diff --git a/2019/12/07/JVM-G1-Part-1/index.html b/2019/12/07/JVM-G1-Part-1/index.html
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+++ b/2019/12/07/JVM-G1-Part-1/index.html
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JVM源码分析之G1垃圾收集器分析一 | Nicksxs's Blog
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 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 , 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 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 ) { _result = g1h->attempt_allocation_at_safepoint (_word_size, false ); if (_result != NULL ) { _pause_succeeded = true ; return ; } } GCCauseSetter x (g1h, _gc_cause) ; if (_should_initiate_conc_mark) { _old_marking_cycles_completed_before = g1h->old_marking_cycles_completed (); bool res = g1h->g1_policy ()->force_initial_mark_if_outside_cycle(_gc_cause); 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 ; } } _pause_succeeded = g1h->do_collection_pause_at_safepoint (_target_pause_time_ms); if (_pause_succeeded) { if (_word_size > 0 ) { _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) { log_info (gc, ergo)("Attempting maximally compacting collection" ); _pause_succeeded = g1h->do_full_collection (false , true ); } } guarantee (_pause_succeeded, "Elevated collections during the safepoint must always succeed." ); } else { assert (_result == NULL , "invariant" ); _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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 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 (); if (!_cm_thread->should_terminate ()) { g1_policy ()->decide_on_conc_mark_initiation (); } assert (!collector_state ()->in_initial_mark_gc () || collector_state ()->in_young_only_phase (), "sanity" ); assert (!collector_state ()->mark_or_rebuild_in_progress () || collector_state ()->in_young_only_phase (), "sanity" ); bool should_start_conc_mark = collector_state ()->in_initial_mark_gc (); { EvacuationInfo evacuation_info; if (collector_state ()->in_initial_mark_gc ()) { 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 ) ; G1HeapTransition heap_transition (this ) ; size_t heap_used_bytes_before_gc = used (); { 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 _ref_processor_stw->enable_discovery (); { NoRefDiscovery no_cm_discovery (_ref_processor_cm) ; _allocator->release_mutator_alloc_region (); 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 ()); g1_rem_set ()->cleanupHRRS (); register_humongous_regions_with_cset (); assert (_verifier->check_cset_fast_test (), "Inconsistency in the InCSetState table." ); _cm->verify_no_cset_oops (); if (_hr_printer.is_active ()) { G1PrintCollectionSetClosure cl (&_hr_printer) ; _collection_set.iterate (&cl); } _allocator->init_gc_alloc_regions (evacuation_info); G1ParScanThreadStateSet per_thread_states (this , workers()->active_workers(), collection_set()->young_region_length()) ; pre_evacuate_collection_set (); 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 { increase_used (g1_policy ()->bytes_copied_during_gc ()); } if (collector_state ()->in_initial_mark_gc ()) { concurrent_mark ()->post_initial_mark (); } 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 (); double expand_ms; if (!expand (expand_bytes, _workers, &expand_ms)) { } g1_policy ()->phase_times ()->record_expand_heap_time (expand_ms); } } _cm->verify_no_cset_oops (); 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 (); } #ifdef TRACESPINNING ParallelTaskTerminator::print_termination_counts (); #endif gc_epilogue (false ); } if (evacuation_failed ()) { log_info (gc)("To-space exhausted" ); } g1_policy ()->print_phases (); heap_transition.print (); _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); 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 ()); } if (should_start_conc_mark) { 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1 2 3 4 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 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); 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 (); 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); 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 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) { 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 ; } 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) { 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) { if (old_region_length () >= min_old_cset_length) { 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 ; } expensive_region_num += 1 ; } } else { if (old_region_length () >= min_old_cset_length) { 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 ; } } time_remaining_ms = MAX2 (time_remaining_ms - predicted_time_ms, 0.0 ); predicted_old_time_ms += predicted_time_ms; cset_chooser ()->pop (); _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 ) { log_debug (gc, ergo, cset)("Added expensive regions to CSet (old CSet region num not reached min)." "old: %u regions, expensive: %u regions, min: %u regions, remaining time: %1.2fms" , old_region_length (), expensive_region_num, min_old_cset_length, time_remaining_ms); } cset_chooser ()->verify (); } stop_incremental_building (); log_debug (gc, ergo, cset)("Finish choosing CSet. old: %u regions, predicted old region time: %1.2fms, time remaining: %1.2f" , old_region_length (), predicted_old_time_ms, time_remaining_ms); double non_young_end_time_sec = os::elapsedTime (); phase_times ()->record_non_young_cset_choice_time_ms ((non_young_end_time_sec - non_young_start_time_sec) * 1000.0 ); QuickSort::sort (_collection_set_regions, _collection_set_cur_length, compare_region_idx, true ); }
上面第三行是个判断,当前是否是 mixed 回收阶段,如果不是的话其实是没有老年代什么事的,所以可以看到代码基本是从这个 if 判断if (collector_state()->in_mixed_phase()) {开始往下走的 先写到这,偏向于做笔记用,有错轻拍
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a:hover{background:var(--body-bg-color)}.main-inner{background:var(--content-bg-color);box-shadow:initial;padding:40px}@media (max-width:991px){.main-inner{padding:20px}}.sub-menu{border-bottom:1px solid #ddd}.post-block:first-of-type{padding-top:40px}@media (max-width:767px){.pagination{margin-bottom:10px}}JVM源码分析之G1垃圾收集器分析一 | Nicksxs's Blog
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 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 , 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 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 ) { _result = g1h->attempt_allocation_at_safepoint (_word_size, false ); if (_result != NULL ) { _pause_succeeded = true ; return ; } } GCCauseSetter x (g1h, _gc_cause) ; if (_should_initiate_conc_mark) { _old_marking_cycles_completed_before = g1h->old_marking_cycles_completed (); bool res = g1h->g1_policy ()->force_initial_mark_if_outside_cycle(_gc_cause); 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 ; } } _pause_succeeded = g1h->do_collection_pause_at_safepoint (_target_pause_time_ms); if (_pause_succeeded) { if (_word_size > 0 ) { _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) { log_info (gc, ergo)("Attempting maximally compacting collection" ); _pause_succeeded = g1h->do_full_collection (false , true ); } } guarantee (_pause_succeeded, "Elevated collections during the safepoint must always succeed." ); } else { assert (_result == NULL , "invariant" ); _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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 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 (); if (!_cm_thread->should_terminate ()) { g1_policy ()->decide_on_conc_mark_initiation (); } assert (!collector_state ()->in_initial_mark_gc () || collector_state ()->in_young_only_phase (), "sanity" ); assert (!collector_state ()->mark_or_rebuild_in_progress () || collector_state ()->in_young_only_phase (), "sanity" ); bool should_start_conc_mark = collector_state ()->in_initial_mark_gc (); { EvacuationInfo evacuation_info; if (collector_state ()->in_initial_mark_gc ()) { 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 ) ; G1HeapTransition heap_transition (this ) ; size_t heap_used_bytes_before_gc = used (); { 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 _ref_processor_stw->enable_discovery (); { NoRefDiscovery no_cm_discovery (_ref_processor_cm) ; _allocator->release_mutator_alloc_region (); 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 ()); g1_rem_set ()->cleanupHRRS (); register_humongous_regions_with_cset (); assert (_verifier->check_cset_fast_test (), "Inconsistency in the InCSetState table." ); _cm->verify_no_cset_oops (); if (_hr_printer.is_active ()) { G1PrintCollectionSetClosure cl (&_hr_printer) ; _collection_set.iterate (&cl); } _allocator->init_gc_alloc_regions (evacuation_info); G1ParScanThreadStateSet per_thread_states (this , workers()->active_workers(), collection_set()->young_region_length()) ; pre_evacuate_collection_set (); 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 { increase_used (g1_policy ()->bytes_copied_during_gc ()); } if (collector_state ()->in_initial_mark_gc ()) { concurrent_mark ()->post_initial_mark (); } 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 (); double expand_ms; if (!expand (expand_bytes, _workers, &expand_ms)) { } g1_policy ()->phase_times ()->record_expand_heap_time (expand_ms); } } _cm->verify_no_cset_oops (); 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 (); } #ifdef TRACESPINNING ParallelTaskTerminator::print_termination_counts (); #endif gc_epilogue (false ); } if (evacuation_failed ()) { log_info (gc)("To-space exhausted" ); } g1_policy ()->print_phases (); heap_transition.print (); _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); 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 ()); } if (should_start_conc_mark) { 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1 2 3 4 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 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); 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 (); 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); 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 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) { 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 ; } 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) { 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) { if (old_region_length () >= min_old_cset_length) { 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 ; } expensive_region_num += 1 ; } } else { if (old_region_length () >= min_old_cset_length) { 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 ; } } time_remaining_ms = MAX2 (time_remaining_ms - predicted_time_ms, 0.0 ); predicted_old_time_ms += predicted_time_ms; cset_chooser ()->pop (); _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 ) { 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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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
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
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 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, 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: 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 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, 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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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}}关于读书打卡与分享 | Nicksxs's Blog
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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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}}关于读书打卡与分享 | Nicksxs's Blog
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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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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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}}《长安的荔枝》读后感 | Nicksxs's Blog
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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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}}《长安的荔枝》读后感 | Nicksxs's Blog
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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diff --git a/leancloud_counter_security_urls.json b/leancloud_counter_security_urls.json
index 131b3d0e13..b907c17ba0 100644
--- a/leancloud_counter_security_urls.json
+++ b/leancloud_counter_security_urls.json
@@ -1 +1 @@
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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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 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 (); final Condition notFull = lock.newCondition(); final Condition notEmpty = lock.newCondition(); final Object[] items = new Object [100 ]; int putptr, takeptr, count; public void put (Object x) throws InterruptedException { lock.lock(); try { while (count == items.length) notFull.await(); items[putptr] = x; if (++putptr == items.length) putptr = 0 ; ++count; notEmpty.signal(); } finally { lock.unlock(); } } public Object take () throws InterruptedException { lock.lock(); try { while (count == 0 ) notEmpty.await(); Object x = items[takeptr]; if (++takeptr == items.length) takeptr = 0 ; --count; notFull.signal(); return x; } finally { lock.unlock(); } } }
