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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

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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

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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. 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. 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，

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    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

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    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. 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. 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. 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. 发现一个不错的openresty站点
    地址

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Nicksxs's Blog

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

openresty

发表于 更新于 分类于 阅读次数： 阅读次数： Disqus：

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

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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

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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. 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. 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，

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    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

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    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. 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. 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. 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. 发现一个不错的openresty站点
    地址

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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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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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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}}村上春树《1Q84》读后感 | Nicksxs's Blog

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

村上春树《1Q84》读后感

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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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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}}村上春树《1Q84》读后感 | Nicksxs's Blog

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

村上春树《1Q84》读后感

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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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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}}G1收集器概述 | Nicksxs's Blog

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G1收集器概述

发表于 分类于 ， ， ， 阅读次数： 阅读次数： Disqus：

G1: The Garbage-First Collector, 垃圾回收优先的垃圾回收器，目标是用户多核 cpu 和大内存的机器，最大的特点就是可预测的停顿时间，官方给出的介绍是提供一个用户在大的堆内存情况下一个低延迟表现的解决方案，通常是 6GB 及以上的堆大小，有低于 0.5 秒稳定的可预测的停顿时间。

这里主要介绍这个比较新的垃圾回收器，在 G1 之前的垃圾回收器都是基于如下图的内存结构分布，有新生代，老年代和永久代（jdk8 之前），然后G1 往前的那些垃圾回收器都有个分代，比如 serial，parallel 等，一般有个应用的组合，最初的 serial 和 serial old，因为新生代和老年代的收集方式不太一样，新生代主要是标记复制，所以有 eden 跟两个 survival区，老年代一般用标记整理方式，而 G1 对这个不太一样。

看一下 G1 的内存分布

可以看到这有很大的不同，G1 通过将内存分成大小相等的 region，每个region是存在于一个连续的虚拟内存范围，对于某个 region 来说其角色是类似于原来的收集器的Eden、Survivor、Old Generation，这个具体在代码层面

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// We encode the value of the heap region type so the generation can be
 // determined quickly. The tag is split into two parts:
 //
 //   major type (young, old, humongous, archive)           : top N-1 bits
 //   minor type (eden / survivor, starts / cont hum, etc.) : bottom 1 bit
 //
 // If there's need to increase the number of minor types in the
 // future, we'll have to increase the size of the latter and hence
 // decrease the size of the former.
 //
 // 00000 0 [ 0] Free
 //
 // 00001 0 [ 2] Young Mask
 // 00001 0 [ 2] Eden
 // 00001 1 [ 3] Survivor
 //
 // 00010 0 [ 4] Humongous Mask
 // 00100 0 [ 8] Pinned Mask
 // 00110 0 [12] Starts Humongous
 // 00110 1 [13] Continues Humongous
 //
 // 01000 0 [16] Old Mask
 //
 // 10000 0 [32] Archive Mask
 // 11100 0 [56] Open Archive
 // 11100 1 [57] Closed Archive
 //
 typedef enum {
   FreeTag               = 0,

   YoungMask             = 2,
   EdenTag               = YoungMask,
   SurvTag               = YoungMask + 1,

   HumongousMask         = 4,
   PinnedMask            = 8,
   StartsHumongousTag    = HumongousMask | PinnedMask,
   ContinuesHumongousTag = HumongousMask | PinnedMask + 1,

   OldMask               = 16,
   OldTag                = OldMask,

   // Archive regions are regions with immutable content (i.e. not reclaimed, and
   // not allocated into during regular operation). They differ in the kind of references
   // allowed for the contained objects:
   // - Closed archive regions form a separate self-contained (closed) object graph
   // within the set of all of these regions. No references outside of closed
   // archive regions are allowed.
   // - Open archive regions have no restrictions on the references of their objects.
   // Objects within these regions are allowed to have references to objects
   // contained in any other kind of regions.
   ArchiveMask           = 32,
   OpenArchiveTag        = ArchiveMask | PinnedMask | OldMask,
   ClosedArchiveTag      = ArchiveMask | PinnedMask | OldMask + 1
 } Tag;

hotspot/share/gc/g1/heapRegionType.hpp

当执行垃圾收集时，G1以类似于CMS收集器的方式运行。 G1执行并发全局标记阶段，以确定整个堆中对象的存活性。标记阶段完成后，G1知道哪些region是基本空的。它首先收集这些region，通常会产生大量的可用空间。这就是为什么这种垃圾收集方法称为“垃圾优先”的原因。顾名思义，G1将其收集和压缩活动集中在可能充满可回收对象（即垃圾）的堆区域。 G1使用暂停预测模型来满足用户定义的暂停时间目标，并根据指定的暂停时间目标选择要收集的区域数。

由G1标识为可回收的区域是使用撤离的方式(Evacuation)。 G1将对象从堆的一个或多个区域复制到堆上的单个区域，并在此过程中压缩并释放内存。撤离是在多处理器上并行执行的，以减少暂停时间并增加吞吐量。因此，对于每次垃圾收集，G1都在用户定义的暂停时间内连续工作以减少碎片。这是优于前面两种方法的。 CMS（并发标记扫描）垃圾收集器不进行压缩。 ParallelOld垃圾回收仅执行整个堆压缩，这导致相当长的暂停时间。

需要重点注意的是，G1不是实时收集器。它很有可能达到设定的暂停时间目标，但并非绝对确定。 G1根据先前收集的数据，估算在用户指定的目标时间内可以收集多少个区域。因此，收集器具有收集区域成本的合理准确的模型，并且收集器使用此模型来确定要收集哪些和多少个区域，同时保持在暂停时间目标之内。

注意：G1同时具有并发（与应用程序线程一起运行，例如优化，标记，清理）和并行（多线程，例如stw）阶段。Full GC仍然是单线程的，但是如果正确调优，您的应用程序应该可以避免Full GC。

在前面那篇中在代码层面简单的了解了这个可预测时间的过程，这也是 G1 的一大特点。

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G1收集器概述

发表于 分类于 ， ， ， 阅读次数： 阅读次数： Disqus：

G1: The Garbage-First Collector, 垃圾回收优先的垃圾回收器，目标是用户多核 cpu 和大内存的机器，最大的特点就是可预测的停顿时间，官方给出的介绍是提供一个用户在大的堆内存情况下一个低延迟表现的解决方案，通常是 6GB 及以上的堆大小，有低于 0.5 秒稳定的可预测的停顿时间。

这里主要介绍这个比较新的垃圾回收器，在 G1 之前的垃圾回收器都是基于如下图的内存结构分布，有新生代，老年代和永久代（jdk8 之前），然后G1 往前的那些垃圾回收器都有个分代，比如 serial，parallel 等，一般有个应用的组合，最初的 serial 和 serial old，因为新生代和老年代的收集方式不太一样，新生代主要是标记复制，所以有 eden 跟两个 survival区，老年代一般用标记整理方式，而 G1 对这个不太一样。

看一下 G1 的内存分布

可以看到这有很大的不同，G1 通过将内存分成大小相等的 region，每个region是存在于一个连续的虚拟内存范围，对于某个 region 来说其角色是类似于原来的收集器的Eden、Survivor、Old Generation，这个具体在代码层面

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// We encode the value of the heap region type so the generation can be
 // determined quickly. The tag is split into two parts:
 //
 //   major type (young, old, humongous, archive)           : top N-1 bits
 //   minor type (eden / survivor, starts / cont hum, etc.) : bottom 1 bit
 //
 // If there's need to increase the number of minor types in the
 // future, we'll have to increase the size of the latter and hence
 // decrease the size of the former.
 //
 // 00000 0 [ 0] Free
 //
 // 00001 0 [ 2] Young Mask
 // 00001 0 [ 2] Eden
 // 00001 1 [ 3] Survivor
 //
 // 00010 0 [ 4] Humongous Mask
 // 00100 0 [ 8] Pinned Mask
 // 00110 0 [12] Starts Humongous
 // 00110 1 [13] Continues Humongous
 //
 // 01000 0 [16] Old Mask
 //
 // 10000 0 [32] Archive Mask
 // 11100 0 [56] Open Archive
 // 11100 1 [57] Closed Archive
 //
 typedef enum {
   FreeTag               = 0,

   YoungMask             = 2,
   EdenTag               = YoungMask,
   SurvTag               = YoungMask + 1,

   HumongousMask         = 4,
   PinnedMask            = 8,
   StartsHumongousTag    = HumongousMask | PinnedMask,
   ContinuesHumongousTag = HumongousMask | PinnedMask + 1,

   OldMask               = 16,
   OldTag                = OldMask,

   // Archive regions are regions with immutable content (i.e. not reclaimed, and
   // not allocated into during regular operation). They differ in the kind of references
   // allowed for the contained objects:
   // - Closed archive regions form a separate self-contained (closed) object graph
   // within the set of all of these regions. No references outside of closed
   // archive regions are allowed.
   // - Open archive regions have no restrictions on the references of their objects.
   // Objects within these regions are allowed to have references to objects
   // contained in any other kind of regions.
   ArchiveMask           = 32,
   OpenArchiveTag        = ArchiveMask | PinnedMask | OldMask,
   ClosedArchiveTag      = ArchiveMask | PinnedMask | OldMask + 1
 } Tag;

hotspot/share/gc/g1/heapRegionType.hpp

当执行垃圾收集时，G1以类似于CMS收集器的方式运行。 G1执行并发全局标记阶段，以确定整个堆中对象的存活性。标记阶段完成后，G1知道哪些region是基本空的。它首先收集这些region，通常会产生大量的可用空间。这就是为什么这种垃圾收集方法称为“垃圾优先”的原因。顾名思义，G1将其收集和压缩活动集中在可能充满可回收对象（即垃圾）的堆区域。 G1使用暂停预测模型来满足用户定义的暂停时间目标，并根据指定的暂停时间目标选择要收集的区域数。

由G1标识为可回收的区域是使用撤离的方式(Evacuation)。 G1将对象从堆的一个或多个区域复制到堆上的单个区域，并在此过程中压缩并释放内存。撤离是在多处理器上并行执行的，以减少暂停时间并增加吞吐量。因此，对于每次垃圾收集，G1都在用户定义的暂停时间内连续工作以减少碎片。这是优于前面两种方法的。 CMS（并发标记扫描）垃圾收集器不进行压缩。 ParallelOld垃圾回收仅执行整个堆压缩，这导致相当长的暂停时间。

需要重点注意的是，G1不是实时收集器。它很有可能达到设定的暂停时间目标，但并非绝对确定。 G1根据先前收集的数据，估算在用户指定的目标时间内可以收集多少个区域。因此，收集器具有收集区域成本的合理准确的模型，并且收集器使用此模型来确定要收集哪些和多少个区域，同时保持在暂停时间目标之内。

注意：G1同时具有并发（与应用程序线程一起运行，例如优化，标记，清理）和并行（多线程，例如stw）阶段。Full GC仍然是单线程的，但是如果正确调优，您的应用程序应该可以避免Full GC。

在前面那篇中在代码层面简单的了解了这个可预测时间的过程，这也是 G1 的一大特点。

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Leetcode 25 Reverse Nodes in k-Group 题解分析

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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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Leetcode 2 Add Two Numbers 题解分析

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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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Leetcode 2 Add Two Numbers 题解分析

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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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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 155 最小栈(Min Stack) 题解分析

发表于 分类于 ， ， ， ， 阅读次数： 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();
        }
    }
0%
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Leetcode 155 最小栈(Min Stack) 题解分析

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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();
        }
    }
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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

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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

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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

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

关于读书打卡与分享

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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！

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

关于读书打卡与分享

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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！

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Leetcode 121 买卖股票的最佳时机(Best Time to Buy and Sell Stock) 题解分析

发表于 分类于 ， ， ， ， 阅读次数： 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

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Leetcode 121 买卖股票的最佳时机(Best Time to Buy and Sell Stock) 题解分析

发表于 分类于 ， ， ， ， 阅读次数： 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

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Leetcode 48 旋转图像(Rotate Image) 题解分析

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题目介绍

You are given an n x n 2D matrix representing an image, rotate the image by 90 degrees (clockwise).

You have to rotate the image in-place, which means you have to modify the input 2D matrix directly. DO NOT allocate another 2D matrix and do the rotation.

如图，这道题以前做过，其实一看有点蒙，好像规则很容易描述，但是代码很难写，因为要类似于贪吃蛇那样，后来想着应该会有一些特殊的技巧，比如翻转等

代码

直接上码

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public void rotate(int[][] matrix) {
        // 这里真的傻了，长宽应该是一致的，所以取一次就够了
        int lengthX = matrix[0].length;
        int lengthY = matrix.length;
        int temp;
        System.out.println(lengthY - (lengthY % 2) / 2);
        // 这里除错了，应该是减掉余数再除 2
//        for (int i = 0; i < lengthY - (lengthY % 2) / 2; i++) {
        /**
         * 1 2 3             7 8 9
         * 4 5 6     =>      4 5 6     先沿着 4 5 6 上下交换
         * 7 8 9             1 2 3
         */
        for (int i = 0; i < (lengthY - (lengthY % 2)) / 2; i++) {
            for (int j = 0; j < lengthX; j++) {
                temp = matrix[i][j];
                matrix[i][j] = matrix[lengthY-i-1][j];
                matrix[lengthY-i-1][j] = temp;
            }
        }

        /**
         * 7 8 9               7 4 1
         * 4 5 6     =>        8 5 2   这里再沿着 7 5 3 这条对角线交换
         * 1 2 3               9 6 3
         */
        for (int i = 0; i < lengthX; i++) {
            for (int j = 0; j <= i; j++) {
                if (i == j) {
                    continue;
                }
                temp = matrix[i][j];
                matrix[i][j] = matrix[j][i];
                matrix[j][i] = temp;
            }
        }
    }

还没到可以直接归纳题目类型的水平，主要是几年前做过，可能有那么点模糊的记忆，当然应该也有直接转的方法

0%
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Leetcode 48 旋转图像(Rotate Image) 题解分析

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题目介绍

You are given an n x n 2D matrix representing an image, rotate the image by 90 degrees (clockwise).

You have to rotate the image in-place, which means you have to modify the input 2D matrix directly. DO NOT allocate another 2D matrix and do the rotation.

如图，这道题以前做过，其实一看有点蒙，好像规则很容易描述，但是代码很难写，因为要类似于贪吃蛇那样，后来想着应该会有一些特殊的技巧，比如翻转等

代码

直接上码

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public void rotate(int[][] matrix) {
        // 这里真的傻了，长宽应该是一致的，所以取一次就够了
        int lengthX = matrix[0].length;
        int lengthY = matrix.length;
        int temp;
        System.out.println(lengthY - (lengthY % 2) / 2);
        // 这里除错了，应该是减掉余数再除 2
//        for (int i = 0; i < lengthY - (lengthY % 2) / 2; i++) {
        /**
         * 1 2 3             7 8 9
         * 4 5 6     =>      4 5 6     先沿着 4 5 6 上下交换
         * 7 8 9             1 2 3
         */
        for (int i = 0; i < (lengthY - (lengthY % 2)) / 2; i++) {
            for (int j = 0; j < lengthX; j++) {
                temp = matrix[i][j];
                matrix[i][j] = matrix[lengthY-i-1][j];
                matrix[lengthY-i-1][j] = temp;
            }
        }

        /**
         * 7 8 9               7 4 1
         * 4 5 6     =>        8 5 2   这里再沿着 7 5 3 这条对角线交换
         * 1 2 3               9 6 3
         */
        for (int i = 0; i < lengthX; i++) {
            for (int j = 0; j <= i; j++) {
                if (i == j) {
                    continue;
                }
                temp = matrix[i][j];
                matrix[i][j] = matrix[j][i];
                matrix[j][i] = temp;
            }
        }
    }

还没到可以直接归纳题目类型的水平，主要是几年前做过，可能有那么点模糊的记忆，当然应该也有直接转的方法

0%
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Leetcode 236 二叉树的最近公共祖先(Lowest Common Ancestor of a Binary Tree) 题解分析

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题目介绍

Given a binary tree, find the lowest common ancestor (LCA) of two given nodes in the tree.

According to the definition of LCA on Wikipedia: “The lowest common ancestor is defined between two nodes p and q as the lowest node in T that has both p and q as descendants (where we allow a node to be a descendant of itself).”

给定一个二叉树, 找到该树中两个指定节点的最近公共祖先。

百度百科中最近公共祖先的定义为：“对于有根树 T 的两个节点 p、q，最近公共祖先表示为一个节点 x，满足 x 是 p、q 的祖先且 x 的深度尽可能大（一个节点也可以是它自己的祖先）。”

代码

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public TreeNode lowestCommonAncestor(TreeNode root, TreeNode p, TreeNode q) {
        // 如果当前节点就是 p 或者是 q 的时候，就直接返回了
        // 当没找到，即 root == null 的时候也会返回 null，这是个重要的点
        if (root == null || root == p || root == q) return root;
        // 在左子树中找 p 和 q
        TreeNode left = lowestCommonAncestor(root.left, p, q);
        // 在右子树中找 p 和 q
        TreeNode right = lowestCommonAncestor(root.right, p, q);
        // 当左边是 null 就直接返回右子树，但是这里不表示右边不是 null，所以这个顺序是不影响的
        // 考虑一种情况，如果一个节点的左右子树都是 null，那么其实对于这个节点来说首先两个子树分别调用
        // lowestCommonAncestor会在开头就返回 null，那么就是上面 left 跟 right 都是 null，然后走下面的判断的时候
        // 其实第一个 if 就返回了 null，如此递归返回就能达到当子树中没有找到 p 或者 q 的时候只返回 null
        if (left == null) {
            return right;
        } else if (right == null) {
            return left;
        } else {
            return root;
        }
//        if (right == null) {
//            return left;
//        } else if (left == null) {
//            return right;
//        } else {
//            return root;
//        }
//        return left == null ? right : right == null ? left : root;
    }
0%
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Leetcode 236 二叉树的最近公共祖先(Lowest Common Ancestor of a Binary Tree) 题解分析

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题目介绍

Given a binary tree, find the lowest common ancestor (LCA) of two given nodes in the tree.

According to the definition of LCA on Wikipedia: “The lowest common ancestor is defined between two nodes p and q as the lowest node in T that has both p and q as descendants (where we allow a node to be a descendant of itself).”

给定一个二叉树, 找到该树中两个指定节点的最近公共祖先。

百度百科中最近公共祖先的定义为：“对于有根树 T 的两个节点 p、q，最近公共祖先表示为一个节点 x，满足 x 是 p、q 的祖先且 x 的深度尽可能大（一个节点也可以是它自己的祖先）。”

代码

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public TreeNode lowestCommonAncestor(TreeNode root, TreeNode p, TreeNode q) {
        // 如果当前节点就是 p 或者是 q 的时候，就直接返回了
        // 当没找到，即 root == null 的时候也会返回 null，这是个重要的点
        if (root == null || root == p || root == q) return root;
        // 在左子树中找 p 和 q
        TreeNode left = lowestCommonAncestor(root.left, p, q);
        // 在右子树中找 p 和 q
        TreeNode right = lowestCommonAncestor(root.right, p, q);
        // 当左边是 null 就直接返回右子树，但是这里不表示右边不是 null，所以这个顺序是不影响的
        // 考虑一种情况，如果一个节点的左右子树都是 null，那么其实对于这个节点来说首先两个子树分别调用
        // lowestCommonAncestor会在开头就返回 null，那么就是上面 left 跟 right 都是 null，然后走下面的判断的时候
        // 其实第一个 if 就返回了 null，如此递归返回就能达到当子树中没有找到 p 或者 q 的时候只返回 null
        if (left == null) {
            return right;
        } else if (right == null) {
            return left;
        } else {
            return root;
        }
//        if (right == null) {
//            return left;
//        } else if (left == null) {
//            return right;
//        } else {
//            return root;
//        }
//        return left == null ? right : right == null ? left : root;
    }
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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;
0%
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What hurts more, the pain of hard work or the pain of regret?

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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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}}2021 年中总结 | Nicksxs's Blog

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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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}}2021 年中总结 | Nicksxs's Blog

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 021 合并两个有序链表 ( Merge Two Sorted Lists ) 题解分析

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题目介绍

Merge two sorted linked lists and return it as a sorted list. The list should be made by splicing together the nodes of the first two lists.

将两个升序链表合并为一个新的 升序 链表并返回。新链表是通过拼接给定的两个链表的所有节点组成的。

示例 1

输入：l1 = [1,2,4], l2 = [1,3,4]
输出：[1,1,2,3,4,4]

示例 2

输入: l1 = [], l2 = []
输出: []

示例 3

输入: l1 = [], l2 = [0]
输出: [0]

简要分析

这题是 Easy 的，看着也挺简单，两个链表进行合并，就是比较下大小，可能将就点的话最好就在两个链表中原地合并

题解代码

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public ListNode mergeTwoLists(ListNode l1, ListNode l2) {
        // 下面两个if判断了入参的边界，如果其一为null，直接返回另一个就可以了
        if (l1 == null) {
            return l2;
        }
        if (l2 == null) {
            return l1;
        }
        // new 一个合并后的头结点
        ListNode merged = new ListNode();
        // 这个是当前节点
        ListNode current = merged;
        // 一开始给这个while加了l1和l2不全为null的条件，后面想了下不需要
        // 因为内部前两个if就是跳出条件
        while (true) {
            if (l1 == null) {
                // 这里其实跟开头类似，只不过这里需要将l2剩余部分接到merged链表后面
                // 所以不能是直接current = l2，这样就是把后面的直接丢了
                current.val = l2.val;
                current.next = l2.next;
                break;
            }
            if (l2 == null) {
                current.val = l1.val;
                current.next = l1.next;
                break;
            }
            // 这里是两个链表都不为空的时候，就比较下大小
            if (l1.val < l2.val) {
                current.val = l1.val;
                l1 = l1.next;
            } else {
                current.val = l2.val;
                l2 = l2.next;
            }
            // 这里是new个新的，其实也可以放在循环头上
            current.next = new ListNode();
            current = current.next;
        }
        current = null;
        // 返回这个头结点
        return merged;
    }

结果

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Leetcode 021 合并两个有序链表 ( Merge Two Sorted Lists ) 题解分析

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题目介绍

Merge two sorted linked lists and return it as a sorted list. The list should be made by splicing together the nodes of the first two lists.

将两个升序链表合并为一个新的 升序 链表并返回。新链表是通过拼接给定的两个链表的所有节点组成的。

示例 1

输入：l1 = [1,2,4], l2 = [1,3,4]
输出：[1,1,2,3,4,4]

示例 2

输入: l1 = [], l2 = []
输出: []

示例 3

输入: l1 = [], l2 = [0]
输出: [0]

简要分析

这题是 Easy 的，看着也挺简单，两个链表进行合并，就是比较下大小，可能将就点的话最好就在两个链表中原地合并

题解代码

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public ListNode mergeTwoLists(ListNode l1, ListNode l2) {
        // 下面两个if判断了入参的边界，如果其一为null，直接返回另一个就可以了
        if (l1 == null) {
            return l2;
        }
        if (l2 == null) {
            return l1;
        }
        // new 一个合并后的头结点
        ListNode merged = new ListNode();
        // 这个是当前节点
        ListNode current = merged;
        // 一开始给这个while加了l1和l2不全为null的条件，后面想了下不需要
        // 因为内部前两个if就是跳出条件
        while (true) {
            if (l1 == null) {
                // 这里其实跟开头类似，只不过这里需要将l2剩余部分接到merged链表后面
                // 所以不能是直接current = l2，这样就是把后面的直接丢了
                current.val = l2.val;
                current.next = l2.next;
                break;
            }
            if (l2 == null) {
                current.val = l1.val;
                current.next = l1.next;
                break;
            }
            // 这里是两个链表都不为空的时候，就比较下大小
            if (l1.val < l2.val) {
                current.val = l1.val;
                l1 = l1.next;
            } else {
                current.val = l2.val;
                l2 = l2.next;
            }
            // 这里是new个新的，其实也可以放在循环头上
            current.next = new ListNode();
            current = current.next;
        }
        current = null;
        // 返回这个头结点
        return merged;
    }

结果

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

发表于 分类于 ， 阅读次数： 阅读次数： Disqus：

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

发表于 分类于 ， 阅读次数： 阅读次数： Disqus：

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 349 两个数组的交集 ( Intersection of Two Arrays *Easy* ) 题解分析

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题目介绍

给定两个数组 nums1 和 nums2 ，返回 它们的交集 。输出结果中的每个元素一定是 唯一 的。我们可以 不考虑输出结果的顺序 。

 

示例

示例 1：

输入：nums1 = [1,2,2,1], nums2 = [2,2]
输出：[2]

示例 2：

输入：nums1 = [4,9,5], nums2 = [9,4,9,8,4]
输出：[9,4]
解释：[4,9] 也是可通过的  

提示：

  • 1 <= nums1.length, nums2.length <= 1000
  • 0 <= nums1[i], nums2[i] <= 1000

分析与题解

两个数组的交集，最简单就是两层循环了把两个都存在的找出来，不过还有个要去重的问题，稍微思考下可以使用集合 set 来处理，先把一个数组全丢进去，再对比另外一个，如果出现在第一个集合里就丢进一个新的集合，最后转换成数组，这次我稍微取了个巧，因为看到了提示里的条件，两个数组中的元素都是不大于 1000 的，所以就搞了个 1000 长度的数组，如果在第一个数组出现，就在对应的下标设置成 1，如果在第二个数组也出现了就加 1，

code

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public int[] intersection(int[] nums1, int[] nums2) {
    // 大小是 1000 的数组，如果没有提示的条件就没法这么做
    // define a array which size is 1000, and can not be done like this without the condition in notice
        int[] inter = new int[1000];
        int[] outer;
        int m = 0;
        for (int j : nums1) {
            //  这里得是设置成 1，因为有可能 nums1 就出现了重复元素，如果直接++会造成结果重复
            // need to be set 1, cause element in nums1 can be duplicated
            inter[j] = 1;
        }
        for (int j : nums2) {
            if (inter[j] > 0) {
                // 这里可以直接+1，因为后面判断只需要判断大于 1
                // just plus 1, cause we can judge with condition that larger than  1
                inter[j] += 1;
            }
        }
        for (int i = 0; i < inter.length; i++) {
            // 统计下元素数量
            // count distinct elements
            if (inter[i] > 1) {
                m++;
            }
        }
        // initial a array of size m
        outer = new int[m];
        m = 0;
        for (int i = 0; i < inter.length; i++) {
            if (inter[i] > 1) {
                // add to outer
                outer[m++] = i;
            }
        }
        return outer;
    }
0%
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Leetcode 349 两个数组的交集 ( Intersection of Two Arrays *Easy* ) 题解分析

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题目介绍

给定两个数组 nums1 和 nums2 ，返回 它们的交集 。输出结果中的每个元素一定是 唯一 的。我们可以 不考虑输出结果的顺序 。

 

示例

示例 1：

输入：nums1 = [1,2,2,1], nums2 = [2,2]
输出：[2]

示例 2：

输入：nums1 = [4,9,5], nums2 = [9,4,9,8,4]
输出：[9,4]
解释：[4,9] 也是可通过的  

提示：

  • 1 <= nums1.length, nums2.length <= 1000
  • 0 <= nums1[i], nums2[i] <= 1000

分析与题解

两个数组的交集，最简单就是两层循环了把两个都存在的找出来，不过还有个要去重的问题，稍微思考下可以使用集合 set 来处理，先把一个数组全丢进去，再对比另外一个，如果出现在第一个集合里就丢进一个新的集合，最后转换成数组，这次我稍微取了个巧，因为看到了提示里的条件，两个数组中的元素都是不大于 1000 的，所以就搞了个 1000 长度的数组，如果在第一个数组出现，就在对应的下标设置成 1，如果在第二个数组也出现了就加 1，

code

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public int[] intersection(int[] nums1, int[] nums2) {
    // 大小是 1000 的数组，如果没有提示的条件就没法这么做
    // define a array which size is 1000, and can not be done like this without the condition in notice
        int[] inter = new int[1000];
        int[] outer;
        int m = 0;
        for (int j : nums1) {
            //  这里得是设置成 1，因为有可能 nums1 就出现了重复元素，如果直接++会造成结果重复
            // need to be set 1, cause element in nums1 can be duplicated
            inter[j] = 1;
        }
        for (int j : nums2) {
            if (inter[j] > 0) {
                // 这里可以直接+1，因为后面判断只需要判断大于 1
                // just plus 1, cause we can judge with condition that larger than  1
                inter[j] += 1;
            }
        }
        for (int i = 0; i < inter.length; i++) {
            // 统计下元素数量
            // count distinct elements
            if (inter[i] > 1) {
                m++;
            }
        }
        // initial a array of size m
        outer = new int[m];
        m = 0;
        for (int i = 0; i < inter.length; i++) {
            if (inter[i] > 1) {
                // add to outer
                outer[m++] = i;
            }
        }
        return outer;
    }
0%
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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

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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

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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 4 寻找两个正序数组的中位数 ( Median of Two Sorted Arrays *Hard* ) 题解分析

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给定两个大小分别为 `m` 和 `n` 的正序（从小到大）数组 `nums1` 和 `nums2`。请你找出并返回这两个正序数组的 **中位数** 。

题目介绍

给定两个大小分别为 m 和 n 的正序（从小到大）数组 nums1 和 nums2。请你找出并返回这两个正序数组的 中位数 。

算法的时间复杂度应该为 O(log (m+n)) 。

示例 1：

输入：nums1 = [1,3], nums2 = [2]
输出：2.00000
解释：合并数组 = [1,2,3] ，中位数 2

示例 2：

输入：nums1 = [1,2], nums2 = [3,4]
输出：2.50000
解释：合并数组 = [1,2,3,4] ，中位数 (2 + 3) / 2 = 2.5

分析与题解

这个题也是我随机出来的，之前都是随机到 easy 的，而且是序号这么靠前的，然后翻一下，之前应该是用 C++做过的，具体的方法其实可以从他的算法时间复杂度要求看出来，大概率是要二分法这种，后面就结合代码来讲了

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public double findMedianSortedArrays(int[] nums1, int[] nums2) {
        int n1 = nums1.length;
        int n2 = nums2.length;
        if (n1 > n2) {
            return findMedianSortedArrays(nums2, nums1);
        }

        // 找到两个数组的中点下标
        int k = (n1 + n2 + 1 ) / 2;
        // 使用一个类似于二分法的查找方法
        // 起始值就是 num1 的头跟尾
        int left = 0;
        int right = n1;
        while (left < right) {
            // m1 表示我取的是 nums1 的中点，即二分法的方式
            int m1 = left + (right - left) / 2;
            // *** 这里是重点，因为这个问题也可以转换成找成 n1 + n2 那么多个数中的前 (n1 + n2 + 1) / 2 个
            // *** 因为两个数组都是排好序的，那么我从 num1 中取了 m1 个，从 num2 中就是去 k - m1 个
            // *** 但是不知道取出来大小是否正好是整体排序的第 (n1 + n2 + 1) / 2 个，所以需要二分法上下对比
            int m2 = k - m1;
            // 如果 nums1[m1] 小，那我在第一个数组 nums1 的二分查找就要把左端点改成前一次的中点 + 1 (不然就进死循环了
            if (nums1[m1] < nums2[m2 - 1]) {
                left = m1 + 1;
            } else {
                right = m1;
            }
        }

        // 因为对比后其实我们只是拿到了一个位置，具体哪个是第 k 个就需要继续判断
        int m1 = left;
        int m2 = k - left;
        // 如果 m1 或者 m2 有小于等于 0 的，那这个值可以先抛弃
        // m1 如果等于 0，就是 num1[0] 都比 nums2 中所有值都要大
        // m2 等于 0 的话 刚好相反
        // 可以这么推断，当其中一个是 0 的时候那么另一个 mx 值肯定是> 0 的，那么就是取的对应的这个下标的值
        int c1 = Math.max( m1 <= 0 ? Integer.MIN_VALUE : nums1[m1 - 1] , m2 <= 0 ?  Integer.MIN_VALUE : nums2[m2 - 1]);
        // 如果两个数组的元素数量和是奇数，那就直接可以返回了，因为 m1 + m2 就是 k, 如果是一个数组，那这个元素其实就是 nums[k - 1]
        // 如果 m1 或者 m2 是 0，那另一个就是 k，取 mx - 1的下标就等于是 k - 1
        // 如果都不是 0，那就是取的了 nums1[m1 - 1] 与 nums2[m2 - 1]中的较大者，如果取得是后者，那么也就是 m1 + m2 - 1 的下标就是 k - 1
        if ((n1 + n2) % 2 == 1) {
            return c1;
        }
        // 如果是偶数个，那还要取两个数组后面的较小者，然后求平均值
        int c2 = Math.min(m1 >= n1 ? Integer.MAX_VALUE : nums1[m1], m2 >= n2 ? Integer.MAX_VALUE : nums2[m2]);
        return (c1 + c2) / 2.0;
    }

前面考虑的方法还是比较繁琐，考虑了两个数组的各种交叉情况，后面这个参考了一些网上的解法，代码比较简洁，但是可能不容易一下子就搞明白，所以配合了比较多的注释。

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

Leetcode 4 寻找两个正序数组的中位数 ( Median of Two Sorted Arrays *Hard* ) 题解分析

发表于 分类于 ， 阅读次数： 阅读次数： Disqus：
给定两个大小分别为 `m` 和 `n` 的正序（从小到大）数组 `nums1` 和 `nums2`。请你找出并返回这两个正序数组的 **中位数** 。

题目介绍

给定两个大小分别为 m 和 n 的正序（从小到大）数组 nums1 和 nums2。请你找出并返回这两个正序数组的 中位数 。

算法的时间复杂度应该为 O(log (m+n)) 。

示例 1：

输入：nums1 = [1,3], nums2 = [2]
输出：2.00000
解释：合并数组 = [1,2,3] ，中位数 2

示例 2：

输入：nums1 = [1,2], nums2 = [3,4]
输出：2.50000
解释：合并数组 = [1,2,3,4] ，中位数 (2 + 3) / 2 = 2.5

分析与题解

这个题也是我随机出来的，之前都是随机到 easy 的，而且是序号这么靠前的，然后翻一下，之前应该是用 C++做过的，具体的方法其实可以从他的算法时间复杂度要求看出来，大概率是要二分法这种，后面就结合代码来讲了

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public double findMedianSortedArrays(int[] nums1, int[] nums2) {
        int n1 = nums1.length;
        int n2 = nums2.length;
        if (n1 > n2) {
            return findMedianSortedArrays(nums2, nums1);
        }

        // 找到两个数组的中点下标
        int k = (n1 + n2 + 1 ) / 2;
        // 使用一个类似于二分法的查找方法
        // 起始值就是 num1 的头跟尾
        int left = 0;
        int right = n1;
        while (left < right) {
            // m1 表示我取的是 nums1 的中点，即二分法的方式
            int m1 = left + (right - left) / 2;
            // *** 这里是重点，因为这个问题也可以转换成找成 n1 + n2 那么多个数中的前 (n1 + n2 + 1) / 2 个
            // *** 因为两个数组都是排好序的，那么我从 num1 中取了 m1 个，从 num2 中就是去 k - m1 个
            // *** 但是不知道取出来大小是否正好是整体排序的第 (n1 + n2 + 1) / 2 个，所以需要二分法上下对比
            int m2 = k - m1;
            // 如果 nums1[m1] 小，那我在第一个数组 nums1 的二分查找就要把左端点改成前一次的中点 + 1 (不然就进死循环了
            if (nums1[m1] < nums2[m2 - 1]) {
                left = m1 + 1;
            } else {
                right = m1;
            }
        }

        // 因为对比后其实我们只是拿到了一个位置，具体哪个是第 k 个就需要继续判断
        int m1 = left;
        int m2 = k - left;
        // 如果 m1 或者 m2 有小于等于 0 的，那这个值可以先抛弃
        // m1 如果等于 0，就是 num1[0] 都比 nums2 中所有值都要大
        // m2 等于 0 的话 刚好相反
        // 可以这么推断，当其中一个是 0 的时候那么另一个 mx 值肯定是> 0 的，那么就是取的对应的这个下标的值
        int c1 = Math.max( m1 <= 0 ? Integer.MIN_VALUE : nums1[m1 - 1] , m2 <= 0 ?  Integer.MIN_VALUE : nums2[m2 - 1]);
        // 如果两个数组的元素数量和是奇数，那就直接可以返回了，因为 m1 + m2 就是 k, 如果是一个数组，那这个元素其实就是 nums[k - 1]
        // 如果 m1 或者 m2 是 0，那另一个就是 k，取 mx - 1的下标就等于是 k - 1
        // 如果都不是 0，那就是取的了 nums1[m1 - 1] 与 nums2[m2 - 1]中的较大者，如果取得是后者，那么也就是 m1 + m2 - 1 的下标就是 k - 1
        if ((n1 + n2) % 2 == 1) {
            return c1;
        }
        // 如果是偶数个，那还要取两个数组后面的较小者，然后求平均值
        int c2 = Math.min(m1 >= n1 ? Integer.MAX_VALUE : nums1[m1], m2 >= n2 ? Integer.MAX_VALUE : nums2[m2]);
        return (c1 + c2) / 2.0;
    }

前面考虑的方法还是比较繁琐，考虑了两个数组的各种交叉情况，后面这个参考了一些网上的解法，代码比较简洁，但是可能不容易一下子就搞明白，所以配合了比较多的注释。

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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 698 划分为k个相等的子集 ( Partition to K Equal Sum Subsets *Medium* ) 题解分析

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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

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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

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Leetcode 698 划分为k个相等的子集 ( Partition to K Equal Sum Subsets *Medium* ) 题解分析

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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

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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

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

《长安的荔枝》读后感

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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。

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

《长安的荔枝》读后感

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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。

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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

发表于 分类于 ， 阅读次数： 阅读次数： Disqus：

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

发表于 分类于 ， 阅读次数： 阅读次数： Disqus：

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

0%
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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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Leetcode 885 螺旋矩阵 III ( Spiral Matrix III *Medium* ) 题解分析

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题目介绍

You start at the cell (rStart, cStart) of an rows x cols grid facing east. The northwest corner is at the first row and column in the grid, and the southeast corner is at the last row and column.

You will walk in a clockwise spiral shape to visit every position in this grid. Whenever you move outside the grid’s boundary, we continue our walk outside the grid (but may return to the grid boundary later.). Eventually, we reach all rows * cols spaces of the grid.

Return an array of coordinates representing the positions of the grid in the order you visited them.

Example 1:

Input: rows = 1, cols = 4, rStart = 0, cStart = 0
Output: [[0,0],[0,1],[0,2],[0,3]]

Example 2:

Input: rows = 5, cols = 6, rStart = 1, cStart = 4
Output: [[1,4],[1,5],[2,5],[2,4],[2,3],[1,3],[0,3],[0,4],[0,5],[3,5],[3,4],[3,3],[3,2],[2,2],[1,2],[0,2],[4,5],[4,4],[4,3],[4,2],[4,1],[3,1],[2,1],[1,1],[0,1],[4,0],[3,0],[2,0],[1,0],[0,0]]

Constraints:

  • 1 <= rows, cols <= 100
  • 0 <= rStart < rows
  • 0 <= cStart < cols

简析

这个题主要是要相同螺旋矩阵的转变方向的边界判断，已经相同步长会行进两次这个规律，写代码倒不复杂

代码

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public int[][] spiralMatrixIII(int rows, int cols, int rStart, int cStart) {
        int size = rows * cols;
        int x = rStart, y = cStart;
        // 返回的二维矩阵
        int[][] matrix = new int[size][2];
        // 传入的参数就是入口第一个
        matrix[0][0] = rStart;
        matrix[0][1] = cStart;
        // 作为数量
        int z = 1;
        // 步进，1，1，2，2，3，3，4 ... 螺旋矩阵的增长
        int a = 1;
        // 方向 1 表示右，2 表示下，3 表示左，4 表示上
        int dir = 1;
        while (z < size) {
            for (int i = 0; i < 2; i++) {
                for (int j= 0; j < a; j++) {
                    // 处理方向
                    if (dir % 4 == 1) {
                        y++;
                    } else if (dir % 4 == 2) {
                        x++;
                    } else if (dir % 4 == 3) {
                        y--;
                    } else {
                        x--;
                    }
                    // 如果在实际矩阵内
                    if (x < rows && y < cols && x >= 0 && y >= 0) {
                        matrix[z][0] = x;
                        matrix[z][1] = y;
                        z++;
                    }
                }
                // 转变方向
                dir++;
            }
            // 步进++
            a++;
        }
        return matrix;
    }

结果

0%
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Leetcode 885 螺旋矩阵 III ( Spiral Matrix III *Medium* ) 题解分析

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题目介绍

You start at the cell (rStart, cStart) of an rows x cols grid facing east. The northwest corner is at the first row and column in the grid, and the southeast corner is at the last row and column.

You will walk in a clockwise spiral shape to visit every position in this grid. Whenever you move outside the grid’s boundary, we continue our walk outside the grid (but may return to the grid boundary later.). Eventually, we reach all rows * cols spaces of the grid.

Return an array of coordinates representing the positions of the grid in the order you visited them.

Example 1:

Input: rows = 1, cols = 4, rStart = 0, cStart = 0
Output: [[0,0],[0,1],[0,2],[0,3]]

Example 2:

Input: rows = 5, cols = 6, rStart = 1, cStart = 4
Output: [[1,4],[1,5],[2,5],[2,4],[2,3],[1,3],[0,3],[0,4],[0,5],[3,5],[3,4],[3,3],[3,2],[2,2],[1,2],[0,2],[4,5],[4,4],[4,3],[4,2],[4,1],[3,1],[2,1],[1,1],[0,1],[4,0],[3,0],[2,0],[1,0],[0,0]]

Constraints:

  • 1 <= rows, cols <= 100
  • 0 <= rStart < rows
  • 0 <= cStart < cols

简析

这个题主要是要相同螺旋矩阵的转变方向的边界判断，已经相同步长会行进两次这个规律，写代码倒不复杂

代码

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public int[][] spiralMatrixIII(int rows, int cols, int rStart, int cStart) {
        int size = rows * cols;
        int x = rStart, y = cStart;
        // 返回的二维矩阵
        int[][] matrix = new int[size][2];
        // 传入的参数就是入口第一个
        matrix[0][0] = rStart;
        matrix[0][1] = cStart;
        // 作为数量
        int z = 1;
        // 步进，1，1，2，2，3，3，4 ... 螺旋矩阵的增长
        int a = 1;
        // 方向 1 表示右，2 表示下，3 表示左，4 表示上
        int dir = 1;
        while (z < size) {
            for (int i = 0; i < 2; i++) {
                for (int j= 0; j < a; j++) {
                    // 处理方向
                    if (dir % 4 == 1) {
                        y++;
                    } else if (dir % 4 == 2) {
                        x++;
                    } else if (dir % 4 == 3) {
                        y--;
                    } else {
                        x--;
                    }
                    // 如果在实际矩阵内
                    if (x < rows && y < cols && x >= 0 && y >= 0) {
                        matrix[z][0] = x;
                        matrix[z][1] = y;
                        z++;
                    }
                }
                // 转变方向
                dir++;
            }
            // 步进++
            a++;
        }
        return matrix;
    }

结果

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Leetcode 1862 向下取整数对和 ( Sum of Floored Pairs *Hard* ) 题解分析

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题目介绍

Given an integer array nums, return the sum of floor(nums[i] / nums[j]) for all pairs of indices 0 <= i, j < nums.length in the array. Since the answer may be too large, return it modulo 10^9 + 7.

The floor() function returns the integer part of the division.

对应中文
给你一个整数数组 nums ，请你返回所有下标对 0 <= i, j < nums.length 的 floor(nums[i] / nums[j]) 结果之和。由于答案可能会很大，请你返回答案对10^9 + 7 取余 的结果。

函数 floor() 返回输入数字的整数部分。

示例

Example 1:

Input: nums = [2,5,9]
Output: 10
Explanation:
floor(2 / 5) = floor(2 / 9) = floor(5 / 9) = 0
floor(2 / 2) = floor(5 / 5) = floor(9 / 9) = 1
floor(5 / 2) = 2
floor(9 / 2) = 4
floor(9 / 5) = 1
We calculate the floor of the division for every pair of indices in the array then sum them up.

Example 2:

Input: nums = [7,7,7,7,7,7,7]
Output: 49

Constraints:

  • 1 <= nums.length <= 10^5
  • 1 <= nums[i] <= 10^5

简析

这题不愧是 hard，要不是看了讨论区的一个大神的解答感觉从头做得想好久，
主要是两点，对于任何一个在里面的数，随便举个例子是 k，最简单的就是循环所有数对 k 除一下，
这样效率会很低，那么对于 k 有什么规律呢，就是对于所有小于 k 的数，往下取整都是 0，所以不用考虑，
对于所有大于 k 的数我们可以分成一个个的区间，[k,2k-1),[2k,3k-1),[3k,4k-1)……对于这些区间的
除了 k 往下取整，每个区间内的都是一样的，所以可以简化为对于任意一个 k，我只要知道与k 相同的有多少个，然后比 k 大的各个区间各有多少个数就可以了

代码

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static final int MAXE5 = 100_000;

static final int MODULUSE9 = 1_000_000_000 + 7;

public int sumOfFlooredPairs(int[] nums) {
    int[] counts = new int[MAXE5+1];
    for (int num : nums) {
        counts[num]++;
    }
    // 这里就是很巧妙的给后一个加上前一个的值，这样其实前后任意两者之差就是这中间的元素数量
    for (int i = 1; i <= MAXE5; i++) {
        counts[i] += counts[i - 1];
    }
    long total = 0;
    for (int i = 1; i <= MAXE5; i++) {
        long sum = 0;
        if (counts[i] == counts[i-1]) {
            continue;
        }
        for (int j = 1; i*j <= MAXE5; j++) {
            int min = i * j - 1;
            int upper = i * (j + 1) - 1;
            // 在每一个区间内的数量，
            sum += (counts[Math.min(upper, MAXE5)] - counts[min]) * (long)j;
        }
        // 左边乘数的数量，即 i 位置的元素数量
        total = (total + (sum % MODULUSE9  ) * (counts[i] - counts[i-1])) % MODULUSE9;
    }
    return (int)total;
}

贴出来大神的解析,解析

结果

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

Leetcode 1862 向下取整数对和 ( Sum of Floored Pairs *Hard* ) 题解分析

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题目介绍

Given an integer array nums, return the sum of floor(nums[i] / nums[j]) for all pairs of indices 0 <= i, j < nums.length in the array. Since the answer may be too large, return it modulo 10^9 + 7.

The floor() function returns the integer part of the division.

对应中文
给你一个整数数组 nums ，请你返回所有下标对 0 <= i, j < nums.length 的 floor(nums[i] / nums[j]) 结果之和。由于答案可能会很大，请你返回答案对10^9 + 7 取余 的结果。

函数 floor() 返回输入数字的整数部分。

示例

Example 1:

Input: nums = [2,5,9]
Output: 10
Explanation:
floor(2 / 5) = floor(2 / 9) = floor(5 / 9) = 0
floor(2 / 2) = floor(5 / 5) = floor(9 / 9) = 1
floor(5 / 2) = 2
floor(9 / 2) = 4
floor(9 / 5) = 1
We calculate the floor of the division for every pair of indices in the array then sum them up.

Example 2:

Input: nums = [7,7,7,7,7,7,7]
Output: 49

Constraints:

  • 1 <= nums.length <= 10^5
  • 1 <= nums[i] <= 10^5

简析

这题不愧是 hard，要不是看了讨论区的一个大神的解答感觉从头做得想好久，
主要是两点，对于任何一个在里面的数，随便举个例子是 k，最简单的就是循环所有数对 k 除一下，
这样效率会很低，那么对于 k 有什么规律呢，就是对于所有小于 k 的数，往下取整都是 0，所以不用考虑，
对于所有大于 k 的数我们可以分成一个个的区间，[k,2k-1),[2k,3k-1),[3k,4k-1)……对于这些区间的
除了 k 往下取整，每个区间内的都是一样的，所以可以简化为对于任意一个 k，我只要知道与k 相同的有多少个，然后比 k 大的各个区间各有多少个数就可以了

代码

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static final int MAXE5 = 100_000;

static final int MODULUSE9 = 1_000_000_000 + 7;

public int sumOfFlooredPairs(int[] nums) {
    int[] counts = new int[MAXE5+1];
    for (int num : nums) {
        counts[num]++;
    }
    // 这里就是很巧妙的给后一个加上前一个的值，这样其实前后任意两者之差就是这中间的元素数量
    for (int i = 1; i <= MAXE5; i++) {
        counts[i] += counts[i - 1];
    }
    long total = 0;
    for (int i = 1; i <= MAXE5; i++) {
        long sum = 0;
        if (counts[i] == counts[i-1]) {
            continue;
        }
        for (int j = 1; i*j <= MAXE5; j++) {
            int min = i * j - 1;
            int upper = i * (j + 1) - 1;
            // 在每一个区间内的数量，
            sum += (counts[Math.min(upper, MAXE5)] - counts[min]) * (long)j;
        }
        // 左边乘数的数量，即 i 位置的元素数量
        total = (total + (sum % MODULUSE9  ) * (counts[i] - counts[i-1])) % MODULUSE9;
    }
    return (int)total;
}

贴出来大神的解析,解析

结果

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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

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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

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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

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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

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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

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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

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Leetcode 25 Reverse Nodes in k-Group 题解分析-再解分析

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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

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cur.next = next.next;
next.next = pre.next;
pre.next = next;
next = cur.next;


在备注下代码

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Leetcode 25 Reverse Nodes in k-Group 题解分析-再解分析

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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

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cur.next = next.next;
next.next = pre.next;
pre.next = next;
next = cur.next;


在备注下代码

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Leetcode 572 Subtree of Another Tree 题解分析

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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

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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);
   }
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Leetcode 572 Subtree of Another Tree 题解分析

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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

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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);
   }
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Leetcode 31 Next Permutation 题解分析

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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

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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

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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

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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

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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

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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

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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);
    }
0%
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Leetcode 31 Next Permutation 题解分析

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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

1
2
3
4
5
6
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
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
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
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
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

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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);
    }
0%
\ No newline at end of file diff --git a/baidusitemap.xml b/baidusitemap.xml index e030ee5968..33b77f2751 100644 --- a/baidusitemap.xml +++ b/baidusitemap.xml @@ -557,11 +557,11 @@ 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/2022/02/13/Disruptor-%E7%B3%BB%E5%88%97%E4%B8%80/ 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 @@ -628,10 +628,6 @@ https://nicksxs.me/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/ 2022-06-11 - - https://nicksxs.me/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/ - 2022-06-11 - 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https://nicksxs.me/2020/08/02/%E8%81%8A%E8%81%8A-Java-%E8%87%AA%E5%B8%A6%E7%9A%84%E9%82%A3%E4%BA%9B%E9%80%86%E5%A4%A9%E5%B7%A5%E5%85%B7/ + https://nicksxs.me/2021/06/13/%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%E4%BA%8C/ 2022-06-11 @@ -673,11 +673,11 @@ 2022-06-11 - https://nicksxs.me/2021/12/26/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E7%9A%84%E7%AE%80%E5%8D%95%E5%8E%9F%E7%90%86%E5%88%9D%E7%AF%87/ + 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 - 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/2021/12/26/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E7%9A%84%E7%AE%80%E5%8D%95%E5%8E%9F%E7%90%86%E5%88%9D%E7%AF%87/ 2022-06-11 @@ -1109,11 +1109,11 @@ 2020-01-12 - https://nicksxs.me/2019/12/26/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D/ + https://nicksxs.me/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/ 2020-01-12 - https://nicksxs.me/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/ + https://nicksxs.me/2019/12/26/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D/ 2020-01-12 diff --git a/leancloud.memo b/leancloud.memo index 0b6c93fcbf..ed97eac80c 100644 --- a/leancloud.memo +++ b/leancloud.memo @@ -298,4 +298,5 @@ {"title":"git小技巧之查看git commit","url":"/2024/11/02/git小技巧之查看git-commit/"}, {"title":"git小技巧之git cherry-pick","url":"/2024/11/09/git小技巧之git-cherry-pick/"}, {"title":"java小技巧之获取调用来源","url":"/2024/11/16/java小技巧之获取调用来源/"}, +{"title":"grep小技巧之匹配到二进制文件","url":"/2024/11/24/grep小技巧之匹配到二进制文件/"}, ] \ No newline at end of file diff --git a/leancloud_counter_security_urls.json b/leancloud_counter_security_urls.json index a7342e59c7..67cb018362 100644 --- a/leancloud_counter_security_urls.json +++ b/leancloud_counter_security_urls.json @@ -1 +1 @@ -[{"title":"村上春树《1Q84》读后感","url":"/2019/12/18/1Q84读后感/"},{"title":"2020 年终总结","url":"/2021/03/31/2020-年终总结/"},{"title":"2019年终总结","url":"/2020/02/01/2019年终总结/"},{"title":"2021 年中总结","url":"/2021/07/18/2021-年中总结/"},{"title":"2021 年终总结","url":"/2022/01/22/2021-年终总结/"},{"title":"2020年中总结","url":"/2020/07/11/2020年中总结/"},{"title":"2022 年终总结","url":"/2023/01/15/2022-年终总结/"},{"title":"AQS篇二 之 Condition 浅析笔记","url":"/2021/02/21/AQS-之-Condition-浅析笔记/"},{"title":"34_Search_for_a_Range","url":"/2016/08/14/34-Search-for-a-Range/"},{"title":"add-two-number","url":"/2015/04/14/Add-Two-Number/"},{"title":"Apollo 如何获取当前环境","url":"/2022/09/04/Apollo-如何获取当前环境/"},{"title":"Apollo 客户端启动过程分析","url":"/2022/09/18/Apollo-客户端启动过程分析/"},{"title":"Apollo 的 value 注解是怎么自动更新的","url":"/2020/11/01/Apollo-的-value-注解是怎么自动更新的/"},{"title":"AbstractQueuedSynchronizer","url":"/2019/09/23/AbstractQueuedSynchronizer/"},{"title":"Clone Graph Part 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题解分析","url":"/2020/12/13/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/"},{"title":"Leetcode 1115 交替打印 FooBar ( Print FooBar Alternately *Medium* ) 题解分析","url":"/2022/05/01/Leetcode-1115-交替打印-FooBar-Print-FooBar-Alternately-Medium-题解分析/"},{"title":"Java 线程池系列-准备篇","url":"/2024/02/04/Java-线程池系列-准备篇/"},{"title":"Leetcode 124 二叉树中的最大路径和(Binary Tree Maximum Path Sum) 题解分析","url":"/2021/01/24/Leetcode-124-二叉树中的最大路径和-Binary-Tree-Maximum-Path-Sum-题解分析/"},{"title":"Leetcode 121 买卖股票的最佳时机(Best Time to Buy and Sell Stock) 题解分析","url":"/2021/03/14/Leetcode-121-买卖股票的最佳时机-Best-Time-to-Buy-and-Sell-Stock-题解分析/"},{"title":"Leetcode 1260 二维网格迁移 ( Shift 2D Grid *Easy* ) 题解分析","url":"/2022/07/22/Leetcode-1260-二维网格迁移-Shift-2D-Grid-Easy-题解分析/"},{"title":"Leetcode 16 最接近的三数之和 ( 3Sum Closest *Medium* ) 题解分析","url":"/2022/08/06/Leetcode-16-最接近的三数之和-3Sum-Closest-Medium-题解分析/"},{"title":"Leetcode 155 最小栈(Min Stack) 题解分析","url":"/2020/12/06/Leetcode-155-最小栈-Min-Stack-题解分析/"},{"title":"Leetcode 160 相交链表(intersection-of-two-linked-lists) 题解分析","url":"/2021/01/10/Leetcode-160-相交链表-intersection-of-two-linked-lists-题解分析/"},{"title":"Leetcode 1862 向下取整数对和 ( Sum of Floored Pairs *Hard* ) 题解分析","url":"/2022/09/11/Leetcode-1862-向下取整数对和-Sum-of-Floored-Pairs-Hard-题解分析/"},{"title":"Java 线程池系列-第二篇","url":"/2024/02/18/Java-线程池系列-第二篇/"},{"title":"Leetcode 20 有效的括号 ( Valid Parentheses *Easy* ) 题解分析","url":"/2022/07/02/Leetcode-20-有效的括号-Valid-Parentheses-Easy-题解分析/"},{"title":"Leetcode 2 Add Two Numbers 题解分析","url":"/2020/10/11/Leetcode-2-Add-Two-Numbers-题解分析/"},{"title":"Leetcode 236 二叉树的最近公共祖先(Lowest Common Ancestor of a Binary Tree) 题解分析","url":"/2021/05/23/Leetcode-236-二叉树的最近公共祖先-Lowest-Common-Ancestor-of-a-Binary-Tree-题解分析/"},{"title":"Leetcode 234 回文链表(Palindrome Linked List) 题解分析","url":"/2020/11/15/Leetcode-234-回文联表-Palindrome-Linked-List-题解分析/"},{"title":"Leetcode 278 第一个错误的版本 ( First Bad Version *Easy* ) 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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：

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🤦‍♂️

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;
  }

}

0%
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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：

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🤦‍♂️

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;
  }

}

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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：

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);
    }

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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！

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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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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~

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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：

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);
    }

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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！

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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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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~

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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：

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。

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;
    }
}
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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：

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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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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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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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

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;
    }
}

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。

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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

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;
    }
}

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。

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

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

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();
  }
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What hurts more, the pain of hard work or the pain of regret?

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
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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

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();
  }
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What hurts more, the pain of hard work or the pain of regret?

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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

发表于 分类于 ， ， ， 阅读次数： Disqus：

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。

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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：

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

发表于 分类于 ， ， ， 阅读次数： Disqus：

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。

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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：

因为最近想搭一个phabricator用来做看板和任务管理，一开始了解这个是Easy大大有在微博推荐过，后来苏洋也在群里和博客里说到了，看上去还不错的样子，因为主角是docker所以就不介绍太多，后面有机会写一下。

docker最开始是之前在某位大佬的博客看到的，看上去有点神奇，感觉是一种轻量级的虚拟机，但是能做的事情好像差不多，那时候是在Ubuntu系统的vps里起一个Ubuntu的docker，然后在里面装个nginx，配置端口映射就可以访问了，后来也草草写过一篇使用docker搭建mysql集群，但是最近看了下好像是因为装docker的大佬做了一些别名还是什么操作，导致里面用的操作都不具有普遍性，而且主要是把搭的过程写了下，属于囫囵吞枣，没理解docker是干啥的，为啥用docker，就是操作了下，这几天借着搭phabricator的过程，把一些原来不理解，或者原来理解错误的地方重新理一下。

之前写的 mysql 集群，一主二备，这种架构在很多小型应用里都是这么配置的，而且一般是直接在三台 vps 里启动三个 mysql 实例，但是如果换成 docker 会有什么好处呢，其实就是方便部署，比如其中一台备库挂了，我要加一台，或者说备库的 qps 太高了，需要再加一个，如果要在 vps 上搭建的话，首先要买一台机器，等初始化，然后在上面修改源，更新，装 mysql ，然后配置主从，可能还要处理防火墙等等，如果把这些打包成一个 docker 镜像，并且放在自己的 docker registry，那就直接run 一下就可以了；还有比如在公司要给一个新同学整一套开发测试环境，以 Java 开发为例，要装 git，maven，jdk，配置 maven settings 和各种 rc，整合在一个镜像里的话，就会很方便了；再比如微服务的水平扩展。

但是为啥 docker 会有这种优势，听起来好像虚拟机也可以干这个事，但是虚拟机动辄上 G，而且需要 VMware，virtual box 等支持，不适合在Linux服务器环境使用，而且占用资源也会非常大。说得这么好，那么 docker 是啥呢

docker 主要使用 Linux 中已经存在的两种技术的一个整合升级，一个是 namespace，一个是cgroups，相比于虚拟机需要完整虚拟出一个操作系统运行基础，docker 基于宿主机内核，通过 namespace 和 cgroups 分隔进程，理念就是提供一个隔离的最小化运行依赖，这样子相对于虚拟机就有了巨大的便利性，具体的 namespace 和 cgroups 就先不展开讲，可以参考耗子叔的文章

安装

那么我们先安装下 docker，参考官方的教程，安装，我的系统是 ubuntu 的，就贴了 ubuntu 的链接，用其他系统的可以找到对应的系统文档安装，安装完了的话看看 docker 的信息

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sudo docker info

输出以下信息

简单运行

然后再来运行个 hello world 呗，

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sudo docker run hello-world

输出了这些

看看这个运行命令是怎么用的，一般都会看到这样子的，sudo docker run -it ubuntu bash, 前面的 docker run 反正就是运行一个容器的意思，-it是啥呢，还有这个什么 ubuntu bash，来看看docker run`的命令帮助信息

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-i, --interactive                    Keep STDIN open even if not attached

就是要有输入，我们运行的时候能输入

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-t, --tty                            Allocate a pseudo-TTY

要有个虚拟终端，

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Usage:	docker run [OPTIONS] IMAGE [COMMAND] [ARG...]

