t != node; t = t.prev)
                if (t.waitStatus <= 0)
                    s = t;
        }
        if (s != null)
            // 将其唤醒
            LockSupport.unpark(s.thread);
    }
+ + -

在 BatchEventProcessor 在类内有个定义 sequence

-
private final Sequence sequence = new Sequence(Sequencer.INITIAL_CURSOR_VALUE);
-

然后在上面循环中的这一句取出来

-
processorSequences[i] = batchEventProcessor.getSequence();
-

调用com.lmax.disruptor.dsl.Disruptor#updateGatingSequencesForNextInChain 方法

-
private void updateGatingSequencesForNextInChain(final Sequence[] barrierSequences, final Sequence[] processorSequences)
    {
        if (processorSequences.length > 0)
        {
            // 然后在这里添加
            ringBuffer.addGatingSequences(processorSequences);
            for (final Sequence barrierSequence : barrierSequences)
            {
                ringBuffer.removeGatingSequence(barrierSequence);
            }
            consumerRepository.unMarkEventProcessorsAsEndOfChain(barrierSequences);
        }
    }
-

而如何更新则是在处理器 com.lmax.disruptor.BatchEventProcessor#run 中

-
public void run()
    {
        if (running.compareAndSet(IDLE, RUNNING))
        {
            sequenceBarrier.clearAlert();

            notifyStart();
            try
            {
                if (running.get() == RUNNING)
                {
                    processEvents();
                }
            }
            finally
            {
                notifyShutdown();
                running.set(IDLE);
            }
        }
        else
        {
            // This is a little bit of guess work.  The running state could of changed to HALTED by
            // this point.  However, Java does not have compareAndExchange which is the only way
            // to get it exactly correct.
            if (running.get() == RUNNING)
            {
                throw new IllegalStateException("Thread is already running");
            }
            else
            {
                earlyExit();
            }
        }
    }
-

然后是

-
private void processEvents()
    {
        T event = null;
        long nextSequence = sequence.get() + 1L;

        while (true)
        {
            try
            {
                final long availableSequence = sequenceBarrier.waitFor(nextSequence);
                if (batchStartAware != null)
                {
                    batchStartAware.onBatchStart(availableSequence - nextSequence + 1);
                }

                while (nextSequence <= availableSequence)
                {
                    event = dataProvider.get(nextSequence);
                    eventHandler.onEvent(event, nextSequence, nextSequence == availableSequence);
                    nextSequence++;
                }
                // 如果正常处理完，那就是会更新为 availableSequence，因为都处理好了
                sequence.set(availableSequence);
            }
            catch (final TimeoutException e)
            {
                notifyTimeout(sequence.get());
            }
            catch (final AlertException ex)
            {
                if (running.get() != RUNNING)
                {
                    break;
                }
            }
            catch (final Throwable ex)
            {
                handleEventException(ex, nextSequence, event);
                // 如果是异常就只是 nextSequence
                sequence.set(nextSequence);
                nextSequence++;
            }
        }
    }
]]> Java + 并发 - Java - Disruptor + java + 并发 + j.u.c + aqs - Disruptor 系列二 - /2022/02/27/Disruptor-%E7%B3%BB%E5%88%97%E4%BA%8C/ - 这里开始慢慢深入的讲一下 disruptor，首先是 lock free , 相比于前面介绍的两个阻塞队列，
disruptor 本身是不直接使用锁的，因为本身的设计是单个线程去生产，通过 cas 来维护头指针，
不直接维护尾指针，这样就减少了锁的使用，提升了性能；第二个是这次介绍的重点，
减少 false sharing 的情况，也就是常说的 伪共享 问题，那么什么叫 伪共享 呢，
这里要扯到一些 cpu 缓存的知识，

譬如我在用的这个笔记本

这里就可能看到 L2 Cache 就是针对每个核的

这里可以看到现代 CPU 的结构里，分为三级缓存，越靠近 cpu 的速度越快，存储容量越小，
而 L1 跟 L2 是 CPU 核专属的每个核都有自己的 L1 和 L2 的，其中 L1 还分为数据和指令，
像我上面的图中显示的 L1 Cache 只有 64KB 大小，其中数据 32KB，指令 32KB，
而 L2 则有 256KB，L3 有 4MB，其中的 Line Size 是我们这里比较重要的一个值，
CPU 其实会就近地从 Cache 中读取数据，碰到 Cache Miss 就再往下一级 Cache 读取，
每次读取是按照缓存行 Cache Line 读取，并且也遵循了“就近原则”，
也就是相近的数据有可能也会马上被读取，所以以行的形式读取，然而这也造成了 false sharing，
因为类似于 ArrayBlockingQueue，需要有 takeIndex , putIndex , count , 因为在同一个类中，
很有可能存在于同一个 Cache Line 中，但是这几个值会被不同的线程修改，
导致从 Cache 取出来以后立马就会被失效，所谓的就近原则也就没用了，
因为需要反复地标记 dirty 脏位，然后把 Cache 刷掉，就造成了false sharing这种情况
而在 disruptor 中则使用了填充的方式，让我的头指针能够不产生false sharing

-
class LhsPadding
{
    protected long p1, p2, p3, p4, p5, p6, p7;
}

class Value extends LhsPadding
{
    protected volatile long value;
}

class RhsPadding extends Value
{
    protected long p9, p10, p11, p12, p13, p14, p15;
}

/**
 * <p>Concurrent sequence class used for tracking the progress of
 * the ring buffer and event processors.  Support a number
 * of concurrent operations including CAS and order writes.
 *
 * <p>Also attempts to be more efficient with regards to false
 * sharing by adding padding around the volatile field.
 */
public class Sequence extends RhsPadding
{
-

通过代码可以看到，sequence 中其实真正有意义的是 value 字段，因为需要在多线程环境下可见也
使用了volatile 关键字，而 LhsPadding 和 RhsPadding 分别在value 前后填充了各
7 个 long 型的变量，long 型的变量在 Java 中是占用 8 bytes，这样就相当于不管怎么样，
value 都会单独使用一个缓存行，使得其不会产生 false sharing 的问题。

+ Disruptor 系列一 + /2022/02/13/Disruptor-%E7%B3%BB%E5%88%97%E4%B8%80/ + 很久之前就听说过这个框架，不过之前有点跟消息队列混起来，这个也是种队列，但不是跟 rocketmq，nsq 那种一样的，而是在进程内部提供队列服务的，偏向于取代ArrayBlockingQueue，因为这个阻塞队列是使用了锁来控制阻塞，关于并发其实有一些通用的最佳实践，就是用锁，即使是 JDK 提供的锁，也是比较耗资源的，当然这是跟不加锁的对比，同样是锁，JDK 的实现还是性能比较优秀的。常见的阻塞队列中例如 ArrayBlockingQueue 和 LinkedBlockingQueue 都有锁的身影的存在，区别在于 ArrayBlockingQueue 是一把锁，后者是两把锁，不过重点不在几把锁，这里其实是两个问题，一个是所谓的 lock free, 对于一个单生产者的 disruptor 来说，因为写入是只有一个线程的，是可以不用加锁，多生产者的时候使用的是 cas 来获取对应的写入坑位，另一个是解决“伪共享”问题，后面可以详细点分析，先介绍下使用
首先是数据源

+
public class LongEvent {
    private long value;

    public void set(long value) {
        this.value = value;
    }

    public long getValue() {
        return value;
    }

    public void setValue(long value) {
        this.value = value;
    }
}
+

事件生产

+
public class LongEventFactory implements EventFactory<LongEvent>
{
    public LongEvent newInstance()
    {
        return new LongEvent();
    }
}
+

事件处理器

+
public class LongEventHandler implements EventHandler<LongEvent> {

    // event 事件，
    // sequence 当前的序列 
    // 是否当前批次最后一个数据
    public void onEvent(LongEvent event, long sequence, boolean endOfBatch)
    {
        String str = String.format("long event : %s l:%s b:%s", event.getValue(), sequence, endOfBatch);
        System.out.println(str);
    }
}

+

主方法代码

+
package disruptor;

import com.lmax.disruptor.RingBuffer;
import com.lmax.disruptor.dsl.Disruptor;
import com.lmax.disruptor.util.DaemonThreadFactory;

import java.nio.ByteBuffer;

public class LongEventMain
{
    public static void main(String[] args) throws Exception
    {
        // 这个需要是 2 的幂次，这样在定位的时候只需要位移操作，也能减少各种计算操作
        int bufferSize = 1024; 

        Disruptor<LongEvent> disruptor = 
                new Disruptor<>(LongEvent::new, bufferSize, DaemonThreadFactory.INSTANCE);

        // 类似于注册处理器
        disruptor.handleEventsWith(new LongEventHandler());
        // 或者直接用 lambda
        disruptor.handleEventsWith((event, sequence, endOfBatch) ->
                System.out.println("Event: " + event));
        // 启动我们的 disruptor
        disruptor.start(); 


        RingBuffer<LongEvent> ringBuffer = disruptor.getRingBuffer(); 
        ByteBuffer bb = ByteBuffer.allocate(8);
        for (long l = 0; true; l++)
        {
            bb.putLong(0, l);
            // 生产事件
            ringBuffer.publishEvent((event, sequence, buffer) -> event.set(buffer.getLong(0)), bb);
            Thread.sleep(1000);
        }
    }
}
+

运行下可以看到运行结果

这里其实就只是最简单的使用，生产者只有一个，然后也不是批量的。

]]> Java @@ -397,25 +395,18 @@ - Dubbo 使用的几个记忆点 - /2022/04/02/Dubbo-%E4%BD%BF%E7%94%A8%E7%9A%84%E5%87%A0%E4%B8%AA%E8%AE%B0%E5%BF%86%E7%82%B9/ - 因为后台使用的 dubbo 作为 rpc 框架，并且会有一些日常使用情景有一些小的技巧，在这里做下记录作笔记用

-

dubbo 只拉取不注册

<dubbo:registry address="zookeeper://127.0.0.1:2181" register="false" />
-

就是只要 register="false" 就可以了，这样比如我们在开发环境想运行服务，但又不想让开发环境正常的请求调用到本地来，当然这不是唯一的方式，通过 dubbo 2.7 以上的 tag 路由也可以实现或者自行改造拉取和注册服务的逻辑，因为注册到注册中心的其实是一串带参数的 url，还是比较方便改造的。相反的就是只注册，不拉取

-

dubbo 只注册不拉取

<dubbo:registry address="zookeeper://127.0.0.1:2181" subscribe="false" />
-

这个使用场景就是如果我这个服务只作为 provider，没有任何调用其他的服务，其实就可以这么设置

-

权重配置

<dubbo:provider loadbalance="random" weight="50"/>
-

首先这是在使用了随机的负载均衡策略的时候可以进行配置，并且是对于多个 provider 的情况下，这样其实也可以部分解决上面的只拉取不注册的问题，我把自己的权重调成 0 或者很低

+ Disruptor 系列二 + /2022/02/27/Disruptor-%E7%B3%BB%E5%88%97%E4%BA%8C/ + 这里开始慢慢深入的讲一下 disruptor，首先是 lock free , 相比于前面介绍的两个阻塞队列，
disruptor 本身是不直接使用锁的，因为本身的设计是单个线程去生产，通过 cas 来维护头指针，
不直接维护尾指针，这样就减少了锁的使用，提升了性能；第二个是这次介绍的重点，
减少 false sharing 的情况，也就是常说的 伪共享 问题，那么什么叫 伪共享 呢，
这里要扯到一些 cpu 缓存的知识，

譬如我在用的这个笔记本

这里就可能看到 L2 Cache 就是针对每个核的

这里可以看到现代 CPU 的结构里，分为三级缓存，越靠近 cpu 的速度越快，存储容量越小，
而 L1 跟 L2 是 CPU 核专属的每个核都有自己的 L1 和 L2 的，其中 L1 还分为数据和指令，
像我上面的图中显示的 L1 Cache 只有 64KB 大小，其中数据 32KB，指令 32KB，
而 L2 则有 256KB，L3 有 4MB，其中的 Line Size 是我们这里比较重要的一个值，
CPU 其实会就近地从 Cache 中读取数据，碰到 Cache Miss 就再往下一级 Cache 读取，
每次读取是按照缓存行 Cache Line 读取，并且也遵循了“就近原则”，
也就是相近的数据有可能也会马上被读取，所以以行的形式读取，然而这也造成了 false sharing，
因为类似于 ArrayBlockingQueue，需要有 takeIndex , putIndex , count , 因为在同一个类中，
很有可能存在于同一个 Cache Line 中，但是这几个值会被不同的线程修改，
导致从 Cache 取出来以后立马就会被失效，所谓的就近原则也就没用了，
因为需要反复地标记 dirty 脏位，然后把 Cache 刷掉，就造成了false sharing这种情况
而在 disruptor 中则使用了填充的方式，让我的头指针能够不产生false sharing

+
class LhsPadding
{
    protected long p1, p2, p3, p4, p5, p6, p7;
}

class Value extends LhsPadding
{
    protected volatile long value;
}

class RhsPadding extends Value
{
    protected long p9, p10, p11, p12, p13, p14, p15;
}

/**
 * <p>Concurrent sequence class used for tracking the progress of
 * the ring buffer and event processors.  Support a number
 * of concurrent operations including CAS and order writes.
 *
 * <p>Also attempts to be more efficient with regards to false
 * sharing by adding padding around the volatile field.
 */
public class Sequence extends RhsPadding
{
+

通过代码可以看到，sequence 中其实真正有意义的是 value 字段，因为需要在多线程环境下可见也
使用了volatile 关键字，而 LhsPadding 和 RhsPadding 分别在value 前后填充了各
7 个 long 型的变量，long 型的变量在 Java 中是占用 8 bytes，这样就相当于不管怎么样，
value 都会单独使用一个缓存行，使得其不会产生 false sharing 的问题。

]]> Java - Dubbo Java - Dubbo - RPC - 负载均衡 + Disruptor @@ -452,6 +443,45 @@ nullsfirst + + Dubbo 使用的几个记忆点 + /2022/04/02/Dubbo-%E4%BD%BF%E7%94%A8%E7%9A%84%E5%87%A0%E4%B8%AA%E8%AE%B0%E5%BF%86%E7%82%B9/ + 因为后台使用的 dubbo 作为 rpc 框架，并且会有一些日常使用情景有一些小的技巧，在这里做下记录作笔记用

+

dubbo 只拉取不注册

<dubbo:registry address="zookeeper://127.0.0.1:2181" register="false" />
+

就是只要 register="false" 就可以了，这样比如我们在开发环境想运行服务，但又不想让开发环境正常的请求调用到本地来，当然这不是唯一的方式，通过 dubbo 2.7 以上的 tag 路由也可以实现或者自行改造拉取和注册服务的逻辑，因为注册到注册中心的其实是一串带参数的 url，还是比较方便改造的。相反的就是只注册，不拉取

+

dubbo 只注册不拉取

<dubbo:registry address="zookeeper://127.0.0.1:2181" subscribe="false" />
+

这个使用场景就是如果我这个服务只作为 provider，没有任何调用其他的服务，其实就可以这么设置

+

权重配置

<dubbo:provider loadbalance="random" weight="50"/>
+

首先这是在使用了随机的负载均衡策略的时候可以进行配置，并且是对于多个 provider 的情况下，这样其实也可以部分解决上面的只拉取不注册的问题，我把自己的权重调成 0 或者很低

+]]> + + Java + Dubbo + + + Java + Dubbo + RPC + 负载均衡 + + + + Clone Graph Part I + /2014/12/30/Clone-Graph-Part-I/ + problem
Clone a graph. Input is a Node pointer. Return the Node pointer of the cloned graph.

A graph is defined below:
struct Node {
vector neighbors;
}
+ + +

code

typedef unordered_map<Node *, Node *> Map;
 
Node *clone(Node *graph) {
    if (!graph) return NULL;
 
    Map map;
    queue<Node *> q;
    q.push(graph);
 
    Node *graphCopy = new Node();
    map[graph] = graphCopy;
 
    while (!q.empty()) {
        Node *node = q.front();
        q.pop();
        int n = node->neighbors.size();
        for (int i = 0; i < n; i++) {
            Node *neighbor = node->neighbors[i];
            // no copy exists
            if (map.find(neighbor) == map.end()) {
                Node *p = new Node();
                map[node]->neighbors.push_back(p);
                map[neighbor] = p;
                q.push(neighbor);
            } else {     // a copy already exists
                map[node]->neighbors.push_back(map[neighbor]);
            }
        }
    }
 
    return graphCopy;
}
+

anlysis

using the Breadth-first traversal
and use a map to save the neighbors not to be duplicated.

+]]> + + leetcode + + + leetcode + C++ + + Filter, Interceptor, Aop, 啥, 啥, 啥? 这些都是啥? /2020/08/22/Filter-Intercepter-Aop-%E5%95%A5-%E5%95%A5-%E5%95%A5-%E8%BF%99%E4%BA%9B%E9%83%BD%E6%98%AF%E5%95%A5/ @@ -506,8 +536,8 @@ Java JVM - GC C++ + GC Java @@ -519,77 +549,22 @@ - Headscale初体验以及踩坑记 - /2023/01/22/Headscale%E5%88%9D%E4%BD%93%E9%AA%8C%E4%BB%A5%E5%8F%8A%E8%B8%A9%E5%9D%91%E8%AE%B0/ - 最近或者说很久以前就想着能够把几个散装服务器以及家里的网络连起来，譬如一些remote desktop的访问，之前搞了下frp，因为家里电脑没怎么注意安全性就被搞了一下，所以还是想用相对更安全的方式，比如限定ip和端口进行访问，但是感觉ip也不固定就比较难搞，后来看到了 Tailscale 和 Headscale 的方式，就想着试试看，没想到一开始就踩了几个比较莫名其妙的坑。
可以按官方文档去搭建，也可以在网上找一些其他人搭建的教程。我碰到的主要是关于配置文件的问题

-

第一个问题

Error initializing error="failed to read or create private key: failed to save private key to disk: open /etc/headscale/private.key: read-only file system"
-

其实一开始看到这个我都有点懵了，咋回事呢，read-only file system一般有可能是文件系统出问题了，不可写入，需要重启或者修改挂载方式，被这个错误的错误日志给误导了，后面才知道是配置文件，在另一个教程中也有个类似的回复，一开始没注意，其实就是同一个问题。
默认的配置文件是这样的

-
---
# headscale will look for a configuration file named `config.yaml` (or `config.json`) in the following order:
#
# - `/etc/headscale`
# - `~/.headscale`
# - current working directory

# The url clients will connect to.
# Typically this will be a domain like:
#
# https://myheadscale.example.com:443
#
server_url: http://127.0.0.1:8080

# Address to listen to / bind to on the server
#
# For production:
# listen_addr: 0.0.0.0:8080
listen_addr: 127.0.0.1:8080

# Address to listen to /metrics, you may want
# to keep this endpoint private to your internal
# network
#
metrics_listen_addr: 127.0.0.1:9090

# Address to listen for gRPC.
# gRPC is used for controlling a headscale server
# remotely with the CLI
# Note: Remote access _only_ works if you have
# valid certificates.
#
# For production:
# grpc_listen_addr: 0.0.0.0:50443
grpc_listen_addr: 127.0.0.1:50443

# Allow the gRPC admin interface to run in INSECURE
# mode. This is not recommended as the traffic will
# be unencrypted. Only enable if you know what you
# are doing.
grpc_allow_insecure: false

# Private key used to encrypt the traffic between headscale
# and Tailscale clients.
# The private key file will be autogenerated if it's missing.
#
# For production:
# /var/lib/headscale/private.key
private_key_path: ./private.key

# The Noise section includes specific configuration for the
# TS2021 Noise protocol
noise:
  # The Noise private key is used to encrypt the
  # traffic between headscale and Tailscale clients when
  # using the new Noise-based protocol. It must be different
  # from the legacy private key.
  #
  # For production:
  # private_key_path: /var/lib/headscale/noise_private.key
  private_key_path: ./noise_private.key

# List of IP prefixes to allocate tailaddresses from.
# Each prefix consists of either an IPv4 or IPv6 address,
# and the associated prefix length, delimited by a slash.
# While this looks like it can take arbitrary values, it
# needs to be within IP ranges supported by the Tailscale
# client.
# IPv6: https://github.com/tailscale/tailscale/blob/22ebb25e833264f58d7c3f534a8b166894a89536/net/tsaddr/tsaddr.go#LL81C52-L81C71
# IPv4: https://github.com/tailscale/tailscale/blob/22ebb25e833264f58d7c3f534a8b166894a89536/net/tsaddr/tsaddr.go#L33
ip_prefixes:
  - fd7a:115c:a1e0::/48
  - 100.64.0.0/10

# DERP is a relay system that Tailscale uses when a direct
# connection cannot be established.
# https://tailscale.com/blog/how-tailscale-works/#encrypted-tcp-relays-derp
#
# headscale needs a list of DERP servers that can be presented
# to the clients.
derp:
  server:
    # If enabled, runs the embedded DERP server and merges it into the rest of the DERP config
    # The Headscale server_url defined above MUST be using https, DERP requires TLS to be in place
    enabled: false

    # Region ID to use for the embedded DERP server.
    # The local DERP prevails if the region ID collides with other region ID coming from
    # the regular DERP config.
    region_id: 999

    # Region code and name are displayed in the Tailscale UI to identify a DERP region
    region_code: "headscale"
    region_name: "Headscale Embedded DERP"

    # Listens over UDP at the configured address for STUN connections - to help with NAT traversal.
    # When the embedded DERP server is enabled stun_listen_addr MUST be defined.
    #
    # For more details on how this works, check this great article: https://tailscale.com/blog/how-tailscale-works/
    stun_listen_addr: "0.0.0.0:3478"

  # List of externally available DERP maps encoded in JSON
  urls:
    - https://controlplane.tailscale.com/derpmap/default

  # Locally available DERP map files encoded in YAML
  #
  # This option is mostly interesting for people hosting
  # their own DERP servers:
  # https://tailscale.com/kb/1118/custom-derp-servers/
  #
  # paths:
  #   - /etc/headscale/derp-example.yaml
  paths: []

  # If enabled, a worker will be set up to periodically
  # refresh the given sources and update the derpmap
  # will be set up.
  auto_update_enabled: true

  # How often should we check for DERP updates?
  update_frequency: 24h

# Disables the automatic check for headscale updates on startup
disable_check_updates: false

# Time before an inactive ephemeral node is deleted?
ephemeral_node_inactivity_timeout: 30m

# Period to check for node updates within the tailnet. A value too low will severely affect
# CPU consumption of Headscale. A value too high (over 60s) will cause problems
# for the nodes, as they won't get updates or keep alive messages frequently enough.
# In case of doubts, do not touch the default 10s.
node_update_check_interval: 10s

# SQLite config
db_type: sqlite3

# For production:
# db_path: /var/lib/headscale/db.sqlite
db_path: ./db.sqlite

# # Postgres config
# If using a Unix socket to connect to Postgres, set the socket path in the 'host' field and leave 'port' blank.
# db_type: postgres
# db_host: localhost
# db_port: 5432
# db_name: headscale
# db_user: foo
# db_pass: bar

# If other 'sslmode' is required instead of 'require(true)' and 'disabled(false)', set the 'sslmode' you need
# in the 'db_ssl' field. Refers to https://www.postgresql.org/docs/current/libpq-ssl.html Table 34.1.
# db_ssl: false

### TLS configuration
#
## Let's encrypt / ACME
#
# headscale supports automatically requesting and setting up
# TLS for a domain with Let's Encrypt.
#
# URL to ACME directory
acme_url: https://acme-v02.api.letsencrypt.org/directory

# Email to register with ACME provider
acme_email: ""

# Domain name to request a TLS certificate for:
tls_letsencrypt_hostname: ""

# Path to store certificates and metadata needed by
# letsencrypt
# For production:
# tls_letsencrypt_cache_dir: /var/lib/headscale/cache
tls_letsencrypt_cache_dir: ./cache

# Type of ACME challenge to use, currently supported types:
# HTTP-01 or TLS-ALPN-01
# See [docs/tls.md](docs/tls.md) for more information
tls_letsencrypt_challenge_type: HTTP-01
# When HTTP-01 challenge is chosen, letsencrypt must set up a
# verification endpoint, and it will be listening on:
# :http = port 80
tls_letsencrypt_listen: ":http"

## Use already defined certificates:
tls_cert_path: ""
tls_key_path: ""

log:
  # Output formatting for logs: text or json
  format: text
  level: info

# Path to a file containg ACL policies.
# ACLs can be defined as YAML or HUJSON.
# https://tailscale.com/kb/1018/acls/
acl_policy_path: ""

## DNS
#
# headscale supports Tailscale's DNS configuration and MagicDNS.
# Please have a look to their KB to better understand the concepts:
#
# - https://tailscale.com/kb/1054/dns/
# - https://tailscale.com/kb/1081/magicdns/
# - https://tailscale.com/blog/2021-09-private-dns-with-magicdns/
#
dns_config:
  # Whether to prefer using Headscale provided DNS or use local.
  override_local_dns: true

  # List of DNS servers to expose to clients.
  nameservers:
    - 1.1.1.1

  # NextDNS (see https://tailscale.com/kb/1218/nextdns/).
  # "abc123" is example NextDNS ID, replace with yours.
  #
  # With metadata sharing:
  # nameservers:
  #   - https://dns.nextdns.io/abc123
  #
  # Without metadata sharing:
  # nameservers:
  #   - 2a07:a8c0::ab:c123
  #   - 2a07:a8c1::ab:c123

  # Split DNS (see https://tailscale.com/kb/1054/dns/),
  # list of search domains and the DNS to query for each one.
  #
  # restricted_nameservers:
  #   foo.bar.com:
  #     - 1.1.1.1
  #   darp.headscale.net:
  #     - 1.1.1.1
  #     - 8.8.8.8

  # Search domains to inject.
  domains: []

  # Extra DNS records
  # so far only A-records are supported (on the tailscale side)
  # See https://github.com/juanfont/headscale/blob/main/docs/dns-records.md#Limitations
  # extra_records:
  #   - name: "grafana.myvpn.example.com"
  #     type: "A"
  #     value: "100.64.0.3"
  #
  #   # you can also put it in one line
  #   - { name: "prometheus.myvpn.example.com", type: "A", value: "100.64.0.3" }

  # Whether to use [MagicDNS](https://tailscale.com/kb/1081/magicdns/).
  # Only works if there is at least a nameserver defined.
  magic_dns: true

  # Defines the base domain to create the hostnames for MagicDNS.
  # `base_domain` must be a FQDNs, without the trailing dot.
  # The FQDN of the hosts will be
  # `hostname.user.base_domain` (e.g., _myhost.myuser.example.com_).
  base_domain: example.com

# Unix socket used for the CLI to connect without authentication
# Note: for production you will want to set this to something like:
# unix_socket: /var/run/headscale.sock
unix_socket: ./headscale.sock
unix_socket_permission: "0770"
#
# headscale supports experimental OpenID connect support,
# it is still being tested and might have some bugs, please
# help us test it.
# OpenID Connect
# oidc:
#   only_start_if_oidc_is_available: true
#   issuer: "https://your-oidc.issuer.com/path"
#   client_id: "your-oidc-client-id"
#   client_secret: "your-oidc-client-secret"
#   # Alternatively, set `client_secret_path` to read the secret from the file.
#   # It resolves environment variables, making integration to systemd's
#   # `LoadCredential` straightforward:
#   client_secret_path: "${CREDENTIALS_DIRECTORY}/oidc_client_secret"
#   # client_secret and client_secret_path are mutually exclusive.
#
#   Customize the scopes used in the OIDC flow, defaults to "openid", "profile" and "email" and add custom query
#   parameters to the Authorize Endpoint request. Scopes default to "openid", "profile" and "email".
#
#   scope: ["openid", "profile", "email", "custom"]
#   extra_params:
#     domain_hint: example.com
#
#   List allowed principal domains and/or users. If an authenticated user's domain is not in this list, the
#   authentication request will be rejected.
#
#   allowed_domains:
#     - example.com
# Groups from keycloak have a leading '/'
#   allowed_groups:
#     - /headscale
#   allowed_users:
#     - alice@example.com
#
#   If `strip_email_domain` is set to `true`, the domain part of the username email address will be removed.
#   This will transform `first-name.last-name@example.com` to the user `first-name.last-name`
#   If `strip_email_domain` is set to `false` the domain part will NOT be removed resulting to the following
#   user: `first-name.last-name.example.com`
#
#   strip_email_domain: true

# Logtail configuration
# Logtail is Tailscales logging and auditing infrastructure, it allows the control panel
# to instruct tailscale nodes to log their activity to a remote server.
logtail:
  # Enable logtail for this headscales clients.
  # As there is currently no support for overriding the log server in headscale, this is
  # disabled by default. Enabling this will make your clients send logs to Tailscale Inc.
  enabled: false

# Enabling this option makes devices prefer a random port for WireGuard traffic over the
# default static port 41641. This option is intended as a workaround for some buggy
# firewall devices. See https://tailscale.com/kb/1181/firewalls/ for more information.
randomize_client_port: false
- -

问题就是出在几个文件路径的配置，默认都是当前目录，也就是headscale的可执行文件所在目录，需要按它配置说明中的生产配置进行修改

-
# For production:
# /var/lib/headscale/private.key
private_key_path: /var/lib/headscale/private.key
-

直接改成绝对路径就好了，还有两个文件路径
另一个也是个秘钥的路径问题

-
noise:
  # The Noise private key is used to encrypt the
  # traffic between headscale and Tailscale clients when
  # using the new Noise-based protocol. It must be different
  # from the legacy private key.
  #
  # For production:
  # private_key_path: /var/lib/headscale/noise_private.key
  private_key_path: /var/lib/headscale/noise_private.key
-

第二个问题

这个问题也是一种误导，
错误信息是

-
Error initializing error="unable to open database file: out of memory (14)"
-

这就是个文件，内存也完全没有被占满的迹象，原来也是文件路径的问题

-
# For production:
# db_path: /var/lib/headscale/db.sqlite
db_path: /var/lib/headscale/db.sqlite
-

都改成绝对路径就可以了，然后这里还有个就是要对/var/lib/headscale/和/etc/headscale/等路径赋予headscale用户权限，有时候对这类问题的排查真的蛮头疼，日志报错都不是真实的错误信息，开源项目对这些错误的提示真的也需要优化，后续的譬如mac也加入节点等后面再开篇讲

-]]> - - headscale - - - headscale - - - - AQS篇一 - /2021/02/14/AQS%E7%AF%87%E4%B8%80/ - 很多东西都是时看时新，而且时间长了也会忘，所以再来复习下，也会有一些新的角度看法这次来聊下AQS的内容，主要是这几个点，

-

第一个线程

第一个线程抢到锁了，此时state跟阻塞队列是怎么样的，其实这里是之前没理解对的地方

-
/**
         * Fair version of tryAcquire.  Don't grant access unless
         * recursive call or no waiters or is first.
         */
        protected final boolean tryAcquire(int acquires) {
            final Thread current = Thread.currentThread();
            int c = getState();
            // 这里如果state还是0说明锁还空着
            if (c == 0) {
                // 因为是公平锁版本的，先去看下是否阻塞队列里有排着队的
                if (!hasQueuedPredecessors() &&
                    compareAndSetState(0, acquires)) {
                    // 没有排队的，并且state使用cas设置成功的就标记当前占有锁的线程是我
                    setExclusiveOwnerThread(current);
                    // 然后其实就返回了，包括阻塞队列的head和tail节点和waitStatus都没有设置
                    return true;
                }
            }
            else if (current == getExclusiveOwnerThread()) {
                int nextc = c + acquires;
                if (nextc < 0)
                    throw new Error("Maximum lock count exceeded");
                setState(nextc);
                return true;
            }
            // 这里就是第二个线程会返回false
            return false;
        }
    }
- -

第二个线程

当第二个线程进来的时候应该是怎么样，结合代码来看

-
/**
     * Acquires in exclusive mode, ignoring interrupts.  Implemented
     * by invoking at least once {@link #tryAcquire},
     * returning on success.  Otherwise the thread is queued, possibly
     * repeatedly blocking and unblocking, invoking {@link
     * #tryAcquire} until success.  This method can be used
     * to implement method {@link Lock#lock}.
     *
     * @param arg the acquire argument.  This value is conveyed to
     *        {@link #tryAcquire} but is otherwise uninterpreted and
     *        can represent anything you like.
     */
    public final void acquire(int arg) {
        // 前面第一种情况是tryAcquire直接成功了，这个if判断第一个条件就是false，就不往下执行了
        // 如果是第二个线程，第一个条件获取锁不成功，条件判断!tryAcquire(arg) == true，就会走
        // acquireQueued(addWaiter(Node.EXCLUSIVE), arg)
        if (!tryAcquire(arg) &&
            acquireQueued(addWaiter(Node.EXCLUSIVE), arg))
            selfInterrupt();
    }
- -

然后来看下addWaiter的逻辑

-
/**
     * Creates and enqueues node for current thread and given mode.
     *
     * @param mode Node.EXCLUSIVE for exclusive, Node.SHARED for shared
     * @return the new node
     */
    private Node addWaiter(Node mode) {
        // 这里是包装成一个node
        Node node = new Node(Thread.currentThread(), mode);
        // Try the fast path of enq; backup to full enq on failure
        // 最快的方式就是把当前线程的节点放在阻塞队列的最后
        Node pred = tail;
        // 只有当tail，也就是pred不为空的时候可以直接接上
        if (pred != null) {
            node.prev = pred;
            // 如果这里cas成功了，就直接接上返回了
            if (compareAndSetTail(pred, node)) {
                pred.next = node;
                return node;
            }
        }
        // 不然就会继续走到这里
        enq(node);
        return node;
    }
- -

然后就是enq的逻辑了

-
/**
     * Inserts node into queue, initializing if necessary. See picture above.
     * @param node the node to insert
     * @return node's predecessor
     */
    private Node enq(final Node node) {
        for (;;) {
            // 如果状态没变化的话，tail这时还是null的
            Node t = tail;
            if (t == null) { // Must initialize
                // 这里就会初始化头结点，就是个空节点
                if (compareAndSetHead(new Node()))
                    // tail也赋值成head
                    tail = head;
            } else {
                // 这里就设置tail了
                node.prev = t;
                if (compareAndSetTail(t, node)) {
                    t.next = node;
                    return t;
                }
            }
        }
    }
- -

所以从这里可以看出来，其实head头结点不是个真实的带有线程的节点，并且不是在第一个线程进来的时候设置的

-

解锁

通过代码来看下

-
/**
     * Attempts to release this lock.
     *
     * <p>If the current thread is the holder of this lock then the hold
     * count is decremented.  If the hold count is now zero then the lock
     * is released.  If the current thread is not the holder of this
     * lock then {@link IllegalMonitorStateException} is thrown.
     *
     * @throws IllegalMonitorStateException if the current thread does not
     *         hold this lock
     */
    public void unlock() {
        // 释放锁
        sync.release(1);
    }
/**
     * Releases in exclusive mode.  Implemented by unblocking one or
     * more threads if {@link #tryRelease} returns true.
     * This method can be used to implement method {@link Lock#unlock}.
     *
     * @param arg the release argument.  This value is conveyed to
     *        {@link #tryRelease} but is otherwise uninterpreted and
     *        can represent anything you like.
     * @return the value returned from {@link #tryRelease}
     */
    public final boolean release(int arg) {
        // 尝试去释放
        if (tryRelease(arg)) {
            Node h = head;
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }
protected final boolean tryRelease(int releases) {
            int c = getState() - releases;
            if (Thread.currentThread() != getExclusiveOwnerThread())
                throw new IllegalMonitorStateException();
            boolean free = false;
    		// 判断是否完全释放锁，因为可重入
            if (c == 0) {
                free = true;
                setExclusiveOwnerThread(null);
            }
            setState(c);
            return free;
        }
// 这段代码和上面的一致，只是为了顺序性，又拷下来看下

public final boolean release(int arg) {
        // 尝试去释放，如果是完全释放，返回的就是true，否则是false
        if (tryRelease(arg)) {
            Node h = head;
            // 这里判断头结点是否为空以及waitStatus的状态，前面说了head节点其实是
            // 在第二个线程进来的时候初始化的，如果是空的话说明没后续节点，并且waitStatus
            // 也表示了后续的等待状态
            if (h != null && h.waitStatus != 0)
                unparkSuccessor(h);
            return true;
        }
        return false;
    }

/**
     * Wakes up node's successor, if one exists.
     *
     * @param node the node
     */
// 唤醒后继节点
    private void unparkSuccessor(Node node) {
        /*
         * If status is negative (i.e., possibly needing signal) try
         * to clear in anticipation of signalling.  It is OK if this
         * fails or if status is changed by waiting thread.
         */
        int ws = node.waitStatus;
        if (ws < 0)
            compareAndSetWaitStatus(node, ws, 0);

        /*
         * Thread to unpark is held in successor, which is normally
         * just the next node.  But if cancelled or apparently null,
         * traverse backwards from tail to find the actual
         * non-cancelled successor.
         */
        Node s = node.next;
        // 如果后继节点是空或者当前节点取消等待了
        if (s == null || s.waitStatus > 0) {
            s = null;
            // 从后往前找，找到非取消的节点，注意这里不是找到就退出，而是一直找到头
            // 所以不必担心中间有取消的
            for (Node t = tail; t != null && t != node; t = t.prev)
                if (t.waitStatus <= 0)
                    s = t;
        }
        if (s != null)
            // 将其唤醒
            LockSupport.unpark(s.thread);
    }
- - + Disruptor 系列三 + /2022/09/25/Disruptor-%E7%B3%BB%E5%88%97%E4%B8%89/ + 原来一直有点被误导，
gatingSequences用来标识每个 processer 的操作位点，但是怎么记录更新有点搞不清楚
其实问题在于 gatingSequences 是个 Sequence 数组，首先要看下怎么加进去的，
可以看到是在 com.lmax.disruptor.RingBuffer#addGatingSequences 这个方法里添加
首先是 com.lmax.disruptor.dsl.Disruptor#handleEventsWith(com.lmax.disruptor.EventHandler<? super T>...)
然后执行 com.lmax.disruptor.dsl.Disruptor#createEventProcessors(com.lmax.disruptor.Sequence[], com.lmax.disruptor.EventHandler<? super T>[])

+
EventHandlerGroup<T> createEventProcessors(
        final Sequence[] barrierSequences,
        final EventHandler<? super T>[] eventHandlers)
    {
        checkNotStarted();

        final Sequence[] processorSequences = new Sequence[eventHandlers.length];
        final SequenceBarrier barrier = ringBuffer.newBarrier(barrierSequences);

        for (int i = 0, eventHandlersLength = eventHandlers.length; i < eventHandlersLength; i++)
        {
            final EventHandler<? super T> eventHandler = eventHandlers[i];

            // 这里将 handler 包装成一个 BatchEventProcessor
            final BatchEventProcessor<T> batchEventProcessor =
                new BatchEventProcessor<>(ringBuffer, barrier, eventHandler);

            if (exceptionHandler != null)
            {
                batchEventProcessor.setExceptionHandler(exceptionHandler);
            }

            consumerRepository.add(batchEventProcessor, eventHandler, barrier);
            processorSequences[i] = batchEventProcessor.getSequence();
        }

        updateGatingSequencesForNextInChain(barrierSequences, processorSequences);

        return new EventHandlerGroup<>(this, consumerRepository, processorSequences);
    }
+

在 BatchEventProcessor 在类内有个定义 sequence

+
private final Sequence sequence = new Sequence(Sequencer.INITIAL_CURSOR_VALUE);
+

然后在上面循环中的这一句取出来

+
processorSequences[i] = batchEventProcessor.getSequence();
+

调用com.lmax.disruptor.dsl.Disruptor#updateGatingSequencesForNextInChain 方法

+
private void updateGatingSequencesForNextInChain(final Sequence[] barrierSequences, final Sequence[] processorSequences)
    {
        if (processorSequences.length > 0)
        {
            // 然后在这里添加
            ringBuffer.addGatingSequences(processorSequences);
            for (final Sequence barrierSequence : barrierSequences)
            {
                ringBuffer.removeGatingSequence(barrierSequence);
            }
            consumerRepository.unMarkEventProcessorsAsEndOfChain(barrierSequences);
        }
    }
-]]> - - Java - 并发 - - - java - 并发 - j.u.c - aqs - - - - Disruptor 系列一 - /2022/02/13/Disruptor-%E7%B3%BB%E5%88%97%E4%B8%80/ - 很久之前就听说过这个框架，不过之前有点跟消息队列混起来，这个也是种队列，但不是跟 rocketmq，nsq 那种一样的，而是在进程内部提供队列服务的，偏向于取代ArrayBlockingQueue，因为这个阻塞队列是使用了锁来控制阻塞，关于并发其实有一些通用的最佳实践，就是用锁，即使是 JDK 提供的锁，也是比较耗资源的，当然这是跟不加锁的对比，同样是锁，JDK 的实现还是性能比较优秀的。常见的阻塞队列中例如 ArrayBlockingQueue 和 LinkedBlockingQueue 都有锁的身影的存在，区别在于 ArrayBlockingQueue 是一把锁，后者是两把锁，不过重点不在几把锁，这里其实是两个问题，一个是所谓的 lock free, 对于一个单生产者的 disruptor 来说，因为写入是只有一个线程的，是可以不用加锁，多生产者的时候使用的是 cas 来获取对应的写入坑位，另一个是解决“伪共享”问题，后面可以详细点分析，先介绍下使用
首先是数据源

-
public class LongEvent {
    private long value;

    public void set(long value) {
        this.value = value;
    }

    public long getValue() {
        return value;
    }

    public void setValue(long value) {
        this.value = value;
    }
}
-

事件生产

-
public class LongEventFactory implements EventFactory<LongEvent>
{
    public LongEvent newInstance()
    {
        return new LongEvent();
    }
}
-

事件处理器

-
public class LongEventHandler implements EventHandler<LongEvent> {

    // event 事件，
    // sequence 当前的序列 
    // 是否当前批次最后一个数据
    public void onEvent(LongEvent event, long sequence, boolean endOfBatch)
    {
        String str = String.format("long event : %s l:%s b:%s", event.getValue(), sequence, endOfBatch);
        System.out.println(str);
    }
}

-

主方法代码

-
package disruptor;

import com.lmax.disruptor.RingBuffer;
import com.lmax.disruptor.dsl.Disruptor;
import com.lmax.disruptor.util.DaemonThreadFactory;

import java.nio.ByteBuffer;

public class LongEventMain
{
    public static void main(String[] args) throws Exception
    {
        // 这个需要是 2 的幂次，这样在定位的时候只需要位移操作，也能减少各种计算操作
        int bufferSize = 1024; 

        Disruptor<LongEvent> disruptor = 
                new Disruptor<>(LongEvent::new, bufferSize, DaemonThreadFactory.INSTANCE);

        // 类似于注册处理器
        disruptor.handleEventsWith(new LongEventHandler());
        // 或者直接用 lambda
        disruptor.handleEventsWith((event, sequence, endOfBatch) ->
                System.out.println("Event: " + event));
        // 启动我们的 disruptor
        disruptor.start(); 


        RingBuffer<LongEvent> ringBuffer = disruptor.getRingBuffer(); 
        ByteBuffer bb = ByteBuffer.allocate(8);
        for (long l = 0; true; l++)
        {
            bb.putLong(0, l);
            // 生产事件
            ringBuffer.publishEvent((event, sequence, buffer) -> event.set(buffer.getLong(0)), bb);
            Thread.sleep(1000);
        }
    }
}
-

运行下可以看到运行结果

这里其实就只是最简单的使用，生产者只有一个，然后也不是批量的。

+

而如何更新则是在处理器 com.lmax.disruptor.BatchEventProcessor#run 中

+
public void run()
    {
        if (running.compareAndSet(IDLE, RUNNING))
        {
            sequenceBarrier.clearAlert();

            notifyStart();
            try
            {
                if (running.get() == RUNNING)
                {
                    processEvents();
                }
            }
            finally
            {
                notifyShutdown();
                running.set(IDLE);
            }
        }
        else
        {
            // This is a little bit of guess work.  The running state could of changed to HALTED by
            // this point.  However, Java does not have compareAndExchange which is the only way
            // to get it exactly correct.
            if (running.get() == RUNNING)
            {
                throw new IllegalStateException("Thread is already running");
            }
            else
            {
                earlyExit();
            }
        }
    }
+

然后是

+
private void processEvents()
    {
        T event = null;
        long nextSequence = sequence.get() + 1L;

        while (true)
        {
            try
            {
                final long availableSequence = sequenceBarrier.waitFor(nextSequence);
                if (batchStartAware != null)
                {
                    batchStartAware.onBatchStart(availableSequence - nextSequence + 1);
                }

                while (nextSequence <= availableSequence)
                {
                    event = dataProvider.get(nextSequence);
                    eventHandler.onEvent(event, nextSequence, nextSequence == availableSequence);
                    nextSequence++;
                }
                // 如果正常处理完，那就是会更新为 availableSequence，因为都处理好了
                sequence.set(availableSequence);
            }
            catch (final TimeoutException e)
            {
                notifyTimeout(sequence.get());
            }
            catch (final AlertException ex)
            {
                if (running.get() != RUNNING)
                {
                    break;
                }
            }
            catch (final Throwable ex)
            {
                handleEventException(ex, nextSequence, event);
                // 如果是异常就只是 nextSequence
                sequence.set(nextSequence);
                nextSequence++;
            }
        }
    }
]]> Java @@ -600,13 +575,22 @@ - Headscale渐入佳境补充篇-自定义中转derper的证书问题 - /2024/05/26/Headscale%E6%B8%90%E5%85%A5%E4%BD%B3%E5%A2%83%E8%A1%A5%E5%85%85%E7%AF%87-%E8%87%AA%E5%AE%9A%E4%B9%89%E4%B8%AD%E8%BD%ACderper%E7%9A%84%E8%AF%81%E4%B9%A6%E9%97%AE%E9%A2%98/ - 之前自定义部署的derper在Mac端和Windows使用时没啥问题，但是在我想作为网关的Armbian小机器上一直会报 “x509: certificate signed by unknown authority”
错误，原因在于之前在通过acme生成证书以后我是直接把cer证书重命名成crt就映射到derper的docker目录里，对于Mac和Windows客户端应该是有兼容逻辑，而实际使用需要完整的证书去识别认证机构，否则就会被认为是自定义证书，在一些常规的证书认证逻辑里就会报上面的问题
这边需要先把生成的 fullchain.cer 完整证书转成crt格式的

-
openssl x509 -inform PEM -in fullchain.cer -out derper.demo.com.crt
-

这边是PEM格式的证书，如果是DER的话就改一下

-
openssl x509 -inform DER -in certificate.cer -out certificate.crt
-

转换完成后就重新启动下docker
这里的原因主要是fullchain.cer是包含了完整的证书链，而ca.cer只是包含用户证书，所以需要替换一下，这算是个小问题

+ Headscale初体验以及踩坑记 + /2023/01/22/Headscale%E5%88%9D%E4%BD%93%E9%AA%8C%E4%BB%A5%E5%8F%8A%E8%B8%A9%E5%9D%91%E8%AE%B0/ + 最近或者说很久以前就想着能够把几个散装服务器以及家里的网络连起来，譬如一些remote desktop的访问，之前搞了下frp，因为家里电脑没怎么注意安全性就被搞了一下，所以还是想用相对更安全的方式，比如限定ip和端口进行访问，但是感觉ip也不固定就比较难搞，后来看到了 Tailscale 和 Headscale 的方式，就想着试试看，没想到一开始就踩了几个比较莫名其妙的坑。
可以按官方文档去搭建，也可以在网上找一些其他人搭建的教程。我碰到的主要是关于配置文件的问题

+

第一个问题

Error initializing error="failed to read or create private key: failed to save private key to disk: open /etc/headscale/private.key: read-only file system"
+

其实一开始看到这个我都有点懵了，咋回事呢，read-only file system一般有可能是文件系统出问题了，不可写入，需要重启或者修改挂载方式，被这个错误的错误日志给误导了，后面才知道是配置文件，在另一个教程中也有个类似的回复，一开始没注意，其实就是同一个问题。
默认的配置文件是这样的

+
---
# headscale will look for a configuration file named `config.yaml` (or `config.json`) in the following order:
#
# - `/etc/headscale`
# - `~/.headscale`
# - current working directory

# The url clients will connect to.
# Typically this will be a domain like:
#
# https://myheadscale.example.com:443
#
server_url: http://127.0.0.1:8080

# Address to listen to / bind to on the server
#
# For production:
# listen_addr: 0.0.0.0:8080
listen_addr: 127.0.0.1:8080

# Address to listen to /metrics, you may want
# to keep this endpoint private to your internal
# network
#
metrics_listen_addr: 127.0.0.1:9090

# Address to listen for gRPC.
# gRPC is used for controlling a headscale server
# remotely with the CLI
# Note: Remote access _only_ works if you have
# valid certificates.
#
# For production:
# grpc_listen_addr: 0.0.0.0:50443
grpc_listen_addr: 127.0.0.1:50443

# Allow the gRPC admin interface to run in INSECURE
# mode. This is not recommended as the traffic will
# be unencrypted. Only enable if you know what you
# are doing.
grpc_allow_insecure: false

# Private key used to encrypt the traffic between headscale
# and Tailscale clients.
# The private key file will be autogenerated if it's missing.
#
# For production:
# /var/lib/headscale/private.key
private_key_path: ./private.key

# The Noise section includes specific configuration for the
# TS2021 Noise protocol
noise:
  # The Noise private key is used to encrypt the
  # traffic between headscale and Tailscale clients when
  # using the new Noise-based protocol. It must be different
  # from the legacy private key.
  #
  # For production:
  # private_key_path: /var/lib/headscale/noise_private.key
  private_key_path: ./noise_private.key

# List of IP prefixes to allocate tailaddresses from.
# Each prefix consists of either an IPv4 or IPv6 address,
# and the associated prefix length, delimited by a slash.
# While this looks like it can take arbitrary values, it
# needs to be within IP ranges supported by the Tailscale
# client.
# IPv6: https://github.com/tailscale/tailscale/blob/22ebb25e833264f58d7c3f534a8b166894a89536/net/tsaddr/tsaddr.go#LL81C52-L81C71
# IPv4: https://github.com/tailscale/tailscale/blob/22ebb25e833264f58d7c3f534a8b166894a89536/net/tsaddr/tsaddr.go#L33
ip_prefixes:
  - fd7a:115c:a1e0::/48
  - 100.64.0.0/10

# DERP is a relay system that Tailscale uses when a direct
# connection cannot be established.
# https://tailscale.com/blog/how-tailscale-works/#encrypted-tcp-relays-derp
#
# headscale needs a list of DERP servers that can be presented
# to the clients.
derp:
  server:
    # If enabled, runs the embedded DERP server and merges it into the rest of the DERP config
    # The Headscale server_url defined above MUST be using https, DERP requires TLS to be in place
    enabled: false

    # Region ID to use for the embedded DERP server.
    # The local DERP prevails if the region ID collides with other region ID coming from
    # the regular DERP config.
    region_id: 999

    # Region code and name are displayed in the Tailscale UI to identify a DERP region
    region_code: "headscale"
    region_name: "Headscale Embedded DERP"

    # Listens over UDP at the configured address for STUN connections - to help with NAT traversal.
    # When the embedded DERP server is enabled stun_listen_addr MUST be defined.
    #
    # For more details on how this works, check this great article: https://tailscale.com/blog/how-tailscale-works/
    stun_listen_addr: "0.0.0.0:3478"

  # List of externally available DERP maps encoded in JSON
  urls:
    - https://controlplane.tailscale.com/derpmap/default

  # Locally available DERP map files encoded in YAML
  #
  # This option is mostly interesting for people hosting
  # their own DERP servers:
  # https://tailscale.com/kb/1118/custom-derp-servers/
  #
  # paths:
  #   - /etc/headscale/derp-example.yaml
  paths: []

  # If enabled, a worker will be set up to periodically
  # refresh the given sources and update the derpmap
  # will be set up.
  auto_update_enabled: true

  # How often should we check for DERP updates?
  update_frequency: 24h

# Disables the automatic check for headscale updates on startup
disable_check_updates: false

# Time before an inactive ephemeral node is deleted?
ephemeral_node_inactivity_timeout: 30m

# Period to check for node updates within the tailnet. A value too low will severely affect
# CPU consumption of Headscale. A value too high (over 60s) will cause problems
# for the nodes, as they won't get updates or keep alive messages frequently enough.
# In case of doubts, do not touch the default 10s.
node_update_check_interval: 10s

# SQLite config
db_type: sqlite3

# For production:
# db_path: /var/lib/headscale/db.sqlite
db_path: ./db.sqlite

# # Postgres config
# If using a Unix socket to connect to Postgres, set the socket path in the 'host' field and leave 'port' blank.
# db_type: postgres
# db_host: localhost
# db_port: 5432
# db_name: headscale
# db_user: foo
# db_pass: bar

# If other 'sslmode' is required instead of 'require(true)' and 'disabled(false)', set the 'sslmode' you need
# in the 'db_ssl' field. Refers to https://www.postgresql.org/docs/current/libpq-ssl.html Table 34.1.
# db_ssl: false

### TLS configuration
#
## Let's encrypt / ACME
#
# headscale supports automatically requesting and setting up
# TLS for a domain with Let's Encrypt.
#
# URL to ACME directory
acme_url: https://acme-v02.api.letsencrypt.org/directory

# Email to register with ACME provider
acme_email: ""

# Domain name to request a TLS certificate for:
tls_letsencrypt_hostname: ""

# Path to store certificates and metadata needed by
# letsencrypt
# For production:
# tls_letsencrypt_cache_dir: /var/lib/headscale/cache
tls_letsencrypt_cache_dir: ./cache

# Type of ACME challenge to use, currently supported types:
# HTTP-01 or TLS-ALPN-01
# See [docs/tls.md](docs/tls.md) for more information
tls_letsencrypt_challenge_type: HTTP-01
# When HTTP-01 challenge is chosen, letsencrypt must set up a
# verification endpoint, and it will be listening on:
# :http = port 80
tls_letsencrypt_listen: ":http"

## Use already defined certificates:
tls_cert_path: ""
tls_key_path: ""

log:
  # Output formatting for logs: text or json
  format: text
  level: info

# Path to a file containg ACL policies.
# ACLs can be defined as YAML or HUJSON.
# https://tailscale.com/kb/1018/acls/
acl_policy_path: ""

## DNS
#
# headscale supports Tailscale's DNS configuration and MagicDNS.
# Please have a look to their KB to better understand the concepts:
#
# - https://tailscale.com/kb/1054/dns/
# - https://tailscale.com/kb/1081/magicdns/
# - https://tailscale.com/blog/2021-09-private-dns-with-magicdns/
#
dns_config:
  # Whether to prefer using Headscale provided DNS or use local.
  override_local_dns: true

  # List of DNS servers to expose to clients.
  nameservers:
    - 1.1.1.1

  # NextDNS (see https://tailscale.com/kb/1218/nextdns/).
  # "abc123" is example NextDNS ID, replace with yours.
  #
  # With metadata sharing:
  # nameservers:
  #   - https://dns.nextdns.io/abc123
  #
  # Without metadata sharing:
  # nameservers:
  #   - 2a07:a8c0::ab:c123
  #   - 2a07:a8c1::ab:c123

  # Split DNS (see https://tailscale.com/kb/1054/dns/),
  # list of search domains and the DNS to query for each one.
  #
  # restricted_nameservers:
  #   foo.bar.com:
  #     - 1.1.1.1
  #   darp.headscale.net:
  #     - 1.1.1.1
  #     - 8.8.8.8

  # Search domains to inject.
  domains: []

  # Extra DNS records
  # so far only A-records are supported (on the tailscale side)
  # See https://github.com/juanfont/headscale/blob/main/docs/dns-records.md#Limitations
  # extra_records:
  #   - name: "grafana.myvpn.example.com"
  #     type: "A"
  #     value: "100.64.0.3"
  #
  #   # you can also put it in one line
  #   - { name: "prometheus.myvpn.example.com", type: "A", value: "100.64.0.3" }

  # Whether to use [MagicDNS](https://tailscale.com/kb/1081/magicdns/).
  # Only works if there is at least a nameserver defined.
  magic_dns: true

  # Defines the base domain to create the hostnames for MagicDNS.
  # `base_domain` must be a FQDNs, without the trailing dot.
  # The FQDN of the hosts will be
  # `hostname.user.base_domain` (e.g., _myhost.myuser.example.com_).
  base_domain: example.com

# Unix socket used for the CLI to connect without authentication
# Note: for production you will want to set this to something like:
# unix_socket: /var/run/headscale.sock
unix_socket: ./headscale.sock
unix_socket_permission: "0770"
#
# headscale supports experimental OpenID connect support,
# it is still being tested and might have some bugs, please
# help us test it.
# OpenID Connect
# oidc:
#   only_start_if_oidc_is_available: true
#   issuer: "https://your-oidc.issuer.com/path"
#   client_id: "your-oidc-client-id"
#   client_secret: "your-oidc-client-secret"
#   # Alternatively, set `client_secret_path` to read the secret from the file.
#   # It resolves environment variables, making integration to systemd's
#   # `LoadCredential` straightforward:
#   client_secret_path: "${CREDENTIALS_DIRECTORY}/oidc_client_secret"
#   # client_secret and client_secret_path are mutually exclusive.
#
#   Customize the scopes used in the OIDC flow, defaults to "openid", "profile" and "email" and add custom query
#   parameters to the Authorize Endpoint request. Scopes default to "openid", "profile" and "email".
#
#   scope: ["openid", "profile", "email", "custom"]
#   extra_params:
#     domain_hint: example.com
#
#   List allowed principal domains and/or users. If an authenticated user's domain is not in this list, the
#   authentication request will be rejected.
#
#   allowed_domains:
#     - example.com
# Groups from keycloak have a leading '/'
#   allowed_groups:
#     - /headscale
#   allowed_users:
#     - alice@example.com
#
#   If `strip_email_domain` is set to `true`, the domain part of the username email address will be removed.
#   This will transform `first-name.last-name@example.com` to the user `first-name.last-name`
#   If `strip_email_domain` is set to `false` the domain part will NOT be removed resulting to the following
#   user: `first-name.last-name.example.com`
#
#   strip_email_domain: true

# Logtail configuration
# Logtail is Tailscales logging and auditing infrastructure, it allows the control panel
# to instruct tailscale nodes to log their activity to a remote server.
logtail:
  # Enable logtail for this headscales clients.
  # As there is currently no support for overriding the log server in headscale, this is
  # disabled by default. Enabling this will make your clients send logs to Tailscale Inc.
  enabled: false

# Enabling this option makes devices prefer a random port for WireGuard traffic over the
# default static port 41641. This option is intended as a workaround for some buggy
# firewall devices. See https://tailscale.com/kb/1181/firewalls/ for more information.
randomize_client_port: false
+ +

问题就是出在几个文件路径的配置，默认都是当前目录，也就是headscale的可执行文件所在目录，需要按它配置说明中的生产配置进行修改

+
# For production:
# /var/lib/headscale/private.key
private_key_path: /var/lib/headscale/private.key
+

直接改成绝对路径就好了，还有两个文件路径
另一个也是个秘钥的路径问题

+
noise:
  # The Noise private key is used to encrypt the
  # traffic between headscale and Tailscale clients when
  # using the new Noise-based protocol. It must be different
  # from the legacy private key.
  #
  # For production:
  # private_key_path: /var/lib/headscale/noise_private.key
  private_key_path: /var/lib/headscale/noise_private.key
+

第二个问题

这个问题也是一种误导，
错误信息是

+
Error initializing error="unable to open database file: out of memory (14)"
+

这就是个文件，内存也完全没有被占满的迹象，原来也是文件路径的问题

+
# For production:
# db_path: /var/lib/headscale/db.sqlite
db_path: /var/lib/headscale/db.sqlite
+

都改成绝对路径就可以了，然后这里还有个就是要对/var/lib/headscale/和/etc/headscale/等路径赋予headscale用户权限，有时候对这类问题的排查真的蛮头疼，日志报错都不是真实的错误信息，开源项目对这些错误的提示真的也需要优化，后续的譬如mac也加入节点等后面再开篇讲

]]> headscale @@ -635,8 +619,8 @@ Java JVM - GC C++ + GC Java @@ -722,15 +706,48 @@ - Java 线程池系列-第三篇 - /2024/02/24/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E4%B8%89%E7%AF%87/ - 第三篇我们要来讲下 Worker 这个“打工人”，线程池里实际在干活的同学
Worker 实现了 Runnable 接口，所以在前面介绍的 addWorker 中
线程启动其实就调用了下面的 run 方法，而 run 方法则调用了内部的
runWorker 方法

-

private final class Worker
    extends AbstractQueuedSynchronizer
    implements Runnable
{
    /**
     * This class will never be serialized, but we provide a
     * serialVersionUID to suppress a javac warning.
     */
    private static final long serialVersionUID = 6138294804551838833L;

    /** Thread this worker is running in.  Null if factory fails. */
    final Thread thread;
    /** Initial task to run.  Possibly null. */
    Runnable firstTask;
    /** Per-thread task counter */
    volatile long completedTasks;

    /**
     * Creates with given first task and thread from ThreadFactory.
     * @param firstTask the first task (null if none)
     */
    Worker(Runnable firstTask) {
        setState(-1); // inhibit interrupts until runWorker
        this.firstTask = firstTask;
        this.thread = getThreadFactory().newThread(this);
    }

    /** Delegates main run loop to outer runWorker  */
    // 启动线程 Worker
    public void run() {
        runWorker(this);
    }
-

介绍下这个 runWorker 方法
这里就会先处理 firstTask，如果 firstTask 是 null 则会去队列里取
里面会有两个钩子，一个是执行前，一个是执行后，注意这个循环是没有常规
退出逻辑的，说明是一直在往队列里获取，除非获取队列里的任务超时失败了
这样就到最后会去执行 processWorkerExit

-
final void runWorker(Worker w) {
        Thread wt = Thread.currentThread();
        Runnable task = w.firstTask;
        w.firstTask = null;
        w.unlock(); // allow interrupts
        boolean completedAbruptly = true;
        try {
            // firstTask 不为空或者从队列里获取到任务
            while (task != null || (task = getTask()) != null) {
                w.lock();
                // If pool is stopping, ensure thread is interrupted;
                // if not, ensure thread is not interrupted.  This
                // requires a recheck in second case to deal with
                // shutdownNow race while clearing interrupt
                if ((runStateAtLeast(ctl.get(), STOP) ||
                     (Thread.interrupted() &&
                      runStateAtLeast(ctl.get(), STOP))) &&
                    !wt.isInterrupted())
                    wt.interrupt();
                try {
                    // 前置执行钩子
                    beforeExecute(wt, task);
                    Throwable thrown = null;
                    try {
                        // 任务执行
                        task.run();
                    } catch (RuntimeException x) {
                        thrown = x; throw x;
                    } catch (Error x) {
                        thrown = x; throw x;
                    } catch (Throwable x) {
                        thrown = x; throw new Error(x);
                    } finally {
                        // 执行完后的钩子
                        afterExecute(task, thrown);
                    }
                } finally {
                    // 最后会执行到这，清空任务
                    task = null;
                    w.completedTasks++;
                    w.unlock();
                }
            }
            completedAbruptly = false;
        } finally {
            // 循环的最后就会去退出Worker
            processWorkerExit(w, completedAbruptly);
        }
    }
-

而这个 processWorkerExit 也有个比较特殊的逻辑
照理我们会想到这里可能就是线程在处理完队列里的任务
以后，就会判断下是否要退出，如果是核心线程的话就不用
如果是非核心线程就会退出了，但是这里其实有个替换逻辑
就是最后有个 addWorker 调用

-
private void processWorkerExit(Worker w, boolean completedAbruptly) {
    if (completedAbruptly) // If abrupt, then workerCount wasn't adjusted
        decrementWorkerCount();

    final ReentrantLock mainLock = this.mainLock;
    mainLock.lock();
    try {
        completedTaskCount += w.completedTasks;
        workers.remove(w);
    } finally {
        mainLock.unlock();
    }

    tryTerminate();

    int c = ctl.get();
    // 判断状态
    if (runStateLessThan(c, STOP)) {
        if (!completedAbruptly) {
            // 核心线程是否也需要退出
            int min = allowCoreThreadTimeOut ? 0 : corePoolSize;
            if (min == 0 && ! workQueue.isEmpty())
                min = 1;
            // 这里代表非核心线程就可以不用替换，也就是回收线程了
            if (workerCountOf(c) >= min)
                return; // replacement not needed
        }
        // 否则是替换线程
        addWorker(null, false);
    }
}
-

这里主要讲了线程 Worker 运行的逻辑

+ Headscale渐入佳境补充篇-自定义中转derper的证书问题 + /2024/05/26/Headscale%E6%B8%90%E5%85%A5%E4%BD%B3%E5%A2%83%E8%A1%A5%E5%85%85%E7%AF%87-%E8%87%AA%E5%AE%9A%E4%B9%89%E4%B8%AD%E8%BD%ACderper%E7%9A%84%E8%AF%81%E4%B9%A6%E9%97%AE%E9%A2%98/ + 之前自定义部署的derper在Mac端和Windows使用时没啥问题，但是在我想作为网关的Armbian小机器上一直会报 “x509: certificate signed by unknown authority”
错误，原因在于之前在通过acme生成证书以后我是直接把cer证书重命名成crt就映射到derper的docker目录里，对于Mac和Windows客户端应该是有兼容逻辑，而实际使用需要完整的证书去识别认证机构，否则就会被认为是自定义证书，在一些常规的证书认证逻辑里就会报上面的问题
这边需要先把生成的 fullchain.cer 完整证书转成crt格式的

+
openssl x509 -inform PEM -in fullchain.cer -out derper.demo.com.crt
+

这边是PEM格式的证书，如果是DER的话就改一下

+
openssl x509 -inform DER -in certificate.cer -out certificate.crt
+

转换完成后就重新启动下docker
这里的原因主要是fullchain.cer是包含了完整的证书链，而ca.cer只是包含用户证书，所以需要替换一下，这算是个小问题

+]]> + + headscale + + + headscale + + + + Java 线程池系列-第四篇 + /2024/03/03/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E5%9B%9B%E7%AF%87/ + 这一篇主要补充两个内容，第一部分就是获取任务的逻辑
首先是状态判断，如果是停止了，SHUTDOWN或更大的了，就需要减小工作线程数量
并返回 null，使得工作线程 worker 退出，然后再判断线程数量和超时，同样如果超过了就会返回 null
然后就是去阻塞队列里获取任务，这里是阻塞着获取的

+
private Runnable getTask() {
        boolean timedOut = false; // Did the last poll() time out?

        for (;;) {
            int c = ctl.get();
            int rs = runStateOf(c);

            // Check if queue empty only if necessary.
            // 状态异常，如果是大于等于 SHUTDOWN 的，则协助关闭线程池
            if (rs >= SHUTDOWN && (rs >= STOP || workQueue.isEmpty())) {
                decrementWorkerCount();
                return null;
            }

            int wc = workerCountOf(c);

            // Are workers subject to culling?
            boolean timed = allowCoreThreadTimeOut || wc > corePoolSize;

            // 如果线程数量超过核心线程数，则帮助减少线程
            if ((wc > maximumPoolSize || (timed && timedOut))
                && (wc > 1 || workQueue.isEmpty())) {
                if (compareAndDecrementWorkerCount(c))
                    return null;
                continue;
            }

            // 如果前面的不符合，则从阻塞队列获取任务
            try {
                Runnable r = timed ?
                    workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) :
                    workQueue.take();
                if (r != null)
                    return r;
                timedOut = true;
            } catch (InterruptedException retry) {
                timedOut = false;
            }
        }
    }
+ +

阻塞队列的 poll 主要是通过锁，和notEmpty这个 condition 来等待制定的时间
指定时间后开始 dequeue 出队

+
public E poll(long timeout, TimeUnit unit) throws InterruptedException {
    long nanos = unit.toNanos(timeout);
    final ReentrantLock lock = this.lock;
    lock.lockInterruptibly();
    try {
        while (count == 0) {
            if (nanos <= 0)
                return null;
            nanos = notEmpty.awaitNanos(nanos);
        }
        return dequeue();
    } finally {
        lock.unlock();
    }
}
+

第二部分比较重要，有的同学的问题是为什么不一开始开到最大线程，而是达到核心线程数后就进队列了，
其实池化技术最简单的原因就是希望能够复用这些线程，因为创建销毁他们的成本太大了，如果直接最大线程数的话
其实都不用用到线程池技术了，直接有多少任务就开多少线程，用完就丢了，阐述下我认为比较重要的概念点

+]]> + + Java + + + Java + + + + Java 线程池系列-第一篇 + /2024/02/11/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E4%B8%80%E7%AF%87/ + 这一篇我们继续聊线程池，一般线程池会介绍我们的参数，我先不一样一些
我们先来翻译一下

+
 An {@link ExecutorService} that executes each submitted task using
 one of possibly several pooled threads, normally configured
 using {@link Executors} factory methods.
 // executorService 使用一个线程池中的线程来执行每个提交的任务，经常使用 Executors 工厂方法

 <p>Thread pools address two different problems: they usually
 provide improved performance when executing large numbers of
 asynchronous tasks, due to reduced per-task invocation overhead,
 and they provide a means of bounding and managing the resources,
 including threads, consumed when executing a collection of tasks.
 Each {@code ThreadPoolExecutor} also maintains some basic
 statistics, such as the number of completed tasks.
 // 线程池解决两个不同的问题，通常是为了在提交大量异步任务时，通过减少每个任务的调度开销来提高性能
 // 并且提供了一种管理和约束资源，包括线程消耗的方法，每个 ThreadPoolExecutor还会管理一些基础的统计信息
 // 比如完成的任务数量
 

 <p>To be useful across a wide range of contexts, this class
 provides many adjustable parameters and extensibility
 hooks. However, programmers are urged to use the more convenient
 {@link Executors} factory methods {@link
 Executors#newCachedThreadPool} (unbounded thread pool, with
 automatic thread reclamation), {@link Executors#newFixedThreadPool}
 (fixed size thread pool) and {@link
 Executors#newSingleThreadExecutor} (single background thread), that
 preconfigure settings for the most common usage
 scenarios. Otherwise, use the following guide when manually
 configuring and tuning this class:
 // 为了在各种情况下都能派上用场，该类提供了许多可调整的参数和扩展钩子。不过，我们建议程序员使用更方便的 Executors 工厂方法 Executors.
 // newCachedThreadPool（无限制线程池，可自动回收线程）、Executors.newFixedThreadPool（固定大小线程池）和 Executors.
 // newSingleThreadExecutor（单后台线程），这些方法可为最常见的使用场景预先配置设置。否则，在手动配置和调整该类时，请使用以下指南：

 Core and maximum pool sizes
 // 核心和最大线程池大小
    A ThreadPoolExecutor will automatically adjust the pool size (see getPoolSize) according to the bounds set by corePoolSize (see getCorePoolSize) and maximumPoolSize (see getMaximumPoolSize). When a new task is submitted in method execute(Runnable), and fewer than corePoolSize threads are running, a new thread is created to handle the request, even if other worker threads are idle. If there are more than corePoolSize but less than maximumPoolSize threads running, a new thread will be created only if the queue is full. By setting corePoolSize and maximumPoolSize the same, you create a fixed-size thread pool. By setting maximumPoolSize to an essentially unbounded value such as Integer.MAX_VALUE, you allow the pool to accommodate an arbitrary number of concurrent tasks. Most typically, core and maximum pool sizes are set only upon construction, but they may also be changed dynamically using setCorePoolSize and setMaximumPoolSize.
    // ThreadPoolExecutor 会根据 corePoolSize（参见 getCorePoolSize）和 maximumPoolSize（参见 getMaximumPoolSize）设置的界限自动调整池大小（参见 getPoolSize）。在方法 execute(Runnable) 中提交新任务时，如果运行的线程少于 corePoolSize，即使其他工作线程处于空闲状态，也会创建一个新线程来处理请求。如果运行的线程多于 corePoolSize 但少于 maximumPoolSize，则只有在队列已满时才会创建新线程。如果将 corePoolSize 和 maximumPoolSize 设置为相同，就可以创建一个固定大小的线程池。如果将 maximumPoolSize 设置为 Integer.MAX_VALUE 等基本无限制的值，就可以让池容纳任意数量的并发任务。通常，核心和最大线程池大小只在构建时设置，但也可以使用 setCorePoolSize 和 setMaximumPoolSize 动态更改。

On-demand construction
// 按需构建
    By default, even core threads are initially created and started only when new tasks arrive, but this can be overridden dynamically using method prestartCoreThread or prestartAllCoreThreads. You probably want to prestart threads if you construct the pool with a non-empty queue.
    // 默认情况下，即使是核心线程也是只有当新任务到来时才会初始创建和启动，但这可以使用方法 prestartCoreThread 或 prestartAllCoreThreads 进行动态重载。如果使用非空队列构建线程池，则可能需要预启动线程。
Creating new threads
// 创建新线程
    /** New threads are created using a ThreadFactory. If not otherwise specified, a Executors.defaultThreadFactory is used, that creates threads to all be in the same ThreadGroup and with the same NORM_PRIORITY priority and non-daemon status. By supplying a different ThreadFactory, you can alter the thread's name, thread group, priority, daemon status, etc. If a ThreadFactory fails to create a thread when asked by returning null from newThread, the executor will continue, but might not be able to execute any tasks. Threads should possess the "modifyThread" RuntimePermission. If worker threads or other threads using the pool do not possess this permission, service may be degraded: configuration changes may not take effect in a timely manner, and a shutdown pool may remain in a state in which termination is possible but not completed. */
    // 新线程是使用线程工厂（ThreadFactory）创建的。如果未另行指定，将使用 Executors.defaultThreadFactory 创建线程，创建的线程将全部位于同一线程组，并具有相同的 NORM_PRIORITY 优先级和非守护进程状态。通过提供不同的 ThreadFactory，可以更改线程的名称、线程组、优先级、守护进程状态等。如果线程工厂无法按要求创建线程，从 newThread 返回空值，执行器将继续运行，但可能无法执行任何任务。线程应拥有 "modifyThread "运行时权限。如果工作线程或使用池的其他线程不具备此权限，服务可能会降级：配置更改可能无法及时生效，关闭的池可能会一直处于可以终止但无法完成的状态。
Keep-alive times
// 保活时间
    If the pool currently has more than corePoolSize threads, excess threads will be terminated if they have been idle for more than the keepAliveTime (see getKeepAliveTime(TimeUnit)). This provides a means of reducing resource consumption when the pool is not being actively used. If the pool becomes more active later, new threads will be constructed. This parameter can also be changed dynamically using method setKeepAliveTime(long, TimeUnit). Using a value of Long.MAX_VALUE TimeUnit.NANOSECONDS effectively disables idle threads from ever terminating prior to shut down. By default, the keep-alive policy applies only when there are more than corePoolSize threads. But method allowCoreThreadTimeOut(boolean) can be used to apply this time-out policy to core threads as well, so long as the keepAliveTime value is non-zero.
    // 如果当前池中的线程数超过 corePoolSize，多余的线程在闲置时间超过 keepAliveTime（请参阅 getKeepAliveTime(TimeUnit)）时将被终止。这提供了一种在线程池未被频繁使用时减少资源消耗的方法。如果以后池变得更加活跃，就会构建新线程。也可以使用方法 setKeepAliveTime(long, TimeUnit) 动态更改该参数。使用 Long.MAX_VALUE TimeUnit.NANOSECONDS 值可有效禁止闲置线程在关闭前终止。默认情况下，只有当线程数超过 corePoolSize 时，才会采用保持连接策略。但只要 keepAliveTime 值不为零，就可以使用方法 allowCoreThreadTimeOut(boolean) 将超时策略也应用到核心线程。
Queuing
// 队列
    Any BlockingQueue may be used to transfer and hold submitted tasks. The use of this queue interacts with pool sizing:
    // 任何阻塞队列（BlockingQueue）都可用于传输和保留已提交的任务。该队列的使用与线程池大小有关：
    If fewer than corePoolSize threads are running, the Executor always prefers adding a new thread rather than queuing.
    // 如果运行的线程少于 corePoolSize，执行器总是倾向于添加新线程而不是队列。
    If corePoolSize or more threads are running, the Executor always prefers queuing a request rather than adding a new thread.
    // 如果运行的线程数大于或等于 corePoolSize，执行器总是倾向于将请求排队，而不是添加新线程。
    If a request cannot be queued, a new thread is created unless this would exceed maximumPoolSize, in which case, the task will be rejected.
    // 如果请求无法进队列，执行器会创建一个新线程，除非这样做会超过最大线程池大小（maximumPoolSize），在这种情况下，任务会被拒绝。
    There are three general strategies for queuing:
    // 入队一般有三种策略：
    Direct handoffs. A good default choice for a work queue is a SynchronousQueue that hands off tasks to threads without otherwise holding them. Here, an attempt to queue a task will fail if no threads are immediately available to run it, so a new thread will be constructed. This policy avoids lockups when handling sets of requests that might have internal dependencies. Direct handoffs generally require unbounded maximumPoolSizes to avoid rejection of new submitted tasks. This in turn admits the possibility of unbounded thread growth when commands continue to arrive on average faster than they can be processed.
    // 直接交接。工作队列的一个很好的默认选择是同步队列（SynchronousQueue），它可以将任务移交给线程，而不会以其他方式保留任务。在这里，如果没有线程可立即运行任务，则队列任务的尝试将失败，因此会构建一个新的线程。在处理可能存在内部依赖关系的请求集时，这种策略可以避免死锁。直接交接通常需要无限制的最大线程池大小，以避免新提交的任务被拒绝。反过来，当命令的平均到达速度超过其处理速度时，就有可能导致线程无限制增长。
    /** Unbounded queues. Using an unbounded queue (for example a LinkedBlockingQueue without a predefined capacity) will cause new tasks to wait in the queue when all corePoolSize threads are busy. Thus, no more than corePoolSize threads will ever be created. (And the value of the maximumPoolSize therefore doesn't have any effect.) This may be appropriate when each task is completely independent of others, so tasks cannot affect each others execution; for example, in a web page server. While this style of queuing can be useful in smoothing out transient bursts of requests, it admits the possibility of unbounded work queue growth when commands continue to arrive on average faster than they can be processed. */
    // 无界队列。使用无界队列（例如没有预定义容量的 LinkedBlockingQueue）会导致新任务在所有 corePoolSize 线程都繁忙时在队列中等待。因此，创建的线程不会超过 corePoolSize。(当每个任务都完全独立于其他任务，因此任务之间不会相互影响执行时，例如在网页服务器中，这种方式可能比较合适。虽然这种队列方式在平滑瞬时突发请求方面很有用，但当命令的平均到达速度持续超过其处理速度时，就会导致工作队列无限制地增长。
    Bounded queues. A bounded queue (for example, an ArrayBlockingQueue) helps prevent resource exhaustion when used with finite maximumPoolSizes, but can be more difficult to tune and control. Queue sizes and maximum pool sizes may be traded off for each other: Using large queues and small pools minimizes CPU usage, OS resources, and context-switching overhead, but can lead to artificially low throughput. If tasks frequently block (for example if they are I/O bound), a system may be able to schedule time for more threads than you otherwise allow. Use of small queues generally requires larger pool sizes, which keeps CPUs busier but may encounter unacceptable scheduling overhead, which also decreases throughput.
    // 有界队列 有界队列（例如 ArrayBlockingQueue）与有限的最大线程池大小一起使用时，有助于防止资源耗尽，但可能更难调整和控制。队列大小和最大池大小可以相互权衡： 使用大队列和小池可以最大限度地减少 CPU 占用率、操作系统资源和上下文切换开销，但会导致人为的低吞吐量。如果任务经常出现阻塞（例如，如果任务受 I/O 约束），系统可能会为更多线程安排时间，而不考虑其他因素。使用小队列通常需要更大的池规模，这将使 CPU 更繁忙，但可能会遇到无法接受的调度开销，这也会降低吞吐量。
Rejected tasks
// 被拒绝的任务
    New tasks submitted in method execute(Runnable) will be rejected when the Executor has been shut down, and also when the Executor uses finite bounds for both maximum threads and work queue capacity, and is saturated. In either case, the execute method invokes the RejectedExecutionHandler.rejectedExecution(Runnable, ThreadPoolExecutor) method of its RejectedExecutionHandler. Four predefined handler policies are provided:
    // 如果执行器已关闭，或者执行器使用了最大线程数和工作队列容量的有限界限，并已达到饱和，那么在 execute(Runnable) 方法中提交的新任务将被拒绝。在这两种情况下，execute 方法都会调用其 RejectedExecutionHandler 的 RejectedExecutionHandler.rejectedExecution(Runnable, ThreadPoolExecutor) 方法。提供了四种预定义的处理程序策略：
    In the default ThreadPoolExecutor.AbortPolicy, the handler throws a runtime RejectedExecutionException upon rejection.
    // 在默认的 ThreadPoolExecutor.AbortPolicy 中，处理程序会在拒绝时抛出运行时 RejectedExecutionException。
    In ThreadPoolExecutor.CallerRunsPolicy, the thread that invokes execute itself runs the task. This provides a simple feedback control mechanism that will slow down the rate that new tasks are submitted.
    // 在 ThreadPoolExecutor.CallerRunsPolicy 中，调用执行本身的线程会运行任务。这提供了一个简单的反馈控制机制，可减慢提交新任务的速度。
    In ThreadPoolExecutor.DiscardPolicy, a task that cannot be executed is simply dropped.
    // 在 ThreadPoolExecutor.DiscardPolicy 中，无法执行的任务会被直接丢弃。
    In ThreadPoolExecutor.DiscardOldestPolicy, if the executor is not shut down, the task at the head of the work queue is dropped, and then execution is retried (which can fail again, causing this to be repeated.)
    // 在 ThreadPoolExecutor.DiscardOldestPolicy 中，如果执行器没有关闭，工作队列头部的任务就会被丢弃，然后重新执行（可能会再次失败，导致重复执行）。
    It is possible to define and use other kinds of RejectedExecutionHandler classes. Doing so requires some care especially when policies are designed to work only under particular capacity or queuing policies.
    // 我们还可以定义和使用其他类型的 RejectedExecutionHandler 类。这样做需要小心谨慎，尤其是当策略设计为仅在特定容量或队列策略下运行时。
Hook methods
// 钩子方法
    This class provides protected overridable beforeExecute(Thread, Runnable) and afterExecute(Runnable, Throwable) methods that are called before and after execution of each task. These can be used to manipulate the execution environment; for example, reinitializing ThreadLocals, gathering statistics, or adding log entries. Additionally, method terminated can be overridden to perform any special processing that needs to be done once the Executor has fully terminated.
    // 该类提供 protected 的可重载 beforeExecute(Thread, Runnable) 和 afterExecute(Runnable, Throwable) 方法，可在每个任务执行前后调用。这些方法可用于操纵执行环境，例如，重新初始化线程位置、收集统计数据或添加日志条目。此外，还可以重写终止方法，以便在执行器完全终止后执行任何需要进行的特殊处理。(一些动态线程池技术就用到了这两个方法)
    If hook or callback methods throw exceptions, internal worker threads may in turn fail and abruptly terminate.
    // 如果钩子或回调方法抛出异常，内部工作线程可能会失败并突然终止。
Queue maintenance
// 队列维护
    Method getQueue() allows access to the work queue for purposes of monitoring and debugging. Use of this method for any other purpose is strongly discouraged. Two supplied methods, remove(Runnable) and purge are available to assist in storage reclamation when large numbers of queued tasks become cancelled.
    // 方法 getQueue() 允许访问工作队列，以便进行监控和调试。强烈建议不要将此方法用于任何其他目的。当大量队列任务被取消时，可使用 remove(Runnable) 和 purge 这两个提供的方法来帮助回收存储空间。(不建议直接将获取到的队列进行修改操作)
Finalization
// 终止
    A pool that is no longer referenced in a program AND has no remaining threads will be shutdown automatically. If you would like to ensure that unreferenced pools are reclaimed even if users forget to call shutdown, then you must arrange that unused threads eventually die, by setting appropriate keep-alive times, using a lower bound of zero core threads and/or setting allowCoreThreadTimeOut(boolean).
    // 程序中不再引用且没有剩余线程的池将自动关闭。如果要确保即使用户忘记调用 shutdown 也能回收未引用的池，则必须通过设置适当的保持连接时间、使用零核心线程下限和/或设置 allowCoreThreadTimeOut(boolean) 来使得未使用的线程最终被停止。
+

通过前面的简单翻译我们就能大致略窥得线程池配置全貌，继续看一个构造方法

+
public ThreadPoolExecutor(int corePoolSize,
                          int maximumPoolSize,
                          long keepAliveTime,
                          TimeUnit unit,
                          BlockingQueue<Runnable> workQueue) {
    this(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue,
         Executors.defaultThreadFactory(), defaultHandler);
}
private static final RejectedExecutionHandler defaultHandler =
    new AbortPolicy();
+

这个构造方法就主要配置了 corePoolSize 核心线程数，maximumPoolSize 最大线程数, keepAliveTime 线程保活时间，unit 时间单位，workQueue 工作队列，而传入后可以看到使用了默认的线程工厂和默认的拒绝处理器

+
public ThreadPoolExecutor(int corePoolSize,
                          int maximumPoolSize,
                          long keepAliveTime,
                          TimeUnit unit,
                          BlockingQueue<Runnable> workQueue,
                          ThreadFactory threadFactory,
                          RejectedExecutionHandler handler) {
    if (corePoolSize < 0 ||
        maximumPoolSize <= 0 ||
        maximumPoolSize < corePoolSize ||
        keepAliveTime < 0)
        throw new IllegalArgumentException();
    if (workQueue == null || threadFactory == null || handler == null)
        throw new NullPointerException();
    this.acc = System.getSecurityManager() == null ?
            null :
            AccessController.getContext();
    this.corePoolSize = corePoolSize;
    this.maximumPoolSize = maximumPoolSize;
    this.workQueue = workQueue;
    this.keepAliveTime = unit.toNanos(keepAliveTime);
    this.threadFactory = threadFactory;
    this.handler = handler;
}
+

最终会执行到这里做一些校验判断，包括几个参数校验和配置赋值

]]> Java @@ -764,28 +781,11 @@ leetcode - leetcode java + leetcode 题解 - - Java 线程池系列-第四篇 - /2024/03/03/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E5%9B%9B%E7%AF%87/ - 这一篇主要补充两个内容，第一部分就是获取任务的逻辑
首先是状态判断，如果是停止了，SHUTDOWN或更大的了，就需要减小工作线程数量
并返回 null，使得工作线程 worker 退出，然后再判断线程数量和超时，同样如果超过了就会返回 null
然后就是去阻塞队列里获取任务，这里是阻塞着获取的

-
private Runnable getTask() {
        boolean timedOut = false; // Did the last poll() time out?

        for (;;) {
            int c = ctl.get();
            int rs = runStateOf(c);

            // Check if queue empty only if necessary.
            // 状态异常，如果是大于等于 SHUTDOWN 的，则协助关闭线程池
            if (rs >= SHUTDOWN && (rs >= STOP || workQueue.isEmpty())) {
                decrementWorkerCount();
                return null;
            }

            int wc = workerCountOf(c);

            // Are workers subject to culling?
            boolean timed = allowCoreThreadTimeOut || wc > corePoolSize;

            // 如果线程数量超过核心线程数，则帮助减少线程
            if ((wc > maximumPoolSize || (timed && timedOut))
                && (wc > 1 || workQueue.isEmpty())) {
                if (compareAndDecrementWorkerCount(c))
                    return null;
                continue;
            }

            // 如果前面的不符合，则从阻塞队列获取任务
            try {
                Runnable r = timed ?
                    workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) :
                    workQueue.take();
                if (r != null)
                    return r;
                timedOut = true;
            } catch (InterruptedException retry) {
                timedOut = false;
            }
        }
    }
- -

阻塞队列的 poll 主要是通过锁，和notEmpty这个 condition 来等待制定的时间
指定时间后开始 dequeue 出队

-
public E poll(long timeout, TimeUnit unit) throws InterruptedException {
    long nanos = unit.toNanos(timeout);
    final ReentrantLock lock = this.lock;
    lock.lockInterruptibly();
    try {
        while (count == 0) {
            if (nanos <= 0)
                return null;
            nanos = notEmpty.awaitNanos(nanos);
        }
        return dequeue();
    } finally {
        lock.unlock();
    }
}
-

第二部分比较重要，有的同学的问题是为什么不一开始开到最大线程，而是达到核心线程数后就进队列了，
其实池化技术最简单的原因就是希望能够复用这些线程，因为创建销毁他们的成本太大了，如果直接最大线程数的话
其实都不用用到线程池技术了，直接有多少任务就开多少线程，用完就丢了，阐述下我认为比较重要的概念点

-]]> - - Java - - - Java - - Leetcode 028 实现 strStr() ( Implement strStr() ) 题解分析 /2021/10/31/Leetcode-028-%E5%AE%9E%E7%8E%B0-strStr-Implement-strStr-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ @@ -807,8 +807,37 @@ leetcode + java leetcode + 题解 + + + + Leetcode 053 最大子序和 ( Maximum Subarray ) 题解分析 + /2021/11/28/Leetcode-053-%E6%9C%80%E5%A4%A7%E5%AD%90%E5%BA%8F%E5%92%8C-Maximum-Subarray-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

Given an integer array nums, find the contiguous subarray (containing at least one number) which has the largest sum and return its sum.

+

A subarray is a contiguous part of an array.

+

示例

Example 1:

+
+

Input: nums = [-2,1,-3,4,-1,2,1,-5,4]
Output: 6
Explanation: [4,-1,2,1] has the largest sum = 6.

+
+

Example 2:

+
+

Input: nums = [1]
Output: 1

+
+

Example 3:

+
+

Input: nums = [5,4,-1,7,8]
Output: 23

+
+

说起来这个题其实非常有渊源，大学数据结构的第一个题就是这个，而最佳的算法就是传说中的 online 算法，就是遍历一次就完了，最基本的做法就是记下来所有的连续子数组，然后求出最大的那个。

+

代码

public int maxSubArray(int[] nums) {
        int max = nums[0];
        int sum = nums[0];
        for (int i = 1; i < nums.length; i++) {
            // 这里最重要的就是这一行了，其实就是如果前面的 sum 是小于 0 的，那么就不需要前面的 sum，反正加上了还不如不加大
            sum = Math.max(nums[i], sum + nums[i]);
            // max 是用来承载最大值的
            max = Math.max(max, sum);
        }
        return max;
    }
]]> + + Java + leetcode + + java + leetcode 题解 @@ -825,16 +854,16 @@

分析

其实对于树这类题，一般是以递归形式比较方便，只是要注意退出条件

]]> - leetcode Java + leetcode Binary Tree java Binary Tree DFS - leetcode java + leetcode 题解 Binary Tree DFS @@ -842,32 +871,21 @@ - Leetcode 053 最大子序和 ( Maximum Subarray ) 题解分析 - /2021/11/28/Leetcode-053-%E6%9C%80%E5%A4%A7%E5%AD%90%E5%BA%8F%E5%92%8C-Maximum-Subarray-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

Given an integer array nums, find the contiguous subarray (containing at least one number) which has the largest sum and return its sum.

-

A subarray is a contiguous part of an array.

-

示例

Example 1:

-
-

Input: nums = [-2,1,-3,4,-1,2,1,-5,4]
Output: 6
Explanation: [4,-1,2,1] has the largest sum = 6.

-
-

Example 2:

-
-

Input: nums = [1]
Output: 1

-
-

Example 3:

-
-

Input: nums = [5,4,-1,7,8]
Output: 23

-
-

说起来这个题其实非常有渊源，大学数据结构的第一个题就是这个，而最佳的算法就是传说中的 online 算法，就是遍历一次就完了，最基本的做法就是记下来所有的连续子数组，然后求出最大的那个。

-

代码

public int maxSubArray(int[] nums) {
        int max = nums[0];
        int sum = nums[0];
        for (int i = 1; i < nums.length; i++) {
            // 这里最重要的就是这一行了，其实就是如果前面的 sum 是小于 0 的，那么就不需要前面的 sum，反正加上了还不如不加大
            sum = Math.max(nums[i], sum + nums[i]);
            // max 是用来承载最大值的
            max = Math.max(max, sum);
        }
        return max;
    }
]]> + Java 线程池系列-第三篇 + /2024/02/24/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E4%B8%89%E7%AF%87/ + 第三篇我们要来讲下 Worker 这个“打工人”，线程池里实际在干活的同学
Worker 实现了 Runnable 接口，所以在前面介绍的 addWorker 中
线程启动其实就调用了下面的 run 方法，而 run 方法则调用了内部的
runWorker 方法

+

private final class Worker
    extends AbstractQueuedSynchronizer
    implements Runnable
{
    /**
     * This class will never be serialized, but we provide a
     * serialVersionUID to suppress a javac warning.
     */
    private static final long serialVersionUID = 6138294804551838833L;

    /** Thread this worker is running in.  Null if factory fails. */
    final Thread thread;
    /** Initial task to run.  Possibly null. */
    Runnable firstTask;
    /** Per-thread task counter */
    volatile long completedTasks;

    /**
     * Creates with given first task and thread from ThreadFactory.
     * @param firstTask the first task (null if none)
     */
    Worker(Runnable firstTask) {
        setState(-1); // inhibit interrupts until runWorker
        this.firstTask = firstTask;
        this.thread = getThreadFactory().newThread(this);
    }

    /** Delegates main run loop to outer runWorker  */
    // 启动线程 Worker
    public void run() {
        runWorker(this);
    }
+

介绍下这个 runWorker 方法
这里就会先处理 firstTask，如果 firstTask 是 null 则会去队列里取
里面会有两个钩子，一个是执行前，一个是执行后，注意这个循环是没有常规
退出逻辑的，说明是一直在往队列里获取，除非获取队列里的任务超时失败了
这样就到最后会去执行 processWorkerExit

+
final void runWorker(Worker w) {
        Thread wt = Thread.currentThread();
        Runnable task = w.firstTask;
        w.firstTask = null;
        w.unlock(); // allow interrupts
        boolean completedAbruptly = true;
        try {
            // firstTask 不为空或者从队列里获取到任务
            while (task != null || (task = getTask()) != null) {
                w.lock();
                // If pool is stopping, ensure thread is interrupted;
                // if not, ensure thread is not interrupted.  This
                // requires a recheck in second case to deal with
                // shutdownNow race while clearing interrupt
                if ((runStateAtLeast(ctl.get(), STOP) ||
                     (Thread.interrupted() &&
                      runStateAtLeast(ctl.get(), STOP))) &&
                    !wt.isInterrupted())
                    wt.interrupt();
                try {
                    // 前置执行钩子
                    beforeExecute(wt, task);
                    Throwable thrown = null;
                    try {
                        // 任务执行
                        task.run();
                    } catch (RuntimeException x) {
                        thrown = x; throw x;
                    } catch (Error x) {
                        thrown = x; throw x;
                    } catch (Throwable x) {
                        thrown = x; throw new Error(x);
                    } finally {
                        // 执行完后的钩子
                        afterExecute(task, thrown);
                    }
                } finally {
                    // 最后会执行到这，清空任务
                    task = null;
                    w.completedTasks++;
                    w.unlock();
                }
            }
            completedAbruptly = false;
        } finally {
            // 循环的最后就会去退出Worker
            processWorkerExit(w, completedAbruptly);
        }
    }
+

而这个 processWorkerExit 也有个比较特殊的逻辑
照理我们会想到这里可能就是线程在处理完队列里的任务
以后，就会判断下是否要退出，如果是核心线程的话就不用
如果是非核心线程就会退出了，但是这里其实有个替换逻辑
就是最后有个 addWorker 调用

+
private void processWorkerExit(Worker w, boolean completedAbruptly) {
    if (completedAbruptly) // If abrupt, then workerCount wasn't adjusted
        decrementWorkerCount();

    final ReentrantLock mainLock = this.mainLock;
    mainLock.lock();
    try {
        completedTaskCount += w.completedTasks;
        workers.remove(w);
    } finally {
        mainLock.unlock();
    }

    tryTerminate();

    int c = ctl.get();
    // 判断状态
    if (runStateLessThan(c, STOP)) {
        if (!completedAbruptly) {
            // 核心线程是否也需要退出
            int min = allowCoreThreadTimeOut ? 0 : corePoolSize;
            if (min == 0 && ! workQueue.isEmpty())
                min = 1;
            // 这里代表非核心线程就可以不用替换，也就是回收线程了
            if (workerCountOf(c) >= min)
                return; // replacement not needed
        }
        // 否则是替换线程
        addWorker(null, false);
    }
}
+

这里主要讲了线程 Worker 运行的逻辑

+]]> Java - leetcode - leetcode - java - 题解 + Java @@ -894,86 +912,73 @@ leetcode - leetcode java + leetcode 题解 Print FooBar Alternately - Java 线程池系列-第一篇 - /2024/02/11/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E4%B8%80%E7%AF%87/ - 这一篇我们继续聊线程池，一般线程池会介绍我们的参数，我先不一样一些
我们先来翻译一下

-
 An {@link ExecutorService} that executes each submitted task using
 one of possibly several pooled threads, normally configured
 using {@link Executors} factory methods.
 // executorService 使用一个线程池中的线程来执行每个提交的任务，经常使用 Executors 工厂方法

 <p>Thread pools address two different problems: they usually
 provide improved performance when executing large numbers of
 asynchronous tasks, due to reduced per-task invocation overhead,
 and they provide a means of bounding and managing the resources,
 including threads, consumed when executing a collection of tasks.
 Each {@code ThreadPoolExecutor} also maintains some basic
 statistics, such as the number of completed tasks.
 // 线程池解决两个不同的问题，通常是为了在提交大量异步任务时，通过减少每个任务的调度开销来提高性能
 // 并且提供了一种管理和约束资源，包括线程消耗的方法，每个 ThreadPoolExecutor还会管理一些基础的统计信息
 // 比如完成的任务数量
 

 <p>To be useful across a wide range of contexts, this class
 provides many adjustable parameters and extensibility
 hooks. However, programmers are urged to use the more convenient
 {@link Executors} factory methods {@link
 Executors#newCachedThreadPool} (unbounded thread pool, with
 automatic thread reclamation), {@link Executors#newFixedThreadPool}
 (fixed size thread pool) and {@link
 Executors#newSingleThreadExecutor} (single background thread), that
 preconfigure settings for the most common usage
 scenarios. Otherwise, use the following guide when manually
 configuring and tuning this class:
 // 为了在各种情况下都能派上用场，该类提供了许多可调整的参数和扩展钩子。不过，我们建议程序员使用更方便的 Executors 工厂方法 Executors.
 // newCachedThreadPool（无限制线程池，可自动回收线程）、Executors.newFixedThreadPool（固定大小线程池）和 Executors.
 // newSingleThreadExecutor（单后台线程），这些方法可为最常见的使用场景预先配置设置。否则，在手动配置和调整该类时，请使用以下指南：

 Core and maximum pool sizes
 // 核心和最大线程池大小
    A ThreadPoolExecutor will automatically adjust the pool size (see getPoolSize) according to the bounds set by corePoolSize (see getCorePoolSize) and maximumPoolSize (see getMaximumPoolSize). When a new task is submitted in method execute(Runnable), and fewer than corePoolSize threads are running, a new thread is created to handle the request, even if other worker threads are idle. If there are more than corePoolSize but less than maximumPoolSize threads running, a new thread will be created only if the queue is full. By setting corePoolSize and maximumPoolSize the same, you create a fixed-size thread pool. By setting maximumPoolSize to an essentially unbounded value such as Integer.MAX_VALUE, you allow the pool to accommodate an arbitrary number of concurrent tasks. Most typically, core and maximum pool sizes are set only upon construction, but they may also be changed dynamically using setCorePoolSize and setMaximumPoolSize.
    // ThreadPoolExecutor 会根据 corePoolSize（参见 getCorePoolSize）和 maximumPoolSize（参见 getMaximumPoolSize）设置的界限自动调整池大小（参见 getPoolSize）。在方法 execute(Runnable) 中提交新任务时，如果运行的线程少于 corePoolSize，即使其他工作线程处于空闲状态，也会创建一个新线程来处理请求。如果运行的线程多于 corePoolSize 但少于 maximumPoolSize，则只有在队列已满时才会创建新线程。如果将 corePoolSize 和 maximumPoolSize 设置为相同，就可以创建一个固定大小的线程池。如果将 maximumPoolSize 设置为 Integer.MAX_VALUE 等基本无限制的值，就可以让池容纳任意数量的并发任务。通常，核心和最大线程池大小只在构建时设置，但也可以使用 setCorePoolSize 和 setMaximumPoolSize 动态更改。

On-demand construction
// 按需构建
    By default, even core threads are initially created and started only when new tasks arrive, but this can be overridden dynamically using method prestartCoreThread or prestartAllCoreThreads. You probably want to prestart threads if you construct the pool with a non-empty queue.
    // 默认情况下，即使是核心线程也是只有当新任务到来时才会初始创建和启动，但这可以使用方法 prestartCoreThread 或 prestartAllCoreThreads 进行动态重载。如果使用非空队列构建线程池，则可能需要预启动线程。
Creating new threads
// 创建新线程
    /** New threads are created using a ThreadFactory. If not otherwise specified, a Executors.defaultThreadFactory is used, that creates threads to all be in the same ThreadGroup and with the same NORM_PRIORITY priority and non-daemon status. By supplying a different ThreadFactory, you can alter the thread's name, thread group, priority, daemon status, etc. If a ThreadFactory fails to create a thread when asked by returning null from newThread, the executor will continue, but might not be able to execute any tasks. Threads should possess the "modifyThread" RuntimePermission. If worker threads or other threads using the pool do not possess this permission, service may be degraded: configuration changes may not take effect in a timely manner, and a shutdown pool may remain in a state in which termination is possible but not completed. */
    // 新线程是使用线程工厂（ThreadFactory）创建的。如果未另行指定，将使用 Executors.defaultThreadFactory 创建线程，创建的线程将全部位于同一线程组，并具有相同的 NORM_PRIORITY 优先级和非守护进程状态。通过提供不同的 ThreadFactory，可以更改线程的名称、线程组、优先级、守护进程状态等。如果线程工厂无法按要求创建线程，从 newThread 返回空值，执行器将继续运行，但可能无法执行任何任务。线程应拥有 "modifyThread "运行时权限。如果工作线程或使用池的其他线程不具备此权限，服务可能会降级：配置更改可能无法及时生效，关闭的池可能会一直处于可以终止但无法完成的状态。
Keep-alive times
// 保活时间
    If the pool currently has more than corePoolSize threads, excess threads will be terminated if they have been idle for more than the keepAliveTime (see getKeepAliveTime(TimeUnit)). This provides a means of reducing resource consumption when the pool is not being actively used. If the pool becomes more active later, new threads will be constructed. This parameter can also be changed dynamically using method setKeepAliveTime(long, TimeUnit). Using a value of Long.MAX_VALUE TimeUnit.NANOSECONDS effectively disables idle threads from ever terminating prior to shut down. By default, the keep-alive policy applies only when there are more than corePoolSize threads. But method allowCoreThreadTimeOut(boolean) can be used to apply this time-out policy to core threads as well, so long as the keepAliveTime value is non-zero.
    // 如果当前池中的线程数超过 corePoolSize，多余的线程在闲置时间超过 keepAliveTime（请参阅 getKeepAliveTime(TimeUnit)）时将被终止。这提供了一种在线程池未被频繁使用时减少资源消耗的方法。如果以后池变得更加活跃，就会构建新线程。也可以使用方法 setKeepAliveTime(long, TimeUnit) 动态更改该参数。使用 Long.MAX_VALUE TimeUnit.NANOSECONDS 值可有效禁止闲置线程在关闭前终止。默认情况下，只有当线程数超过 corePoolSize 时，才会采用保持连接策略。但只要 keepAliveTime 值不为零，就可以使用方法 allowCoreThreadTimeOut(boolean) 将超时策略也应用到核心线程。
Queuing
// 队列
    Any BlockingQueue may be used to transfer and hold submitted tasks. The use of this queue interacts with pool sizing:
    // 任何阻塞队列（BlockingQueue）都可用于传输和保留已提交的任务。该队列的使用与线程池大小有关：
    If fewer than corePoolSize threads are running, the Executor always prefers adding a new thread rather than queuing.
    // 如果运行的线程少于 corePoolSize，执行器总是倾向于添加新线程而不是队列。
    If corePoolSize or more threads are running, the Executor always prefers queuing a request rather than adding a new thread.
    // 如果运行的线程数大于或等于 corePoolSize，执行器总是倾向于将请求排队，而不是添加新线程。
    If a request cannot be queued, a new thread is created unless this would exceed maximumPoolSize, in which case, the task will be rejected.
    // 如果请求无法进队列，执行器会创建一个新线程，除非这样做会超过最大线程池大小（maximumPoolSize），在这种情况下，任务会被拒绝。
    There are three general strategies for queuing:
    // 入队一般有三种策略：
    Direct handoffs. A good default choice for a work queue is a SynchronousQueue that hands off tasks to threads without otherwise holding them. Here, an attempt to queue a task will fail if no threads are immediately available to run it, so a new thread will be constructed. This policy avoids lockups when handling sets of requests that might have internal dependencies. Direct handoffs generally require unbounded maximumPoolSizes to avoid rejection of new submitted tasks. This in turn admits the possibility of unbounded thread growth when commands continue to arrive on average faster than they can be processed.
    // 直接交接。工作队列的一个很好的默认选择是同步队列（SynchronousQueue），它可以将任务移交给线程，而不会以其他方式保留任务。在这里，如果没有线程可立即运行任务，则队列任务的尝试将失败，因此会构建一个新的线程。在处理可能存在内部依赖关系的请求集时，这种策略可以避免死锁。直接交接通常需要无限制的最大线程池大小，以避免新提交的任务被拒绝。反过来，当命令的平均到达速度超过其处理速度时，就有可能导致线程无限制增长。
    /** Unbounded queues. Using an unbounded queue (for example a LinkedBlockingQueue without a predefined capacity) will cause new tasks to wait in the queue when all corePoolSize threads are busy. Thus, no more than corePoolSize threads will ever be created. (And the value of the maximumPoolSize therefore doesn't have any effect.) This may be appropriate when each task is completely independent of others, so tasks cannot affect each others execution; for example, in a web page server. While this style of queuing can be useful in smoothing out transient bursts of requests, it admits the possibility of unbounded work queue growth when commands continue to arrive on average faster than they can be processed. */
    // 无界队列。使用无界队列（例如没有预定义容量的 LinkedBlockingQueue）会导致新任务在所有 corePoolSize 线程都繁忙时在队列中等待。因此，创建的线程不会超过 corePoolSize。(当每个任务都完全独立于其他任务，因此任务之间不会相互影响执行时，例如在网页服务器中，这种方式可能比较合适。虽然这种队列方式在平滑瞬时突发请求方面很有用，但当命令的平均到达速度持续超过其处理速度时，就会导致工作队列无限制地增长。
    Bounded queues. A bounded queue (for example, an ArrayBlockingQueue) helps prevent resource exhaustion when used with finite maximumPoolSizes, but can be more difficult to tune and control. Queue sizes and maximum pool sizes may be traded off for each other: Using large queues and small pools minimizes CPU usage, OS resources, and context-switching overhead, but can lead to artificially low throughput. If tasks frequently block (for example if they are I/O bound), a system may be able to schedule time for more threads than you otherwise allow. Use of small queues generally requires larger pool sizes, which keeps CPUs busier but may encounter unacceptable scheduling overhead, which also decreases throughput.
    // 有界队列 有界队列（例如 ArrayBlockingQueue）与有限的最大线程池大小一起使用时，有助于防止资源耗尽，但可能更难调整和控制。队列大小和最大池大小可以相互权衡： 使用大队列和小池可以最大限度地减少 CPU 占用率、操作系统资源和上下文切换开销，但会导致人为的低吞吐量。如果任务经常出现阻塞（例如，如果任务受 I/O 约束），系统可能会为更多线程安排时间，而不考虑其他因素。使用小队列通常需要更大的池规模，这将使 CPU 更繁忙，但可能会遇到无法接受的调度开销，这也会降低吞吐量。
Rejected tasks
// 被拒绝的任务
    New tasks submitted in method execute(Runnable) will be rejected when the Executor has been shut down, and also when the Executor uses finite bounds for both maximum threads and work queue capacity, and is saturated. In either case, the execute method invokes the RejectedExecutionHandler.rejectedExecution(Runnable, ThreadPoolExecutor) method of its RejectedExecutionHandler. Four predefined handler policies are provided:
    // 如果执行器已关闭，或者执行器使用了最大线程数和工作队列容量的有限界限，并已达到饱和，那么在 execute(Runnable) 方法中提交的新任务将被拒绝。在这两种情况下，execute 方法都会调用其 RejectedExecutionHandler 的 RejectedExecutionHandler.rejectedExecution(Runnable, ThreadPoolExecutor) 方法。提供了四种预定义的处理程序策略：
    In the default ThreadPoolExecutor.AbortPolicy, the handler throws a runtime RejectedExecutionException upon rejection.
    // 在默认的 ThreadPoolExecutor.AbortPolicy 中，处理程序会在拒绝时抛出运行时 RejectedExecutionException。
    In ThreadPoolExecutor.CallerRunsPolicy, the thread that invokes execute itself runs the task. This provides a simple feedback control mechanism that will slow down the rate that new tasks are submitted.
    // 在 ThreadPoolExecutor.CallerRunsPolicy 中，调用执行本身的线程会运行任务。这提供了一个简单的反馈控制机制，可减慢提交新任务的速度。
    In ThreadPoolExecutor.DiscardPolicy, a task that cannot be executed is simply dropped.
    // 在 ThreadPoolExecutor.DiscardPolicy 中，无法执行的任务会被直接丢弃。
    In ThreadPoolExecutor.DiscardOldestPolicy, if the executor is not shut down, the task at the head of the work queue is dropped, and then execution is retried (which can fail again, causing this to be repeated.)
    // 在 ThreadPoolExecutor.DiscardOldestPolicy 中，如果执行器没有关闭，工作队列头部的任务就会被丢弃，然后重新执行（可能会再次失败，导致重复执行）。
    It is possible to define and use other kinds of RejectedExecutionHandler classes. Doing so requires some care especially when policies are designed to work only under particular capacity or queuing policies.
    // 我们还可以定义和使用其他类型的 RejectedExecutionHandler 类。这样做需要小心谨慎，尤其是当策略设计为仅在特定容量或队列策略下运行时。
Hook methods
// 钩子方法
    This class provides protected overridable beforeExecute(Thread, Runnable) and afterExecute(Runnable, Throwable) methods that are called before and after execution of each task. These can be used to manipulate the execution environment; for example, reinitializing ThreadLocals, gathering statistics, or adding log entries. Additionally, method terminated can be overridden to perform any special processing that needs to be done once the Executor has fully terminated.
    // 该类提供 protected 的可重载 beforeExecute(Thread, Runnable) 和 afterExecute(Runnable, Throwable) 方法，可在每个任务执行前后调用。这些方法可用于操纵执行环境，例如，重新初始化线程位置、收集统计数据或添加日志条目。此外，还可以重写终止方法，以便在执行器完全终止后执行任何需要进行的特殊处理。(一些动态线程池技术就用到了这两个方法)
    If hook or callback methods throw exceptions, internal worker threads may in turn fail and abruptly terminate.
    // 如果钩子或回调方法抛出异常，内部工作线程可能会失败并突然终止。
Queue maintenance
// 队列维护
    Method getQueue() allows access to the work queue for purposes of monitoring and debugging. Use of this method for any other purpose is strongly discouraged. Two supplied methods, remove(Runnable) and purge are available to assist in storage reclamation when large numbers of queued tasks become cancelled.
    // 方法 getQueue() 允许访问工作队列，以便进行监控和调试。强烈建议不要将此方法用于任何其他目的。当大量队列任务被取消时，可使用 remove(Runnable) 和 purge 这两个提供的方法来帮助回收存储空间。(不建议直接将获取到的队列进行修改操作)
Finalization
// 终止
    A pool that is no longer referenced in a program AND has no remaining threads will be shutdown automatically. If you would like to ensure that unreferenced pools are reclaimed even if users forget to call shutdown, then you must arrange that unused threads eventually die, by setting appropriate keep-alive times, using a lower bound of zero core threads and/or setting allowCoreThreadTimeOut(boolean).
    // 程序中不再引用且没有剩余线程的池将自动关闭。如果要确保即使用户忘记调用 shutdown 也能回收未引用的池，则必须通过设置适当的保持连接时间、使用零核心线程下限和/或设置 allowCoreThreadTimeOut(boolean) 来使得未使用的线程最终被停止。
-

通过前面的简单翻译我们就能大致略窥得线程池配置全貌，继续看一个构造方法

-
public ThreadPoolExecutor(int corePoolSize,
                          int maximumPoolSize,
                          long keepAliveTime,
                          TimeUnit unit,
                          BlockingQueue<Runnable> workQueue) {
    this(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue,
         Executors.defaultThreadFactory(), defaultHandler);
}
private static final RejectedExecutionHandler defaultHandler =
    new AbortPolicy();
-

这个构造方法就主要配置了 corePoolSize 核心线程数，maximumPoolSize 最大线程数, keepAliveTime 线程保活时间，unit 时间单位，workQueue 工作队列，而传入后可以看到使用了默认的线程工厂和默认的拒绝处理器

-
public ThreadPoolExecutor(int corePoolSize,
                          int maximumPoolSize,
                          long keepAliveTime,
                          TimeUnit unit,
                          BlockingQueue<Runnable> workQueue,
                          ThreadFactory threadFactory,
                          RejectedExecutionHandler handler) {
    if (corePoolSize < 0 ||
        maximumPoolSize <= 0 ||
        maximumPoolSize < corePoolSize ||
        keepAliveTime < 0)
        throw new IllegalArgumentException();
    if (workQueue == null || threadFactory == null || handler == null)
        throw new NullPointerException();
    this.acc = System.getSecurityManager() == null ?
            null :
            AccessController.getContext();
    this.corePoolSize = corePoolSize;
    this.maximumPoolSize = maximumPoolSize;
    this.workQueue = workQueue;
    this.keepAliveTime = unit.toNanos(keepAliveTime);
    this.threadFactory = threadFactory;
    this.handler = handler;
}
-

最终会执行到这里做一些校验判断，包括几个参数校验和配置赋值

-]]> - - Java - - - Java - - - - Leetcode 124 二叉树中的最大路径和(Binary Tree Maximum Path Sum) 题解分析 - /2021/01/24/Leetcode-124-%E4%BA%8C%E5%8F%89%E6%A0%91%E4%B8%AD%E7%9A%84%E6%9C%80%E5%A4%A7%E8%B7%AF%E5%BE%84%E5%92%8C-Binary-Tree-Maximum-Path-Sum-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

A path in a binary tree is a sequence of nodes where each pair of adjacent nodes in the sequence has an edge connecting them. A node can only appear in the sequence at most once. Note that the path does not need to pass through the root.

-

The path sum of a path is the sum of the node’s values in the path.

-

Given the root of a binary tree, return the maximum path sum of any path.

-

路径 被定义为一条从树中任意节点出发，沿父节点-子节点连接，达到任意节点的序列。该路径 至少包含一个 节点，且不一定经过根节点。

-

路径和 是路径中各节点值的总和。

-

给你一个二叉树的根节点 root ，返回其 最大路径和

-

简要分析

其实这个题目会被误解成比较简单，左子树最大的，或者右子树最大的，或者两边加一下，仔细想想都不对，其实有可能是产生于左子树中，或者右子树中，这两个都是指跟左子树根还有右子树根没关系的，这么说感觉不太容易理解，画个图

可以看到图里，其实最长路径和是左边这个子树组成的，跟根节点还有右子树完全没关系，然后再想一种情况，如果是整棵树就是图中的左子树，那么这个最长路径和就是左子树加右子树加根节点了，所以不是我一开始想得那么简单，在代码实现中也需要一些技巧

-

代码

int ansNew = Integer.MIN_VALUE;
public int maxPathSum(TreeNode root) {
        maxSumNew(root);
        return ansNew;
    }
    
public int maxSumNew(TreeNode root) {
    if (root == null) {
        return 0;
    }
    // 这里是个简单的递归，就是去递归左右子树，但是这里其实有个概念，当这样处理时，其实相当于把子树的内部的最大路径和已经算出来了
    int left = maxSumNew(root.left);
    int right = maxSumNew(root.right);
    // 这里前面我有点没想明白，但是看到 ansNew 的比较，其实相当于，返回的是三种情况里的最大值，一个是左子树+根，一个是右子树+根，一个是单独根节点，
    // 这样这个递归的返回才会有意义，不然像原来的方法，它可能是跳着的，但是这种情况其实是借助于 ansNew 这个全局的最大值，因为原来我觉得要比较的是
    // left, right, left + root , right + root, root, left + right + root 这些的最大值，这里是分成了两个阶段，left 跟 right 的最大值已经在上面的
    // 调用过程中赋值给 ansNew 了    
    int currentSum = Math.max(Math.max(root.val + left , root.val + right), root.val);
    // 这边返回的是 currentSum，然后再用它跟 left + right + root 进行对比，然后再去更新 ans
    // PS: 有个小点也是这边的破局点，就是这个 ansNew
    int res = Math.max(left + right + root.val, currentSum);
    ans = Math.max(res, ans);
    return currentSum;
}
+ Leetcode 105 从前序与中序遍历序列构造二叉树(Construct Binary Tree from Preorder and Inorder Traversal) 题解分析 + /2020/12/13/Leetcode-105-%E4%BB%8E%E5%89%8D%E5%BA%8F%E4%B8%8E%E4%B8%AD%E5%BA%8F%E9%81%8D%E5%8E%86%E5%BA%8F%E5%88%97%E6%9E%84%E9%80%A0%E4%BA%8C%E5%8F%89%E6%A0%91-Construct-Binary-Tree-from-Preorder-and-Inorder-Traversal-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

Given preorder and inorder traversal of a tree, construct the binary tree.
给定一棵树的前序和中序遍历，构造出一棵二叉树

+

注意

You may assume that duplicates do not exist in the tree.
你可以假设树中没有重复的元素。(PS: 不然就没法做了呀)

+

例子:

preorder = [3,9,20,15,7]
inorder = [9,3,15,20,7]
+

返回的二叉树

+
  3
 / \
9  20
  /  \
 15   7
-

这里非常重要的就是 ansNew 是最后的一个结果，而对于 maxSumNew 这个函数的返回值其实是需要包含了一个连续结果，因为要返回继续去算路径和，所以返回的是 currentSum，最终结果是 ansNew

-

结果图

难得有个 100%，贴个图哈哈

-]]> + +

简要分析

看到这个题可以想到一个比较常规的解法就是递归拆树，前序就是根左右，中序就是左根右，然后就是通过前序已经确定的根在中序中找到，然后去划分左右子树，这个例子里是 3，找到中序中的位置，那么就可以确定，9 是左子树，15,20,7是右子树，然后对应的可以根据左右子树的元素数量在前序中划分左右子树，再继续递归就行

+
class Solution {
    public TreeNode buildTree(int[] preorder, int[] inorder) {
      // 获取下数组长度
        int n = preorder.length;
        // 排除一下异常和边界
        if (n != inorder.length) {
            return null;
        }
        if (n == 0) {
            return null;
        }
        if (n == 1) {
            return new TreeNode(preorder[0]);
        }
        // 获得根节点
        TreeNode node = new TreeNode(preorder[0]);
        int pos = 0;
        // 找到中序中的位置
        for (int i = 0; i < inorder.length; i++) {
            if (node.val == inorder[i]) {
                pos = i;
                break;
            }
        }
        // 划分左右再进行递归，注意下`Arrays.copyOfRange`的用法
        node.left = buildTree(Arrays.copyOfRange(preorder, 1, pos + 1), Arrays.copyOfRange(inorder, 0, pos));
        node.right = buildTree(Arrays.copyOfRange(preorder, pos + 1, n), Arrays.copyOfRange(inorder, pos + 1, n));
        return node;
    }
}
]]> - leetcode Java + leetcode Binary Tree java Binary Tree + DFS - leetcode java + leetcode 题解 Binary Tree 二叉树 + 递归 + Preorder Traversal + Inorder Traversal + 前序 + 中序 - Leetcode 121 买卖股票的最佳时机(Best Time to Buy and Sell Stock) 题解分析 - /2021/03/14/Leetcode-121-%E4%B9%B0%E5%8D%96%E8%82%A1%E7%A5%A8%E7%9A%84%E6%9C%80%E4%BD%B3%E6%97%B6%E6%9C%BA-Best-Time-to-Buy-and-Sell-Stock-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

You are given an array prices where prices[i] is the price of a given stock on the ith day.

-

You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock.

-

Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0.

-

给定一个数组 prices ，它的第 i 个元素 prices[i] 表示一支给定股票第 i 天的价格。

-

你只能选择 某一天 买入这只股票，并选择在 未来的某一个不同的日子 卖出该股票。设计一个算法来计算你所能获取的最大利润。

-

返回你可以从这笔交易中获取的最大利润。如果你不能获取任何利润，返回 0 。

-

简单分析

其实这个跟二叉树的最长路径和有点类似，需要找到整体的最大收益，但是在迭代过程中需要一个当前的值

-
int maxSofar = 0;
public int maxProfit(int[] prices) {
    if (prices.length <= 1) {
        return 0;
    }
    int maxIn = prices[0];
    int maxOut = prices[0];
    for (int i = 1; i < prices.length; i++) {
        if (maxIn > prices[i]) {
            // 当循环当前值小于之前的买入值时就当成买入值，同时卖出也要更新
            maxIn = prices[i];
            maxOut = prices[i];
        }
        if (prices[i] > maxOut) {
            // 表示一个可卖出点，即比买入值高时
            maxOut = prices[i];
            // 需要设置一个历史值
            maxSofar = Math.max(maxSofar, maxOut - maxIn);
        }
    }
    return maxSofar;
}
+ Leetcode 124 二叉树中的最大路径和(Binary Tree Maximum Path Sum) 题解分析 + /2021/01/24/Leetcode-124-%E4%BA%8C%E5%8F%89%E6%A0%91%E4%B8%AD%E7%9A%84%E6%9C%80%E5%A4%A7%E8%B7%AF%E5%BE%84%E5%92%8C-Binary-Tree-Maximum-Path-Sum-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

A path in a binary tree is a sequence of nodes where each pair of adjacent nodes in the sequence has an edge connecting them. A node can only appear in the sequence at most once. Note that the path does not need to pass through the root.

+

The path sum of a path is the sum of the node’s values in the path.

+

Given the root of a binary tree, return the maximum path sum of any path.

+

路径 被定义为一条从树中任意节点出发，沿父节点-子节点连接，达到任意节点的序列。该路径 至少包含一个 节点，且不一定经过根节点。

+

路径和 是路径中各节点值的总和。

+

给你一个二叉树的根节点 root ，返回其 最大路径和

+

简要分析

其实这个题目会被误解成比较简单，左子树最大的，或者右子树最大的，或者两边加一下，仔细想想都不对，其实有可能是产生于左子树中，或者右子树中，这两个都是指跟左子树根还有右子树根没关系的，这么说感觉不太容易理解，画个图

可以看到图里，其实最长路径和是左边这个子树组成的，跟根节点还有右子树完全没关系，然后再想一种情况，如果是整棵树就是图中的左子树，那么这个最长路径和就是左子树加右子树加根节点了，所以不是我一开始想得那么简单，在代码实现中也需要一些技巧

+

代码

int ansNew = Integer.MIN_VALUE;
public int maxPathSum(TreeNode root) {
        maxSumNew(root);
        return ansNew;
    }
    
public int maxSumNew(TreeNode root) {
    if (root == null) {
        return 0;
    }
    // 这里是个简单的递归，就是去递归左右子树，但是这里其实有个概念，当这样处理时，其实相当于把子树的内部的最大路径和已经算出来了
    int left = maxSumNew(root.left);
    int right = maxSumNew(root.right);
    // 这里前面我有点没想明白，但是看到 ansNew 的比较，其实相当于，返回的是三种情况里的最大值，一个是左子树+根，一个是右子树+根，一个是单独根节点，
    // 这样这个递归的返回才会有意义，不然像原来的方法，它可能是跳着的，但是这种情况其实是借助于 ansNew 这个全局的最大值，因为原来我觉得要比较的是
    // left, right, left + root , right + root, root, left + right + root 这些的最大值，这里是分成了两个阶段，left 跟 right 的最大值已经在上面的
    // 调用过程中赋值给 ansNew 了    
    int currentSum = Math.max(Math.max(root.val + left , root.val + right), root.val);
    // 这边返回的是 currentSum，然后再用它跟 left + right + root 进行对比，然后再去更新 ans
    // PS: 有个小点也是这边的破局点，就是这个 ansNew
    int res = Math.max(left + right + root.val, currentSum);
    ans = Math.max(res, ans);
    return currentSum;
}
-

总结下

一开始看到 easy 就觉得是很简单，就没有 maxSofar ，但是一提交就出现问题了
对于[2, 4, 1]这种就会变成 0，所以还是需要一个历史值来存放历史最大值，这题有点动态规划的意思

+

这里非常重要的就是 ansNew 是最后的一个结果，而对于 maxSumNew 这个函数的返回值其实是需要包含了一个连续结果，因为要返回继续去算路径和，所以返回的是 currentSum，最终结果是 ansNew

+

结果图

难得有个 100%，贴个图哈哈

]]> - leetcode Java - DP + leetcode + Binary Tree java - DP + Binary Tree - leetcode java + leetcode 题解 - DP + Binary Tree + 二叉树 @@ -1011,44 +1016,38 @@ leetcode - leetcode java + leetcode 题解 Shift 2D Grid - Leetcode 16 最接近的三数之和 ( 3Sum Closest *Medium* ) 题解分析 - /2022/08/06/Leetcode-16-%E6%9C%80%E6%8E%A5%E8%BF%91%E7%9A%84%E4%B8%89%E6%95%B0%E4%B9%8B%E5%92%8C-3Sum-Closest-Medium-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

Given an integer array nums of length n and an integer target, find three integers in nums such that the sum is closest to target.

-

Return the sum of the three integers.

-

You may assume that each input would have exactly one solution.

-

简单解释下就是之前是要三数之和等于目标值，现在是找到最接近的三数之和。

-

示例

Example 1:

-

Input: nums = [-1,2,1,-4], target = 1
Output: 2
Explanation: The sum that is closest to the target is 2. (-1 + 2 + 1 = 2).

-
-

Example 2:

-

Input: nums = [0,0,0], target = 1
Output: 0

-
-

Constraints:

    -
  • 3 <= nums.length <= 1000
    • -
  • -1000 <= nums[i] <= 1000
    • -
  • -10^4 <= target <= 10^4
    • -
-

简单解析

这个题思路上来讲不难，也是用原来三数之和的方式去做，利用”双指针法”或者其它描述法，但是需要简化逻辑

-

code

public int threeSumClosest(int[] nums, int target) {
        Arrays.sort(nums);
        // 当前最近的和
        int closestSum = nums[0] + nums[1] + nums[nums.length - 1];
        for (int i = 0; i < nums.length - 2; i++) {
            if (i == 0 || nums[i] != nums[i - 1]) {
                // 左指针
                int left = i + 1;
                // 右指针
                int right = nums.length - 1;
                // 判断是否遍历完了
                while (left < right) {
                    // 当前的和
                    int sum = nums[i] + nums[left] + nums[right];
                    // 小优化，相等就略过了
                    while (left < right && nums[left] == nums[left + 1]) {
                        left++;
                    }
                    while (left < right && nums[right] == nums[right - 1]) {
                        right--;
                    }
                    // 这里判断，其实也还是希望趋近目标值
                    if (sum < target) {
                        left++;
                    } else {
                        right--;
                    }
                    // 判断是否需要替换
                    if (Math.abs(sum - target) < Math.abs(closestSum - target)) {
                        closestSum = sum;
                    }
                }
            }
        }
        return closestSum;
    }
+ Leetcode 121 买卖股票的最佳时机(Best Time to Buy and Sell Stock) 题解分析 + /2021/03/14/Leetcode-121-%E4%B9%B0%E5%8D%96%E8%82%A1%E7%A5%A8%E7%9A%84%E6%9C%80%E4%BD%B3%E6%97%B6%E6%9C%BA-Best-Time-to-Buy-and-Sell-Stock-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

You are given an array prices where prices[i] is the price of a given stock on the ith day.

+

You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock.

+

Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0.

+

给定一个数组 prices ，它的第 i 个元素 prices[i] 表示一支给定股票第 i 天的价格。

+

你只能选择 某一天 买入这只股票，并选择在 未来的某一个不同的日子 卖出该股票。设计一个算法来计算你所能获取的最大利润。

+

返回你可以从这笔交易中获取的最大利润。如果你不能获取任何利润，返回 0 。

+

简单分析

其实这个跟二叉树的最长路径和有点类似，需要找到整体的最大收益，但是在迭代过程中需要一个当前的值

+
int maxSofar = 0;
public int maxProfit(int[] prices) {
    if (prices.length <= 1) {
        return 0;
    }
    int maxIn = prices[0];
    int maxOut = prices[0];
    for (int i = 1; i < prices.length; i++) {
        if (maxIn > prices[i]) {
            // 当循环当前值小于之前的买入值时就当成买入值，同时卖出也要更新
            maxIn = prices[i];
            maxOut = prices[i];
        }
        if (prices[i] > maxOut) {
            // 表示一个可卖出点，即比买入值高时
            maxOut = prices[i];
            // 需要设置一个历史值
            maxSofar = Math.max(maxSofar, maxOut - maxIn);
        }
    }
    return maxSofar;
}
-

结果

+

总结下

一开始看到 easy 就觉得是很简单，就没有 maxSofar ，但是一提交就出现问题了
对于[2, 4, 1]这种就会变成 0，所以还是需要一个历史值来存放历史最大值，这题有点动态规划的意思

]]> Java leetcode + DP + java + DP - leetcode java + leetcode 题解 - 3Sum Closest + DP @@ -1069,15 +1068,15 @@ ]]> - leetcode Java + leetcode stack java stack - leetcode java + leetcode 题解 stack min stack @@ -1085,6 +1084,40 @@ leetcode 155 + + Leetcode 16 最接近的三数之和 ( 3Sum Closest *Medium* ) 题解分析 + /2022/08/06/Leetcode-16-%E6%9C%80%E6%8E%A5%E8%BF%91%E7%9A%84%E4%B8%89%E6%95%B0%E4%B9%8B%E5%92%8C-3Sum-Closest-Medium-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

Given an integer array nums of length n and an integer target, find three integers in nums such that the sum is closest to target.

+

Return the sum of the three integers.

+

You may assume that each input would have exactly one solution.

+

简单解释下就是之前是要三数之和等于目标值，现在是找到最接近的三数之和。

+

示例

Example 1:

+

Input: nums = [-1,2,1,-4], target = 1
Output: 2
Explanation: The sum that is closest to the target is 2. (-1 + 2 + 1 = 2).

+
+

Example 2:

+

Input: nums = [0,0,0], target = 1
Output: 0

+
+

Constraints:

    +
  • 3 <= nums.length <= 1000
    • +
  • -1000 <= nums[i] <= 1000
    • +
  • -10^4 <= target <= 10^4
    • +
+

简单解析

这个题思路上来讲不难，也是用原来三数之和的方式去做，利用”双指针法”或者其它描述法，但是需要简化逻辑

+

code

public int threeSumClosest(int[] nums, int target) {
        Arrays.sort(nums);
        // 当前最近的和
        int closestSum = nums[0] + nums[1] + nums[nums.length - 1];
        for (int i = 0; i < nums.length - 2; i++) {
            if (i == 0 || nums[i] != nums[i - 1]) {
                // 左指针
                int left = i + 1;
                // 右指针
                int right = nums.length - 1;
                // 判断是否遍历完了
                while (left < right) {
                    // 当前的和
                    int sum = nums[i] + nums[left] + nums[right];
                    // 小优化，相等就略过了
                    while (left < right && nums[left] == nums[left + 1]) {
                        left++;
                    }
                    while (left < right && nums[right] == nums[right - 1]) {
                        right--;
                    }
                    // 这里判断，其实也还是希望趋近目标值
                    if (sum < target) {
                        left++;
                    } else {
                        right--;
                    }
                    // 判断是否需要替换
                    if (Math.abs(sum - target) < Math.abs(closestSum - target)) {
                        closestSum = sum;
                    }
                }
            }
        }
        return closestSum;
    }
+ +

结果

+]]> + + Java + leetcode + + + java + leetcode + 题解 + 3Sum Closest + + Leetcode 160 相交链表(intersection-of-two-linked-lists) 题解分析 /2021/01/10/Leetcode-160-%E7%9B%B8%E4%BA%A4%E9%93%BE%E8%A1%A8-intersection-of-two-linked-lists-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ @@ -1097,15 +1130,15 @@

总结

可能缺少这种思维，做的还是比较少，所以没法一下子反应过来，需要锻炼，我的第一反应是两重遍历，不过那样复杂度就高了，这里应该是只有 O(N) 的复杂度。

]]> - leetcode Java + leetcode Linked List java Linked List - leetcode java + leetcode 题解 Linked List @@ -1138,8 +1171,8 @@ leetcode - leetcode java + leetcode 题解 @@ -1159,54 +1192,19 @@

这里唯二需要注意的就是两个点，一个是循环条件需要包含进位值还存在的情况，还有一个是最后一个节点，如果是空的了，就不要在 new 一个出来了，写的比较挫

]]> - leetcode Java + leetcode linked list java linked list - leetcode java + leetcode 题解 linked list - - Leetcode 20 有效的括号 ( Valid Parentheses *Easy* ) 题解分析 - /2022/07/02/Leetcode-20-%E6%9C%89%E6%95%88%E7%9A%84%E6%8B%AC%E5%8F%B7-Valid-Parentheses-Easy-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.

-

An input string is valid if:

-
    -
  1. Open brackets must be closed by the same type of brackets.
    2. -
  2. Open brackets must be closed in the correct order.
    3. -
-

示例

Example 1:

-

Input: s = “()”
Output: true

-
-

Example 2:

-

Input: s = “()[]{}”
Output: true

-
-

Example 3:

-

Input: s = “(]”
Output: false

-
-

Constraints:

    -
  • 1 <= s.length <= 10^4
    • -
  • s consists of parentheses only '()[]{}'.
    • -
-

解析

easy题，并且看起来也是比较简单的，三种括号按对匹配，直接用栈来做，栈里面存的是括号的类型，如果是左括号，就放入栈中，如果是右括号，就把栈顶的元素弹出，如果弹出的元素不是左括号，就返回false，如果弹出的元素是左括号，就继续往下走，如果遍历完了，如果栈里面还有元素，就返回false，如果遍历完了，如果栈里面没有元素，就返回true

-

代码

class Solution {
    public boolean isValid(String s) {

        if (s.length() % 2 != 0) {
            return false;
        }
        Stack<String> stk = new Stack<>();
        for (int i = 0; i < s.length(); i++) {
            if (s.charAt(i) == '{' || s.charAt(i) == '(' || s.charAt(i) == '[') {
                stk.push(String.valueOf(s.charAt(i)));
                continue;
            }
            if (s.charAt(i) == '}') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '{') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }
            if (s.charAt(i) == ']') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '[') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }
            if (s.charAt(i) == ')') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '(') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }

        }
        return stk.size() == 0;
    }
}
- -]]> - - Java - leetcode - - - leetcode - java - - Leetcode 234 回文链表(Palindrome Linked List) 题解分析 /2020/11/15/Leetcode-234-%E5%9B%9E%E6%96%87%E8%81%94%E8%A1%A8-Palindrome-Linked-List-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ @@ -1217,15 +1215,15 @@

简要分析

首先这是个单向链表，如果是双向的就可以一个从头到尾，一个从尾到头，显然那样就没啥意思了，然后想过要不找到中点，然后用一个栈，把前一半塞进栈里，但是这种其实也比较麻烦，比如长度是奇偶数，然后如何找到中点，这倒是可以借助于双指针，还是比较麻烦，再想一想，回文链表，就跟最开始的一样，链表只有单向的，我用个栈不就可以逆向了么，先把链表整个塞进栈里，然后在一个个 pop 出来跟链表从头开始比较，全对上了就是回文了

/**
 * Definition for singly-linked list.
 * public class ListNode {
 *     int val;
 *     ListNode next;
 *     ListNode() {}
 *     ListNode(int val) { this.val = val; }
 *     ListNode(int val, ListNode next) { this.val = val; this.next = next; }
 * }
 */
class Solution {
    public boolean isPalindrome(ListNode head) {
        if (head == null) {
            return true;
        }
        ListNode tail = head;
        LinkedList<Integer> stack = new LinkedList<>();
        // 这里就是一个循环，将所有元素依次压入栈
        while (tail != null) {
            stack.push(tail.val);
            tail = tail.next;
        }
        // 在逐个 pop 出来，其实这个出来的顺序就等于链表从尾到头遍历，同时跟链表从头到尾遍历进行逐对对比
        while (!stack.isEmpty()) {
            if (stack.peekFirst() == head.val) {
                stack.pollFirst();
                head = head.next;
            } else {
                return false;
            }
        }
        return true;
    }
}
]]> - leetcode Java + leetcode Linked List java Linked List - leetcode java + leetcode 题解 Linked List @@ -1245,59 +1243,62 @@ Lowest Common Ancestor of a Binary Tree - leetcode java + leetcode 题解 Lowest Common Ancestor of a Binary Tree - Java 线程池系列-第二篇 - /2024/02/18/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E4%BA%8C%E7%AF%87/ - 介绍了线程池的目的和实现概述，以及如何初始化的，我们就来开始看看线程池最重要的执行过程
老规矩，还是先把注释翻译下，这个对理解逻辑其实非常重要，后面可以循着注释的逻辑来看代码
第一步，如果是少于核心线程数的线程正在运行，那么尝试去开启一个新线程，并把提交的命令
command 最为 first task，这里调用了 addWorker，会自动检查运行状态和线程数，并通过
返回 false 来提示不应该增加线程，比如已经有其他任务先创建了线程导致已经超过了核心线程数
第二步，在前面不符合的情况下，也就是线程数已经大于等于核心线程数了或者在调用 addWorker
的时候校验发现线程数已经大于等于核心线程数或者线程池运行状态不是正在运行了就会进入下一个判断
首先还是判断线程池是否正在运行，然后将 command 放进队列，如果成功进队了，还需要再次进行校验
如果线程池状态不是正在运行了，则需要再出队，然后执行拒绝策略，如果状态正常，但是线程被回收完了
那需要创建线程
第三步，如果不能正常进队列，会尝试再启动一个新线程，这里表示核心线程数满了，并且队列也满了，
则需要再开启一个新线程，如果开启失败则执行拒绝策略
代码注释基本已经把代码讲得很详细了

-
public void execute(Runnable command) {
        if (command == null)
            throw new NullPointerException();
        /*
         * Proceed in 3 steps:
         *
         * 1. If fewer than corePoolSize threads are running, try to
         * start a new thread with the given command as its first
         * task.  The call to addWorker atomically checks runState and
         * workerCount, and so prevents false alarms that would add
         * threads when it shouldn't, by returning false.
         *
         * 2. If a task can be successfully queued, then we still need
         * to double-check whether we should have added a thread
         * (because existing ones died since last checking) or that
         * the pool shut down since entry into this method. So we
         * recheck state and if necessary roll back the enqueuing if
         * stopped, or start a new thread if there are none.
         *
         * 3. If we cannot queue task, then we try to add a new
         * thread.  If it fails, we know we are shut down or saturated
         * and so reject the task.
         */
        int c = ctl.get();
        // 这就是第一步
        if (workerCountOf(c) < corePoolSize) {
            if (addWorker(command, true))
                return;
            // 注意这里是第一步可能失败了，获取一下最新的状态
            c = ctl.get();
        }
        // 这里是第二步
        if (isRunning(c) && workQueue.offer(command)) {
            // 每次都获取最新的状态
            int recheck = ctl.get();
            if (! isRunning(recheck) && remove(command))
                reject(command);
            else if (workerCountOf(recheck) == 0)
                // 这里是判断如果一个线程都没了的话，注意添加的是非核心线程
                addWorker(null, false);
        }
        // 这里第三步是添加线程，并且是非核心线程
        else if (!addWorker(command, false))
            reject(command);
    }
-

这里面比较重要的就是 addWorker 方法，我们也来看一下
这里用到了 break retry; 就是在循环中跳出到这个 retry 的位置继续执行
然后还是获取运行状态，如果是非运行状态，
并且rs == SHUTDOWN 与 firstTask == null 和 ! workQueue.isEmpty()，至少一个为false
因为只有当 SHUTDOWN 状态才会出现后面两者为 false 的情况，如果是 SHUTDOWN ，队列不为空还是需要执行完队列里的任务
然后是判断线程数量与传入参数 core 还有核心线程数跟最大线程数的对比，如果是超过了就返回 false 也就是外层的第一步的内部
if 就会继续执行后面的获取线程池状态，然后是 cas 增加线程数量，如果失败则 break 跳到方法开头，如果成功则继续重新获取 c
判断线程池运行状态，如果不一致则从外层继续进入
后面的就是新建一个 worker，然后获取锁，继续判断线程池状态，如果在运行中或者队列不为空（隐含条件）则继续执行添加 worker
并判断是否需要更新历史最大线程数，更改线程添加状态，然后启动线程，标记线程启动状态

-
private boolean addWorker(Runnable firstTask, boolean core) {
        retry:
        for (;;) {
            int c = ctl.get();
            int rs = runStateOf(c);

            // Check if queue empty only if necessary.
            if (rs >= SHUTDOWN && ! (rs == SHUTDOWN && firstTask == null && ! workQueue.isEmpty()))
                return false;

            for (;;) {
                int wc = workerCountOf(c);
                if (wc >= CAPACITY ||
                    wc >= (core ? corePoolSize : maximumPoolSize))
                    return false;
                if (compareAndIncrementWorkerCount(c))
                    break retry;
                c = ctl.get();  // Re-read ctl
                if (runStateOf(c) != rs)
                    continue retry;
                // else CAS failed due to workerCount change; retry inner loop
            }
        }

        boolean workerStarted = false;
        boolean workerAdded = false;
        Worker w = null;
        try {
            w = new Worker(firstTask);
            final Thread t = w.thread;
            if (t != null) {
                final ReentrantLock mainLock = this.mainLock;
                mainLock.lock();
                try {
                    // Recheck while holding lock.
                    // Back out on ThreadFactory failure or if
                    // shut down before lock acquired.
                    int rs = runStateOf(ctl.get());

                    if (rs < SHUTDOWN ||
                        (rs == SHUTDOWN && firstTask == null)) {
                        if (t.isAlive()) // precheck that t is startable
                            throw new IllegalThreadStateException();
                        workers.add(w);
                        int s = workers.size();
                        if (s > largestPoolSize)
                            largestPoolSize = s;
                        workerAdded = true;
                    }
                } finally {
                    mainLock.unlock();
                }
                if (workerAdded) {
                    t.start();
                    workerStarted = true;
                }
            }
        } finally {
            if (! workerStarted)
                addWorkerFailed(w);
        }
        return workerStarted;
    }
-

后面会继续讲下线程如何执行队列里的任务

+ Leetcode 20 有效的括号 ( Valid Parentheses *Easy* ) 题解分析 + /2022/07/02/Leetcode-20-%E6%9C%89%E6%95%88%E7%9A%84%E6%8B%AC%E5%8F%B7-Valid-Parentheses-Easy-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.