介绍下 Condition 的结构1 2 3 4 5 6 public class ConditionObject implements Condition , java.io.Serializable { private static final long serialVersionUID = 1173984872572414699L ; private transient Node firstWaiter; 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 await1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 public final void await () throws InterruptedException { if (Thread.interrupted()) throw new InterruptedException (); Node node = addConditionWaiter(); int savedState = fullyRelease(node); int interruptMode = 0 ; while (!isOnSyncQueue(node)) { LockSupport.park(this ); if ((interruptMode = checkInterruptWhileWaiting(node)) != 0 ) break ; } if (acquireQueued(node, savedState) && interruptMode != THROW_IE) interruptMode = REINTERRUPT; if (node.nextWaiter != null ) unlinkCancelledWaiters(); if (interruptMode != 0 ) reportInterruptAfterWait(interruptMode); }
添加条件队列节点1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 private Node addConditionWaiter () { Node t = lastWaiter; if (t != null && t.waitStatus != Node.CONDITION) { unlinkCancelledWaiters(); t = lastWaiter; } Node node = new Node (Thread.currentThread(), Node.CONDITION); if (t == null ) firstWaiter = node; else t.nextWaiter = node; lastWaiter = node; return node; }
清理取消的节点1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 private void unlinkCancelledWaiters () { Node t = firstWaiter; Node trail = null ; while (t != null ) { Node next = t.nextWaiter; 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 final int fullyRelease (Node node) { boolean failed = true ; try { int savedState = getState(); if (release(savedState)) { failed = false ; return savedState; } else { throw new IllegalMonitorStateException (); } } finally { if (failed) node.waitStatus = Node.CANCELLED; } }
判断是否在阻塞队列中1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 final boolean isOnSyncQueue (Node node) { if (node.waitStatus == Node.CONDITION || node.prev == null ) return false ; if (node.next != null ) return true ; return findNodeFromTail(node); } private boolean findNodeFromTail (Node node) { Node t = tail; 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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 public final void signal () { if (!isHeldExclusively()) throw new IllegalMonitorStateException (); Node first = firstWaiter; if (first != null ) doSignal(first); } private void doSignal (Node first) { do { if ( (firstWaiter = first.nextWaiter) == null ) lastWaiter = null ; first.nextWaiter = null ; } while (!transferForSignal(first) && (first = firstWaiter) != null ); } final boolean transferForSignal (Node node) { if (!compareAndSetWaitStatus(node, Node.CONDITION, 0 )) return false ; Node p = enq(node); int ws = p.waitStatus; if (ws > 0 || !compareAndSetWaitStatus(p, ws, Node.SIGNAL)) LockSupport.unpark(node.thread); 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 BoundedBuffer1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 package EDU.oswego.cs.dl.util.concurrent;public class BoundedBuffer implements BoundedChannel { protected final Object[] array_; protected int takePtr_ = 0 ; protected int putPtr_ = 0 ; protected int usedSlots_ = 0 ; protected int emptySlots_; protected final Object putMonitor_ = new Object (); public BoundedBuffer (int capacity) throws IllegalArgumentException { if (capacity <= 0 ) throw new IllegalArgumentException (); array_ = new Object [capacity]; emptySlots_ = capacity; } public BoundedBuffer () { this (DefaultChannelCapacity.get()); } 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) { --emptySlots_; array_[putPtr_] = x; if (++putPtr_ >= array_.length) putPtr_ = 0 ; } protected final Object extract () { --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; } }
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 protected final boolean tryAcquire (int acquires) { final Thread current = Thread.currentThread(); int c = getState(); if (c == 0 ) { if (!hasQueuedPredecessors() && compareAndSetState(0 , acquires)) { setExclusiveOwnerThread(current); return true ; } } else if (current == getExclusiveOwnerThread()) { int nextc = c + acquires; if (nextc < 0 ) throw new Error ("Maximum lock count exceeded" ); setState(nextc); return true ; } 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 public final void acquire (int arg) { if (!tryAcquire(arg) && acquireQueued(addWaiter(Node.EXCLUSIVE), arg)) selfInterrupt(); }
然后来看下addWaiter的逻辑1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 private Node addWaiter (Node mode) { Node node = new Node (Thread.currentThread(), mode); Node pred = tail; if (pred != null ) { node.prev = pred; if (compareAndSetTail(pred, node)) { pred.next = node; return node; } } enq(node); return node; }
然后就是enq的逻辑了1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 private Node enq (final Node node) { for (;;) { Node t = tail; if (t == null ) { if (compareAndSetHead(new Node ())) tail = head; } else { 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 public void unlock () { sync.release(1 ); } public final boolean release (int arg) { 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 ; if (c == 0 ) { free = true ; setExclusiveOwnerThread(null ); } setState(c); return free; } public final boolean release (int arg) { if (tryRelease(arg)) { Node h = head; if (h != null && h.waitStatus != 0 ) unparkSuccessor(h); return true ; } return false ; } private void unparkSuccessor (Node node) { int ws = node.waitStatus; if (ws < 0 ) compareAndSetWaitStatus(node, ws, 0 ); Node s = node.next; if (s == null || s.waitStatus > 0 ) { s = null ; for (Node t = tail; t != null && t != node; t = t.prev) if (t.waitStatus <= 0 ) s = t; } if (s != null ) LockSupport.unpark(s.thread); }
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!
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。
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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 int ansNew = Integer.MIN_VALUE;public int maxPathSum (TreeNode root) { maxSumNew(root); return ansNew; } public int maxSumNew (TreeNode root) { if (root == null ) { return 0 ; } int left = maxSumNew(root.left); int right = maxSumNew(root.right); int currentSum = Math.max(Math.max(root.val + left , root.val + right), root.val); 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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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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 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 (); final Condition notFull = lock.newCondition(); final Condition notEmpty = lock.newCondition(); final Object[] items = new Object [100 ]; int putptr, takeptr, count; public void put (Object x) throws InterruptedException { lock.lock(); try { while (count == items.length) notFull.await(); items[putptr] = x; if (++putptr == items.length) putptr = 0 ; ++count; notEmpty.signal(); } finally { lock.unlock(); } } public Object take () throws InterruptedException { lock.lock(); try { while (count == 0 ) notEmpty.await(); Object x = items[takeptr]; if (++takeptr == items.length) takeptr = 0 ; --count; notFull.signal(); return x; } finally { lock.unlock(); } } }
介绍下 Condition 的结构1 2 3 4 5 6 public class ConditionObject implements Condition , java.io.Serializable { private static final long serialVersionUID = 1173984872572414699L ; private transient Node firstWaiter; 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 await1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 public final void await () throws InterruptedException { if (Thread.interrupted()) throw new InterruptedException (); Node node = addConditionWaiter(); int savedState = fullyRelease(node); int interruptMode = 0 ; while (!isOnSyncQueue(node)) { LockSupport.park(this ); if ((interruptMode = checkInterruptWhileWaiting(node)) != 0 ) break ; } if (acquireQueued(node, savedState) && interruptMode != THROW_IE) interruptMode = REINTERRUPT; if (node.nextWaiter != null ) unlinkCancelledWaiters(); if (interruptMode != 0 ) reportInterruptAfterWait(interruptMode); }
添加条件队列节点1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 private Node addConditionWaiter () { Node t = lastWaiter; if (t != null && t.waitStatus != Node.CONDITION) { unlinkCancelledWaiters(); t = lastWaiter; } Node node = new Node (Thread.currentThread(), Node.CONDITION); if (t == null ) firstWaiter = node; else t.nextWaiter = node; lastWaiter = node; return node; }
清理取消的节点1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 private void unlinkCancelledWaiters () { Node t = firstWaiter; Node trail = null ; while (t != null ) { Node next = t.nextWaiter; 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 final int fullyRelease (Node node) { boolean failed = true ; try { int savedState = getState(); if (release(savedState)) { failed = false ; return savedState; } else { throw new IllegalMonitorStateException (); } } finally { if (failed) node.waitStatus = Node.CANCELLED; } }
判断是否在阻塞队列中1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 final boolean isOnSyncQueue (Node node) { if (node.waitStatus == Node.CONDITION || node.prev == null ) return false ; if (node.next != null ) return true ; return findNodeFromTail(node); } private boolean findNodeFromTail (Node node) { Node t = tail; 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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 public final void signal () { if (!isHeldExclusively()) throw new IllegalMonitorStateException (); Node first = firstWaiter; if (first != null ) doSignal(first); } private void doSignal (Node first) { do { if ( (firstWaiter = first.nextWaiter) == null ) lastWaiter = null ; first.nextWaiter = null ; } while (!transferForSignal(first) && (first = firstWaiter) != null ); } final boolean transferForSignal (Node node) { if (!compareAndSetWaitStatus(node, Node.CONDITION, 0 )) return false ; Node p = enq(node); int ws = p.waitStatus; if (ws > 0 || !compareAndSetWaitStatus(p, ws, Node.SIGNAL)) LockSupport.unpark(node.thread); 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 BoundedBuffer1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 package EDU.oswego.cs.dl.util.concurrent;public class BoundedBuffer implements BoundedChannel { protected final Object[] array_; protected int takePtr_ = 0 ; protected int putPtr_ = 0 ; protected int usedSlots_ = 0 ; protected int emptySlots_; protected final Object putMonitor_ = new Object (); public BoundedBuffer (int capacity) throws IllegalArgumentException { if (capacity <= 0 ) throw new IllegalArgumentException (); array_ = new Object [capacity]; emptySlots_ = capacity; } public BoundedBuffer () { this (DefaultChannelCapacity.get()); } 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) { --emptySlots_; array_[putPtr_] = x; if (++putPtr_ >= array_.length) putPtr_ = 0 ; } protected final Object extract () { --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; } }
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 protected final boolean tryAcquire (int acquires) { final Thread current = Thread.currentThread(); int c = getState(); if (c == 0 ) { if (!hasQueuedPredecessors() && compareAndSetState(0 , acquires)) { setExclusiveOwnerThread(current); return true ; } } else if (current == getExclusiveOwnerThread()) { int nextc = c + acquires; if (nextc < 0 ) throw new Error ("Maximum lock count exceeded" ); setState(nextc); return true ; } 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 public final void acquire (int arg) { if (!tryAcquire(arg) && acquireQueued(addWaiter(Node.EXCLUSIVE), arg)) selfInterrupt(); }
然后来看下addWaiter的逻辑1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 private Node addWaiter (Node mode) { Node node = new Node (Thread.currentThread(), mode); Node pred = tail; if (pred != null ) { node.prev = pred; if (compareAndSetTail(pred, node)) { pred.next = node; return node; } } enq(node); return node; }
然后就是enq的逻辑了1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 private Node enq (final Node node) { for (;;) { Node t = tail; if (t == null ) { if (compareAndSetHead(new Node ())) tail = head; } else { 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 public void unlock () { sync.release(1 ); } public final boolean release (int arg) { 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 ; if (c == 0 ) { free = true ; setExclusiveOwnerThread(null ); } setState(c); return free; } public final boolean release (int arg) { if (tryRelease(arg)) { Node h = head; if (h != null && h.waitStatus != 0 ) unparkSuccessor(h); return true ; } return false ; } private void unparkSuccessor (Node node) { int ws = node.waitStatus; if (ws < 0 ) compareAndSetWaitStatus(node, ws, 0 ); Node s = node.next; if (s == null || s.waitStatus > 0 ) { s = null ; for (Node t = tail; t != null && t != node; t = t.prev) if (t.waitStatus <= 0 ) s = t; } if (s != null ) LockSupport.unpark(s.thread); }
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!