Run a command in a new container

镜像

上面说的-it 就是这里的 options，后面那个 ubuntu 就是 image 辣，image 是啥呢

Docker 把应用程序及其依赖，打包在 image 文件里面，可以把它理解成为类似于虚拟机的镜像或者运行一个进程的代码，跑起来了的叫docker 容器或者进程，比如我们将要运行的docker run -it ubuntu bash的ubuntu 就是个 ubuntu 容器的镜像，将这个镜像运行起来后，我们可以进入容器像使用 ubuntu 一样使用它，来看下我们的镜像，使用sudo docker image ls就能列出我们宿主机上的 docker 镜像了

一个 ubuntu 镜像才 64MB，非常小巧，然后是后面的bash，我通过交互式启动了一个 ubuntu 容器，然后在这个启动的容器里运行了 bash 命令，这样就可以在容器里玩一下了

在容器里看下进程，

只有刚才运行容器的 bash 进程和我刚执行的 ps，这里有个可以注意下的，bash 这个进程的 pid 是 1，其实这里就用到了 linux 中的PID Namespace，容器会隔离出一个 pid 的名字空间，这里面的进程跟外部的 pid 命名独立

查看宿主机上的容器

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sudo docker ps -a

如何进入一个正在运行中的 docker 容器

这个应该是比较常用的，因为比如是一个微服务容器，有时候就像看下运行状态，日志啥的

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sudo docker exec -it [containerID] bash

查看日志

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sudo docker logs [containerID]

我在运行容器的终端里胡乱输入点啥，然后通过上面的命令就可以看到啦

发表于 分类于 ， ， 阅读次数： Disqus：

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。

发表于 更新于 分类于 阅读次数： Disqus：

众所周知，我是个前端彩笔，但是也想做点自己可以用的工具页面，所以就让朋友推荐了蚂蚁出品的 ant design，说基本可以直接 ctrl-c ctrl-v，实测对我这种来说还是有点难的，不过也能写点，但是现在碰到的问题是怎么部署到自己的服务器上去
用 ant design 写的是个单页应用，实际来说就是一个 html 加 css 跟 js，最初的时候是直接 build 完就 scp 上去，也考虑过 rsync 之类的，但是都感觉不够自动化，正好自己还没这方面的经验就想折腾下，因为我自己搭的仓库应用是 gogs，搜了一下主要是跟 drones 配合做 ci/cd，研究了一下发现其实这个事情没必要这么搞(PS：drone 也不好用)，整个 hook 就可以了, 但是实际上呢，这东西也不是那么简单
首先是需要在服务器上装 webhook，这个我一开始用 snap 安装，但是出现问题，run 的时候会出现后面参数带的 hooks.json 文件找不到，然后索性就直接 github 上下最新版，放 /usr/local/bin 了，webhook 的原理呢其实也比较简单，就是起一个 http 服务，通过 post 请求调用，解析下参数，如果跟配置的参数一致，就调用对应的命令或者脚本。

配置 hooks.json

webhook 的配置，需要的两个文件，一个是 hooks.json，这个是 webhook 服务的配置文件，像这样

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[
  {
    "id": "redeploy-app",
    "execute-command": "/opt/scripts/redeploy.sh",
    "command-working-directory": "/opt/scripts",
    "pass-arguments-to-command":
    [
      {
        "source": "payload",
        "name": "head_commit.message"
      },
      {
        "source": "payload",
        "name": "pusher.name"
      },
      {
        "source": "payload",
        "name": "head_commit.id"
      }
    ],
    "trigger-rule":
    {
      "and":
      [
        {
          "match":
          {
            "type": "payload-hash-sha1",
            "secret": "your-github-secret",
            "parameter":
            {
              "source": "header",
              "name": "X-Hub-Signature"
            }
          }
        },
        {
          "match":
          {
            "type": "value",
            "value": "refs/heads/master",
            "parameter":
            {
              "source": "payload",
              "name": "ref"
            }
          }
        }
      ]
    }
  }
]

这是个跟 github搭配的示例，首先 id 表示的是这个对应 hook 的识别 id，也可以看到这个 hooks.json 的结构是这样的一个数组，然后就是要执行的命令和命令执行的参数，值得注意的是这个trigger-rule,就是请求进来了回去匹配里面的，比如前一个是一个加密的，放在请求头里，第二个 match 表示请求里的 ref 是个 master 分支，就可以区分分支进行不同操作，但是前面的加密配合 gogs 使用的时候有个问题(PS: webhook 的文档是真的烂)，gogs 设置 webhook 的加密是用的

密钥文本将被用于计算推送内容的 SHA256 HMAC 哈希值，并设置为 X-Gogs-Signature 请求头的值。

这种加密方式，所以 webhook 的这个示例的加密方式不行，但这货的文档里居然没有说明支持哪些加密，神TM，后来还是翻 issue 翻到了, 需要使用这个payload-hash-sha256

执行脚本 redeploy.sh

脚本类似于这样

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#!/bin/bash -e

function cleanup {
      echo "Error occoured"
}
trap cleanup ERR

commit_message=$1 # head_commit.message
pusher_name=$2 # pusher.name
commit_id=$3 # head_commit.id


cd ~/do-react-example-app/
git pull origin master
yarn && yarn build

就是简单的拉代码，然后构建下，真实使用时可能不是这样，因为页面会部署在 nginx 的作用目录，还需要 rsync 过去，这部分可能还涉及到两个问题第一个是使用 rsync 还是其他的 cp，不过这个无所谓；第二个是目录权限的问题，以我的系统ubuntu 为例，默认用户是 ubuntu，nginx 部署的目录是 www，所以需要切换用户等操作，一开始是想用在shell 文件中直接写了密码，但是不知道咋传，查了下是类似于这样 echo "passwd" | sudo -S cmd，通过管道命令往后传，然后就是这个-S, 参数的解释是-S, --stdin read password from standard input,但是这样么也不是太安全的赶脚，又看了下还有两种方法，

  • 就是给root 设置一个不需要密码的命令类似于这样，

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    myusername ALL = (ALL) ALL
    myusername ALL = (root) NOPASSWD: /path/to/my/program

  • 另一种就是把默认用户跟 root 设置成同一个 group 的

使用

真正实操的时候其实还有不少问题，首先运行 webhook 就碰到了我前面说的，使用 snap 运行的时候会找不到前面的 hooks.json配置文件，执行snap run webhook -hooks /opt/hooks/hooks.json -verbose就碰到下面的couldn't load hooks from file! open /opt/hooks/hooks.json: no such file or directory，后来直接下了个官方最新的 release，就直接执行 webhook -hooks /opt/hooks/hooks.json -verbose 就可以了，然后是前面的示例配置文件里的几个参数，比如head_commit.message 其实 gogs 推过来的根本没这玩意，而且都是数组，不知道咋取，烂文档，不过总比搭个 drone 好一点就忍了。补充一点就是在 debug 的时候需要看下问题出在哪，看看脚本有没有执行，所以需要在前面的 json 里加这个参数"include-command-output-in-response": true, 就能输出来脚本执行结果

发表于 分类于 ， 阅读次数： Disqus：

Maven 翻译为”专家”、”内行”，是 Apache 下的一个纯 Java 开发的开源项目。基于项目对象模型（缩写：POM）概念，Maven利用一个中央信息片断能管理一个项目的构建、报告和文档等步骤。

Maven 是一个项目管理工具，可以对 Java 项目进行构建、依赖管理。

Maven 也可被用于构建和管理各种项目，例如 C#，Ruby，Scala 和其他语言编写的项目。Maven 曾是 Jakarta 项目的子项目，现为由 Apache 软件基金会主持的独立 Apache 项目。

maven也是我们日常项目中实用的包管理工具，相比以前需要用把包下载下来，放进 lib 中，在平时工作中使用的话，其实像 idea 这样的 ide 工具都会自带 maven 工具和插件

maven的基本操作

  • mvn -v
    查看 maven 信息
  • mvn compile
    将 Java 编译成 class 文件
  • mvn test
    执行 test 包下的测试用例
  • mvn package
    将项目打成 jar 包
  • mvn clean
    删除package 在 target 目录下面打出来的 jar 包和 target 目录
  • mvn install
    将打出来的 jar 包复制到 maven 的本地仓库里
  • mvn deploy
    将打出来的 jar 包上传到远程仓库里

与 composer 对比

因为我也是个 PHP 程序员，所以对比一下两种语言，很容易想到在 PHP 的 composer 跟 Java 的 maven 是比较类似的作用，有一点两者是非常相似的，就是原仓库都是因为某些原因连接拉取都会很慢，所以像 composer 会有一些国内源，前阵子阿里也出了一个，类似的 maven 一般也会使用阿里的镜像仓库，通过在 setting.xml 文件中的设置

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<mirrors>
    <mirror>
        <id>aliyun</id>
        <name>aliyun maven</name>
        <url>http://maven.aliyun.com/nexus/content/groups/public/</url>
        <mirrorOf>central</mirrorOf>
    </mirror> 
</mirrors>

这算是个尴尬的共同点，然后因为 PHP 是解释型脚本语言，所以 php 打出来的 composer 包其实就是个 php 代码包，使用SPL Autoload等方式加载代码包，maven 包则是经过编译的 class 包，还有一点是 composer 也可以直接使用 github 地址作为包代码的拉取源，这点也是比较大的区别，maven使用 pom 文件管理依赖

maven 的个人小技巧

  • maven 拉取依赖时，同时将 snapshot 也更新了，就是 mvn compile加个-U参数，如果还不行就需要将本地仓库的 snapshot 删掉，
    这个命令的 help 命令解释是 -U,–update-snapshots Forces a check for missing releases and updated snapshots on
    remote repositories，这个在日常使用中还是很经常使用的
  • maven 出现依赖冲突的时候的解决方法
    首先是依赖分析，使用mvn dependency:tree分析下依赖关系，如果要找具体某个包的依赖引用关系可以使用mvn dependency:tree -Dverbose -Dincludes=org.springframework:spring-webmvc命令进行分析，如果发现有冲突的依赖关系，本身 maven 中依赖引用有相对的顺序，大致来说是引用路径短的优先，pom 文件中定义的顺序优先，如果要把冲突的包排除掉可以在 pom 中用
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    <exclusions>
      <exclusion>
          <groupId>ch.qos.logback</groupId>
          <artifactId>logback-classic</artifactId>
      </exclusion>
    </exclusions>
    将冲突的包排除掉
  • maven 依赖的 jdk 版本管理
    前面介绍的mvn -v可以查看 maven 的安装信息
    比如
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    Apache Maven 3.6.3 (cecedd343002696d0abb50b32b541b8a6ba2883f)
    Maven home: /usr/local/Cellar/maven/3.6.3_1/libexec
    Java version: 1.8.0_201, vendor: Oracle Corporation, runtime: /Library/Java/JavaVirtualMachines/jdk1.8.0_201.jdk/Contents/Home/jre
    Default locale: zh_CN, platform encoding: UTF-8
    OS name: "mac os x", version: "10.14.6", arch: "x86_64", family: "mac"
    这里可以看到用了 mac 自带的 jdk1.8，结合我之前碰到的一个问题，因为使用 homebrew 升级了 gradle，而 gradle 又依赖了 jdk13，因为这个 mvn 的 Java version 也变成 jdk13 了，然后 mvn 编译的时候出现了 java.lang.ExceptionInInitializerError: com.sun.tools.javac.code.TypeTags这个问题，所以需要把这个版本给改回来，但是咋改呢，网上搜来的一大堆都是在 pom 文件里的
    source和 target 版本
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    <build>
      <plugins>
    <plugin>
    	<groupId>org.apache.maven.plugins</groupId>
    	<artifactId>maven-compiler-plugin</artifactId>
    	<configuration>
    		<source>1.8</source>
    		<target>1.8</target>
    		<encoding>UTF-8</encoding>
    	</configuration>
    </plugin>
      </plugins>
    <build>
    或者修改 maven 的 setting.xml中的
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    <profiles>
          <profile>
              <id>ngmm-nexus</id>
            <activation>
              <jdk>1.8</jdk>
            </activation>
            <properties>
              <maven.compiler.source>1.8</maven.compiler.source>
              <maven.compiler.target>1.8</maven.compiler.target>
              <maven.compiler.compilerVersion>1.8</maven.compiler.compilerVersion>
          </properties>
          </profile>
    </profiles>
    但是这些都没啥用啊，真正有办法的是建个 .mavenrc，这个顾名思义就是 maven 的资源文件，类似于 .bashrc和.zshrc，在里面添加 MAVEN_HOME 和 JAVA_HOME,然后执行 source .mavenrc就 OK 啦

发表于 分类于 ， ， ， 阅读次数： Disqus：

G1: The Garbage-First Collector, 垃圾回收优先的垃圾回收器，目标是用户多核 cpu 和大内存的机器，最大的特点就是可预测的停顿时间，官方给出的介绍是提供一个用户在大的堆内存情况下一个低延迟表现的解决方案，通常是 6GB 及以上的堆大小，有低于 0.5 秒稳定的可预测的停顿时间。

这里主要介绍这个比较新的垃圾回收器，在 G1 之前的垃圾回收器都是基于如下图的内存结构分布，有新生代，老年代和永久代（jdk8 之前），然后G1 往前的那些垃圾回收器都有个分代，比如 serial，parallel 等，一般有个应用的组合，最初的 serial 和 serial old，因为新生代和老年代的收集方式不太一样，新生代主要是标记复制，所以有 eden 跟两个 survival区，老年代一般用标记整理方式，而 G1 对这个不太一样。

看一下 G1 的内存分布

可以看到这有很大的不同，G1 通过将内存分成大小相等的 region，每个region是存在于一个连续的虚拟内存范围，对于某个 region 来说其角色是类似于原来的收集器的Eden、Survivor、Old Generation，这个具体在代码层面

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// We encode the value of the heap region type so the generation can be
 // determined quickly. The tag is split into two parts:
 //
 //   major type (young, old, humongous, archive)           : top N-1 bits
 //   minor type (eden / survivor, starts / cont hum, etc.) : bottom 1 bit
 //
 // If there's need to increase the number of minor types in the
 // future, we'll have to increase the size of the latter and hence
 // decrease the size of the former.
 //
 // 00000 0 [ 0] Free
 //
 // 00001 0 [ 2] Young Mask
 // 00001 0 [ 2] Eden
 // 00001 1 [ 3] Survivor
 //
 // 00010 0 [ 4] Humongous Mask
 // 00100 0 [ 8] Pinned Mask
 // 00110 0 [12] Starts Humongous
 // 00110 1 [13] Continues Humongous
 //
 // 01000 0 [16] Old Mask
 //
 // 10000 0 [32] Archive Mask
 // 11100 0 [56] Open Archive
 // 11100 1 [57] Closed Archive
 //
 typedef enum {
   FreeTag               = 0,

   YoungMask             = 2,
   EdenTag               = YoungMask,
   SurvTag               = YoungMask + 1,

   HumongousMask         = 4,
   PinnedMask            = 8,
   StartsHumongousTag    = HumongousMask | PinnedMask,
   ContinuesHumongousTag = HumongousMask | PinnedMask + 1,

   OldMask               = 16,
   OldTag                = OldMask,

   // Archive regions are regions with immutable content (i.e. not reclaimed, and
   // not allocated into during regular operation). They differ in the kind of references
   // allowed for the contained objects:
   // - Closed archive regions form a separate self-contained (closed) object graph
   // within the set of all of these regions. No references outside of closed
   // archive regions are allowed.
   // - Open archive regions have no restrictions on the references of their objects.
   // Objects within these regions are allowed to have references to objects
   // contained in any other kind of regions.
   ArchiveMask           = 32,
   OpenArchiveTag        = ArchiveMask | PinnedMask | OldMask,
   ClosedArchiveTag      = ArchiveMask | PinnedMask | OldMask + 1
 } Tag;

hotspot/share/gc/g1/heapRegionType.hpp

当执行垃圾收集时，G1以类似于CMS收集器的方式运行。 G1执行并发全局标记阶段，以确定整个堆中对象的存活性。标记阶段完成后，G1知道哪些region是基本空的。它首先收集这些region，通常会产生大量的可用空间。这就是为什么这种垃圾收集方法称为“垃圾优先”的原因。顾名思义，G1将其收集和压缩活动集中在可能充满可回收对象（即垃圾）的堆区域。 G1使用暂停预测模型来满足用户定义的暂停时间目标，并根据指定的暂停时间目标选择要收集的区域数。

由G1标识为可回收的区域是使用撤离的方式(Evacuation)。 G1将对象从堆的一个或多个区域复制到堆上的单个区域，并在此过程中压缩并释放内存。撤离是在多处理器上并行执行的，以减少暂停时间并增加吞吐量。因此，对于每次垃圾收集，G1都在用户定义的暂停时间内连续工作以减少碎片。这是优于前面两种方法的。 CMS（并发标记扫描）垃圾收集器不进行压缩。 ParallelOld垃圾回收仅执行整个堆压缩，这导致相当长的暂停时间。

需要重点注意的是，G1不是实时收集器。它很有可能达到设定的暂停时间目标，但并非绝对确定。 G1根据先前收集的数据，估算在用户指定的目标时间内可以收集多少个区域。因此，收集器具有收集区域成本的合理准确的模型，并且收集器使用此模型来确定要收集哪些和多少个区域，同时保持在暂停时间目标之内。

注意：G1同时具有并发（与应用程序线程一起运行，例如优化，标记，清理）和并行（多线程，例如stw）阶段。Full GC仍然是单线程的，但是如果正确调优，您的应用程序应该可以避免Full GC。

在前面那篇中在代码层面简单的了解了这个可预测时间的过程，这也是 G1 的一大特点。

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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：

因为最近想搭一个phabricator用来做看板和任务管理，一开始了解这个是Easy大大有在微博推荐过，后来苏洋也在群里和博客里说到了，看上去还不错的样子，因为主角是docker所以就不介绍太多，后面有机会写一下。

docker最开始是之前在某位大佬的博客看到的，看上去有点神奇，感觉是一种轻量级的虚拟机，但是能做的事情好像差不多，那时候是在Ubuntu系统的vps里起一个Ubuntu的docker，然后在里面装个nginx，配置端口映射就可以访问了，后来也草草写过一篇使用docker搭建mysql集群，但是最近看了下好像是因为装docker的大佬做了一些别名还是什么操作，导致里面用的操作都不具有普遍性，而且主要是把搭的过程写了下，属于囫囵吞枣，没理解docker是干啥的，为啥用docker，就是操作了下，这几天借着搭phabricator的过程，把一些原来不理解，或者原来理解错误的地方重新理一下。

之前写的 mysql 集群，一主二备，这种架构在很多小型应用里都是这么配置的，而且一般是直接在三台 vps 里启动三个 mysql 实例，但是如果换成 docker 会有什么好处呢，其实就是方便部署，比如其中一台备库挂了，我要加一台，或者说备库的 qps 太高了，需要再加一个，如果要在 vps 上搭建的话，首先要买一台机器，等初始化，然后在上面修改源，更新，装 mysql ，然后配置主从，可能还要处理防火墙等等，如果把这些打包成一个 docker 镜像，并且放在自己的 docker registry，那就直接run 一下就可以了；还有比如在公司要给一个新同学整一套开发测试环境，以 Java 开发为例，要装 git，maven，jdk，配置 maven settings 和各种 rc，整合在一个镜像里的话，就会很方便了；再比如微服务的水平扩展。

但是为啥 docker 会有这种优势，听起来好像虚拟机也可以干这个事，但是虚拟机动辄上 G，而且需要 VMware，virtual box 等支持，不适合在Linux服务器环境使用，而且占用资源也会非常大。说得这么好，那么 docker 是啥呢

docker 主要使用 Linux 中已经存在的两种技术的一个整合升级，一个是 namespace，一个是cgroups，相比于虚拟机需要完整虚拟出一个操作系统运行基础，docker 基于宿主机内核，通过 namespace 和 cgroups 分隔进程，理念就是提供一个隔离的最小化运行依赖，这样子相对于虚拟机就有了巨大的便利性，具体的 namespace 和 cgroups 就先不展开讲，可以参考耗子叔的文章

安装

那么我们先安装下 docker，参考官方的教程，安装，我的系统是 ubuntu 的，就贴了 ubuntu 的链接，用其他系统的可以找到对应的系统文档安装，安装完了的话看看 docker 的信息

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sudo docker info

输出以下信息

简单运行

然后再来运行个 hello world 呗，

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sudo docker run hello-world

输出了这些

看看这个运行命令是怎么用的，一般都会看到这样子的，sudo docker run -it ubuntu bash, 前面的 docker run 反正就是运行一个容器的意思，-it是啥呢，还有这个什么 ubuntu bash，来看看docker run`的命令帮助信息

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-i, --interactive                    Keep STDIN open even if not attached

就是要有输入，我们运行的时候能输入

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-t, --tty                            Allocate a pseudo-TTY

要有个虚拟终端，

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Usage:	docker run [OPTIONS] IMAGE [COMMAND] [ARG...]

Run a command in a new container

镜像

上面说的-it 就是这里的 options，后面那个 ubuntu 就是 image 辣，image 是啥呢

Docker 把应用程序及其依赖，打包在 image 文件里面，可以把它理解成为类似于虚拟机的镜像或者运行一个进程的代码，跑起来了的叫docker 容器或者进程，比如我们将要运行的docker run -it ubuntu bash的ubuntu 就是个 ubuntu 容器的镜像，将这个镜像运行起来后，我们可以进入容器像使用 ubuntu 一样使用它，来看下我们的镜像，使用sudo docker image ls就能列出我们宿主机上的 docker 镜像了

一个 ubuntu 镜像才 64MB，非常小巧，然后是后面的bash，我通过交互式启动了一个 ubuntu 容器，然后在这个启动的容器里运行了 bash 命令，这样就可以在容器里玩一下了

在容器里看下进程，

只有刚才运行容器的 bash 进程和我刚执行的 ps，这里有个可以注意下的，bash 这个进程的 pid 是 1，其实这里就用到了 linux 中的PID Namespace，容器会隔离出一个 pid 的名字空间，这里面的进程跟外部的 pid 命名独立

查看宿主机上的容器

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sudo docker ps -a

如何进入一个正在运行中的 docker 容器

这个应该是比较常用的，因为比如是一个微服务容器，有时候就像看下运行状态，日志啥的

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sudo docker exec -it [containerID] bash

查看日志

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sudo docker logs [containerID]

我在运行容器的终端里胡乱输入点啥，然后通过上面的命令就可以看到啦

发表于 分类于 ， ， 阅读次数： Disqus：

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。

发表于 更新于 分类于 阅读次数： Disqus：

众所周知，我是个前端彩笔，但是也想做点自己可以用的工具页面，所以就让朋友推荐了蚂蚁出品的 ant design，说基本可以直接 ctrl-c ctrl-v，实测对我这种来说还是有点难的，不过也能写点，但是现在碰到的问题是怎么部署到自己的服务器上去
用 ant design 写的是个单页应用，实际来说就是一个 html 加 css 跟 js，最初的时候是直接 build 完就 scp 上去，也考虑过 rsync 之类的，但是都感觉不够自动化，正好自己还没这方面的经验就想折腾下，因为我自己搭的仓库应用是 gogs，搜了一下主要是跟 drones 配合做 ci/cd，研究了一下发现其实这个事情没必要这么搞(PS：drone 也不好用)，整个 hook 就可以了, 但是实际上呢，这东西也不是那么简单
首先是需要在服务器上装 webhook，这个我一开始用 snap 安装，但是出现问题，run 的时候会出现后面参数带的 hooks.json 文件找不到，然后索性就直接 github 上下最新版，放 /usr/local/bin 了，webhook 的原理呢其实也比较简单，就是起一个 http 服务，通过 post 请求调用，解析下参数，如果跟配置的参数一致，就调用对应的命令或者脚本。

配置 hooks.json

webhook 的配置，需要的两个文件，一个是 hooks.json，这个是 webhook 服务的配置文件，像这样

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[
  {
    "id": "redeploy-app",
    "execute-command": "/opt/scripts/redeploy.sh",
    "command-working-directory": "/opt/scripts",
    "pass-arguments-to-command":
    [
      {
        "source": "payload",
        "name": "head_commit.message"
      },
      {
        "source": "payload",
        "name": "pusher.name"
      },
      {
        "source": "payload",
        "name": "head_commit.id"
      }
    ],
    "trigger-rule":
    {
      "and":
      [
        {
          "match":
          {
            "type": "payload-hash-sha1",
            "secret": "your-github-secret",
            "parameter":
            {
              "source": "header",
              "name": "X-Hub-Signature"
            }
          }
        },
        {
          "match":
          {
            "type": "value",
            "value": "refs/heads/master",
            "parameter":
            {
              "source": "payload",
              "name": "ref"
            }
          }
        }
      ]
    }
  }
]

这是个跟 github搭配的示例，首先 id 表示的是这个对应 hook 的识别 id，也可以看到这个 hooks.json 的结构是这样的一个数组，然后就是要执行的命令和命令执行的参数，值得注意的是这个trigger-rule,就是请求进来了回去匹配里面的，比如前一个是一个加密的，放在请求头里，第二个 match 表示请求里的 ref 是个 master 分支，就可以区分分支进行不同操作，但是前面的加密配合 gogs 使用的时候有个问题(PS: webhook 的文档是真的烂)，gogs 设置 webhook 的加密是用的

密钥文本将被用于计算推送内容的 SHA256 HMAC 哈希值，并设置为 X-Gogs-Signature 请求头的值。

这种加密方式，所以 webhook 的这个示例的加密方式不行，但这货的文档里居然没有说明支持哪些加密，神TM，后来还是翻 issue 翻到了, 需要使用这个payload-hash-sha256

执行脚本 redeploy.sh

脚本类似于这样

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#!/bin/bash -e

function cleanup {
      echo "Error occoured"
}
trap cleanup ERR

commit_message=$1 # head_commit.message
pusher_name=$2 # pusher.name
commit_id=$3 # head_commit.id


cd ~/do-react-example-app/
git pull origin master
yarn && yarn build

就是简单的拉代码，然后构建下，真实使用时可能不是这样，因为页面会部署在 nginx 的作用目录，还需要 rsync 过去，这部分可能还涉及到两个问题第一个是使用 rsync 还是其他的 cp，不过这个无所谓；第二个是目录权限的问题，以我的系统ubuntu 为例，默认用户是 ubuntu，nginx 部署的目录是 www，所以需要切换用户等操作，一开始是想用在shell 文件中直接写了密码，但是不知道咋传，查了下是类似于这样 echo "passwd" | sudo -S cmd，通过管道命令往后传，然后就是这个-S, 参数的解释是-S, --stdin read password from standard input,但是这样么也不是太安全的赶脚，又看了下还有两种方法，

  • 就是给root 设置一个不需要密码的命令类似于这样，

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    myusername ALL = (ALL) ALL
    myusername ALL = (root) NOPASSWD: /path/to/my/program

  • 另一种就是把默认用户跟 root 设置成同一个 group 的

使用

真正实操的时候其实还有不少问题，首先运行 webhook 就碰到了我前面说的，使用 snap 运行的时候会找不到前面的 hooks.json配置文件，执行snap run webhook -hooks /opt/hooks/hooks.json -verbose就碰到下面的couldn't load hooks from file! open /opt/hooks/hooks.json: no such file or directory，后来直接下了个官方最新的 release，就直接执行 webhook -hooks /opt/hooks/hooks.json -verbose 就可以了，然后是前面的示例配置文件里的几个参数，比如head_commit.message 其实 gogs 推过来的根本没这玩意，而且都是数组，不知道咋取，烂文档，不过总比搭个 drone 好一点就忍了。补充一点就是在 debug 的时候需要看下问题出在哪，看看脚本有没有执行，所以需要在前面的 json 里加这个参数"include-command-output-in-response": true, 就能输出来脚本执行结果

发表于 分类于 ， 阅读次数： Disqus：

Maven 翻译为”专家”、”内行”，是 Apache 下的一个纯 Java 开发的开源项目。基于项目对象模型（缩写：POM）概念，Maven利用一个中央信息片断能管理一个项目的构建、报告和文档等步骤。

Maven 是一个项目管理工具，可以对 Java 项目进行构建、依赖管理。

Maven 也可被用于构建和管理各种项目，例如 C#，Ruby，Scala 和其他语言编写的项目。Maven 曾是 Jakarta 项目的子项目，现为由 Apache 软件基金会主持的独立 Apache 项目。

maven也是我们日常项目中实用的包管理工具，相比以前需要用把包下载下来，放进 lib 中，在平时工作中使用的话，其实像 idea 这样的 ide 工具都会自带 maven 工具和插件

maven的基本操作

  • mvn -v
    查看 maven 信息
  • mvn compile
    将 Java 编译成 class 文件
  • mvn test
    执行 test 包下的测试用例
  • mvn package
    将项目打成 jar 包
  • mvn clean
    删除package 在 target 目录下面打出来的 jar 包和 target 目录
  • mvn install
    将打出来的 jar 包复制到 maven 的本地仓库里
  • mvn deploy
    将打出来的 jar 包上传到远程仓库里

与 composer 对比

因为我也是个 PHP 程序员，所以对比一下两种语言，很容易想到在 PHP 的 composer 跟 Java 的 maven 是比较类似的作用，有一点两者是非常相似的，就是原仓库都是因为某些原因连接拉取都会很慢，所以像 composer 会有一些国内源，前阵子阿里也出了一个，类似的 maven 一般也会使用阿里的镜像仓库，通过在 setting.xml 文件中的设置

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<mirrors>
    <mirror>
        <id>aliyun</id>
        <name>aliyun maven</name>
        <url>http://maven.aliyun.com/nexus/content/groups/public/</url>
        <mirrorOf>central</mirrorOf>
    </mirror> 
</mirrors>

这算是个尴尬的共同点，然后因为 PHP 是解释型脚本语言，所以 php 打出来的 composer 包其实就是个 php 代码包，使用SPL Autoload等方式加载代码包，maven 包则是经过编译的 class 包，还有一点是 composer 也可以直接使用 github 地址作为包代码的拉取源，这点也是比较大的区别，maven使用 pom 文件管理依赖

maven 的个人小技巧

  • maven 拉取依赖时，同时将 snapshot 也更新了，就是 mvn compile加个-U参数，如果还不行就需要将本地仓库的 snapshot 删掉，
    这个命令的 help 命令解释是 -U,–update-snapshots Forces a check for missing releases and updated snapshots on
    remote repositories，这个在日常使用中还是很经常使用的
  • maven 出现依赖冲突的时候的解决方法
    首先是依赖分析，使用mvn dependency:tree分析下依赖关系，如果要找具体某个包的依赖引用关系可以使用mvn dependency:tree -Dverbose -Dincludes=org.springframework:spring-webmvc命令进行分析，如果发现有冲突的依赖关系，本身 maven 中依赖引用有相对的顺序，大致来说是引用路径短的优先，pom 文件中定义的顺序优先，如果要把冲突的包排除掉可以在 pom 中用
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    <exclusions>
      <exclusion>
          <groupId>ch.qos.logback</groupId>
          <artifactId>logback-classic</artifactId>
      </exclusion>
    </exclusions>
    将冲突的包排除掉
  • maven 依赖的 jdk 版本管理
    前面介绍的mvn -v可以查看 maven 的安装信息
    比如
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    Apache Maven 3.6.3 (cecedd343002696d0abb50b32b541b8a6ba2883f)
    Maven home: /usr/local/Cellar/maven/3.6.3_1/libexec
    Java version: 1.8.0_201, vendor: Oracle Corporation, runtime: /Library/Java/JavaVirtualMachines/jdk1.8.0_201.jdk/Contents/Home/jre
    Default locale: zh_CN, platform encoding: UTF-8
    OS name: "mac os x", version: "10.14.6", arch: "x86_64", family: "mac"
    这里可以看到用了 mac 自带的 jdk1.8，结合我之前碰到的一个问题，因为使用 homebrew 升级了 gradle，而 gradle 又依赖了 jdk13，因为这个 mvn 的 Java version 也变成 jdk13 了，然后 mvn 编译的时候出现了 java.lang.ExceptionInInitializerError: com.sun.tools.javac.code.TypeTags这个问题，所以需要把这个版本给改回来，但是咋改呢，网上搜来的一大堆都是在 pom 文件里的
    source和 target 版本
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    <build>
      <plugins>
    <plugin>
    	<groupId>org.apache.maven.plugins</groupId>
    	<artifactId>maven-compiler-plugin</artifactId>
    	<configuration>
    		<source>1.8</source>
    		<target>1.8</target>
    		<encoding>UTF-8</encoding>
    	</configuration>
    </plugin>
      </plugins>
    <build>
    或者修改 maven 的 setting.xml中的
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    <profiles>
          <profile>
              <id>ngmm-nexus</id>
            <activation>
              <jdk>1.8</jdk>
            </activation>
            <properties>
              <maven.compiler.source>1.8</maven.compiler.source>
              <maven.compiler.target>1.8</maven.compiler.target>
              <maven.compiler.compilerVersion>1.8</maven.compiler.compilerVersion>
          </properties>
          </profile>
    </profiles>
    但是这些都没啥用啊，真正有办法的是建个 .mavenrc，这个顾名思义就是 maven 的资源文件，类似于 .bashrc和.zshrc，在里面添加 MAVEN_HOME 和 JAVA_HOME,然后执行 source .mavenrc就 OK 啦

发表于 分类于 ， ， ， 阅读次数： Disqus：

G1: The Garbage-First Collector, 垃圾回收优先的垃圾回收器，目标是用户多核 cpu 和大内存的机器，最大的特点就是可预测的停顿时间，官方给出的介绍是提供一个用户在大的堆内存情况下一个低延迟表现的解决方案，通常是 6GB 及以上的堆大小，有低于 0.5 秒稳定的可预测的停顿时间。

这里主要介绍这个比较新的垃圾回收器，在 G1 之前的垃圾回收器都是基于如下图的内存结构分布，有新生代，老年代和永久代（jdk8 之前），然后G1 往前的那些垃圾回收器都有个分代，比如 serial，parallel 等，一般有个应用的组合，最初的 serial 和 serial old，因为新生代和老年代的收集方式不太一样，新生代主要是标记复制，所以有 eden 跟两个 survival区，老年代一般用标记整理方式，而 G1 对这个不太一样。

看一下 G1 的内存分布

可以看到这有很大的不同，G1 通过将内存分成大小相等的 region，每个region是存在于一个连续的虚拟内存范围，对于某个 region 来说其角色是类似于原来的收集器的Eden、Survivor、Old Generation，这个具体在代码层面

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// We encode the value of the heap region type so the generation can be
 // determined quickly. The tag is split into two parts:
 //
 //   major type (young, old, humongous, archive)           : top N-1 bits
 //   minor type (eden / survivor, starts / cont hum, etc.) : bottom 1 bit
 //
 // If there's need to increase the number of minor types in the
 // future, we'll have to increase the size of the latter and hence
 // decrease the size of the former.
 //
 // 00000 0 [ 0] Free
 //
 // 00001 0 [ 2] Young Mask
 // 00001 0 [ 2] Eden
 // 00001 1 [ 3] Survivor
 //
 // 00010 0 [ 4] Humongous Mask
 // 00100 0 [ 8] Pinned Mask
 // 00110 0 [12] Starts Humongous
 // 00110 1 [13] Continues Humongous
 //
 // 01000 0 [16] Old Mask
 //
 // 10000 0 [32] Archive Mask
 // 11100 0 [56] Open Archive
 // 11100 1 [57] Closed Archive
 //
 typedef enum {
   FreeTag               = 0,

   YoungMask             = 2,
   EdenTag               = YoungMask,
   SurvTag               = YoungMask + 1,

   HumongousMask         = 4,
   PinnedMask            = 8,
   StartsHumongousTag    = HumongousMask | PinnedMask,
   ContinuesHumongousTag = HumongousMask | PinnedMask + 1,

   OldMask               = 16,
   OldTag                = OldMask,

   // Archive regions are regions with immutable content (i.e. not reclaimed, and
   // not allocated into during regular operation). They differ in the kind of references
   // allowed for the contained objects:
   // - Closed archive regions form a separate self-contained (closed) object graph
   // within the set of all of these regions. No references outside of closed
   // archive regions are allowed.
   // - Open archive regions have no restrictions on the references of their objects.
   // Objects within these regions are allowed to have references to objects
   // contained in any other kind of regions.
   ArchiveMask           = 32,
   OpenArchiveTag        = ArchiveMask | PinnedMask | OldMask,
   ClosedArchiveTag      = ArchiveMask | PinnedMask | OldMask + 1
 } Tag;

hotspot/share/gc/g1/heapRegionType.hpp

当执行垃圾收集时，G1以类似于CMS收集器的方式运行。 G1执行并发全局标记阶段，以确定整个堆中对象的存活性。标记阶段完成后，G1知道哪些region是基本空的。它首先收集这些region，通常会产生大量的可用空间。这就是为什么这种垃圾收集方法称为“垃圾优先”的原因。顾名思义，G1将其收集和压缩活动集中在可能充满可回收对象（即垃圾）的堆区域。 G1使用暂停预测模型来满足用户定义的暂停时间目标，并根据指定的暂停时间目标选择要收集的区域数。

由G1标识为可回收的区域是使用撤离的方式(Evacuation)。 G1将对象从堆的一个或多个区域复制到堆上的单个区域，并在此过程中压缩并释放内存。撤离是在多处理器上并行执行的，以减少暂停时间并增加吞吐量。因此，对于每次垃圾收集，G1都在用户定义的暂停时间内连续工作以减少碎片。这是优于前面两种方法的。 CMS（并发标记扫描）垃圾收集器不进行压缩。 ParallelOld垃圾回收仅执行整个堆压缩，这导致相当长的暂停时间。

需要重点注意的是，G1不是实时收集器。它很有可能达到设定的暂停时间目标，但并非绝对确定。 G1根据先前收集的数据，估算在用户指定的目标时间内可以收集多少个区域。因此，收集器具有收集区域成本的合理准确的模型，并且收集器使用此模型来确定要收集哪些和多少个区域，同时保持在暂停时间目标之内。

注意：G1同时具有并发（与应用程序线程一起运行，例如优化，标记，清理）和并行（多线程，例如stw）阶段。Full GC仍然是单线程的，但是如果正确调优，您的应用程序应该可以避免Full GC。

在前面那篇中在代码层面简单的了解了这个可预测时间的过程，这也是 G1 的一大特点。

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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：

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~

发表于 更新于 分类于 ， ， ， 阅读次数： Disqus：

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

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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

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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。
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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：

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~

发表于 更新于 分类于 ， ， ， 阅读次数： Disqus：

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

1
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

1
2
3
4
5
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。
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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：

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

发表于 更新于 分类于 阅读次数： Disqus：

最近看了大神的 AQS 的文章，之前总是断断续续地看一点，每次都知难而退，下次看又从头开始，昨天总算硬着头皮看完了第一部分
首先 AQS 只要有这些属性

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// 头结点，你直接把它当做 当前持有锁的线程 可能是最好理解的
private transient volatile Node head;

// 阻塞的尾节点，每个新的节点进来，都插入到最后，也就形成了一个链表
private transient volatile Node tail;

// 这个是最重要的，代表当前锁的状态，0代表没有被占用，大于 0 代表有线程持有当前锁
// 这个值可以大于 1，是因为锁可以重入，每次重入都加上 1
private volatile int state;

// 代表当前持有独占锁的线程，举个最重要的使用例子，因为锁可以重入
// reentrantLock.lock()可以嵌套调用多次，所以每次用这个来判断当前线程是否已经拥有了锁
// if (currentThread == getExclusiveOwnerThread()) {state++}
private transient Thread exclusiveOwnerThread; //继承自AbstractOwnableSynchronizer

大概了解了 aqs 底层的双向等待队列，
结构是这样的

每个 node 里面主要是的代码结构也比较简单

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static final class Node {
    // 标识节点当前在共享模式下
    static final Node SHARED = new Node();
    // 标识节点当前在独占模式下
    static final Node EXCLUSIVE = null;

    // ======== 下面的几个int常量是给waitStatus用的 ===========
    /** waitStatus value to indicate thread has cancelled */
    // 代码此线程取消了争抢这个锁
    static final int CANCELLED =  1;
    /** waitStatus value to indicate successor's thread needs unparking */
    // 官方的描述是，其表示当前node的后继节点对应的线程需要被唤醒
    static final int SIGNAL    = -1;
    /** waitStatus value to indicate thread is waiting on condition */
    // 本文不分析condition，所以略过吧，下一篇文章会介绍这个
    static final int CONDITION = -2;
    /**
     * waitStatus value to indicate the next acquireShared should
     * unconditionally propagate
     */
    // 同样的不分析，略过吧
    static final int PROPAGATE = -3;
    // =====================================================


    // 取值为上面的1、-1、-2、-3，或者0(以后会讲到)
    // 这么理解，暂时只需要知道如果这个值 大于0 代表此线程取消了等待，
    //    ps: 半天抢不到锁，不抢了，ReentrantLock是可以指定timeouot的。。。
    volatile int waitStatus;
    // 前驱节点的引用
    volatile Node prev;
    // 后继节点的引用
    volatile Node next;
    // 这个就是线程本尊
    volatile Thread thread;

}

其实可以主要关注这个 waitStatus 因为这个是后面的节点给前面的节点设置的，等于-1 的时候代表后面有节点等待，需要去唤醒，
这里使用了一个变种的 CLH 队列实现，CLH 队列相关内容可以查看这篇 自旋锁、排队自旋锁、MCS锁、CLH锁

发表于 更新于 分类于 阅读次数： Disqus：

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

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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

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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. 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. 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，

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    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

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    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. 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. 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. 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. 发现一个不错的openresty站点
    地址

发表于 更新于 分类于 阅读次数： Disqus：

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

发表于 更新于 分类于 阅读次数： Disqus：

rabbitmq 介绍

接触了一下rabbitmq，原来在选型的时候是在rabbitmq跟kafka之间做选择，网上搜了一下之后发现kafka的优势在于吞吐量，而rabbitmq相对注重可靠性，因为应用在im上，需要保证消息不能丢失所以就暂时选定rabbitmq，
Message Queue的需求由来已久，80年代最早在金融交易中，高盛等公司采用Teknekron公司的产品，当时的Message queuing软件叫做：the information bus（TIB）。 TIB被电信和通讯公司采用，路透社收购了Teknekron公司。之后，IBM开发了MQSeries，微软开发了Microsoft Message Queue（MSMQ）。这些商业MQ供应商的问题是厂商锁定，价格高昂。2001年，Java Message queuing试图解决锁定和交互性的问题，但对应用来说反而更加麻烦了。
RabbitMQ采用Erlang语言开发。Erlang语言由Ericson设计，专门为开发concurrent和distribution系统的一种语言，在电信领域使用广泛。OTP（Open Telecom Platform）作为Erlang语言的一部分，包含了很多基于Erlang开发的中间件／库／工具，如mnesia／SASL，极大方便了Erlang应用的开发。OTP就类似于Python语言中众多的module，用户借助这些module可以很方便的开发应用。
于是2004年，摩根大通和iMatrix开始着手Advanced Message Queuing Protocol （AMQP）开放标准的开发。2006年，AMQP规范发布。2007年，Rabbit技术公司基于AMQP标准开发的RabbitMQ 1.0 发布。所有主要的编程语言均有与代理接口通讯的客户端库。

简单的使用经验

通俗的理解

这里介绍下其中的一些概念，connection表示和队列服务器的连接，一般情况下是tcp连接， channel表示通道，可以在一个连接上建立多个通道，这里主要是节省了tcp连接握手的成本，exchange可以理解成一个路由器，将消息推送给对应的队列queue，其实是像一个订阅的模式。

集群经验

rabbitmqctl stop这个是关闭rabbitmq，在搭建集群时候先关闭服务，然后使用rabbitmq-server -detached静默启动，这时候使用rabbitmqctl cluster_status查看集群状态，因为还没将节点加入集群，所以只能看到类似

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Cluster status of node rabbit@rabbit1 ...
[{nodes,[{disc,[rabbit@rabbit1,rabbit@rabbit2,rabbit@rabbit3]}]},
 {running_nodes,[rabbit@rabbit2,rabbit@rabbit1]}]
...done.

然后就可以把当前节点加入集群，

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rabbit2$ rabbitmqctl stop_app #这个stop_app与stop的区别是前者停的是rabbitmq应用，保留erlang节点，
                              #后者是停止了rabbitmq和erlang节点
Stopping node rabbit@rabbit2 ...done.
rabbit2$ rabbitmqctl join_cluster rabbit@rabbit1 #这里可以用--ram指定将当前节点作为内存节点加入集群
Clustering node rabbit@rabbit2 with [rabbit@rabbit1] ...done.
rabbit2$ rabbitmqctl start_app
Starting node rabbit@rabbit2 ...done.

其他可以参考官方文档

一些坑

消息丢失

这里碰到过一个坑，对于使用exchange来做消息路由的，会有一个情况，就是在routing_key没被订阅的时候，会将该条找不到路由对应的queue的消息丢掉What happens if we break our contract and send a message with one or four words, like "orange" or "quick.orange.male.rabbit"? Well, these messages won't match any bindings and will be lost.对应链接,而当使用空的exchange时，会保留消息，当出现消费者的时候就可以将收到之前生产者所推送的消息对应链接,这里就是用了空的exchange。

集群搭建

集群搭建的时候有个erlang vm生成的random cookie，这个是用来做集群之间认证的，相同的cookie才能连接，但是如果通过vim打开复制后在其他几点新建文件写入会多一个换行，导致集群建立是报错，所以这里最好使用scp等传输命令直接传输cookie文件，同时要注意下cookie的文件权限。
另外在集群搭建的时候如果更改过hostname，那么要把rabbitmq的数据库删除，否则启动后会马上挂掉

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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：

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

发表于 更新于 分类于 阅读次数： Disqus：

最近看了大神的 AQS 的文章，之前总是断断续续地看一点，每次都知难而退，下次看又从头开始，昨天总算硬着头皮看完了第一部分
首先 AQS 只要有这些属性

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// 头结点，你直接把它当做 当前持有锁的线程 可能是最好理解的
private transient volatile Node head;

// 阻塞的尾节点，每个新的节点进来，都插入到最后，也就形成了一个链表
private transient volatile Node tail;

// 这个是最重要的，代表当前锁的状态，0代表没有被占用，大于 0 代表有线程持有当前锁
// 这个值可以大于 1，是因为锁可以重入，每次重入都加上 1
private volatile int state;

// 代表当前持有独占锁的线程，举个最重要的使用例子，因为锁可以重入
// reentrantLock.lock()可以嵌套调用多次，所以每次用这个来判断当前线程是否已经拥有了锁
// if (currentThread == getExclusiveOwnerThread()) {state++}
private transient Thread exclusiveOwnerThread; //继承自AbstractOwnableSynchronizer

大概了解了 aqs 底层的双向等待队列，
结构是这样的

每个 node 里面主要是的代码结构也比较简单

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static final class Node {
    // 标识节点当前在共享模式下
    static final Node SHARED = new Node();
    // 标识节点当前在独占模式下
    static final Node EXCLUSIVE = null;

    // ======== 下面的几个int常量是给waitStatus用的 ===========
    /** waitStatus value to indicate thread has cancelled */
    // 代码此线程取消了争抢这个锁
    static final int CANCELLED =  1;
    /** waitStatus value to indicate successor's thread needs unparking */
    // 官方的描述是，其表示当前node的后继节点对应的线程需要被唤醒
    static final int SIGNAL    = -1;
    /** waitStatus value to indicate thread is waiting on condition */
    // 本文不分析condition，所以略过吧，下一篇文章会介绍这个
    static final int CONDITION = -2;
    /**
     * waitStatus value to indicate the next acquireShared should
     * unconditionally propagate
     */
    // 同样的不分析，略过吧
    static final int PROPAGATE = -3;
    // =====================================================


    // 取值为上面的1、-1、-2、-3，或者0(以后会讲到)
    // 这么理解，暂时只需要知道如果这个值 大于0 代表此线程取消了等待，
    //    ps: 半天抢不到锁，不抢了，ReentrantLock是可以指定timeouot的。。。
    volatile int waitStatus;
    // 前驱节点的引用
    volatile Node prev;
    // 后继节点的引用
    volatile Node next;
    // 这个就是线程本尊
    volatile Thread thread;

}

其实可以主要关注这个 waitStatus 因为这个是后面的节点给前面的节点设置的，等于-1 的时候代表后面有节点等待，需要去唤醒，
这里使用了一个变种的 CLH 队列实现，CLH 队列相关内容可以查看这篇 自旋锁、排队自旋锁、MCS锁、CLH锁

发表于 更新于 分类于 阅读次数： Disqus：

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

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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

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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. 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. 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，

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    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

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    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. 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. 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. 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. 发现一个不错的openresty站点
    地址

发表于 更新于 分类于 阅读次数： Disqus：

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

发表于 更新于 分类于 阅读次数： Disqus：

rabbitmq 介绍

接触了一下rabbitmq，原来在选型的时候是在rabbitmq跟kafka之间做选择，网上搜了一下之后发现kafka的优势在于吞吐量，而rabbitmq相对注重可靠性，因为应用在im上，需要保证消息不能丢失所以就暂时选定rabbitmq，
Message Queue的需求由来已久，80年代最早在金融交易中，高盛等公司采用Teknekron公司的产品，当时的Message queuing软件叫做：the information bus（TIB）。 TIB被电信和通讯公司采用，路透社收购了Teknekron公司。之后，IBM开发了MQSeries，微软开发了Microsoft Message Queue（MSMQ）。这些商业MQ供应商的问题是厂商锁定，价格高昂。2001年，Java Message queuing试图解决锁定和交互性的问题，但对应用来说反而更加麻烦了。
RabbitMQ采用Erlang语言开发。Erlang语言由Ericson设计，专门为开发concurrent和distribution系统的一种语言，在电信领域使用广泛。OTP（Open Telecom Platform）作为Erlang语言的一部分，包含了很多基于Erlang开发的中间件／库／工具，如mnesia／SASL，极大方便了Erlang应用的开发。OTP就类似于Python语言中众多的module，用户借助这些module可以很方便的开发应用。
于是2004年，摩根大通和iMatrix开始着手Advanced Message Queuing Protocol （AMQP）开放标准的开发。2006年，AMQP规范发布。2007年，Rabbit技术公司基于AMQP标准开发的RabbitMQ 1.0 发布。所有主要的编程语言均有与代理接口通讯的客户端库。

简单的使用经验

通俗的理解

这里介绍下其中的一些概念，connection表示和队列服务器的连接，一般情况下是tcp连接， channel表示通道，可以在一个连接上建立多个通道，这里主要是节省了tcp连接握手的成本，exchange可以理解成一个路由器，将消息推送给对应的队列queue，其实是像一个订阅的模式。

集群经验

rabbitmqctl stop这个是关闭rabbitmq，在搭建集群时候先关闭服务，然后使用rabbitmq-server -detached静默启动，这时候使用rabbitmqctl cluster_status查看集群状态，因为还没将节点加入集群，所以只能看到类似

1
2
3
4
Cluster status of node rabbit@rabbit1 ...
[{nodes,[{disc,[rabbit@rabbit1,rabbit@rabbit2,rabbit@rabbit3]}]},
 {running_nodes,[rabbit@rabbit2,rabbit@rabbit1]}]
...done.

然后就可以把当前节点加入集群，

1
2
3
4
5
6
7
rabbit2$ rabbitmqctl stop_app #这个stop_app与stop的区别是前者停的是rabbitmq应用，保留erlang节点，
                              #后者是停止了rabbitmq和erlang节点
Stopping node rabbit@rabbit2 ...done.
rabbit2$ rabbitmqctl join_cluster rabbit@rabbit1 #这里可以用--ram指定将当前节点作为内存节点加入集群
Clustering node rabbit@rabbit2 with [rabbit@rabbit1] ...done.
rabbit2$ rabbitmqctl start_app
Starting node rabbit@rabbit2 ...done.

其他可以参考官方文档

一些坑

消息丢失

这里碰到过一个坑，对于使用exchange来做消息路由的，会有一个情况，就是在routing_key没被订阅的时候，会将该条找不到路由对应的queue的消息丢掉What happens if we break our contract and send a message with one or four words, like "orange" or "quick.orange.male.rabbit"? Well, these messages won't match any bindings and will be lost.对应链接,而当使用空的exchange时，会保留消息，当出现消费者的时候就可以将收到之前生产者所推送的消息对应链接,这里就是用了空的exchange。

集群搭建

集群搭建的时候有个erlang vm生成的random cookie，这个是用来做集群之间认证的，相同的cookie才能连接，但是如果通过vim打开复制后在其他几点新建文件写入会多一个换行，导致集群建立是报错，所以这里最好使用scp等传输命令直接传输cookie文件，同时要注意下cookie的文件权限。
另外在集群搭建的时候如果更改过hostname，那么要把rabbitmq的数据库删除，否则启动后会马上挂掉

0%
\ No newline at end of file diff --git a/search.xml b/search.xml index 3cf54cd82d..b9fcac0a37 100644 --- a/search.xml +++ b/search.xml @@ -1,5 +1,34 @@ + + 2019年终总结 + /2020/02/01/2019%E5%B9%B4%E7%BB%88%E6%80%BB%E7%BB%93/ + 今天是农历初八了，年前一个月的时候就准备做下今年的年终总结，可是写了一点觉得太情绪化了，希望后面写个平淡点的，正好最近技术方面还没有看到一个完整成文的内容，就来写一下这一年的总结，尽量少写一点太情绪化的东西。

+

跳槽

年初换了个公司，也算换了个环境，跟前公司不太一样，做的事情方向也不同，可能是侧重点不同，一开始有些不适应，主要是压力上，会觉得压力比较大，但是总体来说与人相处的部分还是不错的，做的技术方向还是Java，这里也感谢前东家让我有机会转了Java，个人感觉杭州整个市场还是Java比较有优势，不过在开始的时候总觉得对Java有点不适应，应该值得深究的东西还是很多的，而且对于面试来说，也是有很多可以问的，后面慢慢发现除开某里等一线超一线互联网公司之外，大部分的面试还是有大概的套路跟大纲的，不过更细致的则因人而异了，面试有时候也还看缘分，面试官关注的点跟应试者比较契合的话就很容易通过面试，不然的话总会有能刁难或者理性化地说比较难回答的问题。这个后面可以单独说一下，先按下不表。
刚进公司没多久就负责比较重要的项目，工期也比较紧张，整体来说那段时间的压力的确是比较大的，不过总算最后结果不坏，这里应该说对一些原来在前东家都是掌握的不太好的部分，比如maven，其实maven对于java程序员来说还是很重要的，但是我碰到过的面试基本没问过这个，我自己也在后面的面试中没问过相关的，不知道咋问，比如dependence分析、冲突解决，比如对bean的理解，这个算是我一直以来的疑问点，因为以前刚开始学Java学spring，上来就是bean，但是bean到底是啥，IOC是啥，可能网上的文章跟大多数书籍跟我的理解思路不太match，导致一直不能很好的理解这玩意，到后面才理解，要理解这个bean，需要有两个基本概念，一个是面向对象，一个是对象容器跟依赖反转，还是只说到这，后面可以有专题说一下，总之自认为技术上有了不小的长进了，方向上应该是偏实用的。这个重要的项目完成后慢慢能喘口气了，后面也有一些比较紧急且工作量大的，不过在我TL的帮助下还是能尽量协调好资源。

+

面试

后面因为项目比较多，缺少开发，所以也参与帮忙做一些面试，这里总体感觉是面的候选人还是比较多样的，有些工作了蛮多年但是一些基础问题回答的不好，有些还是在校学生，但是面试技巧不错，针对常见的面试题都有不错的准备，不过还是觉得光靠这些面试题不能完全说明问题，真正工作了需要的是解决问题的人，而不是会背题的，退一步来说能好好准备面试还是比较重要的，也是双向选择中的基本尊重，印象比较深刻的是参加了去杭州某高校的校招面试，感觉参加校招的同学还是很多的，大部分是20年将毕业的研究生，挺多都是基础很扎实，对比起我刚要毕业时还是很汗颜，挺多来面试的同学都非常不错，那天强度也很大，从下午到那开始一直面到六七点，在这祝福那些来面试的同学，也都不容易的，能找到心仪的工作。

+

技术方向

这一年前大半部分还是比较焦虑不能恢复那种主动找时间学习的状态，可能换了公司是主要的原因，初期有个适应的过程也比较正常，总体来说可能是到九十月份开始慢慢有所改善，对这些方面有学习了下，

+
    +
  • spring方向，spring真的是个庞然大物，但是还是要先抓住根本，慢慢发散去了解其他的细节，抓住bean的生命周期，当然也不是死记硬背，让我一个个背下来我也不行，但是知道它究竟是干嘛的，有啥用，并且在工作中能用起来是最重要的
    • +
  • mysql数据库，这部分主要是关注了mvcc，知道了个大概，源码实现细节还没具体研究，有时间可以来个专题（一大堆待写的内容）
    • +
  • java的一些源码，比如aqs这种，结合文章看了下源码，一开始总感觉静不下心来看，然后有一次被LD刺激了下就看完了，包括conditionObject等
    • +
  • redis的源码，这里包括了Redis分布式锁和redis的数据结构源码，已经写成文章，不过比较着急成文，所以质量不是特别好，希望后面再来补补
    • +
  • jvm源码，这部分正好是想了解下g1收集器，大概把周志明的书看完了，但是还没完整的理解掌握，还有就是g1收集器的部分，一是概念部分大概理解了，后面是就是想从源码层面去学习理解，这也是新一年的主要计划
    • +
  • mq的部分是了解了zero copy，sendfile等，跟消息队列主题关系不大🤦‍♂️
    这么看还是学了点东西的，希望新一年再接再厉。
    • +
+

生活

住的地方没变化，主要是周边设施比较方便，暂时没找到更好的就没打算换，主要的问题是没电梯，一开始没觉得有啥，真正住起来还是觉得比较累的，希望后面租的可以有电梯，或者楼层低一点，还有就是要通下水道，第一次让师傅上门，花了两百大洋，后来自学成才了，让师傅通了一次才撑了一个月就不行了，后面自己通的差不多可以撑半年，还是比较有成就感的😀，然后就是跑步了，年初的时候去了紫金港跑步，后面因为工作的原因没去了，但是公司的跑步机倒是让我重拾起这个唯一的运动健身项目，后面因为肠胃问题，体重也需要控制，所以就周末回来也在家这边坚持跑步，下半年的话基本保持每周一次以上，比较那些跑马拉松的大牛还是差距很大，不过也是突破自我了，有一次跑了12公里，最远的距离，而且后面感觉跑十公里也不是特别吃不消了，这一年达成了300公里的目标，体重也稍有下降，比较满意的结果。

+

期待

希望工作方面技术方面能有所长进，生活上能多点时间陪家人，继续跑步减肥，家人健健康康的，嗯

+]]> + + 生活 + 年终总结 + 2019 + + + 生活 + 年终总结 + 2019 + + 村上春树《1Q84》读后感 /2019/12/18/1Q84%E8%AF%BB%E5%90%8E%E6%84%9F/ @@ -12,8 +41,8 @@

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~

]]> - 读后感 生活 + 读后感 村上春树 @@ -46,32 +75,38 @@ - 2019年终总结 - /2020/02/01/2019%E5%B9%B4%E7%BB%88%E6%80%BB%E7%BB%93/ - 今天是农历初八了，年前一个月的时候就准备做下今年的年终总结，可是写了一点觉得太情绪化了，希望后面写个平淡点的，正好最近技术方面还没有看到一个完整成文的内容，就来写一下这一年的总结，尽量少写一点太情绪化的东西。

-

跳槽

年初换了个公司，也算换了个环境，跟前公司不太一样，做的事情方向也不同，可能是侧重点不同，一开始有些不适应，主要是压力上，会觉得压力比较大，但是总体来说与人相处的部分还是不错的，做的技术方向还是Java，这里也感谢前东家让我有机会转了Java，个人感觉杭州整个市场还是Java比较有优势，不过在开始的时候总觉得对Java有点不适应，应该值得深究的东西还是很多的，而且对于面试来说，也是有很多可以问的，后面慢慢发现除开某里等一线超一线互联网公司之外，大部分的面试还是有大概的套路跟大纲的，不过更细致的则因人而异了，面试有时候也还看缘分，面试官关注的点跟应试者比较契合的话就很容易通过面试，不然的话总会有能刁难或者理性化地说比较难回答的问题。这个后面可以单独说一下，先按下不表。
刚进公司没多久就负责比较重要的项目，工期也比较紧张，整体来说那段时间的压力的确是比较大的，不过总算最后结果不坏，这里应该说对一些原来在前东家都是掌握的不太好的部分，比如maven，其实maven对于java程序员来说还是很重要的，但是我碰到过的面试基本没问过这个，我自己也在后面的面试中没问过相关的，不知道咋问，比如dependence分析、冲突解决，比如对bean的理解，这个算是我一直以来的疑问点，因为以前刚开始学Java学spring，上来就是bean，但是bean到底是啥，IOC是啥，可能网上的文章跟大多数书籍跟我的理解思路不太match，导致一直不能很好的理解这玩意，到后面才理解，要理解这个bean，需要有两个基本概念，一个是面向对象，一个是对象容器跟依赖反转，还是只说到这，后面可以有专题说一下，总之自认为技术上有了不小的长进了，方向上应该是偏实用的。这个重要的项目完成后慢慢能喘口气了，后面也有一些比较紧急且工作量大的，不过在我TL的帮助下还是能尽量协调好资源。

-

面试

后面因为项目比较多，缺少开发，所以也参与帮忙做一些面试，这里总体感觉是面的候选人还是比较多样的，有些工作了蛮多年但是一些基础问题回答的不好，有些还是在校学生，但是面试技巧不错，针对常见的面试题都有不错的准备，不过还是觉得光靠这些面试题不能完全说明问题，真正工作了需要的是解决问题的人，而不是会背题的，退一步来说能好好准备面试还是比较重要的，也是双向选择中的基本尊重，印象比较深刻的是参加了去杭州某高校的校招面试，感觉参加校招的同学还是很多的，大部分是20年将毕业的研究生，挺多都是基础很扎实，对比起我刚要毕业时还是很汗颜，挺多来面试的同学都非常不错，那天强度也很大，从下午到那开始一直面到六七点，在这祝福那些来面试的同学，也都不容易的，能找到心仪的工作。

-

技术方向

这一年前大半部分还是比较焦虑不能恢复那种主动找时间学习的状态，可能换了公司是主要的原因，初期有个适应的过程也比较正常，总体来说可能是到九十月份开始慢慢有所改善，对这些方面有学习了下，

-
    -
  • spring方向，spring真的是个庞然大物，但是还是要先抓住根本，慢慢发散去了解其他的细节，抓住bean的生命周期，当然也不是死记硬背，让我一个个背下来我也不行，但是知道它究竟是干嘛的，有啥用，并且在工作中能用起来是最重要的
    • -
  • mysql数据库，这部分主要是关注了mvcc，知道了个大概，源码实现细节还没具体研究，有时间可以来个专题（一大堆待写的内容）
    • -
  • java的一些源码，比如aqs这种，结合文章看了下源码，一开始总感觉静不下心来看，然后有一次被LD刺激了下就看完了，包括conditionObject等
    • -
  • redis的源码，这里包括了Redis分布式锁和redis的数据结构源码，已经写成文章，不过比较着急成文，所以质量不是特别好，希望后面再来补补
    • -
  • jvm源码，这部分正好是想了解下g1收集器，大概把周志明的书看完了，但是还没完整的理解掌握，还有就是g1收集器的部分，一是概念部分大概理解了，后面是就是想从源码层面去学习理解，这也是新一年的主要计划
    • -
  • mq的部分是了解了zero copy，sendfile等，跟消息队列主题关系不大🤦‍♂️
    这么看还是学了点东西的，希望新一年再接再厉。
    • -
-

生活

住的地方没变化，主要是周边设施比较方便，暂时没找到更好的就没打算换，主要的问题是没电梯，一开始没觉得有啥，真正住起来还是觉得比较累的，希望后面租的可以有电梯，或者楼层低一点，还有就是要通下水道，第一次让师傅上门，花了两百大洋，后来自学成才了，让师傅通了一次才撑了一个月就不行了，后面自己通的差不多可以撑半年，还是比较有成就感的😀，然后就是跑步了，年初的时候去了紫金港跑步，后面因为工作的原因没去了，但是公司的跑步机倒是让我重拾起这个唯一的运动健身项目，后面因为肠胃问题，体重也需要控制，所以就周末回来也在家这边坚持跑步，下半年的话基本保持每周一次以上，比较那些跑马拉松的大牛还是差距很大，不过也是突破自我了，有一次跑了12公里，最远的距离，而且后面感觉跑十公里也不是特别吃不消了，这一年达成了300公里的目标，体重也稍有下降，比较满意的结果。

-

期待

希望工作方面技术方面能有所长进，生活上能多点时间陪家人，继续跑步减肥，家人健健康康的，嗯

+ 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 + 年中总结 + 2020 + + + 生活 + 2020 + 年中总结 + + + + 2022 年终总结 + /2023/01/15/2022-%E5%B9%B4%E7%BB%88%E6%80%BB%E7%BB%93/ + 一年又一年，时间匆匆，这一年过得不太容易，很多事情都是来得猝不及防，很多规划也照例是没有完成，今年更多了一些，又是比较丧的一篇总结
工作上的变化让我多理解了一些社会跟职场的现实吧，可能的确是我不够优秀，也可能是其他，说回我自身，在工作中今年应该是收获比较一般的一年，不能说没有，对原先不熟悉的业务的掌握程度有了比较大的提升，只是问题依旧存在，也挺难推动完全改变，只能尽自己所能，而这一点也主要是在团队中的定位因为前面说的一些原因，在前期不明确，限制比较大，虽然现在并没有完全解决，但也有了一些明显的改善，如果明年继续为这家公司服务，希望能有所突破，在人心沟通上的技巧总是比较反感，可也是不得不使用或者说被迫学习使用的，LD说我的对错观太强了，拗不过来，希望能有所改变。
长远的规划上没有什么明确的想法，很容易否定原来的各种想法，见识过各种现实的残酷，明白以前的一些想法不够全面或者比较幼稚，想有更上一层楼的机会，只是不希望是通过自己不认可的方式。比较能接受的是通过提升自己的技术和执行力，能够有更进一步的可能。
技术上是挺失败的去年跟前年还是能看一些书，学一些东西，今年少了很多，可能对原来比较熟悉的都有些遗忘，最近有在改善博客的内容，能更多的是系列化的，由浅入深，只是还很不完善，没什么规划，体系上也还不完整，不过还是以mybatis作为一个开头，后续新开始的内容或者原先写过的相关的都能做个整理，不再是想到啥就写点啥。最近的一个重点是在k8s上，学习方式跟一些特别优秀的人比起来还是会慢一些，不过也是自己的方法，能够更深入的理解整个体系，并讲解出来，可能会尝试采用视频的方式，对一些比较好的内容做尝试，看看会不会有比较好的数据和反馈，在22年还苟着周更的独立技术博客也算是比较稀有了的，其他站的发布也要勤一些，形成所谓的“矩阵”。
跑步减肥这个么还是比较惨，22年只跑了368公里，比21年少了85公里，有一些客观但很多是主观的原因，还是需要跑起来，只是减肥也很迫切，体重比较大跑步还是有些压力的，买了动感单车，就是时间稍长屁股痛这个目前比较难解决，骑还是每天在骑就是强度跟时间不太够，要保证每天30分钟的量可能会比较好。
加油吧，愿23年家人和自己都健康，顺遂。大家也一样。