+

An input string is valid if:

+
    +
  1. Open brackets must be closed by the same type of brackets.
    2. +
  2. Open brackets must be closed in the correct order.
    3. +
+

示例

Example 1:

+

Input: s = “()”
Output: true

+
+

Example 2:

+

Input: s = “()[]{}”
Output: true

+
+

Example 3:

+

Input: s = “(]”
Output: false

+
+

Constraints:

    +
  • 1 <= s.length <= 10^4
    • +
  • s consists of parentheses only '()[]{}'.
    • +
+

解析

easy题，并且看起来也是比较简单的，三种括号按对匹配，直接用栈来做，栈里面存的是括号的类型，如果是左括号，就放入栈中，如果是右括号，就把栈顶的元素弹出，如果弹出的元素不是左括号，就返回false，如果弹出的元素是左括号，就继续往下走，如果遍历完了，如果栈里面还有元素，就返回false，如果遍历完了，如果栈里面没有元素，就返回true

+

代码

class Solution {
    public boolean isValid(String s) {

        if (s.length() % 2 != 0) {
            return false;
        }
        Stack<String> stk = new Stack<>();
        for (int i = 0; i < s.length(); i++) {
            if (s.charAt(i) == '{' || s.charAt(i) == '(' || s.charAt(i) == '[') {
                stk.push(String.valueOf(s.charAt(i)));
                continue;
            }
            if (s.charAt(i) == '}') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '{') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }
            if (s.charAt(i) == ']') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '[') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }
            if (s.charAt(i) == ')') {
                if (stk.isEmpty()) {
                    return false;
                }
                String cur = stk.peek();
                if (cur.charAt(0) != '(') {
                    return false;
                } else {
                    stk.pop();
                }
                continue;
            }

        }
        return stk.size() == 0;
    }
}
+ ]]> Java + leetcode - Java + java + leetcode - Leetcode 105 从前序与中序遍历序列构造二叉树(Construct Binary Tree from Preorder and Inorder Traversal) 题解分析 - /2020/12/13/Leetcode-105-%E4%BB%8E%E5%89%8D%E5%BA%8F%E4%B8%8E%E4%B8%AD%E5%BA%8F%E9%81%8D%E5%8E%86%E5%BA%8F%E5%88%97%E6%9E%84%E9%80%A0%E4%BA%8C%E5%8F%89%E6%A0%91-Construct-Binary-Tree-from-Preorder-and-Inorder-Traversal-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

Given preorder and inorder traversal of a tree, construct the binary tree.
给定一棵树的前序和中序遍历，构造出一棵二叉树

-

注意

You may assume that duplicates do not exist in the tree.
你可以假设树中没有重复的元素。(PS: 不然就没法做了呀)

-

例子:

preorder = [3,9,20,15,7]
inorder = [9,3,15,20,7]
-

返回的二叉树

-
  3
 / \
9  20
  /  \
 15   7
- + Headscale渐入佳境-路由设置 + /2024/04/07/Headscale%E6%B8%90%E5%85%A5%E4%BD%B3%E5%A2%83-%E8%B7%AF%E7%94%B1%E8%AE%BE%E7%BD%AE/ + headscale 在前面两篇初体验和添加节点 主要是介绍了如何搭建和使用，但是这里有个很大的缺点，比如我在想要两地的网络能够连通，但是两地的网络内都不止一个机器，那想让他们连通就需要把它们都加入这个 headscale 所组成的虚拟局域网，并且每个都是这个虚拟局域网的ip，跟原来的 ip 地址不一样会比较难记忆，所以有没有办法不用这么麻烦呢，当然是有的，那就是 headscale 的路由功能。
原先比如我们有两个设备，在两地，通过headscale我把这两个异地节点都添加进去了，节点 A 的 ip比如是 10.0.0.1，另一个节点 B 是 10.0.0.2，在之前的状态下，我的节点 A 只能通过 10.0.0.2 来连接节点 B，如果节点 A 想要连接节点 B 所在地的局域网内部其他节点，我们就可以通过节点 B 的客户端登录的时候开启路由转发，这样节点 B 就会充当一个路由转发的功能，并且不需要通过10.0.0这个网段来访问，直接可以通过节点 B所在的局域网地址来访问，比如192.168.2.10，具体操作就是把客户端启动方式改成命令行的，主要就是 --advertise-routes, 指明需要转发的网段

+
tailscale up --advertise-routes=192.168.xx.0/24 --accept-dns=false --login-server=http://serverIp:serverPort --unattended
+

前面的 192.168.xx.0 就是节点 B 所在局域网的网段，然后 login-server 是 headscale 的服务 ip 跟端口，这个本身客户端就会使用，另外这个还有一个前提的，需要开启服务器的端口转发，对于 Windows 其实默认就可以，Linux 的话就需要设置下

+
$ echo 'net.ipv4.ip_forward = 1' | tee /etc/sysctl.d/ipforwarding.conf
$ echo 'net.ipv6.conf.all.forwarding = 1' | tee -a /etc/sysctl.d/ipforwarding.conf
$ sysctl -p /etc/sysctl.d/ipforwarding.conf
-

简要分析

看到这个题可以想到一个比较常规的解法就是递归拆树，前序就是根左右，中序就是左根右，然后就是通过前序已经确定的根在中序中找到，然后去划分左右子树，这个例子里是 3，找到中序中的位置，那么就可以确定，9 是左子树，15,20,7是右子树，然后对应的可以根据左右子树的元素数量在前序中划分左右子树，再继续递归就行

-
class Solution {
    public TreeNode buildTree(int[] preorder, int[] inorder) {
      // 获取下数组长度
        int n = preorder.length;
        // 排除一下异常和边界
        if (n != inorder.length) {
            return null;
        }
        if (n == 0) {
            return null;
        }
        if (n == 1) {
            return new TreeNode(preorder[0]);
        }
        // 获得根节点
        TreeNode node = new TreeNode(preorder[0]);
        int pos = 0;
        // 找到中序中的位置
        for (int i = 0; i < inorder.length; i++) {
            if (node.val == inorder[i]) {
                pos = i;
                break;
            }
        }
        // 划分左右再进行递归，注意下`Arrays.copyOfRange`的用法
        node.left = buildTree(Arrays.copyOfRange(preorder, 1, pos + 1), Arrays.copyOfRange(inorder, 0, pos));
        node.right = buildTree(Arrays.copyOfRange(preorder, pos + 1, n), Arrays.copyOfRange(inorder, pos + 1, n));
        return node;
    }
}
]]> +

到这里我们只完成了第一步，第二步需要在 headscale 服务端启用这条路由配置
首先可以用 sudo headscale routes 查看当前有的路由
sudo headscale routes enable -h 这里看下怎么启用，因为网上有些是用-i 指定哪一条路由的，但是我使用的这个版本是用-r 的
sudo headscale routes enable -r 1 然后用这个命令启用就行了


这样我们就能够很方便地在单个节点互连之后访问到对方局域网内的其他节点

+]]> - leetcode - Java - Binary Tree - java - Binary Tree - DFS + headscale - leetcode - java - 题解 - Binary Tree - 二叉树 - 递归 - Preorder Traversal - Inorder Traversal - 前序 - 中序 + headscale @@ -1323,29 +1324,12 @@ leetcode - leetcode java + leetcode 题解 First Bad Version - - Headscale渐入佳境-路由设置 - /2024/04/07/Headscale%E6%B8%90%E5%85%A5%E4%BD%B3%E5%A2%83-%E8%B7%AF%E7%94%B1%E8%AE%BE%E7%BD%AE/ - headscale 在前面两篇初体验和添加节点 主要是介绍了如何搭建和使用，但是这里有个很大的缺点，比如我在想要两地的网络能够连通，但是两地的网络内都不止一个机器，那想让他们连通就需要把它们都加入这个 headscale 所组成的虚拟局域网，并且每个都是这个虚拟局域网的ip，跟原来的 ip 地址不一样会比较难记忆，所以有没有办法不用这么麻烦呢，当然是有的，那就是 headscale 的路由功能。
原先比如我们有两个设备，在两地，通过headscale我把这两个异地节点都添加进去了，节点 A 的 ip比如是 10.0.0.1，另一个节点 B 是 10.0.0.2，在之前的状态下，我的节点 A 只能通过 10.0.0.2 来连接节点 B，如果节点 A 想要连接节点 B 所在地的局域网内部其他节点，我们就可以通过节点 B 的客户端登录的时候开启路由转发，这样节点 B 就会充当一个路由转发的功能，并且不需要通过10.0.0这个网段来访问，直接可以通过节点 B所在的局域网地址来访问，比如192.168.2.10，具体操作就是把客户端启动方式改成命令行的，主要就是 --advertise-routes, 指明需要转发的网段

-
tailscale up --advertise-routes=192.168.xx.0/24 --accept-dns=false --login-server=http://serverIp:serverPort --unattended
-

前面的 192.168.xx.0 就是节点 B 所在局域网的网段，然后 login-server 是 headscale 的服务 ip 跟端口，这个本身客户端就会使用，另外这个还有一个前提的，需要开启服务器的端口转发，对于 Windows 其实默认就可以，Linux 的话就需要设置下

-
$ echo 'net.ipv4.ip_forward = 1' | tee /etc/sysctl.d/ipforwarding.conf
$ echo 'net.ipv6.conf.all.forwarding = 1' | tee -a /etc/sysctl.d/ipforwarding.conf
$ sysctl -p /etc/sysctl.d/ipforwarding.conf
- -

到这里我们只完成了第一步，第二步需要在 headscale 服务端启用这条路由配置
首先可以用 sudo headscale routes 查看当前有的路由
sudo headscale routes enable -h 这里看下怎么启用，因为网上有些是用-i 指定哪一条路由的，但是我使用的这个版本是用-r 的
sudo headscale routes enable -r 1 然后用这个命令启用就行了


这样我们就能够很方便地在单个节点互连之后访问到对方局域网内的其他节点

-]]> - - headscale - - - headscale - - Leetcode 3 Longest Substring Without Repeating Characters 题解分析 /2020/09/20/Leetcode-3-Longest-Substring-Without-Repeating-Characters-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ @@ -1362,19 +1346,62 @@

注释应该写的比较清楚了。

]]> - leetcode Java + leetcode 字符串 - online java string - leetcode java + leetcode 题解 string + + Leetcode 25 Reverse Nodes in k-Group 题解分析 + /2020/08/16/Leetcode-25-Reverse-Nodes-in-k-Group-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90-1/ + Given a linked list, reverse the nodes of a linked list k at a time and return its modified list.

+

k is a positive integer and is less than or equal to the length of the linked list. If the number of nodes is not a multiple of k then left-out nodes in the end should remain as it is.

+

Example:

Given this linked list: 1->2->3->4->5

+

For k = 2, you should return: 2->1->4->3->5

+

For k = 3, you should return: 3->2->1->4->5

+

Note:

    +
  • Only constant extra memory is allowed.
    • +
  • You may not alter the values in the list’s nodes, only nodes itself may be changed.
    • +
+

这个题也算是经典中的经典了，各种算法题中的保留曲目，可能不全一样，不过反正都是逆转，这个算是比较难度大的，普通的应该就是整个链表反转，或者只对一个链表中的 m 到 n 位做翻转，这里是 k 个一组做翻转，其实这道题比较难的点是两个，一个是想清楚怎么处理，一个是代码的处理
简单来看下

+
public ListNode reverseKGroup(ListNode head, int k) {
    if (k <= 1) {
        return head;
    }
    if (head == null) {
        return null;
    }
    ListNode tempEnd = head;
    ListNode tempHead = head;
    ListNode lastHead = null;
    boolean isHeadSet = false;
    while (true) {
        // k 个一组获取头尾
        for (int i = 0; i < k - 1; i++) {
            System.out.println(tempEnd.val);
            if (tempEnd.next != null) {
                tempEnd  = tempEnd.next;
            } else {
                tempEnd = null;
                break;
            }
        }
        if (tempEnd != null) {
            if (!isHeadSet) {
                // 只有在第一组返回的时候需要赋值给 head
                head = reverse(null, tempHead, tempEnd);
                isHeadSet = !isHeadSet;
            } else {
                // 后面的需要将前一组的尾巴传进去，作为后一组头
                reverse(lastHead, tempHead, tempEnd);
            }
            if (tempHead.next != null) {
                // 如果后续节点还有
                lastHead = tempHead;
                tempEnd = tempHead.next;
                tempHead = tempHead.next;
            } else {
                break;
            }
        } else {
            break;
        }
    }

    return head;
}
public ListNode reverse(ListNode lastHead, ListNode tempHead, ListNode tempEnd) {
    // k 个一组的头尾传进来
    ListNode tail = null;
    while (tail != tempEnd) {
        // tail 表示是将头往后换时，把后面那个先拿着
        tail = tempHead.next;
        // 然后就是交换
        tempHead.next = tempEnd.next;
        tempEnd.next = tempHead;
        tempHead = tail;
    }
    if (lastHead !=null) {
        // 如果不是第一组，则需要接上前一组的尾巴
        lastHead.next = tail;
    }
    return tail;
}
]]> + + Java + leetcode + + + java + leetcode + 题解 + + + + Leetcode 25 Reverse Nodes in k-Group 题解分析-再解分析 + /2024/01/21/Leetcode-25-Reverse-Nodes-in-k-Group-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 上一次主要是给了一个解题方案，没有具体讲解，这次又做到了就来看下几种方案，链表转置一直是我比较疑惑的问题，特别是边界处理，而这个问题要把难度加大了
我先讲一下我一开始的思路和解题方法，首先就是写一个转置方法，就处理 k 个一组的内部转置，然后外部循环处理分组以及前后连接等问题，但是这里就涉及到一个问题，就是前后连接的话对于整个链表头，也就是第一个 k 元素组来说，头是个空的，就需要额外处理，一开始就是用判空做额外处理，然后在前后连接的时候也有一些困扰，看了自己的代码库发现其实之前也做过，而且发现这个思路跟以前做的还是一样的，只不过在处理 k 个元素内部的转置的时候有了点差异

一开始的思路是这样的，想了下其实还是比较有问题，从处理难度上来说，在 k 组内处理的时候一开始 A 节点会是悬空的，然后得处理到组内最后一个元素的时候再接上，逻辑会更复杂，而另一种思路就是直接把 A 先挪到 C 后面，这样每次只需要移动一个，可能思路上会更清晰一点

也就是这样的，这种方案就是之前发过的题解代码，上一篇

+

而最后这种则代码会简单一些，但是需要一定的理解成本，比较重要的一点是我们加了个虚拟的头结点，第一步会先获取下链表的总长度，然后在内循环里处理转置，重点就是分四步，
也就是图里的，第一步先把 cur 的下一个节点设置成 next 的 next 节点，这里就是图里 A 连接到 C，第二步是把 next 的 next 节点设置成虚拟头的 next节点，第三步是把 pre 的next 节点设置成 next，第四步是 next 往后移动

+
cur.next = next.next;
next.next = pre.next;
pre.next = next;
next = cur.next;
+ +


在备注下代码

+]]> + + Java + leetcode + + + java + leetcode + 题解 + + Leetcode 349 两个数组的交集 ( Intersection of Two Arrays *Easy* ) 题解分析 /2022/03/07/Leetcode-349-%E4%B8%A4%E4%B8%AA%E6%95%B0%E7%BB%84%E7%9A%84%E4%BA%A4%E9%9B%86-Intersection-of-Two-Arrays-Easy-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ @@ -1400,8 +1427,8 @@ leetcode - leetcode java + leetcode 题解 Intersection of Two Arrays @@ -1426,8 +1453,8 @@ leetcode - leetcode java + leetcode 题解 Median of Two Sorted Arrays @@ -1447,40 +1474,13 @@ Rotate Image - leetcode java + leetcode 题解 Rotate Image 矩阵 - - Leetcode 31 Next Permutation 题解分析 - /2024/05/06/Leetcode-31-Next-Permutation-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 这题的题目介绍有一定的误导性，没有把字典序说明白，虽然的确是常规意义的字典序，但是题目介绍给的序列顺序反而会让人觉得那样不对
这里我们逐步深入地来讲这个题，也类似于找规律
注意：这里的递减递增序列都是从前往后看的
字典序可以就当查字典，或者默认的字符串排序方式，就是对于一个序列，逐个字符对比，同一位的相同再对比下一位的，例如 11在12前面，12在13前面，
那对于同一组字符或者像题中的数字，以最简单的12来分析，因为这个就两种排列，12跟21，按字典序排列，12在前，21在后
通过这个仔细点也能发现，或者再举个例子123，这个排列就会多一些了

-
123
132
213
231
312
321
-

一共有这么几种，不过举这个例子是为了更具体的发现规律，对于第一个数字相同的情况，比如123跟132，是132在后，发现规律没，递增序列是最小的，递减序列是最大的，因为2个数字 只有两种排列，扩展到3个数字，也可以把它拆分开，同样是第一个数字是1，后面两个数字就又回到了前面12序列的情况，
所以就可以把问题分成两部分来解决，首先从后往前找到递减序列
通过例子来讲

-
1 4 6 5 3 2
-

为什么要找到递减序列，因为递减序列已经是最大的了，要做调整要在这个之前调整，并且要找的是下一个序列，所以要是最接近的那个，否则我找到的可能就是下N个了，这个有点类似于数学里进位的意思，比如十九，九已经是个位里最大的了，找下一个比它大的只能是前面一位加一，但是加一以后需要九变到这一位的最小，也就是零。
对于上面的例子，递减序列就是6532，第一个非递减的是4，我要找下一个就是要比当前这个大，那我就去后面找比4大的，至于为什么能找到，因为这是递减序列边界之外的，说明肯定有比它大的，那要找哪个呢，要找比4大的数字里最小的，这样才是最近的下一个序列，并且因为后面是递减序列，所以可以直接从后往前找第一个
也就是会找到 5
找到 5 之后就做交换

-
1 5 6 4 3 2
-

这样是不是就行了呢，不行，因为这不是下一个，而是下N个，当4变成5之后，这个序列不管5后面的数字怎么变都是比 146532 大的，但是要找的是下一个，最接近的那个，则需要后面的序列变成最小，也就是变成递增序列，也就是

-
1 5 2 3 4 6
-

这里有两种例外情况，或者严格来说是一种
第一种是本身整个就是递减序列了，这在题中说了，下一个就是从头再开始，那就也很简单整个排序成递增的就行了
第二种是本身就是个完全递增的，那其实就交换末两位，但这种跟上面的可以糅合在一起，比如就是

-
1 2 3 4 5 6
-

单个6构不成递增或递减，找到的第一个非递减的数字就是 5 ，那就是在 5 后面找一个比它大的做交换，这时只有 6 ，所以就 5 跟 6 交换下，然后单个 5 做排序也等于不用做
再结合代码来看

-
public void nextPermutation(int[] nums) {
        int n = nums.length, i = n - 2, j = n - 1;
        // 这个while循环是为了从后往前找到递减序列的分界点
        // 循环完成后i就是递减序列左边的第一个数字位置
        while (i >= 0 && nums[i] >= nums[i + 1]) --i;
        if (i >= 0) {
            // 表示有找到分界点，然后就从后往前找到第一个比nums[i]大的做交换
            while (nums[j] <= nums[i]) --j;
            // 这个while循环在找到第一个nums[j] > nums[i]时退出
            // 下面就是做交换
            int temp = nums[i];
            nums[i] = nums[j];
            nums[j] = temp;
        }
        // 因为前面已经保证了逆序，这里只要排成正序就是最小的了
        Arrays.sort(nums, i+1, nums.length);
    }
- -]]> - - Java - leetcode - - - leetcode - java - 题解 - - Leetcode 42 接雨水 (Trapping Rain Water) 题解分析 /2021/07/04/Leetcode-42-%E6%8E%A5%E9%9B%A8%E6%B0%B4-Trapping-Rain-Water-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ @@ -1496,8 +1496,8 @@ leetcode - leetcode java + leetcode dp 代码题解 Trapping Rain Water @@ -1505,6 +1505,68 @@ Leetcode 42 + + Leetcode 698 划分为k个相等的子集 ( Partition to K Equal Sum Subsets *Medium* ) 题解分析 + /2022/06/19/Leetcode-698-%E5%88%92%E5%88%86%E4%B8%BAk%E4%B8%AA%E7%9B%B8%E7%AD%89%E7%9A%84%E5%AD%90%E9%9B%86-Partition-to-K-Equal-Sum-Subsets-Medium-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

Given an integer array nums and an integer k, return true if it is possible to divide this array into k non-empty subsets whose sums are all equal.

+

示例

Example 1:

+
+

Input: nums = [4,3,2,3,5,2,1], k = 4
Output: true
Explanation: It is possible to divide it into 4 subsets (5), (1, 4), (2,3), (2,3) with equal sums.

+
+

Example 2:

+
+

Input: nums = [1,2,3,4], k = 3
Output: false

+
+

Constraints:

+
    +
  • 1 <= k <= nums.length <= 16
    • +
  • 1 <= nums[i] <= 10^4
    • +
  • The frequency of each element is in the range [1, 4].
    • +
+

解析

看到这个题一开始以为挺简单，但是仔细想想问题还是挺多的，首先是分成 k 组，但是数量不限，应该需要用到回溯的方式，同时对于时间和空间复杂度也有要求，一开始这个代码是超时的，我也试了下 leetcode 上 discussion 里 vote 最高的提交也是超时的，不过看 discussion 里的帖子，貌似是后面加了一些条件，可以帮忙提高执行效率，第三条提示不太清楚意图，具体可以看下代码

+

代码

public boolean canPartitionKSubsets(int[] nums, int k) {
    if (k == 1) {
        return true;
    }
    int sum = 0, n;
    n = nums.length;
    for (int num : nums) {
        sum += num;
    }
    if (sum % k != 0) {
        return false;
    }

    int avg = sum / k;
    // 排序
    Arrays.sort(nums);
    // 做个前置判断，如果最大值超过分组平均值了就可以返回 false 了
    if (nums[n - 1] > avg) {
        return false;
    }
    // 这里取了个巧，先将数组中元素就等于分组平均值的直接排除了
    int calculated = 0;
    for (int i = n - 1; i > 0; i--) {
        if (nums[i] == avg) {
            k--;
            calculated++;
        }
    }

    int[] bucket = new int[k];
    // 初始化 bucket
    for (int i = 0; i < k; i++) {
        bucket[i] = avg;
    }

    // 提前做下边界判断
    if (nums[n - 1] > avg) {
        return false;
    }

    return backTraversal(nums, bucket, k, n - 1 - calculated);
}

private boolean backTraversal(int[] nums, int[] bucket, int k, int cur) {
    if (cur < 0) {
        return true;
    }
    for (int i = 0; i < k; i++) {
        if (bucket[i] == nums[cur] || bucket[i] >= nums[cur] + nums[0]) {
            // 判断如果当前 bucket[i] 剩余的数字等于nums[cur], 即当前bucket已经满了
            // 或者如果当前 bucket[i] 剩余的数字大于等于 nums[cur] + nums[0] ，
            // 因为nums 在经过排序后 nums[0]是最小值，如果加上 nums[0] 都已经超过bucket[i] 了，
            // 那当前bucket[i] 肯定是没法由包含 nums[cur] 的组合组成一个满足和为前面 s/k 的组合了
            // 这里判断的是 nums[cur] ,如果第一次 k 次循环都不符合其实就返回 false 了

            // 而如果符合，就将 bucket[i] 减去 nums[cur] 再次进入递归，
            // 这里进入递归有个收敛参数就是 cur - 1，因为其实判断 cur 递减作为一个结束条件
            bucket[i] -= nums[cur];
            // 符合条件，这里对应着入口，当 cur 被减到 0 了，就表示都符合了因为是根据所有值的和 s 和 k 组除出来的平均值，当所有数都通过前面的 if 判断符合了，并且每个数字都使用了，
            // 即说明已经符合要求了
            if (backTraversal(nums, bucket, k, cur - 1)) return true;
            // 这边是个回退机制，如果前面 nums[cur]没办法组合成和为平均值的话就减掉进入下一个循环
            bucket[i] += nums[cur];
        }
    }
    return false;
}
+ +

最后贴个图

+]]> + + Java + leetcode + + + java + leetcode + + + + Leetcode 747 至少是其他数字两倍的最大数 ( Largest Number At Least Twice of Others *Easy* ) 题解分析 + /2022/10/02/Leetcode-747-%E8%87%B3%E5%B0%91%E6%98%AF%E5%85%B6%E4%BB%96%E6%95%B0%E5%AD%97%E4%B8%A4%E5%80%8D%E7%9A%84%E6%9C%80%E5%A4%A7%E6%95%B0-Largest-Number-At-Least-Twice-of-Others-Easy-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

You are given an integer array nums where the largest integer is unique.

+

Determine whether the largest element in the array is at least twice as much as every other number in the array. If it is, return the index of the largest element, or return -1 otherwise.
确认在数组中的最大数是否是其余任意数的两倍大及以上，如果是返回索引，如果不是返回-1

+

示例

Example 1:

+

Input: nums = [3,6,1,0]
Output: 1
Explanation: 6 is the largest integer.
For every other number in the array x, 6 is at least twice as big as x.
The index of value 6 is 1, so we return 1.

+
+

Example 2:

+

Input: nums = [1,2,3,4]
Output: -1
Explanation: 4 is less than twice the value of 3, so we return -1.

+
+

提示:

    +
  • 2 <= nums.length <= 50
    • +
  • 0 <= nums[i] <= 100
    • +
  • The largest element in nums is unique.
    • +
+

简要解析

这个题easy是题意也比较简单，找最大值，并且最大值是其他任意值的两倍及以上，其实就是找最大值跟次大值，比较下就好了

+

代码

public int dominantIndex(int[] nums) {
    int largest = Integer.MIN_VALUE;
    int second = Integer.MIN_VALUE;
    int largestIndex = -1;
    for (int i = 0; i < nums.length; i++) {
        // 如果有最大的就更新，同时更新最大值和第二大的
        if (nums[i] > largest) {
            second = largest;
            largest = nums[i];
            largestIndex = i;
        } else if (nums[i] > second) {
            // 没有超过最大的，但是比第二大的更大就更新第二大的
            second = nums[i];
        }
    }

    // 判断下是否符合题目要求，要是所有值的两倍及以上
    if (largest >= 2 * second) {
        return largestIndex;
    } else {
        return -1;
    }
}
+

通过图

第一次错了是把第二大的情况只考虑第一种，也有可能最大值完全没经过替换就变成最大值了

+]]> + + Java + leetcode + + + java + leetcode + 题解 + + Leetcode 83 删除排序链表中的重复元素 ( Remove Duplicates from Sorted List *Easy* ) 题解分析 /2022/03/13/Leetcode-83-%E5%88%A0%E9%99%A4%E6%8E%92%E5%BA%8F%E9%93%BE%E8%A1%A8%E4%B8%AD%E7%9A%84%E9%87%8D%E5%A4%8D%E5%85%83%E7%B4%A0-Remove-Duplicates-from-Sorted-List-Easy-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ @@ -1531,8 +1593,8 @@ leetcode - leetcode java + leetcode 题解 Remove Duplicates from Sorted List @@ -1565,38 +1627,8 @@ leetcode - leetcode java - 题解 - - - - Leetcode 747 至少是其他数字两倍的最大数 ( Largest Number At Least Twice of Others *Easy* ) 题解分析 - /2022/10/02/Leetcode-747-%E8%87%B3%E5%B0%91%E6%98%AF%E5%85%B6%E4%BB%96%E6%95%B0%E5%AD%97%E4%B8%A4%E5%80%8D%E7%9A%84%E6%9C%80%E5%A4%A7%E6%95%B0-Largest-Number-At-Least-Twice-of-Others-Easy-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

You are given an integer array nums where the largest integer is unique.

-

Determine whether the largest element in the array is at least twice as much as every other number in the array. If it is, return the index of the largest element, or return -1 otherwise.
确认在数组中的最大数是否是其余任意数的两倍大及以上，如果是返回索引，如果不是返回-1

-

示例

Example 1:

-

Input: nums = [3,6,1,0]
Output: 1
Explanation: 6 is the largest integer.
For every other number in the array x, 6 is at least twice as big as x.
The index of value 6 is 1, so we return 1.

-
-

Example 2:

-

Input: nums = [1,2,3,4]
Output: -1
Explanation: 4 is less than twice the value of 3, so we return -1.

-
-

提示:

    -
  • 2 <= nums.length <= 50
    • -
  • 0 <= nums[i] <= 100
    • -
  • The largest element in nums is unique.
    • -
-

简要解析

这个题easy是题意也比较简单，找最大值，并且最大值是其他任意值的两倍及以上，其实就是找最大值跟次大值，比较下就好了

-

代码

public int dominantIndex(int[] nums) {
    int largest = Integer.MIN_VALUE;
    int second = Integer.MIN_VALUE;
    int largestIndex = -1;
    for (int i = 0; i < nums.length; i++) {
        // 如果有最大的就更新，同时更新最大值和第二大的
        if (nums[i] > largest) {
            second = largest;
            largest = nums[i];
            largestIndex = i;
        } else if (nums[i] > second) {
            // 没有超过最大的，但是比第二大的更大就更新第二大的
            second = nums[i];
        }
    }

    // 判断下是否符合题目要求，要是所有值的两倍及以上
    if (largest >= 2 * second) {
        return largestIndex;
    } else {
        return -1;
    }
}
-

通过图

第一次错了是把第二大的情况只考虑第一种，也有可能最大值完全没经过替换就变成最大值了

-]]> - - Java - leetcode - - leetcode - java 题解 @@ -1617,38 +1649,6 @@ c++ - - Leetcode 698 划分为k个相等的子集 ( Partition to K Equal Sum Subsets *Medium* ) 题解分析 - /2022/06/19/Leetcode-698-%E5%88%92%E5%88%86%E4%B8%BAk%E4%B8%AA%E7%9B%B8%E7%AD%89%E7%9A%84%E5%AD%90%E9%9B%86-Partition-to-K-Equal-Sum-Subsets-Medium-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

Given an integer array nums and an integer k, return true if it is possible to divide this array into k non-empty subsets whose sums are all equal.

-

示例

Example 1:

-
-

Input: nums = [4,3,2,3,5,2,1], k = 4
Output: true
Explanation: It is possible to divide it into 4 subsets (5), (1, 4), (2,3), (2,3) with equal sums.

-
-

Example 2:

-
-

Input: nums = [1,2,3,4], k = 3
Output: false

-
-

Constraints:

-
    -
  • 1 <= k <= nums.length <= 16
    • -
  • 1 <= nums[i] <= 10^4
    • -
  • The frequency of each element is in the range [1, 4].
    • -
-

解析

看到这个题一开始以为挺简单，但是仔细想想问题还是挺多的，首先是分成 k 组，但是数量不限，应该需要用到回溯的方式，同时对于时间和空间复杂度也有要求，一开始这个代码是超时的，我也试了下 leetcode 上 discussion 里 vote 最高的提交也是超时的，不过看 discussion 里的帖子，貌似是后面加了一些条件，可以帮忙提高执行效率，第三条提示不太清楚意图，具体可以看下代码

-

代码

public boolean canPartitionKSubsets(int[] nums, int k) {
    if (k == 1) {
        return true;
    }
    int sum = 0, n;
    n = nums.length;
    for (int num : nums) {
        sum += num;
    }
    if (sum % k != 0) {
        return false;
    }

    int avg = sum / k;
    // 排序
    Arrays.sort(nums);
    // 做个前置判断，如果最大值超过分组平均值了就可以返回 false 了
    if (nums[n - 1] > avg) {
        return false;
    }
    // 这里取了个巧，先将数组中元素就等于分组平均值的直接排除了
    int calculated = 0;
    for (int i = n - 1; i > 0; i--) {
        if (nums[i] == avg) {
            k--;
            calculated++;
        }
    }

    int[] bucket = new int[k];
    // 初始化 bucket
    for (int i = 0; i < k; i++) {
        bucket[i] = avg;
    }

    // 提前做下边界判断
    if (nums[n - 1] > avg) {
        return false;
    }

    return backTraversal(nums, bucket, k, n - 1 - calculated);
}

private boolean backTraversal(int[] nums, int[] bucket, int k, int cur) {
    if (cur < 0) {
        return true;
    }
    for (int i = 0; i < k; i++) {
        if (bucket[i] == nums[cur] || bucket[i] >= nums[cur] + nums[0]) {
            // 判断如果当前 bucket[i] 剩余的数字等于nums[cur], 即当前bucket已经满了
            // 或者如果当前 bucket[i] 剩余的数字大于等于 nums[cur] + nums[0] ，
            // 因为nums 在经过排序后 nums[0]是最小值，如果加上 nums[0] 都已经超过bucket[i] 了，
            // 那当前bucket[i] 肯定是没法由包含 nums[cur] 的组合组成一个满足和为前面 s/k 的组合了
            // 这里判断的是 nums[cur] ,如果第一次 k 次循环都不符合其实就返回 false 了

            // 而如果符合，就将 bucket[i] 减去 nums[cur] 再次进入递归，
            // 这里进入递归有个收敛参数就是 cur - 1，因为其实判断 cur 递减作为一个结束条件
            bucket[i] -= nums[cur];
            // 符合条件，这里对应着入口，当 cur 被减到 0 了，就表示都符合了因为是根据所有值的和 s 和 k 组除出来的平均值，当所有数都通过前面的 if 判断符合了，并且每个数字都使用了，
            // 即说明已经符合要求了
            if (backTraversal(nums, bucket, k, cur - 1)) return true;
            // 这边是个回退机制，如果前面 nums[cur]没办法组合成和为平均值的话就减掉进入下一个循环
            bucket[i] += nums[cur];
        }
    }
    return false;
}
- -

最后贴个图

-]]> - - Java - leetcode - - - leetcode - java - - Linux 下 grep 命令的一点小技巧 /2020/08/06/Linux-%E4%B8%8B-grep-%E5%91%BD%E4%BB%A4%E7%9A%84%E4%B8%80%E7%82%B9%E5%B0%8F%E6%8A%80%E5%B7%A7/ @@ -1733,21 +1733,29 @@ - Leetcode 25 Reverse Nodes in k-Group 题解分析-再解分析 - /2024/01/21/Leetcode-25-Reverse-Nodes-in-k-Group-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 上一次主要是给了一个解题方案，没有具体讲解，这次又做到了就来看下几种方案，链表转置一直是我比较疑惑的问题，特别是边界处理，而这个问题要把难度加大了
我先讲一下我一开始的思路和解题方法，首先就是写一个转置方法，就处理 k 个一组的内部转置，然后外部循环处理分组以及前后连接等问题，但是这里就涉及到一个问题，就是前后连接的话对于整个链表头，也就是第一个 k 元素组来说，头是个空的，就需要额外处理，一开始就是用判空做额外处理，然后在前后连接的时候也有一些困扰，看了自己的代码库发现其实之前也做过，而且发现这个思路跟以前做的还是一样的，只不过在处理 k 个元素内部的转置的时候有了点差异

一开始的思路是这样的，想了下其实还是比较有问题，从处理难度上来说，在 k 组内处理的时候一开始 A 节点会是悬空的，然后得处理到组内最后一个元素的时候再接上，逻辑会更复杂，而另一种思路就是直接把 A 先挪到 C 后面，这样每次只需要移动一个，可能思路上会更清晰一点

也就是这样的，这种方案就是之前发过的题解代码，上一篇

-

而最后这种则代码会简单一些，但是需要一定的理解成本，比较重要的一点是我们加了个虚拟的头结点，第一步会先获取下链表的总长度，然后在内循环里处理转置，重点就是分四步，
也就是图里的，第一步先把 cur 的下一个节点设置成 next 的 next 节点，这里就是图里 A 连接到 C，第二步是把 next 的 next 节点设置成虚拟头的 next节点，第三步是把 pre 的next 节点设置成 next，第四步是 next 往后移动

-
cur.next = next.next;
next.next = pre.next;
pre.next = next;
next = cur.next;
+ Leetcode 31 Next Permutation 题解分析 + /2024/05/06/Leetcode-31-Next-Permutation-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 这题的题目介绍有一定的误导性，没有把字典序说明白，虽然的确是常规意义的字典序，但是题目介绍给的序列顺序反而会让人觉得那样不对
这里我们逐步深入地来讲这个题，也类似于找规律
注意：这里的递减递增序列都是从前往后看的
字典序可以就当查字典，或者默认的字符串排序方式，就是对于一个序列，逐个字符对比，同一位的相同再对比下一位的，例如 11在12前面，12在13前面，
那对于同一组字符或者像题中的数字，以最简单的12来分析，因为这个就两种排列，12跟21，按字典序排列，12在前，21在后
通过这个仔细点也能发现，或者再举个例子123，这个排列就会多一些了

+
123
132
213
231
312
321
+

一共有这么几种，不过举这个例子是为了更具体的发现规律，对于第一个数字相同的情况，比如123跟132，是132在后，发现规律没，递增序列是最小的，递减序列是最大的，因为2个数字 只有两种排列，扩展到3个数字，也可以把它拆分开，同样是第一个数字是1，后面两个数字就又回到了前面12序列的情况，
所以就可以把问题分成两部分来解决，首先从后往前找到递减序列
通过例子来讲

+
1 4 6 5 3 2
+

为什么要找到递减序列，因为递减序列已经是最大的了，要做调整要在这个之前调整，并且要找的是下一个序列，所以要是最接近的那个，否则我找到的可能就是下N个了，这个有点类似于数学里进位的意思，比如十九，九已经是个位里最大的了，找下一个比它大的只能是前面一位加一，但是加一以后需要九变到这一位的最小，也就是零。
对于上面的例子，递减序列就是6532，第一个非递减的是4，我要找下一个就是要比当前这个大，那我就去后面找比4大的，至于为什么能找到，因为这是递减序列边界之外的，说明肯定有比它大的，那要找哪个呢，要找比4大的数字里最小的，这样才是最近的下一个序列，并且因为后面是递减序列，所以可以直接从后往前找第一个
也就是会找到 5
找到 5 之后就做交换

+
1 5 6 4 3 2
+

这样是不是就行了呢，不行，因为这不是下一个，而是下N个，当4变成5之后，这个序列不管5后面的数字怎么变都是比 146532 大的，但是要找的是下一个，最接近的那个，则需要后面的序列变成最小，也就是变成递增序列，也就是

+
1 5 2 3 4 6
+

这里有两种例外情况，或者严格来说是一种
第一种是本身整个就是递减序列了，这在题中说了，下一个就是从头再开始，那就也很简单整个排序成递增的就行了
第二种是本身就是个完全递增的，那其实就交换末两位，但这种跟上面的可以糅合在一起，比如就是

+
1 2 3 4 5 6
+

单个6构不成递增或递减，找到的第一个非递减的数字就是 5 ，那就是在 5 后面找一个比它大的做交换，这时只有 6 ，所以就 5 跟 6 交换下，然后单个 5 做排序也等于不用做
再结合代码来看

+
public void nextPermutation(int[] nums) {
        int n = nums.length, i = n - 2, j = n - 1;
        // 这个while循环是为了从后往前找到递减序列的分界点
        // 循环完成后i就是递减序列左边的第一个数字位置
        while (i >= 0 && nums[i] >= nums[i + 1]) --i;
        if (i >= 0) {
            // 表示有找到分界点，然后就从后往前找到第一个比nums[i]大的做交换
            while (nums[j] <= nums[i]) --j;
            // 这个while循环在找到第一个nums[j] > nums[i]时退出
            // 下面就是做交换
            int temp = nums[i];
            nums[i] = nums[j];
            nums[j] = temp;
        }
        // 因为前面已经保证了逆序，这里只要排成正序就是最小的了
        Arrays.sort(nums, i+1, nums.length);
    }
-


在备注下代码

]]> Java leetcode - leetcode java + leetcode 题解 @@ -1767,30 +1775,6 @@ c++ - - Leetcode 25 Reverse Nodes in k-Group 题解分析 - /2020/08/16/Leetcode-25-Reverse-Nodes-in-k-Group-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90-1/ - Given a linked list, reverse the nodes of a linked list k at a time and return its modified list.

-

k is a positive integer and is less than or equal to the length of the linked list. If the number of nodes is not a multiple of k then left-out nodes in the end should remain as it is.

-

Example:

Given this linked list: 1->2->3->4->5

-

For k = 2, you should return: 2->1->4->3->5

-

For k = 3, you should return: 3->2->1->4->5

-

Note:

    -
  • Only constant extra memory is allowed.
    • -
  • You may not alter the values in the list’s nodes, only nodes itself may be changed.
    • -
-

这个题也算是经典中的经典了，各种算法题中的保留曲目，可能不全一样，不过反正都是逆转，这个算是比较难度大的，普通的应该就是整个链表反转，或者只对一个链表中的 m 到 n 位做翻转，这里是 k 个一组做翻转，其实这道题比较难的点是两个，一个是想清楚怎么处理，一个是代码的处理
简单来看下

-
public ListNode reverseKGroup(ListNode head, int k) {
    if (k <= 1) {
        return head;
    }
    if (head == null) {
        return null;
    }
    ListNode tempEnd = head;
    ListNode tempHead = head;
    ListNode lastHead = null;
    boolean isHeadSet = false;
    while (true) {
        // k 个一组获取头尾
        for (int i = 0; i < k - 1; i++) {
            System.out.println(tempEnd.val);
            if (tempEnd.next != null) {
                tempEnd  = tempEnd.next;
            } else {
                tempEnd = null;
                break;
            }
        }
        if (tempEnd != null) {
            if (!isHeadSet) {
                // 只有在第一组返回的时候需要赋值给 head
                head = reverse(null, tempHead, tempEnd);
                isHeadSet = !isHeadSet;
            } else {
                // 后面的需要将前一组的尾巴传进去，作为后一组头
                reverse(lastHead, tempHead, tempEnd);
            }
            if (tempHead.next != null) {
                // 如果后续节点还有
                lastHead = tempHead;
                tempEnd = tempHead.next;
                tempHead = tempHead.next;
            } else {
                break;
            }
        } else {
            break;
        }
    }

    return head;
}
public ListNode reverse(ListNode lastHead, ListNode tempHead, ListNode tempEnd) {
    // k 个一组的头尾传进来
    ListNode tail = null;
    while (tail != tempEnd) {
        // tail 表示是将头往后换时，把后面那个先拿着
        tail = tempHead.next;
        // 然后就是交换
        tempHead.next = tempEnd.next;
        tempEnd.next = tempHead;
        tempHead = tail;
    }
    if (lastHead !=null) {
        // 如果不是第一组，则需要接上前一组的尾巴
        lastHead.next = tail;
    }
    return tail;
}
]]> - - Java - leetcode - - - leetcode - java - 题解 - - Path Sum /2015/01/04/Path-Sum/ @@ -1810,6 +1794,41 @@ c++ + + Redis_分布式锁 + /2019/12/10/Redis-Part-1/ + 今天看了一下 redis 分布式锁 redlock 的实现，简单记录下，

+

加锁

原先我对 redis 锁的概念就是加锁使用 setnx，解锁使用 lua 脚本，但是 setnx 具体是啥，lua 脚本是啥不是很清楚
首先简单思考下这个问题，首先为啥不是先 get 一下 key 存不存在，然后再 set 一个 key value，因为加锁这个操作我们是要保证两点，一个是不能中途被打断，也就是说要原子性，如果是先 get 一下 key，如果不存在再 set 值的话，那就不是原子操作了；第二个是可不可以直接 set 值呢，显然不行，锁要保证唯一性，有且只能有一个线程或者其他应用单位获得该锁，正好 setnx 给了我们这种原子命令

+

然后是 setnx 的键和值分别是啥，键比较容易想到是要锁住的资源，比如 user_id, 这里有个我自己之前比较容易陷进去的误区，但是这个误区后
面再说，这里其实是把user_id 作为要锁住的资源，在我获得锁的时候别的线程不允许操作，以此保证业务的正确性，不会被多个线程同时修改，确定了键，再来看看值是啥，其实原先我认为值是啥都没关系，我只要锁住了，光键就够我用了，但是考虑下多个线程的问题，如果我这个线程加了锁，然后我因为 gc 停顿等原因卡死了，这个时候redis 的锁或者说就是 redis 的缓存已经过期了，这时候另一个线程获得锁成功，然后我这个线程又活过来了，然后我就仍然认为我拿着锁，我去对数据进行修改或者释放锁，是不是就出现问题了，所以是不是我们还需要一个东西来区分这个锁是哪个线程加的，所以我们可以将值设置成为一个线程独有识别的值，至少在相对长的一段时间内不会重复。

+

上面其实还有两个问题，一个是当 gc 超时时，我这个线程如何知道我手里的锁已经过期了，一种方法是我在加好锁之后就维护一个超时时间，这里其实还有个问题，不过跟第二个问题相关，就一起说了，就是设置超时时间，有些对于不是锁的 redis 缓存操作可以是先设置好值，然后在设置过期时间，那么这就又有上面说到的不是原子性的问题，那么就需要在同一条指令里把超时时间也设置了，幸好 redis 提供了这种支持

+
SET resource_name my_random_value NX PX 30000
+

这里借鉴一下解释下，resource_name就是 key，代表要锁住的东西，my_random_value就是识别我这个线程的，NX代表只有在不存在的时候才设置，然后PX 30000表示超时时间是 30秒自动过期

+

PS：记录下我原先有的一个误区，是不是要用 key 来区分加锁的线程，这样只有一个用处，就是自身线程可以识别是否是自己加的锁，但是最大的问题是别的线程不知道，其实这个用户的出发点是我在担心前面提过的一个问题，就是当 gc 停顿后，我要去判断当前的这个锁是否是我加的，还有就是当释放锁的时候，如果保证不会错误释放了其他线程加的锁，但是这样附带很多其他问题，最大的就是其他线程怎么知道能不能加这个锁。

+

解锁

当线程在锁过期之前就处理完了业务逻辑，那就可以提前释放这个锁，那么提前释放要怎么操作，直接del key显然是不行的，因为这样就是我前面想用线程随机值加资源名作为锁的初衷，我不能去释放别的线程加的锁，那么我要怎么办呢，先 get 一下看是不是我的？那又变成非原子的操作了，幸好redis 也考虑到了这个问题，给了lua 脚本来操作这种

+
if redis.call("get",KEYS[1]) == ARGV[1] then
    return redis.call("del",KEYS[1])
else
    return 0
end
+

这里的KEYS[1]就是前面加锁的resource_name,ARGV[1]就是线程的随机值my_random_value

+

多节点

前面说的其实是单节点 redis 作为分布式锁的情况，那么当我们的 redis 有多节点的情况呢，如果多节点下处于加锁或者解锁或者锁有效情况下
redis 的某个节点宕掉了怎么办，这里就有一些需要思考的地方，是否单独搞一个单节点的 redis作为分布式锁专用的，但是如果这个单节点的挂了呢，还有就是成本问题，所以我们需要一个多节点的分布式锁方案
这里就引出了开头说到的redlock，这个可是 redis的作者写的, 他的加锁过程是分以下几步去做这个事情

+
    +
  • 获取当前时间（毫秒数）。
    • +
  • 按顺序依次向N个Redis节点执行获取锁的操作。这个获取操作跟前面基于单Redis节点的获取锁的过程相同，包含随机字符串my_random_value，也包含过期时间(比如PX 30000，即锁的有效时间)。为了保证在某个Redis节点不可用的时候算法能够继续运行，这个获取锁的操作还有一个超时时间(time out)，它要远小于锁的有效时间（几十毫秒量级）。客户端在向某个Redis节点获取锁失败以后，应该立即尝试下一个Redis节点。这里的失败，应该包含任何类型的失败，比如该Redis节点不可用，或者该Redis节点上的锁已经被其它客户端持有（注：Redlock原文中这里只提到了Redis节点不可用的情况，但也应该包含其它的失败情况）。
    • +
  • 计算整个获取锁的过程总共消耗了多长时间，计算方法是用当前时间减去第1步记录的时间。如果客户端从大多数Redis节点（>= N/2+1）成功获取到了锁，并且获取锁总共消耗的时间没有超过锁的有效时间(lock validity time)，那么这时客户端才认为最终获取锁成功；否则，认为最终获取锁失败。
    • +
  • 如果最终获取锁成功了，那么这个锁的有效时间应该重新计算，它等于最初的锁的有效时间减去第3步计算出来的获取锁消耗的时间。
    • +
  • 如果最终获取锁失败了（可能由于获取到锁的Redis节点个数少于N/2+1，或者整个获取锁的过程消耗的时间超过了锁的最初有效时间），那么客户端应该立即向所有Redis节点发起释放锁的操作（即前面介绍的Redis Lua脚本）。
    释放锁的过程比较简单：客户端向所有Redis节点发起释放锁的操作，不管这些节点当时在获取锁的时候成功与否。这里为什么要向所有的节点发送释放锁的操作呢，这里是因为有部分的节点的失败原因可能是加锁时阻塞，加锁成功的结果没有及时返回，所以为了防止这种情况还是需要向所有发起这个释放锁的操作。
    初步记录就先到这。
    • +
+]]> + + Redis + Distributed Lock + C + Redis + + + C + Redis + Distributed Lock + 分布式锁 + + Reverse Bits /2015/03/11/Reverse-Bits/ @@ -1827,32 +1846,6 @@ c++ - - Leetcode 572 Subtree of Another Tree 题解分析 - /2024/01/28/Leetcode-572-Subtree-of-Another-Tree-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

Given the roots of two binary trees root and subRoot, return true if there is a subtree of root with the same structure and node values of subRoot and false otherwise.

-

A subtree of a binary tree tree is a tree that consists of a node in tree and all of this node’s descendants. The tree tree could also be considered as a subtree of itself.

-

示例 1

-
-

Input: root = [3,4,5,1,2], subRoot = [4,1,2]
Output: true

-
-

示例 2

-
-

Input: root = [3,4,5,1,2,null,null,null,null,0], subRoot = [4,1,2]
Output: false

-
-

解析

需要判断subRoot 为根节点的这棵树是不是 root 为根节点的这个树的子树，题应该不是特别难理解，只是需要注意，这里是子树，而不是一个子结构，也就是示例 2 的判断结果是 false，因为左边子树还有个 0，只能认为是左边的一部分，但不是子树，思路分析也不难，就是递归去判断，只不过是两种维度，第一种就是找到子树的根节点，也就是在 root 树找到 subRoot 的根节点相同的节点，这个就会用到递归，如果找到了一个，就开始递归的对比所有子节点，必须完全一样

-

代码

public boolean isSubtree(TreeNode root, TreeNode subRoot) {
       if (subRoot == null || root == null) {
           return false;
       }
       if (root.val == subRoot.val) {
           // 如果 root 节点跟 subRoot 节点相同，就开始递归对比所有的子节点
           if (isSameTree(root.left, subRoot.left) && isSameTree(root.right, subRoot.right)) {
               return true;
           }
       }
       // 如果 root 节点不同，就开始递归往下找与 subRoot 相同的起始节点，这里是或的关系，因为在左右子树任一找到一个就行
       return isSubtree(root.left, subRoot) || isSubtree(root.right, subRoot);
   }
   // 需要完全相同，也就是说不能像示例 2 那样多一个子节点这种，以及其他不同的情况，反正就是要完全一样
   public boolean isSameTree(TreeNode root, TreeNode subTree) {
       // 如果递归到这个节点都为空也认为是相同的
       if (root == null && subTree == null) {
           return true;
       }
       // 因为有前面的判断，这里就隐含了 subTree != null 这个条件
       if (root == null) {
           return false;
       }
       // 也因为有前面的判断，这里就隐含了  root != null 这个条件
       if (subTree == null) {
           return false;
       }
       // 两个都不为空，但是值不相同，也是 false
       if (root.val != subTree.val) {
           return false;
       }
       // 跟节点相同，就开始递归判断左右子树，必须两者都相同
       return isSameTree(root.left, subTree.left) && isSameTree(root.right, subTree.right);
   }
-]]> - - Java - leetcode - - - leetcode - java - 题解 - - Reverse Integer /2015/03/13/Reverse-Integer/ @@ -1874,38 +1867,19 @@ - Redis_分布式锁 - /2019/12/10/Redis-Part-1/ - 今天看了一下 redis 分布式锁 redlock 的实现，简单记录下，

-

加锁

原先我对 redis 锁的概念就是加锁使用 setnx，解锁使用 lua 脚本，但是 setnx 具体是啥，lua 脚本是啥不是很清楚
首先简单思考下这个问题，首先为啥不是先 get 一下 key 存不存在，然后再 set 一个 key value，因为加锁这个操作我们是要保证两点，一个是不能中途被打断，也就是说要原子性，如果是先 get 一下 key，如果不存在再 set 值的话，那就不是原子操作了；第二个是可不可以直接 set 值呢，显然不行，锁要保证唯一性，有且只能有一个线程或者其他应用单位获得该锁，正好 setnx 给了我们这种原子命令

-

然后是 setnx 的键和值分别是啥，键比较容易想到是要锁住的资源，比如 user_id, 这里有个我自己之前比较容易陷进去的误区，但是这个误区后
面再说，这里其实是把user_id 作为要锁住的资源，在我获得锁的时候别的线程不允许操作，以此保证业务的正确性，不会被多个线程同时修改，确定了键，再来看看值是啥，其实原先我认为值是啥都没关系，我只要锁住了，光键就够我用了，但是考虑下多个线程的问题，如果我这个线程加了锁，然后我因为 gc 停顿等原因卡死了，这个时候redis 的锁或者说就是 redis 的缓存已经过期了，这时候另一个线程获得锁成功，然后我这个线程又活过来了，然后我就仍然认为我拿着锁，我去对数据进行修改或者释放锁，是不是就出现问题了，所以是不是我们还需要一个东西来区分这个锁是哪个线程加的，所以我们可以将值设置成为一个线程独有识别的值，至少在相对长的一段时间内不会重复。

-

上面其实还有两个问题，一个是当 gc 超时时，我这个线程如何知道我手里的锁已经过期了，一种方法是我在加好锁之后就维护一个超时时间，这里其实还有个问题，不过跟第二个问题相关，就一起说了，就是设置超时时间，有些对于不是锁的 redis 缓存操作可以是先设置好值，然后在设置过期时间，那么这就又有上面说到的不是原子性的问题，那么就需要在同一条指令里把超时时间也设置了，幸好 redis 提供了这种支持

-
SET resource_name my_random_value NX PX 30000
-

这里借鉴一下解释下，resource_name就是 key，代表要锁住的东西，my_random_value就是识别我这个线程的，NX代表只有在不存在的时候才设置，然后PX 30000表示超时时间是 30秒自动过期

-

PS：记录下我原先有的一个误区，是不是要用 key 来区分加锁的线程，这样只有一个用处，就是自身线程可以识别是否是自己加的锁，但是最大的问题是别的线程不知道，其实这个用户的出发点是我在担心前面提过的一个问题，就是当 gc 停顿后，我要去判断当前的这个锁是否是我加的，还有就是当释放锁的时候，如果保证不会错误释放了其他线程加的锁，但是这样附带很多其他问题，最大的就是其他线程怎么知道能不能加这个锁。

-

解锁

当线程在锁过期之前就处理完了业务逻辑，那就可以提前释放这个锁，那么提前释放要怎么操作，直接del key显然是不行的，因为这样就是我前面想用线程随机值加资源名作为锁的初衷，我不能去释放别的线程加的锁，那么我要怎么办呢，先 get 一下看是不是我的？那又变成非原子的操作了，幸好redis 也考虑到了这个问题，给了lua 脚本来操作这种

-
if redis.call("get",KEYS[1]) == ARGV[1] then
    return redis.call("del",KEYS[1])
else
    return 0
end
-

这里的KEYS[1]就是前面加锁的resource_name,ARGV[1]就是线程的随机值my_random_value

-

多节点

前面说的其实是单节点 redis 作为分布式锁的情况，那么当我们的 redis 有多节点的情况呢，如果多节点下处于加锁或者解锁或者锁有效情况下
redis 的某个节点宕掉了怎么办，这里就有一些需要思考的地方，是否单独搞一个单节点的 redis作为分布式锁专用的，但是如果这个单节点的挂了呢，还有就是成本问题，所以我们需要一个多节点的分布式锁方案
这里就引出了开头说到的redlock，这个可是 redis的作者写的, 他的加锁过程是分以下几步去做这个事情

-
    -
  • 获取当前时间（毫秒数）。
    • -
  • 按顺序依次向N个Redis节点执行获取锁的操作。这个获取操作跟前面基于单Redis节点的获取锁的过程相同，包含随机字符串my_random_value，也包含过期时间(比如PX 30000，即锁的有效时间)。为了保证在某个Redis节点不可用的时候算法能够继续运行，这个获取锁的操作还有一个超时时间(time out)，它要远小于锁的有效时间（几十毫秒量级）。客户端在向某个Redis节点获取锁失败以后，应该立即尝试下一个Redis节点。这里的失败，应该包含任何类型的失败，比如该Redis节点不可用，或者该Redis节点上的锁已经被其它客户端持有（注：Redlock原文中这里只提到了Redis节点不可用的情况，但也应该包含其它的失败情况）。
    • -
  • 计算整个获取锁的过程总共消耗了多长时间，计算方法是用当前时间减去第1步记录的时间。如果客户端从大多数Redis节点（>= N/2+1）成功获取到了锁，并且获取锁总共消耗的时间没有超过锁的有效时间(lock validity time)，那么这时客户端才认为最终获取锁成功；否则，认为最终获取锁失败。
    • -
  • 如果最终获取锁成功了，那么这个锁的有效时间应该重新计算，它等于最初的锁的有效时间减去第3步计算出来的获取锁消耗的时间。
    • -
  • 如果最终获取锁失败了（可能由于获取到锁的Redis节点个数少于N/2+1，或者整个获取锁的过程消耗的时间超过了锁的最初有效时间），那么客户端应该立即向所有Redis节点发起释放锁的操作（即前面介绍的Redis Lua脚本）。
    释放锁的过程比较简单：客户端向所有Redis节点发起释放锁的操作，不管这些节点当时在获取锁的时候成功与否。这里为什么要向所有的节点发送释放锁的操作呢，这里是因为有部分的节点的失败原因可能是加锁时阻塞，加锁成功的结果没有及时返回，所以为了防止这种情况还是需要向所有发起这个释放锁的操作。
    初步记录就先到这。
    • -
+ Java 线程池系列-第二篇 + /2024/02/18/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E4%BA%8C%E7%AF%87/ + 介绍了线程池的目的和实现概述，以及如何初始化的，我们就来开始看看线程池最重要的执行过程
老规矩，还是先把注释翻译下，这个对理解逻辑其实非常重要，后面可以循着注释的逻辑来看代码
第一步，如果是少于核心线程数的线程正在运行，那么尝试去开启一个新线程，并把提交的命令
command 最为 first task，这里调用了 addWorker，会自动检查运行状态和线程数，并通过
返回 false 来提示不应该增加线程，比如已经有其他任务先创建了线程导致已经超过了核心线程数
第二步，在前面不符合的情况下，也就是线程数已经大于等于核心线程数了或者在调用 addWorker
的时候校验发现线程数已经大于等于核心线程数或者线程池运行状态不是正在运行了就会进入下一个判断
首先还是判断线程池是否正在运行，然后将 command 放进队列，如果成功进队了，还需要再次进行校验
如果线程池状态不是正在运行了，则需要再出队，然后执行拒绝策略，如果状态正常，但是线程被回收完了
那需要创建线程
第三步，如果不能正常进队列，会尝试再启动一个新线程，这里表示核心线程数满了，并且队列也满了，
则需要再开启一个新线程，如果开启失败则执行拒绝策略
代码注释基本已经把代码讲得很详细了

+
public void execute(Runnable command) {
        if (command == null)
            throw new NullPointerException();
        /*
         * Proceed in 3 steps:
         *
         * 1. If fewer than corePoolSize threads are running, try to
         * start a new thread with the given command as its first
         * task.  The call to addWorker atomically checks runState and
         * workerCount, and so prevents false alarms that would add
         * threads when it shouldn't, by returning false.
         *
         * 2. If a task can be successfully queued, then we still need
         * to double-check whether we should have added a thread
         * (because existing ones died since last checking) or that
         * the pool shut down since entry into this method. So we
         * recheck state and if necessary roll back the enqueuing if
         * stopped, or start a new thread if there are none.
         *
         * 3. If we cannot queue task, then we try to add a new
         * thread.  If it fails, we know we are shut down or saturated
         * and so reject the task.
         */
        int c = ctl.get();
        // 这就是第一步
        if (workerCountOf(c) < corePoolSize) {
            if (addWorker(command, true))
                return;
            // 注意这里是第一步可能失败了，获取一下最新的状态
            c = ctl.get();
        }
        // 这里是第二步
        if (isRunning(c) && workQueue.offer(command)) {
            // 每次都获取最新的状态
            int recheck = ctl.get();
            if (! isRunning(recheck) && remove(command))
                reject(command);
            else if (workerCountOf(recheck) == 0)
                // 这里是判断如果一个线程都没了的话，注意添加的是非核心线程
                addWorker(null, false);
        }
        // 这里第三步是添加线程，并且是非核心线程
        else if (!addWorker(command, false))
            reject(command);
    }
+

这里面比较重要的就是 addWorker 方法，我们也来看一下
这里用到了 break retry; 就是在循环中跳出到这个 retry 的位置继续执行
然后还是获取运行状态，如果是非运行状态，
并且rs == SHUTDOWN 与 firstTask == null 和 ! workQueue.isEmpty()，至少一个为false
因为只有当 SHUTDOWN 状态才会出现后面两者为 false 的情况，如果是 SHUTDOWN ，队列不为空还是需要执行完队列里的任务
然后是判断线程数量与传入参数 core 还有核心线程数跟最大线程数的对比，如果是超过了就返回 false 也就是外层的第一步的内部
if 就会继续执行后面的获取线程池状态，然后是 cas 增加线程数量，如果失败则 break 跳到方法开头，如果成功则继续重新获取 c
判断线程池运行状态，如果不一致则从外层继续进入
后面的就是新建一个 worker，然后获取锁，继续判断线程池状态，如果在运行中或者队列不为空（隐含条件）则继续执行添加 worker
并判断是否需要更新历史最大线程数，更改线程添加状态，然后启动线程，标记线程启动状态

+
private boolean addWorker(Runnable firstTask, boolean core) {
        retry:
        for (;;) {
            int c = ctl.get();
            int rs = runStateOf(c);

            // Check if queue empty only if necessary.
            if (rs >= SHUTDOWN && ! (rs == SHUTDOWN && firstTask == null && ! workQueue.isEmpty()))
                return false;

            for (;;) {
                int wc = workerCountOf(c);
                if (wc >= CAPACITY ||
                    wc >= (core ? corePoolSize : maximumPoolSize))
                    return false;
                if (compareAndIncrementWorkerCount(c))
                    break retry;
                c = ctl.get();  // Re-read ctl
                if (runStateOf(c) != rs)
                    continue retry;
                // else CAS failed due to workerCount change; retry inner loop
            }
        }

        boolean workerStarted = false;
        boolean workerAdded = false;
        Worker w = null;
        try {
            w = new Worker(firstTask);
            final Thread t = w.thread;
            if (t != null) {
                final ReentrantLock mainLock = this.mainLock;
                mainLock.lock();
                try {
                    // Recheck while holding lock.
                    // Back out on ThreadFactory failure or if
                    // shut down before lock acquired.
                    int rs = runStateOf(ctl.get());

                    if (rs < SHUTDOWN ||
                        (rs == SHUTDOWN && firstTask == null)) {
                        if (t.isAlive()) // precheck that t is startable
                            throw new IllegalThreadStateException();
                        workers.add(w);
                        int s = workers.size();
                        if (s > largestPoolSize)
                            largestPoolSize = s;
                        workerAdded = true;
                    }
                } finally {
                    mainLock.unlock();
                }
                if (workerAdded) {
                    t.start();
                    workerStarted = true;
                }
            }
        } finally {
            if (! workerStarted)
                addWorkerFailed(w);
        }
        return workerStarted;
    }
+

后面会继续讲下线程如何执行队列里的任务

]]> - Redis - Distributed Lock - C - Redis + Java - C - Redis - Distributed Lock - 分布式锁 + Java @@ -2155,37 +2129,6 @@

就是在 org.apache.catalina.mapper.Mapper#addContextVersion 方法中

public void addContextVersion(String hostName, Host host, String path,
        String version, Context context, String[] welcomeResources,
        WebResourceRoot resources, Collection<WrapperMappingInfo> wrappers) {

    hostName = renameWildcardHost(hostName);

    MappedHost mappedHost  = exactFind(hosts, hostName);
    if (mappedHost == null) {
        addHost(hostName, new String[0], host);
        mappedHost = exactFind(hosts, hostName);
        if (mappedHost == null) {
            log.error(sm.getString("mapper.addContext.noHost", hostName));
            return;
        }
    }
    if (mappedHost.isAlias()) {
        log.error(sm.getString("mapper.addContext.hostIsAlias", hostName));
        return;
    }
    int slashCount = slashCount(path);
    synchronized (mappedHost) {
        ContextVersion newContextVersion = new ContextVersion(version,
                path, slashCount, context, resources, welcomeResources);
        if (wrappers != null) {
            addWrappers(newContextVersion, wrappers);
        }

大致介绍了下 Mapper 的逻辑

-]]> - - Java - SpringBoot - Tomcat - - - Java - Tomcat - SpringBoot - - - - Tomcat 系列篇十一-介绍下 Tomcat 里的后台处理和热加载 - /2023/12/10/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%8D%81%E4%B8%80-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E9%87%8C%E7%9A%84%E5%90%8E%E5%8F%B0%E5%A4%84%E7%90%86%E5%92%8C%E7%83%AD%E5%8A%A0%E8%BD%BD/ - 这部分其实之前在讲线程池的时候也有点带到了， 主要是在这个类里
org.apache.catalina.core.ContainerBase.ContainerBackgroundProcessor

-
protected class ContainerBackgroundProcessor implements Runnable {

        @Override
        public void run() {
            processChildren(ContainerBase.this);
        }

        protected void processChildren(Container container) {
            ClassLoader originalClassLoader = null;

            try {
                if (container instanceof Context) {
                    Loader loader = ((Context) container).getLoader();
                    // Loader will be null for FailedContext instances
                    if (loader == null) {
                        return;
                    }

                    // Ensure background processing for Contexts and Wrappers
                    // is performed under the web app's class loader
                    originalClassLoader = ((Context) container).bind(false, null);
                }
                // 调用 Container 的 backgroundProcess
                container.backgroundProcess();
                // 然后寻找 children
                Container[] children = container.findChildren();
                for (Container child : children) {
                    // 如果 backgroundProcessorDelay <= 0 就调用执行
                    // 否则代表这个 Container 有之前第八篇说的 StartChild 这种
                    if (child.getBackgroundProcessorDelay() <= 0) {
                        processChildren(child);
                    }
                }
            } catch (Throwable t) {
                ExceptionUtils.handleThrowable(t);
                log.error(sm.getString("containerBase.backgroundProcess.error"), t);
            } finally {
                if (container instanceof Context) {
                    ((Context) container).unbind(false, originalClassLoader);
                }
            }
        }
    }
-

这个触发方式是在 ContainerBase 里的

-
protected class ContainerBackgroundProcessorMonitor implements Runnable {
        @Override
        public void run() {
            if (getState().isAvailable()) {
                threadStart();
            }
        }
    }
- -

而在这个 threadStart 里

-
protected void threadStart() {
    if (backgroundProcessorDelay > 0
            && (getState().isAvailable() || LifecycleState.STARTING_PREP.equals(getState()))
            && (backgroundProcessorFuture == null || backgroundProcessorFuture.isDone())) {
        if (backgroundProcessorFuture != null && backgroundProcessorFuture.isDone()) {
            // There was an error executing the scheduled task, get it and log it
            try {
                backgroundProcessorFuture.get();
            } catch (InterruptedException | ExecutionException e) {
                log.error(sm.getString("containerBase.backgroundProcess.error"), e);
            }
        }
        backgroundProcessorFuture = Container.getService(this).getServer().getUtilityExecutor()
                .scheduleWithFixedDelay(new ContainerBackgroundProcessor(),
                        backgroundProcessorDelay, backgroundProcessorDelay,
                        TimeUnit.SECONDS);
    }
}
-

就调用了线程池的 scheduleWithFixedDelay 方法提交了这个 ContainerBackgroundProcessor，
仔细看代码会发现，

-
public StandardEngine() {

    super();
    pipeline.setBasic(new StandardEngineValve());
    /* Set the jmvRoute using the system property jvmRoute */
    try {
        setJvmRoute(System.getProperty("jvmRoute"));
    } catch(Exception ex) {
        log.warn(sm.getString("standardEngine.jvmRouteFail"));
    }
    // By default, the engine will hold the reloading thread
    backgroundProcessorDelay = 10;

}
-

这个就不用开启后台热加载，而主要的热加载同学应该是
org.apache.catalina.core.StandardContext#backgroundProcess

-
public void backgroundProcess() {

        if (!getState().isAvailable()) {
            return;
        }

        Loader loader = getLoader();
        if (loader != null) {
            try {
                // 这里就用了 loader 的 backgroundProcess
                loader.backgroundProcess();
            } catch (Exception e) {
                log.warn(sm.getString(
                        "standardContext.backgroundProcess.loader", loader), e);
            }
        }
        Manager manager = getManager();
        if (manager != null) {
            try {
                manager.backgroundProcess();
            } catch (Exception e) {
                log.warn(sm.getString(
                        "standardContext.backgroundProcess.manager", manager),
                        e);
            }
        }
        WebResourceRoot resources = getResources();
        if (resources != null) {
            try {
                resources.backgroundProcess();
            } catch (Exception e) {
                log.warn(sm.getString(
                        "standardContext.backgroundProcess.resources",
                        resources), e);
            }
        }
        InstanceManager instanceManager = getInstanceManager();
        if (instanceManager != null) {
            try {
                instanceManager.backgroundProcess();
            } catch (Exception e) {
                log.warn(sm.getString(
                        "standardContext.backgroundProcess.instanceManager",
                        resources), e);
            }
        }
        super.backgroundProcess();
    }
-

loader 的后台处理就是

-
@Override
    public void backgroundProcess() {
        if (reloadable && modified()) {
            try {
                Thread.currentThread().setContextClassLoader
                    (WebappLoader.class.getClassLoader());
                if (context != null) {
                    context.reload();
                }
            } finally {
                if (context != null && context.getLoader() != null) {
                    Thread.currentThread().setContextClassLoader
                        (context.getLoader().getClassLoader());
                }
            }
        }
    }
-

然后又会回到 context 的 reload，也就是 StandardContext 的 reload

-
@Override
public synchronized void reload() {

    // Validate our current component state
    if (!getState().isAvailable()) {
        throw new IllegalStateException
            (sm.getString("standardContext.notStarted", getName()));
    }

    if(log.isInfoEnabled()) {
        log.info(sm.getString("standardContext.reloadingStarted",
                getName()));
    }

    // Stop accepting requests temporarily.
    setPaused(true);

    try {
        stop();
    } catch (LifecycleException e) {
        log.error(
            sm.getString("standardContext.stoppingContext", getName()), e);
    }

    try {
        start();
    } catch (LifecycleException e) {
        log.error(
            sm.getString("standardContext.startingContext", getName()), e);
    }

    setPaused(false);

    if(log.isInfoEnabled()) {
        log.info(sm.getString("standardContext.reloadingCompleted",
                getName()));
    }

}
-

这样就是线程池结合后台处理，还是有些复杂的。

]]> Java @@ -2239,18 +2182,6 @@ SpringBoot - - NX30Pro 刷成 Openwrt-ImmortalWrt 后作为有线中继的配置方法 - /2024/06/16/NX30Pro-%E5%88%B7%E6%88%90-Openwrt-ImmortalWrt-%E5%90%8E%E4%BD%9C%E4%B8%BA%E6%9C%89%E7%BA%BF%E4%B8%AD%E7%BB%A7%E7%9A%84%E9%85%8D%E7%BD%AE%E6%96%B9%E6%B3%95/ - 因为买了两个NX30Pro，主要是奔着可以刷Openwrt，然后把两个都刷成了ImmortalWrt，结果发现要作为有线中继的话设置还有点麻烦，所以记录下
首先要设置个与主路由相同网段的静态地址，方便管理
在网络-LAN-基本设置-协议设置成“静态地址”
然后IPv4地址就是刚才说的静态地址，可以设个好记的
子网掩码就默认的，255.255.255.0，
IPv4网关就是主路由地址，DNS也设置成主路由地址
基础设置改成忽略此接口-不提供DHCP服务，由主路由提供
然后在高级设置中将IPv6的 路由通告服务和DHCPv6服务跟NDP代理禁用掉，
物理设置中勾选桥接接口
目前我是这样设置可以作为有线中继连通网络了，后续有变化继续更新

-]]> - - openwrt - - - openwrt - - Tomcat 系列篇十二-番外介绍下 Tomcat 的上传文件限制 /2023/12/17/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%8D%81%E4%BA%8C-%E7%95%AA%E5%A4%96%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E7%9A%84%E4%B8%8A%E4%BC%A0%E6%96%87%E4%BB%B6%E9%99%90%E5%88%B6/ @@ -2309,16 +2240,29 @@ - Windows 莫名重启问题解决 - /2023/12/03/Windows-%E8%8E%AB%E5%90%8D%E9%87%8D%E5%90%AF%E9%97%AE%E9%A2%98%E8%A7%A3%E5%86%B3/ - 之前在 Windows 里用 vmware workstation 搭了个黑裙，然后硬盘直通，硬盘跑着倒还好，但是宿主机 Windows 隔一段时间就会重启，就去搜索了下，发现其实 Windows 里的事件查看器就有点像是 Linux 系统里的 dmesg 或者 journal 日志，然后根据重启的时间排查事件查看器里，Windows 日志 –> 系统，

然后可以在右侧 “筛选当前日志”里选择 eventlog 类型的，发现有这样一条事件
进程 C:\Windows\system32\svchost.exe (APP) 为用户 NT AUTHORITY\SYSTEM 开始计算机 APP 的 重新启动，原因如下: 操作系统: 恢复(计划内)
然后在网上搜索的时候发现有一些相关解答
可能的一种原因是系统在应用一些更新失败时，默认设置的策略是失败后重启，我们可以自己选择不重启，因为老是重启在我虚拟机里的黑裙没关机的情况下直接就断电重启了，还是可能会造成一些影响，
可以在资源管理器里右键计算机，选左侧的”高级系统设置” –> 然后在”高级” tab 下面有个”启动和故障恢复” –> “设置”
在弹出弹窗里将 “自动重新启动” 取消勾选

这样到目前还没继续发生过重启

+ Leetcode 572 Subtree of Another Tree 题解分析 + /2024/01/28/Leetcode-572-Subtree-of-Another-Tree-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

Given the roots of two binary trees root and subRoot, return true if there is a subtree of root with the same structure and node values of subRoot and false otherwise.

+

A subtree of a binary tree tree is a tree that consists of a node in tree and all of this node’s descendants. The tree tree could also be considered as a subtree of itself.

+

示例 1

+
+

Input: root = [3,4,5,1,2], subRoot = [4,1,2]
Output: true

+
+

示例 2

+
+

Input: root = [3,4,5,1,2,null,null,null,null,0], subRoot = [4,1,2]
Output: false

+
+

解析

需要判断subRoot 为根节点的这棵树是不是 root 为根节点的这个树的子树，题应该不是特别难理解，只是需要注意，这里是子树，而不是一个子结构，也就是示例 2 的判断结果是 false，因为左边子树还有个 0，只能认为是左边的一部分，但不是子树，思路分析也不难，就是递归去判断，只不过是两种维度，第一种就是找到子树的根节点，也就是在 root 树找到 subRoot 的根节点相同的节点，这个就会用到递归，如果找到了一个，就开始递归的对比所有子节点，必须完全一样

+

代码

public boolean isSubtree(TreeNode root, TreeNode subRoot) {
       if (subRoot == null || root == null) {
           return false;
       }
       if (root.val == subRoot.val) {
           // 如果 root 节点跟 subRoot 节点相同，就开始递归对比所有的子节点
           if (isSameTree(root.left, subRoot.left) && isSameTree(root.right, subRoot.right)) {
               return true;
           }
       }
       // 如果 root 节点不同，就开始递归往下找与 subRoot 相同的起始节点，这里是或的关系，因为在左右子树任一找到一个就行
       return isSubtree(root.left, subRoot) || isSubtree(root.right, subRoot);
   }
   // 需要完全相同，也就是说不能像示例 2 那样多一个子节点这种，以及其他不同的情况，反正就是要完全一样
   public boolean isSameTree(TreeNode root, TreeNode subTree) {
       // 如果递归到这个节点都为空也认为是相同的
       if (root == null && subTree == null) {
           return true;
       }
       // 因为有前面的判断，这里就隐含了 subTree != null 这个条件
       if (root == null) {
           return false;
       }
       // 也因为有前面的判断，这里就隐含了  root != null 这个条件
       if (subTree == null) {
           return false;
       }
       // 两个都不为空，但是值不相同，也是 false
       if (root.val != subTree.val) {
           return false;
       }
       // 跟节点相同，就开始递归判断左右子树，必须两者都相同
       return isSameTree(root.left, subTree.left) && isSameTree(root.right, subTree.right);
   }
]]> - Windows - 小技巧 + Java + leetcode - Windows + java + leetcode + 题解 @@ -2348,47 +2292,24 @@ - two sum - /2015/01/14/Two-Sum/ - problem

Given an array of integers, find two numbers such that they add up to a specific target number.

-

The function twoSum should return indices of the two numbers such that they add up to the target, where index1 must be less than index2. Please note that your returned answers (both index1 and index2) are not zero-based.

- -

You may assume that each input would have exactly one solution.

-

Input: numbers={2, 7, 11, 15}, target=9
Output: index1=1, index2=2

-

code

struct Node
{
    int num, pos;
};
bool cmp(Node a, Node b)
{
    return a.num < b.num;
}
class Solution {
public:
    vector<int> twoSum(vector<int> &numbers, int target) {
        // Start typing your C/C++ solution below
        // DO NOT write int main() function
        vector<int> result;
        vector<Node> array;
        for (int i = 0; i < numbers.size(); i++)
        {
            Node temp;
            temp.num = numbers[i];
            temp.pos = i;
            array.push_back(temp);
        }

        sort(array.begin(), array.end(), cmp);
        for (int i = 0, j = array.size() - 1; i != j;)
        {
            int sum = array[i].num + array[j].num;
            if (sum == target)
            {
                if (array[i].pos < array[j].pos)
                {
                    result.push_back(array[i].pos + 1);
                    result.push_back(array[j].pos + 1);
                } else
                {
                    result.push_back(array[j].pos + 1);
                    result.push_back(array[i].pos + 1);
                }
                break;
            } else if (sum < target)
            {
                i++;
            } else if (sum > target)
            {
                j--;
            }
        }
        return result;
    }
};
+ Tomcat 系列篇十一-介绍下 Tomcat 里的后台处理和热加载 + /2023/12/10/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%8D%81%E4%B8%80-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E9%87%8C%E7%9A%84%E5%90%8E%E5%8F%B0%E5%A4%84%E7%90%86%E5%92%8C%E7%83%AD%E5%8A%A0%E8%BD%BD/ + 这部分其实之前在讲线程池的时候也有点带到了， 主要是在这个类里
org.apache.catalina.core.ContainerBase.ContainerBackgroundProcessor

+
protected class ContainerBackgroundProcessor implements Runnable {

        @Override
        public void run() {
            processChildren(ContainerBase.this);
        }

        protected void processChildren(Container container) {
            ClassLoader originalClassLoader = null;

            try {
                if (container instanceof Context) {
                    Loader loader = ((Context) container).getLoader();
                    // Loader will be null for FailedContext instances
                    if (loader == null) {
                        return;
                    }

                    // Ensure background processing for Contexts and Wrappers
                    // is performed under the web app's class loader
                    originalClassLoader = ((Context) container).bind(false, null);
                }
                // 调用 Container 的 backgroundProcess
                container.backgroundProcess();
                // 然后寻找 children
                Container[] children = container.findChildren();
                for (Container child : children) {
                    // 如果 backgroundProcessorDelay <= 0 就调用执行
                    // 否则代表这个 Container 有之前第八篇说的 StartChild 这种
                    if (child.getBackgroundProcessorDelay() <= 0) {
                        processChildren(child);
                    }
                }
            } catch (Throwable t) {
                ExceptionUtils.handleThrowable(t);
                log.error(sm.getString("containerBase.backgroundProcess.error"), t);
            } finally {
                if (container instanceof Context) {
                    ((Context) container).unbind(false, originalClassLoader);
                }
            }
        }
    }
+

这个触发方式是在 ContainerBase 里的

+
protected class ContainerBackgroundProcessorMonitor implements Runnable {
        @Override
        public void run() {
            if (getState().isAvailable()) {
                threadStart();
            }
        }
    }
-

Analysis

sort the array, then test from head and end, until catch the right answer

-]]> - - leetcode - - - leetcode - c++ - - - - Tomcat 系列篇四-介绍下 Valve 架构 - /2023/10/01/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%9B%9B-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Valve-%E6%9E%B6%E6%9E%84/ - valve 是 Tomcat 架构中比较重要的一个组成部分，
之前说到

-
connector.getService().getContainer().getPipeline().getFirst().invoke(
                        request, response);
-

这段代码是通过 CoyoteAdapter 将请求处理往 container 传，这里就有个 pipeline 机制，这个 pipeline 可以看一下接口

-
public interface Pipeline extends Contained {

    public Valve getBasic();

    public void setBasic(Valve valve);

    public void addValve(Valve valve);

    public Valve[] getValves();

    public void removeValve(Valve valve);

    public Valve getFirst();

    public boolean isAsyncSupported();

    public void findNonAsyncValves(Set<String> result);
}
-

这里可以往 pipeline 里添加 valve，然后看下 valve 的接口

-
public interface Valve {

    public Valve getNext();

    public void setNext(Valve valve);

    public void backgroundProcess();

    public void invoke(Request request, Response response)
        throws IOException, ServletException;


    public boolean isAsyncSupported();
}
-

这里主要看的就是 getNext 跟 setNext，就变成了一个有序的 pipeline，然后就是 invoke 方法，其实 pipeline 是由两部分组成，valve 在其中起到了前后衔接的重要作用，而且可以再 invoke 中进一步串联调用

图中我们可以看到，对于 container 这个 pipeline，是没设置 first 的，只有 basic，basic 就是个兜底的 valve，在 StandardPipeline 中的 getFirst 实现

-
@Override
public Valve getFirst() {
    if (first != null) {
        return first;
    }

    return basic;
}
-

取不到 first 就降级到 basic，也就是这里的 StandardEngineValve，
它的 invoke 我们来看下

-
@Override
public final void invoke(Request request, Response response)
    throws IOException, ServletException {

    // Select the Host to be used for this Request
    Host host = request.getHost();
    if (host == null) {
        // HTTP 0.9 or HTTP 1.0 request without a host when no default host
        // is defined.
        // Don't overwrite an existing error
        if (!response.isError()) {
            response.sendError(404);
        }
        return;
    }
    if (request.isAsyncSupported()) {
        request.setAsyncSupported(host.getPipeline().isAsyncSupported());
    }

    // Ask this Host to process this request
    host.getPipeline().getFirst().invoke(request, response);
}
-

比较简单，就是调用 host 中的 pipeline 里的第一个 valve 来处理

第一个是 org.apache.catalina.valves.ErrorReportValve，
这里处理的其实是先调用了 next

-
@Override
    public void invoke(Request request, Response response) throws IOException, ServletException {

        // Perform the request
        getNext().invoke(request, response);

        if (response.isCommitted()) {
            if (response.setErrorReported()) {
                // Error wasn't previously reported but we can't write an error
                // page because the response has already been committed.

                // See if IO is allowed
                AtomicBoolean ioAllowed = new AtomicBoolean(true);
                response.getCoyoteResponse().action(ActionCode.IS_IO_ALLOWED, ioAllowed);

                if (ioAllowed.get()) {
                    // I/O is currently still allowed. Flush any data that is
                    // still to be written to the client.
                    try {
                        response.flushBuffer();
                    } catch (Throwable t) {
                        ExceptionUtils.handleThrowable(t);
                    }
                    // Now close immediately to signal to the client that
                    // something went wrong
                    response.getCoyoteResponse().action(ActionCode.CLOSE_NOW,
                            request.getAttribute(RequestDispatcher.ERROR_EXCEPTION));
                }
            }
            return;
        }

        Throwable throwable = (Throwable) request.getAttribute(RequestDispatcher.ERROR_EXCEPTION);

        // If an async request is in progress and is not going to end once this
        // container thread finishes, do not process any error page here.
        if (request.isAsync() && !request.isAsyncCompleting()) {
            return;
        }

        if (throwable != null && !response.isError()) {
            // Make sure that the necessary methods have been called on the
            // response. (It is possible a component may just have set the
            // Throwable. Tomcat won't do that but other components might.)
            // These are safe to call at this point as we know that the response
            // has not been committed.
            response.reset();
            response.sendError(HttpServletResponse.SC_INTERNAL_SERVER_ERROR);
        }

        // One way or another, response.sendError() will have been called before
        // execution reaches this point and suspended the response. Need to
        // reverse that so this valve can write to the response.
        response.setSuspended(false);

        try {
            report(request, response, throwable);
        } catch (Throwable tt) {
            ExceptionUtils.handleThrowable(tt);
        }
    }
-

虽然是放在 first，实际是先调用 next 的 invoke，也就是 org.apache.catalina.core.StandardHostValve 的invoke 方法

-
@Override
    public final void invoke(Request request, Response response)
        throws IOException, ServletException {

        // Select the Context to be used for this Request
        Context context = request.getContext();
        if (context == null) {
            // Don't overwrite an existing error
            if (!response.isError()) {
                response.sendError(404);
            }
            return;
        }

        if (request.isAsyncSupported()) {
            request.setAsyncSupported(context.getPipeline().isAsyncSupported());
        }

        boolean asyncAtStart = request.isAsync();

        try {
            context.bind(Globals.IS_SECURITY_ENABLED, MY_CLASSLOADER);

            if (!asyncAtStart && !context.fireRequestInitEvent(request.getRequest())) {
                // Don't fire listeners during async processing (the listener
                // fired for the request that called startAsync()).
                // If a request init listener throws an exception, the request
                // is aborted.
                return;
            }

            // Ask this Context to process this request. Requests that are
            // already in error must have been routed here to check for
            // application defined error pages so DO NOT forward them to the the
            // application for processing.
            try {
                if (!response.isErrorReportRequired()) {
                    // 交给 context 去处理请求了
                    context.getPipeline().getFirst().invoke(request, response);
                }
            } catch (Throwable t) {
                ExceptionUtils.handleThrowable(t);
                container.getLogger().error("Exception Processing " + request.getRequestURI(), t);
                // If a new error occurred while trying to report a previous
                // error allow the original error to be reported.
                if (!response.isErrorReportRequired()) {
                    request.setAttribute(RequestDispatcher.ERROR_EXCEPTION, t);
                    throwable(request, response, t);
                }
            }

            // Now that the request/response pair is back under container
            // control lift the suspension so that the error handling can
            // complete and/or the container can flush any remaining data
            response.setSuspended(false);

            Throwable t = (Throwable) request.getAttribute(RequestDispatcher.ERROR_EXCEPTION);

            // Protect against NPEs if the context was destroyed during a
            // long running request.
            if (!context.getState().isAvailable()) {
                return;
            }

            // Look for (and render if found) an application level error page
            if (response.isErrorReportRequired()) {
                // If an error has occurred that prevents further I/O, don't waste time
                // producing an error report that will never be read
                AtomicBoolean result = new AtomicBoolean(false);
                response.getCoyoteResponse().action(ActionCode.IS_IO_ALLOWED, result);
                if (result.get()) {
                    if (t != null) {
                        throwable(request, response, t);
                    } else {
                        status(request, response);
                    }
                }
            }

            if (!request.isAsync() && !asyncAtStart) {
                context.fireRequestDestroyEvent(request.getRequest());
            }
        } finally {
            // Access a session (if present) to update last accessed time, based
            // on a strict interpretation of the specification
            if (ACCESS_SESSION) {
                request.getSession(false);
            }

            context.unbind(Globals.IS_SECURITY_ENABLED, MY_CLASSLOADER);
        }
    }
-

这里的往下就是调用 context 的 pipeline 去处理请求了, StandardContext 的 pipeline 里的 first 是
org.apache.catalina.authenticator.NonLoginAuthenticator，处理认证相关的，然后 basic 就是
org.apache.catalina.core.StandardContextValve, 这里的来看下

-
@Override
    public final void invoke(Request request, Response response)
        throws IOException, ServletException {

        // Disallow any direct access to resources under WEB-INF or META-INF
        MessageBytes requestPathMB = request.getRequestPathMB();
        if ((requestPathMB.startsWithIgnoreCase("/META-INF/", 0))
                || (requestPathMB.equalsIgnoreCase("/META-INF"))
                || (requestPathMB.startsWithIgnoreCase("/WEB-INF/", 0))
                || (requestPathMB.equalsIgnoreCase("/WEB-INF"))) {
            response.sendError(HttpServletResponse.SC_NOT_FOUND);
            return;
        }

        // Select the Wrapper to be used for this Request
        Wrapper wrapper = request.getWrapper();
        if (wrapper == null || wrapper.isUnavailable()) {
            response.sendError(HttpServletResponse.SC_NOT_FOUND);
            return;
        }

        // Acknowledge the request
        try {
            response.sendAcknowledgement(ContinueResponseTiming.IMMEDIATELY);
        } catch (IOException ioe) {
            container.getLogger().error(sm.getString(
                    "standardContextValve.acknowledgeException"), ioe);
            request.setAttribute(RequestDispatcher.ERROR_EXCEPTION, ioe);
            response.sendError(HttpServletResponse.SC_INTERNAL_SERVER_ERROR);
            return;
        }

        if (request.isAsyncSupported()) {
            request.setAsyncSupported(wrapper.getPipeline().isAsyncSupported());
        }
        wrapper.getPipeline().getFirst().invoke(request, response);
    }
-

会调用 wrapper 的 pipeline 去处理请求，这里也只有一个
org.apache.catalina.core.StandardWrapperValve
这部分的逻辑比较长，因为要串联后面的 filter 流程

-
@Override
    public final void invoke(Request request, Response response)
        throws IOException, ServletException {

        // Initialize local variables we may need
        boolean unavailable = false;
        Throwable throwable = null;
        // This should be a Request attribute...
        long t1=System.currentTimeMillis();
        requestCount.incrementAndGet();
        StandardWrapper wrapper = (StandardWrapper) getContainer();
        Servlet servlet = null;
        Context context = (Context) wrapper.getParent();

        // Check for the application being marked unavailable
        if (!context.getState().isAvailable()) {
            response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                           sm.getString("standardContext.isUnavailable"));
            unavailable = true;
        }

        // Check for the servlet being marked unavailable
        if (!unavailable && wrapper.isUnavailable()) {
            container.getLogger().info(sm.getString("standardWrapper.isUnavailable",
                    wrapper.getName()));
            long available = wrapper.getAvailable();
            if ((available > 0L) && (available < Long.MAX_VALUE)) {
                response.setDateHeader("Retry-After", available);
                response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                        sm.getString("standardWrapper.isUnavailable",
                                wrapper.getName()));
            } else if (available == Long.MAX_VALUE) {
                response.sendError(HttpServletResponse.SC_NOT_FOUND,
                        sm.getString("standardWrapper.notFound",
                                wrapper.getName()));
            }
            unavailable = true;
        }

        // Allocate a servlet instance to process this request
        try {
            if (!unavailable) {
                servlet = wrapper.allocate();
            }
        } catch (UnavailableException e) {
            container.getLogger().error(
                    sm.getString("standardWrapper.allocateException",
                            wrapper.getName()), e);
            long available = wrapper.getAvailable();
            if ((available > 0L) && (available < Long.MAX_VALUE)) {
                response.setDateHeader("Retry-After", available);
                response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                           sm.getString("standardWrapper.isUnavailable",
                                        wrapper.getName()));
            } else if (available == Long.MAX_VALUE) {
                response.sendError(HttpServletResponse.SC_NOT_FOUND,
                           sm.getString("standardWrapper.notFound",
                                        wrapper.getName()));
            }
        } catch (ServletException e) {
            container.getLogger().error(sm.getString("standardWrapper.allocateException",
                             wrapper.getName()), StandardWrapper.getRootCause(e));
            throwable = e;
            exception(request, response, e);
        } catch (Throwable e) {
            ExceptionUtils.handleThrowable(e);
            container.getLogger().error(sm.getString("standardWrapper.allocateException",
                             wrapper.getName()), e);
            throwable = e;
            exception(request, response, e);
            servlet = null;
        }

        MessageBytes requestPathMB = request.getRequestPathMB();
        DispatcherType dispatcherType = DispatcherType.REQUEST;
        if (request.getDispatcherType()==DispatcherType.ASYNC) {
            dispatcherType = DispatcherType.ASYNC;
        }
        request.setAttribute(Globals.DISPATCHER_TYPE_ATTR,dispatcherType);
        request.setAttribute(Globals.DISPATCHER_REQUEST_PATH_ATTR,
                requestPathMB);
        // Create the filter chain for this request
        ApplicationFilterChain filterChain =
                ApplicationFilterFactory.createFilterChain(request, wrapper, servlet);

        // Call the filter chain for this request
        // NOTE: This also calls the servlet's service() method
        Container container = this.container;
        try {
            if ((servlet != null) && (filterChain != null)) {
                // Swallow output if needed
                if (context.getSwallowOutput()) {
                    try {
                        SystemLogHandler.startCapture();
                        if (request.isAsyncDispatching()) {
                            request.getAsyncContextInternal().doInternalDispatch();
                        } else {
                            filterChain.doFilter(request.getRequest(),
                                    response.getResponse());
                        }
                    } finally {
                        String log = SystemLogHandler.stopCapture();
                        if (log != null && log.length() > 0) {
                            context.getLogger().info(log);
                        }
                    }
                } else {
                    if (request.isAsyncDispatching()) {
                        request.getAsyncContextInternal().doInternalDispatch();
                    } else {
                        filterChain.doFilter
                            (request.getRequest(), response.getResponse());
                    }
                }

            }
        } catch (ClientAbortException | CloseNowException e) {
            if (container.getLogger().isDebugEnabled()) {
                container.getLogger().debug(sm.getString(
                        "standardWrapper.serviceException", wrapper.getName(),
                        context.getName()), e);
            }
            throwable = e;
            exception(request, response, e);
        } catch (IOException e) {
            container.getLogger().error(sm.getString(
                    "standardWrapper.serviceException", wrapper.getName(),
                    context.getName()), e);
            throwable = e;
            exception(request, response, e);
        } catch (UnavailableException e) {
            container.getLogger().error(sm.getString(
                    "standardWrapper.serviceException", wrapper.getName(),
                    context.getName()), e);
            //            throwable = e;
            //            exception(request, response, e);
            wrapper.unavailable(e);
            long available = wrapper.getAvailable();
            if ((available > 0L) && (available < Long.MAX_VALUE)) {
                response.setDateHeader("Retry-After", available);
                response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                           sm.getString("standardWrapper.isUnavailable",
                                        wrapper.getName()));
            } else if (available == Long.MAX_VALUE) {
                response.sendError(HttpServletResponse.SC_NOT_FOUND,
                            sm.getString("standardWrapper.notFound",
                                        wrapper.getName()));
            }
            // Do not save exception in 'throwable', because we
            // do not want to do exception(request, response, e) processing
        } catch (ServletException e) {
            Throwable rootCause = StandardWrapper.getRootCause(e);
            if (!(rootCause instanceof ClientAbortException)) {
                container.getLogger().error(sm.getString(
                        "standardWrapper.serviceExceptionRoot",
                        wrapper.getName(), context.getName(), e.getMessage()),
                        rootCause);
            }
            throwable = e;
            exception(request, response, e);
        } catch (Throwable e) {
            ExceptionUtils.handleThrowable(e);
            container.getLogger().error(sm.getString(
                    "standardWrapper.serviceException", wrapper.getName(),
                    context.getName()), e);
            throwable = e;
            exception(request, response, e);
        } finally {
            // Release the filter chain (if any) for this request
            if (filterChain != null) {
                filterChain.release();
            }

            // Deallocate the allocated servlet instance
            try {
                if (servlet != null) {
                    wrapper.deallocate(servlet);
                }
            } catch (Throwable e) {
                ExceptionUtils.handleThrowable(e);
                container.getLogger().error(sm.getString("standardWrapper.deallocateException",
                                 wrapper.getName()), e);
                if (throwable == null) {
                    throwable = e;
                    exception(request, response, e);
                }
            }

            // If this servlet has been marked permanently unavailable,
            // unload it and release this instance
            try {
                if ((servlet != null) &&
                    (wrapper.getAvailable() == Long.MAX_VALUE)) {
                    wrapper.unload();
                }
            } catch (Throwable e) {
                ExceptionUtils.handleThrowable(e);
                container.getLogger().error(sm.getString("standardWrapper.unloadException",
                                 wrapper.getName()), e);
                if (throwable == null) {
                    exception(request, response, e);
                }
            }
            long t2=System.currentTimeMillis();

            long time=t2-t1;
            processingTime += time;
            if( time > maxTime) {
                maxTime=time;
            }
            if( time < minTime) {
                minTime=time;
            }
        }
    }
-

这里就会创建 ApplicationFilterChain 然后进行
filterChain.doFilter(request.getRequest(), response.getResponse());
doFilter 处理

+

而在这个 threadStart 里

+
protected void threadStart() {
    if (backgroundProcessorDelay > 0
            && (getState().isAvailable() || LifecycleState.STARTING_PREP.equals(getState()))
            && (backgroundProcessorFuture == null || backgroundProcessorFuture.isDone())) {
        if (backgroundProcessorFuture != null && backgroundProcessorFuture.isDone()) {
            // There was an error executing the scheduled task, get it and log it
            try {
                backgroundProcessorFuture.get();
            } catch (InterruptedException | ExecutionException e) {
                log.error(sm.getString("containerBase.backgroundProcess.error"), e);
            }
        }
        backgroundProcessorFuture = Container.getService(this).getServer().getUtilityExecutor()
                .scheduleWithFixedDelay(new ContainerBackgroundProcessor(),
                        backgroundProcessorDelay, backgroundProcessorDelay,
                        TimeUnit.SECONDS);
    }
}
+

就调用了线程池的 scheduleWithFixedDelay 方法提交了这个 ContainerBackgroundProcessor，
仔细看代码会发现，

+
public StandardEngine() {

    super();
    pipeline.setBasic(new StandardEngineValve());
    /* Set the jmvRoute using the system property jvmRoute */
    try {
        setJvmRoute(System.getProperty("jvmRoute"));
    } catch(Exception ex) {
        log.warn(sm.getString("standardEngine.jvmRouteFail"));
    }
    // By default, the engine will hold the reloading thread
    backgroundProcessorDelay = 10;

}
+

这个就不用开启后台热加载，而主要的热加载同学应该是
org.apache.catalina.core.StandardContext#backgroundProcess

+
public void backgroundProcess() {

        if (!getState().isAvailable()) {
            return;
        }

        Loader loader = getLoader();
        if (loader != null) {
            try {
                // 这里就用了 loader 的 backgroundProcess
                loader.backgroundProcess();
            } catch (Exception e) {
                log.warn(sm.getString(
                        "standardContext.backgroundProcess.loader", loader), e);
            }
        }
        Manager manager = getManager();
        if (manager != null) {
            try {
                manager.backgroundProcess();
            } catch (Exception e) {
                log.warn(sm.getString(
                        "standardContext.backgroundProcess.manager", manager),
                        e);
            }
        }
        WebResourceRoot resources = getResources();
        if (resources != null) {
            try {
                resources.backgroundProcess();
            } catch (Exception e) {
                log.warn(sm.getString(
                        "standardContext.backgroundProcess.resources",
                        resources), e);
            }
        }
        InstanceManager instanceManager = getInstanceManager();
        if (instanceManager != null) {
            try {
                instanceManager.backgroundProcess();
            } catch (Exception e) {
                log.warn(sm.getString(
                        "standardContext.backgroundProcess.instanceManager",
                        resources), e);
            }
        }
        super.backgroundProcess();
    }
+

loader 的后台处理就是

+
@Override
    public void backgroundProcess() {
        if (reloadable && modified()) {
            try {
                Thread.currentThread().setContextClassLoader
                    (WebappLoader.class.getClassLoader());
                if (context != null) {
                    context.reload();
                }
            } finally {
                if (context != null && context.getLoader() != null) {
                    Thread.currentThread().setContextClassLoader
                        (context.getLoader().getClassLoader());
                }
            }
        }
    }
+

然后又会回到 context 的 reload，也就是 StandardContext 的 reload

+
@Override
public synchronized void reload() {

    // Validate our current component state
    if (!getState().isAvailable()) {
        throw new IllegalStateException
            (sm.getString("standardContext.notStarted", getName()));
    }

    if(log.isInfoEnabled()) {
        log.info(sm.getString("standardContext.reloadingStarted",
                getName()));
    }

    // Stop accepting requests temporarily.
    setPaused(true);

    try {
        stop();
    } catch (LifecycleException e) {
        log.error(
            sm.getString("standardContext.stoppingContext", getName()), e);
    }

    try {
        start();
    } catch (LifecycleException e) {
        log.error(
            sm.getString("standardContext.startingContext", getName()), e);
    }

    setPaused(false);

    if(log.isInfoEnabled()) {
        log.info(sm.getString("standardContext.reloadingCompleted",
                getName()));
    }

}
+

这样就是线程池结合后台处理，还是有些复杂的。

]]> Java @@ -2402,21 +2323,16 @@ - ambari-summary - /2017/05/09/ambari-summary/ - 初识ambari

ambari是一个大数据平台的管理工具，包含了hadoop, yarn, hive, hbase, spark等大数据的基础架构和工具，简化了数据平台的搭建，之前只是在同事搭建好平台后的一些使用，这次有机会从头开始用ambari来搭建一个测试的数据平台，过程中也踩到不少坑，简单记录下。

-

简单过程

    -
  • 第一个坑
    在刚开始是按照官网的指南，用maven构建，因为GFW的原因，导致反复失败等待，也就是这个guide，因为对maven不熟悉导致有些按图索骥，浪费了很多时间，之后才知道可以直接加repo用yum安装，然而用yum安装马上就出现了第二个坑。
    • -
  • 第二个坑
    因为在线的repo还是因为网络原因很慢很慢，用proxychains勉强把ambari-server本身安装好了，ambari.repo将这个放进/etc/yum.repos.d/路径下，然后yum update && yum install ambari-server安装即可，如果有条件就用proxychains走下代理。
    • -
  • 第三步
    安装好ambari-server后先执行ambari-server setup做一些初始化设置，其中包含了JDK路径的设置，数据库设置，设置好就OK了，然后执行ambari-server start启动服务，这里有个小插曲，因为ambari-server涉及到这么多服务，所以管理控制监控之类的模块是必不可少的，这部分可以在ambari-server的web ui界面安装，也可以命令行提前安装，这部分被称为HDF Management Pack，运行ambari-server install-mpack \ --mpack=http://public-repo-1.hortonworks.com/HDF/centos7/2.x/updates/2.1.4.0/tars/hdf_ambari_mp/hdf-ambari-mpack-2.1.4.0-5.tar.gz \ --purge \ --verbose
    安装，当然这个压缩包可以下载之后指到本地路径安装，然后就可以重启ambari-server
    • -
+ Windows 莫名重启问题解决 + /2023/12/03/Windows-%E8%8E%AB%E5%90%8D%E9%87%8D%E5%90%AF%E9%97%AE%E9%A2%98%E8%A7%A3%E5%86%B3/ + 之前在 Windows 里用 vmware workstation 搭了个黑裙，然后硬盘直通，硬盘跑着倒还好，但是宿主机 Windows 隔一段时间就会重启，就去搜索了下，发现其实 Windows 里的事件查看器就有点像是 Linux 系统里的 dmesg 或者 journal 日志，然后根据重启的时间排查事件查看器里，Windows 日志 –> 系统，

然后可以在右侧 “筛选当前日志”里选择 eventlog 类型的，发现有这样一条事件
进程 C:\Windows\system32\svchost.exe (APP) 为用户 NT AUTHORITY\SYSTEM 开始计算机 APP 的 重新启动，原因如下: 操作系统: 恢复(计划内)
然后在网上搜索的时候发现有一些相关解答
可能的一种原因是系统在应用一些更新失败时，默认设置的策略是失败后重启，我们可以自己选择不重启，因为老是重启在我虚拟机里的黑裙没关机的情况下直接就断电重启了，还是可能会造成一些影响，
可以在资源管理器里右键计算机，选左侧的”高级系统设置” –> 然后在”高级” tab 下面有个”启动和故障恢复” –> “设置”
在弹出弹窗里将 “自动重新启动” 取消勾选

这样到目前还没继续发生过重启

]]> - data analysis + Windows + 小技巧 - hadoop - cluster + Windows @@ -2443,6 +2359,60 @@ c++ + + two sum + /2015/01/14/Two-Sum/ + problem

Given an array of integers, find two numbers such that they add up to a specific target number.

+

The function twoSum should return indices of the two numbers such that they add up to the target, where index1 must be less than index2. Please note that your returned answers (both index1 and index2) are not zero-based.

+ +

You may assume that each input would have exactly one solution.

+

Input: numbers={2, 7, 11, 15}, target=9
Output: index1=1, index2=2

+

code

struct Node
{
    int num, pos;
};
bool cmp(Node a, Node b)
{
    return a.num < b.num;
}
class Solution {
public:
    vector<int> twoSum(vector<int> &numbers, int target) {
        // Start typing your C/C++ solution below
        // DO NOT write int main() function
        vector<int> result;
        vector<Node> array;
        for (int i = 0; i < numbers.size(); i++)
        {
            Node temp;
            temp.num = numbers[i];
            temp.pos = i;
            array.push_back(temp);
        }

        sort(array.begin(), array.end(), cmp);
        for (int i = 0, j = array.size() - 1; i != j;)
        {
            int sum = array[i].num + array[j].num;
            if (sum == target)
            {
                if (array[i].pos < array[j].pos)
                {
                    result.push_back(array[i].pos + 1);
                    result.push_back(array[j].pos + 1);
                } else
                {
                    result.push_back(array[j].pos + 1);
                    result.push_back(array[i].pos + 1);
                }
                break;
            } else if (sum < target)
            {
                i++;
            } else if (sum > target)
            {
                j--;
            }
        }
        return result;
    }
};
+ +

Analysis

sort the array, then test from head and end, until catch the right answer

+]]> + + leetcode + + + leetcode + c++ + + + + Tomcat 系列篇四-介绍下 Valve 架构 + /2023/10/01/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%9B%9B-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Valve-%E6%9E%B6%E6%9E%84/ + valve 是 Tomcat 架构中比较重要的一个组成部分，
之前说到

+
connector.getService().getContainer().getPipeline().getFirst().invoke(
                        request, response);
+

这段代码是通过 CoyoteAdapter 将请求处理往 container 传，这里就有个 pipeline 机制，这个 pipeline 可以看一下接口

+
public interface Pipeline extends Contained {

    public Valve getBasic();

    public void setBasic(Valve valve);

    public void addValve(Valve valve);

    public Valve[] getValves();

    public void removeValve(Valve valve);

    public Valve getFirst();

    public boolean isAsyncSupported();

    public void findNonAsyncValves(Set<String> result);
}
+

这里可以往 pipeline 里添加 valve，然后看下 valve 的接口

+
public interface Valve {

    public Valve getNext();

    public void setNext(Valve valve);

    public void backgroundProcess();

    public void invoke(Request request, Response response)
        throws IOException, ServletException;


    public boolean isAsyncSupported();
}
+

这里主要看的就是 getNext 跟 setNext，就变成了一个有序的 pipeline，然后就是 invoke 方法，其实 pipeline 是由两部分组成，valve 在其中起到了前后衔接的重要作用，而且可以再 invoke 中进一步串联调用

图中我们可以看到，对于 container 这个 pipeline，是没设置 first 的，只有 basic，basic 就是个兜底的 valve，在 StandardPipeline 中的 getFirst 实现

+
@Override
public Valve getFirst() {
    if (first != null) {
        return first;
    }

    return basic;
}
+

取不到 first 就降级到 basic，也就是这里的 StandardEngineValve，
它的 invoke 我们来看下

+
@Override
public final void invoke(Request request, Response response)
    throws IOException, ServletException {

    // Select the Host to be used for this Request
    Host host = request.getHost();
    if (host == null) {
        // HTTP 0.9 or HTTP 1.0 request without a host when no default host
        // is defined.
        // Don't overwrite an existing error
        if (!response.isError()) {
            response.sendError(404);
        }
        return;
    }
    if (request.isAsyncSupported()) {
        request.setAsyncSupported(host.getPipeline().isAsyncSupported());
    }

    // Ask this Host to process this request
    host.getPipeline().getFirst().invoke(request, response);
}
+

比较简单，就是调用 host 中的 pipeline 里的第一个 valve 来处理

第一个是 org.apache.catalina.valves.ErrorReportValve，
这里处理的其实是先调用了 next

+
@Override
    public void invoke(Request request, Response response) throws IOException, ServletException {

        // Perform the request
        getNext().invoke(request, response);

        if (response.isCommitted()) {
            if (response.setErrorReported()) {
                // Error wasn't previously reported but we can't write an error
                // page because the response has already been committed.

                // See if IO is allowed
                AtomicBoolean ioAllowed = new AtomicBoolean(true);
                response.getCoyoteResponse().action(ActionCode.IS_IO_ALLOWED, ioAllowed);

                if (ioAllowed.get()) {
                    // I/O is currently still allowed. Flush any data that is
                    // still to be written to the client.
                    try {
                        response.flushBuffer();
                    } catch (Throwable t) {
                        ExceptionUtils.handleThrowable(t);
                    }
                    // Now close immediately to signal to the client that
                    // something went wrong
                    response.getCoyoteResponse().action(ActionCode.CLOSE_NOW,
                            request.getAttribute(RequestDispatcher.ERROR_EXCEPTION));
                }
            }
            return;
        }

        Throwable throwable = (Throwable) request.getAttribute(RequestDispatcher.ERROR_EXCEPTION);

        // If an async request is in progress and is not going to end once this
        // container thread finishes, do not process any error page here.
        if (request.isAsync() && !request.isAsyncCompleting()) {
            return;
        }

        if (throwable != null && !response.isError()) {
            // Make sure that the necessary methods have been called on the
            // response. (It is possible a component may just have set the
            // Throwable. Tomcat won't do that but other components might.)
            // These are safe to call at this point as we know that the response
            // has not been committed.
            response.reset();
            response.sendError(HttpServletResponse.SC_INTERNAL_SERVER_ERROR);
        }

        // One way or another, response.sendError() will have been called before
        // execution reaches this point and suspended the response. Need to
        // reverse that so this valve can write to the response.
        response.setSuspended(false);

        try {
            report(request, response, throwable);
        } catch (Throwable tt) {
            ExceptionUtils.handleThrowable(tt);
        }
    }
+

虽然是放在 first，实际是先调用 next 的 invoke，也就是 org.apache.catalina.core.StandardHostValve 的invoke 方法

+
@Override
    public final void invoke(Request request, Response response)
        throws IOException, ServletException {

        // Select the Context to be used for this Request
        Context context = request.getContext();
        if (context == null) {
            // Don't overwrite an existing error
            if (!response.isError()) {
                response.sendError(404);
            }
            return;
        }

        if (request.isAsyncSupported()) {
            request.setAsyncSupported(context.getPipeline().isAsyncSupported());
        }

        boolean asyncAtStart = request.isAsync();

        try {
            context.bind(Globals.IS_SECURITY_ENABLED, MY_CLASSLOADER);

            if (!asyncAtStart && !context.fireRequestInitEvent(request.getRequest())) {
                // Don't fire listeners during async processing (the listener
                // fired for the request that called startAsync()).
                // If a request init listener throws an exception, the request
                // is aborted.
                return;
            }

            // Ask this Context to process this request. Requests that are
            // already in error must have been routed here to check for
            // application defined error pages so DO NOT forward them to the the
            // application for processing.
            try {
                if (!response.isErrorReportRequired()) {
                    // 交给 context 去处理请求了
                    context.getPipeline().getFirst().invoke(request, response);
                }
            } catch (Throwable t) {
                ExceptionUtils.handleThrowable(t);
                container.getLogger().error("Exception Processing " + request.getRequestURI(), t);
                // If a new error occurred while trying to report a previous
                // error allow the original error to be reported.
                if (!response.isErrorReportRequired()) {
                    request.setAttribute(RequestDispatcher.ERROR_EXCEPTION, t);
                    throwable(request, response, t);
                }
            }

            // Now that the request/response pair is back under container
            // control lift the suspension so that the error handling can
            // complete and/or the container can flush any remaining data
            response.setSuspended(false);

            Throwable t = (Throwable) request.getAttribute(RequestDispatcher.ERROR_EXCEPTION);

            // Protect against NPEs if the context was destroyed during a
            // long running request.
            if (!context.getState().isAvailable()) {
                return;
            }

            // Look for (and render if found) an application level error page
            if (response.isErrorReportRequired()) {
                // If an error has occurred that prevents further I/O, don't waste time
                // producing an error report that will never be read
                AtomicBoolean result = new AtomicBoolean(false);
                response.getCoyoteResponse().action(ActionCode.IS_IO_ALLOWED, result);
                if (result.get()) {
                    if (t != null) {
                        throwable(request, response, t);
                    } else {
                        status(request, response);
                    }
                }
            }

            if (!request.isAsync() && !asyncAtStart) {
                context.fireRequestDestroyEvent(request.getRequest());
            }
        } finally {
            // Access a session (if present) to update last accessed time, based
            // on a strict interpretation of the specification
            if (ACCESS_SESSION) {
                request.getSession(false);
            }

            context.unbind(Globals.IS_SECURITY_ENABLED, MY_CLASSLOADER);
        }
    }
+

这里的往下就是调用 context 的 pipeline 去处理请求了, StandardContext 的 pipeline 里的 first 是
org.apache.catalina.authenticator.NonLoginAuthenticator，处理认证相关的，然后 basic 就是
org.apache.catalina.core.StandardContextValve, 这里的来看下

+
@Override
    public final void invoke(Request request, Response response)
        throws IOException, ServletException {

        // Disallow any direct access to resources under WEB-INF or META-INF
        MessageBytes requestPathMB = request.getRequestPathMB();
        if ((requestPathMB.startsWithIgnoreCase("/META-INF/", 0))
                || (requestPathMB.equalsIgnoreCase("/META-INF"))
                || (requestPathMB.startsWithIgnoreCase("/WEB-INF/", 0))
                || (requestPathMB.equalsIgnoreCase("/WEB-INF"))) {
            response.sendError(HttpServletResponse.SC_NOT_FOUND);
            return;
        }

        // Select the Wrapper to be used for this Request
        Wrapper wrapper = request.getWrapper();
        if (wrapper == null || wrapper.isUnavailable()) {
            response.sendError(HttpServletResponse.SC_NOT_FOUND);
            return;
        }

        // Acknowledge the request
        try {
            response.sendAcknowledgement(ContinueResponseTiming.IMMEDIATELY);
        } catch (IOException ioe) {
            container.getLogger().error(sm.getString(
                    "standardContextValve.acknowledgeException"), ioe);
            request.setAttribute(RequestDispatcher.ERROR_EXCEPTION, ioe);
            response.sendError(HttpServletResponse.SC_INTERNAL_SERVER_ERROR);
            return;
        }

        if (request.isAsyncSupported()) {
            request.setAsyncSupported(wrapper.getPipeline().isAsyncSupported());
        }
        wrapper.getPipeline().getFirst().invoke(request, response);
    }
+

会调用 wrapper 的 pipeline 去处理请求，这里也只有一个
org.apache.catalina.core.StandardWrapperValve
这部分的逻辑比较长，因为要串联后面的 filter 流程

+
@Override
    public final void invoke(Request request, Response response)
        throws IOException, ServletException {

        // Initialize local variables we may need
        boolean unavailable = false;
        Throwable throwable = null;
        // This should be a Request attribute...
        long t1=System.currentTimeMillis();
        requestCount.incrementAndGet();
        StandardWrapper wrapper = (StandardWrapper) getContainer();
        Servlet servlet = null;
        Context context = (Context) wrapper.getParent();

        // Check for the application being marked unavailable
        if (!context.getState().isAvailable()) {
            response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                           sm.getString("standardContext.isUnavailable"));
            unavailable = true;
        }

        // Check for the servlet being marked unavailable
        if (!unavailable && wrapper.isUnavailable()) {
            container.getLogger().info(sm.getString("standardWrapper.isUnavailable",
                    wrapper.getName()));
            long available = wrapper.getAvailable();
            if ((available > 0L) && (available < Long.MAX_VALUE)) {
                response.setDateHeader("Retry-After", available);
                response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                        sm.getString("standardWrapper.isUnavailable",
                                wrapper.getName()));
            } else if (available == Long.MAX_VALUE) {
                response.sendError(HttpServletResponse.SC_NOT_FOUND,
                        sm.getString("standardWrapper.notFound",
                                wrapper.getName()));
            }
            unavailable = true;
        }

        // Allocate a servlet instance to process this request
        try {
            if (!unavailable) {
                servlet = wrapper.allocate();
            }
        } catch (UnavailableException e) {
            container.getLogger().error(
                    sm.getString("standardWrapper.allocateException",
                            wrapper.getName()), e);
            long available = wrapper.getAvailable();
            if ((available > 0L) && (available < Long.MAX_VALUE)) {
                response.setDateHeader("Retry-After", available);
                response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                           sm.getString("standardWrapper.isUnavailable",
                                        wrapper.getName()));
            } else if (available == Long.MAX_VALUE) {
                response.sendError(HttpServletResponse.SC_NOT_FOUND,
                           sm.getString("standardWrapper.notFound",
                                        wrapper.getName()));
            }
        } catch (ServletException e) {
            container.getLogger().error(sm.getString("standardWrapper.allocateException",
                             wrapper.getName()), StandardWrapper.getRootCause(e));
            throwable = e;
            exception(request, response, e);
        } catch (Throwable e) {
            ExceptionUtils.handleThrowable(e);
            container.getLogger().error(sm.getString("standardWrapper.allocateException",
                             wrapper.getName()), e);
            throwable = e;
            exception(request, response, e);
            servlet = null;
        }

        MessageBytes requestPathMB = request.getRequestPathMB();
        DispatcherType dispatcherType = DispatcherType.REQUEST;
        if (request.getDispatcherType()==DispatcherType.ASYNC) {
            dispatcherType = DispatcherType.ASYNC;
        }
        request.setAttribute(Globals.DISPATCHER_TYPE_ATTR,dispatcherType);
        request.setAttribute(Globals.DISPATCHER_REQUEST_PATH_ATTR,
                requestPathMB);
        // Create the filter chain for this request
        ApplicationFilterChain filterChain =
                ApplicationFilterFactory.createFilterChain(request, wrapper, servlet);

        // Call the filter chain for this request
        // NOTE: This also calls the servlet's service() method
        Container container = this.container;
        try {
            if ((servlet != null) && (filterChain != null)) {
                // Swallow output if needed
                if (context.getSwallowOutput()) {
                    try {
                        SystemLogHandler.startCapture();
                        if (request.isAsyncDispatching()) {
                            request.getAsyncContextInternal().doInternalDispatch();
                        } else {
                            filterChain.doFilter(request.getRequest(),
                                    response.getResponse());
                        }
                    } finally {
                        String log = SystemLogHandler.stopCapture();
                        if (log != null && log.length() > 0) {
                            context.getLogger().info(log);
                        }
                    }
                } else {
                    if (request.isAsyncDispatching()) {
                        request.getAsyncContextInternal().doInternalDispatch();
                    } else {
                        filterChain.doFilter
                            (request.getRequest(), response.getResponse());
                    }
                }

            }
        } catch (ClientAbortException | CloseNowException e) {
            if (container.getLogger().isDebugEnabled()) {
                container.getLogger().debug(sm.getString(
                        "standardWrapper.serviceException", wrapper.getName(),
                        context.getName()), e);
            }
            throwable = e;
            exception(request, response, e);
        } catch (IOException e) {
            container.getLogger().error(sm.getString(
                    "standardWrapper.serviceException", wrapper.getName(),
                    context.getName()), e);
            throwable = e;
            exception(request, response, e);
        } catch (UnavailableException e) {
            container.getLogger().error(sm.getString(
                    "standardWrapper.serviceException", wrapper.getName(),
                    context.getName()), e);
            //            throwable = e;
            //            exception(request, response, e);
            wrapper.unavailable(e);
            long available = wrapper.getAvailable();
            if ((available > 0L) && (available < Long.MAX_VALUE)) {
                response.setDateHeader("Retry-After", available);
                response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE,
                           sm.getString("standardWrapper.isUnavailable",
                                        wrapper.getName()));
            } else if (available == Long.MAX_VALUE) {
                response.sendError(HttpServletResponse.SC_NOT_FOUND,
                            sm.getString("standardWrapper.notFound",
                                        wrapper.getName()));
            }
            // Do not save exception in 'throwable', because we
            // do not want to do exception(request, response, e) processing
        } catch (ServletException e) {
            Throwable rootCause = StandardWrapper.getRootCause(e);
            if (!(rootCause instanceof ClientAbortException)) {
                container.getLogger().error(sm.getString(
                        "standardWrapper.serviceExceptionRoot",
                        wrapper.getName(), context.getName(), e.getMessage()),
                        rootCause);
            }
            throwable = e;
            exception(request, response, e);
        } catch (Throwable e) {
            ExceptionUtils.handleThrowable(e);
            container.getLogger().error(sm.getString(
                    "standardWrapper.serviceException", wrapper.getName(),
                    context.getName()), e);
            throwable = e;
            exception(request, response, e);
        } finally {
            // Release the filter chain (if any) for this request
            if (filterChain != null) {
                filterChain.release();
            }

            // Deallocate the allocated servlet instance
            try {
                if (servlet != null) {
                    wrapper.deallocate(servlet);
                }
            } catch (Throwable e) {
                ExceptionUtils.handleThrowable(e);
                container.getLogger().error(sm.getString("standardWrapper.deallocateException",
                                 wrapper.getName()), e);
                if (throwable == null) {
                    throwable = e;
                    exception(request, response, e);
                }
            }

            // If this servlet has been marked permanently unavailable,
            // unload it and release this instance
            try {
                if ((servlet != null) &&
                    (wrapper.getAvailable() == Long.MAX_VALUE)) {
                    wrapper.unload();
                }
            } catch (Throwable e) {
                ExceptionUtils.handleThrowable(e);
                container.getLogger().error(sm.getString("standardWrapper.unloadException",
                                 wrapper.getName()), e);
                if (throwable == null) {
                    exception(request, response, e);
                }
            }
            long t2=System.currentTimeMillis();

            long time=t2-t1;
            processingTime += time;
            if( time > maxTime) {
                maxTime=time;
            }
            if( time < minTime) {
                minTime=time;
            }
        }
    }
+

这里就会创建 ApplicationFilterChain 然后进行
filterChain.doFilter(request.getRequest(), response.getResponse());
doFilter 处理

+]]> + + Java + SpringBoot + Tomcat + + + Java + Tomcat + SpringBoot + + dnsmasq的一个使用注意点 /2023/04/16/dnsmasq%E7%9A%84%E4%B8%80%E4%B8%AA%E4%BD%BF%E7%94%A8%E6%B3%A8%E6%84%8F%E7%82%B9/ @@ -2564,6 +2534,70 @@ cgroup + + ambari-summary + /2017/05/09/ambari-summary/ + 初识ambari

ambari是一个大数据平台的管理工具，包含了hadoop, yarn, hive, hbase, spark等大数据的基础架构和工具，简化了数据平台的搭建，之前只是在同事搭建好平台后的一些使用，这次有机会从头开始用ambari来搭建一个测试的数据平台，过程中也踩到不少坑，简单记录下。

+

简单过程

    +
  • 第一个坑
    在刚开始是按照官网的指南，用maven构建，因为GFW的原因，导致反复失败等待，也就是这个guide，因为对maven不熟悉导致有些按图索骥，浪费了很多时间，之后才知道可以直接加repo用yum安装，然而用yum安装马上就出现了第二个坑。
    • +
  • 第二个坑
    因为在线的repo还是因为网络原因很慢很慢，用proxychains勉强把ambari-server本身安装好了，ambari.repo将这个放进/etc/yum.repos.d/路径下，然后yum update && yum install ambari-server安装即可，如果有条件就用proxychains走下代理。
    • +
  • 第三步
    安装好ambari-server后先执行ambari-server setup做一些初始化设置，其中包含了JDK路径的设置，数据库设置，设置好就OK了，然后执行ambari-server start启动服务，这里有个小插曲，因为ambari-server涉及到这么多服务，所以管理控制监控之类的模块是必不可少的，这部分可以在ambari-server的web ui界面安装，也可以命令行提前安装，这部分被称为HDF Management Pack，运行ambari-server install-mpack \ --mpack=http://public-repo-1.hortonworks.com/HDF/centos7/2.x/updates/2.1.4.0/tars/hdf_ambari_mp/hdf-ambari-mpack-2.1.4.0-5.tar.gz \ --purge \ --verbose
    安装，当然这个压缩包可以下载之后指到本地路径安装，然后就可以重启ambari-server
    • +
+]]> + + data analysis + + + hadoop + cluster + + + + docker比一般多一点的初学者介绍四 + /2022/12/25/docker%E6%AF%94%E4%B8%80%E8%88%AC%E5%A4%9A%E4%B8%80%E7%82%B9%E7%9A%84%E5%88%9D%E5%AD%A6%E8%80%85%E4%BB%8B%E7%BB%8D%E5%9B%9B/ + 这次单独介绍下docker体系里非常重要的cgroup，docker对资源的限制也是基于cgroup构建的，
简单尝试
新建一个shell脚本

+
#!/bin/bash
while true;do
    echo "1"
done
+ +

直接执行的话就是单核100%的cpu

+

首先在cgroup下面建个目录

+
mkdir -p /sys/fs/cgroup/cpu/sxs_test/
+

查看目录下的文件

其中cpuacct开头的表示cpu相关的统计信息，
我们要配置cpu的额度，是在cpu.cfs_quota_us中

+
echo 2000 > /sys/fs/cgroup/cpu/sxs_test/cpu.cfs_quota_us
+

这样表示可以使用2%的cpu，总的配额是在cpu.cfs_period_us中

+

然后将当前进程输入到cgroup.procs,

+
echo $$ > /sys/fs/cgroup/cpu/sxs_test/cgroup.procs
+

这样就会自动继承当前进程产生的新进程
再次执行就可以看到cpu被限制了

+]]> + + Docker + + + Docker + + + + docker使用中发现的echo命令的一个小技巧及其他 + /2020/03/29/echo%E5%91%BD%E4%BB%A4%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E6%8A%80%E5%B7%A7/ + echo 实操技巧

最近做 docker 系列，会经常需要进到 docker 内部，如上一篇介绍的，这些镜像一般都有用 ubuntu 或者alpine 这样的 Linux 系统作为底包，如果构建镜像的时候没有替换源的话，因为特殊的网络原因，在内部想编辑下东西要安装个类似于 vim 这样的编辑器就会很慢很慢，像视频里 two thousand years later~ 而且如果在容器内部想改源配置的话也要编辑器，就陷入了一个鸡生蛋，跟蛋生鸡的死锁问题中，对于 linux 大神来说应该有一万种方法解决这个问题，对于我这个渣渣来说可能只想到了这个土方法，先 cp backup 一下 sources.list, 再 echo “xxx” > sources.list, 这里就碰到了一个问题，这个 sources.list 一般不止一行，直接 echo 的话就解析不了了，不过 echo 可以支持”\n”转义，就是加-e看一下解释和示例，我这里使用了 tldr ，可以用 npm install -g tldr 安装，也可以直接用man， 或者–help 来查看使用方式

+

查看镜像底包

还有一点也是在这个时候要安装 vim 之类的，得知道是什么镜像底包，如果是用 uname 指令，其实看到的是宿主机的系统，得用cat /etc/issue

+


这里稍稍记一下

+

寻找系统镜像源

目前国内系统源用得比较多的是阿里云源，不过这里也推荐清华源, 中科大源, 浙大源 这里不要脸的推荐下母校的源，不过还不是很完善，尽情期待下。

+]]> + + Linux + Docker + 命令 + echo + 发行版本 + + + linux + Docker + echo + uname + 发行版 + + docker比一般多一点的初学者介绍二 /2020/03/15/docker%E6%AF%94%E4%B8%80%E8%88%AC%E5%A4%9A%E4%B8%80%E7%82%B9%E7%9A%84%E5%88%9D%E5%AD%A6%E8%80%85%E4%BB%8B%E7%BB%8D%E4%BA%8C/ @@ -2615,55 +2649,6 @@ Dubbo - - docker比一般多一点的初学者介绍四 - /2022/12/25/docker%E6%AF%94%E4%B8%80%E8%88%AC%E5%A4%9A%E4%B8%80%E7%82%B9%E7%9A%84%E5%88%9D%E5%AD%A6%E8%80%85%E4%BB%8B%E7%BB%8D%E5%9B%9B/ - 这次单独介绍下docker体系里非常重要的cgroup，docker对资源的限制也是基于cgroup构建的，
简单尝试
新建一个shell脚本

-
#!/bin/bash
while true;do
    echo "1"
done
- -

直接执行的话就是单核100%的cpu

-

首先在cgroup下面建个目录

-
mkdir -p /sys/fs/cgroup/cpu/sxs_test/
-

查看目录下的文件

其中cpuacct开头的表示cpu相关的统计信息，
我们要配置cpu的额度，是在cpu.cfs_quota_us中

-
echo 2000 > /sys/fs/cgroup/cpu/sxs_test/cpu.cfs_quota_us
-

这样表示可以使用2%的cpu，总的配额是在cpu.cfs_period_us中

-

然后将当前进程输入到cgroup.procs,

-
echo $$ > /sys/fs/cgroup/cpu/sxs_test/cgroup.procs
-

这样就会自动继承当前进程产生的新进程
再次执行就可以看到cpu被限制了

-]]> - - Docker - - - Docker - - - - github 小技巧-更新 github host key - /2023/03/28/github-%E5%B0%8F%E6%8A%80%E5%B7%A7-%E6%9B%B4%E6%96%B0-github-host-key/ - 最近一次推送博客，发现报了个错推不上去，

-
WARNING: REMOTE HOST IDENTIFICATION HAS CHANGED!

IT IS POSSIBLE THAT SOMEONE IS DOING SOMETHING NASTY!
Someone could be eavesdropping on you right now (man-in-the-middle attack)!
It is also possible that a host key has just been changed.
-

错误信息是这样，有点奇怪也没干啥，网上一搜发现是We updated our RSA SSH host key
简单翻一下就是

-
-

在3月24日协调世界时大约05:00时，出于谨慎，我们更换了用于保护 GitHub.com 的 Git 操作的 RSA SSH 主机密钥。我们这样做是为了保护我们的用户免受任何对手模仿 GitHub 或通过 SSH 窃听他们的 Git 操作的机会。此密钥不授予对 GitHub 基础设施或客户数据的访问权限。此更改仅影响通过使用 RSA 的 SSH 进行的 Git 操作。GitHub.com 和 HTTPS Git 操作的网络流量不受影响。

-
-

要解决也比较简单就是重置下 host key，

-
-

Host Key是服务器用来证明自己身份的一个永久性的非对称密钥

-
-

使用

-
ssh-keygen -R github.com
-

然后在首次建立连接的时候同意下就可以了

-]]> - - ssh - 技巧 - - - ssh - 端口转发 - - gogs使用webhook部署react单页应用 /2020/02/22/gogs%E4%BD%BF%E7%94%A8webhook%E9%83%A8%E7%BD%B2react%E5%8D%95%E9%A1%B5%E5%BA%94%E7%94%A8/ @@ -2698,21 +2683,29 @@ - headscale 添加节点 - /2023/07/09/headscale-%E6%B7%BB%E5%8A%A0%E8%8A%82%E7%82%B9/ - 添加节点

添加节点非常简单，比如 app store 或者官网可以下载 mac 的安装包，

-

安装包直接下载可以在这里，下载安装完后还需要做一些处理，才能让 Tailscale 使用 Headscale 作为控制服务器。当然，Headscale 已经给我们提供了详细的操作步骤，你只需要在浏览器中打开 URL：http://<HEADSCALE_PUB_IP>:<HEADSCALE_PUB_PORT>/apple，记得端口替换成自己的，就会看到这样的说明页

-

image

-

然后对于像我这样自己下载的客户端安装包，也就是standalone client，就可以用下面的命令

-

defaults write io.tailscale.ipn.macsys ControlURL http://<HEADSCALE_PUB_IP>:<HEADSCALE_PUB_PORT> 类似于 Windows 客户端需要写入注册表，就是把控制端的地址改成了我们自己搭建的 headscale 的，设置完以后就打开 tailscale 客户端右键点击 login，就会弹出一个浏览器地址

-

image

-

按照这个里面的命令去 headscale 的机器上执行，注意要替换 namespace，对于最新的 headscale 已经把 namespace 废弃改成 user 了，这点要注意了，其他客户端也同理，现在还有个好消息，安卓和 iOS 客户端也已经都可以用了，后面可以在介绍下局域网怎么部分打通和自建 derper。

+ github 小技巧-更新 github host key + /2023/03/28/github-%E5%B0%8F%E6%8A%80%E5%B7%A7-%E6%9B%B4%E6%96%B0-github-host-key/ + 最近一次推送博客，发现报了个错推不上去，

+
WARNING: REMOTE HOST IDENTIFICATION HAS CHANGED!

IT IS POSSIBLE THAT SOMEONE IS DOING SOMETHING NASTY!
Someone could be eavesdropping on you right now (man-in-the-middle attack)!
It is also possible that a host key has just been changed.
+

错误信息是这样，有点奇怪也没干啥，网上一搜发现是We updated our RSA SSH host key
简单翻一下就是

+
+

在3月24日协调世界时大约05:00时，出于谨慎，我们更换了用于保护 GitHub.com 的 Git 操作的 RSA SSH 主机密钥。我们这样做是为了保护我们的用户免受任何对手模仿 GitHub 或通过 SSH 窃听他们的 Git 操作的机会。此密钥不授予对 GitHub 基础设施或客户数据的访问权限。此更改仅影响通过使用 RSA 的 SSH 进行的 Git 操作。GitHub.com 和 HTTPS Git 操作的网络流量不受影响。

+
+

要解决也比较简单就是重置下 host key，

+
+

Host Key是服务器用来证明自己身份的一个永久性的非对称密钥

+
+

使用

+
ssh-keygen -R github.com
+

然后在首次建立连接的时候同意下就可以了

]]> - headscale + ssh + 技巧 - headscale + ssh + 端口转发 @@ -2737,17 +2730,21 @@ - java 中发起 http 请求时证书问题解决记录 - /2023/07/29/java-%E4%B8%AD%E5%8F%91%E8%B5%B7-http-%E8%AF%B7%E6%B1%82%E6%97%B6%E8%AF%81%E4%B9%A6%E9%97%AE%E9%A2%98%E8%A7%A3%E5%86%B3%E8%AE%B0%E5%BD%95/ - 再一次环境部署是发现了个问题，就是在请求微信 https 请求的时候，出现了个错误
No appropriate protocol (protocol is disabled or cipher suites are inappropriate)
一开始以为是环境问题，从 oracle 的 jdk 换成了基于 openjdk 的底包，没有 javax 的关系，
完整的提示包含了 javax 的异常
java.lang.RuntimeException: javax.net.ssl.SSLHandshakeException: No appropriate protocol (protocol is disabled or cipher suites are inappropriate)
后面再看了下，是不是也可能是证书的问题，然后就去找了下是不是证书相关的，
可以看到在 /usr/lib/jvm/java-8-openjdk-amd64/jre/lib/security 路径下的 java.security 中
jdk.tls.disabledAlgorithms=SSLv3, TLSv1, TLSv1.1, RC4, DES, MD5withRSA,
而正好在我们代码里 createSocketFactory 的时候使用了 TLSv1 这个证书协议

-
SSLContext sslContext = SSLContext.getInstance("TLS");
sslContext.init(kmf.getKeyManagers(), null, new SecureRandom());
return new SSLConnectionSocketFactory(sslContext, new String[]{"TLSv1"}, null, new DefaultHostnameVerifier());
-

所以就有两种方案，一个是使用更新版本的 TLS 或者另一个就是使用比较久的 jdk，这也说明其实即使都是 jdk8 的，不同的小版本差异还是会有些影响，有的时候对于这些错误还是需要更深入地学习，不能一概而之认为就是 jdk 用的是 oracle 还是 openjdk 的，不同的错误可能就需要仔细确认原因所在。

+ headscale 添加节点 + /2023/07/09/headscale-%E6%B7%BB%E5%8A%A0%E8%8A%82%E7%82%B9/ + 添加节点

添加节点非常简单，比如 app store 或者官网可以下载 mac 的安装包，

+

安装包直接下载可以在这里，下载安装完后还需要做一些处理，才能让 Tailscale 使用 Headscale 作为控制服务器。当然，Headscale 已经给我们提供了详细的操作步骤，你只需要在浏览器中打开 URL：http://<HEADSCALE_PUB_IP>:<HEADSCALE_PUB_PORT>/apple，记得端口替换成自己的，就会看到这样的说明页

+

image

+

然后对于像我这样自己下载的客户端安装包，也就是standalone client，就可以用下面的命令

+

defaults write io.tailscale.ipn.macsys ControlURL http://<HEADSCALE_PUB_IP>:<HEADSCALE_PUB_PORT> 类似于 Windows 客户端需要写入注册表，就是把控制端的地址改成了我们自己搭建的 headscale 的，设置完以后就打开 tailscale 客户端右键点击 login，就会弹出一个浏览器地址

+

image

+

按照这个里面的命令去 headscale 的机器上执行，注意要替换 namespace，对于最新的 headscale 已经把 namespace 废弃改成 user 了，这点要注意了，其他客户端也同理，现在还有个好消息，安卓和 iOS 客户端也已经都可以用了，后面可以在介绍下局域网怎么部分打通和自建 derper。

]]> - java + headscale - java + headscale @@ -2780,6 +2777,20 @@ c++ + + java 中发起 http 请求时证书问题解决记录 + /2023/07/29/java-%E4%B8%AD%E5%8F%91%E8%B5%B7-http-%E8%AF%B7%E6%B1%82%E6%97%B6%E8%AF%81%E4%B9%A6%E9%97%AE%E9%A2%98%E8%A7%A3%E5%86%B3%E8%AE%B0%E5%BD%95/ + 再一次环境部署是发现了个问题，就是在请求微信 https 请求的时候，出现了个错误
No appropriate protocol (protocol is disabled or cipher suites are inappropriate)
一开始以为是环境问题，从 oracle 的 jdk 换成了基于 openjdk 的底包，没有 javax 的关系，
完整的提示包含了 javax 的异常
java.lang.RuntimeException: javax.net.ssl.SSLHandshakeException: No appropriate protocol (protocol is disabled or cipher suites are inappropriate)
后面再看了下，是不是也可能是证书的问题，然后就去找了下是不是证书相关的，
可以看到在 /usr/lib/jvm/java-8-openjdk-amd64/jre/lib/security 路径下的 java.security 中
jdk.tls.disabledAlgorithms=SSLv3, TLSv1, TLSv1.1, RC4, DES, MD5withRSA,
而正好在我们代码里 createSocketFactory 的时候使用了 TLSv1 这个证书协议

+
SSLContext sslContext = SSLContext.getInstance("TLS");
sslContext.init(kmf.getKeyManagers(), null, new SecureRandom());
return new SSLConnectionSocketFactory(sslContext, new String[]{"TLSv1"}, null, new DefaultHostnameVerifier());
+

所以就有两种方案，一个是使用更新版本的 TLS 或者另一个就是使用比较久的 jdk，这也说明其实即使都是 jdk8 的，不同的小版本差异还是会有些影响，有的时候对于这些错误还是需要更深入地学习，不能一概而之认为就是 jdk 用的是 oracle 还是 openjdk 的，不同的错误可能就需要仔细确认原因所在。

+]]> + + java + + + java + + minimum-size-subarray-sum-209 /2016/10/11/minimum-size-subarray-sum-209/ @@ -2796,6 +2807,19 @@ c++ + + C++ 指针使用中的一个小问题 + /2014/12/23/my-new-post/ + 在工作中碰到的一点C++指针上的一点小问题
+

在C++中，应该是从C语言就开始了，除了void型指针之外都是需要有分配对应的内存才可以使用，同时malloc与free成对使用，new与delete成对使用，否则造成内存泄漏。

+]]> + + C++ + + + 博客，文章 + + mybatis 的 foreach 使用的注意点 /2022/07/09/mybatis-%E7%9A%84-foreach-%E4%BD%BF%E7%94%A8%E7%9A%84%E6%B3%A8%E6%84%8F%E7%82%B9/ @@ -2819,66 +2843,6 @@ Mybatis - - C++ 指针使用中的一个小问题 - /2014/12/23/my-new-post/ - 在工作中碰到的一点C++指针上的一点小问题
-

在C++中，应该是从C语言就开始了，除了void型指针之外都是需要有分配对应的内存才可以使用，同时malloc与free成对使用，new与delete成对使用，否则造成内存泄漏。

-]]> - - C++ - - - 博客，文章 - - - - mybatis 的 $ 和 # 是有啥区别 - /2020/09/06/mybatis-%E7%9A%84-%E5%92%8C-%E6%98%AF%E6%9C%89%E5%95%A5%E5%8C%BA%E5%88%AB/ - 这个问题也是面试中常被问到的，就抽空来了解下这个，跳过一大段前面初始化的逻辑，
对于一条select * from t1 where id = #{id}这样的 sql，在初始化扫描 mapper 的xml文件的时候会根据是否是 dynamic 来判断生成 DynamicSqlSource 还是 RawSqlSource，这里它是一条 RawSqlSource，
在这里做了替换，将#{}替换成了?