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。
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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 int ansNew = Integer.MIN_VALUE;public int maxPathSum (TreeNode root) { maxSumNew(root); return ansNew; } public int maxSumNew (TreeNode root) { if (root == null ) { return 0 ; } int left = maxSumNew(root.left); int right = maxSumNew(root.right); int currentSum = Math.max(Math.max(root.val + left , root.val + right), root.val); 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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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1 2 3 4 5 6 7 8 9 10 11 12 13 14 typedef struct intset { uint32_t encoding; uint32_t length; int8_t contents[]; } intset; #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:
1 2 3 4 struct vectord { size_t len; double arr[]; };
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
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 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1 2 3 4 5 6 7 #define ZSKIPLIST_P 0.25 int zslRandomLevel (void ) { int level = 1 ; while ((random()&0xFFFF ) < (ZSKIPLIST_P * 0xFFFF )) level += 1 ; return (level<ZSKIPLIST_MAXLEVEL) ? level : ZSKIPLIST_MAXLEVEL; }
当随机值跟0xFFFF进行与操作小于ZSKIPLIST_P * 0xFFFF时才会增大 level 的值,因此保持了一个相对递减的概率 可以简单分析下,当 random() 的值小于 0xFFFF 的 1/4,才会 level + 1,就意味着当有 1 - 1/4也就是3/4的概率是直接跳出,所以一层的概率是3/4,也就是 1-P,二层的概率是 P*(1-P),三层的概率是 P² * (1-P) 依次递推。
redis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1 2 3 4 5 6 7 8 struct sdshdr { int len; int free ; 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 typedef struct listNode { struct listNode *prev ; struct listNode *next ; void *value; } listNode; typedef struct list { listNode *head; listNode *tail; void *(*dup)(void *ptr); void (*free )(void *ptr); int (*match)(void *ptr, void *key); 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 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; 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; int iterators; } 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。
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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 public class Singleton1 { private Singleton1 () {}; private static Singleton instance = new Singleton1 (); public static Singleton1 getInstance () { return instance; } 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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 public class Singleton2 { private Singleton2 () {} private static volatile Singleton2 instance = null ; private int m = 9 ; public static Singleton getInstance () { if (instance == null ) { synchronized (Singleton2.class) { 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 1 2 3 4 5 6 7 8 9 10 11 public class Singleton3 { private Singleton3 () {} 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 1 2 3 4 5 6 7 public enum Singleton { INSTANCE; public void 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。
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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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1 2 3 4 5 6 7 8 9 10 11 12 13 14 typedef struct intset { uint32_t encoding; uint32_t length; int8_t contents[]; } intset; #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:
1 2 3 4 struct vectord { size_t len; double arr[]; };
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
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 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1 2 3 4 5 6 7 #define ZSKIPLIST_P 0.25 int zslRandomLevel (void ) { int level = 1 ; while ((random()&0xFFFF ) < (ZSKIPLIST_P * 0xFFFF )) level += 1 ; return (level<ZSKIPLIST_MAXLEVEL) ? level : ZSKIPLIST_MAXLEVEL; }
当随机值跟0xFFFF进行与操作小于ZSKIPLIST_P * 0xFFFF时才会增大 level 的值,因此保持了一个相对递减的概率 可以简单分析下,当 random() 的值小于 0xFFFF 的 1/4,才会 level + 1,就意味着当有 1 - 1/4也就是3/4的概率是直接跳出,所以一层的概率是3/4,也就是 1-P,二层的概率是 P*(1-P),三层的概率是 P² * (1-P) 依次递推。
redis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1 2 3 4 5 6 7 8 struct sdshdr { int len; int free ; 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 typedef struct listNode { struct listNode *prev ; struct listNode *next ; void *value; } listNode; typedef struct list { listNode *head; listNode *tail; void *(*dup)(void *ptr); void (*free )(void *ptr); int (*match)(void *ptr, void *key); 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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 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; 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; int iterators; } 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。
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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 public class Singleton1 { private Singleton1 () {}; private static Singleton instance = new Singleton1 (); public static Singleton1 getInstance () { return instance; } 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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 public class Singleton2 { private Singleton2 () {} private static volatile Singleton2 instance = null ; private int m = 9 ; public static Singleton getInstance () { if (instance == null ) { synchronized (Singleton2.class) { 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 1 2 3 4 5 6 7 8 9 10 11 public class Singleton3 { private Singleton3 () {} 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 1 2 3 4 5 6 7 public enum Singleton { INSTANCE; public void 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。
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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-
- 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/
@@ -41,8 +12,8 @@
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~
]]>
- 生活
读后感
+ 生活
村上春树
@@ -74,6 +45,58 @@
拖更
+
+ 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
+
+
+
+ 2021 年中总结
+ /2021/07/18/2021-%E5%B9%B4%E4%B8%AD%E6%80%BB%E7%BB%93/
+ 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。
+]]>
+
+ 生活
+ 年中总结
+ 2021
+
+
+ 生活
+ 2021
+ 技术
+ 读书
+ 年中总结
+
+
2020年中总结
/2020/07/11/2020%E5%B9%B4%E4%B8%AD%E6%80%BB%E7%BB%93/
@@ -93,6 +116,23 @@
年中总结
+
+ 2021 年终总结
+ /2022/01/22/2021-%E5%B9%B4%E7%BB%88%E6%80%BB%E7%BB%93/
+ 又是一年年终总结,本着极度讨厌实时需求的理念,我还是 T+N 发布这个年终总结