]]> 生活 年终总结 - 2019 生活 年终总结 - 2019 + 2022 + 2023 @@ -92,9 +127,9 @@ 生活 2021 + 年中总结 技术 读书 - 年中总结 @@ -115,38 +150,21 @@ - 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 - 年中总结 - 2020 - - - 生活 - 2020 - 年中总结 - - - - 2022 年终总结 - /2023/01/15/2022-%E5%B9%B4%E7%BB%88%E6%80%BB%E7%BB%93/ - 一年又一年，时间匆匆，这一年过得不太容易，很多事情都是来得猝不及防，很多规划也照例是没有完成，今年更多了一些，又是比较丧的一篇总结
工作上的变化让我多理解了一些社会跟职场的现实吧，可能的确是我不够优秀，也可能是其他，说回我自身，在工作中今年应该是收获比较一般的一年，不能说没有，对原先不熟悉的业务的掌握程度有了比较大的提升，只是问题依旧存在，也挺难推动完全改变，只能尽自己所能，而这一点也主要是在团队中的定位因为前面说的一些原因，在前期不明确，限制比较大，虽然现在并没有完全解决，但也有了一些明显的改善，如果明年继续为这家公司服务，希望能有所突破，在人心沟通上的技巧总是比较反感，可也是不得不使用或者说被迫学习使用的，LD说我的对错观太强了，拗不过来，希望能有所改变。
长远的规划上没有什么明确的想法，很容易否定原来的各种想法，见识过各种现实的残酷，明白以前的一些想法不够全面或者比较幼稚，想有更上一层楼的机会，只是不希望是通过自己不认可的方式。比较能接受的是通过提升自己的技术和执行力，能够有更进一步的可能。
技术上是挺失败的去年跟前年还是能看一些书，学一些东西，今年少了很多，可能对原来比较熟悉的都有些遗忘，最近有在改善博客的内容，能更多的是系列化的，由浅入深，只是还很不完善，没什么规划，体系上也还不完整，不过还是以mybatis作为一个开头，后续新开始的内容或者原先写过的相关的都能做个整理，不再是想到啥就写点啥。最近的一个重点是在k8s上，学习方式跟一些特别优秀的人比起来还是会慢一些，不过也是自己的方法，能够更深入的理解整个体系，并讲解出来，可能会尝试采用视频的方式，对一些比较好的内容做尝试，看看会不会有比较好的数据和反馈，在22年还苟着周更的独立技术博客也算是比较稀有了的，其他站的发布也要勤一些，形成所谓的“矩阵”。
跑步减肥这个么还是比较惨，22年只跑了368公里，比21年少了85公里，有一些客观但很多是主观的原因，还是需要跑起来，只是减肥也很迫切，体重比较大跑步还是有些压力的，买了动感单车，就是时间稍长屁股痛这个目前比较难解决，骑还是每天在骑就是强度跟时间不太够，要保证每天30分钟的量可能会比较好。
加油吧，愿23年家人和自己都健康，顺遂。大家也一样。

-]]> + 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;
    }
};
]]> - 生活 - 年终总结 + leetcode - 生活 - 年终总结 - 2022 - 2023 + leetcode + c++ @@ -190,24 +208,6 @@ unlock - - 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 - - - leetcode - c++ - - add-two-number /2015/04/14/Add-Two-Number/ @@ -248,6 +248,47 @@ environment + + Apollo 的 value 注解是怎么自动更新的 + /2020/11/01/Apollo-%E7%9A%84-value-%E6%B3%A8%E8%A7%A3%E6%98%AF%E6%80%8E%E4%B9%88%E8%87%AA%E5%8A%A8%E6%9B%B4%E6%96%B0%E7%9A%84/ + 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

+
@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

+
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

+
@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

+]]> + + Java + Apollo + value + + + Java + Apollo + environment + value + 注解 + + + + AbstractQueuedSynchronizer + /2019/09/23/AbstractQueuedSynchronizer/ + 最近看了大神的 AQS 的文章，之前总是断断续续地看一点，每次都知难而退，下次看又从头开始，昨天总算硬着头皮看完了第一部分
首先 AQS 只要有这些属性

+
// 头结点，你直接把它当做 当前持有锁的线程 可能是最好理解的
private transient volatile Node head;

// 阻塞的尾节点，每个新的节点进来，都插入到最后，也就形成了一个链表
private transient volatile Node tail;

// 这个是最重要的，代表当前锁的状态，0代表没有被占用，大于 0 代表有线程持有当前锁
// 这个值可以大于 1，是因为锁可以重入，每次重入都加上 1
private volatile int state;

// 代表当前持有独占锁的线程，举个最重要的使用例子，因为锁可以重入
// reentrantLock.lock()可以嵌套调用多次，所以每次用这个来判断当前线程是否已经拥有了锁
// if (currentThread == getExclusiveOwnerThread()) {state++}
private transient Thread exclusiveOwnerThread; //继承自AbstractOwnableSynchronizer
+

大概了解了 aqs 底层的双向等待队列，
结构是这样的

每个 node 里面主要是的代码结构也比较简单

+
static final class Node {
    // 标识节点当前在共享模式下
    static final Node SHARED = new Node();
    // 标识节点当前在独占模式下
    static final Node EXCLUSIVE = null;

    // ======== 下面的几个int常量是给waitStatus用的 ===========
    /** waitStatus value to indicate thread has cancelled */
    // 代码此线程取消了争抢这个锁
    static final int CANCELLED =  1;
    /** waitStatus value to indicate successor's thread needs unparking */
    // 官方的描述是，其表示当前node的后继节点对应的线程需要被唤醒
    static final int SIGNAL    = -1;
    /** waitStatus value to indicate thread is waiting on condition */
    // 本文不分析condition，所以略过吧，下一篇文章会介绍这个
    static final int CONDITION = -2;
    /**
     * waitStatus value to indicate the next acquireShared should
     * unconditionally propagate
     */
    // 同样的不分析，略过吧
    static final int PROPAGATE = -3;
    // =====================================================


    // 取值为上面的1、-1、-2、-3，或者0(以后会讲到)
    // 这么理解，暂时只需要知道如果这个值 大于0 代表此线程取消了等待，
    //    ps: 半天抢不到锁，不抢了，ReentrantLock是可以指定timeouot的。。。
    volatile int waitStatus;
    // 前驱节点的引用
    volatile Node prev;
    // 后继节点的引用
    volatile Node next;
    // 这个就是线程本尊
    volatile Thread thread;

}
+

其实可以主要关注这个 waitStatus 因为这个是后面的节点给前面的节点设置的，等于-1 的时候代表后面有节点等待，需要去唤醒，
这里使用了一个变种的 CLH 队列实现，CLH 队列相关内容可以查看这篇 自旋锁、排队自旋锁、MCS锁、CLH锁

+]]> + + java + + + java + aqs + + 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/ @@ -298,43 +339,37 @@ - Apollo 的 value 注解是怎么自动更新的 - /2020/11/01/Apollo-%E7%9A%84-value-%E6%B3%A8%E8%A7%A3%E6%98%AF%E6%80%8E%E4%B9%88%E8%87%AA%E5%8A%A8%E6%9B%B4%E6%96%B0%E7%9A%84/ - 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

-
@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

-
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

-
@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

+ 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);
    }
+ + + + ]]> Java - Apollo - value - - - Java - Apollo - environment - value - 注解 - - - - AbstractQueuedSynchronizer - /2019/09/23/AbstractQueuedSynchronizer/ - 最近看了大神的 AQS 的文章，之前总是断断续续地看一点，每次都知难而退，下次看又从头开始，昨天总算硬着头皮看完了第一部分
首先 AQS 只要有这些属性

-
// 头结点，你直接把它当做 当前持有锁的线程 可能是最好理解的
private transient volatile Node head;

// 阻塞的尾节点，每个新的节点进来，都插入到最后，也就形成了一个链表
private transient volatile Node tail;

// 这个是最重要的，代表当前锁的状态，0代表没有被占用，大于 0 代表有线程持有当前锁
// 这个值可以大于 1，是因为锁可以重入，每次重入都加上 1
private volatile int state;

// 代表当前持有独占锁的线程，举个最重要的使用例子，因为锁可以重入
// reentrantLock.lock()可以嵌套调用多次，所以每次用这个来判断当前线程是否已经拥有了锁
// if (currentThread == getExclusiveOwnerThread()) {state++}
private transient Thread exclusiveOwnerThread; //继承自AbstractOwnableSynchronizer
-

大概了解了 aqs 底层的双向等待队列，
结构是这样的

每个 node 里面主要是的代码结构也比较简单

-
static final class Node {
    // 标识节点当前在共享模式下
    static final Node SHARED = new Node();
    // 标识节点当前在独占模式下
    static final Node EXCLUSIVE = null;

    // ======== 下面的几个int常量是给waitStatus用的 ===========
    /** waitStatus value to indicate thread has cancelled */
    // 代码此线程取消了争抢这个锁
    static final int CANCELLED =  1;
    /** waitStatus value to indicate successor's thread needs unparking */
    // 官方的描述是，其表示当前node的后继节点对应的线程需要被唤醒
    static final int SIGNAL    = -1;
    /** waitStatus value to indicate thread is waiting on condition */
    // 本文不分析condition，所以略过吧，下一篇文章会介绍这个
    static final int CONDITION = -2;
    /**
     * waitStatus value to indicate the next acquireShared should
     * unconditionally propagate
     */
    // 同样的不分析，略过吧
    static final int PROPAGATE = -3;
    // =====================================================


    // 取值为上面的1、-1、-2、-3，或者0(以后会讲到)
    // 这么理解，暂时只需要知道如果这个值 大于0 代表此线程取消了等待，
    //    ps: 半天抢不到锁，不抢了，ReentrantLock是可以指定timeouot的。。。
    volatile int waitStatus;
    // 前驱节点的引用
    volatile Node prev;
    // 后继节点的引用
    volatile Node next;
    // 这个就是线程本尊
    volatile Thread thread;

}
-

其实可以主要关注这个 waitStatus 因为这个是后面的节点给前面的节点设置的，等于-1 的时候代表后面有节点等待，需要去唤醒，
这里使用了一个变种的 CLH 队列实现，CLH 队列相关内容可以查看这篇 自旋锁、排队自旋锁、MCS锁、CLH锁

-]]> - - java + 并发 java + 并发 + j.u.c aqs @@ -395,6 +430,27 @@ 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 + + + Java + Disruptor + + + + 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 @@ -464,59 +520,32 @@ - 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);
    }
- - - + G1收集器概述 + /2020/02/09/G1%E6%94%B6%E9%9B%86%E5%99%A8%E6%A6%82%E8%BF%B0/ + G1: The Garbage-First Collector, 垃圾回收优先的垃圾回收器，目标是用户多核 cpu 和大内存的机器，最大的特点就是可预测的停顿时间，官方给出的介绍是提供一个用户在大的堆内存情况下一个低延迟表现的解决方案，通常是 6GB 及以上的堆大小，有低于 0.5 秒稳定的可预测的停顿时间。

+

这里主要介绍这个比较新的垃圾回收器，在 G1 之前的垃圾回收器都是基于如下图的内存结构分布，有新生代，老年代和永久代（jdk8 之前），然后G1 往前的那些垃圾回收器都有个分代，比如 serial，parallel 等，一般有个应用的组合，最初的 serial 和 serial old，因为新生代和老年代的收集方式不太一样，新生代主要是标记复制，所以有 eden 跟两个 survival区，老年代一般用标记整理方式，而 G1 对这个不太一样。

看一下 G1 的内存分布

可以看到这有很大的不同，G1 通过将内存分成大小相等的 region，每个region是存在于一个连续的虚拟内存范围，对于某个 region 来说其角色是类似于原来的收集器的Eden、Survivor、Old Generation，这个具体在代码层面

+
// We encode the value of the heap region type so the generation can be
 // determined quickly. The tag is split into two parts:
 //
 //   major type (young, old, humongous, archive)           : top N-1 bits
 //   minor type (eden / survivor, starts / cont hum, etc.) : bottom 1 bit
 //
 // If there's need to increase the number of minor types in the
 // future, we'll have to increase the size of the latter and hence
 // decrease the size of the former.
 //
 // 00000 0 [ 0] Free
 //
 // 00001 0 [ 2] Young Mask
 // 00001 0 [ 2] Eden
 // 00001 1 [ 3] Survivor
 //
 // 00010 0 [ 4] Humongous Mask
 // 00100 0 [ 8] Pinned Mask
 // 00110 0 [12] Starts Humongous
 // 00110 1 [13] Continues Humongous
 //
 // 01000 0 [16] Old Mask
 //
 // 10000 0 [32] Archive Mask
 // 11100 0 [56] Open Archive
 // 11100 1 [57] Closed Archive
 //
 typedef enum {
   FreeTag               = 0,

   YoungMask             = 2,
   EdenTag               = YoungMask,
   SurvTag               = YoungMask + 1,

   HumongousMask         = 4,
   PinnedMask            = 8,
   StartsHumongousTag    = HumongousMask | PinnedMask,
   ContinuesHumongousTag = HumongousMask | PinnedMask + 1,

   OldMask               = 16,
   OldTag                = OldMask,

   // Archive regions are regions with immutable content (i.e. not reclaimed, and
   // not allocated into during regular operation). They differ in the kind of references
   // allowed for the contained objects:
   // - Closed archive regions form a separate self-contained (closed) object graph
   // within the set of all of these regions. No references outside of closed
   // archive regions are allowed.
   // - Open archive regions have no restrictions on the references of their objects.
   // Objects within these regions are allowed to have references to objects
   // contained in any other kind of regions.
   ArchiveMask           = 32,
   OpenArchiveTag        = ArchiveMask | PinnedMask | OldMask,
   ClosedArchiveTag      = ArchiveMask | PinnedMask | OldMask + 1
 } Tag;
+

hotspot/share/gc/g1/heapRegionType.hpp

+

当执行垃圾收集时，G1以类似于CMS收集器的方式运行。 G1执行并发全局标记阶段，以确定整个堆中对象的存活性。标记阶段完成后，G1知道哪些region是基本空的。它首先收集这些region，通常会产生大量的可用空间。这就是为什么这种垃圾收集方法称为“垃圾优先”的原因。顾名思义，G1将其收集和压缩活动集中在可能充满可回收对象（即垃圾）的堆区域。 G1使用暂停预测模型来满足用户定义的暂停时间目标，并根据指定的暂停时间目标选择要收集的区域数。

+

由G1标识为可回收的区域是使用撤离的方式(Evacuation)。 G1将对象从堆的一个或多个区域复制到堆上的单个区域，并在此过程中压缩并释放内存。撤离是在多处理器上并行执行的，以减少暂停时间并增加吞吐量。因此，对于每次垃圾收集，G1都在用户定义的暂停时间内连续工作以减少碎片。这是优于前面两种方法的。 CMS（并发标记扫描）垃圾收集器不进行压缩。 ParallelOld垃圾回收仅执行整个堆压缩，这导致相当长的暂停时间。

+

需要重点注意的是，G1不是实时收集器。它很有可能达到设定的暂停时间目标，但并非绝对确定。 G1根据先前收集的数据，估算在用户指定的目标时间内可以收集多少个区域。因此，收集器具有收集区域成本的合理准确的模型，并且收集器使用此模型来确定要收集哪些和多少个区域，同时保持在暂停时间目标之内。

+

注意：G1同时具有并发（与应用程序线程一起运行，例如优化，标记，清理）和并行（多线程，例如stw）阶段。Full GC仍然是单线程的，但是如果正确调优，您的应用程序应该可以避免Full GC。

+

在前面那篇中在代码层面简单的了解了这个可预测时间的过程，这也是 G1 的一大特点。

]]> 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。

-]]> - - Java + JVM + C++ + GC Java - Disruptor + C++ + JVM + G1 + GC + Garbage-First Collector @@ -545,35 +574,6 @@ Disruptor - - G1收集器概述 - /2020/02/09/G1%E6%94%B6%E9%9B%86%E5%99%A8%E6%A6%82%E8%BF%B0/ - G1: The Garbage-First Collector, 垃圾回收优先的垃圾回收器，目标是用户多核 cpu 和大内存的机器，最大的特点就是可预测的停顿时间，官方给出的介绍是提供一个用户在大的堆内存情况下一个低延迟表现的解决方案，通常是 6GB 及以上的堆大小，有低于 0.5 秒稳定的可预测的停顿时间。

-

这里主要介绍这个比较新的垃圾回收器，在 G1 之前的垃圾回收器都是基于如下图的内存结构分布，有新生代，老年代和永久代（jdk8 之前），然后G1 往前的那些垃圾回收器都有个分代，比如 serial，parallel 等，一般有个应用的组合，最初的 serial 和 serial old，因为新生代和老年代的收集方式不太一样，新生代主要是标记复制，所以有 eden 跟两个 survival区，老年代一般用标记整理方式，而 G1 对这个不太一样。

看一下 G1 的内存分布

可以看到这有很大的不同，G1 通过将内存分成大小相等的 region，每个region是存在于一个连续的虚拟内存范围，对于某个 region 来说其角色是类似于原来的收集器的Eden、Survivor、Old Generation，这个具体在代码层面

-
// We encode the value of the heap region type so the generation can be
 // determined quickly. The tag is split into two parts:
 //
 //   major type (young, old, humongous, archive)           : top N-1 bits
 //   minor type (eden / survivor, starts / cont hum, etc.) : bottom 1 bit
 //
 // If there's need to increase the number of minor types in the
 // future, we'll have to increase the size of the latter and hence
 // decrease the size of the former.
 //
 // 00000 0 [ 0] Free
 //
 // 00001 0 [ 2] Young Mask
 // 00001 0 [ 2] Eden
 // 00001 1 [ 3] Survivor
 //
 // 00010 0 [ 4] Humongous Mask
 // 00100 0 [ 8] Pinned Mask
 // 00110 0 [12] Starts Humongous
 // 00110 1 [13] Continues Humongous
 //
 // 01000 0 [16] Old Mask
 //
 // 10000 0 [32] Archive Mask
 // 11100 0 [56] Open Archive
 // 11100 1 [57] Closed Archive
 //
 typedef enum {
   FreeTag               = 0,

   YoungMask             = 2,
   EdenTag               = YoungMask,
   SurvTag               = YoungMask + 1,

   HumongousMask         = 4,
   PinnedMask            = 8,
   StartsHumongousTag    = HumongousMask | PinnedMask,
   ContinuesHumongousTag = HumongousMask | PinnedMask + 1,

   OldMask               = 16,
   OldTag                = OldMask,

   // Archive regions are regions with immutable content (i.e. not reclaimed, and
   // not allocated into during regular operation). They differ in the kind of references
   // allowed for the contained objects:
   // - Closed archive regions form a separate self-contained (closed) object graph
   // within the set of all of these regions. No references outside of closed
   // archive regions are allowed.
   // - Open archive regions have no restrictions on the references of their objects.
   // Objects within these regions are allowed to have references to objects
   // contained in any other kind of regions.
   ArchiveMask           = 32,
   OpenArchiveTag        = ArchiveMask | PinnedMask | OldMask,
   ClosedArchiveTag      = ArchiveMask | PinnedMask | OldMask + 1
 } Tag;
- -

hotspot/share/gc/g1/heapRegionType.hpp

-

当执行垃圾收集时，G1以类似于CMS收集器的方式运行。 G1执行并发全局标记阶段，以确定整个堆中对象的存活性。标记阶段完成后，G1知道哪些region是基本空的。它首先收集这些region，通常会产生大量的可用空间。这就是为什么这种垃圾收集方法称为“垃圾优先”的原因。顾名思义，G1将其收集和压缩活动集中在可能充满可回收对象（即垃圾）的堆区域。 G1使用暂停预测模型来满足用户定义的暂停时间目标，并根据指定的暂停时间目标选择要收集的区域数。

-

由G1标识为可回收的区域是使用撤离的方式(Evacuation)。 G1将对象从堆的一个或多个区域复制到堆上的单个区域，并在此过程中压缩并释放内存。撤离是在多处理器上并行执行的，以减少暂停时间并增加吞吐量。因此，对于每次垃圾收集，G1都在用户定义的暂停时间内连续工作以减少碎片。这是优于前面两种方法的。 CMS（并发标记扫描）垃圾收集器不进行压缩。 ParallelOld垃圾回收仅执行整个堆压缩，这导致相当长的暂停时间。

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需要重点注意的是，G1不是实时收集器。它很有可能达到设定的暂停时间目标，但并非绝对确定。 G1根据先前收集的数据，估算在用户指定的目标时间内可以收集多少个区域。因此，收集器具有收集区域成本的合理准确的模型，并且收集器使用此模型来确定要收集哪些和多少个区域，同时保持在暂停时间目标之内。

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注意：G1同时具有并发（与应用程序线程一起运行，例如优化，标记，清理）和并行（多线程，例如stw）阶段。Full GC仍然是单线程的，但是如果正确调优，您的应用程序应该可以避免Full GC。

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在前面那篇中在代码层面简单的了解了这个可预测时间的过程，这也是 G1 的一大特点。

-]]> - - Java - JVM - GC - C++ - - - Java - C++ - JVM - G1 - GC - Garbage-First Collector - - 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/ @@ -599,51 +599,6 @@ headscale - - 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

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openssl x509 -inform PEM -in fullchain.cer -out derper.demo.com.crt
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这边是PEM格式的证书，如果是DER的话就改一下

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openssl x509 -inform DER -in certificate.cer -out certificate.crt
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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 - - - - JVM源码分析之G1垃圾收集器分析一 - /2019/12/07/JVM-G1-Part-1/ - 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;
}
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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

-
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

-
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

-
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

-]]> - - Java - JVM - GC - C++ - - - Java - C++ - JVM - - Headscale下篇-自定义中转derper /2024/04/14/Headscale%E4%B8%8B%E7%AF%87-%E8%87%AA%E5%AE%9A%E4%B9%89%E4%B8%AD%E8%BD%ACderper/ @@ -674,6 +629,23 @@

image

上面的 urls 注释掉，后面的 paths 添加刚才的配置文件路径，这样就可以了，至于连接是不是走的这个

可以用 tailscale netcheck 查看最近的 derper 节点已经延迟，看到使用了自己的 derper 就对了

+]]> + + headscale + + + headscale + + + + 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 @@ -694,6 +666,51 @@

然后找到进程对应的sock文件，把它映射进去
接下去就是做反向代理，第一步先把域名解析做好，不然caddy没法做证书的，感觉caddy真的是懒人福音

derper.domain.com {
	# 反向代理的地址
	reverse_proxy 127.0.0.1:12345
}

就这几行，是不是超方便，这样就省去了隔段时间要去转换下证书，重启derper的麻烦了，虽然也可以自动化，但是作为一个shell不那么熟练的，又怕权限控制不好，重要目录被删掉的，还是这样比较省力

+]]> + + headscale + + + headscale + + + + JVM源码分析之G1垃圾收集器分析一 + /2019/12/07/JVM-G1-Part-1/ + 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

+
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

+
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

+
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

+
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

+
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

+
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

+]]> + + Java + JVM + C++ + GC + + + Java + C++ + JVM + + + + 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 @@ -728,15 +745,14 @@ - 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 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 @@ -746,37 +762,47 @@ - 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

+ 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 + Java - headscale + Java - 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;
            }
        }
    }
+ 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/ + 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:

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

Example 2:

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

Example 3:

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

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

-]]> +

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

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;
    }
]]> Java + leetcode - Java + leetcode + java + 题解 @@ -804,35 +830,85 @@ leetcode - java leetcode + java 题解 - 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/ - 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:

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

Example 2:

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

Example 3:

-
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

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;
    }
]]> + 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 1115 交替打印 FooBar ( Print FooBar Alternately *Medium* ) 题解分析 + /2022/05/01/Leetcode-1115-%E4%BA%A4%E6%9B%BF%E6%89%93%E5%8D%B0-FooBar-Print-FooBar-Alternately-Medium-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 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:

+
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

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();
        }
    }
}
+]]> Java leetcode + leetcode java + 题解 + Print FooBar Alternately + + + + 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;
}
+ +

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

+]]> + + leetcode + Java + DP + java + DP + + leetcode + java 题解 + DP @@ -859,8 +935,8 @@ leetcode - java leetcode + java 题解 @@ -877,16 +953,16 @@

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

]]> - Java leetcode + Java Binary Tree java Binary Tree DFS - java leetcode + java 题解 Binary Tree DFS @@ -906,16 +982,16 @@

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;
    }
}
]]> - Java leetcode + Java Binary Tree java Binary Tree DFS - java leetcode + java 题解 Binary Tree 二叉树 @@ -926,108 +1002,6 @@ 中序 - - Leetcode 1115 交替打印 FooBar ( Print FooBar Alternately *Medium* ) 题解分析 - /2022/05/01/Leetcode-1115-%E4%BA%A4%E6%9B%BF%E6%89%93%E5%8D%B0-FooBar-Print-FooBar-Alternately-Medium-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 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:

-
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

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();
        }
    }
}
-]]> - - Java - leetcode - - - java - leetcode - 题解 - Print FooBar Alternately - - - - Java 线程池系列-准备篇 - /2024/02/04/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E5%87%86%E5%A4%87%E7%AF%87/ - Java 线程池是 Java 并发体系里的重要组成部分，不过说起线程池一般都是几个参数，以及参数的含义和逻辑，为了更适合刚开始学习的同学或者说有一些会对这么个习惯性的概括讲法有点疑惑的，我们就从零开始，从最基础的开始看起，先来看下里面用来判断线程池状态和线程数量的部分

-
private final AtomicInteger ctl = new AtomicInteger(ctlOf(RUNNING, 0));
private static final int COUNT_BITS = Integer.SIZE - 3;
private static final int CAPACITY   = (1 << COUNT_BITS) - 1;

// runState is stored in the high-order bits
private static final int RUNNING    = -1 << COUNT_BITS;
private static final int SHUTDOWN   =  0 << COUNT_BITS;
private static final int STOP       =  1 << COUNT_BITS;
private static final int TIDYING    =  2 << COUNT_BITS;
private static final int TERMINATED =  3 << COUNT_BITS;

// Packing and unpacking ctl
private static int runStateOf(int c)     { return c & ~CAPACITY; }
private static int workerCountOf(int c)  { return c & CAPACITY; }
private static int ctlOf(int rs, int wc) { return rs | wc; }
-

这里我们先看下 COUNT_BITS 它是 Integer.SIZE - 3，Integer.SIZE 这个是 32，也就是代表了 Java 用了四个 Byte 来存一个 Integer, 那么 COUNT_BITS 就是 29，
这个 29 是用了干嘛的呢，逐步往下看，为什么先介绍 COUNT_BITS，因为第一行的 ctl 的值是通过 ctlOf(RUNNING, 0) 算出来的，而 RUNNING 又是通过等于 -1 左移 COUNT_BITS 位来得到，移动 29 位后，还剩下三位，而这三位只需要表示后面五种状态，其中只有 RUNNING 是负值，这样就巧妙的只利用了 ctl 的前三位来存储线程池的状态而后面的 29 位就是用来存储线程数量的，具体怎么取呢，我们在来看下 CAPACITY 它是 (1 << COUNT_BITS) - 1 把 1 左移 29 位后减 1，也就是 536870911，这里再补充下，左移一位就等于是乘以 2，左移 29 位就是乘以 2 的 29 次，减了 1 就是 29 位能存的最大值，代表线程池最大的线程容量，用二进制来表示就是 32 位长度的int，前三位是 0，后 29 位是 1，代表了线程池的容量，那么 runStateOf 和 workerCountOf 的理解就很简单了，
runStateOf 是 c 跟 CAPACITY 取反之后的值做”与”操作，CAPACITY 取反就变成了前三位是 1，后面的二十九位是 0，而对于”与”操作，后面的二十九位无论是什么都会被忽略，也就是取了前三位的值
workerCountOf 正好相反，直接跟 CAPACITY 做 “与” 操作，也就是取后二十九位的值
ctlOf 就是直接对 rs 与 wc 做了 “或” 操作
而前面也提到了只有 RUNNING 是负数，那么就可以直接做大小比较来判断线程池状态
这个可能对很多同学来说是非常简单的，但是对于一部分同学来说就是个拦路虎，希望能对这部分同学有所帮助。

-]]> - - 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;
}
- -

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

-]]> - - Java - leetcode - Binary Tree - java - Binary Tree - - - java - leetcode - 题解 - Binary Tree - 二叉树 - - - - 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;
}
- -

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 - - - java - leetcode - 题解 - DP - - Leetcode 1260 二维网格迁移 ( Shift 2D Grid *Easy* ) 题解分析 /2022/07/22/Leetcode-1260-%E4%BA%8C%E7%BB%B4%E7%BD%91%E6%A0%BC%E8%BF%81%E7%A7%BB-Shift-2D-Grid-Easy-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ @@ -1063,44 +1037,40 @@ leetcode - java leetcode + java 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 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;
}
-

结果

+

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

]]> - Java leetcode + Java + Binary Tree + java + Binary Tree - java leetcode + java 题解 - 3Sum Closest + Binary Tree + 二叉树 @@ -1121,15 +1091,15 @@ ]]> - Java leetcode + Java stack java stack - java leetcode + java 题解 stack min stack @@ -1137,6 +1107,40 @@ 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 + + + leetcode + java + 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/ @@ -1149,15 +1153,15 @@

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。

]]> - Java leetcode + Java Linked List java Linked List - java leetcode + java 题解 Linked List @@ -1190,27 +1194,11 @@ leetcode - java leetcode + java 题解 - - 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

-]]> - - Java - - - Java - - 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/ @@ -1242,8 +1230,31 @@ 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/ + 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

+
/**
 * 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 + Linked List + java + Linked List + + leetcode + java + 题解 + Linked List @@ -1262,15 +1273,15 @@

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

]]> - Java leetcode + Java linked list java linked list - java leetcode + java 题解 linked list @@ -1290,33 +1301,24 @@ Lowest Common Ancestor of a Binary Tree - java leetcode + java 题解 Lowest Common Ancestor of a Binary Tree - 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/ - 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

-
/**
 * 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;
    }
}
]]> + Java 线程池系列-准备篇 + /2024/02/04/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E5%87%86%E5%A4%87%E7%AF%87/ + Java 线程池是 Java 并发体系里的重要组成部分，不过说起线程池一般都是几个参数，以及参数的含义和逻辑，为了更适合刚开始学习的同学或者说有一些会对这么个习惯性的概括讲法有点疑惑的，我们就从零开始，从最基础的开始看起，先来看下里面用来判断线程池状态和线程数量的部分

+
private final AtomicInteger ctl = new AtomicInteger(ctlOf(RUNNING, 0));
private static final int COUNT_BITS = Integer.SIZE - 3;
private static final int CAPACITY   = (1 << COUNT_BITS) - 1;

// runState is stored in the high-order bits
private static final int RUNNING    = -1 << COUNT_BITS;
private static final int SHUTDOWN   =  0 << COUNT_BITS;
private static final int STOP       =  1 << COUNT_BITS;
private static final int TIDYING    =  2 << COUNT_BITS;
private static final int TERMINATED =  3 << COUNT_BITS;

// Packing and unpacking ctl
private static int runStateOf(int c)     { return c & ~CAPACITY; }
private static int workerCountOf(int c)  { return c & CAPACITY; }
private static int ctlOf(int rs, int wc) { return rs | wc; }
+

这里我们先看下 COUNT_BITS 它是 Integer.SIZE - 3，Integer.SIZE 这个是 32，也就是代表了 Java 用了四个 Byte 来存一个 Integer, 那么 COUNT_BITS 就是 29，
这个 29 是用了干嘛的呢，逐步往下看，为什么先介绍 COUNT_BITS，因为第一行的 ctl 的值是通过 ctlOf(RUNNING, 0) 算出来的，而 RUNNING 又是通过等于 -1 左移 COUNT_BITS 位来得到，移动 29 位后，还剩下三位，而这三位只需要表示后面五种状态，其中只有 RUNNING 是负值，这样就巧妙的只利用了 ctl 的前三位来存储线程池的状态而后面的 29 位就是用来存储线程数量的，具体怎么取呢，我们在来看下 CAPACITY 它是 (1 << COUNT_BITS) - 1 把 1 左移 29 位后减 1，也就是 536870911，这里再补充下，左移一位就等于是乘以 2，左移 29 位就是乘以 2 的 29 次，减了 1 就是 29 位能存的最大值，代表线程池最大的线程容量，用二进制来表示就是 32 位长度的int，前三位是 0，后 29 位是 1，代表了线程池的容量，那么 runStateOf 和 workerCountOf 的理解就很简单了，
runStateOf 是 c 跟 CAPACITY 取反之后的值做”与”操作，CAPACITY 取反就变成了前三位是 1，后面的二十九位是 0，而对于”与”操作，后面的二十九位无论是什么都会被忽略，也就是取了前三位的值
workerCountOf 正好相反，直接跟 CAPACITY 做 “与” 操作，也就是取后二十九位的值
ctlOf 就是直接对 rs 与 wc 做了 “或” 操作
而前面也提到了只有 RUNNING 是负数，那么就可以直接做大小比较来判断线程池状态
这个可能对很多同学来说是非常简单的，但是对于一部分同学来说就是个拦路虎，希望能对这部分同学有所帮助。

+]]> Java - leetcode - Linked List - java - Linked List - java - leetcode - 题解 - Linked List + Java @@ -1342,8 +1344,8 @@ leetcode - java leetcode + java 题解 First Bad Version @@ -1364,35 +1366,60 @@

注释应该写的比较清楚了。

]]> - Java leetcode + Java 字符串 - online java string - java leetcode + java 题解 string - 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

+ 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 + java + 题解 + + + + 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 @@ -1420,79 +1447,36 @@ leetcode - java - leetcode - 题解 - Intersection of Two Arrays - - - - Leetcode 4 寻找两个正序数组的中位数 ( Median of Two Sorted Arrays *Hard* ) 题解分析 - /2022/03/27/Leetcode-4-%E5%AF%BB%E6%89%BE%E4%B8%A4%E4%B8%AA%E6%AD%A3%E5%BA%8F%E6%95%B0%E7%BB%84%E7%9A%84%E4%B8%AD%E4%BD%8D%E6%95%B0-Median-of-Two-Sorted-Arrays-Hard-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ - 题目介绍

给定两个大小分别为 m 和 n 的正序（从小到大）数组 nums1 和 nums2。请你找出并返回这两个正序数组的 中位数 。

-

算法的时间复杂度应该为 O(log (m+n)) 。

-

示例 1：

-

输入：nums1 = [1,3], nums2 = [2]
输出：2.00000
解释：合并数组 = [1,2,3] ，中位数 2

-
-

示例 2：

-

输入：nums1 = [1,2], nums2 = [3,4]
输出：2.50000
解释：合并数组 = [1,2,3,4] ，中位数 (2 + 3) / 2 = 2.5

-
-

分析与题解

这个题也是我随机出来的，之前都是随机到 easy 的，而且是序号这么靠前的，然后翻一下，之前应该是用 C++做过的，具体的方法其实可以从他的算法时间复杂度要求看出来，大概率是要二分法这种，后面就结合代码来讲了

-
public double findMedianSortedArrays(int[] nums1, int[] nums2) {
        int n1 = nums1.length;
        int n2 = nums2.length;
        if (n1 > n2) {
            return findMedianSortedArrays(nums2, nums1);
        }

        // 找到两个数组的中点下标
        int k = (n1 + n2 + 1 ) / 2;
        // 使用一个类似于二分法的查找方法
        // 起始值就是 num1 的头跟尾
        int left = 0;
        int right = n1;
        while (left < right) {
            // m1 表示我取的是 nums1 的中点，即二分法的方式
            int m1 = left + (right - left) / 2;
            // *** 这里是重点，因为这个问题也可以转换成找成 n1 + n2 那么多个数中的前 (n1 + n2 + 1) / 2 个
            // *** 因为两个数组都是排好序的，那么我从 num1 中取了 m1 个，从 num2 中就是去 k - m1 个
            // *** 但是不知道取出来大小是否正好是整体排序的第 (n1 + n2 + 1) / 2 个，所以需要二分法上下对比
            int m2 = k - m1;
            // 如果 nums1[m1] 小，那我在第一个数组 nums1 的二分查找就要把左端点改成前一次的中点 + 1 (不然就进死循环了
            if (nums1[m1] < nums2[m2 - 1]) {
                left = m1 + 1;
            } else {
                right = m1;
            }
        }

        // 因为对比后其实我们只是拿到了一个位置，具体哪个是第 k 个就需要继续判断
        int m1 = left;
        int m2 = k - left;
        // 如果 m1 或者 m2 有小于等于 0 的，那这个值可以先抛弃
        // m1 如果等于 0，就是 num1[0] 都比 nums2 中所有值都要大
        // m2 等于 0 的话 刚好相反
        // 可以这么推断，当其中一个是 0 的时候那么另一个 mx 值肯定是> 0 的，那么就是取的对应的这个下标的值
        int c1 = Math.max( m1 <= 0 ? Integer.MIN_VALUE : nums1[m1 - 1] , m2 <= 0 ?  Integer.MIN_VALUE : nums2[m2 - 1]);
        // 如果两个数组的元素数量和是奇数，那就直接可以返回了，因为 m1 + m2 就是 k, 如果是一个数组，那这个元素其实就是 nums[k - 1]
        // 如果 m1 或者 m2 是 0，那另一个就是 k，取 mx - 1的下标就等于是 k - 1
        // 如果都不是 0，那就是取的了 nums1[m1 - 1] 与 nums2[m2 - 1]中的较大者，如果取得是后者，那么也就是 m1 + m2 - 1 的下标就是 k - 1
        if ((n1 + n2) % 2 == 1) {
            return c1;
        }
        // 如果是偶数个，那还要取两个数组后面的较小者，然后求平均值
        int c2 = Math.min(m1 >= n1 ? Integer.MAX_VALUE : nums1[m1], m2 >= n2 ? Integer.MAX_VALUE : nums2[m2]);
        return (c1 + c2) / 2.0;
    }
-

前面考虑的方法还是比较繁琐，考虑了两个数组的各种交叉情况，后面这个参考了一些网上的解法，代码比较简洁，但是可能不容易一下子就搞明白，所以配合了比较多的注释。

-]]> - - Java - leetcode - - - java - leetcode - 题解 - Median of Two Sorted Arrays - - - - 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 + java 题解 + Intersection of Two Arrays - 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

+ Leetcode 4 寻找两个正序数组的中位数 ( Median of Two Sorted Arrays *Hard* ) 题解分析 + /2022/03/27/Leetcode-4-%E5%AF%BB%E6%89%BE%E4%B8%A4%E4%B8%AA%E6%AD%A3%E5%BA%8F%E6%95%B0%E7%BB%84%E7%9A%84%E4%B8%AD%E4%BD%8D%E6%95%B0-Median-of-Two-Sorted-Arrays-Hard-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/ + 题目介绍

给定两个大小分别为 m 和 n 的正序（从小到大）数组 nums1 和 nums2。请你找出并返回这两个正序数组的 中位数 。

+

算法的时间复杂度应该为 O(log (m+n)) 。

+

示例 1：

+

输入：nums1 = [1,3], nums2 = [2]
输出：2.00000
解释：合并数组 = [1,2,3] ，中位数 2

+
+

示例 2：

+

输入：nums1 = [1,2], nums2 = [3,4]
输出：2.50000
解释：合并数组 = [1,2,3,4] ，中位数 (2 + 3) / 2 = 2.5

+
+

分析与题解

这个题也是我随机出来的，之前都是随机到 easy 的，而且是序号这么靠前的，然后翻一下，之前应该是用 C++做过的，具体的方法其实可以从他的算法时间复杂度要求看出来，大概率是要二分法这种，后面就结合代码来讲了

+
public double findMedianSortedArrays(int[] nums1, int[] nums2) {
        int n1 = nums1.length;
        int n2 = nums2.length;
        if (n1 > n2) {
            return findMedianSortedArrays(nums2, nums1);
        }

        // 找到两个数组的中点下标
        int k = (n1 + n2 + 1 ) / 2;
        // 使用一个类似于二分法的查找方法
        // 起始值就是 num1 的头跟尾
        int left = 0;
        int right = n1;
        while (left < right) {
            // m1 表示我取的是 nums1 的中点，即二分法的方式
            int m1 = left + (right - left) / 2;
            // *** 这里是重点，因为这个问题也可以转换成找成 n1 + n2 那么多个数中的前 (n1 + n2 + 1) / 2 个
            // *** 因为两个数组都是排好序的，那么我从 num1 中取了 m1 个，从 num2 中就是去 k - m1 个
            // *** 但是不知道取出来大小是否正好是整体排序的第 (n1 + n2 + 1) / 2 个，所以需要二分法上下对比
            int m2 = k - m1;
            // 如果 nums1[m1] 小，那我在第一个数组 nums1 的二分查找就要把左端点改成前一次的中点 + 1 (不然就进死循环了
            if (nums1[m1] < nums2[m2 - 1]) {
                left = m1 + 1;
            } else {
                right = m1;
            }
        }

        // 因为对比后其实我们只是拿到了一个位置，具体哪个是第 k 个就需要继续判断
        int m1 = left;
        int m2 = k - left;
        // 如果 m1 或者 m2 有小于等于 0 的，那这个值可以先抛弃
        // m1 如果等于 0，就是 num1[0] 都比 nums2 中所有值都要大
        // m2 等于 0 的话 刚好相反
        // 可以这么推断，当其中一个是 0 的时候那么另一个 mx 值肯定是> 0 的，那么就是取的对应的这个下标的值
        int c1 = Math.max( m1 <= 0 ? Integer.MIN_VALUE : nums1[m1 - 1] , m2 <= 0 ?  Integer.MIN_VALUE : nums2[m2 - 1]);
        // 如果两个数组的元素数量和是奇数，那就直接可以返回了，因为 m1 + m2 就是 k, 如果是一个数组，那这个元素其实就是 nums[k - 1]
        // 如果 m1 或者 m2 是 0，那另一个就是 k，取 mx - 1的下标就等于是 k - 1
        // 如果都不是 0，那就是取的了 nums1[m1 - 1] 与 nums2[m2 - 1]中的较大者，如果取得是后者，那么也就是 m1 + m2 - 1 的下标就是 k - 1
        if ((n1 + n2) % 2 == 1) {
            return c1;
        }
        // 如果是偶数个，那还要取两个数组后面的较小者，然后求平均值
        int c2 = Math.min(m1 >= n1 ? Integer.MAX_VALUE : nums1[m1], m2 >= n2 ? Integer.MAX_VALUE : nums2[m2]);
        return (c1 + c2) / 2.0;
    }
+

前面考虑的方法还是比较繁琐，考虑了两个数组的各种交叉情况，后面这个参考了一些网上的解法，代码比较简洁，但是可能不容易一下子就搞明白，所以配合了比较多的注释。

]]> Java leetcode - java leetcode + java 题解 + Median of Two Sorted Arrays @@ -1510,8 +1494,8 @@ leetcode - java leetcode + java dp 代码题解 Trapping Rain Water @@ -1534,8 +1518,8 @@ Rotate Image - java leetcode + java 题解 Rotate Image 矩阵 @@ -1569,8 +1553,8 @@ leetcode - java leetcode + java @@ -1598,8 +1582,8 @@ leetcode - java leetcode + java 题解 @@ -1629,8 +1613,8 @@ leetcode - java leetcode + java 题解 Remove Duplicates from Sorted List @@ -1663,11 +1647,28 @@ leetcode - java leetcode + java 题解 + + leetcode no.3 + /2015/04/15/Leetcode-No-3/ + **Longest Substring Without Repeating Characters **

+ +

description

Given a string, find the length of the longest substring without repeating characters.
For example, the longest substring without repeating letters for “abcabcbb” is “abc”,
which the length is 3. For “bbbbb” the longest substring is “b”, with the length of 1.

+

分析

源码这次是参考了这个代码，
tail 表示的当前子串的起始点位置，tail从-1开始就包括的串的长度是1的边界。其实我
也是猜的（逃

+
int ct[256];
    memset(ct, -1, sizeof(ct));
	int tail = -1;
	int max = 0;
	for (int i = 0; i < s.size(); i++){
		if (ct[s[i]] > tail)
			tail = ct[s[i]];
		if (i - tail > max)
			max = i - tail;
		ct[s[i]] = i;
	}
	return max;
+]]> + + leetcode + + + leetcode + c++ + + 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/ @@ -1698,23 +1699,6 @@ 转义 - - leetcode no.3 - /2015/04/15/Leetcode-No-3/ - **Longest Substring Without Repeating Characters **

- -

description

Given a string, find the length of the longest substring without repeating characters.
For example, the longest substring without repeating letters for “abcabcbb” is “abc”,
which the length is 3. For “bbbbb” the longest substring is “b”, with the length of 1.

-

分析

源码这次是参考了这个代码，
tail 表示的当前子串的起始点位置，tail从-1开始就包括的串的长度是1的边界。其实我
也是猜的（逃

-
int ct[256];
    memset(ct, -1, sizeof(ct));
	int tail = -1;
	int max = 0;
	for (int i = 0; i < s.size(); i++){
		if (ct[s[i]] > tail)
			tail = ct[s[i]];
		if (i - tail > max)
			max = i - tail;
		ct[s[i]] = i;
	}
	return max;
-]]> - - leetcode - - - leetcode - c++ - - MFC 模态对话框 /2014/12/24/MFC%20%E6%A8%A1%E6%80%81%E5%AF%B9%E8%AF%9D%E6%A1%86/ @@ -1768,6 +1752,22 @@ Maven + + 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

+]]> + + Java + + + Java + + Number of 1 Bits /2015/03/11/Number-Of-1-Bits/ @@ -1803,6 +1803,33 @@ c++ + + 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 + + Redis_分布式锁 /2019/12/10/Redis-Part-1/ @@ -1855,33 +1882,6 @@ c++ - - 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 - - - java - leetcode - 题解 - - Reverse Integer /2015/03/13/Reverse-Integer/ @@ -1962,37 +1962,29 @@ - Tomcat 系列篇七-介绍下 Filter 注册逻辑 - /2023/10/29/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E4%B8%83-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Filter-%E6%B3%A8%E5%86%8C%E9%80%BB%E8%BE%91/ - 在
org.springframework.boot.web.servlet.context.ServletWebServerApplicationContext#createWebServer 的时候会调用

-
this.webServer = factory.getWebServer(getSelfInitializer());
-

就会调用到之前说到的

-
private org.springframework.boot.web.servlet.ServletContextInitializer getSelfInitializer() {
		return this::selfInitialize;
	}

	private void selfInitialize(ServletContext servletContext) throws ServletException {
		prepareWebApplicationContext(servletContext);
		registerApplicationScope(servletContext);
		WebApplicationContextUtils.registerEnvironmentBeans(getBeanFactory(), servletContext);
		for (ServletContextInitializer beans : getServletContextInitializerBeans()) {
			beans.onStartup(servletContext);
		}
	}
-

而这里有个
org.springframework.boot.web.servlet.context.ServletWebServerApplicationContext#getServletContextInitializerBeans
获取初始化所需要的 bean

-
protected Collection<ServletContextInitializer> getServletContextInitializerBeans() {
	return new ServletContextInitializerBeans(getBeanFactory());
}
-

里面就是 new 了这个 org.springframework.boot.web.servlet.ServletContextInitializerBeans#ServletContextInitializerBeans

-
public ServletContextInitializerBeans(ListableBeanFactory beanFactory,
			Class<? extends ServletContextInitializer>... initializerTypes) {
		this.initializers = new LinkedMultiValueMap<>();
		this.initializerTypes = (initializerTypes.length != 0) ? Arrays.asList(initializerTypes)
				: Collections.singletonList(ServletContextInitializer.class);
        // 这里是添加 ServletContextInitializer
		addServletContextInitializerBeans(beanFactory);
        // filter 是在这里
		addAdaptableBeans(beanFactory);
		List<ServletContextInitializer> sortedInitializers = this.initializers.values().stream()
				.flatMap((value) -> value.stream().sorted(AnnotationAwareOrderComparator.INSTANCE))
				.collect(Collectors.toList());
		this.sortedList = Collections.unmodifiableList(sortedInitializers);
		logMappings(this.initializers);
	}
-

具体来看看 org.springframework.boot.web.servlet.ServletContextInitializerBeans#addAdaptableBeans

-
protected void addAdaptableBeans(ListableBeanFactory beanFactory) {
	MultipartConfigElement multipartConfig = getMultipartConfig(beanFactory);
       // 这里把 Servlet 作为 RegistrationBean
	addAsRegistrationBean(beanFactory, Servlet.class, new ServletRegistrationBeanAdapter(multipartConfig));
       // 这里把 Filter 作为 RegistrationBean
	addAsRegistrationBean(beanFactory, Filter.class, new FilterRegistrationBeanAdapter());
	for (Class<?> listenerType : ServletListenerRegistrationBean.getSupportedTypes()) {
		addAsRegistrationBean(beanFactory, EventListener.class, (Class<EventListener>) listenerType,
				new ServletListenerRegistrationBeanAdapter());
	}
}
-

里面是这两段逻辑，先从 beanfactory 里获取到该类型的 bean，然后包装成 RegistrationBean，添加到 initializers

-
protected <T> void addAsRegistrationBean(ListableBeanFactory beanFactory, Class<T> type,
		RegistrationBeanAdapter<T> adapter) {
	addAsRegistrationBean(beanFactory, type, type, adapter);
}

private <T, B extends T> void addAsRegistrationBean(ListableBeanFactory beanFactory, Class<T> type,
		Class<B> beanType, RegistrationBeanAdapter<T> adapter) {
	List<Map.Entry<String, B>> entries = getOrderedBeansOfType(beanFactory, beanType, this.seen);
	for (Entry<String, B> entry : entries) {
		String beanName = entry.getKey();
		B bean = entry.getValue();
		if (this.seen.add(bean)) {
			// One that we haven't already seen
			RegistrationBean registration = adapter.createRegistrationBean(beanName, bean, entries.size());
			int order = getOrder(bean);
			registration.setOrder(order);
			this.initializers.add(type, registration);
			if (logger.isTraceEnabled()) {
				logger.trace("Created " + type.getSimpleName() + " initializer for bean '" + beanName + "'; order="
						+ order + ", resource=" + getResourceDescription(beanName, beanFactory));
			}
		}
	}
}
-

然后再回到第一段，其中的循环里面，因为 ServletContextInitializerBeans 继承了 AbstractCollection 并且实现了 iterator 接口

-
for (ServletContextInitializer beans : getServletContextInitializerBeans()) {
			beans.onStartup(servletContext);
		}
-

在 for 循环中会把 Initializer 排序后的 sortedList 返回 iterator，也就是前面把 filter 包装成的 RegistrationBean

-
public Iterator<ServletContextInitializer> iterator() {
		return this.sortedList.iterator();
	}
-

然后调用了 onStartup 方法，

-
public final void onStartup(ServletContext servletContext) throws ServletException {
	String description = getDescription();
	if (!isEnabled()) {
		logger.info(StringUtils.capitalize(description) + " was not registered (disabled)");
		return;
	}
	register(description, servletContext);
}
-

就会去执行注册逻辑

+ 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 - SpringBoot - Tomcat + leetcode - Java - Tomcat - SpringBoot + leetcode + java + 题解 @@ -2034,51 +2026,27 @@ - Tomcat 系列篇九-介绍下 Tomcat 里的 ContainerBase - /2023/11/19/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E4%B9%9D-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E9%87%8C%E7%9A%84-ContainerBase/ - 前面介绍过 Tomcat 的层次结构，

-
<Server>
    <Service>
        <Connector />
        <Connector />
        <Engine>
            <Host>
                <Context />
            </Host>
        </Engine>
    </Service>
</Server>
-

参考这个 xml，而对于这些组件中，有一类有相同的基类，也就是这次要介绍的 ContainerBase，

包括 engine，host，context 还有 wrapper ，都是同样的容器组件，
而他们共同实现的接口就是 Container ，
主要包含了

-
public static final String ADD_CHILD_EVENT = "addChild";


/**
 * The ContainerEvent event type sent when a valve is added
 * by <code>addValve()</code>, if this Container supports pipelines.
 */
public static final String ADD_VALVE_EVENT = "addValve";


/**
 * The ContainerEvent event type sent when a child container is removed
 * by <code>removeChild()</code>.
 */
public static final String REMOVE_CHILD_EVENT = "removeChild";


/**
 * The ContainerEvent event type sent when a valve is removed
 * by <code>removeValve()</code>, if this Container supports pipelines.
 */
public static final String REMOVE_VALVE_EVENT = "removeValve";
-

这几个事件类型，

-
public Pipeline getPipeline();

public Container getParent();

public void addChild(Container child);

public Container[] findChildren();

public int getStartStopThreads();

public void setStartStopThreads(int startStopThreads);


-

包括 pipeline 和获取 parent，添加 child 跟查找 child，然后设置线程池的线程数等
而对于 ContainerBase
也包含了 Lifecycle 的常规方法

-
@Override
protected void initInternal() throws LifecycleException {
    reconfigureStartStopExecutor(getStartStopThreads());
    super.initInternal();
}


private void reconfigureStartStopExecutor(int threads) {
    if (threads == 1) {
        // Use a fake executor
        if (!(startStopExecutor instanceof InlineExecutorService)) {
            startStopExecutor = new InlineExecutorService();
        }
    } else {
        // Delegate utility execution to the Service
        Server server = Container.getService(this).getServer();
        server.setUtilityThreads(threads);
        startStopExecutor = server.getUtilityExecutor();
    }
}
-

初始化方法主要就是之前讲过的线程池
后面就开始 startInternal

-
@Override
    protected synchronized void startInternal() throws LifecycleException {

        // Start our subordinate components, if any
        logger = null;
        getLogger();
        Cluster cluster = getClusterInternal();
        if (cluster instanceof Lifecycle) {
            ((Lifecycle) cluster).start();
        }
        Realm realm = getRealmInternal();
        if (realm instanceof Lifecycle) {
            ((Lifecycle) realm).start();
        }

        // Start our child containers, if any
        Container children[] = findChildren();
        List<Future<Void>> results = new ArrayList<>();
        for (Container child : children) {
            results.add(startStopExecutor.submit(new StartChild(child)));
        }

        MultiThrowable multiThrowable = null;

        for (Future<Void> result : results) {
            try {
                result.get();
            } catch (Throwable e) {
                log.error(sm.getString("containerBase.threadedStartFailed"), e);
                if (multiThrowable == null) {
                    multiThrowable = new MultiThrowable();
                }
                multiThrowable.add(e);
            }

        }
        if (multiThrowable != null) {
            throw new LifecycleException(sm.getString("containerBase.threadedStartFailed"),
                    multiThrowable.getThrowable());
        }

        // Start the Valves in our pipeline (including the basic), if any
        if (pipeline instanceof Lifecycle) {
            ((Lifecycle) pipeline).start();
        }

        setState(LifecycleState.STARTING);

        // Start our thread
        if (backgroundProcessorDelay > 0) {
            monitorFuture = Container.getService(ContainerBase.this).getServer()
                    .getUtilityExecutor().scheduleWithFixedDelay(
                            new ContainerBackgroundProcessorMonitor(), 0, 60, TimeUnit.SECONDS);
        }
    }
-

这里包括了 cluster 的启动和 realm 的启动，然后将 child 子组件添加到线程池里启动，然后循环 get 结果，接下去就是 pipeline 的启动，设置开始中状态，后面是 ContainerBackgroundProcessor 的启动
然后是停止方法

-
@Override
    protected synchronized void stopInternal() throws LifecycleException {

        // Stop our thread
        if (monitorFuture != null) {
            monitorFuture.cancel(true);
            monitorFuture = null;
        }
        threadStop();

        setState(LifecycleState.STOPPING);

        // Stop the Valves in our pipeline (including the basic), if any
        if (pipeline instanceof Lifecycle &&
                ((Lifecycle) pipeline).getState().isAvailable()) {
            ((Lifecycle) pipeline).stop();
        }

        // Stop our child containers, if any
        Container children[] = findChildren();
        List<Future<Void>> results = new ArrayList<>();
        for (Container child : children) {
            results.add(startStopExecutor.submit(new StopChild(child)));
        }

        boolean fail = false;
        for (Future<Void> result : results) {
            try {
                result.get();
            } catch (Exception e) {
                log.error(sm.getString("containerBase.threadedStopFailed"), e);
                fail = true;
            }
        }
        if (fail) {
            throw new LifecycleException(
                    sm.getString("containerBase.threadedStopFailed"));
        }

        // Stop our subordinate components, if any
        Realm realm = getRealmInternal();
        if (realm instanceof Lifecycle) {
            ((Lifecycle) realm).stop();
        }
        Cluster cluster = getClusterInternal();
        if (cluster instanceof Lifecycle) {
            ((Lifecycle) cluster).stop();
        }
    }
-

也是类似的，只不过停止顺序和刚才开始的顺序反了一下

-]]> - - Java - SpringBoot - Tomcat - - - Java - Tomcat - SpringBoot - - - - Tomcat 系列篇二-介绍下整体架构 - /2023/09/16/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E4%BA%8C-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Engine/ - 前面那一篇感觉上来的有点突兀，还是应该按照架构去慢慢解析，所以这里回归下我们整体的 Tomcat 架构，这里我们通过一个 Tomcat 的配置文件来看看

-
<Server>
    <Service>
        <Connector />
        <Connector />
        <Engine>
            <Host>
                <Context />
            </Host>
        </Engine>
    </Service>
</Server>
-

上次我们讲解了 connector，也提到了初始化的流程，在
org.springframework.boot.web.embedded.tomcat.TomcatServletWebServerFactory#getWebServer
代码中我们就能略窥一斑，

-
Tomcat tomcat = new Tomcat();
        File baseDir = this.baseDirectory != null ? this.baseDirectory : this.createTempDir("tomcat");
        tomcat.setBaseDir(baseDir.getAbsolutePath());
        Connector connector = new Connector(this.protocol);
        connector.setThrowOnFailure(true);
        tomcat.getService().addConnector(connector);
        this.customizeConnector(connector);
        tomcat.setConnector(connector);
        tomcat.getHost().setAutoDeploy(false);
        this.configureEngine(tomcat.getEngine());
-

这里的 connector 是在 service 中的，而
tomcat.getService().addConnector(connector); 这一行
具体来看下

-
public Service getService() {
        return getServer().findServices()[0];
    }
-

又调用了，org.apache.catalina.startup.Tomcat#getServer

-
public Server getServer() {

        if (server != null) {
            return server;
        }

        System.setProperty("catalina.useNaming", "false");

        server = new StandardServer();

        initBaseDir();

        // Set configuration source
        ConfigFileLoader.setSource(new CatalinaBaseConfigurationSource(new File(basedir), null));

        server.setPort( -1 );

        Service service = new StandardService();
        service.setName("Tomcat");
        server.addService(service);
        return server;
    }
-

可以看到，先 new 了 StandardServer，再 new 了 StandardService，可以理解为创建 Server 后具体是由 service 进行服务，
而在 service 中就是上面的配置文件里显示的，service 包含了 connector，可以是多个connector，负责接入，具体内容可以参看上一篇
而后是 Engine，Engine 的关系是一个 Service 有一个 Engine，Engine 负责处理真正的逻辑，

-
public Engine getEngine() {
        Service service = getServer().findServices()[0];
        if (service.getContainer() != null) {
            return service.getContainer();
        }
        Engine engine = new StandardEngine();
        engine.setName( "Tomcat" );
        engine.setDefaultHost(hostname);
        engine.setRealm(createDefaultRealm());
        service.setContainer(engine);
        return engine;
    }
-

Engine 一般默认我们初始化的都是 StandardEngine，包括前面的 StandardServer 和StandardService，
而对于 host 来说，Engine 中可以包含多个 host，也就是可以处理多个虚拟主机的业务逻辑，

-
public Host getHost() {
        Engine engine = getEngine();
        if (engine.findChildren().length > 0) {
            return (Host) engine.findChildren()[0];
        }

        Host host = new StandardHost();
        host.setName(hostname);
        getEngine().addChild(host);
        return host;
    }
-

tomcat的特点也都是常规的懒加载，在 get 的第一次请求里进行初始化，这边同样创建了 StandardHost，对于可以有多个的 host，在 Engine 中添加也变成了addChild，而对于常规的 Tomcat 来说，往下一层就是 context 了，这个可以支持多个 web 应用，所以也是可以添加多个 context，但我这边以 springboot 嵌入的 Tomcat 举例，他是内嵌的 context

-
protected void prepareContext(Host host, ServletContextInitializer[] initializers) {
        File documentRoot = this.getValidDocumentRoot();
        TomcatEmbeddedContext context = new TomcatEmbeddedContext();
        if (documentRoot != null) {
            context.setResources(new LoaderHidingResourceRoot(context));
        }

        context.setName(this.getContextPath());
        context.setDisplayName(this.getDisplayName());
        context.setPath(this.getContextPath());
        File docBase = documentRoot != null ? documentRoot : this.createTempDir("tomcat-docbase");
        context.setDocBase(docBase.getAbsolutePath());
        context.addLifecycleListener(new Tomcat.FixContextListener());
        context.setParentClassLoader(this.resourceLoader != null ? this.resourceLoader.getClassLoader() : ClassUtils.getDefaultClassLoader());
        this.resetDefaultLocaleMapping(context);
        this.addLocaleMappings(context);

        try {
            context.setCreateUploadTargets(true);
        } catch (NoSuchMethodError var8) {
        }

        this.configureTldPatterns(context);
        WebappLoader loader = new WebappLoader();
        loader.setLoaderClass(TomcatEmbeddedWebappClassLoader.class.getName());
        loader.setDelegate(true);
        context.setLoader(loader);
        if (this.isRegisterDefaultServlet()) {
            this.addDefaultServlet(context);
        }

        if (this.shouldRegisterJspServlet()) {
            this.addJspServlet(context);
            this.addJasperInitializer(context);
        }

        context.addLifecycleListener(new StaticResourceConfigurer(context));
        ServletContextInitializer[] initializersToUse = this.mergeInitializers(initializers);
        host.addChild(context);
        this.configureContext(context, initializersToUse);
        this.postProcessContext(context);
    }
-

是一个 TomcatEmbeddedContext，这一点比较特殊，希望这样会有个一个大致的概念。

+ Tomcat 系列篇七-介绍下 Filter 注册逻辑 + /2023/10/29/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E4%B8%83-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Filter-%E6%B3%A8%E5%86%8C%E9%80%BB%E8%BE%91/ + 在
org.springframework.boot.web.servlet.context.ServletWebServerApplicationContext#createWebServer 的时候会调用

+
this.webServer = factory.getWebServer(getSelfInitializer());
+

就会调用到之前说到的

+
private org.springframework.boot.web.servlet.ServletContextInitializer getSelfInitializer() {
		return this::selfInitialize;
	}

	private void selfInitialize(ServletContext servletContext) throws ServletException {
		prepareWebApplicationContext(servletContext);
		registerApplicationScope(servletContext);
		WebApplicationContextUtils.registerEnvironmentBeans(getBeanFactory(), servletContext);
		for (ServletContextInitializer beans : getServletContextInitializerBeans()) {
			beans.onStartup(servletContext);
		}
	}
+

而这里有个
org.springframework.boot.web.servlet.context.ServletWebServerApplicationContext#getServletContextInitializerBeans
获取初始化所需要的 bean

+
protected Collection<ServletContextInitializer> getServletContextInitializerBeans() {
	return new ServletContextInitializerBeans(getBeanFactory());
}
+

里面就是 new 了这个 org.springframework.boot.web.servlet.ServletContextInitializerBeans#ServletContextInitializerBeans

+
public ServletContextInitializerBeans(ListableBeanFactory beanFactory,
			Class<? extends ServletContextInitializer>... initializerTypes) {
		this.initializers = new LinkedMultiValueMap<>();
		this.initializerTypes = (initializerTypes.length != 0) ? Arrays.asList(initializerTypes)
				: Collections.singletonList(ServletContextInitializer.class);
        // 这里是添加 ServletContextInitializer
		addServletContextInitializerBeans(beanFactory);
        // filter 是在这里
		addAdaptableBeans(beanFactory);
		List<ServletContextInitializer> sortedInitializers = this.initializers.values().stream()
				.flatMap((value) -> value.stream().sorted(AnnotationAwareOrderComparator.INSTANCE))
				.collect(Collectors.toList());
		this.sortedList = Collections.unmodifiableList(sortedInitializers);
		logMappings(this.initializers);
	}
+

具体来看看 org.springframework.boot.web.servlet.ServletContextInitializerBeans#addAdaptableBeans