前面说的是否 dynamic 就是在这里进行判断

-
// org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseScriptNode
public SqlSource parseScriptNode() {
    MixedSqlNode rootSqlNode = parseDynamicTags(context);
    SqlSource sqlSource;
    if (isDynamic) {
      sqlSource = new DynamicSqlSource(configuration, rootSqlNode);
    } else {
      sqlSource = new RawSqlSource(configuration, rootSqlNode, parameterType);
    }
    return sqlSource;
  }
// org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseDynamicTags
protected MixedSqlNode parseDynamicTags(XNode node) {
    List<SqlNode> contents = new ArrayList<>();
    NodeList children = node.getNode().getChildNodes();
    for (int i = 0; i < children.getLength(); i++) {
      XNode child = node.newXNode(children.item(i));
      if (child.getNode().getNodeType() == Node.CDATA_SECTION_NODE || child.getNode().getNodeType() == Node.TEXT_NODE) {
        String data = child.getStringBody("");
        TextSqlNode textSqlNode = new TextSqlNode(data);
        if (textSqlNode.isDynamic()) {
          contents.add(textSqlNode);
          isDynamic = true;
        } else {
          contents.add(new StaticTextSqlNode(data));
        }
      } else if (child.getNode().getNodeType() == Node.ELEMENT_NODE) { // issue #628
        String nodeName = child.getNode().getNodeName();
        NodeHandler handler = nodeHandlerMap.get(nodeName);
        if (handler == null) {
          throw new BuilderException("Unknown element <" + nodeName + "> in SQL statement.");
        }
        handler.handleNode(child, contents);
        isDynamic = true;
      }
    }
    return new MixedSqlNode(contents);
  }
// org.apache.ibatis.scripting.xmltags.TextSqlNode#isDynamic
  public boolean isDynamic() {
    DynamicCheckerTokenParser checker = new DynamicCheckerTokenParser();
    GenericTokenParser parser = createParser(checker);
    parser.parse(text);
    return checker.isDynamic();
  }
    private GenericTokenParser createParser(TokenHandler handler) {
    return new GenericTokenParser("${", "}", handler);
  }
-

可以看到其中一个条件就是是否有${}这种占位符，假如说上面的 sql 换成 ${}，那么可以看到它会在这里创建一个 dynamicSqlSource,

-
// org.apache.ibatis.scripting.xmltags.DynamicSqlSource
public class DynamicSqlSource implements SqlSource {

  private final Configuration configuration;
  private final SqlNode rootSqlNode;

  public DynamicSqlSource(Configuration configuration, SqlNode rootSqlNode) {
    this.configuration = configuration;
    this.rootSqlNode = rootSqlNode;
  }

  @Override
  public BoundSql getBoundSql(Object parameterObject) {
    DynamicContext context = new DynamicContext(configuration, parameterObject);
    rootSqlNode.apply(context);
    SqlSourceBuilder sqlSourceParser = new SqlSourceBuilder(configuration);
    Class<?> parameterType = parameterObject == null ? Object.class : parameterObject.getClass();
    SqlSource sqlSource = sqlSourceParser.parse(context.getSql(), parameterType, context.getBindings());
    BoundSql boundSql = sqlSource.getBoundSql(parameterObject);
    context.getBindings().forEach(boundSql::setAdditionalParameter);
    return boundSql;
  }

}

-

这里眼尖的同学可能就可以看出来了，RawSqlSource 在初始化的时候已经经过了 parse，把#{}替换成了?占位符，但是 DynamicSqlSource 并没有
再看这个图，我们发现在这的时候还没有进行替换
然后往里跟
好像是这里了

这里 rootSqlNode.apply 其实是一个对原来 sql 的解析结果的一个循环调用，不同类型的标签会构成不同的 node，像这里就是一个 textSqlNode

可以发现到这我们的 sql 已经被替换了，而且是直接作为 string 类型替换的，所以可以明白了这个问题所在，就是注入，不过细心的同学发现其实这里是有个

理论上还是可以做过滤的，不过好像现在没用起来。
我们前面可以发现对于#{}是在启动扫描 mapper的 xml 文件就替换成了 ?,然后是在什么时候变成实际的值的呢

发现到这的时候还是没有替换，其实说白了也就是 prepareStatement 那一套，

在这里进行替换，会拿到 org.apache.ibatis.mapping.ParameterMapping，然后进行替换，因为会带着类型信息，所以不用担心注入咯

-]]> - - Java - Mybatis - Mysql - Spring - Sql注入 - Mybatis - - - Java - Mysql - Mybatis - Sql注入 - - - - docker使用中发现的echo命令的一个小技巧及其他 - /2020/03/29/echo%E5%91%BD%E4%BB%A4%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E6%8A%80%E5%B7%A7/ - echo 实操技巧

最近做 docker 系列，会经常需要进到 docker 内部，如上一篇介绍的，这些镜像一般都有用 ubuntu 或者alpine 这样的 Linux 系统作为底包，如果构建镜像的时候没有替换源的话，因为特殊的网络原因，在内部想编辑下东西要安装个类似于 vim 这样的编辑器就会很慢很慢，像视频里 two thousand years later~ 而且如果在容器内部想改源配置的话也要编辑器，就陷入了一个鸡生蛋，跟蛋生鸡的死锁问题中，对于 linux 大神来说应该有一万种方法解决这个问题，对于我这个渣渣来说可能只想到了这个土方法，先 cp backup 一下 sources.list, 再 echo “xxx” > sources.list, 这里就碰到了一个问题，这个 sources.list 一般不止一行，直接 echo 的话就解析不了了，不过 echo 可以支持”\n”转义，就是加-e看一下解释和示例，我这里使用了 tldr ，可以用 npm install -g tldr 安装，也可以直接用man， 或者–help 来查看使用方式

-

查看镜像底包

还有一点也是在这个时候要安装 vim 之类的，得知道是什么镜像底包，如果是用 uname 指令，其实看到的是宿主机的系统，得用cat /etc/issue

-


这里稍稍记一下

-

寻找系统镜像源

目前国内系统源用得比较多的是阿里云源，不过这里也推荐清华源, 中科大源, 浙大源 这里不要脸的推荐下母校的源，不过还不是很完善，尽情期待下。

-]]> - - Linux - Docker - 命令 - echo - 发行版本 - - - linux - Docker - echo - uname - 发行版 - - mybatis 的缓存是怎么回事 /2020/10/03/mybatis-%E7%9A%84%E7%BC%93%E5%AD%98%E6%98%AF%E6%80%8E%E4%B9%88%E5%9B%9E%E4%BA%8B/ @@ -2912,8 +2876,8 @@ Java Mybatis - Spring Mybatis + Spring 缓存 Mybatis @@ -2924,6 +2888,117 @@ 缓存 + + mybatis系列-mybatis是如何初始化mapper的 + /2022/12/04/mybatis%E6%98%AF%E5%A6%82%E4%BD%95%E5%88%9D%E5%A7%8B%E5%8C%96mapper%E7%9A%84/ + 前一篇讲了mybatis的初始化使用，如果我第一次看到这个使用入门文档，比较会产生疑惑的是配置了mapper，怎么就能通过selectOne跟语句id就能执行sql了，那么第一个问题，就是mapper是怎么被解析的，存在哪里，怎么被拿出来的

+

添加解析mapper

org.apache.ibatis.session.SqlSessionFactoryBuilder#build(java.io.InputStream)
public SqlSessionFactory build(InputStream inputStream) {
  return build(inputStream, null, null);
}
+ +

通过读取mybatis-config.xml来构建SqlSessionFactory，

+
public SqlSessionFactory build(InputStream inputStream, String environment, Properties properties) {
  try {
    // 创建下xml的解析器
    XMLConfigBuilder parser = new XMLConfigBuilder(inputStream, environment, properties);
    // 进行解析，后再构建
    return build(parser.parse());
  } catch (Exception e) {
    throw ExceptionFactory.wrapException("Error building SqlSession.", e);
  } finally {
    ErrorContext.instance().reset();
    try {
       if (inputStream != null) {
         inputStream.close();
       }
    } catch (IOException e) {
      // Intentionally ignore. Prefer previous error.
    }
  }
+ +

创建XMLConfigBuilder

+
public XMLConfigBuilder(InputStream inputStream, String environment, Properties props) {
    // --------> 创建 XPathParser
  this(new XPathParser(inputStream, true, props, new XMLMapperEntityResolver()), environment, props);
}

public XPathParser(InputStream inputStream, boolean validation, Properties variables, EntityResolver entityResolver) {
    commonConstructor(validation, variables, entityResolver);
    this.document = createDocument(new InputSource(inputStream));
  }

private XMLConfigBuilder(XPathParser parser, String environment, Properties props) {
  super(new Configuration());
  ErrorContext.instance().resource("SQL Mapper Configuration");
  this.configuration.setVariables(props);
  this.parsed = false;
  this.environment = environment;
  this.parser = parser;
}
+ +

这里主要是创建了Builder包含了Parser
然后调用parse方法

+
public Configuration parse() {
  if (parsed) {
    throw new BuilderException("Each XMLConfigBuilder can only be used once.");
  }
  // 标记下是否已解析，但是这里是否有线程安全问题
  parsed = true;
  // --------> 解析配置
  parseConfiguration(parser.evalNode("/configuration"));
  return configuration;
}
+ +

实际的解析区分了各类标签

+
private void parseConfiguration(XNode root) {
  try {
    // issue #117 read properties first
    // 解析properties，这个不是spring自带的，需要额外配置，并且在config文件里应该放在最前
    propertiesElement(root.evalNode("properties"));
    Properties settings = settingsAsProperties(root.evalNode("settings"));
    loadCustomVfs(settings);
    loadCustomLogImpl(settings);
    typeAliasesElement(root.evalNode("typeAliases"));
    pluginElement(root.evalNode("plugins"));
    objectFactoryElement(root.evalNode("objectFactory"));
    objectWrapperFactoryElement(root.evalNode("objectWrapperFactory"));
    reflectorFactoryElement(root.evalNode("reflectorFactory"));
    settingsElement(settings);
    // read it after objectFactory and objectWrapperFactory issue #631
    environmentsElement(root.evalNode("environments"));
    databaseIdProviderElement(root.evalNode("databaseIdProvider"));
    typeHandlerElement(root.evalNode("typeHandlers"));
    // ----------> 我们需要关注的是mapper的处理
    mapperElement(root.evalNode("mappers"));
  } catch (Exception e) {
    throw new BuilderException("Error parsing SQL Mapper Configuration. Cause: " + e, e);
  }
}
+ +

然后就是调用到mapperElement方法了

+
private void mapperElement(XNode parent) throws Exception {
  if (parent != null) {
    for (XNode child : parent.getChildren()) {
      if ("package".equals(child.getName())) {
        String mapperPackage = child.getStringAttribute("name");
        configuration.addMappers(mapperPackage);
      } else {
        String resource = child.getStringAttribute("resource");
        String url = child.getStringAttribute("url");
        String mapperClass = child.getStringAttribute("class");
        if (resource != null && url == null && mapperClass == null) {
          ErrorContext.instance().resource(resource);
          try(InputStream inputStream = Resources.getResourceAsStream(resource)) {
            XMLMapperBuilder mapperParser = new XMLMapperBuilder(inputStream, configuration, resource, configuration.getSqlFragments());
            // --------> 我们这没有指定package，所以是走到这
            mapperParser.parse();
          }
        } else if (resource == null && url != null && mapperClass == null) {
          ErrorContext.instance().resource(url);
          try(InputStream inputStream = Resources.getUrlAsStream(url)){
            XMLMapperBuilder mapperParser = new XMLMapperBuilder(inputStream, configuration, url, configuration.getSqlFragments());
            mapperParser.parse();
          }
        } else if (resource == null && url == null && mapperClass != null) {
          Class<?> mapperInterface = Resources.classForName(mapperClass);
          configuration.addMapper(mapperInterface);
        } else {
          throw new BuilderException("A mapper element may only specify a url, resource or class, but not more than one.");
        }
      }
    }
  }
}
+ +

核心就在这个parse()方法

+
public void parse() {
  if (!configuration.isResourceLoaded(resource)) {
    // -------> 然后就是走到这里,配置xml的mapper节点的内容
    configurationElement(parser.evalNode("/mapper"));
    configuration.addLoadedResource(resource);
    bindMapperForNamespace();
  }

  parsePendingResultMaps();
  parsePendingCacheRefs();
  parsePendingStatements();
}
+ +

具体的处理逻辑

+
private void configurationElement(XNode context) {
  try {
    String namespace = context.getStringAttribute("namespace");
    if (namespace == null || namespace.isEmpty()) {
      throw new BuilderException("Mapper's namespace cannot be empty");
    }
    builderAssistant.setCurrentNamespace(namespace);
    cacheRefElement(context.evalNode("cache-ref"));
    cacheElement(context.evalNode("cache"));
    parameterMapElement(context.evalNodes("/mapper/parameterMap"));
    resultMapElements(context.evalNodes("/mapper/resultMap"));
    sqlElement(context.evalNodes("/mapper/sql"));
    // ------->  走到这，从上下文构建statement
    buildStatementFromContext(context.evalNodes("select|insert|update|delete"));
  } catch (Exception e) {
    throw new BuilderException("Error parsing Mapper XML. The XML location is '" + resource + "'. Cause: " + e, e);
  }
}
+ +

具体代码在这，从上下文构建statement，只不过区分了下databaseId

+
private void buildStatementFromContext(List<XNode> list) {
  if (configuration.getDatabaseId() != null) {
    buildStatementFromContext(list, configuration.getDatabaseId());
  }
  // -----> 判断databaseId
  buildStatementFromContext(list, null);
}
+ +

判断下databaseId

+
private void buildStatementFromContext(List<XNode> list, String requiredDatabaseId) {
  for (XNode context : list) {
    final XMLStatementBuilder statementParser = new XMLStatementBuilder(configuration, builderAssistant, context, requiredDatabaseId);
    try {
      // -------> 解析statement节点
      statementParser.parseStatementNode();
    } catch (IncompleteElementException e) {
      configuration.addIncompleteStatement(statementParser);
    }
  }
}
+ +

接下来就是真正处理的xml语句内容的，各个节点的信息内容

+
public void parseStatementNode() {
  String id = context.getStringAttribute("id");
  String databaseId = context.getStringAttribute("databaseId");

  if (!databaseIdMatchesCurrent(id, databaseId, this.requiredDatabaseId)) {
    return;
  }

  String nodeName = context.getNode().getNodeName();
  SqlCommandType sqlCommandType = SqlCommandType.valueOf(nodeName.toUpperCase(Locale.ENGLISH));
  boolean isSelect = sqlCommandType == SqlCommandType.SELECT;
  boolean flushCache = context.getBooleanAttribute("flushCache", !isSelect);
  boolean useCache = context.getBooleanAttribute("useCache", isSelect);
  boolean resultOrdered = context.getBooleanAttribute("resultOrdered", false);

  // Include Fragments before parsing
  XMLIncludeTransformer includeParser = new XMLIncludeTransformer(configuration, builderAssistant);
  includeParser.applyIncludes(context.getNode());

  String parameterType = context.getStringAttribute("parameterType");
  Class<?> parameterTypeClass = resolveClass(parameterType);

  String lang = context.getStringAttribute("lang");
  LanguageDriver langDriver = getLanguageDriver(lang);

  // Parse selectKey after includes and remove them.
  processSelectKeyNodes(id, parameterTypeClass, langDriver);

  // Parse the SQL (pre: <selectKey> and <include> were parsed and removed)
  KeyGenerator keyGenerator;
  String keyStatementId = id + SelectKeyGenerator.SELECT_KEY_SUFFIX;
  keyStatementId = builderAssistant.applyCurrentNamespace(keyStatementId, true);
  if (configuration.hasKeyGenerator(keyStatementId)) {
    keyGenerator = configuration.getKeyGenerator(keyStatementId);
  } else {
    keyGenerator = context.getBooleanAttribute("useGeneratedKeys",
        configuration.isUseGeneratedKeys() && SqlCommandType.INSERT.equals(sqlCommandType))
        ? Jdbc3KeyGenerator.INSTANCE : NoKeyGenerator.INSTANCE;
  }

  // 语句的主要参数解析
  SqlSource sqlSource = langDriver.createSqlSource(configuration, context, parameterTypeClass);
  StatementType statementType = StatementType.valueOf(context.getStringAttribute("statementType", StatementType.PREPARED.toString()));
  Integer fetchSize = context.getIntAttribute("fetchSize");
  Integer timeout = context.getIntAttribute("timeout");
  String parameterMap = context.getStringAttribute("parameterMap");
  String resultType = context.getStringAttribute("resultType");
  Class<?> resultTypeClass = resolveClass(resultType);
  String resultMap = context.getStringAttribute("resultMap");
  String resultSetType = context.getStringAttribute("resultSetType");
  ResultSetType resultSetTypeEnum = resolveResultSetType(resultSetType);
  if (resultSetTypeEnum == null) {
    resultSetTypeEnum = configuration.getDefaultResultSetType();
  }
  String keyProperty = context.getStringAttribute("keyProperty");
  String keyColumn = context.getStringAttribute("keyColumn");
  String resultSets = context.getStringAttribute("resultSets");

  // --------> 添加映射的statement
  builderAssistant.addMappedStatement(id, sqlSource, statementType, sqlCommandType,
      fetchSize, timeout, parameterMap, parameterTypeClass, resultMap, resultTypeClass,
      resultSetTypeEnum, flushCache, useCache, resultOrdered,
      keyGenerator, keyProperty, keyColumn, databaseId, langDriver, resultSets);
}
+ + +

添加的逻辑具体可以看下

+
public MappedStatement addMappedStatement(
    String id,
    SqlSource sqlSource,
    StatementType statementType,
    SqlCommandType sqlCommandType,
    Integer fetchSize,
    Integer timeout,
    String parameterMap,
    Class<?> parameterType,
    String resultMap,
    Class<?> resultType,
    ResultSetType resultSetType,
    boolean flushCache,
    boolean useCache,
    boolean resultOrdered,
    KeyGenerator keyGenerator,
    String keyProperty,
    String keyColumn,
    String databaseId,
    LanguageDriver lang,
    String resultSets) {

  if (unresolvedCacheRef) {
    throw new IncompleteElementException("Cache-ref not yet resolved");
  }

  id = applyCurrentNamespace(id, false);
  boolean isSelect = sqlCommandType == SqlCommandType.SELECT;

  MappedStatement.Builder statementBuilder = new MappedStatement.Builder(configuration, id, sqlSource, sqlCommandType)
      .resource(resource)
      .fetchSize(fetchSize)
      .timeout(timeout)
      .statementType(statementType)
      .keyGenerator(keyGenerator)
      .keyProperty(keyProperty)
      .keyColumn(keyColumn)
      .databaseId(databaseId)
      .lang(lang)
      .resultOrdered(resultOrdered)
      .resultSets(resultSets)
      .resultMaps(getStatementResultMaps(resultMap, resultType, id))
      .resultSetType(resultSetType)
      .flushCacheRequired(valueOrDefault(flushCache, !isSelect))
      .useCache(valueOrDefault(useCache, isSelect))
      .cache(currentCache);

  ParameterMap statementParameterMap = getStatementParameterMap(parameterMap, parameterType, id);
  if (statementParameterMap != null) {
    statementBuilder.parameterMap(statementParameterMap);
  }

  MappedStatement statement = statementBuilder.build();
  // ------>  正好是这里在configuration中添加了映射好的statement
  configuration.addMappedStatement(statement);
  return statement;
}
+ +

而里面就是往map里添加

+
public void addMappedStatement(MappedStatement ms) {
  mappedStatements.put(ms.getId(), ms);
}
+ +

获取mapper

StudentDO studentDO = session.selectOne("com.nicksxs.mybatisdemo.StudentMapper.selectStudent", 1);
+ +

就是调用了 org.apache.ibatis.session.defaults.DefaultSqlSession#selectOne(java.lang.String, java.lang.Object)

+
public <T> T selectOne(String statement, Object parameter) {
  // Popular vote was to return null on 0 results and throw exception on too many.
  List<T> list = this.selectList(statement, parameter);
  if (list.size() == 1) {
    return list.get(0);
  } else if (list.size() > 1) {
    throw new TooManyResultsException("Expected one result (or null) to be returned by selectOne(), but found: " + list.size());
  } else {
    return null;
  }
}
+ +

调用实际的实现方法

+
public <E> List<E> selectList(String statement, Object parameter) {
  return this.selectList(statement, parameter, RowBounds.DEFAULT);
}
+ +

这里还有一层

+
public <E> List<E> selectList(String statement, Object parameter, RowBounds rowBounds) {
  return selectList(statement, parameter, rowBounds, Executor.NO_RESULT_HANDLER);
}
+ + +

根本的就是从configuration里获取了mappedStatement

+
private <E> List<E> selectList(String statement, Object parameter, RowBounds rowBounds, ResultHandler handler) {
  try {
    // 这里进行了获取
    MappedStatement ms = configuration.getMappedStatement(statement);
    return executor.query(ms, wrapCollection(parameter), rowBounds, handler);
  } catch (Exception e) {
    throw ExceptionFactory.wrapException("Error querying database.  Cause: " + e, e);
  } finally {
    ErrorContext.instance().reset();
  }
}
+]]> + + Java + Mybatis + + + Java + Mysql + Mybatis + + + + mybatis系列-connection连接池解析 + /2023/02/19/mybatis%E7%B3%BB%E5%88%97-connection%E8%BF%9E%E6%8E%A5%E6%B1%A0%E8%A7%A3%E6%9E%90/ + 连接池主要是两个逻辑，首先是获取连接的逻辑，结合代码来讲一讲

+
private PooledConnection popConnection(String username, String password) throws SQLException {
    boolean countedWait = false;
    PooledConnection conn = null;
    long t = System.currentTimeMillis();
    int localBadConnectionCount = 0;

    // 循环获取连接
    while (conn == null) {
      // 加锁
      lock.lock();
      try {
        // 如果闲置的连接列表不为空
        if (!state.idleConnections.isEmpty()) {
          // Pool has available connection
          // 连接池有可用的连接
          conn = state.idleConnections.remove(0);
          if (log.isDebugEnabled()) {
            log.debug("Checked out connection " + conn.getRealHashCode() + " from pool.");
          }
        } else {
          // Pool does not have available connection
          // 进入这个分支表示没有空闲连接，但是活跃连接数还没达到最大活跃连接数上限，那么这时候就可以创建一个新连接
          if (state.activeConnections.size() < poolMaximumActiveConnections) {
            // Can create new connection
            // 这里创建连接我们之前讲过，
            conn = new PooledConnection(dataSource.getConnection(), this);
            if (log.isDebugEnabled()) {
              log.debug("Created connection " + conn.getRealHashCode() + ".");
            }
          } else {
            // Cannot create new connection
            // 进到这个分支了就表示没法创建新连接了，那么怎么办呢，这里引入了一个 poolMaximumCheckoutTime，这代表了我去控制连接一次被使用的最长时间，如果超过这个时间了，我就要去关闭失效它
            PooledConnection oldestActiveConnection = state.activeConnections.get(0);
            long longestCheckoutTime = oldestActiveConnection.getCheckoutTime();
            if (longestCheckoutTime > poolMaximumCheckoutTime) {
              // Can claim overdue connection
              // 所有超时连接从池中被借出的次数+1
              state.claimedOverdueConnectionCount++;
              // 所有超时连接从池中被借出并归还的时间总和 + 当前连接借出时间
              state.accumulatedCheckoutTimeOfOverdueConnections += longestCheckoutTime;
              // 所有连接从池中被借出并归还的时间总和 + 当前连接借出时间
              state.accumulatedCheckoutTime += longestCheckoutTime;
              // 从活跃连接数中移除此连接
              state.activeConnections.remove(oldestActiveConnection);
              // 如果该连接不是自动提交的，则尝试回滚
              if (!oldestActiveConnection.getRealConnection().getAutoCommit()) {
                try {
                  oldestActiveConnection.getRealConnection().rollback();
                } catch (SQLException e) {
                  /*
                     Just log a message for debug and continue to execute the following
                     statement like nothing happened.
                     Wrap the bad connection with a new PooledConnection, this will help
                     to not interrupt current executing thread and give current thread a
                     chance to join the next competition for another valid/good database
                     connection. At the end of this loop, bad {@link @conn} will be set as null.
                   */
                  log.debug("Bad connection. Could not roll back");
                }
              }
              // 用此连接的真实连接再创建一个连接，并设置时间
              conn = new PooledConnection(oldestActiveConnection.getRealConnection(), this);
              conn.setCreatedTimestamp(oldestActiveConnection.getCreatedTimestamp());
              conn.setLastUsedTimestamp(oldestActiveConnection.getLastUsedTimestamp());
              oldestActiveConnection.invalidate();
              if (log.isDebugEnabled()) {
                log.debug("Claimed overdue connection " + conn.getRealHashCode() + ".");
              }
            } else {
              // Must wait
              // 这样还是获取不到连接就只能等待了
              try {
                // 标记状态，然后把等待计数+1
                if (!countedWait) {
                  state.hadToWaitCount++;
                  countedWait = true;
                }
                if (log.isDebugEnabled()) {
                  log.debug("Waiting as long as " + poolTimeToWait + " milliseconds for connection.");
                }
                long wt = System.currentTimeMillis();
                // 等待 poolTimeToWait 时间
                condition.await(poolTimeToWait, TimeUnit.MILLISECONDS);
                // 记录等待时间
                state.accumulatedWaitTime += System.currentTimeMillis() - wt;
              } catch (InterruptedException e) {
                // set interrupt flag
                Thread.currentThread().interrupt();
                break;
              }
            }
          }
        }
        // 如果连接不为空
        if (conn != null) {
          // ping to server and check the connection is valid or not
          // 判断是否有效
          if (conn.isValid()) {
            if (!conn.getRealConnection().getAutoCommit()) {
              // 回滚未提交的
              conn.getRealConnection().rollback();
            }
            conn.setConnectionTypeCode(assembleConnectionTypeCode(dataSource.getUrl(), username, password));
            // 设置时间
            conn.setCheckoutTimestamp(System.currentTimeMillis());
            conn.setLastUsedTimestamp(System.currentTimeMillis());
            // 添加进活跃连接
            state.activeConnections.add(conn);
            state.requestCount++;
            state.accumulatedRequestTime += System.currentTimeMillis() - t;
          } else {
            if (log.isDebugEnabled()) {
              log.debug("A bad connection (" + conn.getRealHashCode() + ") was returned from the pool, getting another connection.");
            }
            // 连接无效，坏连接+1
            state.badConnectionCount++;
            localBadConnectionCount++;
            conn = null;
            // 如果坏连接已经超过了容忍上限，就抛异常
            if (localBadConnectionCount > (poolMaximumIdleConnections + poolMaximumLocalBadConnectionTolerance)) {
              if (log.isDebugEnabled()) {
                log.debug("PooledDataSource: Could not get a good connection to the database.");
              }
              throw new SQLException("PooledDataSource: Could not get a good connection to the database.");
            }
          }
        }
      } finally {
        // 释放锁
        lock.unlock();
      }

    }

    if (conn == null) {
      // 连接仍为空
      if (log.isDebugEnabled()) {
        log.debug("PooledDataSource: Unknown severe error condition.  The connection pool returned a null connection.");
      }
      // 抛出异常
      throw new SQLException("PooledDataSource: Unknown severe error condition.  The connection pool returned a null connection.");
    }
    // fanhui 
    return conn;
  }
+ +

然后是还回连接

+
protected void pushConnection(PooledConnection conn) throws SQLException {
    // 加锁
    lock.lock();
    try {
      // 从活跃连接中移除当前连接
      state.activeConnections.remove(conn);
      if (conn.isValid()) {
        // 当前的空闲连接数小于连接池中允许的最大空闲连接数
        if (state.idleConnections.size() < poolMaximumIdleConnections && conn.getConnectionTypeCode() == expectedConnectionTypeCode) {
          // 记录借出时间
          state.accumulatedCheckoutTime += conn.getCheckoutTime();
          if (!conn.getRealConnection().getAutoCommit()) {
            // 同样是做回滚
            conn.getRealConnection().rollback();
          }
          // 新建一个连接
          PooledConnection newConn = new PooledConnection(conn.getRealConnection(), this);
          // 加入到空闲连接列表中
          state.idleConnections.add(newConn);
          newConn.setCreatedTimestamp(conn.getCreatedTimestamp());
          newConn.setLastUsedTimestamp(conn.getLastUsedTimestamp());
          // 原连接失效
          conn.invalidate();
          if (log.isDebugEnabled()) {
            log.debug("Returned connection " + newConn.getRealHashCode() + " to pool.");
          }
          // 提醒前面等待的
          condition.signal();
        } else {
          // 上面是相同的，就是这里是空闲连接数已经超过上限
          state.accumulatedCheckoutTime += conn.getCheckoutTime();
          if (!conn.getRealConnection().getAutoCommit()) {
            conn.getRealConnection().rollback();
          }
          conn.getRealConnection().close();
          if (log.isDebugEnabled()) {
            log.debug("Closed connection " + conn.getRealHashCode() + ".");
          }
          conn.invalidate();
        }
      } else {
        if (log.isDebugEnabled()) {
          log.debug("A bad connection (" + conn.getRealHashCode() + ") attempted to return to the pool, discarding connection.");
        }
        state.badConnectionCount++;
      }
    } finally {
      lock.unlock();
    }
  }
+]]> + + Java + Mybatis + + + Java + Mysql + Mybatis + + + + mybatis 的 $ 和 # 是有啥区别 + /2020/09/06/mybatis-%E7%9A%84-%E5%92%8C-%E6%98%AF%E6%9C%89%E5%95%A5%E5%8C%BA%E5%88%AB/ + 这个问题也是面试中常被问到的，就抽空来了解下这个，跳过一大段前面初始化的逻辑，
对于一条select * from t1 where id = #{id}这样的 sql，在初始化扫描 mapper 的xml文件的时候会根据是否是 dynamic 来判断生成 DynamicSqlSource 还是 RawSqlSource，这里它是一条 RawSqlSource，
在这里做了替换，将#{}替换成了?

前面说的是否 dynamic 就是在这里进行判断

+
// org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseScriptNode
public SqlSource parseScriptNode() {
    MixedSqlNode rootSqlNode = parseDynamicTags(context);
    SqlSource sqlSource;
    if (isDynamic) {
      sqlSource = new DynamicSqlSource(configuration, rootSqlNode);
    } else {
      sqlSource = new RawSqlSource(configuration, rootSqlNode, parameterType);
    }
    return sqlSource;
  }
// org.apache.ibatis.scripting.xmltags.XMLScriptBuilder#parseDynamicTags
protected MixedSqlNode parseDynamicTags(XNode node) {
    List<SqlNode> contents = new ArrayList<>();
    NodeList children = node.getNode().getChildNodes();
    for (int i = 0; i < children.getLength(); i++) {
      XNode child = node.newXNode(children.item(i));
      if (child.getNode().getNodeType() == Node.CDATA_SECTION_NODE || child.getNode().getNodeType() == Node.TEXT_NODE) {
        String data = child.getStringBody("");
        TextSqlNode textSqlNode = new TextSqlNode(data);
        if (textSqlNode.isDynamic()) {
          contents.add(textSqlNode);
          isDynamic = true;
        } else {
          contents.add(new StaticTextSqlNode(data));
        }
      } else if (child.getNode().getNodeType() == Node.ELEMENT_NODE) { // issue #628
        String nodeName = child.getNode().getNodeName();
        NodeHandler handler = nodeHandlerMap.get(nodeName);
        if (handler == null) {
          throw new BuilderException("Unknown element <" + nodeName + "> in SQL statement.");
        }
        handler.handleNode(child, contents);
        isDynamic = true;
      }
    }
    return new MixedSqlNode(contents);
  }
// org.apache.ibatis.scripting.xmltags.TextSqlNode#isDynamic
  public boolean isDynamic() {
    DynamicCheckerTokenParser checker = new DynamicCheckerTokenParser();
    GenericTokenParser parser = createParser(checker);
    parser.parse(text);
    return checker.isDynamic();
  }
    private GenericTokenParser createParser(TokenHandler handler) {
    return new GenericTokenParser("${", "}", handler);
  }
+

可以看到其中一个条件就是是否有${}这种占位符，假如说上面的 sql 换成 ${}，那么可以看到它会在这里创建一个 dynamicSqlSource,

+
// org.apache.ibatis.scripting.xmltags.DynamicSqlSource
public class DynamicSqlSource implements SqlSource {

  private final Configuration configuration;
  private final SqlNode rootSqlNode;

  public DynamicSqlSource(Configuration configuration, SqlNode rootSqlNode) {
    this.configuration = configuration;
    this.rootSqlNode = rootSqlNode;
  }

  @Override
  public BoundSql getBoundSql(Object parameterObject) {
    DynamicContext context = new DynamicContext(configuration, parameterObject);
    rootSqlNode.apply(context);
    SqlSourceBuilder sqlSourceParser = new SqlSourceBuilder(configuration);
    Class<?> parameterType = parameterObject == null ? Object.class : parameterObject.getClass();
    SqlSource sqlSource = sqlSourceParser.parse(context.getSql(), parameterType, context.getBindings());
    BoundSql boundSql = sqlSource.getBoundSql(parameterObject);
    context.getBindings().forEach(boundSql::setAdditionalParameter);
    return boundSql;
  }

}

+

这里眼尖的同学可能就可以看出来了，RawSqlSource 在初始化的时候已经经过了 parse，把#{}替换成了?占位符，但是 DynamicSqlSource 并没有
再看这个图，我们发现在这的时候还没有进行替换
然后往里跟
好像是这里了

这里 rootSqlNode.apply 其实是一个对原来 sql 的解析结果的一个循环调用，不同类型的标签会构成不同的 node，像这里就是一个 textSqlNode

可以发现到这我们的 sql 已经被替换了，而且是直接作为 string 类型替换的，所以可以明白了这个问题所在，就是注入，不过细心的同学发现其实这里是有个

理论上还是可以做过滤的，不过好像现在没用起来。
我们前面可以发现对于#{}是在启动扫描 mapper的 xml 文件就替换成了 ?,然后是在什么时候变成实际的值的呢

发现到这的时候还是没有替换，其实说白了也就是 prepareStatement 那一套，

在这里进行替换，会拿到 org.apache.ibatis.mapping.ParameterMapping，然后进行替换，因为会带着类型信息，所以不用担心注入咯

+]]> + + Java + Mybatis + Mysql + Spring + Sql注入 + Mybatis + + + Java + Mysql + Mybatis + Sql注入 + + mybatis系列-dataSource解析 /2023/01/08/mybatis%E7%B3%BB%E5%88%97-dataSource%E8%A7%A3%E6%9E%90/ @@ -2953,25 +3028,6 @@

它的入口不是个get方法，而是pop，从含义来来讲就不一样
org.apache.ibatis.datasource.pooled.PooledDataSource#getConnection()

@Override
public Connection getConnection() throws SQLException {
  return popConnection(dataSource.getUsername(), dataSource.getPassword()).getProxyConnection();
}

对于具体怎么获取连接我们可以下一篇具体讲下

-]]> - - Java - Mybatis - - - Java - Mysql - Mybatis - - - - mybatis系列-connection连接池解析 - /2023/02/19/mybatis%E7%B3%BB%E5%88%97-connection%E8%BF%9E%E6%8E%A5%E6%B1%A0%E8%A7%A3%E6%9E%90/ - 连接池主要是两个逻辑，首先是获取连接的逻辑，结合代码来讲一讲

-
private PooledConnection popConnection(String username, String password) throws SQLException {
    boolean countedWait = false;
    PooledConnection conn = null;
    long t = System.currentTimeMillis();
    int localBadConnectionCount = 0;

    // 循环获取连接
    while (conn == null) {
      // 加锁
      lock.lock();
      try {
        // 如果闲置的连接列表不为空
        if (!state.idleConnections.isEmpty()) {
          // Pool has available connection
          // 连接池有可用的连接
          conn = state.idleConnections.remove(0);
          if (log.isDebugEnabled()) {
            log.debug("Checked out connection " + conn.getRealHashCode() + " from pool.");
          }
        } else {
          // Pool does not have available connection
          // 进入这个分支表示没有空闲连接，但是活跃连接数还没达到最大活跃连接数上限，那么这时候就可以创建一个新连接
          if (state.activeConnections.size() < poolMaximumActiveConnections) {
            // Can create new connection
            // 这里创建连接我们之前讲过，
            conn = new PooledConnection(dataSource.getConnection(), this);
            if (log.isDebugEnabled()) {
              log.debug("Created connection " + conn.getRealHashCode() + ".");
            }
          } else {
            // Cannot create new connection
            // 进到这个分支了就表示没法创建新连接了，那么怎么办呢，这里引入了一个 poolMaximumCheckoutTime，这代表了我去控制连接一次被使用的最长时间，如果超过这个时间了，我就要去关闭失效它
            PooledConnection oldestActiveConnection = state.activeConnections.get(0);
            long longestCheckoutTime = oldestActiveConnection.getCheckoutTime();
            if (longestCheckoutTime > poolMaximumCheckoutTime) {
              // Can claim overdue connection
              // 所有超时连接从池中被借出的次数+1
              state.claimedOverdueConnectionCount++;
              // 所有超时连接从池中被借出并归还的时间总和 + 当前连接借出时间
              state.accumulatedCheckoutTimeOfOverdueConnections += longestCheckoutTime;
              // 所有连接从池中被借出并归还的时间总和 + 当前连接借出时间
              state.accumulatedCheckoutTime += longestCheckoutTime;
              // 从活跃连接数中移除此连接
              state.activeConnections.remove(oldestActiveConnection);
              // 如果该连接不是自动提交的，则尝试回滚
              if (!oldestActiveConnection.getRealConnection().getAutoCommit()) {
                try {
                  oldestActiveConnection.getRealConnection().rollback();
                } catch (SQLException e) {
                  /*
                     Just log a message for debug and continue to execute the following
                     statement like nothing happened.
                     Wrap the bad connection with a new PooledConnection, this will help
                     to not interrupt current executing thread and give current thread a
                     chance to join the next competition for another valid/good database
                     connection. At the end of this loop, bad {@link @conn} will be set as null.
                   */
                  log.debug("Bad connection. Could not roll back");
                }
              }
              // 用此连接的真实连接再创建一个连接，并设置时间
              conn = new PooledConnection(oldestActiveConnection.getRealConnection(), this);
              conn.setCreatedTimestamp(oldestActiveConnection.getCreatedTimestamp());
              conn.setLastUsedTimestamp(oldestActiveConnection.getLastUsedTimestamp());
              oldestActiveConnection.invalidate();
              if (log.isDebugEnabled()) {
                log.debug("Claimed overdue connection " + conn.getRealHashCode() + ".");
              }
            } else {
              // Must wait
              // 这样还是获取不到连接就只能等待了
              try {
                // 标记状态，然后把等待计数+1
                if (!countedWait) {
                  state.hadToWaitCount++;
                  countedWait = true;
                }
                if (log.isDebugEnabled()) {
                  log.debug("Waiting as long as " + poolTimeToWait + " milliseconds for connection.");
                }
                long wt = System.currentTimeMillis();
                // 等待 poolTimeToWait 时间
                condition.await(poolTimeToWait, TimeUnit.MILLISECONDS);
                // 记录等待时间
                state.accumulatedWaitTime += System.currentTimeMillis() - wt;
              } catch (InterruptedException e) {
                // set interrupt flag
                Thread.currentThread().interrupt();
                break;
              }
            }
          }
        }
        // 如果连接不为空
        if (conn != null) {
          // ping to server and check the connection is valid or not
          // 判断是否有效
          if (conn.isValid()) {
            if (!conn.getRealConnection().getAutoCommit()) {
              // 回滚未提交的
              conn.getRealConnection().rollback();
            }
            conn.setConnectionTypeCode(assembleConnectionTypeCode(dataSource.getUrl(), username, password));
            // 设置时间
            conn.setCheckoutTimestamp(System.currentTimeMillis());
            conn.setLastUsedTimestamp(System.currentTimeMillis());
            // 添加进活跃连接
            state.activeConnections.add(conn);
            state.requestCount++;
            state.accumulatedRequestTime += System.currentTimeMillis() - t;
          } else {
            if (log.isDebugEnabled()) {
              log.debug("A bad connection (" + conn.getRealHashCode() + ") was returned from the pool, getting another connection.");
            }
            // 连接无效，坏连接+1
            state.badConnectionCount++;
            localBadConnectionCount++;
            conn = null;
            // 如果坏连接已经超过了容忍上限，就抛异常
            if (localBadConnectionCount > (poolMaximumIdleConnections + poolMaximumLocalBadConnectionTolerance)) {
              if (log.isDebugEnabled()) {
                log.debug("PooledDataSource: Could not get a good connection to the database.");
              }
              throw new SQLException("PooledDataSource: Could not get a good connection to the database.");
            }
          }
        }
      } finally {
        // 释放锁
        lock.unlock();
      }

    }

    if (conn == null) {
      // 连接仍为空
      if (log.isDebugEnabled()) {
        log.debug("PooledDataSource: Unknown severe error condition.  The connection pool returned a null connection.");
      }
      // 抛出异常
      throw new SQLException("PooledDataSource: Unknown severe error condition.  The connection pool returned a null connection.");
    }
    // fanhui 
    return conn;
  }
- -

然后是还回连接

-
protected void pushConnection(PooledConnection conn) throws SQLException {
    // 加锁
    lock.lock();
    try {
      // 从活跃连接中移除当前连接
      state.activeConnections.remove(conn);
      if (conn.isValid()) {
        // 当前的空闲连接数小于连接池中允许的最大空闲连接数
        if (state.idleConnections.size() < poolMaximumIdleConnections && conn.getConnectionTypeCode() == expectedConnectionTypeCode) {
          // 记录借出时间
          state.accumulatedCheckoutTime += conn.getCheckoutTime();
          if (!conn.getRealConnection().getAutoCommit()) {
            // 同样是做回滚
            conn.getRealConnection().rollback();
          }
          // 新建一个连接
          PooledConnection newConn = new PooledConnection(conn.getRealConnection(), this);
          // 加入到空闲连接列表中
          state.idleConnections.add(newConn);
          newConn.setCreatedTimestamp(conn.getCreatedTimestamp());
          newConn.setLastUsedTimestamp(conn.getLastUsedTimestamp());
          // 原连接失效
          conn.invalidate();
          if (log.isDebugEnabled()) {
            log.debug("Returned connection " + newConn.getRealHashCode() + " to pool.");
          }
          // 提醒前面等待的
          condition.signal();
        } else {
          // 上面是相同的，就是这里是空闲连接数已经超过上限
          state.accumulatedCheckoutTime += conn.getCheckoutTime();
          if (!conn.getRealConnection().getAutoCommit()) {
            conn.getRealConnection().rollback();
          }
          conn.getRealConnection().close();
          if (log.isDebugEnabled()) {
            log.debug("Closed connection " + conn.getRealHashCode() + ".");
          }
          conn.invalidate();
        }
      } else {
        if (log.isDebugEnabled()) {
          log.debug("A bad connection (" + conn.getRealHashCode() + ") attempted to return to the pool, discarding connection.");
        }
        state.badConnectionCount++;
      }
    } finally {
      lock.unlock();
    }
  }
]]> Java @@ -3022,6 +3078,39 @@

image

真实的从 ? 替换成具体的变量值，是在这里
org.apache.ibatis.executor.SimpleExecutor#doQuery
调用了 

private Statement prepareStatement(StatementHandler handler, Log statementLog) throws SQLException {
    Statement stmt;
    Connection connection = getConnection(statementLog);
    stmt = handler.prepare(connection, transaction.getTimeout());
    handler.parameterize(stmt);
    return stmt;
  }
  @Override
  public void parameterize(Statement statement) throws SQLException {
    parameterHandler.setParameters((PreparedStatement) statement);
  }
    @Override
  public void setParameters(PreparedStatement ps) {
    ErrorContext.instance().activity("setting parameters").object(mappedStatement.getParameterMap().getId());
    List<ParameterMapping> parameterMappings = boundSql.getParameterMappings();
    if (parameterMappings != null) {
      for (int i = 0; i < parameterMappings.size(); i++) {
        ParameterMapping parameterMapping = parameterMappings.get(i);
        if (parameterMapping.getMode() != ParameterMode.OUT) {
          Object value;
          String propertyName = parameterMapping.getProperty();
          if (boundSql.hasAdditionalParameter(propertyName)) { // issue #448 ask first for additional params
            value = boundSql.getAdditionalParameter(propertyName);
          } else if (parameterObject == null) {
            value = null;
          } else if (typeHandlerRegistry.hasTypeHandler(parameterObject.getClass())) {
            value = parameterObject;
          } else {
            MetaObject metaObject = configuration.newMetaObject(parameterObject);
            value = metaObject.getValue(propertyName);
          }
          TypeHandler typeHandler = parameterMapping.getTypeHandler();
          JdbcType jdbcType = parameterMapping.getJdbcType();
          if (value == null && jdbcType == null) {
            jdbcType = configuration.getJdbcTypeForNull();
          }
          try {
            // --------------------------> 
            // 替换变量
            typeHandler.setParameter(ps, i + 1, value, jdbcType);
          } catch (TypeException | SQLException e) {
            throw new TypeException("Could not set parameters for mapping: " + parameterMapping + ". Cause: " + e, e);
          }
        }
      }
    }
  }
+]]> + + Java + Mybatis + + + Java + Mysql + Mybatis + + + + mybatis系列-sql 类的简要分析 + /2023/03/19/mybatis%E7%B3%BB%E5%88%97-sql-%E7%B1%BB%E7%9A%84%E7%AE%80%E8%A6%81%E5%88%86%E6%9E%90/ + 上次就比较简单的讲了使用，这块也比较简单，因为封装得不是很复杂，首先我们从 select 作为入口来看看，这个具体的实现，

+
String selectSql = new SQL() {{
            SELECT("id", "name");
            FROM("student");
            WHERE("id = #{id}");
        }}.toString();
+

SELECT 方法的实现，

+
public T SELECT(String... columns) {
  sql().statementType = SQLStatement.StatementType.SELECT;
  sql().select.addAll(Arrays.asList(columns));
  return getSelf();
}
+

statementType是个枚举

+
public enum StatementType {
  DELETE, INSERT, SELECT, UPDATE
}
+

那这个就是个 select 语句，然后会把参数转成 list 添加到 select 变量里，
然后是 from 语句，这个大概也能猜到就是设置下表名，

+
public T FROM(String table) {
  sql().tables.add(table);
  return getSelf();
}
+

往 tables 里添加了 table，这个 tables 是什么呢
这里也可以看下所有的变量，

+
StatementType statementType;
List<String> sets = new ArrayList<>();
List<String> select = new ArrayList<>();
List<String> tables = new ArrayList<>();
List<String> join = new ArrayList<>();
List<String> innerJoin = new ArrayList<>();
List<String> outerJoin = new ArrayList<>();
List<String> leftOuterJoin = new ArrayList<>();
List<String> rightOuterJoin = new ArrayList<>();
List<String> where = new ArrayList<>();
List<String> having = new ArrayList<>();
List<String> groupBy = new ArrayList<>();
List<String> orderBy = new ArrayList<>();
List<String> lastList = new ArrayList<>();
List<String> columns = new ArrayList<>();
List<List<String>> valuesList = new ArrayList<>();
+

可以看到是一堆 List 先暂存这些sql 片段，然后再拼装成 sql 语句，
因为它重写了 toString 方法

+
@Override
public String toString() {
  StringBuilder sb = new StringBuilder();
  sql().sql(sb);
  return sb.toString();
}
+

调用的 sql 方法是

+
public String sql(Appendable a) {
      SafeAppendable builder = new SafeAppendable(a);
      if (statementType == null) {
        return null;
      }

      String answer;

      switch (statementType) {
        case DELETE:
          answer = deleteSQL(builder);
          break;

        case INSERT:
          answer = insertSQL(builder);
          break;

        case SELECT:
          answer = selectSQL(builder);
          break;

        case UPDATE:
          answer = updateSQL(builder);
          break;

        default:
          answer = null;
      }

      return answer;
    }
+

根据上面的 statementType判断是个什么 sql，我们这个是 selectSQL 就走的 SELECT 这个分支

+
private String selectSQL(SafeAppendable builder) {
  if (distinct) {
    sqlClause(builder, "SELECT DISTINCT", select, "", "", ", ");
  } else {
    sqlClause(builder, "SELECT", select, "", "", ", ");
  }

  sqlClause(builder, "FROM", tables, "", "", ", ");
  joins(builder);
  sqlClause(builder, "WHERE", where, "(", ")", " AND ");
  sqlClause(builder, "GROUP BY", groupBy, "", "", ", ");
  sqlClause(builder, "HAVING", having, "(", ")", " AND ");
  sqlClause(builder, "ORDER BY", orderBy, "", "", ", ");
  limitingRowsStrategy.appendClause(builder, offset, limit);
  return builder.toString();
}
+

上面的可以看出来就是按我们常规的 sql 理解顺序来处理
就是select ... from ... where ... 这样子
再看下 sqlClause 的代码

+
private void sqlClause(SafeAppendable builder, String keyword, List<String> parts, String open, String close,
                           String conjunction) {
      if (!parts.isEmpty()) {
        if (!builder.isEmpty()) {
          builder.append("\n");
        }
        builder.append(keyword);
        builder.append(" ");
        builder.append(open);
        String last = "________";
        for (int i = 0, n = parts.size(); i < n; i++) {
          String part = parts.get(i);
          if (i > 0 && !part.equals(AND) && !part.equals(OR) && !last.equals(AND) && !last.equals(OR)) {
            builder.append(conjunction);
          }
          builder.append(part);
          last = part;
        }
        builder.append(close);
      }
    }
+

这里的拼接方式还需要判断 AND 和 OR 的判断逻辑，其他就没什么特别的了，只是where 语句中的 lastList 不知道是干嘛的，好像只有添加跟赋值的操作，有知道的大神也可以评论指导下

]]> Java @@ -3052,138 +3141,6 @@

delete语句

String deleteSql = new SQL() {{
            DELETE_FROM("student");
            WHERE("id = #{id}");
        }}.toString();
        System.out.println(deleteSql);

打印输出

DELETE FROM student
WHERE (id = #{id})
-]]> - - Java - Mybatis - - - Java - Mysql - Mybatis - - - - mybatis系列-入门篇 - /2022/11/27/mybatis%E7%B3%BB%E5%88%97-%E5%85%A5%E9%97%A8%E7%AF%87/ - mybatis是我们比较常用的orm框架，下面是官网的介绍

-
-

MyBatis 是一款优秀的持久层框架，它支持自定义 SQL、存储过程以及高级映射。MyBatis 免除了几乎所有的 JDBC 代码以及设置参数和获取结果集的工作。MyBatis 可以通过简单的 XML 或注解来配置和映射原始类型、接口和 Java POJO（Plain Old Java Objects，普通老式 Java 对象）为数据库中的记录。

-
-

mybatis一大特点，或者说比较为人熟知的应该就是比 hibernate 是更轻量化，为国人所爱好的orm框架，对于hibernate目前还没有深入的拆解过，后续可以也写一下，在使用体验上觉得是个比较精巧的框架，看代码也比较容易，所以就想写个系列，第一篇先是介绍下使用
根据官网的文档上我们先来尝试一下简单使用
首先我们有个简单的配置，这个文件是mybatis-config.xml

-
<?xml version="1.0" encoding="UTF-8" ?>
<!DOCTYPE configuration
        PUBLIC "-//mybatis.org//DTD Config 3.0//EN"
        "https://mybatis.org/dtd/mybatis-3-config.dtd">
<configuration>
    <!-- 需要加入的properties-->
    <properties resource="application-development.properties"/>
    <!-- 指出使用哪个环境，默认是development-->
    <environments default="development">
        <environment id="development">
        <!-- 指定事务管理器类型-->
            <transactionManager type="JDBC"/>
            <!-- 指定数据源类型-->
            <dataSource type="POOLED">
                <!-- 下面就是具体的参数占位了-->
                <property name="driver" value="${driver}"/>
                <property name="url" value="${url}"/>
                <property name="username" value="${username}"/>
                <property name="password" value="${password}"/>
            </dataSource>
        </environment>
    </environments>
    <mappers>
        <!-- 指定mapper xml的位置或文件-->
        <mapper resource="mapper/StudentMapper.xml"/>
    </mappers>
</configuration>
-

在代码里创建mybatis里重要入口

-
String resource = "mybatis-config.xml";
InputStream inputStream = Resources.getResourceAsStream(resource);
SqlSessionFactory sqlSessionFactory = new SqlSessionFactoryBuilder().build(inputStream);
-

然后我们上面的StudentMapper.xml

-
<?xml version="1.0" encoding="UTF-8" ?>
<!DOCTYPE mapper
        PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN"
        "https://mybatis.org/dtd/mybatis-3-mapper.dtd">
<mapper namespace="com.nicksxs.mybatisdemo.StudentMapper">
    <select id="selectStudent" resultType="com.nicksxs.mybatisdemo.StudentDO">
        select * from student where id = #{id}
    </select>
</mapper>
-

那么我们就要使用这个mapper，

-
String resource = "mybatis-config.xml";
InputStream inputStream = Resources.getResourceAsStream(resource);
SqlSessionFactory sqlSessionFactory = new SqlSessionFactoryBuilder().build(inputStream);
try (SqlSession session = sqlSessionFactory.openSession()) {
    StudentDO studentDO = session.selectOne("com.nicksxs.mybatisdemo.StudentMapper.selectStudent", 1);
    System.out.println("id is " + studentDO.getId() + " name is " +studentDO.getName());
} catch (Exception e) {
    e.printStackTrace();
}
-

sqlSessionFactory是sqlSession的工厂，我们可以通过sqlSessionFactory来创建sqlSession，而SqlSession 提供了在数据库执行 SQL 命令所需的所有方法。你可以通过 SqlSession 实例来直接执行已映射的 SQL 语句。可以看到mapper.xml中有定义mapper的namespace，就可以通过session.selectOne()传入namespace+id来调用这个方法
但是这样调用比较不合理的点，或者说按后面mybatis优化之后我们可以指定mapper接口

-
public interface StudentMapper {

    public StudentDO selectStudent(Long id);
}
-

就可以可以通过mapper接口获取方法，这样就不用涉及到未知的变量转换等异常

-
try (SqlSession session = sqlSessionFactory.openSession()) {
    StudentMapper mapper = session.getMapper(StudentMapper.class);
    StudentDO studentDO = mapper.selectStudent(1L);
    System.out.println("id is " + studentDO.getId() + " name is " +studentDO.getName());
} catch (Exception e) {
    e.printStackTrace();
}
-

这一篇咱们先介绍下简单的使用，后面可以先介绍下这些的原理。

-]]> - - Java - Mybatis - - - Java - Mysql - Mybatis - - - - mybatis系列-mybatis是如何初始化mapper的 - /2022/12/04/mybatis%E6%98%AF%E5%A6%82%E4%BD%95%E5%88%9D%E5%A7%8B%E5%8C%96mapper%E7%9A%84/ - 前一篇讲了mybatis的初始化使用，如果我第一次看到这个使用入门文档，比较会产生疑惑的是配置了mapper，怎么就能通过selectOne跟语句id就能执行sql了，那么第一个问题，就是mapper是怎么被解析的，存在哪里，怎么被拿出来的

-

添加解析mapper

org.apache.ibatis.session.SqlSessionFactoryBuilder#build(java.io.InputStream)
public SqlSessionFactory build(InputStream inputStream) {
  return build(inputStream, null, null);
}
- -

通过读取mybatis-config.xml来构建SqlSessionFactory，

-
public SqlSessionFactory build(InputStream inputStream, String environment, Properties properties) {
  try {
    // 创建下xml的解析器
    XMLConfigBuilder parser = new XMLConfigBuilder(inputStream, environment, properties);
    // 进行解析，后再构建
    return build(parser.parse());
  } catch (Exception e) {
    throw ExceptionFactory.wrapException("Error building SqlSession.", e);
  } finally {
    ErrorContext.instance().reset();
    try {
       if (inputStream != null) {
         inputStream.close();
       }
    } catch (IOException e) {
      // Intentionally ignore. Prefer previous error.
    }
  }
- -

创建XMLConfigBuilder

-
public XMLConfigBuilder(InputStream inputStream, String environment, Properties props) {
    // --------> 创建 XPathParser
  this(new XPathParser(inputStream, true, props, new XMLMapperEntityResolver()), environment, props);
}

public XPathParser(InputStream inputStream, boolean validation, Properties variables, EntityResolver entityResolver) {
    commonConstructor(validation, variables, entityResolver);
    this.document = createDocument(new InputSource(inputStream));
  }

private XMLConfigBuilder(XPathParser parser, String environment, Properties props) {
  super(new Configuration());
  ErrorContext.instance().resource("SQL Mapper Configuration");
  this.configuration.setVariables(props);
  this.parsed = false;
  this.environment = environment;
  this.parser = parser;
}
- -

这里主要是创建了Builder包含了Parser
然后调用parse方法

-
public Configuration parse() {
  if (parsed) {
    throw new BuilderException("Each XMLConfigBuilder can only be used once.");
  }
  // 标记下是否已解析，但是这里是否有线程安全问题
  parsed = true;
  // --------> 解析配置
  parseConfiguration(parser.evalNode("/configuration"));
  return configuration;
}
- -

实际的解析区分了各类标签

-
private void parseConfiguration(XNode root) {
  try {
    // issue #117 read properties first
    // 解析properties，这个不是spring自带的，需要额外配置，并且在config文件里应该放在最前
    propertiesElement(root.evalNode("properties"));
    Properties settings = settingsAsProperties(root.evalNode("settings"));
    loadCustomVfs(settings);
    loadCustomLogImpl(settings);
    typeAliasesElement(root.evalNode("typeAliases"));
    pluginElement(root.evalNode("plugins"));
    objectFactoryElement(root.evalNode("objectFactory"));
    objectWrapperFactoryElement(root.evalNode("objectWrapperFactory"));
    reflectorFactoryElement(root.evalNode("reflectorFactory"));
    settingsElement(settings);
    // read it after objectFactory and objectWrapperFactory issue #631
    environmentsElement(root.evalNode("environments"));
    databaseIdProviderElement(root.evalNode("databaseIdProvider"));
    typeHandlerElement(root.evalNode("typeHandlers"));
    // ----------> 我们需要关注的是mapper的处理
    mapperElement(root.evalNode("mappers"));
  } catch (Exception e) {
    throw new BuilderException("Error parsing SQL Mapper Configuration. Cause: " + e, e);
  }
}
- -

然后就是调用到mapperElement方法了

-
private void mapperElement(XNode parent) throws Exception {
  if (parent != null) {
    for (XNode child : parent.getChildren()) {
      if ("package".equals(child.getName())) {
        String mapperPackage = child.getStringAttribute("name");
        configuration.addMappers(mapperPackage);
      } else {
        String resource = child.getStringAttribute("resource");
        String url = child.getStringAttribute("url");
        String mapperClass = child.getStringAttribute("class");
        if (resource != null && url == null && mapperClass == null) {
          ErrorContext.instance().resource(resource);
          try(InputStream inputStream = Resources.getResourceAsStream(resource)) {
            XMLMapperBuilder mapperParser = new XMLMapperBuilder(inputStream, configuration, resource, configuration.getSqlFragments());
            // --------> 我们这没有指定package，所以是走到这
            mapperParser.parse();
          }
        } else if (resource == null && url != null && mapperClass == null) {
          ErrorContext.instance().resource(url);
          try(InputStream inputStream = Resources.getUrlAsStream(url)){
            XMLMapperBuilder mapperParser = new XMLMapperBuilder(inputStream, configuration, url, configuration.getSqlFragments());
            mapperParser.parse();
          }
        } else if (resource == null && url == null && mapperClass != null) {
          Class<?> mapperInterface = Resources.classForName(mapperClass);
          configuration.addMapper(mapperInterface);
        } else {
          throw new BuilderException("A mapper element may only specify a url, resource or class, but not more than one.");
        }
      }
    }
  }
}
- -

核心就在这个parse()方法

-
public void parse() {
  if (!configuration.isResourceLoaded(resource)) {
    // -------> 然后就是走到这里,配置xml的mapper节点的内容
    configurationElement(parser.evalNode("/mapper"));
    configuration.addLoadedResource(resource);
    bindMapperForNamespace();
  }

  parsePendingResultMaps();
  parsePendingCacheRefs();
  parsePendingStatements();
}
- -

具体的处理逻辑

-
private void configurationElement(XNode context) {
  try {
    String namespace = context.getStringAttribute("namespace");
    if (namespace == null || namespace.isEmpty()) {
      throw new BuilderException("Mapper's namespace cannot be empty");
    }
    builderAssistant.setCurrentNamespace(namespace);
    cacheRefElement(context.evalNode("cache-ref"));
    cacheElement(context.evalNode("cache"));
    parameterMapElement(context.evalNodes("/mapper/parameterMap"));
    resultMapElements(context.evalNodes("/mapper/resultMap"));
    sqlElement(context.evalNodes("/mapper/sql"));
    // ------->  走到这，从上下文构建statement
    buildStatementFromContext(context.evalNodes("select|insert|update|delete"));
  } catch (Exception e) {
    throw new BuilderException("Error parsing Mapper XML. The XML location is '" + resource + "'. Cause: " + e, e);
  }
}
- -

具体代码在这，从上下文构建statement，只不过区分了下databaseId

-
private void buildStatementFromContext(List<XNode> list) {
  if (configuration.getDatabaseId() != null) {
    buildStatementFromContext(list, configuration.getDatabaseId());
  }
  // -----> 判断databaseId
  buildStatementFromContext(list, null);
}
- -

判断下databaseId

-
private void buildStatementFromContext(List<XNode> list, String requiredDatabaseId) {
  for (XNode context : list) {
    final XMLStatementBuilder statementParser = new XMLStatementBuilder(configuration, builderAssistant, context, requiredDatabaseId);
    try {
      // -------> 解析statement节点
      statementParser.parseStatementNode();
    } catch (IncompleteElementException e) {
      configuration.addIncompleteStatement(statementParser);
    }
  }
}
- -

接下来就是真正处理的xml语句内容的，各个节点的信息内容

-
public void parseStatementNode() {
  String id = context.getStringAttribute("id");
  String databaseId = context.getStringAttribute("databaseId");

  if (!databaseIdMatchesCurrent(id, databaseId, this.requiredDatabaseId)) {
    return;
  }

  String nodeName = context.getNode().getNodeName();
  SqlCommandType sqlCommandType = SqlCommandType.valueOf(nodeName.toUpperCase(Locale.ENGLISH));
  boolean isSelect = sqlCommandType == SqlCommandType.SELECT;
  boolean flushCache = context.getBooleanAttribute("flushCache", !isSelect);
  boolean useCache = context.getBooleanAttribute("useCache", isSelect);
  boolean resultOrdered = context.getBooleanAttribute("resultOrdered", false);

  // Include Fragments before parsing
  XMLIncludeTransformer includeParser = new XMLIncludeTransformer(configuration, builderAssistant);
  includeParser.applyIncludes(context.getNode());

  String parameterType = context.getStringAttribute("parameterType");
  Class<?> parameterTypeClass = resolveClass(parameterType);

  String lang = context.getStringAttribute("lang");
  LanguageDriver langDriver = getLanguageDriver(lang);

  // Parse selectKey after includes and remove them.
  processSelectKeyNodes(id, parameterTypeClass, langDriver);

  // Parse the SQL (pre: <selectKey> and <include> were parsed and removed)
  KeyGenerator keyGenerator;
  String keyStatementId = id + SelectKeyGenerator.SELECT_KEY_SUFFIX;
  keyStatementId = builderAssistant.applyCurrentNamespace(keyStatementId, true);
  if (configuration.hasKeyGenerator(keyStatementId)) {
    keyGenerator = configuration.getKeyGenerator(keyStatementId);
  } else {
    keyGenerator = context.getBooleanAttribute("useGeneratedKeys",
        configuration.isUseGeneratedKeys() && SqlCommandType.INSERT.equals(sqlCommandType))
        ? Jdbc3KeyGenerator.INSTANCE : NoKeyGenerator.INSTANCE;
  }

  // 语句的主要参数解析
  SqlSource sqlSource = langDriver.createSqlSource(configuration, context, parameterTypeClass);
  StatementType statementType = StatementType.valueOf(context.getStringAttribute("statementType", StatementType.PREPARED.toString()));
  Integer fetchSize = context.getIntAttribute("fetchSize");
  Integer timeout = context.getIntAttribute("timeout");
  String parameterMap = context.getStringAttribute("parameterMap");
  String resultType = context.getStringAttribute("resultType");
  Class<?> resultTypeClass = resolveClass(resultType);
  String resultMap = context.getStringAttribute("resultMap");
  String resultSetType = context.getStringAttribute("resultSetType");
  ResultSetType resultSetTypeEnum = resolveResultSetType(resultSetType);
  if (resultSetTypeEnum == null) {
    resultSetTypeEnum = configuration.getDefaultResultSetType();
  }
  String keyProperty = context.getStringAttribute("keyProperty");
  String keyColumn = context.getStringAttribute("keyColumn");
  String resultSets = context.getStringAttribute("resultSets");

  // --------> 添加映射的statement
  builderAssistant.addMappedStatement(id, sqlSource, statementType, sqlCommandType,
      fetchSize, timeout, parameterMap, parameterTypeClass, resultMap, resultTypeClass,
      resultSetTypeEnum, flushCache, useCache, resultOrdered,
      keyGenerator, keyProperty, keyColumn, databaseId, langDriver, resultSets);
}
- - -

添加的逻辑具体可以看下

-
public MappedStatement addMappedStatement(
    String id,
    SqlSource sqlSource,
    StatementType statementType,
    SqlCommandType sqlCommandType,
    Integer fetchSize,
    Integer timeout,
    String parameterMap,
    Class<?> parameterType,
    String resultMap,
    Class<?> resultType,
    ResultSetType resultSetType,
    boolean flushCache,
    boolean useCache,
    boolean resultOrdered,
    KeyGenerator keyGenerator,
    String keyProperty,
    String keyColumn,
    String databaseId,
    LanguageDriver lang,
    String resultSets) {

  if (unresolvedCacheRef) {
    throw new IncompleteElementException("Cache-ref not yet resolved");
  }

  id = applyCurrentNamespace(id, false);
  boolean isSelect = sqlCommandType == SqlCommandType.SELECT;

  MappedStatement.Builder statementBuilder = new MappedStatement.Builder(configuration, id, sqlSource, sqlCommandType)
      .resource(resource)
      .fetchSize(fetchSize)
      .timeout(timeout)
      .statementType(statementType)
      .keyGenerator(keyGenerator)
      .keyProperty(keyProperty)
      .keyColumn(keyColumn)
      .databaseId(databaseId)
      .lang(lang)
      .resultOrdered(resultOrdered)
      .resultSets(resultSets)
      .resultMaps(getStatementResultMaps(resultMap, resultType, id))
      .resultSetType(resultSetType)
      .flushCacheRequired(valueOrDefault(flushCache, !isSelect))
      .useCache(valueOrDefault(useCache, isSelect))
      .cache(currentCache);

  ParameterMap statementParameterMap = getStatementParameterMap(parameterMap, parameterType, id);
  if (statementParameterMap != null) {
    statementBuilder.parameterMap(statementParameterMap);
  }

  MappedStatement statement = statementBuilder.build();
  // ------>  正好是这里在configuration中添加了映射好的statement
  configuration.addMappedStatement(statement);
  return statement;
}
- -

而里面就是往map里添加

-
public void addMappedStatement(MappedStatement ms) {
  mappedStatements.put(ms.getId(), ms);
}
- -

获取mapper

StudentDO studentDO = session.selectOne("com.nicksxs.mybatisdemo.StudentMapper.selectStudent", 1);
- -

就是调用了 org.apache.ibatis.session.defaults.DefaultSqlSession#selectOne(java.lang.String, java.lang.Object)

-
public <T> T selectOne(String statement, Object parameter) {
  // Popular vote was to return null on 0 results and throw exception on too many.
  List<T> list = this.selectList(statement, parameter);
  if (list.size() == 1) {
    return list.get(0);
  } else if (list.size() > 1) {
    throw new TooManyResultsException("Expected one result (or null) to be returned by selectOne(), but found: " + list.size());
  } else {
    return null;
  }
}
- -

调用实际的实现方法

-
public <E> List<E> selectList(String statement, Object parameter) {
  return this.selectList(statement, parameter, RowBounds.DEFAULT);
}
- -

这里还有一层

-
public <E> List<E> selectList(String statement, Object parameter, RowBounds rowBounds) {
  return selectList(statement, parameter, rowBounds, Executor.NO_RESULT_HANDLER);
}
- - -

根本的就是从configuration里获取了mappedStatement

-
private <E> List<E> selectList(String statement, Object parameter, RowBounds rowBounds, ResultHandler handler) {
  try {
    // 这里进行了获取
    MappedStatement ms = configuration.getMappedStatement(statement);
    return executor.query(ms, wrapCollection(parameter), rowBounds, handler);
  } catch (Exception e) {
    throw ExceptionFactory.wrapException("Error querying database.  Cause: " + e, e);
  } finally {
    ErrorContext.instance().reset();
  }
}
-]]> - - Java - Mybatis - - - Java - Mysql - Mybatis - - - - mybatis系列-sql 类的简要分析 - /2023/03/19/mybatis%E7%B3%BB%E5%88%97-sql-%E7%B1%BB%E7%9A%84%E7%AE%80%E8%A6%81%E5%88%86%E6%9E%90/ - 上次就比较简单的讲了使用，这块也比较简单，因为封装得不是很复杂，首先我们从 select 作为入口来看看，这个具体的实现，

-
String selectSql = new SQL() {{
            SELECT("id", "name");
            FROM("student");
            WHERE("id = #{id}");
        }}.toString();
-

SELECT 方法的实现，

-
public T SELECT(String... columns) {
  sql().statementType = SQLStatement.StatementType.SELECT;
  sql().select.addAll(Arrays.asList(columns));
  return getSelf();
}
-

statementType是个枚举

-
public enum StatementType {
  DELETE, INSERT, SELECT, UPDATE
}
-

那这个就是个 select 语句，然后会把参数转成 list 添加到 select 变量里，
然后是 from 语句，这个大概也能猜到就是设置下表名，

-
public T FROM(String table) {
  sql().tables.add(table);
  return getSelf();
}
-

往 tables 里添加了 table，这个 tables 是什么呢
这里也可以看下所有的变量，

-
StatementType statementType;
List<String> sets = new ArrayList<>();
List<String> select = new ArrayList<>();
List<String> tables = new ArrayList<>();
List<String> join = new ArrayList<>();
List<String> innerJoin = new ArrayList<>();
List<String> outerJoin = new ArrayList<>();
List<String> leftOuterJoin = new ArrayList<>();
List<String> rightOuterJoin = new ArrayList<>();
List<String> where = new ArrayList<>();
List<String> having = new ArrayList<>();
List<String> groupBy = new ArrayList<>();
List<String> orderBy = new ArrayList<>();
List<String> lastList = new ArrayList<>();
List<String> columns = new ArrayList<>();
List<List<String>> valuesList = new ArrayList<>();
-

可以看到是一堆 List 先暂存这些sql 片段，然后再拼装成 sql 语句，
因为它重写了 toString 方法

-
@Override
public String toString() {
  StringBuilder sb = new StringBuilder();
  sql().sql(sb);
  return sb.toString();
}
-

调用的 sql 方法是

-
public String sql(Appendable a) {
      SafeAppendable builder = new SafeAppendable(a);
      if (statementType == null) {
        return null;
      }

      String answer;

      switch (statementType) {
        case DELETE:
          answer = deleteSQL(builder);
          break;

        case INSERT:
          answer = insertSQL(builder);
          break;

        case SELECT:
          answer = selectSQL(builder);
          break;

        case UPDATE:
          answer = updateSQL(builder);
          break;

        default:
          answer = null;
      }

      return answer;
    }
-

根据上面的 statementType判断是个什么 sql，我们这个是 selectSQL 就走的 SELECT 这个分支

-
private String selectSQL(SafeAppendable builder) {
  if (distinct) {
    sqlClause(builder, "SELECT DISTINCT", select, "", "", ", ");
  } else {
    sqlClause(builder, "SELECT", select, "", "", ", ");
  }

  sqlClause(builder, "FROM", tables, "", "", ", ");
  joins(builder);
  sqlClause(builder, "WHERE", where, "(", ")", " AND ");
  sqlClause(builder, "GROUP BY", groupBy, "", "", ", ");
  sqlClause(builder, "HAVING", having, "(", ")", " AND ");
  sqlClause(builder, "ORDER BY", orderBy, "", "", ", ");
  limitingRowsStrategy.appendClause(builder, offset, limit);
  return builder.toString();
}
-

上面的可以看出来就是按我们常规的 sql 理解顺序来处理
就是select ... from ... where ... 这样子
再看下 sqlClause 的代码

-
private void sqlClause(SafeAppendable builder, String keyword, List<String> parts, String open, String close,
                           String conjunction) {
      if (!parts.isEmpty()) {
        if (!builder.isEmpty()) {
          builder.append("\n");
        }
        builder.append(keyword);
        builder.append(" ");
        builder.append(open);
        String last = "________";
        for (int i = 0, n = parts.size(); i < n; i++) {
          String part = parts.get(i);
          if (i > 0 && !part.equals(AND) && !part.equals(OR) && !last.equals(AND) && !last.equals(OR)) {
            builder.append(conjunction);
          }
          builder.append(part);
          last = part;
        }
        builder.append(close);
      }
    }
-

这里的拼接方式还需要判断 AND 和 OR 的判断逻辑，其他就没什么特别的了，只是where 语句中的 lastList 不知道是干嘛的，好像只有添加跟赋值的操作，有知道的大神也可以评论指导下

]]> Java @@ -3314,21 +3271,25 @@ - mybatis系列-typeAliases系统 - /2023/01/01/mybatis%E7%B3%BB%E5%88%97-typeAliases%E7%B3%BB%E7%BB%9F/ - 其实前面已经聊到过这个概念，在mybatis的配置中，以及一些初始化逻辑都是用了typeAliases，

-
<typeAliases>
  <typeAlias alias="Author" type="domain.blog.Author"/>
  <typeAlias alias="Blog" type="domain.blog.Blog"/>
  <typeAlias alias="Comment" type="domain.blog.Comment"/>
  <typeAlias alias="Post" type="domain.blog.Post"/>
  <typeAlias alias="Section" type="domain.blog.Section"/>
  <typeAlias alias="Tag" type="domain.blog.Tag"/>
</typeAliases>
-

可以在这里注册类型别名，然后在mybatis中配置使用时，可以简化这些类型的使用，其底层逻辑主要是一个map，

-
public class TypeAliasRegistry {

  private final Map<String, Class<?>> typeAliases = new HashMap<>();
-

以string作为key，class对象作为value，比如我们在一开始使用的配置文件

-
<dataSource type="POOLED">
    <property name="driver" value="${driver}"/>
    <property name="url" value="${url}"/>
    <property name="username" value="${username}"/>
    <property name="password" value="${password}"/>
</dataSource>
-

这里使用的dataSource是POOLED，那它肯定是个别名或者需要对应处理
而这个别名就是在Configuration的构造方法里初始化

-
public Configuration() {
    typeAliasRegistry.registerAlias("JDBC", JdbcTransactionFactory.class);
    typeAliasRegistry.registerAlias("MANAGED", ManagedTransactionFactory.class);

    typeAliasRegistry.registerAlias("JNDI", JndiDataSourceFactory.class);
    typeAliasRegistry.registerAlias("POOLED", PooledDataSourceFactory.class);
    typeAliasRegistry.registerAlias("UNPOOLED", UnpooledDataSourceFactory.class);

    typeAliasRegistry.registerAlias("PERPETUAL", PerpetualCache.class);
    typeAliasRegistry.registerAlias("FIFO", FifoCache.class);
    typeAliasRegistry.registerAlias("LRU", LruCache.class);
    typeAliasRegistry.registerAlias("SOFT", SoftCache.class);
    typeAliasRegistry.registerAlias("WEAK", WeakCache.class);

    typeAliasRegistry.registerAlias("DB_VENDOR", VendorDatabaseIdProvider.class);

    typeAliasRegistry.registerAlias("XML", XMLLanguageDriver.class);
    typeAliasRegistry.registerAlias("RAW", RawLanguageDriver.class);

    typeAliasRegistry.registerAlias("SLF4J", Slf4jImpl.class);
    typeAliasRegistry.registerAlias("COMMONS_LOGGING", JakartaCommonsLoggingImpl.class);
    typeAliasRegistry.registerAlias("LOG4J", Log4jImpl.class);
    typeAliasRegistry.registerAlias("LOG4J2", Log4j2Impl.class);
    typeAliasRegistry.registerAlias("JDK_LOGGING", Jdk14LoggingImpl.class);
    typeAliasRegistry.registerAlias("STDOUT_LOGGING", StdOutImpl.class);
    typeAliasRegistry.registerAlias("NO_LOGGING", NoLoggingImpl.class);

    typeAliasRegistry.registerAlias("CGLIB", CglibProxyFactory.class);
    typeAliasRegistry.registerAlias("JAVASSIST", JavassistProxyFactory.class);

    languageRegistry.setDefaultDriverClass(XMLLanguageDriver.class);
    languageRegistry.register(RawLanguageDriver.class);
  }
-

正是通过typeAliasRegistry.registerAlias("POOLED", PooledDataSourceFactory.class);这一行，注册了
POOLED对应的别名类型是PooledDataSourceFactory.class
具体的注册方法是在

-
public void registerAlias(String alias, Class<?> value) {
  if (alias == null) {
    throw new TypeException("The parameter alias cannot be null");
  }
  // issue #748
  // 转换成小写，
  String key = alias.toLowerCase(Locale.ENGLISH);
  // 判断是否已经注册过了
  if (typeAliases.containsKey(key) && typeAliases.get(key) != null && !typeAliases.get(key).equals(value)) {
    throw new TypeException("The alias '" + alias + "' is already mapped to the value '" + typeAliases.get(key).getName() + "'.");
  }
  // 放进map里
  typeAliases.put(key, value);
}
-

而获取的逻辑在这

-
public <T> Class<T> resolveAlias(String string) {
    try {
      if (string == null) {
        return null;
      }
      // issue #748
      // 同样的转成小写
      String key = string.toLowerCase(Locale.ENGLISH);
      Class<T> value;
      if (typeAliases.containsKey(key)) {
        value = (Class<T>) typeAliases.get(key);
      } else {
        // 这里还有从路径下处理的逻辑
        value = (Class<T>) Resources.classForName(string);
      }
      return value;
    } catch (ClassNotFoundException e) {
      throw new TypeException("Could not resolve type alias '" + string + "'.  Cause: " + e, e);
    }
  }
-

逻辑比较简单，但是在mybatis中也是不可或缺的一块概念

+ mybatis系列-入门篇 + /2022/11/27/mybatis%E7%B3%BB%E5%88%97-%E5%85%A5%E9%97%A8%E7%AF%87/ + mybatis是我们比较常用的orm框架，下面是官网的介绍

+
+

MyBatis 是一款优秀的持久层框架，它支持自定义 SQL、存储过程以及高级映射。MyBatis 免除了几乎所有的 JDBC 代码以及设置参数和获取结果集的工作。MyBatis 可以通过简单的 XML 或注解来配置和映射原始类型、接口和 Java POJO（Plain Old Java Objects，普通老式 Java 对象）为数据库中的记录。

+
+

mybatis一大特点，或者说比较为人熟知的应该就是比 hibernate 是更轻量化，为国人所爱好的orm框架，对于hibernate目前还没有深入的拆解过，后续可以也写一下，在使用体验上觉得是个比较精巧的框架，看代码也比较容易，所以就想写个系列，第一篇先是介绍下使用
根据官网的文档上我们先来尝试一下简单使用
首先我们有个简单的配置，这个文件是mybatis-config.xml

+
<?xml version="1.0" encoding="UTF-8" ?>
<!DOCTYPE configuration
        PUBLIC "-//mybatis.org//DTD Config 3.0//EN"
        "https://mybatis.org/dtd/mybatis-3-config.dtd">
<configuration>
    <!-- 需要加入的properties-->
    <properties resource="application-development.properties"/>
    <!-- 指出使用哪个环境，默认是development-->
    <environments default="development">
        <environment id="development">
        <!-- 指定事务管理器类型-->
            <transactionManager type="JDBC"/>
            <!-- 指定数据源类型-->
            <dataSource type="POOLED">
                <!-- 下面就是具体的参数占位了-->
                <property name="driver" value="${driver}"/>
                <property name="url" value="${url}"/>
                <property name="username" value="${username}"/>
                <property name="password" value="${password}"/>
            </dataSource>
        </environment>
    </environments>
    <mappers>
        <!-- 指定mapper xml的位置或文件-->
        <mapper resource="mapper/StudentMapper.xml"/>
    </mappers>
</configuration>
+

在代码里创建mybatis里重要入口

+
String resource = "mybatis-config.xml";
InputStream inputStream = Resources.getResourceAsStream(resource);
SqlSessionFactory sqlSessionFactory = new SqlSessionFactoryBuilder().build(inputStream);
+

然后我们上面的StudentMapper.xml

+
<?xml version="1.0" encoding="UTF-8" ?>
<!DOCTYPE mapper
        PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN"
        "https://mybatis.org/dtd/mybatis-3-mapper.dtd">
<mapper namespace="com.nicksxs.mybatisdemo.StudentMapper">
    <select id="selectStudent" resultType="com.nicksxs.mybatisdemo.StudentDO">
        select * from student where id = #{id}
    </select>
</mapper>
+

那么我们就要使用这个mapper，

+
String resource = "mybatis-config.xml";
InputStream inputStream = Resources.getResourceAsStream(resource);
SqlSessionFactory sqlSessionFactory = new SqlSessionFactoryBuilder().build(inputStream);
try (SqlSession session = sqlSessionFactory.openSession()) {
    StudentDO studentDO = session.selectOne("com.nicksxs.mybatisdemo.StudentMapper.selectStudent", 1);
    System.out.println("id is " + studentDO.getId() + " name is " +studentDO.getName());
} catch (Exception e) {
    e.printStackTrace();
}
+

sqlSessionFactory是sqlSession的工厂，我们可以通过sqlSessionFactory来创建sqlSession，而SqlSession 提供了在数据库执行 SQL 命令所需的所有方法。你可以通过 SqlSession 实例来直接执行已映射的 SQL 语句。可以看到mapper.xml中有定义mapper的namespace，就可以通过session.selectOne()传入namespace+id来调用这个方法
但是这样调用比较不合理的点，或者说按后面mybatis优化之后我们可以指定mapper接口

+
public interface StudentMapper {

    public StudentDO selectStudent(Long id);
}
+

就可以可以通过mapper接口获取方法，这样就不用涉及到未知的变量转换等异常

+
try (SqlSession session = sqlSessionFactory.openSession()) {
    StudentMapper mapper = session.getMapper(StudentMapper.class);
    StudentDO studentDO = mapper.selectStudent(1L);
    System.out.println("id is " + studentDO.getId() + " name is " +studentDO.getName());
} catch (Exception e) {
    e.printStackTrace();
}
+

这一篇咱们先介绍下简单的使用，后面可以先介绍下这些的原理。

]]> Java @@ -3372,46 +3333,42 @@ - openresty - /2019/06/18/openresty/ - 目前公司要对一些新的产品功能做灰度测试，因为在后端业务代码层面添加判断比较麻烦，所以想在nginx上做点手脚，就想到了openresty
前后也踩了不少坑，这边先写一点

-

首先是日志
error_log logs/error.log debug;
需要nginx开启日志的debug才能看到日志

-

使用 lua_code_cache off即可, 另外注意只有使用 content_by_lua_file 才会生效

-
http {
  lua_code_cache off;
}

location ~* /(\d+-.*)/api/orgunits/load_all(.*) {
   default_type 'application/json;charset=utf-8';
   content_by_lua_file /data/projects/xxx/current/lua/controller/load_data.lua;
}
- -

使用lua给nginx请求response头添加内容可以用这个

-
ngx.header['response'] = 'header'
- - -

使用总结

-

后续：

-
    -

  1. 一开始在本地环境的时候使用content_by_lua_file只关注了头，后来发到测试环境发现请求内容都没代理转发到后端服务上
    网上查了下发现content_by_lua_file是将请求的所有内容包括response都用这里面的lua脚本生成了，content这个词就表示是请求内容
    后来改成了access_by_lua_file就正常了，只是要去获取请求内容和修改响应头，并不是要完整的接管请求

    -
    2. -

  2. 后来又碰到了一个坑是nginx有个client_body_buffer_size的配置参数，nginx在32位和64位系统里有8K和16K两个默认值，当请求内容大于这两个值的时候，会把请求内容放到临时文件里，这个时候openresty里的ngx.req.get_post_args()就会报“failed to get post args: requesty body in temp file not supported”这个错误，将client_body_buffer_size这个参数配置调大一点就好了

    -
    3. -

  3. 还有就是lua的异常捕获，网上看一般是用pcall和xpcall来进行保护调用，因为问题主要出在cjson的decode，这里有两个解决方案，一个就是将cjson.decode使用pcall封装，

    - 
    local decode = require("cjson").decode

    function json_decode( str )
        local ok, t = pcall(decode, str)
        if not ok then
          return nil
        end

        return t
    end
    -

    这个是使用了pcall，称为保护调用，会在内部错误后返回两个参数，第一个是false，第二个是错误信息
    还有一种是使用cjson.safe包

    - 
    local json = require("cjson.safe")
    local str = [[ {"key:"value"} ]]

    local t = json.decode(str)
    if t then
        ngx.say(" --> ", type(t))
    end
    -

    cjson.safe包会在解析失败的时候返回nil

    -
    4. -

  4. 还有一个是redis链接时如果host使用的是域名的话会提示“failed to connect: no resolver defined to resolve “redis.xxxxxx.com””，这里需要使用nginx的resolver指令，
    resolver 8.8.8.8 valid=3600s;

    -
    5. -

  5. 还有一点补充下
    就是业务在使用redis的时候使用了db的特性，所以在lua访问redis的时候也需要执行db，这里lua的redis库也支持了这个特性，可以使用instance:select(config:get(‘db’))来切换db

    -
    6. -

  6. 性能优化tips
    建议是尽量少使用阶段钩子，例如content_by_lua_file，*_by_lua

    -
    7. -

  7. 发现一个不错的openresty站点
    地址

    -
    8. -
+ mybatis系列-typeAliases系统 + /2023/01/01/mybatis%E7%B3%BB%E5%88%97-typeAliases%E7%B3%BB%E7%BB%9F/ + 其实前面已经聊到过这个概念，在mybatis的配置中，以及一些初始化逻辑都是用了typeAliases，

+
<typeAliases>
  <typeAlias alias="Author" type="domain.blog.Author"/>
  <typeAlias alias="Blog" type="domain.blog.Blog"/>
  <typeAlias alias="Comment" type="domain.blog.Comment"/>
  <typeAlias alias="Post" type="domain.blog.Post"/>
  <typeAlias alias="Section" type="domain.blog.Section"/>
  <typeAlias alias="Tag" type="domain.blog.Tag"/>
</typeAliases>
+

可以在这里注册类型别名，然后在mybatis中配置使用时，可以简化这些类型的使用，其底层逻辑主要是一个map，

+
public class TypeAliasRegistry {

  private final Map<String, Class<?>> typeAliases = new HashMap<>();
+

以string作为key，class对象作为value，比如我们在一开始使用的配置文件

+
<dataSource type="POOLED">
    <property name="driver" value="${driver}"/>
    <property name="url" value="${url}"/>
    <property name="username" value="${username}"/>
    <property name="password" value="${password}"/>
</dataSource>
+

这里使用的dataSource是POOLED，那它肯定是个别名或者需要对应处理
而这个别名就是在Configuration的构造方法里初始化

+
public Configuration() {
    typeAliasRegistry.registerAlias("JDBC", JdbcTransactionFactory.class);
    typeAliasRegistry.registerAlias("MANAGED", ManagedTransactionFactory.class);

    typeAliasRegistry.registerAlias("JNDI", JndiDataSourceFactory.class);
    typeAliasRegistry.registerAlias("POOLED", PooledDataSourceFactory.class);
    typeAliasRegistry.registerAlias("UNPOOLED", UnpooledDataSourceFactory.class);

    typeAliasRegistry.registerAlias("PERPETUAL", PerpetualCache.class);
    typeAliasRegistry.registerAlias("FIFO", FifoCache.class);
    typeAliasRegistry.registerAlias("LRU", LruCache.class);
    typeAliasRegistry.registerAlias("SOFT", SoftCache.class);
    typeAliasRegistry.registerAlias("WEAK", WeakCache.class);

    typeAliasRegistry.registerAlias("DB_VENDOR", VendorDatabaseIdProvider.class);

    typeAliasRegistry.registerAlias("XML", XMLLanguageDriver.class);
    typeAliasRegistry.registerAlias("RAW", RawLanguageDriver.class);

    typeAliasRegistry.registerAlias("SLF4J", Slf4jImpl.class);
    typeAliasRegistry.registerAlias("COMMONS_LOGGING", JakartaCommonsLoggingImpl.class);
    typeAliasRegistry.registerAlias("LOG4J", Log4jImpl.class);
    typeAliasRegistry.registerAlias("LOG4J2", Log4j2Impl.class);
    typeAliasRegistry.registerAlias("JDK_LOGGING", Jdk14LoggingImpl.class);
    typeAliasRegistry.registerAlias("STDOUT_LOGGING", StdOutImpl.class);
    typeAliasRegistry.registerAlias("NO_LOGGING", NoLoggingImpl.class);

    typeAliasRegistry.registerAlias("CGLIB", CglibProxyFactory.class);
    typeAliasRegistry.registerAlias("JAVASSIST", JavassistProxyFactory.class);

    languageRegistry.setDefaultDriverClass(XMLLanguageDriver.class);
    languageRegistry.register(RawLanguageDriver.class);
  }
+

正是通过typeAliasRegistry.registerAlias("POOLED", PooledDataSourceFactory.class);这一行，注册了
POOLED对应的别名类型是PooledDataSourceFactory.class
具体的注册方法是在

+
public void registerAlias(String alias, Class<?> value) {
  if (alias == null) {
    throw new TypeException("The parameter alias cannot be null");
  }
  // issue #748
  // 转换成小写，
  String key = alias.toLowerCase(Locale.ENGLISH);
  // 判断是否已经注册过了
  if (typeAliases.containsKey(key) && typeAliases.get(key) != null && !typeAliases.get(key).equals(value)) {
    throw new TypeException("The alias '" + alias + "' is already mapped to the value '" + typeAliases.get(key).getName() + "'.");
  }
  // 放进map里
  typeAliases.put(key, value);
}
+

而获取的逻辑在这

+
public <T> Class<T> resolveAlias(String string) {
    try {
      if (string == null) {
        return null;
      }
      // issue #748
      // 同样的转成小写
      String key = string.toLowerCase(Locale.ENGLISH);
      Class<T> value;
      if (typeAliases.containsKey(key)) {
        value = (Class<T>) typeAliases.get(key);
      } else {
        // 这里还有从路径下处理的逻辑
        value = (Class<T>) Resources.classForName(string);
      }
      return value;
    } catch (ClassNotFoundException e) {
      throw new TypeException("Could not resolve type alias '" + string + "'.  Cause: " + e, e);
    }
  }
+

逻辑比较简单，但是在mybatis中也是不可或缺的一块概念

]]> - nginx + Java + Mybatis - nginx - openresty + Java + Mysql + Mybatis + + + + NX30Pro 刷成 Openwrt-ImmortalWrt 后作为有线中继的配置方法 + /2024/06/16/NX30Pro-%E5%88%B7%E6%88%90-Openwrt-ImmortalWrt-%E5%90%8E%E4%BD%9C%E4%B8%BA%E6%9C%89%E7%BA%BF%E4%B8%AD%E7%BB%A7%E7%9A%84%E9%85%8D%E7%BD%AE%E6%96%B9%E6%B3%95/ + 因为买了两个NX30Pro，主要是奔着可以刷Openwrt，然后把两个都刷成了ImmortalWrt，结果发现要作为有线中继的话设置还有点麻烦，所以记录下
首先要设置个与主路由相同网段的静态地址，方便管理
在网络-LAN-基本设置-协议设置成“静态地址”
然后IPv4地址就是刚才说的静态地址，可以设个好记的
子网掩码就默认的，255.255.255.0，
IPv4网关就是主路由地址，DNS也设置成主路由地址
基础设置改成忽略此接口-不提供DHCP服务，由主路由提供
然后在高级设置中将IPv6的 路由通告服务和DHCPv6服务跟NDP代理禁用掉，
物理设置中勾选桥接接口
目前我是这样设置可以作为有线中继连通网络了，后续有变化继续更新

+]]> + + openwrt + + + openwrt @@ -3506,6 +3463,49 @@ mfc + + powershell 初体验 + /2022/11/13/powershell-%E5%88%9D%E4%BD%93%E9%AA%8C/ + powershell变量

变量命名类似于php

+
PS C:\Users\Nicks> $a=1
PS C:\Users\Nicks> $b=2
PS C:\Users\Nicks> $a*$b
2
+

有一个比较好用的是变量交换
一般的语言做两个变量交换一般需要一个临时变量

+
$tmp=$a
$a=$b
$b=$tmp
+

而在powershell中可以这样

+
$a,$b=$b,$a
PS C:\Users\Nicks> $a,$b=$b,$a
PS C:\Users\Nicks> $a
2
PS C:\Users\Nicks> $b
1
+

还可以通过这个

+
PS C:\Users\Nicks> ls variable:

Name                           Value
----                           -----
$                              $b
?                              True
^                              $b
a                              2
args                           {}
b                              1
+

查看现存的变量
当然一般脚本都是动态类型的，
可以通过
gettype方法

+]]> + + 语言 + + + powershell + + + + php-abstract-class-and-interface + /2016/11/10/php-abstract-class-and-interface/ + PHP抽象类和接口
    +
  • 抽象类与接口
    • +
  • 抽象类内可以包含非抽象函数，即可实现函数
    • +
  • 抽象类内必须包含至少一个抽象方法，抽象类和接口均不能实例化
    • +
  • 抽象类可以设置访问级别，接口默认都是public
    • +
  • 类可以实现多个接口但不能继承多个抽象类
    • +
  • 类必须实现抽象类和接口里的抽象方法，不一定要实现抽象类的非抽象方法
    • +
  • 接口内不能定义变量，但是可以定义常量
    • +
+

示例代码

<?php
interface int1{
    const INTER1 = 111;
    function inter1();
}
interface int2{
    const INTER1 = 222;
    function inter2();
}
abstract class abst1{
    public function abstr1(){
        echo 1111;
    }
    abstract function abstra1(){
        echo 'ahahahha';
    }
}
abstract class abst2{
    public function abstr2(){
        echo 1111;
    }
    abstract function abstra2();
}
class normal1 extends abst1{
    protected function abstr2(){
        echo 222;
    }
}
+ +

result

PHP Fatal error:  Abstract function abst1::abstra1() cannot contain body in new.php on line 17

Fatal error: Abstract function abst1::abstra1() cannot contain body in php on line 17
+]]> + + php + + + php + + php 的调试方法-查看调用堆栈 /2023/12/31/php-%E7%9A%84%E8%B0%83%E8%AF%95%E6%96%B9%E6%B3%95-%E6%9F%A5%E7%9C%8B%E8%B0%83%E7%94%A8%E5%A0%86%E6%A0%88/ @@ -3576,40 +3576,22 @@ - php-abstract-class-and-interface - /2016/11/10/php-abstract-class-and-interface/ - PHP抽象类和接口
    -
  • 抽象类与接口
    • -
  • 抽象类内可以包含非抽象函数，即可实现函数
    • -
  • 抽象类内必须包含至少一个抽象方法，抽象类和接口均不能实例化
    • -
  • 抽象类可以设置访问级别，接口默认都是public
    • -
  • 类可以实现多个接口但不能继承多个抽象类
    • -
  • 类必须实现抽象类和接口里的抽象方法，不一定要实现抽象类的非抽象方法
    • -
  • 接口内不能定义变量，但是可以定义常量
    • -
-

示例代码

<?php
interface int1{
    const INTER1 = 111;
    function inter1();
}
interface int2{
    const INTER1 = 222;
    function inter2();
}
abstract class abst1{
    public function abstr1(){
        echo 1111;
    }
    abstract function abstra1(){
        echo 'ahahahha';
    }
}
abstract class abst2{
    public function abstr2(){
        echo 1111;
    }
    abstract function abstra2();
}
class normal1 extends abst1{
    protected function abstr2(){
        echo 222;
    }
}
- -

result

PHP Fatal error:  Abstract function abst1::abstra1() cannot contain body in new.php on line 17

Fatal error: Abstract function abst1::abstra1() cannot contain body in php on line 17
-]]> - - php - - - php - - - - powershell 初体验 - /2022/11/13/powershell-%E5%88%9D%E4%BD%93%E9%AA%8C/ - powershell变量

变量命名类似于php

-
PS C:\Users\Nicks> $a=1
PS C:\Users\Nicks> $b=2
PS C:\Users\Nicks> $a*$b
2
-

有一个比较好用的是变量交换
一般的语言做两个变量交换一般需要一个临时变量

-
$tmp=$a
$a=$b
$b=$tmp
-

而在powershell中可以这样

-
$a,$b=$b,$a
PS C:\Users\Nicks> $a,$b=$b,$a
PS C:\Users\Nicks> $a
2
PS C:\Users\Nicks> $b
1
-

还可以通过这个

-
PS C:\Users\Nicks> ls variable:

Name                           Value
----                           -----
$                              $b
?                              True
^                              $b
a                              2
args                           {}
b                              1
-

查看现存的变量
当然一般脚本都是动态类型的，
可以通过
gettype方法

+ powershell 初体验二 + /2022/11/20/powershell-%E5%88%9D%E4%BD%93%E9%AA%8C%E4%BA%8C/ + powershell创建数组也很方便
可以这样

+
$nums=2,0,1,2
+

顺便可以用下我们上次学到的gettype()

+

如果是想创建连续数字的数组还可以用这个方便的方法

+
$nums=1..5
+


而且数组还可以存放各种类型的数据

+
$array=1,"哈哈",([System.Guid]::NewGuid()),(get-date)
+


还有判断类型可以用-is

创建一个空数组

+
$array=@()
+


数组添加元素

+
$array+="a"
+


数组删除元素

+
$a=1..4
$a=$a[0..1]+$a[3]
+

]]> 语言 @@ -3619,18 +3601,46 @@ - redis 的 rdb 和 COW 介绍 - /2021/08/15/redis-%E7%9A%84-rdb-%E5%92%8C-COW-%E4%BB%8B%E7%BB%8D/ - redis 在使用 rdb 策略进行备份时，rdb 的意思是会在开启备份的时候将开启时间点的内存数据进行备份，并且可以设置时间，这样子就是这个策略其实还是不完全可靠的，如果是在这个间隔中宕机了，或者间隔过长，不过这个不在这次的要说的内容中，如果自己去写这个 rdb 的策略可能就有点类似于 mvcc 的 redolog，需要知道这个时间点之前的数据是怎么样的，防止后面更改的干扰，但是这样一方面需要有比较复杂的 mvcc 实现，另一方面是很占用存储空间，所以 redis 在这里面使用了 COW (Copy On Write) 技术，这个技术呢以前听过，也大致了解是怎么个意思，这次稍微具体地来看下，其实 redis 的 copy-on-write 就是来自于 linux 的 cow

-

Linux中的CopyOnWrite

fork()之后，kernel把父进程中所有的内存页的权限都设为read-only，然后子进程的地址空间指向父进程。当父子进程都只读内存时，相安无事。当其中某个进程写内存时，CPU硬件检测到内存页是read-only的，于是触发页异常中断（page-fault），陷入kernel的一个中断例程。中断例程中，kernel就会把触发的异常的页复制一份，于是父子进程各自持有独立的一份。这个操作其实可以类比为写屏障，正常的读取是没问题的，当有写入时就会分裂。

-

CopyOnWrite的好处：

1、减少分配和复制资源时带来的瞬时延迟；
2、减少不必要的资源分配；
CopyOnWrite的缺点：
1、如果父子进程都需要进行大量的写操作，会产生大量的分页错误（页异常中断page-fault）;

-

Redis中的CopyOnWrite

Redis在持久化时，如果是采用BGSAVE命令或者BGREWRITEAOF的方式，那Redis会fork出一个子进程来读取数据，从而写到磁盘中。
总体来看，Redis还是读操作比较多。如果子进程存在期间，发生了大量的写操作，那可能就会出现很多的分页错误(页异常中断page-fault)，这样就得耗费不少性能在复制上。
而在rehash阶段上，写操作是无法避免的。所以Redis在fork出子进程之后，将负载因子阈值提高，尽量减少写操作，避免不必要的内存写入操作，最大限度地节约内存。这里其实更巧妙了，在细节上去优化会产生大量页异常中断的情况。

+ openresty + /2019/06/18/openresty/ + 目前公司要对一些新的产品功能做灰度测试，因为在后端业务代码层面添加判断比较麻烦，所以想在nginx上做点手脚，就想到了openresty
前后也踩了不少坑，这边先写一点

+

首先是日志
error_log logs/error.log debug;
需要nginx开启日志的debug才能看到日志

+

使用 lua_code_cache off即可, 另外注意只有使用 content_by_lua_file 才会生效

+
http {
  lua_code_cache off;
}

location ~* /(\d+-.*)/api/orgunits/load_all(.*) {
   default_type 'application/json;charset=utf-8';
   content_by_lua_file /data/projects/xxx/current/lua/controller/load_data.lua;
}
+ +

使用lua给nginx请求response头添加内容可以用这个

+
ngx.header['response'] = 'header'
+ + +

使用总结

+

后续：

+
    +

  1. 一开始在本地环境的时候使用content_by_lua_file只关注了头，后来发到测试环境发现请求内容都没代理转发到后端服务上
    网上查了下发现content_by_lua_file是将请求的所有内容包括response都用这里面的lua脚本生成了，content这个词就表示是请求内容
    后来改成了access_by_lua_file就正常了，只是要去获取请求内容和修改响应头，并不是要完整的接管请求

    +
    2. +

  2. 后来又碰到了一个坑是nginx有个client_body_buffer_size的配置参数，nginx在32位和64位系统里有8K和16K两个默认值，当请求内容大于这两个值的时候，会把请求内容放到临时文件里，这个时候openresty里的ngx.req.get_post_args()就会报“failed to get post args: requesty body in temp file not supported”这个错误，将client_body_buffer_size这个参数配置调大一点就好了

    +
    3. +

  3. 还有就是lua的异常捕获，网上看一般是用pcall和xpcall来进行保护调用，因为问题主要出在cjson的decode，这里有两个解决方案，一个就是将cjson.decode使用pcall封装，

    + 
    local decode = require("cjson").decode

    function json_decode( str )
        local ok, t = pcall(decode, str)
        if not ok then
          return nil
        end

        return t
    end
    +

    这个是使用了pcall，称为保护调用，会在内部错误后返回两个参数，第一个是false，第二个是错误信息
    还有一种是使用cjson.safe包

    + 
    local json = require("cjson.safe")
    local str = [[ {"key:"value"} ]]

    local t = json.decode(str)
    if t then
        ngx.say(" --> ", type(t))
    end
    +

    cjson.safe包会在解析失败的时候返回nil

    +
    4. +

  4. 还有一个是redis链接时如果host使用的是域名的话会提示“failed to connect: no resolver defined to resolve “redis.xxxxxx.com””，这里需要使用nginx的resolver指令，
    resolver 8.8.8.8 valid=3600s;

    +
    5. +

  5. 还有一点补充下
    就是业务在使用redis的时候使用了db的特性，所以在lua访问redis的时候也需要执行db，这里lua的redis库也支持了这个特性，可以使用instance:select(config:get(‘db’))来切换db

    +
    6. +

  6. 性能优化tips
    建议是尽量少使用阶段钩子，例如content_by_lua_file，*_by_lua

    +
    7. +

  7. 发现一个不错的openresty站点
    地址

    +
    8. +
]]> - redis + nginx - redis + nginx + openresty @@ -3655,56 +3665,6 @@ im - - powershell 初体验二 - /2022/11/20/powershell-%E5%88%9D%E4%BD%93%E9%AA%8C%E4%BA%8C/ - powershell创建数组也很方便
可以这样

-
$nums=2,0,1,2
-

顺便可以用下我们上次学到的gettype()

-

如果是想创建连续数字的数组还可以用这个方便的方法

-
$nums=1..5
-


而且数组还可以存放各种类型的数据

-
$array=1,"哈哈",([System.Guid]::NewGuid()),(get-date)
-


还有判断类型可以用-is

创建一个空数组

-
$array=@()
-


数组添加元素

-
$array+="a"
-


数组删除元素

-
$a=1..4
$a=$a[0..1]+$a[3]
-

-]]> - - 语言 - - - powershell - - - - redis数据结构介绍三-第三部分 整数集合 - /2020/01/10/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%B8%89/ - redis中对于 set 其实有两种处理，对于元素均为整型，并且元素数目较少时，使用 intset 作为底层数据结构，否则使用 dict 作为底层数据结构，先看一下代码先

-
typedef struct intset {
    // 编码方式
    uint32_t encoding;
    // 集合包含的元素数量
    uint32_t length;
    // 保存元素的数组
    int8_t contents[];
} intset;

/* Note that these encodings are ordered, so:
 * INTSET_ENC_INT16 < INTSET_ENC_INT32 < INTSET_ENC_INT64. */
#define INTSET_ENC_INT16 (sizeof(int16_t))
#define INTSET_ENC_INT32 (sizeof(int32_t))
#define INTSET_ENC_INT64 (sizeof(int64_t))
-

一眼看，为啥整型还需要编码，然后 int8_t 怎么能存下大整形呢，带着这些疑问，我们一步步分析下去，这里的编码其实指的是这个整型集合里存的究竟是多大的整型，16 位，还是 32 位，还是 64 位，结构体下面的宏定义就是表示了 encoding 的可能取值，INTSET_ENC_INT16 表示每个元素用2个字节存储，INTSET_ENC_INT32 表示每个元素用4个字节存储，INTSET_ENC_INT64 表示每个元素用8个字节存储。因此，intset中存储的整数最多只能占用64bit。length 就是正常的表示集合中元素的数量。最奇怪的应该就是这个 contents 了，是个 int8_t 的数组，那放毛线数据啊，最小的都有 16 位，这里我在看代码和《redis 设计与实现》的时候也有点懵逼，后来查了下发现这是个比较取巧的用法，这里我用自己的理解表述一下，先看看 8，16，32，64 的关系，一眼看就知道都是 2 的 N 次，并且呈两倍关系，而且 8 位刚好一个字节，所以呢其实这里的contents 不是个常规意义上的 int8_t 类型的数组，而是个柔性数组。看下 wiki 的定义

-
-

Flexible array members1 were introduced in the C99 standard of the C programming language (in particular, in section §6.7.2.1, item 16, page 103).2 It is a member of a struct, which is an array without a given dimension. It must be the last member of such a struct and it must be accompanied by at least one other member, as in the following example:

-
-
struct vectord {
    size_t len;
    double arr[]; // the flexible array member must be last
};
-

在初始化这个 intset 的时候，这个contents数组是不占用空间的，后面的反正用到了申请，那么这里就有一个问题，给出了三种可能的 encoding 值，他们能随便换吗，显然不行，首先在 intset 中数据的存放是有序的，这个有部分原因是方便二分查找，然后存放数据其实随着数据的大小不同会有一个升级的过程，看下图

新创建的intset只有一个header，总共8个字节。其中encoding = 2, length = 0, 类型都是uint32_t，各占 4 字节，添加15, 5两个元素之后，因为它们是比较小的整数，都能使用2个字节表示，所以encoding不变，值还是2，也就是默认的 INTSET_ENC_INT16，当添加32768的时候，它不再能用2个字节来表示了（2个字节能表达的数据范围是-215~215-1，而32768等于215，超出范围了），因此encoding必须升级到INTSET_ENC_INT32（值为4），即用4个字节表示一个元素。在添加每个元素的过程中，intset始终保持从小到大有序。与ziplist类似，intset也是按小端（little endian）模式存储的（参见维基百科词条Endianness）。比如，在上图中intset添加完所有数据之后，表示encoding字段的4个字节应该解释成0x00000004，而第4个数据应该解释成0x00008000 = 32768

-]]> - - Redis - 数据结构 - C - 源码 - Redis - - - redis - 数据结构 - 源码 - - redis数据结构介绍-第一部分 SDS，链表，字典 /2019/12/26/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D/ @@ -3733,18 +3693,13 @@ - redis数据结构介绍五-第五部分 对象 - /2020/01/20/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%BA%94/ - 前面说了这么些数据结构，其实大家对于 redis 最初的印象应该就是个 key-value 的缓存，类似于 memcache，redis 其实也是个 key-value，key 还是一样的字符串，或者说就是用 redis 自己的动态字符串实现，但是 value 其实就是前面说的那些数据结构，差不多快说完了，还有个 quicklist 后面还有一篇，这里先介绍下 redis 对于这些不同类型的 value 是怎么实现的，首先看下 redisObject 的源码头文件

-
/* The actual Redis Object */
#define OBJ_STRING 0    /* String object. */
#define OBJ_LIST 1      /* List object. */
#define OBJ_SET 2       /* Set object. */
#define OBJ_ZSET 3      /* Sorted set object. */
#define OBJ_HASH 4      /* Hash object. */
/*
 * Objects encoding. Some kind of objects like Strings and Hashes can be
 * internally represented in multiple ways. The 'encoding' field of the object
 * is set to one of this fields for this object. */
#define OBJ_ENCODING_RAW 0     /* Raw representation */
#define OBJ_ENCODING_INT 1     /* Encoded as integer */
#define OBJ_ENCODING_HT 2      /* Encoded as hash table */
#define OBJ_ENCODING_ZIPMAP 3  /* Encoded as zipmap */
#define OBJ_ENCODING_LINKEDLIST 4 /* No longer used: old list encoding. */
#define OBJ_ENCODING_ZIPLIST 5 /* Encoded as ziplist */
#define OBJ_ENCODING_INTSET 6  /* Encoded as intset */
#define OBJ_ENCODING_SKIPLIST 7  /* Encoded as skiplist */
#define OBJ_ENCODING_EMBSTR 8  /* Embedded sds string encoding */
#define OBJ_ENCODING_QUICKLIST 9 /* Encoded as linked list of ziplists */
#define OBJ_ENCODING_STREAM 10 /* Encoded as a radix tree of listpacks */

#define LRU_BITS 24
#define LRU_CLOCK_MAX ((1<<LRU_BITS)-1) /* Max value of obj->lru */
#define LRU_CLOCK_RESOLUTION 1000 /* LRU clock resolution in ms */

#define OBJ_SHARED_REFCOUNT INT_MAX
typedef struct redisObject {
    unsigned type:4;
    unsigned encoding:4;
    unsigned lru:LRU_BITS; /* LRU time (relative to global lru_clock) or
                            * LFU data (least significant 8 bits frequency
                            * and most significant 16 bits access time). */
    int refcount;
    void *ptr;
} robj;
-

主体结构就是这个 redisObject，

-
    -
  • type： 字段表示对象的类型，它对应的就是 redis 的对外暴露的，或者说用户可以使用的五种类型，OBJ_STRING, OBJ_LIST, OBJ_SET, OBJ_ZSET, OBJ_HASH
    • -
  • encoding： 字段表示这个对象在 redis 内部的编码方式，由OBJ_ENCODING_开头的 11 种
    • -
  • lru: 做LRU替换算法用，占24个bit
    • -
  • refcount: 引用计数。它允许robj对象在某些情况下被共享。
    • -
  • ptr: 指向底层实现数据结构的指针
    当 type 是 OBJ_STRING 时，表示类型是个 string，它的编码方式 encoding 可能有 OBJ_ENCODING_RAW，OBJ_ENCODING_INT，OBJ_ENCODING_EMBSTR 三种
    当 type 是 OBJ_LIST 时，表示类型是 list，它的编码方式 encoding 是 OBJ_ENCODING_QUICKLIST，对于早一些的版本，2.2这种可能还会使用 OBJ_ENCODING_ZIPLIST，OBJ_ENCODING_LINKEDLIST
    当 type 是 OBJ_SET 时，是个集合，但是得看具体元素的类型，有可能使用整数集合，OBJ_ENCODING_INTSET, 如果元素不全是整型或者数量超过一定限制，那么编码就是 OBJ_ENCODING_HT hash table 了
    当 type 是 OBJ_ZSET 时，是个有序集合，它底层有可能使用的是 OBJ_ENCODING_ZIPLIST 或者 OBJ_ENCODING_SKIPLIST
    当 type 是 OBJ_HASH 时，一开始也是 OBJ_ENCODING_ZIPLIST，然后当数据量大于 hash_max_ziplist_entries 时会转成 OBJ_ENCODING_HT
    • -
+ redis数据结构介绍二-第二部分 跳表 + /2020/01/04/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%BA%8C/ + 跳表 skiplist

跳表是个在我们日常的代码中不太常用到的数据结构，相对来讲就没有像数组，链表，字典，散列，树等结构那么熟悉，所以就从头开始分析下，首先是链表，跳表跟链表都有个表字（太硬扯了我🤦‍♀️），注意这是个有序链表

如上图，在这个链表里如果我要找到 23，是不是我需要从3，5，9开始一直往后找直到找到 23，也就是说时间复杂度是 O(N),N 的一次幂复杂度，那么我们来看看第二个

这个结构跟原先有点不一样，它给链表中偶数位的节点又加了一个指针把它们链接起来，这样子当我们要寻找 23 的时候就可以从原来的一个个往下找变成跳着找，先找到 5，然后是 10，接着是 19，然后是 28，这时候发现 28 比 23 大了，那我在退回到 19，然后从下一层原来的链表往前找，

这里毛估估是不是前面的节点我就少找了一半,有那么点二分法的意思。
前面的其实是跳表的引子，真正的跳表其实不是这样，因为上面的其实有个比较大的问题，就是插入一个元素后需要调整每个元素的指针，在 redis 中的跳表其实是做了个随机层数的优化，因为沿着前面的例子，其实当数据量很大的时候，是不是层数越多，其查询效率越高，但是随着层数变多，要保持这种严格的层数规则其实也会增大处理复杂度，所以 redis 插入每个元素的时候都是使用随机的方式，看一眼代码

+
/* ZSETs use a specialized version of Skiplists */
typedef struct zskiplistNode {
    sds ele;
    double score;
    struct zskiplistNode *backward;
    struct zskiplistLevel {
        struct zskiplistNode *forward;
        unsigned long span;
    } level[];
} zskiplistNode;

typedef struct zskiplist {
    struct zskiplistNode *header, *tail;
    unsigned long length;
    int level;
} zskiplist;

typedef struct zset {
    dict *dict;
    zskiplist *zsl;
} zset;
+

忘了说了，redis 是把 skiplist 跳表用在 zset 里，zset 是个有序的集合，可以看到 zskiplist 就是个跳表的结构，里面用 header 保存跳表的表头，tail 保存表尾，还有长度和最大层级，具体的跳表节点元素使用 zskiplistNode 表示，里面包含了 sds 类型的元素值，double 类型的分值，用来排序，一个 backward 后向指针和一个 zskiplistLevel 数组，每个 level 包含了一个前向指针，和一个 span，span 表示的是跳表前向指针的跨度，这里再补充一点，前面说了为了灵活这个跳表的新增修改，redis 使用了随机层高的方式插入新节点，但是如果所有节点都随机到很高的层级或者所有都很低的话，跳表的效率优势就会减小，所以 redis 使用了个小技巧，贴下代码

+
#define ZSKIPLIST_P 0.25      /* Skiplist P = 1/4 */
int zslRandomLevel(void) {
    int level = 1;
    while ((random()&0xFFFF) < (ZSKIPLIST_P * 0xFFFF))
        level += 1;
    return (level<ZSKIPLIST_MAXLEVEL) ? level : ZSKIPLIST_MAXLEVEL;
}
+

当随机值跟0xFFFF进行与操作小于ZSKIPLIST_P * 0xFFFF时才会增大 level 的值，因此保持了一个相对递减的概率
可以简单分析下，当 random() 的值小于 0xFFFF 的 1/4,才会 level + 1，就意味着当有 1 - 1/4也就是3/4的概率是直接跳出，所以一层的概率是3/4,也就是 1-P，二层的概率是 P*(1-P),三层的概率是 P² * (1-P) 依次递推。

]]> Redis @@ -3760,42 +3715,31 @@ - redis数据结构介绍二-第二部分 跳表 - /2020/01/04/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%BA%8C/ - 跳表 skiplist

跳表是个在我们日常的代码中不太常用到的数据结构，相对来讲就没有像数组，链表，字典，散列，树等结构那么熟悉，所以就从头开始分析下，首先是链表，跳表跟链表都有个表字（太硬扯了我🤦‍♀️），注意这是个有序链表

如上图，在这个链表里如果我要找到 23，是不是我需要从3，5，9开始一直往后找直到找到 23，也就是说时间复杂度是 O(N),N 的一次幂复杂度，那么我们来看看第二个

这个结构跟原先有点不一样，它给链表中偶数位的节点又加了一个指针把它们链接起来，这样子当我们要寻找 23 的时候就可以从原来的一个个往下找变成跳着找，先找到 5，然后是 10，接着是 19，然后是 28，这时候发现 28 比 23 大了，那我在退回到 19，然后从下一层原来的链表往前找，

这里毛估估是不是前面的节点我就少找了一半,有那么点二分法的意思。
前面的其实是跳表的引子，真正的跳表其实不是这样，因为上面的其实有个比较大的问题，就是插入一个元素后需要调整每个元素的指针，在 redis 中的跳表其实是做了个随机层数的优化，因为沿着前面的例子，其实当数据量很大的时候，是不是层数越多，其查询效率越高，但是随着层数变多，要保持这种严格的层数规则其实也会增大处理复杂度，所以 redis 插入每个元素的时候都是使用随机的方式，看一眼代码

-
/* ZSETs use a specialized version of Skiplists */
typedef struct zskiplistNode {
    sds ele;
    double score;
    struct zskiplistNode *backward;
    struct zskiplistLevel {
        struct zskiplistNode *forward;
        unsigned long span;
    } level[];
} zskiplistNode;

typedef struct zskiplist {
    struct zskiplistNode *header, *tail;
    unsigned long length;
    int level;
} zskiplist;

typedef struct zset {
    dict *dict;
    zskiplist *zsl;
} zset;
-

忘了说了，redis 是把 skiplist 跳表用在 zset 里，zset 是个有序的集合，可以看到 zskiplist 就是个跳表的结构，里面用 header 保存跳表的表头，tail 保存表尾，还有长度和最大层级，具体的跳表节点元素使用 zskiplistNode 表示，里面包含了 sds 类型的元素值，double 类型的分值，用来排序，一个 backward 后向指针和一个 zskiplistLevel 数组，每个 level 包含了一个前向指针，和一个 span，span 表示的是跳表前向指针的跨度，这里再补充一点，前面说了为了灵活这个跳表的新增修改，redis 使用了随机层高的方式插入新节点，但是如果所有节点都随机到很高的层级或者所有都很低的话，跳表的效率优势就会减小，所以 redis 使用了个小技巧，贴下代码

-
#define ZSKIPLIST_P 0.25      /* Skiplist P = 1/4 */
int zslRandomLevel(void) {
    int level = 1;
    while ((random()&0xFFFF) < (ZSKIPLIST_P * 0xFFFF))
        level += 1;
    return (level<ZSKIPLIST_MAXLEVEL) ? level : ZSKIPLIST_MAXLEVEL;
}
-

当随机值跟0xFFFF进行与操作小于ZSKIPLIST_P * 0xFFFF时才会增大 level 的值，因此保持了一个相对递减的概率
可以简单分析下，当 random() 的值小于 0xFFFF 的 1/4,才会 level + 1，就意味着当有 1 - 1/4也就是3/4的概率是直接跳出，所以一层的概率是3/4,也就是 1-P，二层的概率是 P*(1-P),三层的概率是 P² * (1-P) 依次递推。

+ redis 的 rdb 和 COW 介绍 + /2021/08/15/redis-%E7%9A%84-rdb-%E5%92%8C-COW-%E4%BB%8B%E7%BB%8D/ + redis 在使用 rdb 策略进行备份时，rdb 的意思是会在开启备份的时候将开启时间点的内存数据进行备份，并且可以设置时间，这样子就是这个策略其实还是不完全可靠的，如果是在这个间隔中宕机了，或者间隔过长，不过这个不在这次的要说的内容中，如果自己去写这个 rdb 的策略可能就有点类似于 mvcc 的 redolog，需要知道这个时间点之前的数据是怎么样的，防止后面更改的干扰，但是这样一方面需要有比较复杂的 mvcc 实现，另一方面是很占用存储空间，所以 redis 在这里面使用了 COW (Copy On Write) 技术，这个技术呢以前听过，也大致了解是怎么个意思，这次稍微具体地来看下，其实 redis 的 copy-on-write 就是来自于 linux 的 cow

+

Linux中的CopyOnWrite

fork()之后，kernel把父进程中所有的内存页的权限都设为read-only，然后子进程的地址空间指向父进程。当父子进程都只读内存时，相安无事。当其中某个进程写内存时，CPU硬件检测到内存页是read-only的，于是触发页异常中断（page-fault），陷入kernel的一个中断例程。中断例程中，kernel就会把触发的异常的页复制一份，于是父子进程各自持有独立的一份。这个操作其实可以类比为写屏障，正常的读取是没问题的，当有写入时就会分裂。

+

CopyOnWrite的好处：

1、减少分配和复制资源时带来的瞬时延迟；
2、减少不必要的资源分配；
CopyOnWrite的缺点：
1、如果父子进程都需要进行大量的写操作，会产生大量的分页错误（页异常中断page-fault）;

+

Redis中的CopyOnWrite

Redis在持久化时，如果是采用BGSAVE命令或者BGREWRITEAOF的方式，那Redis会fork出一个子进程来读取数据，从而写到磁盘中。
总体来看，Redis还是读操作比较多。如果子进程存在期间，发生了大量的写操作，那可能就会出现很多的分页错误(页异常中断page-fault)，这样就得耗费不少性能在复制上。
而在rehash阶段上，写操作是无法避免的。所以Redis在fork出子进程之后，将负载因子阈值提高，尽量减少写操作，避免不必要的内存写入操作，最大限度地节约内存。这里其实更巧妙了，在细节上去优化会产生大量页异常中断的情况。

]]> - Redis - 数据结构 - C - 源码 - Redis + redis redis - 数据结构 - 源码 - redis数据结构介绍四-第四部分 压缩表 - /2020/01/19/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E5%9B%9B/ - 在 redis 中还有一类表型数据结构叫压缩表，ziplist，它的目的是替代链表，链表是个很容易理解的数据结构，双向链表有前后指针，有带头结点的有的不带，但是链表有个比较大的问题是相对于普通的数组，它的内存不连续，碎片化的存储，内存利用效率不高，而且指针寻址相对于直接使用偏移量的话，也有一定的效率劣势，当然这不是主要的原因，ziplist 设计的主要目的是让链表的内存使用更高效

+ redis数据结构介绍三-第三部分 整数集合 + /2020/01/10/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%B8%89/ + redis中对于 set 其实有两种处理，对于元素均为整型，并且元素数目较少时，使用 intset 作为底层数据结构，否则使用 dict 作为底层数据结构，先看一下代码先

+
typedef struct intset {
    // 编码方式
    uint32_t encoding;
    // 集合包含的元素数量
    uint32_t length;
    // 保存元素的数组
    int8_t contents[];
} intset;

/* Note that these encodings are ordered, so:
 * INTSET_ENC_INT16 < INTSET_ENC_INT32 < INTSET_ENC_INT64. */
#define INTSET_ENC_INT16 (sizeof(int16_t))
#define INTSET_ENC_INT32 (sizeof(int32_t))
#define INTSET_ENC_INT64 (sizeof(int64_t))
+

一眼看，为啥整型还需要编码，然后 int8_t 怎么能存下大整形呢，带着这些疑问，我们一步步分析下去，这里的编码其实指的是这个整型集合里存的究竟是多大的整型，16 位，还是 32 位，还是 64 位，结构体下面的宏定义就是表示了 encoding 的可能取值，INTSET_ENC_INT16 表示每个元素用2个字节存储，INTSET_ENC_INT32 表示每个元素用4个字节存储，INTSET_ENC_INT64 表示每个元素用8个字节存储。因此，intset中存储的整数最多只能占用64bit。length 就是正常的表示集合中元素的数量。最奇怪的应该就是这个 contents 了，是个 int8_t 的数组，那放毛线数据啊，最小的都有 16 位，这里我在看代码和《redis 设计与实现》的时候也有点懵逼，后来查了下发现这是个比较取巧的用法，这里我用自己的理解表述一下，先看看 8，16，32，64 的关系，一眼看就知道都是 2 的 N 次，并且呈两倍关系，而且 8 位刚好一个字节，所以呢其实这里的contents 不是个常规意义上的 int8_t 类型的数组，而是个柔性数组。看下 wiki 的定义

-

The ziplist is a specially encoded dually linked list that is designed to be very memory efficient.
这是摘自 redis 源码中ziplist.c 文件的注释，也说明了原因，它的大概结构是这样子

+

Flexible array members1 were introduced in the C99 standard of the C programming language (in particular, in section §6.7.2.1, item 16, page 103).2 It is a member of a struct, which is an array without a given dimension. It must be the last member of such a struct and it must be accompanied by at least one other member, as in the following example:

-
<zlbytes> <zltail> <zllen> <entry> <entry> ... <entry> <zlend>
-

其中
<zlbytes>表示 ziplist 占用的字节总数，类型是uint32_t，32 位的无符号整型，当然表示的字节数也包含自己本身占用的 4 个
<zltail> 类型也是是uint32_t，表示ziplist表中最后一项（entry）在ziplist中的偏移字节数。<zltail>的存在，使得我们可以很方便地找到最后一项（不用遍历整个ziplist），从而可以在ziplist尾端快速地执行push或pop操作。
<uint16_t zllen> 表示ziplist 中的数据项个数，因为是 16 位，所以当数量超过所能表示的最大的数量，它的 16 位全会置为 1，但是真实的数量需要遍历整个 ziplist 才能知道
<entry>是具体的数据项，后面解释
<zlend> ziplist 的最后一个字节，固定是255。
再看一下<entry>中的具体结构，

-
<prevlen> <encoding> <entry-data>
-

首先这个<prevlen>有两种情况，一种是前面的元素的长度，如果是小于等于 253的时候就用一个uint8_t 来表示前一元素的长度，如果大于的话他将占用五个字节，第一个字节是 254，即表示这个字节已经表示不下了，需要后面的四个字节帮忙表示
<encoding>这个就比较复杂，把源码的注释放下面先看下

-
* |00pppppp| - 1 byte
*      String value with length less than or equal to 63 bytes (6 bits).
*      "pppppp" represents the unsigned 6 bit length.
* |01pppppp|qqqqqqqq| - 2 bytes
*      String value with length less than or equal to 16383 bytes (14 bits).
*      IMPORTANT: The 14 bit number is stored in big endian.
* |10000000|qqqqqqqq|rrrrrrrr|ssssssss|tttttttt| - 5 bytes
*      String value with length greater than or equal to 16384 bytes.
*      Only the 4 bytes following the first byte represents the length
*      up to 32^2-1. The 6 lower bits of the first byte are not used and
*      are set to zero.
*      IMPORTANT: The 32 bit number is stored in big endian.
* |11000000| - 3 bytes
*      Integer encoded as int16_t (2 bytes).
* |11010000| - 5 bytes
*      Integer encoded as int32_t (4 bytes).
* |11100000| - 9 bytes
*      Integer encoded as int64_t (8 bytes).
* |11110000| - 4 bytes
*      Integer encoded as 24 bit signed (3 bytes).
* |11111110| - 2 bytes
*      Integer encoded as 8 bit signed (1 byte).
* |1111xxxx| - (with xxxx between 0000 and 1101) immediate 4 bit integer.
*      Unsigned integer from 0 to 12. The encoded value is actually from
*      1 to 13 because 0000 and 1111 can not be used, so 1 should be
*      subtracted from the encoded 4 bit value to obtain the right value.
* |11111111| - End of ziplist special entry.
-

首先如果 encoding 的前两位是 00 的话代表这个元素是个 6 位的字符串，即直接将数据保存在 encoding 中，不消耗额外的<entry-data>，如果前两位是 01 的话表示是个 14 位的字符串，如果是 10 的话表示encoding 块之后的四个字节是存放字符串类型的数据，encoding 的剩余 6 位置 0。
如果 encoding 的前两位是 11 的话表示这是个整型，具体的如果后两位是00的话，表示后面是个2字节的 int16_t 类型，如果是01的话，后面是个4字节的int32_t,如果是10的话后面是8字节的int64_t,如果是 11 的话后面是 3 字节的有符号整型，这些都要最后 4 位都是 0 的情况噢
剩下当是11111110时，则表示是一个1 字节的有符号数，如果是 1111xxxx，其中xxxx在0000 到 1101 表示实际的 1 到 13，为啥呢，因为 0000 前面已经用过了，而 1110 跟 1111 也都有用了。
看个具体的例子(上下有点对不齐，将就看)

-
[0f 00 00 00] [0c 00 00 00] [02 00] [00 f3] [02 f6] [ff]
|**zlbytes***|  |***zltail***|  |*zllen*|  |entry1 entry2|  |zlend|
-

第一部分代表整个 ziplist 有 15 个字节，zlbytes 自己占了 4 个 zltail 表示最后一个元素的偏移量，第 13 个字节起，zllen 表示有 2 个元素，第一个元素是00f3,00表示前一个元素长度是 0，本来前面就没元素(不过不知道这个能不能优化这一字节)，然后是 f3，换成二进制就是11110011,对照上面的注释，是落在|1111xxxx|这个类型里，注意这个其实是用 0001 到 1101 也就是 1到 13 来表示 0到 12，所以 f3 应该就是 2，第一个元素是 2，第二个元素呢，02 代表前一个元素也就是刚才说的这个，占用 2 字节，f6 展开也是刚才的类型，实际是 5，ff 表示 ziplist 的结尾，所以这个 ziplist 里面是两个元素，2 跟 5

+
struct vectord {
    size_t len;
    double arr[]; // the flexible array member must be last
};
+

在初始化这个 intset 的时候，这个contents数组是不占用空间的，后面的反正用到了申请，那么这里就有一个问题，给出了三种可能的 encoding 值，他们能随便换吗，显然不行，首先在 intset 中数据的存放是有序的，这个有部分原因是方便二分查找，然后存放数据其实随着数据的大小不同会有一个升级的过程，看下图

新创建的intset只有一个header，总共8个字节。其中encoding = 2, length = 0, 类型都是uint32_t，各占 4 字节，添加15, 5两个元素之后，因为它们是比较小的整数，都能使用2个字节表示，所以encoding不变，值还是2，也就是默认的 INTSET_ENC_INT16，当添加32768的时候，它不再能用2个字节来表示了（2个字节能表达的数据范围是-215~215-1，而32768等于215，超出范围了），因此encoding必须升级到INTSET_ENC_INT32（值为4），即用4个字节表示一个元素。在添加每个元素的过程中，intset始终保持从小到大有序。与ziplist类似，intset也是按小端（little endian）模式存储的（参见维基百科词条Endianness）。比如，在上图中intset添加完所有数据之后，表示encoding字段的4个字节应该解释成0x00000004，而第4个数据应该解释成0x00008000 = 32768

]]> Redis @@ -3852,84 +3796,128 @@ - redis淘汰策略复习 - /2021/08/01/redis%E6%B7%98%E6%B1%B0%E7%AD%96%E7%95%A5%E5%A4%8D%E4%B9%A0/ - 前面复习了 redis 的过期策略，这里再复习下淘汰策略，淘汰跟过期的区别有时候会被混淆了，过期主要针对那些设置了过期时间的 key，应该说是一种逻辑策略，是主动的还是被动的加定时的，两种有各自的取舍，而淘汰也可以看成是一种保持系统稳定的策略，因为如果内存满了，不采取任何策略处理，那大概率会导致系统故障，之前其实主要从源码角度分析过redis 的 LRU 和 LFU，但这个是偏底层的实现，抠得比较细，那么具体的系统层面的配置是有哪些策略，来看下 redis labs 的介绍

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
PolicyDescription
noeviction 不逐出Returns an error if the memory limit has been reached when trying to insert more data，插入更多数据时，如果内存达到上限了，返回错误
allkeys-lru 所有的 key 使用 lru 逐出Evicts the least recently used keys out of all keys 在所有 key 中逐出最近最少使用的
allkeys-lfu 所有的 key 使用 lfu 逐出Evicts the least frequently used keys out of all keys 在所有 key 中逐出最近最不频繁使用的
allkeys-random 所有的 key 中随机逐出Randomly evicts keys out of all keys 在所有 key 中随机逐出
volatile-lruEvicts the least recently used keys out of all keys with an “expire” field set 在设置了过期时间的 key 空间 expire 中使用 lru 策略逐出
volatile-lfuEvicts the least frequently used keys out of all keys with an “expire” field set 在设置了过期时间的 key 空间 expire 中使用 lfu 策略逐出
volatile-randomRandomly evicts keys with an “expire” field set 在设置了过期时间的 key 空间 expire 中随机逐出
volatile-ttlEvicts the shortest time-to-live keys out of all keys with an “expire” field set.在设置了过期时间的 key 空间 expire 中逐出更早过期的
-

而在这其中默认使用的策略是 volatile-lru，对 lru 跟 lfu 想有更多的了解可以看下我之前的文章redis系列介绍八-淘汰策略

+ redis数据结构介绍五-第五部分 对象 + /2020/01/20/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%BA%94/ + ">}
+

这里有几点要注意的，第一是当惰性删除时会根据lazyfree_lazy_expire这个参数去判断是执行同步删除还是异步删除，另外一点是对于 slave，是不需要执行的，因为会在 master 过期时向 slave 发送 del 指令。
光采用这个策略会有什么问题呢，假如一些key 一直未被访问，那这些 key 就不会过期了，导致一直被占用着内存，所以 redis 采取了懒汉式过期加定期过期策略，定期策略是怎么执行的呢

+
/* This function handles 'background' operations we are required to do
 * incrementally in Redis databases, such as active key expiring, resizing,
 * rehashing. */
void databasesCron(void) {
    /* Expire keys by random sampling. Not required for slaves
     * as master will synthesize DELs for us. */
    if (server.active_expire_enabled) {
        if (server.masterhost == NULL) {
            activeExpireCycle(ACTIVE_EXPIRE_CYCLE_SLOW);
        } else {
            expireSlaveKeys();
        }
    }

    /* Defrag keys gradually. */
    activeDefragCycle();

    /* Perform hash tables rehashing if needed, but only if there are no
     * other processes saving the DB on disk. Otherwise rehashing is bad
     * as will cause a lot of copy-on-write of memory pages. */
    if (!hasActiveChildProcess()) {
        /* We use global counters so if we stop the computation at a given
         * DB we'll be able to start from the successive in the next
         * cron loop iteration. */
        static unsigned int resize_db = 0;
        static unsigned int rehash_db = 0;
        int dbs_per_call = CRON_DBS_PER_CALL;
        int j;

        /* Don't test more DBs than we have. */
        if (dbs_per_call > server.dbnum) dbs_per_call = server.dbnum;

        /* Resize */
        for (j = 0; j < dbs_per_call; j++) {
            tryResizeHashTables(resize_db % server.dbnum);
            resize_db++;
        }

        /* Rehash */
        if (server.activerehashing) {
            for (j = 0; j < dbs_per_call; j++) {
                int work_done = incrementallyRehash(rehash_db);
                if (work_done) {
                    /* If the function did some work, stop here, we'll do
                     * more at the next cron loop. */
                    break;
                } else {
                    /* If this db didn't need rehash, we'll try the next one. */
                    rehash_db++;
                    rehash_db %= server.dbnum;
                }
            }
        }
    }
}
/* Try to expire a few timed out keys. The algorithm used is adaptive and
 * will use few CPU cycles if there are few expiring keys, otherwise
 * it will get more aggressive to avoid that too much memory is used by
 * keys that can be removed from the keyspace.
 *
 * Every expire cycle tests multiple databases: the next call will start
 * again from the next db, with the exception of exists for time limit: in that
 * case we restart again from the last database we were processing. Anyway
 * no more than CRON_DBS_PER_CALL databases are tested at every iteration.
 *
 * The function can perform more or less work, depending on the "type"
 * argument. It can execute a "fast cycle" or a "slow cycle". The slow
 * cycle is the main way we collect expired cycles: this happens with
 * the "server.hz" frequency (usually 10 hertz).
 *
 * However the slow cycle can exit for timeout, since it used too much time.
 * For this reason the function is also invoked to perform a fast cycle
 * at every event loop cycle, in the beforeSleep() function. The fast cycle
 * will try to perform less work, but will do it much more often.
 *
 * The following are the details of the two expire cycles and their stop
 * conditions:
 *
 * If type is ACTIVE_EXPIRE_CYCLE_FAST the function will try to run a
 * "fast" expire cycle that takes no longer than EXPIRE_FAST_CYCLE_DURATION
 * microseconds, and is not repeated again before the same amount of time.
 * The cycle will also refuse to run at all if the latest slow cycle did not
 * terminate because of a time limit condition.
 *
 * If type is ACTIVE_EXPIRE_CYCLE_SLOW, that normal expire cycle is
 * executed, where the time limit is a percentage of the REDIS_HZ period
 * as specified by the ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC define. In the
 * fast cycle, the check of every database is interrupted once the number
 * of already expired keys in the database is estimated to be lower than
 * a given percentage, in order to avoid doing too much work to gain too
 * little memory.
 *
 * The configured expire "effort" will modify the baseline parameters in
 * order to do more work in both the fast and slow expire cycles.
 */

#define ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP 20 /* Keys for each DB loop. */
#define ACTIVE_EXPIRE_CYCLE_FAST_DURATION 1000 /* Microseconds. */
#define ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC 25 /* Max % of CPU to use. */
#define ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE 10 /* % of stale keys after which
                                                   we do extra efforts. */
void activeExpireCycle(int type) {
    /* Adjust the running parameters according to the configured expire
     * effort. The default effort is 1, and the maximum configurable effort
     * is 10. */
    unsigned long
    effort = server.active_expire_effort-1, /* Rescale from 0 to 9. */
    config_keys_per_loop = ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP +
                           ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP/4*effort,
    config_cycle_fast_duration = ACTIVE_EXPIRE_CYCLE_FAST_DURATION +
                                 ACTIVE_EXPIRE_CYCLE_FAST_DURATION/4*effort,
    config_cycle_slow_time_perc = ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC +
                                  2*effort,
    config_cycle_acceptable_stale = ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE-
                                    effort;

    /* This function has some global state in order to continue the work
     * incrementally across calls. */
    static unsigned int current_db = 0; /* Last DB tested. */
    static int timelimit_exit = 0;      /* Time limit hit in previous call? */
    static long long last_fast_cycle = 0; /* When last fast cycle ran. */

    int j, iteration = 0;
    int dbs_per_call = CRON_DBS_PER_CALL;
    long long start = ustime(), timelimit, elapsed;

    /* When clients are paused the dataset should be static not just from the
     * POV of clients not being able to write, but also from the POV of
     * expires and evictions of keys not being performed. */
    if (clientsArePaused()) return;

    if (type == ACTIVE_EXPIRE_CYCLE_FAST) {
        /* Don't start a fast cycle if the previous cycle did not exit
         * for time limit, unless the percentage of estimated stale keys is
         * too high. Also never repeat a fast cycle for the same period
         * as the fast cycle total duration itself. */
        if (!timelimit_exit &&
            server.stat_expired_stale_perc < config_cycle_acceptable_stale)
            return;

        if (start < last_fast_cycle + (long long)config_cycle_fast_duration*2)
            return;

        last_fast_cycle = start;
    }

    /* We usually should test CRON_DBS_PER_CALL per iteration, with
     * two exceptions:
     *
     * 1) Don't test more DBs than we have.
     * 2) If last time we hit the time limit, we want to scan all DBs
     * in this iteration, as there is work to do in some DB and we don't want
     * expired keys to use memory for too much time. */
    if (dbs_per_call > server.dbnum || timelimit_exit)
        dbs_per_call = server.dbnum;

    /* We can use at max 'config_cycle_slow_time_perc' percentage of CPU
     * time per iteration. Since this function gets called with a frequency of
     * server.hz times per second, the following is the max amount of
     * microseconds we can spend in this function. */
    timelimit = config_cycle_slow_time_perc*1000000/server.hz/100;
    timelimit_exit = 0;
    if (timelimit <= 0) timelimit = 1;

    if (type == ACTIVE_EXPIRE_CYCLE_FAST)
        timelimit = config_cycle_fast_duration; /* in microseconds. */

    /* Accumulate some global stats as we expire keys, to have some idea
     * about the number of keys that are already logically expired, but still
     * existing inside the database. */
    long total_sampled = 0;
    long total_expired = 0;

    for (j = 0; j < dbs_per_call && timelimit_exit == 0; j++) {
        /* Expired and checked in a single loop. */
        unsigned long expired, sampled;

        redisDb *db = server.db+(current_db % server.dbnum);

        /* Increment the DB now so we are sure if we run out of time
         * in the current DB we'll restart from the next. This allows to
         * distribute the time evenly across DBs. */
        current_db++;

        /* Continue to expire if at the end of the cycle more than 25%
         * of the keys were expired. */
        do {
            unsigned long num, slots;
            long long now, ttl_sum;
            int ttl_samples;
            iteration++;

            /* If there is nothing to expire try next DB ASAP. */
            if ((num = dictSize(db->expires)) == 0) {
                db->avg_ttl = 0;
                break;
            }
            slots = dictSlots(db->expires);
            now = mstime();

            /* When there are less than 1% filled slots, sampling the key
             * space is expensive, so stop here waiting for better times...
             * The dictionary will be resized asap. */
            if (num && slots > DICT_HT_INITIAL_SIZE &&
                (num*100/slots < 1)) break;

            /* The main collection cycle. Sample random keys among keys
             * with an expire set, checking for expired ones. */
            expired = 0;
            sampled = 0;
            ttl_sum = 0;
            ttl_samples = 0;

            if (num > config_keys_per_loop)
                num = config_keys_per_loop;

            /* Here we access the low level representation of the hash table
             * for speed concerns: this makes this code coupled with dict.c,
             * but it hardly changed in ten years.
             *
             * Note that certain places of the hash table may be empty,
             * so we want also a stop condition about the number of
             * buckets that we scanned. However scanning for free buckets
             * is very fast: we are in the cache line scanning a sequential
             * array of NULL pointers, so we can scan a lot more buckets
             * than keys in the same time. */
            long max_buckets = num*20;
            long checked_buckets = 0;

            while (sampled < num && checked_buckets < max_buckets) {
                for (int table = 0; table < 2; table++) {
                    if (table == 1 && !dictIsRehashing(db->expires)) break;

                    unsigned long idx = db->expires_cursor;
                    idx &= db->expires->ht[table].sizemask;
                    dictEntry *de = db->expires->ht[table].table[idx];
                    long long ttl;

                    /* Scan the current bucket of the current table. */
                    checked_buckets++;
                    while(de) {
                        /* Get the next entry now since this entry may get
                         * deleted. */
                        dictEntry *e = de;
                        de = de->next;

                        ttl = dictGetSignedIntegerVal(e)-now;
                        if (activeExpireCycleTryExpire(db,e,now)) expired++;
                        if (ttl > 0) {
                            /* We want the average TTL of keys yet
                             * not expired. */
                            ttl_sum += ttl;
                            ttl_samples++;
                        }
                        sampled++;
                    }
                }
                db->expires_cursor++;
            }
            total_expired += expired;
            total_sampled += sampled;

            /* Update the average TTL stats for this database. */
            if (ttl_samples) {
                long long avg_ttl = ttl_sum/ttl_samples;

                /* Do a simple running average with a few samples.
                 * We just use the current estimate with a weight of 2%
                 * and the previous estimate with a weight of 98%. */
                if (db->avg_ttl == 0) db->avg_ttl = avg_ttl;
                db->avg_ttl = (db->avg_ttl/50)*49 + (avg_ttl/50);
            }

            /* We can't block forever here even if there are many keys to
             * expire. So after a given amount of milliseconds return to the
             * caller waiting for the other active expire cycle. */
            if ((iteration & 0xf) == 0) { /* check once every 16 iterations. */
                elapsed = ustime()-start;
                if (elapsed > timelimit) {
                    timelimit_exit = 1;
                    server.stat_expired_time_cap_reached_count++;
                    break;
                }
            }
            /* We don't repeat the cycle for the current database if there are
             * an acceptable amount of stale keys (logically expired but yet
             * not reclained). */
        } while ((expired*100/sampled) > config_cycle_acceptable_stale);
    }

    elapsed = ustime()-start;
    server.stat_expire_cycle_time_used += elapsed;
    latencyAddSampleIfNeeded("expire-cycle",elapsed/1000);

    /* Update our estimate of keys existing but yet to be expired.
     * Running average with this sample accounting for 5%. */
    double current_perc;
    if (total_sampled) {
        current_perc = (double)total_expired/total_sampled;
    } else
        current_perc = 0;
    server.stat_expired_stale_perc = (current_perc*0.05)+
                                     (server.stat_expired_stale_perc*0.95);
}
+

执行定期清除分成两种类型，快和慢，分别由beforeSleep和databasesCron调用，快版有两个限制，一个是执行时长由ACTIVE_EXPIRE_CYCLE_FAST_DURATION限制，另一个是执行间隔是 2 倍的ACTIVE_EXPIRE_CYCLE_FAST_DURATION，另外这还可以由配置的server.active_expire_effort参数来控制，默认是 1，最大是 10

+
onfig_cycle_fast_duration = ACTIVE_EXPIRE_CYCLE_FAST_DURATION +
                                 ACTIVE_EXPIRE_CYCLE_FAST_DURATION/4*effort
+

然后会从一定数量的 db 中找出一定数量的带过期时间的 key（保存在 expires中），这里的数量是由

+
config_keys_per_loop = ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP +
                           ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP/4*effort
```                                 
控制，慢速的执行时长是
```C
config_cycle_slow_time_perc = ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC +
                                  2*effort
timelimit = config_cycle_slow_time_perc*1000000/server.hz/100;
+

这里还有一个额外的退出条件，如果当前数据库的抽样结果已经达到我们所允许的过期 key 百分比，则下次不再处理当前 db，继续处理下个 db

]]> - redis + Redis + 数据结构 + C + 源码 + Redis redis - 淘汰策略 - 应用 - Evict + 数据结构 + 源码 - redis过期策略复习 - /2021/07/25/redis%E8%BF%87%E6%9C%9F%E7%AD%96%E7%95%A5%E5%A4%8D%E4%B9%A0/ - redis过期策略复习

之前其实写过redis的过期的一些原理，这次主要是记录下，一些使用上的概念，主要是redis使用的过期策略是懒过期和定时清除，懒过期的其实比较简单，即是在key被访问的时候会顺带着判断下这个key是否已过期了，如果已经过期了，就不返回了，但是这种策略有个漏洞是如果有些key之后一直不会被访问了，就等于沉在池底了，所以需要有一个定时的清理机制，去从设置了过期的key池子（expires）里随机地捞key，具体的策略我们看下官网的解释

-
    -
  1. Test 20 random keys from the set of keys with an associated expire.
    2. -
  2. Delete all the keys found expired.
    3. -
  3. If more than 25% of keys were expired, start again from step 1.
    4. -
-

从池子里随机获取20个key，将其中过期的key删掉，如果这其中有超过25%的key已经过期了，那就再来一次，以此保持过期的key不超过25%（左右），并且这个定时策略可以在redis的配置文件

-
# Redis calls an internal function to perform many background tasks, like
# closing connections of clients in timeout, purging expired keys that are
# never requested, and so forth.
#
# Not all tasks are performed with the same frequency, but Redis checks for
# tasks to perform according to the specified "hz" value.
#
# By default "hz" is set to 10. Raising the value will use more CPU when
# Redis is idle, but at the same time will make Redis more responsive when
# there are many keys expiring at the same time, and timeouts may be
# handled with more precision.
#
# The range is between 1 and 500, however a value over 100 is usually not
# a good idea. Most users should use the default of 10 and raise this up to
# 100 only in environments where very low latency is required.
hz 10
- -

可以配置这个hz的值，代表的含义是每秒的执行次数，默认是10，其实也用了hz的普遍含义。有兴趣可以看看之前写的一篇文章redis系列介绍七-过期策略

+ redis数据结构介绍五-第五部分 对象 + /2020/01/20/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%BA%94/ + 前面说了这么些数据结构，其实大家对于 redis 最初的印象应该就是个 key-value 的缓存，类似于 memcache，redis 其实也是个 key-value，key 还是一样的字符串，或者说就是用 redis 自己的动态字符串实现，但是 value 其实就是前面说的那些数据结构，差不多快说完了，还有个 quicklist 后面还有一篇，这里先介绍下 redis 对于这些不同类型的 value 是怎么实现的，首先看下 redisObject 的源码头文件

+
/* The actual Redis Object */
#define OBJ_STRING 0    /* String object. */
#define OBJ_LIST 1      /* List object. */
#define OBJ_SET 2       /* Set object. */
#define OBJ_ZSET 3      /* Sorted set object. */
#define OBJ_HASH 4      /* Hash object. */
/*
 * Objects encoding. Some kind of objects like Strings and Hashes can be
 * internally represented in multiple ways. The 'encoding' field of the object
 * is set to one of this fields for this object. */
#define OBJ_ENCODING_RAW 0     /* Raw representation */
#define OBJ_ENCODING_INT 1     /* Encoded as integer */
#define OBJ_ENCODING_HT 2      /* Encoded as hash table */
#define OBJ_ENCODING_ZIPMAP 3  /* Encoded as zipmap */
#define OBJ_ENCODING_LINKEDLIST 4 /* No longer used: old list encoding. */
#define OBJ_ENCODING_ZIPLIST 5 /* Encoded as ziplist */
#define OBJ_ENCODING_INTSET 6  /* Encoded as intset */
#define OBJ_ENCODING_SKIPLIST 7  /* Encoded as skiplist */
#define OBJ_ENCODING_EMBSTR 8  /* Embedded sds string encoding */
#define OBJ_ENCODING_QUICKLIST 9 /* Encoded as linked list of ziplists */
#define OBJ_ENCODING_STREAM 10 /* Encoded as a radix tree of listpacks */

#define LRU_BITS 24
#define LRU_CLOCK_MAX ((1<<LRU_BITS)-1) /* Max value of obj->lru */
#define LRU_CLOCK_RESOLUTION 1000 /* LRU clock resolution in ms */

#define OBJ_SHARED_REFCOUNT INT_MAX
typedef struct redisObject {
    unsigned type:4;
    unsigned encoding:4;
    unsigned lru:LRU_BITS; /* LRU time (relative to global lru_clock) or
                            * LFU data (least significant 8 bits frequency
                            * and most significant 16 bits access time). */
    int refcount;
    void *ptr;
} robj;
+

主体结构就是这个 redisObject，

+
    +
  • type： 字段表示对象的类型，它对应的就是 redis 的对外暴露的，或者说用户可以使用的五种类型，OBJ_STRING, OBJ_LIST, OBJ_SET, OBJ_ZSET, OBJ_HASH
    • +
  • encoding： 字段表示这个对象在 redis 内部的编码方式，由OBJ_ENCODING_开头的 11 种
    • +
  • lru: 做LRU替换算法用，占24个bit
    • +
  • refcount: 引用计数。它允许robj对象在某些情况下被共享。
    • +
  • ptr: 指向底层实现数据结构的指针
    当 type 是 OBJ_STRING 时，表示类型是个 string，它的编码方式 encoding 可能有 OBJ_ENCODING_RAW，OBJ_ENCODING_INT，OBJ_ENCODING_EMBSTR 三种
    当 type 是 OBJ_LIST 时，表示类型是 list，它的编码方式 encoding 是 OBJ_ENCODING_QUICKLIST，对于早一些的版本，2.2这种可能还会使用 OBJ_ENCODING_ZIPLIST，OBJ_ENCODING_LINKEDLIST
    当 type 是 OBJ_SET 时，是个集合，但是得看具体元素的类型，有可能使用整数集合，OBJ_ENCODING_INTSET, 如果元素不全是整型或者数量超过一定限制，那么编码就是 OBJ_ENCODING_HT hash table 了
    当 type 是 OBJ_ZSET 时，是个有序集合，它底层有可能使用的是 OBJ_ENCODING_ZIPLIST 或者 OBJ_ENCODING_SKIPLIST
    当 type 是 OBJ_HASH 时，一开始也是 OBJ_ENCODING_ZIPLIST，然后当数据量大于 hash_max_ziplist_entries 时会转成 OBJ_ENCODING_HT
    • +
]]> - redis - + Redis + 数据结构 + C + 源码 + Redis + + + redis + 数据结构 + 源码 + + + + redis过期策略复习 + /2021/07/25/redis%E8%BF%87%E6%9C%9F%E7%AD%96%E7%95%A5%E5%A4%8D%E4%B9%A0/ + redis过期策略复习

之前其实写过redis的过期的一些原理，这次主要是记录下，一些使用上的概念，主要是redis使用的过期策略是懒过期和定时清除，懒过期的其实比较简单，即是在key被访问的时候会顺带着判断下这个key是否已过期了，如果已经过期了，就不返回了，但是这种策略有个漏洞是如果有些key之后一直不会被访问了，就等于沉在池底了，所以需要有一个定时的清理机制，去从设置了过期的key池子（expires）里随机地捞key，具体的策略我们看下官网的解释

+
    +
  1. Test 20 random keys from the set of keys with an associated expire.
    2. +
  2. Delete all the keys found expired.
    3. +
  3. If more than 25% of keys were expired, start again from step 1.
    4. +
+

从池子里随机获取20个key，将其中过期的key删掉，如果这其中有超过25%的key已经过期了，那就再来一次，以此保持过期的key不超过25%（左右），并且这个定时策略可以在redis的配置文件

+
# Redis calls an internal function to perform many background tasks, like
# closing connections of clients in timeout, purging expired keys that are
# never requested, and so forth.
#
# Not all tasks are performed with the same frequency, but Redis checks for
# tasks to perform according to the specified "hz" value.
#
# By default "hz" is set to 10. Raising the value will use more CPU when
# Redis is idle, but at the same time will make Redis more responsive when
# there are many keys expiring at the same time, and timeouts may be
# handled with more precision.
#
# The range is between 1 and 500, however a value over 100 is usually not
# a good idea. Most users should use the default of 10 and raise this up to
# 100 only in environments where very low latency is required.
hz 10
+ +

可以配置这个hz的值，代表的含义是每秒的执行次数，默认是10，其实也用了hz的普遍含义。有兴趣可以看看之前写的一篇文章redis系列介绍七-过期策略

+]]> + + redis + redis 应用 过期策略 + + redis系列介绍八-淘汰策略 + /2020/04/18/redis%E7%B3%BB%E5%88%97%E4%BB%8B%E7%BB%8D%E5%85%AB/ + LRU

说完了过期策略再说下淘汰策略，redis 使用的策略是近似的 lru 策略，为什么是近似的呢，先来看下什么是 lru，看下 wiki 的介绍
，图中一共有四个槽的存储空间，依次访问顺序是 A B C D E D F，
当第一次访问 D 时刚好占满了坑，并且值是 4，这个值越小代表越先被淘汰，当 E 进来时，看了下已经存在的四个里 A 是最小的，代表是最早存在并且最早被访问的，那就先淘汰它了，E 占领了 A 的位置，并设置值为 4，然后又访问 D 了，D 已经存在了，不过又被访问到了，得更新值为 5，然后是 F 进来了，这时 B 是最老的且最近未被访问，所以就淘汰它了。以上是一个 lru 的简要说明，但是 redis 没有严格按照这个去执行，理由跟前面过期策略一致，最严格的过期策略应该是每个 key 都有对应的定时器，当超时时马上就能清除，但是问题是这样的cpu 消耗太大，所换来的内存效率不太值得，淘汰策略也是这样，类似于上图，要维护所有 key 的一个有序 lru 值，并且遍历将最小的淘汰，redis 采用的是抽样的形式，最初的实现方式是随机从 dict 抽取 5 个 key，淘汰一个 lru 最小的，这样子勉强能达到淘汰的目的，但是效果不是特别好，后面在 redis 3.0开始，将随机抽取改成了维护一个 pool，pool 的大小默认是 16，每次放入的都是按lru 值有序排列好，每一次放入的必须是 lru小于 pool 中最小的 lru 才允许放入，直到放满，后面再有新的就会将大的踢出。
redis 针对这个策略的改进做了一个实验，这里借用下图