+工作篇 工作没什么大变化,有了些微的提升,可能因为是来了之后做了些项目对比公司与来还算是比较重要的,但是技术难度上没有特别突出的点,可能最开始用 openresty+lua 做了个 ab 测的工具,还是让我比较满意的,后面一般都是业务型的需求,今年可能在业务相关的技术逻辑上有了一些深度的了解,而原来一直想做的业务架构升级和通用型技术中间件这样的优化还是停留在想象中,前面说的 ab 测应该算是个半成品,还是没能多走出这一步,得需要多做一些实在的事情,比如轻量级的业务框架,能够对原先不熟悉的业务逻辑,代码逻辑有比较深入的理解,而不是一直都是让特定的同学负责特定的逻辑,很多时候还是在偷懒,习惯以一些简单安全的方案去做事情,在技术上还是要有所追求,还有就是能够在新语言,主要是 rust,swift 这类的能有些小玩具可以做,rust 的话是因为今年看了一本相关的书,后面三分之一其实消化得不好,这本书整体来说是很不错的,只是 rust 本身在所有权这块,还有引用包装等方面是设计得比较难懂,也可能是我基础差,所以还是想在复习下,可以做一个简单的命令行工具这种,然后 swift 是想说可以做点 mac 的小软件,原生的毕竟性能好点,又小。基于 web 做的客户端大部分都是又丑又大,极少数能好看点,但也是很重,起码 7~80M 的大小,原生的估计能除以 10。 整体的职业规划貌似陷入了比较大的困惑期,在目前公司发展前景不是很大,但是出去貌似也没有比较适合我的机会,总的来说还是杭州比较卷,个人觉得有自己的时间是非常重要的,而且这个不光是用来自我提升的,还是让自己有足够的时间做缓冲,有足够的时间锻炼减肥,时间少的情况下,不光会在仅有的时间里暴饮暴食,还没空锻炼,身体是革命的本钱,现在其实能特别明显地感觉到身体状态下滑,容易疲劳,焦虑。所以是否也许有可能以后要往外企这类的方向去发展。 工作上其实还是有个不大不小的缺点,就是容易激动,容易焦虑,前一点可能有稍稍地改观,因为工作中的很多现状其实是我个人难以改变的,即使觉得不合理,但是结构在那里,还不如自己放宽心,尽量做好事情就行。第二点的话还是做得比较差,一直以来抗压能力都比较差,跟成长环境,家庭环境都有比较大的关系,而且说实在的特别是父母,基本也没有在这方面给我正向的帮助,比较擅长给我施压,从小就是通过压力让我好好读书,当个乖学生,考个好学校,并没有能真正地理解我的压力,教我或者帮助我解压,只会在那说着不着边际的空话,甚至经常反过来对我施压。还是希望能慢慢解开,这点可能对我身体也有影响,也许需要看一些心理疏导相关的书籍。工作篇暂时到这,后续还有其他篇,未完待续哈哈😀
+]]>
+
+ 生活
+ 年终总结
+
+
+ 生活
+ 年终总结
+ 2021
+ 拖更
+
+
2022 年终总结
/2023/01/15/2022-%E5%B9%B4%E7%BB%88%E6%80%BB%E7%BB%93/
@@ -128,43 +168,38 @@
- 2021 年中总结
- /2021/07/18/2021-%E5%B9%B4%E4%B8%AD%E6%80%BB%E7%BB%93/
- 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。
-]]>
-
- 生活
- 年中总结
- 2021
-
-
- 生活
- 2021
- 年中总结
- 技术
- 读书
-
-
-
- 2021 年终总结
- /2022/01/22/2021-%E5%B9%B4%E7%BB%88%E6%80%BB%E7%BB%93/
- 又是一年年终总结,本着极度讨厌实时需求的理念,我还是 T+N 发布这个年终总结
-工作篇 工作没什么大变化,有了些微的提升,可能因为是来了之后做了些项目对比公司与来还算是比较重要的,但是技术难度上没有特别突出的点,可能最开始用 openresty+lua 做了个 ab 测的工具,还是让我比较满意的,后面一般都是业务型的需求,今年可能在业务相关的技术逻辑上有了一些深度的了解,而原来一直想做的业务架构升级和通用型技术中间件这样的优化还是停留在想象中,前面说的 ab 测应该算是个半成品,还是没能多走出这一步,得需要多做一些实在的事情,比如轻量级的业务框架,能够对原先不熟悉的业务逻辑,代码逻辑有比较深入的理解,而不是一直都是让特定的同学负责特定的逻辑,很多时候还是在偷懒,习惯以一些简单安全的方案去做事情,在技术上还是要有所追求,还有就是能够在新语言,主要是 rust,swift 这类的能有些小玩具可以做,rust 的话是因为今年看了一本相关的书,后面三分之一其实消化得不好,这本书整体来说是很不错的,只是 rust 本身在所有权这块,还有引用包装等方面是设计得比较难懂,也可能是我基础差,所以还是想在复习下,可以做一个简单的命令行工具这种,然后 swift 是想说可以做点 mac 的小软件,原生的毕竟性能好点,又小。基于 web 做的客户端大部分都是又丑又大,极少数能好看点,但也是很重,起码 7~80M 的大小,原生的估计能除以 10。 整体的职业规划貌似陷入了比较大的困惑期,在目前公司发展前景不是很大,但是出去貌似也没有比较适合我的机会,总的来说还是杭州比较卷,个人觉得有自己的时间是非常重要的,而且这个不光是用来自我提升的,还是让自己有足够的时间做缓冲,有足够的时间锻炼减肥,时间少的情况下,不光会在仅有的时间里暴饮暴食,还没空锻炼,身体是革命的本钱,现在其实能特别明显地感觉到身体状态下滑,容易疲劳,焦虑。所以是否也许有可能以后要往外企这类的方向去发展。 工作上其实还是有个不大不小的缺点,就是容易激动,容易焦虑,前一点可能有稍稍地改观,因为工作中的很多现状其实是我个人难以改变的,即使觉得不合理,但是结构在那里,还不如自己放宽心,尽量做好事情就行。第二点的话还是做得比较差,一直以来抗压能力都比较差,跟成长环境,家庭环境都有比较大的关系,而且说实在的特别是父母,基本也没有在这方面给我正向的帮助,比较擅长给我施压,从小就是通过压力让我好好读书,当个乖学生,考个好学校,并没有能真正地理解我的压力,教我或者帮助我解压,只会在那说着不着边际的空话,甚至经常反过来对我施压。还是希望能慢慢解开,这点可能对我身体也有影响,也许需要看一些心理疏导相关的书籍。工作篇暂时到这,后续还有其他篇,未完待续哈哈😀
+ 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
+ protected final boolean tryAcquire (int acquires) { final Thread current = Thread.currentThread(); int c = getState(); if (c == 0 ) { if (!hasQueuedPredecessors() && compareAndSetState(0 , acquires)) { setExclusiveOwnerThread(current); return true ; } } else if (current == getExclusiveOwnerThread()) { int nextc = c + acquires; if (nextc < 0 ) throw new Error ("Maximum lock count exceeded" ); setState(nextc); return true ; } 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
+ public final void acquire (int arg) { if (!tryAcquire(arg) && acquireQueued(addWaiter(Node.EXCLUSIVE), arg)) selfInterrupt(); }
+
+然后来看下addWaiter的逻辑
+ private Node addWaiter (Node mode) { Node node = new Node (Thread.currentThread(), mode); Node pred = tail; if (pred != null ) { node.prev = pred; if (compareAndSetTail(pred, node)) { pred.next = node; return node; } } enq(node); return node; }
+
+然后就是enq的逻辑了
+ private Node enq (final Node node) { for (;;) { Node t = tail; if (t == null ) { if (compareAndSetHead(new Node ())) tail = head; } else { 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
+ public void unlock () { sync.release(1 ); } public final boolean release (int arg) { 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 ; if (c == 0 ) { free = true ; setExclusiveOwnerThread(null ); } setState(c); return free; } public final boolean release (int arg) { if (tryRelease(arg)) { Node h = head; if (h != null && h.waitStatus != 0 ) unparkSuccessor(h); return true ; } return false ; } private void unparkSuccessor (Node node) { int ws = node.waitStatus; if (ws < 0 ) compareAndSetWaitStatus(node, ws, 0 ); Node s = node.next; if (s == null || s.waitStatus > 0 ) { s = null ; for (Node t = tail; t != null && t != node; t = t.prev) if (t.waitStatus <= 0 ) s = t; } if (s != null ) LockSupport.unpark(s.thread); }
+
+
+
+
]]>
- 生活
- 年终总结
+ Java
+ 并发
- 生活
- 年终总结
- 2021
- 拖更
+ java
+ 并发
+ j.u.c
+ aqs
@@ -208,6 +243,23 @@
unlock
+
+ 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
+private transient volatile Node head;private transient volatile Node tail;private volatile int state;private transient Thread exclusiveOwnerThread;
+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 { static final Node SHARED = new Node (); static final Node EXCLUSIVE = null ; static final int CANCELLED = 1 ; static final int SIGNAL = -1 ; static final int CONDITION = -2 ; static final int PROPAGATE = -3 ; volatile int waitStatus; volatile Node prev; volatile Node next; 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
+
+
add-two-number
/2015/04/14/Add-Two-Number/
@@ -228,23 +280,6 @@
c++
-
- 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
-private transient volatile Node head;private transient volatile Node tail;private volatile int state;private transient Thread exclusiveOwnerThread;
-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 { static final Node SHARED = new Node (); static final Node EXCLUSIVE = null ; static final int CANCELLED = 1 ; static final int SIGNAL = -1 ; static final int CONDITION = -2 ; static final int PROPAGATE = -3 ; volatile int waitStatus; volatile Node prev; volatile Node next; 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 如何获取当前环境
/2022/09/04/Apollo-%E5%A6%82%E4%BD%95%E8%8E%B7%E5%8F%96%E5%BD%93%E5%89%8D%E7%8E%AF%E5%A2%83/
@@ -338,6 +373,23 @@
注解
+
+ Clone Graph Part I
+ /2014/12/30/Clone-Graph-Part-I/
+ problemClone a graph. Input is a Node pointer. Return the Node pointer of the cloned graph. A graph is defined below: struct Node { vector neighbors; }
+
+
+code typedef unordered_map<Node *, Node *> Map; Node *clone (Node *graph) { if (!graph) return NULL ; Map map; queue<Node *> q; q.push (graph); Node *graphCopy = new Node (); map[graph] = graphCopy; while (!q.empty ()) { Node *node = q.front (); q.pop (); int n = node->neighbors.size (); for (int i = 0 ; i < n; i++) { Node *neighbor = node->neighbors[i]; if (map.find (neighbor) == map.end ()) { Node *p = new Node (); map[node]->neighbors.push_back (p); map[neighbor] = p; q.push (neighbor); } else { map[node]->neighbors.push_back (map[neighbor]); } } } return graphCopy; }
+anlysis using the Breadth-first traversal and use a map to save the neighbors not to be duplicated.