+
protected void addAdaptableBeans(ListableBeanFactory beanFactory) {
	MultipartConfigElement multipartConfig = getMultipartConfig(beanFactory);
       // 这里把 Servlet 作为 RegistrationBean
	addAsRegistrationBean(beanFactory, Servlet.class, new ServletRegistrationBeanAdapter(multipartConfig));
       // 这里把 Filter 作为 RegistrationBean
	addAsRegistrationBean(beanFactory, Filter.class, new FilterRegistrationBeanAdapter());
	for (Class<?> listenerType : ServletListenerRegistrationBean.getSupportedTypes()) {
		addAsRegistrationBean(beanFactory, EventListener.class, (Class<EventListener>) listenerType,
				new ServletListenerRegistrationBeanAdapter());
	}
}
+

里面是这两段逻辑，先从 beanfactory 里获取到该类型的 bean，然后包装成 RegistrationBean，添加到 initializers

+
protected <T> void addAsRegistrationBean(ListableBeanFactory beanFactory, Class<T> type,
		RegistrationBeanAdapter<T> adapter) {
	addAsRegistrationBean(beanFactory, type, type, adapter);
}

private <T, B extends T> void addAsRegistrationBean(ListableBeanFactory beanFactory, Class<T> type,
		Class<B> beanType, RegistrationBeanAdapter<T> adapter) {
	List<Map.Entry<String, B>> entries = getOrderedBeansOfType(beanFactory, beanType, this.seen);
	for (Entry<String, B> entry : entries) {
		String beanName = entry.getKey();
		B bean = entry.getValue();
		if (this.seen.add(bean)) {
			// One that we haven't already seen
			RegistrationBean registration = adapter.createRegistrationBean(beanName, bean, entries.size());
			int order = getOrder(bean);
			registration.setOrder(order);
			this.initializers.add(type, registration);
			if (logger.isTraceEnabled()) {
				logger.trace("Created " + type.getSimpleName() + " initializer for bean '" + beanName + "'; order="
						+ order + ", resource=" + getResourceDescription(beanName, beanFactory));
			}
		}
	}
}
+

然后再回到第一段，其中的循环里面，因为 ServletContextInitializerBeans 继承了 AbstractCollection 并且实现了 iterator 接口

+
for (ServletContextInitializer beans : getServletContextInitializerBeans()) {
			beans.onStartup(servletContext);
		}
+

在 for 循环中会把 Initializer 排序后的 sortedList 返回 iterator，也就是前面把 filter 包装成的 RegistrationBean

+
public Iterator<ServletContextInitializer> iterator() {
		return this.sortedList.iterator();
	}
+

然后调用了 onStartup 方法，

+
public final void onStartup(ServletContext servletContext) throws ServletException {
	String description = getDescription();
	if (!isEnabled()) {
		logger.info(StringUtils.capitalize(description) + " was not registered (disabled)");
		return;
	}
	register(description, servletContext);
}
+

就会去执行注册逻辑

]]> Java @@ -2111,6 +2079,36 @@

然后进行执行，

@Override
public void execute(Runnable command) {
    synchronized (lock) {
        if (shutdown) {
            throw new RejectedExecutionException();
        }
        taskRunning = true;
    }
    command.run();
    synchronized (lock) {
        taskRunning = false;
        if (shutdown) {
            terminated = true;
            lock.notifyAll();
        }
    }
}

这个就是我们 startStopExecutor 的主要作用，帮我们启动子组件
对于 Container 来说启动的就是 host，而其实 host 也是继承了 ContainerBase 的，后面可以再继续介绍下

+]]> + + Java + SpringBoot + Tomcat + + + Java + Tomcat + SpringBoot + + + + Tomcat 系列篇二-介绍下整体架构 + /2023/09/16/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E4%BA%8C-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Engine/ + 前面那一篇感觉上来的有点突兀，还是应该按照架构去慢慢解析，所以这里回归下我们整体的 Tomcat 架构，这里我们通过一个 Tomcat 的配置文件来看看

+
<Server>
    <Service>
        <Connector />
        <Connector />
        <Engine>
            <Host>
                <Context />
            </Host>
        </Engine>
    </Service>
</Server>
+

上次我们讲解了 connector，也提到了初始化的流程，在
org.springframework.boot.web.embedded.tomcat.TomcatServletWebServerFactory#getWebServer
代码中我们就能略窥一斑，

+
Tomcat tomcat = new Tomcat();
        File baseDir = this.baseDirectory != null ? this.baseDirectory : this.createTempDir("tomcat");
        tomcat.setBaseDir(baseDir.getAbsolutePath());
        Connector connector = new Connector(this.protocol);
        connector.setThrowOnFailure(true);
        tomcat.getService().addConnector(connector);
        this.customizeConnector(connector);
        tomcat.setConnector(connector);
        tomcat.getHost().setAutoDeploy(false);
        this.configureEngine(tomcat.getEngine());
+

这里的 connector 是在 service 中的，而
tomcat.getService().addConnector(connector); 这一行
具体来看下

+
public Service getService() {
        return getServer().findServices()[0];
    }
+

又调用了，org.apache.catalina.startup.Tomcat#getServer

+
public Server getServer() {

        if (server != null) {
            return server;
        }

        System.setProperty("catalina.useNaming", "false");

        server = new StandardServer();

        initBaseDir();

        // Set configuration source
        ConfigFileLoader.setSource(new CatalinaBaseConfigurationSource(new File(basedir), null));

        server.setPort( -1 );

        Service service = new StandardService();
        service.setName("Tomcat");
        server.addService(service);
        return server;
    }
+

可以看到，先 new 了 StandardServer，再 new 了 StandardService，可以理解为创建 Server 后具体是由 service 进行服务，
而在 service 中就是上面的配置文件里显示的，service 包含了 connector，可以是多个connector，负责接入，具体内容可以参看上一篇
而后是 Engine，Engine 的关系是一个 Service 有一个 Engine，Engine 负责处理真正的逻辑，

+
public Engine getEngine() {
        Service service = getServer().findServices()[0];
        if (service.getContainer() != null) {
            return service.getContainer();
        }
        Engine engine = new StandardEngine();
        engine.setName( "Tomcat" );
        engine.setDefaultHost(hostname);
        engine.setRealm(createDefaultRealm());
        service.setContainer(engine);
        return engine;
    }
+

Engine 一般默认我们初始化的都是 StandardEngine，包括前面的 StandardServer 和StandardService，
而对于 host 来说，Engine 中可以包含多个 host，也就是可以处理多个虚拟主机的业务逻辑，

+
public Host getHost() {
        Engine engine = getEngine();
        if (engine.findChildren().length > 0) {
            return (Host) engine.findChildren()[0];
        }

        Host host = new StandardHost();
        host.setName(hostname);
        getEngine().addChild(host);
        return host;
    }
+

tomcat的特点也都是常规的懒加载，在 get 的第一次请求里进行初始化，这边同样创建了 StandardHost，对于可以有多个的 host，在 Engine 中添加也变成了addChild，而对于常规的 Tomcat 来说，往下一层就是 context 了，这个可以支持多个 web 应用，所以也是可以添加多个 context，但我这边以 springboot 嵌入的 Tomcat 举例，他是内嵌的 context

+
protected void prepareContext(Host host, ServletContextInitializer[] initializers) {
        File documentRoot = this.getValidDocumentRoot();
        TomcatEmbeddedContext context = new TomcatEmbeddedContext();
        if (documentRoot != null) {
            context.setResources(new LoaderHidingResourceRoot(context));
        }

        context.setName(this.getContextPath());
        context.setDisplayName(this.getDisplayName());
        context.setPath(this.getContextPath());
        File docBase = documentRoot != null ? documentRoot : this.createTempDir("tomcat-docbase");
        context.setDocBase(docBase.getAbsolutePath());
        context.addLifecycleListener(new Tomcat.FixContextListener());
        context.setParentClassLoader(this.resourceLoader != null ? this.resourceLoader.getClassLoader() : ClassUtils.getDefaultClassLoader());
        this.resetDefaultLocaleMapping(context);
        this.addLocaleMappings(context);

        try {
            context.setCreateUploadTargets(true);
        } catch (NoSuchMethodError var8) {
        }

        this.configureTldPatterns(context);
        WebappLoader loader = new WebappLoader();
        loader.setLoaderClass(TomcatEmbeddedWebappClassLoader.class.getName());
        loader.setDelegate(true);
        context.setLoader(loader);
        if (this.isRegisterDefaultServlet()) {
            this.addDefaultServlet(context);
        }

        if (this.shouldRegisterJspServlet()) {
            this.addJspServlet(context);
            this.addJasperInitializer(context);
        }

        context.addLifecycleListener(new StaticResourceConfigurer(context));
        ServletContextInitializer[] initializersToUse = this.mergeInitializers(initializers);
        host.addChild(context);
        this.configureContext(context, initializersToUse);
        this.postProcessContext(context);
    }
+

是一个 TomcatEmbeddedContext，这一点比较特殊，希望这样会有个一个大致的概念。

]]> Java @@ -2152,6 +2150,34 @@

start 方法也是类似的，但是更复杂，第一步也是状态判断，是不是已经被启动过了，如果还是 new 状态的话就先调用 init，如果已经 failed 启动失败了就直接调用 stop 结束了，如果是非已初始化和非已停止的状态则是错误的状态转变
如果是正常情况，那就是先设置成 STARTING_PREP，然后调用实现类的 startInternal，如果启动失败了就调用 stop，此时理论上应该是启动中 STARTING 状态，否则就是异常状态转变，剩下的就是状态启动完的状态，

@Override
public final synchronized void start() throws LifecycleException {

    if (LifecycleState.STARTING_PREP.equals(state) || LifecycleState.STARTING.equals(state) ||
            LifecycleState.STARTED.equals(state)) {

        if (log.isDebugEnabled()) {
            Exception e = new LifecycleException();
            log.debug(sm.getString("lifecycleBase.alreadyStarted", toString()), e);
        } else if (log.isInfoEnabled()) {
            log.info(sm.getString("lifecycleBase.alreadyStarted", toString()));
        }

        return;
    }

    if (state.equals(LifecycleState.NEW)) {
        init();
    } else if (state.equals(LifecycleState.FAILED)) {
        stop();
    } else if (!state.equals(LifecycleState.INITIALIZED) &&
            !state.equals(LifecycleState.STOPPED)) {
        invalidTransition(Lifecycle.BEFORE_START_EVENT);
    }

    try {
        setStateInternal(LifecycleState.STARTING_PREP, null, false);
        startInternal();
        if (state.equals(LifecycleState.FAILED)) {
            // This is a 'controlled' failure. The component put itself into the
            // FAILED state so call stop() to complete the clean-up.
            stop();
        } else if (!state.equals(LifecycleState.STARTING)) {
            // Shouldn't be necessary but acts as a check that sub-classes are
            // doing what they are supposed to.
            invalidTransition(Lifecycle.AFTER_START_EVENT);
        } else {
            setStateInternal(LifecycleState.STARTED, null, false);
        }
    } catch (Throwable t) {
        // This is an 'uncontrolled' failure so put the component into the
        // FAILED state and throw an exception.
        handleSubClassException(t, "lifecycleBase.startFail", toString());
    }
}

stop 跟 destroy 也类似的

+]]> + + Java + SpringBoot + Tomcat + + + Java + Tomcat + SpringBoot + + + + Tomcat 系列篇九-介绍下 Tomcat 里的 ContainerBase + /2023/11/19/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E4%B9%9D-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E9%87%8C%E7%9A%84-ContainerBase/ + 前面介绍过 Tomcat 的层次结构，

+
<Server>
    <Service>
        <Connector />
        <Connector />
        <Engine>
            <Host>
                <Context />
            </Host>
        </Engine>
    </Service>
</Server>
+

参考这个 xml，而对于这些组件中，有一类有相同的基类，也就是这次要介绍的 ContainerBase，

包括 engine，host，context 还有 wrapper ，都是同样的容器组件，
而他们共同实现的接口就是 Container ，
主要包含了

+
public static final String ADD_CHILD_EVENT = "addChild";


/**
 * The ContainerEvent event type sent when a valve is added
 * by <code>addValve()</code>, if this Container supports pipelines.
 */
public static final String ADD_VALVE_EVENT = "addValve";


/**
 * The ContainerEvent event type sent when a child container is removed
 * by <code>removeChild()</code>.
 */
public static final String REMOVE_CHILD_EVENT = "removeChild";


/**
 * The ContainerEvent event type sent when a valve is removed
 * by <code>removeValve()</code>, if this Container supports pipelines.
 */
public static final String REMOVE_VALVE_EVENT = "removeValve";
+

这几个事件类型，

+
public Pipeline getPipeline();

public Container getParent();

public void addChild(Container child);

public Container[] findChildren();

public int getStartStopThreads();

public void setStartStopThreads(int startStopThreads);


+

包括 pipeline 和获取 parent，添加 child 跟查找 child，然后设置线程池的线程数等
而对于 ContainerBase
也包含了 Lifecycle 的常规方法

+
@Override
protected void initInternal() throws LifecycleException {
    reconfigureStartStopExecutor(getStartStopThreads());
    super.initInternal();
}


private void reconfigureStartStopExecutor(int threads) {
    if (threads == 1) {
        // Use a fake executor
        if (!(startStopExecutor instanceof InlineExecutorService)) {
            startStopExecutor = new InlineExecutorService();
        }
    } else {
        // Delegate utility execution to the Service
        Server server = Container.getService(this).getServer();
        server.setUtilityThreads(threads);
        startStopExecutor = server.getUtilityExecutor();
    }
}
+

初始化方法主要就是之前讲过的线程池
后面就开始 startInternal

+
@Override
    protected synchronized void startInternal() throws LifecycleException {

        // Start our subordinate components, if any
        logger = null;
        getLogger();
        Cluster cluster = getClusterInternal();
        if (cluster instanceof Lifecycle) {
            ((Lifecycle) cluster).start();
        }
        Realm realm = getRealmInternal();
        if (realm instanceof Lifecycle) {
            ((Lifecycle) realm).start();
        }

        // Start our child containers, if any
        Container children[] = findChildren();
        List<Future<Void>> results = new ArrayList<>();
        for (Container child : children) {
            results.add(startStopExecutor.submit(new StartChild(child)));
        }

        MultiThrowable multiThrowable = null;

        for (Future<Void> result : results) {
            try {
                result.get();
            } catch (Throwable e) {
                log.error(sm.getString("containerBase.threadedStartFailed"), e);
                if (multiThrowable == null) {
                    multiThrowable = new MultiThrowable();
                }
                multiThrowable.add(e);
            }

        }
        if (multiThrowable != null) {
            throw new LifecycleException(sm.getString("containerBase.threadedStartFailed"),
                    multiThrowable.getThrowable());
        }

        // Start the Valves in our pipeline (including the basic), if any
        if (pipeline instanceof Lifecycle) {
            ((Lifecycle) pipeline).start();
        }

        setState(LifecycleState.STARTING);

        // Start our thread
        if (backgroundProcessorDelay > 0) {
            monitorFuture = Container.getService(ContainerBase.this).getServer()
                    .getUtilityExecutor().scheduleWithFixedDelay(
                            new ContainerBackgroundProcessorMonitor(), 0, 60, TimeUnit.SECONDS);
        }
    }
+

这里包括了 cluster 的启动和 realm 的启动，然后将 child 子组件添加到线程池里启动，然后循环 get 结果，接下去就是 pipeline 的启动，设置开始中状态，后面是 ContainerBackgroundProcessor 的启动
然后是停止方法

+
@Override
    protected synchronized void stopInternal() throws LifecycleException {

        // Stop our thread
        if (monitorFuture != null) {
            monitorFuture.cancel(true);
            monitorFuture = null;
        }
        threadStop();

        setState(LifecycleState.STOPPING);

        // Stop the Valves in our pipeline (including the basic), if any
        if (pipeline instanceof Lifecycle &&
                ((Lifecycle) pipeline).getState().isAvailable()) {
            ((Lifecycle) pipeline).stop();
        }

        // Stop our child containers, if any
        Container children[] = findChildren();
        List<Future<Void>> results = new ArrayList<>();
        for (Container child : children) {
            results.add(startStopExecutor.submit(new StopChild(child)));
        }

        boolean fail = false;
        for (Future<Void> result : results) {
            try {
                result.get();
            } catch (Exception e) {
                log.error(sm.getString("containerBase.threadedStopFailed"), e);
                fail = true;
            }
        }
        if (fail) {
            throw new LifecycleException(
                    sm.getString("containerBase.threadedStopFailed"));
        }

        // Stop our subordinate components, if any
        Realm realm = getRealmInternal();
        if (realm instanceof Lifecycle) {
            ((Lifecycle) realm).stop();
        }
        Cluster cluster = getClusterInternal();
        if (cluster instanceof Lifecycle) {
            ((Lifecycle) cluster).stop();
        }
    }
+

也是类似的，只不过停止顺序和刚才开始的顺序反了一下

]]> Java @@ -2221,63 +2247,6 @@

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 - SpringBoot - Tomcat - - - Java - Tomcat - SpringBoot - - - - 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/ - 最近同学在把 springboot 升级到 2.x 版本的过程中碰到了小问题，可能升级变更里能找到信息，不过我们以学习为目的，可以看看代码是怎么样的
报错是在这段代码里的
org.apache.tomcat.util.http.fileupload.util.LimitedInputStream#checkLimit

-
private void checkLimit() throws IOException {
    if (count > sizeMax) {
        raiseError(sizeMax, count);
    }
}
-

其中的 raiseError 是个抽象方法

-
protected abstract void raiseError(long pSizeMax, long pCount)
            throws IOException;
-

具体的实现是在

-
public FileItemStreamImpl(FileItemIteratorImpl pFileItemIterator, String pName, String pFieldName, String pContentType, boolean pFormField, long pContentLength) throws FileUploadException, IOException {
        this.fileItemIteratorImpl = pFileItemIterator;
        this.name = pName;
        this.fieldName = pFieldName;
        this.contentType = pContentType;
        this.formField = pFormField;
        long fileSizeMax = this.fileItemIteratorImpl.getFileSizeMax();
        if (fileSizeMax != -1L && pContentLength != -1L && pContentLength > fileSizeMax) {
            FileSizeLimitExceededException e = new FileSizeLimitExceededException(String.format("The field %s exceeds its maximum permitted size of %s bytes.", this.fieldName, fileSizeMax), pContentLength, fileSizeMax);
            e.setFileName(pName);
            e.setFieldName(pFieldName);
            throw new FileUploadIOException(e);
        } else {
            final MultipartStream.ItemInputStream itemStream = this.fileItemIteratorImpl.getMultiPartStream().newInputStream();
            InputStream istream = itemStream;
            if (fileSizeMax != -1L) {
                istream = new LimitedInputStream(itemStream, fileSizeMax) {
                    protected void raiseError(long pSizeMax, long pCount) throws IOException {
                        itemStream.close(true);
                        FileSizeLimitExceededException e = new FileSizeLimitExceededException(String.format("The field %s exceeds its maximum permitted size of %s bytes.", FileItemStreamImpl.this.fieldName, pSizeMax), pCount, pSizeMax);
                        e.setFieldName(FileItemStreamImpl.this.fieldName);
                        e.setFileName(FileItemStreamImpl.this.name);
                        throw new FileUploadIOException(e);
                    }
                };
            }

            this.stream = (InputStream)istream;
        }
    }
-

后面也会介绍到，这里我们其实主要是要找到这个 pSizeMax 是哪里来的
通过阅读代码会发现跟这个类 MultipartConfigElement 有关系
而在升级后的 springboot 中这个类已经有了自动装配类，也就是
org.springframework.boot.autoconfigure.web.servlet.MultipartAutoConfiguration

-

有了这个自动装配

-
@Configuration(
    proxyBeanMethods = false
)
@ConditionalOnClass({Servlet.class, StandardServletMultipartResolver.class, MultipartConfigElement.class})
@ConditionalOnProperty(
    prefix = "spring.servlet.multipart",
    name = {"enabled"},
    matchIfMissing = true
)
@ConditionalOnWebApplication(
    type = Type.SERVLET
)
@EnableConfigurationProperties({MultipartProperties.class})
public class MultipartAutoConfiguration {
    private final MultipartProperties multipartProperties;

    public MultipartAutoConfiguration(MultipartProperties multipartProperties) {
        this.multipartProperties = multipartProperties;
    }

    @Bean
    @ConditionalOnMissingBean({MultipartConfigElement.class, CommonsMultipartResolver.class})
    public MultipartConfigElement multipartConfigElement() {
        return this.multipartProperties.createMultipartConfig();
    }
-

而这个 MultipartProperties 类中

-
@ConfigurationProperties(
    prefix = "spring.servlet.multipart",
    ignoreUnknownFields = false
)
public class MultipartProperties {
    private boolean enabled = true;
    private String location;
    private DataSize maxFileSize = DataSize.ofMegabytes(1L);
    private DataSize maxRequestSize = DataSize.ofMegabytes(10L);
    private DataSize fileSizeThreshold = DataSize.ofBytes(0L);
    private boolean resolveLazily = false;
-

并且在前面 createMultipartConfig 中就使用了这个maxFileSize 的默认值

-
public MultipartConfigElement createMultipartConfig() {
    MultipartConfigFactory factory = new MultipartConfigFactory();
    PropertyMapper map = PropertyMapper.get().alwaysApplyingWhenNonNull();
    map.from(this.fileSizeThreshold).to(factory::setFileSizeThreshold);
    map.from(this.location).whenHasText().to(factory::setLocation);
    map.from(this.maxRequestSize).to(factory::setMaxRequestSize);
    map.from(this.maxFileSize).to(factory::setMaxFileSize);
    return factory.createMultipartConfig();
}
-

而在 org.apache.catalina.connector.Request#parseParts 中，会判断 mce 的配置

-
private void parseParts(boolean explicit) {

        // 省略一部分代码

            ServletFileUpload upload = new ServletFileUpload();
            upload.setFileItemFactory(factory);
            upload.setFileSizeMax(mce.getMaxFileSize());
            upload.setSizeMax(mce.getMaxRequestSize());

            parts = new ArrayList<>();
            try {
                List<FileItem> items =
                        upload.parseRequest(new ServletRequestContext(this));
                int maxPostSize = getConnector().getMaxPostSize();
                int postSize = 0;
                Charset charset = getCharset();
-

主要 org.apache.tomcat.util.http.fileupload.FileUploadBase#parseRequest

-
public List<FileItem> parseRequest(final RequestContext ctx)
            throws FileUploadException {
        final List<FileItem> items = new ArrayList<>();
        boolean successful = false;
        try {
            final FileItemIterator iter = getItemIterator(ctx);
            final FileItemFactory fileItemFactory = Objects.requireNonNull(getFileItemFactory(),
                    "No FileItemFactory has been set.");
            final byte[] buffer = new byte[Streams.DEFAULT_BUFFER_SIZE];
            while (iter.hasNext()) {
                final FileItemStream item = iter.next();
                // Don't use getName() here to prevent an InvalidFileNameException.
                final String fileName = item.getName();
                final FileItem fileItem = fileItemFactory.createItem(item.getFieldName(), item.getContentType(),
                                                   item.isFormField(), fileName);
                items.add(fileItem);
                try {
                    Streams.copy(item.openStream(), fileItem.getOutputStream(), true, buffer);
                } catch (final FileUploadIOException e) {
-

其中 org.apache.tomcat.util.http.fileupload.FileUploadBase#getItemIterator

-
public FileItemIterator getItemIterator(final RequestContext ctx)
throws FileUploadException, IOException {
    try {
        return new FileItemIteratorImpl(this, ctx);
    } catch (final FileUploadIOException e) {
        // unwrap encapsulated SizeException
        throw (FileUploadException) e.getCause();
    }
}
-

这里就创建了 org.apache.tomcat.util.http.fileupload.impl.FileItemIteratorImpl

-
public FileItemIteratorImpl(final FileUploadBase fileUploadBase, final RequestContext requestContext)
    throws FileUploadException, IOException {
    this.fileUploadBase = fileUploadBase;
    sizeMax = fileUploadBase.getSizeMax();
    fileSizeMax = fileUploadBase.getFileSizeMax();
    ctx = Objects.requireNonNull(requestContext, "requestContext");
    skipPreamble = true;
    findNextItem();
}

-

内部使用了前面给 upload 设置的文件大小上限 upload.setFileSizeMax(mce.getMaxFileSize());

-

然后在 findNextItem 里执行了初始化

-
private boolean findNextItem() throws FileUploadException, IOException {
        if (eof) {
            return false;
        }
        if (currentItem != null) {
            currentItem.close();
            currentItem = null;
        }
        final MultipartStream multi = getMultiPartStream();
        for (;;) {
            final boolean nextPart;
            if (skipPreamble) {
                nextPart = multi.skipPreamble();
            } else {
                nextPart = multi.readBoundary();
            }
            if (!nextPart) {
                if (currentFieldName == null) {
                    // Outer multipart terminated -> No more data
                    eof = true;
                    return false;
                }
                // Inner multipart terminated -> Return to parsing the outer
                multi.setBoundary(multiPartBoundary);
                currentFieldName = null;
                continue;
            }
            final FileItemHeaders headers = fileUploadBase.getParsedHeaders(multi.readHeaders());
            if (currentFieldName == null) {
                // We're parsing the outer multipart
                final String fieldName = fileUploadBase.getFieldName(headers);
                if (fieldName != null) {
                    final String subContentType = headers.getHeader(FileUploadBase.CONTENT_TYPE);
                    if (subContentType != null
                            &&  subContentType.toLowerCase(Locale.ENGLISH)
                                    .startsWith(FileUploadBase.MULTIPART_MIXED)) {
                        currentFieldName = fieldName;
                        // Multiple files associated with this field name
                        final byte[] subBoundary = fileUploadBase.getBoundary(subContentType);
                        multi.setBoundary(subBoundary);
                        skipPreamble = true;
                        continue;
                    }
                    final String fileName = fileUploadBase.getFileName(headers);
                    currentItem = new FileItemStreamImpl(this, fileName,
                            fieldName, headers.getHeader(FileUploadBase.CONTENT_TYPE),
                            fileName == null, getContentLength(headers));
                    currentItem.setHeaders(headers);
                    progressNotifier.noteItem();
                    itemValid = true;
                    return true;
                }
            } else {
                final String fileName = fileUploadBase.getFileName(headers);
                if (fileName != null) {
                    currentItem = new FileItemStreamImpl(this, fileName,
                            currentFieldName,
                            headers.getHeader(FileUploadBase.CONTENT_TYPE),
                            false, getContentLength(headers));
                    currentItem.setHeaders(headers);
                    progressNotifier.noteItem();
                    itemValid = true;
                    return true;
                }
            }
            multi.discardBodyData();
        }
    }

-

这里面就会 new 这个 FileItemStreamImpl

-
currentItem = new FileItemStreamImpl(this, fileName,
                            fieldName, headers.getHeader(FileUploadBase.CONTENT_TYPE),
                            fileName == null, getContentLength(headers));

-

构造方法比较长

-
public FileItemStreamImpl(final FileItemIteratorImpl pFileItemIterator, final String pName, final String pFieldName,
            final String pContentType, final boolean pFormField,
            final long pContentLength) throws FileUploadException, IOException {
        fileItemIteratorImpl = pFileItemIterator;
        name = pName;
        fieldName = pFieldName;
        contentType = pContentType;
        formField = pFormField;
        final long fileSizeMax = fileItemIteratorImpl.getFileSizeMax();
        if (fileSizeMax != -1 && pContentLength != -1
                && pContentLength > fileSizeMax) {
            final FileSizeLimitExceededException e =
                    new FileSizeLimitExceededException(
                            String.format("The field %s exceeds its maximum permitted size of %s bytes.",
                                    fieldName, Long.valueOf(fileSizeMax)),
                            pContentLength, fileSizeMax);
            e.setFileName(pName);
            e.setFieldName(pFieldName);
            throw new FileUploadIOException(e);
        }
        // OK to construct stream now
        final ItemInputStream itemStream = fileItemIteratorImpl.getMultiPartStream().newInputStream();
        InputStream istream = itemStream;
        if (fileSizeMax != -1) {
            istream = new LimitedInputStream(istream, fileSizeMax) {
                @Override
                protected void raiseError(final long pSizeMax, final long pCount)
                        throws IOException {
                    itemStream.close(true);
                    final FileSizeLimitExceededException e =
                        new FileSizeLimitExceededException(
                            String.format("The field %s exceeds its maximum permitted size of %s bytes.",
                                   fieldName, Long.valueOf(pSizeMax)),
                            pCount, pSizeMax);
                    e.setFieldName(fieldName);
                    e.setFileName(name);
                    throw new FileUploadIOException(e);
                }
            };
        }
        stream = istream;
    }
-

当 fileSizeMax != 0 的时候就会初始化 LimitedInputStream，这就就是会在前面的

-

org.apache.tomcat.util.http.fileupload.FileUploadBase#parseRequest 中

-
Streams.copy(item.openStream(), fileItem.getOutputStream(), true, buffer);
-

这里的 item

-
final FileItemIterator iter = getItemIterator(ctx);
            final FileItemFactory fileItemFactory = Objects.requireNonNull(getFileItemFactory(),
                    "No FileItemFactory has been set.");
            final byte[] buffer = new byte[Streams.DEFAULT_BUFFER_SIZE];
            while (iter.hasNext()) {
                final FileItemStream item = iter.next();
-

调用了 FileItemIterator 迭代器的 next

-
@Override
    public FileItemStream next() throws FileUploadException, IOException {
        if (eof  ||  (!itemValid && !hasNext())) {
            throw new NoSuchElementException();
        }
        itemValid = false;
        return currentItem;
    }
-

这个 currentItem 就是前面 new 的 FileItemStreamImpl

-

然后在 Streams.copy 的时候调用 openStream 也就是 org.apache.tomcat.util.http.fileupload.impl.FileItemStreamImpl#openStream

-
@Override
    public InputStream openStream() throws IOException {
        if (((Closeable) stream).isClosed()) {
            throw new FileItemStream.ItemSkippedException();
        }
        return stream;
    }
-

这里的 stream 就是 FileItemStreamImpl 构造方法最后赋值的 stream，会在大小超过限制时抛出错误

-

而这个可以通过设置 properties 来修改，spring.servlet.multipart.max-file-size 和 spring.servlet.multipart.max-request-size

-
spring.servlet.multipart.max-file-size=100MB
spring.servlet.multipart.max-request-size=100MB
-

而老版本的 spring.http.multipart.maxFileSize
其实就是配置名称改了下，但是能看一下代码也是有点收获的。

]]> Java @@ -2351,42 +2320,60 @@ - 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);
   }
+ 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/ + 最近同学在把 springboot 升级到 2.x 版本的过程中碰到了小问题，可能升级变更里能找到信息，不过我们以学习为目的，可以看看代码是怎么样的
报错是在这段代码里的
org.apache.tomcat.util.http.fileupload.util.LimitedInputStream#checkLimit

+
private void checkLimit() throws IOException {
    if (count > sizeMax) {
        raiseError(sizeMax, count);
    }
}
+

其中的 raiseError 是个抽象方法

+
protected abstract void raiseError(long pSizeMax, long pCount)
            throws IOException;
+

具体的实现是在

+
public FileItemStreamImpl(FileItemIteratorImpl pFileItemIterator, String pName, String pFieldName, String pContentType, boolean pFormField, long pContentLength) throws FileUploadException, IOException {
        this.fileItemIteratorImpl = pFileItemIterator;
        this.name = pName;
        this.fieldName = pFieldName;
        this.contentType = pContentType;
        this.formField = pFormField;
        long fileSizeMax = this.fileItemIteratorImpl.getFileSizeMax();
        if (fileSizeMax != -1L && pContentLength != -1L && pContentLength > fileSizeMax) {
            FileSizeLimitExceededException e = new FileSizeLimitExceededException(String.format("The field %s exceeds its maximum permitted size of %s bytes.", this.fieldName, fileSizeMax), pContentLength, fileSizeMax);
            e.setFileName(pName);
            e.setFieldName(pFieldName);
            throw new FileUploadIOException(e);
        } else {
            final MultipartStream.ItemInputStream itemStream = this.fileItemIteratorImpl.getMultiPartStream().newInputStream();
            InputStream istream = itemStream;
            if (fileSizeMax != -1L) {
                istream = new LimitedInputStream(itemStream, fileSizeMax) {
                    protected void raiseError(long pSizeMax, long pCount) throws IOException {
                        itemStream.close(true);
                        FileSizeLimitExceededException e = new FileSizeLimitExceededException(String.format("The field %s exceeds its maximum permitted size of %s bytes.", FileItemStreamImpl.this.fieldName, pSizeMax), pCount, pSizeMax);
                        e.setFieldName(FileItemStreamImpl.this.fieldName);
                        e.setFileName(FileItemStreamImpl.this.name);
                        throw new FileUploadIOException(e);
                    }
                };
            }

            this.stream = (InputStream)istream;
        }
    }
+

后面也会介绍到，这里我们其实主要是要找到这个 pSizeMax 是哪里来的
通过阅读代码会发现跟这个类 MultipartConfigElement 有关系
而在升级后的 springboot 中这个类已经有了自动装配类，也就是
org.springframework.boot.autoconfigure.web.servlet.MultipartAutoConfiguration

+

有了这个自动装配

+
@Configuration(
    proxyBeanMethods = false
)
@ConditionalOnClass({Servlet.class, StandardServletMultipartResolver.class, MultipartConfigElement.class})
@ConditionalOnProperty(
    prefix = "spring.servlet.multipart",
    name = {"enabled"},
    matchIfMissing = true
)
@ConditionalOnWebApplication(
    type = Type.SERVLET
)
@EnableConfigurationProperties({MultipartProperties.class})
public class MultipartAutoConfiguration {
    private final MultipartProperties multipartProperties;

    public MultipartAutoConfiguration(MultipartProperties multipartProperties) {
        this.multipartProperties = multipartProperties;
    }

    @Bean
    @ConditionalOnMissingBean({MultipartConfigElement.class, CommonsMultipartResolver.class})
    public MultipartConfigElement multipartConfigElement() {
        return this.multipartProperties.createMultipartConfig();
    }
+

而这个 MultipartProperties 类中

+
@ConfigurationProperties(
    prefix = "spring.servlet.multipart",
    ignoreUnknownFields = false
)
public class MultipartProperties {
    private boolean enabled = true;
    private String location;
    private DataSize maxFileSize = DataSize.ofMegabytes(1L);
    private DataSize maxRequestSize = DataSize.ofMegabytes(10L);
    private DataSize fileSizeThreshold = DataSize.ofBytes(0L);
    private boolean resolveLazily = false;
+

并且在前面 createMultipartConfig 中就使用了这个maxFileSize 的默认值

+
public MultipartConfigElement createMultipartConfig() {
    MultipartConfigFactory factory = new MultipartConfigFactory();
    PropertyMapper map = PropertyMapper.get().alwaysApplyingWhenNonNull();
    map.from(this.fileSizeThreshold).to(factory::setFileSizeThreshold);
    map.from(this.location).whenHasText().to(factory::setLocation);
    map.from(this.maxRequestSize).to(factory::setMaxRequestSize);
    map.from(this.maxFileSize).to(factory::setMaxFileSize);
    return factory.createMultipartConfig();
}
+

而在 org.apache.catalina.connector.Request#parseParts 中，会判断 mce 的配置

+
private void parseParts(boolean explicit) {

        // 省略一部分代码

            ServletFileUpload upload = new ServletFileUpload();
            upload.setFileItemFactory(factory);
            upload.setFileSizeMax(mce.getMaxFileSize());
            upload.setSizeMax(mce.getMaxRequestSize());

            parts = new ArrayList<>();
            try {
                List<FileItem> items =
                        upload.parseRequest(new ServletRequestContext(this));
                int maxPostSize = getConnector().getMaxPostSize();
                int postSize = 0;
                Charset charset = getCharset();
+

主要 org.apache.tomcat.util.http.fileupload.FileUploadBase#parseRequest

+
public List<FileItem> parseRequest(final RequestContext ctx)
            throws FileUploadException {
        final List<FileItem> items = new ArrayList<>();
        boolean successful = false;
        try {
            final FileItemIterator iter = getItemIterator(ctx);
            final FileItemFactory fileItemFactory = Objects.requireNonNull(getFileItemFactory(),
                    "No FileItemFactory has been set.");
            final byte[] buffer = new byte[Streams.DEFAULT_BUFFER_SIZE];
            while (iter.hasNext()) {
                final FileItemStream item = iter.next();
                // Don't use getName() here to prevent an InvalidFileNameException.
                final String fileName = item.getName();
                final FileItem fileItem = fileItemFactory.createItem(item.getFieldName(), item.getContentType(),
                                                   item.isFormField(), fileName);
                items.add(fileItem);
                try {
                    Streams.copy(item.openStream(), fileItem.getOutputStream(), true, buffer);
                } catch (final FileUploadIOException e) {
+

其中 org.apache.tomcat.util.http.fileupload.FileUploadBase#getItemIterator

+
public FileItemIterator getItemIterator(final RequestContext ctx)
throws FileUploadException, IOException {
    try {
        return new FileItemIteratorImpl(this, ctx);
    } catch (final FileUploadIOException e) {
        // unwrap encapsulated SizeException
        throw (FileUploadException) e.getCause();
    }
}
+

这里就创建了 org.apache.tomcat.util.http.fileupload.impl.FileItemIteratorImpl

+
public FileItemIteratorImpl(final FileUploadBase fileUploadBase, final RequestContext requestContext)
    throws FileUploadException, IOException {
    this.fileUploadBase = fileUploadBase;
    sizeMax = fileUploadBase.getSizeMax();
    fileSizeMax = fileUploadBase.getFileSizeMax();
    ctx = Objects.requireNonNull(requestContext, "requestContext");
    skipPreamble = true;
    findNextItem();
}

+

内部使用了前面给 upload 设置的文件大小上限 upload.setFileSizeMax(mce.getMaxFileSize());

+

然后在 findNextItem 里执行了初始化

+
private boolean findNextItem() throws FileUploadException, IOException {
        if (eof) {
            return false;
        }
        if (currentItem != null) {
            currentItem.close();
            currentItem = null;
        }
        final MultipartStream multi = getMultiPartStream();
        for (;;) {
            final boolean nextPart;
            if (skipPreamble) {
                nextPart = multi.skipPreamble();
            } else {
                nextPart = multi.readBoundary();
            }
            if (!nextPart) {
                if (currentFieldName == null) {
                    // Outer multipart terminated -> No more data
                    eof = true;
                    return false;
                }
                // Inner multipart terminated -> Return to parsing the outer
                multi.setBoundary(multiPartBoundary);
                currentFieldName = null;
                continue;
            }
            final FileItemHeaders headers = fileUploadBase.getParsedHeaders(multi.readHeaders());
            if (currentFieldName == null) {
                // We're parsing the outer multipart
                final String fieldName = fileUploadBase.getFieldName(headers);
                if (fieldName != null) {
                    final String subContentType = headers.getHeader(FileUploadBase.CONTENT_TYPE);
                    if (subContentType != null
                            &&  subContentType.toLowerCase(Locale.ENGLISH)
                                    .startsWith(FileUploadBase.MULTIPART_MIXED)) {
                        currentFieldName = fieldName;
                        // Multiple files associated with this field name
                        final byte[] subBoundary = fileUploadBase.getBoundary(subContentType);
                        multi.setBoundary(subBoundary);
                        skipPreamble = true;
                        continue;
                    }
                    final String fileName = fileUploadBase.getFileName(headers);
                    currentItem = new FileItemStreamImpl(this, fileName,
                            fieldName, headers.getHeader(FileUploadBase.CONTENT_TYPE),
                            fileName == null, getContentLength(headers));
                    currentItem.setHeaders(headers);
                    progressNotifier.noteItem();
                    itemValid = true;
                    return true;
                }
            } else {
                final String fileName = fileUploadBase.getFileName(headers);
                if (fileName != null) {
                    currentItem = new FileItemStreamImpl(this, fileName,
                            currentFieldName,
                            headers.getHeader(FileUploadBase.CONTENT_TYPE),
                            false, getContentLength(headers));
                    currentItem.setHeaders(headers);
                    progressNotifier.noteItem();
                    itemValid = true;
                    return true;
                }
            }
            multi.discardBodyData();
        }
    }

+

这里面就会 new 这个 FileItemStreamImpl

+
currentItem = new FileItemStreamImpl(this, fileName,
                            fieldName, headers.getHeader(FileUploadBase.CONTENT_TYPE),
                            fileName == null, getContentLength(headers));

+

构造方法比较长

+
public FileItemStreamImpl(final FileItemIteratorImpl pFileItemIterator, final String pName, final String pFieldName,
            final String pContentType, final boolean pFormField,
            final long pContentLength) throws FileUploadException, IOException {
        fileItemIteratorImpl = pFileItemIterator;
        name = pName;
        fieldName = pFieldName;
        contentType = pContentType;
        formField = pFormField;
        final long fileSizeMax = fileItemIteratorImpl.getFileSizeMax();
        if (fileSizeMax != -1 && pContentLength != -1
                && pContentLength > fileSizeMax) {
            final FileSizeLimitExceededException e =
                    new FileSizeLimitExceededException(
                            String.format("The field %s exceeds its maximum permitted size of %s bytes.",
                                    fieldName, Long.valueOf(fileSizeMax)),
                            pContentLength, fileSizeMax);
            e.setFileName(pName);
            e.setFieldName(pFieldName);
            throw new FileUploadIOException(e);
        }
        // OK to construct stream now
        final ItemInputStream itemStream = fileItemIteratorImpl.getMultiPartStream().newInputStream();
        InputStream istream = itemStream;
        if (fileSizeMax != -1) {
            istream = new LimitedInputStream(istream, fileSizeMax) {
                @Override
                protected void raiseError(final long pSizeMax, final long pCount)
                        throws IOException {
                    itemStream.close(true);
                    final FileSizeLimitExceededException e =
                        new FileSizeLimitExceededException(
                            String.format("The field %s exceeds its maximum permitted size of %s bytes.",
                                   fieldName, Long.valueOf(pSizeMax)),
                            pCount, pSizeMax);
                    e.setFieldName(fieldName);
                    e.setFileName(name);
                    throw new FileUploadIOException(e);
                }
            };
        }
        stream = istream;
    }
+

当 fileSizeMax != 0 的时候就会初始化 LimitedInputStream，这就就是会在前面的

+

org.apache.tomcat.util.http.fileupload.FileUploadBase#parseRequest 中

+
Streams.copy(item.openStream(), fileItem.getOutputStream(), true, buffer);
+

这里的 item

+
final FileItemIterator iter = getItemIterator(ctx);
            final FileItemFactory fileItemFactory = Objects.requireNonNull(getFileItemFactory(),
                    "No FileItemFactory has been set.");
            final byte[] buffer = new byte[Streams.DEFAULT_BUFFER_SIZE];
            while (iter.hasNext()) {
                final FileItemStream item = iter.next();
+

调用了 FileItemIterator 迭代器的 next

+
@Override
    public FileItemStream next() throws FileUploadException, IOException {
        if (eof  ||  (!itemValid && !hasNext())) {
            throw new NoSuchElementException();
        }
        itemValid = false;
        return currentItem;
    }
+

这个 currentItem 就是前面 new 的 FileItemStreamImpl

+

然后在 Streams.copy 的时候调用 openStream 也就是 org.apache.tomcat.util.http.fileupload.impl.FileItemStreamImpl#openStream

+
@Override
    public InputStream openStream() throws IOException {
        if (((Closeable) stream).isClosed()) {
            throw new FileItemStream.ItemSkippedException();
        }
        return stream;
    }
+

这里的 stream 就是 FileItemStreamImpl 构造方法最后赋值的 stream，会在大小超过限制时抛出错误

+

而这个可以通过设置 properties 来修改，spring.servlet.multipart.max-file-size 和 spring.servlet.multipart.max-request-size

+
spring.servlet.multipart.max-file-size=100MB
spring.servlet.multipart.max-request-size=100MB
+

而老版本的 spring.http.multipart.maxFileSize
其实就是配置名称改了下，但是能看一下代码也是有点收获的。

]]> Java - leetcode - - - java - leetcode - 题解 - - - - 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

-]]> - - Windows - 小技巧 + SpringBoot + Tomcat - Windows + Java + Tomcat + SpringBoot @@ -2423,99 +2410,45 @@
  • The hour must not contain a leading zero, for example “01:00” is not valid, it should be “1:00”.
  • The minute must be consist of two digits and may contain a leading zero, for example “10:2” is not valid, it should be “10:02”.
    • -

    题解

    又是参（chao）考（xi）别人的代码，嗯，就是这么不要脸，链接

    -

    Code

    class Solution {
    public:
        vector<string> readBinaryWatch(int num) {
            vector<string> res;
            for (int h = 0; h < 12; ++h) {
                for (int m = 0; m < 60; ++m) {
                    if (bitset<10>((h << 6) + m).count() == num) {
                        res.push_back(to_string(h) + (m < 10 ? ":0" : ":") + to_string(m));
                    }
                }
            }
            return res;
        }
    };
    ]]> - - leetcode - - - leetcode - c++ - -     - - 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 - -     - - 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/ - 在本地使用了 valet 做 php 的开发环境，因为可以指定自定义域名和证书，碰巧最近公司的网络环境比较糟糕，就想要在自定义 dns 上下点功夫，本来我们经常要在 dns 那配置个内部的 dns 地址，就想是不是可以通过 dnsmasq 来解决，
    却在第一步碰到个低级的问题，在 dnsmasq 的主配置文件里
    我配置了解析文件路径配置
    像这样

    -
    resolv-file=/opt/homebrew/etc/dnsmasq.d/resolv.dnsmasq.conf
    -

    结果发现 dnsmasq 就起不来了，因为是 brew 服务的形式起来，发现日志也没有， dnsmasq 配置文件本身也没什么日志，这个是最讨厌的，网上搜了一圈也都没有， brew services 的服务如果启动状态是 error，并且服务本身没有日志的话就是一头雾水，并且对于 plist 来说，即使我手动加了标准输出和错误输出，brew services restart 的时候也是会被重新覆盖，
    后来仔细看了下这个问题，发现它下面有这么一行配置

    -
    conf-dir=/opt/homebrew/etc/dnsmasq.d/,*.conf
    -

    想了一下发现这个问题其实很简单，dnsmasq 应该是不支持同一配置文件加载两次，
    我把 resolv 文件放在了同一个配置文件目录下，所以就被加载了两次，所以改掉目录就行了，但是目前看 dnsmasq 还不符合我的要求，也有可能我还没完全了解 dnsmasq 的使用方法，我想要的是比如按特定的域名后缀来配置对应的 dns 服务器，这样就不太会被影响，可以试试 AdGuard 看

    -]]>     - - dns - - - dnsmasq - -     - - docker比一般多一点的初学者介绍三 - /2020/03/21/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%B8%89/ - 运行第一个 Dockerfile

    上一篇的 Dockerfile 我们停留在构建阶段，现在来把它跑起来

    -
    docker run -d -p 80 --name static_web nicksxs/static_web \
    nginx -g "daemon off;"
    -

    这里的-d表示以分离模型运行docker (detached),然后-p 是表示将容器的 80 端口开放给宿主机，然后容器名就叫 static_web,使用了我们上次构建的 static_web 镜像，后面的是让 nginx 在前台运行

    可以看到返回了个容器 id，但是具体情况没出现，也没连上去，那我们想看看怎么访问在 Dockerfile 里写的静态页面，我们来看下docker 进程

    发现为我们随机分配了一个宿主机的端口，32768，去服务器的防火墙把这个外网端口开一下，看看是不是符合我们的预期呢

    好像不太对额，应该是 ubuntu 安装的 nginx 的默认工作目录不对，我们来进容器看看，再熟悉下命令docker exec -it 4792455ca2ed /bin/bash
    记得容器 id 换成自己的，进入容器后得找找 nginx 的配置文件，通常在/etc/nginx,/usr/local/etc等目录下，然后找到我们的目录是在这

    所以把刚才的内容复制过去再试试

    目标达成，give me five✌️

    -

    第二个 Dockerfile

    然后就想来动态一点的，毕竟写过 PHP，就来试试 PHP
    再建一个目录叫 dynamic_web，里面创建 src 目录，放一个 index.php
    内容是

    -
    <?php
    echo "Hello World!";
    -

    然后在 dynamic_web 目录下创建 Dockerfile，

    -
    FROM trafex/alpine-nginx-php7:latest
    COPY src/ /var/www/html
    EXPOSE 80
    -

    Dockerfile 虽然只有三行，不过要着重说明下，这个底包其实不是docker 官方的，有两点考虑，一点是官方的基本都是 php apache 的镜像，还有就是 alpine这个，截取一段中文介绍

    -
    -

    Alpine 操作系统是一个面向安全的轻型 Linux 发行版。它不同于通常 Linux 发行版，Alpine 采用了 musl libc 和 busybox 以减小系统的体积和运行时资源消耗，但功能上比 busybox 又完善的多，因此得到开源社区越来越多的青睐。在保持瘦身的同时，Alpine 还提供了自己的包管理工具 apk，可以通过 https://pkgs.alpinelinux.org/packages 网站上查询包信息，也可以直接通过 apk 命令直接查询和安装各种软件。
    Alpine 由非商业组织维护的，支持广泛场景的 Linux发行版，它特别为资深/重度Linux用户而优化，关注安全，性能和资源效能。Alpine 镜像可以适用于更多常用场景，并且是一个优秀的可以适用于生产的基础系统/环境。

    -
    -
    -

    Alpine Docker 镜像也继承了 Alpine Linux 发行版的这些优势。相比于其他 Docker 镜像，它的容量非常小，仅仅只有 5 MB 左右（对比 Ubuntu 系列镜像接近 200 MB），且拥有非常友好的包管理机制。官方镜像来自 docker-alpine 项目。

    -
    -
    -

    目前 Docker 官方已开始推荐使用 Alpine 替代之前的 Ubuntu 做为基础镜像环境。这样会带来多个好处。包括镜像下载速度加快，镜像安全性提高，主机之间的切换更方便，占用更少磁盘空间等。

    -
    -

    一方面在没有镜像的情况下，拉取 docker 镜像还是比较费力的，第二个就是也能节省硬盘空间，所以目前有大部分的 docker 镜像都将 alpine 作为基础镜像了
    然后再来构建下

    这里还有个点，就是上面的那个镜像我们也是 EXPOSE 80端口，然后外部宿主机会随机映射一个端口，为了偷个懒，我们就直接指定外部端口了
    docker run -d -p 80:80 dynamic_web打开浏览器发现访问不了，咋回事呢
    因为我们没看trafex/alpine-nginx-php7:latest这个镜像说明,它内部的服务是 8080 端口的，所以我们映射的暴露端口应该是 8080，再用 docker run -d -p 80:8080 dynamic_web这个启动，

    +

    题解

    又是参（chao）考（xi）别人的代码，嗯，就是这么不要脸，链接

    +

    Code

    class Solution {
    public:
        vector<string> readBinaryWatch(int num) {
            vector<string> res;
            for (int h = 0; h < 12; ++h) {
                for (int m = 0; m < 60; ++m) {
                    if (bitset<10>((h << 6) + m).count() == num) {
                        res.push_back(to_string(h) + (m < 10 ? ":0" : ":") + to_string(m));
                    }
                }
            }
            return res;
        }
    };
    ]]>     + + leetcode + + + leetcode + c++ + +     + + 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

    ]]>     - Docker - 介绍 + Windows + 小技巧 - Docker - namespace - Dockerfile + Windows     - docker-mysql-cluster - /2016/08/14/docker-mysql-cluster/ - docker-mysql-cluster

    基于docker搭了个mysql集群，稍微记一下，
    首先是新建mysql主库容

    docker run -d -e MYSQL_ROOT_PASSWORD=admin --name mysql-master -p 3307:3306 mysql:latest
    -d表示容器运行在后台，-e表示设置环境变量，即MYSQL_ROOT_PASSWORD=admin,设置了mysql的root密码，
    --name表示容器名，-p表示端口映射，将内部mysql:3306映射为外部的3307，最后的mysql:latest表示镜像名
    此外还可以用-v /local_path/my-master.cnf:/etc/mysql/my.cnf来映射配置文件
    然后同理启动从库
    docker run -d -e MYSQL_ROOT_PASSWORD=admin --name mysql-slave -p 3308:3306 mysql:latest
    然后进入主库改下配置文件
    docker-enter mysql-master如果无法进入就用docker ps -a看下容器是否在正常运行，如果status显示
    未正常运行，则用docker logs mysql-master看下日志哪里出错了。
    进入容器后，我这边使用的镜像的mysqld配置文件是在/etc/mysql下面，这个最新版本的mysql的配置文件包含
    三部分，/etc/mysql/my.cnf和/etc/mysql/conf.d/mysql.cnf，还有/etc/mysql/mysql.conf.d/mysqld.cnf
    这里需要改的是最后一个，加上

    -
    log-bin = mysql-bin
    server_id = 1
    -

    保存后退出容器重启主库容器，然后进入从库更改相同文件，

    -
    log-bin = mysql-bin
    server_id = 2
    -

    主从配置

    同样退出重启容器，然后是配置主从，首先进入主库，用命令mysql -u root -pxxxx进入mysql，然后赋予一个同步
    权限GRANT REPLICATION SLAVE ON *.* to 'backup'@'%' identified by '123456';还是同样说明下，ON *.*表示了数
    据库全部的权限，如果要指定数据库/表的话可以使用类似testDb/testTable，然后是'backup'@'%'表示给予同步
    权限的用户名及其主机ip，%表示不限制ip，当然如果有防火墙的话还是会有限制的，最后的identified by '123456'
    表示同步用户的密码，然后就查看下主库的状态信息show master status，如下图：
    9G5FE[9%@7%G(B`Q7]E)5@R.png
    把file跟position记下来，然后再开一个terminal，进入从库容器，登陆mysql，然后设置主库

    -
    change master to
    master_host='xxx.xxx.xxx.xxx',   //如果主从库的容器都在同一个宿主机上，这里的ip是docker容器的ip
    master_user='backup',            //就是上面的赋予权限的用户
    master_password='123456',
    master_log_file='mysql-bin.000004',  //主库中查看到的file
    master_log_pos=312,                  //主库中查看到的position
    master_port=3306;                    //如果是同一宿主机，这里使用真实的3306端口，3308及主库的3307是给外部连接使用的
    -

    通过docker-ip mysql-master可以查看容器的ip
    S(GP)P(M$N3~N1764@OW3E0.png
    这里有一点是要注意的，也是我踩的坑，就是如果是同一宿主机下两个mysql容器互联，我这里只能通过docker-ip和真实
    的3306端口能够连接成功。
    本文参考了这位同学的文章

    + 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
      • +
    ]]>     - docker + data analysis - docker - mysql + hadoop + cluster     @@ -2572,6 +2505,96 @@ cgroup + + 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-mysql-cluster + /2016/08/14/docker-mysql-cluster/ + docker-mysql-cluster

    基于docker搭了个mysql集群，稍微记一下，
    首先是新建mysql主库容

    docker run -d -e MYSQL_ROOT_PASSWORD=admin --name mysql-master -p 3307:3306 mysql:latest
    -d表示容器运行在后台，-e表示设置环境变量，即MYSQL_ROOT_PASSWORD=admin,设置了mysql的root密码，
    --name表示容器名，-p表示端口映射，将内部mysql:3306映射为外部的3307，最后的mysql:latest表示镜像名
    此外还可以用-v /local_path/my-master.cnf:/etc/mysql/my.cnf来映射配置文件
    然后同理启动从库
    docker run -d -e MYSQL_ROOT_PASSWORD=admin --name mysql-slave -p 3308:3306 mysql:latest
    然后进入主库改下配置文件
    docker-enter mysql-master如果无法进入就用docker ps -a看下容器是否在正常运行，如果status显示
    未正常运行，则用docker logs mysql-master看下日志哪里出错了。
    进入容器后，我这边使用的镜像的mysqld配置文件是在/etc/mysql下面，这个最新版本的mysql的配置文件包含
    三部分，/etc/mysql/my.cnf和/etc/mysql/conf.d/mysql.cnf，还有/etc/mysql/mysql.conf.d/mysqld.cnf
    这里需要改的是最后一个，加上

    +
    log-bin = mysql-bin
    server_id = 1
    +

    保存后退出容器重启主库容器，然后进入从库更改相同文件，

    +
    log-bin = mysql-bin
    server_id = 2
    +

    主从配置

    同样退出重启容器，然后是配置主从，首先进入主库，用命令mysql -u root -pxxxx进入mysql，然后赋予一个同步
    权限GRANT REPLICATION SLAVE ON *.* to 'backup'@'%' identified by '123456';还是同样说明下，ON *.*表示了数
    据库全部的权限，如果要指定数据库/表的话可以使用类似testDb/testTable，然后是'backup'@'%'表示给予同步
    权限的用户名及其主机ip，%表示不限制ip，当然如果有防火墙的话还是会有限制的，最后的identified by '123456'
    表示同步用户的密码，然后就查看下主库的状态信息show master status，如下图：
    9G5FE[9%@7%G(B`Q7]E)5@R.png
    把file跟position记下来，然后再开一个terminal，进入从库容器，登陆mysql，然后设置主库

    +
    change master to
    master_host='xxx.xxx.xxx.xxx',   //如果主从库的容器都在同一个宿主机上，这里的ip是docker容器的ip
    master_user='backup',            //就是上面的赋予权限的用户
    master_password='123456',
    master_log_file='mysql-bin.000004',  //主库中查看到的file
    master_log_pos=312,                  //主库中查看到的position
    master_port=3306;                    //如果是同一宿主机，这里使用真实的3306端口，3308及主库的3307是给外部连接使用的
    +

    通过docker-ip mysql-master可以查看容器的ip
    S(GP)P(M$N3~N1764@OW3E0.png
    这里有一点是要注意的，也是我踩的坑，就是如果是同一宿主机下两个mysql容器互联，我这里只能通过docker-ip和真实
    的3306端口能够连接成功。
    本文参考了这位同学的文章

    +]]>     + + docker + + + docker + mysql + +     + + 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/ + 在本地使用了 valet 做 php 的开发环境，因为可以指定自定义域名和证书，碰巧最近公司的网络环境比较糟糕，就想要在自定义 dns 上下点功夫，本来我们经常要在 dns 那配置个内部的 dns 地址，就想是不是可以通过 dnsmasq 来解决，
    却在第一步碰到个低级的问题，在 dnsmasq 的主配置文件里
    我配置了解析文件路径配置
    像这样

    +
    resolv-file=/opt/homebrew/etc/dnsmasq.d/resolv.dnsmasq.conf
    +

    结果发现 dnsmasq 就起不来了，因为是 brew 服务的形式起来，发现日志也没有， dnsmasq 配置文件本身也没什么日志，这个是最讨厌的，网上搜了一圈也都没有， brew services 的服务如果启动状态是 error，并且服务本身没有日志的话就是一头雾水，并且对于 plist 来说，即使我手动加了标准输出和错误输出，brew services restart 的时候也是会被重新覆盖，
    后来仔细看了下这个问题，发现它下面有这么一行配置

    +
    conf-dir=/opt/homebrew/etc/dnsmasq.d/,*.conf
    +

    想了一下发现这个问题其实很简单，dnsmasq 应该是不支持同一配置文件加载两次，
    我把 resolv 文件放在了同一个配置文件目录下，所以就被加载了两次，所以改掉目录就行了，但是目前看 dnsmasq 还不符合我的要求，也有可能我还没完全了解 dnsmasq 的使用方法，我想要的是比如按特定的域名后缀来配置对应的 dns 服务器，这样就不太会被影响，可以试试 AdGuard 看

    +]]>     + + dns + + + dnsmasq + +     + + docker比一般多一点的初学者介绍三 + /2020/03/21/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%B8%89/ + 运行第一个 Dockerfile

    上一篇的 Dockerfile 我们停留在构建阶段，现在来把它跑起来

    +
    docker run -d -p 80 --name static_web nicksxs/static_web \
    nginx -g "daemon off;"
    +

    这里的-d表示以分离模型运行docker (detached),然后-p 是表示将容器的 80 端口开放给宿主机，然后容器名就叫 static_web,使用了我们上次构建的 static_web 镜像，后面的是让 nginx 在前台运行

    可以看到返回了个容器 id，但是具体情况没出现，也没连上去，那我们想看看怎么访问在 Dockerfile 里写的静态页面，我们来看下docker 进程

    发现为我们随机分配了一个宿主机的端口，32768，去服务器的防火墙把这个外网端口开一下，看看是不是符合我们的预期呢

    好像不太对额，应该是 ubuntu 安装的 nginx 的默认工作目录不对，我们来进容器看看，再熟悉下命令docker exec -it 4792455ca2ed /bin/bash
    记得容器 id 换成自己的，进入容器后得找找 nginx 的配置文件，通常在/etc/nginx,/usr/local/etc等目录下，然后找到我们的目录是在这

    所以把刚才的内容复制过去再试试

    目标达成，give me five✌️

    +

    第二个 Dockerfile

    然后就想来动态一点的，毕竟写过 PHP，就来试试 PHP
    再建一个目录叫 dynamic_web，里面创建 src 目录，放一个 index.php
    内容是

    +
    <?php
    echo "Hello World!";
    +

    然后在 dynamic_web 目录下创建 Dockerfile，

    +
    FROM trafex/alpine-nginx-php7:latest
    COPY src/ /var/www/html
    EXPOSE 80
    +

    Dockerfile 虽然只有三行，不过要着重说明下，这个底包其实不是docker 官方的，有两点考虑，一点是官方的基本都是 php apache 的镜像，还有就是 alpine这个，截取一段中文介绍

    +
    +

    Alpine 操作系统是一个面向安全的轻型 Linux 发行版。它不同于通常 Linux 发行版，Alpine 采用了 musl libc 和 busybox 以减小系统的体积和运行时资源消耗，但功能上比 busybox 又完善的多，因此得到开源社区越来越多的青睐。在保持瘦身的同时，Alpine 还提供了自己的包管理工具 apk，可以通过 https://pkgs.alpinelinux.org/packages 网站上查询包信息，也可以直接通过 apk 命令直接查询和安装各种软件。
    Alpine 由非商业组织维护的，支持广泛场景的 Linux发行版，它特别为资深/重度Linux用户而优化，关注安全，性能和资源效能。Alpine 镜像可以适用于更多常用场景，并且是一个优秀的可以适用于生产的基础系统/环境。

    +
    +
    +

    Alpine Docker 镜像也继承了 Alpine Linux 发行版的这些优势。相比于其他 Docker 镜像，它的容量非常小，仅仅只有 5 MB 左右（对比 Ubuntu 系列镜像接近 200 MB），且拥有非常友好的包管理机制。官方镜像来自 docker-alpine 项目。

    +
    +
    +

    目前 Docker 官方已开始推荐使用 Alpine 替代之前的 Ubuntu 做为基础镜像环境。这样会带来多个好处。包括镜像下载速度加快，镜像安全性提高，主机之间的切换更方便，占用更少磁盘空间等。

    +
    +

    一方面在没有镜像的情况下，拉取 docker 镜像还是比较费力的，第二个就是也能节省硬盘空间，所以目前有大部分的 docker 镜像都将 alpine 作为基础镜像了
    然后再来构建下

    这里还有个点，就是上面的那个镜像我们也是 EXPOSE 80端口，然后外部宿主机会随机映射一个端口，为了偷个懒，我们就直接指定外部端口了
    docker run -d -p 80:80 dynamic_web打开浏览器发现访问不了，咋回事呢
    因为我们没看trafex/alpine-nginx-php7:latest这个镜像说明,它内部的服务是 8080 端口的，所以我们映射的暴露端口应该是 8080，再用 docker run -d -p 80:8080 dynamic_web这个启动，

    +]]>     + + Docker + 介绍 + + + Docker + namespace + Dockerfile + +     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/ @@ -2599,26 +2622,52 @@ - 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 + +     + + 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     @@ -2646,29 +2695,6 @@ Dubbo - - 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 - -     git小技巧之git stash /2024/10/27/git%E5%B0%8F%E6%8A%80%E5%B7%A7%E4%B9%8Bgit-stash/ @@ -2691,35 +2717,9 @@ - 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 - -     - - git小技巧之查看git commit - /2024/11/02/git%E5%B0%8F%E6%8A%80%E5%B7%A7%E4%B9%8B%E6%9F%A5%E7%9C%8Bgit-commit/ - 上次说的git stash我觉得是个非常有用的功能，简直是个大杀器，除非不存在需要切换分支的场景，否则在频繁切换的时候，git stash 可以让我们的commit更清晰，不至于写到一半就提交，频繁切换，甚至连commit信息都写不好
    这次的小技巧是属于一个延伸知识，我们知道每次commit都会提交我们的变更，然后我们可以通过git diff .查看当前仓库产生的未提交的变更，如果我想看某一次commit的变更呢，这个功能通过 git 的各种 gui 工具可以很方便的查看，如果通过命令其实也很方便，我们每一次的commit 都会产生一个 commit id , 看过我上一篇的可以联想到，是否 git commit 也有这种 show 命令，答案是肯定的，
    首先我们可以通过 git log 来查看我们的提交记录

    这里我们可以看到我们的提交 commit id
    然后通过 git show xxxxx 来查看我们这次 commit 修改的内容, xxxxx 表示我们的 commit id

    再延伸下，如果想看某个文件的变更呢，也是可以的，就是在 git show xxxxx 后面跟上文件名
    比如
    git show xxxxx 1.txt
    就能看到这一次 commit 对 1.txt 这个文件的更改，都是比较简单入门的 git 命令的小延伸，只是觉得对我有点帮助就分享下

    + git小技巧之git cherry-pick + /2024/11/09/git%E5%B0%8F%E6%8A%80%E5%B7%A7%E4%B9%8Bgit-cherry-pick/ + 上次讲了 git stash 跟 git commit 的小技巧，这次来讲下另一个命令，git cherry-pick，我怀疑这个命令应该叫 choose-pick 才比较好
    这里有个使用场景，比如我们有分支A跟B，分支B想用一下分支A一次变更的代码，正好不用自己再开发了
    比如我在主分支里提交了一个分支B也想要使用的代码或者内容

    然后在后面再提交一次

    切到branchB，做一次提交

    我此时就想把主分支之前那个提交内容给应用到我这个branchB，那就可以用cherry-pick
    找到master分支上那个提交 commit id
    用 git cherry-pick {commit id}

    可以看到这个commit已经被我挑过来应用上了

    然后再深度使用还有比如是否要产生新提交，还是只在暂存区，以及如果发生冲突了是继续还是停止等等

    ]]>     git @@ -2778,6 +2778,19 @@ Webhook     + + git小技巧之查看git commit + /2024/11/02/git%E5%B0%8F%E6%8A%80%E5%B7%A7%E4%B9%8B%E6%9F%A5%E7%9C%8Bgit-commit/ + 上次说的git stash我觉得是个非常有用的功能，简直是个大杀器，除非不存在需要切换分支的场景，否则在频繁切换的时候，git stash 可以让我们的commit更清晰，不至于写到一半就提交，频繁切换，甚至连commit信息都写不好
    这次的小技巧是属于一个延伸知识，我们知道每次commit都会提交我们的变更，然后我们可以通过git diff .查看当前仓库产生的未提交的变更，如果我想看某一次commit的变更呢，这个功能通过 git 的各种 gui 工具可以很方便的查看，如果通过命令其实也很方便，我们每一次的commit 都会产生一个 commit id , 看过我上一篇的可以联想到，是否 git commit 也有这种 show 命令，答案是肯定的，
    首先我们可以通过 git log 来查看我们的提交记录

    这里我们可以看到我们的提交 commit id
    然后通过 git show xxxxx 来查看我们这次 commit 修改的内容, xxxxx 表示我们的 commit id

    再延伸下，如果想看某个文件的变更呢，也是可以的，就是在 git show xxxxx 后面跟上文件名
    比如
    git show xxxxx 1.txt
    就能看到这一次 commit 对 1.txt 这个文件的更改，都是比较简单入门的 git 命令的小延伸，只是觉得对我有点帮助就分享下

    +]]>     + + git + 技巧 + + + git + +     headscale 添加节点 /2023/07/09/headscale-%E6%B7%BB%E5%8A%A0%E8%8A%82%E7%82%B9/ @@ -2796,6 +2809,27 @@ headscale + + hexo 配置系列-接入Algolia搜索 + /2023/04/02/hexo-%E9%85%8D%E7%BD%AE%E7%B3%BB%E5%88%97-%E6%8E%A5%E5%85%A5Algolia%E6%90%9C%E7%B4%A2/ + 博客之前使用的是 local search，最开始感觉使用体验还不错，速度也不慢，最近自己搜了下觉得效果差了很多，不知道是啥原因，所以接入有 next 主题支持的 Algolia 搜索，next 主题的文档已经介绍的很清楚了，这边就记录下，
    首先要去 Algolia 开通下账户，创建一个索引

    创建好后要去找一下 api key 的配置，这个跟 next 主题的说明已经有些不一样了
    在设置里可以找到

    这里默认会有两个 key

    一个是 search only,一个是 admin key，需要再创建一个自定义 key
    这个 key 需要有这些权限，称为 High-privilege API key， 后面有用

    然后就是到博客目录下安装

    +
    cd hexo-site
    npm install hexo-algolia
    +

    然后在 hexo 站点配置中添加

    +
    algolia:
      applicationID: "Application ID"
      apiKey: "Search-only API key"
      indexName: "indexName"
    +

    包括应用 Id，只搜索的 api key（默认给创建好的那个）,indexName 就是最开始创建的 index 名，

    +
    export HEXO_ALGOLIA_INDEXING_KEY=High-privilege API key # Use Git Bash
    # set HEXO_ALGOLIA_INDEXING_KEY=High-privilege API key # Use Windows command line
    hexo clean
    hexo algolia
    +

    然后再到 next 配置中开启 algolia_search

    +
    # Algolia Search
    algolia_search:
      enable: true
      hits:
        per_page: 10
    +

    搜索的界面其实跟 local 的差不多，就是搜索效果会好一些

    也推荐可以搜搜过往的内容，已经左边有个热度的，做了个按阅读量排序的榜单。

    +]]>     + + hexo + 技巧 + + + hexo + +     invert-binary-tree /2015/06/22/invert-binary-tree/ @@ -2826,6 +2860,22 @@ c++ + + minimum-size-subarray-sum-209 + /2016/10/11/minimum-size-subarray-sum-209/ + problem

    Given an array of n positive integers and a positive integer s, find the minimal length of a subarray of which the sum ≥ s. If there isn’t one, return 0 instead.