首先背景是这图中的所有点都对应一个 redis 的 key，灰色部分加入后被顺序访问过一遍，然后又加入了绿色部分，那么按照理论的 lru 算法，应该是图左上中，浅灰色部分全都被淘汰，那么对比来看看图右上，左下和右下，左下表示 2.8 版本就是随机抽样 5 个 key，淘汰其中 lru 最小的一个，发现是灰色和浅灰色的都有被淘汰的，右下的 3.0 版本抽样数量不变的情况下，稍好一些，当 3.0 版本的抽样数量调整成 10 后，已经较为接近理论上的 lru 策略了，通过代码来简要分析下

+
typedef struct redisObject {
    unsigned type:4;
    unsigned encoding:4;
    unsigned lru:LRU_BITS; /* LRU time (relative to global lru_clock) or
                            * LFU data (least significant 8 bits frequency
                            * and most significant 16 bits access time). */
    int refcount;
    void *ptr;
} robj;
+

对于 lru 策略来说，lru 字段记录的就是redisObj 的LRU time，
redis 在访问数据时，都会调用lookupKey方法

+
/* Low level key lookup API, not actually called directly from commands
 * implementations that should instead rely on lookupKeyRead(),
 * lookupKeyWrite() and lookupKeyReadWithFlags(). */
robj *lookupKey(redisDb *db, robj *key, int flags) {
    dictEntry *de = dictFind(db->dict,key->ptr);
    if (de) {
        robj *val = dictGetVal(de);

        /* Update the access time for the ageing algorithm.
         * Don't do it if we have a saving child, as this will trigger
         * a copy on write madness. */
        if (!hasActiveChildProcess() && !(flags & LOOKUP_NOTOUCH)){
            if (server.maxmemory_policy & MAXMEMORY_FLAG_LFU) {
                // 这个是后面一节的内容
                updateLFU(val);
            } else {
                //  对于这个分支，访问时就会去更新 lru 值
                val->lru = LRU_CLOCK();
            }
        }
        return val;
    } else {
        return NULL;
    }
}
/* This function is used to obtain the current LRU clock.
 * If the current resolution is lower than the frequency we refresh the
 * LRU clock (as it should be in production servers) we return the
 * precomputed value, otherwise we need to resort to a system call. */
unsigned int LRU_CLOCK(void) {
    unsigned int lruclock;
    if (1000/server.hz <= LRU_CLOCK_RESOLUTION) {
        // 如果服务器的频率server.hz大于 1 时就是用系统预设的 lruclock
        lruclock = server.lruclock;
    } else {
        lruclock = getLRUClock();
    }
    return lruclock;
}
/* Return the LRU clock, based on the clock resolution. This is a time
 * in a reduced-bits format that can be used to set and check the
 * object->lru field of redisObject structures. */
unsigned int getLRUClock(void) {
    return (mstime()/LRU_CLOCK_RESOLUTION) & LRU_CLOCK_MAX;
}
+

redis 处理命令是在这里processCommand

+
/* If this function gets called we already read a whole
 * command, arguments are in the client argv/argc fields.
 * processCommand() execute the command or prepare the
 * server for a bulk read from the client.
 *
 * If C_OK is returned the client is still alive and valid and
 * other operations can be performed by the caller. Otherwise
 * if C_ERR is returned the client was destroyed (i.e. after QUIT). */
int processCommand(client *c) {
    moduleCallCommandFilters(c);

    

    /* Handle the maxmemory directive.
     *
     * Note that we do not want to reclaim memory if we are here re-entering
     * the event loop since there is a busy Lua script running in timeout
     * condition, to avoid mixing the propagation of scripts with the
     * propagation of DELs due to eviction. */
    if (server.maxmemory && !server.lua_timedout) {
        int out_of_memory = freeMemoryIfNeededAndSafe() == C_ERR;
        /* freeMemoryIfNeeded may flush slave output buffers. This may result
         * into a slave, that may be the active client, to be freed. */
        if (server.current_client == NULL) return C_ERR;

        /* It was impossible to free enough memory, and the command the client
         * is trying to execute is denied during OOM conditions or the client
         * is in MULTI/EXEC context? Error. */
        if (out_of_memory &&
            (c->cmd->flags & CMD_DENYOOM ||
             (c->flags & CLIENT_MULTI &&
              c->cmd->proc != execCommand &&
              c->cmd->proc != discardCommand)))
        {
            flagTransaction(c);
            addReply(c, shared.oomerr);
            return C_OK;
        }
    }
}
+

这里只摘了部分，当需要清理内存时就会调用, 然后调用了freeMemoryIfNeededAndSafe

+
/* This is a wrapper for freeMemoryIfNeeded() that only really calls the
 * function if right now there are the conditions to do so safely:
 *
 * - There must be no script in timeout condition.
 * - Nor we are loading data right now.
 *
 */
int freeMemoryIfNeededAndSafe(void) {
    if (server.lua_timedout || server.loading) return C_OK;
    return freeMemoryIfNeeded();
}
/* This function is periodically called to see if there is memory to free
 * according to the current "maxmemory" settings. In case we are over the
 * memory limit, the function will try to free some memory to return back
 * under the limit.
 *
 * The function returns C_OK if we are under the memory limit or if we
 * were over the limit, but the attempt to free memory was successful.
 * Otehrwise if we are over the memory limit, but not enough memory
 * was freed to return back under the limit, the function returns C_ERR. */
int freeMemoryIfNeeded(void) {
    int keys_freed = 0;
    /* By default replicas should ignore maxmemory
     * and just be masters exact copies. */
    if (server.masterhost && server.repl_slave_ignore_maxmemory) return C_OK;

    size_t mem_reported, mem_tofree, mem_freed;
    mstime_t latency, eviction_latency;
    long long delta;
    int slaves = listLength(server.slaves);

    /* When clients are paused the dataset should be static not just from the
     * POV of clients not being able to write, but also from the POV of
     * expires and evictions of keys not being performed. */
    if (clientsArePaused()) return C_OK;
    if (getMaxmemoryState(&mem_reported,NULL,&mem_tofree,NULL) == C_OK)
        return C_OK;

    mem_freed = 0;

    if (server.maxmemory_policy == MAXMEMORY_NO_EVICTION)
        goto cant_free; /* We need to free memory, but policy forbids. */

    latencyStartMonitor(latency);
    while (mem_freed < mem_tofree) {
        int j, k, i;
        static unsigned int next_db = 0;
        sds bestkey = NULL;
        int bestdbid;
        redisDb *db;
        dict *dict;
        dictEntry *de;

        if (server.maxmemory_policy & (MAXMEMORY_FLAG_LRU|MAXMEMORY_FLAG_LFU) ||
            server.maxmemory_policy == MAXMEMORY_VOLATILE_TTL)
        {
            struct evictionPoolEntry *pool = EvictionPoolLRU;

            while(bestkey == NULL) {
                unsigned long total_keys = 0, keys;

                /* We don't want to make local-db choices when expiring keys,
                 * so to start populate the eviction pool sampling keys from
                 * every DB. */
                for (i = 0; i < server.dbnum; i++) {
                    db = server.db+i;
                    dict = (server.maxmemory_policy & MAXMEMORY_FLAG_ALLKEYS) ?
                            db->dict : db->expires;
                    if ((keys = dictSize(dict)) != 0) {
                        evictionPoolPopulate(i, dict, db->dict, pool);
                        total_keys += keys;
                    }
                }
                if (!total_keys) break; /* No keys to evict. */

                /* Go backward from best to worst element to evict. */
                for (k = EVPOOL_SIZE-1; k >= 0; k--) {
                    if (pool[k].key == NULL) continue;
                    bestdbid = pool[k].dbid;

                    if (server.maxmemory_policy & MAXMEMORY_FLAG_ALLKEYS) {
                        de = dictFind(server.db[pool[k].dbid].dict,
                            pool[k].key);
                    } else {
                        de = dictFind(server.db[pool[k].dbid].expires,
                            pool[k].key);
                    }

                    /* Remove the entry from the pool. */
                    if (pool[k].key != pool[k].cached)
                        sdsfree(pool[k].key);
                    pool[k].key = NULL;
                    pool[k].idle = 0;

                    /* If the key exists, is our pick. Otherwise it is
                     * a ghost and we need to try the next element. */
                    if (de) {
                        bestkey = dictGetKey(de);
                        break;
                    } else {
                        /* Ghost... Iterate again. */
                    }
                }
            }
        }

        /* volatile-random and allkeys-random policy */
        else if (server.maxmemory_policy == MAXMEMORY_ALLKEYS_RANDOM ||
                 server.maxmemory_policy == MAXMEMORY_VOLATILE_RANDOM)
        {
            /* When evicting a random key, we try to evict a key for
             * each DB, so we use the static 'next_db' variable to
             * incrementally visit all DBs. */
            for (i = 0; i < server.dbnum; i++) {
                j = (++next_db) % server.dbnum;
                db = server.db+j;
                dict = (server.maxmemory_policy == MAXMEMORY_ALLKEYS_RANDOM) ?
                        db->dict : db->expires;
                if (dictSize(dict) != 0) {
                    de = dictGetRandomKey(dict);
                    bestkey = dictGetKey(de);
                    bestdbid = j;
                    break;
                }
            }
        }

        /* Finally remove the selected key. */
        if (bestkey) {
            db = server.db+bestdbid;
            robj *keyobj = createStringObject(bestkey,sdslen(bestkey));
            propagateExpire(db,keyobj,server.lazyfree_lazy_eviction);
            /* We compute the amount of memory freed by db*Delete() alone.
             * It is possible that actually the memory needed to propagate
             * the DEL in AOF and replication link is greater than the one
             * we are freeing removing the key, but we can't account for
             * that otherwise we would never exit the loop.
             *
             * AOF and Output buffer memory will be freed eventually so
             * we only care about memory used by the key space. */
            delta = (long long) zmalloc_used_memory();
            latencyStartMonitor(eviction_latency);
            if (server.lazyfree_lazy_eviction)
                dbAsyncDelete(db,keyobj);
            else
                dbSyncDelete(db,keyobj);
            latencyEndMonitor(eviction_latency);
            latencyAddSampleIfNeeded("eviction-del",eviction_latency);
            latencyRemoveNestedEvent(latency,eviction_latency);
            delta -= (long long) zmalloc_used_memory();
            mem_freed += delta;
            server.stat_evictedkeys++;
            notifyKeyspaceEvent(NOTIFY_EVICTED, "evicted",
                keyobj, db->id);
            decrRefCount(keyobj);
            keys_freed++;

            /* When the memory to free starts to be big enough, we may
             * start spending so much time here that is impossible to
             * deliver data to the slaves fast enough, so we force the
             * transmission here inside the loop. */
            if (slaves) flushSlavesOutputBuffers();

            /* Normally our stop condition is the ability to release
             * a fixed, pre-computed amount of memory. However when we
             * are deleting objects in another thread, it's better to
             * check, from time to time, if we already reached our target
             * memory, since the "mem_freed" amount is computed only
             * across the dbAsyncDelete() call, while the thread can
             * release the memory all the time. */
            if (server.lazyfree_lazy_eviction && !(keys_freed % 16)) {
                if (getMaxmemoryState(NULL,NULL,NULL,NULL) == C_OK) {
                    /* Let's satisfy our stop condition. */
                    mem_freed = mem_tofree;
                }
            }
        } else {
            latencyEndMonitor(latency);
            latencyAddSampleIfNeeded("eviction-cycle",latency);
            goto cant_free; /* nothing to free... */
        }
    }
    latencyEndMonitor(latency);
    latencyAddSampleIfNeeded("eviction-cycle",latency);
    return C_OK;

cant_free:
    /* We are here if we are not able to reclaim memory. There is only one
     * last thing we can try: check if the lazyfree thread has jobs in queue
     * and wait... */
    while(bioPendingJobsOfType(BIO_LAZY_FREE)) {
        if (((mem_reported - zmalloc_used_memory()) + mem_freed) >= mem_tofree)
            break;
        usleep(1000);
    }
    return C_ERR;
}
+

这里就是根据具体策略去淘汰 key，首先是要往 pool 更新 key，更新key 的方法是evictionPoolPopulate

+
void evictionPoolPopulate(int dbid, dict *sampledict, dict *keydict, struct evictionPoolEntry *pool) {
    int j, k, count;
    dictEntry *samples[server.maxmemory_samples];

    count = dictGetSomeKeys(sampledict,samples,server.maxmemory_samples);
    for (j = 0; j < count; j++) {
        unsigned long long idle;
        sds key;
        robj *o;
        dictEntry *de;

        de = samples[j];
        key = dictGetKey(de);

        /* If the dictionary we are sampling from is not the main
         * dictionary (but the expires one) we need to lookup the key
         * again in the key dictionary to obtain the value object. */
        if (server.maxmemory_policy != MAXMEMORY_VOLATILE_TTL) {
            if (sampledict != keydict) de = dictFind(keydict, key);
            o = dictGetVal(de);
        }

        /* Calculate the idle time according to the policy. This is called
         * idle just because the code initially handled LRU, but is in fact
         * just a score where an higher score means better candidate. */
        if (server.maxmemory_policy & MAXMEMORY_FLAG_LRU) {
            idle = estimateObjectIdleTime(o);
        } else if (server.maxmemory_policy & MAXMEMORY_FLAG_LFU) {
            /* When we use an LRU policy, we sort the keys by idle time
             * so that we expire keys starting from greater idle time.
             * However when the policy is an LFU one, we have a frequency
             * estimation, and we want to evict keys with lower frequency
             * first. So inside the pool we put objects using the inverted
             * frequency subtracting the actual frequency to the maximum
             * frequency of 255. */
            idle = 255-LFUDecrAndReturn(o);
        } else if (server.maxmemory_policy == MAXMEMORY_VOLATILE_TTL) {
            /* In this case the sooner the expire the better. */
            idle = ULLONG_MAX - (long)dictGetVal(de);
        } else {
            serverPanic("Unknown eviction policy in evictionPoolPopulate()");
        }

        /* Insert the element inside the pool.
         * First, find the first empty bucket or the first populated
         * bucket that has an idle time smaller than our idle time. */
        k = 0;
        while (k < EVPOOL_SIZE &&
               pool[k].key &&
               pool[k].idle < idle) k++;
        if (k == 0 && pool[EVPOOL_SIZE-1].key != NULL) {
            /* Can't insert if the element is < the worst element we have
             * and there are no empty buckets. */
            continue;
        } else if (k < EVPOOL_SIZE && pool[k].key == NULL) {
            /* Inserting into empty position. No setup needed before insert. */
        } else {
            /* Inserting in the middle. Now k points to the first element
             * greater than the element to insert.  */
            if (pool[EVPOOL_SIZE-1].key == NULL) {
                /* Free space on the right? Insert at k shifting
                 * all the elements from k to end to the right. */

                /* Save SDS before overwriting. */
                sds cached = pool[EVPOOL_SIZE-1].cached;
                memmove(pool+k+1,pool+k,
                    sizeof(pool[0])*(EVPOOL_SIZE-k-1));
                pool[k].cached = cached;
            } else {
                /* No free space on right? Insert at k-1 */
                k--;
                /* Shift all elements on the left of k (included) to the
                 * left, so we discard the element with smaller idle time. */
                sds cached = pool[0].cached; /* Save SDS before overwriting. */
                if (pool[0].key != pool[0].cached) sdsfree(pool[0].key);
                memmove(pool,pool+1,sizeof(pool[0])*k);
                pool[k].cached = cached;
            }
        }

        /* Try to reuse the cached SDS string allocated in the pool entry,
         * because allocating and deallocating this object is costly
         * (according to the profiler, not my fantasy. Remember:
         * premature optimizbla bla bla bla. */
        int klen = sdslen(key);
        if (klen > EVPOOL_CACHED_SDS_SIZE) {
            pool[k].key = sdsdup(key);
        } else {
            memcpy(pool[k].cached,key,klen+1);
            sdssetlen(pool[k].cached,klen);
            pool[k].key = pool[k].cached;
        }
        pool[k].idle = idle;
        pool[k].dbid = dbid;
    }
}
+

Redis随机选择maxmemory_samples数量的key，然后计算这些key的空闲时间idle time，当满足条件时(比pool中的某些键的空闲时间还大)就可以进pool。pool更新之后，就淘汰pool中空闲时间最大的键。

+

estimateObjectIdleTime用来计算Redis对象的空闲时间：

+
/* Given an object returns the min number of milliseconds the object was never
 * requested, using an approximated LRU algorithm. */
unsigned long long estimateObjectIdleTime(robj *o) {
    unsigned long long lruclock = LRU_CLOCK();
    if (lruclock >= o->lru) {
        return (lruclock - o->lru) * LRU_CLOCK_RESOLUTION;
    } else {
        return (lruclock + (LRU_CLOCK_MAX - o->lru)) *
                    LRU_CLOCK_RESOLUTION;
    }
}
+

空闲时间第一种是 lurclock 大于对象的 lru，那么就是减一下乘以精度，因为 lruclock 有可能是已经预生成的，所以会可能走下面这个

+

LFU

上面介绍了LRU 的算法，但是考虑一种场景

+
~~~~~A~~~~~A~~~~~A~~~~A~~~~~A~~~~~A~~|
~~B~~B~~B~~B~~B~~B~~B~~B~~B~~B~~B~~B~|
~~~~~~~~~~C~~~~~~~~~C~~~~~~~~~C~~~~~~|
~~~~~D~~~~~~~~~~D~~~~~~~~~D~~~~~~~~~D|
+

可以发现，当采用 lru 的淘汰策略的时候，D 是最新的，会被认为是最值得保留的，但是事实上还不如 A 跟 B，然后 antirez 大神就想到了LFU (Least Frequently Used) 这个算法, 显然对于上面的四个 key 的访问频率，保留优先级应该是 B > A > C = D
那要怎么来实现这个 LFU 算法呢，其实像LRU，理想的情况就是维护个链表，把最新访问的放到头上去，但是这个会影响访问速度，注意到前面代码的应该可以看到，redisObject 的 lru 字段其实是两用的，当策略是 LFU 时，这个字段就另作他用了，它的 24 位长度被分成两部分

+
      16 bits      8 bits
+----------------+--------+
+ Last decr time | LOG_C  |
+----------------+--------+
+

前16位字段是最后一次递减时间，因此Redis知道 上一次计数器递减，后8位是 计数器 counter。
LFU 的主体策略就是当这个 key 被访问的次数越多频率越高他就越容易被保留下来，并且是最近被访问的频率越高。这其实有两个事情要做，一个是在访问的时候增加计数值，在一定长时间不访问时进行衰减，所以这里用了两个值，前 16 位记录上一次衰减的时间，后 8 位记录具体的计数值。
Redis4.0之后为maxmemory_policy淘汰策略添加了两个LFU模式：

+

volatile-lfu：对有过期时间的key采用LFU淘汰策略
allkeys-lfu：对全部key采用LFU淘汰策略
还有2个配置可以调整LFU算法：

+
lfu-log-factor 10
lfu-decay-time 1
```  
`lfu-log-factor` 可以调整计数器counter的增长速度，lfu-log-factor越大，counter增长的越慢。

`lfu-decay-time`是一个以分钟为单位的数值，可以调整counter的减少速度
这里有个问题是 8 位大小够计么，访问一次加 1 的话的确不够，不过大神就是大神，才不会这么简单的加一。往下看代码
```C
/* Low level key lookup API, not actually called directly from commands
 * implementations that should instead rely on lookupKeyRead(),
 * lookupKeyWrite() and lookupKeyReadWithFlags(). */
robj *lookupKey(redisDb *db, robj *key, int flags) {
    dictEntry *de = dictFind(db->dict,key->ptr);
    if (de) {
        robj *val = dictGetVal(de);

        /* Update the access time for the ageing algorithm.
         * Don't do it if we have a saving child, as this will trigger
         * a copy on write madness. */
        if (!hasActiveChildProcess() && !(flags & LOOKUP_NOTOUCH)){
            if (server.maxmemory_policy & MAXMEMORY_FLAG_LFU) {
                // 当淘汰策略是 LFU 时，就会调用这个updateLFU
                updateLFU(val);
            } else {
                val->lru = LRU_CLOCK();
            }
        }
        return val;
    } else {
        return NULL;
    }
}
+

updateLFU 这个其实个入口，调用了两个重要的方法

+
/* Update LFU when an object is accessed.
 * Firstly, decrement the counter if the decrement time is reached.
 * Then logarithmically increment the counter, and update the access time. */
void updateLFU(robj *val) {
    unsigned long counter = LFUDecrAndReturn(val);
    counter = LFULogIncr(counter);
    val->lru = (LFUGetTimeInMinutes()<<8) | counter;
}
+

首先来看看LFUDecrAndReturn，这个方法的作用是根据上一次衰减时间和系统配置的 lfu-decay-time 参数来确定需要将 counter 减去多少

+
/* If the object decrement time is reached decrement the LFU counter but
 * do not update LFU fields of the object, we update the access time
 * and counter in an explicit way when the object is really accessed.
 * And we will times halve the counter according to the times of
 * elapsed time than server.lfu_decay_time.
 * Return the object frequency counter.
 *
 * This function is used in order to scan the dataset for the best object
 * to fit: as we check for the candidate, we incrementally decrement the
 * counter of the scanned objects if needed. */
unsigned long LFUDecrAndReturn(robj *o) {
    // 右移 8 位，拿到上次衰减时间
    unsigned long ldt = o->lru >> 8;
    // 对 255 做与操作，拿到 counter 值
    unsigned long counter = o->lru & 255;
    // 根据lfu_decay_time来算出过了多少个衰减周期
    unsigned long num_periods = server.lfu_decay_time ? LFUTimeElapsed(ldt) / server.lfu_decay_time : 0;
    if (num_periods)
        counter = (num_periods > counter) ? 0 : counter - num_periods;
    return counter;
}
+

然后是加，调用了LFULogIncr

+
/* Logarithmically increment a counter. The greater is the current counter value
 * the less likely is that it gets really implemented. Saturate it at 255. */
uint8_t LFULogIncr(uint8_t counter) {
    // 最大值就是 255，到顶了就不加了
    if (counter == 255) return 255;
    // 生成个随机小数
    double r = (double)rand()/RAND_MAX;
    // 减去个基础值，LFU_INIT_VAL = 5，防止刚进来就被逐出
    double baseval = counter - LFU_INIT_VAL;
    // 如果是小于 0，
    if (baseval < 0) baseval = 0;
    // 如果 baseval 是 0，那么 p 就是 1了，后面 counter 直接加一，如果不是的话，得看系统参数lfu_log_factor，这个越大，除出来的 p 越小，那么 counter++的可能性也越小，这样子就把前面的疑问给解决了，不是直接+1 的
    double p = 1.0/(baseval*server.lfu_log_factor+1);
    if (r < p) counter++;
    return counter;
}
+

大概的变化速度可以参考

+
+--------+------------+------------+------------+------------+------------+
| factor | 100 hits   | 1000 hits  | 100K hits  | 1M hits    | 10M hits   |
+--------+------------+------------+------------+------------+------------+
| 0      | 104        | 255        | 255        | 255        | 255        |
+--------+------------+------------+------------+------------+------------+
| 1      | 18         | 49         | 255        | 255        | 255        |
+--------+------------+------------+------------+------------+------------+
| 10     | 10         | 18         | 142        | 255        | 255        |
+--------+------------+------------+------------+------------+------------+
| 100    | 8          | 11         | 49         | 143        | 255        |
+--------+------------+------------+------------+------------+------------+
+

简而言之就是 lfu_log_factor 越大变化的越慢

+

总结

总结一下，redis 实现了近似的 lru 淘汰策略，通过增加了淘汰 key 的池子(pool)，并且增大每次抽样的 key 的数量来将淘汰效果更进一步地接近于 lru，这是 lru 策略，但是对于前面举的一个例子，其实 lru 并不能保证 key 的淘汰就如我们预期，所以在后期又引入了 lfu 的策略，lfu的策略比较巧妙，复用了 redis 对象的 lru 字段，并且使用了factor 参数来控制计数器递增的速度，防止 8 位的计数器太早溢出。

+]]> + + Redis + 数据结构 + C + 源码 + Redis + + + redis + 数据结构 + 源码 + + rust学习笔记-所有权三之切片 /2021/05/16/rust%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0-%E6%89%80%E6%9C%89%E6%9D%83%E4%B8%89%E4%B9%8B%E5%88%87%E7%89%87/ @@ -4007,39 +3995,20 @@ - redis系列介绍八-淘汰策略 - /2020/04/18/redis%E7%B3%BB%E5%88%97%E4%BB%8B%E7%BB%8D%E5%85%AB/ - LRU

说完了过期策略再说下淘汰策略，redis 使用的策略是近似的 lru 策略，为什么是近似的呢，先来看下什么是 lru，看下 wiki 的介绍
，图中一共有四个槽的存储空间，依次访问顺序是 A B C D E D F，
当第一次访问 D 时刚好占满了坑，并且值是 4，这个值越小代表越先被淘汰，当 E 进来时，看了下已经存在的四个里 A 是最小的，代表是最早存在并且最早被访问的，那就先淘汰它了，E 占领了 A 的位置，并设置值为 4，然后又访问 D 了，D 已经存在了，不过又被访问到了，得更新值为 5，然后是 F 进来了，这时 B 是最老的且最近未被访问，所以就淘汰它了。以上是一个 lru 的简要说明，但是 redis 没有严格按照这个去执行，理由跟前面过期策略一致，最严格的过期策略应该是每个 key 都有对应的定时器，当超时时马上就能清除，但是问题是这样的cpu 消耗太大，所换来的内存效率不太值得，淘汰策略也是这样，类似于上图，要维护所有 key 的一个有序 lru 值，并且遍历将最小的淘汰，redis 采用的是抽样的形式，最初的实现方式是随机从 dict 抽取 5 个 key，淘汰一个 lru 最小的，这样子勉强能达到淘汰的目的，但是效果不是特别好，后面在 redis 3.0开始，将随机抽取改成了维护一个 pool，pool 的大小默认是 16，每次放入的都是按lru 值有序排列好，每一次放入的必须是 lru小于 pool 中最小的 lru 才允许放入，直到放满，后面再有新的就会将大的踢出。
redis 针对这个策略的改进做了一个实验，这里借用下图

首先背景是这图中的所有点都对应一个 redis 的 key，灰色部分加入后被顺序访问过一遍，然后又加入了绿色部分，那么按照理论的 lru 算法，应该是图左上中，浅灰色部分全都被淘汰，那么对比来看看图右上，左下和右下，左下表示 2.8 版本就是随机抽样 5 个 key，淘汰其中 lru 最小的一个，发现是灰色和浅灰色的都有被淘汰的，右下的 3.0 版本抽样数量不变的情况下，稍好一些，当 3.0 版本的抽样数量调整成 10 后，已经较为接近理论上的 lru 策略了，通过代码来简要分析下

-
typedef struct redisObject {
    unsigned type:4;
    unsigned encoding:4;
    unsigned lru:LRU_BITS; /* LRU time (relative to global lru_clock) or
                            * LFU data (least significant 8 bits frequency
                            * and most significant 16 bits access time). */
    int refcount;
    void *ptr;
} robj;
-

对于 lru 策略来说，lru 字段记录的就是redisObj 的LRU time，
redis 在访问数据时，都会调用lookupKey方法

-
/* Low level key lookup API, not actually called directly from commands
 * implementations that should instead rely on lookupKeyRead(),
 * lookupKeyWrite() and lookupKeyReadWithFlags(). */
robj *lookupKey(redisDb *db, robj *key, int flags) {
    dictEntry *de = dictFind(db->dict,key->ptr);
    if (de) {
        robj *val = dictGetVal(de);

        /* Update the access time for the ageing algorithm.
         * Don't do it if we have a saving child, as this will trigger
         * a copy on write madness. */
        if (!hasActiveChildProcess() && !(flags & LOOKUP_NOTOUCH)){
            if (server.maxmemory_policy & MAXMEMORY_FLAG_LFU) {
                // 这个是后面一节的内容
                updateLFU(val);
            } else {
                //  对于这个分支，访问时就会去更新 lru 值
                val->lru = LRU_CLOCK();
            }
        }
        return val;
    } else {
        return NULL;
    }
}
/* This function is used to obtain the current LRU clock.
 * If the current resolution is lower than the frequency we refresh the
 * LRU clock (as it should be in production servers) we return the
 * precomputed value, otherwise we need to resort to a system call. */
unsigned int LRU_CLOCK(void) {
    unsigned int lruclock;
    if (1000/server.hz <= LRU_CLOCK_RESOLUTION) {
        // 如果服务器的频率server.hz大于 1 时就是用系统预设的 lruclock
        lruclock = server.lruclock;
    } else {
        lruclock = getLRUClock();
    }
    return lruclock;
}
/* Return the LRU clock, based on the clock resolution. This is a time
 * in a reduced-bits format that can be used to set and check the
 * object->lru field of redisObject structures. */
unsigned int getLRUClock(void) {
    return (mstime()/LRU_CLOCK_RESOLUTION) & LRU_CLOCK_MAX;
}
-

redis 处理命令是在这里processCommand

-
/* If this function gets called we already read a whole
 * command, arguments are in the client argv/argc fields.
 * processCommand() execute the command or prepare the
 * server for a bulk read from the client.
 *
 * If C_OK is returned the client is still alive and valid and
 * other operations can be performed by the caller. Otherwise
 * if C_ERR is returned the client was destroyed (i.e. after QUIT). */
int processCommand(client *c) {
    moduleCallCommandFilters(c);

    

    /* Handle the maxmemory directive.
     *
     * Note that we do not want to reclaim memory if we are here re-entering
     * the event loop since there is a busy Lua script running in timeout
     * condition, to avoid mixing the propagation of scripts with the
     * propagation of DELs due to eviction. */
    if (server.maxmemory && !server.lua_timedout) {
        int out_of_memory = freeMemoryIfNeededAndSafe() == C_ERR;
        /* freeMemoryIfNeeded may flush slave output buffers. This may result
         * into a slave, that may be the active client, to be freed. */
        if (server.current_client == NULL) return C_ERR;

        /* It was impossible to free enough memory, and the command the client
         * is trying to execute is denied during OOM conditions or the client
         * is in MULTI/EXEC context? Error. */
        if (out_of_memory &&
            (c->cmd->flags & CMD_DENYOOM ||
             (c->flags & CLIENT_MULTI &&
              c->cmd->proc != execCommand &&
              c->cmd->proc != discardCommand)))
        {
            flagTransaction(c);
            addReply(c, shared.oomerr);
            return C_OK;
        }
    }
}
-

这里只摘了部分，当需要清理内存时就会调用, 然后调用了freeMemoryIfNeededAndSafe

-
/* This is a wrapper for freeMemoryIfNeeded() that only really calls the
 * function if right now there are the conditions to do so safely:
 *
 * - There must be no script in timeout condition.
 * - Nor we are loading data right now.
 *
 */
int freeMemoryIfNeededAndSafe(void) {
    if (server.lua_timedout || server.loading) return C_OK;
    return freeMemoryIfNeeded();
}
/* This function is periodically called to see if there is memory to free
 * according to the current "maxmemory" settings. In case we are over the
 * memory limit, the function will try to free some memory to return back
 * under the limit.
 *
 * The function returns C_OK if we are under the memory limit or if we
 * were over the limit, but the attempt to free memory was successful.
 * Otehrwise if we are over the memory limit, but not enough memory
 * was freed to return back under the limit, the function returns C_ERR. */
int freeMemoryIfNeeded(void) {
    int keys_freed = 0;
    /* By default replicas should ignore maxmemory
     * and just be masters exact copies. */
    if (server.masterhost && server.repl_slave_ignore_maxmemory) return C_OK;

    size_t mem_reported, mem_tofree, mem_freed;
    mstime_t latency, eviction_latency;
    long long delta;
    int slaves = listLength(server.slaves);

    /* When clients are paused the dataset should be static not just from the
     * POV of clients not being able to write, but also from the POV of
     * expires and evictions of keys not being performed. */
    if (clientsArePaused()) return C_OK;
    if (getMaxmemoryState(&mem_reported,NULL,&mem_tofree,NULL) == C_OK)
        return C_OK;

    mem_freed = 0;

    if (server.maxmemory_policy == MAXMEMORY_NO_EVICTION)
        goto cant_free; /* We need to free memory, but policy forbids. */

    latencyStartMonitor(latency);
    while (mem_freed < mem_tofree) {
        int j, k, i;
        static unsigned int next_db = 0;
        sds bestkey = NULL;
        int bestdbid;
        redisDb *db;
        dict *dict;
        dictEntry *de;

        if (server.maxmemory_policy & (MAXMEMORY_FLAG_LRU|MAXMEMORY_FLAG_LFU) ||
            server.maxmemory_policy == MAXMEMORY_VOLATILE_TTL)
        {
            struct evictionPoolEntry *pool = EvictionPoolLRU;

            while(bestkey == NULL) {
                unsigned long total_keys = 0, keys;

                /* We don't want to make local-db choices when expiring keys,
                 * so to start populate the eviction pool sampling keys from
                 * every DB. */
                for (i = 0; i < server.dbnum; i++) {
                    db = server.db+i;
                    dict = (server.maxmemory_policy & MAXMEMORY_FLAG_ALLKEYS) ?
                            db->dict : db->expires;
                    if ((keys = dictSize(dict)) != 0) {
                        evictionPoolPopulate(i, dict, db->dict, pool);
                        total_keys += keys;
                    }
                }
                if (!total_keys) break; /* No keys to evict. */

                /* Go backward from best to worst element to evict. */
                for (k = EVPOOL_SIZE-1; k >= 0; k--) {
                    if (pool[k].key == NULL) continue;
                    bestdbid = pool[k].dbid;

                    if (server.maxmemory_policy & MAXMEMORY_FLAG_ALLKEYS) {
                        de = dictFind(server.db[pool[k].dbid].dict,
                            pool[k].key);
                    } else {
                        de = dictFind(server.db[pool[k].dbid].expires,
                            pool[k].key);
                    }

                    /* Remove the entry from the pool. */
                    if (pool[k].key != pool[k].cached)
                        sdsfree(pool[k].key);
                    pool[k].key = NULL;
                    pool[k].idle = 0;

                    /* If the key exists, is our pick. Otherwise it is
                     * a ghost and we need to try the next element. */
                    if (de) {
                        bestkey = dictGetKey(de);
                        break;
                    } else {
                        /* Ghost... Iterate again. */
                    }
                }
            }
        }

        /* volatile-random and allkeys-random policy */
        else if (server.maxmemory_policy == MAXMEMORY_ALLKEYS_RANDOM ||
                 server.maxmemory_policy == MAXMEMORY_VOLATILE_RANDOM)
        {
            /* When evicting a random key, we try to evict a key for
             * each DB, so we use the static 'next_db' variable to
             * incrementally visit all DBs. */
            for (i = 0; i < server.dbnum; i++) {
                j = (++next_db) % server.dbnum;
                db = server.db+j;
                dict = (server.maxmemory_policy == MAXMEMORY_ALLKEYS_RANDOM) ?
                        db->dict : db->expires;
                if (dictSize(dict) != 0) {
                    de = dictGetRandomKey(dict);
                    bestkey = dictGetKey(de);
                    bestdbid = j;
                    break;
                }
            }
        }

        /* Finally remove the selected key. */
        if (bestkey) {
            db = server.db+bestdbid;
            robj *keyobj = createStringObject(bestkey,sdslen(bestkey));
            propagateExpire(db,keyobj,server.lazyfree_lazy_eviction);
            /* We compute the amount of memory freed by db*Delete() alone.
             * It is possible that actually the memory needed to propagate
             * the DEL in AOF and replication link is greater than the one
             * we are freeing removing the key, but we can't account for
             * that otherwise we would never exit the loop.
             *
             * AOF and Output buffer memory will be freed eventually so
             * we only care about memory used by the key space. */
            delta = (long long) zmalloc_used_memory();
            latencyStartMonitor(eviction_latency);
            if (server.lazyfree_lazy_eviction)
                dbAsyncDelete(db,keyobj);
            else
                dbSyncDelete(db,keyobj);
            latencyEndMonitor(eviction_latency);
            latencyAddSampleIfNeeded("eviction-del",eviction_latency);
            latencyRemoveNestedEvent(latency,eviction_latency);
            delta -= (long long) zmalloc_used_memory();
            mem_freed += delta;
            server.stat_evictedkeys++;
            notifyKeyspaceEvent(NOTIFY_EVICTED, "evicted",
                keyobj, db->id);
            decrRefCount(keyobj);
            keys_freed++;

            /* When the memory to free starts to be big enough, we may
             * start spending so much time here that is impossible to
             * deliver data to the slaves fast enough, so we force the
             * transmission here inside the loop. */
            if (slaves) flushSlavesOutputBuffers();

            /* Normally our stop condition is the ability to release
             * a fixed, pre-computed amount of memory. However when we
             * are deleting objects in another thread, it's better to
             * check, from time to time, if we already reached our target
             * memory, since the "mem_freed" amount is computed only
             * across the dbAsyncDelete() call, while the thread can
             * release the memory all the time. */
            if (server.lazyfree_lazy_eviction && !(keys_freed % 16)) {
                if (getMaxmemoryState(NULL,NULL,NULL,NULL) == C_OK) {
                    /* Let's satisfy our stop condition. */
                    mem_freed = mem_tofree;
                }
            }
        } else {
            latencyEndMonitor(latency);
            latencyAddSampleIfNeeded("eviction-cycle",latency);
            goto cant_free; /* nothing to free... */
        }
    }
    latencyEndMonitor(latency);
    latencyAddSampleIfNeeded("eviction-cycle",latency);
    return C_OK;

cant_free:
    /* We are here if we are not able to reclaim memory. There is only one
     * last thing we can try: check if the lazyfree thread has jobs in queue
     * and wait... */
    while(bioPendingJobsOfType(BIO_LAZY_FREE)) {
        if (((mem_reported - zmalloc_used_memory()) + mem_freed) >= mem_tofree)
            break;
        usleep(1000);
    }
    return C_ERR;
}
-

这里就是根据具体策略去淘汰 key，首先是要往 pool 更新 key，更新key 的方法是evictionPoolPopulate

-
void evictionPoolPopulate(int dbid, dict *sampledict, dict *keydict, struct evictionPoolEntry *pool) {
    int j, k, count;
    dictEntry *samples[server.maxmemory_samples];

    count = dictGetSomeKeys(sampledict,samples,server.maxmemory_samples);
    for (j = 0; j < count; j++) {
        unsigned long long idle;
        sds key;
        robj *o;
        dictEntry *de;

        de = samples[j];
        key = dictGetKey(de);

        /* If the dictionary we are sampling from is not the main
         * dictionary (but the expires one) we need to lookup the key
         * again in the key dictionary to obtain the value object. */
        if (server.maxmemory_policy != MAXMEMORY_VOLATILE_TTL) {
            if (sampledict != keydict) de = dictFind(keydict, key);
            o = dictGetVal(de);
        }

        /* Calculate the idle time according to the policy. This is called
         * idle just because the code initially handled LRU, but is in fact
         * just a score where an higher score means better candidate. */
        if (server.maxmemory_policy & MAXMEMORY_FLAG_LRU) {
            idle = estimateObjectIdleTime(o);
        } else if (server.maxmemory_policy & MAXMEMORY_FLAG_LFU) {
            /* When we use an LRU policy, we sort the keys by idle time
             * so that we expire keys starting from greater idle time.
             * However when the policy is an LFU one, we have a frequency
             * estimation, and we want to evict keys with lower frequency
             * first. So inside the pool we put objects using the inverted
             * frequency subtracting the actual frequency to the maximum
             * frequency of 255. */
            idle = 255-LFUDecrAndReturn(o);
        } else if (server.maxmemory_policy == MAXMEMORY_VOLATILE_TTL) {
            /* In this case the sooner the expire the better. */
            idle = ULLONG_MAX - (long)dictGetVal(de);
        } else {
            serverPanic("Unknown eviction policy in evictionPoolPopulate()");
        }

        /* Insert the element inside the pool.
         * First, find the first empty bucket or the first populated
         * bucket that has an idle time smaller than our idle time. */
        k = 0;
        while (k < EVPOOL_SIZE &&
               pool[k].key &&
               pool[k].idle < idle) k++;
        if (k == 0 && pool[EVPOOL_SIZE-1].key != NULL) {
            /* Can't insert if the element is < the worst element we have
             * and there are no empty buckets. */
            continue;
        } else if (k < EVPOOL_SIZE && pool[k].key == NULL) {
            /* Inserting into empty position. No setup needed before insert. */
        } else {
            /* Inserting in the middle. Now k points to the first element
             * greater than the element to insert.  */
            if (pool[EVPOOL_SIZE-1].key == NULL) {
                /* Free space on the right? Insert at k shifting
                 * all the elements from k to end to the right. */

                /* Save SDS before overwriting. */
                sds cached = pool[EVPOOL_SIZE-1].cached;
                memmove(pool+k+1,pool+k,
                    sizeof(pool[0])*(EVPOOL_SIZE-k-1));
                pool[k].cached = cached;
            } else {
                /* No free space on right? Insert at k-1 */
                k--;
                /* Shift all elements on the left of k (included) to the
                 * left, so we discard the element with smaller idle time. */
                sds cached = pool[0].cached; /* Save SDS before overwriting. */
                if (pool[0].key != pool[0].cached) sdsfree(pool[0].key);
                memmove(pool,pool+1,sizeof(pool[0])*k);
                pool[k].cached = cached;
            }
        }

        /* Try to reuse the cached SDS string allocated in the pool entry,
         * because allocating and deallocating this object is costly
         * (according to the profiler, not my fantasy. Remember:
         * premature optimizbla bla bla bla. */
        int klen = sdslen(key);
        if (klen > EVPOOL_CACHED_SDS_SIZE) {
            pool[k].key = sdsdup(key);
        } else {
            memcpy(pool[k].cached,key,klen+1);
            sdssetlen(pool[k].cached,klen);
            pool[k].key = pool[k].cached;
        }
        pool[k].idle = idle;
        pool[k].dbid = dbid;
    }
}
-

Redis随机选择maxmemory_samples数量的key，然后计算这些key的空闲时间idle time，当满足条件时(比pool中的某些键的空闲时间还大)就可以进pool。pool更新之后，就淘汰pool中空闲时间最大的键。

-

estimateObjectIdleTime用来计算Redis对象的空闲时间：

-
/* Given an object returns the min number of milliseconds the object was never
 * requested, using an approximated LRU algorithm. */
unsigned long long estimateObjectIdleTime(robj *o) {
    unsigned long long lruclock = LRU_CLOCK();
    if (lruclock >= o->lru) {
        return (lruclock - o->lru) * LRU_CLOCK_RESOLUTION;
    } else {
        return (lruclock + (LRU_CLOCK_MAX - o->lru)) *
                    LRU_CLOCK_RESOLUTION;
    }
}
-

空闲时间第一种是 lurclock 大于对象的 lru，那么就是减一下乘以精度，因为 lruclock 有可能是已经预生成的，所以会可能走下面这个

-

LFU

上面介绍了LRU 的算法，但是考虑一种场景

-
~~~~~A~~~~~A~~~~~A~~~~A~~~~~A~~~~~A~~|
~~B~~B~~B~~B~~B~~B~~B~~B~~B~~B~~B~~B~|
~~~~~~~~~~C~~~~~~~~~C~~~~~~~~~C~~~~~~|
~~~~~D~~~~~~~~~~D~~~~~~~~~D~~~~~~~~~D|
-

可以发现，当采用 lru 的淘汰策略的时候，D 是最新的，会被认为是最值得保留的，但是事实上还不如 A 跟 B，然后 antirez 大神就想到了LFU (Least Frequently Used) 这个算法, 显然对于上面的四个 key 的访问频率，保留优先级应该是 B > A > C = D
那要怎么来实现这个 LFU 算法呢，其实像LRU，理想的情况就是维护个链表，把最新访问的放到头上去，但是这个会影响访问速度，注意到前面代码的应该可以看到，redisObject 的 lru 字段其实是两用的，当策略是 LFU 时，这个字段就另作他用了，它的 24 位长度被分成两部分

-
      16 bits      8 bits
+----------------+--------+
+ Last decr time | LOG_C  |
+----------------+--------+
-

前16位字段是最后一次递减时间，因此Redis知道 上一次计数器递减，后8位是 计数器 counter。
LFU 的主体策略就是当这个 key 被访问的次数越多频率越高他就越容易被保留下来，并且是最近被访问的频率越高。这其实有两个事情要做，一个是在访问的时候增加计数值，在一定长时间不访问时进行衰减，所以这里用了两个值，前 16 位记录上一次衰减的时间，后 8 位记录具体的计数值。
Redis4.0之后为maxmemory_policy淘汰策略添加了两个LFU模式：

-

volatile-lfu：对有过期时间的key采用LFU淘汰策略
allkeys-lfu：对全部key采用LFU淘汰策略
还有2个配置可以调整LFU算法：

-
lfu-log-factor 10
lfu-decay-time 1
```  
`lfu-log-factor` 可以调整计数器counter的增长速度，lfu-log-factor越大，counter增长的越慢。

`lfu-decay-time`是一个以分钟为单位的数值，可以调整counter的减少速度
这里有个问题是 8 位大小够计么，访问一次加 1 的话的确不够，不过大神就是大神，才不会这么简单的加一。往下看代码
```C
/* Low level key lookup API, not actually called directly from commands
 * implementations that should instead rely on lookupKeyRead(),
 * lookupKeyWrite() and lookupKeyReadWithFlags(). */
robj *lookupKey(redisDb *db, robj *key, int flags) {
    dictEntry *de = dictFind(db->dict,key->ptr);
    if (de) {
        robj *val = dictGetVal(de);

        /* Update the access time for the ageing algorithm.
         * Don't do it if we have a saving child, as this will trigger
         * a copy on write madness. */
        if (!hasActiveChildProcess() && !(flags & LOOKUP_NOTOUCH)){
            if (server.maxmemory_policy & MAXMEMORY_FLAG_LFU) {
                // 当淘汰策略是 LFU 时，就会调用这个updateLFU
                updateLFU(val);
            } else {
                val->lru = LRU_CLOCK();
            }
        }
        return val;
    } else {
        return NULL;
    }
}
-

updateLFU 这个其实个入口，调用了两个重要的方法

-
/* Update LFU when an object is accessed.
 * Firstly, decrement the counter if the decrement time is reached.
 * Then logarithmically increment the counter, and update the access time. */
void updateLFU(robj *val) {
    unsigned long counter = LFUDecrAndReturn(val);
    counter = LFULogIncr(counter);
    val->lru = (LFUGetTimeInMinutes()<<8) | counter;
}
-

首先来看看LFUDecrAndReturn，这个方法的作用是根据上一次衰减时间和系统配置的 lfu-decay-time 参数来确定需要将 counter 减去多少

-
/* If the object decrement time is reached decrement the LFU counter but
 * do not update LFU fields of the object, we update the access time
 * and counter in an explicit way when the object is really accessed.
 * And we will times halve the counter according to the times of
 * elapsed time than server.lfu_decay_time.
 * Return the object frequency counter.
 *
 * This function is used in order to scan the dataset for the best object
 * to fit: as we check for the candidate, we incrementally decrement the
 * counter of the scanned objects if needed. */
unsigned long LFUDecrAndReturn(robj *o) {
    // 右移 8 位，拿到上次衰减时间
    unsigned long ldt = o->lru >> 8;
    // 对 255 做与操作，拿到 counter 值
    unsigned long counter = o->lru & 255;
    // 根据lfu_decay_time来算出过了多少个衰减周期
    unsigned long num_periods = server.lfu_decay_time ? LFUTimeElapsed(ldt) / server.lfu_decay_time : 0;
    if (num_periods)
        counter = (num_periods > counter) ? 0 : counter - num_periods;
    return counter;
}
-

然后是加，调用了LFULogIncr

-
/* Logarithmically increment a counter. The greater is the current counter value
 * the less likely is that it gets really implemented. Saturate it at 255. */
uint8_t LFULogIncr(uint8_t counter) {
    // 最大值就是 255，到顶了就不加了
    if (counter == 255) return 255;
    // 生成个随机小数
    double r = (double)rand()/RAND_MAX;
    // 减去个基础值，LFU_INIT_VAL = 5，防止刚进来就被逐出
    double baseval = counter - LFU_INIT_VAL;
    // 如果是小于 0，
    if (baseval < 0) baseval = 0;
    // 如果 baseval 是 0，那么 p 就是 1了，后面 counter 直接加一，如果不是的话，得看系统参数lfu_log_factor，这个越大，除出来的 p 越小，那么 counter++的可能性也越小，这样子就把前面的疑问给解决了，不是直接+1 的
    double p = 1.0/(baseval*server.lfu_log_factor+1);
    if (r < p) counter++;
    return counter;
}
-

大概的变化速度可以参考

-
+--------+------------+------------+------------+------------+------------+
| factor | 100 hits   | 1000 hits  | 100K hits  | 1M hits    | 10M hits   |
+--------+------------+------------+------------+------------+------------+
| 0      | 104        | 255        | 255        | 255        | 255        |
+--------+------------+------------+------------+------------+------------+
| 1      | 18         | 49         | 255        | 255        | 255        |
+--------+------------+------------+------------+------------+------------+
| 10     | 10         | 18         | 142        | 255        | 255        |
+--------+------------+------------+------------+------------+------------+
| 100    | 8          | 11         | 49         | 143        | 255        |
+--------+------------+------------+------------+------------+------------+
-

简而言之就是 lfu_log_factor 越大变化的越慢

-

总结

总结一下，redis 实现了近似的 lru 淘汰策略，通过增加了淘汰 key 的池子(pool)，并且增大每次抽样的 key 的数量来将淘汰效果更进一步地接近于 lru，这是 lru 策略，但是对于前面举的一个例子，其实 lru 并不能保证 key 的淘汰就如我们预期，所以在后期又引入了 lfu 的策略，lfu的策略比较巧妙，复用了 redis 对象的 lru 字段，并且使用了factor 参数来控制计数器递增的速度，防止 8 位的计数器太早溢出。

+ redis数据结构介绍四-第四部分 压缩表 + /2020/01/19/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E5%9B%9B/ + 在 redis 中还有一类表型数据结构叫压缩表，ziplist，它的目的是替代链表，链表是个很容易理解的数据结构，双向链表有前后指针，有带头结点的有的不带，但是链表有个比较大的问题是相对于普通的数组，它的内存不连续，碎片化的存储，内存利用效率不高，而且指针寻址相对于直接使用偏移量的话，也有一定的效率劣势，当然这不是主要的原因，ziplist 设计的主要目的是让链表的内存使用更高效

+
+

The ziplist is a specially encoded dually linked list that is designed to be very memory efficient.
这是摘自 redis 源码中ziplist.c 文件的注释，也说明了原因，它的大概结构是这样子

+
+
<zlbytes> <zltail> <zllen> <entry> <entry> ... <entry> <zlend>
+

其中
<zlbytes>表示 ziplist 占用的字节总数，类型是uint32_t，32 位的无符号整型，当然表示的字节数也包含自己本身占用的 4 个
<zltail> 类型也是是uint32_t，表示ziplist表中最后一项（entry）在ziplist中的偏移字节数。<zltail>的存在，使得我们可以很方便地找到最后一项（不用遍历整个ziplist），从而可以在ziplist尾端快速地执行push或pop操作。
<uint16_t zllen> 表示ziplist 中的数据项个数，因为是 16 位，所以当数量超过所能表示的最大的数量，它的 16 位全会置为 1，但是真实的数量需要遍历整个 ziplist 才能知道
<entry>是具体的数据项，后面解释
<zlend> ziplist 的最后一个字节，固定是255。
再看一下<entry>中的具体结构，

+
<prevlen> <encoding> <entry-data>
+

首先这个<prevlen>有两种情况，一种是前面的元素的长度，如果是小于等于 253的时候就用一个uint8_t 来表示前一元素的长度，如果大于的话他将占用五个字节，第一个字节是 254，即表示这个字节已经表示不下了，需要后面的四个字节帮忙表示
<encoding>这个就比较复杂，把源码的注释放下面先看下

+
* |00pppppp| - 1 byte
*      String value with length less than or equal to 63 bytes (6 bits).
*      "pppppp" represents the unsigned 6 bit length.
* |01pppppp|qqqqqqqq| - 2 bytes
*      String value with length less than or equal to 16383 bytes (14 bits).
*      IMPORTANT: The 14 bit number is stored in big endian.
* |10000000|qqqqqqqq|rrrrrrrr|ssssssss|tttttttt| - 5 bytes
*      String value with length greater than or equal to 16384 bytes.
*      Only the 4 bytes following the first byte represents the length
*      up to 32^2-1. The 6 lower bits of the first byte are not used and
*      are set to zero.
*      IMPORTANT: The 32 bit number is stored in big endian.
* |11000000| - 3 bytes
*      Integer encoded as int16_t (2 bytes).
* |11010000| - 5 bytes
*      Integer encoded as int32_t (4 bytes).
* |11100000| - 9 bytes
*      Integer encoded as int64_t (8 bytes).
* |11110000| - 4 bytes
*      Integer encoded as 24 bit signed (3 bytes).
* |11111110| - 2 bytes
*      Integer encoded as 8 bit signed (1 byte).
* |1111xxxx| - (with xxxx between 0000 and 1101) immediate 4 bit integer.
*      Unsigned integer from 0 to 12. The encoded value is actually from
*      1 to 13 because 0000 and 1111 can not be used, so 1 should be
*      subtracted from the encoded 4 bit value to obtain the right value.
* |11111111| - End of ziplist special entry.
+

首先如果 encoding 的前两位是 00 的话代表这个元素是个 6 位的字符串，即直接将数据保存在 encoding 中，不消耗额外的<entry-data>，如果前两位是 01 的话表示是个 14 位的字符串，如果是 10 的话表示encoding 块之后的四个字节是存放字符串类型的数据，encoding 的剩余 6 位置 0。
如果 encoding 的前两位是 11 的话表示这是个整型，具体的如果后两位是00的话，表示后面是个2字节的 int16_t 类型，如果是01的话，后面是个4字节的int32_t,如果是10的话后面是8字节的int64_t,如果是 11 的话后面是 3 字节的有符号整型，这些都要最后 4 位都是 0 的情况噢
剩下当是11111110时，则表示是一个1 字节的有符号数，如果是 1111xxxx，其中xxxx在0000 到 1101 表示实际的 1 到 13，为啥呢，因为 0000 前面已经用过了，而 1110 跟 1111 也都有用了。
看个具体的例子(上下有点对不齐，将就看)

+
[0f 00 00 00] [0c 00 00 00] [02 00] [00 f3] [02 f6] [ff]
|**zlbytes***|  |***zltail***|  |*zllen*|  |entry1 entry2|  |zlend|
+

第一部分代表整个 ziplist 有 15 个字节，zlbytes 自己占了 4 个 zltail 表示最后一个元素的偏移量，第 13 个字节起，zllen 表示有 2 个元素，第一个元素是00f3,00表示前一个元素长度是 0，本来前面就没元素(不过不知道这个能不能优化这一字节)，然后是 f3，换成二进制就是11110011,对照上面的注释，是落在|1111xxxx|这个类型里，注意这个其实是用 0001 到 1101 也就是 1到 13 来表示 0到 12，所以 f3 应该就是 2，第一个元素是 2，第二个元素呢，02 代表前一个元素也就是刚才说的这个，占用 2 字节，f6 展开也是刚才的类型，实际是 5，ff 表示 ziplist 的结尾，所以这个 ziplist 里面是两个元素，2 跟 5

]]> Redis @@ -4055,30 +4024,20 @@ - redis系列介绍七-过期策略 - /2020/04/12/redis%E7%B3%BB%E5%88%97%E4%BB%8B%E7%BB%8D%E4%B8%83/ - 这一篇不再是数据结构介绍了，大致的数据结构基本都介绍了，这一篇主要是查漏补缺，或者说讲一些重要且基本的概念，也可能是经常被忽略的，很多讲 redis 的系列文章可能都会忽略，学习 redis 的时候也会，因为觉得源码学习就是讲主要的数据结构和“算法”学习了就好了。
redis 的主要应用就是拿来作为高性能的缓存，那么缓存一般有些啥需要注意的，首先是访问速度，如果取得跟数据库一样快，那就没什么存在的意义，第二个是缓存的字面意思，我只是为了让数据读取快一些，通常大部分的场景这个是需要更新过期的，这里就把我要讲的第一点引出来了（真累，

-

redis过期策略

redis 是如何过期缓存的，可以猜测下，最无脑的就是每个设置了过期时间的 key 都设个定时器，过期了就删除，这种显然消耗太大，清理地最及时，还有的就是 redis 正在采用的懒汉清理策略和定期清理
懒汉策略就是在使用的时候去检查缓存是否过期，比如 get 操作时，先判断下这个 key 是否已经过期了，如果过期了就删掉，并且返回空，如果没过期则正常返回
主要代码是

-
/* This function is called when we are going to perform some operation
 * in a given key, but such key may be already logically expired even if
 * it still exists in the database. The main way this function is called
 * is via lookupKey*() family of functions.
 *
 * The behavior of the function depends on the replication role of the
 * instance, because slave instances do not expire keys, they wait
 * for DELs from the master for consistency matters. However even
 * slaves will try to have a coherent return value for the function,
 * so that read commands executed in the slave side will be able to
 * behave like if the key is expired even if still present (because the
 * master has yet to propagate the DEL).
 *
 * In masters as a side effect of finding a key which is expired, such
 * key will be evicted from the database. Also this may trigger the
 * propagation of a DEL/UNLINK command in AOF / replication stream.
 *
 * The return value of the function is 0 if the key is still valid,
 * otherwise the function returns 1 if the key is expired. */
int expireIfNeeded(redisDb *db, robj *key) {
    if (!keyIsExpired(db,key)) return 0;

    /* If we are running in the context of a slave, instead of
     * evicting the expired key from the database, we return ASAP:
     * the slave key expiration is controlled by the master that will
     * send us synthesized DEL operations for expired keys.
     *
     * Still we try to return the right information to the caller,
     * that is, 0 if we think the key should be still valid, 1 if
     * we think the key is expired at this time. */
    if (server.masterhost != NULL) return 1;

    /* Delete the key */
    server.stat_expiredkeys++;
    propagateExpire(db,key,server.lazyfree_lazy_expire);
    notifyKeyspaceEvent(NOTIFY_EXPIRED,
        "expired",key,db->id);
    return server.lazyfree_lazy_expire ? dbAsyncDelete(db,key) :
                                         dbSyncDelete(db,key);
}

/* Check if the key is expired. */
int keyIsExpired(redisDb *db, robj *key) {
    mstime_t when = getExpire(db,key);
    mstime_t now;

    if (when < 0) return 0; /* No expire for this key */

    /* Don't expire anything while loading. It will be done later. */
    if (server.loading) return 0;

    /* If we are in the context of a Lua script, we pretend that time is
     * blocked to when the Lua script started. This way a key can expire
     * only the first time it is accessed and not in the middle of the
     * script execution, making propagation to slaves / AOF consistent.
     * See issue #1525 on Github for more information. */
    if (server.lua_caller) {
        now = server.lua_time_start;
    }
    /* If we are in the middle of a command execution, we still want to use
     * a reference time that does not change: in that case we just use the
     * cached time, that we update before each call in the call() function.
     * This way we avoid that commands such as RPOPLPUSH or similar, that
     * may re-open the same key multiple times, can invalidate an already
     * open object in a next call, if the next call will see the key expired,
     * while the first did not. */
    else if (server.fixed_time_expire > 0) {
        now = server.mstime;
    }
    /* For the other cases, we want to use the most fresh time we have. */
    else {
        now = mstime();
    }

    /* The key expired if the current (virtual or real) time is greater
     * than the expire time of the key. */
    return now > when;
}
/* Return the expire time of the specified key, or -1 if no expire
 * is associated with this key (i.e. the key is non volatile) */
long long getExpire(redisDb *db, robj *key) {
    dictEntry *de;

    /* No expire? return ASAP */
    if (dictSize(db->expires) == 0 ||
       (de = dictFind(db->expires,key->ptr)) == NULL) return -1;

    /* The entry was found in the expire dict, this means it should also
     * be present in the main dict (safety check). */
    serverAssertWithInfo(NULL,key,dictFind(db->dict,key->ptr) != NULL);
    return dictGetSignedIntegerVal(de);
}
-

这里有几点要注意的，第一是当惰性删除时会根据lazyfree_lazy_expire这个参数去判断是执行同步删除还是异步删除，另外一点是对于 slave，是不需要执行的，因为会在 master 过期时向 slave 发送 del 指令。
光采用这个策略会有什么问题呢，假如一些key 一直未被访问，那这些 key 就不会过期了，导致一直被占用着内存，所以 redis 采取了懒汉式过期加定期过期策略，定期策略是怎么执行的呢

-
/* This function handles 'background' operations we are required to do
 * incrementally in Redis databases, such as active key expiring, resizing,
 * rehashing. */
void databasesCron(void) {
    /* Expire keys by random sampling. Not required for slaves
     * as master will synthesize DELs for us. */
    if (server.active_expire_enabled) {
        if (server.masterhost == NULL) {
            activeExpireCycle(ACTIVE_EXPIRE_CYCLE_SLOW);
        } else {
            expireSlaveKeys();
        }
    }

    /* Defrag keys gradually. */
    activeDefragCycle();

    /* Perform hash tables rehashing if needed, but only if there are no
     * other processes saving the DB on disk. Otherwise rehashing is bad
     * as will cause a lot of copy-on-write of memory pages. */
    if (!hasActiveChildProcess()) {
        /* We use global counters so if we stop the computation at a given
         * DB we'll be able to start from the successive in the next
         * cron loop iteration. */
        static unsigned int resize_db = 0;
        static unsigned int rehash_db = 0;
        int dbs_per_call = CRON_DBS_PER_CALL;
        int j;

        /* Don't test more DBs than we have. */
        if (dbs_per_call > server.dbnum) dbs_per_call = server.dbnum;

        /* Resize */
        for (j = 0; j < dbs_per_call; j++) {
            tryResizeHashTables(resize_db % server.dbnum);
            resize_db++;
        }

        /* Rehash */
        if (server.activerehashing) {
            for (j = 0; j < dbs_per_call; j++) {
                int work_done = incrementallyRehash(rehash_db);
                if (work_done) {
                    /* If the function did some work, stop here, we'll do
                     * more at the next cron loop. */
                    break;
                } else {
                    /* If this db didn't need rehash, we'll try the next one. */
                    rehash_db++;
                    rehash_db %= server.dbnum;
                }
            }
        }
    }
}
/* Try to expire a few timed out keys. The algorithm used is adaptive and
 * will use few CPU cycles if there are few expiring keys, otherwise
 * it will get more aggressive to avoid that too much memory is used by
 * keys that can be removed from the keyspace.
 *
 * Every expire cycle tests multiple databases: the next call will start
 * again from the next db, with the exception of exists for time limit: in that
 * case we restart again from the last database we were processing. Anyway
 * no more than CRON_DBS_PER_CALL databases are tested at every iteration.
 *
 * The function can perform more or less work, depending on the "type"
 * argument. It can execute a "fast cycle" or a "slow cycle". The slow
 * cycle is the main way we collect expired cycles: this happens with
 * the "server.hz" frequency (usually 10 hertz).
 *
 * However the slow cycle can exit for timeout, since it used too much time.
 * For this reason the function is also invoked to perform a fast cycle
 * at every event loop cycle, in the beforeSleep() function. The fast cycle
 * will try to perform less work, but will do it much more often.
 *
 * The following are the details of the two expire cycles and their stop
 * conditions:
 *
 * If type is ACTIVE_EXPIRE_CYCLE_FAST the function will try to run a
 * "fast" expire cycle that takes no longer than EXPIRE_FAST_CYCLE_DURATION
 * microseconds, and is not repeated again before the same amount of time.
 * The cycle will also refuse to run at all if the latest slow cycle did not
 * terminate because of a time limit condition.
 *
 * If type is ACTIVE_EXPIRE_CYCLE_SLOW, that normal expire cycle is
 * executed, where the time limit is a percentage of the REDIS_HZ period
 * as specified by the ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC define. In the
 * fast cycle, the check of every database is interrupted once the number
 * of already expired keys in the database is estimated to be lower than
 * a given percentage, in order to avoid doing too much work to gain too
 * little memory.
 *
 * The configured expire "effort" will modify the baseline parameters in
 * order to do more work in both the fast and slow expire cycles.
 */

#define ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP 20 /* Keys for each DB loop. */
#define ACTIVE_EXPIRE_CYCLE_FAST_DURATION 1000 /* Microseconds. */
#define ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC 25 /* Max % of CPU to use. */
#define ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE 10 /* % of stale keys after which
                                                   we do extra efforts. */
void activeExpireCycle(int type) {
    /* Adjust the running parameters according to the configured expire
     * effort. The default effort is 1, and the maximum configurable effort
     * is 10. */
    unsigned long
    effort = server.active_expire_effort-1, /* Rescale from 0 to 9. */
    config_keys_per_loop = ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP +
                           ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP/4*effort,
    config_cycle_fast_duration = ACTIVE_EXPIRE_CYCLE_FAST_DURATION +
                                 ACTIVE_EXPIRE_CYCLE_FAST_DURATION/4*effort,
    config_cycle_slow_time_perc = ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC +
                                  2*effort,
    config_cycle_acceptable_stale = ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE-
                                    effort;

    /* This function has some global state in order to continue the work
     * incrementally across calls. */
    static unsigned int current_db = 0; /* Last DB tested. */
    static int timelimit_exit = 0;      /* Time limit hit in previous call? */
    static long long last_fast_cycle = 0; /* When last fast cycle ran. */

    int j, iteration = 0;
    int dbs_per_call = CRON_DBS_PER_CALL;
    long long start = ustime(), timelimit, elapsed;

    /* When clients are paused the dataset should be static not just from the
     * POV of clients not being able to write, but also from the POV of
     * expires and evictions of keys not being performed. */
    if (clientsArePaused()) return;

    if (type == ACTIVE_EXPIRE_CYCLE_FAST) {
        /* Don't start a fast cycle if the previous cycle did not exit
         * for time limit, unless the percentage of estimated stale keys is
         * too high. Also never repeat a fast cycle for the same period
         * as the fast cycle total duration itself. */
        if (!timelimit_exit &&
            server.stat_expired_stale_perc < config_cycle_acceptable_stale)
            return;

        if (start < last_fast_cycle + (long long)config_cycle_fast_duration*2)
            return;

        last_fast_cycle = start;
    }

    /* We usually should test CRON_DBS_PER_CALL per iteration, with
     * two exceptions:
     *
     * 1) Don't test more DBs than we have.
     * 2) If last time we hit the time limit, we want to scan all DBs
     * in this iteration, as there is work to do in some DB and we don't want
     * expired keys to use memory for too much time. */
    if (dbs_per_call > server.dbnum || timelimit_exit)
        dbs_per_call = server.dbnum;

    /* We can use at max 'config_cycle_slow_time_perc' percentage of CPU
     * time per iteration. Since this function gets called with a frequency of
     * server.hz times per second, the following is the max amount of
     * microseconds we can spend in this function. */
    timelimit = config_cycle_slow_time_perc*1000000/server.hz/100;
    timelimit_exit = 0;
    if (timelimit <= 0) timelimit = 1;

    if (type == ACTIVE_EXPIRE_CYCLE_FAST)
        timelimit = config_cycle_fast_duration; /* in microseconds. */

    /* Accumulate some global stats as we expire keys, to have some idea
     * about the number of keys that are already logically expired, but still
     * existing inside the database. */
    long total_sampled = 0;
    long total_expired = 0;

    for (j = 0; j < dbs_per_call && timelimit_exit == 0; j++) {
        /* Expired and checked in a single loop. */
        unsigned long expired, sampled;

        redisDb *db = server.db+(current_db % server.dbnum);

        /* Increment the DB now so we are sure if we run out of time
         * in the current DB we'll restart from the next. This allows to
         * distribute the time evenly across DBs. */
        current_db++;

        /* Continue to expire if at the end of the cycle more than 25%
         * of the keys were expired. */
        do {
            unsigned long num, slots;
            long long now, ttl_sum;
            int ttl_samples;
            iteration++;

            /* If there is nothing to expire try next DB ASAP. */
            if ((num = dictSize(db->expires)) == 0) {
                db->avg_ttl = 0;
                break;
            }
            slots = dictSlots(db->expires);
            now = mstime();

            /* When there are less than 1% filled slots, sampling the key
             * space is expensive, so stop here waiting for better times...
             * The dictionary will be resized asap. */
            if (num && slots > DICT_HT_INITIAL_SIZE &&
                (num*100/slots < 1)) break;

            /* The main collection cycle. Sample random keys among keys
             * with an expire set, checking for expired ones. */
            expired = 0;
            sampled = 0;
            ttl_sum = 0;
            ttl_samples = 0;

            if (num > config_keys_per_loop)
                num = config_keys_per_loop;

            /* Here we access the low level representation of the hash table
             * for speed concerns: this makes this code coupled with dict.c,
             * but it hardly changed in ten years.
             *
             * Note that certain places of the hash table may be empty,
             * so we want also a stop condition about the number of
             * buckets that we scanned. However scanning for free buckets
             * is very fast: we are in the cache line scanning a sequential
             * array of NULL pointers, so we can scan a lot more buckets
             * than keys in the same time. */
            long max_buckets = num*20;
            long checked_buckets = 0;

            while (sampled < num && checked_buckets < max_buckets) {
                for (int table = 0; table < 2; table++) {
                    if (table == 1 && !dictIsRehashing(db->expires)) break;

                    unsigned long idx = db->expires_cursor;
                    idx &= db->expires->ht[table].sizemask;
                    dictEntry *de = db->expires->ht[table].table[idx];
                    long long ttl;

                    /* Scan the current bucket of the current table. */
                    checked_buckets++;
                    while(de) {
                        /* Get the next entry now since this entry may get
                         * deleted. */
                        dictEntry *e = de;
                        de = de->next;

                        ttl = dictGetSignedIntegerVal(e)-now;
                        if (activeExpireCycleTryExpire(db,e,now)) expired++;
                        if (ttl > 0) {
                            /* We want the average TTL of keys yet
                             * not expired. */
                            ttl_sum += ttl;
                            ttl_samples++;
                        }
                        sampled++;
                    }
                }
                db->expires_cursor++;
            }
            total_expired += expired;
            total_sampled += sampled;

            /* Update the average TTL stats for this database. */
            if (ttl_samples) {
                long long avg_ttl = ttl_sum/ttl_samples;

                /* Do a simple running average with a few samples.
                 * We just use the current estimate with a weight of 2%
                 * and the previous estimate with a weight of 98%. */
                if (db->avg_ttl == 0) db->avg_ttl = avg_ttl;
                db->avg_ttl = (db->avg_ttl/50)*49 + (avg_ttl/50);
            }

            /* We can't block forever here even if there are many keys to
             * expire. So after a given amount of milliseconds return to the
             * caller waiting for the other active expire cycle. */
            if ((iteration & 0xf) == 0) { /* check once every 16 iterations. */
                elapsed = ustime()-start;
                if (elapsed > timelimit) {
                    timelimit_exit = 1;
                    server.stat_expired_time_cap_reached_count++;
                    break;
                }
            }
            /* We don't repeat the cycle for the current database if there are
             * an acceptable amount of stale keys (logically expired but yet
             * not reclained). */
        } while ((expired*100/sampled) > config_cycle_acceptable_stale);
    }

    elapsed = ustime()-start;
    server.stat_expire_cycle_time_used += elapsed;
    latencyAddSampleIfNeeded("expire-cycle",elapsed/1000);

    /* Update our estimate of keys existing but yet to be expired.
     * Running average with this sample accounting for 5%. */
    double current_perc;
    if (total_sampled) {
        current_perc = (double)total_expired/total_sampled;
    } else
        current_perc = 0;
    server.stat_expired_stale_perc = (current_perc*0.05)+
                                     (server.stat_expired_stale_perc*0.95);
}
-

执行定期清除分成两种类型，快和慢，分别由beforeSleep和databasesCron调用，快版有两个限制，一个是执行时长由ACTIVE_EXPIRE_CYCLE_FAST_DURATION限制，另一个是执行间隔是 2 倍的ACTIVE_EXPIRE_CYCLE_FAST_DURATION，另外这还可以由配置的server.active_expire_effort参数来控制，默认是 1，最大是 10

-
onfig_cycle_fast_duration = ACTIVE_EXPIRE_CYCLE_FAST_DURATION +
                                 ACTIVE_EXPIRE_CYCLE_FAST_DURATION/4*effort
-

然后会从一定数量的 db 中找出一定数量的带过期时间的 key（保存在 expires中），这里的数量是由

-
config_keys_per_loop = ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP +
                           ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP/4*effort
```                                 
控制，慢速的执行时长是
```C
config_cycle_slow_time_perc = ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC +
                                  2*effort
timelimit = config_cycle_slow_time_perc*1000000/server.hz/100;
-

这里还有一个额外的退出条件，如果当前数据库的抽样结果已经达到我们所允许的过期 key 百分比，则下次不再处理当前 db，继续处理下个 db

+ spark-little-tips + /2017/03/28/spark-little-tips/ + spark 的一些粗浅使用经验

工作中学习使用了一下Spark做数据分析,主要是用spark的python接口，首先是pyspark.SparkContext(appName=xxx)，这是初始化一个Spark应用实例或者说会话，不能重复，
返回的实例句柄就可以调用textFile(path)读取文本文件，这里的文本文件可以是HDFS上的文本文件，也可以普通文本文件，但是需要在Spark的所有集群上都存在，否则会
读取失败，parallelize则可以将python生成的集合数据读取后转换成rdd(A Resilient Distributed Dataset (RDD),一种spark下的基本抽象数据集),基于这个RDD就可以做
数据的流式计算，例如map reduce,在Spark中可以非常方便地实现

+

简单的mapreduce word count示例

textFile = sc.parallelize([(1,1), (2,1), (3,1), (4,1), (5,1),(1,1), (2,1), (3,1), (4,1), (5,1)])
data = textFile.reduceByKey(lambda x, y: x + y).collect()
for _ in data:
    print(_)
+ + +

结果

(3, 2)
(1, 2)
(4, 2)
(2, 2)
(5, 2)
]]> - Redis - 数据结构 - C - 源码 - Redis + data analysis - redis - 数据结构 - 源码 + spark + python @@ -4111,6 +4070,57 @@ 新语言 + + spring boot中的 http 接口返回 json 形式的小注意点 + /2023/06/25/spring-boot%E4%B8%AD%E7%9A%84-http-%E6%8E%A5%E5%8F%A3%E8%BF%94%E5%9B%9E-json-%E5%BD%A2%E5%BC%8F%E7%9A%84%E5%B0%8F%E6%B3%A8%E6%84%8F%E7%82%B9/ + 这个可能是个很简单的点，不过之前碰到了就记录下，我们常规的应用都是使用统一的请求响应转换器去处理请求和响应返回，但是对于有文件上传或者返回的是文件的情况，一般都是不使用统一的处理，但是在响应返回的时候可能会存在这样的情况，如果文件正常被处理那就返回文件，如果处理异常需要给前端返回 json类型的响应，里面能够取到响应码错误描述等

+

比如在请求中参数就使用 httpRequest(HttpServletRequest request, HttpServletResponse response)
然后在返回的时候就使用 response.getOutputStream().write(result)，而如果是要返回 json 形式的话就可以像这个文章说明的
链接

+
Employee employee = new Employee(1, "Karan", "IT", 5000);
String employeeJsonString = this.gson.toJson(employee);

PrintWriter out = response.getWriter();
response.setContentType("application/json");
response.setCharacterEncoding("UTF-8");
out.print(employeeJsonString);
out.flush();
+

一开始我也是这么一搜就用了，后来发现返回的一直是乱码，仔细看了下发现了个问题，就是这个 response 设置 contentType 是在getWriter之后的，这样自然就不会起作用了，所以要在设置 setContentType 和 setCharacterEncoding 之后再 getWriter，之后就可以正常返回了。

+]]> + + Java + SpringBoot + + + Java + Spring + SpringBoot + + + + spring event 介绍 + /2022/01/30/spring-event-%E4%BB%8B%E7%BB%8D/ + spring框架中如果想使用一些一部操作，除了依赖第三方中间件的消息队列，还可以用spring自己的event，简单介绍下使用方法
首先我们可以建一个event，继承ApplicationEvent

+

public class CustomSpringEvent extends ApplicationEvent {

    private String message;

    public CustomSpringEvent(Object source, String message) {
        super(source);
        this.message = message;
    }
    public String getMessage() {
        return message;
    }
}
+

这个 ApplicationEvent 其实也比较简单，内部就一个 Object 类型的 source，可以自行扩展，我们在自定义的这个 Event 里加了个 Message ，只是简单介绍下使用

+
public abstract class ApplicationEvent extends EventObject {
    private static final long serialVersionUID = 7099057708183571937L;
    private final long timestamp;

    public ApplicationEvent(Object source) {
        super(source);
        this.timestamp = System.currentTimeMillis();
    }

    public ApplicationEvent(Object source, Clock clock) {
        super(source);
        this.timestamp = clock.millis();
    }

    public final long getTimestamp() {
        return this.timestamp;
    }
}
+ +

然后就是事件生产者和监听消费者

+
@Component
public class CustomSpringEventPublisher {

    @Resource
    private ApplicationEventPublisher applicationEventPublisher;

    public void publishCustomEvent(final String message) {
        System.out.println("Publishing custom event. ");
        CustomSpringEvent customSpringEvent = new CustomSpringEvent(this, message);
        applicationEventPublisher.publishEvent(customSpringEvent);
    }
}
+

这里的 ApplicationEventPublisher 是 Spring 的方法接口

+
@FunctionalInterface
public interface ApplicationEventPublisher {
    default void publishEvent(ApplicationEvent event) {
        this.publishEvent((Object)event);
    }

    void publishEvent(Object var1);
}
+

具体的是例如 org.springframework.context.support.AbstractApplicationContext#publishEvent(java.lang.Object, org.springframework.core.ResolvableType) 中的实现，后面可以展开讲讲

+

事件监听者：

+
@Component
public class CustomSpringEventListener implements ApplicationListener<CustomSpringEvent> {
    @Override
    public void onApplicationEvent(CustomSpringEvent event) {
        System.out.println("Received spring custom event - " + event.getMessage());
    }
}
+

这里的也是 spring 的一个方法接口

+
@FunctionalInterface
public interface ApplicationListener<E extends ApplicationEvent> extends EventListener {
    void onApplicationEvent(E var1);

    static <T> ApplicationListener<PayloadApplicationEvent<T>> forPayload(Consumer<T> consumer) {
        return (event) -> {
            consumer.accept(event.getPayload());
        };
    }
}
+ +

然后简单包个请求

+

@RequestMapping(value = "/event", method = RequestMethod.GET)
@ResponseBody
public void event() {
    customSpringEventPublisher.publishCustomEvent("hello sprint event");
}
+ +


就能看到接收到消息了。

+]]> + + Java + Spring + + + Java + Spring + Spring Event + + springboot mappings 注册逻辑 /2023/08/13/springboot-mappings-%E6%B3%A8%E5%86%8C%E9%80%BB%E8%BE%91/ @@ -4145,41 +4155,6 @@ SpringBoot - - spark-little-tips - /2017/03/28/spark-little-tips/ - spark 的一些粗浅使用经验

工作中学习使用了一下Spark做数据分析,主要是用spark的python接口，首先是pyspark.SparkContext(appName=xxx)，这是初始化一个Spark应用实例或者说会话，不能重复，
返回的实例句柄就可以调用textFile(path)读取文本文件，这里的文本文件可以是HDFS上的文本文件，也可以普通文本文件，但是需要在Spark的所有集群上都存在，否则会
读取失败，parallelize则可以将python生成的集合数据读取后转换成rdd(A Resilient Distributed Dataset (RDD),一种spark下的基本抽象数据集),基于这个RDD就可以做
数据的流式计算，例如map reduce,在Spark中可以非常方便地实现

-

简单的mapreduce word count示例

textFile = sc.parallelize([(1,1), (2,1), (3,1), (4,1), (5,1),(1,1), (2,1), (3,1), (4,1), (5,1)])
data = textFile.reduceByKey(lambda x, y: x + y).collect()
for _ in data:
    print(_)
- - -

结果

(3, 2)
(1, 2)
(4, 2)
(2, 2)
(5, 2)
-]]> - - data analysis - - - spark - python - - - - spring boot中的 http 接口返回 json 形式的小注意点 - /2023/06/25/spring-boot%E4%B8%AD%E7%9A%84-http-%E6%8E%A5%E5%8F%A3%E8%BF%94%E5%9B%9E-json-%E5%BD%A2%E5%BC%8F%E7%9A%84%E5%B0%8F%E6%B3%A8%E6%84%8F%E7%82%B9/ - 这个可能是个很简单的点，不过之前碰到了就记录下，我们常规的应用都是使用统一的请求响应转换器去处理请求和响应返回，但是对于有文件上传或者返回的是文件的情况，一般都是不使用统一的处理，但是在响应返回的时候可能会存在这样的情况，如果文件正常被处理那就返回文件，如果处理异常需要给前端返回 json类型的响应，里面能够取到响应码错误描述等

-

比如在请求中参数就使用 httpRequest(HttpServletRequest request, HttpServletResponse response)
然后在返回的时候就使用 response.getOutputStream().write(result)，而如果是要返回 json 形式的话就可以像这个文章说明的
链接

-
Employee employee = new Employee(1, "Karan", "IT", 5000);
String employeeJsonString = this.gson.toJson(employee);

PrintWriter out = response.getWriter();
response.setContentType("application/json");
response.setCharacterEncoding("UTF-8");
out.print(employeeJsonString);
out.flush();
-

一开始我也是这么一搜就用了，后来发现返回的一直是乱码，仔细看了下发现了个问题，就是这个 response 设置 contentType 是在getWriter之后的，这样自然就不会起作用了，所以要在设置 setContentType 和 setCharacterEncoding 之后再 getWriter，之后就可以正常返回了。

-]]> - - Java - SpringBoot - - - Java - Spring - SpringBoot - - springboot web server 启动逻辑 /2023/08/20/springboot-web-server-%E5%90%AF%E5%8A%A8%E9%80%BB%E8%BE%91/ @@ -4214,36 +4189,29 @@ - spring event 介绍 - /2022/01/30/spring-event-%E4%BB%8B%E7%BB%8D/ - spring框架中如果想使用一些一部操作，除了依赖第三方中间件的消息队列，还可以用spring自己的event，简单介绍下使用方法
首先我们可以建一个event，继承ApplicationEvent

-

public class CustomSpringEvent extends ApplicationEvent {

    private String message;

    public CustomSpringEvent(Object source, String message) {
        super(source);
        this.message = message;
    }
    public String getMessage() {
        return message;
    }
}
-

这个 ApplicationEvent 其实也比较简单，内部就一个 Object 类型的 source，可以自行扩展，我们在自定义的这个 Event 里加了个 Message ，只是简单介绍下使用

-
public abstract class ApplicationEvent extends EventObject {
    private static final long serialVersionUID = 7099057708183571937L;
    private final long timestamp;

    public ApplicationEvent(Object source) {
        super(source);
        this.timestamp = System.currentTimeMillis();
    }

    public ApplicationEvent(Object source, Clock clock) {
        super(source);
        this.timestamp = clock.millis();
    }

    public final long getTimestamp() {
        return this.timestamp;
    }
}
+ springboot 处理请求的小分支-跳转 & cookie + /2023/09/03/springboot-%E5%A4%84%E7%90%86%E8%AF%B7%E6%B1%82%E7%9A%84%E5%B0%8F%E5%88%86%E6%94%AF-%E8%B7%B3%E8%BD%AC-cookie/ + 首先是跳转，应该说设置状态，其中跳转是其中一个状态，

+
@RequestMapping(value = "request1", method = RequestMethod.GET)
    public void request1(HttpServletRequest request, HttpServletResponse response) {
        response.setStatus(HttpServletResponse.SC_OK);
    }
+

200 状态就很简单，如果想做跳转可以使用 302 并设置 location

+
@RequestMapping(value = "request2", method = RequestMethod.GET)
public void request2(HttpServletRequest request, HttpServletResponse response) {
    response.setHeader("Location", "https://baidu.com");
    response.setStatus(HttpServletResponse.SC_MOVED_TEMPORARILY);
}
+

这里如果设置了 302，但没设置跳转 location，就是 302 found

+
int SC_MOVED_TEMPORARILY = 302;
int SC_FOUND = 302;
-

然后就是事件生产者和监听消费者

-
@Component
public class CustomSpringEventPublisher {

    @Resource
    private ApplicationEventPublisher applicationEventPublisher;

    public void publishCustomEvent(final String message) {
        System.out.println("Publishing custom event. ");
        CustomSpringEvent customSpringEvent = new CustomSpringEvent(this, message);
        applicationEventPublisher.publishEvent(customSpringEvent);
    }
}
-

这里的 ApplicationEventPublisher 是 Spring 的方法接口

-
@FunctionalInterface
public interface ApplicationEventPublisher {
    default void publishEvent(ApplicationEvent event) {
        this.publishEvent((Object)event);
    }

    void publishEvent(Object var1);
}
-

具体的是例如 org.springframework.context.support.AbstractApplicationContext#publishEvent(java.lang.Object, org.springframework.core.ResolvableType) 中的实现，后面可以展开讲讲

-

事件监听者：

-
@Component
public class CustomSpringEventListener implements ApplicationListener<CustomSpringEvent> {
    @Override
    public void onApplicationEvent(CustomSpringEvent event) {
        System.out.println("Received spring custom event - " + event.getMessage());
    }
}
-

这里的也是 spring 的一个方法接口

-
@FunctionalInterface
public interface ApplicationListener<E extends ApplicationEvent> extends EventListener {
    void onApplicationEvent(E var1);

    static <T> ApplicationListener<PayloadApplicationEvent<T>> forPayload(Consumer<T> consumer) {
        return (event) -> {
            consumer.accept(event.getPayload());
        };
    }
}
- -

然后简单包个请求

-

@RequestMapping(value = "/event", method = RequestMethod.GET)
@ResponseBody
public void event() {
    customSpringEventPublisher.publishCustomEvent("hello sprint event");
}
- -


就能看到接收到消息了。

+

而如果是 301 跳转，代表原地址不可用了，永久跳转

+
@RequestMapping(value = "request3", method = RequestMethod.GET)
    public void request3(HttpServletRequest request, HttpServletResponse response) {
        response.setHeader("Location", "https://baidu.com");
        response.setStatus(HttpServletResponse.SC_MOVED_PERMANENTLY);
    }
+

另一个问题是设置 cookie

@RequestMapping(value = "request4", method = RequestMethod.GET)
    public void request4(HttpServletRequest request, HttpServletResponse response) {
        Cookie cookie = new Cookie("a", "b");
        response.addCookie(cookie);
        response.setStatus(HttpServletResponse.SC_OK);
    }
+

这样的设置就能成功设定 cookie，而随着目前的浏览器 cookie 策略，如果要跳转后设置的话，估计是会越来越难，包括

+
response.addHeader("Set-Cookie", "a=b; domain=baidu.com; SameSite=none;Secure");
+

这样的方式也没法实现。

]]> Java - Spring + SpringBoot Java - Spring - Spring Event + SpringBoot @@ -4264,32 +4232,6 @@

第三行

这里拿的是上面的 map 里的 key，也就是 RequestMappingInfo 也就是 org.springframework.web.servlet.mvc.method.RequestMappingInfo
而真的 url 就存在 org.springframework.web.servlet.mvc.condition.PatternsRequestCondition
最终这里面的patterns就是我们要的路径

public class PatternsRequestCondition extends AbstractRequestCondition<PatternsRequestCondition> {

	private final static Set<String> EMPTY_PATH_PATTERN = Collections.singleton("");


	private final Set<String> patterns;

写到这下一篇是不是可以介绍下 mapping 的具体注册逻辑

-]]> - - Java - SpringBoot - - - Java - SpringBoot - - - - springboot 处理请求的小分支-跳转 & cookie - /2023/09/03/springboot-%E5%A4%84%E7%90%86%E8%AF%B7%E6%B1%82%E7%9A%84%E5%B0%8F%E5%88%86%E6%94%AF-%E8%B7%B3%E8%BD%AC-cookie/ - 首先是跳转，应该说设置状态，其中跳转是其中一个状态，

-
@RequestMapping(value = "request1", method = RequestMethod.GET)
    public void request1(HttpServletRequest request, HttpServletResponse response) {
        response.setStatus(HttpServletResponse.SC_OK);
    }
-

200 状态就很简单，如果想做跳转可以使用 302 并设置 location

-
@RequestMapping(value = "request2", method = RequestMethod.GET)
public void request2(HttpServletRequest request, HttpServletResponse response) {
    response.setHeader("Location", "https://baidu.com");
    response.setStatus(HttpServletResponse.SC_MOVED_TEMPORARILY);
}
-

这里如果设置了 302，但没设置跳转 location，就是 302 found

-
int SC_MOVED_TEMPORARILY = 302;
int SC_FOUND = 302;
- -

而如果是 301 跳转，代表原地址不可用了，永久跳转

-
@RequestMapping(value = "request3", method = RequestMethod.GET)
    public void request3(HttpServletRequest request, HttpServletResponse response) {
        response.setHeader("Location", "https://baidu.com");
        response.setStatus(HttpServletResponse.SC_MOVED_PERMANENTLY);
    }
-

另一个问题是设置 cookie

@RequestMapping(value = "request4", method = RequestMethod.GET)
    public void request4(HttpServletRequest request, HttpServletResponse response) {
        Cookie cookie = new Cookie("a", "b");
        response.addCookie(cookie);
        response.setStatus(HttpServletResponse.SC_OK);
    }
-

这样的设置就能成功设定 cookie，而随着目前的浏览器 cookie 策略，如果要跳转后设置的话，估计是会越来越难，包括

-
response.addHeader("Set-Cookie", "a=b; domain=baidu.com; SameSite=none;Secure");
-

这样的方式也没法实现。

]]> Java @@ -4346,6 +4288,73 @@ SpringBoot + + openresty + /2019/06/18/openresty/ + 目前公司要对一些新的产品功能做灰度测试，因为在后端业务代码层面添加判断比较麻烦，所以想在nginx上做点手脚，就想到了openresty
前后也踩了不少坑，这边先写一点

+

首先是日志
error_log logs/error.log debug;
需要nginx开启日志的debug才能看到日志

+

使用 lua_code_cache off即可, 另外注意只有使用 content_by_lua_file 才会生效

+
http {
  lua_code_cache off;
}

location ~* /(\d+-.*)/api/orgunits/load_all(.*) {
   default_type 'application/json;charset=utf-8';
   content_by_lua_file /data/projects/xxx/current/lua/controller/load_data.lua;
}
+ +

使用lua给nginx请求response头添加内容可以用这个

+
ngx.header['response'] = 'header'
+ + +

使用总结

+

后续：

+
    +

  1. 一开始在本地环境的时候使用content_by_lua_file只关注了头，后来发到测试环境发现请求内容都没代理转发到后端服务上
    网上查了下发现content_by_lua_file是将请求的所有内容包括response都用这里面的lua脚本生成了，content这个词就表示是请求内容
    后来改成了access_by_lua_file就正常了，只是要去获取请求内容和修改响应头，并不是要完整的接管请求

    +
    2. +

  2. 后来又碰到了一个坑是nginx有个client_body_buffer_size的配置参数，nginx在32位和64位系统里有8K和16K两个默认值，当请求内容大于这两个值的时候，会把请求内容放到临时文件里，这个时候openresty里的ngx.req.get_post_args()就会报“failed to get post args: requesty body in temp file not supported”这个错误，将client_body_buffer_size这个参数配置调大一点就好了

    +
    3. +

  3. 还有就是lua的异常捕获，网上看一般是用pcall和xpcall来进行保护调用，因为问题主要出在cjson的decode，这里有两个解决方案，一个就是将cjson.decode使用pcall封装，

    + 
    local decode = require("cjson").decode

    function json_decode( str )
        local ok, t = pcall(decode, str)
        if not ok then
          return nil
        end

        return t
    end
    +

    这个是使用了pcall，称为保护调用，会在内部错误后返回两个参数，第一个是false，第二个是错误信息
    还有一种是使用cjson.safe包

    + 
    local json = require("cjson.safe")
    local str = [[ {"key:"value"} ]]

    local t = json.decode(str)
    if t then
        ngx.say(" --> ", type(t))
    end
    +

    cjson.safe包会在解析失败的时候返回nil

    +
    4. +

  4. 还有一个是redis链接时如果host使用的是域名的话会提示“failed to connect: no resolver defined to resolve “redis.xxxxxx.com””，这里需要使用nginx的resolver指令，
    resolver 8.8.8.8 valid=3600s;

    +
    5. +

  5. 还有一点补充下
    就是业务在使用redis的时候使用了db的特性，所以在lua访问redis的时候也需要执行db，这里lua的redis库也支持了这个特性，可以使用instance:select(config:get(‘db’))来切换db

    +
    6. +

  6. 性能优化tips
    建议是尽量少使用阶段钩子，例如content_by_lua_file，*_by_lua

    +
    7. +

  7. 发现一个不错的openresty站点
    地址

    +
    8. +
+]]> + + nginx + + + openresty + nginx + + + + ssh 小技巧-端口转发 + /2023/03/26/ssh-%E5%B0%8F%E6%8A%80%E5%B7%A7-%E7%AB%AF%E5%8F%A3%E8%BD%AC%E5%8F%91/ + 我们在使用 ssh 连接的使用有一个很好用功能，就是端口转发，而且使用的方式也很多样，比如我们经常用 vscode 来做远程开发的话，一般远程连接就可以基于 ssh，前面也介绍过 vscode 的端口转发，并且可以配置到 .ssh/config 配置文件里，只不过最近在一次使用的过程中发现了一个问题，就是在一台 Ubuntu 的某云服务器上想 ssh 到另一台服务器上，并且做下端口映射，但是发现报了个错，

+
bind: Cannot assign requested address
+

查了下这个问题，猜测是不是端口已经被占用了，查了下并不是，然后想到是不是端口是系统保留的，

+
sysctl -a |grep port_range
+

结果中

+
net.ipv4.ip_local_port_range = 50000    65000      -----意味着50000~65000端口可用
+

发现也不是，没有限制，最后才查到这个原因是默认如果有 ipv6 的话会使用 ipv6 的地址做映射
所以如果是命令连接做端口转发的话，

+
ssh -4 -L 11234:localhost:1234 x.x.x.x
+

使用-4来制定通过 ipv4 地址来做映射
如果是在 .ssh/config 中配置的话可以直接指定所有的连接都走 ipv4

+
Host *
    AddressFamily inet
+

inet代表 ipv4，inet6代表 ipv6
AddressFamily 的所有取值范围是：”any”(默认)、”inet”(仅IPv4)、”inet6”(仅IPv6)。
另外此类问题还可以通过 ssh -v 来打印更具体的信息

+]]> + + ssh + 技巧 + + + ssh + 端口转发 + + summary-ranges-228 /2016/10/12/summary-ranges-228/ @@ -4401,27 +4410,110 @@ user3: - ssh 小技巧-端口转发 - /2023/03/26/ssh-%E5%B0%8F%E6%8A%80%E5%B7%A7-%E7%AB%AF%E5%8F%A3%E8%BD%AC%E5%8F%91/ - 我们在使用 ssh 连接的使用有一个很好用功能，就是端口转发，而且使用的方式也很多样，比如我们经常用 vscode 来做远程开发的话，一般远程连接就可以基于 ssh，前面也介绍过 vscode 的端口转发，并且可以配置到 .ssh/config 配置文件里，只不过最近在一次使用的过程中发现了一个问题，就是在一台 Ubuntu 的某云服务器上想 ssh 到另一台服务器上，并且做下端口映射，但是发现报了个错，

-
bind: Cannot assign requested address
-

查了下这个问题，猜测是不是端口已经被占用了，查了下并不是，然后想到是不是端口是系统保留的，

-
sysctl -a |grep port_range
-

结果中

-
net.ipv4.ip_local_port_range = 50000    65000      -----意味着50000~65000端口可用
-

发现也不是，没有限制，最后才查到这个原因是默认如果有 ipv6 的话会使用 ipv6 的地址做映射
所以如果是命令连接做端口转发的话，

-
ssh -4 -L 11234:localhost:1234 x.x.x.x
-

使用-4来制定通过 ipv4 地址来做映射
如果是在 .ssh/config 中配置的话可以直接指定所有的连接都走 ipv4

-
Host *
    AddressFamily inet
-

inet代表 ipv4，inet6代表 ipv6
AddressFamily 的所有取值范围是：”any”(默认)、”inet”(仅IPv4)、”inet6”(仅IPv6)。
另外此类问题还可以通过 ssh -v 来打印更具体的信息

+ php 的调试方法-查看调用堆栈 + /2023/12/31/php-%E7%9A%84%E8%B0%83%E8%AF%95%E6%96%B9%E6%B3%95-%E6%9F%A5%E7%9C%8B%E8%B0%83%E7%94%A8%E5%A0%86%E6%A0%88/ + php 代码调试里的神器就是 echo 111;exit; 但是对于使用了接口和继承比较多的话，有时候比较难找，可能定位到了一段代码但是不知道怎么调用过来的，这时候就可以用这个方法

+

总结下来有三种，

+

第一种是最巧妙的

function a() {
echo 111;exit;
}
+

比如本来是上面这样子，那么其实我们可以主动new 个异常

+
function a() {
$e = new Exception();
print_r($e->getTraceAsString());
echo 111;exit;
}
+

这样我的 trace 调用链路就出来了

+

第二种

这个就是比较简单的，调用 php 自身提供的方法

+
debug_backtrace(int $options = DEBUG_BACKTRACE_PROVIDE_OBJECT, int $limit = 0): array
+

第一个参数是个掩码

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
debug_backtrace()Populates both indexes
debug_backtrace(DEBUG_BACKTRACE_PROVIDE_OBJECT)
debug_backtrace(1)
debug_backtrace(0)Omits index "object" and populates index "args".
debug_backtrace(DEBUG_BACKTRACE_IGNORE_ARGS)Omits index "object" and index "args".
debug_backtrace(2)
debug_backtrace(DEBUG_BACKTRACE_PROVIDE_OBJECTDEBUG_BACKTRACE_IGNORE_ARGS)
debug_backtrace(3)
+

第二个参数是限制栈深度

+

第三种

这个也是用自身的方法

+
debug_print_backtrace(int $options = 0, int $limit = 0): void
+

这里的第一个参数只有一个可以传的

+ + + + + + + +
DEBUG_BACKTRACE_IGNORE_ARGSWhether or not to omit the “args” index, and thus all the function/method arguments, to save memory.
+

就是隐藏参数，不然如果对于一些框架代码，这个打印会非常大，需要注意下

]]> - ssh - 技巧 + php - ssh - 端口转发 + php + + + + wordpress 忘记密码的一种解决方法 + /2021/12/05/wordpress-%E5%BF%98%E8%AE%B0%E5%AF%86%E7%A0%81%E7%9A%84%E4%B8%80%E7%A7%8D%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95/ + 前阵子搭了个 WordPress，但是没怎么用，前两天发现忘了登录密码了，最近不知道是什么情况，chrome 的记住密码跟历史记录感觉有点问题，历史记录丢了不少东西，可能是时间太久了，但是理论上应该有 LRU 这种策略的，有些还比较常用，还有记住密码，因为个人域名都是用子域名分配给各个服务，有些记住了，有些又没记住密码，略蛋疼，所以就找了下这个方案。
当然这个方案不是最优的，有很多限制，首先就是要能够登陆 WordPress 的数据库，不然这个方法是没用的。
首先不管用什么方式（别违法）先登陆数据库，选择 WordPress 的数据库，可以看到里面有几个表，我们的目标就是 wp_users 表，用 select 查询看下可以看到有用户的数据，如果是像我这样搭着玩的没有创建其他用户的话应该就只有一个用户，那我们的表里的用户数据就只会有一条，当然多条的话可以通过用户名来找

然后可能我这个版本是这样，没有装额外的插件，密码只是经过了 MD5 的单向哈希，所以我们可以设定一个新密码，然后用 MD5 编码后直接更新进去

+
UPDATE wp_users SET user_pass = MD5('123456') WHERE ID = 1;
+ +

然后就能用自己的账户跟刚才更新的密码登录了。

+]]> + + 小技巧 + + + WordPress + 小技巧 + + + + 《垃圾回收算法手册读书》笔记之整理算法 + /2021/03/07/%E3%80%8A%E5%9E%83%E5%9C%BE%E5%9B%9E%E6%94%B6%E7%AE%97%E6%B3%95%E6%89%8B%E5%86%8C%E8%AF%BB%E4%B9%A6%E3%80%8B%E7%AC%94%E8%AE%B0%E4%B9%8B%E6%95%B4%E7%90%86%E7%AE%97%E6%B3%95/ + 最近看了下这本垃圾回收算法手册，看到了第三章的标记-整理回收算法，做个简单的读书笔记

+

双指针整理算法

对于一块待整理区域，通过两个指针，free 在区域的起始端，scan 指针在区域的末端，free 指针从前往后知道找到空闲区域，scan 从后往前一直找到存活对象，当 free 指针未与 scan 指针交叉时，会给 scan 位置的对象特定位置标记上 free 的地址，即将要转移的地址，不过这里有个限制，这种整理算法一般会用于对象大小统一的情况，否则 free 指针扫描时还需要匹配scan 指针扫描到的存活对象的大小。

+

Lisp 2 整理算法

需要三次完整遍历堆区域
第一遍是遍历后将计算出所有对象的最终地址（转发地址）
第二遍是使用转发地址更新赋值器线程根以及被标记对象中的引用，该操作将确保它们指向对象的新位置
第三次遍历是relocate最终将存活对象移动到其新的目标位置

+

引线整理算法

这个真的长见识了，

可以看到，原来是 A，B，C 对象引用了 N，这里会在第一次遍历的时候把这种引用反过来，让 N 的对象头部保存下 A 的地址，表示这类引用，然后在遍历到 B 的时候在链起来，到最后就会把所有引用了 N 对象的所有对象通过引线链起来，在第二次遍历的时候就把更新A，B，C 对象引用的 N 地址，并且移动 N 对象

+

单次遍历算法

这个一直提到过位图的实现方式，

可以看到在第一步会先通过位图标记，标记的方式是位图的每一位对应的堆内存的一个字（这里可能指的是 byte 吧），然后将一个存活对象的内存区域的第一个字跟最后一个字标记，这里如果在通过普通的方式就还需要一个地方在存转发地址，但是因为具体的位置可以通过位图算出来，也就不需要额外记录了

+]]> + + Java + gc + jvm + + + java + gc + 标记整理 + 垃圾回收 + jvm @@ -4450,25 +4542,17 @@ user3: - 《垃圾回收算法手册读书》笔记之整理算法 - /2021/03/07/%E3%80%8A%E5%9E%83%E5%9C%BE%E5%9B%9E%E6%94%B6%E7%AE%97%E6%B3%95%E6%89%8B%E5%86%8C%E8%AF%BB%E4%B9%A6%E3%80%8B%E7%AC%94%E8%AE%B0%E4%B9%8B%E6%95%B4%E7%90%86%E7%AE%97%E6%B3%95/ - 最近看了下这本垃圾回收算法手册，看到了第三章的标记-整理回收算法，做个简单的读书笔记

-

双指针整理算法

对于一块待整理区域，通过两个指针，free 在区域的起始端，scan 指针在区域的末端，free 指针从前往后知道找到空闲区域，scan 从后往前一直找到存活对象，当 free 指针未与 scan 指针交叉时，会给 scan 位置的对象特定位置标记上 free 的地址，即将要转移的地址，不过这里有个限制，这种整理算法一般会用于对象大小统一的情况，否则 free 指针扫描时还需要匹配scan 指针扫描到的存活对象的大小。

-

Lisp 2 整理算法

需要三次完整遍历堆区域
第一遍是遍历后将计算出所有对象的最终地址（转发地址）
第二遍是使用转发地址更新赋值器线程根以及被标记对象中的引用，该操作将确保它们指向对象的新位置
第三次遍历是relocate最终将存活对象移动到其新的目标位置

-

引线整理算法

这个真的长见识了，

可以看到，原来是 A，B，C 对象引用了 N，这里会在第一次遍历的时候把这种引用反过来，让 N 的对象头部保存下 A 的地址，表示这类引用，然后在遍历到 B 的时候在链起来，到最后就会把所有引用了 N 对象的所有对象通过引线链起来，在第二次遍历的时候就把更新A，B，C 对象引用的 N 地址，并且移动 N 对象

-

单次遍历算法

这个一直提到过位图的实现方式，

可以看到在第一步会先通过位图标记，标记的方式是位图的每一位对应的堆内存的一个字（这里可能指的是 byte 吧），然后将一个存活对象的内存区域的第一个字跟最后一个字标记，这里如果在通过普通的方式就还需要一个地方在存转发地址，但是因为具体的位置可以通过位图算出来，也就不需要额外记录了

+ 《长安的荔枝》读后感 + /2022/07/17/%E3%80%8A%E9%95%BF%E5%AE%89%E7%9A%84%E8%8D%94%E6%9E%9D%E3%80%8B%E8%AF%BB%E5%90%8E%E6%84%9F/ + 断断续续地看完了马伯庸老师的《长安的荔枝》，一开始是看这本书在排行榜排得很高，又是马伯庸的，之前看过他的《古董局中局》，还是很有意思的，而且正好是比较短的，不过前后也拖了蛮久才看完，看完后读了下马老师自己写的后记，就特别有感触。
整个故事是围绕一个上林署监事李善德被委任一项给贵妃送荔枝的差事展开，“长安回望绣成堆，山顶千门次第开，一骑红尘妃子笑，无人知是荔枝来”，以前没细究过这个送荔枝的过程，但是以以前的运输速度和保鲜条件，感觉也不是太现实，所以主人公一开始就以为只是像以往一样是送荔枝干这种，能比较方便运输，不容易变质的，结果发现其实是同僚在坑他，这次是要在贵妃生辰的时候给贵妃送来新鲜的岭南荔枝，用比较时兴的词来说，这就是个送命题啊，鲜荔枝一日色变，两日香变，三日味变，同僚的还有杜甫跟韩承，都觉得老李可以直接写休书了，保全家人，不然就是全家送命，李善德也觉得基本算是判刑了，而且其实是这事被转了几次，最后到老李所在的上林署，主管为了骗他接下这个活还特意在文书上把荔枝鲜的“鲜”字贴住，那会叫做“贴黄”，变成了荔枝“煎”，所以说官场险恶，大家都想把这烫手山芋丢出去，结果丢到了我们老实的老李头上，但是从接到这个通知到贵妃的生辰六月初一还有挺长的时间，其实这个活虽然送命，但是在前期这个“荔枝使”也基本就是类似带着尚方宝剑，御赐黄马褂的职位，随便申请经费，不必像常规的部门费用需要定预算，申请后再层层审批，而是特事特批特办的耍赖做法，所以在这段时间是能够潇洒挥霍一下的。其实可以好好地捞一波给妻女，然后写下和离，在自己死后能让她们过的好一些，但最后还是在杜甫的一番劝导下做出了尝试一番的决定，因为也没其他办法，既是退无可退，何不向前拼死一搏，其实说到这，我觉得看这本书感觉有所收获的第一点，有时候总觉得事情没戏了，想躺平放弃了，但是这样其实这个结果是不会变好的，尝试努力，拼尽全力搏一搏，说不定会有所改观，至少不会变更坏了。

]]> - Java - gc - jvm + 读后感 + 生活 - java - gc - 标记整理 - 垃圾回收 - jvm + 生活 + 读后感 @@ -4489,174 +4573,47 @@ user3: - 一个经典的fastjson反序列化问题记录 - /2024/08/11/%E4%B8%80%E4%B8%AA%E7%BB%8F%E5%85%B8%E7%9A%84fastjson%E5%8F%8D%E5%BA%8F%E5%88%97%E5%8C%96%E9%97%AE%E9%A2%98%E8%AE%B0%E5%BD%95/ - 最近碰到一个问题，因为一些干扰因素导致排查的时候走了一段歧路，
报错信息是

-
Exception in thread "main" com.alibaba.fastjson.JSONException: scan null error
-

用一个简单的demo来复现下

-
public enum DemoEnum {

    DEMO1("demo1", "desc");
    private String code;

    private String desc;

    DemoEnum(String code, String desc) {
        this.code = code;
        this.desc = desc;
    }
    public static DemoEnum getByCode(String code) {
        for (DemoEnum demoEnum : values()) {
            if (demoEnum.code.equals(code)) {
                return demoEnum;
            }
        }
        return null;
    }

    public String getCode() {
        return code;
    }

    public void setCode(String code) {
        this.code = code;
    }

    public String getDesc() {
        return desc;
    }

    public void setDesc(String desc) {
        this.desc = desc;
    }
}
-

先定义一个枚举类，然后有个demo类

-
public class FastJsonDemo implements Serializable {

    private static final long serialVersionUID = 1131767138182111892L;
    private String name;

    private Map<DemoEnum, String> map = new HashMap<>();

    public String getName() {
        return name;
    }

    public void setName(String name) {
        this.name = name;
    }

    public Map<DemoEnum, String> getMap() {
        return map;
    }

    public void setMap(Map<DemoEnum, String> map) {
        this.map = map;
    }
}
-

其中map的key是上面定义的枚举类，然后在main方法中做一下序列化和反序列化

-
public class demo {

    public static void main(String[] args) {
        FastJsonDemo fastJsonDemo = new FastJsonDemo();
        fastJsonDemo.setName("nick");
        Map<DemoEnum, String> map = new HashMap<>();
        map.put(DemoEnum.getByCode("code"), "null key value");
        fastJsonDemo.setMap(map);
        FastJsonDemo decodeFastJsonDemo = JSONObject.parseObject(JSONObject.toJSONString(fastJsonDemo), FastJsonDemo.class);
    }
}
-

这样就会出现这个异常

-
Exception in thread "main" com.alibaba.fastjson.JSONException: scan null error
	at com.alibaba.fastjson.parser.JSONLexerBase.scanNullOrNew(JSONLexerBase.java:4531)
	at com.alibaba.fastjson.parser.JSONLexerBase.nextToken(JSONLexerBase.java:154)
	at com.alibaba.fastjson.parser.JSONLexerBase.nextToken(JSONLexerBase.java:358)
	at com.alibaba.fastjson.parser.deserializer.MapDeserializer.parseMap(MapDeserializer.java:227)
	at com.alibaba.fastjson.parser.deserializer.MapDeserializer.deserialze(MapDeserializer.java:61)
	at com.alibaba.fastjson.parser.deserializer.MapDeserializer.deserialze(MapDeserializer.java:41)
	at com.alibaba.fastjson.parser.deserializer.FastjsonASMDeserializer_1_FastJsonDemo.deserialze(Unknown Source)
	at com.alibaba.fastjson.parser.deserializer.JavaBeanDeserializer.deserialze(JavaBeanDeserializer.java:269)
	at com.alibaba.fastjson.parser.DefaultJSONParser.parseObject(DefaultJSONParser.java:671)
	at com.alibaba.fastjson.JSON.parseObject(JSON.java:365)
	at com.alibaba.fastjson.JSON.parseObject(JSON.java:269)
	at com.alibaba.fastjson.JSON.parseObject(JSON.java:488)
	at fastjson.demo.main(demo.java:23)
-

这个异常有两个原因，第一当然是在map中出现了null作为key的数据，第二就是这个key是复杂对象
在反序列化以后就会出现这个异常，因为最初出现这个异常是因为我改了另一个字符串字段，并且会反序列化成json就让我判断出现了误差
这边做一个记录。

-]]> - - java - - - java - - - - 介绍一下Java的BiFunction - /2024/06/30/%E4%BB%8B%E7%BB%8D%E4%B8%80%E4%B8%8BJava%E7%9A%84BiFunction/ - java的函数式接口是java8引入的很重要的特性，也让日常代码有了比较大的风格转变
这里介绍下BiFunction，
BiFunction的代码很短

-
@FunctionalInterface
public interface BiFunction<T, U, R> {

    /**
     * Applies this function to the given arguments.
     *
     * @param t the first function argument
     * @param u the second function argument
     * @return the function result
     */
    R apply(T t, U u);

    /**
     * Returns a composed function that first applies this function to
     * its input, and then applies the {@code after} function to the result.
     * If evaluation of either function throws an exception, it is relayed to
     * the caller of the composed function.
     *
     * @param <V> the type of output of the {@code after} function, and of the
     *           composed function
     * @param after the function to apply after this function is applied
     * @return a composed function that first applies this function and then
     * applies the {@code after} function
     * @throws NullPointerException if after is null
     */
    default <V> BiFunction<T, U, V> andThen(Function<? super R, ? extends V> after) {
        Objects.requireNonNull(after);
        return (T t, U u) -> after.apply(apply(t, u));
    }
}
-

默认的方法就跟普通的FunctionalInterface一样，就有一个apply方法，只是这里的特殊点有两个

-

返回值作为参数

这个BiFunction有三个泛型参数，T，U跟R，而这个R其实是具体方法的返回值
比如我们简单实现一个字符串拼接

-
BiFunction<String, String, String> biDemo = (s1, s2) -> s1 + s2;
System.out.println(biDemo.apply("hello ", "world"));
-

apply返回的就是参数R

-

andThen

这个方法是可以让结果再调用传入的方法，并返回结果

-
Function<String, Integer> funcDemo = String::length;
System.out.println(biDemo.andThen(funcDemo).apply("hello ", "world"));
-

定义了个函数接口，并且给了实现，就是字符串获取长度
然后传给biDemo，这样就可以输出拼接后的字符串长度

-

组合使用

public <T1, T2, R1, R2> R2 combination(T1 t1, T2 t2, BiFunction<T1, T2, R1> biFunction, Function<R1, R2> function) {
    return biFunction.andThen(function).apply(t1, t2);
}
-

这样就把前面的两个方法串起来了

-]]> - - java - - - java - stream - - - - 向量数据库学习-介绍一下HNSW算法 - /2024/06/23/%E4%BB%8B%E7%BB%8D%E4%B8%80%E4%B8%8BHNSW%E7%AE%97%E6%B3%95/ - 结合AI写的一篇HNSW的介绍文章，算是向量数据库中比较核心的算法

-

1. 引言

向量数据库在当今人工智能和机器学习领域扮演着越来越重要的角色。随着数据规模的不断扩大和应用场景的日益复杂,传统的数据库系统已经难以满足高维向量数据的快速检索需求。在这个背景下,近似最近邻(Approximate Nearest Neighbor, ANN)搜索问题成为了一个热点研究方向。

-

HNSW(Hierarchical Navigable Small World)算法是解决ANN问题的一种高效方法。它通过构建一个多层次的图结构,结合小世界网络的特性,实现了对高维向量数据的快速、精确检索。本文将深入浅出地介绍HNSW算法的核心思想、工作原理以及实际应用。

-

2. HNSW算法的核心思想

HNSW算法的核心思想可以概括为三个关键点：分层结构、小世界图和近似搜索。

-

分层结构

HNSW采用了一种多层次的图结构。想象一下,如果我们要在一个大城市中找到一个特定的地址,我们通常会先看城市地图,然后逐步缩小范围到区、街道,最后定位到具体门牌号。HNSW的分层结构就类似于这种由粗到细的搜索过程。这个就是之前介绍跳表的目的，就是现在稀疏的结构里可以快速定位，然后再往下层进行搜索，提高了搜索效率

-

小世界图

小世界网络是一种特殊的图结构,它具有较短的平均路径长度和较高的聚类系数。在HNSW中,每一层都是一个小世界图。这种结构使得在图中的任意两点之间都存在一条相对较短的路径,大大提高了搜索效率。

-

近似搜索

与传统的KNN(K-Nearest Neighbors)算法不同,HNSW通过牺牲一小部分精度来换取显著的速度提升。它不保证一定能找到真正的最近邻,但可以以极高的概率找到足够接近的点,而且搜索速度要快得多。

-

3. HNSW的工作原理

数据结构:多层图的构建过程

    -
  1. HNSW从底层开始构建,每个数据点都会出现在底层。
    2. -
  2. 对于每个新插入的点,算法会随机决定它是否应该出现在上一层。这个过程一直持续到某一层,该点不再被选中。
    3. -
  3. 在每一层,新点会与该层的其他点建立连接,形成小世界图结构。
    4. -
-

搜索过程:自顶向下的贪婪搜索

    -
  1. 搜索从最顶层开始,选择一个随机起点。
    2. -
  2. 在当前层进行贪婪搜索,不断移动到离目标更近的邻居。
    3. -
  3. 当在当前层无法找到更近的点时,下降到下一层继续搜索。
    4. -
  4. 重复这个过程直到达到底层,得到最终结果。
    5. -
-

插入新向量的过程

    -
  1. 确定新向量应该出现在哪些层。
    2. -
  2. 从顶层开始,在每一层执行类似于搜索的过程,找到适合连接的邻居点。
    3. -
  3. 建立连接,同时可能需要删除一些现有连接以维持图的结构特性。

    论文中的算法是这样的
    4. -
-
    -

  • 红色节点表示新插入的数据点。在这个例子中,新节点被插入到了层级0和层级1。

    -
    • -

  • 红色实线表示新建立的同层连接。新节点与其邻近节点建立了连接。

    -
    • -

  • 红色虚线表示新建立的跨层连接。新节点在不同层级之间建立了连接。

    -
    • -

  • 绿色虚线箭头表示插入过程的路径。这条路径展示了算法如何从顶层开始,逐层下降,最终确定新节点的插入位置。

    -
    • -

  • 其他颜色的节点和灰色的连接线表示原有的HNSW结构。

    -
    • -

  • 插入过程从顶层开始,沿着绿色虚线所示的路径向下搜索。

    -
    • -

  • 在每一层,算法都会寻找最近的邻居节点,并决定是否在该层插入新节点。

    -
    • -

  • 在本例中,新节点被插入到了层级0(基础层)和层级1。这是因为HNSW使用概率方法来决定节点应该出现在哪些层级。

    -
    • -

  • 插入后,新节点与其邻近节点建立连接(红色实线),以维持图的小世界特性。

    -
    • -

  • 同时,新节点也与其在不同层级的对应节点建立跨层连接(红色虚线),以确保层级之间的快速访问。

    -
    • -
-

4. HNSW的性能分析

时间复杂度分析

    -
  • 搜索时间复杂度: O(log N),其中N是数据点的总数。
    • -
  • 插入时间复杂度: 平均情况下为O(log N)。
    • -
-

空间复杂度分析

    -
  • 空间复杂度: O(N log N),因为上层节点数量呈指数衰减。
    • -
-

与其他ANN算法的比较

相比LSH(Locality-Sensitive Hashing)和Annoy等算法,HNSW在大多数场景下都能提供更好的查询性能和更高的准确率。然而,HNSW的内存消耗相对较高,这是它的一个潜在缺点。

-

5. HNSW的实际应用

在推荐系统中的应用

HNSW可以用于快速检索相似用户或物品,提高推荐系统的响应速度和准确性。例如,在音乐推荐中,可以用HNSW快速找到与用户喜好相似的歌曲。

-

在图像检索中的应用

在大规模图像数据库中,HNSW可以实现快速的相似图像搜索。这在图像搜索引擎、视觉商品搜索等场景中非常有用。

-

在自然语言处理中的应用

HNSW可以用于词嵌入或句子嵌入的快速检索,支持语义相似度计算、文本分类等任务。

-

6. HNSW的优化和变种

参数调优技巧

    -
  • 层数选择:影响搜索速度和内存使用
    • -
  • 每层连接数:影响搜索准确度和构建时间
    • -
  • 候选集大小:影响搜索质量和速度的平衡
    • -
-

常见的HNSW变种算法介绍

    -
  • NSW-G:改进了图的构建方法,提高了搜索效率
    • -
  • HCNNG:结合了层次聚类,提高了在某些数据集上的性能
    • -
-

7. 实现HNSW的挑战与解决方案

大规模数据集的处理

    -
  • 使用外部存储和分批处理
    • -
  • 采用压缩技术减少内存占用
    • -
-

并行化和分布式实现

    -
  • 搜索过程的并行化
    • -
  • 分布式索引构建和查询
    • -
-

动态更新和删除操作的处理

    -
  • 软删除技术
    • -
  • 定期重建索引
    • -
-

8. 总结与展望

HNSW算法通过其独特的分层小世界图结构,在保证高准确率的同时实现了对高维向量数据的快速检索。它在推荐系统、图像检索、自然语言处理等多个领域都有广泛应用。

-

未来,向量数据库技术可能会朝着以下方向发展:

-
    -
  1. 更高效的压缩和索引技术
    2. -
  2. 更好的动态更新支持
    3. -
  3. 与深度学习模型的更紧密集成
    4. -
  4. 针对特定领域的优化变体
    5. -
-

随着人工智能和大数据技术的不断发展,HNSW等高效的向量检索算法将在更广阔的应用场景中发挥重要作用。

+ 上次的其他 外行聊国足 + /2022/03/06/%E4%B8%8A%E6%AC%A1%E7%9A%84%E5%85%B6%E4%BB%96-%E5%A4%96%E8%A1%8C%E8%81%8A%E5%9B%BD%E8%B6%B3/ + 上次本来想在换车牌后面聊下这个话题，为啥要聊这个话题呢，也很简单，在地铁上看到一对猜测是情侣或者比较关系好的男女同学在聊，因为是这位男同学是大学学的工科，然后自己爱好设计绘画相关的，可能还以此赚了点钱，在地铁上讨论男的要不要好好努力把大学课程完成好，大致的观点是没必要，本来就不适合，这一段我就不说了，恋爱人的嘴，信你个鬼。
后面男的说在家里又跟他爹吵了关于男足的，估计是那次输了越南，实话说我不是个足球迷，对各方面技术相关也不熟，只是对包括这个人的解释和网上一些观点的看法，纯主观，这次地铁上这位说的大概意思是足球这个训练什么的很难的，要想赢越南也很难的，不是我们能嘴炮的；在网上看到一个赞同数很多的一个回答，说什么中国是个体育弱国，但是由于有一些乒乓球，跳水等小众项目比较厉害，让民众给误解了，首先我先来反驳下这个偷换概念的观点，第一所谓的体育弱国，跟我们觉得足球不应该这么差没半毛钱关系，因为体育弱国，我们的足球本来就不是顶尖的，也并不是去跟顶尖的球队去争，以足球为例，跟巴西，阿根廷，英国，德国，西班牙，意大利，法国这些足球强国，去比较，我相信没有一个足球迷会这么去做对比，因为我们足球历史最高排名是 1998 年的 37 名，最差是 100 名，把能数出来的强队都数完，估计都还不会到 37，所以根本没有跟强队去做对比，第二体育弱国，我们的体育投入是在逐年降低吗，我们是因战乱没法好好训练踢球？还是这帮傻逼就不争气，前面也说了我们足球世界排名最高 37，最低 100，那么前阵子我们输的越南是第几，目前我们的排名 77 名，越南 92 名，看明白了么，轮排名我们都不至于输越南，然后就是这个排名，这也是我想回应那位地铁上的兄弟，我觉得除了造核弹这种高精尖技术，绝大部分包含足球这类运动，遵循类二八原则，比如满分是 100 分，从 80 提到 90 分或者 90 分提到 100 分非常难，30 分提到 40 分，50 分提到 60 分我觉得都是可以凭后天努力达成的，基本不受天赋限制，这里可以以篮球来类比下，相对足球的确篮球没有那么火，或者行业市值没法比，但是也算是相对大众了，中国在篮球方面相对比较好一点，在 08 年奥运会冲进过八强，那也不是唯一的巅峰，但是我说这个其实是想说明两方面的事情，第一，像篮球一样，状态是有起起伏伏，排名也会变动，但是我觉得至少能维持一个相对稳定的总体排名和持平或者上升的趋势，这恰恰是我们这种所谓的“体育弱国”应该走的路线，第二就是去支持我的类二八原则的，可以看到我们的篮球这两年也很垃圾，排名跌到 29 了，那问题我觉得跟足球是一样的，就是不能脚踏实地，如斯科拉说的，中国篮球太缺少竞争，打得好不好都是这些人打，打输了还是照样拿钱，相对足球，篮球的技术我还是懂一些的，对比 08 年的中国男篮，的确像姚明跟王治郅这样的天赋型+努力型球员少了以后竞争力下降在所难免，但是去对比下基本功，传球，投篮，罚球稳定性，也完全不是一个水平的，这些就是我说的，可以通过努力训练拿 80 分的，只要拿到 80 分，甚至只要拿到 60 分，我觉得应该就还算对得起球迷了，就像 NBA 里球队也会有核心球员的更替，战绩起起伏伏，但是基本功这东西，防守积极性，我觉得不随核心球员的变化而变化，就像姚明这样的天赋，其实他应该还有一些先天缺陷，大脚趾较长等，但是他从 CBA 到 NBA，在 NBA 适应并且打成顶尖中锋，离不开刻苦训练，任何的成功都不是纯天赋的，必须要付出足够的努力。
说回足球，如果像前面那么洗地（体育弱国），那能给我维持住一个稳定的排名我也能接受，问题是我们的经济物质资源比 2000 年前应该有了质的变化，身体素质也越来越好，即使是体育弱国，这么继续走下坡路，半死不活的，不觉得是打了自己的脸么。足球也需要基本功，基本的体能，力量这些，看看现在这些国足运动员的体型，对比下女足，说实话，如果男足这些运动员都练得不错的体脂率，耐力等，成绩即使不好，也不会比现在更差。
纯主观吐槽，勿喷。

]]> - 算法 + 生活 + 运动 - 算法 + 生活 - wordpress 忘记密码的一种解决方法 - /2021/12/05/wordpress-%E5%BF%98%E8%AE%B0%E5%AF%86%E7%A0%81%E7%9A%84%E4%B8%80%E7%A7%8D%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95/ - 前阵子搭了个 WordPress，但是没怎么用，前两天发现忘了登录密码了，最近不知道是什么情况，chrome 的记住密码跟历史记录感觉有点问题，历史记录丢了不少东西，可能是时间太久了，但是理论上应该有 LRU 这种策略的，有些还比较常用，还有记住密码，因为个人域名都是用子域名分配给各个服务，有些记住了，有些又没记住密码，略蛋疼，所以就找了下这个方案。
当然这个方案不是最优的，有很多限制，首先就是要能够登陆 WordPress 的数据库，不然这个方法是没用的。
首先不管用什么方式（别违法）先登陆数据库，选择 WordPress 的数据库，可以看到里面有几个表，我们的目标就是 wp_users 表，用 select 查询看下可以看到有用户的数据，如果是像我这样搭着玩的没有创建其他用户的话应该就只有一个用户，那我们的表里的用户数据就只会有一条，当然多条的话可以通过用户名来找

然后可能我这个版本是这样，没有装额外的插件，密码只是经过了 MD5 的单向哈希，所以我们可以设定一个新密码，然后用 MD5 编码后直接更新进去

-
UPDATE wp_users SET user_pass = MD5('123456') WHERE ID = 1;
+ nginx 日志小记 + /2022/04/17/nginx-%E6%97%A5%E5%BF%97%E5%B0%8F%E8%AE%B0/ + nginx 默认的日志有特定的格式，我们也可以自定义，

+

默认的格式是预定义的 combined

+
log_format combined '$remote_addr - $remote_user [$time_local] '
                    '"$request" $status $body_bytes_sent '
                    '"$http_referer" "$http_user_agent"';
-

然后就能用自己的账户跟刚才更新的密码登录了。

-]]> - - 小技巧 - - - WordPress - 小技巧 - - - - 上次的其他 外行聊国足 - /2022/03/06/%E4%B8%8A%E6%AC%A1%E7%9A%84%E5%85%B6%E4%BB%96-%E5%A4%96%E8%A1%8C%E8%81%8A%E5%9B%BD%E8%B6%B3/ - 上次本来想在换车牌后面聊下这个话题，为啥要聊这个话题呢，也很简单，在地铁上看到一对猜测是情侣或者比较关系好的男女同学在聊，因为是这位男同学是大学学的工科，然后自己爱好设计绘画相关的，可能还以此赚了点钱，在地铁上讨论男的要不要好好努力把大学课程完成好，大致的观点是没必要，本来就不适合，这一段我就不说了，恋爱人的嘴，信你个鬼。
后面男的说在家里又跟他爹吵了关于男足的，估计是那次输了越南，实话说我不是个足球迷，对各方面技术相关也不熟，只是对包括这个人的解释和网上一些观点的看法，纯主观，这次地铁上这位说的大概意思是足球这个训练什么的很难的，要想赢越南也很难的，不是我们能嘴炮的；在网上看到一个赞同数很多的一个回答，说什么中国是个体育弱国，但是由于有一些乒乓球，跳水等小众项目比较厉害，让民众给误解了，首先我先来反驳下这个偷换概念的观点，第一所谓的体育弱国，跟我们觉得足球不应该这么差没半毛钱关系，因为体育弱国，我们的足球本来就不是顶尖的，也并不是去跟顶尖的球队去争，以足球为例，跟巴西，阿根廷，英国，德国，西班牙，意大利，法国这些足球强国，去比较，我相信没有一个足球迷会这么去做对比，因为我们足球历史最高排名是 1998 年的 37 名，最差是 100 名，把能数出来的强队都数完，估计都还不会到 37，所以根本没有跟强队去做对比，第二体育弱国，我们的体育投入是在逐年降低吗，我们是因战乱没法好好训练踢球？还是这帮傻逼就不争气，前面也说了我们足球世界排名最高 37，最低 100，那么前阵子我们输的越南是第几，目前我们的排名 77 名，越南 92 名，看明白了么，轮排名我们都不至于输越南，然后就是这个排名，这也是我想回应那位地铁上的兄弟，我觉得除了造核弹这种高精尖技术，绝大部分包含足球这类运动，遵循类二八原则，比如满分是 100 分，从 80 提到 90 分或者 90 分提到 100 分非常难，30 分提到 40 分，50 分提到 60 分我觉得都是可以凭后天努力达成的，基本不受天赋限制，这里可以以篮球来类比下，相对足球的确篮球没有那么火，或者行业市值没法比，但是也算是相对大众了，中国在篮球方面相对比较好一点，在 08 年奥运会冲进过八强，那也不是唯一的巅峰，但是我说这个其实是想说明两方面的事情，第一，像篮球一样，状态是有起起伏伏，排名也会变动，但是我觉得至少能维持一个相对稳定的总体排名和持平或者上升的趋势，这恰恰是我们这种所谓的“体育弱国”应该走的路线，第二就是去支持我的类二八原则的，可以看到我们的篮球这两年也很垃圾，排名跌到 29 了，那问题我觉得跟足球是一样的，就是不能脚踏实地，如斯科拉说的，中国篮球太缺少竞争，打得好不好都是这些人打，打输了还是照样拿钱，相对足球，篮球的技术我还是懂一些的，对比 08 年的中国男篮，的确像姚明跟王治郅这样的天赋型+努力型球员少了以后竞争力下降在所难免，但是去对比下基本功，传球，投篮，罚球稳定性，也完全不是一个水平的，这些就是我说的，可以通过努力训练拿 80 分的，只要拿到 80 分，甚至只要拿到 60 分，我觉得应该就还算对得起球迷了，就像 NBA 里球队也会有核心球员的更替，战绩起起伏伏，但是基本功这东西，防守积极性，我觉得不随核心球员的变化而变化，就像姚明这样的天赋，其实他应该还有一些先天缺陷，大脚趾较长等，但是他从 CBA 到 NBA，在 NBA 适应并且打成顶尖中锋，离不开刻苦训练，任何的成功都不是纯天赋的，必须要付出足够的努力。
说回足球，如果像前面那么洗地（体育弱国），那能给我维持住一个稳定的排名我也能接受，问题是我们的经济物质资源比 2000 年前应该有了质的变化，身体素质也越来越好，即使是体育弱国，这么继续走下坡路，半死不活的，不觉得是打了自己的脸么。足球也需要基本功，基本的体能，力量这些，看看现在这些国足运动员的体型，对比下女足，说实话，如果男足这些运动员都练得不错的体脂率，耐力等，成绩即使不好，也不会比现在更差。
纯主观吐槽，勿喷。

+

配置的日志可以使用这个默认的，如果满足需求的话

+
Syntax:	access_log path [format [buffer=size] [gzip[=level]] [flush=time] [if=condition]];
        access_log off;
Default: access_log logs/access.log combined;
Context: http, server, location, if in location, limit_except
+ +

而如果需要额外的一些配置的话可以自己定义 log_format ，比如我想要给日志里加上请求时间，那就可以自己定义一个 log_format 比如添加下

+
$request_time
request processing time in seconds with a milliseconds resolution;   
time elapsed between the first bytes were read from the client and the log write after the last bytes were sent to the client
+ +
log_format combined_extend '$remote_addr - $remote_user [$time_local] '
                    '"$request" $status $body_bytes_sent '
                    '"$http_referer" "$http_user_agent" "$request_time"';
+ +

然后其他的比如还有 gzip 压缩，可以设置压缩级别，flush 刷盘时间还有根据条件控制

+

这里的条件控制简单看了下还比较厉害

+

比如我想对2xx 跟 3xx 的访问不记录日志

+
map $status $loggable {
    ~^[23]  0;
    default 1;
}

access_log /path/to/access.log combined if=$loggable;
+ +

当 $loggable 是 0 或者空时表示 if 条件为否，上面的默认就是 1，只有当请求状态 status 是 2xx 或 3xx 时才是 0，代表不用记录，有了这个特性就可以更灵活地配置日志

+

文章主要参考了 nginx 的 log 模块的文档

]]> - 生活 - 运动 + nginx - 生活 + nginx + 日志 @@ -4728,26 +4685,6 @@ user3: 中间件 - - 从丁仲礼被美国制裁聊点啥 - /2020/12/20/%E4%BB%8E%E4%B8%81%E4%BB%B2%E7%A4%BC%E8%A2%AB%E7%BE%8E%E5%9B%BD%E5%88%B6%E8%A3%81%E8%81%8A%E7%82%B9%E5%95%A5/ - 几年前看了柴静的《穹顶之下》觉得这个记者调查得很深入，挺有水平，然后再看到了她跟丁仲礼的采访，其实没看完整，也没试着去理解，就觉得环境问题挺严重的，为啥柴静这个对面的这位好像对这个很不屑的样子，最近因为丁仲礼上了美国制裁名单，B 站又有人把这个视频发了出来，就完整看了下，就觉得自己挺惭愧的，就抱着对柴静的好感而没来由的否定了丁老的看法和说法，所以人也需要不断地学习，改正之前错误的观点，当然不是说我现在说的就是百分百正确，只是个人的一些浅显的见解

-

先聊聊这个事情，整体看下来我的一些理解，IPCC给中国的方案其实是个很大的陷阱，它里面有几个隐藏的点是容易被我们外行忽略的，第一点是基数，首先发达国家目前（指2010年采访或者IPCC方案时间）的人均碳排放量已经是远高于发展中国家的了，这也就导致了所谓的发达国家承诺减排80%是个非常有诚意的承诺其实就是忽悠；第二点是碳排放是个累计过程，从1900年开始到2050年，或者说到2010年，发达国家已经排的量是远超过发展中国家的，这是非常不公平的；第三点其实是通过前两点推导出来的，也就是即使发达国家这么有诚意地说减排80%，扒开这层虚伪的外衣，其实是给他们11亿人分走了48%的碳排放量，留给发展中国家55亿人口的只剩下了52%；第四点，人是否因为国家的发达与否而应受到不平等待遇，如果按国家维度，丁老说的，摩纳哥要跟中国分同样的排放量么，中国人还算不算人；第五点，这点算是我自己想的，也可能是柴静屁股决定脑袋想不到的点，她作为一个物质生活条件已经足够好了，那么对于那些生活在物质条件平均线以下的，他们是否能像城里人那样有空调地暖，洗澡有热水器浴霸，上下班能开车，这些其实都直接或者间接地导致了碳排放；他们有没有改善物质生活条件地权利呢，并且再说回来，其实丁老也给了我们觉得合理地方案，我们保证不管发达国家不管减排多少，我们都不会超过他们的80%，我觉得这是真正的诚意，而不是接着减排80%的噱头来忽悠人，也是像丁老这样的专家才能看破这个陷阱，碳排放权其实就是发展权，就是人权，中国人就不是人了么，或者说站在贫困线以下的人民是否有改善物质条件的权利，而不是说像柴静这样，只是管她自己，可能觉得小孩因为空气污染导致身体不好，所以做了穹顶之下这个纪录片，想去改善这个事情，空气污染不是说对的，只是每个国家都有这个过程，如果不发展，哪里有资源去让人活得好，活得好了是前提，然后再去各方面都改善。

-

对于这个问题其实更想说的是人的认知偏差，之前总觉得美帝是更自由民主，公平啥的，或者说觉得美帝啥都好，有种无脑愤青的感觉，外国的月亮比较圆，但是经历了像川普当选美国总统以来的各种魔幻操作，还有对于疫情的种种不可思议的美国民众的反应，其实更让人明白第一是外国的月亮没比较圆，第二是事情总是没那么简单粗暴非黑即白，美国不像原先设想地那么领先优秀，但是的确有很多方面是全球领先的，天朝也有体制所带来的优势，不可妄自菲薄，也不能忙不自大，还是要多学习知识，提升认知水平。

-]]> - - 生活 - 吐槽 - 疫情 - 美国 - - - 生活 - 吐槽 - 疫情 - 美国 - - 介绍下最近比较实用的端口转发 /2021/11/14/%E4%BB%8B%E7%BB%8D%E4%B8%8B%E6%9C%80%E8%BF%91%E6%AF%94%E8%BE%83%E5%AE%9E%E7%94%A8%E7%9A%84%E7%AB%AF%E5%8F%A3%E8%BD%AC%E5%8F%91/ @@ -4769,17 +4706,23 @@ user3: - 《长安的荔枝》读后感 - /2022/07/17/%E3%80%8A%E9%95%BF%E5%AE%89%E7%9A%84%E8%8D%94%E6%9E%9D%E3%80%8B%E8%AF%BB%E5%90%8E%E6%84%9F/ - 断断续续地看完了马伯庸老师的《长安的荔枝》，一开始是看这本书在排行榜排得很高，又是马伯庸的，之前看过他的《古董局中局》，还是很有意思的，而且正好是比较短的，不过前后也拖了蛮久才看完，看完后读了下马老师自己写的后记，就特别有感触。
整个故事是围绕一个上林署监事李善德被委任一项给贵妃送荔枝的差事展开，“长安回望绣成堆，山顶千门次第开，一骑红尘妃子笑，无人知是荔枝来”，以前没细究过这个送荔枝的过程，但是以以前的运输速度和保鲜条件，感觉也不是太现实，所以主人公一开始就以为只是像以往一样是送荔枝干这种，能比较方便运输，不容易变质的，结果发现其实是同僚在坑他，这次是要在贵妃生辰的时候给贵妃送来新鲜的岭南荔枝，用比较时兴的词来说，这就是个送命题啊，鲜荔枝一日色变，两日香变，三日味变，同僚的还有杜甫跟韩承，都觉得老李可以直接写休书了，保全家人，不然就是全家送命，李善德也觉得基本算是判刑了，而且其实是这事被转了几次，最后到老李所在的上林署，主管为了骗他接下这个活还特意在文书上把荔枝鲜的“鲜”字贴住，那会叫做“贴黄”，变成了荔枝“煎”，所以说官场险恶，大家都想把这烫手山芋丢出去，结果丢到了我们老实的老李头上，但是从接到这个通知到贵妃的生辰六月初一还有挺长的时间，其实这个活虽然送命，但是在前期这个“荔枝使”也基本就是类似带着尚方宝剑，御赐黄马褂的职位，随便申请经费，不必像常规的部门费用需要定预算，申请后再层层审批，而是特事特批特办的耍赖做法，所以在这段时间是能够潇洒挥霍一下的。其实可以好好地捞一波给妻女，然后写下和离，在自己死后能让她们过的好一些，但最后还是在杜甫的一番劝导下做出了尝试一番的决定，因为也没其他办法，既是退无可退，何不向前拼死一搏，其实说到这，我觉得看这本书感觉有所收获的第一点，有时候总觉得事情没戏了，想躺平放弃了，但是这样其实这个结果是不会变好的，尝试努力，拼尽全力搏一搏，说不定会有所改观，至少不会变更坏了。

+ 从丁仲礼被美国制裁聊点啥 + /2020/12/20/%E4%BB%8E%E4%B8%81%E4%BB%B2%E7%A4%BC%E8%A2%AB%E7%BE%8E%E5%9B%BD%E5%88%B6%E8%A3%81%E8%81%8A%E7%82%B9%E5%95%A5/ + 几年前看了柴静的《穹顶之下》觉得这个记者调查得很深入，挺有水平，然后再看到了她跟丁仲礼的采访，其实没看完整，也没试着去理解，就觉得环境问题挺严重的，为啥柴静这个对面的这位好像对这个很不屑的样子，最近因为丁仲礼上了美国制裁名单，B 站又有人把这个视频发了出来，就完整看了下，就觉得自己挺惭愧的，就抱着对柴静的好感而没来由的否定了丁老的看法和说法，所以人也需要不断地学习，改正之前错误的观点，当然不是说我现在说的就是百分百正确，只是个人的一些浅显的见解

+

先聊聊这个事情，整体看下来我的一些理解，IPCC给中国的方案其实是个很大的陷阱，它里面有几个隐藏的点是容易被我们外行忽略的，第一点是基数，首先发达国家目前（指2010年采访或者IPCC方案时间）的人均碳排放量已经是远高于发展中国家的了，这也就导致了所谓的发达国家承诺减排80%是个非常有诚意的承诺其实就是忽悠；第二点是碳排放是个累计过程，从1900年开始到2050年，或者说到2010年，发达国家已经排的量是远超过发展中国家的，这是非常不公平的；第三点其实是通过前两点推导出来的，也就是即使发达国家这么有诚意地说减排80%，扒开这层虚伪的外衣，其实是给他们11亿人分走了48%的碳排放量，留给发展中国家55亿人口的只剩下了52%；第四点，人是否因为国家的发达与否而应受到不平等待遇，如果按国家维度，丁老说的，摩纳哥要跟中国分同样的排放量么，中国人还算不算人；第五点，这点算是我自己想的，也可能是柴静屁股决定脑袋想不到的点，她作为一个物质生活条件已经足够好了，那么对于那些生活在物质条件平均线以下的，他们是否能像城里人那样有空调地暖，洗澡有热水器浴霸，上下班能开车，这些其实都直接或者间接地导致了碳排放；他们有没有改善物质生活条件地权利呢，并且再说回来，其实丁老也给了我们觉得合理地方案，我们保证不管发达国家不管减排多少，我们都不会超过他们的80%，我觉得这是真正的诚意，而不是接着减排80%的噱头来忽悠人，也是像丁老这样的专家才能看破这个陷阱，碳排放权其实就是发展权，就是人权，中国人就不是人了么，或者说站在贫困线以下的人民是否有改善物质条件的权利，而不是说像柴静这样，只是管她自己，可能觉得小孩因为空气污染导致身体不好，所以做了穹顶之下这个纪录片，想去改善这个事情，空气污染不是说对的，只是每个国家都有这个过程，如果不发展，哪里有资源去让人活得好，活得好了是前提，然后再去各方面都改善。

+

对于这个问题其实更想说的是人的认知偏差，之前总觉得美帝是更自由民主，公平啥的，或者说觉得美帝啥都好，有种无脑愤青的感觉，外国的月亮比较圆，但是经历了像川普当选美国总统以来的各种魔幻操作，还有对于疫情的种种不可思议的美国民众的反应，其实更让人明白第一是外国的月亮没比较圆，第二是事情总是没那么简单粗暴非黑即白，美国不像原先设想地那么领先优秀，但是的确有很多方面是全球领先的，天朝也有体制所带来的优势，不可妄自菲薄，也不能忙不自大，还是要多学习知识，提升认知水平。

]]> - 读后感 生活 + 吐槽 + 疫情 + 美国 生活 - 读后感 + 吐槽 + 疫情 + 美国 @@ -4803,23 +4746,19 @@ user3: - 体验下Java 21的虚拟线程-协程 - /2024/08/18/%E4%BD%93%E9%AA%8C%E4%B8%8BJava-21%E7%9A%84%E8%99%9A%E6%8B%9F%E7%BA%BF%E7%A8%8B-%E5%8D%8F%E7%A8%8B/ - Java在后续版本中添加了虚拟线程，也是类似于php跟go的协程，对应操作系统的线程是在线程基础上模拟了一层子线程的逻辑，因为减少了操作系统的线程上下文切换开销，能够在常规业务场景带了比较大的性能提升，但也并非银弹，不能包治百病
首先安装下jdk 21 版本，需要用 /usr/libexec/java_home 切换下JAVA_HOME
然后在PATH中设置好

-
export JAVA_HOME=$(/usr/libexec/java_home -v 21)
-

首先是试一下线程版本

-
long start = System.currentTimeMillis();
ExecutorService executor = Executors.newFixedThreadPool(200);
for (int i = 0; i < 100000; i++) {
    executor.submit(() -> {
        try {
            // 线程睡眠 10ms，可以等同于模拟业务耗时10ms
                TimeUnit.MILLISECONDS.sleep(10);
            } catch (InterruptedException e) {

        }
    });
}
executor.close();
System.out.printf("totalMillis：%dms\n", System.currentTimeMillis() - start);
-


耗时5897ms

-
long start = System.currentTimeMillis();
ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor();
for (int i = 0; i < 10000; i++) {
    executor.submit(() -> {
        // 模拟业务处理
        try {
            TimeUnit.MILLISECONDS.sleep(10);
        } catch (InterruptedException e) {

        }
    });
}
executor.close();
System.out.printf("totalMillis：%dms\n", System.currentTimeMillis() - start);
-


耗时154ms
相对来说还是能快很多的
而核心的虚拟线程实现主要来自于调度

-
static Thread newVirtualThread(Executor scheduler,
                               String name,
                               int characteristics,
                               Runnable task) {
    if (ContinuationSupport.isSupported()) {
        return new VirtualThread(scheduler, name, characteristics, task);
    } else {
        if (scheduler != null)
            throw new UnsupportedOperationException();
        return new BoundVirtualThread(name, characteristics, task);
    }
}
-

在创建虚拟线程过程中，我们需要去处理调度器，初始时调度器为空

-
VirtualThread(Executor scheduler, String name, int characteristics, Runnable task) {
    super(name, characteristics, /*bound*/ false);
    Objects.requireNonNull(task);

    // choose scheduler if not specified
    if (scheduler == null) {
        Thread parent = Thread.currentThread();
        if (parent instanceof VirtualThread vparent) {
            scheduler = vparent.scheduler;
        } else {
            scheduler = DEFAULT_SCHEDULER;
        }
    }

    this.scheduler = scheduler;
    this.cont = new VThreadContinuation(this, task);
    this.runContinuation = this::runContinuation;
}
-

而这个默认调度器就是

-
private static final ForkJoinPool DEFAULT_SCHEDULER = createDefaultScheduler();
-