+]]>
+
+ leetcode
+
+
+ leetcode
+ C++
+
+
Comparator使用小记
/2020/04/05/Comparator%E4%BD%BF%E7%94%A8%E5%B0%8F%E8%AE%B0/
@@ -372,41 +424,6 @@
nullsfirst
-
- 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
- protected final boolean tryAcquire (int acquires) { final Thread current = Thread.currentThread(); int c = getState(); if (c == 0 ) { if (!hasQueuedPredecessors() && compareAndSetState(0 , acquires)) { setExclusiveOwnerThread(current); return true ; } } else if (current == getExclusiveOwnerThread()) { int nextc = c + acquires; if (nextc < 0 ) throw new Error ("Maximum lock count exceeded" ); setState(nextc); return true ; } 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
- public final void acquire (int arg) { if (!tryAcquire(arg) && acquireQueued(addWaiter(Node.EXCLUSIVE), arg)) selfInterrupt(); }
-
-然后来看下addWaiter的逻辑
- private Node addWaiter (Node mode) { Node node = new Node (Thread.currentThread(), mode); Node pred = tail; if (pred != null ) { node.prev = pred; if (compareAndSetTail(pred, node)) { pred.next = node; return node; } } enq(node); return node; }
-
-然后就是enq的逻辑了
- private Node enq (final Node node) { for (;;) { Node t = tail; if (t == null ) { if (compareAndSetHead(new Node ())) tail = head; } else { 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
- public void unlock () { sync.release(1 ); } public final boolean release (int arg) { 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 ; if (c == 0 ) { free = true ; setExclusiveOwnerThread(null ); } setState(c); return free; } public final boolean release (int arg) { if (tryRelease(arg)) { Node h = head; if (h != null && h.waitStatus != 0 ) unparkSuccessor(h); return true ; } return false ; } private void unparkSuccessor (Node node) { int ws = node.waitStatus; if (ws < 0 ) compareAndSetWaitStatus(node, ws, 0 ); Node s = node.next; if (s == null || s.waitStatus > 0 ) { s = null ; for (Node t = tail; t != null && t != node; t = t.prev) if (t.waitStatus <= 0 ) s = t; } if (s != null ) LockSupport.unpark(s.thread); }
-
-
-
-
-]]>
-
- 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/
@@ -454,43 +471,6 @@
Disruptor
-
- Filter, Interceptor, Aop, 啥, 啥, 啥? 这些都是啥?
- /2020/08/22/Filter-Intercepter-Aop-%E5%95%A5-%E5%95%A5-%E5%95%A5-%E8%BF%99%E4%BA%9B%E9%83%BD%E6%98%AF%E5%95%A5/
- 本来是想取个像现在那些公众号转了又转的文章标题,”面试官再问你xxxxx,就把这篇文章甩给他看”这种标题,但是觉得实在太 low 了,还是用一部我比较喜欢的电影里的一句台词,《人在囧途》里王宝强对着那张老板给他的欠条,看不懂字时候说的那句,这些都是些啥(第四声) 当我刚开始面 Java 的时候,其实我真的没注意这方面的东西,实话说就是不知道这些是啥,开发中用过 Interceptor和 Aop,了解 aop 的实现原理,但是不知道 Java web 中的 Filter 是怎么回事,知道 dubbo 的 filter,就这样,所以被问到了的确是回答不出来,可能就觉得这个渣渣,这么简单的都不会,所以还是花点时间来看看这个是个啥,为了避免我口吐芬芳,还是耐下性子来简单说下这几个东西 首先是 servlet,怎么去解释这个呢,因为之前是 PHPer,所以比较喜欢用它来举例子,在普通的 PHP 的 web 应用中一般有几部分组成,接受 HTTP 请求的是前置的 nginx 或者 apache,但是这俩玩意都是只能处理静态的请求,远古时代 PHP 和 HTML 混编是通过 apache 的 php module,跟后来 nginx 使用 php-fpm 其实道理类似,就是把请求中需要 PHP 处理的转发给 PHP,在 Java 中呢,是有个比较牛叉的叫 Tomcat 的,它可以把请求转成 servlet,而 servlet 其实就是一种实现了特定接口的 Java 代码,
-package javax.servlet;import java.io.IOException;public interface Servlet { public void init (ServletConfig config) throws ServletException; public ServletConfig getServletConfig () ; public void service (ServletRequest req, ServletResponse res) throws ServletException, IOException; public String getServletInfo () ; public void destroy () ; }
-重点看 servlet 的 service方法,就是接受请求,处理完了给响应,不说细节,不然光 Tomcat 的能说半年,所以呢再进一步去理解,其实就能知道,就是一个先后的问题,盗个图 filter 跟后两者最大的不一样其实是一个基于 servlet,在非常外层做的处理,然后是 interceptor 的 prehandle 跟 posthandle,接着才是我们常规的 aop,就这么点事情,做个小试验吧(还是先补段代码吧)
-Filter @Override public void doFilter (ServletRequest request, ServletResponse response) throws IOException, ServletException { if ( Globals.IS_SECURITY_ENABLED ) { final ServletRequest req = request; final ServletResponse res = response; try { java.security.AccessController.doPrivileged( new java .security.PrivilegedExceptionAction<Void>() { @Override public Void run () throws ServletException, IOException { internalDoFilter(req,res); return null ; } } ); } catch ( PrivilegedActionException pe) { Exception e = pe.getException(); if (e instanceof ServletException) throw (ServletException) e; else if (e instanceof IOException) throw (IOException) e; else if (e instanceof RuntimeException) throw (RuntimeException) e; else throw new ServletException (e.getMessage(), e); } } else { internalDoFilter(request,response); } } private void internalDoFilter (ServletRequest request, ServletResponse response) throws IOException, ServletException { if (pos < n) { ApplicationFilterConfig filterConfig = filters[pos++]; try { Filter filter = filterConfig.getFilter(); if (request.isAsyncSupported() && "false" .equalsIgnoreCase( filterConfig.getFilterDef().getAsyncSupported())) { request.setAttribute(Globals.ASYNC_SUPPORTED_ATTR, Boolean.FALSE); } if ( Globals.IS_SECURITY_ENABLED ) { final ServletRequest req = request; final ServletResponse res = response; Principal principal = ((HttpServletRequest) req).getUserPrincipal(); Object[] args = new Object []{req, res, this }; SecurityUtil.doAsPrivilege ("doFilter" , filter, classType, args, principal); } else { filter.doFilter(request, response, this ); } } catch (IOException | ServletException | RuntimeException e) { throw e; } catch (Throwable e) { e = ExceptionUtils.unwrapInvocationTargetException(e); ExceptionUtils.handleThrowable(e); throw new ServletException (sm.getString("filterChain.filter" ), e); } return ; } try { if (ApplicationDispatcher.WRAP_SAME_OBJECT) { lastServicedRequest.set(request); lastServicedResponse.set(response); } if (request.isAsyncSupported() && !servletSupportsAsync) { request.setAttribute(Globals.ASYNC_SUPPORTED_ATTR, Boolean.FALSE); } if ((request instanceof HttpServletRequest) && (response instanceof HttpServletResponse) && Globals.IS_SECURITY_ENABLED ) { final ServletRequest req = request; final ServletResponse res = response; Principal principal = ((HttpServletRequest) req).getUserPrincipal(); Object[] args = new Object []{req, res}; SecurityUtil.doAsPrivilege("service" , servlet, classTypeUsedInService, args, principal); } else { servlet.service(request, response); } } catch (IOException | ServletException | RuntimeException e) { throw e; } catch (Throwable e) { e = ExceptionUtils.unwrapInvocationTargetException(e); ExceptionUtils.handleThrowable(e); throw new ServletException (sm.getString("filterChain.servlet" ), e); } finally { if (ApplicationDispatcher.WRAP_SAME_OBJECT) { lastServicedRequest.set(null ); lastServicedResponse.set(null ); } } }
-注意看这一行filter.doFilter(request, response, this); 是不是看懂了,就是个 filter 链,但是这个代码在哪呢,org.apache.catalina.core.ApplicationFilterChain#doFilter 然后是interceptor,
-protected void doDispatch (HttpServletRequest request, HttpServletResponse response) throws Exception { HttpServletRequest processedRequest = request; HandlerExecutionChain mappedHandler = null ; boolean multipartRequestParsed = false ; WebAsyncManager asyncManager = WebAsyncUtils.getAsyncManager(request); try { try { ModelAndView mv = null ; Object dispatchException = null ; try { processedRequest = this .checkMultipart(request); multipartRequestParsed = processedRequest != request; mappedHandler = this .getHandler(processedRequest); if (mappedHandler == null ) { this .noHandlerFound(processedRequest, response); return ; } HandlerAdapter ha = this .getHandlerAdapter(mappedHandler.getHandler()); String method = request.getMethod(); boolean isGet = "GET" .equals(method); if (isGet || "HEAD" .equals(method)) { long lastModified = ha.getLastModified(request, mappedHandler.getHandler()); if ((new ServletWebRequest (request, response)).checkNotModified(lastModified) && isGet) { return ; } } if (!mappedHandler.applyPreHandle(processedRequest, response)) { return ; } mv = ha.handle(processedRequest, response, mappedHandler.getHandler()); if (asyncManager.isConcurrentHandlingStarted()) { return ; } this .applyDefaultViewName(processedRequest, mv); mappedHandler.applyPostHandle(processedRequest, response, mv); } catch (Exception var20) { dispatchException = var20; } catch (Throwable var21) { dispatchException = new NestedServletException ("Handler dispatch failed" , var21); } this .processDispatchResult(processedRequest, response, mappedHandler, mv, (Exception)dispatchException); } catch (Exception var22) { this .triggerAfterCompletion(processedRequest, response, mappedHandler, var22); } catch (Throwable var23) { this .triggerAfterCompletion(processedRequest, response, mappedHandler, new NestedServletException ("Handler processing failed" , var23)); } } finally { if (asyncManager.isConcurrentHandlingStarted()) { if (mappedHandler != null ) { mappedHandler.applyAfterConcurrentHandlingStarted(processedRequest, response); } } else if (multipartRequestParsed) { this .cleanupMultipart(processedRequest); } } }
-代码在哪呢,org.springframework.web.servlet.DispatcherServlet#doDispatch,然后才是我们自己写的 aop,是不是差不多明白了,嗯,接下来是例子 写个 filter
-public class DemoFilter extends HttpServlet implements Filter { @Override public void init (FilterConfig filterConfig) throws ServletException { System.out.println("==>DemoFilter启动" ); } @Override public void doFilter (ServletRequest servletRequest, ServletResponse servletResponse, FilterChain filterChain) throws IOException, ServletException { HttpServletRequest req = (HttpServletRequest) servletRequest; HttpServletResponse resp = (HttpServletResponse) servletResponse; System.out.println("before filter" ); filterChain.doFilter(req, resp); System.out.println("after filter" ); } @Override public void destroy () { } }
-因为用的springboot,所以就不写 web.xml 了,写个配置类
-@Configuration public class FilterConfiguration { @Bean public FilterRegistrationBean filterDemo4Registration () { FilterRegistrationBean registration = new FilterRegistrationBean (); registration.setFilter(new DemoFilter ()); registration.addUrlPatterns("/*" ); registration.setName("DemoFilter" ); registration.setEnabled(true ); registration.setOrder(1 ); return registration; } }
-然后再来个 interceptor 和 aop,以及一个简单的请求处理
-public class DemoInterceptor extends HandlerInterceptorAdapter { @Override public boolean preHandle (HttpServletRequest request, HttpServletResponse response, Object handler) throws Exception { System.out.println("preHandle test" ); return true ; } @Override public void postHandle (HttpServletRequest request, HttpServletResponse response, Object handler, ModelAndView modelAndView) throws Exception { System.out.println("postHandle test" ); } } @Aspect @Component public class DemoAspect { @Pointcut("execution( public * com.nicksxs.springbootdemo.demo.DemoController.*())") public void point () { } @Before("point()") public void doBefore () { System.out.println("==doBefore==" ); } @After("point()") public void doAfter () { System.out.println("==doAfter==" ); } } @RestController public class DemoController { @RequestMapping("/hello") @ResponseBody public String hello () { return "hello world" ; } }
-好了,请求一下,看看 stdout, 搞定完事儿~
-]]>
-
- Java
- Filter
- Interceptor - AOP
- Spring
- Servlet
- Interceptor
- AOP
-
-
- Java
- Filter
- Interceptor
- AOP
- Spring
- Tomcat
- Servlet
- Web
-
-
Disruptor 系列二
/2022/02/27/Disruptor-%E7%B3%BB%E5%88%97%E4%BA%8C/
@@ -528,6 +508,43 @@
负载均衡
+
+ Filter, Interceptor, Aop, 啥, 啥, 啥? 这些都是啥?