    +

    For example, given the array [2,3,1,2,4,3] and s = 7,
    the subarray [4,3] has the minimal length under the problem constraint.

    +

    题解

    参考，滑动窗口，跟之前Data Structure课上的online算法有点像，链接

    +

    Code

    class Solution {
    public:
        int minSubArrayLen(int s, vector<int>& nums) {
            int len = nums.size();
            if(len == 0) return 0;
            int minlen = INT_MAX;
            int sum = 0;
            
            int left = 0;
            int right = -1;
            while(right < len)
            {
                while(sum < s && right < len)
                    sum += nums[++right];
                if(sum >= s)
                {
                    minlen = minlen < right - left + 1 ? minlen : right - left + 1;
                    sum -= nums[left++];
                }
            }
            return minlen > len ? 0 : minlen;
        }
    };
    +]]>     + + leetcode + + + leetcode + c++ + +     java小技巧之获取调用来源 /2024/11/16/java%E5%B0%8F%E6%8A%80%E5%B7%A7%E4%B9%8B%E8%8E%B7%E5%8F%96%E8%B0%83%E7%94%A8%E6%9D%A5%E6%BA%90/ @@ -2848,54 +2898,16 @@ - 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/ - problem

    Given an array of n positive integers and a positive integer s, find the minimal length of a subarray of which the sum ≥ s. If there isn’t one, return 0 instead.

    -

    For example, given the array [2,3,1,2,4,3] and s = 7,
    the subarray [4,3] has the minimal length under the problem constraint.

    -

    题解

    参考，滑动窗口，跟之前Data Structure课上的online算法有点像，链接

    -

    Code

    class Solution {
    public:
        int minSubArrayLen(int s, vector<int>& nums) {
            int len = nums.size();
            if(len == 0) return 0;
            int minlen = INT_MAX;
            int sum = 0;
            
            int left = 0;
            int right = -1;
            while(right < len)
            {
                while(sum < s && right < len)
                    sum += nums[++right];
                if(sum >= s)
                {
                    minlen = minlen < right - left + 1 ? minlen : right - left + 1;
                    sum -= nums[left++];
                }
            }
            return minlen > len ? 0 : minlen;
        }
    };
    -]]>     - - leetcode - - - leetcode - c++ - -     - - hexo 配置系列-接入Algolia搜索 - /2023/04/02/hexo-%E9%85%8D%E7%BD%AE%E7%B3%BB%E5%88%97-%E6%8E%A5%E5%85%A5Algolia%E6%90%9C%E7%B4%A2/ - 博客之前使用的是 local search，最开始感觉使用体验还不错，速度也不慢，最近自己搜了下觉得效果差了很多，不知道是啥原因，所以接入有 next 主题支持的 Algolia 搜索，next 主题的文档已经介绍的很清楚了，这边就记录下，
    首先要去 Algolia 开通下账户，创建一个索引

    创建好后要去找一下 api key 的配置，这个跟 next 主题的说明已经有些不一样了
    在设置里可以找到

    这里默认会有两个 key

    一个是 search only,一个是 admin key，需要再创建一个自定义 key
    这个 key 需要有这些权限，称为 High-privilege API key， 后面有用

    然后就是到博客目录下安装

    -
    cd hexo-site
    npm install hexo-algolia
    -

    然后在 hexo 站点配置中添加

    -
    algolia:
      applicationID: "Application ID"
      apiKey: "Search-only API key"
      indexName: "indexName"
    -

    包括应用 Id，只搜索的 api key（默认给创建好的那个）,indexName 就是最开始创建的 index 名，

    -
    export HEXO_ALGOLIA_INDEXING_KEY=High-privilege API key # Use Git Bash
    # set HEXO_ALGOLIA_INDEXING_KEY=High-privilege API key # Use Windows command line
    hexo clean
    hexo algolia
    -

    然后再到 next 配置中开启 algolia_search

    -
    # Algolia Search
    algolia_search:
      enable: true
      hits:
        per_page: 10
    -

    搜索的界面其实跟 local 的差不多，就是搜索效果会好一些

    也推荐可以搜搜过往的内容，已经左边有个热度的，做了个按阅读量排序的榜单。

    + 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。

    ]]>     - hexo - 技巧 + C++ - hexo + 博客，文章     @@ -2921,19 +2933,6 @@ 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/ @@ -3479,6 +3478,49 @@ Mybatis + + 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"';
    + +

    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

    +]]>     + + nginx + + + nginx + 日志 + +     + + 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 + +     openresty /2019/06/18/openresty/ @@ -3513,44 +3555,13 @@

  • 发现一个不错的openresty站点
    地址

    • -]]> - - nginx - - - openresty - nginx - -     - - 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"';
    - -

    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

    ]]>     nginx nginx - 日志 + openresty     @@ -3585,6 +3596,20 @@ mfc + + 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 + +     php-abstract-class-and-interface /2016/11/10/php-abstract-class-and-interface/ @@ -3677,26 +3702,6 @@ 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

    -]]>     - - 语言 - - - powershell - -     powershell 初体验二 /2022/11/20/powershell-%E5%88%9D%E4%BD%93%E9%AA%8C%E4%BA%8C/ @@ -3723,18 +3728,23 @@ - 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。

    + 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

    ]]>     - redis + 语言 - redis + powershell     @@ -3760,30 +3770,18 @@ - 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/ - 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

    -
    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

    -
    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

    -
    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。

    + 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     @@ -3809,16 +3807,18 @@ - 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/ + 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

    +
    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

    +
    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

    +
    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。

    ]]>     Redis @@ -3846,35 +3846,6 @@
  • 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 - C - 源码 - 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

    -
    -

    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 @@ -3916,6 +3887,35 @@

    插入过程

    简单说下插入元素的过程

    /* Wrapper to allow argument-based switching between HEAD/TAIL pop */
    void quicklistPush(quicklist *quicklist, void *value, const size_t sz,
                       int where) {
        if (where == QUICKLIST_HEAD) {
            quicklistPushHead(quicklist, value, sz);
        } else if (where == QUICKLIST_TAIL) {
            quicklistPushTail(quicklist, value, sz);
        }
    }

    /* Add new entry to head node of quicklist.
     *
     * Returns 0 if used existing head.
     * Returns 1 if new head created. */
    int quicklistPushHead(quicklist *quicklist, void *value, size_t sz) {
        quicklistNode *orig_head = quicklist->head;
        if (likely(
                _quicklistNodeAllowInsert(quicklist->head, quicklist->fill, sz))) {
            quicklist->head->zl =
                ziplistPush(quicklist->head->zl, value, sz, ZIPLIST_HEAD);
            quicklistNodeUpdateSz(quicklist->head);
        } else {
            quicklistNode *node = quicklistCreateNode();
            node->zl = ziplistPush(ziplistNew(), value, sz, ZIPLIST_HEAD);

            quicklistNodeUpdateSz(node);
            _quicklistInsertNodeBefore(quicklist, quicklist->head, node);
        }
        quicklist->count++;
        quicklist->head->count++;
        return (orig_head != quicklist->head);
    }

    /* Add new entry to tail node of quicklist.
     *
     * Returns 0 if used existing tail.
     * Returns 1 if new tail created. */
    int quicklistPushTail(quicklist *quicklist, void *value, size_t sz) {
        quicklistNode *orig_tail = quicklist->tail;
        if (likely(
                _quicklistNodeAllowInsert(quicklist->tail, quicklist->fill, sz))) {
            quicklist->tail->zl =
                ziplistPush(quicklist->tail->zl, value, sz, ZIPLIST_TAIL);
            quicklistNodeUpdateSz(quicklist->tail);
        } else {
            quicklistNode *node = quicklistCreateNode();
            node->zl = ziplistPush(ziplistNew(), value, sz, ZIPLIST_TAIL);

            quicklistNodeUpdateSz(node);
            _quicklistInsertNodeAfter(quicklist, quicklist->tail, node);
        }
        quicklist->count++;
        quicklist->tail->count++;
        return (orig_tail != quicklist->tail);
    }

    /* Wrappers for node inserting around existing node. */
    REDIS_STATIC void _quicklistInsertNodeBefore(quicklist *quicklist,
                                                 quicklistNode *old_node,
                                                 quicklistNode *new_node) {
        __quicklistInsertNode(quicklist, old_node, new_node, 0);
    }

    REDIS_STATIC void _quicklistInsertNodeAfter(quicklist *quicklist,
                                                quicklistNode *old_node,
                                                quicklistNode *new_node) {
        __quicklistInsertNode(quicklist, old_node, new_node, 1);
    }

    /* Insert 'new_node' after 'old_node' if 'after' is 1.
     * Insert 'new_node' before 'old_node' if 'after' is 0.
     * Note: 'new_node' is *always* uncompressed, so if we assign it to
     *       head or tail, we do not need to uncompress it. */
    REDIS_STATIC void __quicklistInsertNode(quicklist *quicklist,
                                            quicklistNode *old_node,
                                            quicklistNode *new_node, int after) {
        if (after) {
            new_node->prev = old_node;
            if (old_node) {
                new_node->next = old_node->next;
                if (old_node->next)
                    old_node->next->prev = new_node;
                old_node->next = new_node;
            }
            if (quicklist->tail == old_node)
                quicklist->tail = new_node;
        } else {
            new_node->next = old_node;
            if (old_node) {
                new_node->prev = old_node->prev;
                if (old_node->prev)
                    old_node->prev->next = new_node;
                old_node->prev = new_node;
            }
            if (quicklist->head == old_node)
                quicklist->head = new_node;
        }
        /* If this insert creates the only element so far, initialize head/tail. */
        if (quicklist->len == 0) {
            quicklist->head = quicklist->tail = new_node;
        }

        if (old_node)
            quicklistCompress(quicklist, old_node);

        quicklist->len++;
    }

    前面第一步先根据插入的是头还是尾选择不同的 push 函数，quicklistPushHead 或者 quicklistPushTail，举例分析下从头插入的 quicklistPushHead，先判断当前的 quicklistNode 节点还能不能允许再往 ziplist 里添加元素，如果可以就添加，如果不允许就新建一个 quicklistNode，然后调用 _quicklistInsertNodeBefore 将节点插进去，具体插入quicklist节点的操作类似链表的插入。

    +]]> + + Redis + 数据结构 + C + 源码 + 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

    +
    +

    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 @@ -3986,19 +3986,6 @@ Evict     - - git小技巧之git cherry-pick - /2024/11/09/git%E5%B0%8F%E6%8A%80%E5%B7%A7%E4%B9%8Bgit-cherry-pick/ - 上次讲了 git stash 跟 git commit 的小技巧，这次来讲下另一个命令，git cherry-pick，我怀疑这个命令应该叫 choose-pick 才比较好
    这里有个使用场景，比如我们有分支A跟B，分支B想用一下分支A一次变更的代码，正好不用自己再开发了
    比如我在主分支里提交了一个分支B也想要使用的代码或者内容

    然后在后面再提交一次

    切到branchB，做一次提交

    我此时就想把主分支之前那个提交内容给应用到我这个branchB，那就可以用cherry-pick
    找到master分支上那个提交 commit id
    用 git cherry-pick {commit id}

    可以看到这个commit已经被我挑过来应用上了

    然后再深度使用还有比如是否要产生新提交，还是只在暂存区，以及如果发生冲突了是继续还是停止等等

    -]]>     - - git - 技巧 - - - git - -     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/ @@ -4012,6 +3999,54 @@

    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 + 数据结构 + C + 源码 + Redis + + + redis + 数据结构 + 源码 + +     + + 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 @@ -4083,39 +4118,16 @@     - 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 @@ -4203,24 +4215,6 @@ 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 - -     spark-little-tips /2017/03/28/spark-little-tips/ @@ -4354,34 +4348,6 @@

    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 - SpringBoot - - - Java - SpringBoot - -     - - springboot 获取 web 应用中所有的接口 url - /2023/08/06/springboot-%E8%8E%B7%E5%8F%96-web-%E5%BA%94%E7%94%A8%E4%B8%AD%E6%89%80%E6%9C%89%E7%9A%84%E6%8E%A5%E5%8F%A3-url/ - 最近有个小需求，要把我们一个 springboot 应用的 request mapping 给导出来，这么说已经是转化过了的，应该是要整理这个应用所有的接口路径，比如我有一个 api.baidu1.com 作为接口域名，然后这个域名下有很多个接口提供服务，这些接口都是写在一个 springboot 应用里，如果本身有网关管理这些接口转发的其实可以通过网关的数据输出，这里就介绍下通过代码来获取

    -
    RequestMappingHandlerMapping mapping = appContext.getBean(RequestMappingHandlerMapping.class);
    Map<RequestMappingInfo, HandlerMethod> map = mapping.getHandlerMethods();
    for (Map.Entry<RequestMappingInfo, HandlerMethod> entry : map.entrySet()) {
        Set<String> urlSet = entry.getKey().getPatternsCondition().getPatterns();
        for (String url : urlSet) {
            System.out.println(url);
        }
    }
    -

    第一行

    逐行来解析下，第一行就是从上下文中获取 RequestMappingHandlerMapping 这个 bean，

    -

    第二行

    然后调用了 getHandlerMethods,
    这里面具体执行了

    -
    public Map<T, HandlerMethod> getHandlerMethods() {
    		this.mappingRegistry.acquireReadLock();
    		try {
    			return Collections.unmodifiableMap(this.mappingRegistry.getMappings());
    		}
    		finally {
    			this.mappingRegistry.releaseReadLock();
    		}
    	}
    -

    前后加了锁，重要的就是从 mappingRegistry 中获取 mappings, 这里获取的就是
    org.springframework.web.servlet.handler.AbstractHandlerMethodMapping.MappingRegistry#getMappings 具体的代码

    -
    public Map<T, HandlerMethod> getMappings() {
    	return this.mappingLookup;
    }
    -

    而这个 mappingLookup 再来看下

    -
    class MappingRegistry {

    	private final Map<T, MappingRegistration<T>> registry = new HashMap<>();

    	private final Map<T, HandlerMethod> mappingLookup = new LinkedHashMap<>();
    -

    可以看到就是在 MappingRegistry 中的一个变量，至于这个变量是怎么来的，简单的考虑下 springboot 处理请求的流程，就是从 Mapping 去找到对应的 Handler，所以就需要提前将这个对应关系存到这个变量里，
    具体可以看这 org.springframework.web.servlet.handler.AbstractHandlerMethodMapping.MappingRegistry#register

    -
    public void register(T mapping, Object handler, Method method) {
    			// Assert that the handler method is not a suspending one.
    			if (KotlinDetector.isKotlinType(method.getDeclaringClass())) {
    				Class<?>[] parameterTypes = method.getParameterTypes();
    				if ((parameterTypes.length > 0) && "kotlin.coroutines.Continuation".equals(parameterTypes[parameterTypes.length - 1].getName())) {
    					throw new IllegalStateException("Unsupported suspending handler method detected: " + method);
    				}
    			}
    			this.readWriteLock.writeLock().lock();
    			try {
    				HandlerMethod handlerMethod = createHandlerMethod(handler, method);
    				validateMethodMapping(handlerMethod, mapping);
    				this.mappingLookup.put(mapping, handlerMethod);
    -

    就是在这里会把 mapping 和 handleMethod 对应关系存进去

    -

    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 @@ -4439,18 +4405,31 @@     - summary-ranges-228 - /2016/10/12/summary-ranges-228/ - problem

    Given a sorted integer array without duplicates, return the summary of its ranges.

    -

    For example, given [0,1,2,4,5,7], return ["0->2","4->5","7"].

    -

    题解

    每一个区间的起点nums[i]加上j是否等于nums[i+j]
    参考

    -

    Code

    class Solution {
    public:
        vector<string> summaryRanges(vector<int>& nums) {
            int i = 0, j = 1, n;
            vector<string> res;
            n = nums.size();
            while(i < n){
                j = 1;
                while(j < n && nums[i+j] - nums[i] == j) j++;
                res.push_back(j <= 1 ? to_string(nums[i]) : to_string(nums[i]) + "->" + to_string(nums[i + j - 1]));
                i += j;
            }
            return res;
        }
    };
    ]]>     + springboot 获取 web 应用中所有的接口 url + /2023/08/06/springboot-%E8%8E%B7%E5%8F%96-web-%E5%BA%94%E7%94%A8%E4%B8%AD%E6%89%80%E6%9C%89%E7%9A%84%E6%8E%A5%E5%8F%A3-url/ + 最近有个小需求，要把我们一个 springboot 应用的 request mapping 给导出来，这么说已经是转化过了的，应该是要整理这个应用所有的接口路径，比如我有一个 api.baidu1.com 作为接口域名，然后这个域名下有很多个接口提供服务，这些接口都是写在一个 springboot 应用里，如果本身有网关管理这些接口转发的其实可以通过网关的数据输出，这里就介绍下通过代码来获取

    +
    RequestMappingHandlerMapping mapping = appContext.getBean(RequestMappingHandlerMapping.class);
    Map<RequestMappingInfo, HandlerMethod> map = mapping.getHandlerMethods();
    for (Map.Entry<RequestMappingInfo, HandlerMethod> entry : map.entrySet()) {
        Set<String> urlSet = entry.getKey().getPatternsCondition().getPatterns();
        for (String url : urlSet) {
            System.out.println(url);
        }
    }
    +

    第一行

    逐行来解析下，第一行就是从上下文中获取 RequestMappingHandlerMapping 这个 bean，

    +

    第二行

    然后调用了 getHandlerMethods,
    这里面具体执行了

    +
    public Map<T, HandlerMethod> getHandlerMethods() {
    		this.mappingRegistry.acquireReadLock();
    		try {
    			return Collections.unmodifiableMap(this.mappingRegistry.getMappings());
    		}
    		finally {
    			this.mappingRegistry.releaseReadLock();
    		}
    	}
    +

    前后加了锁，重要的就是从 mappingRegistry 中获取 mappings, 这里获取的就是
    org.springframework.web.servlet.handler.AbstractHandlerMethodMapping.MappingRegistry#getMappings 具体的代码

    +
    public Map<T, HandlerMethod> getMappings() {
    	return this.mappingLookup;
    }
    +

    而这个 mappingLookup 再来看下

    +
    class MappingRegistry {

    	private final Map<T, MappingRegistration<T>> registry = new HashMap<>();

    	private final Map<T, HandlerMethod> mappingLookup = new LinkedHashMap<>();
    +

    可以看到就是在 MappingRegistry 中的一个变量，至于这个变量是怎么来的，简单的考虑下 springboot 处理请求的流程，就是从 Mapping 去找到对应的 Handler，所以就需要提前将这个对应关系存到这个变量里，
    具体可以看这 org.springframework.web.servlet.handler.AbstractHandlerMethodMapping.MappingRegistry#register

    +
    public void register(T mapping, Object handler, Method method) {
    			// Assert that the handler method is not a suspending one.
    			if (KotlinDetector.isKotlinType(method.getDeclaringClass())) {
    				Class<?>[] parameterTypes = method.getParameterTypes();
    				if ((parameterTypes.length > 0) && "kotlin.coroutines.Continuation".equals(parameterTypes[parameterTypes.length - 1].getName())) {
    					throw new IllegalStateException("Unsupported suspending handler method detected: " + method);
    				}
    			}
    			this.readWriteLock.writeLock().lock();
    			try {
    				HandlerMethod handlerMethod = createHandlerMethod(handler, method);
    				validateMethodMapping(handlerMethod, mapping);
    				this.mappingLookup.put(mapping, handlerMethod);
    +

    就是在这里会把 mapping 和 handleMethod 对应关系存进去

    +

    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

    +]]>     - leetcode + Java + SpringBoot - leetcode - c++ + Java + SpringBoot     @@ -4478,31 +4457,18 @@ - win 下 vmware 虚拟机搭建黑裙 nas 的小思路 - /2023/06/04/win-%E4%B8%8B-vmware-%E8%99%9A%E6%8B%9F%E6%9C%BA%E6%90%AD%E5%BB%BA%E9%BB%91%E8%A3%99-nas-%E7%9A%84%E5%B0%8F%E6%80%9D%E8%B7%AF/ - 上次说 nas 的方案我是在 win10 下使用vmware workstation 搭建的黑裙虚拟机，采用 sata 物理磁盘直通的方式，算是跑通了黑裙的基础使用模式，但是后来发现的一个问题是之前没考虑到的，我买了不带 f 的处理器就是为了核显能做硬解，但是因为 cpu 是通过 vmware 虚拟的，目前看来是没法直通核显的，我是使用的 jellyfin 套件，一开始使用是默认的刮削方式，而且把电视剧当成了电影在刮削，所以基本不能看，后面使用了 tmm 作为刮削工具，可以手动填写 imdb 的id 来进行搜索，一般比较正式的剧都可以在豆瓣上找到的，然后让 jellyfin 只作为媒体管理器，但是前面的问题还是没解决，所以考虑了下可以在win10 下直接运行 jellyfin，媒体目录使用挂载在 nas 里的盘，这样 jellyfin 就能直接调用核显了，也算是把 win10 本身给利用起来了，并且文件的管理还是在黑裙中。
    现在想来其实我这个方案还是不太合理，cpu 性能有点过剩想通过虚拟机的形式进行隔离使用，但是购买带核显的 cpu 最大的目的却没有实现，如果是直接裸机部署黑裙的话，真的是觉得 cpu 有点太浪费了，毕竟 passmark评分有 1w3 的cpu，只用来跑黑裙，所以网上的很多建议也是合理的，不过我可能是 win10 用的比较多了，还是习惯有 win 的环境。

    -]]>     - - nas - - - nas - -     - - 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。

    -]]>     + summary-ranges-228 + /2016/10/12/summary-ranges-228/ + problem

    Given a sorted integer array without duplicates, return the summary of its ranges.

    +

    For example, given [0,1,2,4,5,7], return ["0->2","4->5","7"].

    +

    题解

    每一个区间的起点nums[i]加上j是否等于nums[i+j]
    参考

    +

    Code

    class Solution {
    public:
        vector<string> summaryRanges(vector<int>& nums) {
            int i = 0, j = 1, n;
            vector<string> res;
            n = nums.size();
            while(i < n){
                j = 1;
                while(j < n && nums[i+j] - nums[i] == j) j++;
                res.push_back(j <= 1 ? to_string(nums[i]) : to_string(nums[i]) + "->" + to_string(nums[i + j - 1]));
                i += j;
            }
            return res;
        }
    };
    ]]>     - 小技巧 + leetcode - WordPress - 小技巧 + leetcode + c++     @@ -4544,6 +4510,47 @@ user3: swoole + + win 下 vmware 虚拟机搭建黑裙 nas 的小思路 + /2023/06/04/win-%E4%B8%8B-vmware-%E8%99%9A%E6%8B%9F%E6%9C%BA%E6%90%AD%E5%BB%BA%E9%BB%91%E8%A3%99-nas-%E7%9A%84%E5%B0%8F%E6%80%9D%E8%B7%AF/ + 上次说 nas 的方案我是在 win10 下使用vmware workstation 搭建的黑裙虚拟机，采用 sata 物理磁盘直通的方式，算是跑通了黑裙的基础使用模式，但是后来发现的一个问题是之前没考虑到的，我买了不带 f 的处理器就是为了核显能做硬解，但是因为 cpu 是通过 vmware 虚拟的，目前看来是没法直通核显的，我是使用的 jellyfin 套件，一开始使用是默认的刮削方式，而且把电视剧当成了电影在刮削，所以基本不能看，后面使用了 tmm 作为刮削工具，可以手动填写 imdb 的id 来进行搜索，一般比较正式的剧都可以在豆瓣上找到的，然后让 jellyfin 只作为媒体管理器，但是前面的问题还是没解决，所以考虑了下可以在win10 下直接运行 jellyfin，媒体目录使用挂载在 nas 里的盘，这样 jellyfin 就能直接调用核显了，也算是把 win10 本身给利用起来了，并且文件的管理还是在黑裙中。
    现在想来其实我这个方案还是不太合理，cpu 性能有点过剩想通过虚拟机的形式进行隔离使用，但是购买带核显的 cpu 最大的目的却没有实现，如果是直接裸机部署黑裙的话，真的是觉得 cpu 有点太浪费了，毕竟 passmark评分有 1w3 的cpu，只用来跑黑裙，所以网上的很多建议也是合理的，不过我可能是 win10 用的比较多了，还是习惯有 win 的环境。

    +]]>     + + nas + + + nas + +     + + 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 + +     + + 《寻羊历险记》读后感 + /2023/07/23/%E3%80%8A%E5%AF%BB%E7%BE%8A%E5%8E%86%E9%99%A9%E8%AE%B0%E3%80%8B%E8%AF%BB%E5%90%8E%E6%84%9F/ + 最近本来是在读《舞舞舞》，然后看到有介绍说，这个跟《寻羊历险记》是有情节上的关联，所以就先去看了《寻羊历险记》，《寻羊历险记》也是村上春树的第一本成规模的长篇小说，也可以认为是《舞舞舞》的前篇。
    最开始这个情节跟之前的刺杀骑士团长还是哪本有点类似，都有跟老婆离婚了，主角应该是个跟朋友一起开翻译社的，后面也开始做广告相关的，做到了经济收益还不错的阶段，也有一些挺哲学的对话，朋友觉得这么赚钱不地道（可能也是觉得这样忘了初心），在离婚以后又结交了一个耳朵很好看的女友，这个女友也是个比较抽象的存在，描述中给人感觉是一个外貌很普通的女孩，但是耳朵漂亮的惊为天人，不知道是不是有什么隐喻，感觉现实中没见过这样的人，女友平时把耳朵遮起来不轻易露出来，只有在跟主角做爱的时候才露出来
    主体剧情是因为男主在广告中用了一张一位叫“鼠”的朋友寄给他的一张包含一只特殊的羊的照片，就有个政界大佬的秘书找过来，逼迫主角要找到照片上的羊，这只羊背上有星纹，基本不可能属于在日本当时可能存在的羊的品种，因为这位政界大佬快病危了，所以要求主角必须在一个月内找到这只羊，因为这里把这只神秘的羊塑造成神一样的存在，这位政界大佬在年轻时被这只羊上身，后面就开始在政治事业上大展宏图，就基本成了能左右整个日本走向的巨佬，但近期随着巨佬，身体状态慢慢变差，这只羊就从他身上消失了，所以秘书要主角必须找到这只羊，不然基本会让主角的翻译社完蛋，这样主角就会面临破产赔偿等严重后果，只是说主角本来也一直是这种丧气存在，再说这么茫茫人海找个人都难，还要找这么一只玄乎的不太可能真实存在的羊，所以基本是想要放弃的，结果刚才说的耳朵很漂亮的女友却有着神乎其神的直觉，就觉得能找到，然后他们就踏上了巡羊的旅程，一开始到了札幌，寻找一无所获，然后神奇的是女友推荐一定要住的海豚宾馆，恰恰是一切线索的源头，宾馆老板的父亲正好是羊博士，在年轻的时候被羊上身过，后面离开后就去了那位巨佬身上，让巨佬成了真正控制日本的地下帝国的王，跟羊博士咨询后知道羊可能在十二瀑镇的牧场出现过，所以一路找寻，发现其实这个牧场中有个别墅正好是主角朋友“鼠”的，到了别墅后出现了个神秘的羊男，这个羊男真的不太明白是怎么个存在，就是让主角的女友回去了，然后最后一个肉体承载着“鼠”的灵魂跟主角有了一次接触，主角呆在这个别墅过着百无聊赖的生活，在才到有一次羊男来跟他交流的时候其实是承载着鼠的灵魂，就觉得是不是一切都是在忽悠他，离一个月期限也越来越近了，而在发怒之后，“鼠”真的出现后，但是不是真的“鼠”，而是只有他的灵魂，因为“鼠”已经死了，因为不想被“羊”附身，成为羊壳，并且让主角设定好时钟后的装置后尽快下山，第二天主角离开上了火车后山上牧场就传来爆炸声，“鼠”已经跟羊同归于尽了，免得再有其他人被羊附身，主角也很难过，回到故乡在四周大海已经被填掉了的旧防波堤上大哭悼念逝去的“鼠”。
    其实对于没什么时代概念或者对村上一直以来的观念不是特别敏感的，对这本书讲了啥有点云里雾里，后面看了一些简单的解释就是羊其实代表日本帝国主义和资本主义，可能是村上本人反帝国主义，军国主义和资本主义的一个表征，想来也有些道理，不然“鼠”的牺牲就感觉比较没意义，但是另一方面我的理解也可能是对自由的向往的表达，被羊控制，虽然可以成就“伟大”的事业，但是也丧失了自我。

    +]]>     + + 读后感 + 生活 + + + 读后感 + +     《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/ @@ -4566,20 +4573,6 @@ user3: jvm - - 《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。

    -]]>     - - 读后感 - 生活 - - - 读后感 - 生活 - -     一个 nginx 的简单记忆点 /2022/08/21/%E4%B8%80%E4%B8%AA-nginx-%E7%9A%84%E7%AE%80%E5%8D%95%E8%AE%B0%E5%BF%86%E7%82%B9/ @@ -4598,41 +4591,48 @@ user3: - 《寻羊历险记》读后感 - /2023/07/23/%E3%80%8A%E5%AF%BB%E7%BE%8A%E5%8E%86%E9%99%A9%E8%AE%B0%E3%80%8B%E8%AF%BB%E5%90%8E%E6%84%9F/ - 最近本来是在读《舞舞舞》，然后看到有介绍说，这个跟《寻羊历险记》是有情节上的关联，所以就先去看了《寻羊历险记》，《寻羊历险记》也是村上春树的第一本成规模的长篇小说，也可以认为是《舞舞舞》的前篇。
    最开始这个情节跟之前的刺杀骑士团长还是哪本有点类似，都有跟老婆离婚了，主角应该是个跟朋友一起开翻译社的，后面也开始做广告相关的，做到了经济收益还不错的阶段，也有一些挺哲学的对话，朋友觉得这么赚钱不地道（可能也是觉得这样忘了初心），在离婚以后又结交了一个耳朵很好看的女友，这个女友也是个比较抽象的存在，描述中给人感觉是一个外貌很普通的女孩，但是耳朵漂亮的惊为天人，不知道是不是有什么隐喻，感觉现实中没见过这样的人，女友平时把耳朵遮起来不轻易露出来，只有在跟主角做爱的时候才露出来
    主体剧情是因为男主在广告中用了一张一位叫“鼠”的朋友寄给他的一张包含一只特殊的羊的照片，就有个政界大佬的秘书找过来，逼迫主角要找到照片上的羊，这只羊背上有星纹，基本不可能属于在日本当时可能存在的羊的品种，因为这位政界大佬快病危了，所以要求主角必须在一个月内找到这只羊，因为这里把这只神秘的羊塑造成神一样的存在，这位政界大佬在年轻时被这只羊上身，后面就开始在政治事业上大展宏图，就基本成了能左右整个日本走向的巨佬，但近期随着巨佬，身体状态慢慢变差，这只羊就从他身上消失了，所以秘书要主角必须找到这只羊，不然基本会让主角的翻译社完蛋，这样主角就会面临破产赔偿等严重后果，只是说主角本来也一直是这种丧气存在，再说这么茫茫人海找个人都难，还要找这么一只玄乎的不太可能真实存在的羊，所以基本是想要放弃的，结果刚才说的耳朵很漂亮的女友却有着神乎其神的直觉，就觉得能找到，然后他们就踏上了巡羊的旅程，一开始到了札幌，寻找一无所获，然后神奇的是女友推荐一定要住的海豚宾馆，恰恰是一切线索的源头，宾馆老板的父亲正好是羊博士，在年轻的时候被羊上身过，后面离开后就去了那位巨佬身上，让巨佬成了真正控制日本的地下帝国的王，跟羊博士咨询后知道羊可能在十二瀑镇的牧场出现过，所以一路找寻，发现其实这个牧场中有个别墅正好是主角朋友“鼠”的，到了别墅后出现了个神秘的羊男，这个羊男真的不太明白是怎么个存在，就是让主角的女友回去了，然后最后一个肉体承载着“鼠”的灵魂跟主角有了一次接触，主角呆在这个别墅过着百无聊赖的生活，在才到有一次羊男来跟他交流的时候其实是承载着鼠的灵魂，就觉得是不是一切都是在忽悠他，离一个月期限也越来越近了，而在发怒之后，“鼠”真的出现后，但是不是真的“鼠”，而是只有他的灵魂，因为“鼠”已经死了，因为不想被“羊”附身，成为羊壳，并且让主角设定好时钟后的装置后尽快下山，第二天主角离开上了火车后山上牧场就传来爆炸声，“鼠”已经跟羊同归于尽了，免得再有其他人被羊附身，主角也很难过，回到故乡在四周大海已经被填掉了的旧防波堤上大哭悼念逝去的“鼠”。
    其实对于没什么时代概念或者对村上一直以来的观念不是特别敏感的，对这本书讲了啥有点云里雾里，后面看了一些简单的解释就是羊其实代表日本帝国主义和资本主义，可能是村上本人反帝国主义，军国主义和资本主义的一个表征，想来也有些道理，不然“鼠”的牺牲就感觉比较没意义，但是另一方面我的理解也可能是对自由的向往的表达，被羊控制，虽然可以成就“伟大”的事业，但是也丧失了自我。

    + 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。

    ]]>     - 读后感 - 生活 + Java + SpringBoot - 读后感 + Java + Spring + SpringBoot     - 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。

    + 《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。

    ]]>     + 读后感 生活 - 运动 生活 + 读后感     - 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

    + 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。

    ]]>     - openwrt + 生活 + 运动 - openwrt + 生活     @@ -4705,120 +4705,51 @@ user3: - 不一劳永逸的临时docker镜像拉取解决办法 - /2024/10/13/%E4%B8%8D%E4%B8%80%E5%8A%B3%E6%B0%B8%E9%80%B8%E7%9A%84%E4%B8%B4%E6%97%B6docker%E9%95%9C%E5%83%8F%E6%8B%89%E5%8F%96%E8%A7%A3%E5%86%B3%E5%8A%9E%E6%B3%95/ - docker镜像拉取目前是个大问题，前阵子出现了比较大规模的封禁，导致很多原有的方案无法使用，其中包括阿里云的私有镜像地址，导致我折腾了半天
    本来是可以在 https://cr.console.aliyun.com/cn-beijing/instances/mirrors 上打开，阿里云用户都有各自的私有地址

    -
    https://xxxxxxxx.mirror.aliyuncs.com
    -

    然后可以通过这个方式修改

    -
    sudo mkdir -p /etc/docker
    sudo tee /etc/docker/daemon.json <<-'EOF'
    {
      "registry-mirrors": ["https://xxxxxxxx.mirror.aliyuncs.com"]
    }
    EOF
    sudo systemctl daemon-reload
    sudo systemctl restart docker
    -

    这样修改以后查看下日志

    -
    journalctl -xe --no-pager -u docker
    -

    但还是拉不下来，后来发现还是拉不下来
    应该也是之前被封的原因，
    后面重要找到了一个
    配置使用这个就可以了

    -
    {
      "registry-mirrors": [
            "https://dockerproxy.cn"
        ]
    }
    -

    或者拉取镜像的前缀加上这个地址
    docker pull dockerproxy.cn/yangchuansheng/derper:latest
    也可以这么拉取，说实话docker镜像现在真的很必要，据说这次是因为传了某些敏感的AI语音
    参考下面的图

    -]]>     - - Docker - - - Docker - -     - - 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。

    + 一个Java类型的小问题记录 + /2024/09/01/%E4%B8%80%E4%B8%AAJava%E7%B1%BB%E5%9E%8B%E7%9A%84%E5%B0%8F%E9%97%AE%E9%A2%98%E8%AE%B0%E5%BD%95/ + 最近发现一个小问题，感觉挺有意思，我们来看下代码

    +
    public static void main(String[] args) {
            Integer oneHundredYear = 36500;
            System.out.println(oneHundredYear * 24 * 60 * 60 * 1000);
        }
    +

    定义了个变量，用来表示100年的时间，然后计算出对应的时间戳
    首先看下这个值

    +
    1094004736
    +

    如果对时间戳这些没概念的话，可能没发现有什么问题，但是好像也不对，乘以1000了，但是尾数是736
    这个暂时我们先不管

    +
    public static void main(String[] args) {
            Integer oneHundredYear = 36500;
            System.out.println(oneHundredYear * 24 * 60 * 60 * 1000);
            System.out.println(System.currentTimeMillis() + oneHundredYear * 24 * 60 * 60 * 1000);
        }
    +

    然后来看下

    +
    1094004736
    1726195843465
    +

    然后我们去看下这个时间戳，按正常理解应该是2124年的8月31日

    但实际是不对的，只是12天后，跟100年这个差距天差地别了，这是为啥呢，可能大佬一眼就看出来了这是类型转换问题，但是比如说我这么改

    +
    public static void main(String[] args) {
            Integer oneHundredYear = 36500;
            System.out.println(oneHundredYear * 24 * 60 * 60 * 1000L);
            System.out.println(System.currentTimeMillis() + oneHundredYear * 24 * 60 * 60 * 1000L);
        }
    +

    或者

    +
    public static void main(String[] args) {
            Integer oneHundredYear = 36500;
            System.out.println(Long.valueOf(oneHundredYear * 24 * 60 * 60 * 1000));
            System.out.println(System.currentTimeMillis() + Long.valueOf(oneHundredYear * 24 * 60 * 60 * 1000));
        }
    +

    会发现还是一样，依旧是错误的
    简单来说，就在于类型转换的时机，我们看下这个示例就知道了

    +
    public static void main(String[] args) {
            Integer oneHundredYear = 36500;
            System.out.println(oneHundredYear * 24);
            System.out.println(oneHundredYear * 24 * 60);
            System.out.println(oneHundredYear * 24 * 60 * 60);
            System.out.println(oneHundredYear * 24 * 60 * 60 * 1000);
        }
    +

    结果是这样

    +
    876000
    52560000
    -1141367296
    1094004736
    +

    到乘第二个60的时候已经是负的了，因为已经超过了Integer的范围了
    但是为啥用后面的示例转换成long类型还不行呢
    这个就在于编译器是怎么做类型转换的
    在第一个oneHundredYear跟24相乘的时候是认为两个Integer相乘，并且没有检查范围
    只有在乘以显式申明的最后的1000的时候才会做转换
    我们看下反编译

    可以看到是先做int型的乘法，碰到有参数是long型时才会转类型
    那么理论上其实我们只要在第二个60及之前申明long或者强转long就行了，这也是个很基础的问题，只是有时候写代码的时候直觉会以为加了个L就可以了，但实际是没那么简单

    ]]>     - 算法 + Java - 算法 + Java + +     + + 不一劳永逸的临时docker镜像拉取解决办法 + /2024/10/13/%E4%B8%8D%E4%B8%80%E5%8A%B3%E6%B0%B8%E9%80%B8%E7%9A%84%E4%B8%B4%E6%97%B6docker%E9%95%9C%E5%83%8F%E6%8B%89%E5%8F%96%E8%A7%A3%E5%86%B3%E5%8A%9E%E6%B3%95/ + docker镜像拉取目前是个大问题，前阵子出现了比较大规模的封禁，导致很多原有的方案无法使用，其中包括阿里云的私有镜像地址，导致我折腾了半天
    本来是可以在 https://cr.console.aliyun.com/cn-beijing/instances/mirrors 上打开，阿里云用户都有各自的私有地址

    +
    https://xxxxxxxx.mirror.aliyuncs.com
    +

    然后可以通过这个方式修改

    +
    sudo mkdir -p /etc/docker
    sudo tee /etc/docker/daemon.json <<-'EOF'
    {
      "registry-mirrors": ["https://xxxxxxxx.mirror.aliyuncs.com"]
    }
    EOF
    sudo systemctl daemon-reload
    sudo systemctl restart docker
    +

    这样修改以后查看下日志

    +
    journalctl -xe --no-pager -u docker
    +

    但还是拉不下来，后来发现还是拉不下来
    应该也是之前被封的原因，
    后面重要找到了一个
    配置使用这个就可以了

    +
    {
      "registry-mirrors": [
            "https://dockerproxy.cn"
        ]
    }
    +

    或者拉取镜像的前缀加上这个地址
    docker pull dockerproxy.cn/yangchuansheng/derper:latest
    也可以这么拉取，说实话docker镜像现在真的很必要，据说这次是因为传了某些敏感的AI语音
    参考下面的图

    +]]>     + + Docker + + + Docker     @@ -4841,6 +4772,28 @@ user3: 端口转发 + + 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 + +     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/ @@ -4881,28 +4834,6 @@ user3: 杀人诛心 - - 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 - -     nas 中使用 tmm 刮削视频 /2023/07/02/%E4%BD%BF%E7%94%A8-tmm-%E5%88%AE%E5%89%8A%E8%A7%86%E9%A2%91/ @@ -4915,32 +4846,6 @@ user3: nas - - 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 - -     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/ @@ -4959,6 +4864,22 @@ user3: java + + 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 + +     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/ @@ -4981,14 +4902,111 @@ user3: 健康码 + + 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。

    +]]>     + + 算法 + + + 算法 + +     关于读书打卡与分享 /2021/02/07/%E5%85%B3%E4%BA%8E%E8%AF%BB%E4%B9%A6%E6%89%93%E5%8D%A1%E4%B8%8E%E5%88%86%E4%BA%AB/ 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！

    ]]>     - 读后感 生活 + 读后感 白岩松 幸福了吗 @@ -5001,31 +5019,16 @@ user3:     - 一个Java类型的小问题记录 - /2024/09/01/%E4%B8%80%E4%B8%AAJava%E7%B1%BB%E5%9E%8B%E7%9A%84%E5%B0%8F%E9%97%AE%E9%A2%98%E8%AE%B0%E5%BD%95/ - 最近发现一个小问题，感觉挺有意思，我们来看下代码

    -
    public static void main(String[] args) {
            Integer oneHundredYear = 36500;
            System.out.println(oneHundredYear * 24 * 60 * 60 * 1000);
        }
    -

    定义了个变量，用来表示100年的时间，然后计算出对应的时间戳
    首先看下这个值

    -
    1094004736
    -

    如果对时间戳这些没概念的话，可能没发现有什么问题，但是好像也不对，乘以1000了，但是尾数是736
    这个暂时我们先不管

    -
    public static void main(String[] args) {
            Integer oneHundredYear = 36500;
            System.out.println(oneHundredYear * 24 * 60 * 60 * 1000);
            System.out.println(System.currentTimeMillis() + oneHundredYear * 24 * 60 * 60 * 1000);
        }
    -

    然后来看下

    -
    1094004736
    1726195843465
    -

    然后我们去看下这个时间戳，按正常理解应该是2124年的8月31日

    但实际是不对的，只是12天后，跟100年这个差距天差地别了，这是为啥呢，可能大佬一眼就看出来了这是类型转换问题，但是比如说我这么改

    -
    public static void main(String[] args) {
            Integer oneHundredYear = 36500;
            System.out.println(oneHundredYear * 24 * 60 * 60 * 1000L);
            System.out.println(System.currentTimeMillis() + oneHundredYear * 24 * 60 * 60 * 1000L);
        }
    -

    或者

    -
    public static void main(String[] args) {
            Integer oneHundredYear = 36500;
            System.out.println(Long.valueOf(oneHundredYear * 24 * 60 * 60 * 1000));
            System.out.println(System.currentTimeMillis() + Long.valueOf(oneHundredYear * 24 * 60 * 60 * 1000));
        }
    -

    会发现还是一样，依旧是错误的
    简单来说，就在于类型转换的时机，我们看下这个示例就知道了

    -
    public static void main(String[] args) {
            Integer oneHundredYear = 36500;
            System.out.println(oneHundredYear * 24);
            System.out.println(oneHundredYear * 24 * 60);
            System.out.println(oneHundredYear * 24 * 60 * 60);
            System.out.println(oneHundredYear * 24 * 60 * 60 * 1000);
        }
    -

    结果是这样

    -
    876000
    52560000
    -1141367296
    1094004736
    -

    到乘第二个60的时候已经是负的了，因为已经超过了Integer的范围了
    但是为啥用后面的示例转换成long类型还不行呢
    这个就在于编译器是怎么做类型转换的
    在第一个oneHundredYear跟24相乘的时候是认为两个Integer相乘，并且没有检查范围
    只有在乘以显式申明的最后的1000的时候才会做转换
    我们看下反编译

    可以看到是先做int型的乘法，碰到有参数是long型时才会转类型
    那么理论上其实我们只要在第二个60及之前申明long或者强转long就行了，这也是个很基础的问题，只是有时候写代码的时候直觉会以为加了个L就可以了，但实际是没那么简单

    + 分享一次折腾老旧笔记本的体验-续续篇 + /2023/02/26/%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%BB%AD%E7%AF%87/ + 上周因为一些事情没回去，买好了内存条这周才回去用，结果不知道是 U 盘的问题还是什么其他原因原来装的那个系统起不来，然后想用之前一直用的 LD 的笔记本，结果 LD 的老笔记本也起不来了，一直卡在启动界面，猜测可能是本身也比较老了，用的机械硬盘会不会被我一直动而损坏了，所以就想搞个老毛桃跟新搞一个启动盘，结果这周最大的坑就在这了，在我的 15 寸 mbp 上用etcher做了个 win10 的启动盘，可能是分区表格式是 GUID，不是 MBR 的，老电脑不能识别，就像装个虚拟机来做启动盘，结果像老毛桃不支持这种用虚拟机的，需要本地磁盘，然后我就有用 bootcamp 安装了个 win10，又没网络，查了下网络需要用 bootcamp 下载 Windows Support 软件，下了好久一直没下完，后来又提示连不上苹果服务器，好不容易下完了，开启 win10 后，安装到一半就蓝屏了，真的是够操蛋的，在这个时候真的很需要有一个 Windows 机器的支持，LD 的那个笔记本也跟我差不多老了，虽然比较慢但之前好歹也还可以用，结果这下也被我弄坏了，家里就没有其他机器可以支持了，有点巧妇难为无米之炊的赶脚，而且还是想再折腾下我的老电脑，后面看看打算做几个 U 盘，分工职责明确，一个老毛桃的PE 盘，老毛桃的虽然不是很纯净的，但是是我一直以来用过内置工具比较全，修复启动功能比较强大的一个 pe，没用过 winpe，后续可以考虑使用下，一个 win7 的启动盘，因为是老电脑，如果后面是新的电脑可以考虑是 win10，一个 win to go 盘，这样我的 mbp 就可以直接用 win10 了，不过想来也还是要有一台 win 机器才比较好，不然很多时候都是在折腾工具，工欲善其事必先利其器，后面也可以考虑能够更深入的去了解主板的一些问题，我原来的那个主板可能是一些小的电阻等问题，如果能自己学习解决就更好了，有时候研究下还是有意思的。

    ]]>     - Java + Windows + 小技巧 - Java + Windows     @@ -5042,9 +5045,9 @@ user3: - 分享一次折腾老旧笔记本的体验-续续篇 - /2023/02/26/%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%BB%AD%E7%AF%87/ - 上周因为一些事情没回去，买好了内存条这周才回去用，结果不知道是 U 盘的问题还是什么其他原因原来装的那个系统起不来，然后想用之前一直用的 LD 的笔记本，结果 LD 的老笔记本也起不来了，一直卡在启动界面，猜测可能是本身也比较老了，用的机械硬盘会不会被我一直动而损坏了，所以就想搞个老毛桃跟新搞一个启动盘，结果这周最大的坑就在这了，在我的 15 寸 mbp 上用etcher做了个 win10 的启动盘，可能是分区表格式是 GUID，不是 MBR 的，老电脑不能识别，就像装个虚拟机来做启动盘，结果像老毛桃不支持这种用虚拟机的，需要本地磁盘，然后我就有用 bootcamp 安装了个 win10，又没网络，查了下网络需要用 bootcamp 下载 Windows Support 软件，下了好久一直没下完，后来又提示连不上苹果服务器，好不容易下完了，开启 win10 后，安装到一半就蓝屏了，真的是够操蛋的，在这个时候真的很需要有一个 Windows 机器的支持，LD 的那个笔记本也跟我差不多老了，虽然比较慢但之前好歹也还可以用，结果这下也被我弄坏了，家里就没有其他机器可以支持了，有点巧妇难为无米之炊的赶脚，而且还是想再折腾下我的老电脑，后面看看打算做几个 U 盘，分工职责明确，一个老毛桃的PE 盘，老毛桃的虽然不是很纯净的，但是是我一直以来用过内置工具比较全，修复启动功能比较强大的一个 pe，没用过 winpe，后续可以考虑使用下，一个 win7 的启动盘，因为是老电脑，如果后面是新的电脑可以考虑是 win10，一个 win to go 盘，这样我的 mbp 就可以直接用 win10 了，不过想来也还是要有一台 win 机器才比较好，不然很多时候都是在折腾工具，工欲善其事必先利其器，后面也可以考虑能够更深入的去了解主板的一些问题，我原来的那个主板可能是一些小的电阻等问题，如果能自己学习解决就更好了，有时候研究下还是有意思的。

    + 分享一次折腾老旧笔记本的体验 + /2023/02/05/%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/ + 上大学的时候买了第一个笔记本，是联想的小y，y460，配置应该是 i5+2g 内存，500g 硬盘，ati 5650 的显卡，那时候还是比较时髦的带有集显和独显切换的，一些人也觉得它算是一代“神机”，陪我度过了大学四年，还有毕业后的第一年，记得中间还用的比较曲折，差不多第一学期末的时候硬盘感觉有时候会文件操作卡住，去售后看了下，果然硬盘出问题了，还在当场把硬盘的东西都拷贝到新买的移动硬盘里，不过联想的售后还是比较不错的，在保内可以直接免费换，如果出保了估计就要修不起了，后面出了个很大的问题，就是屏幕花屏了，而且它的花屏是横着的会有那种上升的变化，一开始感觉跟湿度还有温度有点关系，天气冷了就很容易出现，天气热了就好一点，出现的概率小一点，而且是合盖后再开会好，过一会又会上升，只是这么度过了保修期，去售后问了下，修一下要两千多，后来是在玉泉学校里面的维修店，好像是花了六百多，省是省了很多，但是后面使用的时候发现有点漏光，而且两三个亮点，总归还是给我那不富裕的经济条件带来了一个正常屏幕，不过是挺影响使用，严格来说都没法用了，后面基本是大半个屏幕花掉，接近整个屏幕花掉，至此大学就是这么用过去了。
    噢对了，中间在大二的时候加了一条 2g 的内存，因为像操作系统课需要用虚拟机，2g 内存不太够，不过那会用的都是 32 位的 win7 系统，实际使用用不到 4g 内存，得使用破解补丁才能识别到，后来在大学毕业后由于收入也低，想换其他电脑，特别是 mac，买不起，电脑在那会还会玩玩 DNF，但是风扇声很大，也想优化下，就买了个 msata 的固态硬盘，因为刚好有个口子留着，在那之前一直搜到的是把光驱位拆掉换上 sata 固态硬盘，这对于那会的我来说操作难度实在有点大，换上了msata 固态之后还是有比较大的提升的，把操作系统装到固态硬盘后其他盘就只放数据了，后面还装过黑苹果，只是不小心被室友强制关机了，之后就起不来了，也不知道啥原因，后续想继续按原来的操作装，也没成功，再往后就配了一台台式机，这个笔记本就放在那没啥用了，后面偶尔会拿出来用一下，特别是疫情那会充当了 LD 的临时使用机器。
    最近一次就是想把行车记录仪里的视频导出来，结果上传的时候我点了下 win10 的更新，重启后这个机器就起不来了，一开始以为顶多是个重装系统，结果重装也不行，就进不去，插着 U 盘也起不来，一开始还能进 BIOS，后面就直接进不去了，像内存条脏了，擦下金手指什么的，都没搞定，想着也只可能是主板上的部件坏了，所以就开始了这趟半作死之旅，买了块主板来换，拆这个笔记本是真的够难的，开机键那一条是真的拆不开，后面还把两个扣子扳坏了，里面的排线插口还几个也拔坏了，幸好用买来的主板装上还能用，但是后面就很奇怪了，因为这个电脑装了 Ubuntu 跟 win10 的双系统，Ubuntu 能正常起来，但是 win10 就是起不来，插上老毛桃或者 win10 的启动盘都是起不来，老毛桃是能起来，但是进不了 pe，插启动盘又报 0xc00000e9 错误码，现在想着是不是这个固态硬盘有点问题或者还是内存问题，只能后续再看了，拆机的时候也找不到视频，机器实在是太老了，后面再试试看。

    ]]>     Windows @@ -5072,39 +5075,47 @@ user3:     - 分享一次折腾老旧笔记本的体验 - /2023/02/05/%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/ - 上大学的时候买了第一个笔记本，是联想的小y，y460，配置应该是 i5+2g 内存，500g 硬盘，ati 5650 的显卡，那时候还是比较时髦的带有集显和独显切换的，一些人也觉得它算是一代“神机”，陪我度过了大学四年，还有毕业后的第一年，记得中间还用的比较曲折，差不多第一学期末的时候硬盘感觉有时候会文件操作卡住，去售后看了下，果然硬盘出问题了，还在当场把硬盘的东西都拷贝到新买的移动硬盘里，不过联想的售后还是比较不错的，在保内可以直接免费换，如果出保了估计就要修不起了，后面出了个很大的问题，就是屏幕花屏了，而且它的花屏是横着的会有那种上升的变化，一开始感觉跟湿度还有温度有点关系，天气冷了就很容易出现，天气热了就好一点，出现的概率小一点，而且是合盖后再开会好，过一会又会上升，只是这么度过了保修期，去售后问了下，修一下要两千多，后来是在玉泉学校里面的维修店，好像是花了六百多，省是省了很多，但是后面使用的时候发现有点漏光，而且两三个亮点，总归还是给我那不富裕的经济条件带来了一个正常屏幕，不过是挺影响使用，严格来说都没法用了，后面基本是大半个屏幕花掉，接近整个屏幕花掉，至此大学就是这么用过去了。
    噢对了，中间在大二的时候加了一条 2g 的内存，因为像操作系统课需要用虚拟机，2g 内存不太够，不过那会用的都是 32 位的 win7 系统，实际使用用不到 4g 内存，得使用破解补丁才能识别到，后来在大学毕业后由于收入也低，想换其他电脑，特别是 mac，买不起，电脑在那会还会玩玩 DNF，但是风扇声很大，也想优化下，就买了个 msata 的固态硬盘，因为刚好有个口子留着，在那之前一直搜到的是把光驱位拆掉换上 sata 固态硬盘，这对于那会的我来说操作难度实在有点大，换上了msata 固态之后还是有比较大的提升的，把操作系统装到固态硬盘后其他盘就只放数据了，后面还装过黑苹果，只是不小心被室友强制关机了，之后就起不来了，也不知道啥原因，后续想继续按原来的操作装，也没成功，再往后就配了一台台式机，这个笔记本就放在那没啥用了，后面偶尔会拿出来用一下，特别是疫情那会充当了 LD 的临时使用机器。
    最近一次就是想把行车记录仪里的视频导出来，结果上传的时候我点了下 win10 的更新，重启后这个机器就起不来了，一开始以为顶多是个重装系统，结果重装也不行，就进不去，插着 U 盘也起不来，一开始还能进 BIOS，后面就直接进不去了，像内存条脏了，擦下金手指什么的，都没搞定，想着也只可能是主板上的部件坏了，所以就开始了这趟半作死之旅，买了块主板来换，拆这个笔记本是真的够难的，开机键那一条是真的拆不开，后面还把两个扣子扳坏了，里面的排线插口还几个也拔坏了，幸好用买来的主板装上还能用，但是后面就很奇怪了，因为这个电脑装了 Ubuntu 跟 win10 的双系统，Ubuntu 能正常起来，但是 win10 就是起不来，插上老毛桃或者 win10 的启动盘都是起不来，老毛桃是能起来，但是进不了 pe，插启动盘又报 0xc00000e9 错误码，现在想着是不是这个固态硬盘有点问题或者还是内存问题，只能后续再看了，拆机的时候也找不到视频，机器实在是太老了，后面再试试看。

    + 分享记录一下一个 git 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-git-%E6%93%8D%E4%BD%9C%E6%96%B9%E6%B3%95/ + 前阵子一个同事因为发现某个分支上的代码好像有缺失导致无法正常运行，然后就对比了下把缺失的代码从另一个分支上拷了过来，可能有所欠考虑，不过主要是说下操作过程和最后的处理方法，这位同学的操作是改一些代码commit 一下，这样的 commit 了大概五六次，并且已经 push 到了远端，然后就在想要怎么去处理，在本地可以 reset，已经到远端了，一个很不优雅的操作就是本地 reset 了用 force push，这个当然是不可取的，然后就是 revert 了，但是又已经 commit 了好几次了，网上看了下，好像处理方法还挺成熟的，git revert 命令本质上就是一个逆向的 git cherry-pick 操作。 它将你提交中的变更的以完全相反的方式的应用到一个新创建的提交中，本质上就是撤销或者倒转。可以理解为就是提交一个反向的操作，这里其实我们可以用range revert来进行 git revert, 用法就是

    +
    git revert OLDER_COMMIT^..NEWER_COMMIT
    +

    这样就可以解决上面的问题了，但是还有个问题是这样会根据前面的 commit 数量提交对应数量个 revert commit 会显得比较乱，如果要比较干净的 commit 历史，
    可以看下 git revert 命令说明

    然后就可以用 -n 参数，表示不自动提交

    +
    git revert -n OLDER_COMMIT^..NEWER_COMMIT
    git commit -m "revert OLDER_COMMIT to NEWER_COMMIT"
    + + ]]>     - Windows + git 小技巧 - Windows + git     - 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