对应创建的就是个ForkJoinPool

-
private static ForkJoinPool createDefaultScheduler() {
        ForkJoinWorkerThreadFactory factory = pool -> {
            PrivilegedAction<ForkJoinWorkerThread> pa = () -> new CarrierThread(pool);
            return AccessController.doPrivileged(pa);
        };
        PrivilegedAction<ForkJoinPool> pa = () -> {
            int parallelism, maxPoolSize, minRunnable;
            String parallelismValue = System.getProperty("jdk.virtualThreadScheduler.parallelism");
            String maxPoolSizeValue = System.getProperty("jdk.virtualThreadScheduler.maxPoolSize");
            String minRunnableValue = System.getProperty("jdk.virtualThreadScheduler.minRunnable");
            if (parallelismValue != null) {
                parallelism = Integer.parseInt(parallelismValue);
            } else {
                parallelism = Runtime.getRuntime().availableProcessors();
            }
            if (maxPoolSizeValue != null) {
                maxPoolSize = Integer.parseInt(maxPoolSizeValue);
                parallelism = Integer.min(parallelism, maxPoolSize);
            } else {
                maxPoolSize = Integer.max(parallelism, 256);
            }
            if (minRunnableValue != null) {
                minRunnable = Integer.parseInt(minRunnableValue);
            } else {
                minRunnable = Integer.max(parallelism / 2, 1);
            }
            Thread.UncaughtExceptionHandler handler = (t, e) -> { };
            boolean asyncMode = true; // FIFO
            return new ForkJoinPool(parallelism, factory, handler, asyncMode,
                         0, maxPoolSize, minRunnable, pool -> true, 30, SECONDS);
        };
        return AccessController.doPrivileged(pa);
    }
-

可以看到参数都是通过系统参数获取，或者用系统的cpu数量来决定并行度，主体的逻辑就是既然系统线程开销大，那我就在系统线程内部模拟一个更小颗粒度的，在线程内部进行调度的模型，以此来减少系统切换开销，只不过细节还有很多需要研究，有兴趣的可以留言探讨

+ 一个经典的fastjson反序列化问题记录 + /2024/08/11/%E4%B8%80%E4%B8%AA%E7%BB%8F%E5%85%B8%E7%9A%84fastjson%E5%8F%8D%E5%BA%8F%E5%88%97%E5%8C%96%E9%97%AE%E9%A2%98%E8%AE%B0%E5%BD%95/ + 最近碰到一个问题，因为一些干扰因素导致排查的时候走了一段歧路，
报错信息是

+
Exception in thread "main" com.alibaba.fastjson.JSONException: scan null error
+

用一个简单的demo来复现下

+
public enum DemoEnum {

    DEMO1("demo1", "desc");
    private String code;

    private String desc;

    DemoEnum(String code, String desc) {
        this.code = code;
        this.desc = desc;
    }
    public static DemoEnum getByCode(String code) {
        for (DemoEnum demoEnum : values()) {
            if (demoEnum.code.equals(code)) {
                return demoEnum;
            }
        }
        return null;
    }

    public String getCode() {
        return code;
    }

    public void setCode(String code) {
        this.code = code;
    }

    public String getDesc() {
        return desc;
    }

    public void setDesc(String desc) {
        this.desc = desc;
    }
}
+

先定义一个枚举类，然后有个demo类

+
public class FastJsonDemo implements Serializable {

    private static final long serialVersionUID = 1131767138182111892L;
    private String name;

    private Map<DemoEnum, String> map = new HashMap<>();

    public String getName() {
        return name;
    }

    public void setName(String name) {
        this.name = name;
    }

    public Map<DemoEnum, String> getMap() {
        return map;
    }

    public void setMap(Map<DemoEnum, String> map) {
        this.map = map;
    }
}
+

其中map的key是上面定义的枚举类，然后在main方法中做一下序列化和反序列化

+
public class demo {

    public static void main(String[] args) {
        FastJsonDemo fastJsonDemo = new FastJsonDemo();
        fastJsonDemo.setName("nick");
        Map<DemoEnum, String> map = new HashMap<>();
        map.put(DemoEnum.getByCode("code"), "null key value");
        fastJsonDemo.setMap(map);
        FastJsonDemo decodeFastJsonDemo = JSONObject.parseObject(JSONObject.toJSONString(fastJsonDemo), FastJsonDemo.class);
    }
}
+

这样就会出现这个异常

+
Exception in thread "main" com.alibaba.fastjson.JSONException: scan null error
	at com.alibaba.fastjson.parser.JSONLexerBase.scanNullOrNew(JSONLexerBase.java:4531)
	at com.alibaba.fastjson.parser.JSONLexerBase.nextToken(JSONLexerBase.java:154)
	at com.alibaba.fastjson.parser.JSONLexerBase.nextToken(JSONLexerBase.java:358)
	at com.alibaba.fastjson.parser.deserializer.MapDeserializer.parseMap(MapDeserializer.java:227)
	at com.alibaba.fastjson.parser.deserializer.MapDeserializer.deserialze(MapDeserializer.java:61)
	at com.alibaba.fastjson.parser.deserializer.MapDeserializer.deserialze(MapDeserializer.java:41)
	at com.alibaba.fastjson.parser.deserializer.FastjsonASMDeserializer_1_FastJsonDemo.deserialze(Unknown Source)
	at com.alibaba.fastjson.parser.deserializer.JavaBeanDeserializer.deserialze(JavaBeanDeserializer.java:269)
	at com.alibaba.fastjson.parser.DefaultJSONParser.parseObject(DefaultJSONParser.java:671)
	at com.alibaba.fastjson.JSON.parseObject(JSON.java:365)
	at com.alibaba.fastjson.JSON.parseObject(JSON.java:269)
	at com.alibaba.fastjson.JSON.parseObject(JSON.java:488)
	at fastjson.demo.main(demo.java:23)
+

这个异常有两个原因，第一当然是在map中出现了null作为key的数据，第二就是这个key是复杂对象
在反序列化以后就会出现这个异常，因为最初出现这个异常是因为我改了另一个字符串字段，并且会反序列化成json就让我判断出现了误差
这边做一个记录。

]]> java @@ -4841,21 +4780,25 @@ user3: - 关于 npe 的一个小记忆点 - /2023/07/16/%E5%85%B3%E4%BA%8E-npe-%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E8%AE%B0%E5%BF%86%E7%82%B9/ - Java 中最常见的一类问题或者说异常就是 NullPointerException，而往往这种异常我们在查看日志的时候都是根据打印出来的异常堆栈找到对应的代码以确定问题，但是有一种情况会出现一个比较奇怪的情况，虽然我们在日志中打印了异常

-
log.error("something error", e);
-

这样是可以打印出来日志，譬如 slf4j 就可以把异常作为最后一个参数传入

-
void error(String var1, Throwable var2);
-

有的时候如果不小心把异常通过占位符传入就可能出现异常被吞的情况，不过这点不是这次的重点
即使我们正常作为最后一个参数传入了，也会出现只打印出来

-
something error, java.lang.NullPointerException: null
-

这样就导致了如果我们这个日志所代表的异常包含的代码范围比较大的话，就不确定具体是哪一行出现的异常，除非对这些代码逻辑非常清楚，这个是为什么呢，其实这也是 jvm 的一种优化机制，像我们目前主要还在用的 jdk8中使用的 jvm 虚拟机 hotspot 的一个热点异常机制，对于这样一直出现的相同异常，就认为是热点异常，后面只会打印异常名，不再打印异常堆栈，这样对于 io 性能能够提供一定的优化保证，这个可以通过 jvm 启动参数
-XX:-OmitStackTraceInFastThrow 来关闭优化，这个问题其实在一开始造成了一定困扰，找不准具体是哪一行代码，不过在知道这个之后简单的重启也能暂时解决问题。

-]]> - - java + 关于公共交通再吐个槽 + /2021/03/21/%E5%85%B3%E4%BA%8E%E5%85%AC%E5%85%B1%E4%BA%A4%E9%80%9A%E5%86%8D%E5%90%90%E4%B8%AA%E6%A7%BD/ + 事情源于周末来回家发生的两件事情，先是回去的时候从高铁下车要坐公交，现在算是有个比较好的临时候车点了，但是可能由于疫情好转，晚上都不用检查健康码就可以进候车点，但是上公交的时候还是需要看健康码，一般情况下从高铁下来的，各个地方的人都有，而且也不太清楚这边上公交车需要查验健康码，我的一个看法是候车的时候就应该放个横幅告示，或者再配合喇叭循环播放，请提前准备好健康码，上车前需要查验，因为像这周的情况，我乘坐的那辆车是间隔时间比较长，而且终点是工业开发区，可能是比较多外来务工人员的目的地，正好这部分人可能对于操作手机检验健康码这个事情不太熟悉，所以结果就是头上几个不知道怎么搞出来健康码，然后让几乎有满满一整车的人在后面堵着，司机又非常厌烦那些没有提前出示健康码的，有位乘客搞得久了点，还被误以为没刷卡买票，差点吵起来，其实公共交通或者高铁站负责的在公交指引路线上多写一句上车前需要查验健康码，可能就能改善比较多，还有就是那个积水的路，这个吐槽起来就一大坨了，整个绍兴像 dayuejin 一样到处都是破路。
第二个就是来杭州的时候，经过人行横道，远处车道的公交车停下来等我们了，为了少添麻烦总想快点穿过去，但是这时靠近我们的车道（晚上光线较暗，可见度不佳）有一辆从远处来的奥迪 A4 还是 A5 这种的车反而想加速冲过去，如果少看一下可能我已经残了，交规考的靠近人行道要减速好像基本都是个摆设了，杭州也只有公交车因为一些考核指标原因会主动礼让，人其实需要有同理心，虽然可能有些人是开车多于骑车走路的，但是总也不可能永远不穿人行道吧，甚至这些人可能还会在人行道红灯的时候走过去。这个事情不是吐槽公共交通的，只是也有些许关系，想起来还有一件事也是刚才来杭州的时候看到的，等公交的时候看到有辆路虎要加塞，而目标车道刚好是辆大货车，大货车看到按了喇叭，路虎犹豫了下还是挤进去了，可能是对路虎的扛撞性能非常自信吧，反正我是挺后怕的，这种级别的车，被撞了的话估计还是鸡蛋撞石头，吨位惯性在那，这里再延伸下，挺多开豪车的人好像都觉得这路上路权更大一些，谁谁都得让着他，可能实际吃亏的不多，所以越加巩固了这种思维，当真的碰到不管的可能就会明白了，路权这个事情在天朝也基本没啥人重视，也没想说个结论，就到这吧

+]]> + + 生活 + 公交 - java + 生活 + 开车 + 加塞 + 糟心事 + 规则 + 公交 + 路政规划 + 基础设施 + 杭州 + 健康码 @@ -4877,6 +4820,32 @@ user3: 足球 + + 体验下Java 21的虚拟线程-协程 + /2024/08/18/%E4%BD%93%E9%AA%8C%E4%B8%8BJava-21%E7%9A%84%E8%99%9A%E6%8B%9F%E7%BA%BF%E7%A8%8B-%E5%8D%8F%E7%A8%8B/ + Java在后续版本中添加了虚拟线程，也是类似于php跟go的协程，对应操作系统的线程是在线程基础上模拟了一层子线程的逻辑，因为减少了操作系统的线程上下文切换开销，能够在常规业务场景带了比较大的性能提升，但也并非银弹，不能包治百病
首先安装下jdk 21 版本，需要用 /usr/libexec/java_home 切换下JAVA_HOME
然后在PATH中设置好

+
export JAVA_HOME=$(/usr/libexec/java_home -v 21)
+

首先是试一下线程版本

+
long start = System.currentTimeMillis();
ExecutorService executor = Executors.newFixedThreadPool(200);
for (int i = 0; i < 100000; i++) {
    executor.submit(() -> {
        try {
            // 线程睡眠 10ms，可以等同于模拟业务耗时10ms
                TimeUnit.MILLISECONDS.sleep(10);
            } catch (InterruptedException e) {

        }
    });
}
executor.close();
System.out.printf("totalMillis：%dms\n", System.currentTimeMillis() - start);
+


耗时5897ms

+
long start = System.currentTimeMillis();
ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor();
for (int i = 0; i < 10000; i++) {
    executor.submit(() -> {
        // 模拟业务处理
        try {
            TimeUnit.MILLISECONDS.sleep(10);
        } catch (InterruptedException e) {

        }
    });
}
executor.close();
System.out.printf("totalMillis：%dms\n", System.currentTimeMillis() - start);
+


耗时154ms
相对来说还是能快很多的
而核心的虚拟线程实现主要来自于调度

+
static Thread newVirtualThread(Executor scheduler,
                               String name,
                               int characteristics,
                               Runnable task) {
    if (ContinuationSupport.isSupported()) {
        return new VirtualThread(scheduler, name, characteristics, task);
    } else {
        if (scheduler != null)
            throw new UnsupportedOperationException();
        return new BoundVirtualThread(name, characteristics, task);
    }
}
+

在创建虚拟线程过程中，我们需要去处理调度器，初始时调度器为空

+
VirtualThread(Executor scheduler, String name, int characteristics, Runnable task) {
    super(name, characteristics, /*bound*/ false);
    Objects.requireNonNull(task);

    // choose scheduler if not specified
    if (scheduler == null) {
        Thread parent = Thread.currentThread();
        if (parent instanceof VirtualThread vparent) {
            scheduler = vparent.scheduler;
        } else {
            scheduler = DEFAULT_SCHEDULER;
        }
    }

    this.scheduler = scheduler;
    this.cont = new VThreadContinuation(this, task);
    this.runContinuation = this::runContinuation;
}
+

而这个默认调度器就是

+
private static final ForkJoinPool DEFAULT_SCHEDULER = createDefaultScheduler();
+

对应创建的就是个ForkJoinPool

+
private static ForkJoinPool createDefaultScheduler() {
        ForkJoinWorkerThreadFactory factory = pool -> {
            PrivilegedAction<ForkJoinWorkerThread> pa = () -> new CarrierThread(pool);
            return AccessController.doPrivileged(pa);
        };
        PrivilegedAction<ForkJoinPool> pa = () -> {
            int parallelism, maxPoolSize, minRunnable;
            String parallelismValue = System.getProperty("jdk.virtualThreadScheduler.parallelism");
            String maxPoolSizeValue = System.getProperty("jdk.virtualThreadScheduler.maxPoolSize");
            String minRunnableValue = System.getProperty("jdk.virtualThreadScheduler.minRunnable");
            if (parallelismValue != null) {
                parallelism = Integer.parseInt(parallelismValue);
            } else {
                parallelism = Runtime.getRuntime().availableProcessors();
            }
            if (maxPoolSizeValue != null) {
                maxPoolSize = Integer.parseInt(maxPoolSizeValue);
                parallelism = Integer.min(parallelism, maxPoolSize);
            } else {
                maxPoolSize = Integer.max(parallelism, 256);
            }
            if (minRunnableValue != null) {
                minRunnable = Integer.parseInt(minRunnableValue);
            } else {
                minRunnable = Integer.max(parallelism / 2, 1);
            }
            Thread.UncaughtExceptionHandler handler = (t, e) -> { };
            boolean asyncMode = true; // FIFO
            return new ForkJoinPool(parallelism, factory, handler, asyncMode,
                         0, maxPoolSize, minRunnable, pool -> true, 30, SECONDS);
        };
        return AccessController.doPrivileged(pa);
    }
+

可以看到参数都是通过系统参数获取，或者用系统的cpu数量来决定并行度，主体的逻辑就是既然系统线程开销大，那我就在系统线程内部模拟一个更小颗粒度的，在线程内部进行调度的模型，以此来减少系统切换开销，只不过细节还有很多需要研究，有兴趣的可以留言探讨

+]]> + + java + + + java + + 分享一次折腾老旧笔记本的体验-续篇 /2023/02/12/%E5%88%86%E4%BA%AB%E4%B8%80%E6%AC%A1%E6%8A%98%E8%85%BE%E8%80%81%E6%97%A7%E7%AC%94%E8%AE%B0%E6%9C%AC%E7%9A%84%E4%BD%93%E9%AA%8C-%E7%BB%AD%E7%AF%87/ @@ -4917,25 +4886,20 @@ user3: - 关于公共交通再吐个槽 - /2021/03/21/%E5%85%B3%E4%BA%8E%E5%85%AC%E5%85%B1%E4%BA%A4%E9%80%9A%E5%86%8D%E5%90%90%E4%B8%AA%E6%A7%BD/ - 事情源于周末来回家发生的两件事情，先是回去的时候从高铁下车要坐公交，现在算是有个比较好的临时候车点了，但是可能由于疫情好转，晚上都不用检查健康码就可以进候车点，但是上公交的时候还是需要看健康码，一般情况下从高铁下来的，各个地方的人都有，而且也不太清楚这边上公交车需要查验健康码，我的一个看法是候车的时候就应该放个横幅告示，或者再配合喇叭循环播放，请提前准备好健康码，上车前需要查验，因为像这周的情况，我乘坐的那辆车是间隔时间比较长，而且终点是工业开发区，可能是比较多外来务工人员的目的地，正好这部分人可能对于操作手机检验健康码这个事情不太熟悉，所以结果就是头上几个不知道怎么搞出来健康码，然后让几乎有满满一整车的人在后面堵着，司机又非常厌烦那些没有提前出示健康码的，有位乘客搞得久了点，还被误以为没刷卡买票，差点吵起来，其实公共交通或者高铁站负责的在公交指引路线上多写一句上车前需要查验健康码，可能就能改善比较多，还有就是那个积水的路，这个吐槽起来就一大坨了，整个绍兴像 dayuejin 一样到处都是破路。
第二个就是来杭州的时候，经过人行横道，远处车道的公交车停下来等我们了，为了少添麻烦总想快点穿过去，但是这时靠近我们的车道（晚上光线较暗，可见度不佳）有一辆从远处来的奥迪 A4 还是 A5 这种的车反而想加速冲过去，如果少看一下可能我已经残了，交规考的靠近人行道要减速好像基本都是个摆设了，杭州也只有公交车因为一些考核指标原因会主动礼让，人其实需要有同理心，虽然可能有些人是开车多于骑车走路的，但是总也不可能永远不穿人行道吧，甚至这些人可能还会在人行道红灯的时候走过去。这个事情不是吐槽公共交通的，只是也有些许关系，想起来还有一件事也是刚才来杭州的时候看到的，等公交的时候看到有辆路虎要加塞，而目标车道刚好是辆大货车，大货车看到按了喇叭，路虎犹豫了下还是挤进去了，可能是对路虎的扛撞性能非常自信吧，反正我是挺后怕的，这种级别的车，被撞了的话估计还是鸡蛋撞石头，吨位惯性在那，这里再延伸下，挺多开豪车的人好像都觉得这路上路权更大一些，谁谁都得让着他，可能实际吃亏的不多，所以越加巩固了这种思维，当真的碰到不管的可能就会明白了，路权这个事情在天朝也基本没啥人重视，也没想说个结论，就到这吧

+ 分享一次比较诡异的 Windows 下 U盘无法退出的经历 + /2023/01/29/%E5%88%86%E4%BA%AB%E4%B8%80%E6%AC%A1%E6%AF%94%E8%BE%83%E8%AF%A1%E5%BC%82%E7%9A%84-Windows-%E4%B8%8B-U%E7%9B%98%E6%97%A0%E6%B3%95%E9%80%80%E5%87%BA%E7%9A%84%E7%BB%8F%E5%8E%86/ + 作为一个 Windows 的老用户，并且也算是 Windows 系统的半个粉丝，但是秉承一贯的优缺点都应该说的原则，Windows 系统有一点缺点是真的挺难受，相信 Windows 用过比较久的都会经历过，就是 U盘无法退出的问题，在比较远古时代，这个问题似乎能采取的措施不多，关机再拔 U盘的方式是一种比较保险的方式，其他貌似有 360这种可以解除占用的，但是需要安装 360 软件，对于目前的使用环境来说有点得不偿失，也是比较流氓的一类软件了，目前在 Windows 环境我主要就安装了个火绒，或者就用 Windows 自带的 defender。

+

第一种

最近这次经历也是有火绒的一定责任，在我尝试推出 U盘的时候提示了我被另一个大流氓软件，XlibabaProtect.exe 占用了，这个流氓软件真的是充分展示了某里的技术实力，试过 N 多种办法都关不掉也删不掉，尝试了很多种办法也没办法删除，但是后面换了种思路，一般这种情况肯定是有进程在占用 U盘里的内容，最新版本的 Powertoys 会在文件的右键菜单里添加一个叫 File Locksmith 的功能，可以用于检查正在使用哪些文件以及由哪些进程使用，但是可能是我的使用姿势不对，没有仔细看文档，它里面有个”以管理员身份重启”,可能会有用。
这算是第一种方式，

+

第二种

第二种方式是 Windows 任务管理器中性能 tab 下的”打开资源监视器”，
,假如我的 U 盘的盘符是F:
就可以搜索到占用这个盘符下文件的进程，这里千万小心‼️‼️，不可轻易杀掉这些进程，有些系统进程如果轻易杀掉会导致蓝屏等问题，不可轻易尝试，除非能确认这些进程的作用。
对于前两种方式对我来说都无效，

+

第三种

所以尝试了第三种，
就是磁盘脱机的方式，在”计算机”右键管理，点击”磁盘管理”,可以找到 U 盘盘符右键，点击”脱机”，然后再”推出”，这个对我来说也不行

+

第四种

这种是唯一对我有效的，在开始菜单搜索”event”，可以搜到”事件查看器”,
，这个可以看到当前最近 Windows 发生的事件，打开这个后就点击U盘推出，因为推不出来也是一种错误事件，点击下刷新就能在这看到具体是因为什么推出不了，具体的进程信息
最后发现是英特尔的驱动管理程序的一个进程，关掉就退出了，虽然前面说的某里的进程是流氓，但这边是真的冤枉它了

]]> - 生活 - 公交 + Windows + 小技巧 - 生活 - 开车 - 加塞 - 糟心事 - 规则 - 公交 - 路政规划 - 基础设施 - 杭州 - 健康码 + Windows @@ -4957,31 +4921,22 @@ user3: - 向量数据库学习基础之跳表 - /2024/06/16/%E5%90%91%E9%87%8F%E6%95%B0%E6%8D%AE%E5%BA%93%E5%AD%A6%E4%B9%A0%E5%9F%BA%E7%A1%80%E4%B9%8B%E8%B7%B3%E8%A1%A8/ - 在学习向量数据库的时候，发现检索的算法 HNSW 是个比较重要且核心的概念，直接学习（面向小白）可能有一定的门槛，所以我们先通过常见算法的跳表来做个引入
跳表的前置基础就是最常见的序列结构之一的链表，链表的特点是添加插入元素效率非常高，查询检索则需要线性复杂度，比如Java中的hashmap，在jdk1.8以后就把
单纯链表的结构改成了链表和红黑树结合的方式，因为当链表比较长的时候，线性时间复杂度也是个比较慢的方法相比对数时间复杂度，而对于链表，也有直接在它基础
优化而来的跳表结构，跳表是在链表的基础上引入了分层的概念，通过投硬币概率来决定是否生成新层，先用比较通俗的话来讲一下我的理解
因为链表查找只能从前往后一个一个找（单向链表），那么有没有办法可以跳过一些，加速我的查找，那么引入了分层的结构，在更上层以更稀疏的数据链表来索引数据，
举个例子，我原来是一个

-
1 --> 2 --> 3 --> 4 --> 5 --> 6 --> 7 --> 8 --> 9
-

这样的链表，我要查找8个元素是否存在，那我需要查找八次才能找到
如果我把结构改成

-
1 --------------> 4 --------------> 7 ---------> null 
|                 |                 |
1 --> 2 --> 3 --> 4 --> 5 --> 6 --> 7 --> 8 --> 9 --> null
-

变成这样的结构，我现在第一层找最后一个比8小的元素，就是7，然后再到下一层查找，这样我的查找路径就是 1 --> 4 --> 7 --> 8 速度加快了一倍
直观来说这样是能够提升很多查询效率，但是具体怎么实现我们也来看一下
首先定义个简单的Node

-
public class Node {
    public int data = -1;

    public Node[] forwards;

    public Node(int level) {
        forwards = new Node[level];
    }
}
-

接下去是SkipList的主体结构

-
public class SkipList {
    private static final int MAX_LEVEL = 16;

    private int currentLevel = 0;

    private Node head = new Node(MAX_LEVEL);

    private Random random = new Random();
-

包含最大层数，当前层数，头结点，跟随机值
生成随机层数参考了redis的zset实现方法

-
private int generateRandomLevel() {
        // 参考redis sorted set 实现
        int level = 1;
        while ((random.nextInt() & 0xFFFF) < (0.25 * 0xFFFF))
            level += 1;
        return Math.min(level, MAX_LEVEL);
    }
-

接下去先看下

-

插入元素

public void insert(int value) {
        int level = generateRandomLevel();
        if (level >= currentLevel) {
            currentLevel = level;
        }

        Node newNode = new Node(level);
        newNode.setData(value);
        Node[] update = new Node[level];

        Arrays.fill(update, head);

        Node p = head;

        for (int i = level - 1; i >= 0; --i) {
            // 找到应该处在的位置，把前一元素记录为待更新
            while (p.forwards[i] != null && p.forwards[i].data < value) {
                p = p.forwards[i];
            }
            // 对应这一层，更新新的位置p
            update[i] = p;
        }
        
        // 每一层都处理前后链接
        for (int i = 0; i < level; i++) {
            newNode.forwards[i] = update[i].forwards[i];
            update[i].forwards[i] = newNode;
        }
    }
-

看下图片演示,类似于链表的插入，但是需要处理每一层的前后链接

-

查找元素

public Node search(int value) {
        Node p = head;
        // 逐层找到比目标元素小的最后一个元素
        // 然后再往下一层找
        for (int i = currentLevel - 1; i >= 0 ; i--) {
            while (p.forwards[i] != null && p.forwards[i].data < value) {
                p = p.forwards[i];
            }
        }
        // 此时如果前一元素就是目标元素就返回该元素
        if (p.forwards[0] != null && p.forwards[0].data == value) {
            return p.forwards[0];
        } else {
            return null;
        }
    }
-

也可以看下图里的演示，按红色的线寻找元素

-

删除元素

public void delete(int value) {
        Node[] update = new Node[currentLevel];
        Node p = head;
        // 找到待删除元素的前一元素
        for (int i = currentLevel - 1; i >= 0; i--) {
            while (p.forwards[i] != null && p.forwards[i].data < value) {
                p = p.forwards[i];
            }
            update[i] = p;
        }
        // 判断如果该层有这个元素就更新链接
        if (p.forwards[0] != null && p.forwards[0].data == value) {
            for (int i = currentLevel - 1; i >= 0; i--) {
                if (update[i].forwards[i] != null && update[i].forwards[i].data == value) {
                    update[i].forwards[i] = update[i].forwards[i].forwards[i];
                }
            }
        }
    }
-

删除操作就类似于插入操作。了解了跳表原理以后其实HNSW就是个在空间层面的跳表

+ 分享记录一下一个 scp 操作方法 + /2022/02/06/%E5%88%86%E4%BA%AB%E8%AE%B0%E5%BD%95%E4%B8%80%E4%B8%8B%E4%B8%80%E4%B8%AA-scp-%E6%93%8D%E4%BD%9C%E6%96%B9%E6%B3%95/ + scp 是个在服务器之间拷贝文件的一个常用命令，有时候有个场景是比如我们需要拷贝一些带有共同前缀的文件，但是有一个问题是比如我们有使用 zsh 的话，会出现一个报错，

+
zsh: no matches found: root@100.100.100.100://root/prefix*
+

这里就比较奇怪了，这个前缀的文件肯定是有的，这里其实是由于 zsh 会对 * 进行展开，这个可以在例如 ls 命令在使用中就可以发现 zsh 有这个特性
需要使用双引号或单引号将路径包起来或者在*之前加反斜杠\来阻止对*展开和转义

+
scp root@100.100.100.100://root/prefix* .
+

通过使用双引号"进行转义

+
scp root@100.100.100.100:"//root/prefix*" .
+

或者可以将 shell 从 zsh 切换成 bash

]]> - 算法 + shell + 小技巧 - 算法 + scp @@ -5004,51 +4959,6 @@ user3: 算法 - - 分享一次比较诡异的 Windows 下 U盘无法退出的经历 - /2023/01/29/%E5%88%86%E4%BA%AB%E4%B8%80%E6%AC%A1%E6%AF%94%E8%BE%83%E8%AF%A1%E5%BC%82%E7%9A%84-Windows-%E4%B8%8B-U%E7%9B%98%E6%97%A0%E6%B3%95%E9%80%80%E5%87%BA%E7%9A%84%E7%BB%8F%E5%8E%86/ - 作为一个 Windows 的老用户，并且也算是 Windows 系统的半个粉丝，但是秉承一贯的优缺点都应该说的原则，Windows 系统有一点缺点是真的挺难受，相信 Windows 用过比较久的都会经历过，就是 U盘无法退出的问题，在比较远古时代，这个问题似乎能采取的措施不多，关机再拔 U盘的方式是一种比较保险的方式，其他貌似有 360这种可以解除占用的，但是需要安装 360 软件，对于目前的使用环境来说有点得不偿失，也是比较流氓的一类软件了，目前在 Windows 环境我主要就安装了个火绒，或者就用 Windows 自带的 defender。

-

第一种

最近这次经历也是有火绒的一定责任，在我尝试推出 U盘的时候提示了我被另一个大流氓软件，XlibabaProtect.exe 占用了，这个流氓软件真的是充分展示了某里的技术实力，试过 N 多种办法都关不掉也删不掉，尝试了很多种办法也没办法删除，但是后面换了种思路，一般这种情况肯定是有进程在占用 U盘里的内容，最新版本的 Powertoys 会在文件的右键菜单里添加一个叫 File Locksmith 的功能，可以用于检查正在使用哪些文件以及由哪些进程使用，但是可能是我的使用姿势不对，没有仔细看文档，它里面有个”以管理员身份重启”,可能会有用。
这算是第一种方式，

-

第二种

第二种方式是 Windows 任务管理器中性能 tab 下的”打开资源监视器”，
,假如我的 U 盘的盘符是F:
就可以搜索到占用这个盘符下文件的进程，这里千万小心‼️‼️，不可轻易杀掉这些进程，有些系统进程如果轻易杀掉会导致蓝屏等问题，不可轻易尝试，除非能确认这些进程的作用。
对于前两种方式对我来说都无效，

-

第三种

所以尝试了第三种，
就是磁盘脱机的方式，在”计算机”右键管理，点击”磁盘管理”,可以找到 U 盘盘符右键，点击”脱机”，然后再”推出”，这个对我来说也不行

-

第四种

这种是唯一对我有效的，在开始菜单搜索”event”，可以搜到”事件查看器”,
，这个可以看到当前最近 Windows 发生的事件，打开这个后就点击U盘推出，因为推不出来也是一种错误事件，点击下刷新就能在这看到具体是因为什么推出不了，具体的进程信息
最后发现是英特尔的驱动管理程序的一个进程，关掉就退出了，虽然前面说的某里的进程是流氓，但这边是真的冤枉它了

-]]> - - Windows - 小技巧 - - - Windows - - - - 在 wsl 2 中开启 ssh 连接 - /2023/04/23/%E5%9C%A8-wsl-2-%E4%B8%AD%E5%BC%80%E5%90%AF-ssh-%E8%BF%9E%E6%8E%A5/ - 之前在 wsl 1 中开启 ssh 其实很方便，只要把 sshd 服务起来就好了，但是在 wsl 2 中就不太一样了，
我这边使用的是 wsl 2 中的 Ubuntu 20.04，直接启动 sshd 服务是没法让其他机器连接的，而且都没有 ifconfig 命令可以查看 ip
不过可以用直接用 ip a来查看，可以看到这个 ip 是172网段的，而在 wsl 1 中可以看到 ip 就是 win 的 ip，
所以需要做一些操作，首先要安装 openssl-server

-
sudo apt update
sudo apt install openssh-server
-

另外如果需要提高安全性，可以wsl 中配置 hosts.allow

-
sshd:192.168.xx.
-

先定一个子网段，然后对于ssh的配置，可以做以下修改

-
Port 22
PasswordAuthentication yes
-

在进行重启

-
sudo service ssh --full-restart
-

配置以后发现上面的问题，没法远程登录，因为 wsl 2 是基于 hyper-v 虚拟机实现的，并且 ip 使用的是一个虚拟出来的子网 ip，所以需要在 Windows 这一层配置端口的转发，可以通过命令netsh interface portproxy show v4tov4看到

截图是我已经添加好了的，先把原来的删除，再进行添加

-
netsh interface portproxy delete v4tov4 listenaddress=0.0.0.0 listenport=22
netsh interface portproxy add v4tov4 listenaddress=0.0.0.0 listenport=22 connectaddress=172.19.129.207 connectport=22
-

也可以全量删除

-
netsh int portproxy reset all
-

但是这样也不能直接访问了，还需要开启防火墙

-
netsh advfirewall firewall add rule name="WSL SSH" dir=in action=allow protocol=TCP localport=22
- - - -]]> - - wsl - - - wsl - - 周末我在老丈人家打了天小工 /2020/08/16/%E5%91%A8%E6%9C%AB%E6%88%91%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E6%89%93%E4%BA%86%E5%A4%A9%E5%B0%8F%E5%B7%A5/ @@ -5069,123 +4979,191 @@ user3: - 在老丈人家的小工记三 - /2020/09/13/%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E7%9A%84%E5%B0%8F%E5%B7%A5%E8%AE%B0%E4%B8%89/ - 小工记三

前面这两周周末也都去老丈人家帮忙了，上上周周六先是去了那个在装修的旧房子那，把三楼收拾了下，因为要搬进来住，来不及等二楼装修好，就要把三楼里的东西都整理干净，这个活感觉是比较 easy，原来是就准备把三楼当放东西仓储的地方了，我们乡下大部分三层楼都是这么用的，这次也是没办法，之前搬进来的木头什么的都搬出去，主要是这上面灰尘太多，后面清理鼻孔的时候都是黑色的了，把东西都搬出去以后主要是地还是很脏，就扫了地拖了地，因为是水泥地，灰尘又太多了，拖起来都是会灰尘扬起来，整个脱完了的确干净很多，然而这会就出了个大乌龙，我们清理的是三楼的西边一间，结果老丈人上来说要住东边那间的🤦‍♂️，不过其实西边的也得清理，因为还是要放被子什么的，不算是白费功夫，接着清理东边那间，之前这个房子做过群租房，里面有个高低铺的床，当时觉得可以用在放被子什么的就没扔，只是拆掉了放旁边，我们就把它擦干净了又装好，发现螺丝🔩少了几个，亘古不变的真理，拆了以后装要不就多几个要不就少几个，不是很牢靠，不过用来放放被子省得放地上总还是可以的，对了前面还做了个事情就是铺地毯，其实也不是地毯，就是类似于墙布雨篷布那种，别人不用了送给我们的，三楼水泥地也不会铺瓷砖地板了就放一下，干净好看点，不过大小不合适要裁一下，那把剪刀是真的太难用了，我手都要抽筋了，它就是刀口只有一小个点是能剪下来的，其他都是钝的，后来还是用刀片直接裁，铺好以后，真的感觉也不太一样了，焕然一新的感觉
差不多中午了就去吃饭了，之前两次是去了一家小饭店，还是还比较干净，但是店里菜不好吃，还死贵，这次去了一家小快餐店，口味好，便宜，味道是真的不错，带鱼跟黄鱼都好吃，一点都不腥，我对这类比较腥的鱼真的是很挑剔的，基本上除了家里做的很少吃外面的，那天抱着试试的态度吃了下，真的还不错，后来丈母娘说好像这家老板是给别人结婚喜事酒席当厨师的，怪不得做的好吃，其实本来是有一点小抗拒，怕不干净什么的，后来发现菜很好吃，而且可能是老丈人跟干活的师傅去吃的比较多，老板很客气，我们吃完饭，还给我们买了葡萄吃，不过这家店有一个槽点，就是饭比较不好吃，有时候会夹生，不过后面聊起来其实是这种小菜馆饭点的通病，烧的太早太多容易多出来浪费，烧的迟了不够吃，而且大的电饭锅比较不容易烧好。
下午前面还是在处理三楼的，窗户上各种钉子，实在是太多了，我后面在走廊上排了一排🤦‍♂️，有些是直接断了，有些是就撬了出来，感觉我在杭州租房也没有这样子各种钉钉子，挂下衣服什么的也不用这么多吧，比较不能理解，搞得到处都是钉子。那天我爸也去帮忙了，主要是在卫生间里做白缝，其实也是个技术活，印象中好像我小时候自己家里也做过这个事情，但是比较模糊了，后面我们三楼搞完了就去帮我爸了，前面是我老婆二爹在那先刷上白缝，这里叫白缝，有些考究的也叫美缝，就是瓷砖铺完之后的缝，如果不去弄的话，里面水泥的颜色就露出来了，而且容易渗水，所以就要用白水泥加胶水搅拌之后糊在缝上，但是也不是直接糊，先要把缝抠一抠，因为铺瓷砖的还不会仔细到每个缝里的水泥都是一样满，而且也需要一些空间糊上去，不然就太表面的一层很容易被水直接冲掉了，然后这次其实也不是用的白水泥，而是直接现成买来就已经配好的用来填缝的，兑水搅拌均匀就好了，后面就主要是我跟我爸在搞，那个时候真的觉得我实在是太胖了，蹲下去真的没一会就受不了了，膝盖什么的太难受了，后面我跪着刷，然后膝盖又疼，也是比较不容易，不过我爸动作很快，我中间跪累了休息一会，我爸就能搞一大片，后面其实我也没做多少（谦虚一下），总体来讲这次不是很累，就是蹲着跪着腿有点受不了，是应该好好减肥了。

-]]> - - 生活 - 运动 - 跑步 - 干活 - - - 生活 - 小技巧 - 运动 - 减肥 - 跑步 - 干活 - - - - 在老丈人家的小工记五 - /2020/10/18/%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E7%9A%84%E5%B0%8F%E5%B7%A5%E8%AE%B0%E4%BA%94/ - 终于回忆起来了，年纪大了写这种东西真的要立马写，不然很容易想不起来，那天应该是 9 月 12 日，也就是上周六，因为我爸也去了，而且娘亲（丈母娘，LD 这么叫，我也就随了她这么叫，当然是背后，当面就叫妈）也在那，早上一到那二爹就给我爸指挥了活，要挖一条院子的出水道，自己想出来的词，因为觉得下水道是竖的，在那稍稍帮了一会会忙，然后我还是比较惯例的跟着 LD 还有娘亲去住的家里，主要是老丈人可能也不太想让我干太累的活，因为上次已经差不多把三楼都整理干净了，然后就是二楼了，二楼说实话我也帮不上什么忙，主要是衣服被子什么的，正好是有张以前小孩子睡过的那种摇篮床，看上去虽然有一些破损，整体还是不错的，所以打算拿过去，我就负责把它拆掉了，比较简单的是只要拧螺丝就行了，但是其实是用了好多好多工具才搞定的，一开始只要螺丝刀就行了，但是因为年代久了，后面的螺帽也有点锈住或者本身就会串着会一起动，所以拿来了个扳手，大部分的其实都被这两个工具给搞定了，但是后期大概还剩下四分之一的时候，有一颗完全锈住，并且螺纹跟之前那些都不一样，但是这个已经是最大的螺丝刀了，也没办法换个大的了，所以又去找来个一字的，因为十字的不是也可以用一字的拧嘛，结果可能是我买的工具箱里的一字螺丝刀太新了，口子那很锋利，直接把螺丝花纹给划掉了，大的小的都划掉，然后真的变成凹进去一个圆柱体了，然后就想能不能隔一层布去拧，然而因为的确是已经变成圆柱体了，布也不太给力，不放弃的我又去找来了个老虎钳，妄图把划掉的螺丝用老虎钳钳住，另一端用扳手拧开螺帽，但是这个螺丝跟螺帽真的是生锈的太严重了，外加上钳不太牢，完全是两边一起转，实在是没办法了，在征得同意之后，直接掰断了，火死了，一颗螺丝折腾得比我拆一张床还久，那天因为早上去的也比较晚了，然后就快吃午饭了，正好想着带一点东西过去，就把一些脸盆，泡脚桶啥的拿过去了，先是去吃了饭，还是在那家快餐店，菜的口味还是依然不错，就是人比较多，我爸旁边都是素菜，都没怎么吃远一点的荤菜，下次要早点去，把荤菜放我爸旁边😄（PS：他们家饭还是依然尴尬，需要等），吃完就开到在修的房子那把东西拿了出来，我爸已经动作很快的打了一小半的地沟了，说实话那玩意真的是很重，我之前把它从三楼拿下来，就觉得这个太重了，这次还要用起来，感觉我的手会分分钟废掉，不过一开始我还是跟着LD去了住的家里，惯例睡了午觉，那天睡得比较踏实，竟然睡了一个小时，醒了想了下，其实LD她们收拾也用不上我（没啥力气活），我还是去帮我爸他们，跟LD说了下就去了在修的老房子那，两位老爹在一起钻地，看着就很累，我连忙上去想换一会他们，因为刚好是钻到混凝土地线，特别难，力道不够就会滑开，用蛮力就是钻进去拔不出来，原理是因为本身浇的时候就是很紧实的，需要边钻边动，那家伙实在是太重了，真的是汗如雨下，基本是三个人轮流来，我是个添乱的，经常卡住，然后把地线，其实就是一条混凝土横梁，里面还有14跟18的钢筋，需要割断，这个割断也是很有技巧，钢筋本身在里面是受到挤压的，直接用切割的，到快断掉的时候就会崩一下，非常危险，还是老丈人比较有经验，要留一点点，然后直接用榔头敲断就好了，本来以为这个是最难的了，结果下面是一块非常大的青基石，而且也是石头跟石头挤一块，边上一点点打钻有点杯水车薪，后来是用那种螺旋的钻，钻四个洞，相对位置大概是个长方形，这样子把中间这个长方形钻出来就比较容易地能拿出来了，后面的也容易搞出来了，后面的其实难度不是特别大了，主要是地沟打好之后得看看高低是不是符合要求的，不能本来是往外排水的反而外面高，这个怎么看就又很有技巧了，一般在地上的只要侧着看一下就好了，考究点就用下水平尺，但是在地下的，不用水平尺，其实可以借助于地沟里正要放进去的水管，放点水进去，看水往哪流就行了，铺好水管后，就剩填埋的活了，不是太麻烦了，那天真的是累到了，打那个混凝土的时候我真的是把我整个人压上去了，不过也挺爽的，有点把平时无处发泄的蛮力发泄出去了。

+ 介绍一下Java的BiFunction + /2024/06/30/%E4%BB%8B%E7%BB%8D%E4%B8%80%E4%B8%8BJava%E7%9A%84BiFunction/ + java的函数式接口是java8引入的很重要的特性，也让日常代码有了比较大的风格转变
这里介绍下BiFunction，
BiFunction的代码很短

+
@FunctionalInterface
public interface BiFunction<T, U, R> {

    /**
     * Applies this function to the given arguments.
     *
     * @param t the first function argument
     * @param u the second function argument
     * @return the function result
     */
    R apply(T t, U u);

    /**
     * Returns a composed function that first applies this function to
     * its input, and then applies the {@code after} function to the result.
     * If evaluation of either function throws an exception, it is relayed to
     * the caller of the composed function.
     *
     * @param <V> the type of output of the {@code after} function, and of the
     *           composed function
     * @param after the function to apply after this function is applied
     * @return a composed function that first applies this function and then
     * applies the {@code after} function
     * @throws NullPointerException if after is null
     */
    default <V> BiFunction<T, U, V> andThen(Function<? super R, ? extends V> after) {
        Objects.requireNonNull(after);
        return (T t, U u) -> after.apply(apply(t, u));
    }
}
+

默认的方法就跟普通的FunctionalInterface一样，就有一个apply方法，只是这里的特殊点有两个

+

返回值作为参数

这个BiFunction有三个泛型参数，T，U跟R，而这个R其实是具体方法的返回值
比如我们简单实现一个字符串拼接

+
BiFunction<String, String, String> biDemo = (s1, s2) -> s1 + s2;
System.out.println(biDemo.apply("hello ", "world"));
+

apply返回的就是参数R

+

andThen

这个方法是可以让结果再调用传入的方法，并返回结果

+
Function<String, Integer> funcDemo = String::length;
System.out.println(biDemo.andThen(funcDemo).apply("hello ", "world"));
+

定义了个函数接口，并且给了实现，就是字符串获取长度
然后传给biDemo，这样就可以输出拼接后的字符串长度

+

组合使用

public <T1, T2, R1, R2> R2 combination(T1 t1, T2 t2, BiFunction<T1, T2, R1> biFunction, Function<R1, R2> function) {
    return biFunction.andThen(function).apply(t1, t2);
}
+

这样就把前面的两个方法串起来了

]]> - 生活 - 运动 - 跑步 - 干活 + java - 生活 - 小技巧 - 运动 - 减肥 - 跑步 - 干活 + java + stream - 在老丈人家的小工记四 - /2020/09/26/%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E7%9A%84%E5%B0%8F%E5%B7%A5%E8%AE%B0%E5%9B%9B/ - 小工记四

第四周去的时候让我们去了现在在住的房子里，去三楼整理东西了，蛮多的东西需要收拾整理，有些需要丢一下，以前往往是把不太要用的东西就放三楼了，但是后面就不会再去收拾整理，LD 跟丈母娘负责收拾，我不太知道哪些还要的，哪些不要了，而且本来也不擅长这种收拾🤦‍♂️，然后就变成她们收拾出来废纸箱，我负责拆掉，压平，这时候终于觉得体重还算是有点作用，总体来说这个事情我其实也不擅长，不擅长的主要是捆起来，可能我总是小题大做，因为纸箱大小不一，如果不做一下分类，然后把大的折小一些的话，直接绑起来，容易拎起来就散掉了，而且一些鞋盒子这种小件的纸盒会比较薄，冰箱这种大件的比较厚，厚的比较不容易变形，需要大力踩踏，而且扎的时候需要用体重压住捆实了之后那样子才是真的捆实的，不然待会又是松松垮垮容易滑出来散架，因为压住了捆好后，下来了之后箱子就会弹开了把绳子崩紧实，感觉又是掌握到生活小技巧了😃，我这里其实比较单调无聊，然后 LD 那可以说非常厉害了，一共理出来 11 把旧电扇，还有好多没用过全新的不锈钢脸盆大大小小的，感觉比店里在卖的还多，还有是有比较多小时候的东西，特别多小时候的衣服，其实这种对我来说最难了，可能需要读一下断舍离，蛮多东西都舍不得扔，但是其实是没啥用了，然后还占地方，这天应该算是比较轻松的一天了，上午主要是把收拾出来要的和不要的搬下楼，然后下午要去把纸板给卖掉。中午还是去小快餐店吃的，在住的家里理东西还有个好处就是中午吃完饭可以小憩一下，因为我个人是非常依赖午休的，不然下午完全没精神，而且心态也会比较烦躁，一方面是客观的的确比较疲惫，另一方面应该主观心理作用也有点影响，就像上班的时候也是觉得不午睡就会很难受，心理作用也有一点，不过总之能睡还是睡一会，真的没办法就心态好点，吃完午饭之后我们就推着小平板车去收废品的地方卖掉了上午我收拾捆起来的纸板，好像卖了一百多，都是直接过地磅了，不用一捆一捆地称，不过有个小插曲，那里另外一个大爷在倒他的三轮车的时候撞了我一下，还好车速慢，屁股上肉垫后，接下来就比较麻烦了，是LD 她们两姐妹从小到大的书，也要去卖掉，小平板车就载不下了，而且着实也不太好推，轮子不太平，导致推着很累，书有好多箱，本来是想去亲戚家借电动三轮车，因为不会开摩托的那种，摩托的那种 LD 邻居家就有，可是到了那发现那个也是很大，而且刹车是用脚踩的那种，俺爹不太放心，就说第二天周日他有空会帮忙去载了卖掉的，然后比较搞笑的来了，丈母娘看错了时间，以为已经快五点了，就让我们顺便在车里带点东西去在修的房子，放到那边三楼去，到了那还跟老丈人说已经这么迟了要赶紧去菜场买菜了，结果我们回来以后才发现看错了一个小时🤦‍♂️。
前面可以没提，前三周去的我们一般就周六去一天，然后周日因为要早点回杭州，而且可能想让我们周日能休息下，但是这周就因为周日的时候我爸要去帮忙载书，然后 LD 姐姐也会过来收拾东西，我们周日就又去整理收拾了，周日由于俺爹去的很早，我过去的时候书已经木有了，主要是去收拾东西了，把一些有用没用的继续整理，基本上三楼的就处理完毕了，舒了一大口气，毕竟让丈母娘一个人收拾实在是太累了，但是要扔掉的衣服比较棘手，附近知道的青蛙回收桶被推倒了，其他地方也不知道哪里有，我们就先载了一些东西去在修的房子那，然后去找青蛙桶，结果一个小区可以进，但是已经满了，另一个不让进，后来只能让 LD 姐姐带去她们小区扔了，塞了满满一车。因为要赶回杭州的车就没有等我爸一起回来，他还在那帮忙搞卫生间的墙缝。
虽然这两天不太热，活也不算很吃力，不过我这个体重和易出汗的体质，还是让短袖不知道湿透了多少次，灌了好多水和冰红茶（下午能提提神），回来周一早上称体重也比较喜人，差一点就达到阶段目标，可以想想去哪里吃之前想好的烤肉跟火锅了（估计吃完立马回到解放前）。

+ 关于 npe 的一个小记忆点 + /2023/07/16/%E5%85%B3%E4%BA%8E-npe-%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E8%AE%B0%E5%BF%86%E7%82%B9/ + Java 中最常见的一类问题或者说异常就是 NullPointerException，而往往这种异常我们在查看日志的时候都是根据打印出来的异常堆栈找到对应的代码以确定问题，但是有一种情况会出现一个比较奇怪的情况，虽然我们在日志中打印了异常

+
log.error("something error", e);
+

这样是可以打印出来日志，譬如 slf4j 就可以把异常作为最后一个参数传入

+
void error(String var1, Throwable var2);
+

有的时候如果不小心把异常通过占位符传入就可能出现异常被吞的情况，不过这点不是这次的重点
即使我们正常作为最后一个参数传入了，也会出现只打印出来

+
something error, java.lang.NullPointerException: null
+

这样就导致了如果我们这个日志所代表的异常包含的代码范围比较大的话，就不确定具体是哪一行出现的异常，除非对这些代码逻辑非常清楚，这个是为什么呢，其实这也是 jvm 的一种优化机制，像我们目前主要还在用的 jdk8中使用的 jvm 虚拟机 hotspot 的一个热点异常机制，对于这样一直出现的相同异常，就认为是热点异常，后面只会打印异常名，不再打印异常堆栈，这样对于 io 性能能够提供一定的优化保证，这个可以通过 jvm 启动参数
-XX:-OmitStackTraceInFastThrow 来关闭优化，这个问题其实在一开始造成了一定困扰，找不准具体是哪一行代码，不过在知道这个之后简单的重启也能暂时解决问题。

]]> - 生活 - 运动 - 跑步 - 干活 + java - 生活 - 小技巧 - 运动 - 减肥 - 跑步 - 干活 + java - 分享记录一下一个 scp 操作方法 - /2022/02/06/%E5%88%86%E4%BA%AB%E8%AE%B0%E5%BD%95%E4%B8%80%E4%B8%8B%E4%B8%80%E4%B8%AA-scp-%E6%93%8D%E4%BD%9C%E6%96%B9%E6%B3%95/ - scp 是个在服务器之间拷贝文件的一个常用命令，有时候有个场景是比如我们需要拷贝一些带有共同前缀的文件，但是有一个问题是比如我们有使用 zsh 的话，会出现一个报错，

-
zsh: no matches found: root@100.100.100.100://root/prefix*
-

这里就比较奇怪了，这个前缀的文件肯定是有的，这里其实是由于 zsh 会对 * 进行展开，这个可以在例如 ls 命令在使用中就可以发现 zsh 有这个特性
需要使用双引号或单引号将路径包起来或者在*之前加反斜杠\来阻止对*展开和转义

-
scp root@100.100.100.100://root/prefix* .
-

通过使用双引号"进行转义

-
scp root@100.100.100.100:"//root/prefix*" .
-

或者可以将 shell 从 zsh 切换成 bash

+ 在 wsl 2 中开启 ssh 连接 + /2023/04/23/%E5%9C%A8-wsl-2-%E4%B8%AD%E5%BC%80%E5%90%AF-ssh-%E8%BF%9E%E6%8E%A5/ + 之前在 wsl 1 中开启 ssh 其实很方便，只要把 sshd 服务起来就好了，但是在 wsl 2 中就不太一样了，
我这边使用的是 wsl 2 中的 Ubuntu 20.04，直接启动 sshd 服务是没法让其他机器连接的，而且都没有 ifconfig 命令可以查看 ip
不过可以用直接用 ip a来查看，可以看到这个 ip 是172网段的，而在 wsl 1 中可以看到 ip 就是 win 的 ip，
所以需要做一些操作，首先要安装 openssl-server

+
sudo apt update
sudo apt install openssh-server
+

另外如果需要提高安全性，可以wsl 中配置 hosts.allow

+
sshd:192.168.xx.
+

先定一个子网段，然后对于ssh的配置，可以做以下修改

+
Port 22
PasswordAuthentication yes
+

在进行重启

+
sudo service ssh --full-restart
+

配置以后发现上面的问题，没法远程登录，因为 wsl 2 是基于 hyper-v 虚拟机实现的，并且 ip 使用的是一个虚拟出来的子网 ip，所以需要在 Windows 这一层配置端口的转发，可以通过命令netsh interface portproxy show v4tov4看到

截图是我已经添加好了的，先把原来的删除，再进行添加

+
netsh interface portproxy delete v4tov4 listenaddress=0.0.0.0 listenport=22
netsh interface portproxy add v4tov4 listenaddress=0.0.0.0 listenport=22 connectaddress=172.19.129.207 connectport=22
+

也可以全量删除

+
netsh int portproxy reset all
+

但是这样也不能直接访问了，还需要开启防火墙

+
netsh advfirewall firewall add rule name="WSL SSH" dir=in action=allow protocol=TCP localport=22
+ + + ]]> - shell - 小技巧 + wsl - scp + wsl - 寄生虫观后感 - /2020/03/01/%E5%AF%84%E7%94%9F%E8%99%AB%E8%A7%82%E5%90%8E%E6%84%9F/ - 寄生虫这部电影在获得奥斯卡之前就有关注了，豆瓣评分很高，一开始看到这个片名以为是像《铁线虫入侵》那种灾难片，后来看到男主，宋康昊，也是老面孔了，从高中时候在学校操场组织看的《汉江怪物》，有点二的感觉，后来在大学寝室电脑上重新看的时候，室友跟我说是韩国国宝级演员，真人不可貌相，感觉是个呆子的形象。

-

但是你说这不是个灾难片，而是个反映社会问题的，就业比较容易往这个方向猜，只是剧情会是怎么样的，一时也没啥头绪，后来不知道哪里看了下一个剧情透露，是一个穷人给富人做家教，然后把自己一家都带进富人家，如果是这样的话可能会把这个怎么带进去作为一个主线，不过事实告诉我，这没那么重要，从第一步朋友的介绍，就显得无比顺利，要去当家教了，作为一个穷成这样的人，瞬间转变成一个衣着得体，言行举止都没让富人家看出破绽的延世大学学生，这真的挺难让人理解，所谓江山易改，本性难移，还有就是这人也正好有那么好能力去辅导，并且诡异的是，多惠也是瞬间就喜欢上了男主，多惠跟将男主介绍给她做家教，也就是多惠原来的家教敏赫，应该也有不少的相处时间，这变了有点大了吧，当然这里也可能因为时长需要，如果说这一点是因为时长，那可能我所有的槽点都是因为这个吧，因为我理解的应该是把家里的人如何一步步地带进富人家，这应该是整个剧情的一个需要更多铺垫去克服这个矛盾点，有时候也想过如果我去当导演，是能拍出个啥，没这个机会，可能有也会是很扯淡的，当然这也不能阻拦我谈谈对这个点的一些看法，毕竟评价一台电冰箱不是说我必须得自己会制冷对吧，这大概是我觉得这个电影的第一个槽点，接下去接二连三的，就是我说的这个最核心的矛盾点，不知道谁说过，这种影视剧应该是源自于生活又高于生活，越是好的作品，越要接近生活，这样子才更能有感同身受。

-

接下去的点是金基宇介绍金基婷去给多颂当美术家教，这一步又是我理解的败笔吧，就怎么说呢，没什么铺垫，突然从一个社会底层的穷姑娘，转变成一个气场爆表，把富人家太太唬得一愣一愣的，如果说富太太是比较简单无脑的，那富人自己应该是比较有见识而且是做 IT 的，给自己儿子女儿做家教的，查查底细也很正常吧，但是啥都没有，然后呢，她又开始耍司机的心机了，真的是莫名其妙了，司机真的很惨，窈窕淑女君子好逑，而且这个操作也让我摸不着头脑，这是多腹黑并且有经验才会这么操作，脱内裤真的是让我看得一愣愣的，更看得我一愣一愣的，富人竟然也完全按着这个思路去想了，完全没有别的可能呢，甚至可以去查下行车记录仪或者怎样的，或者有没有毛发体液啥的去检验下，毕竟金基婷也乘坐过这辆车，但是最最让我不懂的还是脱内裤这个操作，究竟是什么样的人才会的呢，值得思考。

-

金基泽和忠淑的点也是比较奇怪，首先是金基泽，引起最后那个杀人事件的一个由头，大部分观点都是人为朴社长在之前跟老婆啪啪啪的时候说金基泽的身上有股乘地铁的人的味道，简而言之就是穷人的味道，还有去雯光丈夫身下拿钥匙是对金基泽和雯光丈夫身上的味道的鄙夷，可是这个原因真的站不住脚，即使是同样经济水平，如果身上有比较重的异味，背后讨论下，或者闻到了比较重的味道，有不适的表情和动作很正常吧，像雯光丈夫，在地下室里呆了那么久，身上有异味并且比较重太正常了，就跟在厕所呆久了不会觉得味道大，但是从没味道的地方一进有点味道的厕所就会觉得异样，略尴尬的理由；再说忠淑呢，感觉是太厉害了，能胜任这么一家有钱人的各种挑剔的饮食口味要求的保姆职位，也是让人看懵逼了，看到了不禁想到一个问题，这家人开头是那么地穷，不堪，突然转变成这么地像骗子家族，如果有这么好的骗人能力，应该不会到这种地步吧，如果真的是那么穷，没能力，没志气，又怎么会突然变成这么厉害呢，一家人各司其职，把富人家唬得团团转，而这个前提是，这些人的确能胜任这四个位置，这就是我非常不能理解的点。

-

然后说回这个标题，寄生虫，不知道是不是翻译过来不准确，如果真的是叫寄生虫的话，这个寄生虫智商未免也太低了，没有像新冠那样机制，致死率低一点，传染能力强一点，潜伏期也能传染，这个寄生虫第一次受到免疫系统的攻击就自爆了；还有呢，作为一个社会比较低层的打工者，乡下人，对这个审题也是不太审的清，是指这一家人是社会的寄生虫，不思进取，并且死的应该，富人是傻白甜，又有钱又善良，这是给有钱人洗地了还是啥，这个奥斯卡真不知道是怎么得的，总觉得奥斯卡，甚至低一点，豆瓣，得奖的，评分高的都是被一群“精英党”把持的，有黑人主角的，得分高；有同性恋的，得分高；结局惨的，得分高；看不懂的，得分高；就像肖申克的救赎，真不知道是哪里好了，最近看了关于明朝那些事的三杨，杨溥的经历应该比这个厉害吧，可是外国人看不懂，就像外国人不懂中国为什么有反分裂国家法，经历了鸦片战争，八国联军，抗日战争等等，其实跟外国对于黑人的权益的问题，因为有南北战争，所以极度重视这个问题，相应的中国也有自己的历史，请理解。

-

简而言之我对寄生虫的评分大概 5~6 分吧。

+ 向量数据库学习-介绍一下HNSW算法 + /2024/06/23/%E4%BB%8B%E7%BB%8D%E4%B8%80%E4%B8%8BHNSW%E7%AE%97%E6%B3%95/ + 结合AI写的一篇HNSW的介绍文章，算是向量数据库中比较核心的算法

+

1. 引言

向量数据库在当今人工智能和机器学习领域扮演着越来越重要的角色。随着数据规模的不断扩大和应用场景的日益复杂,传统的数据库系统已经难以满足高维向量数据的快速检索需求。在这个背景下,近似最近邻(Approximate Nearest Neighbor, ANN)搜索问题成为了一个热点研究方向。

+

HNSW(Hierarchical Navigable Small World)算法是解决ANN问题的一种高效方法。它通过构建一个多层次的图结构,结合小世界网络的特性,实现了对高维向量数据的快速、精确检索。本文将深入浅出地介绍HNSW算法的核心思想、工作原理以及实际应用。

+

2. HNSW算法的核心思想

HNSW算法的核心思想可以概括为三个关键点：分层结构、小世界图和近似搜索。

+

分层结构

HNSW采用了一种多层次的图结构。想象一下,如果我们要在一个大城市中找到一个特定的地址,我们通常会先看城市地图,然后逐步缩小范围到区、街道,最后定位到具体门牌号。HNSW的分层结构就类似于这种由粗到细的搜索过程。这个就是之前介绍跳表的目的，就是现在稀疏的结构里可以快速定位，然后再往下层进行搜索，提高了搜索效率

+

小世界图

小世界网络是一种特殊的图结构,它具有较短的平均路径长度和较高的聚类系数。在HNSW中,每一层都是一个小世界图。这种结构使得在图中的任意两点之间都存在一条相对较短的路径,大大提高了搜索效率。

+

近似搜索

与传统的KNN(K-Nearest Neighbors)算法不同,HNSW通过牺牲一小部分精度来换取显著的速度提升。它不保证一定能找到真正的最近邻,但可以以极高的概率找到足够接近的点,而且搜索速度要快得多。

+

3. HNSW的工作原理

数据结构:多层图的构建过程

    +
  1. HNSW从底层开始构建,每个数据点都会出现在底层。
    2. +
  2. 对于每个新插入的点,算法会随机决定它是否应该出现在上一层。这个过程一直持续到某一层,该点不再被选中。
    3. +
  3. 在每一层,新点会与该层的其他点建立连接,形成小世界图结构。
    4. +
+

搜索过程:自顶向下的贪婪搜索

    +
  1. 搜索从最顶层开始,选择一个随机起点。
    2. +
  2. 在当前层进行贪婪搜索,不断移动到离目标更近的邻居。
    3. +
  3. 当在当前层无法找到更近的点时,下降到下一层继续搜索。
    4. +
  4. 重复这个过程直到达到底层,得到最终结果。
    5. +
+

插入新向量的过程

    +
  1. 确定新向量应该出现在哪些层。
    2. +
  2. 从顶层开始,在每一层执行类似于搜索的过程,找到适合连接的邻居点。
    3. +
  3. 建立连接,同时可能需要删除一些现有连接以维持图的结构特性。

    论文中的算法是这样的
    4. +
+
    +

  • 红色节点表示新插入的数据点。在这个例子中,新节点被插入到了层级0和层级1。

    +
    • +

  • 红色实线表示新建立的同层连接。新节点与其邻近节点建立了连接。

    +
    • +

  • 红色虚线表示新建立的跨层连接。新节点在不同层级之间建立了连接。

    +
    • +

  • 绿色虚线箭头表示插入过程的路径。这条路径展示了算法如何从顶层开始,逐层下降,最终确定新节点的插入位置。

    +
    • +

  • 其他颜色的节点和灰色的连接线表示原有的HNSW结构。

    +
    • +

  • 插入过程从顶层开始,沿着绿色虚线所示的路径向下搜索。

    +
    • +

  • 在每一层,算法都会寻找最近的邻居节点,并决定是否在该层插入新节点。

    +
    • +

  • 在本例中,新节点被插入到了层级0(基础层)和层级1。这是因为HNSW使用概率方法来决定节点应该出现在哪些层级。

    +
    • +

  • 插入后,新节点与其邻近节点建立连接(红色实线),以维持图的小世界特性。

    +
    • +

  • 同时,新节点也与其在不同层级的对应节点建立跨层连接(红色虚线),以确保层级之间的快速访问。

    +
    • +
+

4. HNSW的性能分析

时间复杂度分析

    +
  • 搜索时间复杂度: O(log N),其中N是数据点的总数。
    • +
  • 插入时间复杂度: 平均情况下为O(log N)。
    • +
+

空间复杂度分析

    +
  • 空间复杂度: O(N log N),因为上层节点数量呈指数衰减。
    • +
+

与其他ANN算法的比较

相比LSH(Locality-Sensitive Hashing)和Annoy等算法,HNSW在大多数场景下都能提供更好的查询性能和更高的准确率。然而,HNSW的内存消耗相对较高,这是它的一个潜在缺点。

+

5. HNSW的实际应用

在推荐系统中的应用

HNSW可以用于快速检索相似用户或物品,提高推荐系统的响应速度和准确性。例如,在音乐推荐中,可以用HNSW快速找到与用户喜好相似的歌曲。

+

在图像检索中的应用

在大规模图像数据库中,HNSW可以实现快速的相似图像搜索。这在图像搜索引擎、视觉商品搜索等场景中非常有用。

+

在自然语言处理中的应用

HNSW可以用于词嵌入或句子嵌入的快速检索,支持语义相似度计算、文本分类等任务。

+

6. HNSW的优化和变种

参数调优技巧

    +
  • 层数选择:影响搜索速度和内存使用
    • +
  • 每层连接数:影响搜索准确度和构建时间
    • +
  • 候选集大小:影响搜索质量和速度的平衡
    • +
+

常见的HNSW变种算法介绍

    +
  • NSW-G:改进了图的构建方法,提高了搜索效率
    • +
  • HCNNG:结合了层次聚类,提高了在某些数据集上的性能
    • +
+

7. 实现HNSW的挑战与解决方案

大规模数据集的处理

    +
  • 使用外部存储和分批处理
    • +
  • 采用压缩技术减少内存占用
    • +
+

并行化和分布式实现

    +
  • 搜索过程的并行化
    • +
  • 分布式索引构建和查询
    • +
+

动态更新和删除操作的处理

    +
  • 软删除技术
    • +
  • 定期重建索引
    • +
+

8. 总结与展望

HNSW算法通过其独特的分层小世界图结构,在保证高准确率的同时实现了对高维向量数据的快速检索。它在推荐系统、图像检索、自然语言处理等多个领域都有广泛应用。

+

未来,向量数据库技术可能会朝着以下方向发展:

+
    +
  1. 更高效的压缩和索引技术
    2. +
  2. 更好的动态更新支持
    3. +
  3. 与深度学习模型的更紧密集成
    4. +
  4. 针对特定领域的优化变体
    5. +
+

随着人工智能和大数据技术的不断发展,HNSW等高效的向量检索算法将在更广阔的应用场景中发挥重要作用。

]]> - 生活 - 影评 - 2020 + 算法 - 生活 - 影评 - 寄生虫 + 算法 - 尝试下图片向量化 - /2024/07/28/%E5%B0%9D%E8%AF%95%E4%B8%8B%E5%9B%BE%E7%89%87%E5%90%91%E9%87%8F%E5%8C%96/ - 之前觉得谷歌的以图搜图很厉害，现在似乎这个路径还毕竟清晰了，首先要有图片库，把它们向量化以后存储起来，然后对于目标图片也做向量化，再做检索
那么我们先来做重要的这一步，图片的向量化，因为向量化以后就跟图片没关系了，直接用前面讲到的向量的近似搜索就可以做到以图搜图了
这边我们用到了towhee工具，towhee是个机器学习的pipeline工具，可以做数据源（文件，图片，媒体，文本）–> 模型 –> 向量。
首先我们安装towhee

-
pip install towhee towhee.models
-

这里有第一个坑，因为有torch依赖，但是目前torch支持的python版本是最高3.9，再往上可能就有问题了
所以我们要先创建一个3.9的环境

-
conda create -n py9 python=3.9
-

然后我们会遇到第二个坑
就是milvus的客户端

-
pip install pymilvus==2.3.0
-

再早的客户端会有依赖不支持，更新的客户端会连接不上
然后再在milvus创建一个Collection

然后这个是不对的，因为我使用attu界面创建的，默认第二个字段一定得是向量字段，所以又用了上次的Java代码来创建

-
CreateCollectionReq.CollectionSchema collectionSchema = clientV2.createSchema();
// add two fileds, id and vector
        Integer dim = 2048;
        collectionSchema.addField(AddFieldReq.builder().fieldName("url").dataType(DataType.VarChar).build());
        collectionSchema.addField(AddFieldReq.builder().fieldName("embedding").dataType(DataType.FloatVector).dimension(dim).build());
        collectionSchema.addField(AddFieldReq.builder().fieldName("id").dataType(DataType.Int64).isPrimaryKey(Boolean.TRUE).autoID(Boolean.TRUE).description("id").build());

        CreateCollectionReq req = CreateCollectionReq
                .builder()
                .collectionSchema(collectionSchema)
                .collectionName("text_image_search")
                .dimension(dim).build();
        clientV2.createCollection(req);
-

并且注意字段顺序不能错，这个顺序在towhee的demo中也没地方直接修改
看一下towhee的demo
地址在这
https://towhee.io/tasks/detail/pipeline/text-image-search

-
from towhee import AutoPipes, AutoConfig

# set MilvusInsertConfig for the built-in insert_milvus pipeline
insert_conf = AutoConfig.load_config('insert_milvus')
insert_conf.collection_name = 'text_image_search'

insert_pipe = AutoPipes.pipeline('insert_milvus', insert_conf)

# generate embedding
embedding = image_embedding('./test1.png').get()[0]

# insert text and embedding into Milvus
insert_pipe(['./test1.png', embedding])
-

第三个坑，这并不能跑起来

-
 embedding = insert_pipe.image_embedding('./test.jpg').get()[0]
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'RuntimePipeline' object has no attribute 'image_embedding'
-

因为找不到这个 image_embedding
估计是这个也是包路径变更了，但是并没有什么文档可以找到，之前LlamaIndex还有点更新指南，后来是问了Claude才知道了

-
from towhee import AutoPipes, AutoConfig, ops


# set MilvusInsertConfig for the built-in insert_milvus pipeline
insert_conf = AutoConfig.load_config('insert_milvus')
insert_conf.collection_name = 'text_image_search'


insert_pipe = AutoPipes.pipeline('insert_milvus', insert_conf)

# 创建图像嵌入管道
image_embedding_pipe = AutoPipes.pipeline('image-embedding')

# 生成嵌入
embedding = image_embedding_pipe('./test.jpg').get()[0]


# insert text and embedding into Milvus
insert_pipe(['./test.jpg', embedding])
-

需要从 AutoPipes 中把这个 image-embedding 找出来
然后就是上面说到的字段映射问题，默认是先url，再embedding字段，并且主键字段必须是autoId，不然也会缺少默认值，还是感叹下，工程化的东西还是要工程化的质量保证，否则变更都无从知晓，现在人工智能大热，大家都在追风，只是基础的软件还是要稳扎稳打，这样我们工程人员才能把它们更好的用起来，PS：娃真可爱，但真的好累

+ 向量数据库学习基础之跳表 + /2024/06/16/%E5%90%91%E9%87%8F%E6%95%B0%E6%8D%AE%E5%BA%93%E5%AD%A6%E4%B9%A0%E5%9F%BA%E7%A1%80%E4%B9%8B%E8%B7%B3%E8%A1%A8/ + 在学习向量数据库的时候，发现检索的算法 HNSW 是个比较重要且核心的概念，直接学习（面向小白）可能有一定的门槛，所以我们先通过常见算法的跳表来做个引入
跳表的前置基础就是最常见的序列结构之一的链表，链表的特点是添加插入元素效率非常高，查询检索则需要线性复杂度，比如Java中的hashmap，在jdk1.8以后就把
单纯链表的结构改成了链表和红黑树结合的方式，因为当链表比较长的时候，线性时间复杂度也是个比较慢的方法相比对数时间复杂度，而对于链表，也有直接在它基础
优化而来的跳表结构，跳表是在链表的基础上引入了分层的概念，通过投硬币概率来决定是否生成新层，先用比较通俗的话来讲一下我的理解
因为链表查找只能从前往后一个一个找（单向链表），那么有没有办法可以跳过一些，加速我的查找，那么引入了分层的结构，在更上层以更稀疏的数据链表来索引数据，
举个例子，我原来是一个

+
1 --> 2 --> 3 --> 4 --> 5 --> 6 --> 7 --> 8 --> 9
+

这样的链表，我要查找8个元素是否存在，那我需要查找八次才能找到
如果我把结构改成

+
1 --------------> 4 --------------> 7 ---------> null 
|                 |                 |
1 --> 2 --> 3 --> 4 --> 5 --> 6 --> 7 --> 8 --> 9 --> null
+

变成这样的结构，我现在第一层找最后一个比8小的元素，就是7，然后再到下一层查找，这样我的查找路径就是 1 --> 4 --> 7 --> 8 速度加快了一倍
直观来说这样是能够提升很多查询效率，但是具体怎么实现我们也来看一下
首先定义个简单的Node

+
public class Node {
    public int data = -1;

    public Node[] forwards;

    public Node(int level) {
        forwards = new Node[level];
    }
}
+

接下去是SkipList的主体结构

+
public class SkipList {
    private static final int MAX_LEVEL = 16;

    private int currentLevel = 0;

    private Node head = new Node(MAX_LEVEL);

    private Random random = new Random();
+

包含最大层数，当前层数，头结点，跟随机值
生成随机层数参考了redis的zset实现方法

+
private int generateRandomLevel() {
        // 参考redis sorted set 实现
        int level = 1;
        while ((random.nextInt() & 0xFFFF) < (0.25 * 0xFFFF))
            level += 1;
        return Math.min(level, MAX_LEVEL);
    }
+

接下去先看下

+

插入元素

public void insert(int value) {
        int level = generateRandomLevel();
        if (level >= currentLevel) {
            currentLevel = level;
        }

        Node newNode = new Node(level);
        newNode.setData(value);
        Node[] update = new Node[level];

        Arrays.fill(update, head);

        Node p = head;

        for (int i = level - 1; i >= 0; --i) {
            // 找到应该处在的位置，把前一元素记录为待更新
            while (p.forwards[i] != null && p.forwards[i].data < value) {
                p = p.forwards[i];
            }
            // 对应这一层，更新新的位置p
            update[i] = p;
        }
        
        // 每一层都处理前后链接
        for (int i = 0; i < level; i++) {
            newNode.forwards[i] = update[i].forwards[i];
            update[i].forwards[i] = newNode;
        }
    }
+

看下图片演示,类似于链表的插入，但是需要处理每一层的前后链接

+

查找元素

public Node search(int value) {
        Node p = head;
        // 逐层找到比目标元素小的最后一个元素
        // 然后再往下一层找
        for (int i = currentLevel - 1; i >= 0 ; i--) {
            while (p.forwards[i] != null && p.forwards[i].data < value) {
                p = p.forwards[i];
            }
        }
        // 此时如果前一元素就是目标元素就返回该元素
        if (p.forwards[0] != null && p.forwards[0].data == value) {
            return p.forwards[0];
        } else {
            return null;
        }
    }
+

也可以看下图里的演示，按红色的线寻找元素

+

删除元素

public void delete(int value) {
        Node[] update = new Node[currentLevel];
        Node p = head;
        // 找到待删除元素的前一元素
        for (int i = currentLevel - 1; i >= 0; i--) {
            while (p.forwards[i] != null && p.forwards[i].data < value) {
                p = p.forwards[i];
            }
            update[i] = p;
        }
        // 判断如果该层有这个元素就更新链接
        if (p.forwards[0] != null && p.forwards[0].data == value) {
            for (int i = currentLevel - 1; i >= 0; i--) {
                if (update[i].forwards[i] != null && update[i].forwards[i].data == value) {
                    update[i].forwards[i] = update[i].forwards[i].forwards[i];
                }
            }
        }
    }
+

删除操作就类似于插入操作。了解了跳表原理以后其实HNSW就是个在空间层面的跳表

]]> 算法 @@ -5194,6 +5172,66 @@ user3: 算法 + + 在老丈人家的小工记三 + /2020/09/13/%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E7%9A%84%E5%B0%8F%E5%B7%A5%E8%AE%B0%E4%B8%89/ + 小工记三

前面这两周周末也都去老丈人家帮忙了，上上周周六先是去了那个在装修的旧房子那，把三楼收拾了下，因为要搬进来住，来不及等二楼装修好，就要把三楼里的东西都整理干净，这个活感觉是比较 easy，原来是就准备把三楼当放东西仓储的地方了，我们乡下大部分三层楼都是这么用的，这次也是没办法，之前搬进来的木头什么的都搬出去，主要是这上面灰尘太多，后面清理鼻孔的时候都是黑色的了，把东西都搬出去以后主要是地还是很脏，就扫了地拖了地，因为是水泥地，灰尘又太多了，拖起来都是会灰尘扬起来，整个脱完了的确干净很多，然而这会就出了个大乌龙，我们清理的是三楼的西边一间，结果老丈人上来说要住东边那间的🤦‍♂️，不过其实西边的也得清理，因为还是要放被子什么的，不算是白费功夫，接着清理东边那间，之前这个房子做过群租房，里面有个高低铺的床，当时觉得可以用在放被子什么的就没扔，只是拆掉了放旁边，我们就把它擦干净了又装好，发现螺丝🔩少了几个，亘古不变的真理，拆了以后装要不就多几个要不就少几个，不是很牢靠，不过用来放放被子省得放地上总还是可以的，对了前面还做了个事情就是铺地毯，其实也不是地毯，就是类似于墙布雨篷布那种，别人不用了送给我们的，三楼水泥地也不会铺瓷砖地板了就放一下，干净好看点，不过大小不合适要裁一下，那把剪刀是真的太难用了，我手都要抽筋了，它就是刀口只有一小个点是能剪下来的，其他都是钝的，后来还是用刀片直接裁，铺好以后，真的感觉也不太一样了，焕然一新的感觉
差不多中午了就去吃饭了，之前两次是去了一家小饭店，还是还比较干净，但是店里菜不好吃，还死贵，这次去了一家小快餐店，口味好，便宜，味道是真的不错，带鱼跟黄鱼都好吃，一点都不腥，我对这类比较腥的鱼真的是很挑剔的，基本上除了家里做的很少吃外面的，那天抱着试试的态度吃了下，真的还不错，后来丈母娘说好像这家老板是给别人结婚喜事酒席当厨师的，怪不得做的好吃，其实本来是有一点小抗拒，怕不干净什么的，后来发现菜很好吃，而且可能是老丈人跟干活的师傅去吃的比较多，老板很客气，我们吃完饭，还给我们买了葡萄吃，不过这家店有一个槽点，就是饭比较不好吃，有时候会夹生，不过后面聊起来其实是这种小菜馆饭点的通病，烧的太早太多容易多出来浪费，烧的迟了不够吃，而且大的电饭锅比较不容易烧好。
下午前面还是在处理三楼的，窗户上各种钉子，实在是太多了，我后面在走廊上排了一排🤦‍♂️，有些是直接断了，有些是就撬了出来，感觉我在杭州租房也没有这样子各种钉钉子，挂下衣服什么的也不用这么多吧，比较不能理解，搞得到处都是钉子。那天我爸也去帮忙了，主要是在卫生间里做白缝，其实也是个技术活，印象中好像我小时候自己家里也做过这个事情，但是比较模糊了，后面我们三楼搞完了就去帮我爸了，前面是我老婆二爹在那先刷上白缝，这里叫白缝，有些考究的也叫美缝，就是瓷砖铺完之后的缝，如果不去弄的话，里面水泥的颜色就露出来了，而且容易渗水，所以就要用白水泥加胶水搅拌之后糊在缝上，但是也不是直接糊，先要把缝抠一抠，因为铺瓷砖的还不会仔细到每个缝里的水泥都是一样满，而且也需要一些空间糊上去，不然就太表面的一层很容易被水直接冲掉了，然后这次其实也不是用的白水泥，而是直接现成买来就已经配好的用来填缝的，兑水搅拌均匀就好了，后面就主要是我跟我爸在搞，那个时候真的觉得我实在是太胖了，蹲下去真的没一会就受不了了，膝盖什么的太难受了，后面我跪着刷，然后膝盖又疼，也是比较不容易，不过我爸动作很快，我中间跪累了休息一会，我爸就能搞一大片，后面其实我也没做多少（谦虚一下），总体来讲这次不是很累，就是蹲着跪着腿有点受不了，是应该好好减肥了。

+]]> + + 生活 + 运动 + 跑步 + 干活 + + + 生活 + 小技巧 + 运动 + 减肥 + 跑步 + 干活 + + + + 在老丈人家的小工记五 + /2020/10/18/%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E7%9A%84%E5%B0%8F%E5%B7%A5%E8%AE%B0%E4%BA%94/ + 终于回忆起来了，年纪大了写这种东西真的要立马写，不然很容易想不起来，那天应该是 9 月 12 日，也就是上周六，因为我爸也去了，而且娘亲（丈母娘，LD 这么叫，我也就随了她这么叫，当然是背后，当面就叫妈）也在那，早上一到那二爹就给我爸指挥了活，要挖一条院子的出水道，自己想出来的词，因为觉得下水道是竖的，在那稍稍帮了一会会忙，然后我还是比较惯例的跟着 LD 还有娘亲去住的家里，主要是老丈人可能也不太想让我干太累的活，因为上次已经差不多把三楼都整理干净了，然后就是二楼了，二楼说实话我也帮不上什么忙，主要是衣服被子什么的，正好是有张以前小孩子睡过的那种摇篮床，看上去虽然有一些破损，整体还是不错的，所以打算拿过去，我就负责把它拆掉了，比较简单的是只要拧螺丝就行了，但是其实是用了好多好多工具才搞定的，一开始只要螺丝刀就行了，但是因为年代久了，后面的螺帽也有点锈住或者本身就会串着会一起动，所以拿来了个扳手，大部分的其实都被这两个工具给搞定了，但是后期大概还剩下四分之一的时候，有一颗完全锈住，并且螺纹跟之前那些都不一样，但是这个已经是最大的螺丝刀了，也没办法换个大的了，所以又去找来个一字的，因为十字的不是也可以用一字的拧嘛，结果可能是我买的工具箱里的一字螺丝刀太新了，口子那很锋利，直接把螺丝花纹给划掉了，大的小的都划掉，然后真的变成凹进去一个圆柱体了，然后就想能不能隔一层布去拧，然而因为的确是已经变成圆柱体了，布也不太给力，不放弃的我又去找来了个老虎钳，妄图把划掉的螺丝用老虎钳钳住，另一端用扳手拧开螺帽，但是这个螺丝跟螺帽真的是生锈的太严重了，外加上钳不太牢，完全是两边一起转，实在是没办法了，在征得同意之后，直接掰断了，火死了，一颗螺丝折腾得比我拆一张床还久，那天因为早上去的也比较晚了，然后就快吃午饭了，正好想着带一点东西过去，就把一些脸盆，泡脚桶啥的拿过去了，先是去吃了饭，还是在那家快餐店，菜的口味还是依然不错，就是人比较多，我爸旁边都是素菜，都没怎么吃远一点的荤菜，下次要早点去，把荤菜放我爸旁边😄（PS：他们家饭还是依然尴尬，需要等），吃完就开到在修的房子那把东西拿了出来，我爸已经动作很快的打了一小半的地沟了，说实话那玩意真的是很重，我之前把它从三楼拿下来，就觉得这个太重了，这次还要用起来，感觉我的手会分分钟废掉，不过一开始我还是跟着LD去了住的家里，惯例睡了午觉，那天睡得比较踏实，竟然睡了一个小时，醒了想了下，其实LD她们收拾也用不上我（没啥力气活），我还是去帮我爸他们，跟LD说了下就去了在修的老房子那，两位老爹在一起钻地，看着就很累，我连忙上去想换一会他们，因为刚好是钻到混凝土地线，特别难，力道不够就会滑开，用蛮力就是钻进去拔不出来，原理是因为本身浇的时候就是很紧实的，需要边钻边动，那家伙实在是太重了，真的是汗如雨下，基本是三个人轮流来，我是个添乱的，经常卡住，然后把地线，其实就是一条混凝土横梁，里面还有14跟18的钢筋，需要割断，这个割断也是很有技巧，钢筋本身在里面是受到挤压的，直接用切割的，到快断掉的时候就会崩一下，非常危险，还是老丈人比较有经验，要留一点点，然后直接用榔头敲断就好了，本来以为这个是最难的了，结果下面是一块非常大的青基石，而且也是石头跟石头挤一块，边上一点点打钻有点杯水车薪，后来是用那种螺旋的钻，钻四个洞，相对位置大概是个长方形，这样子把中间这个长方形钻出来就比较容易地能拿出来了，后面的也容易搞出来了，后面的其实难度不是特别大了，主要是地沟打好之后得看看高低是不是符合要求的，不能本来是往外排水的反而外面高，这个怎么看就又很有技巧了，一般在地上的只要侧着看一下就好了，考究点就用下水平尺，但是在地下的，不用水平尺，其实可以借助于地沟里正要放进去的水管，放点水进去，看水往哪流就行了，铺好水管后，就剩填埋的活了，不是太麻烦了，那天真的是累到了，打那个混凝土的时候我真的是把我整个人压上去了，不过也挺爽的，有点把平时无处发泄的蛮力发泄出去了。

+]]> + + 生活 + 运动 + 跑步 + 干活 + + + 生活 + 小技巧 + 运动 + 减肥 + 跑步 + 干活 + + + + 在老丈人家的小工记四 + /2020/09/26/%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E7%9A%84%E5%B0%8F%E5%B7%A5%E8%AE%B0%E5%9B%9B/ + 小工记四

第四周去的时候让我们去了现在在住的房子里，去三楼整理东西了，蛮多的东西需要收拾整理，有些需要丢一下，以前往往是把不太要用的东西就放三楼了，但是后面就不会再去收拾整理，LD 跟丈母娘负责收拾，我不太知道哪些还要的，哪些不要了，而且本来也不擅长这种收拾🤦‍♂️，然后就变成她们收拾出来废纸箱，我负责拆掉，压平，这时候终于觉得体重还算是有点作用，总体来说这个事情我其实也不擅长，不擅长的主要是捆起来，可能我总是小题大做，因为纸箱大小不一，如果不做一下分类，然后把大的折小一些的话，直接绑起来，容易拎起来就散掉了，而且一些鞋盒子这种小件的纸盒会比较薄，冰箱这种大件的比较厚，厚的比较不容易变形，需要大力踩踏，而且扎的时候需要用体重压住捆实了之后那样子才是真的捆实的，不然待会又是松松垮垮容易滑出来散架，因为压住了捆好后，下来了之后箱子就会弹开了把绳子崩紧实，感觉又是掌握到生活小技巧了😃，我这里其实比较单调无聊，然后 LD 那可以说非常厉害了，一共理出来 11 把旧电扇，还有好多没用过全新的不锈钢脸盆大大小小的，感觉比店里在卖的还多，还有是有比较多小时候的东西，特别多小时候的衣服，其实这种对我来说最难了，可能需要读一下断舍离，蛮多东西都舍不得扔，但是其实是没啥用了，然后还占地方，这天应该算是比较轻松的一天了，上午主要是把收拾出来要的和不要的搬下楼，然后下午要去把纸板给卖掉。中午还是去小快餐店吃的，在住的家里理东西还有个好处就是中午吃完饭可以小憩一下，因为我个人是非常依赖午休的，不然下午完全没精神，而且心态也会比较烦躁，一方面是客观的的确比较疲惫，另一方面应该主观心理作用也有点影响，就像上班的时候也是觉得不午睡就会很难受，心理作用也有一点，不过总之能睡还是睡一会，真的没办法就心态好点，吃完午饭之后我们就推着小平板车去收废品的地方卖掉了上午我收拾捆起来的纸板，好像卖了一百多，都是直接过地磅了，不用一捆一捆地称，不过有个小插曲，那里另外一个大爷在倒他的三轮车的时候撞了我一下，还好车速慢，屁股上肉垫后，接下来就比较麻烦了，是LD 她们两姐妹从小到大的书，也要去卖掉，小平板车就载不下了，而且着实也不太好推，轮子不太平，导致推着很累，书有好多箱，本来是想去亲戚家借电动三轮车，因为不会开摩托的那种，摩托的那种 LD 邻居家就有，可是到了那发现那个也是很大，而且刹车是用脚踩的那种，俺爹不太放心，就说第二天周日他有空会帮忙去载了卖掉的，然后比较搞笑的来了，丈母娘看错了时间，以为已经快五点了，就让我们顺便在车里带点东西去在修的房子，放到那边三楼去，到了那还跟老丈人说已经这么迟了要赶紧去菜场买菜了，结果我们回来以后才发现看错了一个小时🤦‍♂️。
前面可以没提，前三周去的我们一般就周六去一天，然后周日因为要早点回杭州，而且可能想让我们周日能休息下，但是这周就因为周日的时候我爸要去帮忙载书，然后 LD 姐姐也会过来收拾东西，我们周日就又去整理收拾了，周日由于俺爹去的很早，我过去的时候书已经木有了，主要是去收拾东西了，把一些有用没用的继续整理，基本上三楼的就处理完毕了，舒了一大口气，毕竟让丈母娘一个人收拾实在是太累了，但是要扔掉的衣服比较棘手，附近知道的青蛙回收桶被推倒了，其他地方也不知道哪里有，我们就先载了一些东西去在修的房子那，然后去找青蛙桶，结果一个小区可以进，但是已经满了，另一个不让进，后来只能让 LD 姐姐带去她们小区扔了，塞了满满一车。因为要赶回杭州的车就没有等我爸一起回来，他还在那帮忙搞卫生间的墙缝。
虽然这两天不太热，活也不算很吃力，不过我这个体重和易出汗的体质，还是让短袖不知道湿透了多少次，灌了好多水和冰红茶（下午能提提神），回来周一早上称体重也比较喜人，差一点就达到阶段目标，可以想想去哪里吃之前想好的烤肉跟火锅了（估计吃完立马回到解放前）。

+]]> + + 生活 + 运动 + 跑步 + 干活 + + + 生活 + 小技巧 + 运动 + 减肥 + 跑步 + 干活 + + 小工周记一 /2023/03/05/%E5%B0%8F%E5%B7%A5%E5%91%A8%E8%AE%B0%E4%B8%80/ @@ -5212,16 +5250,24 @@ user3: - 屯菜惊魂记 - /2022/04/24/%E5%B1%AF%E8%8F%9C%E6%83%8A%E9%AD%82%E8%AE%B0/ - 因某国际大都市的给力表现，昨儿旁边行政区启动应急响应，同事早上就在群里说要去超市买菜了，到了超市人还特别多，由于来的就是我们经常去的那家超市，一方面为了安全，另一方面是怕已经抢不到了，就去了另一家比较远的超市，开车怕没车位就骑了小电驴，还下着小雨，结果到了超市差不多 12 点多，超市里出来的人都是推着一整车一整车的物资，有些比较像我，整箱的泡面，好几提纸巾，还有各种吃的，都是整箱整箱的，进了超市发现结账包括自助结账的都排很长的队，到了蔬菜货架附近，差点哼起那首歌“空空如也~”，新鲜蔬菜基本已被抢空，只剩下一些卖相不太好的土豆番薯之类的，也算是意料之外情理之中了，本来以为这家超市稍微离封控区远一些会空一点，结果就是所谓的某大都市封控了等物资，杭州市是屯了物资等封控，新鲜蔬菜没了我们也只能买点其他的，神奇的是水果基本都在，可能困难时期水果不算必需品了？还是水果基本人人都已经储备了很多，不太能理解，虽然水果还在，但是称重的地方也还有好多人排队，我们采取了并行策略，LD 在那排队，遥控指挥我去拿其他物资，拿了点碱水面，黑米，那黑米的时候还闹了个乌龙，因为前面就是散装鸡蛋的堆货的地方，结果我们以为是在那后面排队，结果称重那个在那散步了，我们还在那排队，看到后面排队，那几个挑的人也该提醒下吧，几个鸡蛋挑了半天，看看人家大妈，直接拿了四盘，看了下牛奶货架也比较空，不过还有致优跟优倍，不过不算很实惠，本来想买，只是后来赶着去结账，就给忘了，称好了黑米去看了下肉，结果肉也没了，都在买猪蹄，我们也不太爱吃猪蹄，就买了点鸡胸肉，整体看起来我们买的东西真的有点格格不入，不买泡面（因为 LD 不让买了），也不屯啥米和鸡蛋，其实鸡蛋已经买了，米也买了，其他的本身冰箱小也放不下太多东西，我是觉得还可能在屯一点这那的，LD 觉得太多了，基本的米面油有了，其他调味品什么也有了。后面就是排队结账，我去排的时候刚好前面一个小伙子跟大妈在争执，大妈说我们差不多时间来的，你要排前面就前面，小伙子有点不高兴，觉得她就是插队，哈哈，平时一般这种剧情都是发生在我身上的，这会看着前面的吵起来还是很开心的，终于有跟我一样较真的人了，有时候总觉得我是个很纠结，很较真的人，但是我现在慢慢认可了这种较真，如果没有人指出来这种是插队行为，是不对的，就会有越来越多的人觉得是可以随意插队的，正确的事应该要坚持，很多情况大家总是觉得多一事不如少一事，鸡毛蒜皮的没什么好计较的，正是这种想法，那么多人才不管任何规则，反而搞得像遵守规则都是傻 X 似的。回到屯物资，后面结账排到队了也没来得及买原来想买的花生牛奶什么的，毕竟那么多人排着队，回家后因为没有蔬菜，结果就只能吃干菜汤和饭了

+ 寄生虫观后感 + /2020/03/01/%E5%AF%84%E7%94%9F%E8%99%AB%E8%A7%82%E5%90%8E%E6%84%9F/ + 寄生虫这部电影在获得奥斯卡之前就有关注了，豆瓣评分很高，一开始看到这个片名以为是像《铁线虫入侵》那种灾难片，后来看到男主，宋康昊，也是老面孔了，从高中时候在学校操场组织看的《汉江怪物》，有点二的感觉，后来在大学寝室电脑上重新看的时候，室友跟我说是韩国国宝级演员，真人不可貌相，感觉是个呆子的形象。

+

但是你说这不是个灾难片，而是个反映社会问题的，就业比较容易往这个方向猜，只是剧情会是怎么样的，一时也没啥头绪，后来不知道哪里看了下一个剧情透露，是一个穷人给富人做家教，然后把自己一家都带进富人家，如果是这样的话可能会把这个怎么带进去作为一个主线，不过事实告诉我，这没那么重要，从第一步朋友的介绍，就显得无比顺利，要去当家教了，作为一个穷成这样的人，瞬间转变成一个衣着得体，言行举止都没让富人家看出破绽的延世大学学生，这真的挺难让人理解，所谓江山易改，本性难移，还有就是这人也正好有那么好能力去辅导，并且诡异的是，多惠也是瞬间就喜欢上了男主，多惠跟将男主介绍给她做家教，也就是多惠原来的家教敏赫，应该也有不少的相处时间，这变了有点大了吧，当然这里也可能因为时长需要，如果说这一点是因为时长，那可能我所有的槽点都是因为这个吧，因为我理解的应该是把家里的人如何一步步地带进富人家，这应该是整个剧情的一个需要更多铺垫去克服这个矛盾点，有时候也想过如果我去当导演，是能拍出个啥，没这个机会，可能有也会是很扯淡的，当然这也不能阻拦我谈谈对这个点的一些看法，毕竟评价一台电冰箱不是说我必须得自己会制冷对吧，这大概是我觉得这个电影的第一个槽点，接下去接二连三的，就是我说的这个最核心的矛盾点，不知道谁说过，这种影视剧应该是源自于生活又高于生活，越是好的作品，越要接近生活，这样子才更能有感同身受。

+

接下去的点是金基宇介绍金基婷去给多颂当美术家教，这一步又是我理解的败笔吧，就怎么说呢，没什么铺垫，突然从一个社会底层的穷姑娘，转变成一个气场爆表，把富人家太太唬得一愣一愣的，如果说富太太是比较简单无脑的，那富人自己应该是比较有见识而且是做 IT 的，给自己儿子女儿做家教的，查查底细也很正常吧，但是啥都没有，然后呢，她又开始耍司机的心机了，真的是莫名其妙了，司机真的很惨，窈窕淑女君子好逑，而且这个操作也让我摸不着头脑，这是多腹黑并且有经验才会这么操作，脱内裤真的是让我看得一愣愣的，更看得我一愣一愣的，富人竟然也完全按着这个思路去想了，完全没有别的可能呢，甚至可以去查下行车记录仪或者怎样的，或者有没有毛发体液啥的去检验下，毕竟金基婷也乘坐过这辆车，但是最最让我不懂的还是脱内裤这个操作，究竟是什么样的人才会的呢，值得思考。

+

金基泽和忠淑的点也是比较奇怪，首先是金基泽，引起最后那个杀人事件的一个由头，大部分观点都是人为朴社长在之前跟老婆啪啪啪的时候说金基泽的身上有股乘地铁的人的味道，简而言之就是穷人的味道，还有去雯光丈夫身下拿钥匙是对金基泽和雯光丈夫身上的味道的鄙夷，可是这个原因真的站不住脚，即使是同样经济水平，如果身上有比较重的异味，背后讨论下，或者闻到了比较重的味道，有不适的表情和动作很正常吧，像雯光丈夫，在地下室里呆了那么久，身上有异味并且比较重太正常了，就跟在厕所呆久了不会觉得味道大，但是从没味道的地方一进有点味道的厕所就会觉得异样，略尴尬的理由；再说忠淑呢，感觉是太厉害了，能胜任这么一家有钱人的各种挑剔的饮食口味要求的保姆职位，也是让人看懵逼了，看到了不禁想到一个问题，这家人开头是那么地穷，不堪，突然转变成这么地像骗子家族，如果有这么好的骗人能力，应该不会到这种地步吧，如果真的是那么穷，没能力，没志气，又怎么会突然变成这么厉害呢，一家人各司其职，把富人家唬得团团转，而这个前提是，这些人的确能胜任这四个位置，这就是我非常不能理解的点。

+

然后说回这个标题，寄生虫，不知道是不是翻译过来不准确，如果真的是叫寄生虫的话，这个寄生虫智商未免也太低了，没有像新冠那样机制，致死率低一点，传染能力强一点，潜伏期也能传染，这个寄生虫第一次受到免疫系统的攻击就自爆了；还有呢，作为一个社会比较低层的打工者，乡下人，对这个审题也是不太审的清，是指这一家人是社会的寄生虫，不思进取，并且死的应该，富人是傻白甜，又有钱又善良，这是给有钱人洗地了还是啥，这个奥斯卡真不知道是怎么得的，总觉得奥斯卡，甚至低一点，豆瓣，得奖的，评分高的都是被一群“精英党”把持的，有黑人主角的，得分高；有同性恋的，得分高；结局惨的，得分高；看不懂的，得分高；就像肖申克的救赎，真不知道是哪里好了，最近看了关于明朝那些事的三杨，杨溥的经历应该比这个厉害吧，可是外国人看不懂，就像外国人不懂中国为什么有反分裂国家法，经历了鸦片战争，八国联军，抗日战争等等，其实跟外国对于黑人的权益的问题，因为有南北战争，所以极度重视这个问题，相应的中国也有自己的历史，请理解。

+

简而言之我对寄生虫的评分大概 5~6 分吧。

]]> 生活 + 影评 + 2020 生活 - 囤物资 + 影评 + 寄生虫 @@ -5245,6 +5291,35 @@ user3: 跑步 + + 使用milvus和towhee实现简陋版的以图搜图 + /2024/08/04/%E4%BD%BF%E7%94%A8milvus%E5%92%8Ctowhee%E5%AE%9E%E7%8E%B0%E7%AE%80%E9%99%8B%E7%89%88%E7%9A%84%E4%BB%A5%E5%9B%BE%E6%90%9C%E5%9B%BE/ + 上次我们尝试用 towhee 跟 milvus 实现了图片的向量化，那么顺势我们就能在这个基础上实现以图搜图
首先我们找一些图片，

然后我们先把他们都向量化，存储在milvus里

+
from towhee import AutoPipes, AutoConfig, ops
import towhee
import os
from pymilvus import MilvusClient
import json
 
# 1. 设置一个Milvus客户端
client = MilvusClient(
    uri="http://localhost:19530"
)
insert_conf = AutoConfig.load_config('insert_milvus')
insert_conf.collection_name = 'text_image_search'


insert_pipe = AutoPipes.pipeline('insert_milvus', insert_conf)

# 创建图像嵌入管道
image_embedding_pipe = AutoPipes.pipeline('image-embedding')
files = os.listdir("./images")
for file in files: 
    file_path = os.path.join("./images", file)
    if os.path.isfile(file_path):
        embedding = image_embedding_pipe(file_path).get()[0]
        insert_pipe([file_path, embedding])
+

然后我们找一张神仙姐姐的其他图片，先把它 embedding 后在 milvus 里进行向量检索

+
image_embedding_pipe = AutoPipes.pipeline('image-embedding')

# 生成嵌入
embedding = image_embedding_pipe('./to_search.jpg').get()[0]
print(embedding)
res = client.search(
    collection_name="text_image_search",
    data=[embedding],
    limit=5,
    search_params={"metric_type": "IP", "params": {}}
)
result = json.dumps(res, indent=4)
print(result)
+

我们检索出来5个结果，

可以通过distance找到距离最近的这个是id=451291280409722600
可以发现也是神仙姐姐的,只是作为参考
to_search 目标图片是

搜索的最短距离就是id对应的图片

这张图片就是神仙姐姐

+]]> + + 算法 + + + 算法 + + + + 屯菜惊魂记 + /2022/04/24/%E5%B1%AF%E8%8F%9C%E6%83%8A%E9%AD%82%E8%AE%B0/ + 因某国际大都市的给力表现，昨儿旁边行政区启动应急响应，同事早上就在群里说要去超市买菜了，到了超市人还特别多，由于来的就是我们经常去的那家超市，一方面为了安全，另一方面是怕已经抢不到了，就去了另一家比较远的超市，开车怕没车位就骑了小电驴，还下着小雨，结果到了超市差不多 12 点多，超市里出来的人都是推着一整车一整车的物资，有些比较像我，整箱的泡面，好几提纸巾，还有各种吃的，都是整箱整箱的，进了超市发现结账包括自助结账的都排很长的队，到了蔬菜货架附近，差点哼起那首歌“空空如也~”，新鲜蔬菜基本已被抢空，只剩下一些卖相不太好的土豆番薯之类的，也算是意料之外情理之中了，本来以为这家超市稍微离封控区远一些会空一点，结果就是所谓的某大都市封控了等物资，杭州市是屯了物资等封控，新鲜蔬菜没了我们也只能买点其他的，神奇的是水果基本都在，可能困难时期水果不算必需品了？还是水果基本人人都已经储备了很多，不太能理解，虽然水果还在，但是称重的地方也还有好多人排队，我们采取了并行策略，LD 在那排队，遥控指挥我去拿其他物资，拿了点碱水面，黑米，那黑米的时候还闹了个乌龙，因为前面就是散装鸡蛋的堆货的地方，结果我们以为是在那后面排队，结果称重那个在那散步了，我们还在那排队，看到后面排队，那几个挑的人也该提醒下吧，几个鸡蛋挑了半天，看看人家大妈，直接拿了四盘，看了下牛奶货架也比较空，不过还有致优跟优倍，不过不算很实惠，本来想买，只是后来赶着去结账，就给忘了，称好了黑米去看了下肉，结果肉也没了，都在买猪蹄，我们也不太爱吃猪蹄，就买了点鸡胸肉，整体看起来我们买的东西真的有点格格不入，不买泡面（因为 LD 不让买了），也不屯啥米和鸡蛋，其实鸡蛋已经买了，米也买了，其他的本身冰箱小也放不下太多东西，我是觉得还可能在屯一点这那的，LD 觉得太多了，基本的米面油有了，其他调味品什么也有了。后面就是排队结账，我去排的时候刚好前面一个小伙子跟大妈在争执，大妈说我们差不多时间来的，你要排前面就前面，小伙子有点不高兴，觉得她就是插队，哈哈，平时一般这种剧情都是发生在我身上的，这会看着前面的吵起来还是很开心的，终于有跟我一样较真的人了，有时候总觉得我是个很纠结，很较真的人，但是我现在慢慢认可了这种较真，如果没有人指出来这种是插队行为，是不对的，就会有越来越多的人觉得是可以随意插队的，正确的事应该要坚持，很多情况大家总是觉得多一事不如少一事，鸡毛蒜皮的没什么好计较的，正是这种想法，那么多人才不管任何规则，反而搞得像遵守规则都是傻 X 似的。回到屯物资，后面结账排到队了也没来得及买原来想买的花生牛奶什么的，毕竟那么多人排着队，回家后因为没有蔬菜，结果就只能吃干菜汤和饭了

+]]> + + 生活 + + + 生活 + 囤物资 + + 折腾记-给玩客云装上casaos /2024/04/28/%E6%8A%98%E8%85%BE%E8%AE%B0-%E7%BB%99%E7%8E%A9%E5%AE%A2%E4%BA%91%E8%A3%85%E4%B8%8Acasaos/ @@ -5279,9 +5354,13 @@ user3: - 折腾记-讲一下iptables的一个小问题 - /2024/05/05/%E6%8A%98%E8%85%BE%E8%AE%B0-%E8%AE%B2%E4%B8%80%E4%B8%8Biptables%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E9%97%AE%E9%A2%98/ - 很早之前就有用过iptables，那时候是早期版本的Ubuntu系统，配置防火墙的时候，是挺复杂的，就是在reject之前添加accept规则，并且当时很多网上资料都是在后续添加就行，这个其实在当时是有问题的，所以比较印象深刻。
这次碰到的问题是对iptables的概念不熟悉有关，因为iptables的逻辑其实也有一定问题我觉得，比如最基本的命令，iptables -L
这里help显示的是会展示所有chain的rules

但是这样完全把table概念给忽略了，如果不注明理论上应该把所有table的展示出来，或者把包含这个参数并且是默认给出filter这个table的写清楚
所以这次要补一下这部分概念
iptables里首先就是有四种table，分别是 Filter, NAT, Mangle, Raw四种内建表
并且层次结构是 iptables -> Tables -> Chains -> Rules.
filter:
This is the default table (if no -t option is passed). It contains the built-in chains INPUT (for packets destined to local sockets), FORWARD (for packets being routed through the box), and OUTPUT (for locally-generated packets).
包含 INPUT，FORWORD和OUPUT三个chain，默认就是展示这个，所以就会导致用iptables -L 显示不了mangle table的规则
nat:
This table is consulted when a packet that creates a new connection is encountered. It consists of three built-ins: PREROUTING (for altering packets as soon as they come in), OUTPUT (for altering locally-generated packets before routing), and POSTROUTING (for altering packets as they are about to go out).
nat包含 PREROUTING，OUTPUT，POSTROUTING三个chain
mangle:
This table is used for specialized packet alteration. Until kernel 2.4.17 it had two built-in chains: PREROUTING (for altering incoming packets before routing) and OUTPUT (for altering locally-generated packets before routing). Since kernel 2.4.18, three other built-in chains are also supported: INPUT (for packets coming into the box itself), FORWARD (for altering packets being routed through the box), and POSTROUTING (for altering packets as they are about to go out).
mangle主要用于专门的数据包更改，包含 PREROUTING，INPUT，OUTPUT，FORWARD，POSTROUTING 五个chain，注意有内核版本区别，只是现在基本没那么老的内核了
raw:
This table is used mainly for configuring exemptions from connection tracking in combination with the NOTRACK target. It registers at the netfilter hooks with higher priority and is thus called before ip_conntrack, or any other IP tables. It provides the following built-in chains: PREROUTING (for packets arriving via any network interface) OUTPUT (for packets generated by local processes)
包含 PREROUTING和OUTPUT，主要用来配置例外
chain内部会有实际的rule规则，这个具体后面可以介绍

+ 折腾记-给玩客云刷上Armbian + /2024/03/31/%E6%8A%98%E8%85%BE%E8%AE%B0-%E7%BB%99%E7%8E%A9%E5%AE%A2%E4%BA%91%E5%88%B7%E4%B8%8AArmbian/ + 之前买来玩客云作为轻nas使用，但是由于只能外接硬盘，并且是usb2.0的接口，使用体验不是特别好，并且前期作为老母鸡还能兑换视频会员这种，后面取消了就不怎么使用了，前阵子玩客云停止提供服务了，所以就想可以折腾下，比如Armbian这样的，可以作为轻量服务器使用，功耗也比较低，跑一些简单的docker还是可以的，

配置是这样的，存储小了点，其他就是一个普通小服务器的配置了，usb2.0有点弱，但是现在有一些是正常退役，还有很多是PCDN被禁了宽带以后咸鱼出售的，带上电源跟网线才30左右，还是比较有性价比的
首先机器有几个版本，主要是1.0跟1.3，要注意1.3版本的主板上有个混淆点，会有一个1.2的版本，指的是具体存储或者网卡等组件的版本，玩客云版本是较大的 V1.3，而1.0则是比较小的字体，而且据网上的资料，1.0版本是有个MAC地址的贴条

可以看上图的标示，箭头指向的是短接点，这是1.0版本的，1.3版本是找不到这个短接点，而是在反面（有S805芯片和存储的一面）
新版手边没有，借用一张恩山的图

这个在最开始我刷第一个的时候比较困扰我，因为不确定版本，网上又有很多都是只截一小块区域的图，也不知道相对位置
首先如果是从闲鱼买来的已经刷成PCDN，某心云的需要通过短接刷底包之后再刷入系统，可以先用吹风机热风吹一会后盖，然后用一字螺丝刀从SD卡槽把外层的一片壳撬开，然后通过十字螺丝刀拧开之后就可以把主板拿出来
然后需要镊子或者其他铁丝，能够短接的就行，外加一个双公头USB线
刷机工具包可以用我这个分享的

+
https://www.alipan.com/s/sd4Wi6TDzeP
提取码: 84or
+

第一步打开USB Burning Tool, 要短接刷入update.img

通过文件导入网盘里的 update.img，然后先用双公头USB线连接电脑的USB口和玩客云靠近HDMI口的USB口，如果方便最好接到电脑后置的USB口，防止不稳定刷机终端，接下来就需要短接了，这里的姿势要先短接好，保持稳定，然后插上电源线，这里如果连接成功就会在软件左上会显示一栏连接成功，如果电脑连接音响也会有叮咚这样的声音，接下去就可以点开始进行刷入底包了，这里最大的困难点就是保持短接稳定后插电源，并且继续保持稳定刷入底包，底包刷入会比较快，等到刷机进度条走完刷成功后就点停止，然后关闭软件，拔掉玩客云电源，拔掉USB
第二步是刷入Armbian系统，根据一些教程有些说需要先刷入5.8，我这边是直接刷入了5.9的系统，也就多一步，准备一个U盘，16G或者8G都行，先用Etcher把5.8系统刷入U盘，刷入完成后再关闭软件，推出U盘，将U盘插入玩客云上靠近网口的USB口，然后再通电，指示灯会蓝色-> 绿色 -> 蓝紫闪烁 -> 蓝色，这样就是刷机成功了，会需要一点时间，刷机完成后再把5.9的的刷入U盘，再继续刚才的步骤，此时需要把网线插入玩客云，等待正常亮灯显示后，在路由器上可以找到一个aml-s812的设备，在通过ssh连接，账号是root，密码是1234，进入系统后通过以下命令就可以刷入emmc了

+
cd /boot/install  sudo ./install.sh
+

完成后会自动断开，拔掉U盘后再用同样方法连接就可以了，后续怎么使用就是Linux相关的了，后面也可以讲下docker的操作，但要注意架构，这个是arm架构32位的

]]> 折腾 @@ -5379,15 +5458,15 @@ user3: - 是何原因竟让两人深夜奔袭十公里 - /2022/06/05/%E6%98%AF%E4%BD%95%E5%8E%9F%E5%9B%A0%E7%AB%9F%E8%AE%A9%E4%B8%A4%E4%BA%BA%E6%B7%B1%E5%A4%9C%E5%A5%94%E8%A2%AD%E5%8D%81%E5%85%AC%E9%87%8C/ - 偶尔来个标题党，不过也是一次比较神奇的经历
上周五下班后跟 LD 约好去吃牛蛙，某个朋友好像对这类都不太能接受，我以前小时候也不常吃，但是这类其实都是口味比较重，没有那种肉本身的腥味，而且肉质比较特殊，吃过几次以后就有点爱上了，这次刚好是 LD 买的新店开业券，比较优惠（我们俩都是有点勤俭持家的，想着小电驴还有三格电，这家店又有点远，骑车单趟大概要 10 公里左右，有点担心，LD 说应该可以的，就一起骑了过去（跟她轮换着骑电驴和共享单车），结果大概离吃牛蛙的店还有一辆公里的时候，电量就报警了，只有最后一个红色的了，一共是五格，最后一格是红色的，提示我们该充电了，这样子是真的有点慌了，之前开了几个月都是还有一两格电的时候就充电了，没有试验过究竟这最后一格电能开多远，总之先到了再说。
这家牛蛙没想到还挺热闹的，我们到那已经快八点了，还有十几个排队的，有个人还想插队（向来是不惯着这种，一边去），旁边刚好是有些商店就逛了下，就跟常规的商业中心差不多，开业的比较早也算是这一边比较核心的商业综合体了，各种品牌都有，而且还有彩票售卖点的，只是不太理解现在的彩票都是兑图案的，而且要 10 块钱一张，我的概念里还是以前 2 块钱一张的双色球，偶尔能中个五块十块的。排队还剩四五个的时候我们就去门口坐着等了，又等了大概二十分钟才排到我们，靠近我们等的里面的位置，好像好几个小女生在那还叫了外卖奶茶，然后各种拍照，小朋友的生活还是丰富多彩的，我们到了就点了蒜蓉的，没有点传说中紫苏的，菜单上画了 N 个🌶，LD 还是想体验下说下次人多点可以试试，我们俩吃怕太辣了吃不消，口味还是不错的，这家貌似是 LD 闺蜜推荐的，口碑有保证。两个人光吃一个蛙锅就差不多了，本来还想再点个其他的，后面实在吃不下了就没点，吃完还是惯例点了个奶茶，不过是真的不好找，太大了。
本来是就回个家的事了，结果就因为前面铺垫的小电驴已经只有一格电了，标题的深夜奔袭十公里就出现了，这个电驴估计续航也虚标挺严重的，电量也是这样，骑的时候显示只有一格电，关掉再开起来又有三格，然后我们回去骑了没一公里就没电了，这下是真的完球了，觉得车子也比较新，直接停外面也不放心，就开始了深夜的十公里推电驴奔袭，LD 看我太累还帮我中间推了一段，虽然是跑过十公里的，但是推着个没电的电驴，还是着实不容易的，LD 也是陪我推着车走，中间好几次说我们把电驴停着打车回去，把电池带回去充满了明天再过来骑车，可能是心态已经转变了，这应该算是一次很特殊的体验，从我们吃完出来大概十点，到最后我们推到小区，大概是过了两个小时的样子，说句深夜也不太过分，把这次这么推车看成了一种意志力的考验，很多事情也都是怕坚持，或者说怕不能坚持，想走得远，没有持续的努力坚持肯定是不行的，所以还是坚持着把车推回来（好吧，我其实主要是怕车被偷，毕竟刚来杭州上学没多久就被偷了自行车留下了阴影），中间感谢 LD，跟我轮着推了一段路，有些下坡的时候还在那坐着用脚蹬一下，离家里大概还有一公里的时候，有个骑电瓶车的大叔还停下来问我们是车破了还是没电了，应该是出于好意吧，最后快到的时候真的非常渴，买了2.5 升的水被我一口气喝了大半瓶，奶茶已经不能起到解渴的作用了，本来以为这样能消耗很多，结果第二天一称还重了，（我的称一定有问题 233

+ 折腾记-讲一下iptables的一个小问题 + /2024/05/05/%E6%8A%98%E8%85%BE%E8%AE%B0-%E8%AE%B2%E4%B8%80%E4%B8%8Biptables%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E9%97%AE%E9%A2%98/ + 很早之前就有用过iptables，那时候是早期版本的Ubuntu系统，配置防火墙的时候，是挺复杂的，就是在reject之前添加accept规则，并且当时很多网上资料都是在后续添加就行，这个其实在当时是有问题的，所以比较印象深刻。
这次碰到的问题是对iptables的概念不熟悉有关，因为iptables的逻辑其实也有一定问题我觉得，比如最基本的命令，iptables -L
这里help显示的是会展示所有chain的rules

但是这样完全把table概念给忽略了，如果不注明理论上应该把所有table的展示出来，或者把包含这个参数并且是默认给出filter这个table的写清楚
所以这次要补一下这部分概念
iptables里首先就是有四种table，分别是 Filter, NAT, Mangle, Raw四种内建表
并且层次结构是 iptables -> Tables -> Chains -> Rules.
filter:
This is the default table (if no -t option is passed). It contains the built-in chains INPUT (for packets destined to local sockets), FORWARD (for packets being routed through the box), and OUTPUT (for locally-generated packets).
包含 INPUT，FORWORD和OUPUT三个chain，默认就是展示这个，所以就会导致用iptables -L 显示不了mangle table的规则
nat:
This table is consulted when a packet that creates a new connection is encountered. It consists of three built-ins: PREROUTING (for altering packets as soon as they come in), OUTPUT (for altering locally-generated packets before routing), and POSTROUTING (for altering packets as they are about to go out).
nat包含 PREROUTING，OUTPUT，POSTROUTING三个chain
mangle:
This table is used for specialized packet alteration. Until kernel 2.4.17 it had two built-in chains: PREROUTING (for altering incoming packets before routing) and OUTPUT (for altering locally-generated packets before routing). Since kernel 2.4.18, three other built-in chains are also supported: INPUT (for packets coming into the box itself), FORWARD (for altering packets being routed through the box), and POSTROUTING (for altering packets as they are about to go out).
mangle主要用于专门的数据包更改，包含 PREROUTING，INPUT，OUTPUT，FORWARD，POSTROUTING 五个chain，注意有内核版本区别，只是现在基本没那么老的内核了
raw:
This table is used mainly for configuring exemptions from connection tracking in combination with the NOTRACK target. It registers at the netfilter hooks with higher priority and is thus called before ip_conntrack, or any other IP tables. It provides the following built-in chains: PREROUTING (for packets arriving via any network interface) OUTPUT (for packets generated by local processes)
包含 PREROUTING和OUTPUT，主要用来配置例外
chain内部会有实际的rule规则，这个具体后面可以介绍

]]> - 生活 + 折腾 - 生活 + 折腾 @@ -5403,22 +5482,15 @@ user3: - 用 ollama 本地运行谷歌开源大模型 Gemma - /2024/03/24/%E7%94%A8-ollama-%E6%9C%AC%E5%9C%B0%E8%BF%90%E8%A1%8C%E5%A4%A7%E6%A8%A1%E5%9E%8B/ - 原先在 23 年初的时候调研过一些国产的大模型，包括复旦开源的 MOSS 和清华的 ChatGLM，那时候还是早期版本，需要在 Linux 上，并且有比较好的显卡，而且一般来讲都得是 N 卡，过程中需要安装 pytorch和比较多依赖，并且当时的效果也还比较差，所以后面就没有长期使用。
最近看到谷歌在 2 月份开源了大模型 Gemma ，gemma 的博客在这里，想要在本地运行这个模型在现在这个阶段也变得简单很多，因为我们有了 ollama 工具

可以通过这个工具来运行大模型，并且已经支持了谷歌开源的 Gemma

我这边本地是 MacBook Pro 14 寸的，m3 pro 的处理器，18g 内存，刚好可以用 7b 量化的模型

这里有推荐的模型和内存推荐匹配规则，16g 可以运行 13B 及以下模型
下载安装完后我们可以用以下命令

-
ollama run gemma:7b
-

这里需要拉取模型，约5.2g 大小，考虑网络原因可能会比较慢

我们可以简单来试试问个问题


看出来回答的还是比较丰富的，谷歌出品还是比较有水平的，不至于像 ChatGLM 最初版本的在不做调优的情况下甚至有点前言不搭后语
对于想使用 chatgpt 但是没条件，这也算是个低配平替了, 并且已经是个比较可用的了，同时也方便进行学习调优等
如果想要类似于 chatgpt 那样的网页版，可以安装 open-webui
可以通过 webui 访问 ollama 运行的大模型，
用 docker 启动的命令也贴一下

-
docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
-

不过有个小问题就是 docker 镜像拉取会有点慢，可以添加下国内镜像加速

-
{
    "registry-mirrors": [
        "https://dockerproxy.com",
        "https://docker.mirrors.ustc.edu.cn",
        "https://docker.nju.edu.cn"
    ]
}
-


这里有一个小区别，Gemma 在多轮会话的时候会在前面的答案基础上再完善。

-

补充一个在 windows 环境下，cpu 跑模型的也是可行的

现在是大模型可以深入千家万户了，大家都可以尝试下，如果对日常的工作学习有一些效率上的提升也是好的

+ 是何原因竟让两人深夜奔袭十公里 + /2022/06/05/%E6%98%AF%E4%BD%95%E5%8E%9F%E5%9B%A0%E7%AB%9F%E8%AE%A9%E4%B8%A4%E4%BA%BA%E6%B7%B1%E5%A4%9C%E5%A5%94%E8%A2%AD%E5%8D%81%E5%85%AC%E9%87%8C/ + 偶尔来个标题党，不过也是一次比较神奇的经历
上周五下班后跟 LD 约好去吃牛蛙，某个朋友好像对这类都不太能接受，我以前小时候也不常吃，但是这类其实都是口味比较重，没有那种肉本身的腥味，而且肉质比较特殊，吃过几次以后就有点爱上了，这次刚好是 LD 买的新店开业券，比较优惠（我们俩都是有点勤俭持家的，想着小电驴还有三格电，这家店又有点远，骑车单趟大概要 10 公里左右，有点担心，LD 说应该可以的，就一起骑了过去（跟她轮换着骑电驴和共享单车），结果大概离吃牛蛙的店还有一辆公里的时候，电量就报警了，只有最后一个红色的了，一共是五格，最后一格是红色的，提示我们该充电了，这样子是真的有点慌了，之前开了几个月都是还有一两格电的时候就充电了，没有试验过究竟这最后一格电能开多远，总之先到了再说。
这家牛蛙没想到还挺热闹的，我们到那已经快八点了，还有十几个排队的，有个人还想插队（向来是不惯着这种，一边去），旁边刚好是有些商店就逛了下，就跟常规的商业中心差不多，开业的比较早也算是这一边比较核心的商业综合体了，各种品牌都有，而且还有彩票售卖点的，只是不太理解现在的彩票都是兑图案的，而且要 10 块钱一张，我的概念里还是以前 2 块钱一张的双色球，偶尔能中个五块十块的。排队还剩四五个的时候我们就去门口坐着等了，又等了大概二十分钟才排到我们，靠近我们等的里面的位置，好像好几个小女生在那还叫了外卖奶茶，然后各种拍照，小朋友的生活还是丰富多彩的，我们到了就点了蒜蓉的，没有点传说中紫苏的，菜单上画了 N 个🌶，LD 还是想体验下说下次人多点可以试试，我们俩吃怕太辣了吃不消，口味还是不错的，这家貌似是 LD 闺蜜推荐的，口碑有保证。两个人光吃一个蛙锅就差不多了，本来还想再点个其他的，后面实在吃不下了就没点，吃完还是惯例点了个奶茶，不过是真的不好找，太大了。
本来是就回个家的事了，结果就因为前面铺垫的小电驴已经只有一格电了，标题的深夜奔袭十公里就出现了，这个电驴估计续航也虚标挺严重的，电量也是这样，骑的时候显示只有一格电，关掉再开起来又有三格，然后我们回去骑了没一公里就没电了，这下是真的完球了，觉得车子也比较新，直接停外面也不放心，就开始了深夜的十公里推电驴奔袭，LD 看我太累还帮我中间推了一段，虽然是跑过十公里的，但是推着个没电的电驴，还是着实不容易的，LD 也是陪我推着车走，中间好几次说我们把电驴停着打车回去，把电池带回去充满了明天再过来骑车，可能是心态已经转变了，这应该算是一次很特殊的体验，从我们吃完出来大概十点，到最后我们推到小区，大概是过了两个小时的样子，说句深夜也不太过分，把这次这么推车看成了一种意志力的考验，很多事情也都是怕坚持，或者说怕不能坚持，想走得远，没有持续的努力坚持肯定是不行的，所以还是坚持着把车推回来（好吧，我其实主要是怕车被偷，毕竟刚来杭州上学没多久就被偷了自行车留下了阴影），中间感谢 LD，跟我轮着推了一段路，有些下坡的时候还在那坐着用脚蹬一下，离家里大概还有一公里的时候，有个骑电瓶车的大叔还停下来问我们是车破了还是没电了，应该是出于好意吧，最后快到的时候真的非常渴，买了2.5 升的水被我一口气喝了大半瓶，奶茶已经不能起到解渴的作用了，本来以为这样能消耗很多，结果第二天一称还重了，（我的称一定有问题 233

]]> - LLM + 生活 - LLM + 生活 @@ -5451,34 +5523,6 @@ user3: 生活 - - 给局域网中的Ubuntu固定下ip - /2024/06/02/%E7%BB%99%E5%B1%80%E5%9F%9F%E7%BD%91%E4%B8%AD%E7%9A%84Ubuntu%E5%9B%BA%E5%AE%9A%E4%B8%8Bip/ - 将一个小点，在局域网中包括类似于homelab的或者仅仅只是搭一个用来当实验环境的，在局域网内机器比较少的时候大部分DHCP会给分配相同的ip，但是这不是一定的，当机器比较多，并且上下线比较频繁时就会出现ip地址变动，这对于一些使用场景比较讨厌，所以我想把ip固定下来，先简单引用下DHCP的原理，他分为下面几个阶段

-

DHCP发现（DISCOVER）

client在物理子网上发送广播来寻找可用的服务器。网络管理员可以配置一个本地路由来转发DHCP包给另一个子网上的DHCP服务器。该client实现生成一个目的地址为255.255.255.255或者一个子网广播地址的UDP包。

-

客户也可以申请它使用的最后一个IP地址（在下面的例子里为192.168.1.100）。如果该客户所在的网络中此IP仍然可用，服务器就可以准许该申请。否则，就要看该服务器是授权的还是非授权的。授权服务器会拒绝请求，使得客户立刻申请一个新的IP。非授权服务器仅仅忽略掉请求，导致一个客户端请求的超时，于是客户端就会放弃此请求而去申请一个新的IP地址。

-

DHCP提供（OFFER）

当DHCP服务器收到一个来自客户的IP租约请求时，它会提供一个IP租约。DHCP为客户保留一个IP地址，然后通过网络单播一个DHCPOFFER消息给客户。该消息包含客户的MAC地址、服务器提供的IP地址、子网掩码、租期以及提供IP的DHCP服务器的IP。

-

服务器基于在CHADDR字段指定的客户硬件地址来检查配置。这里的服务器，192.168.1.1，将IP地址指定于YIADDR字段。

-

DHCP请求（REQUEST）

当客户PC收到一个IP租约提供时，它必须告诉所有其他的DHCP服务器它已经接受了一个租约提供。因此，该客户会发送一个DHCPREQUEST消息，其中包含提供租约的服务器的IP。当其他DHCP服务器收到了该消息后，它们会收回所有可能已提供给该客户的租约。然后它们把曾经给该客户保留的那个地址重新放回到可用地址池中，这样，它们就可以为其他计算机分配这个地址。任意数量的DHCP服务器都可以响应同一个IP租约请求，但是每一个客户网卡只能接受一个租约提供。

-

DHCP确认（Acknowledge，ACK）

当DHCP服务器收到来自客户的REQUEST消息后，它就开始了配置过程的最后阶段。这个响应阶段包括发送一个DHCPACK包给客户。这个包包含租期和客户可能请求的其他所有配置信息。这时候，TCP/IP配置过程就完成了。

-

该服务器响应请求并发送响应给客户。整个系统期望客户来根据选项来配置其网卡。

-

DHCP释放（RELEASE）

客户端向DHCP服务器发送一个请求以释放DHCP资源，并注销其IP地址。鉴于客户端更多的时候并不清楚何时用户会将其从网络中移除，此协议不会托管“DHCP释放的发送”。

-

DHCP NAK

服务器回复客户，客户要求的IP不能被分配。

-

Ubuntu

而对于Ubuntu，现在较新版本的设置方式也有点变化，比如我用的是22.04

-
cd /etc/netplan/
-

先去这个目录下查看配置文件
我们要找的是这个 00-installer-config.yaml 文件

原本的是长这样

默认是通过dhcp分配ip
我们要改成静态ip

-
network:
  renderer: networkd
  ethernets:
    ens33:
      addresses:
        - 192.168.1.xx/24
      nameservers:
        addresses: [114.114.114.114, 233.5.5.5]
      routes:
        - to: default
          via: 192.168.1.1
  version: 2
-

解释一下 ethernets中的ens33表示我们的网卡接口的标识
可以通过 ip addr 查看

第一个是本地回环，第二个可以看下是否跟内网ip对应上，如果是的话就这个ens33
然后就是addresses填的地址，就是我们想要固定的ip地址，记得得是自己局域网网段的
上面只是个示例，然后是nameservers里的是dns服务器，也是按需填写
这里的114.114.114.114跟233.5.5.5分别是电信跟阿里云的，仅供参考
然后是routes就是网关地址，按自己的环境填写，网络没有任何特殊处理的一般就是路由器
接下去就是应用这个配置

-
sudo netplan apply
-

这样ip配置就生效了
可以通过
ip addr show ens33查看

-]]> - - linux - - - linux - - 学习下LlamaIndex-初始篇 /2024/04/21/%E5%AD%A6%E4%B9%A0%E4%B8%8BLlamaIndex-%E5%88%9D%E5%A7%8B%E7%AF%87/ @@ -5547,13 +5591,118 @@ user3: - 使用milvus和towhee实现简陋版的以图搜图 - /2024/08/04/%E4%BD%BF%E7%94%A8milvus%E5%92%8Ctowhee%E5%AE%9E%E7%8E%B0%E7%AE%80%E9%99%8B%E7%89%88%E7%9A%84%E4%BB%A5%E5%9B%BE%E6%90%9C%E5%9B%BE/ - 上次我们尝试用 towhee 跟 milvus 实现了图片的向量化，那么顺势我们就能在这个基础上实现以图搜图
首先我们找一些图片，

然后我们先把他们都向量化，存储在milvus里

-
from towhee import AutoPipes, AutoConfig, ops
import towhee
import os
from pymilvus import MilvusClient
import json
 
# 1. 设置一个Milvus客户端
client = MilvusClient(
    uri="http://localhost:19530"
)
insert_conf = AutoConfig.load_config('insert_milvus')
insert_conf.collection_name = 'text_image_search'


insert_pipe = AutoPipes.pipeline('insert_milvus', insert_conf)

# 创建图像嵌入管道
image_embedding_pipe = AutoPipes.pipeline('image-embedding')
files = os.listdir("./images")
for file in files: 
    file_path = os.path.join("./images", file)
    if os.path.isfile(file_path):
        embedding = image_embedding_pipe(file_path).get()[0]
        insert_pipe([file_path, embedding])
-

然后我们找一张神仙姐姐的其他图片，先把它 embedding 后在 milvus 里进行向量检索

-
image_embedding_pipe = AutoPipes.pipeline('image-embedding')

# 生成嵌入
embedding = image_embedding_pipe('./to_search.jpg').get()[0]
print(embedding)
res = client.search(
    collection_name="text_image_search",
    data=[embedding],
    limit=5,
    search_params={"metric_type": "IP", "params": {}}
)
result = json.dumps(res, indent=4)
print(result)
-

我们检索出来5个结果，

可以通过distance找到距离最近的这个是id=451291280409722600
可以发现也是神仙姐姐的,只是作为参考
to_search 目标图片是

搜索的最短距离就是id对应的图片

这张图片就是神仙姐姐

+ 给局域网中的Ubuntu固定下ip + /2024/06/02/%E7%BB%99%E5%B1%80%E5%9F%9F%E7%BD%91%E4%B8%AD%E7%9A%84Ubuntu%E5%9B%BA%E5%AE%9A%E4%B8%8Bip/ + 将一个小点，在局域网中包括类似于homelab的或者仅仅只是搭一个用来当实验环境的，在局域网内机器比较少的时候大部分DHCP会给分配相同的ip，但是这不是一定的，当机器比较多，并且上下线比较频繁时就会出现ip地址变动，这对于一些使用场景比较讨厌，所以我想把ip固定下来，先简单引用下DHCP的原理，他分为下面几个阶段

+

DHCP发现（DISCOVER）

client在物理子网上发送广播来寻找可用的服务器。网络管理员可以配置一个本地路由来转发DHCP包给另一个子网上的DHCP服务器。该client实现生成一个目的地址为255.255.255.255或者一个子网广播地址的UDP包。

+

客户也可以申请它使用的最后一个IP地址（在下面的例子里为192.168.1.100）。如果该客户所在的网络中此IP仍然可用，服务器就可以准许该申请。否则，就要看该服务器是授权的还是非授权的。授权服务器会拒绝请求，使得客户立刻申请一个新的IP。非授权服务器仅仅忽略掉请求，导致一个客户端请求的超时，于是客户端就会放弃此请求而去申请一个新的IP地址。

+

DHCP提供（OFFER）

当DHCP服务器收到一个来自客户的IP租约请求时，它会提供一个IP租约。DHCP为客户保留一个IP地址，然后通过网络单播一个DHCPOFFER消息给客户。该消息包含客户的MAC地址、服务器提供的IP地址、子网掩码、租期以及提供IP的DHCP服务器的IP。

+

服务器基于在CHADDR字段指定的客户硬件地址来检查配置。这里的服务器，192.168.1.1，将IP地址指定于YIADDR字段。

+

DHCP请求（REQUEST）

当客户PC收到一个IP租约提供时，它必须告诉所有其他的DHCP服务器它已经接受了一个租约提供。因此，该客户会发送一个DHCPREQUEST消息，其中包含提供租约的服务器的IP。当其他DHCP服务器收到了该消息后，它们会收回所有可能已提供给该客户的租约。然后它们把曾经给该客户保留的那个地址重新放回到可用地址池中，这样，它们就可以为其他计算机分配这个地址。任意数量的DHCP服务器都可以响应同一个IP租约请求，但是每一个客户网卡只能接受一个租约提供。

+

DHCP确认（Acknowledge，ACK）

当DHCP服务器收到来自客户的REQUEST消息后，它就开始了配置过程的最后阶段。这个响应阶段包括发送一个DHCPACK包给客户。这个包包含租期和客户可能请求的其他所有配置信息。这时候，TCP/IP配置过程就完成了。

+

该服务器响应请求并发送响应给客户。整个系统期望客户来根据选项来配置其网卡。

+

DHCP释放（RELEASE）

客户端向DHCP服务器发送一个请求以释放DHCP资源，并注销其IP地址。鉴于客户端更多的时候并不清楚何时用户会将其从网络中移除，此协议不会托管“DHCP释放的发送”。

+

DHCP NAK

服务器回复客户，客户要求的IP不能被分配。

+

Ubuntu

而对于Ubuntu，现在较新版本的设置方式也有点变化，比如我用的是22.04

+
cd /etc/netplan/
+

先去这个目录下查看配置文件
我们要找的是这个 00-installer-config.yaml 文件

原本的是长这样

默认是通过dhcp分配ip
我们要改成静态ip

+
network:
  renderer: networkd
  ethernets:
    ens33:
      addresses:
        - 192.168.1.xx/24
      nameservers:
        addresses: [114.114.114.114, 233.5.5.5]
      routes:
        - to: default
          via: 192.168.1.1
  version: 2
+

解释一下 ethernets中的ens33表示我们的网卡接口的标识
可以通过 ip addr 查看

第一个是本地回环，第二个可以看下是否跟内网ip对应上，如果是的话就这个ens33
然后就是addresses填的地址，就是我们想要固定的ip地址，记得得是自己局域网网段的
上面只是个示例，然后是nameservers里的是dns服务器，也是按需填写
这里的114.114.114.114跟233.5.5.5分别是电信跟阿里云的，仅供参考
然后是routes就是网关地址，按自己的环境填写，网络没有任何特殊处理的一般就是路由器
接下去就是应用这个配置

+
sudo netplan apply
+

这样ip配置就生效了
可以通过
ip addr show ens33查看

+]]> + + linux + + + linux + + + + 聊一下 Java 的日志系列二 + /2024/01/14/%E8%81%8A%E4%B8%80%E4%B8%8B-Java-%E7%9A%84%E6%97%A5%E5%BF%97%E7%B3%BB%E5%88%97%E4%BA%8C/ + log 初始化过程，首先是在启动类里会获取 logger

+
private static final Log logger = LogFactory.getLog(SpringApplication.class);
+

然后是

+
public static Log getLog(Class clazz) throws LogConfigurationException {
        return getFactory().getInstance(clazz);
    }
+

继续下去是
org.apache.commons.logging.impl.SLF4JLogFactory#getInstance(java.lang.Class)

+
public Log getInstance(Class clazz) throws LogConfigurationException {
    return this.getInstance(clazz.getName());
}
public Log getInstance(String name) throws LogConfigurationException {
    Log instance = (Log)this.loggerMap.get(name);
    if (instance != null) {
        return instance;
    } else {
        Logger slf4jLogger = LoggerFactory.getLogger(name);
        Object newInstance;
        if (slf4jLogger instanceof LocationAwareLogger) {
            newInstance = new SLF4JLocationAwareLog((LocationAwareLogger)slf4jLogger);
        } else {
            newInstance = new SLF4JLog(slf4jLogger);
        }

        Log oldInstance = (Log)this.loggerMap.putIfAbsent(name, newInstance);
        return (Log)(oldInstance == null ? newInstance : oldInstance);
    }
}
+

然后再往下处理就是 LoggerFactory.getLogger(name); 了，跟普通的获取 logger 一样

+
public static Logger getLogger(String name) {
    ILoggerFactory iLoggerFactory = getILoggerFactory();
    return iLoggerFactory.getLogger(name);
}
+

继续下去会先判断是否已初始化

+
public static ILoggerFactory getILoggerFactory() {
        if (INITIALIZATION_STATE == UNINITIALIZED) {
            synchronized (LoggerFactory.class) {
                if (INITIALIZATION_STATE == UNINITIALIZED) {
                    INITIALIZATION_STATE = ONGOING_INITIALIZATION;
                    performInitialization();
                }
            }
        }
        switch (INITIALIZATION_STATE) {
        case SUCCESSFUL_INITIALIZATION:
            return StaticLoggerBinder.getSingleton().getLoggerFactory();
        case NOP_FALLBACK_INITIALIZATION:
            return NOP_FALLBACK_FACTORY;
        case FAILED_INITIALIZATION:
            throw new IllegalStateException(UNSUCCESSFUL_INIT_MSG);
        case ONGOING_INITIALIZATION:
            // support re-entrant behavior.
            // See also http://jira.qos.ch/browse/SLF4J-97
            return SUBST_FACTORY;
        }
        throw new IllegalStateException("Unreachable code");
    }
+

第一次调用的话就会走到 org.slf4j.LoggerFactory#performInitialization 里面是先调用绑定，这也是 slf4j 门面模式的特点

+
private final static void performInitialization() {
    bind();
    if (INITIALIZATION_STATE == SUCCESSFUL_INITIALIZATION) {
        versionSanityCheck();
    }
}
+ + +
private final static void bind() {
        try {
            Set<URL> staticLoggerBinderPathSet = null;
            // skip check under android, see also
            // http://jira.qos.ch/browse/SLF4J-328
            if (!isAndroid()) {
                staticLoggerBinderPathSet = findPossibleStaticLoggerBinderPathSet();
                reportMultipleBindingAmbiguity(staticLoggerBinderPathSet);
            }
            // the next line does the binding
            StaticLoggerBinder.getSingleton();
            INITIALIZATION_STATE = SUCCESSFUL_INITIALIZATION;
            reportActualBinding(staticLoggerBinderPathSet);
            fixSubstituteLoggers();
            replayEvents();
            // release all resources in SUBST_FACTORY
            SUBST_FACTORY.clear();
        } catch (NoClassDefFoundError ncde) {
            String msg = ncde.getMessage();
            if (messageContainsOrgSlf4jImplStaticLoggerBinder(msg)) {
                INITIALIZATION_STATE = NOP_FALLBACK_INITIALIZATION;
                Util.report("Failed to load class \"org.slf4j.impl.StaticLoggerBinder\".");
                Util.report("Defaulting to no-operation (NOP) logger implementation");
                Util.report("See " + NO_STATICLOGGERBINDER_URL + " for further details.");
            } else {
                failedBinding(ncde);
                throw ncde;
            }
        } catch (java.lang.NoSuchMethodError nsme) {
            String msg = nsme.getMessage();
            if (msg != null && msg.contains("org.slf4j.impl.StaticLoggerBinder.getSingleton()")) {
                INITIALIZATION_STATE = FAILED_INITIALIZATION;
                Util.report("slf4j-api 1.6.x (or later) is incompatible with this binding.");
                Util.report("Your binding is version 1.5.5 or earlier.");
                Util.report("Upgrade your binding to version 1.6.x.");
            }
            throw nsme;
        } catch (Exception e) {
            failedBinding(e);
            throw new IllegalStateException("Unexpected initialization failure", e);
        }
    }
+

非安卓环境会进入第一个 if 逻辑，先要去找static_logger_binder

+
static Set<URL> findPossibleStaticLoggerBinderPathSet() {
        // use Set instead of list in order to deal with bug #138
        // LinkedHashSet appropriate here because it preserves insertion order
        // during iteration
        Set<URL> staticLoggerBinderPathSet = new LinkedHashSet<URL>();
        try {
            ClassLoader loggerFactoryClassLoader = LoggerFactory.class.getClassLoader();
            Enumeration<URL> paths;
            if (loggerFactoryClassLoader == null) {
                paths = ClassLoader.getSystemResources(STATIC_LOGGER_BINDER_PATH);
            } else {
                paths = loggerFactoryClassLoader.getResources(STATIC_LOGGER_BINDER_PATH);
            }
            while (paths.hasMoreElements()) {
                URL path = paths.nextElement();
                staticLoggerBinderPathSet.add(path);
            }
        } catch (IOException ioe) {
            Util.report("Error getting resources from path", ioe);
        }
        return staticLoggerBinderPathSet;
    }
+

上面的 STATIC_LOGGER_BINDER_PATH
就是 org/slf4j/impl/StaticLoggerBinder.class 如果找不到在外层方法就会抛出 NoSuchMethodError 错误，然后就是通过 StaticLoggerBinder.getSingleton(); 获取 StaticLoggerBinder 实例了

+
public static StaticLoggerBinder getSingleton() {
    return SINGLETON;
}
private static StaticLoggerBinder SINGLETON = new StaticLoggerBinder();
+

比较重要的是这里的 static 代码块还有一个逻辑，就是下面这个初始化逻辑

+
static {
    SINGLETON.init();
}
void init() {
    try {
        try {
            new ContextInitializer(defaultLoggerContext).autoConfig();
        } catch (JoranException je) {
            Util.report("Failed to auto configure default logger context", je);
        }
        // logback-292
        if (!StatusUtil.contextHasStatusListener(defaultLoggerContext)) {
            StatusPrinter.printInCaseOfErrorsOrWarnings(defaultLoggerContext);
        }
        contextSelectorBinder.init(defaultLoggerContext, KEY);
        initialized = true;
    } catch (Exception t) { // see LOGBACK-1159
        Util.report("Failed to instantiate [" + LoggerContext.class.getName() + "]", t);
    }
}
+

上面的 defaultLoggerContext 也是通过静态代码块处理的

+
private LoggerContext defaultLoggerContext = new LoggerContext();
+

然后是调用 ch.qos.logback.classic.util.ContextInitializer#autoConfig 方法

+
public void autoConfig() throws JoranException {
        StatusListenerConfigHelper.installIfAsked(loggerContext);
        URL url = findURLOfDefaultConfigurationFile(true);
        if (url != null) {
            configureByResource(url);
        } else {
            Configurator c = EnvUtil.loadFromServiceLoader(Configurator.class);
            if (c != null) {
                try {
                    c.setContext(loggerContext);
                    c.configure(loggerContext);
                } catch (Exception e) {
                    throw new LogbackException(String.format("Failed to initialize Configurator: %s using ServiceLoader", c != null ? c.getClass()
                                    .getCanonicalName() : "null"), e);
                }
            } else {
                BasicConfigurator basicConfigurator = new BasicConfigurator();
                basicConfigurator.setContext(loggerContext);
                basicConfigurator.configure(loggerContext);
            }
        }
    }
+

前面会先查找配置文件路径
URL url = findURLOfDefaultConfigurationFile(true); 还是调用这个类内的 ch.qos.logback.classic.util.ContextInitializer#findURLOfDefaultConfigurationFile 方法

+
public URL findURLOfDefaultConfigurationFile(boolean updateStatus) {
    ClassLoader myClassLoader = Loader.getClassLoaderOfObject(this);
    URL url = findConfigFileURLFromSystemProperties(myClassLoader, updateStatus);
    if (url != null) {
        return url;
    }

    url = getResource(TEST_AUTOCONFIG_FILE, myClassLoader, updateStatus);
    if (url != null) {
        return url;
    }

    url = getResource(GROOVY_AUTOCONFIG_FILE, myClassLoader, updateStatus);
    if (url != null) {
        return url;
    }

    return getResource(AUTOCONFIG_FILE, myClassLoader, updateStatus);
}
+

才发现原来这里也是硬编码了文件名

+
public class ContextInitializer {

    final public static String GROOVY_AUTOCONFIG_FILE = "logback.groovy";
    final public static String AUTOCONFIG_FILE = "logback.xml";
    final public static String TEST_AUTOCONFIG_FILE = "logback-test.xml";
    final public static String CONFIG_FILE_PROPERTY = "logback.configurationFile";

+

如果不为空就调用了
ch.qos.logback.classic.util.ContextInitializer#configureByResource 这边是根据后缀判断处理方法

+
public void configureByResource(URL url) throws JoranException {
        if (url == null) {
            throw new IllegalArgumentException("URL argument cannot be null");
        }
        final String urlString = url.toString();
        if (urlString.endsWith("groovy")) {
            if (EnvUtil.isGroovyAvailable()) {
                // avoid directly referring to GafferConfigurator so as to avoid
                // loading groovy.lang.GroovyObject . See also http://jira.qos.ch/browse/LBCLASSIC-214
                GafferUtil.runGafferConfiguratorOn(loggerContext, this, url);
            } else {
                StatusManager sm = loggerContext.getStatusManager();
                sm.add(new ErrorStatus("Groovy classes are not available on the class path. ABORTING INITIALIZATION.", loggerContext));
            }
        } else if (urlString.endsWith("xml")) {
            JoranConfigurator configurator = new JoranConfigurator();
            configurator.setContext(loggerContext);
            configurator.doConfigure(url);
        } else {
            throw new LogbackException("Unexpected filename extension of file [" + url.toString() + "]. Should be either .groovy or .xml");
        }
    }
+

因为我们找到的是 logback.xml，所以走的是
ch.qos.logback.classic.joran.JoranConfigurator ，
然后调用了
ch.qos.logback.core.joran.GenericConfigurator#doConfigure(java.net.URL)
这个 GenericConfigurator 是 JoranConfigurator 的父类