+ /2020/08/22/Filter-Intercepter-Aop-%E5%95%A5-%E5%95%A5-%E5%95%A5-%E8%BF%99%E4%BA%9B%E9%83%BD%E6%98%AF%E5%95%A5/
+ 本来是想取个像现在那些公众号转了又转的文章标题,”面试官再问你xxxxx,就把这篇文章甩给他看”这种标题,但是觉得实在太 low 了,还是用一部我比较喜欢的电影里的一句台词,《人在囧途》里王宝强对着那张老板给他的欠条,看不懂字时候说的那句,这些都是些啥(第四声) 当我刚开始面 Java 的时候,其实我真的没注意这方面的东西,实话说就是不知道这些是啥,开发中用过 Interceptor和 Aop,了解 aop 的实现原理,但是不知道 Java web 中的 Filter 是怎么回事,知道 dubbo 的 filter,就这样,所以被问到了的确是回答不出来,可能就觉得这个渣渣,这么简单的都不会,所以还是花点时间来看看这个是个啥,为了避免我口吐芬芳,还是耐下性子来简单说下这几个东西 首先是 servlet,怎么去解释这个呢,因为之前是 PHPer,所以比较喜欢用它来举例子,在普通的 PHP 的 web 应用中一般有几部分组成,接受 HTTP 请求的是前置的 nginx 或者 apache,但是这俩玩意都是只能处理静态的请求,远古时代 PHP 和 HTML 混编是通过 apache 的 php module,跟后来 nginx 使用 php-fpm 其实道理类似,就是把请求中需要 PHP 处理的转发给 PHP,在 Java 中呢,是有个比较牛叉的叫 Tomcat 的,它可以把请求转成 servlet,而 servlet 其实就是一种实现了特定接口的 Java 代码,
+package javax.servlet;import java.io.IOException;public interface Servlet { public void init (ServletConfig config) throws ServletException; public ServletConfig getServletConfig () ; public void service (ServletRequest req, ServletResponse res) throws ServletException, IOException; public String getServletInfo () ; public void destroy () ; }
+重点看 servlet 的 service方法,就是接受请求,处理完了给响应,不说细节,不然光 Tomcat 的能说半年,所以呢再进一步去理解,其实就能知道,就是一个先后的问题,盗个图 filter 跟后两者最大的不一样其实是一个基于 servlet,在非常外层做的处理,然后是 interceptor 的 prehandle 跟 posthandle,接着才是我们常规的 aop,就这么点事情,做个小试验吧(还是先补段代码吧)
+Filter @Override public void doFilter (ServletRequest request, ServletResponse response) throws IOException, ServletException { if ( Globals.IS_SECURITY_ENABLED ) { final ServletRequest req = request; final ServletResponse res = response; try { java.security.AccessController.doPrivileged( new java .security.PrivilegedExceptionAction<Void>() { @Override public Void run () throws ServletException, IOException { internalDoFilter(req,res); return null ; } } ); } catch ( PrivilegedActionException pe) { Exception e = pe.getException(); if (e instanceof ServletException) throw (ServletException) e; else if (e instanceof IOException) throw (IOException) e; else if (e instanceof RuntimeException) throw (RuntimeException) e; else throw new ServletException (e.getMessage(), e); } } else { internalDoFilter(request,response); } } private void internalDoFilter (ServletRequest request, ServletResponse response) throws IOException, ServletException { if (pos < n) { ApplicationFilterConfig filterConfig = filters[pos++]; try { Filter filter = filterConfig.getFilter(); if (request.isAsyncSupported() && "false" .equalsIgnoreCase( filterConfig.getFilterDef().getAsyncSupported())) { request.setAttribute(Globals.ASYNC_SUPPORTED_ATTR, Boolean.FALSE); } if ( Globals.IS_SECURITY_ENABLED ) { final ServletRequest req = request; final ServletResponse res = response; Principal principal = ((HttpServletRequest) req).getUserPrincipal(); Object[] args = new Object []{req, res, this }; SecurityUtil.doAsPrivilege ("doFilter" , filter, classType, args, principal); } else { filter.doFilter(request, response, this ); } } catch (IOException | ServletException | RuntimeException e) { throw e; } catch (Throwable e) { e = ExceptionUtils.unwrapInvocationTargetException(e); ExceptionUtils.handleThrowable(e); throw new ServletException (sm.getString("filterChain.filter" ), e); } return ; } try { if (ApplicationDispatcher.WRAP_SAME_OBJECT) { lastServicedRequest.set(request); lastServicedResponse.set(response); } if (request.isAsyncSupported() && !servletSupportsAsync) { request.setAttribute(Globals.ASYNC_SUPPORTED_ATTR, Boolean.FALSE); } if ((request instanceof HttpServletRequest) && (response instanceof HttpServletResponse) && Globals.IS_SECURITY_ENABLED ) { final ServletRequest req = request; final ServletResponse res = response; Principal principal = ((HttpServletRequest) req).getUserPrincipal(); Object[] args = new Object []{req, res}; SecurityUtil.doAsPrivilege("service" , servlet, classTypeUsedInService, args, principal); } else { servlet.service(request, response); } } catch (IOException | ServletException | RuntimeException e) { throw e; } catch (Throwable e) { e = ExceptionUtils.unwrapInvocationTargetException(e); ExceptionUtils.handleThrowable(e); throw new ServletException (sm.getString("filterChain.servlet" ), e); } finally { if (ApplicationDispatcher.WRAP_SAME_OBJECT) { lastServicedRequest.set(null ); lastServicedResponse.set(null ); } } }
+注意看这一行filter.doFilter(request, response, this); 是不是看懂了,就是个 filter 链,但是这个代码在哪呢,org.apache.catalina.core.ApplicationFilterChain#doFilter 然后是interceptor,
+protected void doDispatch (HttpServletRequest request, HttpServletResponse response) throws Exception { HttpServletRequest processedRequest = request; HandlerExecutionChain mappedHandler = null ; boolean multipartRequestParsed = false ; WebAsyncManager asyncManager = WebAsyncUtils.getAsyncManager(request); try { try { ModelAndView mv = null ; Object dispatchException = null ; try { processedRequest = this .checkMultipart(request); multipartRequestParsed = processedRequest != request; mappedHandler = this .getHandler(processedRequest); if (mappedHandler == null ) { this .noHandlerFound(processedRequest, response); return ; } HandlerAdapter ha = this .getHandlerAdapter(mappedHandler.getHandler()); String method = request.getMethod(); boolean isGet = "GET" .equals(method); if (isGet || "HEAD" .equals(method)) { long lastModified = ha.getLastModified(request, mappedHandler.getHandler()); if ((new ServletWebRequest (request, response)).checkNotModified(lastModified) && isGet) { return ; } } if (!mappedHandler.applyPreHandle(processedRequest, response)) { return ; } mv = ha.handle(processedRequest, response, mappedHandler.getHandler()); if (asyncManager.isConcurrentHandlingStarted()) { return ; } this .applyDefaultViewName(processedRequest, mv); mappedHandler.applyPostHandle(processedRequest, response, mv); } catch (Exception var20) { dispatchException = var20; } catch (Throwable var21) { dispatchException = new NestedServletException ("Handler dispatch failed" , var21); } this .processDispatchResult(processedRequest, response, mappedHandler, mv, (Exception)dispatchException); } catch (Exception var22) { this .triggerAfterCompletion(processedRequest, response, mappedHandler, var22); } catch (Throwable var23) { this .triggerAfterCompletion(processedRequest, response, mappedHandler, new NestedServletException ("Handler processing failed" , var23)); } } finally { if (asyncManager.isConcurrentHandlingStarted()) { if (mappedHandler != null ) { mappedHandler.applyAfterConcurrentHandlingStarted(processedRequest, response); } } else if (multipartRequestParsed) { this .cleanupMultipart(processedRequest); } } }