    + 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 - stream     @@ -5126,6 +5137,25 @@ user3: scp + + 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/ + 这周回家提前约好了要去老丈人家帮下忙，因为在翻修下老房子，活不是特别整的那种，所以大部分都是自己干，或者找个大工临时干几天（我们这那种比较专业的泥工匠叫做大工），像我这样去帮忙的，就是干点小工（把给大工帮忙的，干些偏体力活的叫做小工）的活。从大学毕业以后真的蛮少帮家里干活了，以前上学的时候放假还是帮家里淘个米，简单的扫地拖地啥的，当然刚高考完的时候，还去我爸厂里帮忙干了几天的活，实在是比较累，不过现在想着是觉得自己那时候比较牛，而不是特别排斥这个活，相对于现在的工作来说，导致了一系列的职业病，颈椎腰背都很僵硬，眼镜也不好，还有反流，像我爸那种活反而是脑力加体力的比较好的结合。
    这一天的活前半部分主要是在清理厨房，瓷砖上的油污和墙上天花板上即将脱落的石灰或者白色涂料层，这种活特别是瓷砖上的油污，之前在自己家里也干活，还是比较熟悉的，不过前面主要是LD 在干，我主要是先搞墙上和天花板上的，干活还是很需要技巧的，如果直接去铲，那基本我会变成一个灰人，而且吸一鼻子灰，老丈人比较专业，先接上软管用水冲，一冲效果特别好，有些石灰涂料层直接就冲掉了，冲完之后先用带加长杆的刀片铲铲了一圈墙面，说实话因为老房子之前租出去了，所以墙面什么的被糟蹋的比较难看，一层一层的，不过这还算还好，后面主要是天花板上的，这可难倒我了，从小我爸妈是比较把我当小孩管着，爬上爬下的基本都是我爸搞定，但是到了老丈人家也只得硬着头皮上了，爬到跳（一种建筑工地用的架子）上，还有点晃，小心脏扑通扑通跳，而且带加长杆的铲子还是比较重的，铲一会手也有点累，不过坚持着铲完了，上面还是比较平整的，不过下来的时候又把我难住了🤦‍♂️，往下爬的时候有根杆子要跨过去，由于裤子比较紧，强行一把跨过去怕抽筋，所以以一个非常尴尬的姿势停留休息了一会，再跨了过去，幸好事后问 LD，他们都没看到，哈哈哈，然后就是帮忙一起搞瓷砖上的油污，这个太有经验了，不过老丈人更有意思，一会试试啤酒，一会用用沙子，后面在午饭前基本就弄的比较干净了，就坐着等吃饭了，下午午休了会，就继续干活了。
    下午是我这次体验的重点了，因为要清理以前贴的墙纸，真的是个很麻烦的活，只能说贴墙纸的师傅活干得太好了，基本不可能整个撕下来，想用铲子一点点铲下来也不行，太轻了就只铲掉表面一层，太重了就把墙纸跟墙面的石灰啥的整个铲下来了，而且手又累又酸，后来想着是不是继续用水冲一下，对着一小面墙试验了下，效果还不错，但是又发现了个问题，那一面墙又有一块是后面糊上去的，铲掉外层的石灰后不平，然后就是最最重头的，也是让我后遗症持续到第二天的，要把那一块糊上去的水泥敲下来，毛估下大概是敲了80%左右，剩下的我的手已经不会用力了，因为那一块应该是要糊上去的始作俑者，就一块里面凹进去的，我拿着榔头敲到我手已经没法使劲了，而且大下午，感觉没五分钟，我的汗已经糊满脸，眼睛也睁不开，不然就流到眼睛里了，此处获得成就一：用榔头敲墙壁，也是个技术加体力的活，而且需要非常好的技巧，否则手马上就废了，敲下去的反作用力，没一会就不行了，然后是看着老丈人兄弟帮忙拆一个柜子，在我看来是个几天都搞不定的活，他轻轻松松在我敲墙的那会就搞定了，以前总觉得我干的活非常有技术含量，可是这个事情真的也是很有技巧啊，它是个把一间房间分隔开的柜子，从底到顶上，还带着门，我还在旁边帮忙撬一下脚踢，一根木条撬半天，唉，成就二：专业的人就是不一样。
    最后就是成就三了：我之前沾沾自喜的跑了多少步，做了什么锻炼，其实都是渣渣，像这样干一天活，没经历过的，基本大半天就废了，反过来说，如果能经常去这么干一天活，跑步啥的都是渣渣，消耗的能量远远超过跑个十公里啥的。

    +]]>     + + 生活 + 运动 + 跑步 + 干活 + + + 生活 + 运动 + 减肥 + 跑步 + 干活 + +     向量数据库 Milvus 安装初体验 /2024/07/21/%E5%90%91%E9%87%8F%E6%95%B0%E6%8D%AE%E5%BA%93-Milvus-%E5%AE%89%E8%A3%85%E5%88%9D%E4%BD%93%E9%AA%8C/ @@ -5146,43 +5176,6 @@ user3: 算法 - - 分享记录一下一个 git 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-git-%E6%93%8D%E4%BD%9C%E6%96%B9%E6%B3%95/ - 前阵子一个同事因为发现某个分支上的代码好像有缺失导致无法正常运行，然后就对比了下把缺失的代码从另一个分支上拷了过来，可能有所欠考虑，不过主要是说下操作过程和最后的处理方法，这位同学的操作是改一些代码commit 一下，这样的 commit 了大概五六次，并且已经 push 到了远端，然后就在想要怎么去处理，在本地可以 reset，已经到远端了，一个很不优雅的操作就是本地 reset 了用 force push，这个当然是不可取的，然后就是 revert 了，但是又已经 commit 了好几次了，网上看了下，好像处理方法还挺成熟的，git revert 命令本质上就是一个逆向的 git cherry-pick 操作。 它将你提交中的变更的以完全相反的方式的应用到一个新创建的提交中，本质上就是撤销或者倒转。可以理解为就是提交一个反向的操作，这里其实我们可以用range revert来进行 git revert, 用法就是

    -
    git revert OLDER_COMMIT^..NEWER_COMMIT
    -

    这样就可以解决上面的问题了，但是还有个问题是这样会根据前面的 commit 数量提交对应数量个 revert commit 会显得比较乱，如果要比较干净的 commit 历史，
    可以看下 git revert 命令说明

    然后就可以用 -n 参数，表示不自动提交

    -
    git revert -n OLDER_COMMIT^..NEWER_COMMIT
    git commit -m "revert OLDER_COMMIT to NEWER_COMMIT"
    - - -]]>     - - git - 小技巧 - - - git - -     - - 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/ - 这周回家提前约好了要去老丈人家帮下忙，因为在翻修下老房子，活不是特别整的那种，所以大部分都是自己干，或者找个大工临时干几天（我们这那种比较专业的泥工匠叫做大工），像我这样去帮忙的，就是干点小工（把给大工帮忙的，干些偏体力活的叫做小工）的活。从大学毕业以后真的蛮少帮家里干活了，以前上学的时候放假还是帮家里淘个米，简单的扫地拖地啥的，当然刚高考完的时候，还去我爸厂里帮忙干了几天的活，实在是比较累，不过现在想着是觉得自己那时候比较牛，而不是特别排斥这个活，相对于现在的工作来说，导致了一系列的职业病，颈椎腰背都很僵硬，眼镜也不好，还有反流，像我爸那种活反而是脑力加体力的比较好的结合。
    这一天的活前半部分主要是在清理厨房，瓷砖上的油污和墙上天花板上即将脱落的石灰或者白色涂料层，这种活特别是瓷砖上的油污，之前在自己家里也干活，还是比较熟悉的，不过前面主要是LD 在干，我主要是先搞墙上和天花板上的，干活还是很需要技巧的，如果直接去铲，那基本我会变成一个灰人，而且吸一鼻子灰，老丈人比较专业，先接上软管用水冲，一冲效果特别好，有些石灰涂料层直接就冲掉了，冲完之后先用带加长杆的刀片铲铲了一圈墙面，说实话因为老房子之前租出去了，所以墙面什么的被糟蹋的比较难看，一层一层的，不过这还算还好，后面主要是天花板上的，这可难倒我了，从小我爸妈是比较把我当小孩管着，爬上爬下的基本都是我爸搞定，但是到了老丈人家也只得硬着头皮上了，爬到跳（一种建筑工地用的架子）上，还有点晃，小心脏扑通扑通跳，而且带加长杆的铲子还是比较重的，铲一会手也有点累，不过坚持着铲完了，上面还是比较平整的，不过下来的时候又把我难住了🤦‍♂️，往下爬的时候有根杆子要跨过去，由于裤子比较紧，强行一把跨过去怕抽筋，所以以一个非常尴尬的姿势停留休息了一会，再跨了过去，幸好事后问 LD，他们都没看到，哈哈哈，然后就是帮忙一起搞瓷砖上的油污，这个太有经验了，不过老丈人更有意思，一会试试啤酒，一会用用沙子，后面在午饭前基本就弄的比较干净了，就坐着等吃饭了，下午午休了会，就继续干活了。
    下午是我这次体验的重点了，因为要清理以前贴的墙纸，真的是个很麻烦的活，只能说贴墙纸的师傅活干得太好了，基本不可能整个撕下来，想用铲子一点点铲下来也不行，太轻了就只铲掉表面一层，太重了就把墙纸跟墙面的石灰啥的整个铲下来了，而且手又累又酸，后来想着是不是继续用水冲一下，对着一小面墙试验了下，效果还不错，但是又发现了个问题，那一面墙又有一块是后面糊上去的，铲掉外层的石灰后不平，然后就是最最重头的，也是让我后遗症持续到第二天的，要把那一块糊上去的水泥敲下来，毛估下大概是敲了80%左右，剩下的我的手已经不会用力了，因为那一块应该是要糊上去的始作俑者，就一块里面凹进去的，我拿着榔头敲到我手已经没法使劲了，而且大下午，感觉没五分钟，我的汗已经糊满脸，眼睛也睁不开，不然就流到眼睛里了，此处获得成就一：用榔头敲墙壁，也是个技术加体力的活，而且需要非常好的技巧，否则手马上就废了，敲下去的反作用力，没一会就不行了，然后是看着老丈人兄弟帮忙拆一个柜子，在我看来是个几天都搞不定的活，他轻轻松松在我敲墙的那会就搞定了，以前总觉得我干的活非常有技术含量，可是这个事情真的也是很有技巧啊，它是个把一间房间分隔开的柜子，从底到顶上，还带着门，我还在旁边帮忙撬一下脚踢，一根木条撬半天，唉，成就二：专业的人就是不一样。
    最后就是成就三了：我之前沾沾自喜的跑了多少步，做了什么锻炼，其实都是渣渣，像这样干一天活，没经历过的，基本大半天就废了，反过来说，如果能经常去这么干一天活，跑步啥的都是渣渣，消耗的能量远远超过跑个十公里啥的。

    -]]>     - - 生活 - 运动 - 跑步 - 干活 - - - 生活 - 运动 - 减肥 - 跑步 - 干活 - -     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/ @@ -5211,22 +5204,6 @@ user3: wsl - - 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 - -     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/ @@ -5308,23 +5285,6 @@ user3: 寄生虫 - - 小工周记一 - /2023/03/05/%E5%B0%8F%E5%B7%A5%E5%91%A8%E8%AE%B0%E4%B8%80/ - 开始修老房子又可以更新这个系列了，比较无聊，就是帮着干点零活的记录，这次过去起的比较早，前几天是在翻新瓦片，到这次周六是收尾了，到了的时候先是继续筛了沙子，上周也筛了，就是只筛了一点点，筛沙子的那个像纱窗一样的还是用一扇中空的中间有一根竖档的门钉上铁丝网做的，就是沙子一直放在外面，原来是有袋子装好的，后来是风吹雨打在上面的都已经破掉，还夹杂了很多树叶什么的，需要过下筛，并且前面都是下雨天，沙子都是湿的，不太像我以前看村里有人造房子筛沙子那样，用铲子铲上去就自己都下去的，湿的就是会在一坨，所以需要铲得比较少，然后撒的比较开，这个需要一点经验，然后如果有人一起的话就可以用扫把按住扫一下，这样就会筛得比较有效，不至于都滑下去，沙子本来大部分是可以筛出来的，还有一点就是这种情况网筛需要放得坡度小一点，不然就更容易直接往下调，袋子没破的就不用过筛了，只是湿掉了的是真的重，筛完了那些破掉了的袋子里的沙子，就没有特别的事情要做了，看到大工在那打墙，有一些敲下来的好的砖头就留着，需要削一下上面的混凝土，据大工说现在砖头要七八毛一块了，后面能够重新利用还是挺值钱的，我跟 LD 就用泥刀和铁锹的在那慢慢削，砖头上的泥灰有的比较牢固有的就像直接是沙子，泥刀刮一下就下来了，有的就结合得比较牢固，不过据说以前的砖头工艺上还比较落后，这个房子差不多是三十年前了的，砖头表面都是有点不平，甚至变形，那时候可能砖头是手工烧制的，现在的砖头比较工艺可好多了，不过可能也贵了很多，后来老丈人也过来了，指导了我们拌泥灰，就是水泥和黄沙混合，以前小时候可喜欢玩这个了，可是就也搞不清楚这个是怎么搅拌的，只看见是水泥跟黄沙围城一圈，中间放水，然后一点点搬进去，首先需要先把干的水泥跟黄沙进行混合，具体的比例是老丈人说的，拌的方式有点像堆沙堆，把水泥黄沙铲起来堆起来，就一直要往这个混合堆的尖尖上堆，这样子它自己滑下来能更好地混合，来回两趟就基本混合均匀了，然后就是跟以前看过的，中间扒拉出一个空间放水，然后慢慢把周围的混合好的泥沙推进去，需要注意不要太着急，旁边的推进去太多太快就会漏水出来，一个是会把旁边的地给弄脏，另一个也铲不回水，然后就是推完所有的都混合水了，就铲起来倒一下，再将铲子翻过来捣几下，后面我们就去吃饭了，去了一家叫金记的，又贵又不太好吃的店，就是离得比较近，六七个人只有六七个菜，吃完要四百多，也是有点离谱了。
    下午是重头戏，其实本来倒没啥事，就说帮忙搞下靠背（就是踢脚线上面到窗台附近的用木头还有其他材料的装饰性的），都撬撬掉，但是真的是有点离谱了，首先是撬棒真的很重，20 斤的重量（网上查的，没有真的查过），抡起来还要用力地铲进去，因为就是破坏性的要把整个都撬掉，对于我这种又没技巧又没力气的非专业选手，抡起撬棍铲两下手就开始痛了，只是也比较犟，不想刚开始弄就说太重了要休息，后面都完全靠的一点倔强劲撑着，看着里面的工艺感觉也是不容易的，直着横着的木条有好多，竖的一整条，每隔三五十公分，横着的就是三五十公分，每根都要用钉子钉起来，然后外层好像是贴上去，在同一个面的开了头之后就能靠着蛮力往下撬，但是到了转角就又要重新开头，而且最上面一根横条跟紧邻的那一块，大概十几公分，是横着的三条钉在一起，真的是大力都出不了奇迹了，用撬棍的一头用力地敲打都很震手，要从下面往上铲进去撬开一点，然后再从上面往下敲打，这里比较重要的是要小心钉子，我这次运气比较好，踩下去已经扎到了，不过正好在脚趾缝里，没有扎到脚，还是要小心的，做完这个我的手真的是差不多废了，上臂的疼痛已经动一下就受不了了，后面有撬下了最下面当踢脚线的小瓷砖，这个房子估计中间修过一次，两批水泥糊的，新的那批粘的特别牢，敲敲打打了半天才下来一点点，锤子敲上去跟一整块石头一样，震手又没有进展，整个搞完，楼上又在敲墙了，下面的灰尘也是从没见过，我一直在那洒水都完全没有缓解，就上去跟 LD 一起拣砖头，手痛到只能抬两块砖头都会痛了。
    回到家里开始越来越痛，两个手就完全没法动了，应该也是肌肉拉伤了，我这样是没足够的力气也不会什么技巧，像大工说的，他们也累也难，只是为了赚钱，不过他们有了经验跟技巧，会注意怎么使力不容易受伤，怎么样比较省力，还有一点就是即使这么累，他们一般也下午五点半就下班了，真的很累了，至少还有不少时间可以回家休息，而我们的职业呢，就像 LD 说的回家就像住酒店，就只是来洗澡睡个觉，希望能改善吧

    -]]>     - - 生活 - - - 生活 - 小技巧 - 运动 - 减肥 - 跑步 - 干活 - -     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/ @@ -5353,6 +5313,46 @@ user3: 算法 + + 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 + +     + + 小工周记一 + /2023/03/05/%E5%B0%8F%E5%B7%A5%E5%91%A8%E8%AE%B0%E4%B8%80/ + 开始修老房子又可以更新这个系列了，比较无聊，就是帮着干点零活的记录，这次过去起的比较早，前几天是在翻新瓦片，到这次周六是收尾了，到了的时候先是继续筛了沙子，上周也筛了，就是只筛了一点点，筛沙子的那个像纱窗一样的还是用一扇中空的中间有一根竖档的门钉上铁丝网做的，就是沙子一直放在外面，原来是有袋子装好的，后来是风吹雨打在上面的都已经破掉，还夹杂了很多树叶什么的，需要过下筛，并且前面都是下雨天，沙子都是湿的，不太像我以前看村里有人造房子筛沙子那样，用铲子铲上去就自己都下去的，湿的就是会在一坨，所以需要铲得比较少，然后撒的比较开，这个需要一点经验，然后如果有人一起的话就可以用扫把按住扫一下，这样就会筛得比较有效，不至于都滑下去，沙子本来大部分是可以筛出来的，还有一点就是这种情况网筛需要放得坡度小一点，不然就更容易直接往下调，袋子没破的就不用过筛了，只是湿掉了的是真的重，筛完了那些破掉了的袋子里的沙子，就没有特别的事情要做了，看到大工在那打墙，有一些敲下来的好的砖头就留着，需要削一下上面的混凝土，据大工说现在砖头要七八毛一块了，后面能够重新利用还是挺值钱的，我跟 LD 就用泥刀和铁锹的在那慢慢削，砖头上的泥灰有的比较牢固有的就像直接是沙子，泥刀刮一下就下来了，有的就结合得比较牢固，不过据说以前的砖头工艺上还比较落后，这个房子差不多是三十年前了的，砖头表面都是有点不平，甚至变形，那时候可能砖头是手工烧制的，现在的砖头比较工艺可好多了，不过可能也贵了很多，后来老丈人也过来了，指导了我们拌泥灰，就是水泥和黄沙混合，以前小时候可喜欢玩这个了，可是就也搞不清楚这个是怎么搅拌的，只看见是水泥跟黄沙围城一圈，中间放水，然后一点点搬进去，首先需要先把干的水泥跟黄沙进行混合，具体的比例是老丈人说的，拌的方式有点像堆沙堆，把水泥黄沙铲起来堆起来，就一直要往这个混合堆的尖尖上堆，这样子它自己滑下来能更好地混合，来回两趟就基本混合均匀了，然后就是跟以前看过的，中间扒拉出一个空间放水，然后慢慢把周围的混合好的泥沙推进去，需要注意不要太着急，旁边的推进去太多太快就会漏水出来，一个是会把旁边的地给弄脏，另一个也铲不回水，然后就是推完所有的都混合水了，就铲起来倒一下，再将铲子翻过来捣几下，后面我们就去吃饭了，去了一家叫金记的，又贵又不太好吃的店，就是离得比较近，六七个人只有六七个菜，吃完要四百多，也是有点离谱了。
    下午是重头戏，其实本来倒没啥事，就说帮忙搞下靠背（就是踢脚线上面到窗台附近的用木头还有其他材料的装饰性的），都撬撬掉，但是真的是有点离谱了，首先是撬棒真的很重，20 斤的重量（网上查的，没有真的查过），抡起来还要用力地铲进去，因为就是破坏性的要把整个都撬掉，对于我这种又没技巧又没力气的非专业选手，抡起撬棍铲两下手就开始痛了，只是也比较犟，不想刚开始弄就说太重了要休息，后面都完全靠的一点倔强劲撑着，看着里面的工艺感觉也是不容易的，直着横着的木条有好多，竖的一整条，每隔三五十公分，横着的就是三五十公分，每根都要用钉子钉起来，然后外层好像是贴上去，在同一个面的开了头之后就能靠着蛮力往下撬，但是到了转角就又要重新开头，而且最上面一根横条跟紧邻的那一块，大概十几公分，是横着的三条钉在一起，真的是大力都出不了奇迹了，用撬棍的一头用力地敲打都很震手，要从下面往上铲进去撬开一点，然后再从上面往下敲打，这里比较重要的是要小心钉子，我这次运气比较好，踩下去已经扎到了，不过正好在脚趾缝里，没有扎到脚，还是要小心的，做完这个我的手真的是差不多废了，上臂的疼痛已经动一下就受不了了，后面有撬下了最下面当踢脚线的小瓷砖，这个房子估计中间修过一次，两批水泥糊的，新的那批粘的特别牢，敲敲打打了半天才下来一点点，锤子敲上去跟一整块石头一样，震手又没有进展，整个搞完，楼上又在敲墙了，下面的灰尘也是从没见过，我一直在那洒水都完全没有缓解，就上去跟 LD 一起拣砖头，手痛到只能抬两块砖头都会痛了。
    回到家里开始越来越痛，两个手就完全没法动了，应该也是肌肉拉伤了，我这样是没足够的力气也不会什么技巧，像大工说的，他们也累也难，只是为了赚钱，不过他们有了经验跟技巧，会注意怎么使力不容易受伤，怎么样比较省力，还有一点就是即使这么累，他们一般也下午五点半就下班了，真的很累了，至少还有不少时间可以回家休息，而我们的职业呢，就像 LD 说的回家就像住酒店，就只是来洗澡睡个觉，希望能改善吧

    +]]>     + + 生活 + + + 生活 + 小技巧 + 运动 + 减肥 + 跑步 + 干活 + +     基于Caddy的快速反向代理 /2024/10/06/%E5%9F%BA%E4%BA%8ECaddy%E7%9A%84%E5%BF%AB%E9%80%9F%E5%8F%8D%E5%90%91%E4%BB%A3%E7%90%86/ @@ -5382,30 +5382,44 @@ user3: - 小技巧之-安卓终端工具Termux访问手机文件 - /2024/09/22/%E5%B0%8F%E6%8A%80%E5%B7%A7%E4%B9%8B-%E5%AE%89%E5%8D%93%E7%BB%88%E7%AB%AF%E5%B7%A5%E5%85%B7Termux%E8%AE%BF%E9%97%AE%E6%89%8B%E6%9C%BA%E6%96%87%E4%BB%B6/ - Termux是安卓下一个终端工具，一开始以为就是一些极客的高端玩具，在安卓下编程用，实际的实用性不太强，直到之前稍微研究了下，还真的是个神器，这个神器的原因在于三方面
    第一点，它其实不只是个终端工具，而是个类似于iterm2 + brew的组合拳神器
    自带了pkg包管理工具

    还可以通过termux-change-repo来更改包的安装源
    有了这个工具，我们就可以在手机里运行一些简单的程序，比如跑个php脚本，因为可以安装php

    甚至可以安装nginx，在手机里跑个web应用，可以通过安装git来拉取我们的代码，实现手机开发可能比较难，但是在手机上运行代码还是可以的
    第二点是这个工具可以开启安卓文件的访问，默认它是限定在应用目录下的，我们可以通过以下命令来请求访问安卓文件目录权限

    -
    termux-setup-storage
    -

    然后我们去到 /storage/emulated/0 目录下，就发现安卓的文件就在这个目录下，举个例子这样我们就可以用脚本来处理我的照片文件
    第三点是其实目前的手机处理器性能已经到了一个非常不错的阶段，比如我看了下我的渣渣安卓机是高通骁龙870处理器，它是个21年出的手机处理器，性能大致接近于桌面端i5 4590
    870的geekbench跑分，单核1141，多核3317
    i5 4590的geekbench跑分，单核1142，3165
    单核非常接近，多核还稍微领先，并且最近也看到有用最新的高通骁龙8gen3来跑端侧大模型的，其实说明了目前手机的性能真的达到了很高的水平，提供了更多的可玩性，虽然从另一个角度来讲，870在运行现在最新版本的手机应用时会发烫变卡，那是因为手机应用往往要做非常多的事情，后台保活，前台的各种图形渲染，包括微信，支付宝都有了内置的小程序，那么都需要一套资源隔离机制来实现运行态的维护，并且各种内容也搞得花里胡哨都需要资源，我们只是运行简单的脚本，手机的可用性不比很多云服务器差，一般云服务器我们个人用户都买的是比较低配的，最低1核1g的配置，远不如现在的手机强大，可玩性是真的不错的。

    + 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

    ]]>     - 小技巧 + 生活 - 小技巧 + 生活 + 囤物资     - 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

    + 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/ + LlamaIndex 是目前比较新的大模型RAG框架，RAG是指 检索增强生成 (Retrieval-Augmented Generation, RAG)是指在利用大语言模型回答问题之前，先从外部知识库检索相关信息。RAG 被证明能显著提升答案的准确性，并特别是在知识密集型任务上减少模型的错误输出。通过引用信息来源，用户可以核实答案的准确性，从而增强对模型输出的信任。
    此外，RAG 有助于快速更新知识并引入特定领域的专业知识。
    RAG 有效结合了大语言模型的参数化知识和非参数化的外部知识库，成为实施大语言模型的关键方法之一。本文概述了 RAG 在大语言模型时代的发展模式，总结了三种模式：初级 RAG、高级 RAG 和模块化 RAG。
    首先我们可以了解下LlamaIndex的几大模块，
    使用大型语言模型(LLMs):无论是OpenAI还是任何托管的LLM,亦或是您自己本地运行的模型,LLM都被用于从索引和存储到查询和解析数据的每一个步骤。LlamaIndex提供了大量可靠经过测试的提示,我们也将向您展示如何自定义您自己的提示。
    LlamaIndex 主要有这五大块能力

    +
      +
    • Data connectors
      • +
    • Data indexes
      • +
    • Engines
      • +
    • Data agents
      • +
    • Application integrations
      • +
    +

    其中加载部分可以认为是初始的部分，我们可以从各个数据源去获取信息，比如各种文档，或者confluence，甚至api接口，只需要合适的数据加载器，我们就能将其转化为我们要的数据
    比如我想要将我博客里的Markdown文件作为知识库加载

    +
    reader = SimpleDirectoryReader(
        input_dir="./somedir/source/_posts/",
        required_exts=[".md"],
        recursive=True
    )
    docs = reader.load_data()
    +

    然后由文档来就构建我们的索引

    +
    index = VectorStoreIndex.from_documents(docs)
    +

    但是这里我们需要提前设置好对应的 embedding 模型，这个很重要，如果有条件推荐使用OpenAI的，如果想用本地的可以考虑使用BAAI智源出品的，只是要注意是否支持多语言，如果要使用本地embedding的话需要安装

    +
    pip install llama-index-embeddings-huggingface
    +

    这里我选了BAAI也就是智源研究所的bge-m3模型，因为这个是支持多语言的，如果是纯英文的可以用他们 BAAI/bge-small-en-v1.5

    +
    from llama_index.core import Settings
    from llama_index.embeddings.huggingface import HuggingFaceEmbedding
    Settings.llm = Ollama(model="gemma:7b", request_timeout=60.0)
    Settings.embed_model = HuggingFaceEmbedding(
        model_name="BAAI/bge-m3"
    )
    index = VectorStoreIndex.from_documents(docs)
    query_engine = index.as_query_engine(llm=gemma_7b)
    response = query_engine.query("数据库主从延迟")
    print(response)
    +

    另外LLM 我使用了本地部署的 gemma:7b，因为想尝试下本地的链路，需要注意的是一些文档还是用的原来的service_context，在新版本中这个已经被改掉了，都是通过 Settings 来设置，此外还有诸如 SimpleDirectoryReader 这些包也被挪到了 llama_index.core 中，需要注意下
    然后我们就能使用这种方式来增强LLM，把我们的本地文档作为知识库来搜索，结合了大模型的能力。

    ]]>     - 生活 + LLM - 生活 - 囤物资 + LLM     @@ -5430,35 +5444,29 @@ 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

    -]]>     - - 折腾 - - - 折腾 - -     - - 小技巧-用iptables统计网速占用 - /2024/09/28/%E5%B0%8F%E6%8A%80%E5%B7%A7-%E7%94%A8iptables%E7%BB%9F%E8%AE%A1%E7%BD%91%E9%80%9F%E5%8D%A0%E7%94%A8/ - 目前我在家里用的路由器是个装了ImmortalWrt的NX30 Pro路由器，由于内存只有256兆，默认没带网速统计和限制网速的插件，对于网络速度限制就有点困难，刚好这次网上找到了一个可以用iptables统计网速的脚本，简单记录下

    -

    #!/bin/sh
    echo "Collecting data..."
    echo ""
    cat /proc/net/arp | grep : | grep ^192 | grep -v 00:00:00:00:00:00| awk '{print $1}'> mac-arp
    iptables -N UPLOAD
    iptables -N DOWNLOAD
    while read line;do iptables -I FORWARD 1 -s $line -j UPLOAD;done < mac-arp
    while read line;do iptables -I FORWARD 1 -d $line -j DOWNLOAD;done < mac-arp
    sleep 1
    echo "Download speed:"
    echo ""
    iptables -nvx -L FORWARD | grep DOWNLOAD | awk '{print $2/1024/1" KB/s ",$1/10" packets/s", $9}' | sort -n -r
    echo ""
    echo "Upload speed:"
    echo ""
    iptables -nvx -L FORWARD | grep UPLOAD | awk '{print $2/1024/1" KB/s ",$1/10" packets/s", $8}' | sort -n -r
    while read line;do iptables -D FORWARD -s $line -j UPLOAD;done < mac-arp
    while read line;do iptables -D FORWARD -d $line -j DOWNLOAD;done < mac-arp
    iptables -X UPLOAD
    iptables -X DOWNLOAD
    -

    首先是通过arp记录内网ip地址，然后添加UPLOAD和DOWNLOAD链，
    然后为mac-arp文件中每个ip地址添加FORWARD规则，将流量导到UPLOAD和DOWNLOAD链
    等待1秒，统计下载和上传速度，计算KB/s,packets/s，再清理规则
    然后对于上传占用高的，我们可以用

    -
    iptables -t mangle -I FORWARD 1 -s 192.168.x.x -m limit --limit 800/s --limit-burst 1000 -j ACCEPT
    iptables -t mangle -I FORWARD 2 -s 192.168.x.x -j DROP
    -

    限制通过包的数量为800，不过这个不绝对精确

    + 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

    ]]>     - 小技巧 + 算法 - 小技巧 + 算法     @@ -5488,10 +5496,26 @@ user3:

    逻辑复杂负载比较高的就算了，考虑N1或者其他性能更好的机器

    ]]> - 折腾 + 折腾 + + + 折腾 + +     + + 小技巧-用iptables统计网速占用 + /2024/09/28/%E5%B0%8F%E6%8A%80%E5%B7%A7-%E7%94%A8iptables%E7%BB%9F%E8%AE%A1%E7%BD%91%E9%80%9F%E5%8D%A0%E7%94%A8/ + 目前我在家里用的路由器是个装了ImmortalWrt的NX30 Pro路由器，由于内存只有256兆，默认没带网速统计和限制网速的插件，对于网络速度限制就有点困难，刚好这次网上找到了一个可以用iptables统计网速的脚本，简单记录下

    +

    #!/bin/sh
    echo "Collecting data..."
    echo ""
    cat /proc/net/arp | grep : | grep ^192 | grep -v 00:00:00:00:00:00| awk '{print $1}'> mac-arp
    iptables -N UPLOAD
    iptables -N DOWNLOAD
    while read line;do iptables -I FORWARD 1 -s $line -j UPLOAD;done < mac-arp
    while read line;do iptables -I FORWARD 1 -d $line -j DOWNLOAD;done < mac-arp
    sleep 1
    echo "Download speed:"
    echo ""
    iptables -nvx -L FORWARD | grep DOWNLOAD | awk '{print $2/1024/1" KB/s ",$1/10" packets/s", $9}' | sort -n -r
    echo ""
    echo "Upload speed:"
    echo ""
    iptables -nvx -L FORWARD | grep UPLOAD | awk '{print $2/1024/1" KB/s ",$1/10" packets/s", $8}' | sort -n -r
    while read line;do iptables -D FORWARD -s $line -j UPLOAD;done < mac-arp
    while read line;do iptables -D FORWARD -d $line -j DOWNLOAD;done < mac-arp
    iptables -X UPLOAD
    iptables -X DOWNLOAD
    +

    首先是通过arp记录内网ip地址，然后添加UPLOAD和DOWNLOAD链，
    然后为mac-arp文件中每个ip地址添加FORWARD规则，将流量导到UPLOAD和DOWNLOAD链
    等待1秒，统计下载和上传速度，计算KB/s,packets/s，再清理规则
    然后对于上传占用高的，我们可以用

    +
    iptables -t mangle -I FORWARD 1 -s 192.168.x.x -m limit --limit 800/s --limit-burst 1000 -j ACCEPT
    iptables -t mangle -I FORWARD 2 -s 192.168.x.x -j DROP
    +

    限制通过包的数量为800，不过这个不绝对精确

    +]]>     + + 小技巧 - 折腾 + 小技巧     @@ -5582,6 +5606,18 @@ user3: utf8mb4_general_ci + + 深度学习入门初认识 + /2023/04/30/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%85%A5%E9%97%A8%E5%88%9D%E8%AE%A4%E8%AF%86/ + 对于深度学习只能说我是个门外汉，开始学习，不过很多还搞不懂，做个记录和分享，基于《深度学习入门：基于 Python 的理论与实现》，
    第一章 Python 入门就不介绍了，不是重点，不过完全没有 Python 基础的可以看下，我之前算是学过一点点，
    第二章我觉得入门的方式比较不错，从感知机入手，有一些顾名思义，感知一些状态（输入），来做出反应，简单的就是比如通电了，我的灯就亮起来了，并且往后就是延伸到了计算机的最基础组成，与或门，与门与或门是最基础的，配合非门进行组成，可以作为计算机基础单元ALU 的组成基础，而更复杂的也可以由此进行搭建，这是个比较通俗的解释，根据书中的定义，感知机是具有输入和输出的算法。给定一个输入后，将输出一个既定的值，单层的感知机无法实现更复杂的异或门，但是可以通过 2 层感知机来实现，也就是一个与非门，一个或门，作为第一层的感知机，他们的输出作为与门的输入，就可以成为一个异或门。理论上多层感知机可以表示计算机。
    而后第三章引出了激活函数，也就是在前面的与或门和基础的感知机的基础上加上了输出的条件，前面与门或门都是最基础的 0,1 游戏，现在可以加上更复杂的判断条件，在输入的基础上配以权重，再加上偏置参数，表示被激活的容易程度，这种激活函数可以被称为阶跃函数，如果超过了一定的值就代表被激活，没有则不激活，但是实际在神经元中被使用的主要是用 sigmoid 函数，相比阶跃函数，sigmoid 函数是一个平滑的曲线，随着输入变化而连续变化，因为相对感知机，神经元需要的信号是连续的实数值信号，再往后则是对输出层的介绍，如果是回归问题，也就是根据输入预测一个（连续的）数值的问题，属于回归问题，可以用恒等函数，而对于分类问题，则使用 softmax 函数，这个函数的一个重要的点在于也是区分于简单的二元分类器，softmax 是将多个结果概率进行数值处理（归一化），也叫做归一化指数函数，对于不同的结果概率是将概率最大的进行放大，凸显其中最大的值并抑制远低于最大值的其他分量。使得其他概率值也能够被使用，但是减弱其份额权重。

    +]]>     + + 深度学习 + + + 深度学习 + +     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/ @@ -5595,15 +5631,17 @@ user3: - 深度学习入门初认识 - /2023/04/30/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%85%A5%E9%97%A8%E5%88%9D%E8%AE%A4%E8%AF%86/ - 对于深度学习只能说我是个门外汉，开始学习，不过很多还搞不懂，做个记录和分享，基于《深度学习入门：基于 Python 的理论与实现》，
    第一章 Python 入门就不介绍了，不是重点，不过完全没有 Python 基础的可以看下，我之前算是学过一点点，
    第二章我觉得入门的方式比较不错，从感知机入手，有一些顾名思义，感知一些状态（输入），来做出反应，简单的就是比如通电了，我的灯就亮起来了，并且往后就是延伸到了计算机的最基础组成，与或门，与门与或门是最基础的，配合非门进行组成，可以作为计算机基础单元ALU 的组成基础，而更复杂的也可以由此进行搭建，这是个比较通俗的解释，根据书中的定义，感知机是具有输入和输出的算法。给定一个输入后，将输出一个既定的值，单层的感知机无法实现更复杂的异或门，但是可以通过 2 层感知机来实现，也就是一个与非门，一个或门，作为第一层的感知机，他们的输出作为与门的输入，就可以成为一个异或门。理论上多层感知机可以表示计算机。
    而后第三章引出了激活函数，也就是在前面的与或门和基础的感知机的基础上加上了输出的条件，前面与门或门都是最基础的 0,1 游戏，现在可以加上更复杂的判断条件，在输入的基础上配以权重，再加上偏置参数，表示被激活的容易程度，这种激活函数可以被称为阶跃函数，如果超过了一定的值就代表被激活，没有则不激活，但是实际在神经元中被使用的主要是用 sigmoid 函数，相比阶跃函数，sigmoid 函数是一个平滑的曲线，随着输入变化而连续变化，因为相对感知机，神经元需要的信号是连续的实数值信号，再往后则是对输出层的介绍，如果是回归问题，也就是根据输入预测一个（连续的）数值的问题，属于回归问题，可以用恒等函数，而对于分类问题，则使用 softmax 函数，这个函数的一个重要的点在于也是区分于简单的二元分类器，softmax 是将多个结果概率进行数值处理（归一化），也叫做归一化指数函数，对于不同的结果概率是将概率最大的进行放大，凸显其中最大的值并抑制远低于最大值的其他分量。使得其他概率值也能够被使用，但是减弱其份额权重。

    + 小技巧之-安卓终端工具Termux访问手机文件 + /2024/09/22/%E5%B0%8F%E6%8A%80%E5%B7%A7%E4%B9%8B-%E5%AE%89%E5%8D%93%E7%BB%88%E7%AB%AF%E5%B7%A5%E5%85%B7Termux%E8%AE%BF%E9%97%AE%E6%89%8B%E6%9C%BA%E6%96%87%E4%BB%B6/ + Termux是安卓下一个终端工具，一开始以为就是一些极客的高端玩具，在安卓下编程用，实际的实用性不太强，直到之前稍微研究了下，还真的是个神器，这个神器的原因在于三方面
    第一点，它其实不只是个终端工具，而是个类似于iterm2 + brew的组合拳神器
    自带了pkg包管理工具

    还可以通过termux-change-repo来更改包的安装源
    有了这个工具，我们就可以在手机里运行一些简单的程序，比如跑个php脚本，因为可以安装php

    甚至可以安装nginx，在手机里跑个web应用，可以通过安装git来拉取我们的代码，实现手机开发可能比较难，但是在手机上运行代码还是可以的
    第二点是这个工具可以开启安卓文件的访问，默认它是限定在应用目录下的，我们可以通过以下命令来请求访问安卓文件目录权限

    +
    termux-setup-storage
    +

    然后我们去到 /storage/emulated/0 目录下，就发现安卓的文件就在这个目录下，举个例子这样我们就可以用脚本来处理我的照片文件
    第三点是其实目前的手机处理器性能已经到了一个非常不错的阶段，比如我看了下我的渣渣安卓机是高通骁龙870处理器，它是个21年出的手机处理器，性能大致接近于桌面端i5 4590
    870的geekbench跑分，单核1141，多核3317
    i5 4590的geekbench跑分，单核1142，3165
    单核非常接近，多核还稍微领先，并且最近也看到有用最新的高通骁龙8gen3来跑端侧大模型的，其实说明了目前手机的性能真的达到了很高的水平，提供了更多的可玩性，虽然从另一个角度来讲，870在运行现在最新版本的手机应用时会发烫变卡，那是因为手机应用往往要做非常多的事情，后台保活，前台的各种图形渲染，包括微信，支付宝都有了内置的小程序，那么都需要一套资源隔离机制来实现运行态的维护，并且各种内容也搞得花里胡哨都需要资源，我们只是运行简单的脚本，手机的可用性不比很多云服务器差，一般云服务器我们个人用户都买的是比较低配的，最低1核1g的配置，远不如现在的手机强大，可玩性是真的不错的。

    ]]>     - 深度学习 + 小技巧 - 深度学习 + 小技巧     @@ -5619,50 +5657,33 @@ user3: - 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/ - LlamaIndex 是目前比较新的大模型RAG框架，RAG是指 检索增强生成 (Retrieval-Augmented Generation, RAG)是指在利用大语言模型回答问题之前，先从外部知识库检索相关信息。RAG 被证明能显著提升答案的准确性，并特别是在知识密集型任务上减少模型的错误输出。通过引用信息来源，用户可以核实答案的准确性，从而增强对模型输出的信任。
    此外，RAG 有助于快速更新知识并引入特定领域的专业知识。
    RAG 有效结合了大语言模型的参数化知识和非参数化的外部知识库，成为实施大语言模型的关键方法之一。本文概述了 RAG 在大语言模型时代的发展模式，总结了三种模式：初级 RAG、高级 RAG 和模块化 RAG。
    首先我们可以了解下LlamaIndex的几大模块，
    使用大型语言模型(LLMs):无论是OpenAI还是任何托管的LLM,亦或是您自己本地运行的模型,LLM都被用于从索引和存储到查询和解析数据的每一个步骤。LlamaIndex提供了大量可靠经过测试的提示,我们也将向您展示如何自定义您自己的提示。
    LlamaIndex 主要有这五大块能力

    -
      -
    • Data connectors
      • -
    • Data indexes
      • -
    • Engines
      • -
    • Data agents
      • -
    • Application integrations
      • -
    -

    其中加载部分可以认为是初始的部分，我们可以从各个数据源去获取信息，比如各种文档，或者confluence，甚至api接口，只需要合适的数据加载器，我们就能将其转化为我们要的数据
    比如我想要将我博客里的Markdown文件作为知识库加载

    -
    reader = SimpleDirectoryReader(
        input_dir="./somedir/source/_posts/",
        required_exts=[".md"],
        recursive=True
    )
    docs = reader.load_data()
    -

    然后由文档来就构建我们的索引

    -
    index = VectorStoreIndex.from_documents(docs)
    -

    但是这里我们需要提前设置好对应的 embedding 模型，这个很重要，如果有条件推荐使用OpenAI的，如果想用本地的可以考虑使用BAAI智源出品的，只是要注意是否支持多语言，如果要使用本地embedding的话需要安装

    -
    pip install llama-index-embeddings-huggingface
    -

    这里我选了BAAI也就是智源研究所的bge-m3模型，因为这个是支持多语言的，如果是纯英文的可以用他们 BAAI/bge-small-en-v1.5

    -
    from llama_index.core import Settings
    from llama_index.embeddings.huggingface import HuggingFaceEmbedding
    Settings.llm = Ollama(model="gemma:7b", request_timeout=60.0)
    Settings.embed_model = HuggingFaceEmbedding(
        model_name="BAAI/bge-m3"
    )
    index = VectorStoreIndex.from_documents(docs)
    query_engine = index.as_query_engine(llm=gemma_7b)
    response = query_engine.query("数据库主从延迟")
    print(response)
    -

    另外LLM 我使用了本地部署的 gemma:7b，因为想尝试下本地的链路，需要注意的是一些文档还是用的原来的service_context，在新版本中这个已经被改掉了，都是通过 Settings 来设置，此外还有诸如 SimpleDirectoryReader 这些包也被挪到了 llama_index.core 中，需要注意下
    然后我们就能使用这种方式来增强LLM，把我们的本地文档作为知识库来搜索，结合了大模型的能力。

    + 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

    ]]>     - LLM + 折腾 - LLM + 折腾     - 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

    + 给小电驴上牌 + /2022/03/20/%E7%BB%99%E5%B0%8F%E7%94%B5%E9%A9%B4%E4%B8%8A%E7%89%8C/ + 三八节活动的时候下决心买了个小电驴，主要是上下班路上现在通勤条件越来越恶劣了，之前都是觉得坐公交就行了，实际路程就比较短，但是现在或者说大概是年前那两个月差不多就开始了，基本是堵一路，个人感觉是天目山路那边在修地铁，而且蚂蚁的几个空间都在那，上班的时间点都差不多，前一个修地铁感觉挺久了，机动车保有量也越来越多，总体是古墩路就越来越堵，还有个原因就是早上上班的点共享单车都被骑走了，有时候整整走一路都没一辆，有时候孤零零地有一辆基本都是破的；走路其实也是一种选择，但是因为要赶着上班，走得太慢就要很久，可能要 45 分钟这样，走得比较快就一身汗挺难受的。所以考虑自行车和电动车，这里还有一点就是不管是乘公交还是骑共享单车，其实都要从楼下走出去蛮远，公司回来也是，也就是这种通勤方式在准备阶段就花了比较多时间，比如总的从下班到到家的时间是半小时，可能在骑共享单车和公交车上的时间都不到十分钟，就比较难受。觉得这种比例太浪费时间，如果能有这种比较点对点的方式，估计能省时省力不少，前面说的骑共享单车的方式其实在之前是比较可行的，但是后来越来越少车，基本都是每周的前几天，周一到周三都是没有车，走路到公司再冷的天都是走出一身的汗，下雨天就更难受，本来下雨天应该是优先选择坐公交，但是一般下雨天堵车会更严重，而且车子到我上车的那个站，下雨天就挤得不行，总体说下来感觉事情都不打，但是几年下来，还是会挺不爽的。

    +

    电驴看的比较草率，主要是考虑续航，然后锂电池外加 48v 和 24AH，这样一般来讲还是价格比较高的，只是原来没预料到这个限速，以为现在的车子都比较快，但是现在的新国标车子都是 25km/h 的限速，然后 15km/h 都是会要提醒，虽然说有一些特殊的解除限速的方法，但是解了也就 35km/h ，差距不是特别大，而且现在的车子都是比较小，也不太能载东西，特别是上下班路程也不远的情况下，其实不是那么需要速度，就像我朋友说的，可能骑车的时间还不如等红绿灯多，所以就还好，也不打算解除限速，只是品牌上也仔细看，后来选了绿源，目前大部分还是雅迪，爱玛，台羚，绿源，小牛等，路上看的话还是雅迪比较多，不过价格也比较贵一点，还有就是小牛了，是比较新兴的品牌，手机 App 什么的做得比较好，而且也比较贵，最后以相对比较便宜的价格买了个锂电 48V24AH 的小车子，后来发现还是有点不方便的点就是没有比较大的筐，也不好装，这样就是下雨天雨衣什么的比较不方便放。

    +

    聊回来主题上牌这个事情，这个事情也是颇费心力，提车的时候店里的让我跟他早上一起去，但是因为不确定时间，也比较远就没跟着去，因为我是线上买的，线下自提，线下的店可能没啥利润可以拿，就不肯帮忙代上牌，朋友说在线下店里买是可以代上的，自己上牌过程也比较曲折，一开始是头盔没到，然后是等开发票，主要的东西就是需要骑着车子去车管所，不能只自己去，然后需要预约，附近比较近的都是提前一周就预约完了号了，要提前在支付宝上进行预约，比较空的就是店里推荐的景区大队，但是随之而来就是比较蛋疼的，这个景区大队太远了，看下骑车距离有十几公里，所以就有点拖延症，但是总归要上的，不然一直不能开是白买了，上牌的材料主要是车辆合格证，发票，然后车子上的浙品码，在车架上和电池上，然后车架号什么的都要跟合格证上完全对应，整体车子要跟合格证上一毛一样，如果有额外的反光镜，后面副座都需要拆掉，脚踏板要装上，到了那其实还比较顺利，就是十几公里外加那天比较冷，吹得头疼。

    ]]>     - LLM + 生活 - LLM + 生活     @@ -5682,43 +5703,21 @@ user3: - 给小电驴上牌 - /2022/03/20/%E7%BB%99%E5%B0%8F%E7%94%B5%E9%A9%B4%E4%B8%8A%E7%89%8C/ - 三八节活动的时候下决心买了个小电驴，主要是上下班路上现在通勤条件越来越恶劣了，之前都是觉得坐公交就行了，实际路程就比较短，但是现在或者说大概是年前那两个月差不多就开始了，基本是堵一路，个人感觉是天目山路那边在修地铁，而且蚂蚁的几个空间都在那，上班的时间点都差不多，前一个修地铁感觉挺久了，机动车保有量也越来越多，总体是古墩路就越来越堵，还有个原因就是早上上班的点共享单车都被骑走了，有时候整整走一路都没一辆，有时候孤零零地有一辆基本都是破的；走路其实也是一种选择，但是因为要赶着上班，走得太慢就要很久，可能要 45 分钟这样，走得比较快就一身汗挺难受的。所以考虑自行车和电动车，这里还有一点就是不管是乘公交还是骑共享单车，其实都要从楼下走出去蛮远，公司回来也是，也就是这种通勤方式在准备阶段就花了比较多时间，比如总的从下班到到家的时间是半小时，可能在骑共享单车和公交车上的时间都不到十分钟，就比较难受。觉得这种比例太浪费时间，如果能有这种比较点对点的方式，估计能省时省力不少，前面说的骑共享单车的方式其实在之前是比较可行的，但是后来越来越少车，基本都是每周的前几天，周一到周三都是没有车，走路到公司再冷的天都是走出一身的汗，下雨天就更难受，本来下雨天应该是优先选择坐公交，但是一般下雨天堵车会更严重，而且车子到我上车的那个站，下雨天就挤得不行，总体说下来感觉事情都不打，但是几年下来，还是会挺不爽的。

    -

    电驴看的比较草率，主要是考虑续航，然后锂电池外加 48v 和 24AH，这样一般来讲还是价格比较高的，只是原来没预料到这个限速，以为现在的车子都比较快，但是现在的新国标车子都是 25km/h 的限速，然后 15km/h 都是会要提醒，虽然说有一些特殊的解除限速的方法，但是解了也就 35km/h ，差距不是特别大，而且现在的车子都是比较小，也不太能载东西，特别是上下班路程也不远的情况下，其实不是那么需要速度，就像我朋友说的，可能骑车的时间还不如等红绿灯多，所以就还好，也不打算解除限速，只是品牌上也仔细看，后来选了绿源，目前大部分还是雅迪，爱玛，台羚，绿源，小牛等，路上看的话还是雅迪比较多，不过价格也比较贵一点，还有就是小牛了，是比较新兴的品牌，手机 App 什么的做得比较好，而且也比较贵，最后以相对比较便宜的价格买了个锂电 48V24AH 的小车子，后来发现还是有点不方便的点就是没有比较大的筐，也不好装，这样就是下雨天雨衣什么的比较不方便放。

    -

    聊回来主题上牌这个事情，这个事情也是颇费心力，提车的时候店里的让我跟他早上一起去，但是因为不确定时间，也比较远就没跟着去，因为我是线上买的，线下自提，线下的店可能没啥利润可以拿，就不肯帮忙代上牌，朋友说在线下店里买是可以代上的，自己上牌过程也比较曲折，一开始是头盔没到，然后是等开发票，主要的东西就是需要骑着车子去车管所，不能只自己去，然后需要预约，附近比较近的都是提前一周就预约完了号了，要提前在支付宝上进行预约，比较空的就是店里推荐的景区大队，但是随之而来就是比较蛋疼的，这个景区大队太远了，看下骑车距离有十几公里，所以就有点拖延症，但是总归要上的，不然一直不能开是白买了，上牌的材料主要是车辆合格证，发票，然后车子上的浙品码，在车架上和电池上，然后车架号什么的都要跟合格证上完全对应，整体车子要跟合格证上一毛一样，如果有额外的反光镜，后面副座都需要拆掉，脚踏板要装上，到了那其实还比较顺利，就是十几公里外加那天比较冷，吹得头疼。

    -]]>     - - 生活 - - - 生活 - -     - - 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

    + 用netty实现一个简单的http server + /2024/09/08/%E7%94%A8netty%E5%AE%9E%E7%8E%B0%E4%B8%80%E4%B8%AA%E7%AE%80%E5%8D%95%E7%9A%84http-server/ + netty是java网络框架中非常有名，因为它把各种网络类型，阻塞非阻塞的都做了上层的抽象，非常值得学习，这边就先以一个简单的http server来做下实践，
    主体的server代码

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

            // boss 线程组
            EventLoopGroup boss = new NioEventLoopGroup();
            // worker 线程组
            EventLoopGroup work = new NioEventLoopGroup();
            try {
                // 服务端启动类
                ServerBootstrap bootstrap = new ServerBootstrap();
                // 绑定线程组
                bootstrap.group(boss, work)
                        // 设置服务端的tcp连接的最大排队连接数
                        .option(ChannelOption.SO_BACKLOG, 1024)
                        // 设置日志处理器
                        .handler(new LoggingHandler(LogLevel.DEBUG))
                        // 绑定Channel
                        .channel(NioServerSocketChannel.class)
                        // 实际的逻辑handler
                        .childHandler(new HttpServerInitializer());

                // 绑定端口
                ChannelFuture f = bootstrap.bind(new InetSocketAddress(8082)).sync();
                System.out.println("server start up on port 8080");
                f.channel().closeFuture().sync();
            } finally {
                boss.shutdownGracefully();
                work.shutdownGracefully();
            }
        }
    +

    然后来看下 HttpServerInitializer

    +
    public class HttpServerInitializer extends ChannelInitializer<SocketChannel> {
        @Override
        protected void initChannel(SocketChannel socketChannel) throws Exception {
            ChannelPipeline pipeline = socketChannel.pipeline();
            // 在这个Channel的pipeline上添加处理器
            // 前三个都是netty提供的
            pipeline.addLast(new HttpServerCodec());
            pipeline.addLast(new HttpContentCompressor((CompressionOptions[]) null ));
            pipeline.addLast(new HttpServerExpectContinueHandler());
            // 这个是我们自己实现的逻辑
            pipeline.addLast(new SimpleHttpServerHandler());
        }
    }
    +

    实际的代码也是很简略，我们用一个hello world统一响应所有的请求

    +
    public class SimpleHttpServerHandler extends SimpleChannelInboundHandler<HttpObject> {
        private static final byte[] CONTENT = { 'H', 'e', 'l', 'l', 'o', ' ', 'W', 'o', 'r', 'l', 'd' };

        @Override
        public void channelReadComplete(ChannelHandlerContext ctx) {
            // 刷写响应
            ctx.flush();
        }

        @Override
        protected void channelRead0(ChannelHandlerContext channelHandlerContext, HttpObject msg) throws Exception {
            // 先是从这个Channel读取到内容
            System.out.println("channel read");
            if (msg instanceof HttpRequest) {
                // 判断是否为HttpRequest，这个其实依赖于前面netty自带的编码器
                HttpRequest httpRequest = (HttpRequest) msg;
                boolean keepAlive = HttpUtil.isKeepAlive(httpRequest);

                // 包装http响应
                FullHttpResponse response = new DefaultFullHttpResponse(httpRequest.protocolVersion(), OK, Unpooled.wrappedBuffer(CONTENT));

                // 设置请求头
                response.headers()
                        .set(CONTENT_TYPE, TEXT_PLAIN)
                        .setInt(CONTENT_LENGTH, response.content().readableBytes());

                // 设置是否要连接保活
                if (keepAlive) {
                    if (!httpRequest.protocolVersion().isKeepAliveDefault()) {
                        response.headers().set(CONNECTION, KEEP_ALIVE);
                    }
                } else {
                    response.headers().set(CONNECTION, CLOSE);
                }

                // 写入channelFuture
                ChannelFuture future = channelHandlerContext.write(response);

                // 如果不保活，添加监听器
                if (!keepAlive) {
                    future.addListener(ChannelFutureListener.CLOSE);
                }
            }
        }

        @Override
        public void exceptionCaught(ChannelHandlerContext ctx, Throwable cause) {
            cause.printStackTrace();
            ctx.close();
        }
    }
    +

    上面就是这个简单的示例了，只是我在 channelRead0 入口建了个打印，但是这个再接收请求时会打印两次，可以作为个探讨题，我们下次来分析下

    ]]>     - 算法 + Java - 算法 + Java     @@ -5761,142 +5760,54 @@ user3: - 用netty实现一个简单的http server-深入理解下 - /2024/09/15/%E7%94%A8netty%E5%AE%9E%E7%8E%B0%E4%B8%80%E4%B8%AA%E7%AE%80%E5%8D%95%E7%9A%84http-server-%E6%B7%B1%E5%85%A5%E7%90%86%E8%A7%A3%E4%B8%8B/ - 上次用netty写的一个玩具http server，发现了一个问题，为啥channelRead0方法会被调用两次，这里我们来研究下
    我们在收到http请求的时候需要经过的一个必要的过程就是编解码，而这里我们用的是 io.netty.handler.codec.http.HttpServerCodec

    -
    pipeline.addLast(new HttpServerCodec());
    -

    它继承了 io.netty.channel.CombinedChannelDuplexHandler

    -
    public HttpServerCodec(HttpDecoderConfig config) {
        this.queue = new ArrayDeque();
        this.init(new HttpServerRequestDecoder(config), new HttpServerResponseEncoder());
    }
    -

    在初始化的时候调用了 io.netty.channel.CombinedChannelDuplexHandler#init
    传入参数

    -
    public HttpServerCodec(HttpDecoderConfig config) {
        this.queue = new ArrayDeque();
        this.init(new HttpServerRequestDecoder(config), new HttpServerResponseEncoder());
    }
    -

    这个 HttpServerRequestDecoder

    -
    private final class HttpServerRequestDecoder extends HttpRequestDecoder {
        HttpServerRequestDecoder(HttpDecoderConfig config) {
            super(config);
        }

        protected void decode(ChannelHandlerContext ctx, ByteBuf buffer, List<Object> out) throws Exception {
            int oldSize = out.size();
            super.decode(ctx, buffer, out);
            int size = out.size();

            for(int i = oldSize; i < size; ++i) {
                Object obj = out.get(i);
                if (obj instanceof HttpRequest) {
                    HttpServerCodec.this.queue.add(((HttpRequest)obj).method());
                }
            }

        }
    -

    这个decode方法就会调用到父类的decode方法 io.netty.handler.codec.http.HttpObjectDecoder#decode

    -
    protected void decode(ChannelHandlerContext ctx, ByteBuf buffer, List<Object> out) throws Exception {
            if (this.resetRequested.get()) {
                this.resetNow();
            }

            int toRead;
            int toRead;
            ByteBuf line;
            switch (this.currentState) {
                case SKIP_CONTROL_CHARS:
                case READ_INITIAL:
                    try {
                        line = this.lineParser.parse(buffer);
                        if (line == null) {
                            return;
                        }

                        String[] initialLine = this.splitInitialLine(line);

                        assert initialLine.length == 3 : "initialLine::length must be 3";

                        this.message = this.createMessage(initialLine);
                        this.currentState = HttpObjectDecoder.State.READ_HEADER;
                    } catch (Exception var9) {
                        out.add(this.invalidMessage(this.message, buffer, var9));
                        return;
                    }
                case READ_HEADER:
                    try {
                        State nextState = this.readHeaders(buffer);
                        if (nextState == null) {
                            return;
                        }

                        this.currentState = nextState;
                        switch (nextState) {
                            case SKIP_CONTROL_CHARS:
                                this.addCurrentMessage(out);
                                out.add(LastHttpContent.EMPTY_LAST_CONTENT);
                                this.resetNow();
                                return;
                            case READ_CHUNK_SIZE:
                                if (!this.chunkedSupported) {
                                    throw new IllegalArgumentException("Chunked messages not supported");
                                }

                                this.addCurrentMessage(out);
                                return;
                            default:
                                if (this.contentLength == 0L || this.contentLength == -1L && this.isDecodingRequest()) {
                                    this.addCurrentMessage(out);
                                    out.add(LastHttpContent.EMPTY_LAST_CONTENT);
                                    this.resetNow();
                                    return;
                                }

                                assert nextState == HttpObjectDecoder.State.READ_FIXED_LENGTH_CONTENT || nextState == HttpObjectDecoder.State.READ_VARIABLE_LENGTH_CONTENT;

                                this.addCurrentMessage(out);
                                if (nextState == HttpObjectDecoder.State.READ_FIXED_LENGTH_CONTENT) {
                                    this.chunkSize = this.contentLength;
                                }

                                return;
                        }
                    } catch (Exception var10) {
                        out.add(this.invalidMessage(this.message, buffer, var10));
                        return;
                    }
                case READ_CHUNK_SIZE:
                    try {
                        line = this.lineParser.parse(buffer);
                        if (line == null) {
                            return;
                        }

                        toRead = getChunkSize(line.array(), line.arrayOffset() + line.readerIndex(), line.readableBytes());
                        this.chunkSize = (long)toRead;
                        if (toRead == 0) {
                            this.currentState = HttpObjectDecoder.State.READ_CHUNK_FOOTER;
                            return;
                        }

                        this.currentState = HttpObjectDecoder.State.READ_CHUNKED_CONTENT;
                    } catch (Exception var8) {
                        out.add(this.invalidChunk(buffer, var8));
                        return;
                    }
                case READ_CHUNKED_CONTENT:
                    assert this.chunkSize <= 2147483647L;

                    toRead = Math.min((int)this.chunkSize, this.maxChunkSize);
                    if (!this.allowPartialChunks && buffer.readableBytes() < toRead) {
                        return;
                    }

                    toRead = Math.min(toRead, buffer.readableBytes());
                    if (toRead == 0) {
                        return;
                    }

                    HttpContent chunk = new DefaultHttpContent(buffer.readRetainedSlice(toRead));
                    this.chunkSize -= (long)toRead;
                    out.add(chunk);
                    if (this.chunkSize != 0L) {
                        return;
                    }

                    this.currentState = HttpObjectDecoder.State.READ_CHUNK_DELIMITER;
                case READ_CHUNK_DELIMITER:
                    toRead = buffer.writerIndex();
                    toRead = buffer.readerIndex();

                    while(toRead > toRead) {
                        byte next = buffer.getByte(toRead++);
                        if (next == 10) {
                            this.currentState = HttpObjectDecoder.State.READ_CHUNK_SIZE;
                            break;
                        }
                    }

                    buffer.readerIndex(toRead);
                    return;
                case READ_VARIABLE_LENGTH_CONTENT:
                    toRead = Math.min(buffer.readableBytes(), this.maxChunkSize);
                    if (toRead > 0) {
                        ByteBuf content = buffer.readRetainedSlice(toRead);
                        out.add(new DefaultHttpContent(content));
                    }

                    return;
                case READ_FIXED_LENGTH_CONTENT:
                    toRead = buffer.readableBytes();
                    if (toRead == 0) {
                        return;
                    }

                    toRead = Math.min(toRead, this.maxChunkSize);
                    if ((long)toRead > this.chunkSize) {
                        toRead = (int)this.chunkSize;
                    }

                    ByteBuf content = buffer.readRetainedSlice(toRead);
                    this.chunkSize -= (long)toRead;
                    if (this.chunkSize == 0L) {
                        out.add(new DefaultLastHttpContent(content, this.trailersFactory));
                        this.resetNow();
                    } else {
                        out.add(new DefaultHttpContent(content));
                    }

                    return;
                case READ_CHUNK_FOOTER:
                    try {
                        LastHttpContent trailer = this.readTrailingHeaders(buffer);
                        if (trailer == null) {
                            return;
                        }

                        out.add(trailer);
                        this.resetNow();
                        return;
                    } catch (Exception var7) {
                        out.add(this.invalidChunk(buffer, var7));
                        return;
                    }
                case BAD_MESSAGE:
                    buffer.skipBytes(buffer.readableBytes());
                    break;
                case UPGRADED:
                    toRead = buffer.readableBytes();
                    if (toRead > 0) {
                        out.add(buffer.readBytes(toRead));
                    }
            }

        }
    -

    这个方法里会做实际的byte转换成message或者说string的转换
    而在我们把消息转换完成，

    会在最后加一个 io.netty.handler.codec.http.LastHttpContent#EMPTY_LAST_CONTENT
    这样其实我的message就会变成两条，也就是为啥channelRead0会被调用两次，这样的目的也是让我的业务代码可以分辨是否请求已经读完，并且可以在读完以后有一些其他操作空间
    可以看到第一次调用channelRead0的msg

    第二次就是

    -]]>     - - Java - - - Java - -     - - 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 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 - -     - - 用netty实现一个简单的http server - /2024/09/08/%E7%94%A8netty%E5%AE%9E%E7%8E%B0%E4%B8%80%E4%B8%AA%E7%AE%80%E5%8D%95%E7%9A%84http-server/ - netty是java网络框架中非常有名，因为它把各种网络类型，阻塞非阻塞的都做了上层的抽象，非常值得学习，这边就先以一个简单的http server来做下实践，
    主体的server代码

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

            // boss 线程组
            EventLoopGroup boss = new NioEventLoopGroup();
            // worker 线程组
            EventLoopGroup work = new NioEventLoopGroup();
            try {
                // 服务端启动类
                ServerBootstrap bootstrap = new ServerBootstrap();
                // 绑定线程组
                bootstrap.group(boss, work)
                        // 设置服务端的tcp连接的最大排队连接数
                        .option(ChannelOption.SO_BACKLOG, 1024)
                        // 设置日志处理器
                        .handler(new LoggingHandler(LogLevel.DEBUG))
                        // 绑定Channel
                        .channel(NioServerSocketChannel.class)
                        // 实际的逻辑handler
                        .childHandler(new HttpServerInitializer());