+
public final void doConfigure(URL url) throws JoranException {
        InputStream in = null;
        try {
            informContextOfURLUsedForConfiguration(getContext(), url);
            URLConnection urlConnection = url.openConnection();
            // per http://jira.qos.ch/browse/LBCORE-105
            // per http://jira.qos.ch/browse/LBCORE-127
            urlConnection.setUseCaches(false);

            in = urlConnection.getInputStream();
            doConfigure(in, url.toExternalForm());
        } catch (IOException ioe) {
            String errMsg = "Could not open URL [" + url + "].";
            addError(errMsg, ioe);
            throw new JoranException(errMsg, ioe);
        } finally {
            if (in != null) {
                try {
                    in.close();
                } catch (IOException ioe) {
                    String errMsg = "Could not close input stream";
                    addError(errMsg, ioe);
                    throw new JoranException(errMsg, ioe);
                }
            }
        }
    }
+

然后继续调用
ch.qos.logback.core.joran.GenericConfigurator#doConfigure(java.io.InputStream, java.lang.String) 处理具体的内容，前面处理了内容获取，因为如果是走的 url 文件就需要从网络获取文件流，

+
public final void doConfigure(InputStream inputStream, String systemId) throws JoranException {
    InputSource inputSource = new InputSource(inputStream);
    inputSource.setSystemId(systemId);
    doConfigure(inputSource);
}
+

而实际的处理 xml 内容则是在
ch.qos.logback.core.joran.GenericConfigurator#doConfigure(org.xml.sax.InputSource)

+
public final void doConfigure(final InputSource inputSource) throws JoranException {

    long threshold = System.currentTimeMillis();
    // if (!ConfigurationWatchListUtil.wasConfigurationWatchListReset(context)) {
    // informContextOfURLUsedForConfiguration(getContext(), null);
    // }
    SaxEventRecorder recorder = new SaxEventRecorder(context);
    recorder.recordEvents(inputSource);
    doConfigure(recorder.saxEventList);
    // no exceptions a this level
    StatusUtil statusUtil = new StatusUtil(context);
    if (statusUtil.noXMLParsingErrorsOccurred(threshold)) {
        addInfo("Registering current configuration as safe fallback point");
        registerSafeConfiguration(recorder.saxEventList);
    }
}
+

需要解析 xml 了，先是调用 buildInterpreter 构建了 Interpreter

+
public void doConfigure(final List<SaxEvent> eventList) throws JoranException {
    buildInterpreter();
    // disallow simultaneous configurations of the same context
    synchronized (context.getConfigurationLock()) {
        interpreter.getEventPlayer().play(eventList);
    }
}
+

build 里还有一些逻辑，就是匹配后面要处理的规则集，就是要确认那些标签要处理

+
protected void buildInterpreter() {
        RuleStore rs = new SimpleRuleStore(context);
        addInstanceRules(rs);
        this.interpreter = new Interpreter(context, rs, initialElementPath());
        InterpretationContext interpretationContext = interpreter.getInterpretationContext();
        interpretationContext.setContext(context);
        addImplicitRules(interpreter);
        addDefaultNestedComponentRegistryRules(interpretationContext.getDefaultNestedComponentRegistry());
    }
+

第一步就是先 new 一个 ch.qos.logback.core.joran.spi.SimpleRuleStore 然后调用了 ch.qos.logback.classic.joran.JoranConfigurator#addInstanceRules 来添加规则，就是下面的，可以看到对于 logger,appender 这些的配置，其中前面点的父类也又一些规则 ,ch.qos.logback.core.joran.JoranConfiguratorBase 逻辑类似就不展开了，这里的规则就对应了后面的各种 Action，就是对应的处理逻辑

+
public void addInstanceRules(RuleStore rs) {
    // parent rules already added
    super.addInstanceRules(rs);

    rs.addRule(new ElementSelector("configuration"), new ConfigurationAction());

    rs.addRule(new ElementSelector("configuration/contextName"), new ContextNameAction());
    rs.addRule(new ElementSelector("configuration/contextListener"), new LoggerContextListenerAction());
    rs.addRule(new ElementSelector("configuration/insertFromJNDI"), new InsertFromJNDIAction());
    rs.addRule(new ElementSelector("configuration/evaluator"), new EvaluatorAction());

    rs.addRule(new ElementSelector("configuration/appender/sift"), new SiftAction());
    rs.addRule(new ElementSelector("configuration/appender/sift/*"), new NOPAction());

    rs.addRule(new ElementSelector("configuration/logger"), new LoggerAction());
    rs.addRule(new ElementSelector("configuration/logger/level"), new LevelAction());

    rs.addRule(new ElementSelector("configuration/root"), new RootLoggerAction());
    rs.addRule(new ElementSelector("configuration/root/level"), new LevelAction());
    rs.addRule(new ElementSelector("configuration/logger/appender-ref"), new AppenderRefAction<ILoggingEvent>());
    rs.addRule(new ElementSelector("configuration/root/appender-ref"), new AppenderRefAction<ILoggingEvent>());

    // add if-then-else support
    rs.addRule(new ElementSelector("*/if"), new IfAction());
    rs.addRule(new ElementSelector("*/if/then"), new ThenAction());
    rs.addRule(new ElementSelector("*/if/then/*"), new NOPAction());
    rs.addRule(new ElementSelector("*/if/else"), new ElseAction());
    rs.addRule(new ElementSelector("*/if/else/*"), new NOPAction());

    // add jmxConfigurator only if we have JMX available.
    // If running under JDK 1.4 (retrotranslateed logback) then we
    // might not have JMX.
    if (PlatformInfo.hasJMXObjectName()) {
        rs.addRule(new ElementSelector("configuration/jmxConfigurator"), new JMXConfiguratorAction());
    }
    rs.addRule(new ElementSelector("configuration/include"), new IncludeAction());

    rs.addRule(new ElementSelector("configuration/consolePlugin"), new ConsolePluginAction());

    rs.addRule(new ElementSelector("configuration/receiver"), new ReceiverAction());

}
+

然后调用了 interpreter 的 eventPlayer 处理 xml 的各个标签

+
public void play(List<SaxEvent> aSaxEventList) {
        eventList = aSaxEventList;
        SaxEvent se;
        for (currentIndex = 0; currentIndex < eventList.size(); currentIndex++) {
            se = eventList.get(currentIndex);

            if (se instanceof StartEvent) {
                interpreter.startElement((StartEvent) se);
                // invoke fireInPlay after startElement processing
                interpreter.getInterpretationContext().fireInPlay(se);
            }
            if (se instanceof BodyEvent) {
                // invoke fireInPlay before characters processing
                interpreter.getInterpretationContext().fireInPlay(se);
                interpreter.characters((BodyEvent) se);
            }
            if (se instanceof EndEvent) {
                // invoke fireInPlay before endElement processing
                interpreter.getInterpretationContext().fireInPlay(se);
                interpreter.endElement((EndEvent) se);
            }

        }
    }
+

接下去就在 startElement 中调用会先获取 getApplicableActionList 适配的 Action， callBeginAction

+
private void startElement(String namespaceURI, String localName, String qName, Attributes atts) {

        String tagName = getTagName(localName, qName);
        elementPath.push(tagName);

        if (skip != null) {
            // every startElement pushes an action list
            pushEmptyActionList();
            return;
        }

        List<Action> applicableActionList = getApplicableActionList(elementPath, atts);
        if (applicableActionList != null) {
            actionListStack.add(applicableActionList);
            callBeginAction(applicableActionList, tagName, atts);
        } else {
            // every startElement pushes an action list
            pushEmptyActionList();
            String errMsg = "no applicable action for [" + tagName + "], current ElementPath  is [" + elementPath + "]";
            cai.addError(errMsg);
        }
    }
+

里面就是用了 ruleStore 来判断是否适配，并且取出对应的 Action

+
List<Action> getApplicableActionList(ElementPath elementPath, Attributes attributes) {
    List<Action> applicableActionList = ruleStore.matchActions(elementPath);

    // logger.debug("set of applicable patterns: " + applicableActionList);
    if (applicableActionList == null) {
        applicableActionList = lookupImplicitAction(elementPath, attributes, interpretationContext);
    }

    return applicableActionList;
}
+

比如我这边往下处理，通过规则匹配拿到了 LoggerAction, 然后调用 Action.begin 方法

+
void callBeginAction(List<Action> applicableActionList, String tagName, Attributes atts) {
        if (applicableActionList == null) {
            return;
        }

        Iterator<Action> i = applicableActionList.iterator();
        while (i.hasNext()) {
            Action action = (Action) i.next();
            // now let us invoke the action. We catch and report any eventual
            // exceptions
            try {
                action.begin(interpretationContext, tagName, atts);
            } catch (ActionException e) {
                skip = elementPath.duplicate();
                cai.addError("ActionException in Action for tag [" + tagName + "]", e);
            } catch (RuntimeException e) {
                skip = elementPath.duplicate();
                cai.addError("RuntimeException in Action for tag [" + tagName + "]", e);
            }
        }
    }
+

就是下面这个

+
public void begin(InterpretationContext ec, String name, Attributes attributes) {
        // Let us forget about previous errors (in this object)
        inError = false;
        logger = null;

        LoggerContext loggerContext = (LoggerContext) this.context;

        String loggerName = ec.subst(attributes.getValue(NAME_ATTRIBUTE));

        if (OptionHelper.isEmpty(loggerName)) {
            inError = true;
            String aroundLine = getLineColStr(ec);
            String errorMsg = "No 'name' attribute in element " + name + ", around " + aroundLine;
            addError(errorMsg);
            return;
        }

        logger = loggerContext.getLogger(loggerName);

        String levelStr = ec.subst(attributes.getValue(LEVEL_ATTRIBUTE));

        if (!OptionHelper.isEmpty(levelStr)) {
            if (ActionConst.INHERITED.equalsIgnoreCase(levelStr) || ActionConst.NULL.equalsIgnoreCase(levelStr)) {
                addInfo("Setting level of logger [" + loggerName + "] to null, i.e. INHERITED");
                logger.setLevel(null);
            } else {
                Level level = Level.toLevel(levelStr);
                addInfo("Setting level of logger [" + loggerName + "] to " + level);
                logger.setLevel(level);
            }
        }

        String additivityStr = ec.subst(attributes.getValue(ActionConst.ADDITIVITY_ATTRIBUTE));
        if (!OptionHelper.isEmpty(additivityStr)) {
            boolean additive = OptionHelper.toBoolean(additivityStr, true);
            addInfo("Setting additivity of logger [" + loggerName + "] to " + additive);
            logger.setAdditive(additive);
        }
        ec.pushObject(logger);
    }
+

这里就会处理 logger 获取逻辑

+
public final Logger getLogger(final String name) {

        if (name == null) {
            throw new IllegalArgumentException("name argument cannot be null");
        }

        // if we are asking for the root logger, then let us return it without
        // wasting time
        if (Logger.ROOT_LOGGER_NAME.equalsIgnoreCase(name)) {
            return root;
        }

        int i = 0;
        Logger logger = root;

        // check if the desired logger exists, if it does, return it
        // without further ado.
        Logger childLogger = (Logger) loggerCache.get(name);
        // if we have the child, then let us return it without wasting time
        if (childLogger != null) {
            return childLogger;
        }

        // if the desired logger does not exist, them create all the loggers
        // in between as well (if they don't already exist)
        String childName;
        while (true) {
            int h = LoggerNameUtil.getSeparatorIndexOf(name, i);
            if (h == -1) {
                childName = name;
            } else {
                childName = name.substring(0, h);
            }
            // move i left of the last point
            i = h + 1;
            synchronized (logger) {
                childLogger = logger.getChildByName(childName);
                if (childLogger == null) {
                    childLogger = logger.createChildByName(childName);
                    loggerCache.put(childName, childLogger);
                    incSize();
                }
            }
            logger = childLogger;
            if (h == -1) {
                return childLogger;
            }
        }
    }
+

如果从 loggerCache 中能取到就直接获取，如果不能就会去创建

+]]> + + Java + + + Java + + + + 尝试下图片向量化 + /2024/07/28/%E5%B0%9D%E8%AF%95%E4%B8%8B%E5%9B%BE%E7%89%87%E5%90%91%E9%87%8F%E5%8C%96/ + 之前觉得谷歌的以图搜图很厉害，现在似乎这个路径还毕竟清晰了，首先要有图片库，把它们向量化以后存储起来，然后对于目标图片也做向量化，再做检索
那么我们先来做重要的这一步，图片的向量化，因为向量化以后就跟图片没关系了，直接用前面讲到的向量的近似搜索就可以做到以图搜图了
这边我们用到了towhee工具，towhee是个机器学习的pipeline工具，可以做数据源（文件，图片，媒体，文本）–> 模型 –> 向量。
首先我们安装towhee

+
pip install towhee towhee.models
+

这里有第一个坑，因为有torch依赖，但是目前torch支持的python版本是最高3.9，再往上可能就有问题了
所以我们要先创建一个3.9的环境

+
conda create -n py9 python=3.9
+

然后我们会遇到第二个坑
就是milvus的客户端

+
pip install pymilvus==2.3.0
+

再早的客户端会有依赖不支持，更新的客户端会连接不上
然后再在milvus创建一个Collection

然后这个是不对的，因为我使用attu界面创建的，默认第二个字段一定得是向量字段，所以又用了上次的Java代码来创建

+
CreateCollectionReq.CollectionSchema collectionSchema = clientV2.createSchema();
// add two fileds, id and vector
        Integer dim = 2048;
        collectionSchema.addField(AddFieldReq.builder().fieldName("url").dataType(DataType.VarChar).build());
        collectionSchema.addField(AddFieldReq.builder().fieldName("embedding").dataType(DataType.FloatVector).dimension(dim).build());
        collectionSchema.addField(AddFieldReq.builder().fieldName("id").dataType(DataType.Int64).isPrimaryKey(Boolean.TRUE).autoID(Boolean.TRUE).description("id").build());

        CreateCollectionReq req = CreateCollectionReq
                .builder()
                .collectionSchema(collectionSchema)
                .collectionName("text_image_search")
                .dimension(dim).build();
        clientV2.createCollection(req);
+

并且注意字段顺序不能错，这个顺序在towhee的demo中也没地方直接修改
看一下towhee的demo
地址在这
https://towhee.io/tasks/detail/pipeline/text-image-search

+
from towhee import AutoPipes, AutoConfig

# set MilvusInsertConfig for the built-in insert_milvus pipeline
insert_conf = AutoConfig.load_config('insert_milvus')
insert_conf.collection_name = 'text_image_search'

insert_pipe = AutoPipes.pipeline('insert_milvus', insert_conf)

# generate embedding
embedding = image_embedding('./test1.png').get()[0]

# insert text and embedding into Milvus
insert_pipe(['./test1.png', embedding])
+

第三个坑，这并不能跑起来

+
 embedding = insert_pipe.image_embedding('./test.jpg').get()[0]
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'RuntimePipeline' object has no attribute 'image_embedding'
+

因为找不到这个 image_embedding
估计是这个也是包路径变更了，但是并没有什么文档可以找到，之前LlamaIndex还有点更新指南，后来是问了Claude才知道了

+
from towhee import AutoPipes, AutoConfig, ops


# set MilvusInsertConfig for the built-in insert_milvus pipeline
insert_conf = AutoConfig.load_config('insert_milvus')
insert_conf.collection_name = 'text_image_search'


insert_pipe = AutoPipes.pipeline('insert_milvus', insert_conf)

# 创建图像嵌入管道
image_embedding_pipe = AutoPipes.pipeline('image-embedding')

# 生成嵌入
embedding = image_embedding_pipe('./test.jpg').get()[0]


# insert text and embedding into Milvus
insert_pipe(['./test.jpg', embedding])
+

需要从 AutoPipes 中把这个 image-embedding 找出来
然后就是上面说到的字段映射问题，默认是先url，再embedding字段，并且主键字段必须是autoId，不然也会缺少默认值，还是感叹下，工程化的东西还是要工程化的质量保证，否则变更都无从知晓，现在人工智能大热，大家都在追风，只是基础的软件还是要稳扎稳打，这样我们工程人员才能把它们更好的用起来，PS：娃真可爱，但真的好累

]]> 算法 @@ -5563,15 +5712,57 @@ user3: - 聊一下 Linux 中 dmesg 跟 journal 的差别 - /2024/07/14/%E8%81%8A%E4%B8%80%E4%B8%8B-Linux-%E4%B8%AD-dmesg-%E8%B7%9F-journal-%E7%9A%84%E5%B7%AE%E5%88%AB/ - 最近在迁移一个自己用的mysql实例，发现用 portainer 安装 mysql 一直失败，还以为是配置了自定义端口映射被系统防火墙限制，但后面不映射端口也是不行，一开始查看 journal日志也没什么发现，因为之前在腾讯云的小机器也是正常的部署，所以没觉得是可能会oom，一时间就有点没头绪，结果也是好奇就看了下dmesg，发现里面都是oom，给了1g的内存的ubuntu虚拟机，安装了一两个docker就不行了，不过这里比较重要的就是记录下dmesg 跟 journal日志的这点区别
dmesg 打印了内核的日志缓冲，所以这个跟journal差别的第一点就是一个是个会被刷掉的缓冲区，另一个是记在文件，但更重要的是dmesg是打印的内核日志，而journal是会通过systemd收集日志，journal可以收集内核日志，但是可以进行配置，并且可能会晚于dmesg

而对于这个oom，如果是系统的oom killer，那的确是有可能dmesg会能查看到，而journal看不到
简单记录下，PS：奶娃是有点累，一天都没啥时间了

+ 聊一下 RocketMQ 的 DefaultMQPushConsumer 源码 + /2020/06/26/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84-Consumer/ + 首先看下官方的小 demo

+
public static void main(String[] args) throws InterruptedException, MQClientException {

        /*
         * Instantiate with specified consumer group name.
         * 首先是new 一个对象出来，然后指定 Consumer 的 Group
         * 同一类Consumer的集合，这类Consumer通常消费同一类消息且消费逻辑一致。消费者组使得在消息消费方面，实现负载均衡和容错的目标变得非常容易。要注意的是，消费者组的消费者实例必须订阅完全相同的Topic。RocketMQ 支持两种消息模式：集群消费（Clustering）和广播消费（Broadcasting）。
         */
        DefaultMQPushConsumer consumer = new DefaultMQPushConsumer("please_rename_unique_group_name_4");

        /*
         * Specify name server addresses.
         * <p/>
         * 这里可以通知指定环境变量或者设置对象参数的形式指定名字空间服务的地址
         *
         * Alternatively, you may specify name server addresses via exporting environmental variable: NAMESRV_ADDR
         * <pre>
         * {@code
         * consumer.setNamesrvAddr("name-server1-ip:9876;name-server2-ip:9876");
         * }
         * </pre>
         */

        /*
         * Specify where to start in case the specified consumer group is a brand new one.
         * 指定消费起始点
         */
        consumer.setConsumeFromWhere(ConsumeFromWhere.CONSUME_FROM_FIRST_OFFSET);

        /*
         * Subscribe one more more topics to consume.
         * 指定订阅的 topic 跟 tag，注意后面的是个表达式，可以以 tag1 || tag2 || tag3 传入
         */
        consumer.subscribe("TopicTest", "*");

        /*
         *  Register callback to execute on arrival of messages fetched from brokers.
         *  注册具体获得消息后的处理方法
         */
        consumer.registerMessageListener(new MessageListenerConcurrently() {

            @Override
            public ConsumeConcurrentlyStatus consumeMessage(List<MessageExt> msgs,
                ConsumeConcurrentlyContext context) {
                System.out.printf("%s Receive New Messages: %s %n", Thread.currentThread().getName(), msgs);
                return ConsumeConcurrentlyStatus.CONSUME_SUCCESS;
            }
        });

        /*
         *  Launch the consumer instance.
         * 启动消费者
         */
        consumer.start();

        System.out.printf("Consumer Started.%n");
    }
+ +

然后就是看看 start 的过程了

+
/**
     * This method gets internal infrastructure readily to serve. Instances must call this method after configuration.
     *
     * @throws MQClientException if there is any client error.
     */
    @Override
    public void start() throws MQClientException {
        setConsumerGroup(NamespaceUtil.wrapNamespace(this.getNamespace(), this.consumerGroup));
        this.defaultMQPushConsumerImpl.start();
        if (null != traceDispatcher) {
            try {
                traceDispatcher.start(this.getNamesrvAddr(), this.getAccessChannel());
            } catch (MQClientException e) {
                log.warn("trace dispatcher start failed ", e);
            }
        }
    }
+

具体的逻辑在this.defaultMQPushConsumerImpl.start()，这个 defaultMQPushConsumerImpl 就是

+
/**
     * Internal implementation. Most of the functions herein are delegated to it.
     */
    protected final transient DefaultMQPushConsumerImpl defaultMQPushConsumerImpl;
+ +
public synchronized void start() throws MQClientException {
        switch (this.serviceState) {
            case CREATE_JUST:
                log.info("the consumer [{}] start beginning. messageModel={}, isUnitMode={}", this.defaultMQPushConsumer.getConsumerGroup(),
                    this.defaultMQPushConsumer.getMessageModel(), this.defaultMQPushConsumer.isUnitMode());
                // 这里比较巧妙，相当于想设立了个屏障，防止并发启动，不过这里并不是悲观锁，也不算个严格的乐观锁
                this.serviceState = ServiceState.START_FAILED;

                this.checkConfig();

                this.copySubscription();

                if (this.defaultMQPushConsumer.getMessageModel() == MessageModel.CLUSTERING) {
                    this.defaultMQPushConsumer.changeInstanceNameToPID();
                }

                // 这个mQClientFactory，负责管理client（consumer、producer），并提供多中功能接口供各个Service（Rebalance、PullMessage等）调用；大部分逻辑均在这个类中完成
                this.mQClientFactory = MQClientManager.getInstance().getOrCreateMQClientInstance(this.defaultMQPushConsumer, this.rpcHook);

                // 这个 rebalanceImpl 主要负责决定，当前的consumer应该从哪些Queue中消费消息；
                this.rebalanceImpl.setConsumerGroup(this.defaultMQPushConsumer.getConsumerGroup());
                this.rebalanceImpl.setMessageModel(this.defaultMQPushConsumer.getMessageModel());
                this.rebalanceImpl.setAllocateMessageQueueStrategy(this.defaultMQPushConsumer.getAllocateMessageQueueStrategy());
                this.rebalanceImpl.setmQClientFactory(this.mQClientFactory);

                // 长连接，负责从broker处拉取消息，然后利用ConsumeMessageService回调用户的Listener执行消息消费逻辑
                this.pullAPIWrapper = new PullAPIWrapper(
                    mQClientFactory,
                    this.defaultMQPushConsumer.getConsumerGroup(), isUnitMode());
                this.pullAPIWrapper.registerFilterMessageHook(filterMessageHookList);

                if (this.defaultMQPushConsumer.getOffsetStore() != null) {
                    this.offsetStore = this.defaultMQPushConsumer.getOffsetStore();
                } else {
                    switch (this.defaultMQPushConsumer.getMessageModel()) {
                        case BROADCASTING:
                            this.offsetStore = new LocalFileOffsetStore(this.mQClientFactory, this.defaultMQPushConsumer.getConsumerGroup());
                            break;
                        case CLUSTERING:
                            this.offsetStore = new RemoteBrokerOffsetStore(this.mQClientFactory, this.defaultMQPushConsumer.getConsumerGroup());
                            break;
                        default:
                            break;
                    }
                    this.defaultMQPushConsumer.setOffsetStore(this.offsetStore);
                }
                // offsetStore 维护当前consumer的消费记录（offset）；有两种实现，Local和Rmote，Local存储在本地磁盘上，适用于BROADCASTING广播消费模式；而Remote则将消费进度存储在Broker上，适用于CLUSTERING集群消费模式；
                this.offsetStore.load();

                if (this.getMessageListenerInner() instanceof MessageListenerOrderly) {
                    this.consumeOrderly = true;
                    this.consumeMessageService =
                        new ConsumeMessageOrderlyService(this, (MessageListenerOrderly) this.getMessageListenerInner());
                } else if (this.getMessageListenerInner() instanceof MessageListenerConcurrently) {
                    this.consumeOrderly = false;
                    this.consumeMessageService =
                        new ConsumeMessageConcurrentlyService(this, (MessageListenerConcurrently) this.getMessageListenerInner());
                }

                // 实现所谓的"Push-被动"消费机制；从Broker拉取的消息后，封装成ConsumeRequest提交给ConsumeMessageSerivce，此service负责回调用户的Listener消费消息；
                this.consumeMessageService.start();

                boolean registerOK = mQClientFactory.registerConsumer(this.defaultMQPushConsumer.getConsumerGroup(), this);
                if (!registerOK) {
                    this.serviceState = ServiceState.CREATE_JUST;
                    this.consumeMessageService.shutdown();
                    throw new MQClientException("The consumer group[" + this.defaultMQPushConsumer.getConsumerGroup()
                        + "] has been created before, specify another name please." + FAQUrl.suggestTodo(FAQUrl.GROUP_NAME_DUPLICATE_URL),
                        null);
                }

                mQClientFactory.start();
                log.info("the consumer [{}] start OK.", this.defaultMQPushConsumer.getConsumerGroup());
                this.serviceState = ServiceState.RUNNING;
                break;
            case RUNNING:
            case START_FAILED:
            case SHUTDOWN_ALREADY:
                throw new MQClientException("The PushConsumer service state not OK, maybe started once, "
                    + this.serviceState
                    + FAQUrl.suggestTodo(FAQUrl.CLIENT_SERVICE_NOT_OK),
                    null);
            default:
                break;
        }

        this.updateTopicSubscribeInfoWhenSubscriptionChanged();
        this.mQClientFactory.checkClientInBroker();
        this.mQClientFactory.sendHeartbeatToAllBrokerWithLock();
        this.mQClientFactory.rebalanceImmediately();
    }
+

然后我们往下看主要的目光聚焦mQClientFactory.start()

+
public void start() throws MQClientException {

        synchronized (this) {
            switch (this.serviceState) {
                case CREATE_JUST:
                    this.serviceState = ServiceState.START_FAILED;
                    // If not specified,looking address from name server
                    if (null == this.clientConfig.getNamesrvAddr()) {
                        this.mQClientAPIImpl.fetchNameServerAddr();
                    }
                    // Start request-response channel
                    // 这里主要是初始化了个网络客户端
                    this.mQClientAPIImpl.start();
                    // Start various schedule tasks
                    // 定时任务
                    this.startScheduledTask();
                    // Start pull service
                    // 这里重点说下
                    this.pullMessageService.start();
                    // Start rebalance service
                    this.rebalanceService.start();
                    // Start push service
                    this.defaultMQProducer.getDefaultMQProducerImpl().start(false);
                    log.info("the client factory [{}] start OK", this.clientId);
                    this.serviceState = ServiceState.RUNNING;
                    break;
                case START_FAILED:
                    throw new MQClientException("The Factory object[" + this.getClientId() + "] has been created before, and failed.", null);
                default:
                    break;
            }
        }
    }
+

我们来看下这个 pullMessageService，org.apache.rocketmq.client.impl.consumer.PullMessageService，

实现了 runnable 接口，
然后可以看到 run 方法

+
public void run() {
        log.info(this.getServiceName() + " service started");

        while (!this.isStopped()) {
            try {
                PullRequest pullRequest = this.pullRequestQueue.take();
                this.pullMessage(pullRequest);
            } catch (InterruptedException ignored) {
            } catch (Exception e) {
                log.error("Pull Message Service Run Method exception", e);
            }
        }

        log.info(this.getServiceName() + " service end");
    }
+

接着在看 pullMessage 方法

+
private void pullMessage(final PullRequest pullRequest) {
        final MQConsumerInner consumer = this.mQClientFactory.selectConsumer(pullRequest.getConsumerGroup());
        if (consumer != null) {
            DefaultMQPushConsumerImpl impl = (DefaultMQPushConsumerImpl) consumer;
            impl.pullMessage(pullRequest);
        } else {
            log.warn("No matched consumer for the PullRequest {}, drop it", pullRequest);
        }
    }
+

实际上调用了这个方法，这个方法很长，我在代码里注释下下每一段的功能

+
public void pullMessage(final PullRequest pullRequest) {
        final ProcessQueue processQueue = pullRequest.getProcessQueue();
        // 这里开始就是检查状态，确定是否往下执行
        if (processQueue.isDropped()) {
            log.info("the pull request[{}] is dropped.", pullRequest.toString());
            return;
        }

        pullRequest.getProcessQueue().setLastPullTimestamp(System.currentTimeMillis());

        try {
            this.makeSureStateOK();
        } catch (MQClientException e) {
            log.warn("pullMessage exception, consumer state not ok", e);
            this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
            return;
        }

        if (this.isPause()) {
            log.warn("consumer was paused, execute pull request later. instanceName={}, group={}", this.defaultMQPushConsumer.getInstanceName(), this.defaultMQPushConsumer.getConsumerGroup());
            this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_SUSPEND);
            return;
        }

        // 这块其实是个类似于限流的功能块，对消息数量和消息大小做限制
        long cachedMessageCount = processQueue.getMsgCount().get();
        long cachedMessageSizeInMiB = processQueue.getMsgSize().get() / (1024 * 1024);

        if (cachedMessageCount > this.defaultMQPushConsumer.getPullThresholdForQueue()) {
            this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_FLOW_CONTROL);
            if ((queueFlowControlTimes++ % 1000) == 0) {
                log.warn(
                    "the cached message count exceeds the threshold {}, so do flow control, minOffset={}, maxOffset={}, count={}, size={} MiB, pullRequest={}, flowControlTimes={}",
                    this.defaultMQPushConsumer.getPullThresholdForQueue(), processQueue.getMsgTreeMap().firstKey(), processQueue.getMsgTreeMap().lastKey(), cachedMessageCount, cachedMessageSizeInMiB, pullRequest, queueFlowControlTimes);
            }
            return;
        }

        if (cachedMessageSizeInMiB > this.defaultMQPushConsumer.getPullThresholdSizeForQueue()) {
            this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_FLOW_CONTROL);
            if ((queueFlowControlTimes++ % 1000) == 0) {
                log.warn(
                    "the cached message size exceeds the threshold {} MiB, so do flow control, minOffset={}, maxOffset={}, count={}, size={} MiB, pullRequest={}, flowControlTimes={}",
                    this.defaultMQPushConsumer.getPullThresholdSizeForQueue(), processQueue.getMsgTreeMap().firstKey(), processQueue.getMsgTreeMap().lastKey(), cachedMessageCount, cachedMessageSizeInMiB, pullRequest, queueFlowControlTimes);
            }
            return;
        }

        // 若不是顺序消费（即DefaultMQPushConsumerImpl.consumeOrderly等于false），则检查ProcessQueue对象的msgTreeMap:TreeMap<Long,MessageExt>变量的第一个key值与最后一个key值之间的差额，该key值表示查询的队列偏移量queueoffset；若差额大于阈值（由DefaultMQPushConsumer. consumeConcurrentlyMaxSpan指定，默认是2000），则调用PullMessageService.executePullRequestLater方法，在50毫秒之后重新将该PullRequest请求放入PullMessageService.pullRequestQueue队列中；并跳出该方法；这里的意思主要就是消息有堆积了，等会再来拉取
        if (!this.consumeOrderly) {
            if (processQueue.getMaxSpan() > this.defaultMQPushConsumer.getConsumeConcurrentlyMaxSpan()) {
                this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_FLOW_CONTROL);
                if ((queueMaxSpanFlowControlTimes++ % 1000) == 0) {
                    log.warn(
                        "the queue's messages, span too long, so do flow control, minOffset={}, maxOffset={}, maxSpan={}, pullRequest={}, flowControlTimes={}",
                        processQueue.getMsgTreeMap().firstKey(), processQueue.getMsgTreeMap().lastKey(), processQueue.getMaxSpan(),
                        pullRequest, queueMaxSpanFlowControlTimes);
                }
                return;
            }
        } else {
            if (processQueue.isLocked()) {
                if (!pullRequest.isLockedFirst()) {
                    final long offset = this.rebalanceImpl.computePullFromWhere(pullRequest.getMessageQueue());
                    boolean brokerBusy = offset < pullRequest.getNextOffset();
                    log.info("the first time to pull message, so fix offset from broker. pullRequest: {} NewOffset: {} brokerBusy: {}",
                        pullRequest, offset, brokerBusy);
                    if (brokerBusy) {
                        log.info("[NOTIFYME]the first time to pull message, but pull request offset larger than broker consume offset. pullRequest: {} NewOffset: {}",
                            pullRequest, offset);
                    }

                    pullRequest.setLockedFirst(true);
                    pullRequest.setNextOffset(offset);
                }
            } else {
                this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
                log.info("pull message later because not locked in broker, {}", pullRequest);
                return;
            }
        }

        // 以PullRequest.messageQueue对象的topic值为参数从RebalanceImpl.subscriptionInner: ConcurrentHashMap, SubscriptionData>中获取对应的SubscriptionData对象，若该对象为null，考虑到并发的关系，调用executePullRequestLater方法，稍后重试；并跳出该方法；
        final SubscriptionData subscriptionData = this.rebalanceImpl.getSubscriptionInner().get(pullRequest.getMessageQueue().getTopic());
        if (null == subscriptionData) {
            this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
            log.warn("find the consumer's subscription failed, {}", pullRequest);
            return;
        }

        final long beginTimestamp = System.currentTimeMillis();

        // 异步拉取回调，先不讨论细节
        PullCallback pullCallback = new PullCallback() {
            @Override
            public void onSuccess(PullResult pullResult) {
                if (pullResult != null) {
                    pullResult = DefaultMQPushConsumerImpl.this.pullAPIWrapper.processPullResult(pullRequest.getMessageQueue(), pullResult,
                        subscriptionData);

                    switch (pullResult.getPullStatus()) {
                        case FOUND:
                            long prevRequestOffset = pullRequest.getNextOffset();
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());
                            long pullRT = System.currentTimeMillis() - beginTimestamp;
                            DefaultMQPushConsumerImpl.this.getConsumerStatsManager().incPullRT(pullRequest.getConsumerGroup(),
                                pullRequest.getMessageQueue().getTopic(), pullRT);

                            long firstMsgOffset = Long.MAX_VALUE;
                            if (pullResult.getMsgFoundList() == null || pullResult.getMsgFoundList().isEmpty()) {
                                DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            } else {
                                firstMsgOffset = pullResult.getMsgFoundList().get(0).getQueueOffset();

                                DefaultMQPushConsumerImpl.this.getConsumerStatsManager().incPullTPS(pullRequest.getConsumerGroup(),
                                    pullRequest.getMessageQueue().getTopic(), pullResult.getMsgFoundList().size());

                                boolean dispatchToConsume = processQueue.putMessage(pullResult.getMsgFoundList());
                                DefaultMQPushConsumerImpl.this.consumeMessageService.submitConsumeRequest(
                                    pullResult.getMsgFoundList(),
                                    processQueue,
                                    pullRequest.getMessageQueue(),
                                    dispatchToConsume);

                                if (DefaultMQPushConsumerImpl.this.defaultMQPushConsumer.getPullInterval() > 0) {
                                    DefaultMQPushConsumerImpl.this.executePullRequestLater(pullRequest,
                                        DefaultMQPushConsumerImpl.this.defaultMQPushConsumer.getPullInterval());
                                } else {
                                    DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                                }
                            }

                            if (pullResult.getNextBeginOffset() < prevRequestOffset
                                || firstMsgOffset < prevRequestOffset) {
                                log.warn(
                                    "[BUG] pull message result maybe data wrong, nextBeginOffset: {} firstMsgOffset: {} prevRequestOffset: {}",
                                    pullResult.getNextBeginOffset(),
                                    firstMsgOffset,
                                    prevRequestOffset);
                            }

                            break;
                        case NO_NEW_MSG:
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());

                            DefaultMQPushConsumerImpl.this.correctTagsOffset(pullRequest);

                            DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            break;
                        case NO_MATCHED_MSG:
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());

                            DefaultMQPushConsumerImpl.this.correctTagsOffset(pullRequest);

                            DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            break;
                        case OFFSET_ILLEGAL:
                            log.warn("the pull request offset illegal, {} {}",
                                pullRequest.toString(), pullResult.toString());
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());

                            pullRequest.getProcessQueue().setDropped(true);
                            DefaultMQPushConsumerImpl.this.executeTaskLater(new Runnable() {

                                @Override
                                public void run() {
                                    try {
                                        DefaultMQPushConsumerImpl.this.offsetStore.updateOffset(pullRequest.getMessageQueue(),
                                            pullRequest.getNextOffset(), false);

                                        DefaultMQPushConsumerImpl.this.offsetStore.persist(pullRequest.getMessageQueue());

                                        DefaultMQPushConsumerImpl.this.rebalanceImpl.removeProcessQueue(pullRequest.getMessageQueue());

                                        log.warn("fix the pull request offset, {}", pullRequest);
                                    } catch (Throwable e) {
                                        log.error("executeTaskLater Exception", e);
                                    }
                                }
                            }, 10000);
                            break;
                        default:
                            break;
                    }
                }
            }

            @Override
            public void onException(Throwable e) {
                if (!pullRequest.getMessageQueue().getTopic().startsWith(MixAll.RETRY_GROUP_TOPIC_PREFIX)) {
                    log.warn("execute the pull request exception", e);
                }

                DefaultMQPushConsumerImpl.this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
            }
        };
        // 如果为集群模式，即可置commitOffsetEnable为 true
        boolean commitOffsetEnable = false;
        long commitOffsetValue = 0L;
        if (MessageModel.CLUSTERING == this.defaultMQPushConsumer.getMessageModel()) {
            commitOffsetValue = this.offsetStore.readOffset(pullRequest.getMessageQueue(), ReadOffsetType.READ_FROM_MEMORY);
            if (commitOffsetValue > 0) {
                commitOffsetEnable = true;
            }
        }

        // 将上面获得的commitOffsetEnable更新到订阅关系里
        String subExpression = null;
        boolean classFilter = false;
        SubscriptionData sd = this.rebalanceImpl.getSubscriptionInner().get(pullRequest.getMessageQueue().getTopic());
        if (sd != null) {
            if (this.defaultMQPushConsumer.isPostSubscriptionWhenPull() && !sd.isClassFilterMode()) {
                subExpression = sd.getSubString();
            }

            classFilter = sd.isClassFilterMode();
        }

        // 组成 sysFlag
        int sysFlag = PullSysFlag.buildSysFlag(
            commitOffsetEnable, // commitOffset
            true, // suspend
            subExpression != null, // subscription
            classFilter // class filter
        );
        // 调用真正的拉取消息接口
        try {
            this.pullAPIWrapper.pullKernelImpl(
                pullRequest.getMessageQueue(),
                subExpression,
                subscriptionData.getExpressionType(),
                subscriptionData.getSubVersion(),
                pullRequest.getNextOffset(),
                this.defaultMQPushConsumer.getPullBatchSize(),
                sysFlag,
                commitOffsetValue,
                BROKER_SUSPEND_MAX_TIME_MILLIS,
                CONSUMER_TIMEOUT_MILLIS_WHEN_SUSPEND,
                CommunicationMode.ASYNC,
                pullCallback
            );
        } catch (Exception e) {
            log.error("pullKernelImpl exception", e);
            this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
        }
    }
+

以下就是拉取消息的底层 api，不够不是特别复杂，主要是在找 broker，和设置请求参数

+
public PullResult pullKernelImpl(
    final MessageQueue mq,
    final String subExpression,
    final String expressionType,
    final long subVersion,
    final long offset,
    final int maxNums,
    final int sysFlag,
    final long commitOffset,
    final long brokerSuspendMaxTimeMillis,
    final long timeoutMillis,
    final CommunicationMode communicationMode,
    final PullCallback pullCallback
) throws MQClientException, RemotingException, MQBrokerException, InterruptedException {
    FindBrokerResult findBrokerResult =
        this.mQClientFactory.findBrokerAddressInSubscribe(mq.getBrokerName(),
            this.recalculatePullFromWhichNode(mq), false);
    if (null == findBrokerResult) {
        this.mQClientFactory.updateTopicRouteInfoFromNameServer(mq.getTopic());
        findBrokerResult =
            this.mQClientFactory.findBrokerAddressInSubscribe(mq.getBrokerName(),
                this.recalculatePullFromWhichNode(mq), false);
    }

    if (findBrokerResult != null) {
        {
            // check version
            if (!ExpressionType.isTagType(expressionType)
                && findBrokerResult.getBrokerVersion() < MQVersion.Version.V4_1_0_SNAPSHOT.ordinal()) {
                throw new MQClientException("The broker[" + mq.getBrokerName() + ", "
                    + findBrokerResult.getBrokerVersion() + "] does not upgrade to support for filter message by " + expressionType, null);
            }
        }
        int sysFlagInner = sysFlag;

        if (findBrokerResult.isSlave()) {
            sysFlagInner = PullSysFlag.clearCommitOffsetFlag(sysFlagInner);
        }

        PullMessageRequestHeader requestHeader = new PullMessageRequestHeader();
        requestHeader.setConsumerGroup(this.consumerGroup);
        requestHeader.setTopic(mq.getTopic());
        requestHeader.setQueueId(mq.getQueueId());
        requestHeader.setQueueOffset(offset);
        requestHeader.setMaxMsgNums(maxNums);
        requestHeader.setSysFlag(sysFlagInner);
        requestHeader.setCommitOffset(commitOffset);
        requestHeader.setSuspendTimeoutMillis(brokerSuspendMaxTimeMillis);
        requestHeader.setSubscription(subExpression);
        requestHeader.setSubVersion(subVersion);
        requestHeader.setExpressionType(expressionType);

        String brokerAddr = findBrokerResult.getBrokerAddr();
        if (PullSysFlag.hasClassFilterFlag(sysFlagInner)) {
            brokerAddr = computPullFromWhichFilterServer(mq.getTopic(), brokerAddr);
        }

        PullResult pullResult = this.mQClientFactory.getMQClientAPIImpl().pullMessage(
            brokerAddr,
            requestHeader,
            timeoutMillis,
            communicationMode,
            pullCallback);

        return pullResult;
    }

    throw new MQClientException("The broker[" + mq.getBrokerName() + "] not exist", null);
}
+

再看下一步的

+
public PullResult pullMessage(
    final String addr,
    final PullMessageRequestHeader requestHeader,
    final long timeoutMillis,
    final CommunicationMode communicationMode,
    final PullCallback pullCallback
) throws RemotingException, MQBrokerException, InterruptedException {
    RemotingCommand request = RemotingCommand.createRequestCommand(RequestCode.PULL_MESSAGE, requestHeader);

    switch (communicationMode) {
        case ONEWAY:
            assert false;
            return null;
        case ASYNC:
            this.pullMessageAsync(addr, request, timeoutMillis, pullCallback);
            return null;
        case SYNC:
            return this.pullMessageSync(addr, request, timeoutMillis);
        default:
            assert false;
            break;
    }

    return null;
}
+

通过 communicationMode 判断是同步拉取还是异步拉取，异步就调用

+
private void pullMessageAsync(
        final String addr,
        final RemotingCommand request,
        final long timeoutMillis,
        final PullCallback pullCallback
    ) throws RemotingException, InterruptedException {
        this.remotingClient.invokeAsync(addr, request, timeoutMillis, new InvokeCallback() {
            @Override
            public void operationComplete(ResponseFuture responseFuture) {
                异步
                RemotingCommand response = responseFuture.getResponseCommand();
                if (response != null) {
                    try {
                        PullResult pullResult = MQClientAPIImpl.this.processPullResponse(response);
                        assert pullResult != null;
                        pullCallback.onSuccess(pullResult);
                    } catch (Exception e) {
                        pullCallback.onException(e);
                    }
                } else {
                    if (!responseFuture.isSendRequestOK()) {
                        pullCallback.onException(new MQClientException("send request failed to " + addr + ". Request: " + request, responseFuture.getCause()));
                    } else if (responseFuture.isTimeout()) {
                        pullCallback.onException(new MQClientException("wait response from " + addr + " timeout :" + responseFuture.getTimeoutMillis() + "ms" + ". Request: " + request,
                            responseFuture.getCause()));
                    } else {
                        pullCallback.onException(new MQClientException("unknown reason. addr: " + addr + ", timeoutMillis: " + timeoutMillis + ". Request: " + request, responseFuture.getCause()));
                    }
                }
            }
        });
    }
+

并且会调用前面 pullCallback 的onSuccess和onException方法，同步的就是调用

+
private PullResult pullMessageSync(
        final String addr,
        final RemotingCommand request,
        final long timeoutMillis
    ) throws RemotingException, InterruptedException, MQBrokerException {
        RemotingCommand response = this.remotingClient.invokeSync(addr, request, timeoutMillis);
        assert response != null;
        return this.processPullResponse(response);
    }
+

然后就是这个 remotingClient 的 invokeAsync 跟 invokeSync 方法

+
@Override
    public void invokeAsync(String addr, RemotingCommand request, long timeoutMillis, InvokeCallback invokeCallback)
        throws InterruptedException, RemotingConnectException, RemotingTooMuchRequestException, RemotingTimeoutException,
        RemotingSendRequestException {
        long beginStartTime = System.currentTimeMillis();
        final Channel channel = this.getAndCreateChannel(addr);
        if (channel != null && channel.isActive()) {
            try {
                doBeforeRpcHooks(addr, request);
                long costTime = System.currentTimeMillis() - beginStartTime;
                if (timeoutMillis < costTime) {
                    throw new RemotingTooMuchRequestException("invokeAsync call timeout");
                }
                this.invokeAsyncImpl(channel, request, timeoutMillis - costTime, invokeCallback);
            } catch (RemotingSendRequestException e) {
                log.warn("invokeAsync: send request exception, so close the channel[{}]", addr);
                this.closeChannel(addr, channel);
                throw e;
            }
        } else {
            this.closeChannel(addr, channel);
            throw new RemotingConnectException(addr);
        }
    }
@Override
    public RemotingCommand invokeSync(String addr, final RemotingCommand request, long timeoutMillis)
        throws InterruptedException, RemotingConnectException, RemotingSendRequestException, RemotingTimeoutException {
        long beginStartTime = System.currentTimeMillis();
        final Channel channel = this.getAndCreateChannel(addr);
        if (channel != null && channel.isActive()) {
            try {
                doBeforeRpcHooks(addr, request);
                long costTime = System.currentTimeMillis() - beginStartTime;
                if (timeoutMillis < costTime) {
                    throw new RemotingTimeoutException("invokeSync call timeout");
                }
                RemotingCommand response = this.invokeSyncImpl(channel, request, timeoutMillis - costTime);
                doAfterRpcHooks(RemotingHelper.parseChannelRemoteAddr(channel), request, response);
                return response;
            } catch (RemotingSendRequestException e) {
                log.warn("invokeSync: send request exception, so close the channel[{}]", addr);
                this.closeChannel(addr, channel);
                throw e;
            } catch (RemotingTimeoutException e) {
                if (nettyClientConfig.isClientCloseSocketIfTimeout()) {
                    this.closeChannel(addr, channel);
                    log.warn("invokeSync: close socket because of timeout, {}ms, {}", timeoutMillis, addr);
                }
                log.warn("invokeSync: wait response timeout exception, the channel[{}]", addr);
                throw e;
            }
        } else {
            this.closeChannel(addr, channel);
            throw new RemotingConnectException(addr);
        }
    }
+

再往下看

+
public RemotingCommand invokeSyncImpl(final Channel channel, final RemotingCommand request,
        final long timeoutMillis)
        throws InterruptedException, RemotingSendRequestException, RemotingTimeoutException {
        final int opaque = request.getOpaque();

        try {
            同步跟异步都是会把结果用ResponseFuture抱起来
            final ResponseFuture responseFuture = new ResponseFuture(channel, opaque, timeoutMillis, null, null);
            this.responseTable.put(opaque, responseFuture);
            final SocketAddress addr = channel.remoteAddress();
            channel.writeAndFlush(request).addListener(new ChannelFutureListener() {
                @Override
                public void operationComplete(ChannelFuture f) throws Exception {
                    if (f.isSuccess()) {
                        responseFuture.setSendRequestOK(true);
                        return;
                    } else {
                        responseFuture.setSendRequestOK(false);
                    }

                    responseTable.remove(opaque);
                    responseFuture.setCause(f.cause());
                    responseFuture.putResponse(null);
                    log.warn("send a request command to channel <" + addr + "> failed.");
                }
            });
            // 区别是同步的是在这等待
            RemotingCommand responseCommand = responseFuture.waitResponse(timeoutMillis);
            if (null == responseCommand) {
                if (responseFuture.isSendRequestOK()) {
                    throw new RemotingTimeoutException(RemotingHelper.parseSocketAddressAddr(addr), timeoutMillis,
                        responseFuture.getCause());
                } else {
                    throw new RemotingSendRequestException(RemotingHelper.parseSocketAddressAddr(addr), responseFuture.getCause());
                }
            }

            return responseCommand;
        } finally {
            this.responseTable.remove(opaque);
        }
    }

    public void invokeAsyncImpl(final Channel channel, final RemotingCommand request, final long timeoutMillis,
        final InvokeCallback invokeCallback)
        throws InterruptedException, RemotingTooMuchRequestException, RemotingTimeoutException, RemotingSendRequestException {
        long beginStartTime = System.currentTimeMillis();
        final int opaque = request.getOpaque();
        boolean acquired = this.semaphoreAsync.tryAcquire(timeoutMillis, TimeUnit.MILLISECONDS);
        if (acquired) {
            final SemaphoreReleaseOnlyOnce once = new SemaphoreReleaseOnlyOnce(this.semaphoreAsync);
            long costTime = System.currentTimeMillis() - beginStartTime;
            if (timeoutMillis < costTime) {
                once.release();
                throw new RemotingTimeoutException("invokeAsyncImpl call timeout");
            }

            final ResponseFuture responseFuture = new ResponseFuture(channel, opaque, timeoutMillis - costTime, invokeCallback, once);
            this.responseTable.put(opaque, responseFuture);
            try {
                channel.writeAndFlush(request).addListener(new ChannelFutureListener() {
                    @Override
                    public void operationComplete(ChannelFuture f) throws Exception {
                        if (f.isSuccess()) {
                            responseFuture.setSendRequestOK(true);
                            return;
                        }
                        requestFail(opaque);
                        log.warn("send a request command to channel <{}> failed.", RemotingHelper.parseChannelRemoteAddr(channel));
                    }
                });
            } catch (Exception e) {
                responseFuture.release();
                log.warn("send a request command to channel <" + RemotingHelper.parseChannelRemoteAddr(channel) + "> Exception", e);
                throw new RemotingSendRequestException(RemotingHelper.parseChannelRemoteAddr(channel), e);
            }
        } else {
            if (timeoutMillis <= 0) {
                throw new RemotingTooMuchRequestException("invokeAsyncImpl invoke too fast");
            } else {
                String info =
                    String.format("invokeAsyncImpl tryAcquire semaphore timeout, %dms, waiting thread nums: %d semaphoreAsyncValue: %d",
                        timeoutMillis,
                        this.semaphoreAsync.getQueueLength(),
                        this.semaphoreAsync.availablePermits()
                    );
                log.warn(info);
                throw new RemotingTimeoutException(info);
            }
        }
    }
+ + + ]]> - linux + MQ + RocketMQ + 消息队列 + RocketMQ + 中间件 + RocketMQ - linux + MQ + 消息队列 + RocketMQ + 削峰填谷 + 中间件 + DefaultMQPushConsumer + 源码解析 @@ -5632,69 +5823,15 @@ user3: RocketMQ 削峰填谷 中间件 - NameServer 源码解析 + NameServer - 聊一下 RocketMQ 的 DefaultMQPushConsumer 源码 - /2020/06/26/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84-Consumer/ - 首先看下官方的小 demo

-
public static void main(String[] args) throws InterruptedException, MQClientException {

        /*
         * Instantiate with specified consumer group name.
         * 首先是new 一个对象出来，然后指定 Consumer 的 Group
         * 同一类Consumer的集合，这类Consumer通常消费同一类消息且消费逻辑一致。消费者组使得在消息消费方面，实现负载均衡和容错的目标变得非常容易。要注意的是，消费者组的消费者实例必须订阅完全相同的Topic。RocketMQ 支持两种消息模式：集群消费（Clustering）和广播消费（Broadcasting）。
         */
        DefaultMQPushConsumer consumer = new DefaultMQPushConsumer("please_rename_unique_group_name_4");

        /*
         * Specify name server addresses.
         * <p/>
         * 这里可以通知指定环境变量或者设置对象参数的形式指定名字空间服务的地址
         *
         * Alternatively, you may specify name server addresses via exporting environmental variable: NAMESRV_ADDR
         * <pre>
         * {@code
         * consumer.setNamesrvAddr("name-server1-ip:9876;name-server2-ip:9876");
         * }
         * </pre>
         */

        /*
         * Specify where to start in case the specified consumer group is a brand new one.
         * 指定消费起始点
         */
        consumer.setConsumeFromWhere(ConsumeFromWhere.CONSUME_FROM_FIRST_OFFSET);

        /*
         * Subscribe one more more topics to consume.
         * 指定订阅的 topic 跟 tag，注意后面的是个表达式，可以以 tag1 || tag2 || tag3 传入
         */
        consumer.subscribe("TopicTest", "*");

        /*
         *  Register callback to execute on arrival of messages fetched from brokers.
         *  注册具体获得消息后的处理方法
         */
        consumer.registerMessageListener(new MessageListenerConcurrently() {

            @Override
            public ConsumeConcurrentlyStatus consumeMessage(List<MessageExt> msgs,
                ConsumeConcurrentlyContext context) {
                System.out.printf("%s Receive New Messages: %s %n", Thread.currentThread().getName(), msgs);
                return ConsumeConcurrentlyStatus.CONSUME_SUCCESS;
            }
        });

        /*
         *  Launch the consumer instance.
         * 启动消费者
         */
        consumer.start();

        System.out.printf("Consumer Started.%n");
    }
- -

然后就是看看 start 的过程了

-
/**
     * This method gets internal infrastructure readily to serve. Instances must call this method after configuration.
     *
     * @throws MQClientException if there is any client error.
     */
    @Override
    public void start() throws MQClientException {
        setConsumerGroup(NamespaceUtil.wrapNamespace(this.getNamespace(), this.consumerGroup));
        this.defaultMQPushConsumerImpl.start();
        if (null != traceDispatcher) {
            try {
                traceDispatcher.start(this.getNamesrvAddr(), this.getAccessChannel());
            } catch (MQClientException e) {
                log.warn("trace dispatcher start failed ", e);
            }
        }
    }
-

具体的逻辑在this.defaultMQPushConsumerImpl.start()，这个 defaultMQPushConsumerImpl 就是

-
/**
     * Internal implementation. Most of the functions herein are delegated to it.
     */
    protected final transient DefaultMQPushConsumerImpl defaultMQPushConsumerImpl;
- -
public synchronized void start() throws MQClientException {
        switch (this.serviceState) {
            case CREATE_JUST:
                log.info("the consumer [{}] start beginning. messageModel={}, isUnitMode={}", this.defaultMQPushConsumer.getConsumerGroup(),
                    this.defaultMQPushConsumer.getMessageModel(), this.defaultMQPushConsumer.isUnitMode());
                // 这里比较巧妙，相当于想设立了个屏障，防止并发启动，不过这里并不是悲观锁，也不算个严格的乐观锁
                this.serviceState = ServiceState.START_FAILED;

                this.checkConfig();

                this.copySubscription();

                if (this.defaultMQPushConsumer.getMessageModel() == MessageModel.CLUSTERING) {
                    this.defaultMQPushConsumer.changeInstanceNameToPID();
                }

                // 这个mQClientFactory，负责管理client（consumer、producer），并提供多中功能接口供各个Service（Rebalance、PullMessage等）调用；大部分逻辑均在这个类中完成
                this.mQClientFactory = MQClientManager.getInstance().getOrCreateMQClientInstance(this.defaultMQPushConsumer, this.rpcHook);

                // 这个 rebalanceImpl 主要负责决定，当前的consumer应该从哪些Queue中消费消息；
                this.rebalanceImpl.setConsumerGroup(this.defaultMQPushConsumer.getConsumerGroup());
                this.rebalanceImpl.setMessageModel(this.defaultMQPushConsumer.getMessageModel());
                this.rebalanceImpl.setAllocateMessageQueueStrategy(this.defaultMQPushConsumer.getAllocateMessageQueueStrategy());
                this.rebalanceImpl.setmQClientFactory(this.mQClientFactory);

                // 长连接，负责从broker处拉取消息，然后利用ConsumeMessageService回调用户的Listener执行消息消费逻辑
                this.pullAPIWrapper = new PullAPIWrapper(
                    mQClientFactory,
                    this.defaultMQPushConsumer.getConsumerGroup(), isUnitMode());
                this.pullAPIWrapper.registerFilterMessageHook(filterMessageHookList);

                if (this.defaultMQPushConsumer.getOffsetStore() != null) {
                    this.offsetStore = this.defaultMQPushConsumer.getOffsetStore();
                } else {
                    switch (this.defaultMQPushConsumer.getMessageModel()) {
                        case BROADCASTING:
                            this.offsetStore = new LocalFileOffsetStore(this.mQClientFactory, this.defaultMQPushConsumer.getConsumerGroup());
                            break;
                        case CLUSTERING:
                            this.offsetStore = new RemoteBrokerOffsetStore(this.mQClientFactory, this.defaultMQPushConsumer.getConsumerGroup());
                            break;
                        default:
                            break;
                    }
                    this.defaultMQPushConsumer.setOffsetStore(this.offsetStore);
                }
                // offsetStore 维护当前consumer的消费记录（offset）；有两种实现，Local和Rmote，Local存储在本地磁盘上，适用于BROADCASTING广播消费模式；而Remote则将消费进度存储在Broker上，适用于CLUSTERING集群消费模式；
                this.offsetStore.load();

                if (this.getMessageListenerInner() instanceof MessageListenerOrderly) {
                    this.consumeOrderly = true;
                    this.consumeMessageService =
                        new ConsumeMessageOrderlyService(this, (MessageListenerOrderly) this.getMessageListenerInner());
                } else if (this.getMessageListenerInner() instanceof MessageListenerConcurrently) {
                    this.consumeOrderly = false;
                    this.consumeMessageService =
                        new ConsumeMessageConcurrentlyService(this, (MessageListenerConcurrently) this.getMessageListenerInner());
                }

                // 实现所谓的"Push-被动"消费机制；从Broker拉取的消息后，封装成ConsumeRequest提交给ConsumeMessageSerivce，此service负责回调用户的Listener消费消息；
                this.consumeMessageService.start();

                boolean registerOK = mQClientFactory.registerConsumer(this.defaultMQPushConsumer.getConsumerGroup(), this);
                if (!registerOK) {
                    this.serviceState = ServiceState.CREATE_JUST;
                    this.consumeMessageService.shutdown();
                    throw new MQClientException("The consumer group[" + this.defaultMQPushConsumer.getConsumerGroup()
                        + "] has been created before, specify another name please." + FAQUrl.suggestTodo(FAQUrl.GROUP_NAME_DUPLICATE_URL),
                        null);
                }

                mQClientFactory.start();
                log.info("the consumer [{}] start OK.", this.defaultMQPushConsumer.getConsumerGroup());
                this.serviceState = ServiceState.RUNNING;
                break;
            case RUNNING:
            case START_FAILED:
            case SHUTDOWN_ALREADY:
                throw new MQClientException("The PushConsumer service state not OK, maybe started once, "
                    + this.serviceState
                    + FAQUrl.suggestTodo(FAQUrl.CLIENT_SERVICE_NOT_OK),
                    null);
            default:
                break;
        }

        this.updateTopicSubscribeInfoWhenSubscriptionChanged();
        this.mQClientFactory.checkClientInBroker();
        this.mQClientFactory.sendHeartbeatToAllBrokerWithLock();
        this.mQClientFactory.rebalanceImmediately();
    }
-

然后我们往下看主要的目光聚焦mQClientFactory.start()

-
public void start() throws MQClientException {

        synchronized (this) {
            switch (this.serviceState) {
                case CREATE_JUST:
                    this.serviceState = ServiceState.START_FAILED;
                    // If not specified,looking address from name server
                    if (null == this.clientConfig.getNamesrvAddr()) {
                        this.mQClientAPIImpl.fetchNameServerAddr();
                    }
                    // Start request-response channel
                    // 这里主要是初始化了个网络客户端
                    this.mQClientAPIImpl.start();
                    // Start various schedule tasks
                    // 定时任务
                    this.startScheduledTask();
                    // Start pull service
                    // 这里重点说下
                    this.pullMessageService.start();
                    // Start rebalance service
                    this.rebalanceService.start();
                    // Start push service
                    this.defaultMQProducer.getDefaultMQProducerImpl().start(false);
                    log.info("the client factory [{}] start OK", this.clientId);
                    this.serviceState = ServiceState.RUNNING;
                    break;
                case START_FAILED:
                    throw new MQClientException("The Factory object[" + this.getClientId() + "] has been created before, and failed.", null);
                default:
                    break;
            }
        }
    }
-

我们来看下这个 pullMessageService，org.apache.rocketmq.client.impl.consumer.PullMessageService，

实现了 runnable 接口，
然后可以看到 run 方法

-
public void run() {
        log.info(this.getServiceName() + " service started");

        while (!this.isStopped()) {
            try {
                PullRequest pullRequest = this.pullRequestQueue.take();
                this.pullMessage(pullRequest);
            } catch (InterruptedException ignored) {
            } catch (Exception e) {
                log.error("Pull Message Service Run Method exception", e);
            }
        }

        log.info(this.getServiceName() + " service end");
    }
-

接着在看 pullMessage 方法

-
private void pullMessage(final PullRequest pullRequest) {
        final MQConsumerInner consumer = this.mQClientFactory.selectConsumer(pullRequest.getConsumerGroup());
        if (consumer != null) {
            DefaultMQPushConsumerImpl impl = (DefaultMQPushConsumerImpl) consumer;
            impl.pullMessage(pullRequest);
        } else {
            log.warn("No matched consumer for the PullRequest {}, drop it", pullRequest);
        }
    }
-

实际上调用了这个方法，这个方法很长，我在代码里注释下下每一段的功能

-
public void pullMessage(final PullRequest pullRequest) {
        final ProcessQueue processQueue = pullRequest.getProcessQueue();
        // 这里开始就是检查状态，确定是否往下执行
        if (processQueue.isDropped()) {
            log.info("the pull request[{}] is dropped.", pullRequest.toString());
            return;
        }

        pullRequest.getProcessQueue().setLastPullTimestamp(System.currentTimeMillis());

        try {
            this.makeSureStateOK();
        } catch (MQClientException e) {
            log.warn("pullMessage exception, consumer state not ok", e);
            this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
            return;
        }

        if (this.isPause()) {
            log.warn("consumer was paused, execute pull request later. instanceName={}, group={}", this.defaultMQPushConsumer.getInstanceName(), this.defaultMQPushConsumer.getConsumerGroup());
            this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_SUSPEND);
            return;
        }

        // 这块其实是个类似于限流的功能块，对消息数量和消息大小做限制
        long cachedMessageCount = processQueue.getMsgCount().get();
        long cachedMessageSizeInMiB = processQueue.getMsgSize().get() / (1024 * 1024);

        if (cachedMessageCount > this.defaultMQPushConsumer.getPullThresholdForQueue()) {
            this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_FLOW_CONTROL);
            if ((queueFlowControlTimes++ % 1000) == 0) {
                log.warn(
                    "the cached message count exceeds the threshold {}, so do flow control, minOffset={}, maxOffset={}, count={}, size={} MiB, pullRequest={}, flowControlTimes={}",
                    this.defaultMQPushConsumer.getPullThresholdForQueue(), processQueue.getMsgTreeMap().firstKey(), processQueue.getMsgTreeMap().lastKey(), cachedMessageCount, cachedMessageSizeInMiB, pullRequest, queueFlowControlTimes);
            }
            return;
        }

        if (cachedMessageSizeInMiB > this.defaultMQPushConsumer.getPullThresholdSizeForQueue()) {
            this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_FLOW_CONTROL);
            if ((queueFlowControlTimes++ % 1000) == 0) {
                log.warn(
                    "the cached message size exceeds the threshold {} MiB, so do flow control, minOffset={}, maxOffset={}, count={}, size={} MiB, pullRequest={}, flowControlTimes={}",
                    this.defaultMQPushConsumer.getPullThresholdSizeForQueue(), processQueue.getMsgTreeMap().firstKey(), processQueue.getMsgTreeMap().lastKey(), cachedMessageCount, cachedMessageSizeInMiB, pullRequest, queueFlowControlTimes);
            }
            return;
        }

        // 若不是顺序消费（即DefaultMQPushConsumerImpl.consumeOrderly等于false），则检查ProcessQueue对象的msgTreeMap:TreeMap<Long,MessageExt>变量的第一个key值与最后一个key值之间的差额，该key值表示查询的队列偏移量queueoffset；若差额大于阈值（由DefaultMQPushConsumer. consumeConcurrentlyMaxSpan指定，默认是2000），则调用PullMessageService.executePullRequestLater方法，在50毫秒之后重新将该PullRequest请求放入PullMessageService.pullRequestQueue队列中；并跳出该方法；这里的意思主要就是消息有堆积了，等会再来拉取
        if (!this.consumeOrderly) {
            if (processQueue.getMaxSpan() > this.defaultMQPushConsumer.getConsumeConcurrentlyMaxSpan()) {
                this.executePullRequestLater(pullRequest, PULL_TIME_DELAY_MILLS_WHEN_FLOW_CONTROL);
                if ((queueMaxSpanFlowControlTimes++ % 1000) == 0) {
                    log.warn(
                        "the queue's messages, span too long, so do flow control, minOffset={}, maxOffset={}, maxSpan={}, pullRequest={}, flowControlTimes={}",
                        processQueue.getMsgTreeMap().firstKey(), processQueue.getMsgTreeMap().lastKey(), processQueue.getMaxSpan(),
                        pullRequest, queueMaxSpanFlowControlTimes);
                }
                return;
            }
        } else {
            if (processQueue.isLocked()) {
                if (!pullRequest.isLockedFirst()) {
                    final long offset = this.rebalanceImpl.computePullFromWhere(pullRequest.getMessageQueue());
                    boolean brokerBusy = offset < pullRequest.getNextOffset();
                    log.info("the first time to pull message, so fix offset from broker. pullRequest: {} NewOffset: {} brokerBusy: {}",
                        pullRequest, offset, brokerBusy);
                    if (brokerBusy) {
                        log.info("[NOTIFYME]the first time to pull message, but pull request offset larger than broker consume offset. pullRequest: {} NewOffset: {}",
                            pullRequest, offset);
                    }

                    pullRequest.setLockedFirst(true);
                    pullRequest.setNextOffset(offset);
                }
            } else {
                this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
                log.info("pull message later because not locked in broker, {}", pullRequest);
                return;
            }
        }

        // 以PullRequest.messageQueue对象的topic值为参数从RebalanceImpl.subscriptionInner: ConcurrentHashMap, SubscriptionData>中获取对应的SubscriptionData对象，若该对象为null，考虑到并发的关系，调用executePullRequestLater方法，稍后重试；并跳出该方法；
        final SubscriptionData subscriptionData = this.rebalanceImpl.getSubscriptionInner().get(pullRequest.getMessageQueue().getTopic());
        if (null == subscriptionData) {
            this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
            log.warn("find the consumer's subscription failed, {}", pullRequest);
            return;
        }

        final long beginTimestamp = System.currentTimeMillis();

        // 异步拉取回调，先不讨论细节
        PullCallback pullCallback = new PullCallback() {
            @Override
            public void onSuccess(PullResult pullResult) {
                if (pullResult != null) {
                    pullResult = DefaultMQPushConsumerImpl.this.pullAPIWrapper.processPullResult(pullRequest.getMessageQueue(), pullResult,
                        subscriptionData);

                    switch (pullResult.getPullStatus()) {
                        case FOUND:
                            long prevRequestOffset = pullRequest.getNextOffset();
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());
                            long pullRT = System.currentTimeMillis() - beginTimestamp;
                            DefaultMQPushConsumerImpl.this.getConsumerStatsManager().incPullRT(pullRequest.getConsumerGroup(),
                                pullRequest.getMessageQueue().getTopic(), pullRT);

                            long firstMsgOffset = Long.MAX_VALUE;
                            if (pullResult.getMsgFoundList() == null || pullResult.getMsgFoundList().isEmpty()) {
                                DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            } else {
                                firstMsgOffset = pullResult.getMsgFoundList().get(0).getQueueOffset();

                                DefaultMQPushConsumerImpl.this.getConsumerStatsManager().incPullTPS(pullRequest.getConsumerGroup(),
                                    pullRequest.getMessageQueue().getTopic(), pullResult.getMsgFoundList().size());

                                boolean dispatchToConsume = processQueue.putMessage(pullResult.getMsgFoundList());
                                DefaultMQPushConsumerImpl.this.consumeMessageService.submitConsumeRequest(
                                    pullResult.getMsgFoundList(),
                                    processQueue,
                                    pullRequest.getMessageQueue(),
                                    dispatchToConsume);

                                if (DefaultMQPushConsumerImpl.this.defaultMQPushConsumer.getPullInterval() > 0) {
                                    DefaultMQPushConsumerImpl.this.executePullRequestLater(pullRequest,
                                        DefaultMQPushConsumerImpl.this.defaultMQPushConsumer.getPullInterval());
                                } else {
                                    DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                                }
                            }

                            if (pullResult.getNextBeginOffset() < prevRequestOffset
                                || firstMsgOffset < prevRequestOffset) {
                                log.warn(
                                    "[BUG] pull message result maybe data wrong, nextBeginOffset: {} firstMsgOffset: {} prevRequestOffset: {}",
                                    pullResult.getNextBeginOffset(),
                                    firstMsgOffset,
                                    prevRequestOffset);
                            }

                            break;
                        case NO_NEW_MSG:
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());

                            DefaultMQPushConsumerImpl.this.correctTagsOffset(pullRequest);

                            DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            break;
                        case NO_MATCHED_MSG:
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());

                            DefaultMQPushConsumerImpl.this.correctTagsOffset(pullRequest);

                            DefaultMQPushConsumerImpl.this.executePullRequestImmediately(pullRequest);
                            break;
                        case OFFSET_ILLEGAL:
                            log.warn("the pull request offset illegal, {} {}",
                                pullRequest.toString(), pullResult.toString());
                            pullRequest.setNextOffset(pullResult.getNextBeginOffset());

                            pullRequest.getProcessQueue().setDropped(true);
                            DefaultMQPushConsumerImpl.this.executeTaskLater(new Runnable() {

                                @Override
                                public void run() {
                                    try {
                                        DefaultMQPushConsumerImpl.this.offsetStore.updateOffset(pullRequest.getMessageQueue(),
                                            pullRequest.getNextOffset(), false);

                                        DefaultMQPushConsumerImpl.this.offsetStore.persist(pullRequest.getMessageQueue());

                                        DefaultMQPushConsumerImpl.this.rebalanceImpl.removeProcessQueue(pullRequest.getMessageQueue());

                                        log.warn("fix the pull request offset, {}", pullRequest);
                                    } catch (Throwable e) {
                                        log.error("executeTaskLater Exception", e);
                                    }
                                }
                            }, 10000);
                            break;
                        default:
                            break;
                    }
                }
            }

            @Override
            public void onException(Throwable e) {
                if (!pullRequest.getMessageQueue().getTopic().startsWith(MixAll.RETRY_GROUP_TOPIC_PREFIX)) {
                    log.warn("execute the pull request exception", e);
                }

                DefaultMQPushConsumerImpl.this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
            }
        };
        // 如果为集群模式，即可置commitOffsetEnable为 true
        boolean commitOffsetEnable = false;
        long commitOffsetValue = 0L;
        if (MessageModel.CLUSTERING == this.defaultMQPushConsumer.getMessageModel()) {
            commitOffsetValue = this.offsetStore.readOffset(pullRequest.getMessageQueue(), ReadOffsetType.READ_FROM_MEMORY);
            if (commitOffsetValue > 0) {
                commitOffsetEnable = true;
            }
        }

        // 将上面获得的commitOffsetEnable更新到订阅关系里
        String subExpression = null;
        boolean classFilter = false;
        SubscriptionData sd = this.rebalanceImpl.getSubscriptionInner().get(pullRequest.getMessageQueue().getTopic());
        if (sd != null) {
            if (this.defaultMQPushConsumer.isPostSubscriptionWhenPull() && !sd.isClassFilterMode()) {
                subExpression = sd.getSubString();
            }

            classFilter = sd.isClassFilterMode();
        }

        // 组成 sysFlag
        int sysFlag = PullSysFlag.buildSysFlag(
            commitOffsetEnable, // commitOffset
            true, // suspend
            subExpression != null, // subscription
            classFilter // class filter
        );
        // 调用真正的拉取消息接口
        try {
            this.pullAPIWrapper.pullKernelImpl(
                pullRequest.getMessageQueue(),
                subExpression,
                subscriptionData.getExpressionType(),
                subscriptionData.getSubVersion(),
                pullRequest.getNextOffset(),
                this.defaultMQPushConsumer.getPullBatchSize(),
                sysFlag,
                commitOffsetValue,
                BROKER_SUSPEND_MAX_TIME_MILLIS,
                CONSUMER_TIMEOUT_MILLIS_WHEN_SUSPEND,
                CommunicationMode.ASYNC,
                pullCallback
            );
        } catch (Exception e) {
            log.error("pullKernelImpl exception", e);
            this.executePullRequestLater(pullRequest, pullTimeDelayMillsWhenException);
        }
    }
-

以下就是拉取消息的底层 api，不够不是特别复杂，主要是在找 broker，和设置请求参数

-
public PullResult pullKernelImpl(
    final MessageQueue mq,
    final String subExpression,
    final String expressionType,
    final long subVersion,
    final long offset,
    final int maxNums,
    final int sysFlag,
    final long commitOffset,
    final long brokerSuspendMaxTimeMillis,
    final long timeoutMillis,
    final CommunicationMode communicationMode,
    final PullCallback pullCallback
) throws MQClientException, RemotingException, MQBrokerException, InterruptedException {
    FindBrokerResult findBrokerResult =
        this.mQClientFactory.findBrokerAddressInSubscribe(mq.getBrokerName(),
            this.recalculatePullFromWhichNode(mq), false);
    if (null == findBrokerResult) {
        this.mQClientFactory.updateTopicRouteInfoFromNameServer(mq.getTopic());
        findBrokerResult =
            this.mQClientFactory.findBrokerAddressInSubscribe(mq.getBrokerName(),
                this.recalculatePullFromWhichNode(mq), false);
    }

    if (findBrokerResult != null) {
        {
            // check version
            if (!ExpressionType.isTagType(expressionType)
                && findBrokerResult.getBrokerVersion() < MQVersion.Version.V4_1_0_SNAPSHOT.ordinal()) {
                throw new MQClientException("The broker[" + mq.getBrokerName() + ", "
                    + findBrokerResult.getBrokerVersion() + "] does not upgrade to support for filter message by " + expressionType, null);
            }
        }
        int sysFlagInner = sysFlag;

        if (findBrokerResult.isSlave()) {
            sysFlagInner = PullSysFlag.clearCommitOffsetFlag(sysFlagInner);
        }

        PullMessageRequestHeader requestHeader = new PullMessageRequestHeader();
        requestHeader.setConsumerGroup(this.consumerGroup);
        requestHeader.setTopic(mq.getTopic());
        requestHeader.setQueueId(mq.getQueueId());
        requestHeader.setQueueOffset(offset);
        requestHeader.setMaxMsgNums(maxNums);
        requestHeader.setSysFlag(sysFlagInner);
        requestHeader.setCommitOffset(commitOffset);
        requestHeader.setSuspendTimeoutMillis(brokerSuspendMaxTimeMillis);
        requestHeader.setSubscription(subExpression);
        requestHeader.setSubVersion(subVersion);
        requestHeader.setExpressionType(expressionType);

        String brokerAddr = findBrokerResult.getBrokerAddr();
        if (PullSysFlag.hasClassFilterFlag(sysFlagInner)) {
            brokerAddr = computPullFromWhichFilterServer(mq.getTopic(), brokerAddr);
        }

        PullResult pullResult = this.mQClientFactory.getMQClientAPIImpl().pullMessage(
            brokerAddr,
            requestHeader,
            timeoutMillis,
            communicationMode,
            pullCallback);

        return pullResult;
    }

    throw new MQClientException("The broker[" + mq.getBrokerName() + "] not exist", null);
}
-

再看下一步的

-
public PullResult pullMessage(
    final String addr,
    final PullMessageRequestHeader requestHeader,
    final long timeoutMillis,
    final CommunicationMode communicationMode,
    final PullCallback pullCallback
) throws RemotingException, MQBrokerException, InterruptedException {
    RemotingCommand request = RemotingCommand.createRequestCommand(RequestCode.PULL_MESSAGE, requestHeader);

    switch (communicationMode) {
        case ONEWAY:
            assert false;
            return null;
        case ASYNC:
            this.pullMessageAsync(addr, request, timeoutMillis, pullCallback);
            return null;
        case SYNC:
            return this.pullMessageSync(addr, request, timeoutMillis);
        default:
            assert false;
            break;
    }

    return null;
}
-

通过 communicationMode 判断是同步拉取还是异步拉取，异步就调用

-
private void pullMessageAsync(
        final String addr,
        final RemotingCommand request,
        final long timeoutMillis,
        final PullCallback pullCallback
    ) throws RemotingException, InterruptedException {
        this.remotingClient.invokeAsync(addr, request, timeoutMillis, new InvokeCallback() {
            @Override
            public void operationComplete(ResponseFuture responseFuture) {
                异步
                RemotingCommand response = responseFuture.getResponseCommand();
                if (response != null) {
                    try {
                        PullResult pullResult = MQClientAPIImpl.this.processPullResponse(response);
                        assert pullResult != null;
                        pullCallback.onSuccess(pullResult);
                    } catch (Exception e) {
                        pullCallback.onException(e);
                    }
                } else {
                    if (!responseFuture.isSendRequestOK()) {
                        pullCallback.onException(new MQClientException("send request failed to " + addr + ". Request: " + request, responseFuture.getCause()));
                    } else if (responseFuture.isTimeout()) {
                        pullCallback.onException(new MQClientException("wait response from " + addr + " timeout :" + responseFuture.getTimeoutMillis() + "ms" + ". Request: " + request,
                            responseFuture.getCause()));
                    } else {
                        pullCallback.onException(new MQClientException("unknown reason. addr: " + addr + ", timeoutMillis: " + timeoutMillis + ". Request: " + request, responseFuture.getCause()));
                    }
                }
            }
        });
    }
-

并且会调用前面 pullCallback 的onSuccess和onException方法，同步的就是调用

-
private PullResult pullMessageSync(
        final String addr,
        final RemotingCommand request,
        final long timeoutMillis
    ) throws RemotingException, InterruptedException, MQBrokerException {
        RemotingCommand response = this.remotingClient.invokeSync(addr, request, timeoutMillis);
        assert response != null;
        return this.processPullResponse(response);
    }
-

然后就是这个 remotingClient 的 invokeAsync 跟 invokeSync 方法

-
@Override
    public void invokeAsync(String addr, RemotingCommand request, long timeoutMillis, InvokeCallback invokeCallback)
        throws InterruptedException, RemotingConnectException, RemotingTooMuchRequestException, RemotingTimeoutException,
        RemotingSendRequestException {
        long beginStartTime = System.currentTimeMillis();
        final Channel channel = this.getAndCreateChannel(addr);
        if (channel != null && channel.isActive()) {
            try {
                doBeforeRpcHooks(addr, request);
                long costTime = System.currentTimeMillis() - beginStartTime;
                if (timeoutMillis < costTime) {
                    throw new RemotingTooMuchRequestException("invokeAsync call timeout");
                }
                this.invokeAsyncImpl(channel, request, timeoutMillis - costTime, invokeCallback);
            } catch (RemotingSendRequestException e) {
                log.warn("invokeAsync: send request exception, so close the channel[{}]", addr);
                this.closeChannel(addr, channel);
                throw e;
            }
        } else {
            this.closeChannel(addr, channel);
            throw new RemotingConnectException(addr);
        }
    }
@Override
    public RemotingCommand invokeSync(String addr, final RemotingCommand request, long timeoutMillis)
        throws InterruptedException, RemotingConnectException, RemotingSendRequestException, RemotingTimeoutException {
        long beginStartTime = System.currentTimeMillis();
        final Channel channel = this.getAndCreateChannel(addr);
        if (channel != null && channel.isActive()) {
            try {
                doBeforeRpcHooks(addr, request);
                long costTime = System.currentTimeMillis() - beginStartTime;
                if (timeoutMillis < costTime) {
                    throw new RemotingTimeoutException("invokeSync call timeout");
                }
                RemotingCommand response = this.invokeSyncImpl(channel, request, timeoutMillis - costTime);
                doAfterRpcHooks(RemotingHelper.parseChannelRemoteAddr(channel), request, response);
                return response;
            } catch (RemotingSendRequestException e) {
                log.warn("invokeSync: send request exception, so close the channel[{}]", addr);
                this.closeChannel(addr, channel);
                throw e;
            } catch (RemotingTimeoutException e) {
                if (nettyClientConfig.isClientCloseSocketIfTimeout()) {
                    this.closeChannel(addr, channel);
                    log.warn("invokeSync: close socket because of timeout, {}ms, {}", timeoutMillis, addr);
                }
                log.warn("invokeSync: wait response timeout exception, the channel[{}]", addr);
                throw e;
            }
        } else {
            this.closeChannel(addr, channel);
            throw new RemotingConnectException(addr);
        }
    }
-

再往下看

-
public RemotingCommand invokeSyncImpl(final Channel channel, final RemotingCommand request,
        final long timeoutMillis)
        throws InterruptedException, RemotingSendRequestException, RemotingTimeoutException {
        final int opaque = request.getOpaque();

        try {
            同步跟异步都是会把结果用ResponseFuture抱起来
            final ResponseFuture responseFuture = new ResponseFuture(channel, opaque, timeoutMillis, null, null);
            this.responseTable.put(opaque, responseFuture);
            final SocketAddress addr = channel.remoteAddress();
            channel.writeAndFlush(request).addListener(new ChannelFutureListener() {
                @Override
                public void operationComplete(ChannelFuture f) throws Exception {
                    if (f.isSuccess()) {
                        responseFuture.setSendRequestOK(true);
                        return;
                    } else {
                        responseFuture.setSendRequestOK(false);
                    }

                    responseTable.remove(opaque);
                    responseFuture.setCause(f.cause());
                    responseFuture.putResponse(null);
                    log.warn("send a request command to channel <" + addr + "> failed.");
                }
            });
            // 区别是同步的是在这等待
            RemotingCommand responseCommand = responseFuture.waitResponse(timeoutMillis);
            if (null == responseCommand) {
                if (responseFuture.isSendRequestOK()) {
                    throw new RemotingTimeoutException(RemotingHelper.parseSocketAddressAddr(addr), timeoutMillis,
                        responseFuture.getCause());
                } else {
                    throw new RemotingSendRequestException(RemotingHelper.parseSocketAddressAddr(addr), responseFuture.getCause());
                }
            }

            return responseCommand;
        } finally {
            this.responseTable.remove(opaque);
        }
    }

    public void invokeAsyncImpl(final Channel channel, final RemotingCommand request, final long timeoutMillis,
        final InvokeCallback invokeCallback)
        throws InterruptedException, RemotingTooMuchRequestException, RemotingTimeoutException, RemotingSendRequestException {
        long beginStartTime = System.currentTimeMillis();
        final int opaque = request.getOpaque();
        boolean acquired = this.semaphoreAsync.tryAcquire(timeoutMillis, TimeUnit.MILLISECONDS);
        if (acquired) {
            final SemaphoreReleaseOnlyOnce once = new SemaphoreReleaseOnlyOnce(this.semaphoreAsync);
            long costTime = System.currentTimeMillis() - beginStartTime;
            if (timeoutMillis < costTime) {
                once.release();
                throw new RemotingTimeoutException("invokeAsyncImpl call timeout");
            }

            final ResponseFuture responseFuture = new ResponseFuture(channel, opaque, timeoutMillis - costTime, invokeCallback, once);
            this.responseTable.put(opaque, responseFuture);
            try {
                channel.writeAndFlush(request).addListener(new ChannelFutureListener() {
                    @Override
                    public void operationComplete(ChannelFuture f) throws Exception {
                        if (f.isSuccess()) {
                            responseFuture.setSendRequestOK(true);
                            return;
                        }
                        requestFail(opaque);
                        log.warn("send a request command to channel <{}> failed.", RemotingHelper.parseChannelRemoteAddr(channel));
                    }
                });
            } catch (Exception e) {
                responseFuture.release();
                log.warn("send a request command to channel <" + RemotingHelper.parseChannelRemoteAddr(channel) + "> Exception", e);
                throw new RemotingSendRequestException(RemotingHelper.parseChannelRemoteAddr(channel), e);
            }
        } else {
            if (timeoutMillis <= 0) {
                throw new RemotingTooMuchRequestException("invokeAsyncImpl invoke too fast");
            } else {
                String info =
                    String.format("invokeAsyncImpl tryAcquire semaphore timeout, %dms, waiting thread nums: %d semaphoreAsyncValue: %d",
                        timeoutMillis,
                        this.semaphoreAsync.getQueueLength(),
                        this.semaphoreAsync.availablePermits()
                    );
                log.warn(info);
                throw new RemotingTimeoutException(info);
            }
        }
    }
- - - -]]> - - MQ - RocketMQ - 消息队列 - RocketMQ - 中间件 - RocketMQ - - - MQ - 消息队列 - RocketMQ - 削峰填谷 - 中间件 - 源码解析 - DefaultMQPushConsumer - - - - 聊一下 RocketMQ 的消息存储之 MMAP - /2021/09/04/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8/ - 这是个很大的话题了，可能会分成两部分说，第一部分就是所谓的零拷贝 ( zero-copy )，这一块其实也不新鲜，我对零拷贝的概念主要来自这篇文章，个人感觉写得非常好，在 rocketmq 中，最大的一块存储就是消息存储，也就是 CommitLog ，当然还有 ConsumeQueue 和 IndexFile，以及其他一些文件，CommitLog 的存储是以一个 1G 大小的文件作为存储单位，写完了就再建一个，那么如何提高这 1G 文件的读写效率呢，就是 mmap，传统意义的读写文件，read，write 都需要由系统调用，来回地在用户态跟内核态进行拷贝切换，

-
read(file, tmp_buf, len);
write(socket, tmp_buf, len);
+ 聊一下 RocketMQ 的消息存储之 MMAP + /2021/09/04/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8/ + 这是个很大的话题了，可能会分成两部分说，第一部分就是所谓的零拷贝 ( zero-copy )，这一块其实也不新鲜，我对零拷贝的概念主要来自这篇文章，个人感觉写得非常好，在 rocketmq 中，最大的一块存储就是消息存储，也就是 CommitLog ，当然还有 ConsumeQueue 和 IndexFile，以及其他一些文件，CommitLog 的存储是以一个 1G 大小的文件作为存储单位，写完了就再建一个，那么如何提高这 1G 文件的读写效率呢，就是 mmap，传统意义的读写文件，read，write 都需要由系统调用，来回地在用户态跟内核态进行拷贝切换，

+
read(file, tmp_buf, len);
write(socket, tmp_buf, len);
@@ -5737,6 +5874,44 @@ user3: RocketMQ + + 聊一下 Java 的日志系列三 + /2024/01/21/%E8%81%8A%E4%B8%80%E4%B8%8B-Java-%E7%9A%84%E6%97%A5%E5%BF%97%E7%B3%BB%E5%88%97%E4%B8%89/ + 上周因为一些事情没有更新在这里，是因为新电脑还没到，手头没有把移动硬盘里的 time machine 恢复出来的机器，所以单独更了一篇在新建的一个 cloudflare page 服务上，总体体验还可以，就是有个小点后面可以讲一下，继续完善下 Java 的这个日志，或者说主要讲的是 logback
前面讲了logback 的初始化逻辑，后面就是我最开始来看这个的逻辑，日志的滚动策略的触发逻辑
我们常规的记录日志的方式比如

+
logger.info("log some thing and biz info: {}",biz);
+

这里就调用了
ch.qos.logback.classic.Logger#info(java.lang.String, java.lang.Object)

+
public void info(String format, Object arg) {
    filterAndLog_1(FQCN, null, Level.INFO, format, arg, null);
}
+

这里的 FQCN 就是这个类的全限定名

+
public static final String FQCN = ch.qos.logback.classic.Logger.class.getName();
+

这个具体的 filterAndLog_1 方法还是在同一个 Logger 类里的
ch.qos.logback.classic.Logger#filterAndLog_1

+
private void filterAndLog_1(final String localFQCN, final Marker marker, final Level level, final String msg, final Object param, final Throwable t) {

    final FilterReply decision = loggerContext.getTurboFilterChainDecision_1(marker, this, level, msg, param, t);

    if (decision == FilterReply.NEUTRAL) {
        if (effectiveLevelInt > level.levelInt) {
            return;
        }
    } else if (decision == FilterReply.DENY) {
        return;
    }

    buildLoggingEventAndAppend(localFQCN, marker, level, msg, new Object[] { param }, t);
}
+

这里面先是走过滤器逻辑，如果是拒绝的，那就不往下执行，也就不执行下面的写日志逻辑了，如果是中性的，那就判断日志级别是否符合，如果直接是拒绝就 return 了，如果是 ACCEPT 就往下执行了

+
public enum FilterReply {
    DENY, NEUTRAL, ACCEPT;
}
+

然后继续调用同样的类的方法
ch.qos.logback.classic.Logger#buildLoggingEventAndAppend

+
private void buildLoggingEventAndAppend(final String localFQCN, final Marker marker, final Level level, final String msg, final Object[] params,
                final Throwable t) {
    LoggingEvent le = new LoggingEvent(localFQCN, this, level, msg, t, params);
    le.setMarker(marker);
    callAppenders(le);
}
+

先把日志包装成 LoggingEvent ，然后调用
ch.qos.logback.classic.Logger#callAppenders

+
public void callAppenders(ILoggingEvent event) {
    int writes = 0;
    for (Logger l = this; l != null; l = l.parent) {
        writes += l.appendLoopOnAppenders(event);
        if (!l.additive) {
            break;
        }
    }
    // No appenders in hierarchy
    if (writes == 0) {
        loggerContext.noAppenderDefinedWarning(this);
    }
}
+

这里会按 Logger 调用 appendLoopOnAppenders

+
private int appendLoopOnAppenders(ILoggingEvent event) {
    if (aai != null) {
        return aai.appendLoopOnAppenders(event);
    } else {
        return 0;
    }
+

继续调用事件的
ch.qos.logback.core.spi.AppenderAttachableImpl#appendLoopOnAppenders

+
public int appendLoopOnAppenders(E e) {
    int size = 0;
    final Appender<E>[] appenderArray = appenderList.asTypedArray();
    final int len = appenderArray.length;
    for (int i = 0; i < len; i++) {
        appenderArray[i].doAppend(e);
        size++;
    }
    return size;
}
+

因为 Logger 里有关联 appender，就具体调用关联的 appender 来添加日志
然后就接着调用
ch.qos.logback.core.UnsynchronizedAppenderBase#doAppend 方法

+
public void doAppend(E eventObject) {
        // WARNING: The guard check MUST be the first statement in the
        // doAppend() method.

        // prevent re-entry.
        if (Boolean.TRUE.equals(guard.get())) {
            return;
        }

        try {
            guard.set(Boolean.TRUE);

            if (!this.started) {
                if (statusRepeatCount++ < ALLOWED_REPEATS) {
                    addStatus(new WarnStatus("Attempted to append to non started appender [" + name + "].", this));
                }
                return;
            }

            if (getFilterChainDecision(eventObject) == FilterReply.DENY) {
                return;
            }

            // ok, we now invoke derived class' implementation of append
            this.append(eventObject);

        } catch (Exception e) {
            if (exceptionCount++ < ALLOWED_REPEATS) {
                addError("Appender [" + name + "] failed to append.", e);
            }
        } finally {
            guard.set(Boolean.FALSE);
        }
    }
+

这里先判断了 guard 的状态，guard 是个 ThreadLocal，是为了拦截异常的无限循环调用，这里有些疑惑的，看了一圈可能相关的是前面的 Logger 会往父级查找，然后如果不同层级的 Logger 关联了相同的 appender 的话，或者这个 doAppend 又被其他 appender 调用了，就可能会出现循环调用，然后会在开始实际的 append的之前先设置 guard 状态，
然后再执行 append，这里会走到
ch.qos.logback.core.OutputStreamAppender#append

+
@Override
protected void append(E eventObject) {
    if (!isStarted()) {
        return;
    }

    subAppend(eventObject);
}
+

忘了补充下，我们这里使用的是
ch.qos.logback.core.rolling.RollingFileAppender
接下去就是调用的 RollingFileAppender 的 subAppend 方法

+
@Override
protected void subAppend(E event) {
    // The roll-over check must precede actual writing. This is the
    // only correct behavior for time driven triggers.

    // We need to synchronize on triggeringPolicy so that only one rollover
    // occurs at a time
    synchronized (triggeringPolicy) {
        if (triggeringPolicy.isTriggeringEvent(currentlyActiveFile, event)) {
            rollover();
        }
    }

    super.subAppend(event);
}
+

这里就会先判断是否触发事件，
而这里就是主要的逻辑，看是否需要走 rollover，也就是具体的滚动逻辑

+
@Override
    public boolean isTriggeringEvent(File activeFile, final E event) {

        long time = getCurrentTime();

        // first check for roll-over based on time
        if (time >= nextCheck) {
            Date dateInElapsedPeriod = dateInCurrentPeriod;
            elapsedPeriodsFileName = tbrp.fileNamePatternWithoutCompSuffix.convertMultipleArguments(dateInElapsedPeriod, currentPeriodsCounter);
            currentPeriodsCounter = 0;
            setDateInCurrentPeriod(time);
            computeNextCheck();
            return true;
        }

        // next check for roll-over based on size
        if (invocationGate.isTooSoon(time)) {
            return false;
        }

        if (activeFile == null) {
            addWarn("activeFile == null");
            return false;
        }
        if (maxFileSize == null) {
            addWarn("maxFileSize = null");
            return false;
        }
        if (activeFile.length() >= maxFileSize.getSize()) {

            elapsedPeriodsFileName = tbrp.fileNamePatternWithoutCompSuffix.convertMultipleArguments(dateInCurrentPeriod, currentPeriodsCounter);
            currentPeriodsCounter++;
            return true;
        }

        return false;
    }
+

这里主要就用到了 nextCheck 判断，如果通过判断就可以执行滚动了，然后就是后面的判断，是否太快了，也就是 isTooSoon 然后再看当前的活跃日志以及最大文件大小是否设置，然后如果当前的活跃日志的大小超过了配置也会触发滚动

+]]> + + Java + + + Java + + 聊一下 RocketMQ 的消息存储三 /2021/10/03/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8%E4%B8%89/ @@ -5760,44 +5935,6 @@ user3:

这里也可以看到 ConsumeQueue 的存储格式，

AA6Tve

偏移量，消息大小，跟 tag 的 hashCode

-]]> - - MQ - RocketMQ - 消息队列 - - - MQ - 消息队列 - RocketMQ - - - - 聊一下 RocketMQ 的消息存储二 - /2021/09/12/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8%E4%BA%8C/ - CommitLog 结构

CommitLog 是 rocketmq 的服务端，也就是 broker 存储消息的的文件，跟 kafka 一样，也是顺序写入，当然消息是变长的，生成的规则是每个文件的默认1G =1024 * 1024 * 1024，commitlog的文件名fileName，名字长度为20位，左边补零，剩余为起始偏移量；比如00000000000000000000代表了第一个文件，起始偏移量为0，文件大小为1G=1 073 741 824Byte；当这个文件满了，第二个文件名字为00000000001073741824，起始偏移量为1073741824, 消息存储的时候会顺序写入文件，当文件满了则写入下一个文件，代码中的定义

-
// CommitLog file size,default is 1G
private int mapedFileSizeCommitLog = 1024 * 1024 * 1024;
- -

kLahwW

-

本地跑个 demo 验证下，也是这样，这里奇妙有几个比较巧妙的点（个人观点），首先文件就刚好是 1G，并且按照大小偏移量去生成下一个文件，这样获取消息的时候按大小算一下就知道在哪个文件里了，

-

代码中写入 CommitLog 的逻辑可以从这开始看

-
public PutMessageResult putMessage(final MessageExtBrokerInner msg) {
        // Set the storage time
        msg.setStoreTimestamp(System.currentTimeMillis());
        // Set the message body BODY CRC (consider the most appropriate setting
        // on the client)
        msg.setBodyCRC(UtilAll.crc32(msg.getBody()));
        // Back to Results
        AppendMessageResult result = null;

        StoreStatsService storeStatsService = this.defaultMessageStore.getStoreStatsService();

        String topic = msg.getTopic();
        int queueId = msg.getQueueId();

        final int tranType = MessageSysFlag.getTransactionValue(msg.getSysFlag());
        if (tranType == MessageSysFlag.TRANSACTION_NOT_TYPE
            || tranType == MessageSysFlag.TRANSACTION_COMMIT_TYPE) {
            // Delay Delivery
            if (msg.getDelayTimeLevel() > 0) {
                if (msg.getDelayTimeLevel() > this.defaultMessageStore.getScheduleMessageService().getMaxDelayLevel()) {
                    msg.setDelayTimeLevel(this.defaultMessageStore.getScheduleMessageService().getMaxDelayLevel());
                }

                topic = ScheduleMessageService.SCHEDULE_TOPIC;
                queueId = ScheduleMessageService.delayLevel2QueueId(msg.getDelayTimeLevel());

                // Backup real topic, queueId
                MessageAccessor.putProperty(msg, MessageConst.PROPERTY_REAL_TOPIC, msg.getTopic());
                MessageAccessor.putProperty(msg, MessageConst.PROPERTY_REAL_QUEUE_ID, String.valueOf(msg.getQueueId()));
                msg.setPropertiesString(MessageDecoder.messageProperties2String(msg.getProperties()));

                msg.setTopic(topic);
                msg.setQueueId(queueId);
            }
        }

        long eclipseTimeInLock = 0;
        MappedFile unlockMappedFile = null;
        MappedFile mappedFile = this.mappedFileQueue.getLastMappedFile();

        putMessageLock.lock(); //spin or ReentrantLock ,depending on store config
        try {
            long beginLockTimestamp = this.defaultMessageStore.getSystemClock().now();
            this.beginTimeInLock = beginLockTimestamp;

            // Here settings are stored timestamp, in order to ensure an orderly
            // global
            msg.setStoreTimestamp(beginLockTimestamp);

            if (null == mappedFile || mappedFile.isFull()) {
                mappedFile = this.mappedFileQueue.getLastMappedFile(0); // Mark: NewFile may be cause noise
            }
            if (null == mappedFile) {
                log.error("create mapped file1 error, topic: " + msg.getTopic() + " clientAddr: " + msg.getBornHostString());
                beginTimeInLock = 0;
                return new PutMessageResult(PutMessageStatus.CREATE_MAPEDFILE_FAILED, null);
            }

            result = mappedFile.appendMessage(msg, this.appendMessageCallback);
            switch (result.getStatus()) {
                case PUT_OK:
                    break;
                case END_OF_FILE:
                    unlockMappedFile = mappedFile;
                    // Create a new file, re-write the message
                    mappedFile = this.mappedFileQueue.getLastMappedFile(0);
                    if (null == mappedFile) {
                        // XXX: warn and notify me
                        log.error("create mapped file2 error, topic: " + msg.getTopic() + " clientAddr: " + msg.getBornHostString());
                        beginTimeInLock = 0;
                        return new PutMessageResult(PutMessageStatus.CREATE_MAPEDFILE_FAILED, result);
                    }
                    result = mappedFile.appendMessage(msg, this.appendMessageCallback);
                    break;
                case MESSAGE_SIZE_EXCEEDED:
                case PROPERTIES_SIZE_EXCEEDED:
                    beginTimeInLock = 0;
                    return new PutMessageResult(PutMessageStatus.MESSAGE_ILLEGAL, result);
                case UNKNOWN_ERROR:
                    beginTimeInLock = 0;
                    return new PutMessageResult(PutMessageStatus.UNKNOWN_ERROR, result);
                default:
                    beginTimeInLock = 0;
                    return new PutMessageResult(PutMessageStatus.UNKNOWN_ERROR, result);
            }

            eclipseTimeInLock = this.defaultMessageStore.getSystemClock().now() - beginLockTimestamp;
            beginTimeInLock = 0;
        } finally {
            putMessageLock.unlock();
        }

        if (eclipseTimeInLock > 500) {
            log.warn("[NOTIFYME]putMessage in lock cost time(ms)={}, bodyLength={} AppendMessageResult={}", eclipseTimeInLock, msg.getBody().length, result);
        }

        if (null != unlockMappedFile && this.defaultMessageStore.getMessageStoreConfig().isWarmMapedFileEnable()) {
            this.defaultMessageStore.unlockMappedFile(unlockMappedFile);
        }

        PutMessageResult putMessageResult = new PutMessageResult(PutMessageStatus.PUT_OK, result);

        // Statistics
        storeStatsService.getSinglePutMessageTopicTimesTotal(msg.getTopic()).incrementAndGet();
        storeStatsService.getSinglePutMessageTopicSizeTotal(topic).addAndGet(result.getWroteBytes());

        handleDiskFlush(result, putMessageResult, msg);
        handleHA(result, putMessageResult, msg);

        return putMessageResult;
    }
- -

前面也看到在CommitLog 目录下是有大小为 1G 的文件组成，在实现逻辑中，其实是通过 org.apache.rocketmq.store.MappedFileQueue ，内部是存的一个MappedFile的队列，对于写入的场景每次都是通过org.apache.rocketmq.store.MappedFileQueue#getLastMappedFile() 获取最后一个文件，如果还没有创建，或者最后这个文件已经满了，那就调用 org.apache.rocketmq.store.MappedFileQueue#getLastMappedFile(long)

-
public MappedFile getLastMappedFile(final long startOffset, boolean needCreate) {
        long createOffset = -1;
  			// 调用前面的方法，只是从 mappedFileQueue 获取最后一个
        MappedFile mappedFileLast = getLastMappedFile();

        // 如果为空，计算下创建的偏移量
        if (mappedFileLast == null) {
            createOffset = startOffset - (startOffset % this.mappedFileSize);
        }
  
				// 如果不为空，但是当前的文件写满了
        if (mappedFileLast != null && mappedFileLast.isFull()) {
            // 前一个的偏移量加上单个文件的偏移量，也就是 1G
            createOffset = mappedFileLast.getFileFromOffset() + this.mappedFileSize;
        }

        if (createOffset != -1 && needCreate) {
            // 根据 createOffset 转换成文件名进行创建
            String nextFilePath = this.storePath + File.separator + UtilAll.offset2FileName(createOffset);
            String nextNextFilePath = this.storePath + File.separator
                + UtilAll.offset2FileName(createOffset + this.mappedFileSize);
            MappedFile mappedFile = null;

          	// 这里如果allocateMappedFileService 存在，就提交请求
            if (this.allocateMappedFileService != null) {
                mappedFile = this.allocateMappedFileService.putRequestAndReturnMappedFile(nextFilePath,
                    nextNextFilePath, this.mappedFileSize);
            } else {
                try {
                  // 否则就直接创建
                    mappedFile = new MappedFile(nextFilePath, this.mappedFileSize);
                } catch (IOException e) {
                    log.error("create mappedFile exception", e);
                }
            }

            if (mappedFile != null) {
                if (this.mappedFiles.isEmpty()) {
                    mappedFile.setFirstCreateInQueue(true);
                }
                this.mappedFiles.add(mappedFile);
            }

            return mappedFile;
        }

        return mappedFileLast;
    }
- -

首先看下直接创建的，

-
public MappedFile(final String fileName, final int fileSize) throws IOException {
        init(fileName, fileSize);
    }
private void init(final String fileName, final int fileSize) throws IOException {
        this.fileName = fileName;
        this.fileSize = fileSize;
        this.file = new File(fileName);
        this.fileFromOffset = Long.parseLong(this.file.getName());
        boolean ok = false;

        ensureDirOK(this.file.getParent());

        try {
          // 通过 RandomAccessFile 创建 fileChannel
            this.fileChannel = new RandomAccessFile(this.file, "rw").getChannel();
          // 做 mmap 映射
            this.mappedByteBuffer = this.fileChannel.map(MapMode.READ_WRITE, 0, fileSize);
            TOTAL_MAPPED_VIRTUAL_MEMORY.addAndGet(fileSize);
            TOTAL_MAPPED_FILES.incrementAndGet();
            ok = true;
        } catch (FileNotFoundException e) {
            log.error("create file channel " + this.fileName + " Failed. ", e);
            throw e;
        } catch (IOException e) {
            log.error("map file " + this.fileName + " Failed. ", e);
            throw e;
        } finally {
            if (!ok && this.fileChannel != null) {
                this.fileChannel.close();
            }
        }
    }
- -

如果是提交给AllocateMappedFileService的话就用到了一些异步操作

-
public MappedFile putRequestAndReturnMappedFile(String nextFilePath, String nextNextFilePath, int fileSize) {
        int canSubmitRequests = 2;
        if (this.messageStore.getMessageStoreConfig().isTransientStorePoolEnable()) {
            if (this.messageStore.getMessageStoreConfig().isFastFailIfNoBufferInStorePool()
                && BrokerRole.SLAVE != this.messageStore.getMessageStoreConfig().getBrokerRole()) { //if broker is slave, don't fast fail even no buffer in pool
                canSubmitRequests = this.messageStore.getTransientStorePool().remainBufferNumbs() - this.requestQueue.size();
            }
        }
				// 将请求放在 requestTable 中
        AllocateRequest nextReq = new AllocateRequest(nextFilePath, fileSize);
        boolean nextPutOK = this.requestTable.putIfAbsent(nextFilePath, nextReq) == null;
        // requestTable 使用了 concurrentHashMap，用文件名作为 key，防止并发
        if (nextPutOK) {
            // 这里判断了是否可以提交到 TransientStorePool，涉及读写分离，后面再细聊
            if (canSubmitRequests <= 0) {
                log.warn("[NOTIFYME]TransientStorePool is not enough, so create mapped file error, " +
                    "RequestQueueSize : {}, StorePoolSize: {}", this.requestQueue.size(), this.messageStore.getTransientStorePool().remainBufferNumbs());
                this.requestTable.remove(nextFilePath);
                return null;
            }
          // 塞到阻塞队列中
            boolean offerOK = this.requestQueue.offer(nextReq);
            if (!offerOK) {
                log.warn("never expected here, add a request to preallocate queue failed");
            }
            canSubmitRequests--;
        }

        // 这里的两个提交我猜测是为了多生成一个 CommitLog，
        AllocateRequest nextNextReq = new AllocateRequest(nextNextFilePath, fileSize);
        boolean nextNextPutOK = this.requestTable.putIfAbsent(nextNextFilePath, nextNextReq) == null;
        if (nextNextPutOK) {
            if (canSubmitRequests <= 0) {
                log.warn("[NOTIFYME]TransientStorePool is not enough, so skip preallocate mapped file, " +
                    "RequestQueueSize : {}, StorePoolSize: {}", this.requestQueue.size(), this.messageStore.getTransientStorePool().remainBufferNumbs());
                this.requestTable.remove(nextNextFilePath);
            } else {
                boolean offerOK = this.requestQueue.offer(nextNextReq);
                if (!offerOK) {
                    log.warn("never expected here, add a request to preallocate queue failed");
                }
            }
        }

        if (hasException) {
            log.warn(this.getServiceName() + " service has exception. so return null");
            return null;
        }

        AllocateRequest result = this.requestTable.get(nextFilePath);
        try {
          // 这里就异步等着
            if (result != null) {
                boolean waitOK = result.getCountDownLatch().await(waitTimeOut, TimeUnit.MILLISECONDS);
                if (!waitOK) {
                    log.warn("create mmap timeout " + result.getFilePath() + " " + result.getFileSize());
                    return null;
                } else {
                    this.requestTable.remove(nextFilePath);
                    return result.getMappedFile();
                }
            } else {
                log.error("find preallocate mmap failed, this never happen");
            }
        } catch (InterruptedException e) {
            log.warn(this.getServiceName() + " service has exception. ", e);
        }

        return null;
    }
- -

而真正去执行文件操作的就是 AllocateMappedFileService的 run 方法

-
public void run() {
        log.info(this.getServiceName() + " service started");

        while (!this.isStopped() && this.mmapOperation()) {

        }
        log.info(this.getServiceName() + " service end");
    }
private boolean mmapOperation() {
        boolean isSuccess = false;
        AllocateRequest req = null;
        try {
          // 从阻塞队列里获取请求
            req = this.requestQueue.take();
            AllocateRequest expectedRequest = this.requestTable.get(req.getFilePath());
            if (null == expectedRequest) {
                log.warn("this mmap request expired, maybe cause timeout " + req.getFilePath() + " "
                    + req.getFileSize());
                return true;
            }
            if (expectedRequest != req) {
                log.warn("never expected here,  maybe cause timeout " + req.getFilePath() + " "
                    + req.getFileSize() + ", req:" + req + ", expectedRequest:" + expectedRequest);
                return true;
            }

            if (req.getMappedFile() == null) {
                long beginTime = System.currentTimeMillis();

                MappedFile mappedFile;
                if (messageStore.getMessageStoreConfig().isTransientStorePoolEnable()) {
                    try {
                      // 通过 transientStorePool 创建
                        mappedFile = ServiceLoader.load(MappedFile.class).iterator().next();
                        mappedFile.init(req.getFilePath(), req.getFileSize(), messageStore.getTransientStorePool());
                    } catch (RuntimeException e) {
                        log.warn("Use default implementation.");
                      // 默认创建
                        mappedFile = new MappedFile(req.getFilePath(), req.getFileSize(), messageStore.getTransientStorePool());
                    }
                } else {
                  // 默认创建
                    mappedFile = new MappedFile(req.getFilePath(), req.getFileSize());
                }

                long eclipseTime = UtilAll.computeEclipseTimeMilliseconds(beginTime);
                if (eclipseTime > 10) {
                    int queueSize = this.requestQueue.size();
                    log.warn("create mappedFile spent time(ms) " + eclipseTime + " queue size " + queueSize
                        + " " + req.getFilePath() + " " + req.getFileSize());
                }

                // pre write mappedFile
                if (mappedFile.getFileSize() >= this.messageStore.getMessageStoreConfig()
                    .getMapedFileSizeCommitLog()
                    &&
                    this.messageStore.getMessageStoreConfig().isWarmMapedFileEnable()) {
                    mappedFile.warmMappedFile(this.messageStore.getMessageStoreConfig().getFlushDiskType(),
                        this.messageStore.getMessageStoreConfig().getFlushLeastPagesWhenWarmMapedFile());
                }

                req.setMappedFile(mappedFile);
                this.hasException = false;
                isSuccess = true;
            }
        } catch (InterruptedException e) {
            log.warn(this.getServiceName() + " interrupted, possibly by shutdown.");
            this.hasException = true;
            return false;
        } catch (IOException e) {
            log.warn(this.getServiceName() + " service has exception. ", e);
            this.hasException = true;
            if (null != req) {
                requestQueue.offer(req);
                try {
                    Thread.sleep(1);
                } catch (InterruptedException ignored) {
                }
            }
        } finally {
            if (req != null && isSuccess)
              // 通知前面等待的
                req.getCountDownLatch().countDown();
        }
        return true;
    }
- - - - ]]> MQ @@ -5824,44 +5961,6 @@ user3: 
public boolean putKey(final String key, final long phyOffset, final long storeTimestamp) {
        if (this.indexHeader.getIndexCount() < this.indexNum) {
          // 获取 key 的 hash
            int keyHash = indexKeyHashMethod(key);
          // 计算属于哪个 slot
            int slotPos = keyHash % this.hashSlotNum;
          // 计算 slot 位置 因为结构是有个 indexHead，主要是分为三段 header，slot 和 index
            int absSlotPos = IndexHeader.INDEX_HEADER_SIZE + slotPos * hashSlotSize;

            FileLock fileLock = null;

            try {

                // fileLock = this.fileChannel.lock(absSlotPos, hashSlotSize,
                // false);
                int slotValue = this.mappedByteBuffer.getInt(absSlotPos);
                if (slotValue <= invalidIndex || slotValue > this.indexHeader.getIndexCount()) {
                    slotValue = invalidIndex;
                }

                long timeDiff = storeTimestamp - this.indexHeader.getBeginTimestamp();

                timeDiff = timeDiff / 1000;

                if (this.indexHeader.getBeginTimestamp() <= 0) {
                    timeDiff = 0;
                } else if (timeDiff > Integer.MAX_VALUE) {
                    timeDiff = Integer.MAX_VALUE;
                } else if (timeDiff < 0) {
                    timeDiff = 0;
                }

              // 计算索引存放位置，头部 + slot 数量 * slot 大小 + 已有的 index 数量 + index 大小
                int absIndexPos =
                    IndexHeader.INDEX_HEADER_SIZE + this.hashSlotNum * hashSlotSize
                        + this.indexHeader.getIndexCount() * indexSize;
							
                this.mappedByteBuffer.putInt(absIndexPos, keyHash);
                this.mappedByteBuffer.putLong(absIndexPos + 4, phyOffset);
                this.mappedByteBuffer.putInt(absIndexPos + 4 + 8, (int) timeDiff);
                this.mappedByteBuffer.putInt(absIndexPos + 4 + 8 + 4, slotValue);

              // 存放的是数量位移，不是绝对位置
                this.mappedByteBuffer.putInt(absSlotPos, this.indexHeader.getIndexCount());

                if (this.indexHeader.getIndexCount() <= 1) {
                    this.indexHeader.setBeginPhyOffset(phyOffset);
                    this.indexHeader.setBeginTimestamp(storeTimestamp);
                }

                this.indexHeader.incHashSlotCount();
                this.indexHeader.incIndexCount();
                this.indexHeader.setEndPhyOffset(phyOffset);
                this.indexHeader.setEndTimestamp(storeTimestamp);

                return true;
            } catch (Exception e) {
                log.error("putKey exception, Key: " + key + " KeyHashCode: " + key.hashCode(), e);
            } finally {
                if (fileLock != null) {
                    try {
                        fileLock.release();
                    } catch (IOException e) {
                        log.error("Failed to release the lock", e);
                    }
                }
            }
        } else {
            log.warn("Over index file capacity: index count = " + this.indexHeader.getIndexCount()
                + "; index max num = " + this.indexNum);
        }

        return false;
    }

具体可以看一下这个简略的示意图

-]]> - - MQ - RocketMQ - 消息队列 - - - MQ - 消息队列 - RocketMQ - - - - 聊一下 RocketMQ 的顺序消息 - /2021/08/29/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E9%A1%BA%E5%BA%8F%E6%B6%88%E6%81%AF/ - rocketmq 里有一种比较特殊的用法，就是顺序消息，比如订单的生命周期里，在创建，支付，签收等状态轮转中，会发出来对应的消息，这里面就比较需要去保证他们的顺序，当然在处理的业务代码也可以做对应的处理，结合消息重投，但是如果这里消息就能保证顺序性了，那么业务代码就能更好的关注业务代码的处理。

-

首先有一种情况是全局的有序，比如对于一个 topic 里就得发送线程保证只有一个，内部的 queue 也只有一个，消费线程也只有一个，这样就能比较容易的保证全局顺序性了，但是这里的问题就是完全限制了性能，不是很现实，在真实场景里很多都是比如对于同一个订单，需要去保证状态的轮转是按照预期的顺序来，而不必要全局的有序性。

-

对于这类的有序性，需要在发送和接收方都有对应的处理，在发送消息中，需要去指定 selector，即MessageQueueSelector，能够以固定的方式是分配到对应的 MessageQueue

-

比如像 RocketMQ 中的示例

-
SendResult sendResult = producer.send(msg, new MessageQueueSelector() {
                @Override
                public MessageQueue select(List<MessageQueue> mqs, Message msg, Object arg) {
                    Long id = (Long) arg;  //message queue is selected by #salesOrderID
                    long index = id % mqs.size();
                    return mqs.get((int) index);
                }
            }, orderList.get(i).getOrderId());
- -

而在消费侧有几个点比较重要，首先我们要保证一个 MessageQueue只被一个消费者消费，消费队列存在broker端，要保证 MessageQueue 只被一个消费者消费，那么消费者在进行消息拉取消费时就必须向mq服务器申请队列锁，消费者申请队列锁的代码存在于RebalanceService消息队列负载的实现代码中。

-
List<PullRequest> pullRequestList = new ArrayList<PullRequest>();
        for (MessageQueue mq : mqSet) {
            if (!this.processQueueTable.containsKey(mq)) {
              // 判断是否顺序，如果是顺序消费的，则需要加锁
                if (isOrder && !this.lock(mq)) {
                    log.warn("doRebalance, {}, add a new mq failed, {}, because lock failed", consumerGroup, mq);
                    continue;
                }

                this.removeDirtyOffset(mq);
                ProcessQueue pq = new ProcessQueue();
                long nextOffset = this.computePullFromWhere(mq);
                if (nextOffset >= 0) {
                    ProcessQueue pre = this.processQueueTable.putIfAbsent(mq, pq);
                    if (pre != null) {
                        log.info("doRebalance, {}, mq already exists, {}", consumerGroup, mq);
                    } else {
                        log.info("doRebalance, {}, add a new mq, {}", consumerGroup, mq);
                        PullRequest pullRequest = new PullRequest();
                        pullRequest.setConsumerGroup(consumerGroup);
                        pullRequest.setNextOffset(nextOffset);
                        pullRequest.setMessageQueue(mq);
                        pullRequest.setProcessQueue(pq);
                        pullRequestList.add(pullRequest);
                        changed = true;
                    }
                } else {
                    log.warn("doRebalance, {}, add new mq failed, {}", consumerGroup, mq);
                }
            }
        }
- -

在申请到锁之后会创建 pullRequest 进行消息拉取，消息拉取部分的代码实现在PullMessageService中，

-
@Override
    public void run() {
        log.info(this.getServiceName() + " service started");

        while (!this.isStopped()) {
            try {
                PullRequest pullRequest = this.pullRequestQueue.take();
                this.pullMessage(pullRequest);
            } catch (InterruptedException ignored) {
            } catch (Exception e) {
                log.error("Pull Message Service Run Method exception", e);
            }
        }

        log.info(this.getServiceName() + " service end");
    }
- -

消息拉取完后，需要提交到ConsumeMessageService中进行消费，顺序消费的实现为ConsumeMessageOrderlyService，提交消息进行消费的方法为ConsumeMessageOrderlyService#submitConsumeRequest，具体实现如下：

-
@Override
public void submitConsumeRequest(
    final List<MessageExt> msgs,
    final ProcessQueue processQueue,
    final MessageQueue messageQueue,
    final boolean dispathToConsume) {
    if (dispathToConsume) {
        ConsumeRequest consumeRequest = new ConsumeRequest(processQueue, messageQueue);
        this.consumeExecutor.submit(consumeRequest);
    }
}
- -

构建了一个ConsumeRequest对象，它是个实现了 runnable 接口的类，并提交给了线程池来并行消费，看下顺序消费的ConsumeRequest的run方法实现：

-
@Override
        public void run() {
            if (this.processQueue.isDropped()) {
                log.warn("run, the message queue not be able to consume, because it's dropped. {}", this.messageQueue);
                return;
            }
						// 获得 Consumer 消息队列锁，即单个线程独占
            final Object objLock = messageQueueLock.fetchLockObject(this.messageQueue);
            synchronized (objLock) {
              // (广播模式) 或者 (集群模式 && Broker消息队列锁有效)
                if (MessageModel.BROADCASTING.equals(ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.messageModel())
                    || (this.processQueue.isLocked() && !this.processQueue.isLockExpired())) {
                    final long beginTime = System.currentTimeMillis();
                  // 循环
                    for (boolean continueConsume = true; continueConsume; ) {
                        if (this.processQueue.isDropped()) {
                            log.warn("the message queue not be able to consume, because it's dropped. {}", this.messageQueue);
                            break;
                        }

                      // 消息队列分布式锁未锁定，提交延迟获得锁并消费请求
                        if (MessageModel.CLUSTERING.equals(ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.messageModel())
                            && !this.processQueue.isLocked()) {
                            log.warn("the message queue not locked, so consume later, {}", this.messageQueue);
                            ConsumeMessageOrderlyService.this.tryLockLaterAndReconsume(this.messageQueue, this.processQueue, 10);
                            break;
                        }

                      // 消息队列分布式锁已经过期，提交延迟获得锁并消费请求
                        if (MessageModel.CLUSTERING.equals(ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.messageModel())
                            && this.processQueue.isLockExpired()) {
                            log.warn("the message queue lock expired, so consume later, {}", this.messageQueue);
                            ConsumeMessageOrderlyService.this.tryLockLaterAndReconsume(this.messageQueue, this.processQueue, 10);
                            break;
                        }
												// 当前周期消费时间超过连续时长，默认：60s，提交延迟消费请求。默认情况下，每消费1分钟休息10ms。
                        long interval = System.currentTimeMillis() - beginTime;
                        if (interval > MAX_TIME_CONSUME_CONTINUOUSLY) {
                            ConsumeMessageOrderlyService.this.submitConsumeRequestLater(processQueue, messageQueue, 10);
                            break;
                        }
												// 获取消费消息。此处和并发消息请求不同，并发消息请求已经带了消费哪些消息。
                        final int consumeBatchSize =
                            ConsumeMessageOrderlyService.this.defaultMQPushConsumer.getConsumeMessageBatchMaxSize();

                        List<MessageExt> msgs = this.processQueue.takeMessags(consumeBatchSize);
                        defaultMQPushConsumerImpl.resetRetryAndNamespace(msgs, defaultMQPushConsumer.getConsumerGroup());
                        if (!msgs.isEmpty()) {
                            final ConsumeOrderlyContext context = new ConsumeOrderlyContext(this.messageQueue);

                            ConsumeOrderlyStatus status = null;

                            ConsumeMessageContext consumeMessageContext = null;
                            if (ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.hasHook()) {
                                consumeMessageContext = new ConsumeMessageContext();
                                consumeMessageContext
                                    .setConsumerGroup(ConsumeMessageOrderlyService.this.defaultMQPushConsumer.getConsumerGroup());
                                consumeMessageContext.setNamespace(defaultMQPushConsumer.getNamespace());
                                consumeMessageContext.setMq(messageQueue);
                                consumeMessageContext.setMsgList(msgs);
                                consumeMessageContext.setSuccess(false);
                                // init the consume context type
                                consumeMessageContext.setProps(new HashMap<String, String>());
                                ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.executeHookBefore(consumeMessageContext);
                            }
														// 执行消费
                            long beginTimestamp = System.currentTimeMillis();
                            ConsumeReturnType returnType = ConsumeReturnType.SUCCESS;
                            boolean hasException = false;
                            try {
                                this.processQueue.getLockConsume().lock(); // 锁定处理队列
                                if (this.processQueue.isDropped()) {
                                    log.warn("consumeMessage, the message queue not be able to consume, because it's dropped. {}",
                                        this.messageQueue);
                                    break;
                                }

                                status = messageListener.consumeMessage(Collections.unmodifiableList(msgs), context);
                            } catch (Throwable e) {
                                log.warn("consumeMessage exception: {} Group: {} Msgs: {} MQ: {}",
                                    RemotingHelper.exceptionSimpleDesc(e),
                                    ConsumeMessageOrderlyService.this.consumerGroup,
                                    msgs,
                                    messageQueue);
                                hasException = true;
                            } finally {
                                this.processQueue.getLockConsume().unlock();  // 解锁
                            }

                            if (null == status
                                || ConsumeOrderlyStatus.ROLLBACK == status
                                || ConsumeOrderlyStatus.SUSPEND_CURRENT_QUEUE_A_MOMENT == status) {
                                log.warn("consumeMessage Orderly return not OK, Group: {} Msgs: {} MQ: {}",
                                    ConsumeMessageOrderlyService.this.consumerGroup,
                                    msgs,
                                    messageQueue);
                            }

                            long consumeRT = System.currentTimeMillis() - beginTimestamp;
                            if (null == status) {
                                if (hasException) {
                                    returnType = ConsumeReturnType.EXCEPTION;
                                } else {
                                    returnType = ConsumeReturnType.RETURNNULL;
                                }
                            } else if (consumeRT >= defaultMQPushConsumer.getConsumeTimeout() * 60 * 1000) {
                                returnType = ConsumeReturnType.TIME_OUT;
                            } else if (ConsumeOrderlyStatus.SUSPEND_CURRENT_QUEUE_A_MOMENT == status) {
                                returnType = ConsumeReturnType.FAILED;
                            } else if (ConsumeOrderlyStatus.SUCCESS == status) {
                                returnType = ConsumeReturnType.SUCCESS;
                            }

                            if (ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.hasHook()) {
                                consumeMessageContext.getProps().put(MixAll.CONSUME_CONTEXT_TYPE, returnType.name());
                            }

                            if (null == status) {
                                status = ConsumeOrderlyStatus.SUSPEND_CURRENT_QUEUE_A_MOMENT;
                            }

                            if (ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.hasHook()) {
                                consumeMessageContext.setStatus(status.toString());
                                consumeMessageContext
                                    .setSuccess(ConsumeOrderlyStatus.SUCCESS == status || ConsumeOrderlyStatus.COMMIT == status);
                                ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.executeHookAfter(consumeMessageContext);
                            }

                            ConsumeMessageOrderlyService.this.getConsumerStatsManager()
                                .incConsumeRT(ConsumeMessageOrderlyService.this.consumerGroup, messageQueue.getTopic(), consumeRT);

                            continueConsume = ConsumeMessageOrderlyService.this.processConsumeResult(msgs, status, context, this);
                        } else {
                            continueConsume = false;
                        }
                    }
                } else {
                    if (this.processQueue.isDropped()) {
                        log.warn("the message queue not be able to consume, because it's dropped. {}", this.messageQueue);
                        return;
                    }

                    ConsumeMessageOrderlyService.this.tryLockLaterAndReconsume(this.messageQueue, this.processQueue, 100);
                }
            }
        }
- -

获取到锁对象后，使用synchronized尝试申请线程级独占锁。

-

如果加锁成功，同一时刻只有一个线程进行消息消费。

-

如果加锁失败，会延迟100ms重新尝试向broker端申请锁定messageQueue，锁定成功后重新提交消费请求

-

创建消息拉取任务时，消息客户端向broker端申请锁定MessageQueue，使得一个MessageQueue同一个时刻只能被一个消费客户端消费。

-

消息消费时，多线程针对同一个消息队列的消费先尝试使用synchronized申请独占锁，加锁成功才能进行消费，使得一个MessageQueue同一个时刻只能被一个消费客户端中一个线程消费。
这里其实还有很重要的一点是对processQueue的加锁，这里其实是保证了在 rebalance的过程中如果 processQueue 被分配给了另一个 consumer，但是当前已经被我这个 consumer 再消费，但是没提交，就有可能出现被两个消费者消费，所以得进行加锁保证不受 rebalance 影响。

]]> MQ @@ -5917,19 +6016,41 @@ user3: - 折腾记-给玩客云刷上Armbian - /2024/03/31/%E6%8A%98%E8%85%BE%E8%AE%B0-%E7%BB%99%E7%8E%A9%E5%AE%A2%E4%BA%91%E5%88%B7%E4%B8%8AArmbian/ - 之前买来玩客云作为轻nas使用，但是由于只能外接硬盘，并且是usb2.0的接口，使用体验不是特别好，并且前期作为老母鸡还能兑换视频会员这种，后面取消了就不怎么使用了，前阵子玩客云停止提供服务了，所以就想可以折腾下，比如Armbian这样的，可以作为轻量服务器使用，功耗也比较低，跑一些简单的docker还是可以的，

配置是这样的，存储小了点，其他就是一个普通小服务器的配置了，usb2.0有点弱，但是现在有一些是正常退役，还有很多是PCDN被禁了宽带以后咸鱼出售的，带上电源跟网线才30左右，还是比较有性价比的
首先机器有几个版本，主要是1.0跟1.3，要注意1.3版本的主板上有个混淆点，会有一个1.2的版本，指的是具体存储或者网卡等组件的版本，玩客云版本是较大的 V1.3，而1.0则是比较小的字体，而且据网上的资料，1.0版本是有个MAC地址的贴条

可以看上图的标示，箭头指向的是短接点，这是1.0版本的，1.3版本是找不到这个短接点，而是在反面（有S805芯片和存储的一面）
新版手边没有，借用一张恩山的图

这个在最开始我刷第一个的时候比较困扰我，因为不确定版本，网上又有很多都是只截一小块区域的图，也不知道相对位置
首先如果是从闲鱼买来的已经刷成PCDN，某心云的需要通过短接刷底包之后再刷入系统，可以先用吹风机热风吹一会后盖，然后用一字螺丝刀从SD卡槽把外层的一片壳撬开，然后通过十字螺丝刀拧开之后就可以把主板拿出来
然后需要镊子或者其他铁丝，能够短接的就行，外加一个双公头USB线
刷机工具包可以用我这个分享的

-
https://www.alipan.com/s/sd4Wi6TDzeP
提取码: 84or
-

第一步打开USB Burning Tool, 要短接刷入update.img

通过文件导入网盘里的 update.img，然后先用双公头USB线连接电脑的USB口和玩客云靠近HDMI口的USB口，如果方便最好接到电脑后置的USB口，防止不稳定刷机终端，接下来就需要短接了，这里的姿势要先短接好，保持稳定，然后插上电源线，这里如果连接成功就会在软件左上会显示一栏连接成功，如果电脑连接音响也会有叮咚这样的声音，接下去就可以点开始进行刷入底包了，这里最大的困难点就是保持短接稳定后插电源，并且继续保持稳定刷入底包，底包刷入会比较快，等到刷机进度条走完刷成功后就点停止，然后关闭软件，拔掉玩客云电源，拔掉USB
第二步是刷入Armbian系统，根据一些教程有些说需要先刷入5.8，我这边是直接刷入了5.9的系统，也就多一步，准备一个U盘，16G或者8G都行，先用Etcher把5.8系统刷入U盘，刷入完成后再关闭软件，推出U盘，将U盘插入玩客云上靠近网口的USB口，然后再通电，指示灯会蓝色-> 绿色 -> 蓝紫闪烁 -> 蓝色，这样就是刷机成功了，会需要一点时间，刷机完成后再把5.9的的刷入U盘，再继续刚才的步骤，此时需要把网线插入玩客云，等待正常亮灯显示后，在路由器上可以找到一个aml-s812的设备，在通过ssh连接，账号是root，密码是1234，进入系统后通过以下命令就可以刷入emmc了

-
cd /boot/install  sudo ./install.sh
-

完成后会自动断开，拔掉U盘后再用同样方法连接就可以了，后续怎么使用就是Linux相关的了，后面也可以讲下docker的操作，但要注意架构，这个是arm架构32位的

+ 聊一下 RocketMQ 的消息存储二 + /2021/09/12/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E6%B6%88%E6%81%AF%E5%AD%98%E5%82%A8%E4%BA%8C/ + CommitLog 结构

CommitLog 是 rocketmq 的服务端，也就是 broker 存储消息的的文件，跟 kafka 一样，也是顺序写入，当然消息是变长的，生成的规则是每个文件的默认1G =1024 * 1024 * 1024，commitlog的文件名fileName，名字长度为20位，左边补零，剩余为起始偏移量；比如00000000000000000000代表了第一个文件，起始偏移量为0，文件大小为1G=1 073 741 824Byte；当这个文件满了，第二个文件名字为00000000001073741824，起始偏移量为1073741824, 消息存储的时候会顺序写入文件，当文件满了则写入下一个文件，代码中的定义

+
// CommitLog file size,default is 1G
private int mapedFileSizeCommitLog = 1024 * 1024 * 1024;
+ +

kLahwW

+

本地跑个 demo 验证下，也是这样，这里奇妙有几个比较巧妙的点（个人观点），首先文件就刚好是 1G，并且按照大小偏移量去生成下一个文件，这样获取消息的时候按大小算一下就知道在哪个文件里了，

+

代码中写入 CommitLog 的逻辑可以从这开始看

+
public PutMessageResult putMessage(final MessageExtBrokerInner msg) {
        // Set the storage time
        msg.setStoreTimestamp(System.currentTimeMillis());
        // Set the message body BODY CRC (consider the most appropriate setting
        // on the client)
        msg.setBodyCRC(UtilAll.crc32(msg.getBody()));
        // Back to Results
        AppendMessageResult result = null;

        StoreStatsService storeStatsService = this.defaultMessageStore.getStoreStatsService();

        String topic = msg.getTopic();
        int queueId = msg.getQueueId();

        final int tranType = MessageSysFlag.getTransactionValue(msg.getSysFlag());
        if (tranType == MessageSysFlag.TRANSACTION_NOT_TYPE
            || tranType == MessageSysFlag.TRANSACTION_COMMIT_TYPE) {
            // Delay Delivery
            if (msg.getDelayTimeLevel() > 0) {
                if (msg.getDelayTimeLevel() > this.defaultMessageStore.getScheduleMessageService().getMaxDelayLevel()) {
                    msg.setDelayTimeLevel(this.defaultMessageStore.getScheduleMessageService().getMaxDelayLevel());
                }

                topic = ScheduleMessageService.SCHEDULE_TOPIC;
                queueId = ScheduleMessageService.delayLevel2QueueId(msg.getDelayTimeLevel());

                // Backup real topic, queueId
                MessageAccessor.putProperty(msg, MessageConst.PROPERTY_REAL_TOPIC, msg.getTopic());
                MessageAccessor.putProperty(msg, MessageConst.PROPERTY_REAL_QUEUE_ID, String.valueOf(msg.getQueueId()));
                msg.setPropertiesString(MessageDecoder.messageProperties2String(msg.getProperties()));

                msg.setTopic(topic);
                msg.setQueueId(queueId);
            }
        }

        long eclipseTimeInLock = 0;
        MappedFile unlockMappedFile = null;
        MappedFile mappedFile = this.mappedFileQueue.getLastMappedFile();

        putMessageLock.lock(); //spin or ReentrantLock ,depending on store config
        try {
            long beginLockTimestamp = this.defaultMessageStore.getSystemClock().now();
            this.beginTimeInLock = beginLockTimestamp;

            // Here settings are stored timestamp, in order to ensure an orderly
            // global
            msg.setStoreTimestamp(beginLockTimestamp);

            if (null == mappedFile || mappedFile.isFull()) {
                mappedFile = this.mappedFileQueue.getLastMappedFile(0); // Mark: NewFile may be cause noise
            }
            if (null == mappedFile) {
                log.error("create mapped file1 error, topic: " + msg.getTopic() + " clientAddr: " + msg.getBornHostString());
                beginTimeInLock = 0;
                return new PutMessageResult(PutMessageStatus.CREATE_MAPEDFILE_FAILED, null);
            }

            result = mappedFile.appendMessage(msg, this.appendMessageCallback);
            switch (result.getStatus()) {
                case PUT_OK:
                    break;
                case END_OF_FILE:
                    unlockMappedFile = mappedFile;
                    // Create a new file, re-write the message
                    mappedFile = this.mappedFileQueue.getLastMappedFile(0);
                    if (null == mappedFile) {
                        // XXX: warn and notify me
                        log.error("create mapped file2 error, topic: " + msg.getTopic() + " clientAddr: " + msg.getBornHostString());
                        beginTimeInLock = 0;
                        return new PutMessageResult(PutMessageStatus.CREATE_MAPEDFILE_FAILED, result);
                    }
                    result = mappedFile.appendMessage(msg, this.appendMessageCallback);
                    break;
                case MESSAGE_SIZE_EXCEEDED:
                case PROPERTIES_SIZE_EXCEEDED:
                    beginTimeInLock = 0;
                    return new PutMessageResult(PutMessageStatus.MESSAGE_ILLEGAL, result);
                case UNKNOWN_ERROR:
                    beginTimeInLock = 0;
                    return new PutMessageResult(PutMessageStatus.UNKNOWN_ERROR, result);
                default:
                    beginTimeInLock = 0;
                    return new PutMessageResult(PutMessageStatus.UNKNOWN_ERROR, result);
            }

            eclipseTimeInLock = this.defaultMessageStore.getSystemClock().now() - beginLockTimestamp;
            beginTimeInLock = 0;
        } finally {
            putMessageLock.unlock();
        }

        if (eclipseTimeInLock > 500) {
            log.warn("[NOTIFYME]putMessage in lock cost time(ms)={}, bodyLength={} AppendMessageResult={}", eclipseTimeInLock, msg.getBody().length, result);
        }

        if (null != unlockMappedFile && this.defaultMessageStore.getMessageStoreConfig().isWarmMapedFileEnable()) {
            this.defaultMessageStore.unlockMappedFile(unlockMappedFile);
        }

        PutMessageResult putMessageResult = new PutMessageResult(PutMessageStatus.PUT_OK, result);

        // Statistics
        storeStatsService.getSinglePutMessageTopicTimesTotal(msg.getTopic()).incrementAndGet();
        storeStatsService.getSinglePutMessageTopicSizeTotal(topic).addAndGet(result.getWroteBytes());

        handleDiskFlush(result, putMessageResult, msg);
        handleHA(result, putMessageResult, msg);

        return putMessageResult;
    }
+ +

前面也看到在CommitLog 目录下是有大小为 1G 的文件组成，在实现逻辑中，其实是通过 org.apache.rocketmq.store.MappedFileQueue ，内部是存的一个MappedFile的队列，对于写入的场景每次都是通过org.apache.rocketmq.store.MappedFileQueue#getLastMappedFile() 获取最后一个文件，如果还没有创建，或者最后这个文件已经满了，那就调用 org.apache.rocketmq.store.MappedFileQueue#getLastMappedFile(long)

+
public MappedFile getLastMappedFile(final long startOffset, boolean needCreate) {
        long createOffset = -1;
  			// 调用前面的方法，只是从 mappedFileQueue 获取最后一个
        MappedFile mappedFileLast = getLastMappedFile();

        // 如果为空，计算下创建的偏移量
        if (mappedFileLast == null) {
            createOffset = startOffset - (startOffset % this.mappedFileSize);
        }
  
				// 如果不为空，但是当前的文件写满了
        if (mappedFileLast != null && mappedFileLast.isFull()) {
            // 前一个的偏移量加上单个文件的偏移量，也就是 1G
            createOffset = mappedFileLast.getFileFromOffset() + this.mappedFileSize;
        }

        if (createOffset != -1 && needCreate) {
            // 根据 createOffset 转换成文件名进行创建
            String nextFilePath = this.storePath + File.separator + UtilAll.offset2FileName(createOffset);
            String nextNextFilePath = this.storePath + File.separator
                + UtilAll.offset2FileName(createOffset + this.mappedFileSize);
            MappedFile mappedFile = null;

          	// 这里如果allocateMappedFileService 存在，就提交请求
            if (this.allocateMappedFileService != null) {
                mappedFile = this.allocateMappedFileService.putRequestAndReturnMappedFile(nextFilePath,
                    nextNextFilePath, this.mappedFileSize);
            } else {
                try {
                  // 否则就直接创建
                    mappedFile = new MappedFile(nextFilePath, this.mappedFileSize);
                } catch (IOException e) {
                    log.error("create mappedFile exception", e);
                }
            }

            if (mappedFile != null) {
                if (this.mappedFiles.isEmpty()) {
                    mappedFile.setFirstCreateInQueue(true);
                }
                this.mappedFiles.add(mappedFile);
            }

            return mappedFile;
        }

        return mappedFileLast;
    }
+ +

首先看下直接创建的，

+
public MappedFile(final String fileName, final int fileSize) throws IOException {
        init(fileName, fileSize);
    }
private void init(final String fileName, final int fileSize) throws IOException {
        this.fileName = fileName;
        this.fileSize = fileSize;
        this.file = new File(fileName);
        this.fileFromOffset = Long.parseLong(this.file.getName());
        boolean ok = false;

        ensureDirOK(this.file.getParent());

        try {
          // 通过 RandomAccessFile 创建 fileChannel
            this.fileChannel = new RandomAccessFile(this.file, "rw").getChannel();
          // 做 mmap 映射
            this.mappedByteBuffer = this.fileChannel.map(MapMode.READ_WRITE, 0, fileSize);
            TOTAL_MAPPED_VIRTUAL_MEMORY.addAndGet(fileSize);
            TOTAL_MAPPED_FILES.incrementAndGet();
            ok = true;
        } catch (FileNotFoundException e) {
            log.error("create file channel " + this.fileName + " Failed. ", e);
            throw e;
        } catch (IOException e) {
            log.error("map file " + this.fileName + " Failed. ", e);
            throw e;
        } finally {
            if (!ok && this.fileChannel != null) {
                this.fileChannel.close();
            }
        }
    }
+ +

如果是提交给AllocateMappedFileService的话就用到了一些异步操作

+
public MappedFile putRequestAndReturnMappedFile(String nextFilePath, String nextNextFilePath, int fileSize) {
        int canSubmitRequests = 2;
        if (this.messageStore.getMessageStoreConfig().isTransientStorePoolEnable()) {
            if (this.messageStore.getMessageStoreConfig().isFastFailIfNoBufferInStorePool()
                && BrokerRole.SLAVE != this.messageStore.getMessageStoreConfig().getBrokerRole()) { //if broker is slave, don't fast fail even no buffer in pool
                canSubmitRequests = this.messageStore.getTransientStorePool().remainBufferNumbs() - this.requestQueue.size();
            }
        }
				// 将请求放在 requestTable 中
        AllocateRequest nextReq = new AllocateRequest(nextFilePath, fileSize);
        boolean nextPutOK = this.requestTable.putIfAbsent(nextFilePath, nextReq) == null;
        // requestTable 使用了 concurrentHashMap，用文件名作为 key，防止并发
        if (nextPutOK) {
            // 这里判断了是否可以提交到 TransientStorePool，涉及读写分离，后面再细聊
            if (canSubmitRequests <= 0) {
                log.warn("[NOTIFYME]TransientStorePool is not enough, so create mapped file error, " +
                    "RequestQueueSize : {}, StorePoolSize: {}", this.requestQueue.size(), this.messageStore.getTransientStorePool().remainBufferNumbs());
                this.requestTable.remove(nextFilePath);
                return null;
            }
          // 塞到阻塞队列中
            boolean offerOK = this.requestQueue.offer(nextReq);
            if (!offerOK) {
                log.warn("never expected here, add a request to preallocate queue failed");
            }
            canSubmitRequests--;
        }

        // 这里的两个提交我猜测是为了多生成一个 CommitLog，
        AllocateRequest nextNextReq = new AllocateRequest(nextNextFilePath, fileSize);
        boolean nextNextPutOK = this.requestTable.putIfAbsent(nextNextFilePath, nextNextReq) == null;
        if (nextNextPutOK) {
            if (canSubmitRequests <= 0) {
                log.warn("[NOTIFYME]TransientStorePool is not enough, so skip preallocate mapped file, " +
                    "RequestQueueSize : {}, StorePoolSize: {}", this.requestQueue.size(), this.messageStore.getTransientStorePool().remainBufferNumbs());
                this.requestTable.remove(nextNextFilePath);
            } else {
                boolean offerOK = this.requestQueue.offer(nextNextReq);
                if (!offerOK) {
                    log.warn("never expected here, add a request to preallocate queue failed");
                }
            }
        }

        if (hasException) {
            log.warn(this.getServiceName() + " service has exception. so return null");
            return null;
        }

        AllocateRequest result = this.requestTable.get(nextFilePath);
        try {
          // 这里就异步等着
            if (result != null) {
                boolean waitOK = result.getCountDownLatch().await(waitTimeOut, TimeUnit.MILLISECONDS);
                if (!waitOK) {
                    log.warn("create mmap timeout " + result.getFilePath() + " " + result.getFileSize());
                    return null;
                } else {
                    this.requestTable.remove(nextFilePath);
                    return result.getMappedFile();
                }
            } else {
                log.error("find preallocate mmap failed, this never happen");
            }
        } catch (InterruptedException e) {
            log.warn(this.getServiceName() + " service has exception. ", e);
        }

        return null;
    }
+ +

而真正去执行文件操作的就是 AllocateMappedFileService的 run 方法

+
public void run() {
        log.info(this.getServiceName() + " service started");

        while (!this.isStopped() && this.mmapOperation()) {

        }
        log.info(this.getServiceName() + " service end");
    }
private boolean mmapOperation() {
        boolean isSuccess = false;
        AllocateRequest req = null;
        try {
          // 从阻塞队列里获取请求
            req = this.requestQueue.take();
            AllocateRequest expectedRequest = this.requestTable.get(req.getFilePath());
            if (null == expectedRequest) {
                log.warn("this mmap request expired, maybe cause timeout " + req.getFilePath() + " "
                    + req.getFileSize());
                return true;
            }
            if (expectedRequest != req) {
                log.warn("never expected here,  maybe cause timeout " + req.getFilePath() + " "
                    + req.getFileSize() + ", req:" + req + ", expectedRequest:" + expectedRequest);
                return true;
            }

            if (req.getMappedFile() == null) {
                long beginTime = System.currentTimeMillis();

                MappedFile mappedFile;
                if (messageStore.getMessageStoreConfig().isTransientStorePoolEnable()) {
                    try {
                      // 通过 transientStorePool 创建
                        mappedFile = ServiceLoader.load(MappedFile.class).iterator().next();
                        mappedFile.init(req.getFilePath(), req.getFileSize(), messageStore.getTransientStorePool());
                    } catch (RuntimeException e) {
                        log.warn("Use default implementation.");
                      // 默认创建
                        mappedFile = new MappedFile(req.getFilePath(), req.getFileSize(), messageStore.getTransientStorePool());
                    }
                } else {
                  // 默认创建
                    mappedFile = new MappedFile(req.getFilePath(), req.getFileSize());
                }

                long eclipseTime = UtilAll.computeEclipseTimeMilliseconds(beginTime);
                if (eclipseTime > 10) {
                    int queueSize = this.requestQueue.size();
                    log.warn("create mappedFile spent time(ms) " + eclipseTime + " queue size " + queueSize
                        + " " + req.getFilePath() + " " + req.getFileSize());
                }

                // pre write mappedFile
                if (mappedFile.getFileSize() >= this.messageStore.getMessageStoreConfig()
                    .getMapedFileSizeCommitLog()
                    &&
                    this.messageStore.getMessageStoreConfig().isWarmMapedFileEnable()) {
                    mappedFile.warmMappedFile(this.messageStore.getMessageStoreConfig().getFlushDiskType(),
                        this.messageStore.getMessageStoreConfig().getFlushLeastPagesWhenWarmMapedFile());
                }

                req.setMappedFile(mappedFile);
                this.hasException = false;
                isSuccess = true;
            }
        } catch (InterruptedException e) {
            log.warn(this.getServiceName() + " interrupted, possibly by shutdown.");
            this.hasException = true;
            return false;
        } catch (IOException e) {
            log.warn(this.getServiceName() + " service has exception. ", e);
            this.hasException = true;
            if (null != req) {
                requestQueue.offer(req);
                try {
                    Thread.sleep(1);
                } catch (InterruptedException ignored) {
                }
            }
        } finally {
            if (req != null && isSuccess)
              // 通知前面等待的
                req.getCountDownLatch().countDown();
        }
        return true;
    }
+ + + + ]]> - 折腾 + MQ + RocketMQ + 消息队列 - 折腾 + MQ + 消息队列 + RocketMQ @@ -5981,6 +6102,44 @@ user3: 数据源动态切换 + + 聊一下 RocketMQ 的顺序消息 + /2021/08/29/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84%E9%A1%BA%E5%BA%8F%E6%B6%88%E6%81%AF/ + rocketmq 里有一种比较特殊的用法，就是顺序消息，比如订单的生命周期里，在创建，支付，签收等状态轮转中，会发出来对应的消息，这里面就比较需要去保证他们的顺序，当然在处理的业务代码也可以做对应的处理，结合消息重投，但是如果这里消息就能保证顺序性了，那么业务代码就能更好的关注业务代码的处理。