+代码在哪呢,org.springframework.web.servlet.DispatcherServlet#doDispatch,然后才是我们自己写的 aop,是不是差不多明白了,嗯,接下来是例子 写个 filter
+public class DemoFilter extends HttpServlet implements Filter { @Override public void init (FilterConfig filterConfig) throws ServletException { System.out.println("==>DemoFilter启动" ); } @Override public void doFilter (ServletRequest servletRequest, ServletResponse servletResponse, FilterChain filterChain) throws IOException, ServletException { HttpServletRequest req = (HttpServletRequest) servletRequest; HttpServletResponse resp = (HttpServletResponse) servletResponse; System.out.println("before filter" ); filterChain.doFilter(req, resp); System.out.println("after filter" ); } @Override public void destroy () { } }
+因为用的springboot,所以就不写 web.xml 了,写个配置类
+@Configuration public class FilterConfiguration { @Bean public FilterRegistrationBean filterDemo4Registration () { FilterRegistrationBean registration = new FilterRegistrationBean (); registration.setFilter(new DemoFilter ()); registration.addUrlPatterns("/*" ); registration.setName("DemoFilter" ); registration.setEnabled(true ); registration.setOrder(1 ); return registration; } }
+然后再来个 interceptor 和 aop,以及一个简单的请求处理
+public class DemoInterceptor extends HandlerInterceptorAdapter { @Override public boolean preHandle (HttpServletRequest request, HttpServletResponse response, Object handler) throws Exception { System.out.println("preHandle test" ); return true ; } @Override public void postHandle (HttpServletRequest request, HttpServletResponse response, Object handler, ModelAndView modelAndView) throws Exception { System.out.println("postHandle test" ); } } @Aspect @Component public class DemoAspect { @Pointcut("execution( public * com.nicksxs.springbootdemo.demo.DemoController.*())") public void point () { } @Before("point()") public void doBefore () { System.out.println("==doBefore==" ); } @After("point()") public void doAfter () { System.out.println("==doAfter==" ); } } @RestController public class DemoController { @RequestMapping("/hello") @ResponseBody public String hello () { return "hello world" ; } }
+好了,请求一下,看看 stdout, 搞定完事儿~
+]]>
+
+ Java
+ Filter
+ Interceptor - AOP
+ Spring
+ Servlet
+ Interceptor
+ AOP
+
+
+ Java
+ Filter
+ Interceptor
+ AOP
+ Spring
+ Tomcat
+ Servlet
+ Web
+
+
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/
@@ -550,13 +567,51 @@
Java
- JVM
C++
+ JVM
G1
GC
Garbage-First Collector
+
+ 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/
+ derper是 tailscale 已经开源的 tailscale 中转服务,可以进行自己搭建,不然就是使用 tailscale 提供的 derper 节点,我的体验是在晚上高峰期会比较卡,所以有能力可以自建的话也是比较推荐的,不过相对比前面的设置会麻烦点
+第一个就是默认这个是需要 https 的,所以需要给 derper 服务申请个域名,或者有了域名就加个解析,比如 dp.hello.com,申请完域名就需要申请证书了,这个刚好就学习下使用 acme.sh这个工具,还是非常不错的,我用的是腾讯云的域名解析服务,其实是收购的 dnspod 的服务,acme.sh 安装就很简单,一条命令
+`
+curl https://get.acme.sh | sh
+然后成功了之后就要把.zshrc 生效下,默认会把脚本路径加入到当前的 shell 的 rc 文件里,然后就是申请证书,但是有两种方式,一种就是在域名指定的 ip 机器对应的访问路径下放置验证的文件
+acme.sh --issue -d domain.tld -d www.domain.tld --webroot /home/wwwroot/domain.tld/
+比如这样指定 webroot,另一个中更方便的方式就是通过 dns 认证的方式,我这边以 dnspod 举例,这边要特别注意的是 dnspod 虽然被腾讯云收购,但是这个方式还是需要用 dnspod 原来自己的 api token 验证的
+
+然后拿到生成的 id 跟 token 设置到环境变量里,需要保存好这个 token,后面是看不到了的
+export DP_Id=123456export DP_Key=xxxxxx
+然后就可以用命令进行验证了
+acme.sh --issue --dns dns_dp -d xxx.hello.com
+这个是 dnspod 家的,其他的也可以在这里 找到验证方式
+申请完成后会把证书放在用户目录下的.acme.sh 目录中,记得把cer文件改成 crt,并且文件名只能是域名 xxx.hello.com.crt,我没试过其他的行不行,从别的文章里看到需要一致,然后就是启动 derper 了
+我们通过 docker 的方式,
+sudo docker run --restart always \ --name derper -p 12345:12345 -p 3478:3478/udp \ -v /$dir /.acme.sh/xxxxx.hello.com_ecc/:/app/certs \ -v /run/tailscale/tailscaled.sock:/var/run/tailscale/tailscaled.sock \ -e DERP_CERT_MODE=manual \ -e DERP_ADDR=:12345 \ -e DERP_DOMAIN=xxxxx.hello.com \ -e DERP_VERIFY_CLIENTS=true \ -d ghcr.io/yangchuansheng/derper:latest
+ 前一个端口 12345可以自己随意定,后面$dir改成自己的.acme.sh目录所在的路径,然后
+ -v /run/tailscale/tailscaled.sock:/var/run/tailscale/tailscaled.sock \ 这一行 和-e DERP_VERIFY_CLIENTS=true \ 是为了能够让 derper 能够安全一些,就是在本机开一个 tailscale 客户端,连接接到 headscale 服务,可以参考前面的添加节点 ,然后把这个启动客户端的进程映射进docker里面,因为本身 derper 就没有特别的认证逻辑,所以就让同一个headscale服务的客户端进程来验证是属于同一个headscale服务的节点才允许加入中转,实际这样就跑起来了一个 derper 服务这样可以用 docker 命令查看日志
+sudo docker logs -f derper 查看是不是有问题,然后如果没有问题的话还不一定代表 derper 起来了,比较直接的方法就是直接打开域名加上端口的地址,比如 xxxx.hello.com:12345 这个,
+
+显示这个就代表服务已经正常启动运行了,然后就是需要修改 headscale 的配置文件了,首先要设置 derper 的配置文件,类似这样
+regions: 100: regionid: 100 regioncode: ahk regionname: Aliyun Hongkong nodes: - name: 100a regionid: 100 hostname: xxxxx.hello.com ipv4: stunport: 3478 stunonly: false derpport: 12345
+regionid表示区域 id,一个区域下可以后多个 node,node的 name 是节点的唯一识别码,ipv4 可以不填,有 hostname 就可以,然后stunonly 是 false 表示不只使用 stun还可以使用 derp
+然后就是修改 headscale 自身的配置文件,把共用的 derper 配置改成
+
+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/
@@ -583,52 +638,50 @@
- Clone Graph Part I
- /2014/12/30/Clone-Graph-Part-I/
- problemClone a graph. Input is a Node pointer. Return the Node pointer of the cloned graph. A graph is defined below: struct Node { vector neighbors; }
+ 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
-
-code typedef unordered_map<Node *, Node *> Map; Node *clone (Node *graph) { if (!graph) return NULL ; Map map; queue<Node *> q; q.push (graph); Node *graphCopy = new Node (); map[graph] = graphCopy; while (!q.empty ()) { Node *node = q.front (); q.pop (); int n = node->neighbors.size (); for (int i = 0 ; i < n; i++) { Node *neighbor = node->neighbors[i]; if (map.find (neighbor) == map.end ()) { Node *p = new Node (); map[node]->neighbors.push_back (p); map[neighbor] = p; q.push (neighbor); } else { map[node]->neighbors.push_back (map[neighbor]); } } } return graphCopy; }
-anlysis using the Breadth-first traversal and use a map to save the neighbors not to be duplicated.