                // 绑定端口
                ChannelFuture f = bootstrap.bind(new InetSocketAddress(8082)).sync();
                System.out.println("server start up on port 8080");
                f.channel().closeFuture().sync();
            } finally {
                boss.shutdownGracefully();
                work.shutdownGracefully();
            }
        }
    -

    然后来看下 HttpServerInitializer

    -
    public class HttpServerInitializer extends ChannelInitializer<SocketChannel> {
        @Override
        protected void initChannel(SocketChannel socketChannel) throws Exception {
            ChannelPipeline pipeline = socketChannel.pipeline();
            // 在这个Channel的pipeline上添加处理器
            // 前三个都是netty提供的
            pipeline.addLast(new HttpServerCodec());
            pipeline.addLast(new HttpContentCompressor((CompressionOptions[]) null ));
            pipeline.addLast(new HttpServerExpectContinueHandler());
            // 这个是我们自己实现的逻辑
            pipeline.addLast(new SimpleHttpServerHandler());
        }
    }
    -

    实际的代码也是很简略，我们用一个hello world统一响应所有的请求

    -
    public class SimpleHttpServerHandler extends SimpleChannelInboundHandler<HttpObject> {
        private static final byte[] CONTENT = { 'H', 'e', 'l', 'l', 'o', ' ', 'W', 'o', 'r', 'l', 'd' };

        @Override
        public void channelReadComplete(ChannelHandlerContext ctx) {
            // 刷写响应
            ctx.flush();
        }

        @Override
        protected void channelRead0(ChannelHandlerContext channelHandlerContext, HttpObject msg) throws Exception {
            // 先是从这个Channel读取到内容
            System.out.println("channel read");
            if (msg instanceof HttpRequest) {
                // 判断是否为HttpRequest，这个其实依赖于前面netty自带的编码器
                HttpRequest httpRequest = (HttpRequest) msg;
                boolean keepAlive = HttpUtil.isKeepAlive(httpRequest);

                // 包装http响应
                FullHttpResponse response = new DefaultFullHttpResponse(httpRequest.protocolVersion(), OK, Unpooled.wrappedBuffer(CONTENT));

                // 设置请求头
                response.headers()
                        .set(CONTENT_TYPE, TEXT_PLAIN)
                        .setInt(CONTENT_LENGTH, response.content().readableBytes());

                // 设置是否要连接保活
                if (keepAlive) {
                    if (!httpRequest.protocolVersion().isKeepAliveDefault()) {
                        response.headers().set(CONNECTION, KEEP_ALIVE);
                    }
                } else {
                    response.headers().set(CONNECTION, CLOSE);
                }

                // 写入channelFuture
                ChannelFuture future = channelHandlerContext.write(response);

                // 如果不保活，添加监听器
                if (!keepAlive) {
                    future.addListener(ChannelFutureListener.CLOSE);
                }
            }
        }

        @Override
        public void exceptionCaught(ChannelHandlerContext ctx, Throwable cause) {
            cause.printStackTrace();
            ctx.close();
        }
    }
    -

    上面就是这个简单的示例了，只是我在 channelRead0 入口建了个打印，但是这个再接收请求时会打印两次，可以作为个探讨题，我们下次来分析下

    + 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 @@ -5905,6 +5816,34 @@ user3: Java     + + 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 RocketMQ 的 DefaultMQPushConsumer 源码 /2020/06/26/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84-Consumer/ @@ -6056,41 +5995,6 @@ user3:

    第一种方法是为SIGBUS信号安装一个信号处理程序，然后在处理程序中简单地调用返回。通过这样做，写系统调用会返回它在被打断之前所写的字节数，并将errno设置为成功。让我指出，这将是一个糟糕的解决方案，一个治标不治本的解决方案。因为SIGBUS预示着进程出了严重的问题，所以不鼓励使用这种解决方案。

    第二个解决方案涉及内核的文件租赁（在Windows中称为 “机会锁”）。这是解决这个问题的正确方法。通过在文件描述符上使用租赁，你与内核在一个特定的文件上达成租约。然后你可以向内核请求一个读/写租约。当另一个进程试图截断你正在传输的文件时，内核会向你发送一个实时信号，即RT_SIGNAL_LEASE信号。它告诉你内核即将终止你对该文件的写或读租约。在你的程序访问一个无效的地址和被SIGBUS信号杀死之前，你的写调用会被打断了。写入调用的返回值是中断前写入的字节数，errno将被设置为成功。下面是一些示例代码，显示了如何从内核中获得租约。

    if(fcntl(fd, F_SETSIG, RT_SIGNAL_LEASE) == -1) {
        perror("kernel lease set signal");
        return -1;
    }
    /* l_type can be F_RDLCK F_WRLCK */
    if(fcntl(fd, F_SETLEASE, l_type)){
        perror("kernel lease set type");
        return -1;
    }
    -]]> - - MQ - RocketMQ - 消息队列 - - - MQ - 消息队列 - RocketMQ - -     - - 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/ - ConsumeQueue 其实是定位到一个 topic 下的消息在 CommitLog 下的偏移量，它也是固定大小的

    -
    // ConsumeQueue file size,default is 30W
    private int mapedFileSizeConsumeQueue = 300000 * ConsumeQueue.CQ_STORE_UNIT_SIZE;

    public static final int CQ_STORE_UNIT_SIZE = 20;
    - -

    所以文件大小是5.7M 左右

    -

    5udpag

    -

    ConsumeQueue 的构建是通过org.apache.rocketmq.store.DefaultMessageStore.ReputMessageService运行后的 doReput 方法，而启动是的 reputFromOffset 则是通过org.apache.rocketmq.store.DefaultMessageStore#start中下面代码设置并启动

    -
    log.info("[SetReputOffset] maxPhysicalPosInLogicQueue={} clMinOffset={} clMaxOffset={} clConfirmedOffset={}",
                    maxPhysicalPosInLogicQueue, this.commitLog.getMinOffset(), this.commitLog.getMaxOffset(), this.commitLog.getConfirmOffset());
                this.reputMessageService.setReputFromOffset(maxPhysicalPosInLogicQueue);
                this.reputMessageService.start();
    - -

    看一下 doReput 的逻辑

    -
    private void doReput() {
                if (this.reputFromOffset < DefaultMessageStore.this.commitLog.getMinOffset()) {
                    log.warn("The reputFromOffset={} is smaller than minPyOffset={}, this usually indicate that the dispatch behind too much and the commitlog has expired.",
                        this.reputFromOffset, DefaultMessageStore.this.commitLog.getMinOffset());
                    this.reputFromOffset = DefaultMessageStore.this.commitLog.getMinOffset();
                }
                for (boolean doNext = true; this.isCommitLogAvailable() && doNext; ) {

                    if (DefaultMessageStore.this.getMessageStoreConfig().isDuplicationEnable()
                        && this.reputFromOffset >= DefaultMessageStore.this.getConfirmOffset()) {
                        break;
                    }

                  // 根据偏移量获取消息
                    SelectMappedBufferResult result = DefaultMessageStore.this.commitLog.getData(reputFromOffset);
                    if (result != null) {
                        try {
                            this.reputFromOffset = result.getStartOffset();

                            for (int readSize = 0; readSize < result.getSize() && doNext; ) {
                              // 消息校验和转换
                                DispatchRequest dispatchRequest =
                                    DefaultMessageStore.this.commitLog.checkMessageAndReturnSize(result.getByteBuffer(), false, false);
                                int size = dispatchRequest.getBufferSize() == -1 ? dispatchRequest.getMsgSize() : dispatchRequest.getBufferSize();

                                if (dispatchRequest.isSuccess()) {
                                    if (size > 0) {
                                      // 进行分发处理，包括 ConsumeQueue 和 IndexFile
                                        DefaultMessageStore.this.doDispatch(dispatchRequest);

                                        if (BrokerRole.SLAVE != DefaultMessageStore.this.getMessageStoreConfig().getBrokerRole()
                                            && DefaultMessageStore.this.brokerConfig.isLongPollingEnable()) {
                                            DefaultMessageStore.this.messageArrivingListener.arriving(dispatchRequest.getTopic(),
                                                dispatchRequest.getQueueId(), dispatchRequest.getConsumeQueueOffset() + 1,
                                                dispatchRequest.getTagsCode(), dispatchRequest.getStoreTimestamp(),
                                                dispatchRequest.getBitMap(), dispatchRequest.getPropertiesMap());
                                        }

                                        this.reputFromOffset += size;
                                        readSize += size;
                                        if (DefaultMessageStore.this.getMessageStoreConfig().getBrokerRole() == BrokerRole.SLAVE) {
                                            DefaultMessageStore.this.storeStatsService
                                                .getSinglePutMessageTopicTimesTotal(dispatchRequest.getTopic()).incrementAndGet();
                                            DefaultMessageStore.this.storeStatsService
                                                .getSinglePutMessageTopicSizeTotal(dispatchRequest.getTopic())
                                                .addAndGet(dispatchRequest.getMsgSize());
                                        }
                                    } else if (size == 0) {
                                        this.reputFromOffset = DefaultMessageStore.this.commitLog.rollNextFile(this.reputFromOffset);
                                        readSize = result.getSize();
                                    }
                                } else if (!dispatchRequest.isSuccess()) {

                                    if (size > 0) {
                                        log.error("[BUG]read total count not equals msg total size. reputFromOffset={}", reputFromOffset);
                                        this.reputFromOffset += size;
                                    } else {
                                        doNext = false;
                                        // If user open the dledger pattern or the broker is master node,
                                        // it will not ignore the exception and fix the reputFromOffset variable
                                        if (DefaultMessageStore.this.getMessageStoreConfig().isEnableDLegerCommitLog() ||
                                            DefaultMessageStore.this.brokerConfig.getBrokerId() == MixAll.MASTER_ID) {
                                            log.error("[BUG]dispatch message to consume queue error, COMMITLOG OFFSET: {}",
                                                this.reputFromOffset);
                                            this.reputFromOffset += result.getSize() - readSize;
                                        }
                                    }
                                }
                            }
                        } finally {
                            result.release();
                        }
                    } else {
                        doNext = false;
                    }
                }
            }
    - -

    分发的逻辑看到这

    -
        class CommitLogDispatcherBuildConsumeQueue implements CommitLogDispatcher {

            @Override
            public void dispatch(DispatchRequest request) {
                final int tranType = MessageSysFlag.getTransactionValue(request.getSysFlag());
                switch (tranType) {
                    case MessageSysFlag.TRANSACTION_NOT_TYPE:
                    case MessageSysFlag.TRANSACTION_COMMIT_TYPE:
                        DefaultMessageStore.this.putMessagePositionInfo(request);
                        break;
                    case MessageSysFlag.TRANSACTION_PREPARED_TYPE:
                    case MessageSysFlag.TRANSACTION_ROLLBACK_TYPE:
                        break;
                }
            }
        }
    public void putMessagePositionInfo(DispatchRequest dispatchRequest) {
            ConsumeQueue cq = this.findConsumeQueue(dispatchRequest.getTopic(), dispatchRequest.getQueueId());
            cq.putMessagePositionInfoWrapper(dispatchRequest);
        }
    - -

    真正存储的是在这

    -
    private boolean putMessagePositionInfo(final long offset, final int size, final long tagsCode,
        final long cqOffset) {

        if (offset + size <= this.maxPhysicOffset) {
            log.warn("Maybe try to build consume queue repeatedly maxPhysicOffset={} phyOffset={}", maxPhysicOffset, offset);
            return true;
        }

        this.byteBufferIndex.flip();
        this.byteBufferIndex.limit(CQ_STORE_UNIT_SIZE);
        this.byteBufferIndex.putLong(offset);
        this.byteBufferIndex.putInt(size);
        this.byteBufferIndex.putLong(tagsCode);
    - -

    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 @@ -6205,6 +6109,73 @@ user3: RocketMQ     + + 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 + +     聊一下 SpringBoot 中使用的 cglib 作为动态代理中的一个注意点 /2021/09/19/%E8%81%8A%E4%B8%80%E4%B8%8B-SpringBoot-%E4%B8%AD%E4%BD%BF%E7%94%A8%E7%9A%84-cglib-%E4%BD%9C%E4%B8%BA%E5%8A%A8%E6%80%81%E4%BB%A3%E7%90%86%E4%B8%AD%E7%9A%84%E4%B8%80%E4%B8%AA%E6%B3%A8%E6%84%8F%E7%82%B9/ @@ -6247,34 +6218,6 @@ user3: 事务 - - 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 SpringBoot 中动态切换数据源的方法 /2021/09/26/%E8%81%8A%E4%B8%80%E4%B8%8B-SpringBoot-%E4%B8%AD%E5%8A%A8%E6%80%81%E5%88%87%E6%8D%A2%E6%95%B0%E6%8D%AE%E6%BA%90%E7%9A%84%E6%96%B9%E6%B3%95/ @@ -6294,34 +6237,91 @@ user3:

    这边可以看到就是通过动态地通过putDatabaseType设置lookupKey来进行数据源切换，要通过接口注解配置来进行设置的话，我们就需要一个注解

    @Retention(RetentionPolicy.RUNTIME)
    @Target(ElementType.METHOD)
    public @interface DataSource {
        String value();
    }
    -

    这个注解可以配置在我的接口方法上，比如这样

    -
    public interface StudentService {

        @DataSource("master1")
        public Student queryOne();

        @DataSource("master2")
        public Student queryAnother();

    }
    +

    这个注解可以配置在我的接口方法上，比如这样

    +
    public interface StudentService {

        @DataSource("master1")
        public Student queryOne();

        @DataSource("master2")
        public Student queryAnother();

    }
    + +

    通过切面来进行数据源的设置

    +
    @Aspect
    @Component
    @Order(-1)
    public class DataSourceAspect {

        @Pointcut("execution(* com.nicksxs.springdemo.service..*.*(..))")
        public void pointCut() {

        }


        @Before("pointCut()")
        public void before(JoinPoint point)
        {
            Object target = point.getTarget();
            System.out.println(target.toString());
            String method = point.getSignature().getName();
            System.out.println(method);
            Class<?>[] classz = target.getClass().getInterfaces();
            Class<?>[] parameterTypes = ((MethodSignature) point.getSignature())
                    .getMethod().getParameterTypes();
            try {
                Method m = classz[0].getMethod(method, parameterTypes);
                System.out.println("method"+ m.getName());
                if (m.isAnnotationPresent(DataSource.class)) {
                    DataSource data = m.getAnnotation(DataSource.class);
                    System.out.println("dataSource："+data.value());
                    DatabaseContextHolder.putDatabaseType(DatabaseType.getDatabaseType(data.value()));
                }

            } catch (Exception e) {
                e.printStackTrace();
            }
        }

        @After("pointCut()")
        public void after() {
    				DatabaseContextHolder.clearDatabaseType();
        }
    }
    + +

    通过接口判断是否带有注解跟是注解的值，DatabaseType 的配置不太好，不过先忽略了，然后在切点后进行清理

    +

    这是我 master1 的数据，

    +

    +

    master2 的数据

    +

    +

    然后跑一下简单的 demo，

    +
    @Override
    public void run(String...args) {
    	LOGGER.info("run here");
    	System.out.println(studentService.queryOne());
    	System.out.println(studentService.queryAnother());

    }
    + +

    看一下运行结果

    +

    +

    其实这个方法应用场景不止可以用来迁移数据库，还能实现精细化的读写数据源分离之类的，算是做个简单记录和分享。

    +]]> + + Java + SpringBoot + + + Java + Spring + SpringBoot + Druid + 数据源动态切换 + +     + + 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/ + ConsumeQueue 其实是定位到一个 topic 下的消息在 CommitLog 下的偏移量，它也是固定大小的

    +
    // ConsumeQueue file size,default is 30W
    private int mapedFileSizeConsumeQueue = 300000 * ConsumeQueue.CQ_STORE_UNIT_SIZE;

    public static final int CQ_STORE_UNIT_SIZE = 20;
    + +

    所以文件大小是5.7M 左右

    +

    5udpag

    +

    ConsumeQueue 的构建是通过org.apache.rocketmq.store.DefaultMessageStore.ReputMessageService运行后的 doReput 方法，而启动是的 reputFromOffset 则是通过org.apache.rocketmq.store.DefaultMessageStore#start中下面代码设置并启动

    +
    log.info("[SetReputOffset] maxPhysicalPosInLogicQueue={} clMinOffset={} clMaxOffset={} clConfirmedOffset={}",
                    maxPhysicalPosInLogicQueue, this.commitLog.getMinOffset(), this.commitLog.getMaxOffset(), this.commitLog.getConfirmOffset());
                this.reputMessageService.setReputFromOffset(maxPhysicalPosInLogicQueue);
                this.reputMessageService.start();
    + +

    看一下 doReput 的逻辑

    +
    private void doReput() {
                if (this.reputFromOffset < DefaultMessageStore.this.commitLog.getMinOffset()) {
                    log.warn("The reputFromOffset={} is smaller than minPyOffset={}, this usually indicate that the dispatch behind too much and the commitlog has expired.",
                        this.reputFromOffset, DefaultMessageStore.this.commitLog.getMinOffset());
                    this.reputFromOffset = DefaultMessageStore.this.commitLog.getMinOffset();
                }
                for (boolean doNext = true; this.isCommitLogAvailable() && doNext; ) {

                    if (DefaultMessageStore.this.getMessageStoreConfig().isDuplicationEnable()
                        && this.reputFromOffset >= DefaultMessageStore.this.getConfirmOffset()) {
                        break;
                    }

                  // 根据偏移量获取消息
                    SelectMappedBufferResult result = DefaultMessageStore.this.commitLog.getData(reputFromOffset);
                    if (result != null) {
                        try {
                            this.reputFromOffset = result.getStartOffset();

                            for (int readSize = 0; readSize < result.getSize() && doNext; ) {
                              // 消息校验和转换
                                DispatchRequest dispatchRequest =
                                    DefaultMessageStore.this.commitLog.checkMessageAndReturnSize(result.getByteBuffer(), false, false);
                                int size = dispatchRequest.getBufferSize() == -1 ? dispatchRequest.getMsgSize() : dispatchRequest.getBufferSize();

                                if (dispatchRequest.isSuccess()) {
                                    if (size > 0) {
                                      // 进行分发处理，包括 ConsumeQueue 和 IndexFile
                                        DefaultMessageStore.this.doDispatch(dispatchRequest);

                                        if (BrokerRole.SLAVE != DefaultMessageStore.this.getMessageStoreConfig().getBrokerRole()
                                            && DefaultMessageStore.this.brokerConfig.isLongPollingEnable()) {
                                            DefaultMessageStore.this.messageArrivingListener.arriving(dispatchRequest.getTopic(),
                                                dispatchRequest.getQueueId(), dispatchRequest.getConsumeQueueOffset() + 1,
                                                dispatchRequest.getTagsCode(), dispatchRequest.getStoreTimestamp(),
                                                dispatchRequest.getBitMap(), dispatchRequest.getPropertiesMap());
                                        }

                                        this.reputFromOffset += size;
                                        readSize += size;
                                        if (DefaultMessageStore.this.getMessageStoreConfig().getBrokerRole() == BrokerRole.SLAVE) {
                                            DefaultMessageStore.this.storeStatsService
                                                .getSinglePutMessageTopicTimesTotal(dispatchRequest.getTopic()).incrementAndGet();
                                            DefaultMessageStore.this.storeStatsService
                                                .getSinglePutMessageTopicSizeTotal(dispatchRequest.getTopic())
                                                .addAndGet(dispatchRequest.getMsgSize());
                                        }
                                    } else if (size == 0) {
                                        this.reputFromOffset = DefaultMessageStore.this.commitLog.rollNextFile(this.reputFromOffset);
                                        readSize = result.getSize();
                                    }
                                } else if (!dispatchRequest.isSuccess()) {

                                    if (size > 0) {
                                        log.error("[BUG]read total count not equals msg total size. reputFromOffset={}", reputFromOffset);
                                        this.reputFromOffset += size;
                                    } else {
                                        doNext = false;
                                        // If user open the dledger pattern or the broker is master node,
                                        // it will not ignore the exception and fix the reputFromOffset variable
                                        if (DefaultMessageStore.this.getMessageStoreConfig().isEnableDLegerCommitLog() ||
                                            DefaultMessageStore.this.brokerConfig.getBrokerId() == MixAll.MASTER_ID) {
                                            log.error("[BUG]dispatch message to consume queue error, COMMITLOG OFFSET: {}",
                                                this.reputFromOffset);
                                            this.reputFromOffset += result.getSize() - readSize;
                                        }
                                    }
                                }
                            }
                        } finally {
                            result.release();
                        }
                    } else {
                        doNext = false;
                    }
                }
            }
    -

    通过切面来进行数据源的设置

    -
    @Aspect
    @Component
    @Order(-1)
    public class DataSourceAspect {

        @Pointcut("execution(* com.nicksxs.springdemo.service..*.*(..))")
        public void pointCut() {

        }


        @Before("pointCut()")
        public void before(JoinPoint point)
        {
            Object target = point.getTarget();
            System.out.println(target.toString());
            String method = point.getSignature().getName();
            System.out.println(method);
            Class<?>[] classz = target.getClass().getInterfaces();
            Class<?>[] parameterTypes = ((MethodSignature) point.getSignature())
                    .getMethod().getParameterTypes();
            try {
                Method m = classz[0].getMethod(method, parameterTypes);
                System.out.println("method"+ m.getName());
                if (m.isAnnotationPresent(DataSource.class)) {
                    DataSource data = m.getAnnotation(DataSource.class);
                    System.out.println("dataSource："+data.value());
                    DatabaseContextHolder.putDatabaseType(DatabaseType.getDatabaseType(data.value()));
                }

            } catch (Exception e) {
                e.printStackTrace();
            }
        }

        @After("pointCut()")
        public void after() {
    				DatabaseContextHolder.clearDatabaseType();
        }
    }
    +

    分发的逻辑看到这

    +
        class CommitLogDispatcherBuildConsumeQueue implements CommitLogDispatcher {

            @Override
            public void dispatch(DispatchRequest request) {
                final int tranType = MessageSysFlag.getTransactionValue(request.getSysFlag());
                switch (tranType) {
                    case MessageSysFlag.TRANSACTION_NOT_TYPE:
                    case MessageSysFlag.TRANSACTION_COMMIT_TYPE:
                        DefaultMessageStore.this.putMessagePositionInfo(request);
                        break;
                    case MessageSysFlag.TRANSACTION_PREPARED_TYPE:
                    case MessageSysFlag.TRANSACTION_ROLLBACK_TYPE:
                        break;
                }
            }
        }
    public void putMessagePositionInfo(DispatchRequest dispatchRequest) {
            ConsumeQueue cq = this.findConsumeQueue(dispatchRequest.getTopic(), dispatchRequest.getQueueId());
            cq.putMessagePositionInfoWrapper(dispatchRequest);
        }
    -

    通过接口判断是否带有注解跟是注解的值，DatabaseType 的配置不太好，不过先忽略了，然后在切点后进行清理

    -

    这是我 master1 的数据，

    -

    -

    master2 的数据

    -

    -

    然后跑一下简单的 demo，

    -
    @Override
    public void run(String...args) {
    	LOGGER.info("run here");
    	System.out.println(studentService.queryOne());
    	System.out.println(studentService.queryAnother());

    }
    +

    真正存储的是在这

    +
    private boolean putMessagePositionInfo(final long offset, final int size, final long tagsCode,
        final long cqOffset) {

        if (offset + size <= this.maxPhysicOffset) {
            log.warn("Maybe try to build consume queue repeatedly maxPhysicOffset={} phyOffset={}", maxPhysicOffset, offset);
            return true;
        }

        this.byteBufferIndex.flip();
        this.byteBufferIndex.limit(CQ_STORE_UNIT_SIZE);
        this.byteBufferIndex.putLong(offset);
        this.byteBufferIndex.putInt(size);
        this.byteBufferIndex.putLong(tagsCode);
    -

    看一下运行结果

    -

    -

    其实这个方法应用场景不止可以用来迁移数据库，还能实现精细化的读写数据源分离之类的，算是做个简单记录和分享。

    +

    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 + +     + + 用netty实现一个简单的http server-深入理解下 + /2024/09/15/%E7%94%A8netty%E5%AE%9E%E7%8E%B0%E4%B8%80%E4%B8%AA%E7%AE%80%E5%8D%95%E7%9A%84http-server-%E6%B7%B1%E5%85%A5%E7%90%86%E8%A7%A3%E4%B8%8B/ + 上次用netty写的一个玩具http server，发现了一个问题，为啥channelRead0方法会被调用两次，这里我们来研究下
    我们在收到http请求的时候需要经过的一个必要的过程就是编解码，而这里我们用的是 io.netty.handler.codec.http.HttpServerCodec

    +
    pipeline.addLast(new HttpServerCodec());
    +

    它继承了 io.netty.channel.CombinedChannelDuplexHandler

    +
    public HttpServerCodec(HttpDecoderConfig config) {
        this.queue = new ArrayDeque();
        this.init(new HttpServerRequestDecoder(config), new HttpServerResponseEncoder());
    }
    +

    在初始化的时候调用了 io.netty.channel.CombinedChannelDuplexHandler#init
    传入参数

    +
    public HttpServerCodec(HttpDecoderConfig config) {
        this.queue = new ArrayDeque();
        this.init(new HttpServerRequestDecoder(config), new HttpServerResponseEncoder());
    }
    +

    这个 HttpServerRequestDecoder

    +
    private final class HttpServerRequestDecoder extends HttpRequestDecoder {
        HttpServerRequestDecoder(HttpDecoderConfig config) {
            super(config);
        }

        protected void decode(ChannelHandlerContext ctx, ByteBuf buffer, List<Object> out) throws Exception {
            int oldSize = out.size();
            super.decode(ctx, buffer, out);
            int size = out.size();

            for(int i = oldSize; i < size; ++i) {
                Object obj = out.get(i);
                if (obj instanceof HttpRequest) {
                    HttpServerCodec.this.queue.add(((HttpRequest)obj).method());
                }
            }

        }
    +

    这个decode方法就会调用到父类的decode方法 io.netty.handler.codec.http.HttpObjectDecoder#decode

    +
    protected void decode(ChannelHandlerContext ctx, ByteBuf buffer, List<Object> out) throws Exception {
            if (this.resetRequested.get()) {
                this.resetNow();
            }

            int toRead;
            int toRead;
            ByteBuf line;
            switch (this.currentState) {
                case SKIP_CONTROL_CHARS:
                case READ_INITIAL:
                    try {
                        line = this.lineParser.parse(buffer);
                        if (line == null) {
                            return;
                        }

                        String[] initialLine = this.splitInitialLine(line);

                        assert initialLine.length == 3 : "initialLine::length must be 3";

                        this.message = this.createMessage(initialLine);
                        this.currentState = HttpObjectDecoder.State.READ_HEADER;
                    } catch (Exception var9) {
                        out.add(this.invalidMessage(this.message, buffer, var9));
                        return;
                    }
                case READ_HEADER:
                    try {
                        State nextState = this.readHeaders(buffer);
                        if (nextState == null) {
                            return;
                        }

                        this.currentState = nextState;
                        switch (nextState) {
                            case SKIP_CONTROL_CHARS:
                                this.addCurrentMessage(out);
                                out.add(LastHttpContent.EMPTY_LAST_CONTENT);
                                this.resetNow();
                                return;
                            case READ_CHUNK_SIZE:
                                if (!this.chunkedSupported) {
                                    throw new IllegalArgumentException("Chunked messages not supported");
                                }

                                this.addCurrentMessage(out);
                                return;
                            default:
                                if (this.contentLength == 0L || this.contentLength == -1L && this.isDecodingRequest()) {
                                    this.addCurrentMessage(out);
                                    out.add(LastHttpContent.EMPTY_LAST_CONTENT);
                                    this.resetNow();
                                    return;
                                }

                                assert nextState == HttpObjectDecoder.State.READ_FIXED_LENGTH_CONTENT || nextState == HttpObjectDecoder.State.READ_VARIABLE_LENGTH_CONTENT;

                                this.addCurrentMessage(out);
                                if (nextState == HttpObjectDecoder.State.READ_FIXED_LENGTH_CONTENT) {
                                    this.chunkSize = this.contentLength;
                                }

                                return;
                        }
                    } catch (Exception var10) {
                        out.add(this.invalidMessage(this.message, buffer, var10));
                        return;
                    }
                case READ_CHUNK_SIZE:
                    try {
                        line = this.lineParser.parse(buffer);
                        if (line == null) {
                            return;
                        }

                        toRead = getChunkSize(line.array(), line.arrayOffset() + line.readerIndex(), line.readableBytes());
                        this.chunkSize = (long)toRead;
                        if (toRead == 0) {
                            this.currentState = HttpObjectDecoder.State.READ_CHUNK_FOOTER;
                            return;
                        }

                        this.currentState = HttpObjectDecoder.State.READ_CHUNKED_CONTENT;
                    } catch (Exception var8) {
                        out.add(this.invalidChunk(buffer, var8));
                        return;
                    }
                case READ_CHUNKED_CONTENT:
                    assert this.chunkSize <= 2147483647L;

                    toRead = Math.min((int)this.chunkSize, this.maxChunkSize);
                    if (!this.allowPartialChunks && buffer.readableBytes() < toRead) {
                        return;
                    }

                    toRead = Math.min(toRead, buffer.readableBytes());
                    if (toRead == 0) {
                        return;
                    }

                    HttpContent chunk = new DefaultHttpContent(buffer.readRetainedSlice(toRead));
                    this.chunkSize -= (long)toRead;
                    out.add(chunk);
                    if (this.chunkSize != 0L) {
                        return;
                    }

                    this.currentState = HttpObjectDecoder.State.READ_CHUNK_DELIMITER;
                case READ_CHUNK_DELIMITER:
                    toRead = buffer.writerIndex();
                    toRead = buffer.readerIndex();

                    while(toRead > toRead) {
                        byte next = buffer.getByte(toRead++);
                        if (next == 10) {
                            this.currentState = HttpObjectDecoder.State.READ_CHUNK_SIZE;
                            break;
                        }
                    }

                    buffer.readerIndex(toRead);
                    return;
                case READ_VARIABLE_LENGTH_CONTENT:
                    toRead = Math.min(buffer.readableBytes(), this.maxChunkSize);
                    if (toRead > 0) {
                        ByteBuf content = buffer.readRetainedSlice(toRead);
                        out.add(new DefaultHttpContent(content));
                    }

                    return;
                case READ_FIXED_LENGTH_CONTENT:
                    toRead = buffer.readableBytes();
                    if (toRead == 0) {
                        return;
                    }

                    toRead = Math.min(toRead, this.maxChunkSize);
                    if ((long)toRead > this.chunkSize) {
                        toRead = (int)this.chunkSize;
                    }

                    ByteBuf content = buffer.readRetainedSlice(toRead);
                    this.chunkSize -= (long)toRead;
                    if (this.chunkSize == 0L) {
                        out.add(new DefaultLastHttpContent(content, this.trailersFactory));
                        this.resetNow();
                    } else {
                        out.add(new DefaultHttpContent(content));
                    }

                    return;
                case READ_CHUNK_FOOTER:
                    try {
                        LastHttpContent trailer = this.readTrailingHeaders(buffer);
                        if (trailer == null) {
                            return;
                        }

                        out.add(trailer);
                        this.resetNow();
                        return;
                    } catch (Exception var7) {
                        out.add(this.invalidChunk(buffer, var7));
                        return;
                    }
                case BAD_MESSAGE:
                    buffer.skipBytes(buffer.readableBytes());
                    break;
                case UPGRADED:
                    toRead = buffer.readableBytes();
                    if (toRead > 0) {
                        out.add(buffer.readBytes(toRead));
                    }
            }

        }
    +

    这个方法里会做实际的byte转换成message或者说string的转换
    而在我们把消息转换完成，

    会在最后加一个 io.netty.handler.codec.http.LastHttpContent#EMPTY_LAST_CONTENT
    这样其实我的message就会变成两条，也就是为啥channelRead0会被调用两次，这样的目的也是让我的业务代码可以分辨是否请求已经读完，并且可以在读完以后有一些其他操作空间
    可以看到第一次调用channelRead0的msg

    第二次就是

    ]]>     Java - SpringBoot Java - Spring - SpringBoot - Druid - 数据源动态切换     @@ -6363,27 +6363,103 @@ 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

    + 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

    +

    ]]>     - java + 网络 - java + http     @@ -6582,6 +6658,30 @@ user3: 自旋 + + 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 + +     聊聊 Java 中绕不开的 Synchronized 关键字 /2021/06/20/%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/ @@ -6606,18 +6706,6 @@ user3: 自旋 - - 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

    -]]>     - - linux - - - linux - -     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/ @@ -6675,6 +6763,31 @@ user3: 类加载 + + 聊聊 Java 自带的那些*逆天*工具 + /2020/08/02/%E8%81%8A%E8%81%8A-Java-%E8%87%AA%E5%B8%A6%E7%9A%84%E9%82%A3%E4%BA%9B%E9%80%86%E5%A4%A9%E5%B7%A5%E5%85%B7/ + 原谅我的标题党，其实这些工具的确很厉害，之前其实介绍过一点相关的，是从我一次问题排查的过程中用到的，但是最近又有碰到一次排查问题，发现其实用 idea 直接 dump thread 是不现实的，毕竟服务器环境的没法这么操作，那就得用 Java 的那些工具了

    +

    jstack & jps

    譬如 jstack，这个命令其实不能更简单了
    看看 help 信息

    用-l参数可以打出锁的额外信息，然后后面的 pid 就是进程 id 咯，机智的小伙伴会问了（就你这个小白才问这么蠢的问题🤦‍♂️），怎么看 Java 应用的进程呢
    那就是 jps 了，命令也很简单，一般直接用 jps命令就好了，不过也可以 help 看一下

    稍微解释下，-q是只显示进程 id，-m是输出给main 方法的参数，比如我在配置中加给参数

    然后用 jps -m查看

    -v加上小 v 的话就是打印 jvm 参数

    还是有点东西，然后就继续介绍 jstack 了，然后我们看看 jstack 出来是啥，为了加点内容我加了个死锁

    +
    public static void main(String[] args) throws InterruptedException {
            SpringApplication.run(ThreadDumpDemoApplication.class, args);
            ReentrantLock lock1 = new ReentrantLock();
            ReentrantLock lock2 = new ReentrantLock();
            Thread t1 = new Thread() {
                @Override
                public void run() {
                    try {
                        lock1.lock();
                        TimeUnit.SECONDS.sleep(1);
                        lock2.lock();
                    } catch (InterruptedException e) {
                        e.printStackTrace();
                    }
                }
            };
            Thread t2 = new Thread() {
                @Override
                public void run() {
                    try {
                        lock2.lock();
                        TimeUnit.SECONDS.sleep(1);
                        lock1.lock();
                    } catch (InterruptedException e) {
                        e.printStackTrace();
                    }
                }
            };
            t1.setName("mythread1");
            t2.setName("mythread2");
            t1.start();
            t2.start();
            Thread.sleep(10000);
        }
    +

    然后看看出来时怎么样的

    +
    2020-08-02 21:50:32
    Full thread dump Java HotSpot(TM) 64-Bit Server VM (25.201-b09 mixed mode):

    "DestroyJavaVM" #147 prio=5 os_prio=31 tid=0x00007fc9dd807000 nid=0x2603 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "mythread2" #140 prio=5 os_prio=31 tid=0x00007fc9dd877000 nid=0x9903 waiting on condition [0x0000700006fb9000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4330> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$2.run(ThreadDumpDemoApplication.java:34)

       Locked ownable synchronizers:
            - <0x000000076f5d4360> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)

    "mythread1" #139 prio=5 os_prio=31 tid=0x00007fc9de873800 nid=0x9a03 waiting on condition [0x0000700006eb6000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4360> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$1.run(ThreadDumpDemoApplication.java:22)

       Locked ownable synchronizers:
            - <0x000000076f5d4330> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)

    "http-nio-8080-Acceptor" #137 daemon prio=5 os_prio=31 tid=0x00007fc9de1ac000 nid=0x9b03 runnable [0x0000700006db3000]
       java.lang.Thread.State: RUNNABLE
            at sun.nio.ch.ServerSocketChannelImpl.accept0(Native Method)
            at sun.nio.ch.ServerSocketChannelImpl.accept(ServerSocketChannelImpl.java:422)
            at sun.nio.ch.ServerSocketChannelImpl.accept(ServerSocketChannelImpl.java:250)
            - locked <0x000000076f1e4820> (a java.lang.Object)
            at org.apache.tomcat.util.net.NioEndpoint.serverSocketAccept(NioEndpoint.java:469)
            at org.apache.tomcat.util.net.NioEndpoint.serverSocketAccept(NioEndpoint.java:71)
            at org.apache.tomcat.util.net.Acceptor.run(Acceptor.java:95)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-ClientPoller" #136 daemon prio=5 os_prio=31 tid=0x00007fc9dd876800 nid=0x6503 runnable [0x0000700006cb0000]
       java.lang.Thread.State: RUNNABLE
            at sun.nio.ch.KQueueArrayWrapper.kevent0(Native Method)
            at sun.nio.ch.KQueueArrayWrapper.poll(KQueueArrayWrapper.java:198)
            at sun.nio.ch.KQueueSelectorImpl.doSelect(KQueueSelectorImpl.java:117)
            at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86)
            - locked <0x000000076f2978c8> (a sun.nio.ch.Util$3)
            - locked <0x000000076f2978b8> (a java.util.Collections$UnmodifiableSet)
            - locked <0x000000076f297798> (a sun.nio.ch.KQueueSelectorImpl)
            at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)
            at org.apache.tomcat.util.net.NioEndpoint$Poller.run(NioEndpoint.java:709)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-10" #135 daemon prio=5 os_prio=31 tid=0x00007fc9de1af000 nid=0x9d03 waiting on condition [0x0000700006bad000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-9" #134 daemon prio=5 os_prio=31 tid=0x00007fc9de1ab800 nid=0x6403 waiting on condition [0x0000700006aaa000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-8" #133 daemon prio=5 os_prio=31 tid=0x00007fc9de873000 nid=0x9f03 waiting on condition [0x00007000069a7000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-7" #132 daemon prio=5 os_prio=31 tid=0x00007fc9df0a1800 nid=0xa103 waiting on condition [0x00007000068a4000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-6" #131 daemon prio=5 os_prio=31 tid=0x00007fc9df242800 nid=0x6103 waiting on condition [0x00007000067a1000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-5" #130 daemon prio=5 os_prio=31 tid=0x00007fc9de872000 nid=0x5f03 waiting on condition [0x000070000669e000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-4" #129 daemon prio=5 os_prio=31 tid=0x00007fc9de1a6000 nid=0x5e03 waiting on condition [0x000070000659b000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-3" #128 daemon prio=5 os_prio=31 tid=0x00007fc9de871800 nid=0x5c03 waiting on condition [0x0000700006498000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-2" #127 daemon prio=5 os_prio=31 tid=0x00007fc9dead9000 nid=0x5b03 waiting on condition [0x0000700006395000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-1" #126 daemon prio=5 os_prio=31 tid=0x00007fc9ddb00000 nid=0x5a03 waiting on condition [0x0000700006292000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-BlockPoller" #125 daemon prio=5 os_prio=31 tid=0x00007fc9df242000 nid=0xa503 runnable [0x000070000618f000]
       java.lang.Thread.State: RUNNABLE
            at sun.nio.ch.KQueueArrayWrapper.kevent0(Native Method)
            at sun.nio.ch.KQueueArrayWrapper.poll(KQueueArrayWrapper.java:198)
            at sun.nio.ch.KQueueSelectorImpl.doSelect(KQueueSelectorImpl.java:117)
            at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86)
            - locked <0x000000076f1eea30> (a sun.nio.ch.Util$3)
            - locked <0x000000076f1ee198> (a java.util.Collections$UnmodifiableSet)
            - locked <0x000000076f1ee010> (a sun.nio.ch.KQueueSelectorImpl)
            at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)
            at org.apache.tomcat.util.net.NioBlockingSelector$BlockPoller.run(NioBlockingSelector.java:313)

       Locked ownable synchronizers:
            - None

    "container-0" #124 prio=5 os_prio=31 tid=0x00007fc9df06a000 nid=0x5803 waiting on condition [0x000070000608c000]
       java.lang.Thread.State: TIMED_WAITING (sleeping)
            at java.lang.Thread.sleep(Native Method)
            at org.apache.catalina.core.StandardServer.await(StandardServer.java:570)
            at org.springframework.boot.web.embedded.tomcat.TomcatWebServer$1.run(TomcatWebServer.java:197)

       Locked ownable synchronizers:
            - None

    "Catalina-utility-2" #123 prio=1 os_prio=31 tid=0x00007fc9de886000 nid=0xa80f waiting on condition [0x0000700005f89000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076c88ab58> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1088)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "Catalina-utility-1" #122 prio=1 os_prio=31 tid=0x00007fc9de884000 nid=0x5667 waiting on condition [0x0000700005e86000]
       java.lang.Thread.State: TIMED_WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076c88ab58> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1093)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "RMI Scheduler(0)" #15 daemon prio=5 os_prio=31 tid=0x00007fc9de9ee000 nid=0x5503 waiting on condition [0x0000700005d83000]
       java.lang.Thread.State: TIMED_WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x00000006c0015410> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1093)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "Attach Listener" #13 daemon prio=9 os_prio=31 tid=0x00007fc9df149800 nid=0x3c07 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "RMI TCP Accept-0" #11 daemon prio=5 os_prio=31 tid=0x00007fc9df100000 nid=0x4003 runnable [0x0000700005977000]
       java.lang.Thread.State: RUNNABLE
            at java.net.PlainSocketImpl.socketAccept(Native Method)
            at java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:409)
            at java.net.ServerSocket.implAccept(ServerSocket.java:545)
            at java.net.ServerSocket.accept(ServerSocket.java:513)
            at sun.management.jmxremote.LocalRMIServerSocketFactory$1.accept(LocalRMIServerSocketFactory.java:52)
            at sun.rmi.transport.tcp.TCPTransport$AcceptLoop.executeAcceptLoop(TCPTransport.java:405)
            at sun.rmi.transport.tcp.TCPTransport$AcceptLoop.run(TCPTransport.java:377)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "Service Thread" #9 daemon prio=9 os_prio=31 tid=0x00007fc9df0ce800 nid=0x4103 runnable [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "C1 CompilerThread2" #8 daemon prio=9 os_prio=31 tid=0x00007fc9df0ce000 nid=0x4203 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "C2 CompilerThread1" #7 daemon prio=9 os_prio=31 tid=0x00007fc9de0a3800 nid=0x3503 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "C2 CompilerThread0" #6 daemon prio=9 os_prio=31 tid=0x00007fc9de89b000 nid=0x3403 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "Monitor Ctrl-Break" #5 daemon prio=5 os_prio=31 tid=0x00007fc9df0ca000 nid=0x3303 runnable [0x0000700005468000]
       java.lang.Thread.State: RUNNABLE
            at java.net.SocketInputStream.socketRead0(Native Method)
            at java.net.SocketInputStream.socketRead(SocketInputStream.java:116)
            at java.net.SocketInputStream.read(SocketInputStream.java:171)
            at java.net.SocketInputStream.read(SocketInputStream.java:141)
            at sun.nio.cs.StreamDecoder.readBytes(StreamDecoder.java:284)
            at sun.nio.cs.StreamDecoder.implRead(StreamDecoder.java:326)
            at sun.nio.cs.StreamDecoder.read(StreamDecoder.java:178)
            - locked <0x00000006c001b760> (a java.io.InputStreamReader)
            at java.io.InputStreamReader.read(InputStreamReader.java:184)
            at java.io.BufferedReader.fill(BufferedReader.java:161)
            at java.io.BufferedReader.readLine(BufferedReader.java:324)
            - locked <0x00000006c001b760> (a java.io.InputStreamReader)
            at java.io.BufferedReader.readLine(BufferedReader.java:389)
            at com.intellij.rt.execution.application.AppMainV2$1.run(AppMainV2.java:64)

       Locked ownable synchronizers:
            - None

    "Signal Dispatcher" #4 daemon prio=9 os_prio=31 tid=0x00007fc9de824000 nid=0x4503 runnable [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "Finalizer" #3 daemon prio=8 os_prio=31 tid=0x00007fc9dd811800 nid=0x4f03 in Object.wait() [0x0000700005262000]
       java.lang.Thread.State: WAITING (on object monitor)
            at java.lang.Object.wait(Native Method)
            - waiting on <0x00000006c0008348> (a java.lang.ref.ReferenceQueue$Lock)
            at java.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:144)
            - locked <0x00000006c0008348> (a java.lang.ref.ReferenceQueue$Lock)
            at java.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:165)
            at java.lang.ref.Finalizer$FinalizerThread.run(Finalizer.java:216)

       Locked ownable synchronizers:
            - None

    "Reference Handler" #2 daemon prio=10 os_prio=31 tid=0x00007fc9de02a000 nid=0x5003 in Object.wait() [0x000070000515f000]
       java.lang.Thread.State: WAITING (on object monitor)
            at java.lang.Object.wait(Native Method)
            - waiting on <0x00000006c001b940> (a java.lang.ref.Reference$Lock)
            at java.lang.Object.wait(Object.java:502)
            at java.lang.ref.Reference.tryHandlePending(Reference.java:191)
            - locked <0x00000006c001b940> (a java.lang.ref.Reference$Lock)
            at java.lang.ref.Reference$ReferenceHandler.run(Reference.java:153)

       Locked ownable synchronizers:
            - None

    "VM Thread" os_prio=31 tid=0x00007fc9df00b800 nid=0x2c03 runnable 

    "GC task thread#0 (ParallelGC)" os_prio=31 tid=0x00007fc9de805000 nid=0x1e07 runnable 

    "GC task thread#1 (ParallelGC)" os_prio=31 tid=0x00007fc9de003800 nid=0x2a03 runnable 

    "GC task thread#2 (ParallelGC)" os_prio=31 tid=0x00007fc9df002000 nid=0x5403 runnable 

    "GC task thread#3 (ParallelGC)" os_prio=31 tid=0x00007fc9df002800 nid=0x5203 runnable 

    "VM Periodic Task Thread" os_prio=31 tid=0x00007fc9df11a800 nid=0x3a03 waiting on condition 

    JNI global references: 1087


    Found one Java-level deadlock:
    =============================
    "mythread2":
      waiting for ownable synchronizer 0x000000076f5d4330, (a java.util.concurrent.locks.ReentrantLock$NonfairSync),
      which is held by "mythread1"
    "mythread1":
      waiting for ownable synchronizer 0x000000076f5d4360, (a java.util.concurrent.locks.ReentrantLock$NonfairSync),
      which is held by "mythread2"

    Java stack information for the threads listed above:
    ===================================================
    "mythread2":
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4330> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$2.run(ThreadDumpDemoApplication.java:34)
    "mythread1":
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4360> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$1.run(ThreadDumpDemoApplication.java:22)

    Found 1 deadlock.
    +

    前面的信息其实上次就看过了，后面就可以发现有个死锁了，

    上面比较长，把主要的截出来，就是这边的，这点就很强大了。

    +

    jmap

    惯例还是看一下帮助信息

    这个相对命令比较多，不过因为现在 dump 下来我们可能会用文件模式，然后将文件下载下来使用 mat 进行分析，所以可以使用
    jmap -dump:live,format=b,file=heap.bin <pid>
    命令照着上面看的就是打印活着的对象，然后以二进制格式，文件名叫 heap.bin 然后最后就是进程 id，打印出来以后可以用 mat 打开

    这样就可以很清晰的看到应用里的各种信息，jmap 直接在命令中还可以看很多信息，比如使用jmap -histo <pid>打印对象的实例数和对象占用的内存

    jmap -finalizerinfo <pid> 打印正在等候回收的对象

    +

    小tips

    对于一些应用内存已经占满了，jstack 和 jmap 可能会连不上的情况，可以使用-F参数强制打印线程或者 dump 文件，但是要注意这两者使用的用户必须与 java 进程启动用户一致，并且使用的 jdk 也要一致

    +]]>     + + Java + Thread dump + 工具 + 问题排查 + + + Java + JPS + JStack + JMap + +     聊聊 Java 的类加载机制二 /2021/06/13/%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%E4%BA%8C/ @@ -6693,43 +6806,18 @@ user3:

    破坏双亲委派

    关于破坏双亲委派模型，第一次是在 JDK1.2 之后引入了双亲委派模型之前，那么在那之前已经有了类加载器，所以java.lang.ClassLoader 中添加了一个 protected 方法 findClass()，并引导用户编写的类加载逻辑时尽可能去重写这个方法，而不是在 loadClass()中编写代码。这个跟上面的逻辑其实类似，当父类加载失败，会调用 findClass()来完成加载；第二次是因为这个模型本身还有一些不足之处，比如 SPI 这种，所以有设计了线程下上下文类加载器(Thread Context ClassLoader)。这个类加载器可以通过 java.lang.Thread 类的 java.lang.Thread#setContextClassLoader() 进行设置，然后第三种是为了追求程序动态性，这里有涉及到了 osgi 等概念，就不展开了

    ]]> - Java - - - Java - 类加载 - 加载 - 验证 - 准备 - 解析 - 初始化 - 链接 - 双亲委派 - -     - - 聊聊 Java 自带的那些*逆天*工具 - /2020/08/02/%E8%81%8A%E8%81%8A-Java-%E8%87%AA%E5%B8%A6%E7%9A%84%E9%82%A3%E4%BA%9B%E9%80%86%E5%A4%A9%E5%B7%A5%E5%85%B7/ - 原谅我的标题党，其实这些工具的确很厉害，之前其实介绍过一点相关的，是从我一次问题排查的过程中用到的，但是最近又有碰到一次排查问题，发现其实用 idea 直接 dump thread 是不现实的，毕竟服务器环境的没法这么操作，那就得用 Java 的那些工具了

    -

    jstack & jps

    譬如 jstack，这个命令其实不能更简单了
    看看 help 信息

    用-l参数可以打出锁的额外信息，然后后面的 pid 就是进程 id 咯，机智的小伙伴会问了（就你这个小白才问这么蠢的问题🤦‍♂️），怎么看 Java 应用的进程呢
    那就是 jps 了，命令也很简单，一般直接用 jps命令就好了，不过也可以 help 看一下

    稍微解释下，-q是只显示进程 id，-m是输出给main 方法的参数，比如我在配置中加给参数

    然后用 jps -m查看

    -v加上小 v 的话就是打印 jvm 参数

    还是有点东西，然后就继续介绍 jstack 了，然后我们看看 jstack 出来是啥，为了加点内容我加了个死锁

    -
    public static void main(String[] args) throws InterruptedException {
            SpringApplication.run(ThreadDumpDemoApplication.class, args);
            ReentrantLock lock1 = new ReentrantLock();
            ReentrantLock lock2 = new ReentrantLock();
            Thread t1 = new Thread() {
                @Override
                public void run() {
                    try {
                        lock1.lock();
                        TimeUnit.SECONDS.sleep(1);
                        lock2.lock();
                    } catch (InterruptedException e) {
                        e.printStackTrace();
                    }
                }
            };
            Thread t2 = new Thread() {
                @Override
                public void run() {
                    try {
                        lock2.lock();
                        TimeUnit.SECONDS.sleep(1);
                        lock1.lock();
                    } catch (InterruptedException e) {
                        e.printStackTrace();
                    }
                }
            };
            t1.setName("mythread1");
            t2.setName("mythread2");
            t1.start();
            t2.start();
            Thread.sleep(10000);
        }
    -

    然后看看出来时怎么样的

    -
    2020-08-02 21:50:32
    Full thread dump Java HotSpot(TM) 64-Bit Server VM (25.201-b09 mixed mode):

    "DestroyJavaVM" #147 prio=5 os_prio=31 tid=0x00007fc9dd807000 nid=0x2603 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "mythread2" #140 prio=5 os_prio=31 tid=0x00007fc9dd877000 nid=0x9903 waiting on condition [0x0000700006fb9000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4330> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$2.run(ThreadDumpDemoApplication.java:34)

       Locked ownable synchronizers:
            - <0x000000076f5d4360> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)

    "mythread1" #139 prio=5 os_prio=31 tid=0x00007fc9de873800 nid=0x9a03 waiting on condition [0x0000700006eb6000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4360> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$1.run(ThreadDumpDemoApplication.java:22)

       Locked ownable synchronizers:
            - <0x000000076f5d4330> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)

    "http-nio-8080-Acceptor" #137 daemon prio=5 os_prio=31 tid=0x00007fc9de1ac000 nid=0x9b03 runnable [0x0000700006db3000]
       java.lang.Thread.State: RUNNABLE
            at sun.nio.ch.ServerSocketChannelImpl.accept0(Native Method)
            at sun.nio.ch.ServerSocketChannelImpl.accept(ServerSocketChannelImpl.java:422)
            at sun.nio.ch.ServerSocketChannelImpl.accept(ServerSocketChannelImpl.java:250)
            - locked <0x000000076f1e4820> (a java.lang.Object)
            at org.apache.tomcat.util.net.NioEndpoint.serverSocketAccept(NioEndpoint.java:469)
            at org.apache.tomcat.util.net.NioEndpoint.serverSocketAccept(NioEndpoint.java:71)
            at org.apache.tomcat.util.net.Acceptor.run(Acceptor.java:95)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-ClientPoller" #136 daemon prio=5 os_prio=31 tid=0x00007fc9dd876800 nid=0x6503 runnable [0x0000700006cb0000]
       java.lang.Thread.State: RUNNABLE
            at sun.nio.ch.KQueueArrayWrapper.kevent0(Native Method)
            at sun.nio.ch.KQueueArrayWrapper.poll(KQueueArrayWrapper.java:198)
            at sun.nio.ch.KQueueSelectorImpl.doSelect(KQueueSelectorImpl.java:117)
            at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86)
            - locked <0x000000076f2978c8> (a sun.nio.ch.Util$3)
            - locked <0x000000076f2978b8> (a java.util.Collections$UnmodifiableSet)
            - locked <0x000000076f297798> (a sun.nio.ch.KQueueSelectorImpl)
            at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)
            at org.apache.tomcat.util.net.NioEndpoint$Poller.run(NioEndpoint.java:709)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-10" #135 daemon prio=5 os_prio=31 tid=0x00007fc9de1af000 nid=0x9d03 waiting on condition [0x0000700006bad000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-9" #134 daemon prio=5 os_prio=31 tid=0x00007fc9de1ab800 nid=0x6403 waiting on condition [0x0000700006aaa000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-8" #133 daemon prio=5 os_prio=31 tid=0x00007fc9de873000 nid=0x9f03 waiting on condition [0x00007000069a7000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-7" #132 daemon prio=5 os_prio=31 tid=0x00007fc9df0a1800 nid=0xa103 waiting on condition [0x00007000068a4000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-6" #131 daemon prio=5 os_prio=31 tid=0x00007fc9df242800 nid=0x6103 waiting on condition [0x00007000067a1000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-5" #130 daemon prio=5 os_prio=31 tid=0x00007fc9de872000 nid=0x5f03 waiting on condition [0x000070000669e000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-4" #129 daemon prio=5 os_prio=31 tid=0x00007fc9de1a6000 nid=0x5e03 waiting on condition [0x000070000659b000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-3" #128 daemon prio=5 os_prio=31 tid=0x00007fc9de871800 nid=0x5c03 waiting on condition [0x0000700006498000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-2" #127 daemon prio=5 os_prio=31 tid=0x00007fc9dead9000 nid=0x5b03 waiting on condition [0x0000700006395000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-exec-1" #126 daemon prio=5 os_prio=31 tid=0x00007fc9ddb00000 nid=0x5a03 waiting on condition [0x0000700006292000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f26aa00> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:107)
            at org.apache.tomcat.util.threads.TaskQueue.take(TaskQueue.java:33)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "http-nio-8080-BlockPoller" #125 daemon prio=5 os_prio=31 tid=0x00007fc9df242000 nid=0xa503 runnable [0x000070000618f000]
       java.lang.Thread.State: RUNNABLE
            at sun.nio.ch.KQueueArrayWrapper.kevent0(Native Method)
            at sun.nio.ch.KQueueArrayWrapper.poll(KQueueArrayWrapper.java:198)
            at sun.nio.ch.KQueueSelectorImpl.doSelect(KQueueSelectorImpl.java:117)
            at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86)
            - locked <0x000000076f1eea30> (a sun.nio.ch.Util$3)
            - locked <0x000000076f1ee198> (a java.util.Collections$UnmodifiableSet)
            - locked <0x000000076f1ee010> (a sun.nio.ch.KQueueSelectorImpl)
            at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)
            at org.apache.tomcat.util.net.NioBlockingSelector$BlockPoller.run(NioBlockingSelector.java:313)

       Locked ownable synchronizers:
            - None

    "container-0" #124 prio=5 os_prio=31 tid=0x00007fc9df06a000 nid=0x5803 waiting on condition [0x000070000608c000]
       java.lang.Thread.State: TIMED_WAITING (sleeping)
            at java.lang.Thread.sleep(Native Method)
            at org.apache.catalina.core.StandardServer.await(StandardServer.java:570)
            at org.springframework.boot.web.embedded.tomcat.TomcatWebServer$1.run(TomcatWebServer.java:197)

       Locked ownable synchronizers:
            - None

    "Catalina-utility-2" #123 prio=1 os_prio=31 tid=0x00007fc9de886000 nid=0xa80f waiting on condition [0x0000700005f89000]
       java.lang.Thread.State: WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076c88ab58> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1088)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "Catalina-utility-1" #122 prio=1 os_prio=31 tid=0x00007fc9de884000 nid=0x5667 waiting on condition [0x0000700005e86000]
       java.lang.Thread.State: TIMED_WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076c88ab58> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1093)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "RMI Scheduler(0)" #15 daemon prio=5 os_prio=31 tid=0x00007fc9de9ee000 nid=0x5503 waiting on condition [0x0000700005d83000]
       java.lang.Thread.State: TIMED_WAITING (parking)
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x00000006c0015410> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
            at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1093)
            at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)
            at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)
            at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)
            at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "Attach Listener" #13 daemon prio=9 os_prio=31 tid=0x00007fc9df149800 nid=0x3c07 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "RMI TCP Accept-0" #11 daemon prio=5 os_prio=31 tid=0x00007fc9df100000 nid=0x4003 runnable [0x0000700005977000]
       java.lang.Thread.State: RUNNABLE
            at java.net.PlainSocketImpl.socketAccept(Native Method)
            at java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:409)
            at java.net.ServerSocket.implAccept(ServerSocket.java:545)
            at java.net.ServerSocket.accept(ServerSocket.java:513)
            at sun.management.jmxremote.LocalRMIServerSocketFactory$1.accept(LocalRMIServerSocketFactory.java:52)
            at sun.rmi.transport.tcp.TCPTransport$AcceptLoop.executeAcceptLoop(TCPTransport.java:405)
            at sun.rmi.transport.tcp.TCPTransport$AcceptLoop.run(TCPTransport.java:377)
            at java.lang.Thread.run(Thread.java:748)

       Locked ownable synchronizers:
            - None

    "Service Thread" #9 daemon prio=9 os_prio=31 tid=0x00007fc9df0ce800 nid=0x4103 runnable [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "C1 CompilerThread2" #8 daemon prio=9 os_prio=31 tid=0x00007fc9df0ce000 nid=0x4203 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "C2 CompilerThread1" #7 daemon prio=9 os_prio=31 tid=0x00007fc9de0a3800 nid=0x3503 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "C2 CompilerThread0" #6 daemon prio=9 os_prio=31 tid=0x00007fc9de89b000 nid=0x3403 waiting on condition [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "Monitor Ctrl-Break" #5 daemon prio=5 os_prio=31 tid=0x00007fc9df0ca000 nid=0x3303 runnable [0x0000700005468000]
       java.lang.Thread.State: RUNNABLE
            at java.net.SocketInputStream.socketRead0(Native Method)
            at java.net.SocketInputStream.socketRead(SocketInputStream.java:116)
            at java.net.SocketInputStream.read(SocketInputStream.java:171)
            at java.net.SocketInputStream.read(SocketInputStream.java:141)
            at sun.nio.cs.StreamDecoder.readBytes(StreamDecoder.java:284)
            at sun.nio.cs.StreamDecoder.implRead(StreamDecoder.java:326)
            at sun.nio.cs.StreamDecoder.read(StreamDecoder.java:178)
            - locked <0x00000006c001b760> (a java.io.InputStreamReader)
            at java.io.InputStreamReader.read(InputStreamReader.java:184)
            at java.io.BufferedReader.fill(BufferedReader.java:161)
            at java.io.BufferedReader.readLine(BufferedReader.java:324)
            - locked <0x00000006c001b760> (a java.io.InputStreamReader)
            at java.io.BufferedReader.readLine(BufferedReader.java:389)
            at com.intellij.rt.execution.application.AppMainV2$1.run(AppMainV2.java:64)

       Locked ownable synchronizers:
            - None

    "Signal Dispatcher" #4 daemon prio=9 os_prio=31 tid=0x00007fc9de824000 nid=0x4503 runnable [0x0000000000000000]
       java.lang.Thread.State: RUNNABLE

       Locked ownable synchronizers:
            - None

    "Finalizer" #3 daemon prio=8 os_prio=31 tid=0x00007fc9dd811800 nid=0x4f03 in Object.wait() [0x0000700005262000]
       java.lang.Thread.State: WAITING (on object monitor)
            at java.lang.Object.wait(Native Method)
            - waiting on <0x00000006c0008348> (a java.lang.ref.ReferenceQueue$Lock)
            at java.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:144)
            - locked <0x00000006c0008348> (a java.lang.ref.ReferenceQueue$Lock)
            at java.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:165)
            at java.lang.ref.Finalizer$FinalizerThread.run(Finalizer.java:216)

       Locked ownable synchronizers:
            - None

    "Reference Handler" #2 daemon prio=10 os_prio=31 tid=0x00007fc9de02a000 nid=0x5003 in Object.wait() [0x000070000515f000]
       java.lang.Thread.State: WAITING (on object monitor)
            at java.lang.Object.wait(Native Method)
            - waiting on <0x00000006c001b940> (a java.lang.ref.Reference$Lock)
            at java.lang.Object.wait(Object.java:502)
            at java.lang.ref.Reference.tryHandlePending(Reference.java:191)
            - locked <0x00000006c001b940> (a java.lang.ref.Reference$Lock)
            at java.lang.ref.Reference$ReferenceHandler.run(Reference.java:153)

       Locked ownable synchronizers:
            - None

    "VM Thread" os_prio=31 tid=0x00007fc9df00b800 nid=0x2c03 runnable 

    "GC task thread#0 (ParallelGC)" os_prio=31 tid=0x00007fc9de805000 nid=0x1e07 runnable 

    "GC task thread#1 (ParallelGC)" os_prio=31 tid=0x00007fc9de003800 nid=0x2a03 runnable 

    "GC task thread#2 (ParallelGC)" os_prio=31 tid=0x00007fc9df002000 nid=0x5403 runnable 

    "GC task thread#3 (ParallelGC)" os_prio=31 tid=0x00007fc9df002800 nid=0x5203 runnable 

    "VM Periodic Task Thread" os_prio=31 tid=0x00007fc9df11a800 nid=0x3a03 waiting on condition 

    JNI global references: 1087


    Found one Java-level deadlock:
    =============================
    "mythread2":
      waiting for ownable synchronizer 0x000000076f5d4330, (a java.util.concurrent.locks.ReentrantLock$NonfairSync),
      which is held by "mythread1"
    "mythread1":
      waiting for ownable synchronizer 0x000000076f5d4360, (a java.util.concurrent.locks.ReentrantLock$NonfairSync),
      which is held by "mythread2"

    Java stack information for the threads listed above:
    ===================================================
    "mythread2":
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4330> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$2.run(ThreadDumpDemoApplication.java:34)
    "mythread1":
            at sun.misc.Unsafe.park(Native Method)
            - parking to wait for  <0x000000076f5d4360> (a java.util.concurrent.locks.ReentrantLock$NonfairSync)
            at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireQueued(AbstractQueuedSynchronizer.java:870)
            at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquire(AbstractQueuedSynchronizer.java:1199)
            at java.util.concurrent.locks.ReentrantLock$NonfairSync.lock(ReentrantLock.java:209)
            at java.util.concurrent.locks.ReentrantLock.lock(ReentrantLock.java:285)
            at com.nicksxs.thread_dump_demo.ThreadDumpDemoApplication$1.run(ThreadDumpDemoApplication.java:22)

    Found 1 deadlock.
    -

    前面的信息其实上次就看过了，后面就可以发现有个死锁了，

    上面比较长，把主要的截出来，就是这边的，这点就很强大了。

    -

    jmap

    惯例还是看一下帮助信息

    这个相对命令比较多，不过因为现在 dump 下来我们可能会用文件模式，然后将文件下载下来使用 mat 进行分析，所以可以使用
    jmap -dump:live,format=b,file=heap.bin <pid>
    命令照着上面看的就是打印活着的对象，然后以二进制格式，文件名叫 heap.bin 然后最后就是进程 id，打印出来以后可以用 mat 打开

    这样就可以很清晰的看到应用里的各种信息，jmap 直接在命令中还可以看很多信息，比如使用jmap -histo <pid>打印对象的实例数和对象占用的内存

    jmap -finalizerinfo <pid> 打印正在等候回收的对象

    -

    小tips

    对于一些应用内存已经占满了，jstack 和 jmap 可能会连不上的情况，可以使用-F参数强制打印线程或者 dump 文件，但是要注意这两者使用的用户必须与 java 进程启动用户一致，并且使用的 jdk 也要一致

    -]]>     - - Java - Thread dump - 工具 - 问题排查 + Java Java - JPS - JStack - JMap + 类加载 + 加载 + 验证 + 准备 + 解析 + 初始化 + 链接 + 双亲委派     @@ -6840,106 +6928,6 @@ user3: Broker - - 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 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/ @@ -6958,32 +6946,6 @@ user3:

    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。

    -]]> - - Java - - - Java - Sharding-Jdbc - -     - - 聊聊 Sharding-Jdbc 的简单原理初篇 - /2021/12/26/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E7%9A%84%E7%AE%80%E5%8D%95%E5%8E%9F%E7%90%86%E5%88%9D%E7%AF%87/ - 在上一篇 sharding-jdbc 的介绍中其实碰到过一个问题，这里也引出了一个比较有意思的话题
    就是我在执行 query 的时候犯过一个比较难发现的错误，

    -
    ResultSet resultSet = ps.executeQuery(sql);
    -

    实际上应该是

    -
    ResultSet resultSet = ps.executeQuery();
    -

    而这里的差别就是，是否传 sql 这个参数，首先我们要知道这个 ps 是什么，它也是个接口java.sql.PreparedStatement,而真正的实现类是org.apache.shardingsphere.driver.jdbc.core.statement.ShardingSpherePreparedStatement,我们来看下继承关系

    这里可以看到继承关系里有org.apache.shardingsphere.driver.jdbc.unsupported.AbstractUnsupportedOperationPreparedStatement
    那么在我上面的写错的代码里

    -
    @Override
    public final ResultSet executeQuery(final String sql) throws SQLException {
        throw new SQLFeatureNotSupportedException("executeQuery with SQL for PreparedStatement");
    }
    -

    这个报错一开始让我有点懵，后来点进去了发现是这么个异常，但是我其实一开始是用的更新语句，以为更新不支持，因为平时使用没有深究过，以为是不是需要使用 Mybatis 才可以执行更新，但是理论上也不应该，再往上看原来这些异常是由 sharding-jdbc 包装的，也就是在上面说的AbstractUnsupportedOperationPreparedStatement，这其实也是一种设计思想，本身 jdbc 提供了一系列接口，由各家去支持，包括 mysql，sql server，oracle 等，而正因为这个设计，所以 sharding-jdbc 也可以在此基础上进行设计，我们可以总体地看下 sharding-jdbc 的实现基础

    看了前面ShardingSpherePreparedStatement的继承关系，应该也能猜到这里的几个类都是实现了 jdbc 的基础接口，

    在前一篇的 demo 中的

    -
    Connection conn = dataSource.getConnection();
    -

    其实就获得了org.apache.shardingsphere.driver.jdbc.core.connection.ShardingSphereConnection#ShardingSphereConnection
    然后获得java.sql.PreparedStatement

    -
    PreparedStatement ps = conn.prepareStatement(sql)
    -

    就是获取了org.apache.shardingsphere.driver.jdbc.core.statement.ShardingSpherePreparedStatement
    然后就是执行

    -
    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 @@ -7015,38 +6977,29 @@ user3:     - 聊聊 mysql 的 MVCC 续续篇之锁分析 - /2020/05/10/%E8%81%8A%E8%81%8A-mysql-%E7%9A%84-MVCC-%E7%BB%AD%E7%BB%AD%E7%AF%87%E4%B9%8B%E5%8A%A0%E9%94%81%E5%88%86%E6%9E%90/ - 看完前面两篇水文之后，感觉不得不来分析下 mysql 的锁了，其实前面说到幻读的时候是有个前提没提到的，比如一个select * from table1 where id = 1这种查询语句其实是不会加传说中的锁的，当然这里是指在 RR 或者 RC 隔离级别下，
    看一段 mysql官方文档

    -
    -

    SELECT ... FROM is a consistent read, reading a snapshot of the database and setting no locks unless the transaction isolation level is set to SERIALIZABLE. For SERIALIZABLE level, the search sets shared next-key locks on the index records it encounters. However, only an index record lock is required for statements that lock rows using a unique index to search for a unique row.