+

首先有一种情况是全局的有序，比如对于一个 topic 里就得发送线程保证只有一个，内部的 queue 也只有一个，消费线程也只有一个，这样就能比较容易的保证全局顺序性了，但是这里的问题就是完全限制了性能，不是很现实，在真实场景里很多都是比如对于同一个订单，需要去保证状态的轮转是按照预期的顺序来，而不必要全局的有序性。

+

对于这类的有序性，需要在发送和接收方都有对应的处理，在发送消息中，需要去指定 selector，即MessageQueueSelector，能够以固定的方式是分配到对应的 MessageQueue

+

比如像 RocketMQ 中的示例

+
SendResult sendResult = producer.send(msg, new MessageQueueSelector() {
                @Override
                public MessageQueue select(List<MessageQueue> mqs, Message msg, Object arg) {
                    Long id = (Long) arg;  //message queue is selected by #salesOrderID
                    long index = id % mqs.size();
                    return mqs.get((int) index);
                }
            }, orderList.get(i).getOrderId());
+ +

而在消费侧有几个点比较重要，首先我们要保证一个 MessageQueue只被一个消费者消费，消费队列存在broker端，要保证 MessageQueue 只被一个消费者消费，那么消费者在进行消息拉取消费时就必须向mq服务器申请队列锁，消费者申请队列锁的代码存在于RebalanceService消息队列负载的实现代码中。

+
List<PullRequest> pullRequestList = new ArrayList<PullRequest>();
        for (MessageQueue mq : mqSet) {
            if (!this.processQueueTable.containsKey(mq)) {
              // 判断是否顺序，如果是顺序消费的，则需要加锁
                if (isOrder && !this.lock(mq)) {
                    log.warn("doRebalance, {}, add a new mq failed, {}, because lock failed", consumerGroup, mq);
                    continue;
                }

                this.removeDirtyOffset(mq);
                ProcessQueue pq = new ProcessQueue();
                long nextOffset = this.computePullFromWhere(mq);
                if (nextOffset >= 0) {
                    ProcessQueue pre = this.processQueueTable.putIfAbsent(mq, pq);
                    if (pre != null) {
                        log.info("doRebalance, {}, mq already exists, {}", consumerGroup, mq);
                    } else {
                        log.info("doRebalance, {}, add a new mq, {}", consumerGroup, mq);
                        PullRequest pullRequest = new PullRequest();
                        pullRequest.setConsumerGroup(consumerGroup);
                        pullRequest.setNextOffset(nextOffset);
                        pullRequest.setMessageQueue(mq);
                        pullRequest.setProcessQueue(pq);
                        pullRequestList.add(pullRequest);
                        changed = true;
                    }
                } else {
                    log.warn("doRebalance, {}, add new mq failed, {}", consumerGroup, mq);
                }
            }
        }
+ +

在申请到锁之后会创建 pullRequest 进行消息拉取，消息拉取部分的代码实现在PullMessageService中，

+
@Override
    public void run() {
        log.info(this.getServiceName() + " service started");

        while (!this.isStopped()) {
            try {
                PullRequest pullRequest = this.pullRequestQueue.take();
                this.pullMessage(pullRequest);
            } catch (InterruptedException ignored) {
            } catch (Exception e) {
                log.error("Pull Message Service Run Method exception", e);
            }
        }

        log.info(this.getServiceName() + " service end");
    }
+ +

消息拉取完后，需要提交到ConsumeMessageService中进行消费，顺序消费的实现为ConsumeMessageOrderlyService，提交消息进行消费的方法为ConsumeMessageOrderlyService#submitConsumeRequest，具体实现如下：

+
@Override
public void submitConsumeRequest(
    final List<MessageExt> msgs,
    final ProcessQueue processQueue,
    final MessageQueue messageQueue,
    final boolean dispathToConsume) {
    if (dispathToConsume) {
        ConsumeRequest consumeRequest = new ConsumeRequest(processQueue, messageQueue);
        this.consumeExecutor.submit(consumeRequest);
    }
}
+ +

构建了一个ConsumeRequest对象，它是个实现了 runnable 接口的类，并提交给了线程池来并行消费，看下顺序消费的ConsumeRequest的run方法实现：

+
@Override
        public void run() {
            if (this.processQueue.isDropped()) {
                log.warn("run, the message queue not be able to consume, because it's dropped. {}", this.messageQueue);
                return;
            }
						// 获得 Consumer 消息队列锁，即单个线程独占
            final Object objLock = messageQueueLock.fetchLockObject(this.messageQueue);
            synchronized (objLock) {
              // (广播模式) 或者 (集群模式 && Broker消息队列锁有效)
                if (MessageModel.BROADCASTING.equals(ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.messageModel())
                    || (this.processQueue.isLocked() && !this.processQueue.isLockExpired())) {
                    final long beginTime = System.currentTimeMillis();
                  // 循环
                    for (boolean continueConsume = true; continueConsume; ) {
                        if (this.processQueue.isDropped()) {
                            log.warn("the message queue not be able to consume, because it's dropped. {}", this.messageQueue);
                            break;
                        }

                      // 消息队列分布式锁未锁定，提交延迟获得锁并消费请求
                        if (MessageModel.CLUSTERING.equals(ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.messageModel())
                            && !this.processQueue.isLocked()) {
                            log.warn("the message queue not locked, so consume later, {}", this.messageQueue);
                            ConsumeMessageOrderlyService.this.tryLockLaterAndReconsume(this.messageQueue, this.processQueue, 10);
                            break;
                        }

                      // 消息队列分布式锁已经过期，提交延迟获得锁并消费请求
                        if (MessageModel.CLUSTERING.equals(ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.messageModel())
                            && this.processQueue.isLockExpired()) {
                            log.warn("the message queue lock expired, so consume later, {}", this.messageQueue);
                            ConsumeMessageOrderlyService.this.tryLockLaterAndReconsume(this.messageQueue, this.processQueue, 10);
                            break;
                        }
												// 当前周期消费时间超过连续时长，默认：60s，提交延迟消费请求。默认情况下，每消费1分钟休息10ms。
                        long interval = System.currentTimeMillis() - beginTime;
                        if (interval > MAX_TIME_CONSUME_CONTINUOUSLY) {
                            ConsumeMessageOrderlyService.this.submitConsumeRequestLater(processQueue, messageQueue, 10);
                            break;
                        }
												// 获取消费消息。此处和并发消息请求不同，并发消息请求已经带了消费哪些消息。
                        final int consumeBatchSize =
                            ConsumeMessageOrderlyService.this.defaultMQPushConsumer.getConsumeMessageBatchMaxSize();

                        List<MessageExt> msgs = this.processQueue.takeMessags(consumeBatchSize);
                        defaultMQPushConsumerImpl.resetRetryAndNamespace(msgs, defaultMQPushConsumer.getConsumerGroup());
                        if (!msgs.isEmpty()) {
                            final ConsumeOrderlyContext context = new ConsumeOrderlyContext(this.messageQueue);

                            ConsumeOrderlyStatus status = null;

                            ConsumeMessageContext consumeMessageContext = null;
                            if (ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.hasHook()) {
                                consumeMessageContext = new ConsumeMessageContext();
                                consumeMessageContext
                                    .setConsumerGroup(ConsumeMessageOrderlyService.this.defaultMQPushConsumer.getConsumerGroup());
                                consumeMessageContext.setNamespace(defaultMQPushConsumer.getNamespace());
                                consumeMessageContext.setMq(messageQueue);
                                consumeMessageContext.setMsgList(msgs);
                                consumeMessageContext.setSuccess(false);
                                // init the consume context type
                                consumeMessageContext.setProps(new HashMap<String, String>());
                                ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.executeHookBefore(consumeMessageContext);
                            }
														// 执行消费
                            long beginTimestamp = System.currentTimeMillis();
                            ConsumeReturnType returnType = ConsumeReturnType.SUCCESS;
                            boolean hasException = false;
                            try {
                                this.processQueue.getLockConsume().lock(); // 锁定处理队列
                                if (this.processQueue.isDropped()) {
                                    log.warn("consumeMessage, the message queue not be able to consume, because it's dropped. {}",
                                        this.messageQueue);
                                    break;
                                }

                                status = messageListener.consumeMessage(Collections.unmodifiableList(msgs), context);
                            } catch (Throwable e) {
                                log.warn("consumeMessage exception: {} Group: {} Msgs: {} MQ: {}",
                                    RemotingHelper.exceptionSimpleDesc(e),
                                    ConsumeMessageOrderlyService.this.consumerGroup,
                                    msgs,
                                    messageQueue);
                                hasException = true;
                            } finally {
                                this.processQueue.getLockConsume().unlock();  // 解锁
                            }

                            if (null == status
                                || ConsumeOrderlyStatus.ROLLBACK == status
                                || ConsumeOrderlyStatus.SUSPEND_CURRENT_QUEUE_A_MOMENT == status) {
                                log.warn("consumeMessage Orderly return not OK, Group: {} Msgs: {} MQ: {}",
                                    ConsumeMessageOrderlyService.this.consumerGroup,
                                    msgs,
                                    messageQueue);
                            }

                            long consumeRT = System.currentTimeMillis() - beginTimestamp;
                            if (null == status) {
                                if (hasException) {
                                    returnType = ConsumeReturnType.EXCEPTION;
                                } else {
                                    returnType = ConsumeReturnType.RETURNNULL;
                                }
                            } else if (consumeRT >= defaultMQPushConsumer.getConsumeTimeout() * 60 * 1000) {
                                returnType = ConsumeReturnType.TIME_OUT;
                            } else if (ConsumeOrderlyStatus.SUSPEND_CURRENT_QUEUE_A_MOMENT == status) {
                                returnType = ConsumeReturnType.FAILED;
                            } else if (ConsumeOrderlyStatus.SUCCESS == status) {
                                returnType = ConsumeReturnType.SUCCESS;
                            }

                            if (ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.hasHook()) {
                                consumeMessageContext.getProps().put(MixAll.CONSUME_CONTEXT_TYPE, returnType.name());
                            }

                            if (null == status) {
                                status = ConsumeOrderlyStatus.SUSPEND_CURRENT_QUEUE_A_MOMENT;
                            }

                            if (ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.hasHook()) {
                                consumeMessageContext.setStatus(status.toString());
                                consumeMessageContext
                                    .setSuccess(ConsumeOrderlyStatus.SUCCESS == status || ConsumeOrderlyStatus.COMMIT == status);
                                ConsumeMessageOrderlyService.this.defaultMQPushConsumerImpl.executeHookAfter(consumeMessageContext);
                            }

                            ConsumeMessageOrderlyService.this.getConsumerStatsManager()
                                .incConsumeRT(ConsumeMessageOrderlyService.this.consumerGroup, messageQueue.getTopic(), consumeRT);

                            continueConsume = ConsumeMessageOrderlyService.this.processConsumeResult(msgs, status, context, this);
                        } else {
                            continueConsume = false;
                        }
                    }
                } else {
                    if (this.processQueue.isDropped()) {
                        log.warn("the message queue not be able to consume, because it's dropped. {}", this.messageQueue);
                        return;
                    }

                    ConsumeMessageOrderlyService.this.tryLockLaterAndReconsume(this.messageQueue, this.processQueue, 100);
                }
            }
        }
+ +

获取到锁对象后，使用synchronized尝试申请线程级独占锁。

+

如果加锁成功，同一时刻只有一个线程进行消息消费。

+

如果加锁失败，会延迟100ms重新尝试向broker端申请锁定messageQueue，锁定成功后重新提交消费请求

+

创建消息拉取任务时，消息客户端向broker端申请锁定MessageQueue，使得一个MessageQueue同一个时刻只能被一个消费客户端消费。

+

消息消费时，多线程针对同一个消息队列的消费先尝试使用synchronized申请独占锁，加锁成功才能进行消费，使得一个MessageQueue同一个时刻只能被一个消费客户端中一个线程消费。
这里其实还有很重要的一点是对processQueue的加锁，这里其实是保证了在 rebalance的过程中如果 processQueue 被分配给了另一个 consumer，但是当前已经被我这个 consumer 再消费，但是没提交，就有可能出现被两个消费者消费，所以得进行加锁保证不受 rebalance 影响。

+]]> + + MQ + RocketMQ + 消息队列 + + + MQ + 消息队列 + RocketMQ + + 聊一下 SpringBoot 设置非 web 应用的方法 /2022/07/31/%E8%81%8A%E4%B8%80%E4%B8%8B-SpringBoot-%E8%AE%BE%E7%BD%AE%E9%9D%9E-web-%E5%BA%94%E7%94%A8%E7%9A%84%E6%96%B9%E6%B3%95/ @@ -6020,12 +6179,31 @@ user3: - 聊在东京奥运会闭幕式这天 - /2021/08/08/%E8%81%8A%E5%9C%A8%E4%B8%9C%E4%BA%AC%E5%A5%A5%E8%BF%90%E4%BC%9A%E9%97%AD%E5%B9%95%E5%BC%8F%E8%BF%99%E5%A4%A9/ - 这届奥运会有可能是我除了 08 年之外关注度最高的一届奥运会，原因可能是因为最近也没什么电影综艺啥的比较好看，前面看跑男倒还行，不是说多好，也就图一乐，最开始看向往的生活觉得也挺不错的，后面变成了统一来了就看黄磊做饭，然后夸黄磊做饭好吃，然后无聊的说这种生活多么多么美好，单调无聊，差不多弃了，这里面还包括大华不在了，大华其实个人还是有点呱噪的，但是挺能搞气氛，并且也有才华，彭彭跟子枫人是不讨厌，但是撑不起来，所以也导致了前面说的结果，都变成了黄磊彩虹屁现场，虽然偶尔怀疑他是否做得好吃，但是整体还是承认的，可对于一个这么多季了的综艺来说，这样也有点单调了。

-

还有奥运会像乒乓球，篮球，跳水这几个都是比较喜欢的项目，篮球🏀是从初中开始就也有在自己在玩的，虽然因为身高啊体质基本没什么天赋，但也算是热爱驱动，差不多到了大学因为比较懒才放下了，初中高中还是有很多时间花在上面，不像别人经常打球跑跑跳跳还能长高，我反而一直都没长个子，也因为这个其实蛮遗憾的，后面想想可能是初中的时候远走他乡去住宿读初中，伙食营养跟不上导致的，可能也是自己的一厢情愿吧，总觉得应该还能再长点个，这一点以后我自己的小孩我应该会特别注意这段时间他/她的营养摄入了；然后像乒乓球🏓的话其实小时候是比较讨厌的，因为家里人，父母都没有这类爱好习惯，我也完全不会，但是小学那会班里的“恶霸”就以公平之名要我们男生每个人都排队打几个，我这种不会的反而又要被嘲笑，这个小时候的阴影让我有了比较不好的印象，对它🏓的改观是在工作以后，前司跟一个同样不会的同事经常在饭点会打打，而且那会因为这个其实身体得到了锻炼，感觉是个不错的健身方式，然后又是中国的优势项目，小时候跟着我爸看孔令辉，那时候完全不懂，印象就觉得老瓦很牛，后面其实也没那么关注，上一届好像看了马龙的比赛；跳水也是中国的优势项目，而且也比较简单，不是说真的很简单，就是我们外行观众看着就看看水花大小图一乐。

-

这次的观赛过程其实主要还是在乒乓球上面，现在都有点怪我的乌鸦嘴，混双我一直就不太放心（关我什么事，我也不专业），然后一直觉得混双是不是不太稳，结果那天看的时候也是因为央视一套跟五套都没放，我家的有线电视又是没有五加体育，然后用电脑投屏就很卡，看得也很不爽，同时那天因为看的时候已经是 2:0还是再后面点了，一方面是不懂每队只有一次暂停，另一方面不知道已经用过暂停了，所以就特别怀疑马林是不是只会无脑鼓掌，感觉作为教练，并且是前冠军，应该也能在擦汗间隙，或者局间休息调整的时候多给些战略战术的指导，类似于后面男团小胖打奥恰洛夫，像解说都看出来了，其实奥恰那会的反手特别顺，打得特别凶，那就不能让他能特别顺手的上反手位，这当然是外行比较粗浅的看法，在混双过程中其实除了这个，还有让人很不爽的就是我们的许昕跟刘诗雯有种拿不出破釜沉舟的勇气的感觉，在气势上完全被对面两位日本乒乓球最讨厌的两位对手压制着，我都要输了，我就每一颗都要不让你好过，因为真的不是说没有实力，对面水谷隼也不是多么多么强的，可能上一届男团许昕输给他还留着阴影，但是以许昕 19 年男单世界第一的实力，目前也排在世界前三，输一场不应该成为这种阻力，有一些失误也很可惜，后面孙颖莎真的打得很解气，第二局一度以为又要被翻盘了，结果来了个大逆转，女团的时候也是，感觉在心态上孙颖莎还是很值得肯定的，少年老成这个词很适合，看其他的视频也觉得莎莎萌萌哒，陈梦总感觉还欠一点王者霸气，王曼昱还是可以的，反手很凶，我觉得其实这一届日本女乒就是打得非常凶，即使像平野这种看着很弱的妹子，打的球可一点都不弱，也是这种凶狠的打法，有点要压制中国的感觉，这方面我觉得是需要改善的，打这种要不就是实力上的完全碾压，要不就是我实力虽然比较没强多少，但是你狠我打得比你还狠，越保守越要输，我不太成熟的想法是这样的，还有就是面对逆境，这个就要说到男队的了，樊振东跟马龙在半决赛的时候，特别是男团的第二盘，樊振东打奥恰很好地表现了这个心态，当然樊振东我不是特别了解，据说他是比较善于打相持，比较善于焦灼的情况，不过整体看下来樊振东还是有一些欠缺，就是面对情况的快速转变应对，这一点也是马龙特别强的，虽然看起来马龙真的是年纪大了点，没有 16 年那会满头发胶，油光锃亮的大背头和满脸胶原蛋白的意气风发，大范围运动能力也弱了一点，但是经验和能力的全面性也让他最终能再次站上巅峰，还是非常佩服的，这里提一下张继科，虽然可能天赋上是张继科更强点，但是男乒一直都是有强者出现，能为国家队付出这么多并且一直坚持的可不是人人都可以，即使现在同台竞技马龙打不过张继科我还是更喜欢马龙。再来说说我们的对手，主要分三部分，德国男乒，里面有波尔（我刚听到的时候在想怎么又出来个叫波尔的，是不是像举重的石智勇一样，又来一个同名的，结果是同一个，已经四十岁了），这真是个让人敬佩的对手，实力强，经验丰富，虽然男单有点可惜，但是帮助男团获得银牌，真的是起到了定海神针的作用；奥恰洛夫，以前完全不认识，或者说看过也忘了，这次是真的有点意外，竟然有这么个马龙护法，其实他也坦言非常想赢一次马龙，并且在半决赛也非常接近赢得比赛，是个实力非常强的对手，就是男团半决赛输给张本智和有点可惜，有点被打蒙的感觉，佛朗西斯卡的话也是实力不错的选手，就是可能被奥恰跟波尔的光芒掩盖了，跟波尔在男团第一盘男双的比赛中打败日本那对男双也是非常给力的，说实话，最后打国乒的时候的确是国乒实力更胜一筹，但是即使德国赢了我也是充满尊敬，拼的就是硬实力，就像第二盘奥恰打樊振东，反手是真的很强，反过来看奥恰可能也不是很善于快速调整，樊振东打出来自己的节奏，主攻奥恰的中路，他好像没什么好办法解决。再来说我最讨厌的日本，嗯，小日本，张本智和、水谷隼、伊藤美诚，一一评价下（我是外行，绝对主观评价），张本智和，父母也是中国人，原来叫张智和，改日本籍后加了个本，被微博网友笑称日本尖叫鸡，男单输给了斯洛文尼亚选手，男团里是赢了两场，但是在我看来其实实力上可能比不上全力的奥恰，主要是特别能叫，会干扰对手，如果觉得这种也是种能力我也无话可说，要是有那种吼声能直接把对手震聋的，都不需要打比赛了，我简单记了下，赢一颗球，他要叫八声，用 LD 的话来说烦都烦死了，心态是在面对一些困境顺境的应对调整适应能力，而不是对这种噪音的适应能力，至少我是这么看的，所以我很期待樊振东能好好地虐虐他，因为其他像林昀儒真的是非常优秀的新选手，所谓的国乒克星估计也是小日本自己说说的，国乒其实有很多对手，马龙跟樊振东在男单半决赛碰到的这两个几乎都差点把他们掀翻了，所以还是练好自己的实力再来吹吧，免得打脸；水谷隼的话真的是长相就是特别地讨厌，还搞出那套不打比赛的姿态，男团里被波尔干掉就是很好的例子，波尔虽然真的很强，但毕竟 40 岁了，跟伊藤美诚一起说了吧，伊藤实力说实话是有的，混双中很大一部分的赢面来自于她，刘诗雯做了手术状态不好，许昕失误稍多，但是这种赢球了就感觉我赢了你一辈子一场没输的感觉，还有那种不知道怎么形容的笑，实力强的正常打比赛的我都佩服，像女团决赛里，平野跟石川佳纯的打法其实也很凶狠，但是都是正常的比赛，即使中国队两位实力不济输了也很正常，这种就真的需要像孙颖莎这样的小魔王无视各种魔法攻击，无视你各种花里胡哨的打法的人好好教训一下，混双输了以后了解了下她，感觉实力真的不错，是个大威胁，但是其实我们孙颖莎也是经历了九个月的继续成长，像张怡宁也评价了她，可能后面就没什么空间了，当然如果由张怡宁来打她就更适合了，净整这些有的没的，就打得你没脾气。

-

乒乓球的说的有点多，就分篇说了，第一篇先到这。

+ 用 ollama 本地运行谷歌开源大模型 Gemma + /2024/03/24/%E7%94%A8-ollama-%E6%9C%AC%E5%9C%B0%E8%BF%90%E8%A1%8C%E5%A4%A7%E6%A8%A1%E5%9E%8B/ + 原先在 23 年初的时候调研过一些国产的大模型，包括复旦开源的 MOSS 和清华的 ChatGLM，那时候还是早期版本，需要在 Linux 上，并且有比较好的显卡，而且一般来讲都得是 N 卡，过程中需要安装 pytorch和比较多依赖，并且当时的效果也还比较差，所以后面就没有长期使用。
最近看到谷歌在 2 月份开源了大模型 Gemma ，gemma 的博客在这里，想要在本地运行这个模型在现在这个阶段也变得简单很多，因为我们有了 ollama 工具

可以通过这个工具来运行大模型，并且已经支持了谷歌开源的 Gemma

我这边本地是 MacBook Pro 14 寸的，m3 pro 的处理器，18g 内存，刚好可以用 7b 量化的模型

这里有推荐的模型和内存推荐匹配规则，16g 可以运行 13B 及以下模型
下载安装完后我们可以用以下命令

+
ollama run gemma:7b
+

这里需要拉取模型，约5.2g 大小，考虑网络原因可能会比较慢

我们可以简单来试试问个问题


看出来回答的还是比较丰富的，谷歌出品还是比较有水平的，不至于像 ChatGLM 最初版本的在不做调优的情况下甚至有点前言不搭后语
对于想使用 chatgpt 但是没条件，这也算是个低配平替了, 并且已经是个比较可用的了，同时也方便进行学习调优等
如果想要类似于 chatgpt 那样的网页版，可以安装 open-webui
可以通过 webui 访问 ollama 运行的大模型，
用 docker 启动的命令也贴一下

+
docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
+

不过有个小问题就是 docker 镜像拉取会有点慢，可以添加下国内镜像加速

+
{
    "registry-mirrors": [
        "https://dockerproxy.com",
        "https://docker.mirrors.ustc.edu.cn",
        "https://docker.nju.edu.cn"
    ]
}
+


这里有一个小区别，Gemma 在多轮会话的时候会在前面的答案基础上再完善。

+

补充一个在 windows 环境下，cpu 跑模型的也是可行的

现在是大模型可以深入千家万户了，大家都可以尝试下，如果对日常的工作学习有一些效率上的提升也是好的

+]]> + + LLM + + + LLM + + + + 聊在东京奥运会闭幕式这天-二 + /2021/08/19/%E8%81%8A%E5%9C%A8%E4%B8%9C%E4%BA%AC%E5%A5%A5%E8%BF%90%E4%BC%9A%E9%97%AD%E5%B9%95%E5%BC%8F%E8%BF%99%E5%A4%A9-%E4%BA%8C/ + 前面主要还是说了乒乓球的，因为整体还是乒乓球的比赛赛程比较长，比较激烈，扣人心弦，记得那会在公司没法看视频直播，就偶尔看看奥运会官网的比分，还几场马龙樊振东，陈梦被赢了一局就吓尿了，已经被混双那场留下了阴影，其实后面去看看16 年的比赛什么的，中国队虽然拿了这么多冠军，但是自改成 11 分制以来，其实都没办法那么完全彻底地碾压，而且像张继科，樊振东，陈梦都多少有些慢热，现在看来是马龙比较全面，不过看过了马龙，刘国梁，许昕等的一些过往经历，都是起起伏伏，即使是张怡宁这样的大魔王，也经历过逢王楠不赢的阶段，心态无法调整好。

+

其实最开始是举重项目，侯志慧是女子 49 公斤级的冠军，这场比赛是全场都看，其实看中国队的举重比赛跟跳水有点像，每一轮都需要到最后才能等到中国队，跳水其实每轮都有，举重会按照自己报的试举重量进行排名，重量大的会在后面举，抓举和挺举各三次试举机会，有时候会看着比较焦虑，一直等不来，怕一上来就没试举成功，而且中国队一般试举重量就是很大的，容易一次试举不成功就马上下一次，连着举其实压力会非常大，说实话真的是外行看热闹，每次都是多懂一点点，这次由于实在是比较无聊，所以看的会比较专心点，对于对应的规则知识点也会多了解一点，同时对于举重，没想到我们国家的这些运动员有这么强，最后八块金牌拿了七块，有一块拿到银牌也是有点因为教练的策略问题，这里其实也稍微知道一点，因为报上去的试举重量是谁小谁先举，并且我们国家都是实力非常强的，所以都会报大一些，并且如果这个项目有实力相近的选手，会比竞对多报一公斤，这样子如果前面竞争对手没举成功，我们把握就很大了，最坏的情况即使对手试举成功了，我们还有机会搏一把，比如谌利军这样的，只是说说感想，举重运动员真的是个比较单纯的群体，而且训练是非常痛苦枯燥的，非常容易受伤，像挺举就有点会压迫呼吸通道，看到好几个都是脸憋得通红，甚至直接因为压迫气道而没法完成后面的挺举，像之前 16 年的举重比赛，有个运动员没成功夺冠就非常愧疚地哭着说对不起祖国，没有获得冠军，这是怎么样的一种歉疚，怎么样的一种纯粹的感情呢，相对应地来说，我又要举男足，男篮的例子了，很多人在那嘲笑我这样对男足男篮愤愤不平的人，说可能我这样的人都没交个税（从缴纳个税的数量比例来算有可能），只是这里有两个打脸的事情，我足额缴纳个税，接近 20%的薪资都缴了个税，并且我买的所有东西都缴了增值税，如果让我这样缴纳了个税，缴纳了增值税的有个人的投票权，我一定会投票不让男足男篮使用我缴纳我的税金，用我们的缴纳的税，打出这么烂的表现，想乒乓球混双，拿个亚军都会被喷，那可是世界第二了，而且是就输了那么一场，足球篮球呢，我觉得是一方面成绩差，因为比赛真的有状态跟心态的影响，偶尔有一场失误非常正常，NBA 被黑八的有这么多强队，但是如果像男足男篮，成绩是越来越差，用范志毅的话来说就是脸都不要了，还有就是精气神，要在比赛中打出胜负欲，保持这种争胜心，才有机会再进步，前火箭队主教练鲁迪·汤姆贾诺维奇的话，“永远不要低估冠军的决心”，即使我现在打不过你，我会在下一次，下下次打败你，竞技体育永远要有这种精神，可以接受一时的失败，但是要保持永远争胜的心。

+

第一块金牌是杨倩拿下的，中国队拿奥运会首金也是有政治任务的，而恰恰杨倩这个金牌也有点碰巧是对手最后一枪失误了，当然竞技体育，特别是射击，真的是容不得一点点失误，像前面几届的美国神通埃蒙斯，失之毫厘差之千里，但是这个具体评价就比较少，唯一一点让我比较出戏的就是杨倩真的非常像王刚的徒弟漆二娃，哈哈，微博上也有挺多人觉得像，射击还是个比较可以接受年纪稍大的运动员，需要经验和稳定性，相对来说爆发力体力稍好一点，像庞伟这样的，混合团体10米气手枪金牌，36 岁可能其他项目已经是年龄很大了，不过前面说的举重的吕小军军神也是年纪蛮大了，但是非常强，而且在油管上简直就是个神，相对来说射击是关注比较少，杨倩的也只是看了后面拿到冠军这个结果，有些因为时间或者电视上没放，但是成绩还是不错的，没多少喷点。

+

第二篇先到这，纯主观，轻喷。

]]> 生活 @@ -6035,55 +6213,17 @@ user3: 生活 运动 东京奥运会 - 乒乓球 - 跳水 - - - - 聊一下 Java 的日志系列三 - /2024/01/21/%E8%81%8A%E4%B8%80%E4%B8%8B-Java-%E7%9A%84%E6%97%A5%E5%BF%97%E7%B3%BB%E5%88%97%E4%B8%89/ - 上周因为一些事情没有更新在这里，是因为新电脑还没到，手头没有把移动硬盘里的 time machine 恢复出来的机器，所以单独更了一篇在新建的一个 cloudflare page 服务上，总体体验还可以，就是有个小点后面可以讲一下，继续完善下 Java 的这个日志，或者说主要讲的是 logback
前面讲了logback 的初始化逻辑，后面就是我最开始来看这个的逻辑，日志的滚动策略的触发逻辑
我们常规的记录日志的方式比如

-
logger.info("log some thing and biz info: {}",biz);
-

这里就调用了
ch.qos.logback.classic.Logger#info(java.lang.String, java.lang.Object)

-
public void info(String format, Object arg) {
    filterAndLog_1(FQCN, null, Level.INFO, format, arg, null);
}
-

这里的 FQCN 就是这个类的全限定名

-
public static final String FQCN = ch.qos.logback.classic.Logger.class.getName();
-

这个具体的 filterAndLog_1 方法还是在同一个 Logger 类里的
ch.qos.logback.classic.Logger#filterAndLog_1

-
private void filterAndLog_1(final String localFQCN, final Marker marker, final Level level, final String msg, final Object param, final Throwable t) {

    final FilterReply decision = loggerContext.getTurboFilterChainDecision_1(marker, this, level, msg, param, t);

    if (decision == FilterReply.NEUTRAL) {
        if (effectiveLevelInt > level.levelInt) {
            return;
        }
    } else if (decision == FilterReply.DENY) {
        return;
    }

    buildLoggingEventAndAppend(localFQCN, marker, level, msg, new Object[] { param }, t);
}
-

这里面先是走过滤器逻辑，如果是拒绝的，那就不往下执行，也就不执行下面的写日志逻辑了，如果是中性的，那就判断日志级别是否符合，如果直接是拒绝就 return 了，如果是 ACCEPT 就往下执行了

-
public enum FilterReply {
    DENY, NEUTRAL, ACCEPT;
}
-

然后继续调用同样的类的方法
ch.qos.logback.classic.Logger#buildLoggingEventAndAppend

-
private void buildLoggingEventAndAppend(final String localFQCN, final Marker marker, final Level level, final String msg, final Object[] params,
                final Throwable t) {
    LoggingEvent le = new LoggingEvent(localFQCN, this, level, msg, t, params);
    le.setMarker(marker);
    callAppenders(le);
}
-

先把日志包装成 LoggingEvent ，然后调用
ch.qos.logback.classic.Logger#callAppenders

-
public void callAppenders(ILoggingEvent event) {
    int writes = 0;
    for (Logger l = this; l != null; l = l.parent) {
        writes += l.appendLoopOnAppenders(event);
        if (!l.additive) {
            break;
        }
    }
    // No appenders in hierarchy
    if (writes == 0) {
        loggerContext.noAppenderDefinedWarning(this);
    }
}
-

这里会按 Logger 调用 appendLoopOnAppenders

-
private int appendLoopOnAppenders(ILoggingEvent event) {
    if (aai != null) {
        return aai.appendLoopOnAppenders(event);
    } else {
        return 0;
    }
-

继续调用事件的
ch.qos.logback.core.spi.AppenderAttachableImpl#appendLoopOnAppenders

-
public int appendLoopOnAppenders(E e) {
    int size = 0;
    final Appender<E>[] appenderArray = appenderList.asTypedArray();
    final int len = appenderArray.length;
    for (int i = 0; i < len; i++) {
        appenderArray[i].doAppend(e);
        size++;
    }
    return size;
}
-

因为 Logger 里有关联 appender，就具体调用关联的 appender 来添加日志
然后就接着调用
ch.qos.logback.core.UnsynchronizedAppenderBase#doAppend 方法

-
public void doAppend(E eventObject) {
        // WARNING: The guard check MUST be the first statement in the
        // doAppend() method.

        // prevent re-entry.
        if (Boolean.TRUE.equals(guard.get())) {
            return;
        }

        try {
            guard.set(Boolean.TRUE);

            if (!this.started) {
                if (statusRepeatCount++ < ALLOWED_REPEATS) {
                    addStatus(new WarnStatus("Attempted to append to non started appender [" + name + "].", this));
                }
                return;
            }

            if (getFilterChainDecision(eventObject) == FilterReply.DENY) {
                return;
            }

            // ok, we now invoke derived class' implementation of append
            this.append(eventObject);

        } catch (Exception e) {
            if (exceptionCount++ < ALLOWED_REPEATS) {
                addError("Appender [" + name + "] failed to append.", e);
            }
        } finally {
            guard.set(Boolean.FALSE);
        }
    }
-

这里先判断了 guard 的状态，guard 是个 ThreadLocal，是为了拦截异常的无限循环调用，这里有些疑惑的，看了一圈可能相关的是前面的 Logger 会往父级查找，然后如果不同层级的 Logger 关联了相同的 appender 的话，或者这个 doAppend 又被其他 appender 调用了，就可能会出现循环调用，然后会在开始实际的 append的之前先设置 guard 状态，
然后再执行 append，这里会走到
ch.qos.logback.core.OutputStreamAppender#append

-
@Override
protected void append(E eventObject) {
    if (!isStarted()) {
        return;
    }

    subAppend(eventObject);
}
-

忘了补充下，我们这里使用的是
ch.qos.logback.core.rolling.RollingFileAppender
接下去就是调用的 RollingFileAppender 的 subAppend 方法

-
@Override
protected void subAppend(E event) {
    // The roll-over check must precede actual writing. This is the
    // only correct behavior for time driven triggers.

    // We need to synchronize on triggeringPolicy so that only one rollover
    // occurs at a time
    synchronized (triggeringPolicy) {
        if (triggeringPolicy.isTriggeringEvent(currentlyActiveFile, event)) {
            rollover();
        }
    }

    super.subAppend(event);
}
-

这里就会先判断是否触发事件，
而这里就是主要的逻辑，看是否需要走 rollover，也就是具体的滚动逻辑

-
@Override
    public boolean isTriggeringEvent(File activeFile, final E event) {

        long time = getCurrentTime();

        // first check for roll-over based on time
        if (time >= nextCheck) {
            Date dateInElapsedPeriod = dateInCurrentPeriod;
            elapsedPeriodsFileName = tbrp.fileNamePatternWithoutCompSuffix.convertMultipleArguments(dateInElapsedPeriod, currentPeriodsCounter);
            currentPeriodsCounter = 0;
            setDateInCurrentPeriod(time);
            computeNextCheck();
            return true;
        }

        // next check for roll-over based on size
        if (invocationGate.isTooSoon(time)) {
            return false;
        }

        if (activeFile == null) {
            addWarn("activeFile == null");
            return false;
        }
        if (maxFileSize == null) {
            addWarn("maxFileSize = null");
            return false;
        }
        if (activeFile.length() >= maxFileSize.getSize()) {

            elapsedPeriodsFileName = tbrp.fileNamePatternWithoutCompSuffix.convertMultipleArguments(dateInCurrentPeriod, currentPeriodsCounter);
            currentPeriodsCounter++;
            return true;
        }

        return false;
    }
-

这里主要就用到了 nextCheck 判断，如果通过判断就可以执行滚动了，然后就是后面的判断，是否太快了，也就是 isTooSoon 然后再看当前的活跃日志以及最大文件大小是否设置，然后如果当前的活跃日志的大小超过了配置也会触发滚动

-]]> - - Java - - - Java + 举重 + 射击 - 聊在东京奥运会闭幕式这天-二 - /2021/08/19/%E8%81%8A%E5%9C%A8%E4%B8%9C%E4%BA%AC%E5%A5%A5%E8%BF%90%E4%BC%9A%E9%97%AD%E5%B9%95%E5%BC%8F%E8%BF%99%E5%A4%A9-%E4%BA%8C/ - 前面主要还是说了乒乓球的，因为整体还是乒乓球的比赛赛程比较长，比较激烈，扣人心弦，记得那会在公司没法看视频直播，就偶尔看看奥运会官网的比分，还几场马龙樊振东，陈梦被赢了一局就吓尿了，已经被混双那场留下了阴影，其实后面去看看16 年的比赛什么的，中国队虽然拿了这么多冠军，但是自改成 11 分制以来，其实都没办法那么完全彻底地碾压，而且像张继科，樊振东，陈梦都多少有些慢热，现在看来是马龙比较全面，不过看过了马龙，刘国梁，许昕等的一些过往经历，都是起起伏伏，即使是张怡宁这样的大魔王，也经历过逢王楠不赢的阶段，心态无法调整好。

-

其实最开始是举重项目，侯志慧是女子 49 公斤级的冠军，这场比赛是全场都看，其实看中国队的举重比赛跟跳水有点像，每一轮都需要到最后才能等到中国队，跳水其实每轮都有，举重会按照自己报的试举重量进行排名，重量大的会在后面举，抓举和挺举各三次试举机会，有时候会看着比较焦虑，一直等不来，怕一上来就没试举成功，而且中国队一般试举重量就是很大的，容易一次试举不成功就马上下一次，连着举其实压力会非常大，说实话真的是外行看热闹，每次都是多懂一点点，这次由于实在是比较无聊，所以看的会比较专心点，对于对应的规则知识点也会多了解一点，同时对于举重，没想到我们国家的这些运动员有这么强，最后八块金牌拿了七块，有一块拿到银牌也是有点因为教练的策略问题，这里其实也稍微知道一点，因为报上去的试举重量是谁小谁先举，并且我们国家都是实力非常强的，所以都会报大一些，并且如果这个项目有实力相近的选手，会比竞对多报一公斤，这样子如果前面竞争对手没举成功，我们把握就很大了，最坏的情况即使对手试举成功了，我们还有机会搏一把，比如谌利军这样的，只是说说感想，举重运动员真的是个比较单纯的群体，而且训练是非常痛苦枯燥的，非常容易受伤，像挺举就有点会压迫呼吸通道，看到好几个都是脸憋得通红，甚至直接因为压迫气道而没法完成后面的挺举，像之前 16 年的举重比赛，有个运动员没成功夺冠就非常愧疚地哭着说对不起祖国，没有获得冠军，这是怎么样的一种歉疚，怎么样的一种纯粹的感情呢，相对应地来说，我又要举男足，男篮的例子了，很多人在那嘲笑我这样对男足男篮愤愤不平的人，说可能我这样的人都没交个税（从缴纳个税的数量比例来算有可能），只是这里有两个打脸的事情，我足额缴纳个税，接近 20%的薪资都缴了个税，并且我买的所有东西都缴了增值税，如果让我这样缴纳了个税，缴纳了增值税的有个人的投票权，我一定会投票不让男足男篮使用我缴纳我的税金，用我们的缴纳的税，打出这么烂的表现，想乒乓球混双，拿个亚军都会被喷，那可是世界第二了，而且是就输了那么一场，足球篮球呢，我觉得是一方面成绩差，因为比赛真的有状态跟心态的影响，偶尔有一场失误非常正常，NBA 被黑八的有这么多强队，但是如果像男足男篮，成绩是越来越差，用范志毅的话来说就是脸都不要了，还有就是精气神，要在比赛中打出胜负欲，保持这种争胜心，才有机会再进步，前火箭队主教练鲁迪·汤姆贾诺维奇的话，“永远不要低估冠军的决心”，即使我现在打不过你，我会在下一次，下下次打败你，竞技体育永远要有这种精神，可以接受一时的失败，但是要保持永远争胜的心。

-

第一块金牌是杨倩拿下的，中国队拿奥运会首金也是有政治任务的，而恰恰杨倩这个金牌也有点碰巧是对手最后一枪失误了，当然竞技体育，特别是射击，真的是容不得一点点失误，像前面几届的美国神通埃蒙斯，失之毫厘差之千里，但是这个具体评价就比较少，唯一一点让我比较出戏的就是杨倩真的非常像王刚的徒弟漆二娃，哈哈，微博上也有挺多人觉得像，射击还是个比较可以接受年纪稍大的运动员，需要经验和稳定性，相对来说爆发力体力稍好一点，像庞伟这样的，混合团体10米气手枪金牌，36 岁可能其他项目已经是年龄很大了，不过前面说的举重的吕小军军神也是年纪蛮大了，但是非常强，而且在油管上简直就是个神，相对来说射击是关注比较少，杨倩的也只是看了后面拿到冠军这个结果，有些因为时间或者电视上没放，但是成绩还是不错的，没多少喷点。

-

第二篇先到这，纯主观，轻喷。

+ 聊在东京奥运会闭幕式这天 + /2021/08/08/%E8%81%8A%E5%9C%A8%E4%B8%9C%E4%BA%AC%E5%A5%A5%E8%BF%90%E4%BC%9A%E9%97%AD%E5%B9%95%E5%BC%8F%E8%BF%99%E5%A4%A9/ + 这届奥运会有可能是我除了 08 年之外关注度最高的一届奥运会，原因可能是因为最近也没什么电影综艺啥的比较好看，前面看跑男倒还行，不是说多好，也就图一乐，最开始看向往的生活觉得也挺不错的，后面变成了统一来了就看黄磊做饭，然后夸黄磊做饭好吃，然后无聊的说这种生活多么多么美好，单调无聊，差不多弃了，这里面还包括大华不在了，大华其实个人还是有点呱噪的，但是挺能搞气氛，并且也有才华，彭彭跟子枫人是不讨厌，但是撑不起来，所以也导致了前面说的结果，都变成了黄磊彩虹屁现场，虽然偶尔怀疑他是否做得好吃，但是整体还是承认的，可对于一个这么多季了的综艺来说，这样也有点单调了。

+

还有奥运会像乒乓球，篮球，跳水这几个都是比较喜欢的项目，篮球🏀是从初中开始就也有在自己在玩的，虽然因为身高啊体质基本没什么天赋，但也算是热爱驱动，差不多到了大学因为比较懒才放下了，初中高中还是有很多时间花在上面，不像别人经常打球跑跑跳跳还能长高，我反而一直都没长个子，也因为这个其实蛮遗憾的，后面想想可能是初中的时候远走他乡去住宿读初中，伙食营养跟不上导致的，可能也是自己的一厢情愿吧，总觉得应该还能再长点个，这一点以后我自己的小孩我应该会特别注意这段时间他/她的营养摄入了；然后像乒乓球🏓的话其实小时候是比较讨厌的，因为家里人，父母都没有这类爱好习惯，我也完全不会，但是小学那会班里的“恶霸”就以公平之名要我们男生每个人都排队打几个，我这种不会的反而又要被嘲笑，这个小时候的阴影让我有了比较不好的印象，对它🏓的改观是在工作以后，前司跟一个同样不会的同事经常在饭点会打打，而且那会因为这个其实身体得到了锻炼，感觉是个不错的健身方式，然后又是中国的优势项目，小时候跟着我爸看孔令辉，那时候完全不懂，印象就觉得老瓦很牛，后面其实也没那么关注，上一届好像看了马龙的比赛；跳水也是中国的优势项目，而且也比较简单，不是说真的很简单，就是我们外行观众看着就看看水花大小图一乐。

+

这次的观赛过程其实主要还是在乒乓球上面，现在都有点怪我的乌鸦嘴，混双我一直就不太放心（关我什么事，我也不专业），然后一直觉得混双是不是不太稳，结果那天看的时候也是因为央视一套跟五套都没放，我家的有线电视又是没有五加体育，然后用电脑投屏就很卡，看得也很不爽，同时那天因为看的时候已经是 2:0还是再后面点了，一方面是不懂每队只有一次暂停，另一方面不知道已经用过暂停了，所以就特别怀疑马林是不是只会无脑鼓掌，感觉作为教练，并且是前冠军，应该也能在擦汗间隙，或者局间休息调整的时候多给些战略战术的指导，类似于后面男团小胖打奥恰洛夫，像解说都看出来了，其实奥恰那会的反手特别顺，打得特别凶，那就不能让他能特别顺手的上反手位，这当然是外行比较粗浅的看法，在混双过程中其实除了这个，还有让人很不爽的就是我们的许昕跟刘诗雯有种拿不出破釜沉舟的勇气的感觉，在气势上完全被对面两位日本乒乓球最讨厌的两位对手压制着，我都要输了，我就每一颗都要不让你好过，因为真的不是说没有实力，对面水谷隼也不是多么多么强的，可能上一届男团许昕输给他还留着阴影，但是以许昕 19 年男单世界第一的实力，目前也排在世界前三，输一场不应该成为这种阻力，有一些失误也很可惜，后面孙颖莎真的打得很解气，第二局一度以为又要被翻盘了，结果来了个大逆转，女团的时候也是，感觉在心态上孙颖莎还是很值得肯定的，少年老成这个词很适合，看其他的视频也觉得莎莎萌萌哒，陈梦总感觉还欠一点王者霸气，王曼昱还是可以的，反手很凶，我觉得其实这一届日本女乒就是打得非常凶，即使像平野这种看着很弱的妹子，打的球可一点都不弱，也是这种凶狠的打法，有点要压制中国的感觉，这方面我觉得是需要改善的，打这种要不就是实力上的完全碾压，要不就是我实力虽然比较没强多少，但是你狠我打得比你还狠，越保守越要输，我不太成熟的想法是这样的，还有就是面对逆境，这个就要说到男队的了，樊振东跟马龙在半决赛的时候，特别是男团的第二盘，樊振东打奥恰很好地表现了这个心态，当然樊振东我不是特别了解，据说他是比较善于打相持，比较善于焦灼的情况，不过整体看下来樊振东还是有一些欠缺，就是面对情况的快速转变应对，这一点也是马龙特别强的，虽然看起来马龙真的是年纪大了点，没有 16 年那会满头发胶，油光锃亮的大背头和满脸胶原蛋白的意气风发，大范围运动能力也弱了一点，但是经验和能力的全面性也让他最终能再次站上巅峰，还是非常佩服的，这里提一下张继科，虽然可能天赋上是张继科更强点，但是男乒一直都是有强者出现，能为国家队付出这么多并且一直坚持的可不是人人都可以，即使现在同台竞技马龙打不过张继科我还是更喜欢马龙。再来说说我们的对手，主要分三部分，德国男乒，里面有波尔（我刚听到的时候在想怎么又出来个叫波尔的，是不是像举重的石智勇一样，又来一个同名的，结果是同一个，已经四十岁了），这真是个让人敬佩的对手，实力强，经验丰富，虽然男单有点可惜，但是帮助男团获得银牌，真的是起到了定海神针的作用；奥恰洛夫，以前完全不认识，或者说看过也忘了，这次是真的有点意外，竟然有这么个马龙护法，其实他也坦言非常想赢一次马龙，并且在半决赛也非常接近赢得比赛，是个实力非常强的对手，就是男团半决赛输给张本智和有点可惜，有点被打蒙的感觉，佛朗西斯卡的话也是实力不错的选手，就是可能被奥恰跟波尔的光芒掩盖了，跟波尔在男团第一盘男双的比赛中打败日本那对男双也是非常给力的，说实话，最后打国乒的时候的确是国乒实力更胜一筹，但是即使德国赢了我也是充满尊敬，拼的就是硬实力，就像第二盘奥恰打樊振东，反手是真的很强，反过来看奥恰可能也不是很善于快速调整，樊振东打出来自己的节奏，主攻奥恰的中路，他好像没什么好办法解决。再来说我最讨厌的日本，嗯，小日本，张本智和、水谷隼、伊藤美诚，一一评价下（我是外行，绝对主观评价），张本智和，父母也是中国人，原来叫张智和，改日本籍后加了个本，被微博网友笑称日本尖叫鸡，男单输给了斯洛文尼亚选手，男团里是赢了两场，但是在我看来其实实力上可能比不上全力的奥恰，主要是特别能叫，会干扰对手，如果觉得这种也是种能力我也无话可说，要是有那种吼声能直接把对手震聋的，都不需要打比赛了，我简单记了下，赢一颗球，他要叫八声，用 LD 的话来说烦都烦死了，心态是在面对一些困境顺境的应对调整适应能力，而不是对这种噪音的适应能力，至少我是这么看的，所以我很期待樊振东能好好地虐虐他，因为其他像林昀儒真的是非常优秀的新选手，所谓的国乒克星估计也是小日本自己说说的，国乒其实有很多对手，马龙跟樊振东在男单半决赛碰到的这两个几乎都差点把他们掀翻了，所以还是练好自己的实力再来吹吧，免得打脸；水谷隼的话真的是长相就是特别地讨厌，还搞出那套不打比赛的姿态，男团里被波尔干掉就是很好的例子，波尔虽然真的很强，但毕竟 40 岁了，跟伊藤美诚一起说了吧，伊藤实力说实话是有的，混双中很大一部分的赢面来自于她，刘诗雯做了手术状态不好，许昕失误稍多，但是这种赢球了就感觉我赢了你一辈子一场没输的感觉，还有那种不知道怎么形容的笑，实力强的正常打比赛的我都佩服，像女团决赛里，平野跟石川佳纯的打法其实也很凶狠，但是都是正常的比赛，即使中国队两位实力不济输了也很正常，这种就真的需要像孙颖莎这样的小魔王无视各种魔法攻击，无视你各种花里胡哨的打法的人好好教训一下，混双输了以后了解了下她，感觉实力真的不错，是个大威胁，但是其实我们孙颖莎也是经历了九个月的继续成长，像张怡宁也评价了她，可能后面就没什么空间了，当然如果由张怡宁来打她就更适合了，净整这些有的没的，就打得你没脾气。

+

乒乓球的说的有点多，就分篇说了，第一篇先到这。

]]> 生活 @@ -6093,8 +6233,8 @@ user3: 生活 运动 东京奥运会 - 举重 - 射击 + 乒乓球 + 跳水 @@ -6163,73 +6303,6 @@ user3: SPI - - 聊一下 Java 的日志系列二 - /2024/01/14/%E8%81%8A%E4%B8%80%E4%B8%8B-Java-%E7%9A%84%E6%97%A5%E5%BF%97%E7%B3%BB%E5%88%97%E4%BA%8C/ - log 初始化过程，首先是在启动类里会获取 logger

-
private static final Log logger = LogFactory.getLog(SpringApplication.class);
-

然后是

-
public static Log getLog(Class clazz) throws LogConfigurationException {
        return getFactory().getInstance(clazz);
    }
-

继续下去是
org.apache.commons.logging.impl.SLF4JLogFactory#getInstance(java.lang.Class)

-
public Log getInstance(Class clazz) throws LogConfigurationException {
    return this.getInstance(clazz.getName());
}
public Log getInstance(String name) throws LogConfigurationException {
    Log instance = (Log)this.loggerMap.get(name);
    if (instance != null) {
        return instance;
    } else {
        Logger slf4jLogger = LoggerFactory.getLogger(name);
        Object newInstance;
        if (slf4jLogger instanceof LocationAwareLogger) {
            newInstance = new SLF4JLocationAwareLog((LocationAwareLogger)slf4jLogger);
        } else {
            newInstance = new SLF4JLog(slf4jLogger);
        }

        Log oldInstance = (Log)this.loggerMap.putIfAbsent(name, newInstance);
        return (Log)(oldInstance == null ? newInstance : oldInstance);
    }
}
-

然后再往下处理就是 LoggerFactory.getLogger(name); 了，跟普通的获取 logger 一样

-
public static Logger getLogger(String name) {
    ILoggerFactory iLoggerFactory = getILoggerFactory();
    return iLoggerFactory.getLogger(name);
}
-

继续下去会先判断是否已初始化

-
public static ILoggerFactory getILoggerFactory() {
        if (INITIALIZATION_STATE == UNINITIALIZED) {
            synchronized (LoggerFactory.class) {
                if (INITIALIZATION_STATE == UNINITIALIZED) {
                    INITIALIZATION_STATE = ONGOING_INITIALIZATION;
                    performInitialization();
                }
            }
        }
        switch (INITIALIZATION_STATE) {
        case SUCCESSFUL_INITIALIZATION:
            return StaticLoggerBinder.getSingleton().getLoggerFactory();
        case NOP_FALLBACK_INITIALIZATION:
            return NOP_FALLBACK_FACTORY;
        case FAILED_INITIALIZATION:
            throw new IllegalStateException(UNSUCCESSFUL_INIT_MSG);
        case ONGOING_INITIALIZATION:
            // support re-entrant behavior.
            // See also http://jira.qos.ch/browse/SLF4J-97
            return SUBST_FACTORY;
        }
        throw new IllegalStateException("Unreachable code");
    }
-

第一次调用的话就会走到 org.slf4j.LoggerFactory#performInitialization 里面是先调用绑定，这也是 slf4j 门面模式的特点

-
private final static void performInitialization() {
    bind();
    if (INITIALIZATION_STATE == SUCCESSFUL_INITIALIZATION) {
        versionSanityCheck();
    }
}
- - -
private final static void bind() {
        try {
            Set<URL> staticLoggerBinderPathSet = null;
            // skip check under android, see also
            // http://jira.qos.ch/browse/SLF4J-328
            if (!isAndroid()) {
                staticLoggerBinderPathSet = findPossibleStaticLoggerBinderPathSet();
                reportMultipleBindingAmbiguity(staticLoggerBinderPathSet);
            }
            // the next line does the binding
            StaticLoggerBinder.getSingleton();
            INITIALIZATION_STATE = SUCCESSFUL_INITIALIZATION;
            reportActualBinding(staticLoggerBinderPathSet);
            fixSubstituteLoggers();
            replayEvents();
            // release all resources in SUBST_FACTORY
            SUBST_FACTORY.clear();
        } catch (NoClassDefFoundError ncde) {
            String msg = ncde.getMessage();
            if (messageContainsOrgSlf4jImplStaticLoggerBinder(msg)) {
                INITIALIZATION_STATE = NOP_FALLBACK_INITIALIZATION;
                Util.report("Failed to load class \"org.slf4j.impl.StaticLoggerBinder\".");
                Util.report("Defaulting to no-operation (NOP) logger implementation");
                Util.report("See " + NO_STATICLOGGERBINDER_URL + " for further details.");
            } else {
                failedBinding(ncde);
                throw ncde;
            }
        } catch (java.lang.NoSuchMethodError nsme) {
            String msg = nsme.getMessage();
            if (msg != null && msg.contains("org.slf4j.impl.StaticLoggerBinder.getSingleton()")) {
                INITIALIZATION_STATE = FAILED_INITIALIZATION;
                Util.report("slf4j-api 1.6.x (or later) is incompatible with this binding.");
                Util.report("Your binding is version 1.5.5 or earlier.");
                Util.report("Upgrade your binding to version 1.6.x.");
            }
            throw nsme;
        } catch (Exception e) {
            failedBinding(e);
            throw new IllegalStateException("Unexpected initialization failure", e);
        }
    }
-

非安卓环境会进入第一个 if 逻辑，先要去找static_logger_binder

-
static Set<URL> findPossibleStaticLoggerBinderPathSet() {
        // use Set instead of list in order to deal with bug #138
        // LinkedHashSet appropriate here because it preserves insertion order
        // during iteration
        Set<URL> staticLoggerBinderPathSet = new LinkedHashSet<URL>();
        try {
            ClassLoader loggerFactoryClassLoader = LoggerFactory.class.getClassLoader();
            Enumeration<URL> paths;
            if (loggerFactoryClassLoader == null) {
                paths = ClassLoader.getSystemResources(STATIC_LOGGER_BINDER_PATH);
            } else {
                paths = loggerFactoryClassLoader.getResources(STATIC_LOGGER_BINDER_PATH);
            }
            while (paths.hasMoreElements()) {
                URL path = paths.nextElement();
                staticLoggerBinderPathSet.add(path);
            }
        } catch (IOException ioe) {
            Util.report("Error getting resources from path", ioe);
        }
        return staticLoggerBinderPathSet;
    }
-

上面的 STATIC_LOGGER_BINDER_PATH
就是 org/slf4j/impl/StaticLoggerBinder.class 如果找不到在外层方法就会抛出 NoSuchMethodError 错误，然后就是通过 StaticLoggerBinder.getSingleton(); 获取 StaticLoggerBinder 实例了

-
public static StaticLoggerBinder getSingleton() {
    return SINGLETON;
}
private static StaticLoggerBinder SINGLETON = new StaticLoggerBinder();
-

比较重要的是这里的 static 代码块还有一个逻辑，就是下面这个初始化逻辑

-
static {
    SINGLETON.init();
}
void init() {
    try {
        try {
            new ContextInitializer(defaultLoggerContext).autoConfig();
        } catch (JoranException je) {
            Util.report("Failed to auto configure default logger context", je);
        }
        // logback-292
        if (!StatusUtil.contextHasStatusListener(defaultLoggerContext)) {
            StatusPrinter.printInCaseOfErrorsOrWarnings(defaultLoggerContext);
        }
        contextSelectorBinder.init(defaultLoggerContext, KEY);
        initialized = true;
    } catch (Exception t) { // see LOGBACK-1159
        Util.report("Failed to instantiate [" + LoggerContext.class.getName() + "]", t);
    }
}
-

上面的 defaultLoggerContext 也是通过静态代码块处理的

-
private LoggerContext defaultLoggerContext = new LoggerContext();
-

然后是调用 ch.qos.logback.classic.util.ContextInitializer#autoConfig 方法

-
public void autoConfig() throws JoranException {
        StatusListenerConfigHelper.installIfAsked(loggerContext);
        URL url = findURLOfDefaultConfigurationFile(true);
        if (url != null) {
            configureByResource(url);
        } else {
            Configurator c = EnvUtil.loadFromServiceLoader(Configurator.class);
            if (c != null) {
                try {
                    c.setContext(loggerContext);
                    c.configure(loggerContext);
                } catch (Exception e) {
                    throw new LogbackException(String.format("Failed to initialize Configurator: %s using ServiceLoader", c != null ? c.getClass()
                                    .getCanonicalName() : "null"), e);
                }
            } else {
                BasicConfigurator basicConfigurator = new BasicConfigurator();
                basicConfigurator.setContext(loggerContext);
                basicConfigurator.configure(loggerContext);
            }
        }
    }
-

前面会先查找配置文件路径
URL url = findURLOfDefaultConfigurationFile(true); 还是调用这个类内的 ch.qos.logback.classic.util.ContextInitializer#findURLOfDefaultConfigurationFile 方法

-
public URL findURLOfDefaultConfigurationFile(boolean updateStatus) {
    ClassLoader myClassLoader = Loader.getClassLoaderOfObject(this);
    URL url = findConfigFileURLFromSystemProperties(myClassLoader, updateStatus);
    if (url != null) {
        return url;
    }

    url = getResource(TEST_AUTOCONFIG_FILE, myClassLoader, updateStatus);
    if (url != null) {
        return url;
    }

    url = getResource(GROOVY_AUTOCONFIG_FILE, myClassLoader, updateStatus);
    if (url != null) {
        return url;
    }

    return getResource(AUTOCONFIG_FILE, myClassLoader, updateStatus);
}
-

才发现原来这里也是硬编码了文件名

-
public class ContextInitializer {

    final public static String GROOVY_AUTOCONFIG_FILE = "logback.groovy";
    final public static String AUTOCONFIG_FILE = "logback.xml";
    final public static String TEST_AUTOCONFIG_FILE = "logback-test.xml";
    final public static String CONFIG_FILE_PROPERTY = "logback.configurationFile";

-

如果不为空就调用了
ch.qos.logback.classic.util.ContextInitializer#configureByResource 这边是根据后缀判断处理方法

-
public void configureByResource(URL url) throws JoranException {
        if (url == null) {
            throw new IllegalArgumentException("URL argument cannot be null");
        }
        final String urlString = url.toString();
        if (urlString.endsWith("groovy")) {
            if (EnvUtil.isGroovyAvailable()) {
                // avoid directly referring to GafferConfigurator so as to avoid
                // loading groovy.lang.GroovyObject . See also http://jira.qos.ch/browse/LBCLASSIC-214
                GafferUtil.runGafferConfiguratorOn(loggerContext, this, url);
            } else {
                StatusManager sm = loggerContext.getStatusManager();
                sm.add(new ErrorStatus("Groovy classes are not available on the class path. ABORTING INITIALIZATION.", loggerContext));
            }
        } else if (urlString.endsWith("xml")) {
            JoranConfigurator configurator = new JoranConfigurator();
            configurator.setContext(loggerContext);
            configurator.doConfigure(url);
        } else {
            throw new LogbackException("Unexpected filename extension of file [" + url.toString() + "]. Should be either .groovy or .xml");
        }
    }
-

因为我们找到的是 logback.xml，所以走的是
ch.qos.logback.classic.joran.JoranConfigurator ，
然后调用了
ch.qos.logback.core.joran.GenericConfigurator#doConfigure(java.net.URL)
这个 GenericConfigurator 是 JoranConfigurator 的父类

-
public final void doConfigure(URL url) throws JoranException {
        InputStream in = null;
        try {
            informContextOfURLUsedForConfiguration(getContext(), url);
            URLConnection urlConnection = url.openConnection();
            // per http://jira.qos.ch/browse/LBCORE-105
            // per http://jira.qos.ch/browse/LBCORE-127
            urlConnection.setUseCaches(false);

            in = urlConnection.getInputStream();
            doConfigure(in, url.toExternalForm());
        } catch (IOException ioe) {
            String errMsg = "Could not open URL [" + url + "].";
            addError(errMsg, ioe);
            throw new JoranException(errMsg, ioe);
        } finally {
            if (in != null) {
                try {
                    in.close();
                } catch (IOException ioe) {
                    String errMsg = "Could not close input stream";
                    addError(errMsg, ioe);
                    throw new JoranException(errMsg, ioe);
                }
            }
        }
    }
-

然后继续调用
ch.qos.logback.core.joran.GenericConfigurator#doConfigure(java.io.InputStream, java.lang.String) 处理具体的内容，前面处理了内容获取，因为如果是走的 url 文件就需要从网络获取文件流，

-
public final void doConfigure(InputStream inputStream, String systemId) throws JoranException {
    InputSource inputSource = new InputSource(inputStream);
    inputSource.setSystemId(systemId);
    doConfigure(inputSource);
}
-

而实际的处理 xml 内容则是在
ch.qos.logback.core.joran.GenericConfigurator#doConfigure(org.xml.sax.InputSource)

-
public final void doConfigure(final InputSource inputSource) throws JoranException {

    long threshold = System.currentTimeMillis();
    // if (!ConfigurationWatchListUtil.wasConfigurationWatchListReset(context)) {
    // informContextOfURLUsedForConfiguration(getContext(), null);
    // }
    SaxEventRecorder recorder = new SaxEventRecorder(context);
    recorder.recordEvents(inputSource);
    doConfigure(recorder.saxEventList);
    // no exceptions a this level
    StatusUtil statusUtil = new StatusUtil(context);
    if (statusUtil.noXMLParsingErrorsOccurred(threshold)) {
        addInfo("Registering current configuration as safe fallback point");
        registerSafeConfiguration(recorder.saxEventList);
    }
}
-

需要解析 xml 了，先是调用 buildInterpreter 构建了 Interpreter

-
public void doConfigure(final List<SaxEvent> eventList) throws JoranException {
    buildInterpreter();
    // disallow simultaneous configurations of the same context
    synchronized (context.getConfigurationLock()) {
        interpreter.getEventPlayer().play(eventList);
    }
}
-

build 里还有一些逻辑，就是匹配后面要处理的规则集，就是要确认那些标签要处理

-
protected void buildInterpreter() {
        RuleStore rs = new SimpleRuleStore(context);
        addInstanceRules(rs);
        this.interpreter = new Interpreter(context, rs, initialElementPath());
        InterpretationContext interpretationContext = interpreter.getInterpretationContext();
        interpretationContext.setContext(context);
        addImplicitRules(interpreter);
        addDefaultNestedComponentRegistryRules(interpretationContext.getDefaultNestedComponentRegistry());
    }
-

第一步就是先 new 一个 ch.qos.logback.core.joran.spi.SimpleRuleStore 然后调用了 ch.qos.logback.classic.joran.JoranConfigurator#addInstanceRules 来添加规则，就是下面的，可以看到对于 logger,appender 这些的配置，其中前面点的父类也又一些规则 ,ch.qos.logback.core.joran.JoranConfiguratorBase 逻辑类似就不展开了，这里的规则就对应了后面的各种 Action，就是对应的处理逻辑

-
public void addInstanceRules(RuleStore rs) {
    // parent rules already added
    super.addInstanceRules(rs);

    rs.addRule(new ElementSelector("configuration"), new ConfigurationAction());

    rs.addRule(new ElementSelector("configuration/contextName"), new ContextNameAction());
    rs.addRule(new ElementSelector("configuration/contextListener"), new LoggerContextListenerAction());
    rs.addRule(new ElementSelector("configuration/insertFromJNDI"), new InsertFromJNDIAction());
    rs.addRule(new ElementSelector("configuration/evaluator"), new EvaluatorAction());

    rs.addRule(new ElementSelector("configuration/appender/sift"), new SiftAction());
    rs.addRule(new ElementSelector("configuration/appender/sift/*"), new NOPAction());

    rs.addRule(new ElementSelector("configuration/logger"), new LoggerAction());
    rs.addRule(new ElementSelector("configuration/logger/level"), new LevelAction());

    rs.addRule(new ElementSelector("configuration/root"), new RootLoggerAction());
    rs.addRule(new ElementSelector("configuration/root/level"), new LevelAction());
    rs.addRule(new ElementSelector("configuration/logger/appender-ref"), new AppenderRefAction<ILoggingEvent>());
    rs.addRule(new ElementSelector("configuration/root/appender-ref"), new AppenderRefAction<ILoggingEvent>());

    // add if-then-else support
    rs.addRule(new ElementSelector("*/if"), new IfAction());
    rs.addRule(new ElementSelector("*/if/then"), new ThenAction());
    rs.addRule(new ElementSelector("*/if/then/*"), new NOPAction());
    rs.addRule(new ElementSelector("*/if/else"), new ElseAction());
    rs.addRule(new ElementSelector("*/if/else/*"), new NOPAction());

    // add jmxConfigurator only if we have JMX available.
    // If running under JDK 1.4 (retrotranslateed logback) then we
    // might not have JMX.
    if (PlatformInfo.hasJMXObjectName()) {
        rs.addRule(new ElementSelector("configuration/jmxConfigurator"), new JMXConfiguratorAction());
    }
    rs.addRule(new ElementSelector("configuration/include"), new IncludeAction());

    rs.addRule(new ElementSelector("configuration/consolePlugin"), new ConsolePluginAction());

    rs.addRule(new ElementSelector("configuration/receiver"), new ReceiverAction());

}
-

然后调用了 interpreter 的 eventPlayer 处理 xml 的各个标签

-
public void play(List<SaxEvent> aSaxEventList) {
        eventList = aSaxEventList;
        SaxEvent se;
        for (currentIndex = 0; currentIndex < eventList.size(); currentIndex++) {
            se = eventList.get(currentIndex);

            if (se instanceof StartEvent) {
                interpreter.startElement((StartEvent) se);
                // invoke fireInPlay after startElement processing
                interpreter.getInterpretationContext().fireInPlay(se);
            }
            if (se instanceof BodyEvent) {
                // invoke fireInPlay before characters processing
                interpreter.getInterpretationContext().fireInPlay(se);
                interpreter.characters((BodyEvent) se);
            }
            if (se instanceof EndEvent) {
                // invoke fireInPlay before endElement processing
                interpreter.getInterpretationContext().fireInPlay(se);
                interpreter.endElement((EndEvent) se);
            }

        }
    }
-

接下去就在 startElement 中调用会先获取 getApplicableActionList 适配的 Action， callBeginAction

-
private void startElement(String namespaceURI, String localName, String qName, Attributes atts) {

        String tagName = getTagName(localName, qName);
        elementPath.push(tagName);

        if (skip != null) {
            // every startElement pushes an action list
            pushEmptyActionList();
            return;
        }

        List<Action> applicableActionList = getApplicableActionList(elementPath, atts);
        if (applicableActionList != null) {
            actionListStack.add(applicableActionList);
            callBeginAction(applicableActionList, tagName, atts);
        } else {
            // every startElement pushes an action list
            pushEmptyActionList();
            String errMsg = "no applicable action for [" + tagName + "], current ElementPath  is [" + elementPath + "]";
            cai.addError(errMsg);
        }
    }
-

里面就是用了 ruleStore 来判断是否适配，并且取出对应的 Action

-
List<Action> getApplicableActionList(ElementPath elementPath, Attributes attributes) {
    List<Action> applicableActionList = ruleStore.matchActions(elementPath);

    // logger.debug("set of applicable patterns: " + applicableActionList);
    if (applicableActionList == null) {
        applicableActionList = lookupImplicitAction(elementPath, attributes, interpretationContext);
    }

    return applicableActionList;
}
-

比如我这边往下处理，通过规则匹配拿到了 LoggerAction, 然后调用 Action.begin 方法

-
void callBeginAction(List<Action> applicableActionList, String tagName, Attributes atts) {
        if (applicableActionList == null) {
            return;
        }

        Iterator<Action> i = applicableActionList.iterator();
        while (i.hasNext()) {
            Action action = (Action) i.next();
            // now let us invoke the action. We catch and report any eventual
            // exceptions
            try {
                action.begin(interpretationContext, tagName, atts);
            } catch (ActionException e) {
                skip = elementPath.duplicate();
                cai.addError("ActionException in Action for tag [" + tagName + "]", e);
            } catch (RuntimeException e) {
                skip = elementPath.duplicate();
                cai.addError("RuntimeException in Action for tag [" + tagName + "]", e);
            }
        }
    }
-

就是下面这个

-
public void begin(InterpretationContext ec, String name, Attributes attributes) {
        // Let us forget about previous errors (in this object)
        inError = false;
        logger = null;

        LoggerContext loggerContext = (LoggerContext) this.context;

        String loggerName = ec.subst(attributes.getValue(NAME_ATTRIBUTE));

        if (OptionHelper.isEmpty(loggerName)) {
            inError = true;
            String aroundLine = getLineColStr(ec);
            String errorMsg = "No 'name' attribute in element " + name + ", around " + aroundLine;
            addError(errorMsg);
            return;
        }

        logger = loggerContext.getLogger(loggerName);

        String levelStr = ec.subst(attributes.getValue(LEVEL_ATTRIBUTE));

        if (!OptionHelper.isEmpty(levelStr)) {
            if (ActionConst.INHERITED.equalsIgnoreCase(levelStr) || ActionConst.NULL.equalsIgnoreCase(levelStr)) {
                addInfo("Setting level of logger [" + loggerName + "] to null, i.e. INHERITED");
                logger.setLevel(null);
            } else {
                Level level = Level.toLevel(levelStr);
                addInfo("Setting level of logger [" + loggerName + "] to " + level);
                logger.setLevel(level);
            }
        }

        String additivityStr = ec.subst(attributes.getValue(ActionConst.ADDITIVITY_ATTRIBUTE));
        if (!OptionHelper.isEmpty(additivityStr)) {
            boolean additive = OptionHelper.toBoolean(additivityStr, true);
            addInfo("Setting additivity of logger [" + loggerName + "] to " + additive);
            logger.setAdditive(additive);
        }
        ec.pushObject(logger);
    }
-

这里就会处理 logger 获取逻辑

-
public final Logger getLogger(final String name) {

        if (name == null) {
            throw new IllegalArgumentException("name argument cannot be null");
        }

        // if we are asking for the root logger, then let us return it without
        // wasting time
        if (Logger.ROOT_LOGGER_NAME.equalsIgnoreCase(name)) {
            return root;
        }

        int i = 0;
        Logger logger = root;

        // check if the desired logger exists, if it does, return it
        // without further ado.
        Logger childLogger = (Logger) loggerCache.get(name);
        // if we have the child, then let us return it without wasting time
        if (childLogger != null) {
            return childLogger;
        }

        // if the desired logger does not exist, them create all the loggers
        // in between as well (if they don't already exist)
        String childName;
        while (true) {
            int h = LoggerNameUtil.getSeparatorIndexOf(name, i);
            if (h == -1) {
                childName = name;
            } else {
                childName = name.substring(0, h);
            }
            // move i left of the last point
            i = h + 1;
            synchronized (logger) {
                childLogger = logger.getChildByName(childName);
                if (childLogger == null) {
                    childLogger = logger.createChildByName(childName);
                    loggerCache.put(childName, childLogger);
                    incSize();
                }
            }
            logger = childLogger;
            if (h == -1) {
                return childLogger;
            }
        }
    }
-

如果从 loggerCache 中能取到就直接获取，如果不能就会去创建

-]]> - - Java - - - Java - - 聊聊 Dubbo 的容错机制 /2020/11/22/%E8%81%8A%E8%81%8A-Dubbo-%E7%9A%84%E5%AE%B9%E9%94%99%E6%9C%BA%E5%88%B6/ @@ -6326,6 +6399,9 @@ user3: Synchronized 关键字在 Java 的并发体系里也是非常重要的一个内容，首先比较常规的是知道它使用的方式，可以锁对象，可以锁代码块，也可以锁方法，看一个简单的 demo

public class SynchronizedDemo {

    public static void main(String[] args) {
        SynchronizedDemo synchronizedDemo = new SynchronizedDemo();
        synchronizedDemo.lockMethod();
    }

    public synchronized void lockMethod() {
        System.out.println("here i'm locked");
    }

    public void lockSynchronizedDemo() {
        synchronized (this) {
            System.out.println("here lock class");
        }
    }
}
+

然后来查看反编译结果，其实代码（日光）之下并无新事，即使是完全不懂的也可以通过一些词义看出一些意义

+
  Last modified 2021年6月20日; size 729 bytes
  MD5 checksum dd9c529863bd7ff839a95481db578ad9
  Compiled from "SynchronizedDemo.java"
public class SynchronizedDemo
  minor version: 0
  major version: 53
  flags: (0x0021) ACC_PUBLIC, ACC_SUPER
  this_class: #2                          // SynchronizedDemo
  super_class: #9                         // java/lang/Object
  interfaces: 0, fields: 0, methods: 4, attributes: 1
Constant pool:
   #1 = Methodref          #9.#22         // java/lang/Object."<init>":()V
   #2 = Class              #23            // SynchronizedDemo
   #3 = Methodref          #2.#22         // SynchronizedDemo."<init>":()V
   #4 = Methodref          #2.#24         // SynchronizedDemo.lockMethod:()V
   #5 = Fieldref           #25.#26        // java/lang/System.out:Ljava/io/PrintStream;
   #6 = String             #27            // here i\'m locked
   #7 = Methodref          #28.#29        // java/io/PrintStream.println:(Ljava/lang/String;)V
   #8 = String             #30            // here lock class
   #9 = Class              #31            // java/lang/Object
  #10 = Utf8               <init>
  #11 = Utf8               ()V
  #12 = Utf8               Code
  #13 = Utf8onized void lockMethod() {
        System.out.println("here i'm locked");
    }

    public void lockSynchronizedDemo() {
        synchronized (this) {
            System.out.println("here lock class");
        }
    }
}
+

然后来查看反编译结果，其实代码（日光）之下并无新事，即使是完全不懂的也可以通过一些词义看出一些意义

  Last modified 2021年6月20日; size 729 bytes
  MD5 checksum dd9c529863bd7ff839a95481db578ad9
  Compiled from "SynchronizedDemo.java"
public class SynchronizedDemo
  minor version: 0
  major version: 53
  flags: (0x0021) ACC_PUBLIC, ACC_SUPER
  this_class: #2                          // SynchronizedDemo
  super_class: #9                         // java/lang/Object
  interfaces: 0, fields: 0, methods: 4, attributes: 1
Constant pool:
   #1 = Methodref          #9.#22         // java/lang/Object."<init>":()V
   #2 = Class              #23            // SynchronizedDemo
   #3 = Methodref          #2.#22         // SynchronizedDemo."<init>":()V
   #4 = Methodref          #2.#24         // SynchronizedDemo.lockMethod:()V
   #5 = Fieldref           #25.#26        // java/lang/System.out:Ljava/io/PrintStream;
   #6 = String             #27            // here i\'m locked
   #7 = Methodref          #28.#29        // java/io/PrintStream.println:(Ljava/lang/String;)V
   #8 = String             #30            // here lock class
   #9 = Class              #31            // java/lang/Object
  #10 = Utf8               <init>
  #11 = Utf8               ()V
  #12 = Utf8               Code
  #13 = Utf8               LineNumberTable
  #14 = Utf8               main
  #15 = Utf8               ([Ljava/lang/String;)V
  #16 = Utf8               lockMethod
  #17 = Utf8               lockSynchronizedDemo
  #18 = Utf8               StackMapTable
  #19 = Class              #32            // java/lang/Throwable
  #20 = Utf8               SourceFile
  #21 = Utf8               SynchronizedDemo.java
  #22 = NameAndType        #10:#11        // "<init>":()V
  #23 = Utf8               SynchronizedDemo
  #24 = NameAndType        #16:#11        // lockMethod:()V
  #25 = Class              #33            // java/lang/System
  #26 = NameAndType        #34:#35        // out:Ljava/io/PrintStream;
  #27 = Utf8               here i\'m locked
  #28 = Class              #36            // java/io/PrintStream
  #29 = NameAndType        #37:#38        // println:(Ljava/lang/String;)V
  #30 = Utf8               here lock class
  #31 = Utf8               java/lang/Object
  #32 = Utf8               java/lang/Throwable
  #33 = Utf8               java/lang/System
  #34 = Utf8               out
  #35 = Utf8               Ljava/io/PrintStream;
  #36 = Utf8               java/io/PrintStream
  #37 = Utf8               println
  #38 = Utf8               (Ljava/lang/String;)V
{
  public SynchronizedDemo();
    descriptor: ()V
    flags: (0x0001) ACC_PUBLIC
    Code:
      stack=1, locals=1, args_size=1
         0: aload_0
         1: invokespecial #1                  // Method java/lang/Object."<init>":()V
         4: return
      LineNumberTable:
        line 5: 0

  public static void main(java.lang.String[]);
    descriptor: ([Ljava/lang/String;)V
    flags: (0x0009) ACC_PUBLIC, ACC_STATIC
    Code:
      stack=2, locals=2, args_size=1
         0: new           #2                  // class SynchronizedDemo
         3: dup
         4: invokespecial #3                  // Method "<init>":()V
         7: astore_1
         8: aload_1
         9: invokevirtual #4                  // Method lockMethod:()V
        12: return
      LineNumberTable:
        line 8: 0
        line 9: 8
        line 10: 12

  public synchronized void lockMethod();
    descriptor: ()V
    flags: (0x0021) ACC_PUBLIC, ACC_SYNCHRONIZED
    Code:
      stack=2, locals=1, args_size=1
         0: getstatic     #5                  // Field java/lang/System.out:Ljava/io/PrintStream;
         3: ldc           #6                  // String here i\'m locked
         5: invokevirtual #7                  // Method java/io/PrintStream.println:(Ljava/lang/String;)V
         8: return
      LineNumberTable:
        line 13: 0
        line 14: 8

  public void lockSynchronizedDemo();
    descriptor: ()V
    flags: (0x0001) ACC_PUBLIC
    Code:
      stack=2, locals=3, args_size=1
         0: aload_0
         1: dup
         2: astore_1
         3: monitorenter
         4: getstatic     #5                  // Field java/lang/System.out:Ljava/io/PrintStream;
         7: ldc           #8                  // String here lock class
         9: invokevirtual #7                  // Method java/io/PrintStream.println:(Ljava/lang/String;)V
        12: aload_1
        13: monitorexit
        14: goto          22
        17: astore_2
        18: aload_1
        19: monitorexit
        20: aload_2
        21: athrow
        22: return
      Exception table:
         from    to  target type
             4    14    17   any
            17    20    17   any
      LineNumberTable:
        line 17: 0
        line 18: 4
        line 19: 12
        line 20: 22
      StackMapTable: number_of_entries = 2
        frame_type = 255 /* full_frame */
          offset_delta = 17
          locals = [ class SynchronizedDemo, class java/lang/Object ]
          stack = [ class java/lang/Throwable ]
        frame_type = 250 /* chop */
          offset_delta = 4
}
SourceFile: "SynchronizedDemo.java"
@@ -6344,22 +6420,6 @@ user3: 自旋 - - 聊聊 Java 的 equals 和 hashCode 方法 - /2021/01/03/%E8%81%8A%E8%81%8A-Java-%E7%9A%84-equals-%E5%92%8C-hashCode-%E6%96%B9%E6%B3%95/ - Java 中的这个话题也是比较常遇到的，关于这块原先也是比较忽略的，但是仔细想想又有点遗忘了就在这里记一下
简单看下代码
java.lang.Object#equals

-
public boolean equals(Object obj) {
        return (this == obj);
    }
-

对于所有对象的父类，equals 方法其实对比的就是对象的地址，也就是是否是同一个对象，试想如果像 Integer 或者 String 这种，我们没有重写 equals，那其实就等于是在用==，可能就没法达到我们的目的，所以像 String 这种常用的 jdk 类都是默认重写了
java.lang.String#equals

-
public boolean equals(Object anObject) {
        if (this == anObject) {
            return true;
        }
        if (anObject instanceof String) {
            String anotherString = (String)anObject;
            int n = value.length;
            if (n == anotherString.value.length) {
                char v1[] = value;
                char v2[] = anotherString.value;
                int i = 0;
                while (n-- != 0) {
                    if (v1[i] != v2[i])
                        return false;
                    i++;
                }
                return true;
            }
        }
        return false;
    }
-

然后呢就是为啥一些书或者 effective java 中写了 equals 跟 hashCode 要一起重写，这里涉及到当对象作为 HashMap 的 key 的时候
首先 HashMap 会使用 hashCode 去判断是否在同一个槽里，然后在通过 equals 去判断是否是同一个 key，是的话就替换，不是的话就链表接下去，如果不重写 hashCode 的话，默认的 object 的hashCode 是 native 方法，根据对象的地址生成的，这样其实对象的值相同的话，因为地址不同，HashMap 也会出现异常，所以需要重写，同时也需要重写 equals 方法，才能确认是同一个 key，而不是落在同一个槽的不同 key.

-]]> - - java - - - java - - 聊聊 Java 的类加载机制一 /2020/11/08/%E8%81%8A%E8%81%8A-Java-%E7%9A%84%E7%B1%BB%E5%8A%A0%E8%BD%BD%E6%9C%BA%E5%88%B6/ @@ -6567,23 +6627,17 @@ user3: - 聊聊 Sharding-Jdbc 分库分表下的分页方案 - /2022/01/09/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E5%88%86%E5%BA%93%E5%88%86%E8%A1%A8%E4%B8%8B%E7%9A%84%E5%88%86%E9%A1%B5%E6%96%B9%E6%A1%88/ - 前面在聊 Sharding-Jdbc 的时候看到了一篇文章，关于一个分页的查询，一直比较直接的想法就是分库分表下的分页是非常不合理的，一般我们的实操方案都是分表加上 ES 搜索做分页，或者通过合表读写分离的方案，因为对于 sharding-jdbc 如果没有带分表键，查询基本都是只能在所有分表都执行一遍，然后再加上分页，基本上是分页越大后续的查询越耗资源，但是仔细的去想这个细节还是这次，就简单说说
首先就是我的分表结构

-
CREATE TABLE `student_time_0` (
  `id` int(11) unsigned NOT NULL AUTO_INCREMENT,
  `user_id` int(11) NOT NULL,
  `name` varchar(200) COLLATE utf8_bin DEFAULT NULL,
  `age` tinyint(3) unsigned DEFAULT NULL,
  `create_time` bigint(20) DEFAULT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=674 DEFAULT CHARSET=utf8 COLLATE=utf8_bin;
-

有这样的三个表，student_time_0, student_time_1, student_time_2, 以 user_id 作为分表键，根据表数量取模作为分表依据
这里先构造点数据，

-
insert into student_time (`name`, `user_id`, `age`, `create_time`) values (?, ?, ?, ?)
-

主要是为了保证 create_time 唯一比较好说明问题，

-
int i = 0;
try (
        Connection conn = dataSource.getConnection();
        PreparedStatement ps = conn.prepareStatement(insertSql)) {
    do {
        ps.setString(1, localName + new Random().nextInt(100));
        ps.setLong(2, 10086L + (new Random().nextInt(100)));
        ps.setInt(3, 18);
        ps.setLong(4, new Date().getTime());


        int result = ps.executeUpdate();
        LOGGER.info("current execute result: {}", result);
        Thread.sleep(new Random().nextInt(100));
        i++;
    } while (i <= 2000);
-

三个表的数据分别是 673，678，650，说明符合预期了，各个表数据不一样，接下来比如我们想要做一个这样的分页查询

-
select * from student_time ORDER BY create_time ASC limit 1000, 5;
-

student_time 对于我们使用的 sharding-jdbc 来说当然是逻辑表，首先从一无所知去想这个查询如果我们自己来处理应该是怎么做，
首先是不是可以每个表都从 333 开始取 5 条数据，类似于下面的查询，然后进行 15 条的合并重排序获取前面的 5 条

-
select * from student_time_0 ORDER BY create_time ASC limit 333, 5;
select * from student_time_1 ORDER BY create_time ASC limit 333, 5;
select * from student_time_2 ORDER BY create_time ASC limit 333, 5;
-

忽略前面 limit 差的 1，这个结果除非三个表的分布是绝对的均匀，否则结果肯定会出现一定的偏差，以为每个表的 333 这个位置对于其他表来说都不一定是一样的，这样对于最后整体的结果，就会出现偏差
因为一直在纠结怎么让这个更直观的表现出来，所以尝试画了个图

黑色的框代表我从每个表里按排序从 334 到 338 的 5 条数据， 他们在每个表里都是代表了各自正确的排序值，但是对于我们想要的其实是合表后的 1001，1005 这五条，然后我们假设总的排序值位于前 1000 的分布是第 0 个表是 320 条，第 1 个表是 340 条，第 2 个表是 340 条，那么可以明显地看出来我这么查的结果简单合并肯定是不对的。
那么 sharding-jdbc 是如何保证这个结果的呢，其实就是我在每个表里都查分页偏移量和分页大小那么多的数据，在我这个例子里就是对于 0，1，2 三个分表每个都查 1005 条数据，即使我的数据不平衡到最极端的情况，前 1005 条数据都出在某个分表中，也可以正确获得最后的结果，但是明显的问题就是大分页，数据较多，就会导致非常大的问题，即使如 sharding-jdbc 对于合并排序的优化做得比较好，也还是需要传输那么大量的数据，并且查询也耗时，那么有没有解决方案呢，应该说有两个，或者说主要是想讲后者
第一个办法是像这种查询，如果业务上不需要进行跳页，而是只给下一页，那么我们就能把前一次的最大偏移量的 create_time 记录下来，下一页就可以拿着这个偏移量进行查询，这个比较简单易懂，就不多说了
第二个办法是看的58 沈剑的一篇文章，尝试理解讲述一下，
这个办法的第一步跟前面那个错误的方法或者说不准确的方法一样，先是将分页偏移量平均后在三个表里进行查询

-
t0
334 10158 nick95  18  1641548941767
335 10098 nick11  18  1641548941879
336 10167 nick51  18  1641548942089
337 10167 nick3 18  1641548942119
338 10170 nick57  18  1641548942169


t1
334 10105 nick98  18  1641548939071   最小
335 10174 nick94  18  1641548939377
336 10129 nick85  18  1641548939442
337 10141 nick84  18  1641548939480
338 10096 nick74  18  1641548939668

t2
334 10184 nick11  18  1641548945075
335 10109 nick93  18  1641548945382
336 10181 nick41  18  1641548945583
337 10130 nick80  18  1641548945993
338 10184 nick19  18  1641548946294  最大
-

然后要做什么呢，其实目标比较明白，因为前面那种方法其实就是我知道了前一页的偏移量，所以可以直接当做条件来进行查询，那这里我也想着拿到这个条件，所以我将第一遍查出来的最小的 create_time 和最大的 create_time 找出来，然后再去三个表里查询，其实主要是最小值，因为我拿着最小值去查以后我就能知道这个最小值在每个表里处在什么位置，

-
t0
322 10161 nick81  18  1641548939284
323 10113 nick16  18  1641548939393
324 10110 nick56  18  1641548939577
325 10116 nick69  18  1641548939588
326 10173 nick51  18  1641548939646

t1
334 10105 nick98  18  1641548939071
335 10174 nick94  18  1641548939377
336 10129 nick85  18  1641548939442
337 10141 nick84  18  1641548939480
338 10096 nick74  18  1641548939668

t2
297 10136 nick28  18  1641548939161
298 10142 nick68  18  1641548939177
299 10124 nick41  18  1641548939237
300 10148 nick87  18  1641548939510
301 10169 nick23  18  1641548939715
-

我只贴了前五条数据，为了方便知道偏移量，每个分表都使用了自增主键，我们可以看到前一次查询的最小值分别在其他两个表里的位置分别是 322-1 和 297-1，那么对于总体来说这个时间应该是在 322 - 1 + 333 + 297 - 1 = 951，那这样子我只要对后面的数据最多每个表查 1000 - 951 + 5 = 54 条数据再进行合并排序就可以获得最终正确的结果。
这个就是传说中的二次查询法。

+ 聊聊 Sharding-Jdbc 的简单使用 + /2021/12/12/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E7%9A%84%E7%AE%80%E5%8D%95%E4%BD%BF%E7%94%A8/ + 我们在日常工作中还是使用比较多的分库分表组件的，其中比较优秀的就有 Sharding-Jdbc，一开始由当当开源，后来捐献给了 Apache，说一下简单使用，因为原来经常的使用都是基于 xml 跟 properties 组合起来使用，这里主要试下用 Java Config 来配置
首先是通过 Spring Initializr 创建个带 jdbc 的 Spring Boot 项目，然后引入主要的依赖

+
<dependency>
    <groupId>org.apache.shardingsphere</groupId>
    <artifactId>shardingsphere-jdbc-core</artifactId>
    <version>5.0.0-beta</version>
</dependency>
+

因为前面有聊过 Spring Boot 的自动加载，在这里 spring 就会自己去找 DataSource 的配置，所以要在入口把它干掉

+
@SpringBootApplication(exclude = { DataSourceAutoConfiguration.class})
public class ShardingJdbcDemoApplication implements CommandLineRunner {
+

然后因为想在入口跑代码，就实现了下 org.springframework.boot.CommandLineRunner 主要是后面的 Java Config 代码

+

// 注意这里的注解，可以让 Spring 自动帮忙加载，也就是 Java Config 的核心
@Configuration
public class MysqlConfig {

    @Bean
    public DataSource dataSource() throws SQLException {
        // Configure actual data sources
        Map<String, DataSource> dataSourceMap = new HashMap<>();

        
        // Configure the first data source
        // 使用了默认的Hikari连接池的 DataSource
        HikariDataSource dataSource1 = new HikariDataSource();
        dataSource1.setDriverClassName("com.mysql.jdbc.Driver");
        dataSource1.setJdbcUrl("jdbc:mysql://localhost:3306/sharding");
        dataSource1.setUsername("username");
        dataSource1.setPassword("password");
        dataSourceMap.put("ds0", dataSource1);

        // Configure student table rule
        // 这里是配置分表逻辑，逻辑表是 student，对应真实的表是 student_0 到 student_1, 这个配置方式就是有多少表可以用 student_$->{0..n}
        ShardingTableRuleConfiguration studentTableRuleConfig = new ShardingTableRuleConfiguration("student", "ds0.student_$->{0..1}");

        // 设置分表字段
        studentTableRuleConfig.setTableShardingStrategy(new StandardShardingStrategyConfiguration("user_id", "tableShardingAlgorithm"));


        // Configure sharding rule
        // 配置 studentTableRuleConfig
        ShardingRuleConfiguration shardingRuleConfig = new ShardingRuleConfiguration();
        shardingRuleConfig.getTables().add(studentTableRuleConfig);

        // Configure table sharding algorithm
        Properties tableShardingAlgorithmrProps = new Properties();
        // 算法表达式就是根据 user_id 对 2 进行取模
        tableShardingAlgorithmrProps.setProperty("algorithm-expression", "student_${user_id % 2}");
        shardingRuleConfig.getShardingAlgorithms().put("tableShardingAlgorithm", new ShardingSphereAlgorithmConfiguration("INLINE", tableShardingAlgorithmrProps));


        // 然后创建这个 DataSource
        return ShardingSphereDataSourceFactory.createDataSource(dataSourceMap, Collections.singleton(shardingRuleConfig), new Properties());

    }
}
+

然后我们就可以在使用这个 DataSource 了，先看下这两个表的数据

+
@Override
    public void run(String... args) {
        LOGGER.info("run here");
        String sql = "SELECT * FROM student WHERE user_id=? ";
        try (
                Connection conn = dataSource.getConnection();
                PreparedStatement ps = conn.prepareStatement(sql)) {
            // 参数就是 user_id，然后也是分表键，对 2 取模就是 1，应该是去 student_1 取数据
            ps.setInt(1, 1001);

            ResultSet resultSet = ps.executeQuery();
            while (resultSet.next()) {
                final int id = resultSet.getInt("id");
                final String name = resultSet.getString("name");
                final int userId = resultSet.getInt("user_id");
                final int age = resultSet.getInt("age");
                System.out.println("奇数表 id：" + id + " 姓名：" + name
                        + " 用户 id：" + userId + " 年龄：" + age );
                System.out.println("=============================");
            }
            // 参数就是 user_id，然后也是分表键，对 2 取模就是 0，应该是去 student_0 取数据
            ps.setInt(1, 1000);
            resultSet = ps.executeQuery();
            while (resultSet.next()) {
                final int id = resultSet.getInt("id");
                final String name = resultSet.getString("name");
                final int userId = resultSet.getInt("user_id");
                final int age = resultSet.getInt("age");
                System.out.println("偶数表 id：" + id + " 姓名：" + name
                        + " 用户 id：" + userId + " 年龄：" + age );
                System.out.println("=============================");
            }
        } catch (SQLException e) {
            e.printStackTrace();
        }
    }
+

看下查询结果

]]> Java @@ -6610,27 +6664,6 @@ user3: 
ResultSet resultSet = ps.executeQuery();

然后获得结果
org.apache.shardingsphere.driver.jdbc.core.resultset.ShardingSphereResultSet

其实像 mybatis 也是基于这样去实现的

-]]> - - Java - - - Java - Sharding-Jdbc - - - - 聊聊 Sharding-Jdbc 的简单使用 - /2021/12/12/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E7%9A%84%E7%AE%80%E5%8D%95%E4%BD%BF%E7%94%A8/ - 我们在日常工作中还是使用比较多的分库分表组件的，其中比较优秀的就有 Sharding-Jdbc，一开始由当当开源，后来捐献给了 Apache，说一下简单使用，因为原来经常的使用都是基于 xml 跟 properties 组合起来使用，这里主要试下用 Java Config 来配置
首先是通过 Spring Initializr 创建个带 jdbc 的 Spring Boot 项目，然后引入主要的依赖

-
<dependency>
    <groupId>org.apache.shardingsphere</groupId>
    <artifactId>shardingsphere-jdbc-core</artifactId>
    <version>5.0.0-beta</version>
</dependency>
-

因为前面有聊过 Spring Boot 的自动加载，在这里 spring 就会自己去找 DataSource 的配置，所以要在入口把它干掉

-
@SpringBootApplication(exclude = { DataSourceAutoConfiguration.class})
public class ShardingJdbcDemoApplication implements CommandLineRunner {
-

然后因为想在入口跑代码，就实现了下 org.springframework.boot.CommandLineRunner 主要是后面的 Java Config 代码

-

// 注意这里的注解，可以让 Spring 自动帮忙加载，也就是 Java Config 的核心
@Configuration
public class MysqlConfig {

    @Bean
    public DataSource dataSource() throws SQLException {
        // Configure actual data sources
        Map<String, DataSource> dataSourceMap = new HashMap<>();

        
        // Configure the first data source
        // 使用了默认的Hikari连接池的 DataSource
        HikariDataSource dataSource1 = new HikariDataSource();
        dataSource1.setDriverClassName("com.mysql.jdbc.Driver");
        dataSource1.setJdbcUrl("jdbc:mysql://localhost:3306/sharding");
        dataSource1.setUsername("username");
        dataSource1.setPassword("password");
        dataSourceMap.put("ds0", dataSource1);

        // Configure student table rule
        // 这里是配置分表逻辑，逻辑表是 student，对应真实的表是 student_0 到 student_1, 这个配置方式就是有多少表可以用 student_$->{0..n}
        ShardingTableRuleConfiguration studentTableRuleConfig = new ShardingTableRuleConfiguration("student", "ds0.student_$->{0..1}");

        // 设置分表字段
        studentTableRuleConfig.setTableShardingStrategy(new StandardShardingStrategyConfiguration("user_id", "tableShardingAlgorithm"));


        // Configure sharding rule
        // 配置 studentTableRuleConfig
        ShardingRuleConfiguration shardingRuleConfig = new ShardingRuleConfiguration();
        shardingRuleConfig.getTables().add(studentTableRuleConfig);

        // Configure table sharding algorithm
        Properties tableShardingAlgorithmrProps = new Properties();
        // 算法表达式就是根据 user_id 对 2 进行取模
        tableShardingAlgorithmrProps.setProperty("algorithm-expression", "student_${user_id % 2}");
        shardingRuleConfig.getShardingAlgorithms().put("tableShardingAlgorithm", new ShardingSphereAlgorithmConfiguration("INLINE", tableShardingAlgorithmrProps));


        // 然后创建这个 DataSource
        return ShardingSphereDataSourceFactory.createDataSource(dataSourceMap, Collections.singleton(shardingRuleConfig), new Properties());

    }
}
-

然后我们就可以在使用这个 DataSource 了，先看下这两个表的数据

-
@Override
    public void run(String... args) {
        LOGGER.info("run here");
        String sql = "SELECT * FROM student WHERE user_id=? ";
        try (
                Connection conn = dataSource.getConnection();
                PreparedStatement ps = conn.prepareStatement(sql)) {
            // 参数就是 user_id，然后也是分表键，对 2 取模就是 1，应该是去 student_1 取数据
            ps.setInt(1, 1001);

            ResultSet resultSet = ps.executeQuery();
            while (resultSet.next()) {
                final int id = resultSet.getInt("id");
                final String name = resultSet.getString("name");
                final int userId = resultSet.getInt("user_id");
                final int age = resultSet.getInt("age");
                System.out.println("奇数表 id：" + id + " 姓名：" + name
                        + " 用户 id：" + userId + " 年龄：" + age );
                System.out.println("=============================");
            }
            // 参数就是 user_id，然后也是分表键，对 2 取模就是 0，应该是去 student_0 取数据
            ps.setInt(1, 1000);
            resultSet = ps.executeQuery();
            while (resultSet.next()) {
                final int id = resultSet.getInt("id");
                final String name = resultSet.getString("name");
                final int userId = resultSet.getInt("user_id");
                final int age = resultSet.getInt("age");
                System.out.println("偶数表 id：" + id + " 姓名：" + name
                        + " 用户 id：" + userId + " 年龄：" + age );
                System.out.println("=============================");
            }
        } catch (SQLException e) {
            e.printStackTrace();
        }
    }
-

看下查询结果

]]> Java @@ -6674,43 +6707,30 @@ user3: - 聊聊 mysql 的 MVCC - /2020/04/26/%E8%81%8A%E8%81%8A-mysql-%E7%9A%84-MVCC/ - 很久以前，有位面试官问到，你知道 mysql 的事务隔离级别吗，“额 O__O …，不太清楚”，完了之后我就去网上找相关的文章，找到了这篇MySQL 四种事务隔离级的说明, 文章写得特别好，看了这个就懂了各个事务隔离级别都是啥，不过看了这个之后多思考一下的话还是会发现问题，这么神奇的事务隔离级别是怎么实现的呢

-

其中 innodb 的事务隔离用到了标题里说到的 mvcc，Multiversion concurrency control, 直译过来就是多版本并发控制，先不讲这个究竟是个啥，考虑下如果纯猜测，这个事务隔离级别应该会是怎么样实现呢，愚钝的我想了下，可以在事务开始的时候拷贝一个表，这个可以支持 RR 级别，RC 级别就不支持了，而且要是个非常大的表，想想就不可行

-

腆着脸说虽然这个不可行，但是思路是对的，具体实行起来需要做一系列（肥肠多）的改动，首先根据我的理解，其实这个拷贝一个表是变成拷贝一条记录，但是如果有多个事务，那就得拷贝多次，这个问题其实可以借助版本管理系统来解释，在用版本管理系统，git 之类的之前，很原始的可能是开发完一个功能后，就打个压缩包用时间等信息命名，然后如果后面要找回这个就直接用这个压缩包的就行了，后来有了 svn，git 中心式和分布式的版本管理系统，它的一个特点是粒度可以控制到文件和代码行级别，对应的我们的 mysql 事务是不是也可以从一开始预想的表级别细化到行的级别，可能之前很多人都了解过，数据库的一行记录除了我们用户自定义的字段，还有一些额外的字段，去源码data0type.h里捞一下

-
/* Precise data types for system columns and the length of those columns;
NOTE: the values must run from 0 up in the order given! All codes must
be less than 256 */
#define DATA_ROW_ID 0     /* row id: a 48-bit integer */
#define DATA_ROW_ID_LEN 6 /* stored length for row id */

/** Transaction id: 6 bytes */
constexpr size_t DATA_TRX_ID = 1;

/** Transaction ID type size in bytes. */
constexpr size_t DATA_TRX_ID_LEN = 6;

/** Rollback data pointer: 7 bytes */
constexpr size_t DATA_ROLL_PTR = 2;

/** Rollback data pointer type size in bytes. */
constexpr size_t DATA_ROLL_PTR_LEN = 7;
- -

一个是 DATA_ROW_ID，这个是在数据没指定主键的时候会生成一个隐藏的，如果用户有指定主键就是主键了

-

一个是 DATA_TRX_ID，这个表示这条记录的事务 ID

-

还有一个是 DATA_ROLL_PTR 指向回滚段的指针

-

指向的回滚段其实就是我们常说的 undo log，这里面的具体结构就是个链表，在 mvcc 里会使用到这个，还有就是这个 DATA_TRX_ID，每条记录都记录了这个事务 ID，表示的是这条记录的当前值是被哪个事务修改的，下面就扯回事务了，我们知道 Read Uncommitted， 其实用不到隔离，直接读取当前值就好了，到了 Read Committed 级别，我们要让事务读取到提交过的值，mysql 使用了一个叫 read view 的玩意，它里面有这些值是我们需要注意的，

-

m_low_limit_id, 这个是 read view 创建时最大的活跃事务 id

-

m_up_limit_id, 这个是 read view 创建时最小的活跃事务 id

-

m_ids, 这个是 read view 创建时所有的活跃事务 id 数组

-

m_creator_trx_id 这个是当前记录的创建事务 id

-

判断事务的可见性主要的逻辑是这样，

-
    -
  1. 当记录的事务 id 小于最小活跃事务 id，说明是可见的，
    2. -
  2. 如果记录的事务 id 等于当前事务 id，说明是自己的更改，可见
    3. -
  3. 如果记录的事务 id 大于最大的活跃事务 id, 不可见
    4. -
  4. 如果记录的事务 id 介于 m_low_limit_id 和 m_up_limit_id 之间，则要判断它是否在 m_ids 中，如果在，不可见，如果不在，表示已提交，可见
    具体的代码捞一下看看
    /** Check whether the changes by id are visible.
      @param[in]	id	transaction id to check against the view
      @param[in]	name	table name
      @return whether the view sees the modifications of id. */
      bool changes_visible(trx_id_t id, const table_name_t &name) const
          MY_ATTRIBUTE((warn_unused_result)) {
        ut_ad(id > 0);

        if (id < m_up_limit_id || id == m_creator_trx_id) {
          return (true);
        }

        check_trx_id_sanity(id, name);

        if (id >= m_low_limit_id) {
          return (false);

        } else if (m_ids.empty()) {
          return (true);
        }

        const ids_t::value_type *p = m_ids.data();

        return (!std::binary_search(p, p + m_ids.size(), id));
      }
    -剩下来一点是啥呢，就是 Read Committed 和 Repeated Read 也不一样，那前面说的 read view 都能支持吗，又是怎么支持呢，假如这个 read view 是在事务一开始就创建，那好像能支持的只是 RR 事务隔离级别，其实呢，这是通过创建 read view的时机，对于 RR 级别，就是在事务的第一个 select 语句是创建，对于 RC 级别，是在每个 select 语句执行前都是创建一次，那样就可以保证能读到所有已提交的数据
    5. -
+ 聊聊 Sharding-Jdbc 分库分表下的分页方案 + /2022/01/09/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E5%88%86%E5%BA%93%E5%88%86%E8%A1%A8%E4%B8%8B%E7%9A%84%E5%88%86%E9%A1%B5%E6%96%B9%E6%A1%88/ + 前面在聊 Sharding-Jdbc 的时候看到了一篇文章，关于一个分页的查询，一直比较直接的想法就是分库分表下的分页是非常不合理的，一般我们的实操方案都是分表加上 ES 搜索做分页，或者通过合表读写分离的方案，因为对于 sharding-jdbc 如果没有带分表键，查询基本都是只能在所有分表都执行一遍，然后再加上分页，基本上是分页越大后续的查询越耗资源，但是仔细的去想这个细节还是这次，就简单说说
首先就是我的分表结构

+
CREATE TABLE `student_time_0` (
  `id` int(11) unsigned NOT NULL AUTO_INCREMENT,
  `user_id` int(11) NOT NULL,
  `name` varchar(200) COLLATE utf8_bin DEFAULT NULL,
  `age` tinyint(3) unsigned DEFAULT NULL,
  `create_time` bigint(20) DEFAULT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=674 DEFAULT CHARSET=utf8 COLLATE=utf8_bin;
+

有这样的三个表，student_time_0, student_time_1, student_time_2, 以 user_id 作为分表键，根据表数量取模作为分表依据
这里先构造点数据，

+
insert into student_time (`name`, `user_id`, `age`, `create_time`) values (?, ?, ?, ?)
+

主要是为了保证 create_time 唯一比较好说明问题，

+
int i = 0;
try (
        Connection conn = dataSource.getConnection();
        PreparedStatement ps = conn.prepareStatement(insertSql)) {
    do {
        ps.setString(1, localName + new Random().nextInt(100));
        ps.setLong(2, 10086L + (new Random().nextInt(100)));
        ps.setInt(3, 18);
        ps.setLong(4, new Date().getTime());


        int result = ps.executeUpdate();
        LOGGER.info("current execute result: {}", result);
        Thread.sleep(new Random().nextInt(100));
        i++;
    } while (i <= 2000);
+

三个表的数据分别是 673，678，650，说明符合预期了，各个表数据不一样，接下来比如我们想要做一个这样的分页查询

+
select * from student_time ORDER BY create_time ASC limit 1000, 5;
+

student_time 对于我们使用的 sharding-jdbc 来说当然是逻辑表，首先从一无所知去想这个查询如果我们自己来处理应该是怎么做，
首先是不是可以每个表都从 333 开始取 5 条数据，类似于下面的查询，然后进行 15 条的合并重排序获取前面的 5 条

+
select * from student_time_0 ORDER BY create_time ASC limit 333, 5;
select * from student_time_1 ORDER BY create_time ASC limit 333, 5;
select * from student_time_2 ORDER BY create_time ASC limit 333, 5;
+

忽略前面 limit 差的 1，这个结果除非三个表的分布是绝对的均匀，否则结果肯定会出现一定的偏差，以为每个表的 333 这个位置对于其他表来说都不一定是一样的，这样对于最后整体的结果，就会出现偏差
因为一直在纠结怎么让这个更直观的表现出来，所以尝试画了个图

黑色的框代表我从每个表里按排序从 334 到 338 的 5 条数据， 他们在每个表里都是代表了各自正确的排序值，但是对于我们想要的其实是合表后的 1001，1005 这五条，然后我们假设总的排序值位于前 1000 的分布是第 0 个表是 320 条，第 1 个表是 340 条，第 2 个表是 340 条，那么可以明显地看出来我这么查的结果简单合并肯定是不对的。
那么 sharding-jdbc 是如何保证这个结果的呢，其实就是我在每个表里都查分页偏移量和分页大小那么多的数据，在我这个例子里就是对于 0，1，2 三个分表每个都查 1005 条数据，即使我的数据不平衡到最极端的情况，前 1005 条数据都出在某个分表中，也可以正确获得最后的结果，但是明显的问题就是大分页，数据较多，就会导致非常大的问题，即使如 sharding-jdbc 对于合并排序的优化做得比较好，也还是需要传输那么大量的数据，并且查询也耗时，那么有没有解决方案呢，应该说有两个，或者说主要是想讲后者
第一个办法是像这种查询，如果业务上不需要进行跳页，而是只给下一页，那么我们就能把前一次的最大偏移量的 create_time 记录下来，下一页就可以拿着这个偏移量进行查询，这个比较简单易懂，就不多说了
第二个办法是看的58 沈剑的一篇文章，尝试理解讲述一下，
这个办法的第一步跟前面那个错误的方法或者说不准确的方法一样，先是将分页偏移量平均后在三个表里进行查询

+
t0
334 10158 nick95  18  1641548941767
335 10098 nick11  18  1641548941879
336 10167 nick51  18  1641548942089
337 10167 nick3 18  1641548942119
338 10170 nick57  18  1641548942169


t1
334 10105 nick98  18  1641548939071   最小
335 10174 nick94  18  1641548939377
336 10129 nick85  18  1641548939442
337 10141 nick84  18  1641548939480
338 10096 nick74  18  1641548939668

t2
334 10184 nick11  18  1641548945075
335 10109 nick93  18  1641548945382
336 10181 nick41  18  1641548945583
337 10130 nick80  18  1641548945993
338 10184 nick19  18  1641548946294  最大
+

然后要做什么呢，其实目标比较明白，因为前面那种方法其实就是我知道了前一页的偏移量，所以可以直接当做条件来进行查询，那这里我也想着拿到这个条件，所以我将第一遍查出来的最小的 create_time 和最大的 create_time 找出来，然后再去三个表里查询，其实主要是最小值，因为我拿着最小值去查以后我就能知道这个最小值在每个表里处在什么位置，

+
t0
322 10161 nick81  18  1641548939284
323 10113 nick16  18  1641548939393
324 10110 nick56  18  1641548939577
325 10116 nick69  18  1641548939588
326 10173 nick51  18  1641548939646

t1
334 10105 nick98  18  1641548939071
335 10174 nick94  18  1641548939377
336 10129 nick85  18  1641548939442
337 10141 nick84  18  1641548939480
338 10096 nick74  18  1641548939668

t2
297 10136 nick28  18  1641548939161
298 10142 nick68  18  1641548939177
299 10124 nick41  18  1641548939237
300 10148 nick87  18  1641548939510
301 10169 nick23  18  1641548939715
+

我只贴了前五条数据，为了方便知道偏移量，每个分表都使用了自增主键，我们可以看到前一次查询的最小值分别在其他两个表里的位置分别是 322-1 和 297-1，那么对于总体来说这个时间应该是在 322 - 1 + 333 + 297 - 1 = 951，那这样子我只要对后面的数据最多每个表查 1000 - 951 + 5 = 54 条数据再进行合并排序就可以获得最终正确的结果。
这个就是传说中的二次查询法。

]]> - Mysql - C - 数据结构 - 源码 - Mysql + Java - mysql - 数据结构 - 源码 - mvcc - read view + Java + Sharding-Jdbc @@ -6779,26 +6799,66 @@ user3: - 聊聊 mysql 索引的一些细节 - /2020/12/27/%E8%81%8A%E8%81%8A-mysql-%E7%B4%A2%E5%BC%95%E7%9A%84%E4%B8%80%E4%BA%9B%E7%BB%86%E8%8A%82/ - 前几天同事问了我个 mysql 索引的问题，虽然大概知道，但是还是想来实践下，就是 is null，is not null 这类查询是否能用索引，可能之前有些网上的文章说都是不能用索引，但是其实不是，我们来看个小试验

-
CREATE TABLE `null_index_t` (
  `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
  `null_key` varchar(255) DEFAULT NULL,
  `null_key1` varchar(255) DEFAULT NULL,
  `null_key2` varchar(255) DEFAULT NULL,
  PRIMARY KEY (`id`),
  KEY `idx_1` (`null_key`) USING BTREE,
  KEY `idx_2` (`null_key1`) USING BTREE,
  KEY `idx_3` (`null_key2`) USING BTREE
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
-

用个存储过程来插入数据

-

delimiter $	#以delimiter来标记用$表示存储过程结束
create procedure nullIndex1()
begin
declare i int;	
declare j int;	
set i=1;
set j=1;
while(i<=100) do	
	while(j<=100) do	
		IF (i % 3 = 0) THEN
	     INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (null , LEFT(MD5(RAND()), 8), LEFT(MD5(RAND()), 8));
    ELSEIF (i % 3 = 1) THEN
			 INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT(MD5(RAND()), 8), NULL, LEFT(MD5(RAND()), 8));
	  ELSE
			 INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT(MD5(RAND()), 8), LEFT(MD5(RAND()), 8), NULL);
    END IF;
		set j=j+1;
	end while;
	set i=i+1;
	set j=1;	
end while;
end 
$
call nullIndex1();
-

然后看下我们的 is null 查询

-
EXPLAIN select * from null_index_t WHERE null_key is null;
-


再来看看另一个

-
EXPLAIN select * from null_index_t WHERE null_key is not null;
-


从这里能看出来啥呢，可以思考下

-

从上面可以发现，is null应该是用上了索引了，所以至少不是一刀切不能用，但是看着is not null好像不太行额
我们在做一点小改动，把这个表里的数据改成 9100 条是 null，剩下 900 条是有值的，然后再执行下

然后再来看看执行结果

-
EXPLAIN select * from null_index_t WHERE null_key is null;
-

-
EXPLAIN select * from null_index_t WHERE null_key is not null;
-


是不是不一样了，这里再补充下我试验使用的 mysql 是 5.7 的，不保证在其他版本的一致性，
其实可以看出随着数据量的变化，mysql 会不会使用索引是会变化的，不是说 is not null 一定会使用，也不是一定不会使用，而是优化器会根据查询成本做个预判，这个预判尽可能会减小查询成本，主要包括回表啥的，但是也不一定完全准确。

-]]> - - Mysql - C + 聊聊 mysql 的 MVCC + /2020/04/26/%E8%81%8A%E8%81%8A-mysql-%E7%9A%84-MVCC/ + 很久以前，有位面试官问到，你知道 mysql 的事务隔离级别吗，“额 O__O …，不太清楚”，完了之后我就去网上找相关的文章，找到了这篇MySQL 四种事务隔离级的说明, 文章写得特别好，看了这个就懂了各个事务隔离级别都是啥，不过看了这个之后多思考一下的话还是会发现问题，这么神奇的事务隔离级别是怎么实现的呢

+

其中 innodb 的事务隔离用到了标题里说到的 mvcc，Multiversion concurrency control, 直译过来就是多版本并发控制，先不讲这个究竟是个啥，考虑下如果纯猜测，这个事务隔离级别应该会是怎么样实现呢，愚钝的我想了下，可以在事务开始的时候拷贝一个表，这个可以支持 RR 级别，RC 级别就不支持了，而且要是个非常大的表，想想就不可行

+

腆着脸说虽然这个不可行，但是思路是对的，具体实行起来需要做一系列（肥肠多）的改动，首先根据我的理解，其实这个拷贝一个表是变成拷贝一条记录，但是如果有多个事务，那就得拷贝多次，这个问题其实可以借助版本管理系统来解释，在用版本管理系统，git 之类的之前，很原始的可能是开发完一个功能后，就打个压缩包用时间等信息命名，然后如果后面要找回这个就直接用这个压缩包的就行了，后来有了 svn，git 中心式和分布式的版本管理系统，它的一个特点是粒度可以控制到文件和代码行级别，对应的我们的 mysql 事务是不是也可以从一开始预想的表级别细化到行的级别，可能之前很多人都了解过，数据库的一行记录除了我们用户自定义的字段，还有一些额外的字段，去源码data0type.h里捞一下

+
/* Precise data types for system columns and the length of those columns;
NOTE: the values must run from 0 up in the order given! All codes must
be less than 256 */
#define DATA_ROW_ID 0     /* row id: a 48-bit integer */
#define DATA_ROW_ID_LEN 6 /* stored length for row id */

/** Transaction id: 6 bytes */
constexpr size_t DATA_TRX_ID = 1;

/** Transaction ID type size in bytes. */
constexpr size_t DATA_TRX_ID_LEN = 6;

/** Rollback data pointer: 7 bytes */
constexpr size_t DATA_ROLL_PTR = 2;

/** Rollback data pointer type size in bytes. */
constexpr size_t DATA_ROLL_PTR_LEN = 7;
+ +

一个是 DATA_ROW_ID，这个是在数据没指定主键的时候会生成一个隐藏的，如果用户有指定主键就是主键了

+

一个是 DATA_TRX_ID，这个表示这条记录的事务 ID

+

还有一个是 DATA_ROLL_PTR 指向回滚段的指针

+

指向的回滚段其实就是我们常说的 undo log，这里面的具体结构就是个链表，在 mvcc 里会使用到这个，还有就是这个 DATA_TRX_ID，每条记录都记录了这个事务 ID，表示的是这条记录的当前值是被哪个事务修改的，下面就扯回事务了，我们知道 Read Uncommitted， 其实用不到隔离，直接读取当前值就好了，到了 Read Committed 级别，我们要让事务读取到提交过的值，mysql 使用了一个叫 read view 的玩意，它里面有这些值是我们需要注意的，

+

m_low_limit_id, 这个是 read view 创建时最大的活跃事务 id

+

m_up_limit_id, 这个是 read view 创建时最小的活跃事务 id

+

m_ids, 这个是 read view 创建时所有的活跃事务 id 数组

+

m_creator_trx_id 这个是当前记录的创建事务 id

+

判断事务的可见性主要的逻辑是这样，

+
    +
  1. 当记录的事务 id 小于最小活跃事务 id，说明是可见的，
    2. +
  2. 如果记录的事务 id 等于当前事务 id，说明是自己的更改，可见
    3. +
  3. 如果记录的事务 id 大于最大的活跃事务 id, 不可见
    4. +
  4. 如果记录的事务 id 介于 m_low_limit_id 和 m_up_limit_id 之间，则要判断它是否在 m_ids 中，如果在，不可见，如果不在，表示已提交，可见
    具体的代码捞一下看看
    /** Check whether the changes by id are visible.
      @param[in]	id	transaction id to check against the view
      @param[in]	name	table name
      @return whether the view sees the modifications of id. */
      bool changes_visible(trx_id_t id, const table_name_t &name) const
          MY_ATTRIBUTE((warn_unused_result)) {
        ut_ad(id > 0);

        if (id < m_up_limit_id || id == m_creator_trx_id) {
          return (true);
        }

        check_trx_id_sanity(id, name);

        if (id >= m_low_limit_id) {
          return (false);

        } else if (m_ids.empty()) {
          return (true);
        }

        const ids_t::value_type *p = m_ids.data();

        return (!std::binary_search(p, p + m_ids.size(), id));
      }
    +剩下来一点是啥呢，就是 Read Committed 和 Repeated Read 也不一样，那前面说的 read view 都能支持吗，又是怎么支持呢，假如这个 read view 是在事务一开始就创建，那好像能支持的只是 RR 事务隔离级别，其实呢，这是通过创建 read view的时机，对于 RR 级别，就是在事务的第一个 select 语句是创建，对于 RC 级别，是在每个 select 语句执行前都是创建一次，那样就可以保证能读到所有已提交的数据
    5. +
+]]> + + Mysql + C + 数据结构 + 源码 + Mysql + + + mysql + 数据结构 + 源码 + mvcc + read view + + + + 聊聊 mysql 索引的一些细节 + /2020/12/27/%E8%81%8A%E8%81%8A-mysql-%E7%B4%A2%E5%BC%95%E7%9A%84%E4%B8%80%E4%BA%9B%E7%BB%86%E8%8A%82/ + 前几天同事问了我个 mysql 索引的问题，虽然大概知道，但是还是想来实践下，就是 is null，is not null 这类查询是否能用索引，可能之前有些网上的文章说都是不能用索引，但是其实不是，我们来看个小试验

+
CREATE TABLE `null_index_t` (
  `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
  `null_key` varchar(255) DEFAULT NULL,
  `null_key1` varchar(255) DEFAULT NULL,
  `null_key2` varchar(255) DEFAULT NULL,
  PRIMARY KEY (`id`),
  KEY `idx_1` (`null_key`) USING BTREE,
  KEY `idx_2` (`null_key1`) USING BTREE,
  KEY `idx_3` (`null_key2`) USING BTREE
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
+

用个存储过程来插入数据

+

delimiter $	#以delimiter来标记用$表示存储过程结束
create procedure nullIndex1()
begin
declare i int;	
declare j int;	
set i=1;
set j=1;
while(i<=100) do	
	while(j<=100) do	
		IF (i % 3 = 0) THEN
	     INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (null , LEFT(MD5(RAND()), 8), LEFT(MD5(RAND()), 8));
    ELSEIF (i % 3 = 1) THEN
			 INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT(MD5(RAND()), 8), NULL, LEFT(MD5(RAND()), 8));
	  ELSE
			 INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT(MD5(RAND()), 8), LEFT(MD5(RAND()), 8), NULL);
    END IF;
		set j=j+1;
	end while;
	set i=i+1;
	set j=1;	
end while;
end 
$
call nullIndex1();
+

然后看下我们的 is null 查询

+
EXPLAIN select * from null_index_t WHERE null_key is null;
+


再来看看另一个

+
EXPLAIN select * from null_index_t WHERE null_key is not null;
+


从这里能看出来啥呢，可以思考下

+

从上面可以发现，is null应该是用上了索引了，所以至少不是一刀切不能用，但是看着is not null好像不太行额
我们在做一点小改动，把这个表里的数据改成 9100 条是 null，剩下 900 条是有值的，然后再执行下

然后再来看看执行结果

+
EXPLAIN select * from null_index_t WHERE null_key is null;
+

+
EXPLAIN select * from null_index_t WHERE null_key is not null;
+


是不是不一样了，这里再补充下我试验使用的 mysql 是 5.7 的，不保证在其他版本的一致性，
其实可以看出随着数据量的变化，mysql 会不会使用索引是会变化的，不是说 is not null 一定会使用，也不是一定不会使用，而是优化器会根据查询成本做个预判，这个预判尽可能会减小查询成本，主要包括回表啥的，但是也不一定完全准确。

+]]> + + Mysql + C 索引 Mysql @@ -6810,6 +6870,32 @@ user3: procedure + + 聊聊Java中的单例模式 + /2019/12/21/%E8%81%8A%E8%81%8AJava%E4%B8%AD%E7%9A%84%E5%8D%95%E4%BE%8B%E6%A8%A1%E5%BC%8F/ + 这是个 Java 面试的高频问题，我也遇到过，以往都是觉得这类题没意思，网上一搜一大堆，也不愿意记，其实说回来，主要还是没静下心来好好去理解，今天无意中看到一个课程，基本帮我把一些疑惑的点讲清楚了，首先单例是啥意思，这个其实是有范围一说，比如我起了个Spring Boot应用，在这个应用范围内，我的常规 bean 是单例的，意味着 getBean 的时候其实永远只会拿到那一个对象，那要怎么来写一个单例呢，首先就是传说中的饿汉模式，也是最简单的

+

饿汉模式

public class Singleton1 {
    // 首先，将构造方法变成私有的
    private Singleton1() {};
    // 创建私有静态实例，这样第一次使用的时候就会进行创建
    private static Singleton instance = new Singleton1();

    // 使用这个对象都是通过这个 getInstance 来获取
    public static Singleton1 getInstance() {
        return instance;
    }
    // 瞎写一个静态方法。这里想说的是，如果我们只是要调用 Singleton.getDate(...)，
    // 本来是不想要生成 Singleton 实例的，不过没办法，已经生成了
    public static Date getDate(String mode) {return new Date();}
}
+

上面借鉴了一些代码，其实这是最基本，也不会错的方法，但是正如其中getDate方法里说的问题，有时候并没有想那这个对象，但是因为我调用了这个类的静态方法，导致对象已经生成了，可能这也是饿汉模式名字的来由，不管三七二十一给你生成个单例就完事了，不管有没有用，但是这种个人觉得也没啥大问题，如果是面试的话最好说出来它的缺点

+

饱汉模式

public class Singleton2 {
    // 首先，也是先堵死 new Singleton() 这条路,将构造方法变成私有
    private Singleton2() {}
    // 和饿汉模式相比，这边不需要先实例化出来，注意这里的 volatile，它是必须的
    private static volatile Singleton2 instance = null;

    private int m = 9;

    public static Singleton getInstance() {
        if (instance == null) {
            // 加锁
            synchronized (Singleton2.class) {
                // 这一次判断也是必须的，不然会有并发问题
                if (instance == null) {
                    instance = new Singleton2();
                }
            }
        }
        return instance;
    }
}
+

这里容易错的有三点，理解了其实就比较好记了

+

第一点，为啥不在 getInstance 上整个代码块加 synchronized，这个其实比较容易理解，就是锁的力度太大，性能太差了，这点其实也要去理解，可以举个夸张的例子，比如我一个电商的服务，如果为了避免一个人的订单出现问题，是不是可以从请求入口就把他锁住，到请求结束释放，那么里面做的事情都有保障，然而这显然不可能，因为我们想要这种竞态条件抢占资源的时间尽量减少，防止其他线程等待。
第二点，为啥synchronized之已经检查了 instance == null,还要在里面再检查一次，这个有个术语，叫 double check lock,但是为啥要这么做呢，其实很简单，想象当有两个线程，都过了第一步为空判断，这个时候只有一个线程能拿到这个锁，另一个线程就等待了，如果不再判断一次，那么第一个线程新建完对象释放锁之后，第二个线程又能拿到锁，再去创建一个对象。
第三点，为啥要volatile关键字，原先对它的理解是它修饰的变量在 JMM 中能及时将变量值写到主存中，但是它还有个很重要的作用，就是防止指令重排序，instance = new Singleton();这行代码其实在底层是分成三条指令执行的，第一条是在堆上申请了一块内存放这个对象，但是对象的字段啥的都还是默认值，第二条是设置对象的值，比如上面的 m 是 9，然后第三条是将这个对象和虚拟机栈上的指针建立引用关联，那么如果我不用volatile关键字，这三条指令就有可能出现重排，比如变成了 1-3-2 这种顺序，当执行完第二步时，有个线程来访问这个对象了，先判断是不是空，发现不是空的，就拿去直接用了，是不是就出现问题了，所以这个volatile也是不可缺少的

+

嵌套类

public class Singleton3 {

    private Singleton3() {}
    // 主要是使用了 嵌套类可以访问外部类的静态属性和静态方法 的特性
    private static class Holder {
        private static Singleton3 instance = new Singleton3();
    }
    public static Singleton3 getInstance() {
        return Holder.instance;
    }
}
+

这个我个人感觉是饿汉模式的升级版，可以在调用getInstance的时候去实例化对象，也是比较推荐的

+

枚举单例

public enum Singleton {
    INSTANCE;
    
    public void doSomething(){
        //todo doSomething
    }
}
+

枚举很特殊，它在类加载的时候会初始化里面的所有的实例，而且 JVM 保证了它们不会再被实例化，所以它天生就是单例的。

+]]> + + Java + Design Patterns + Singleton + + + 设计模式 + Design Patterns + 单例 + Singleton + + 聊聊 redis 缓存的应用问题 /2021/01/31/%E8%81%8A%E8%81%8A-redis-%E7%BC%93%E5%AD%98%E7%9A%84%E5%BA%94%E7%94%A8%E9%97%AE%E9%A2%98/ @@ -6876,6 +6962,106 @@ user3: AutoConfiguration + + 聊一聊 https 的逻辑 + /2024/03/10/%E8%81%8A%E4%B8%80%E8%81%8A-https-%E7%9A%84%E9%80%BB%E8%BE%91/ + https 和 tls

https 我原本的理解来自于几个概念

+

第一个是非对称加密

+

也就是公钥可以公开在网上，然后用公钥加密，由私有的私钥进行解密，然后就对 https 有了初步的偏颇的概念，就是浏览器用公钥进行加密，服务器端用私钥进行解密，这里再仔细想想会延伸出来几个问题，也是逐步完善我的后续理解

+

第二个就是非对称加密以后的对称加密

+

因为对加密的理解是超大的质数（这个还来源于大学的数据结构老师）相乘后会很难做质因数分解，比如最大的质数2^82,589,933 − 1，用十进制表示有24,862,048位，用现在计算机来算也是非常难的，那如果一直采用非对称加密，这个效率也会比较低，所以在网上查了一番之后知道了是先采用非对称加密，然后再用对称加密，因为非对称加密先有了安全保证，后续的消息就可以用对称加密的方式来进行传输安全了

+

第三个是最近一次看到的

+

也是一直缺乏思考的问题，因为 https 我们在使用的时候都会购买证书或者使用免费的证书，那这个证书和前面说的公私钥是什么关系，好像一直是悬空的，没想过这中间的相关性，证书究竟是怎么起作用的

+

下面是摘自 cloudflare 的 tls 握手流程，把证书的使用方式说的比较明白了

+

TLS 握手是由客户端和服务器交换的一系列数据报或消息。TLS 握手涉及多个步骤，因为客户端和服务器要交换完成握手和进行进一步对话所需的信息。

+

TLS 握手中的确切步骤将根据所使用的密钥交换算法的种类和双方支持的密码套件而有所不同。RSA 密钥交换算法虽然现在被认为不安全，但曾在 1.3 之前的 TLS 版本中使用。大致如下：

+
    +
  1. “客户端问候（client hello）” 消息： 客户端通过向服务器发送“问候”消息来开始握手。该消息将包含客户端支持的 TLS 版本，支持的密码套件，以及称为一串称为“客户端随机数（client random）”的随机字节。
    2. +
  2. “服务器问候（server hello）”消息： 作为对 client hello 消息的回复，服务器发送一条消息，内含服务器的 SSL 证书、服务器选择的密码套件，以及“服务器随机数（server random）”，即由服务器生成的另一串随机字节。
    3. +
  3. 身份验证： 客户端使用颁发该证书的证书颁发机构验证服务器的 SSL 证书。此举确认服务器是其声称的身份，且客户端正在与该域的实际所有者进行交互。
    4. +
  4. 预主密钥： 客户端再发送一串随机字节，即“预主密钥（premaster secret）”。预主密钥是使用公钥加密的，只能使用服务器的私钥解密。（客户端从服务器的 SSL 证书中获得公钥。）
    5. +
  5. 私钥被使用：服务器对预主密钥进行解密。
    6. +
  6. 生成会话密钥：客户端和服务器均使用客户端随机数、服务器随机数和预主密钥生成会话密钥。双方应得到相同的结果。
    7. +
  7. 客户端就绪：客户端发送一条“已完成”消息，该消息用会话密钥加密。
    8. +
  8. 服务器就绪：服务器发送一条“已完成”消息，该消息用会话密钥加密。
    9. +
  9. 实现安全对称加密：已完成握手，并使用会话密钥继续进行通信。
    10. +
+

为了学习这个过程，我们尝试用 Chrome 的自带抓包工具，以往这是可以通过在 Chrome 地址栏中输入 chrome://net-internals/#events 来打开，现在变成了两部分，chrome://net-export/ 这里可以开始抓包，然后会记录下一个抓包日志文件里，

+

+

然后再打开

+

https://netlog-viewer.appspot.com/#events 来查看具体的日志

+

这里我们以打开百度为例，即在打开 chrome://net-export/ 并启动抓包后，再在一个新 tab 打开 baidu.com，然后关闭

+

将日志文件在 https://netlog-viewer.appspot.com/#events 打开

+

+

这里可以看到

+

+

--> type 1 表示现在 tls 握手进行到哪一步了, 对应的值表示不同的阶段

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
代码(type)释义
0HelloRequest
1ClientHello
2ServerHello
11Certificate
12ServerKeyExchange
13CertificateRequest
14ServerHelloDone
15CertificateVerify
16ClientKeyExchange
20Finished
+

具体的对应关系是在这边

+

比如 11 之后表示我们从服务端收到了证书

+

+

然后就要去验证证书的可靠性

+

+

后面是验证后的结果

+

+

+

最后就是交换对称会话秘钥然后完成握手

+

+]]> + + 网络 + + + http + + 聊聊一次 brew update 引发的血案 /2020/06/13/%E8%81%8A%E8%81%8A%E4%B8%80%E6%AC%A1-brew-update-%E5%BC%95%E5%8F%91%E7%9A%84%E8%A1%80%E6%A1%88/ @@ -6919,66 +7105,105 @@ user3: - 聊聊Java中的单例模式 - /2019/12/21/%E8%81%8A%E8%81%8AJava%E4%B8%AD%E7%9A%84%E5%8D%95%E4%BE%8B%E6%A8%A1%E5%BC%8F/ - 这是个 Java 面试的高频问题，我也遇到过，以往都是觉得这类题没意思，网上一搜一大堆，也不愿意记，其实说回来，主要还是没静下心来好好去理解，今天无意中看到一个课程，基本帮我把一些疑惑的点讲清楚了，首先单例是啥意思，这个其实是有范围一说，比如我起了个Spring Boot应用，在这个应用范围内，我的常规 bean 是单例的，意味着 getBean 的时候其实永远只会拿到那一个对象，那要怎么来写一个单例呢，首先就是传说中的饿汉模式，也是最简单的

-

饿汉模式

public class Singleton1 {
    // 首先，将构造方法变成私有的
    private Singleton1() {};
    // 创建私有静态实例，这样第一次使用的时候就会进行创建
    private static Singleton instance = new Singleton1();

    // 使用这个对象都是通过这个 getInstance 来获取
    public static Singleton1 getInstance() {
        return instance;
    }
    // 瞎写一个静态方法。这里想说的是，如果我们只是要调用 Singleton.getDate(...)，
    // 本来是不想要生成 Singleton 实例的，不过没办法，已经生成了
    public static Date getDate(String mode) {return new Date();}
}
-

上面借鉴了一些代码，其实这是最基本，也不会错的方法，但是正如其中getDate方法里说的问题，有时候并没有想那这个对象，但是因为我调用了这个类的静态方法，导致对象已经生成了，可能这也是饿汉模式名字的来由，不管三七二十一给你生成个单例就完事了，不管有没有用，但是这种个人觉得也没啥大问题，如果是面试的话最好说出来它的缺点

-

饱汉模式

public class Singleton2 {
    // 首先，也是先堵死 new Singleton() 这条路,将构造方法变成私有
    private Singleton2() {}
    // 和饿汉模式相比，这边不需要先实例化出来，注意这里的 volatile，它是必须的
    private static volatile Singleton2 instance = null;

    private int m = 9;

    public static Singleton getInstance() {
        if (instance == null) {
            // 加锁
            synchronized (Singleton2.class) {
                // 这一次判断也是必须的，不然会有并发问题
                if (instance == null) {
                    instance = new Singleton2();
                }
            }
        }
        return instance;
    }
}
-

这里容易错的有三点，理解了其实就比较好记了

-

第一点，为啥不在 getInstance 上整个代码块加 synchronized，这个其实比较容易理解，就是锁的力度太大，性能太差了，这点其实也要去理解，可以举个夸张的例子，比如我一个电商的服务，如果为了避免一个人的订单出现问题，是不是可以从请求入口就把他锁住，到请求结束释放，那么里面做的事情都有保障，然而这显然不可能，因为我们想要这种竞态条件抢占资源的时间尽量减少，防止其他线程等待。
第二点，为啥synchronized之已经检查了 instance == null,还要在里面再检查一次，这个有个术语，叫 double check lock,但是为啥要这么做呢，其实很简单，想象当有两个线程，都过了第一步为空判断，这个时候只有一个线程能拿到这个锁，另一个线程就等待了，如果不再判断一次，那么第一个线程新建完对象释放锁之后，第二个线程又能拿到锁，再去创建一个对象。
第三点，为啥要volatile关键字，原先对它的理解是它修饰的变量在 JMM 中能及时将变量值写到主存中，但是它还有个很重要的作用，就是防止指令重排序，instance = new Singleton();这行代码其实在底层是分成三条指令执行的，第一条是在堆上申请了一块内存放这个对象，但是对象的字段啥的都还是默认值，第二条是设置对象的值，比如上面的 m 是 9，然后第三条是将这个对象和虚拟机栈上的指针建立引用关联，那么如果我不用volatile关键字，这三条指令就有可能出现重排，比如变成了 1-3-2 这种顺序，当执行完第二步时，有个线程来访问这个对象了，先判断是不是空，发现不是空的，就拿去直接用了，是不是就出现问题了，所以这个volatile也是不可缺少的

-

嵌套类

public class Singleton3 {

    private Singleton3() {}
    // 主要是使用了 嵌套类可以访问外部类的静态属性和静态方法 的特性
    private static class Holder {
        private static Singleton3 instance = new Singleton3();
    }
    public static Singleton3 getInstance() {
        return Holder.instance;
    }
}
-

这个我个人感觉是饿汉模式的升级版，可以在调用getInstance的时候去实例化对象，也是比较推荐的

-

枚举单例

public enum Singleton {
    INSTANCE;
    
    public void doSomething(){
        //todo doSomething
    }
}
-

枚举很特殊，它在类加载的时候会初始化里面的所有的实例，而且 JVM 保证了它们不会再被实例化，所以它天生就是单例的。

+ 聊聊对 FunctionalInterface 注解的一些理解 + /2023/10/15/%E8%81%8A%E8%81%8A%E5%AF%B9-FunctionalInterface-%E6%B3%A8%E8%A7%A3%E7%9A%84%E4%B8%80%E4%BA%9B%E7%90%86%E8%A7%A3/ + 在看 Tomcat 代码过程中碰到了一个小问题，可以用代码来举例讲一下
先定义一个简单的接口

+
@FunctionalInterface
public interface FunctionalInterfaceTest {

    String getInfo();
}
+

functionalInterface本质还是个接口，所以可以用类实现接口的方式
比如像这样

+
public class FunctionalInterfaceImpl implements FunctionalInterfaceTest{
    @Override
    public String getInfo() {
        return "info";
    }
}
+

但是有下面另外两种方式也是可以实现接口的
第一种是 lambda，

+
FunctionalInterfaceTest interfaceTest = () -> "aaa";
System.out.println(interfaceTest.getInfo());
+

这个实现方式就是直接把 lambda 表达式作为接口实现，但是如果这样就要注意如果实在代码中间的话，debug 还是会跳到这个 lambda 的实现，有时候会混乱，因为只是跳转到 lambda 表达式，而不是所在的方法
第二种是这样

+
public static void main(String[] args) {
    FunctionalInterfaceTest interfaceTest1 = demo.getSelfInfo();
    System.out.println(interfaceTest.getInfo());
}

public FunctionalInterfaceTest getSelfInfo() {
    return this::info;
}

public String info() {
    return "ccc";
}
+

前面 getSelfInfo 的返回值就是类里的一个方法，可以用来作为 FunctionalInterface 的实现，这里跟上面有个比较奇怪的，或者说对于常规的接口理解，一般来说实现的方法签名必须和接口的方法签名一致，但是对于函数接口，私域就突破了这个限制，不管是lambda 这种匿名函数或者后面这种方法引用，都没有用 getInfo 这个方法名，这也是一个比较最近的一个理解。

]]> - Java - Design Patterns - Singleton + java - 设计模式 - Design Patterns - 单例 - Singleton + java + stream - 聊聊传说中的 ThreadLocal - /2021/05/30/%E8%81%8A%E8%81%8A%E4%BC%A0%E8%AF%B4%E4%B8%AD%E7%9A%84-ThreadLocal/ - 说来也惭愧，这个 ThreadLocal 其实一直都是一知半解，而且看了一下之后还发现记错了，所以还是记录下
原先记忆里的都是反过来，一个 ThreadLocal 是里面按照 thread 作为 key，存储线程内容的，真的是半解都米有，完全是错的，这样就得用 concurrentHashMap 这种去存储并且要锁定线程了，然后内容也只能存一个了，想想简直智障

-

究竟是啥结构

比如我们在代码中 new 一个 ThreadLocal，

-
public static void main(String[] args) {
        ThreadLocal<Man> tl = new ThreadLocal<>();

        new Thread(() -> {
            try {
                TimeUnit.SECONDS.sleep(2);
            } catch (InterruptedException e) {
                e.printStackTrace();
            }
            System.out.println(tl.get());
        }).start();
        new Thread(() -> {
            try {
                TimeUnit.SECONDS.sleep(1);
            } catch (InterruptedException e) {
                e.printStackTrace();
            }
            tl.set(new Man());
        }).start();
    }

    static class Man {
        String name = "nick";
    }
-

这里构造了两个线程，一个先往里设值，一个后从里取，运行看下结果，

知道这个用法的话肯定知道是取不到值的，只是具体的原理原来搞错了，我们来看下设值 set 方法

-
public void set(T value) {
    Thread t = Thread.currentThread();
    ThreadLocalMap map = getMap(t);
    if (map != null)
        map.set(this, value);
    else
        createMap(t, value);
}
-

写博客这会我才明白我原来咋会错得这么离谱，看到第一行代码 t 就是当前线程，然后第二行就是用这个线程去getMap，然后我是把这个当成从 map 里取值了，其实这里是

-
ThreadLocalMap getMap(Thread t) {
    return t.threadLocals;
}
-

获取 t 的 threadLocals 成员变量，那这个 threadLocals 又是啥呢

它其实是线程 Thread 中的一个类型是java.lang.ThreadLocal.ThreadLocalMap的成员变量
这是 ThreadLocal 的一个静态成员变量

-
static class ThreadLocalMap {

        /**
         * The entries in this hash map extend WeakReference, using
         * its main ref field as the key (which is always a
         * ThreadLocal object).  Note that null keys (i.e. entry.get()
         * == null) mean that the key is no longer referenced, so the
         * entry can be expunged from table.  Such entries are referred to
         * as "stale entries" in the code that follows.
         */
        static class Entry extends WeakReference<ThreadLocal<?>> {
            /** The value associated with this ThreadLocal. */
            Object value;

            Entry(ThreadLocal<?> k, Object v) {
                super(k);
                value = v;
            }
        }
    }
-

全部代码有点长，只截取了一小部分，然后我们再回头来分析前面说的 set 过程，再 copy 下代码

-
public void set(T value) {
    Thread t = Thread.currentThread();
    ThreadLocalMap map = getMap(t);
    if (map != null)
        map.set(this, value);
    else
        createMap(t, value);
}
-

获取到 map 以后呢，如果 map 不为空，就往 map 里 set，这里注意 key 是啥，其实是当前这个 ThreadLocal，这里就比较明白了究竟是啥结构，每个线程都会维护自身的 ThreadLocalMap，它是线程的一个成员变量，当创建 ThreadLocal 的时候，进行设值的时候其实是往这个 map 里以 ThreadLocal 作为 key，往里设 value。

-

内存泄漏是什么鬼

这里又要看下前面的 ThreadLocalMap 结构了，类似 HashMap，它有个 Entry 结构，在设置的时候会先包装成一个 Entry

-
private void set(ThreadLocal<?> key, Object value) {

        // We don't use a fast path as with get() because it is at
        // least as common to use set() to create new entries as
        // it is to replace existing ones, in which case, a fast
        // path would fail more often than not.

        Entry[] tab = table;
        int len = tab.length;
        int i = key.threadLocalHashCode & (len-1);

        for (Entry e = tab[i];
             e != null;
             e = tab[i = nextIndex(i, len)]) {
            ThreadLocal<?> k = e.get();

            if (k == key) {
                e.value = value;
                return;
            }

            if (k == null) {
                replaceStaleEntry(key, value, i);
                return;
            }
        }

        tab[i] = new Entry(key, value);
        int sz = ++size;
        if (!cleanSomeSlots(i, sz) && sz >= threshold)
            rehash();
}
-

这里其实比较重要的就是前面的 Entry 的构造方法，Entry 是个 WeakReference 的子类，然后在构造方法里可以看到 key 会被包装成一个弱引用，这里为什么使用弱引用，其实是方便这个 key 被回收，如果前面的 ThreadLocal tl实例被设置成 null 了，如果这里是直接的强引用的话，就只能等到线程整个回收了，但是其实是弱引用也会有问题，主要是因为这个 value，如果在 ThreadLocal tl 被设置成 null 了，那么其实这个 value 就会没法被访问到，所以最好的操作还是在使用完了就 remove 掉

+ 聊聊如何识别和意识到日常生活中的各类危险 + /2021/06/06/%E8%81%8A%E8%81%8A%E5%A6%82%E4%BD%95%E8%AF%86%E5%88%AB%E5%92%8C%E6%84%8F%E8%AF%86%E5%88%B0%E6%97%A5%E5%B8%B8%E7%94%9F%E6%B4%BB%E4%B8%AD%E7%9A%84%E5%90%84%E7%B1%BB%E5%8D%B1%E9%99%A9/ + 这篇博客的灵感又是来自于我从绍兴来杭州的路上，在我们进站以后上电梯快到的时候，突然前面不动了，右边我能看到的是有个人的行李箱一时拎不起来，另一边后面看到其实是个小孩子在那哭闹，一位妈妈就在那停着安抚或者可能有点手足无措，其实这一点应该是在几年前慢慢意识到是个非常危险的场景，特别是像绍兴北站这样上去站台是非常长的电梯，因为最近扩建改造，车次减少了很多，所以每一班都有很多人，检票上站台的电梯都是满员运转，试想这种情况，如果刚才那位妈妈再多停留一点时间，很可能就会出现后面的人上不来被挤下去，再严重点就是踩踏事件，但是这类情况很少人真的意识到，非常明显的例子就是很多人拿着比较大比较重的行李箱，不走垂梯，并且在快到的时候没有提前准备好，有可能在玩手机啥的，如果提不动，后面又是挤满人了，就很可能出现前面说的这种情况，并且其实这种是非紧急情况，大多数人都没有心理准备，一旦发生后果可能就会很严重，例如火灾地震疏散大部分人或者说负责引导的都是指示要有序撤离，防止踩踏，但是普通坐个扶梯，一般都不会有这个意识，但是如果这个时间比较长，出现了人员站不住往后倒了，真的会很严重。所以如果自己是带娃的或者带了很重的行李箱的，请提前做好准备，看到前面有人带的，最好也保持一定距离。
还有比如日常走路，旁边有车子停着的情况，比较基本的看车灯有没有亮着，亮着的是否是倒车灯，这种应该特别注意远离，至少保持距离，不能挨着走，很多人特别是一些老年人，在一些人比较多的路上，往往完全无视旁边这些车的状态，我走我的路，谁敢阻拦我，管他车在那动不动，其实真的非常危险，车子本身有视线死角，再加上司机的驾驶习惯和状态，想去送死跟碰瓷的除外，还有就是有一些车会比较特殊，车子发动着，但是没灯，可能是车子灯坏了或者司机通过什么方式关了灯，这种比较难避开，不过如果车子打着了，一般会有比较大的热量散发，车子刚灭了也会有，反正能远离点尽量远离，从轿车的车前面走过挨着走要比从屁股后面挨着走稍微安全一些，但也最好不要挨着车走。
最后一点其实是我觉得是我自己比较怕死，一般对来向的车或者从侧面出来的车会做更长的预判距离，特别是电瓶车，一般是不让人的，像送外卖的小哥，的确他们不太容易，但是真的很危险啊，基本就生死看刹车，能刹住就赚了，刹不住就看身子骨扛不扛撞了，只是这里要多说点又要谈到资本的趋利性了，总是想法设法的压榨以获取更多的利益，也不扯远了，能远离就远离吧。

]]> - Java + 生活 - Java - ThreadLocal - 弱引用 - 内存泄漏 - WeakReference + 生活 + 糟心事 + 扶梯 + 踩踏 + 安全 + 电瓶车 - 聊聊厦门旅游的好与不好 - /2021/04/11/%E8%81%8A%E8%81%8A%E5%8E%A6%E9%97%A8%E6%97%85%E6%B8%B8%E7%9A%84%E5%A5%BD%E4%B8%8E%E4%B8%8D%E5%A5%BD/ - 这几天去了趟厦门，原来几年前就想去了，本来都请好假了，后面因为一些事情没去成，这次刚好公司组织，就跟 LD 一起去了厦门，也不洋洋洒洒地写游记了，后面可能会有，今天先来总结下好的地方和比较坑的地方。
这次主要去了中山路、鼓浪屿、曾厝(cuo)垵、植物园、灵玲马戏团，因为住的离环岛路比较近，还有幸现场看了下厦门马拉松，其中

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中山路

这里看上去是有点民国时期的建筑风格，部分像那种电视里的租界啥的，不过这次去的时候都在翻修，路一大半拦起来了，听导游说这里往里面走有个局口街，然后上次听前同事说厦门比较有名的就是沙茶面和海蛎煎，不出意料的不太爱吃，沙茶面比较普通，可能是没吃到正宗的，海蛎煎吃不惯，倒是有个大叔的沙茶里脊还不错，在局口街那，还有小哥在那拍，应该也算是个网红打卡点了，然后吃了个油条麻糍也还不错，总体如果是看建筑的话可能最近不是个好时间，个人也没这方面爱好，吃的话最好多打听打听沙茶面跟海蛎煎哪里正宗。如果不知道哪家好吃，也不爱看这类建筑的可以排个坑。

+ 聊聊我理解的分布式事务 + /2020/05/17/%E8%81%8A%E8%81%8A%E6%88%91%E7%90%86%E8%A7%A3%E7%9A%84%E5%88%86%E5%B8%83%E5%BC%8F%E4%BA%8B%E5%8A%A1/ + 前面说了mysql数据库的事务相关的，那事务是用来干嘛的，这里得补一下ACID，

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+

ACID，是指数据库管理系统（DBMS）在写入或更新资料的过程中，为保证事务（transaction）是正确可靠的，所必须具备的四个特性：原子性（atomicity，或称不可分割性）、一致性（consistency）、隔离性（isolation，又称独立性）、持久性（durability）。

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  • Atomicity（原子性）：一个事务（transaction）中的所有操作，或者全部完成，或者全部不完成，不会结束在中间某个环节。事务在执行过程中发生错误，会被回滚（Rollback）到事务开始前的状态，就像这个事务从来没有执行过一样。即，事务不可分割、不可约简。[1]

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  • Consistency（一致性）：在事务开始之前和事务结束以后，数据库的完整性没有被破坏。这表示写入的资料必须完全符合所有的预设约束、触发器、级联回滚等。[1]

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  • Isolation（隔离性）：数据库允许多个并发事务同时对其数据进行读写和修改的能力，隔离性可以防止多个事务并发执行时由于交叉执行而导致数据的不一致。事务隔离分为不同级别，包括未提交读（Read uncommitted）、提交读（read committed）、可重复读（repeatable read）和串行化（Serializable）。[1]

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  • Durability（持久性）：事务处理结束后，对数据的修改就是永久的，即便系统故障也不会丢失。[1]

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在mysql中，借助于MVCC，各种级别的锁，日志等特性来实现了事务的ACID，但是这个我们通常是对于一个数据库服务的定义，常见的情况下我们的数据库随着业务发展也会从单实例变成多实例，组成主从Master-Slave架构，这个时候其实会有一些问题随之出现，比如说主从同步延迟，假如在业务代码中做了读写分离，对于一些敏感度较低的数据其实问题不是很大，只要主从延迟不到特别夸张的地步一般都是可以忍受的，但是对于一些核心的业务数据，比如订单之类的，不能忍受数据不一致，下了单了，付了款了，一刷订单列表，发现这个订单还没支付，甚至订单都没在，这对于用户来讲是恨不能容忍的错误，那么这里就需要一些措施，要不就不读写分离，要不就在 redis 这类缓存下订单，或者支付后加个延时等，这些都是一些补偿措施，并且这也是一个不太切当的例子，比较合适的例子也可以用这个下单来说，一般在电商平台下单会有挺多要做的事情，比如像下面这个图

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下单的是后要冻结核销优惠券，如果账户里有钱要冻结扣除账户里的钱，如果使用了J 豆也一样，可能还有 E 卡，忽略我借用的平台，因为目前一般后台服务化之后，可能每一项都是对应的一个后台服务，我们期望的执行过程是要不全成功，要不就全保持执行前状态，不能是部分扣减核销成功了，部分还不行，所以我们处理这种情况会引入一些通用的方案，第一种叫二阶段提交,

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二阶段提交（英语：Two-phase Commit）是指在计算机网络以及数据库领域内，为了使基于分布式系统架构下的所有节点在进行事务提交时保持一致性而设计的一种算法。通常，二阶段提交也被称为是一种协议（Protocol）。在分布式系统中，每个节点虽然可以知晓自己的操作时成功或者失败，却无法知道其他节点的操作的成功或失败。当一个事务跨越多个节点时，为了保持事务的ACID特性，需要引入一个作为协调者的组件来统一掌控所有节点（称作参与者）的操作结果并最终指示这些节点是否要把操作结果进行真正的提交（比如将更新后的数据写入磁盘等等）。因此，二阶段提交的算法思路可以概括为： 参与者将操作成败通知协调者，再由协调者根据所有参与者的反馈情报决定各参与者是否要提交操作还是中止操作。

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对于上面的例子，我们将整个过程分成两个阶段，首先是提交请求阶段，这个阶段大概需要做的是确定资源存在，锁定资源，可能还要做好失败后回滚的准备，如果这些都 ok 了那么就响应成功，这里其实用到了一个叫事务的协调者的角色，类似于裁判员，每个节点都反馈第一阶段成功后，开始执行第二阶段，也就是实际执行操作，这里也是需要所有节点都反馈成功后才是执行成功，要不就是失败回滚。其实常用的分布式事务的解决方案主要也是基于此方案的改进，比如后面介绍的三阶段提交，有三阶段提交就是因为二阶段提交比较尴尬的几个点，

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  • 第一是对于两阶段提交，其中默认只有协调者有超时时间，当一个参与者进入卡死状态时只能依赖协调者的超时来结束任务，这中间的时间参与者都是锁定着资源
    • +
  • 第二是协调者的单点问题，万一挂了，参与者就会在那傻等着
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所以三阶段提交引入了各节点的超时机制和一个准备阶段，首先是一个can commit阶段，询问下各个节点有没有资源，能不能进行操作，这个阶段不阻塞，只是提前做个摸底，这个阶段其实人畜无害，但是能提高成功率，在这个阶段如果就有节点反馈是不接受的，那就不用执行下去了，也没有锁资源，然后第二阶段是 pre commit ，这个阶段做的事情跟原来的 第一阶段比较类似，然后是第三阶段do commit，其实三阶段提交我个人觉得只是加了个超时，和准备阶段，好像木有根本性的解决的两阶段提交的问题，后续可以再看看一些论文来思考讨论下。

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2020年05月24日22:11 更新
这里跟朋友讨论了下，好像想通了最核心的一点，对于前面说的那个场景，如果是两阶段提交，如果各个节点中有一个没回应，并且协调者也挂了，这个时候会有什么情况呢，再加一个假设，其实比如这个一阶段其实是检验就失败的，理论上应该大家都释放资源，那么对于这种异常情况，其他的参与者就不知所措了，就傻傻地锁着资源阻塞着，那么三阶段提交的意义就出现了，把第一阶段拆开，那么即使在这个阶段出现上述的异常，即也不会锁定资源，同时参与者也有超时机制，在第二阶段锁定资源出现异常是，其他参与者节点等超时后就自动释放资源了，也就没啥问题了，不过对于那种异常恢复后的一些情况还是没有很好地解决，需要借助 zk 等，后面有空可以讲讲 paxos 跟 raft 等

+]]> + + 分布式事务 + 两阶段提交 + 三阶段提交 + + + 分布式事务 + 两阶段提交 + 三阶段提交 + 2PC + 3PC + + + + 聊聊我的远程工作体验 + /2022/06/26/%E8%81%8A%E8%81%8A%E6%88%91%E7%9A%84%E8%BF%9C%E7%A8%8B%E5%B7%A5%E4%BD%9C%E4%BD%93%E9%AA%8C/ + 发生疫情之后，因为正好是春节假期，假期结束的时候还不具备回工作地点办公的条件，所以史无前例地开始了远程办公，以前对于远程办公的概念还停留在国外一些有“格局”的企业会允许员工远程办公，当然对于远程办公这个事情本身我个人也并不是全然支持的态度，其中涉及到很多方面，首先远程办公并不代表就是不用去办公地点上班，可以在家里摸鱼，相对能够得到较高报酬的能够远程办公的企业需要在远程办公期间能够有高效的产出，并且也需要像在公司办公地点一样，能随时被联系到，第二点是薪资福利之外的社保公积金，除非薪资相比非远程办公的企业高出比较多，不然没法 cover 企业额外缴纳的社保公积金，听说有部分企业也会远程办公点给员工上社保，但是毕竟能做到这点的很少，在允许远程办公的企业数量这个本来就不大的基数里，大概率是少之又少了。
疫情这个特殊原因开始的远程办公体验也算是开了个之前不太容易开的头，也跟我前面说的第一点有关系，大部分的企业也会担心员工远程办公是否有与在公司办公地点办公一样或者比较接近的办公效率。同时我们在开始远程办公的时候也碰到了因为原先没做过相应准备而导致的许多问题，首先基础设施上就有几个问题，第一个是办公电脑的问题，因为整个公司各个部门的工作性质和内容不同，并不是每个部门都是配笔记本的，或者有些部门并不需要想研发一样带上电脑 on call，所以那么使用台式机或者没有将笔记本带回家的则需要自己准备电脑或者让公司邮寄。第二个是远程网络的问题，像我们公司有研发团队平时也已经准备好了 vpn，但是在这种时候我们没准备好的是 vpn 带宽，毕竟平时只会偶尔有需要连一下 vpn 到公司网络，像这样大量员工都需要连接 vpn 进行工作的话，我们的初步体验就是网络卡的不行，一些远程调试工作没法进行，并且还有一些问题是可能只有我们研发会碰到，比如我们的线上测试服务器网络在办公地点是有网络打通的，但是我们在家就没办法连接，还有就是沟通效率相关，因为这是个全国性的情况，线上会议工具原先都是为特定用户使用，并且视频音频实时传输所需要的带宽质量要求也是比较高的，大规模的远程会议沟通需求让这些做线上会议的服务也算是碰上了类似双十一的大考了，我们是先后使用了 zoom，腾讯会议跟钉钉视频会议，使用体验上来说是 zoom 做得相对比较成熟和稳定，不过后面腾讯会议跟钉钉视频会议也开始赶上来。
前面说的这几个点都是得有远程办公经验的公司才会提前做好相应的准备，比如可以做动态网络扩容，能够在需要大量员工连接公司网络的情况下快速响应提升带宽，另一些则是偏软性的，比如如如何在远程办公的条件下控制我们项目进度，如果保证沟通信息是否能像当面沟通那样准确传达，这方面其实我的经验也是边实操边优化的，最开始我们可能为了高效同步消息，会频繁的使用视频会议沟通，这其实并不能解决沟通效率问题，反而打扰了正常的工作，后续我们在特别是做项目过程中就通过相对简单的每日早会和日报机制，将每天的进度与问题风险点进行同步确认，只与相关直接干系人进行视频电话沟通确认，并且要保持一个思维，即远程办公比较适宜的是相对比较成熟的团队，平常工作和合作都已经有默契或者说规则并且能够遵守，在这个前提下，将目光专注于做的事情而不是管到具体的人有没有全天都在高效工作。同样也希望国内的环境能够有更多的远程火种成长起来，让它成为更好的工作方式，WLB！

+]]> + + 生活 + + + 生活 + 远程办公 + + + + 聊聊厦门旅游的好与不好 + /2021/04/11/%E8%81%8A%E8%81%8A%E5%8E%A6%E9%97%A8%E6%97%85%E6%B8%B8%E7%9A%84%E5%A5%BD%E4%B8%8E%E4%B8%8D%E5%A5%BD/ + 这几天去了趟厦门，原来几年前就想去了，本来都请好假了，后面因为一些事情没去成，这次刚好公司组织，就跟 LD 一起去了厦门，也不洋洋洒洒地写游记了，后面可能会有，今天先来总结下好的地方和比较坑的地方。
这次主要去了中山路、鼓浪屿、曾厝(cuo)垵、植物园、灵玲马戏团，因为住的离环岛路比较近，还有幸现场看了下厦门马拉松，其中

+

中山路

这里看上去是有点民国时期的建筑风格，部分像那种电视里的租界啥的，不过这次去的时候都在翻修，路一大半拦起来了，听导游说这里往里面走有个局口街，然后上次听前同事说厦门比较有名的就是沙茶面和海蛎煎，不出意料的不太爱吃，沙茶面比较普通，可能是没吃到正宗的，海蛎煎吃不惯，倒是有个大叔的沙茶里脊还不错，在局口街那，还有小哥在那拍，应该也算是个网红打卡点了，然后吃了个油条麻糍也还不错，总体如果是看建筑的话可能最近不是个好时间，个人也没这方面爱好，吃的话最好多打听打听沙茶面跟海蛎煎哪里正宗。如果不知道哪家好吃，也不爱看这类建筑的可以排个坑。

鼓浪屿

鼓浪屿也是完全没啥概念，需要乘船过去，但是只要二十分钟，岛上没有机动车，基本都靠走，有几个比较有名的地方，菽庄花园，里面有钢琴博物馆，对这个感兴趣的可以去看看，旁边是沙滩还可以逛逛，然后有各种博物馆，风琴啥的，岛上最大的特色是巷子多，道听途说有三百多条小巷，还有几个网红打卡点，周杰伦晴天墙，还有个最美转角，都是挤满了人排队打卡拍照，不过如果不着急，慢慢悠悠逛逛还是不错的，比较推荐，推荐值☆☆

曾厝垵

一直读不对这个字，都是叫：那个曾什么垵，愧对语文老师，这里到算是意外之喜，鼓浪屿回来已经挺累了，不过由于比较饿（什么原因后面说），并且离住的地方不远，就过去逛了逛，东西还蛮好吃的，芒果挺便宜，一大杯才十块，无骨鸡爪很贵，不是特别爱，臭豆腐不错的，也不算很贵，这里想起来，那边八婆婆的豆乳烧仙草还不错的，去中山路那会喝了，来曾厝垵也买了，奶茶爱好者可以试试，含糖量应该很高，不爱甜食或者减肥的同学慎重考虑好了再尝试，晚上那边从牌坊出来，沿着环岛路挺多夜宵店什么的，非常推荐，推荐值☆☆☆☆

植物园

植物园还是挺名副其实的，有热带植物，沙漠多肉，因为赶时间逛得不多，热带雨林植物那太多人了，都是在那拍照，而且我指的拍照都是拍人照，本身就很小的路，各种十八线网红，或者普通游客在那摆 pose 拍照，挺无语的；沙漠多肉比较惊喜，好多比人高的仙人掌，一大片的仙人球，很可恶的是好多大仙人掌上都有人刻字，越来越体会到，我们社会人多了，什么样的都有，而且不少；还看了下百花厅，但没什么特别的，可能赶时间比较着急，没仔细看，比较推荐，推荐值☆☆☆

@@ -7005,24 +7230,33 @@ user3: - 聊聊对 FunctionalInterface 注解的一些理解 - /2023/10/15/%E8%81%8A%E8%81%8A%E5%AF%B9-FunctionalInterface-%E6%B3%A8%E8%A7%A3%E7%9A%84%E4%B8%80%E4%BA%9B%E7%90%86%E8%A7%A3/ - 在看 Tomcat 代码过程中碰到了一个小问题，可以用代码来举例讲一下
先定义一个简单的接口

-
@FunctionalInterface
public interface FunctionalInterfaceTest {

    String getInfo();
}
-

functionalInterface本质还是个接口，所以可以用类实现接口的方式
比如像这样

-
public class FunctionalInterfaceImpl implements FunctionalInterfaceTest{
    @Override
    public String getInfo() {
        return "info";
    }
}
-

但是有下面另外两种方式也是可以实现接口的
第一种是 lambda，

-
FunctionalInterfaceTest interfaceTest = () -> "aaa";
System.out.println(interfaceTest.getInfo());
-

这个实现方式就是直接把 lambda 表达式作为接口实现，但是如果这样就要注意如果实在代码中间的话，debug 还是会跳到这个 lambda 的实现，有时候会混乱，因为只是跳转到 lambda 表达式，而不是所在的方法
第二种是这样

-
public static void main(String[] args) {
    FunctionalInterfaceTest interfaceTest1 = demo.getSelfInfo();
    System.out.println(interfaceTest.getInfo());
}

public FunctionalInterfaceTest getSelfInfo() {
    return this::info;
}

public String info() {
    return "ccc";
}
-

前面 getSelfInfo 的返回值就是类里的一个方法，可以用来作为 FunctionalInterface 的实现，这里跟上面有个比较奇怪的，或者说对于常规的接口理解，一般来说实现的方法签名必须和接口的方法签名一致，但是对于函数接口，私域就突破了这个限制，不管是lambda 这种匿名函数或者后面这种方法引用，都没有用 getInfo 这个方法名，这也是一个比较最近的一个理解。

+ 聊聊传说中的 ThreadLocal + /2021/05/30/%E8%81%8A%E8%81%8A%E4%BC%A0%E8%AF%B4%E4%B8%AD%E7%9A%84-ThreadLocal/ + 说来也惭愧，这个 ThreadLocal 其实一直都是一知半解，而且看了一下之后还发现记错了，所以还是记录下
原先记忆里的都是反过来，一个 ThreadLocal 是里面按照 thread 作为 key，存储线程内容的，真的是半解都米有，完全是错的，这样就得用 concurrentHashMap 这种去存储并且要锁定线程了，然后内容也只能存一个了，想想简直智障

+

究竟是啥结构

比如我们在代码中 new 一个 ThreadLocal，

+
public static void main(String[] args) {
        ThreadLocal<Man> tl = new ThreadLocal<>();

        new Thread(() -> {
            try {
                TimeUnit.SECONDS.sleep(2);
            } catch (InterruptedException e) {
                e.printStackTrace();
            }
            System.out.println(tl.get());
        }).start();
        new Thread(() -> {
            try {
                TimeUnit.SECONDS.sleep(1);
            } catch (InterruptedException e) {
                e.printStackTrace();
            }
            tl.set(new Man());
        }).start();
    }

    static class Man {
        String name = "nick";
    }
+

这里构造了两个线程，一个先往里设值，一个后从里取，运行看下结果，

知道这个用法的话肯定知道是取不到值的，只是具体的原理原来搞错了，我们来看下设值 set 方法

+
public void set(T value) {
    Thread t = Thread.currentThread();
    ThreadLocalMap map = getMap(t);
    if (map != null)
        map.set(this, value);
    else
        createMap(t, value);
}
+

写博客这会我才明白我原来咋会错得这么离谱，看到第一行代码 t 就是当前线程，然后第二行就是用这个线程去getMap，然后我是把这个当成从 map 里取值了，其实这里是

+
ThreadLocalMap getMap(Thread t) {
    return t.threadLocals;
}
+

获取 t 的 threadLocals 成员变量，那这个 threadLocals 又是啥呢

它其实是线程 Thread 中的一个类型是java.lang.ThreadLocal.ThreadLocalMap的成员变量
这是 ThreadLocal 的一个静态成员变量

+
static class ThreadLocalMap {

        /**
         * The entries in this hash map extend WeakReference, using
         * its main ref field as the key (which is always a
         * ThreadLocal object).  Note that null keys (i.e. entry.get()
         * == null) mean that the key is no longer referenced, so the
         * entry can be expunged from table.  Such entries are referred to
         * as "stale entries" in the code that follows.
         */
        static class Entry extends WeakReference<ThreadLocal<?>> {
            /** The value associated with this ThreadLocal. */
            Object value;

            Entry(ThreadLocal<?> k, Object v) {
                super(k);
                value = v;
            }
        }
    }
+

全部代码有点长，只截取了一小部分，然后我们再回头来分析前面说的 set 过程，再 copy 下代码

+
public void set(T value) {
    Thread t = Thread.currentThread();
    ThreadLocalMap map = getMap(t);
    if (map != null)
        map.set(this, value);
    else
        createMap(t, value);
}
+

获取到 map 以后呢，如果 map 不为空，就往 map 里 set，这里注意 key 是啥，其实是当前这个 ThreadLocal，这里就比较明白了究竟是啥结构，每个线程都会维护自身的 ThreadLocalMap，它是线程的一个成员变量，当创建 ThreadLocal 的时候，进行设值的时候其实是往这个 map 里以 ThreadLocal 作为 key，往里设 value。

+

内存泄漏是什么鬼

这里又要看下前面的 ThreadLocalMap 结构了，类似 HashMap，它有个 Entry 结构，在设置的时候会先包装成一个 Entry

+
private void set(ThreadLocal<?> key, Object value) {

        // We don't use a fast path as with get() because it is at
        // least as common to use set() to create new entries as
        // it is to replace existing ones, in which case, a fast
        // path would fail more often than not.

        Entry[] tab = table;
        int len = tab.length;
        int i = key.threadLocalHashCode & (len-1);

        for (Entry e = tab[i];
             e != null;
             e = tab[i = nextIndex(i, len)]) {
            ThreadLocal<?> k = e.get();

            if (k == key) {
                e.value = value;
                return;
            }

            if (k == null) {
                replaceStaleEntry(key, value, i);
                return;
            }
        }

        tab[i] = new Entry(key, value);
        int sz = ++size;
        if (!cleanSomeSlots(i, sz) && sz >= threshold)
            rehash();
}
+

这里其实比较重要的就是前面的 Entry 的构造方法，Entry 是个 WeakReference 的子类，然后在构造方法里可以看到 key 会被包装成一个弱引用，这里为什么使用弱引用，其实是方便这个 key 被回收，如果前面的 ThreadLocal tl实例被设置成 null 了，如果这里是直接的强引用的话，就只能等到线程整个回收了，但是其实是弱引用也会有问题，主要是因为这个 value，如果在 ThreadLocal tl 被设置成 null 了，那么其实这个 value 就会没法被访问到，所以最好的操作还是在使用完了就 remove 掉

]]> - java + Java - java - stream + Java + ThreadLocal + 弱引用 + 内存泄漏 + WeakReference @@ -7044,9 +7278,12 @@ user3: - 聊聊如何识别和意识到日常生活中的各类危险 - /2021/06/06/%E8%81%8A%E8%81%8A%E5%A6%82%E4%BD%95%E8%AF%86%E5%88%AB%E5%92%8C%E6%84%8F%E8%AF%86%E5%88%B0%E6%97%A5%E5%B8%B8%E7%94%9F%E6%B4%BB%E4%B8%AD%E7%9A%84%E5%90%84%E7%B1%BB%E5%8D%B1%E9%99%A9/ - 这篇博客的灵感又是来自于我从绍兴来杭州的路上，在我们进站以后上电梯快到的时候，突然前面不动了，右边我能看到的是有个人的行李箱一时拎不起来，另一边后面看到其实是个小孩子在那哭闹，一位妈妈就在那停着安抚或者可能有点手足无措，其实这一点应该是在几年前慢慢意识到是个非常危险的场景，特别是像绍兴北站这样上去站台是非常长的电梯，因为最近扩建改造，车次减少了很多，所以每一班都有很多人，检票上站台的电梯都是满员运转，试想这种情况，如果刚才那位妈妈再多停留一点时间，很可能就会出现后面的人上不来被挤下去，再严重点就是踩踏事件，但是这类情况很少人真的意识到，非常明显的例子就是很多人拿着比较大比较重的行李箱，不走垂梯，并且在快到的时候没有提前准备好，有可能在玩手机啥的，如果提不动，后面又是挤满人了，就很可能出现前面说的这种情况，并且其实这种是非紧急情况，大多数人都没有心理准备，一旦发生后果可能就会很严重，例如火灾地震疏散大部分人或者说负责引导的都是指示要有序撤离，防止踩踏，但是普通坐个扶梯，一般都不会有这个意识，但是如果这个时间比较长，出现了人员站不住往后倒了，真的会很严重。所以如果自己是带娃的或者带了很重的行李箱的，请提前做好准备，看到前面有人带的，最好也保持一定距离。
还有比如日常走路，旁边有车子停着的情况，比较基本的看车灯有没有亮着，亮着的是否是倒车灯，这种应该特别注意远离，至少保持距离，不能挨着走，很多人特别是一些老年人，在一些人比较多的路上，往往完全无视旁边这些车的状态，我走我的路，谁敢阻拦我，管他车在那动不动，其实真的非常危险，车子本身有视线死角，再加上司机的驾驶习惯和状态，想去送死跟碰瓷的除外，还有就是有一些车会比较特殊，车子发动着，但是没灯，可能是车子灯坏了或者司机通过什么方式关了灯，这种比较难避开，不过如果车子打着了，一般会有比较大的热量散发，车子刚灭了也会有，反正能远离点尽量远离，从轿车的车前面走过挨着走要比从屁股后面挨着走稍微安全一些，但也最好不要挨着车走。
最后一点其实是我觉得是我自己比较怕死，一般对来向的车或者从侧面出来的车会做更长的预判距离，特别是电瓶车，一般是不让人的，像送外卖的小哥，的确他们不太容易，但是真的很危险啊，基本就生死看刹车，能刹住就赚了，刹不住就看身子骨扛不扛撞了，只是这里要多说点又要谈到资本的趋利性了，总是想法设法的压榨以获取更多的利益，也不扯远了，能远离就远离吧。

+ 聊聊最近平淡的生活之又聊通勤 + /2021/11/07/%E8%81%8A%E8%81%8A%E6%9C%80%E8%BF%91%E5%B9%B3%E6%B7%A1%E7%9A%84%E7%94%9F%E6%B4%BB/ + 一直以来过着特别平淡普通的生活，不过大多数人应该都这样吧，也许有些人可以把平凡的生活过得精彩，最简单的说明就是朋友圈吧，看我一年的盆友圈虽然在发，不过大概 90%的都是发发跑步的打卡，偶尔会有稀稀拉拉的点赞，天天上班，也不喜欢发什么状态，觉得没什么人关注，索性不发。

+

只是这么平淡的生活就有一些自己比较心烦纠结的，之前有提到过的交通，最近似乎又发现了一点，就真相总是让人跌破眼镜，以前觉得我可能是胆子比较小，所以会觉得怎么路上这些电瓶都是这么肆无忌惮的往我冲过来，后面慢慢有一种借用电视剧读心神探的概念，安全距离，觉得大部分人跟我一样，骑电瓶车什么的总还是有个安全距离，只是可能这个安全距离对于不同的人不一样，那些骑电瓶车的潜意识里的安全距离是非常短，所以经常会骑车离着你非常近才会刹车，但是这个安全距离理论最近又被推翻了，因为经历过几次电瓶车就是已经跟你有身体接触了，但是没到把人撞倒的程度，似乎这些骑电瓶车的觉得步行的行人在人行道上是空气，蹭一下也无所谓，反正不能挡我的路，总感觉要不是我在前面骑自行车太慢挡着电瓶车，不然他们都能起飞去干掉 F35 解放湾湾了；

+

另一个问题应该是说我们交通规则普及的太少，虽然我们没有路权这个名词概念，但是其实是有这个优先级的，包括像杭州是以公交车在人行道礼让行人闻名的，其实这个文明的行为只限于人行道在直行路中间的，大部分在十字路口，右转的公交车很少会让直行人行道的，前提是直行的绿灯的时候，特别是像公交车这样，车身特别长，右转的时候会有比较大的死角，如果是公交车先转，行人或者自行车很容易被卷进去，非常危险的，私家车就更不用说了，反正右转即使人行道上人非常多要转的也是一秒都不等，所以我自己在开车的时候是尽量在右转的时候等人行道上的行人或者骑车的走完，因为总会觉得我是不是有点双标，骑车走路的时候希望开车的能按规则让我，自己开车的时候又想赶紧开走，所以在开车的时候尽量做到让行车和骑车的。

+

还有个其实是写着写着想起来的，比如我骑车左转的时候，因为我是左转到对角那就到了，跟那些左转后要再直行的不一样，我们应该在学车的时候也学过，超车要从左边超，但是往往那些骑电瓶车的在左转的时候会从我右边超过来再往左边撇过去，如果留的空间大还好，有些电瓶车就是如果车头超过了就不管他的车屁股，如果我不减速，自行车就被刮倒了，可能的确是别人就不是人，只要不把你撞倒就无所谓，反正为了你自己不被撞倒你肯定会让的。

]]> 生活 @@ -7054,23 +7291,9 @@ user3: 生活 糟心事 - 扶梯 - 踩踏 - 安全 + 规则 电瓶车 - - - - 聊聊我的远程工作体验 - /2022/06/26/%E8%81%8A%E8%81%8A%E6%88%91%E7%9A%84%E8%BF%9C%E7%A8%8B%E5%B7%A5%E4%BD%9C%E4%BD%93%E9%AA%8C/ - 发生疫情之后，因为正好是春节假期，假期结束的时候还不具备回工作地点办公的条件，所以史无前例地开始了远程办公，以前对于远程办公的概念还停留在国外一些有“格局”的企业会允许员工远程办公，当然对于远程办公这个事情本身我个人也并不是全然支持的态度，其中涉及到很多方面，首先远程办公并不代表就是不用去办公地点上班，可以在家里摸鱼，相对能够得到较高报酬的能够远程办公的企业需要在远程办公期间能够有高效的产出，并且也需要像在公司办公地点一样，能随时被联系到，第二点是薪资福利之外的社保公积金，除非薪资相比非远程办公的企业高出比较多，不然没法 cover 企业额外缴纳的社保公积金，听说有部分企业也会远程办公点给员工上社保，但是毕竟能做到这点的很少，在允许远程办公的企业数量这个本来就不大的基数里，大概率是少之又少了。
疫情这个特殊原因开始的远程办公体验也算是开了个之前不太容易开的头，也跟我前面说的第一点有关系，大部分的企业也会担心员工远程办公是否有与在公司办公地点办公一样或者比较接近的办公效率。同时我们在开始远程办公的时候也碰到了因为原先没做过相应准备而导致的许多问题，首先基础设施上就有几个问题，第一个是办公电脑的问题，因为整个公司各个部门的工作性质和内容不同，并不是每个部门都是配笔记本的，或者有些部门并不需要想研发一样带上电脑 on call，所以那么使用台式机或者没有将笔记本带回家的则需要自己准备电脑或者让公司邮寄。第二个是远程网络的问题，像我们公司有研发团队平时也已经准备好了 vpn，但是在这种时候我们没准备好的是 vpn 带宽，毕竟平时只会偶尔有需要连一下 vpn 到公司网络，像这样大量员工都需要连接 vpn 进行工作的话，我们的初步体验就是网络卡的不行，一些远程调试工作没法进行，并且还有一些问题是可能只有我们研发会碰到，比如我们的线上测试服务器网络在办公地点是有网络打通的，但是我们在家就没办法连接，还有就是沟通效率相关，因为这是个全国性的情况，线上会议工具原先都是为特定用户使用，并且视频音频实时传输所需要的带宽质量要求也是比较高的，大规模的远程会议沟通需求让这些做线上会议的服务也算是碰上了类似双十一的大考了，我们是先后使用了 zoom，腾讯会议跟钉钉视频会议，使用体验上来说是 zoom 做得相对比较成熟和稳定，不过后面腾讯会议跟钉钉视频会议也开始赶上来。
前面说的这几个点都是得有远程办公经验的公司才会提前做好相应的准备，比如可以做动态网络扩容，能够在需要大量员工连接公司网络的情况下快速响应提升带宽，另一些则是偏软性的，比如如如何在远程办公的条件下控制我们项目进度，如果保证沟通信息是否能像当面沟通那样准确传达，这方面其实我的经验也是边实操边优化的，最开始我们可能为了高效同步消息，会频繁的使用视频会议沟通，这其实并不能解决沟通效率问题，反而打扰了正常的工作，后续我们在特别是做项目过程中就通过相对简单的每日早会和日报机制，将每天的进度与问题风险点进行同步确认，只与相关直接干系人进行视频电话沟通确认，并且要保持一个思维，即远程办公比较适宜的是相对比较成熟的团队，平常工作和合作都已经有默契或者说规则并且能够遵守，在这个前提下，将目光专注于做的事情而不是管到具体的人有没有全天都在高效工作。同样也希望国内的环境能够有更多的远程火种成长起来，让它成为更好的工作方式，WLB！

-]]> - - 生活 - - - 生活 - 远程办公 + 骑车 @@ -7088,66 +7311,28 @@ user3: - 聊聊我理解的分布式事务 - /2020/05/17/%E8%81%8A%E8%81%8A%E6%88%91%E7%90%86%E8%A7%A3%E7%9A%84%E5%88%86%E5%B8%83%E5%BC%8F%E4%BA%8B%E5%8A%A1/ - 前面说了mysql数据库的事务相关的，那事务是用来干嘛的，这里得补一下ACID，

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ACID，是指数据库管理系统（DBMS）在写入或更新资料的过程中，为保证事务（transaction）是正确可靠的，所必须具备的四个特性：原子性（atomicity，或称不可分割性）、一致性（consistency）、隔离性（isolation，又称独立性）、持久性（durability）。

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  • Atomicity（原子性）：一个事务（transaction）中的所有操作，或者全部完成，或者全部不完成，不会结束在中间某个环节。事务在执行过程中发生错误，会被回滚（Rollback）到事务开始前的状态，就像这个事务从来没有执行过一样。即，事务不可分割、不可约简。[1]

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  • Consistency（一致性）：在事务开始之前和事务结束以后，数据库的完整性没有被破坏。这表示写入的资料必须完全符合所有的预设约束、触发器、级联回滚等。[1]

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  • Isolation（隔离性）：数据库允许多个并发事务同时对其数据进行读写和修改的能力，隔离性可以防止多个事务并发执行时由于交叉执行而导致数据的不一致。事务隔离分为不同级别，包括未提交读（Read uncommitted）、提交读（read committed）、可重复读（repeatable read）和串行化（Serializable）。[1]

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  • Durability（持久性）：事务处理结束后，对数据的修改就是永久的，即便系统故障也不会丢失。[1]

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在mysql中，借助于MVCC，各种级别的锁，日志等特性来实现了事务的ACID，但是这个我们通常是对于一个数据库服务的定义，常见的情况下我们的数据库随着业务发展也会从单实例变成多实例，组成主从Master-Slave架构，这个时候其实会有一些问题随之出现，比如说主从同步延迟，假如在业务代码中做了读写分离，对于一些敏感度较低的数据其实问题不是很大，只要主从延迟不到特别夸张的地步一般都是可以忍受的，但是对于一些核心的业务数据，比如订单之类的，不能忍受数据不一致，下了单了，付了款了，一刷订单列表，发现这个订单还没支付，甚至订单都没在，这对于用户来讲是恨不能容忍的错误，那么这里就需要一些措施，要不就不读写分离，要不就在 redis 这类缓存下订单，或者支付后加个延时等，这些都是一些补偿措施，并且这也是一个不太切当的例子，比较合适的例子也可以用这个下单来说，一般在电商平台下单会有挺多要做的事情，比如像下面这个图

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下单的是后要冻结核销优惠券，如果账户里有钱要冻结扣除账户里的钱，如果使用了J 豆也一样，可能还有 E 卡，忽略我借用的平台，因为目前一般后台服务化之后，可能每一项都是对应的一个后台服务，我们期望的执行过程是要不全成功，要不就全保持执行前状态，不能是部分扣减核销成功了，部分还不行，所以我们处理这种情况会引入一些通用的方案，第一种叫二阶段提交,

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二阶段提交（英语：Two-phase Commit）是指在计算机网络以及数据库领域内，为了使基于分布式系统架构下的所有节点在进行事务提交时保持一致性而设计的一种算法。通常，二阶段提交也被称为是一种协议（Protocol）。在分布式系统中，每个节点虽然可以知晓自己的操作时成功或者失败，却无法知道其他节点的操作的成功或失败。当一个事务跨越多个节点时，为了保持事务的ACID特性，需要引入一个作为协调者的组件来统一掌控所有节点（称作参与者）的操作结果并最终指示这些节点是否要把操作结果进行真正的提交（比如将更新后的数据写入磁盘等等）。因此，二阶段提交的算法思路可以概括为： 参与者将操作成败通知协调者，再由协调者根据所有参与者的反馈情报决定各参与者是否要提交操作还是中止操作。

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对于上面的例子，我们将整个过程分成两个阶段，首先是提交请求阶段，这个阶段大概需要做的是确定资源存在，锁定资源，可能还要做好失败后回滚的准备，如果这些都 ok 了那么就响应成功，这里其实用到了一个叫事务的协调者的角色，类似于裁判员，每个节点都反馈第一阶段成功后，开始执行第二阶段，也就是实际执行操作，这里也是需要所有节点都反馈成功后才是执行成功，要不就是失败回滚。其实常用的分布式事务的解决方案主要也是基于此方案的改进，比如后面介绍的三阶段提交，有三阶段提交就是因为二阶段提交比较尴尬的几个点，

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  • 第一是对于两阶段提交，其中默认只有协调者有超时时间，当一个参与者进入卡死状态时只能依赖协调者的超时来结束任务，这中间的时间参与者都是锁定着资源
    • -
  • 第二是协调者的单点问题，万一挂了，参与者就会在那傻等着
    • -
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所以三阶段提交引入了各节点的超时机制和一个准备阶段，首先是一个can commit阶段，询问下各个节点有没有资源，能不能进行操作，这个阶段不阻塞，只是提前做个摸底，这个阶段其实人畜无害，但是能提高成功率，在这个阶段如果就有节点反馈是不接受的，那就不用执行下去了，也没有锁资源，然后第二阶段是 pre commit ，这个阶段做的事情跟原来的 第一阶段比较类似，然后是第三阶段do commit，其实三阶段提交我个人觉得只是加了个超时，和准备阶段，好像木有根本性的解决的两阶段提交的问题，后续可以再看看一些论文来思考讨论下。

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2020年05月24日22:11 更新
这里跟朋友讨论了下，好像想通了最核心的一点，对于前面说的那个场景，如果是两阶段提交，如果各个节点中有一个没回应，并且协调者也挂了，这个时候会有什么情况呢，再加一个假设，其实比如这个一阶段其实是检验就失败的，理论上应该大家都释放资源，那么对于这种异常情况，其他的参与者就不知所措了，就傻傻地锁着资源阻塞着，那么三阶段提交的意义就出现了，把第一阶段拆开，那么即使在这个阶段出现上述的异常，即也不会锁定资源，同时参与者也有超时机制，在第二阶段锁定资源出现异常是，其他参与者节点等超时后就自动释放资源了，也就没啥问题了，不过对于那种异常恢复后的一些情况还是没有很好地解决，需要借助 zk 等，后面有空可以讲讲 paxos 跟 raft 等

+ 聊一下 Linux 中 dmesg 跟 journal 的差别 + /2024/07/14/%E8%81%8A%E4%B8%80%E4%B8%8B-Linux-%E4%B8%AD-dmesg-%E8%B7%9F-journal-%E7%9A%84%E5%B7%AE%E5%88%AB/ + 最近在迁移一个自己用的mysql实例，发现用 portainer 安装 mysql 一直失败，还以为是配置了自定义端口映射被系统防火墙限制，但后面不映射端口也是不行，一开始查看 journal日志也没什么发现，因为之前在腾讯云的小机器也是正常的部署，所以没觉得是可能会oom，一时间就有点没头绪，结果也是好奇就看了下dmesg，发现里面都是oom，给了1g的内存的ubuntu虚拟机，安装了一两个docker就不行了，不过这里比较重要的就是记录下dmesg 跟 journal日志的这点区别
dmesg 打印了内核的日志缓冲，所以这个跟journal差别的第一点就是一个是个会被刷掉的缓冲区，另一个是记在文件，但更重要的是dmesg是打印的内核日志，而journal是会通过systemd收集日志，journal可以收集内核日志，但是可以进行配置，并且可能会晚于dmesg

而对于这个oom，如果是系统的oom killer，那的确是有可能dmesg会能查看到，而journal看不到
简单记录下，PS：奶娃是有点累，一天都没啥时间了

]]> - 分布式事务 - 两阶段提交 - 三阶段提交 + linux - 分布式事务 - 两阶段提交 - 三阶段提交 - 2PC - 3PC + linux - 聊聊最近平淡的生活之又聊通勤 - /2021/11/07/%E8%81%8A%E8%81%8A%E6%9C%80%E8%BF%91%E5%B9%B3%E6%B7%A1%E7%9A%84%E7%94%9F%E6%B4%BB/ - 一直以来过着特别平淡普通的生活，不过大多数人应该都这样吧，也许有些人可以把平凡的生活过得精彩，最简单的说明就是朋友圈吧，看我一年的盆友圈虽然在发，不过大概 90%的都是发发跑步的打卡，偶尔会有稀稀拉拉的点赞，天天上班，也不喜欢发什么状态，觉得没什么人关注，索性不发。

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只是这么平淡的生活就有一些自己比较心烦纠结的，之前有提到过的交通，最近似乎又发现了一点，就真相总是让人跌破眼镜，以前觉得我可能是胆子比较小，所以会觉得怎么路上这些电瓶都是这么肆无忌惮的往我冲过来，后面慢慢有一种借用电视剧读心神探的概念，安全距离，觉得大部分人跟我一样，骑电瓶车什么的总还是有个安全距离，只是可能这个安全距离对于不同的人不一样，那些骑电瓶车的潜意识里的安全距离是非常短，所以经常会骑车离着你非常近才会刹车，但是这个安全距离理论最近又被推翻了，因为经历过几次电瓶车就是已经跟你有身体接触了，但是没到把人撞倒的程度，似乎这些骑电瓶车的觉得步行的行人在人行道上是空气，蹭一下也无所谓，反正不能挡我的路，总感觉要不是我在前面骑自行车太慢挡着电瓶车，不然他们都能起飞去干掉 F35 解放湾湾了；

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另一个问题应该是说我们交通规则普及的太少，虽然我们没有路权这个名词概念，但是其实是有这个优先级的，包括像杭州是以公交车在人行道礼让行人闻名的，其实这个文明的行为只限于人行道在直行路中间的，大部分在十字路口，右转的公交车很少会让直行人行道的，前提是直行的绿灯的时候，特别是像公交车这样，车身特别长，右转的时候会有比较大的死角，如果是公交车先转，行人或者自行车很容易被卷进去，非常危险的，私家车就更不用说了，反正右转即使人行道上人非常多要转的也是一秒都不等，所以我自己在开车的时候是尽量在右转的时候等人行道上的行人或者骑车的走完，因为总会觉得我是不是有点双标，骑车走路的时候希望开车的能按规则让我，自己开车的时候又想赶紧开走，所以在开车的时候尽量做到让行车和骑车的。

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还有个其实是写着写着想起来的，比如我骑车左转的时候，因为我是左转到对角那就到了，跟那些左转后要再直行的不一样，我们应该在学车的时候也学过，超车要从左边超，但是往往那些骑电瓶车的在左转的时候会从我右边超过来再往左边撇过去，如果留的空间大还好，有些电瓶车就是如果车头超过了就不管他的车屁股，如果我不减速，自行车就被刮倒了，可能的确是别人就不是人，只要不把你撞倒就无所谓，反正为了你自己不被撞倒你肯定会让的。

+ 聊聊最近平淡的生活之看《神探狄仁杰》 + /2021/12/19/%E8%81%8A%E8%81%8A%E6%9C%80%E8%BF%91%E5%B9%B3%E6%B7%A1%E7%9A%84%E7%94%9F%E6%B4%BB%E4%B9%8B%E7%9C%8B%E3%80%8A%E7%A5%9E%E6%8E%A2%E7%8B%84%E4%BB%81%E6%9D%B0%E3%80%8B/ + 其实最近看的不止这一部，前面看了《继承者们》，《少年包青天》这些，就一起聊下，其中看《继承者们》算是个人比较喜欢，以前就有这种看剧的习惯，这个跟《一生一世》里任嘉伦说自己看《寻秦记》看了几十遍一样，我看喜欢的剧也基本上会看不止五遍，继承者们是有帅哥美女看，而且印象中剧情也挺甜的，一般情况下最好是已经有点遗忘剧情了，因为我个人觉得看剧分两种，无聊了又心情不太好，可以看些这类轻松又看过的剧，可以不完全专心地看剧，另外有心情专心看的时候，可以看一些需要思考，一些探案类的或者烧脑类。
最近看了《神探狄仁杰》，因为跟前面看的《少年包青天》都是这类古装探案剧，正好有些感想，《少年包青天》算是儿时阴影，本来是不太会去看的，正好有一次有机会跟 LD 一起看了会就也觉得比较有意思就看了下去，不得不说，以前的这些剧还是很不错的，包括剧情和演员，第一部一共是 40 集，看的过程中也发现了大概是五个案子，平均八集一个案子，整体节奏还是比较慢的，但是基本每个案子其实都是构思得很巧妙，很久以前看过但是现在基本不太记得剧情了，每个案子在前面几集的时候基本都猜不到犯案逻辑，但是在看了狄仁杰之后，发现两部剧也有比较大的差别，少年包青天相对来说逻辑性会更强一些，个人主观觉得推理的严谨性更高，可能剧本打磨上更将就一下，而狄仁杰因为要提现他的个人强项，不比少年包青天中有公孙策一时瑜亮的情节，狄仁杰中分工明确，李元芳是个武力担当，曾泰是捧哏的，相对来说是狄仁杰在案子里从始至终地推进案情，有些甚至有些玄乎，会有一些跳脱跟不合理，有些像是狄仁杰的奇遇，不过这些想法是私人的观点，并不是想要评孰优孰劣；第二个感受是不知道是不是年代关系，特别是少年包青天，每个案件的大 boss 基本都不是个完全的坏人，甚至都是比较情有可原的可怜人，因为一些特殊原因，而好几个都是包拯身边的人，这一点其实跟狄仁杰里第一个使团惊魂案件比较类似，虎敬晖也是个人物形象比较丰满的角色，不是个标签化的淡薄形象，跟金木兰的感情和反叛行动在最后都说明的缘由，而且也有随着跟狄仁杰一起办案被其影响感化，最终为了救狄仁杰而被金木兰所杀，只是这样金木兰这个角色就会有些偏执和符号化，当然剧本肯定不是能面面俱到，这样的剧本已经比现在很多流量剧的好很多了。还想到了前阵子看的《指环王》中的白袍萨鲁曼在剧中也是个比较单薄的角色，这样的电影彪炳影史也没办法把个个人物都设计得完整有血有肉，或者说这本来也是应该有侧重点，当然其实我也不觉得指环王就是绝对的最好的，因为相对来说故事情节的复杂性等真的不如西游记，只是在 86 版之后的各种乱七八糟的翻牌和乱拍已经让这个真正的王者神话故事有点力不从心，这里边有部西游记后传是个人还比较喜欢的，虽然武打动作比较鬼畜，但是剧情基本是无敌的，在西游的架构上衍生出来这么完整丰富的故事，人物角色也都有各自的出彩点。
说回狄仁杰，在这之前也看过徐克拍的几部狄仁杰的电影版，第一部刘德华拍得相对完成度更高，故事性也可圈可点，后面几部就是剧情拉胯，靠特效拉回来一点分，虽说这个也是所谓的狄仁杰宇宙的构思在里面但是现在来看基本是跟西游那些差不多，完全没有整体性可言，打一枪换一个地方，演员也没有延续性，剧情也是前后跳脱，没什么关联跟承上启下，导致质量层次不一，更不用谈什么狄仁杰宇宙了，不过这个事情也是难说，原因很多，现在资本都是更加趋利的，一些需要更长久时间才能有回报的投资是很难获得资本青睐，所以只能将重心投给选择一些流量明星，而本来应该将资源投给剧本打磨的基本就没了，再深入说也没意义了，社会现状就是这样。
还有一点感想是，以前的剧里的拍摄环境还是比较惨的，看着一些房子，甚至皇宫都是比较破旧的，地面还是石板这种，想想以前的演员的环境再想想现在的，比如成龙说的，以前他拍剧就是啪摔了，问这条有没有过，过了就直接送医院，而不是现在可能手蹭破点皮就大叫，甚至还有饭圈这些破事。

]]> 生活 生活 - 糟心事 - 规则 - 电瓶车 - 骑车 + 看剧 @@ -7187,49 +7372,50 @@ user3: - 聊聊这次换车牌及其他 - /2022/02/20/%E8%81%8A%E8%81%8A%E8%BF%99%E6%AC%A1%E6%8D%A2%E8%BD%A6%E7%89%8C%E5%8F%8A%E5%85%B6%E4%BB%96/ - 去年 8 月份运气比较好，摇到了车牌，本来其实应该很早就开始摇的，前面第一次换工作没注意社保断缴了一个月，也是大意失荆州，后面到了 17 年社保满两年了，好像只摇了一次，还是就没摇过，有点忘了，好像是什么原因导致那次也没摇成功，但是后面暂住证就取消了，需要居住证，居住证又要一年及以上的租房合同，并且那会买车以后也不怎么开，住的地方车位还好，但是公司车位一个月要两三千，甚至还是打车上下班比较实惠，所以也没放在心上，后面摇到房以后，也觉得应该准备起来车子，就开始办了居住证，居住证其实还可以用劳动合同，而且办起来也挺快，大概是三四月份开始摇，到 8 月份的某一天收到短信说摇到了，一开始还挺开心，不过心里抱着也不怎么开，也没怎么大放在心上，不过这里有一点就是我把那个照片直接发出去，上面有着我的身份证号，被 LD 说了一顿，以后也应该小心点，但是后面不知道是哪里看了下，说杭州上牌已经需要国六标准的车了，瞬间感觉是空欢喜了，可是有同事说是可以的，我就又打了官方的电话，结果说可以的，要先转籍，然后再做上牌。

-

转籍其实是很方便的，在交警 12123 App 上申请就行了，在转籍以后，需要去实地验车，验车的话，在支付宝-杭州交警生活号里进行预约，找就近的车管所就好，需要准备一些东西，首先是行驶证，机动车登记证书，身份证，居住证，还有车上需要准备的东西是要有三脚架和反光背心，反光背心是最近几个月开始要的，问过之前去验车的只需要三脚架就好了，预约好了的话建议是赶上班时间越早越好，不然过去排队时间要很久，而且人多了以后会很乱，各种插队，而且有很多都是汽车销售，一个销售带着一堆车，我们附近那个进去的小路没一会就堵满车，进去需要先排队，然后扫码，接着交资料，这两个都排着队，如果去晚了就要排很久的队，交完资料才是排队等验车，验车就是打开引擎盖，有人会帮忙拓印发动机车架号，然后验车的会各种检查一下，车里面，还有后备箱，建议车内整理干净点，后备箱不要放杂物，检验完了之后，需要把三脚架跟反光背心放在后备箱盖子上，人在旁边拍个照，然后需要把车牌遮住后再拍个车子的照片，再之后就是去把车牌卸了，这个多吐槽下，那边应该是本来那边师傅帮忙卸车牌，结果他就说是教我们拆，虽然也不算难，但是不排除师傅有在偷懒，完了之后就是把旧车牌交回去，然后需要在手机上（警察叔叔 App）提交各种资料，包括身份证，行驶证，机动车登记证书，提交了之后就等寄车牌过来了。

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这里面缺失的一个环节就是选号了，选号杭州有两个方式，一种就是根据交管局定期发布的选号号段，可以自定义拼 20 个号，在手机上的交警 12123 App 上可以三个一组的形式提交，如果有没被选走的，就可以预选到这个了，但是这种就是也需要有一定策略，最新出的号段能选中的概率大一点，然后数字全是 8，6 这种的肯定会一早就被选走，然后如果跟我一样可以提前选下尾号，因为尾号数字影响限号，我比较有可能周五回家，所以得避开 5，0 的，第二种就是 50 选一跟以前新车选号一样，就不介绍了。第一种选中了以后可以在前面交还旧车牌的时候填上等着寄过来了，因为我是第一种选中的，第二种也可以在手机上选，也在可以在交还车牌的时候现场选。

-

总体过程其实是 LD 在各种查资料跟帮我跑来跑去，要不是 LD，估计在交管局那边我就懵逼了，各种插队，而且车子开着车子，也不能随便跑，所以建议办这个的时候有个人一起比较好。

+ 聊聊那些加塞狗 + /2021/01/17/%E8%81%8A%E8%81%8A%E9%82%A3%E4%BA%9B%E5%8A%A0%E5%A1%9E%E7%8B%97/ + 今天真的是被气得不轻，情况是碰到一个有 70 多秒的直行红灯，然后直行就排了很长的队，但是左转车道没车，就有好几辆车占着左转车道，准备往直行车道插队加塞，一般这种加塞的，会挑个不太计较的，如果前面一辆不让的话就再等等，我因为赶着回家，就不想让，结果那辆车几次车头直接往里冲，当时怒气值基本已经蓄满了，我真的是分毫都不想让，如果路上都是让着这种人的，那么这种情况只会越来越严重，我理解的这种心态，就赌你怕麻烦，多一事不如少一事，结果就是每次都能顺利插队加塞，其实延伸到我们社会中的种种实质性的排队或者等同于排队的情况，都已经有这种惯有思维，一方面这种不符合规则，可能在严重程度上容易被很多人所忽视，基本上已经被很多人当成是“合理”行为，另一方面，对于这些“微小”的违规行为，本身管理层面也基本没有想要管的意思，就更多的成为了纵容这些行为的导火索，并且大多数人都是想着如果不让，发生点小剐小蹭的要浪费很多时间精力来处理，甚至会觉得会被别人觉得自己太小气等等，诸多内外成本结合起来，会真的去硬刚的可能少之又少了，这样也就让更多的人觉得这种行为是被默许的，再举个非常小的例子，以我们公司疫情期间的盒饭发放为例，有两个比较“有意思”的事情，第一个就是因为疫情，本来是让排队要间隔一米，但是可能除了我比较怕死会跟前面的人保持点距离基本没别人会不挨着前面的人，甚至我跟我前面的人保持点距离，后面的同学会推着我让我上去；第二个是关于拿饭，这么多人排着队拿饭，然后有部分同学，一个人拿好几份，帮组里其他人的都拿了，有些甚至一个人拿十份，假如这个盒饭发放是说明了可以按部门直接全领了那就没啥问题，但是当时的状况是个人排队领自己的那一份，如果一个同学直接帮着组里十几个人都拿了，后面排队的人是什么感受呢，甚至有些是看到队伍排长了，就找队伍里自己认识的比较靠前的人说你帮我也拿一份，其实作为我这个比较按规矩办事的“愣头青”来说，我是比较不能接受这两件小事里的行为的，再往下说可能就有点偏激了，先说到这~

]]> 生活 + 开车 生活 - 换车牌 + 开车 + 加塞 + 糟心事 + 规则 - 聊聊那些加塞狗 - /2021/01/17/%E8%81%8A%E8%81%8A%E9%82%A3%E4%BA%9B%E5%8A%A0%E5%A1%9E%E7%8B%97/ - 今天真的是被气得不轻，情况是碰到一个有 70 多秒的直行红灯，然后直行就排了很长的队，但是左转车道没车，就有好几辆车占着左转车道，准备往直行车道插队加塞，一般这种加塞的，会挑个不太计较的，如果前面一辆不让的话就再等等，我因为赶着回家，就不想让，结果那辆车几次车头直接往里冲，当时怒气值基本已经蓄满了，我真的是分毫都不想让，如果路上都是让着这种人的，那么这种情况只会越来越严重，我理解的这种心态，就赌你怕麻烦，多一事不如少一事，结果就是每次都能顺利插队加塞，其实延伸到我们社会中的种种实质性的排队或者等同于排队的情况，都已经有这种惯有思维，一方面这种不符合规则，可能在严重程度上容易被很多人所忽视，基本上已经被很多人当成是“合理”行为，另一方面，对于这些“微小”的违规行为，本身管理层面也基本没有想要管的意思，就更多的成为了纵容这些行为的导火索，并且大多数人都是想着如果不让，发生点小剐小蹭的要浪费很多时间精力来处理，甚至会觉得会被别人觉得自己太小气等等，诸多内外成本结合起来，会真的去硬刚的可能少之又少了，这样也就让更多的人觉得这种行为是被默许的，再举个非常小的例子，以我们公司疫情期间的盒饭发放为例，有两个比较“有意思”的事情，第一个就是因为疫情，本来是让排队要间隔一米，但是可能除了我比较怕死会跟前面的人保持点距离基本没别人会不挨着前面的人，甚至我跟我前面的人保持点距离，后面的同学会推着我让我上去；第二个是关于拿饭，这么多人排着队拿饭，然后有部分同学，一个人拿好几份，帮组里其他人的都拿了，有些甚至一个人拿十份，假如这个盒饭发放是说明了可以按部门直接全领了那就没啥问题，但是当时的状况是个人排队领自己的那一份，如果一个同学直接帮着组里十几个人都拿了，后面排队的人是什么感受呢，甚至有些是看到队伍排长了，就找队伍里自己认识的比较靠前的人说你帮我也拿一份，其实作为我这个比较按规矩办事的“愣头青”来说，我是比较不能接受这两件小事里的行为的，再往下说可能就有点偏激了，先说到这~

+ 聊聊这次换车牌及其他 + /2022/02/20/%E8%81%8A%E8%81%8A%E8%BF%99%E6%AC%A1%E6%8D%A2%E8%BD%A6%E7%89%8C%E5%8F%8A%E5%85%B6%E4%BB%96/ + 去年 8 月份运气比较好，摇到了车牌，本来其实应该很早就开始摇的，前面第一次换工作没注意社保断缴了一个月，也是大意失荆州，后面到了 17 年社保满两年了，好像只摇了一次，还是就没摇过，有点忘了，好像是什么原因导致那次也没摇成功，但是后面暂住证就取消了，需要居住证，居住证又要一年及以上的租房合同，并且那会买车以后也不怎么开，住的地方车位还好，但是公司车位一个月要两三千，甚至还是打车上下班比较实惠，所以也没放在心上，后面摇到房以后，也觉得应该准备起来车子，就开始办了居住证，居住证其实还可以用劳动合同，而且办起来也挺快，大概是三四月份开始摇，到 8 月份的某一天收到短信说摇到了，一开始还挺开心，不过心里抱着也不怎么开，也没怎么大放在心上，不过这里有一点就是我把那个照片直接发出去，上面有着我的身份证号，被 LD 说了一顿，以后也应该小心点，但是后面不知道是哪里看了下，说杭州上牌已经需要国六标准的车了，瞬间感觉是空欢喜了，可是有同事说是可以的，我就又打了官方的电话，结果说可以的，要先转籍，然后再做上牌。

+

转籍其实是很方便的，在交警 12123 App 上申请就行了，在转籍以后，需要去实地验车，验车的话，在支付宝-杭州交警生活号里进行预约，找就近的车管所就好，需要准备一些东西，首先是行驶证，机动车登记证书，身份证，居住证，还有车上需要准备的东西是要有三脚架和反光背心，反光背心是最近几个月开始要的，问过之前去验车的只需要三脚架就好了，预约好了的话建议是赶上班时间越早越好，不然过去排队时间要很久，而且人多了以后会很乱，各种插队，而且有很多都是汽车销售，一个销售带着一堆车，我们附近那个进去的小路没一会就堵满车，进去需要先排队，然后扫码，接着交资料，这两个都排着队，如果去晚了就要排很久的队，交完资料才是排队等验车，验车就是打开引擎盖，有人会帮忙拓印发动机车架号，然后验车的会各种检查一下，车里面，还有后备箱，建议车内整理干净点，后备箱不要放杂物，检验完了之后，需要把三脚架跟反光背心放在后备箱盖子上，人在旁边拍个照，然后需要把车牌遮住后再拍个车子的照片，再之后就是去把车牌卸了，这个多吐槽下，那边应该是本来那边师傅帮忙卸车牌，结果他就说是教我们拆，虽然也不算难，但是不排除师傅有在偷懒，完了之后就是把旧车牌交回去，然后需要在手机上（警察叔叔 App）提交各种资料，包括身份证，行驶证，机动车登记证书，提交了之后就等寄车牌过来了。

+

这里面缺失的一个环节就是选号了，选号杭州有两个方式，一种就是根据交管局定期发布的选号号段，可以自定义拼 20 个号，在手机上的交警 12123 App 上可以三个一组的形式提交，如果有没被选走的，就可以预选到这个了，但是这种就是也需要有一定策略，最新出的号段能选中的概率大一点，然后数字全是 8，6 这种的肯定会一早就被选走，然后如果跟我一样可以提前选下尾号，因为尾号数字影响限号，我比较有可能周五回家，所以得避开 5，0 的，第二种就是 50 选一跟以前新车选号一样，就不介绍了。第一种选中了以后可以在前面交还旧车牌的时候填上等着寄过来了，因为我是第一种选中的，第二种也可以在手机上选，也在可以在交还车牌的时候现场选。

+

总体过程其实是 LD 在各种查资料跟帮我跑来跑去，要不是 LD，估计在交管局那边我就懵逼了，各种插队，而且车子开着车子，也不能随便跑，所以建议办这个的时候有个人一起比较好。

]]> 生活 - 开车 生活 - 开车 - 加塞 - 糟心事 - 规则 + 换车牌 - 聊聊最近平淡的生活之看《神探狄仁杰》 - /2021/12/19/%E8%81%8A%E8%81%8A%E6%9C%80%E8%BF%91%E5%B9%B3%E6%B7%A1%E7%9A%84%E7%94%9F%E6%B4%BB%E4%B9%8B%E7%9C%8B%E3%80%8A%E7%A5%9E%E6%8E%A2%E7%8B%84%E4%BB%81%E6%9D%B0%E3%80%8B/ - 其实最近看的不止这一部，前面看了《继承者们》，《少年包青天》这些，就一起聊下，其中看《继承者们》算是个人比较喜欢，以前就有这种看剧的习惯，这个跟《一生一世》里任嘉伦说自己看《寻秦记》看了几十遍一样，我看喜欢的剧也基本上会看不止五遍，继承者们是有帅哥美女看，而且印象中剧情也挺甜的，一般情况下最好是已经有点遗忘剧情了，因为我个人觉得看剧分两种，无聊了又心情不太好，可以看些这类轻松又看过的剧，可以不完全专心地看剧，另外有心情专心看的时候，可以看一些需要思考，一些探案类的或者烧脑类。
最近看了《神探狄仁杰》，因为跟前面看的《少年包青天》都是这类古装探案剧，正好有些感想，《少年包青天》算是儿时阴影，本来是不太会去看的，正好有一次有机会跟 LD 一起看了会就也觉得比较有意思就看了下去，不得不说，以前的这些剧还是很不错的，包括剧情和演员，第一部一共是 40 集，看的过程中也发现了大概是五个案子，平均八集一个案子，整体节奏还是比较慢的，但是基本每个案子其实都是构思得很巧妙，很久以前看过但是现在基本不太记得剧情了，每个案子在前面几集的时候基本都猜不到犯案逻辑，但是在看了狄仁杰之后，发现两部剧也有比较大的差别，少年包青天相对来说逻辑性会更强一些，个人主观觉得推理的严谨性更高，可能剧本打磨上更将就一下，而狄仁杰因为要提现他的个人强项，不比少年包青天中有公孙策一时瑜亮的情节，狄仁杰中分工明确，李元芳是个武力担当，曾泰是捧哏的，相对来说是狄仁杰在案子里从始至终地推进案情，有些甚至有些玄乎，会有一些跳脱跟不合理，有些像是狄仁杰的奇遇，不过这些想法是私人的观点，并不是想要评孰优孰劣；第二个感受是不知道是不是年代关系，特别是少年包青天，每个案件的大 boss 基本都不是个完全的坏人，甚至都是比较情有可原的可怜人，因为一些特殊原因，而好几个都是包拯身边的人，这一点其实跟狄仁杰里第一个使团惊魂案件比较类似，虎敬晖也是个人物形象比较丰满的角色，不是个标签化的淡薄形象，跟金木兰的感情和反叛行动在最后都说明的缘由，而且也有随着跟狄仁杰一起办案被其影响感化，最终为了救狄仁杰而被金木兰所杀，只是这样金木兰这个角色就会有些偏执和符号化，当然剧本肯定不是能面面俱到，这样的剧本已经比现在很多流量剧的好很多了。还想到了前阵子看的《指环王》中的白袍萨鲁曼在剧中也是个比较单薄的角色，这样的电影彪炳影史也没办法把个个人物都设计得完整有血有肉，或者说这本来也是应该有侧重点，当然其实我也不觉得指环王就是绝对的最好的，因为相对来说故事情节的复杂性等真的不如西游记，只是在 86 版之后的各种乱七八糟的翻牌和乱拍已经让这个真正的王者神话故事有点力不从心，这里边有部西游记后传是个人还比较喜欢的，虽然武打动作比较鬼畜，但是剧情基本是无敌的，在西游的架构上衍生出来这么完整丰富的故事，人物角色也都有各自的出彩点。
说回狄仁杰，在这之前也看过徐克拍的几部狄仁杰的电影版，第一部刘德华拍得相对完成度更高，故事性也可圈可点，后面几部就是剧情拉胯，靠特效拉回来一点分，虽说这个也是所谓的狄仁杰宇宙的构思在里面但是现在来看基本是跟西游那些差不多，完全没有整体性可言，打一枪换一个地方，演员也没有延续性，剧情也是前后跳脱，没什么关联跟承上启下，导致质量层次不一，更不用谈什么狄仁杰宇宙了，不过这个事情也是难说，原因很多，现在资本都是更加趋利的，一些需要更长久时间才能有回报的投资是很难获得资本青睐，所以只能将重心投给选择一些流量明星，而本来应该将资源投给剧本打磨的基本就没了，再深入说也没意义了，社会现状就是这样。
还有一点感想是，以前的剧里的拍摄环境还是比较惨的，看着一些房子，甚至皇宫都是比较破旧的，地面还是石板这种，想想以前的演员的环境再想想现在的，比如成龙说的，以前他拍剧就是啪摔了，问这条有没有过，过了就直接送医院，而不是现在可能手蹭破点皮就大叫，甚至还有饭圈这些破事。

+ 聊聊部分公交车的设计bug + /2021/12/05/%E8%81%8A%E8%81%8A%E9%83%A8%E5%88%86%E5%85%AC%E4%BA%A4%E8%BD%A6%E7%9A%84%E8%AE%BE%E8%AE%A1bug/ + 今天惯例坐公交回住的地方，不小心撞了头，原因是我们想坐倒数第二排，然后LD 走在我后面，我就走到最后一排中间等着，但是最后一排是高一截的，等 LD 坐进去以后，我就往前走，结果撞到了车顶的扶手杆子的一端，差点撞昏了去，这里我觉得其实杆子长度应该短一点，不然从最后一排出来，还是有比较大概率因为没注意看而撞到头，特别是没注意看的情况，发力其实会比较大，一头撞上就会像我这样，眼前一黑，又痛得要死。
还有一点就是座位设计了，先来看个图

图里大致画了两条线，因为可能是轮胎还是什么原因，后排中间会有那么大的突起，但是看两条红线可以发现，靠近过道的座位边缘跟地面突起的边缘不是一样宽的，这样导致的结果就是坐着的时候有一个脚没地儿搁，要不就得侧着斜着坐，或者就是一个脚悬空，短程的可能还好，路程远一点还是比较难受的，特别是像我现在这样，大腿外侧有点难受的情况，就会更难受。
虽然说这两个点，基本是屁用没有，但是我也是在自己这个博客说说，也当是个树洞了。

]]> 生活 生活 - 看剧 + 公交 + 杭州 @@ -7280,6 +7466,18 @@ user3: 记录一次折腾自组 nas 的失败经历-续续篇 /2023/05/28/%E8%AE%B0%E5%BD%95%E4%B8%80%E6%AC%A1%E6%8A%98%E8%85%BE%E8%87%AA%E7%BB%84-nas-%E7%9A%84%E5%A4%B1%E8%B4%A5%E7%BB%8F%E5%8E%86-%E7%BB%AD%E7%BB%AD%E7%AF%87/ 之前这个机器已经算是跑起来了，虽然不是很完善也不是最佳实践，不过这篇可能也不算是失败经历了，因为最后成功跑起来了，在没法装最新版的 exsi 的情况下，并且我后面买的华硕 z370 主板点不亮，所以我也有点死心就直接用Windows 下装 vmware workstation 装虚拟机，然后直通硬盘来做 nas，这样可能对于其他人来说是很垃圾的方案，不过因为我很多常用软件的都是在 Windows 环境下的，并且纯黑裙的环境会比较浪费，相比一些同学在群晖里安装 Windows 虚拟机，我觉得还是反过来比较好，毕竟 vmware 做虚拟机应该是比群晖专业点，不过这个方案也有一些问题
第一种方式是直接找网上同学分享的处理好引导的 vmx，这种我碰到了一个问题就是在打开虚拟机安装群晖系统.pat的时候会提示“无法安装此文件，文件可能已损坏”，这其实不是真的文件已损坏，应该是群晖在做校验的时候存在什么条件没有通过，尝试了断网等方式都不成功，所以后来就用了比较釜底抽薪的方案，直接使用大佬开源的 arpl 引导制作工具
第二种方式一开始是躺在我 B 站收藏夹里，有个 up 制作的，做得很细致，也把很多细节也解释了，过程其实不难，就是按步骤一步步执行，但是一开始选择了 918+的系统在我的方案里安装不了，会提示无法安装，经过视频下的评论的知道，尝试使用 920+的系统就顺利安装成功了，这里唯一的区别就是在添加硬盘的时候要选择物理磁盘，然后 vmware 给出的硬盘选项是Physical0， Physical1，记得别选错了，然后在启动后我租了 raid5,4 块 4T 的盘，可以组成一个 10T 多一点的存储空间，打算用来作为比较长读写的区域，更大的盘可能就会作为只读区域，减小写入量。后面还有一些问题待解决，一个是电源，考虑换个稍好一点，因为目前看下来电源风扇的噪音比较大，还有就是主板，最近看中了微星的 z390，不过价格比较贵，打算慢慢蹲蹲看。

+]]> + + nas + + + nas + + + + 记录一次折腾自组 nas 的失败经历 + /2023/05/07/%E8%AE%B0%E5%BD%95%E4%B8%80%E6%AC%A1%E6%8A%98%E8%85%BE%E8%87%AA%E7%BB%84-nas-%E7%9A%84%E5%A4%B1%E8%B4%A5%E7%BB%8F%E5%8E%86/ + 鉴于现在市面上的成品 nas 对于我来说要不就是太贵了，要不就是便宜的盘位少，性能比较差，很多 nas 主打还有用 docker 什么的，但是性能对于我个人比较特殊的使用方式来说还是不太够用的，比如现在比较性能好的 nas 像绿联新出的 DX4600，用的是N5105，passmark 分数还不如我 15 年买的 pc 上的 i5 4590，当然很多人是考虑功耗，这也是萝卜青菜各有所爱，可能我算下来还是觉得没多大必要
然后就是考虑用什么硬件配置了，这个流派也有很多种，
用蜗牛星际的原版硬件其实对于需求不大的也是挺好的，整套的都解决了，cpu 用 j1900 如果就做 nas 应该也够了，我没选的原因一方面是性能不符合我的要求，另一方面是现在市面上的机器大部分都是战损成色，而且也不太便宜，如果成色比较好的能够 400 以内拿下整机的话感觉还算可以，cpu 换成 j4125 或者 j3455 再加个 100 也能接受，但基本比较少有这种价格，之前看到一个换了 j3455 的只要 360，犹豫了下没下手，其他很多的都是 j1900 的都要 600 左右
然后是各类 E3，E5 和商用服务器类型的，这种的特点是功耗大，其实 cpu 很便宜，E5 2630V3 跟 2630V4 都只要十几块钱，性能过得去，没有选的主要是机箱占地方也比较贵，还有是商用的怕很多系统需要自己找驱动什么的，配件比如 HBA 卡这种，买的不兼容什么的还是挺麻烦的，另外噪音也是个比较大的问题，租的房子比较小，即使放客厅也是靠着卧室的墙边，如果以后换大一点的房子倒是可以考虑
最后就是我目前选的方案，就是普通的民用机器，找盘位多一点的机箱，我原来的 4590 的机器的机箱就不错，但是已经停产了，二手的太重了闲鱼都不出外地，cpu 跟主板其实考虑了很久，因为从 4590 开始核显就能硬解 H264 这种常规的视频了，考虑用intel 四代的 i3 或者 i5 应该纯 nas 来讲是足够用了，但是这样就跟我现在已经有的 4590 有点重叠了，而且也觉得最好是能性能好一些的，就开始看一些稍新一点的，很多用的多的有 i3-8100，跟 i3-10100 这种，但是这些已经被炒的价格比较高，寻寻觅觅了很久看中了 i5-8600，这个价格跟 8500 差不多，性能还好一些，主板就是我标题说的最“失败”的一个点了，主要是因为主板自带的网卡是 Realtek 的，至于更具体的后面会专门介绍，机箱是图便宜买的爱国者半岛铁盒的 F10，内存就买了一根光威的 32g 的，装系统的硬盘是用了以前囤的京东京造的麒麟系列，但是现在对这个系列挺不看好，之前有个盘就掉盘了，维修体验一开始也不好，半个多月维修，电源也是图便宜买的一个先马的平头哥系列，额定 550w 只要 140 左右，整体的机器就攒齐了，但是很多问题也随之出现了
第一个问题是买的二手的板 U 套装，结果寄过来的时候没带挡板，导致一开始装上了又要拆下来；第二个问题是主机贪便宜，主机上固定主板螺丝的螺柱拧不进去，后来店家告诉我让我可以用电源固定的螺丝先拧一下才把螺柱拧进去；第三个问题是因为没什么装机经验导致的，散热装的太累了，因为要兼容各种主板，还有各种螺丝，装的时候也着急；第四个问题是电源的比较便宜，一方面比较不放心安全性，另一方面是我想多装几个硬盘，电源直出的只有四个 sata 口，需要买转接线，从 D 型的 4pin 口子转出来；第五个问题也是电源，主板电源线有点不太够，走背线就比较困难
以上主要是装机的困难，下一篇介绍作为 nas 的各种问题吧

]]> nas @@ -7301,15 +7499,24 @@ user3: - 记录一次折腾自组 nas 的失败经历 - /2023/05/07/%E8%AE%B0%E5%BD%95%E4%B8%80%E6%AC%A1%E6%8A%98%E8%85%BE%E8%87%AA%E7%BB%84-nas-%E7%9A%84%E5%A4%B1%E8%B4%A5%E7%BB%8F%E5%8E%86/ - 鉴于现在市面上的成品 nas 对于我来说要不就是太贵了，要不就是便宜的盘位少，性能比较差，很多 nas 主打还有用 docker 什么的，但是性能对于我个人比较特殊的使用方式来说还是不太够用的，比如现在比较性能好的 nas 像绿联新出的 DX4600，用的是N5105，passmark 分数还不如我 15 年买的 pc 上的 i5 4590，当然很多人是考虑功耗，这也是萝卜青菜各有所爱，可能我算下来还是觉得没多大必要
然后就是考虑用什么硬件配置了，这个流派也有很多种，
用蜗牛星际的原版硬件其实对于需求不大的也是挺好的，整套的都解决了，cpu 用 j1900 如果就做 nas 应该也够了，我没选的原因一方面是性能不符合我的要求，另一方面是现在市面上的机器大部分都是战损成色，而且也不太便宜，如果成色比较好的能够 400 以内拿下整机的话感觉还算可以，cpu 换成 j4125 或者 j3455 再加个 100 也能接受，但基本比较少有这种价格，之前看到一个换了 j3455 的只要 360，犹豫了下没下手，其他很多的都是 j1900 的都要 600 左右
然后是各类 E3，E5 和商用服务器类型的，这种的特点是功耗大，其实 cpu 很便宜，E5 2630V3 跟 2630V4 都只要十几块钱，性能过得去，没有选的主要是机箱占地方也比较贵，还有是商用的怕很多系统需要自己找驱动什么的，配件比如 HBA 卡这种，买的不兼容什么的还是挺麻烦的，另外噪音也是个比较大的问题，租的房子比较小，即使放客厅也是靠着卧室的墙边，如果以后换大一点的房子倒是可以考虑
最后就是我目前选的方案，就是普通的民用机器，找盘位多一点的机箱，我原来的 4590 的机器的机箱就不错，但是已经停产了，二手的太重了闲鱼都不出外地，cpu 跟主板其实考虑了很久，因为从 4590 开始核显就能硬解 H264 这种常规的视频了，考虑用intel 四代的 i3 或者 i5 应该纯 nas 来讲是足够用了，但是这样就跟我现在已经有的 4590 有点重叠了，而且也觉得最好是能性能好一些的，就开始看一些稍新一点的，很多用的多的有 i3-8100，跟 i3-10100 这种，但是这些已经被炒的价格比较高，寻寻觅觅了很久看中了 i5-8600，这个价格跟 8500 差不多，性能还好一些，主板就是我标题说的最“失败”的一个点了，主要是因为主板自带的网卡是 Realtek 的，至于更具体的后面会专门介绍，机箱是图便宜买的爱国者半岛铁盒的 F10，内存就买了一根光威的 32g 的，装系统的硬盘是用了以前囤的京东京造的麒麟系列，但是现在对这个系列挺不看好，之前有个盘就掉盘了，维修体验一开始也不好，半个多月维修，电源也是图便宜买的一个先马的平头哥系列，额定 550w 只要 140 左右，整体的机器就攒齐了，但是很多问题也随之出现了
第一个问题是买的二手的板 U 套装，结果寄过来的时候没带挡板，导致一开始装上了又要拆下来；第二个问题是主机贪便宜，主机上固定主板螺丝的螺柱拧不进去，后来店家告诉我让我可以用电源固定的螺丝先拧一下才把螺柱拧进去；第三个问题是因为没什么装机经验导致的，散热装的太累了，因为要兼容各种主板，还有各种螺丝，装的时候也着急；第四个问题是电源的比较便宜，一方面比较不放心安全性，另一方面是我想多装几个硬盘，电源直出的只有四个 sata 口，需要买转接线，从 D 型的 4pin 口子转出来；第五个问题也是电源，主板电源线有点不太够，走背线就比较困难
以上主要是装机的困难，下一篇介绍作为 nas 的各种问题吧

+ 记录下 phpunit 的入门使用方法 + /2022/10/16/%E8%AE%B0%E5%BD%95%E4%B8%8B-phpunit-%E7%9A%84%E5%85%A5%E9%97%A8%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95/ + 这周开始打算写个比较简单的php工具包，然后顺带学习使用下php的单元测试，通过phpunit还是比较方便的，首先就composer require phpunit/phpunit
安装下 phpunit, 前面包就是通过 composer init 创建，装完依赖后就可以把自动加载代码生成下 composer dump-autoload
目录结构差不多这样

+
.
├── composer.json
├── composer.lock
├── oldfile.txt
├── phpunit.xml
├── src
│   └── Rename.php
└── tests
    └── RenameTest.php

2 directories, 6 files
+

src/是源码，tests/是放的单测，比较重要的是phpunit.xml

+
<?xml version="1.0" encoding="UTF-8"?>
<phpunit colors="true" bootstrap="vendor/autoload.php">
    <testsuites>
        <testsuite name="php-rename">
            <directory>./tests/</directory>
        </testsuite>
    </testsuites>
</phpunit>
+

其中bootstrap就是需要把依赖包的自动加载入口配上，因为这个作为一个package，也会指出命名空间
然后就是testsuite的路径，源码中

+
<?php
namespace Nicksxs\PhpRename;

class Rename
{
    public static function renameSingleFile($file, $newFileName): bool
    {
        if(!is_file($file)) {
            echo "it's not a file";
            return false;
        }
        $fileInfo = pathinfo($file);
        return rename($file, $fileInfo["dirname"] . DIRECTORY_SEPARATOR . $newFileName . "." . $fileInfo["extension"]);
    }
}
+

就是一个简单的重命名
然后test代码是这样，

+
<?php

// require_once 'vendor/autoload.php';

use PHPUnit\Framework\TestCase;
use Nicksxs\PhpRename\Rename;
use function PHPUnit\Framework\assertEquals;

class RenameTest extends TestCase 
{
    public function setUp() :void
    {
        $myfile = fopen(__DIR__ . DIRECTORY_SEPARATOR . "oldfile.txt", "w") or die("Unable to open file!");
        $txt = "file test1\n";
        fwrite($myfile, $txt);
        fclose($myfile);
    }
    public function testRename()
    {
        Rename::renameSingleFile(__DIR__ . DIRECTORY_SEPARATOR . "oldfile.txt", "newfile");
        assertEquals(is_file(__DIR__ . DIRECTORY_SEPARATOR . "newfile.txt"), true);
    }

    protected function tearDown(): void
    {
        unlink(__DIR__ . DIRECTORY_SEPARATOR . "newfile.txt");
    }
}
+

setUp 跟 tearDown 就是初始化跟结束清理的，但是注意如果不指明 __DIR__ ,待会的目录就会在执行 vendor/bin/phpunit 下面，
或者也可以指定在一个 tmp/ 目录下
最后就可以通过vendor/bin/phpunit 来执行测试
执行结果

+
❯ vendor/bin/phpunit
PHPUnit 9.5.25 by Sebastian Bergmann and contributors.

.                                                                   1 / 1 (100%)

Time: 00:00.005, Memory: 6.00 MB

OK (1 test, 1 assertion)
]]> - nas + php - nas + php @@ -7344,27 +7551,6 @@ user3: stream - - 记录下 phpunit 的入门使用方法 - /2022/10/16/%E8%AE%B0%E5%BD%95%E4%B8%8B-phpunit-%E7%9A%84%E5%85%A5%E9%97%A8%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95/ - 这周开始打算写个比较简单的php工具包，然后顺带学习使用下php的单元测试，通过phpunit还是比较方便的，首先就composer require phpunit/phpunit
安装下 phpunit, 前面包就是通过 composer init 创建，装完依赖后就可以把自动加载代码生成下 composer dump-autoload
目录结构差不多这样

-
.
├── composer.json
├── composer.lock
├── oldfile.txt
├── phpunit.xml
├── src
│   └── Rename.php
└── tests
    └── RenameTest.php

2 directories, 6 files
-

src/是源码，tests/是放的单测，比较重要的是phpunit.xml

-
<?xml version="1.0" encoding="UTF-8"?>
<phpunit colors="true" bootstrap="vendor/autoload.php">
    <testsuites>
        <testsuite name="php-rename">
            <directory>./tests/</directory>
        </testsuite>
    </testsuites>
</phpunit>
-

其中bootstrap就是需要把依赖包的自动加载入口配上，因为这个作为一个package，也会指出命名空间
然后就是testsuite的路径，源码中

-
<?php
namespace Nicksxs\PhpRename;

class Rename
{
    public static function renameSingleFile($file, $newFileName): bool
    {
        if(!is_file($file)) {
            echo "it's not a file";
            return false;
        }
        $fileInfo = pathinfo($file);
        return rename($file, $fileInfo["dirname"] . DIRECTORY_SEPARATOR . $newFileName . "." . $fileInfo["extension"]);
    }
}
-

就是一个简单的重命名
然后test代码是这样，

-
<?php

// require_once 'vendor/autoload.php';

use PHPUnit\Framework\TestCase;
use Nicksxs\PhpRename\Rename;
use function PHPUnit\Framework\assertEquals;

class RenameTest extends TestCase 
{
    public function setUp() :void
    {
        $myfile = fopen(__DIR__ . DIRECTORY_SEPARATOR . "oldfile.txt", "w") or die("Unable to open file!");
        $txt = "file test1\n";
        fwrite($myfile, $txt);
        fclose($myfile);
    }
    public function testRename()
    {
        Rename::renameSingleFile(__DIR__ . DIRECTORY_SEPARATOR . "oldfile.txt", "newfile");
        assertEquals(is_file(__DIR__ . DIRECTORY_SEPARATOR . "newfile.txt"), true);
    }

    protected function tearDown(): void
    {
        unlink(__DIR__ . DIRECTORY_SEPARATOR . "newfile.txt");
    }
}
-

setUp 跟 tearDown 就是初始化跟结束清理的，但是注意如果不指明 __DIR__ ,待会的目录就会在执行 vendor/bin/phpunit 下面，
或者也可以指定在一个 tmp/ 目录下
最后就可以通过vendor/bin/phpunit 来执行测试
执行结果

-
❯ vendor/bin/phpunit
PHPUnit 9.5.25 by Sebastian Bergmann and contributors.

.                                                                   1 / 1 (100%)

Time: 00:00.005, Memory: 6.00 MB

OK (1 test, 1 assertion)
-]]> - - php - - - php - - 记录下 phpunit 的入门使用方法之setUp和tearDown /2022/10/23/%E8%AE%B0%E5%BD%95%E4%B8%8B-phpunit-%E7%9A%84%E5%85%A5%E9%97%A8%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95%E4%B9%8BsetUp%E5%92%8CtearDown/ @@ -7382,44 +7568,41 @@ user3: - 记录下 redis 的一些使用方法 - /2022/10/30/%E8%AE%B0%E5%BD%95%E4%B8%8B-redis-%E7%9A%84%E4%B8%80%E4%BA%9B%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95/ - 虽然说之前讲解过一些redis源码相关的，但是说实话，redis的各种使用其实有时候有点生疏，或者在一些特定的使用场景中，一些使用方法还是需要学习和记录的

-

获取所有数据

获取list类型的所有元素，可以使用 lrange , 直接用lrange key 0 -1
比如

这里有一些方便的就是可以不用知道长度，直接全返回，或者如果想拿到特定区间的就可以直接指定起止范围，

这样就不用一个个pop出来

-

裁剪list

前面用了lrange取得了一个范围的数据，如果想将数据直接移除，那可以用 ltrim ,

这两个命令就可以从list里取出批量数据，并且能从list里删除这部分数据

+ 聊一下 Spring 的 SmartLifecycle 使用 + /2024/07/07/%E8%81%8A%E4%B8%80%E4%B8%8B-Spring-%E7%9A%84-SmarLifecycle-%E4%BD%BF%E7%94%A8/ + 最近在学习过程中碰到一个比较有意思的Spring特性，就是 SmartLifecycle ，这个可以很轻松的融合进 Spring 生命周期

+
public interface SmartLifecycle extends Lifecycle, Phased {
    int DEFAULT_PHASE = Integer.MAX_VALUE;

    default boolean isAutoStartup() {
        return true;
    }

    default void stop(Runnable callback) {
        this.stop();
        callback.run();
    }

    default int getPhase() {
        return Integer.MAX_VALUE;
    }
}
+

接口继承了 org.springframework.context.Lifecycle 跟 org.springframework.context.Phased
其中默认实现了 isAutoStartup , 并且默认是返回 true，所以相对于 Lifecycle 能更智能些自动启动
然后 Lifecycle 这个接口就比较简单，只有几个接口

+
public interface Lifecycle {
    void start();

    void stop();

    boolean isRunning();
}
+

我们可以是实现下这个接口

+
@Component
public class MySmartLifecycle implements SmartLifecycle {

    private volatile boolean isRunning;

    @Override
    public void start() {
        System.out.println("my smart life cycle start");
        isRunning = true;
    }

    @Override
    public void stop() {
        System.out.println("my smart life cycle end");
        isRunning = false;
    }

    @Override
    public boolean isRunning() {
        return isRunning;
    }
}
+

这个 bean 会在 Spring 启动后被自动调用 start 方法

这个就是在不深入 bean 的生命周期，并且是在 bean 已经都初始化以后调用
同样会在 spring 容器关闭时调用 stop 方法

如果有此类的需求就可以扩展此接口实现
而这个其实是在 spring 生命周期中的 finishRefresh 方法中进行调用

+
protected void finishRefresh() {
    this.clearResourceCaches();
    this.initLifecycleProcessor();
    this.getLifecycleProcessor().onRefresh();
    this.publishEvent((ApplicationEvent)(new ContextRefreshedEvent(this)));
    if (!NativeDetector.inNativeImage()) {
        LiveBeansView.registerApplicationContext(this);
    }

}
+

调用了 LifecycleProcessor 的 onRefresh 方法

+
public void onRefresh() {
    this.startBeans(true);
    this.running = true;
}
+

这其中就会调用 start 方法

+
private void startBeans(boolean autoStartupOnly) {
    // 获取这些bean
    Map<String, Lifecycle> lifecycleBeans = this.getLifecycleBeans();
    Map<Integer, LifecycleGroup> phases = new TreeMap();
    lifecycleBeans.forEach((beanName, bean) -> {
        if (!autoStartupOnly || bean instanceof SmartLifecycle && ((SmartLifecycle)bean).isAutoStartup()) {
            int phase = this.getPhase(bean);
            ((LifecycleGroup)phases.computeIfAbsent(phase, (p) -> {
                return new LifecycleGroup(phase, this.timeoutPerShutdownPhase, lifecycleBeans, autoStartupOnly);
            })).add(beanName, bean);
        }

    });
    if (!phases.isEmpty()) {
        phases.values().forEach(LifecycleGroup::start);
    }

}
+

会先判断 isAutoStartup，然后将不同周期加入分组，然后再循环调用start

]]> - redis + java - redis + java - 记录下 zookeeper 集群迁移和易错点 - /2022/05/29/%E8%AE%B0%E5%BD%95%E4%B8%8B-zookeeper-%E9%9B%86%E7%BE%A4%E8%BF%81%E7%A7%BB/ - 前阵子做了zk 的集群升级迁移，大概情况是原来是一个三节点的 zk 集群（最小可用
大概是

-
zk1 192.168.2.1
zk2 192.168.2.2
zk3 192.168.2.3
-

在 zoo.cfg 中的配置就是如下

-
server.1=192.168.2.1:2888:3888
server.2=192.168.2.2:2888:3888
server.3=192.168.2.3:2888:3888
-

加节点

需要将集群迁移到 192.168.2.4（简称 zk4），192.168.2.5（简称 zk5），192.168.2.6（简称 zk6） 这三台机器上，目前新的这三台机器上是没有 zk 部署的, 我们想要的是数据不丢失，那主要考虑的就是滚动升级，这里我其实犯了几个错误，也特别说明下
首先我们想要新的三台机器加进去，所以我在zk4，zk5，zk6 的配置是这样

-
server.1=192.168.2.1:2888:3888
server.2=192.168.2.2:2888:3888
server.3=192.168.2.3:2888:3888
server.4=192.168.2.4:2888:3888
server.5=192.168.2.5:2888:3888
server.6=192.168.2.6:2888:3888
-

这样起来发现状态是该节点没起来，
PS：查看当前节点状态可以通过 ./zkServer.sh status 来查看
第一个问题是我需要一个myid文件，标识我是哪个节点，里面的内容就写 4或 5 或 6 这样就行了，并且这个文件的路径应该在配置文件中指定的dataDir=数据目录下
第二个问题是困扰我比较久的，我在按上面的配置启动节点后，发现这几个节点都是没起来的，并且有 FastLeaderElection@xxx - Notification time out: 60000 这个报错，一开始以为是网络不通，端口没开这些原因，检查了下都是通的，结果原因其实跟我之前的一个考虑是相关的，当有六个节点的时候，理论上需要有半数以上的节点可用，集群才会是健康的，但是按我这个方式起来，其实我配置了六个节点，但是其中三个都是不可用的（包括自身节点），那么它自然是没办法正常工作，所以这里其实也需要滚动添加，类似于这样
我的 zk4 的配置应该是这样

-
server.1=192.168.2.1:2888:3888
server.2=192.168.2.2:2888:3888
server.3=192.168.2.3:2888:3888
server.4=192.168.2.4:2888:3888
-

然后 zk5 的配置

-
server.1=192.168.2.1:2888:3888
server.2=192.168.2.2:2888:3888
server.3=192.168.2.3:2888:3888
server.4=192.168.2.4:2888:3888
server.5=192.168.2.5:2888:3888
-

接着 zk6 的配置就可以是全部了

-
server.1=192.168.2.1:2888:3888
server.2=192.168.2.2:2888:3888
server.3=192.168.2.3:2888:3888
server.4=192.168.2.4:2888:3888
server.5=192.168.2.5:2888:3888
server.6=192.168.2.6:2888:3888
-

然后为了集群完全更新，就继续在 zk4 和 zk5 加上其他节点，这样我的 6 节点集群就起来了

-

下节点

这里我踩了另外一个坑，或者说没搞清楚两种方式的差别，

-

第一种

首先说说我没采用的第一种方式，（也是比较合理的）其实上面这个集群有个明显的问题，老集群其实还是各自认了一个三节点的集群，其中 zk3 是主节点，对于 zk1，zk2，zk3 来说它们能看到的就只有这三个节点，对于后三个 zk4，zk5，zk6 节点来说他们能连上其余五个节点，可以认为这是个六节点的集群，那么比较合理的操作应该是在老的三节点上把后面三个也都加进来，即每个节点的配置里 server 都有 6 个，然后我再对老的节点进行下线，这里下线需要注意的比较理想的是下一个节点就要修改配置，挪掉下线的节点后进行一遍重启，比如我知道了集群中的 leader 是在 zk3 上面，那么我先将 zk1 和 zk2 下掉，那么在我将 zk1 下线的之后，我将其他的五个节点都删除 zk1 的配置，然后重启，这样其实不是必须，但相对会可靠些，理论上我也可以在下掉 zk1 和 zk2 之后再修改配置重启其余节点。而当只剩下 zk3，zk4，zk5，zk6 四个节点的集群后，并且每个节点里的配置也只有这四个 server，我再下线 zk3 这个 leader 的时候，就会进行选举，再选出新的 leader，因为刚好是三节点，同样保证了最小可用。

-

第二种

这也是我踩坑的一种方式，就是我没有修改原来三节点的配置，并且我一开始以为可以通过下线 zk1，zk2，zk3（进行选举）的方式完成下线，然后再进行重启，但是这种方式就是我上面说的，原来的三节点里我下掉 zk1 还是能够正常运行，但是我下线 zk2 的时候，这个集群就等于是挂了，小于最小可用了，这样三节点都挂了，而且对于新加入的三个节点来说，又回到了最初起不来一样状态，六节点里只有三节点在线，导致整个集群都挂了，所以对于我这样的操作来说，我需要滚动修改启动，在下线 zk1 的时候就需要把 zk4，zk5，zk6 中的 zk1 移除后重启，当然这样唯一的好处就是可以少重启几个，同样继续下线 zk2 的时候，把 zk2 移除掉再重启，其实在移除 zk1 后修改重启后，在下线 zk2 的时候，集群就会重新选举了，因为 zk2 下线的时候，zk3 还是会一起下线。这个是我们需要特别注意的

+ 记录下 redis 的一些使用方法 + /2022/10/30/%E8%AE%B0%E5%BD%95%E4%B8%8B-redis-%E7%9A%84%E4%B8%80%E4%BA%9B%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95/ + 虽然说之前讲解过一些redis源码相关的，但是说实话，redis的各种使用其实有时候有点生疏，或者在一些特定的使用场景中，一些使用方法还是需要学习和记录的

+

获取所有数据

获取list类型的所有元素，可以使用 lrange , 直接用lrange key 0 -1
比如

这里有一些方便的就是可以不用知道长度，直接全返回，或者如果想拿到特定区间的就可以直接指定起止范围，

这样就不用一个个pop出来

+

裁剪list

前面用了lrange取得了一个范围的数据，如果想将数据直接移除，那可以用 ltrim ,

这两个命令就可以从list里取出批量数据，并且能从list里删除这部分数据

]]> - java + redis - zookeeper + redis @@ -7438,68 +7621,83 @@ user3: - 记录下把小米路由器 4A 千兆版刷成 openwrt 的过程 - /2023/05/21/%E8%AE%B0%E5%BD%95%E4%B8%8B%E6%8A%8A%E5%B0%8F%E7%B1%B3%E8%B7%AF%E7%94%B1%E5%99%A8-4A-%E5%8D%83%E5%85%86%E7%89%88%E5%88%B7%E6%88%90-openwrt-%E7%9A%84%E8%BF%87%E7%A8%8B/ - 之前在绍兴家里的一条宽带送了个小米路由器 4A，正好原来的小米路由器 3 不知道为啥经常断流不稳定，而且只支持百兆，这边用了 200M 的宽带，感觉也比较浪费，所以就动了这个心思，但是还是有蛮多坑的，首先是看到了一篇文章，写的比较详细，
看到的就是这篇文章
这里使用的是 OpenWRTInvasion 这个项目来破解 ssh，首先这里有个最常见的一个问题，就是文件拉不到，所以有一些可行的方法就是自己起一个http 服务，可以修改脚本代码，直接从这个启动的 http 服务拉取已经下载下的文件，就这个问题我就尝试了很多次，还有就是这个 OpenWRTInvasion 最后一个支持 Windows 的版本就是 0.0.7，后面的版本其实做了很多的优化解决了文件的问题，一开始碰到的问题是本地起了文件服务但是没请求，或者请求了但后续 ssh 没有正常破解，我就换了 Mac 用最新版本的OpenWRTInvasion来尝试进行破解，发现还是不行，结果查了不少资料发现最根本的问题是这个路由器的新版本就不支持这种破解了，因为这个路由器新的版本都是 v2 版本，也就是2.30.x 版本的系统了，原来支持的是 2.28.x 的这些系统，后来幸好是找到了这个版本的系统支持的另一个恩山大神的文章，根据这个文章提供的工具进行破解就成功了，但是破解要多尝试几次，我第一次是失败的，小米路由器 4A 千兆版的版本号也会写作 R4Av2，在搜索一些资料的时候也可以用这个型号去搜，可能也是另一种黑话，路由器以前刷过梅林，padavan，还是第一次刷 openwrt，都已经忘了以前是怎么刷的来着，感觉现在越来越难刷了，特别是 ssh，想给我的 ax6 刷个 openwrt，发现前提是需要先有一个 openwrt 的路由器，简直了，变成先有鸡还是先有蛋的问题了，所以我把这个小米 4A 刷成 openwrt 也有这个考虑，毕竟 4A 配置上不太高，openwrt 各种插件可能还跑不起来，权当做练手和到时候用来开 AX6 的工具了。

+ 这周末我又在老丈人家打了天小工 + /2020/08/30/%E8%BF%99%E5%91%A8%E6%9C%AB%E6%88%91%E5%8F%88%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E6%89%93%E4%BA%86%E5%A4%A9%E5%B0%8F%E5%B7%A5/ + 因为活实在比较多，也不太好叫大工(活比较杂散)，相比上一次我跟 LD 俩人晚起了一点，我真的是只要有事，早上就醒的很早，准备八点出发的，六点就醒了，然后想继续睡就一直做梦🤦‍♂️，差不多八点半多到的丈人家，他们应该已经干了有一会了，我们到了以后就分配给我撬地板的活，上次说的那个敲掉柜子的房间里，还铺着质地还不错的木地板，但是也不想要了，得撬掉重新铺。
拿着撬棍和榔头就上楼去干了，浙江这几天的天气，最高温度一般 38、9，楼上那个房间也没风扇，有了也不能用，都是灰尘，撬了两下，我感觉我体内的水就像真气爆发一样变成汗炸了出来，眼睛全被汗糊住了，可能大部分人不太了解地板是怎么铺的，一般是在地面先铺一层混凝土，混凝土中间嵌进去规则的长条木条，然后真正的地板一块块的都是钉在那个木条上，用那种气枪钉和普通的钉子，并且块跟块之前还有一个木头的槽结构相互耦合，然后边缘的一圈在用较薄的木板将整个木地板封边(这些词都是我现造的)，边缘的用的钉子会更多，所以那几下真的很用力，而且撬地板，得蹲下起来，如此反复，对于我这个体重快超过身高的中年人来说的确是非常大的挑战，接下来继续撬了几个，已经有种要虚脱晕倒的感觉了，及时去喝水擦了汗，又歇了一会，为啥一上来就这么拼呢，主要是因为那个房间丈人在干活的时候是直接看得到的🤦‍♂️，后来被 LD 一顿教育，本来就是去帮忙的，又不是专业做这个的，急啥。
喝了水之后，又稍稍歇了一会，就开始继续撬了，本来觉得这个地板撬着好像还行，房间不大，没多久就撬完了，撬完之后喝了点饮料(补充点糖分，早餐吃得少，有点低血糖)，然后看到 LD 在撬下面的木条了，这个动作开始了那天最大的经验值收集行动，前面说了这个木条一般是跟混凝土一块铺上去的，但是谁也没想到，这个混凝土铺上去的时候竟然处理的这么随意，根本没考虑跟下面的贴合，所以撬木条的时候直接把木条跟木条中间大块大块的混凝土一块撬起来了，想想那重量，于是我这靠蛮力干活的，就用力把木条带着混凝土一块撬了起来，还沾沾自喜，但是发现结果是撬起来一块之后，体力值瞬间归零，上一篇我也提到了，其实干这类活也是很有技巧性的，但是上次的是我没学会，可能需要花时间学的，但是这次是LD 用她的纤细胳膊教会我的，我在撬的时候，屏住一口气，双手用力，起，大概是吃好几口奶的力气都用出来了，但是 LD 在我休息的时候，慢慢悠悠的，先把撬棍挤到木条或者混凝土跟下层的缝里，然后往下垫一小块混凝土碎石，然后轻轻松松的扳两下，就撬开了，亏我高中的时候引以为傲的物理成绩，作为物理课代表，这么浅显易懂的杠杆原理都完全不会用到生活里，后面在用这个技巧撬的过程中，真的觉得自己蠢到家了，当然在明白了用点杠杆原理之后，撬地板的活就变得慢慢悠悠，悠哉悠哉的了(其实还是很热的，披着毛巾擦眼睛)。
上午的活差不多完了，后面就是把撬出来的混凝土和地板条丢下去，地上铺着不用了的被子，然后就是午饭和午休环节了，午饭换了一家快餐，味道非常可以，下午的活就比较单调了，帮忙清理了上去扔下来的混凝土碎块跟木条，然后稍微打扫了下，老丈人就让我们回家了，接着上次说的，还是觉得比跑步啥的消耗大太多了，那汗流的，一口就能喝完一瓶 500 毫升左右的矿泉水。

]]> 生活 + 运动 + 跑步 + 干活 - 路由器 + 生活 + 运动 + 减肥 + 跑步 + 干活 - 远程桌面工具rustdesk的私有化部署 - /2024/05/12/%E8%BF%9C%E7%A8%8B%E6%A1%8C%E9%9D%A2%E5%B7%A5%E5%85%B7rustdesk%E7%9A%84%E7%A7%81%E6%9C%89%E5%8C%96%E9%83%A8%E7%BD%B2/ - 最近因为利用rustdesk远程操作诈骗太多，所以rustdesk官方直接关闭了国内的公有服务，相对其他比如teamviewer来说，rustdesk还是个比较不错的选择，性能考量也过得去，相对来说在双方都是mac的情况下，不付费的体验来说，比自带的vnc好了很多，vnc目测是直接全量传实时画面数据，带宽占用大，体验差。正巧最近有需求就使用了rustdesk所以记录下部署过程
我使用的较早先版本的部署方式，
首先需要一台有外网ip的服务器，并且需要使用到以下几个端口，如果使用云服务器，需要在控制台开启这些端口
核心端口:

-
TCP 21114-21119
UDP 21116
-

测试有没有开启可以使用这个工具网站 CanYouSeeMe.org ，或者用另一台机器telnet下
需要部署的服务有两个
hbbs - RustDesk ID/Rendezvous server id服务
hbbr - RustDesk Relay server 中继服务
这里使用docker来部署，比较简单，先拉取镜像，没错就一个

-
sudo docker image pull rustdesk/rustdesk-server
-

这边建议创建个rustdesk目录，进入目录后再启动下面的服务
先启动hbbs

-
sudo docker run --name hbbs -p 21115:21115 -p 21116:21116 -p 21116:21116/udp -p 21118:21118 -v `pwd`:/root -td --net=host rustdesk/rustdesk-server hbbs
-

使用 --net=host 是为了让server拿到连接进入的真实来源ip
然后再启动hbbr

-
sudo docker run --name hbbr -p 21117:21117 -p 21119:21119 -v `pwd`:/root -td --net=host rustdesk/rustdesk-server hbbr
-

接下来进到刚才建的目录，会看到有个一对公私钥，把公钥内容保存下来
然后就是客户端登录了，以mac为例

点击这边三点，再点击修改中继设置

在弹出窗口

ID服务器跟中继服务器框填入前面部署服务的服务器外网ip，在Key框里把刚才保存的公钥填进去
每个需要连接的客户端都需要这么设置，然后就可以使用自己的服务器作为ID跟中继服务器了，不会受官方关停国内服务影响

+ 记录下 zookeeper 集群迁移和易错点 + /2022/05/29/%E8%AE%B0%E5%BD%95%E4%B8%8B-zookeeper-%E9%9B%86%E7%BE%A4%E8%BF%81%E7%A7%BB/ + 前阵子做了zk 的集群升级迁移，大概情况是原来是一个三节点的 zk 集群（最小可用
大概是

+
zk1 192.168.2.1
zk2 192.168.2.2
zk3 192.168.2.3
+

在 zoo.cfg 中的配置就是如下

+
server.1=192.168.2.1:2888:3888
server.2=192.168.2.2:2888:3888
server.3=192.168.2.3:2888:3888
+

加节点

需要将集群迁移到 192.168.2.4（简称 zk4），192.168.2.5（简称 zk5），192.168.2.6（简称 zk6） 这三台机器上，目前新的这三台机器上是没有 zk 部署的, 我们想要的是数据不丢失，那主要考虑的就是滚动升级，这里我其实犯了几个错误，也特别说明下
首先我们想要新的三台机器加进去，所以我在zk4，zk5，zk6 的配置是这样

+
server.1=192.168.2.1:2888:3888
server.2=192.168.2.2:2888:3888
server.3=192.168.2.3:2888:3888
server.4=192.168.2.4:2888:3888
server.5=192.168.2.5:2888:3888
server.6=192.168.2.6:2888:3888
+

这样起来发现状态是该节点没起来，
PS：查看当前节点状态可以通过 ./zkServer.sh status 来查看
第一个问题是我需要一个myid文件，标识我是哪个节点，里面的内容就写 4或 5 或 6 这样就行了，并且这个文件的路径应该在配置文件中指定的dataDir=数据目录下
第二个问题是困扰我比较久的，我在按上面的配置启动节点后，发现这几个节点都是没起来的，并且有 FastLeaderElection@xxx - Notification time out: 60000 这个报错，一开始以为是网络不通，端口没开这些原因，检查了下都是通的，结果原因其实跟我之前的一个考虑是相关的，当有六个节点的时候，理论上需要有半数以上的节点可用，集群才会是健康的，但是按我这个方式起来，其实我配置了六个节点，但是其中三个都是不可用的（包括自身节点），那么它自然是没办法正常工作，所以这里其实也需要滚动添加，类似于这样
我的 zk4 的配置应该是这样

+
server.1=192.168.2.1:2888:3888
server.2=192.168.2.2:2888:3888
server.3=192.168.2.3:2888:3888
server.4=192.168.2.4:2888:3888
+

然后 zk5 的配置

+
server.1=192.168.2.1:2888:3888
server.2=192.168.2.2:2888:3888
server.3=192.168.2.3:2888:3888
server.4=192.168.2.4:2888:3888
server.5=192.168.2.5:2888:3888
+

接着 zk6 的配置就可以是全部了

+
server.1=192.168.2.1:2888:3888
server.2=192.168.2.2:2888:3888
server.3=192.168.2.3:2888:3888
server.4=192.168.2.4:2888:3888
server.5=192.168.2.5:2888:3888
server.6=192.168.2.6:2888:3888
+

然后为了集群完全更新，就继续在 zk4 和 zk5 加上其他节点，这样我的 6 节点集群就起来了

+

下节点

这里我踩了另外一个坑，或者说没搞清楚两种方式的差别，

+

第一种

首先说说我没采用的第一种方式，（也是比较合理的）其实上面这个集群有个明显的问题，老集群其实还是各自认了一个三节点的集群，其中 zk3 是主节点，对于 zk1，zk2，zk3 来说它们能看到的就只有这三个节点，对于后三个 zk4，zk5，zk6 节点来说他们能连上其余五个节点，可以认为这是个六节点的集群，那么比较合理的操作应该是在老的三节点上把后面三个也都加进来，即每个节点的配置里 server 都有 6 个，然后我再对老的节点进行下线，这里下线需要注意的比较理想的是下一个节点就要修改配置，挪掉下线的节点后进行一遍重启，比如我知道了集群中的 leader 是在 zk3 上面，那么我先将 zk1 和 zk2 下掉，那么在我将 zk1 下线的之后，我将其他的五个节点都删除 zk1 的配置，然后重启，这样其实不是必须，但相对会可靠些，理论上我也可以在下掉 zk1 和 zk2 之后再修改配置重启其余节点。而当只剩下 zk3，zk4，zk5，zk6 四个节点的集群后，并且每个节点里的配置也只有这四个 server，我再下线 zk3 这个 leader 的时候，就会进行选举，再选出新的 leader，因为刚好是三节点，同样保证了最小可用。

+

第二种

这也是我踩坑的一种方式，就是我没有修改原来三节点的配置，并且我一开始以为可以通过下线 zk1，zk2，zk3（进行选举）的方式完成下线，然后再进行重启，但是这种方式就是我上面说的，原来的三节点里我下掉 zk1 还是能够正常运行，但是我下线 zk2 的时候，这个集群就等于是挂了，小于最小可用了，这样三节点都挂了，而且对于新加入的三个节点来说，又回到了最初起不来一样状态，六节点里只有三节点在线，导致整个集群都挂了，所以对于我这样的操作来说，我需要滚动修改启动，在下线 zk1 的时候就需要把 zk4，zk5，zk6 中的 zk1 移除后重启，当然这样唯一的好处就是可以少重启几个，同样继续下线 zk2 的时候，把 zk2 移除掉再重启，其实在移除 zk1 后修改重启后，在下线 zk2 的时候，集群就会重新选举了，因为 zk2 下线的时候，zk3 还是会一起下线。这个是我们需要特别注意的

]]> - 工具 + java - 工具 + zookeeper - 这周末我又在老丈人家打了天小工 - /2020/08/30/%E8%BF%99%E5%91%A8%E6%9C%AB%E6%88%91%E5%8F%88%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E6%89%93%E4%BA%86%E5%A4%A9%E5%B0%8F%E5%B7%A5/ - 因为活实在比较多，也不太好叫大工(活比较杂散)，相比上一次我跟 LD 俩人晚起了一点，我真的是只要有事，早上就醒的很早，准备八点出发的，六点就醒了，然后想继续睡就一直做梦🤦‍♂️，差不多八点半多到的丈人家，他们应该已经干了有一会了，我们到了以后就分配给我撬地板的活，上次说的那个敲掉柜子的房间里，还铺着质地还不错的木地板，但是也不想要了，得撬掉重新铺。
拿着撬棍和榔头就上楼去干了，浙江这几天的天气，最高温度一般 38、9，楼上那个房间也没风扇，有了也不能用，都是灰尘，撬了两下，我感觉我体内的水就像真气爆发一样变成汗炸了出来，眼睛全被汗糊住了，可能大部分人不太了解地板是怎么铺的，一般是在地面先铺一层混凝土，混凝土中间嵌进去规则的长条木条，然后真正的地板一块块的都是钉在那个木条上，用那种气枪钉和普通的钉子，并且块跟块之前还有一个木头的槽结构相互耦合，然后边缘的一圈在用较薄的木板将整个木地板封边(这些词都是我现造的)，边缘的用的钉子会更多，所以那几下真的很用力，而且撬地板，得蹲下起来，如此反复，对于我这个体重快超过身高的中年人来说的确是非常大的挑战，接下来继续撬了几个，已经有种要虚脱晕倒的感觉了，及时去喝水擦了汗，又歇了一会，为啥一上来就这么拼呢，主要是因为那个房间丈人在干活的时候是直接看得到的🤦‍♂️，后来被 LD 一顿教育，本来就是去帮忙的，又不是专业做这个的，急啥。
喝了水之后，又稍稍歇了一会，就开始继续撬了，本来觉得这个地板撬着好像还行，房间不大，没多久就撬完了，撬完之后喝了点饮料(补充点糖分，早餐吃得少，有点低血糖)，然后看到 LD 在撬下面的木条了，这个动作开始了那天最大的经验值收集行动，前面说了这个木条一般是跟混凝土一块铺上去的，但是谁也没想到，这个混凝土铺上去的时候竟然处理的这么随意，根本没考虑跟下面的贴合，所以撬木条的时候直接把木条跟木条中间大块大块的混凝土一块撬起来了，想想那重量，于是我这靠蛮力干活的，就用力把木条带着混凝土一块撬了起来，还沾沾自喜，但是发现结果是撬起来一块之后，体力值瞬间归零，上一篇我也提到了，其实干这类活也是很有技巧性的，但是上次的是我没学会，可能需要花时间学的，但是这次是LD 用她的纤细胳膊教会我的，我在撬的时候，屏住一口气，双手用力，起，大概是吃好几口奶的力气都用出来了，但是 LD 在我休息的时候，慢慢悠悠的，先把撬棍挤到木条或者混凝土跟下层的缝里，然后往下垫一小块混凝土碎石，然后轻轻松松的扳两下，就撬开了，亏我高中的时候引以为傲的物理成绩，作为物理课代表，这么浅显易懂的杠杆原理都完全不会用到生活里，后面在用这个技巧撬的过程中，真的觉得自己蠢到家了，当然在明白了用点杠杆原理之后，撬地板的活就变得慢慢悠悠，悠哉悠哉的了(其实还是很热的，披着毛巾擦眼睛)。
上午的活差不多完了，后面就是把撬出来的混凝土和地板条丢下去，地上铺着不用了的被子，然后就是午饭和午休环节了，午饭换了一家快餐，味道非常可以，下午的活就比较单调了，帮忙清理了上去扔下来的混凝土碎块跟木条，然后稍微打扫了下，老丈人就让我们回家了，接着上次说的，还是觉得比跑步啥的消耗大太多了，那汗流的，一口就能喝完一瓶 500 毫升左右的矿泉水。

+ 闲聊下乘公交的用户体验 + /2021/02/28/%E9%97%B2%E8%81%8A%E4%B8%8B%E4%B9%98%E5%85%AC%E4%BA%A4%E7%9A%84%E7%94%A8%E6%88%B7%E4%BD%93%E9%AA%8C/ + 新年开工开车来杭州，因为没有车位加限行今天来就没开车来了，从东站做公交回住的地方，这班神奇的车我之前也吐槽过了，有神奇的乘客和神奇的司机，因为基本上这班车是从我毕业就开始乘了，所以也算是比较熟悉了，以前总体感觉不太好的是乘坐时间太长了，不过这个也不能怪车，是我自己住得远（离东站），后来住到了现在的地方，也算是直达，并且 LD 比较喜欢直达的，不爱更快却要换乘的地铁，所以坐的频率比较高，也说过前面那些比较气人的乘客，自己不好好戴口罩，反而联合一起上车的乘客诽谤司机，说他要吃人了要打人了，也正是这个司机比较有意思，上车就让戴好口罩，还给大家讲，哪里哪里又有疫情了，我觉得其实这个司机还是不错的，特殊时期，对于这种公共交通，这样的确是比较负责任的做法，只是说话方式，语气这个因人而异，他也不是来伺候人的，而且这么一大车人，说了一遍不行，再说一遍，三遍以上了，嗓门大一点也属于正常的人的行为。
还是说回今天要说的，今天这位司机我看着跟前面说的那位有点像，因为上车的时候比较暗没看清脸，主要原因是这位司机开车比较猛，比较急，然后车上因为这个时间点，比较多大学开学来的学生，拎着个行李箱，一开始是前面已经都站满了人，后面还有很多空位，因为后面没地方放行李箱，就因为这样前面站着的有几个就在说司机开慢点，结果司机貌似也没听进去，还是我行我素，过了会又有人说司机开稳一点，就在这个人说完没一会，停在红绿灯路口的车里，就有人问有没有垃圾桶，接着又让司机开门，说晕车太严重了，要下车，司机开了门，我望出去两个妹子下了车，好像在路边草丛吐了，前面开门下车的时候就有人说她们第一次来杭州，可能有点责怪司机开的不稳，也影响了杭州交通给新来杭州的人的感受，说完了事情经过，其实我有蛮多感触，对于杭州公交司机，我大概是大一来了没多久，陪室友去文三路买电脑就晕车，下车的时候在公交车站吐了，可能是从大学开始缺乏锻炼，又饮食不规律，更加容易晕车，大部分晕车我觉得都是我自己的原因，有时候是上车前吃太多了，或者早上起太早，没睡好，没吃东西，反正自己也是挺多原因的，说到司机的原因的话，我觉得可能这班车还算好的，最让我难受的还是上下班高峰的时候，因为经过的那条路是比较重要的主干道，路比较老比较窄，并且还有很多人行道，所以经常一脚油门连带着一脚刹车，真的很难受，这种算是我觉得真的是公交体验比较差的一点，但是这一点呢也不能完全怪公交司机，杭州的路政规划是很垃圾，没看错，是垃圾，所以总体结论是公交还行，主要是路政规划就是垃圾，包括这条主干道这么多人行道，并且两边都是老小区，老年人在上班高峰可能要买菜送娃或者其他事情，在通畅的情况下可能只需要六分钟的路程，有时候因为各种原因，半小时都开不完，扯开去一点，杭州的路，核心的高速说封就封，本来是高架可以直接通到城西，结果没造，到了路本已经很拥挤的时候开始来造隧道，各种破坏，隧道接高架的地方，无尽的加塞，对于我这样的小白司机来说真的是太恶心了，所以我一直想说的就是杭州这个地方房价领先基础设施十年，地铁，高架，高速通通不行，地面道路就更不行了。
总结下，其实杭州的真正的公交体验差，应该还是路造成的，对于前面的那两位妹子来说，有可能是她们来自于公交司机都是开的特别稳，并且路况也很好的地方，也或者是我被虐习惯了🤦‍♂️

]]> 生活 - 运动 - 跑步 - 干活 + 公交 生活 - 运动 - 减肥 - 跑步 - 干活 + 开车 + 加塞 + 糟心事 + 规则 + 公交 + 路政规划 + 基础设施 + 杭州 + 高速 - 聊聊部分公交车的设计bug - /2021/12/05/%E8%81%8A%E8%81%8A%E9%83%A8%E5%88%86%E5%85%AC%E4%BA%A4%E8%BD%A6%E7%9A%84%E8%AE%BE%E8%AE%A1bug/ - 今天惯例坐公交回住的地方，不小心撞了头，原因是我们想坐倒数第二排，然后LD 走在我后面，我就走到最后一排中间等着，但是最后一排是高一截的，等 LD 坐进去以后，我就往前走，结果撞到了车顶的扶手杆子的一端，差点撞昏了去，这里我觉得其实杆子长度应该短一点，不然从最后一排出来，还是有比较大概率因为没注意看而撞到头，特别是没注意看的情况，发力其实会比较大，一头撞上就会像我这样，眼前一黑，又痛得要死。
还有一点就是座位设计了，先来看个图

图里大致画了两条线，因为可能是轮胎还是什么原因，后排中间会有那么大的突起，但是看两条红线可以发现，靠近过道的座位边缘跟地面突起的边缘不是一样宽的，这样导致的结果就是坐着的时候有一个脚没地儿搁，要不就得侧着斜着坐，或者就是一个脚悬空，短程的可能还好，路程远一点还是比较难受的，特别是像我现在这样，大腿外侧有点难受的情况，就会更难受。
虽然说这两个点，基本是屁用没有，但是我也是在自己这个博客说说，也当是个树洞了。

+ 记录下把小米路由器 4A 千兆版刷成 openwrt 的过程 + /2023/05/21/%E8%AE%B0%E5%BD%95%E4%B8%8B%E6%8A%8A%E5%B0%8F%E7%B1%B3%E8%B7%AF%E7%94%B1%E5%99%A8-4A-%E5%8D%83%E5%85%86%E7%89%88%E5%88%B7%E6%88%90-openwrt-%E7%9A%84%E8%BF%87%E7%A8%8B/ + 之前在绍兴家里的一条宽带送了个小米路由器 4A，正好原来的小米路由器 3 不知道为啥经常断流不稳定，而且只支持百兆，这边用了 200M 的宽带，感觉也比较浪费，所以就动了这个心思，但是还是有蛮多坑的，首先是看到了一篇文章，写的比较详细，
看到的就是这篇文章
这里使用的是 OpenWRTInvasion 这个项目来破解 ssh，首先这里有个最常见的一个问题，就是文件拉不到，所以有一些可行的方法就是自己起一个http 服务，可以修改脚本代码，直接从这个启动的 http 服务拉取已经下载下的文件，就这个问题我就尝试了很多次，还有就是这个 OpenWRTInvasion 最后一个支持 Windows 的版本就是 0.0.7，后面的版本其实做了很多的优化解决了文件的问题，一开始碰到的问题是本地起了文件服务但是没请求，或者请求了但后续 ssh 没有正常破解，我就换了 Mac 用最新版本的OpenWRTInvasion来尝试进行破解，发现还是不行，结果查了不少资料发现最根本的问题是这个路由器的新版本就不支持这种破解了，因为这个路由器新的版本都是 v2 版本，也就是2.30.x 版本的系统了，原来支持的是 2.28.x 的这些系统，后来幸好是找到了这个版本的系统支持的另一个恩山大神的文章，根据这个文章提供的工具进行破解就成功了，但是破解要多尝试几次，我第一次是失败的，小米路由器 4A 千兆版的版本号也会写作 R4Av2，在搜索一些资料的时候也可以用这个型号去搜，可能也是另一种黑话，路由器以前刷过梅林，padavan，还是第一次刷 openwrt，都已经忘了以前是怎么刷的来着，感觉现在越来越难刷了，特别是 ssh，想给我的 ax6 刷个 openwrt，发现前提是需要先有一个 openwrt 的路由器，简直了，变成先有鸡还是先有蛋的问题了，所以我把这个小米 4A 刷成 openwrt 也有这个考虑，毕竟 4A 配置上不太高，openwrt 各种插件可能还跑不起来，权当做练手和到时候用来开 AX6 的工具了。

]]> 生活 - 生活 - 公交 - 杭州 + 路由器 @@ -7532,28 +7730,6 @@ user3: 生活 - - 闲聊下乘公交的用户体验 - /2021/02/28/%E9%97%B2%E8%81%8A%E4%B8%8B%E4%B9%98%E5%85%AC%E4%BA%A4%E7%9A%84%E7%94%A8%E6%88%B7%E4%BD%93%E9%AA%8C/ - 新年开工开车来杭州，因为没有车位加限行今天来就没开车来了，从东站做公交回住的地方，这班神奇的车我之前也吐槽过了，有神奇的乘客和神奇的司机，因为基本上这班车是从我毕业就开始乘了，所以也算是比较熟悉了，以前总体感觉不太好的是乘坐时间太长了，不过这个也不能怪车，是我自己住得远（离东站），后来住到了现在的地方，也算是直达，并且 LD 比较喜欢直达的，不爱更快却要换乘的地铁，所以坐的频率比较高，也说过前面那些比较气人的乘客，自己不好好戴口罩，反而联合一起上车的乘客诽谤司机，说他要吃人了要打人了，也正是这个司机比较有意思，上车就让戴好口罩，还给大家讲，哪里哪里又有疫情了，我觉得其实这个司机还是不错的，特殊时期，对于这种公共交通，这样的确是比较负责任的做法，只是说话方式，语气这个因人而异，他也不是来伺候人的，而且这么一大车人，说了一遍不行，再说一遍，三遍以上了，嗓门大一点也属于正常的人的行为。
还是说回今天要说的，今天这位司机我看着跟前面说的那位有点像，因为上车的时候比较暗没看清脸，主要原因是这位司机开车比较猛，比较急，然后车上因为这个时间点，比较多大学开学来的学生，拎着个行李箱，一开始是前面已经都站满了人，后面还有很多空位，因为后面没地方放行李箱，就因为这样前面站着的有几个就在说司机开慢点，结果司机貌似也没听进去，还是我行我素，过了会又有人说司机开稳一点，就在这个人说完没一会，停在红绿灯路口的车里，就有人问有没有垃圾桶，接着又让司机开门，说晕车太严重了，要下车，司机开了门，我望出去两个妹子下了车，好像在路边草丛吐了，前面开门下车的时候就有人说她们第一次来杭州，可能有点责怪司机开的不稳，也影响了杭州交通给新来杭州的人的感受，说完了事情经过，其实我有蛮多感触，对于杭州公交司机，我大概是大一来了没多久，陪室友去文三路买电脑就晕车，下车的时候在公交车站吐了，可能是从大学开始缺乏锻炼，又饮食不规律，更加容易晕车，大部分晕车我觉得都是我自己的原因，有时候是上车前吃太多了，或者早上起太早，没睡好，没吃东西，反正自己也是挺多原因的，说到司机的原因的话，我觉得可能这班车还算好的，最让我难受的还是上下班高峰的时候，因为经过的那条路是比较重要的主干道，路比较老比较窄，并且还有很多人行道，所以经常一脚油门连带着一脚刹车，真的很难受，这种算是我觉得真的是公交体验比较差的一点，但是这一点呢也不能完全怪公交司机，杭州的路政规划是很垃圾，没看错，是垃圾，所以总体结论是公交还行，主要是路政规划就是垃圾，包括这条主干道这么多人行道，并且两边都是老小区，老年人在上班高峰可能要买菜送娃或者其他事情，在通畅的情况下可能只需要六分钟的路程，有时候因为各种原因，半小时都开不完，扯开去一点，杭州的路，核心的高速说封就封，本来是高架可以直接通到城西，结果没造，到了路本已经很拥挤的时候开始来造隧道，各种破坏，隧道接高架的地方，无尽的加塞，对于我这样的小白司机来说真的是太恶心了，所以我一直想说的就是杭州这个地方房价领先基础设施十年，地铁，高架，高速通通不行，地面道路就更不行了。
总结下，其实杭州的真正的公交体验差，应该还是路造成的，对于前面的那两位妹子来说，有可能是她们来自于公交司机都是开的特别稳，并且路况也很好的地方，也或者是我被虐习惯了🤦‍♂️

-]]> - - 生活 - 公交 - - - 生活 - 开车 - 加塞 - 糟心事 - 规则 - 公交 - 路政规划 - 基础设施 - 杭州 - 高速 - - 闲话篇-路遇神逻辑骑车带娃爹 /2022/05/08/%E9%97%B2%E8%AF%9D%E7%AF%87-%E8%B7%AF%E9%81%87%E7%A5%9E%E9%80%BB%E8%BE%91%E9%AA%91%E8%BD%A6%E5%B8%A6%E5%A8%83%E7%88%B9/ @@ -7569,21 +7745,6 @@ user3: 生活 - - 阿里云 rds 主从延迟排查 - /2023/12/24/%E9%98%BF%E9%87%8C%E4%BA%91-rds-%E4%B8%BB%E4%BB%8E%E5%BB%B6%E8%BF%9F%E6%8E%92%E6%9F%A5/ - 昨天同学问我是不是数据库主从延迟有点高，可能有一分多钟，然后我就去看了rds 的监控，发现主实例上的监控显示的延迟才 1.2 秒，而且是最高 1.2 秒，感觉这样的话应该就没啥问题，然后同学跟我说他加了日志，大致的逻辑是主库数据落库以后就会发一条 mq 消息出来，然后消费者接收到以后回去从库查一下这个数据，结果发现延迟了 90 多秒才查到数据，这种情况比较可能的猜测是阿里云这个监控的逻辑可能是从库在获得第一条同步数据的时候，而不是最终同步完成，但是跟阿里云咨询了并不是，使用的就是 show slave status 结果里的 Seconds_Behind_Master 指标，那这第一种情况就否定掉了，这里其实是犯了个错误，应该去从库看这个延迟的，不过对于阿里云来说在 rds 监控是看不到从库的监控的，只能到 das，也就是阿里云的数据库自治服务可以看到，这里能看到从库的延迟监控，发现的确有这么高，这样就要考虑为啥会出现这种情况，阿里云同学反馈的是这段时间的 iops 很高，并且 cpu 也比较高，让我排查下 binlog，这里就碰到一个小问题，阿里云 rds 的线上实例我们没法在本地连接，并且密码也是在代码里加密过的，去服务器上连接一方面需要装 mysql 客户端，另一方面是怕拉取日志会有性能影响，幸好阿里云这点做的比较好，在 rds 的”备份恢复”–> “日志备份”菜单里可以找到binlog 文件， 在这里其实大致就发现了问题，因为出问题的时间段内升成 binlog 的量大大超过其他时间段，然后通过内网下载后就可以对 binlog 进行分析了，这里我们用到了 mysqlbinlog 工具，主要就是找具体是哪些写入导致这个问题，mysqlbinlog 可以在 mysql 官网下载 mysql 的压缩包，注意是压缩包，这样解压了直接用就好了，不用完整安装 mysql，一般我们需要对 binlog 进行 base64 的反编码，

-
./mysqlbinlog -vv --base64-output=decode-rows mysql-bin.xxxx | less
- -

这样查看里面的具体信息，发现是大量的 insert 数据，再经过排查发现同一个 binlog 文件近 500M 全是同一个表的数据插入，再根据表对应的业务查找发现是有个业务逻辑会在这个时间点全量删除后在生成插入数据，后续需要进行优化

-]]> - - Mysql - - - mysql - - 难得的大扫除 /2022/04/10/%E9%9A%BE%E5%BE%97%E7%9A%84%E5%A4%A7%E6%89%AB%E9%99%A4/ @@ -7598,127 +7759,18 @@ user3: - 聊一下 Spring 的 SmartLifecycle 使用 - /2024/07/07/%E8%81%8A%E4%B8%80%E4%B8%8B-Spring-%E7%9A%84-SmarLifecycle-%E4%BD%BF%E7%94%A8/ - 最近在学习过程中碰到一个比较有意思的Spring特性，就是 SmartLifecycle ，这个可以很轻松的融合进 Spring 生命周期

-
public interface SmartLifecycle extends Lifecycle, Phased {
    int DEFAULT_PHASE = Integer.MAX_VALUE;

    default boolean isAutoStartup() {
        return true;
    }

    default void stop(Runnable callback) {
        this.stop();
        callback.run();
    }

    default int getPhase() {
        return Integer.MAX_VALUE;
    }
}
-

接口继承了 org.springframework.context.Lifecycle 跟 org.springframework.context.Phased
其中默认实现了 isAutoStartup , 并且默认是返回 true，所以相对于 Lifecycle 能更智能些自动启动
然后 Lifecycle 这个接口就比较简单，只有几个接口

-
public interface Lifecycle {
    void start();

    void stop();

    boolean isRunning();
}
-

我们可以是实现下这个接口

-
@Component
public class MySmartLifecycle implements SmartLifecycle {

    private volatile boolean isRunning;

    @Override
    public void start() {
        System.out.println("my smart life cycle start");
        isRunning = true;
    }

    @Override
    public void stop() {
        System.out.println("my smart life cycle end");
        isRunning = false;
    }

    @Override
    public boolean isRunning() {
        return isRunning;
    }
}
-

这个 bean 会在 Spring 启动后被自动调用 start 方法

这个就是在不深入 bean 的生命周期，并且是在 bean 已经都初始化以后调用
同样会在 spring 容器关闭时调用 stop 方法

如果有此类的需求就可以扩展此接口实现
而这个其实是在 spring 生命周期中的 finishRefresh 方法中进行调用

-
protected void finishRefresh() {
    this.clearResourceCaches();
    this.initLifecycleProcessor();
    this.getLifecycleProcessor().onRefresh();
    this.publishEvent((ApplicationEvent)(new ContextRefreshedEvent(this)));
    if (!NativeDetector.inNativeImage()) {
        LiveBeansView.registerApplicationContext(this);
    }

}
-

调用了 LifecycleProcessor 的 onRefresh 方法

-
public void onRefresh() {
    this.startBeans(true);
    this.running = true;
}
-

这其中就会调用 start 方法

-
private void startBeans(boolean autoStartupOnly) {
    // 获取这些bean
    Map<String, Lifecycle> lifecycleBeans = this.getLifecycleBeans();
    Map<Integer, LifecycleGroup> phases = new TreeMap();
    lifecycleBeans.forEach((beanName, bean) -> {
        if (!autoStartupOnly || bean instanceof SmartLifecycle && ((SmartLifecycle)bean).isAutoStartup()) {
            int phase = this.getPhase(bean);
            ((LifecycleGroup)phases.computeIfAbsent(phase, (p) -> {
                return new LifecycleGroup(phase, this.timeoutPerShutdownPhase, lifecycleBeans, autoStartupOnly);
            })).add(beanName, bean);
        }

    });
    if (!phases.isEmpty()) {
        phases.values().forEach(LifecycleGroup::start);
    }

}
-

会先判断 isAutoStartup，然后将不同周期加入分组，然后再循环调用start

-]]> - - java - - - java - - - - 聊一聊 https 的逻辑 - /2024/03/10/%E8%81%8A%E4%B8%80%E8%81%8A-https-%E7%9A%84%E9%80%BB%E8%BE%91/ - https 和 tls

https 我原本的理解来自于几个概念

-

第一个是非对称加密

-

也就是公钥可以公开在网上，然后用公钥加密，由私有的私钥进行解密，然后就对 https 有了初步的偏颇的概念，就是浏览器用公钥进行加密，服务器端用私钥进行解密，这里再仔细想想会延伸出来几个问题，也是逐步完善我的后续理解

-

第二个就是非对称加密以后的对称加密

-

因为对加密的理解是超大的质数（这个还来源于大学的数据结构老师）相乘后会很难做质因数分解，比如最大的质数2^82,589,933 − 1，用十进制表示有24,862,048位，用现在计算机来算也是非常难的，那如果一直采用非对称加密，这个效率也会比较低，所以在网上查了一番之后知道了是先采用非对称加密，然后再用对称加密，因为非对称加密先有了安全保证，后续的消息就可以用对称加密的方式来进行传输安全了

-

第三个是最近一次看到的

-

也是一直缺乏思考的问题，因为 https 我们在使用的时候都会购买证书或者使用免费的证书，那这个证书和前面说的公私钥是什么关系，好像一直是悬空的，没想过这中间的相关性，证书究竟是怎么起作用的

-

下面是摘自 cloudflare 的 tls 握手流程，把证书的使用方式说的比较明白了

-

TLS 握手是由客户端和服务器交换的一系列数据报或消息。TLS 握手涉及多个步骤，因为客户端和服务器要交换完成握手和进行进一步对话所需的信息。

-

TLS 握手中的确切步骤将根据所使用的密钥交换算法的种类和双方支持的密码套件而有所不同。RSA 密钥交换算法虽然现在被认为不安全，但曾在 1.3 之前的 TLS 版本中使用。大致如下：

-
    -
  1. “客户端问候（client hello）” 消息： 客户端通过向服务器发送“问候”消息来开始握手。该消息将包含客户端支持的 TLS 版本，支持的密码套件，以及称为一串称为“客户端随机数（client random）”的随机字节。
    2. -
  2. “服务器问候（server hello）”消息： 作为对 client hello 消息的回复，服务器发送一条消息，内含服务器的 SSL 证书、服务器选择的密码套件，以及“服务器随机数（server random）”，即由服务器生成的另一串随机字节。
    3. -
  3. 身份验证： 客户端使用颁发该证书的证书颁发机构验证服务器的 SSL 证书。此举确认服务器是其声称的身份，且客户端正在与该域的实际所有者进行交互。
    4. -
  4. 预主密钥： 客户端再发送一串随机字节，即“预主密钥（premaster secret）”。预主密钥是使用公钥加密的，只能使用服务器的私钥解密。（客户端从服务器的 SSL 证书中获得公钥。）
    5. -
  5. 私钥被使用：服务器对预主密钥进行解密。
    6. -
  6. 生成会话密钥：客户端和服务器均使用客户端随机数、服务器随机数和预主密钥生成会话密钥。双方应得到相同的结果。
    7. -
  7. 客户端就绪：客户端发送一条“已完成”消息，该消息用会话密钥加密。
    8. -
  8. 服务器就绪：服务器发送一条“已完成”消息，该消息用会话密钥加密。
    9. -
  9. 实现安全对称加密：已完成握手，并使用会话密钥继续进行通信。
    10. -
-

为了学习这个过程，我们尝试用 Chrome 的自带抓包工具，以往这是可以通过在 Chrome 地址栏中输入 chrome://net-internals/#events 来打开，现在变成了两部分，chrome://net-export/ 这里可以开始抓包，然后会记录下一个抓包日志文件里，

-

-

然后再打开

-

https://netlog-viewer.appspot.com/#events 来查看具体的日志

-

这里我们以打开百度为例，即在打开 chrome://net-export/ 并启动抓包后，再在一个新 tab 打开 baidu.com，然后关闭

-

将日志文件在 https://netlog-viewer.appspot.com/#events 打开

-

-

这里可以看到

-

-

--> type 1 表示现在 tls 握手进行到哪一步了, 对应的值表示不同的阶段

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
代码(type)释义
0HelloRequest
1ClientHello
2ServerHello
11Certificate
12ServerKeyExchange
13CertificateRequest
14ServerHelloDone
15CertificateVerify
16ClientKeyExchange
20Finished
-

具体的对应关系是在这边

-

比如 11 之后表示我们从服务端收到了证书

-

-

然后就要去验证证书的可靠性

-

-

后面是验证后的结果

-

-

-

最后就是交换对称会话秘钥然后完成握手

-

+ 阿里云 rds 主从延迟排查 + /2023/12/24/%E9%98%BF%E9%87%8C%E4%BA%91-rds-%E4%B8%BB%E4%BB%8E%E5%BB%B6%E8%BF%9F%E6%8E%92%E6%9F%A5/ + 昨天同学问我是不是数据库主从延迟有点高，可能有一分多钟，然后我就去看了rds 的监控，发现主实例上的监控显示的延迟才 1.2 秒，而且是最高 1.2 秒，感觉这样的话应该就没啥问题，然后同学跟我说他加了日志，大致的逻辑是主库数据落库以后就会发一条 mq 消息出来，然后消费者接收到以后回去从库查一下这个数据，结果发现延迟了 90 多秒才查到数据，这种情况比较可能的猜测是阿里云这个监控的逻辑可能是从库在获得第一条同步数据的时候，而不是最终同步完成，但是跟阿里云咨询了并不是，使用的就是 show slave status 结果里的 Seconds_Behind_Master 指标，那这第一种情况就否定掉了，这里其实是犯了个错误，应该去从库看这个延迟的，不过对于阿里云来说在 rds 监控是看不到从库的监控的，只能到 das，也就是阿里云的数据库自治服务可以看到，这里能看到从库的延迟监控，发现的确有这么高，这样就要考虑为啥会出现这种情况，阿里云同学反馈的是这段时间的 iops 很高，并且 cpu 也比较高，让我排查下 binlog，这里就碰到一个小问题，阿里云 rds 的线上实例我们没法在本地连接，并且密码也是在代码里加密过的，去服务器上连接一方面需要装 mysql 客户端，另一方面是怕拉取日志会有性能影响，幸好阿里云这点做的比较好，在 rds 的”备份恢复”–> “日志备份”菜单里可以找到binlog 文件， 在这里其实大致就发现了问题，因为出问题的时间段内升成 binlog 的量大大超过其他时间段，然后通过内网下载后就可以对 binlog 进行分析了，这里我们用到了 mysqlbinlog 工具，主要就是找具体是哪些写入导致这个问题，mysqlbinlog 可以在 mysql 官网下载 mysql 的压缩包，注意是压缩包，这样解压了直接用就好了，不用完整安装 mysql，一般我们需要对 binlog 进行 base64 的反编码，

+
./mysqlbinlog -vv --base64-output=decode-rows mysql-bin.xxxx | less
+ +

这样查看里面的具体信息，发现是大量的 insert 数据，再经过排查发现同一个 binlog 文件近 500M 全是同一个表的数据插入，再根据表对应的业务查找发现是有个业务逻辑会在这个时间点全量删除后在生成插入数据，后续需要进行优化

]]> - 网络 + Mysql - http + mysql @@ -7732,6 +7784,26 @@ user3:

-m：禁止硬盘进入休眠。

-s：禁止系统进入睡眠状态，此参数仅在插上电源的时候才有效。

-t：指定命令有效的超时值，以秒为单位。
这样我们就能从rustdesk获得画面进行操作了，希望后面rustdesk能解决掉这个问题，因为这两种方式也不是很完善，如果只有纯远程的话就没办法了

+]]> + + 工具 + + + 工具 + + + + 远程桌面工具rustdesk的私有化部署 + /2024/05/12/%E8%BF%9C%E7%A8%8B%E6%A1%8C%E9%9D%A2%E5%B7%A5%E5%85%B7rustdesk%E7%9A%84%E7%A7%81%E6%9C%89%E5%8C%96%E9%83%A8%E7%BD%B2/ + 最近因为利用rustdesk远程操作诈骗太多，所以rustdesk官方直接关闭了国内的公有服务，相对其他比如teamviewer来说，rustdesk还是个比较不错的选择，性能考量也过得去，相对来说在双方都是mac的情况下，不付费的体验来说，比自带的vnc好了很多，vnc目测是直接全量传实时画面数据，带宽占用大，体验差。正巧最近有需求就使用了rustdesk所以记录下部署过程
我使用的较早先版本的部署方式，
首先需要一台有外网ip的服务器，并且需要使用到以下几个端口，如果使用云服务器，需要在控制台开启这些端口
核心端口:

+
TCP 21114-21119
UDP 21116
+

测试有没有开启可以使用这个工具网站 CanYouSeeMe.org ，或者用另一台机器telnet下
需要部署的服务有两个
hbbs - RustDesk ID/Rendezvous server id服务
hbbr - RustDesk Relay server 中继服务
这里使用docker来部署，比较简单，先拉取镜像，没错就一个

+
sudo docker image pull rustdesk/rustdesk-server
+

这边建议创建个rustdesk目录，进入目录后再启动下面的服务
先启动hbbs

+
sudo docker run --name hbbs -p 21115:21115 -p 21116:21116 -p 21116:21116/udp -p 21118:21118 -v `pwd`:/root -td --net=host rustdesk/rustdesk-server hbbs
+

使用 --net=host 是为了让server拿到连接进入的真实来源ip
然后再启动hbbr

+
sudo docker run --name hbbr -p 21117:21117 -p 21119:21119 -v `pwd`:/root -td --net=host rustdesk/rustdesk-server hbbr
+

接下来进到刚才建的目录，会看到有个一对公私钥，把公钥内容保存下来
然后就是客户端登录了，以mac为例

点击这边三点，再点击修改中继设置

在弹出窗口

ID服务器跟中继服务器框填入前面部署服务的服务器外网ip，在Key框里把刚才保存的公钥填进去
每个需要连接的客户端都需要这么设置，然后就可以使用自己的服务器作为ID跟中继服务器了，不会受官方关停国内服务影响

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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}标签: Java | Nicksxs's Blog

Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

Java 标签

2024
2023
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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

java 标签

2024
2023
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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}标签: Java | Nicksxs's Blog

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2022
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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}标签: Java | Nicksxs's Blog

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What hurts more, the pain of hard work or the pain of regret?

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2023
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Nicksxs's Blog

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2022
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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}标签: Java | Nicksxs's Blog

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

java 标签

2022
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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

Java 标签

2023
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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

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2021
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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

Java 标签

2023
2022
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Nicksxs's Blog

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2021
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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}标签: Java | Nicksxs's Blog

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Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

java 标签

2021
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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}标签: Java | Nicksxs's Blog

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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}标签: Linked List | Nicksxs's Blog

Nicksxs's Blog

What hurts more, the pain of hard work or the pain of regret?

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2021
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.site-nav .animated{animation:none}body.site-nav-on .site-nav{height:var(--scroll-height);visibility:unset}}.menu .menu-item{display:block;margin:0}.menu .menu-item a{padding:5px 20px;position:relative;text-align:left;transition-property:background-color}.menu .menu-item .badge{background:#ccc;border-radius:10px;color:var(--content-bg-color);float:right;padding:2px 5px;text-shadow:1px 1px 0 rgba(0,0,0,.1)}.main-menu .menu-item-active::after{background:#bbb;border-radius:50%;content:' ';height:6px;margin-top:-3px;position:absolute;right:15px;top:50%;width:6px}.sub-menu{margin:0;padding:6px 0}.sub-menu .menu-item{display:inline-block}.sub-menu .menu-item a{background:0 0;margin:5px 10px;padding:initial}.sub-menu .menu-item a:hover{background:0 0;color:#fc6423}.sub-menu .menu-item-active{border-bottom-color:#fc6423;color:#fc6423}.sub-menu .menu-item-active:hover{border-bottom-color:#fc6423}.sidebar{position:-webkit-sticky;position:sticky;top:12px}@media (max-width:991px){.column{width:auto}.site-nav-on .site-brand-container{box-shadow:0 0 16px rgba(0,0,0,.5)}.menu .menu-item.menu-item-search,.sidebar{display:none}}.sidebar-inner{background:var(--content-bg-color);border-radius:initial;box-shadow:initial;box-sizing:border-box;color:var(--text-color);margin-top:12px;max-height:calc(100vh - 24px);visibility:hidden}.site-state-item{padding:0 10px}.sidebar .sidebar-button{border-bottom:1px dotted #ccc;border-top:1px dotted #ccc}.sidebar .sidebar-button button{border:0;color:#fc6423;display:block;width:100%}.sidebar .sidebar-button button:hover{background:0 0;border:0;color:#e34603}.links-of-author{display:flex;flex-wrap:wrap;justify-content:center}.links-of-author-item{margin:5px 0 0;width:50%}.links-of-author-item a{box-sizing:border-box;max-width:100%;overflow:hidden;padding:0 5px;text-overflow:ellipsis;white-space:nowrap;border-bottom:0;border-radius:4px;display:block}.links-of-author-item a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}标签: linked list | Nicksxs's Blog

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