    -
    -

    纯粹的这种一致性读，实际读取的是快照，也就是基于 read view 的读取方式，除非当前隔离级别是SERIALIZABLE
    但是对于以下几类

    -
      -
    • select * from table where ? lock in share mode;
      • -
    • select * from table where ? for update;
      • -
    • insert into table values (...);
      • -
    • update table set ? where ?;
      • -
    • delete from table where ?;
      • -
    -

    除了第一条是 S 锁之外，其他都是 X 排他锁，这边在顺带下，S 锁表示共享锁， X 表示独占锁，同为 S 锁之间不冲突，S 与 X，X 与 S，X 与 X 之间都冲突，也就是加了前者，后者就加不上了
    我们知道对于 RC 级别会出现幻读现象，对于 RR 级别不会出现，主要的区别是 RR 级别下对于以上的加锁读取都根据情况加上了 gap 锁，那么是不是 RR 级别下以上所有的都是要加 gap 锁呢，当然不是
    举个例子，RR 事务隔离级别下，table1 有个主键id 字段
    select * from table1 where id = 10 for update
    这条语句要加 gap 锁吗？
    答案是不需要，这里其实算是我看了这么久的一点自己的理解，啥时候要加 gap 锁，判断的条件是根据我查询的数据是否会因为不加 gap 锁而出现数量的不一致，我上面这条查询语句，在什么情况下会出现查询结果数量不一致呢，只要在这条记录被更新或者删除的时候，有没有可能我第一次查出来一条，第二次变成两条了呢，不可能，因为是主键索引。
    再变更下这个题的条件，当 id 不是主键，但是是唯一索引，这样需要怎么加锁，注意问题是怎么加锁，不是需不需要加 gap 锁，这里呢就是稍微延伸一下，把聚簇索引（主键索引）和二级索引带一下，当 id 不是主键，说明是个二级索引，但是它是唯一索引，体会下，首先对于 id = 10这个二级索引肯定要加锁，要不要锁 gap 呢，不用，因为是唯一索引，id = 10 只可能有这一条记录，然后呢，这样是不是就好了，还不行，因为啥，因为它是二级索引，对应的主键索引的记录才是真正的数据，万一被更新掉了咋办，所以在 id = 10 对应的主键索引上也需要加上锁（默认都是 record lock行锁），那主键索引上要不要加 gap 呢，也不用，也是精确定位到这一条记录
    最后呢，当 id 不是主键，也不是唯一索引，只是个普通的索引，这里就需要大名鼎鼎的 gap 锁了，
    是时候画个图了

    其实核心的目的还是不让这个 id=10 的记录不会出现幻读，那么就需要在 id 这个索引上加上三个 gap 锁，主键索引上就不用了，在 id 索引上已经控制住了id = 10 不会出现幻读，主键索引上这两条对应的记录已经锁了，所以就这样 OK 了

    + 聊聊 Sharding-Jdbc 的简单原理初篇 + /2021/12/26/%E8%81%8A%E8%81%8A-Sharding-Jdbc-%E7%9A%84%E7%AE%80%E5%8D%95%E5%8E%9F%E7%90%86%E5%88%9D%E7%AF%87/ + 在上一篇 sharding-jdbc 的介绍中其实碰到过一个问题，这里也引出了一个比较有意思的话题
    就是我在执行 query 的时候犯过一个比较难发现的错误，

    +
    ResultSet resultSet = ps.executeQuery(sql);
    +

    实际上应该是

    +
    ResultSet resultSet = ps.executeQuery();
    +

    而这里的差别就是，是否传 sql 这个参数，首先我们要知道这个 ps 是什么，它也是个接口java.sql.PreparedStatement,而真正的实现类是org.apache.shardingsphere.driver.jdbc.core.statement.ShardingSpherePreparedStatement,我们来看下继承关系

    这里可以看到继承关系里有org.apache.shardingsphere.driver.jdbc.unsupported.AbstractUnsupportedOperationPreparedStatement
    那么在我上面的写错的代码里

    +
    @Override
    public final ResultSet executeQuery(final String sql) throws SQLException {
        throw new SQLFeatureNotSupportedException("executeQuery with SQL for PreparedStatement");
    }
    +

    这个报错一开始让我有点懵，后来点进去了发现是这么个异常，但是我其实一开始是用的更新语句，以为更新不支持，因为平时使用没有深究过，以为是不是需要使用 Mybatis 才可以执行更新，但是理论上也不应该，再往上看原来这些异常是由 sharding-jdbc 包装的，也就是在上面说的AbstractUnsupportedOperationPreparedStatement，这其实也是一种设计思想，本身 jdbc 提供了一系列接口，由各家去支持，包括 mysql，sql server，oracle 等，而正因为这个设计，所以 sharding-jdbc 也可以在此基础上进行设计，我们可以总体地看下 sharding-jdbc 的实现基础

    看了前面ShardingSpherePreparedStatement的继承关系，应该也能猜到这里的几个类都是实现了 jdbc 的基础接口，

    在前一篇的 demo 中的

    +
    Connection conn = dataSource.getConnection();
    +

    其实就获得了org.apache.shardingsphere.driver.jdbc.core.connection.ShardingSphereConnection#ShardingSphereConnection
    然后获得java.sql.PreparedStatement

    +
    PreparedStatement ps = conn.prepareStatement(sql)
    +

    就是获取了org.apache.shardingsphere.driver.jdbc.core.statement.ShardingSpherePreparedStatement
    然后就是执行

    +
    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

    ]]>     - Mysql - C - 数据结构 - 源码 - Mysql + Java - mysql - 数据结构 - 源码 - mvcc - read view - gap lock - next-key lock - 幻读 + Java + Sharding-Jdbc     @@ -7093,6 +7046,41 @@ user3:

    SELECT … FOR UPDATE sets an exclusive next-key lock on every record the search encounters. However, only an index record lock is required for statements that lock rows using a unique index to search for a unique row.

    对于一个 for update 查询，在 RR 级别下，会设置一个 next-key lock在每一条被查询到的记录上，next-lock 又是啥呢，其实就是 gap 锁和 record 锁的结合体，比如我在数据库里有 id 是 1，3，5，7，10，对于上面那条查询，查出来的结果就是 1，3，5，7，那么按照文档里描述的，对于这几条记录都会加上next-key lock，也就是(-∞, 1], (1, 3], (3, 5], (5, 7], (7, 10) 这些区间和记录会被锁起来，不让插入，再唠叨一下呢，就是其实如果是只读的事务，光 read view 一致性读就够了，如果是有写操作的呢，就需要锁了。

    +]]> + + Mysql + C + 数据结构 + 源码 + Mysql + + + mysql + 数据结构 + 源码 + mvcc + read view + gap lock + next-key lock + 幻读 + +     + + 聊聊 mysql 的 MVCC 续续篇之锁分析 + /2020/05/10/%E8%81%8A%E8%81%8A-mysql-%E7%9A%84-MVCC-%E7%BB%AD%E7%BB%AD%E7%AF%87%E4%B9%8B%E5%8A%A0%E9%94%81%E5%88%86%E6%9E%90/ + 看完前面两篇水文之后，感觉不得不来分析下 mysql 的锁了，其实前面说到幻读的时候是有个前提没提到的，比如一个select * from table1 where id = 1这种查询语句其实是不会加传说中的锁的，当然这里是指在 RR 或者 RC 隔离级别下，
    看一段 mysql官方文档

    +
    +

    SELECT ... FROM is a consistent read, reading a snapshot of the database and setting no locks unless the transaction isolation level is set to SERIALIZABLE. For SERIALIZABLE level, the search sets shared next-key locks on the index records it encounters. However, only an index record lock is required for statements that lock rows using a unique index to search for a unique row.

    +
    +

    纯粹的这种一致性读，实际读取的是快照，也就是基于 read view 的读取方式，除非当前隔离级别是SERIALIZABLE
    但是对于以下几类

    +
      +
    • select * from table where ? lock in share mode;
      • +
    • select * from table where ? for update;
      • +
    • insert into table values (...);
      • +
    • update table set ? where ?;
      • +
    • delete from table where ?;
      • +
    +

    除了第一条是 S 锁之外，其他都是 X 排他锁，这边在顺带下，S 锁表示共享锁， X 表示独占锁，同为 S 锁之间不冲突，S 与 X，X 与 S，X 与 X 之间都冲突，也就是加了前者，后者就加不上了
    我们知道对于 RC 级别会出现幻读现象，对于 RR 级别不会出现，主要的区别是 RR 级别下对于以上的加锁读取都根据情况加上了 gap 锁，那么是不是 RR 级别下以上所有的都是要加 gap 锁呢，当然不是
    举个例子，RR 事务隔离级别下，table1 有个主键id 字段
    select * from table1 where id = 10 for update
    这条语句要加 gap 锁吗？
    答案是不需要，这里其实算是我看了这么久的一点自己的理解，啥时候要加 gap 锁，判断的条件是根据我查询的数据是否会因为不加 gap 锁而出现数量的不一致，我上面这条查询语句，在什么情况下会出现查询结果数量不一致呢，只要在这条记录被更新或者删除的时候，有没有可能我第一次查出来一条，第二次变成两条了呢，不可能，因为是主键索引。
    再变更下这个题的条件，当 id 不是主键，但是是唯一索引，这样需要怎么加锁，注意问题是怎么加锁，不是需不需要加 gap 锁，这里呢就是稍微延伸一下，把聚簇索引（主键索引）和二级索引带一下，当 id 不是主键，说明是个二级索引，但是它是唯一索引，体会下，首先对于 id = 10这个二级索引肯定要加锁，要不要锁 gap 呢，不用，因为是唯一索引，id = 10 只可能有这一条记录，然后呢，这样是不是就好了，还不行，因为啥，因为它是二级索引，对应的主键索引的记录才是真正的数据，万一被更新掉了咋办，所以在 id = 10 对应的主键索引上也需要加上锁（默认都是 record lock行锁），那主键索引上要不要加 gap 呢，也不用，也是精确定位到这一条记录
    最后呢，当 id 不是主键，也不是唯一索引，只是个普通的索引，这里就需要大名鼎鼎的 gap 锁了，
    是时候画个图了

    其实核心的目的还是不让这个 id=10 的记录不会出现幻读，那么就需要在 id 这个索引上加上三个 gap 锁，主键索引上就不用了，在 id 索引上已经控制住了id = 10 不会出现幻读，主键索引上这两条对应的记录已经锁了，所以就这样 OK 了

    ]]>     Mysql @@ -7161,53 +7149,27 @@ user3: 

    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 - - - mysql - 索引 - is null - is not null - 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。

    +


    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。

    ]]>     - Java - Design Patterns - Singleton + Mysql + C + 索引 + Mysql - 设计模式 - Design Patterns - 单例 - Singleton + mysql + 索引 + is null + is not null + procedure     @@ -7241,6 +7203,32 @@ user3: 互斥锁 + + 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 SpringBoot 自动装配 /2021/07/11/%E8%81%8A%E8%81%8ASpringBoot-%E8%87%AA%E5%8A%A8%E8%A3%85%E9%85%8D/ @@ -7318,6 +7306,18 @@ user3: zsh + + 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

    +]]>     + + linux + + + linux + +     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/ @@ -7417,21 +7417,16 @@ user3: - 聊聊我刚学会的应用诊断方法 - /2020/05/22/%E8%81%8A%E8%81%8A%E6%88%91%E5%88%9A%E5%AD%A6%E4%BC%9A%E7%9A%84%E5%BA%94%E7%94%A8%E8%AF%8A%E6%96%AD%E6%96%B9%E6%B3%95/ - 因为传说中的出身问题，我以前写的是PHP，在使用 swoole 之前，基本的应用调试手段就是简单粗暴的 var_dump，exit，对于单进程模型的 PHP 也是简单有效，技术栈换成 Java 之后，就变得没那么容易，一方面是需要编译，另一方面是一般都是基于 spring 的项目，如果问题定位比较模糊，那框架层的是很难靠简单的 System.out.println 或者打 log 解决，(PS：我觉得可能我写的东西比较适合从 PHP 这种弱类型语言转到 Java 的小白同学)这个时候一方面因为是 Java，有了非常好用的 idea IDE 的支持，可以各种花式调试，条件断点尤其牛叉，但是又因为有 Spring+Java 的双重原因，有些情况下单步调试可以把手按废掉，这也是我之前一直比较困惑苦逼的点，后来随着慢慢精（jiang）进（you）之后，比如对于一个 oom 的情况，我们可以通过启动参数加上-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=xx/xx 来配置溢出时的堆dump 日志，获取到这个文件后，我们可以通过像 Memory Analyzer (MAT)[https://www.eclipse.org/mat/] (The Eclipse Memory Analyzer is a fast and feature-rich Java heap analyzer that helps you find memory leaks and reduce memory consumption.)来查看诊断问题所在，之前用到的时候是因为有个死循环一直往链表里塞数据，属于比较简单的，后来一次是由于运维进行应用迁移时按默认的统一配置了堆内存大小，导致内存的确不够用，所以溢出了，
    今天想说的其实主要是我们的 thread dump，这也是我最近才真正用的一个方法，可能真的很小白了，用过 ide 的单步调试其实都知道会有一个一层层的玩意，比如函数从 A，调用了 B，再从 B 调用了 C，一直往下(因为是 Java，所以还有很多🤦‍♂️)，这个其实也是大部分语言的调用模型，利用了栈这个数据结构，通过这个结构我们可以知道代码的调用链路，由于对于一个 spring 应用，在本身框架代码量非常庞大的情况下，外加如果应用代码也是非常多的时候，有时候通过单步调试真的很难短时间定位到问题，需要非常大的耐心和仔细观察，当然不是说完全不行，举个例子当我的应用经常启动需要非常长的时间，因为本身应用有非常多个 bean，比较难说究竟是 bean 的加载的确很慢还是有什么异常原因，这种时候就可以使用 thread dump 了，具体怎么操作呢

    如果在idea 中运行或者调试时，可以直接点击这个照相机一样的按钮，右边就会出现了左边会显示所有的线程，右边会显示线程栈，

    -
    "main@1" prio=5 tid=0x1 nid=NA runnable
      java.lang.Thread.State: RUNNABLE
    	  at TreeDistance.treeDist(TreeDistance.java:64)
    	  at TreeDistance.treeDist(TreeDistance.java:65)
    	  at TreeDistance.treeDist(TreeDistance.java:65)
    	  at TreeDistance.treeDist(TreeDistance.java:65)
    	  at TreeDistance.main(TreeDistance.java:45)
    -

    这就是我们主线程的堆栈信息了，main 表示这个线程名，prio表示优先级，默认是 5，tid 表示线程 id，nid 表示对应的系统线程，后面的runnable 表示目前线程状态，因为是被我打了断点，所以是就许状态，然后下面就是对应的线程栈内容了，在TreeDistance类的 treeDist方法中，对应的文件行数是 64 行。
    这里使用 thread dump一般也不会是上面我截图代码里的这种代码量很少的，一般是大型项目，有时候跑着跑着没反应，又不知道跑到哪了，特别是一些刚接触的大项目或者需要定位一个大项目的一个疑难问题，一时没思路时，可以使用这个方法，个人觉得非常有帮助。

    + 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！

    ]]>     - Java - Thread dump - 工具 - 问题排查 + 生活 - Java - Thread dump + 生活 + 远程办公     @@ -7479,16 +7474,21 @@ user3: - 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！

    + 聊聊我刚学会的应用诊断方法 + /2020/05/22/%E8%81%8A%E8%81%8A%E6%88%91%E5%88%9A%E5%AD%A6%E4%BC%9A%E7%9A%84%E5%BA%94%E7%94%A8%E8%AF%8A%E6%96%AD%E6%96%B9%E6%B3%95/ + 因为传说中的出身问题，我以前写的是PHP，在使用 swoole 之前，基本的应用调试手段就是简单粗暴的 var_dump，exit，对于单进程模型的 PHP 也是简单有效，技术栈换成 Java 之后，就变得没那么容易，一方面是需要编译，另一方面是一般都是基于 spring 的项目，如果问题定位比较模糊，那框架层的是很难靠简单的 System.out.println 或者打 log 解决，(PS：我觉得可能我写的东西比较适合从 PHP 这种弱类型语言转到 Java 的小白同学)这个时候一方面因为是 Java，有了非常好用的 idea IDE 的支持，可以各种花式调试，条件断点尤其牛叉，但是又因为有 Spring+Java 的双重原因，有些情况下单步调试可以把手按废掉，这也是我之前一直比较困惑苦逼的点，后来随着慢慢精（jiang）进（you）之后，比如对于一个 oom 的情况，我们可以通过启动参数加上-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=xx/xx 来配置溢出时的堆dump 日志，获取到这个文件后，我们可以通过像 Memory Analyzer (MAT)[https://www.eclipse.org/mat/] (The Eclipse Memory Analyzer is a fast and feature-rich Java heap analyzer that helps you find memory leaks and reduce memory consumption.)来查看诊断问题所在，之前用到的时候是因为有个死循环一直往链表里塞数据，属于比较简单的，后来一次是由于运维进行应用迁移时按默认的统一配置了堆内存大小，导致内存的确不够用，所以溢出了，
    今天想说的其实主要是我们的 thread dump，这也是我最近才真正用的一个方法，可能真的很小白了，用过 ide 的单步调试其实都知道会有一个一层层的玩意，比如函数从 A，调用了 B，再从 B 调用了 C，一直往下(因为是 Java，所以还有很多🤦‍♂️)，这个其实也是大部分语言的调用模型，利用了栈这个数据结构，通过这个结构我们可以知道代码的调用链路，由于对于一个 spring 应用，在本身框架代码量非常庞大的情况下，外加如果应用代码也是非常多的时候，有时候通过单步调试真的很难短时间定位到问题，需要非常大的耐心和仔细观察，当然不是说完全不行，举个例子当我的应用经常启动需要非常长的时间，因为本身应用有非常多个 bean，比较难说究竟是 bean 的加载的确很慢还是有什么异常原因，这种时候就可以使用 thread dump 了，具体怎么操作呢

    如果在idea 中运行或者调试时，可以直接点击这个照相机一样的按钮，右边就会出现了左边会显示所有的线程，右边会显示线程栈，

    +
    "main@1" prio=5 tid=0x1 nid=NA runnable
      java.lang.Thread.State: RUNNABLE
    	  at TreeDistance.treeDist(TreeDistance.java:64)
    	  at TreeDistance.treeDist(TreeDistance.java:65)
    	  at TreeDistance.treeDist(TreeDistance.java:65)
    	  at TreeDistance.treeDist(TreeDistance.java:65)
    	  at TreeDistance.main(TreeDistance.java:45)
    +

    这就是我们主线程的堆栈信息了，main 表示这个线程名，prio表示优先级，默认是 5，tid 表示线程 id，nid 表示对应的系统线程，后面的runnable 表示目前线程状态，因为是被我打了断点，所以是就许状态，然后下面就是对应的线程栈内容了，在TreeDistance类的 treeDist方法中，对应的文件行数是 64 行。
    这里使用 thread dump一般也不会是上面我截图代码里的这种代码量很少的，一般是大型项目，有时候跑着跑着没反应，又不知道跑到哪了，特别是一些刚接触的大项目或者需要定位一个大项目的一个疑难问题，一时没思路时，可以使用这个方法，个人觉得非常有帮助。

    ]]>     - 生活 + Java + Thread dump + 工具 + 问题排查 - 生活 - 远程办公 + Java + Thread dump     @@ -7515,6 +7515,23 @@ user3: /2021/12/31/%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%E3%80%8A%E8%8A%B1%E6%9D%9F%E8%88%AC%E7%9A%84%E6%81%8B%E7%88%B1%E3%80%8B%E8%A7%82%E5%90%8E%E6%84%9F/ 周末在领导的提议下看了豆瓣的年度榜单，本来感觉没啥心情看的，看到主演有有村架纯就觉得可以看一下，颜值即正义嘛，男主小麦跟女主小娟（后面简称小麦跟小娟）是两个在一次非常偶然的没赶上地铁末班车事件中相识，这里得说下日本这种通宵营业的店好像挺不错的，看着也挺正常，国内估计只有酒吧之类的可以。晚上去的地方是有点暗暗的，好像也有点类似酒吧，旁边有类似于 dj 那种，然后同桌的还有除了男女主的另外一对男女，也是因为没赶上地铁末班车的，但也是陌生人，然后小麦突然看到了有个非常有名的电影人，小娟竟然也认识，然后旁边那对完全不认识，还在那吹自己看过很多电影，比如《肖申克的救赎》，于是男女主都特别鄙夷地看着他们，然后他们又去了另一个有点像泡澡的地方席地而坐，他们发现了自己的鞋子都是一样的，然后在女的去上厕所的时候，小麦暗恋的学姐也来了，然后小麦就去跟学姐他们一起坐了，小娟回来后有点不开心就说去朋友家睡，幸好小麦看出来了（他竟然看出来了，本来以为应该是没填过恋爱很木讷的），就追出去，然后就去了小麦家，到了家小娟发现小麦家的书柜上的书简直就跟她自己家的一模一样，小麦还给小娟吹了头发，一起吃烤饭团，看电影，第二天送小娟上了公交，还约好了一起看木乃伊展，然而并没有交换联系方式，但是他们还是约上了一起看了木乃伊展，在餐馆就出现了片头那一幕的来源，因为餐馆他们想一起听歌，就用有线耳机一人一个耳朵听，但是旁边就有个大叔说“你们是不是不爱音乐，左右耳朵是不一样的，只有一起听才是真正的音乐”这样的话，然后的剧情有点跳，因为是指他们一直在这家餐馆吃饭，中间有他们一起出去玩情节穿插着，也是在这他们确立了关系，可以说主体就是体现了他们非常的合拍和默契，就像一些影评说的，这部电影是说如何跟百分百合拍的人分手，然后就是正常的恋爱开始啪啪啪，一直腻在床上，也没去就业说明会，后面也有讲了一点小麦带着小娟去认识他的朋友，也把小娟介绍给了他们认识，这里算是个小伏笔，后面他们分手也有这里的人的一些关系，接下去的剧情说实话我是不太喜欢的，如果一部八分的电影只是说恋爱被现实打败的话，我觉得在我这是不合格的，但是事实也是这样，小麦其实是有家里的资助，所以后面还是按自己的喜好给一些机构画点插画，小娟则要出去工作，因为小娟家庭观念也是要让她出去有正经工作，用脚指头想也能知道肯定不顺利，然后就是暂时在一家蛋糕店工作，小麦就每天去接小娟，日子过得甜甜蜜蜜，后面小娟在自己的努力下考了个什么资格证，去了一家医院还是什么做前台行政，这中间当然就有父母来见面吃饭了，他们在开始恋爱不久就同居合租了，然后小娟父母就是来说要让她有个正经工作，对男的说的话就是人生就是责任这类的话，而小麦爸爸算是个导火索，因为小麦家里是做烟花生意的，他爸让他就做烟花生意，因为要回老家，并且小麦也不想做，所以就拒绝了，然后他爸就说不给他每个月五万的资助，这也导致了小麦需要去找工作，这个过程也是很辛苦，本来想要年前找好工作，然后事与愿违，后面有一次小娟被同事吐槽怎么从来不去团建，于是她就去了（我以为会拒绝），正在团建的时候小麦给她电话，说找到工作了，是一个创业物流公司这种，这里剧情就是我觉得比较俗套的，小麦各种被虐，累成狗，但是就像小娟爸爸说的话，人生就是责任，所以一直在坚持，但是这样也导致了跟小娟的交流也越来越少，他们原来最爱的漫画，爱玩的游戏，也只剩小娟一个人看，一个人玩，而正是这个时候，小娟说她辞掉了工作，去做一个不是太靠谱的漫画改造的密室逃脱，然后这里其实有一点后面争议很大的，就是这个工作其实是前面小麦介绍给小娟的那些朋友中一个的女朋友介绍的，而在有个剧情就是小娟有一次在这个密室逃脱的老板怀里醒过来，是在 KTV 那样的场景里，这就有很多人觉得小娟是不是出轨了，我觉得其实不那么重要，因为这个离职的事情已经让一切矛盾都摆在眼前，小麦其实是接受这种需要承担责任的生活，也想着要跟小娟结婚，但是小娟似乎还是想要过着那样理想的生活，做自己想做的事情，看自己爱看的漫画，也要小麦能像以前那样一直那么默契的有着相同的爱好，这里的触发点其实还有个是那个小麦的朋友（也就是他女朋友介绍小娟那个不靠谱工作的）的葬礼上，小麦在参加完葬礼后有挺多想倾诉的，而小娟只是想睡了，这个让小麦第二天起来都不想理小娟，只是这里我不太理解，难道这点闹情绪都不能接受吗，所谓的合拍也只是毫无限制的情况下的合拍吧，真正的生活怎么可能如此理想呢，即使没有物质生活的压力，也会有其他的各种压力和限制，在这之后其实小麦想说的是小娟是不是没有想跟自己继续在一起的想法了，而小娟觉得都不说话了，还怎么结婚呢，后面其实导演搞了个小 trick，突然放了异常婚礼，但是不是男女主的，我并不觉得这个桥段很好，在婚礼里男女主都觉得自己想要跟对方说分手了，但是当他们去了最开始一直去的餐馆的时候，一个算是一个现实映照的就是他们一直坐的位子被占了，可能也是导演想通过这个来说明他们已经回不去了，在餐馆交谈的时候，小麦其实是说他们结婚吧，并没有想前面婚礼上预设地要分手，但是小娟放弃了，不想结婚，因为不想过那样的生活了，而小麦觉得可能生活就是那样，不可能一直保持刚恋爱时候的那种感觉，生活就是责任，人生就意味着责任。

    我的一些观点也在前面说了，恋爱到婚姻，即使物质没问题，经济没问题，也会有各种各样的问题，需要一起去解决，因为结婚就意味着需要相互扶持，而不是各取所需，可能我的要求比较高，后面男女主在分手后还一起住了一段时间，我原来还在想会不会通过这个方式让他们继续去磨合同步，只是我失望了，最后给个打分可能是 5 到 6 分吧，勉强及格，好的影视剧应该源于生活高于生活，这一部可能还比不上生活。

    +]]>     + + 生活 + + + 生活 + 看剧 + +     + + 聊聊最近平淡的生活之看看老剧 + /2021/11/21/%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%E7%9C%8B%E8%80%81%E5%89%A7/ + 最近因为也没什么好看的新剧和综艺所以就看看一些以前看过的老剧，我是个非常念旧的人吧，很多剧都会反反复复地看，一方面之前看过觉得好看的的确是一直记着，还有就是平时工作完了回来就想能放松下，剧情太纠结的，太烧脑的都不喜欢，也就是我常挂在口头的不喜欢看费脑子的剧，跟我不喜欢狼人杀的原因也类似。

    +

    前面其实是看的太阳的后裔，跟 LD 一起看的，之前其实算是看过一点，但是没有看的很完整，并且很多剧情也忘了，只是这个我我可能看得更少一点，因为最开始的时候觉得男主应该是男二，可能对长得这样的男主并且是这样的人设有点失望，感觉不是特别像个特种兵，但是由于本来也比较火，而且 LD 比较喜欢就从这个开始看了，有两个点是比较想说的

    +

    韩剧虽然被吐槽的很多，但是很多剧的质量，情节把控还是优于目前非常多国内剧的，相对来说剧情发展的前后承接不是那么硬凹出来的，而且人设都立得住，这个是非常重要的，很多国内剧怎么说呢，就是当爹的看起来就比儿子没大几岁，三四十岁的人去演一个十岁出头的小姑娘，除非容貌异常，比如刘晓庆这种，不然就会觉得导演在把我们观众当傻子。瞬间就没有想看下去的欲望了。

    +

    再一点就是情节是大众都能接受度比较高的，现在有很多普遍会找一些新奇的视角，比如卖腐，想某某令，两部都叫某某令，这其实是一个点，延伸出去就是跟前面说的一点有点类似，xx 老祖，人看着就二三十，叫 xx 老祖，（喜欢的人轻喷哈）然后名字有一堆，同一个人物一会叫这个名字，一会又叫另一个名字，然后一堆死表情。

    +

    因为今天有个特殊的事情发生，所以简短的写（shui）一篇了

    ]]>     生活 @@ -7556,23 +7573,6 @@ user3: 修电脑的     - - 聊聊最近平淡的生活之看看老剧 - /2021/11/21/%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%E7%9C%8B%E8%80%81%E5%89%A7/ - 最近因为也没什么好看的新剧和综艺所以就看看一些以前看过的老剧，我是个非常念旧的人吧，很多剧都会反反复复地看，一方面之前看过觉得好看的的确是一直记着，还有就是平时工作完了回来就想能放松下，剧情太纠结的，太烧脑的都不喜欢，也就是我常挂在口头的不喜欢看费脑子的剧，跟我不喜欢狼人杀的原因也类似。

    -

    前面其实是看的太阳的后裔，跟 LD 一起看的，之前其实算是看过一点，但是没有看的很完整，并且很多剧情也忘了，只是这个我我可能看得更少一点，因为最开始的时候觉得男主应该是男二，可能对长得这样的男主并且是这样的人设有点失望，感觉不是特别像个特种兵，但是由于本来也比较火，而且 LD 比较喜欢就从这个开始看了，有两个点是比较想说的

    -

    韩剧虽然被吐槽的很多，但是很多剧的质量，情节把控还是优于目前非常多国内剧的，相对来说剧情发展的前后承接不是那么硬凹出来的，而且人设都立得住，这个是非常重要的，很多国内剧怎么说呢，就是当爹的看起来就比儿子没大几岁，三四十岁的人去演一个十岁出头的小姑娘，除非容貌异常，比如刘晓庆这种，不然就会觉得导演在把我们观众当傻子。瞬间就没有想看下去的欲望了。

    -

    再一点就是情节是大众都能接受度比较高的，现在有很多普遍会找一些新奇的视角，比如卖腐，想某某令，两部都叫某某令，这其实是一个点，延伸出去就是跟前面说的一点有点类似，xx 老祖，人看着就二三十，叫 xx 老祖，（喜欢的人轻喷哈）然后名字有一堆，同一个人物一会叫这个名字，一会又叫另一个名字，然后一堆死表情。

    -

    因为今天有个特殊的事情发生，所以简短的写（shui）一篇了

    -]]>     - - 生活 - - - 生活 - 看剧 - -     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/ @@ -7732,22 +7732,6 @@ user3: stream - - 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/ - 可能是太久没写单测了，写个单测发现不符合预期，后来验证下才反应过来
    我们来看下demo

    -
    class RenameTest extends TestCase
    {
        public function setUp(): void
        {
            var_dump("setUp");
        }

        public function test1()
        {
            var_dump("test1");
            assertEquals(1, 1);
        }

        public function test2()
        {
            var_dump("test2");
            assertEquals(1, 1);
        }

        protected function tearDown(): void
        {
            var_dump("tearDown");
        }
    }
    -

    因为我是想写个重命名的小工具，希望通过setUp和tearDown做一些文件初始化和清理工作，但是我把两个case的初始化跟清理工作写到了单个setUp和tearDown中，这样就出现了异常的错误
    通过上面的示例代码，可以看到执行结果

    -
    ❯ vendor/bin/phpunit
    PHPUnit 9.5.25 by Sebastian Bergmann and contributors.

    .string(5) "setUp"
    string(5) "test1"
    string(8) "tearDown"
    .                                                                  2 / 2 (100%)string(5) "setUp"
    string(5) "test2"
    string(8) "tearDown"


    Time: 00:00.005, Memory: 6.00 MB

    OK (2 tests, 2 assertions)
    -

    其实就是很简单的会在每个test方法前后都执行setUp和tearDown

    -]]>     - - php - - - php - -     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/ @@ -7825,6 +7809,22 @@ user3: 云服务器 + + 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/ + 可能是太久没写单测了，写个单测发现不符合预期，后来验证下才反应过来
    我们来看下demo

    +
    class RenameTest extends TestCase
    {
        public function setUp(): void
        {
            var_dump("setUp");
        }

        public function test1()
        {
            var_dump("test1");
            assertEquals(1, 1);
        }

        public function test2()
        {
            var_dump("test2");
            assertEquals(1, 1);
        }

        protected function tearDown(): void
        {
            var_dump("tearDown");
        }
    }
    +

    因为我是想写个重命名的小工具，希望通过setUp和tearDown做一些文件初始化和清理工作，但是我把两个case的初始化跟清理工作写到了单个setUp和tearDown中，这样就出现了异常的错误
    通过上面的示例代码，可以看到执行结果

    +
    ❯ vendor/bin/phpunit
    PHPUnit 9.5.25 by Sebastian Bergmann and contributors.

    .string(5) "setUp"
    string(5) "test1"
    string(8) "tearDown"
    .                                                                  2 / 2 (100%)string(5) "setUp"
    string(5) "test2"
    string(8) "tearDown"


    Time: 00:00.005, Memory: 6.00 MB

    OK (2 tests, 2 assertions)
    +

    其实就是很简单的会在每个test方法前后都执行setUp和tearDown

    +]]>     + + php + + + php + +     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/ @@ -7894,20 +7894,6 @@ user3: 高速 - - 闲话篇-也算碰到了为老不尊和坏人变老了的典型案例 - /2022/05/22/%E9%97%B2%E8%AF%9D%E7%AF%87-%E4%B9%9F%E7%AE%97%E7%A2%B0%E5%88%B0%E4%BA%86%E4%B8%BA%E8%80%81%E4%B8%8D%E5%B0%8A%E5%92%8C%E5%9D%8F%E4%BA%BA%E5%8F%98%E8%80%81%E4%BA%86%E7%9A%84%E5%85%B8%E5%9E%8B%E6%A1%88%E4%BE%8B/ - 在目前的房子也差不多租了四五年了，楼下邻居换了两拨了，我们这栋楼装修了不知道多少次，因为是学区的原因，房子交易的频率还是比较高的，不过比较神奇的我们对门的没换过，而且一直也没什么交集（除了后面说的水管爆裂），就进出的时候偶尔看到应该是住着一对老夫妻，感觉年纪也有个七八十了。

    -

    对对面这户人家的印象，就是对面的老头子经常是我出门上班去了他回来，看着他颤颤巍巍地走楼梯，我看到了都靠边走，而且有几次还听见好像是他儿子在说他，”年假这么大了，还是少出去吧”，说实话除了这次的事情，之前就有一次水管阀门爆裂了，算是有点交集，那次大概是去年冬天，天气已经很冷了，我们周日下午回来看到楼梯有点湿，但是没什么特别的异常就没怎么注意，到晚上洗完澡，楼下的邻居就来敲门，说我们门外的水表那一直在流水，出门一看真的是懵了，外面水表那在哗哗哗地流水，导致楼梯那就跟水帘洞一样，仔细看看是对面家的水表阀门那在漏水，我只能先用塑料袋包一下，然后大冬天（刚洗完澡）穿着凉拖跑下去找物业保安，走到一楼的时候发现水一直流到一楼了，楼梯上都是水流下来，五楼那是最惨的，感觉门框周边都浸透了，五楼的也是态度比较差的让我一定要把水弄好了，但是前面也说了谁是从对门那户的水表阀那出来的，理论上应该让对面的处理，结果我敲门敲了半天对面都没反应，想着我放着不管也不太好，就去找了物业保安，保安上来看了只能先把总阀关了，我也打电话给维修自来水管的，自来水公司的人过了会也是真的来修了，我那会是挺怕不来修，自来水公司的师傅到了以后拿开一看是对面那户的有个阀门估计是自己换上去的，跟我们这的完全不一样，看上去就比较劣质，师傅也挺气的，大晚上被叫过来，我又尝试着去敲门也还是没人应，也没办法，对面老人家我敲太响到时候出来说我吓到他们啥的，第二天去说也没现场了。

    -

    前面的这件事是个重要铺垫，前几天 LD 下班后把厨余垃圾套好袋子放在门口，打算等我下班了因为要去做核酸（hz 48 小时核酸）顺便带下去，结果到了七点多，说对面的老太太在那疯狂砸门了，LD 被吓到了不敢开门，老太太在外面一边砸门一边骂，“你们年轻人怎么素质这么差”（他们家也经常在门口放垃圾，我们刚来住的时候在楼梯转角他们就放这个废弃的洗衣机，每次走楼梯带点东西都要小心翼翼地走，不然都过不去，然后我赶紧赶回去，结果她听到我回家了，还特意开门继续骂，“你们年轻人怎么素质这么差，垃圾放在这里”，我说我们刚才放在这，打算待会做核酸的时候去扔掉，结果他们家老头，都已经没了牙齿，在那瞪大眼睛说，“你们早上就放在这了的，”我说是LD 刚才下班了放的，争论了一会，我说这个事情我们门口放了垃圾，这会我就去扔掉了，但是你们家老太太这么砸门总不太好，像之前门口水管爆掉了，我敲了门没人应，我也没要砸门一定把你们叫醒，结果老头老太说我们的水管从来没换过，不可能破的（其实到这，再往后说就没意思了，跟这么不要脸的人说多了也只是瞎扯），一会又回到这个垃圾的问题，那个老头说“你们昨天就放在这里了的”，睁着眼说瞎话可真是 666，感觉不是老太太拦着点他马上就要冲上来揍我了一样，事后我想想，这种情况我大概只能躺地上装死了，当这个事情发生之前我真的快把前面说的事情（水管阀坏了）给忘了，虽然这是理论上不该我来处理，除非是老头老太太请求我帮忙，这事后面我也从没说起过，本来完全没交集，对他们的是怎么样的人也没概念，总觉得年纪大了可能还比较心宽和蔼点，结果没想到就是一典型的坏人变老了，我说你们这么砸门，我老婆都被吓得不敢开门，结果对面老头老太太的儿子也出来了说，“我们就是敲下门，我母亲是机关单位退休的，所以肯定不会敲门很大声的，你老婆觉得吓到了是你们人生观价值观有问题”，听到这话我差点笑出来，连着两个可笑至极的脑残逻辑，无语他妈给无语开门，无语到家了。对门家我们之前有个印象就是因为我们都是顶楼，这边老小区以前都是把前后阳台包进来的，然后社区就来咨询大家的意见是不是统一把包进来的违建拆掉，还特地上来六楼跟他们说，结果对面的老头就说，“我要去住建局投诉你们”，本来这个事情是违法的，但是社区的意思也是征求各位业主的意见，结果感觉是社区上门强拆了一样，为老不尊，坏人变老了的典范了。

    -]]>     - - 生活 - - - 生活 - -     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/ @@ -7939,39 +7925,50 @@ user3: - 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/ - 因为房东要来续签合同，记得之前她说要来看看，后来一直都没来成，一方面我们没打扫过也不想被看到，小房子东西从搬进来以后越来越多，虽然不是脏乱差，但也觉得有点不满意干净状态，这里不得不感叹房东家的有钱程度，买了房子自己都没进房子看过，买来只是为了个学籍，去年前房东把房子卖给新房东后，我们还是比较担心会要换房子了，这里其实是个我们在乎的优点略大于缺点的小房子，面积比较小，但是交通便利以及上下班通勤，周边配套也还不错，有个比较大的菜市场，虽然不常去，因为不太会挑不会还价，还是主要去附近一公里左右的超市，可以安静地挑菜，但是说实在的菜场的菜还是比超市新鲜一些。
    大扫除说实在的住在这边以后就没有一次真正意义上的大扫除，因为平时也有在正常打扫，只有偶尔的厨房煤气灶和厕所专门清理下，平时扫地拖地都有做，但是因为说实在的这房子也比较老了，地板什么的都有明显的老化，表面上的油漆都已经被磨损掉了，一些污渍很难拖干净，而且包括厨房和厕所的瓷砖都是纹路特别多，加上磨损，基本是污渍很多，特别是厨房的，又有油渍，我们搬进来的时候厨房的地就已经不太干净了，还有一点就是虽然不是在乡下的房子，但是旁边有两条主干道，一般只要开着窗没几天就灰尘积起来了，公司的电脑在家两天不到就一层灰，而且有些灰在地上时间久一点就会变成那种棉絮状的，看起来就会觉得更脏，并且地板我们平时就是扫一下，然后拖一下没明显的脏东西跟大灰尘就好了，有一些脏的就很难拖干净。
    这次的算是整体的大扫除，把柜子，桌子，茶几台，窗边的灰尘都要擦掉，有一些角落还是有蛮多灰尘，当然特别难受的就是电脑那些接口，线缆上的，都杂糅在一块，如果要全都解开了理顺了还是比较麻烦，并且得断电，所以还是尽力清理，但没有全弄开了（我承认我是在偷懒，这里得说下清理了键盘，键盘之前都是放着用，也没盖住，按键缝里就很容易积灰也很难清理，这次索性直接把键全拔了，但是里面的清理也还是挺麻烦，因为不是平板一块，而且还有小孔，有些缝隙也比较难擦进去，只能慢慢地用牙线棒裹着抹布还有棉签擦一下，然后把键帽用洗手液什么的都擦一下洗洗干净，最后晾干了装好感觉就是一把新键盘了，后面主要是拖地了，这次最神奇的就是这个拖地，本来我就跟 LD 吹牛说拖地我是专业的，从小拖到大，有些地板缝边上的污渍，我又是用力来回拖，再用脚踩着拖，还是能把一些原来以为拖不掉的污渍给拖干净了，但是后来的厨房就比较难，用洗洁精来回拖感觉一点都起不来，可能是污渍积了太久了，一开始都想要放弃了，就打算拖干就好了，后来突然看到旁边有个洗衣服的板刷，结果竟然能刷起来，这样就停不下来了，说累是真的非常累，感觉刷一块瓷砖就要休息一会，但是整体刷完之后就是焕然一新的赶脚，简直太有成就感了。

    + 闲话篇-也算碰到了为老不尊和坏人变老了的典型案例 + /2022/05/22/%E9%97%B2%E8%AF%9D%E7%AF%87-%E4%B9%9F%E7%AE%97%E7%A2%B0%E5%88%B0%E4%BA%86%E4%B8%BA%E8%80%81%E4%B8%8D%E5%B0%8A%E5%92%8C%E5%9D%8F%E4%BA%BA%E5%8F%98%E8%80%81%E4%BA%86%E7%9A%84%E5%85%B8%E5%9E%8B%E6%A1%88%E4%BE%8B/ + 在目前的房子也差不多租了四五年了，楼下邻居换了两拨了，我们这栋楼装修了不知道多少次，因为是学区的原因，房子交易的频率还是比较高的，不过比较神奇的我们对门的没换过，而且一直也没什么交集（除了后面说的水管爆裂），就进出的时候偶尔看到应该是住着一对老夫妻，感觉年纪也有个七八十了。

    +

    对对面这户人家的印象，就是对面的老头子经常是我出门上班去了他回来，看着他颤颤巍巍地走楼梯，我看到了都靠边走，而且有几次还听见好像是他儿子在说他，”年假这么大了，还是少出去吧”，说实话除了这次的事情，之前就有一次水管阀门爆裂了，算是有点交集，那次大概是去年冬天，天气已经很冷了，我们周日下午回来看到楼梯有点湿，但是没什么特别的异常就没怎么注意，到晚上洗完澡，楼下的邻居就来敲门，说我们门外的水表那一直在流水，出门一看真的是懵了，外面水表那在哗哗哗地流水，导致楼梯那就跟水帘洞一样，仔细看看是对面家的水表阀门那在漏水，我只能先用塑料袋包一下，然后大冬天（刚洗完澡）穿着凉拖跑下去找物业保安，走到一楼的时候发现水一直流到一楼了，楼梯上都是水流下来，五楼那是最惨的，感觉门框周边都浸透了，五楼的也是态度比较差的让我一定要把水弄好了，但是前面也说了谁是从对门那户的水表阀那出来的，理论上应该让对面的处理，结果我敲门敲了半天对面都没反应，想着我放着不管也不太好，就去找了物业保安，保安上来看了只能先把总阀关了，我也打电话给维修自来水管的，自来水公司的人过了会也是真的来修了，我那会是挺怕不来修，自来水公司的师傅到了以后拿开一看是对面那户的有个阀门估计是自己换上去的，跟我们这的完全不一样，看上去就比较劣质，师傅也挺气的，大晚上被叫过来，我又尝试着去敲门也还是没人应，也没办法，对面老人家我敲太响到时候出来说我吓到他们啥的，第二天去说也没现场了。

    +

    前面的这件事是个重要铺垫，前几天 LD 下班后把厨余垃圾套好袋子放在门口，打算等我下班了因为要去做核酸（hz 48 小时核酸）顺便带下去，结果到了七点多，说对面的老太太在那疯狂砸门了，LD 被吓到了不敢开门，老太太在外面一边砸门一边骂，“你们年轻人怎么素质这么差”（他们家也经常在门口放垃圾，我们刚来住的时候在楼梯转角他们就放这个废弃的洗衣机，每次走楼梯带点东西都要小心翼翼地走，不然都过不去，然后我赶紧赶回去，结果她听到我回家了，还特意开门继续骂，“你们年轻人怎么素质这么差，垃圾放在这里”，我说我们刚才放在这，打算待会做核酸的时候去扔掉，结果他们家老头，都已经没了牙齿，在那瞪大眼睛说，“你们早上就放在这了的，”我说是LD 刚才下班了放的，争论了一会，我说这个事情我们门口放了垃圾，这会我就去扔掉了，但是你们家老太太这么砸门总不太好，像之前门口水管爆掉了，我敲了门没人应，我也没要砸门一定把你们叫醒，结果老头老太说我们的水管从来没换过，不可能破的（其实到这，再往后说就没意思了，跟这么不要脸的人说多了也只是瞎扯），一会又回到这个垃圾的问题，那个老头说“你们昨天就放在这里了的”，睁着眼说瞎话可真是 666，感觉不是老太太拦着点他马上就要冲上来揍我了一样，事后我想想，这种情况我大概只能躺地上装死了，当这个事情发生之前我真的快把前面说的事情（水管阀坏了）给忘了，虽然这是理论上不该我来处理，除非是老头老太太请求我帮忙，这事后面我也从没说起过，本来完全没交集，对他们的是怎么样的人也没概念，总觉得年纪大了可能还比较心宽和蔼点，结果没想到就是一典型的坏人变老了，我说你们这么砸门，我老婆都被吓得不敢开门，结果对面老头老太太的儿子也出来了说，“我们就是敲下门，我母亲是机关单位退休的，所以肯定不会敲门很大声的，你老婆觉得吓到了是你们人生观价值观有问题”，听到这话我差点笑出来，连着两个可笑至极的脑残逻辑，无语他妈给无语开门，无语到家了。对门家我们之前有个印象就是因为我们都是顶楼，这边老小区以前都是把前后阳台包进来的，然后社区就来咨询大家的意见是不是统一把包进来的违建拆掉，还特地上来六楼跟他们说，结果对面的老头就说，“我要去住建局投诉你们”，本来这个事情是违法的，但是社区的意思也是征求各位业主的意见，结果感觉是社区上门强拆了一样，为老不尊，坏人变老了的典范了。

    ]]>     生活 生活 - 大扫除     - 聊下apollo配置中心的接入 - /2024/08/25/%E8%81%8A%E4%B8%8Bapollo%E9%85%8D%E7%BD%AE%E4%B8%AD%E5%BF%83%E7%9A%84%E6%8E%A5%E5%85%A5/ - 很多技术栈在优化过程中都会有更便捷的接入方式，或者接入demo，这次想拿apollo来对比一些例如向量数据库的部署方式，对我说的就是milvus，
    apollo如果生产环境部署完全不推荐用这种方式，但是如果为了做个实验，研究下源码还是很方便的，当然前提是有docker环境
    首先下载docker-compose配置文件，如果是x86环境就是这个链接，如果是m1这类的就是用这个链接，然后再下载sql文件夹
    目录结构差不多是这样

    然后在这个目录下执行
    sudo docker-compose -f docker-compose-arm64.yml up -d
    如果是非m1的话直接用
    sudo docker-compose up -d
    就好，因为docker-compose默认识别的文件名就是docker-compose.yml
    然后看下日志

    -
    ==== starting service ====
    Service logging file is ./service/apollo-service.log
    Application is running as root (UID 0). This is considered insecure.
    Started [66]
    Waiting for config service startup...
    Config service started. You may visit http://localhost:8080 for service status now!
    Waiting for admin service startup.
    Admin service started
    ==== starting portal ====
    Portal logging file is ./portal/apollo-portal.log
    Application is running as root (UID 0). This is considered insecure.
    Started [211]
    Waiting for portal startup...
    Portal started. You can visit http://localhost:8070 now!
    -

    就表示启动成功了，然后就可以访问后面那个地址 http://localhost:8070 进入控制台，默认用户名apollo，密码是admin
    然后我们在应用中想要使用，主要是这个几个配置
    第一个就是在resources目录下创建apollo-env.properties
    里面是meta server的地址，比如我这边就是

    -
    dev.meta=http://127.0.0.1:8080
    -

    这表示是对应的spring.profiles.active是dev的配置
    第二步是添加pom依赖

    -
    <dependency>
    	<groupId>com.ctrip.framework.apollo</groupId>
    	<artifactId>apollo-client</artifactId>
    	<version>2.0.1</version>
    </dependency>
    -

    最后一步是在springboot的启动类添加注解

    -
    @EnableApolloConfig
    -

    然后就可以直接使用@Value注解使用配置的值

    请求下这个接口,就可以看到对应的值

    然后我们可以在控制台修改下这个值，发布

    默认日志也会把这个打印出来

    -
    2024-08-24 19:38:30.462  INFO 57920 --- [Apollo-Config-1] c.f.a.s.p.AutoUpdateConfigChangeListener : Auto update apollo changed value successfully, new value: apolloDemoValue, key: demo, beanName: demoController, field: com.nicksxs.spbdemo.controller.DemoController.demo
    -

    对应的请求也会拿到最新的值

    + 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

    ]]>     - Java + 工具 - Java - Apollo + 工具 + +     + + 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/ + 因为房东要来续签合同，记得之前她说要来看看，后来一直都没来成，一方面我们没打扫过也不想被看到，小房子东西从搬进来以后越来越多，虽然不是脏乱差，但也觉得有点不满意干净状态，这里不得不感叹房东家的有钱程度，买了房子自己都没进房子看过，买来只是为了个学籍，去年前房东把房子卖给新房东后，我们还是比较担心会要换房子了，这里其实是个我们在乎的优点略大于缺点的小房子，面积比较小，但是交通便利以及上下班通勤，周边配套也还不错，有个比较大的菜市场，虽然不常去，因为不太会挑不会还价，还是主要去附近一公里左右的超市，可以安静地挑菜，但是说实在的菜场的菜还是比超市新鲜一些。
    大扫除说实在的住在这边以后就没有一次真正意义上的大扫除，因为平时也有在正常打扫，只有偶尔的厨房煤气灶和厕所专门清理下，平时扫地拖地都有做，但是因为说实在的这房子也比较老了，地板什么的都有明显的老化，表面上的油漆都已经被磨损掉了，一些污渍很难拖干净，而且包括厨房和厕所的瓷砖都是纹路特别多，加上磨损，基本是污渍很多，特别是厨房的，又有油渍，我们搬进来的时候厨房的地就已经不太干净了，还有一点就是虽然不是在乡下的房子，但是旁边有两条主干道，一般只要开着窗没几天就灰尘积起来了，公司的电脑在家两天不到就一层灰，而且有些灰在地上时间久一点就会变成那种棉絮状的，看起来就会觉得更脏，并且地板我们平时就是扫一下，然后拖一下没明显的脏东西跟大灰尘就好了，有一些脏的就很难拖干净。
    这次的算是整体的大扫除，把柜子，桌子，茶几台，窗边的灰尘都要擦掉，有一些角落还是有蛮多灰尘，当然特别难受的就是电脑那些接口，线缆上的，都杂糅在一块，如果要全都解开了理顺了还是比较麻烦，并且得断电，所以还是尽力清理，但没有全弄开了（我承认我是在偷懒，这里得说下清理了键盘，键盘之前都是放着用，也没盖住，按键缝里就很容易积灰也很难清理，这次索性直接把键全拔了，但是里面的清理也还是挺麻烦，因为不是平板一块，而且还有小孔，有些缝隙也比较难擦进去，只能慢慢地用牙线棒裹着抹布还有棉签擦一下，然后把键帽用洗手液什么的都擦一下洗洗干净，最后晾干了装好感觉就是一把新键盘了，后面主要是拖地了，这次最神奇的就是这个拖地，本来我就跟 LD 吹牛说拖地我是专业的，从小拖到大，有些地板缝边上的污渍，我又是用力来回拖，再用脚踩着拖，还是能把一些原来以为拖不掉的污渍给拖干净了，但是后来的厨房就比较难，用洗洁精来回拖感觉一点都起不来，可能是污渍积了太久了，一开始都想要放弃了，就打算拖干就好了，后来突然看到旁边有个洗衣服的板刷，结果竟然能刷起来，这样就停不下来了，说累是真的非常累，感觉刷一块瓷砖就要休息一会，但是整体刷完之后就是焕然一新的赶脚，简直太有成就感了。

    +]]>     + + 生活 + + + 生活 + 大扫除     @@ -7994,23 +7991,26 @@ user3: - 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

    + 聊下apollo配置中心的接入 + /2024/08/25/%E8%81%8A%E4%B8%8Bapollo%E9%85%8D%E7%BD%AE%E4%B8%AD%E5%BF%83%E7%9A%84%E6%8E%A5%E5%85%A5/ + 很多技术栈在优化过程中都会有更便捷的接入方式，或者接入demo，这次想拿apollo来对比一些例如向量数据库的部署方式，对我说的就是milvus，
    apollo如果生产环境部署完全不推荐用这种方式，但是如果为了做个实验，研究下源码还是很方便的，当然前提是有docker环境
    首先下载docker-compose配置文件，如果是x86环境就是这个链接，如果是m1这类的就是用这个链接，然后再下载sql文件夹
    目录结构差不多是这样

    然后在这个目录下执行
    sudo docker-compose -f docker-compose-arm64.yml up -d
    如果是非m1的话直接用
    sudo docker-compose up -d
    就好，因为docker-compose默认识别的文件名就是docker-compose.yml
    然后看下日志

    +
    ==== starting service ====
    Service logging file is ./service/apollo-service.log
    Application is running as root (UID 0). This is considered insecure.
    Started [66]
    Waiting for config service startup...
    Config service started. You may visit http://localhost:8080 for service status now!
    Waiting for admin service startup.
    Admin service started
    ==== starting portal ====
    Portal logging file is ./portal/apollo-portal.log
    Application is running as root (UID 0). This is considered insecure.
    Started [211]
    Waiting for portal startup...
    Portal started. You can visit http://localhost:8070 now!
    +

    就表示启动成功了，然后就可以访问后面那个地址 http://localhost:8070 进入控制台，默认用户名apollo，密码是admin
    然后我们在应用中想要使用，主要是这个几个配置
    第一个就是在resources目录下创建apollo-env.properties
    里面是meta server的地址，比如我这边就是

    +
    dev.meta=http://127.0.0.1:8080
    +

    这表示是对应的spring.profiles.active是dev的配置
    第二步是添加pom依赖

    +
    <dependency>
    	<groupId>com.ctrip.framework.apollo</groupId>
    	<artifactId>apollo-client</artifactId>
    	<version>2.0.1</version>
    </dependency>
    +

    最后一步是在springboot的启动类添加注解

    +
    @EnableApolloConfig
    +

    然后就可以直接使用@Value注解使用配置的值

    请求下这个接口,就可以看到对应的值

    然后我们可以在控制台修改下这个值，发布

    默认日志也会把这个打印出来

    +
    2024-08-24 19:38:30.462  INFO 57920 --- [Apollo-Config-1] c.f.a.s.p.AutoUpdateConfigChangeListener : Auto update apollo changed value successfully, new value: apolloDemoValue, key: demo, beanName: demoController, field: com.nicksxs.spbdemo.controller.DemoController.demo
    +

    对应的请求也会拿到最新的值

    ]]>     - 工具 + Java - 工具 + Java + Apollo     diff --git a/sitemap.xml b/sitemap.xml index 5c2f8d6a4d..1cf41794f8 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -1262,7 +1262,7 @@     - 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/2022/02/13/Disruptor-%E7%B3%BB%E5%88%97%E4%B8%80/ 2022-06-11 @@ -1271,7 +1271,7 @@ - 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 @@ -1424,7 +1424,7 @@ - https://nicksxs.me/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/ + https://nicksxs.me/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/ 2022-06-11 @@ -1433,7 +1433,7 @@ - https://nicksxs.me/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/ + https://nicksxs.me/2021/10/17/%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%E5%9B%9B/ 2022-06-11 @@ -1442,7 +1442,7 @@ - https://nicksxs.me/2021/10/17/%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%E5%9B%9B/ + https://nicksxs.me/2021/09/19/%E8%81%8A%E4%B8%80%E4%B8%8B-SpringBoot-%E4%B8%AD%E4%BD%BF%E7%94%A8%E7%9A%84-cglib-%E4%BD%9C%E4%B8%BA%E5%8A%A8%E6%80%81%E4%BB%A3%E7%90%86%E4%B8%AD%E7%9A%84%E4%B8%80%E4%B8%AA%E6%B3%A8%E6%84%8F%E7%82%B9/ 2022-06-11 @@ -1451,7 +1451,7 @@ - https://nicksxs.me/2021/09/19/%E8%81%8A%E4%B8%80%E4%B8%8B-SpringBoot-%E4%B8%AD%E4%BD%BF%E7%94%A8%E7%9A%84-cglib-%E4%BD%9C%E4%B8%BA%E5%8A%A8%E6%80%81%E4%BB%A3%E7%90%86%E4%B8%AD%E7%9A%84%E4%B8%80%E4%B8%AA%E6%B3%A8%E6%84%8F%E7%82%B9/ + https://nicksxs.me/2021/09/26/%E8%81%8A%E4%B8%80%E4%B8%8B-SpringBoot-%E4%B8%AD%E5%8A%A8%E6%80%81%E5%88%87%E6%8D%A2%E6%95%B0%E6%8D%AE%E6%BA%90%E7%9A%84%E6%96%B9%E6%B3%95/ 2022-06-11 @@ -1460,7 +1460,7 @@ - https://nicksxs.me/2021/09/26/%E8%81%8A%E4%B8%80%E4%B8%8B-SpringBoot-%E4%B8%AD%E5%8A%A8%E6%80%81%E5%88%87%E6%8D%A2%E6%95%B0%E6%8D%AE%E6%BA%90%E7%9A%84%E6%96%B9%E6%B3%95/ + https://nicksxs.me/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/ 2022-06-11 @@ -1487,7 +1487,7 @@ - https://nicksxs.me/2021/06/13/%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%E4%BA%8C/ + https://nicksxs.me/2020/08/02/%E8%81%8A%E8%81%8A-Java-%E8%87%AA%E5%B8%A6%E7%9A%84%E9%82%A3%E4%BA%9B%E9%80%86%E5%A4%A9%E5%B7%A5%E5%85%B7/ 2022-06-11 @@ -1496,7 +1496,7 @@ - https://nicksxs.me/2020/08/02/%E8%81%8A%E8%81%8A-Java-%E8%87%AA%E5%B8%A6%E7%9A%84%E9%82%A3%E4%BA%9B%E9%80%86%E5%A4%A9%E5%B7%A5%E5%85%B7/ + 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