+到这里我们只完成了第一步,第二步需要在 headscale 服务端启用这条路由配置 首先可以用 sudo headscale routes 查看当前有的路由sudo headscale routes enable -h 这里看下怎么启用,因为网上有些是用-i 指定哪一条路由的,但是我使用的这个版本是用-r 的sudo headscale routes enable -r 1 然后用这个命令启用就行了 这样我们就能够很方便地在单个节点互连之后访问到对方局域网内的其他节点
]]>
- leetcode
+ headscale
- leetcode
- C++
+ headscale
- 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/
- derper是 tailscale 已经开源的 tailscale 中转服务,可以进行自己搭建,不然就是使用 tailscale 提供的 derper 节点,我的体验是在晚上高峰期会比较卡,所以有能力可以自建的话也是比较推荐的,不过相对比前面的设置会麻烦点
-第一个就是默认这个是需要 https 的,所以需要给 derper 服务申请个域名,或者有了域名就加个解析,比如 dp.hello.com,申请完域名就需要申请证书了,这个刚好就学习下使用 acme.sh这个工具,还是非常不错的,我用的是腾讯云的域名解析服务,其实是收购的 dnspod 的服务,acme.sh 安装就很简单,一条命令
-`
-curl https://get.acme.sh | sh
-然后成功了之后就要把.zshrc 生效下,默认会把脚本路径加入到当前的 shell 的 rc 文件里,然后就是申请证书,但是有两种方式,一种就是在域名指定的 ip 机器对应的访问路径下放置验证的文件
-acme.sh --issue -d domain.tld -d www.domain.tld --webroot /home/wwwroot/domain.tld/
-比如这样指定 webroot,另一个中更方便的方式就是通过 dns 认证的方式,我这边以 dnspod 举例,这边要特别注意的是 dnspod 虽然被腾讯云收购,但是这个方式还是需要用 dnspod 原来自己的 api token 验证的
-
-然后拿到生成的 id 跟 token 设置到环境变量里,需要保存好这个 token,后面是看不到了的
-export DP_Id=123456export DP_Key=xxxxxx
-然后就可以用命令进行验证了
-acme.sh --issue --dns dns_dp -d xxx.hello.com
-这个是 dnspod 家的,其他的也可以在这里 找到验证方式
-申请完成后会把证书放在用户目录下的.acme.sh 目录中,记得把cer文件改成 crt,并且文件名只能是域名 xxx.hello.com.crt,我没试过其他的行不行,从别的文章里看到需要一致,然后就是启动 derper 了
-我们通过 docker 的方式,
-sudo docker run --restart always \ --name derper -p 12345:12345 -p 3478:3478/udp \ -v /$dir /.acme.sh/xxxxx.hello.com_ecc/:/app/certs \ -v /run/tailscale/tailscaled.sock:/var/run/tailscale/tailscaled.sock \ -e DERP_CERT_MODE=manual \ -e DERP_ADDR=:12345 \ -e DERP_DOMAIN=xxxxx.hello.com \ -e DERP_VERIFY_CLIENTS=true \ -d ghcr.io/yangchuansheng/derper:latest
- 前一个端口 12345可以自己随意定,后面$dir改成自己的.acme.sh目录所在的路径,然后
- -v /run/tailscale/tailscaled.sock:/var/run/tailscale/tailscaled.sock \ 这一行 和-e DERP_VERIFY_CLIENTS=true \ 是为了能够让 derper 能够安全一些,就是在本机开一个 tailscale 客户端,连接接到 headscale 服务,可以参考前面的添加节点 ,然后把这个启动客户端的进程映射进docker里面,因为本身 derper 就没有特别的认证逻辑,所以就让同一个headscale服务的客户端进程来验证是属于同一个headscale服务的节点才允许加入中转,实际这样就跑起来了一个 derper 服务这样可以用 docker 命令查看日志
-sudo docker logs -f derper 查看是不是有问题,然后如果没有问题的话还不一定代表 derper 起来了,比较直接的方法就是直接打开域名加上端口的地址,比如 xxxx.hello.com:12345 这个,
-
-显示这个就代表服务已经正常启动运行了,然后就是需要修改 headscale 的配置文件了,首先要设置 derper 的配置文件,类似这样
-regions: 100: regionid: 100 regioncode: ahk regionname: Aliyun Hongkong nodes: - name: 100a regionid: 100 hostname: xxxxx.hello.com ipv4: stunport: 3478 stunonly: false derpport: 12345
-regionid表示区域 id,一个区域下可以后多个 node,node的 name 是节点的唯一识别码,ipv4 可以不填,有 hostname 就可以,然后stunonly 是 false 表示不只使用 stun还可以使用 derp
-然后就是修改 headscale 自身的配置文件,把共用的 derper 配置改成
-
-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渐入佳境补充篇-自定义中转derper使用反向代理
+ /2024/10/20/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%E4%BD%BF%E7%94%A8%E5%8F%8D%E5%90%91%E4%BB%A3%E7%90%86/
+ 之前在使用headscale的自建derper中转的时候,因为使用了acme管理的证书,虽然acme会自动续期,但是由于证书要做转换,没办法很方便的自动更新derper中映射的证书,因为最近在尝试迁移服务器,就在寻找是否有新的方法,正好就结合前面使用的caddy,只需要做好域名解析,caddy 就会自动加上https,那么在docker层面就可以不用额外增加证书啥的,各干各的事 docker还是一样的方式,差别就在于不用映射证书目录了
+docker run --restart always --name derper -p 12345:12345 -p 3478:3478/udp -v /run/tailscale/tailscaled.sock:/var/run/tailscale/tailscaled.sock -e DERP_ADDR=:12345 -e DERP_DOMAIN=derper.domain.com -e DERP_VERIFY_CLIENTS=true -d dockerproxy.cn/yangchuansheng/derper:latest
+derper.domain.com 需要换成自己的域名,端口也可以自己按需,只是还要注意下 /run/tailscale/tailscaled.sock 这个映射是为了让derper能够识别到这是同一个headscale下的客户端链接,防止被滥用,
+curl -fsSL https://tailscale.com/install.sh | sh
+本机通过这个安装下,然后再登录headscale
+tailscale up --login-server=http://headscaleip:headscaleport --accept-routes=true --accept-dns=false
+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 { 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
+
+
+
+ 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
@@ -662,41 +715,8 @@
Java
- JVM
C++
-
-
-
- Headscale渐入佳境补充篇-自定义中转derper的证书问题
- /2024/05/26/Headscale%E6%B8%90%E5%85%A5%E4%BD%B3%E5%A2%83%E8%A1%A5%E5%85%85%E7%AF%87-%E8%87%AA%E5%AE%9A%E4%B9%89%E4%B8%AD%E8%BD%ACderper%E7%9A%84%E8%AF%81%E4%B9%A6%E9%97%AE%E9%A2%98/
- 之前自定义部署的derper在Mac端和Windows使用时没啥问题,但是在我想作为网关的Armbian小机器上一直会报 “x509: certificate signed by unknown authority” 错误,原因在于之前在通过acme生成证书以后我是直接把cer证书重命名成crt就映射到derper的docker目录里,对于Mac和Windows客户端应该是有兼容逻辑,而实际使用需要完整的证书去识别认证机构,否则就会被认为是自定义证书,在一些常规的证书认证逻辑里就会报上面的问题 这边需要先把生成的 fullchain.cer 完整证书转成crt格式的
-openssl x509 -inform PEM -in fullchain.cer -out derper.demo.com.crt
-这边是PEM格式的证书,如果是DER的话就改一下
-openssl x509 -inform DER -in certificate.cer -out certificate.crt
-转换完成后就重新启动下docker 这里的原因主要是fullchain.cer是包含了完整的证书链,而ca.cer只是包含用户证书,所以需要替换一下,这算是个小问题
-]]>
-
- headscale
-
-
- headscale
-
-
-
- Headscale渐入佳境-路由设置
- /2024/04/07/Headscale%E6%B8%90%E5%85%A5%E4%BD%B3%E5%A2%83-%E8%B7%AF%E7%94%B1%E8%AE%BE%E7%BD%AE/
- headscale 在前面两篇初体验 和添加节点 主要是介绍了如何搭建和使用,但是这里有个很大的缺点,比如我在想要两地的网络能够连通,但是两地的网络内都不止一个机器,那想让他们连通就需要把它们都加入这个 headscale 所组成的虚拟局域网,并且每个都是这个虚拟局域网的ip,跟原来的 ip 地址不一样会比较难记忆,所以有没有办法不用这么麻烦呢,当然是有的,那就是 headscale 的路由功能。 原先比如我们有两个设备,在两地,通过headscale我把这两个异地节点都添加进去了,节点 A 的 ip比如是 10.0.0.1,另一个节点 B 是 10.0.0.2,在之前的状态下,我的节点 A 只能通过 10.0.0.2 来连接节点 B,如果节点 A 想要连接节点 B 所在地的局域网内部其他节点,我们就可以通过节点 B 的客户端登录的时候开启路由转发,这样节点 B 就会充当一个路由转发的功能,并且不需要通过10.0.0这个网段来访问,直接可以通过节点 B所在的局域网地址来访问,比如192.168.2.10,具体操作就是把客户端启动方式改成命令行的,主要就是 --advertise-routes, 指明需要转发的网段
-tailscale up --advertise-routes=192.168.xx.0/24 --accept-dns=false --login-server=http://serverIp:serverPort --unattended
-前面的 192.168.xx.0 就是节点 B 所在局域网的网段,然后 login-server 是 headscale 的服务 ip 跟端口,这个本身客户端就会使用,另外这个还有一个前提的,需要开启服务器的端口转发,对于 Windows 其实默认就可以,Linux 的话就需要设置下
-$ echo 'net.ipv4.ip_forward = 1' | tee /etc/sysctl.d/ipforwarding.conf$ echo 'net.ipv6.conf.all.forwarding = 1' | tee -a /etc/sysctl.d/ipforwarding.conf$ sysctl -p /etc/sysctl.d/ipforwarding.conf
-
-到这里我们只完成了第一步,第二步需要在 headscale 服务端启用这条路由配置 首先可以用 sudo headscale routes 查看当前有的路由sudo headscale routes enable -h 这里看下怎么启用,因为网上有些是用-i 指定哪一条路由的,但是我使用的这个版本是用-r 的sudo headscale routes enable -r 1 然后用这个命令启用就行了 这样我们就能够很方便地在单个节点互连之后访问到对方局域网内的其他节点
-]]>
-
- headscale
-
-
- headscale
+ JVM
@@ -730,6 +750,24 @@
public boolean offer (E e) { checkNotNull(e); final ReentrantLock lock = this .lock; lock.lock(); try { if (count == items.length) { return false ; } else { enqueue(e); return true ; } } finally { lock.unlock(); try { Thread.sleep(1000 ); } catch (InterruptedException ex) { throw new RuntimeException (ex); } } }
这样就没啥问题了
除了最后这个加延时,其他的直接用 ArrayBlockingQueue 就可以实验,实操一下会对这个逻辑有更深的理解
+]]>
+
+ Java
+
+
+ Java
+
+
+
+ Java 线程池系列-第一篇
+ /2024/02/11/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E7%AC%AC%E4%B8%80%E7%AF%87/
+ 这一篇我们继续聊线程池,一般线程池会介绍我们的参数,我先不一样一些 我们先来翻译一下
+ An {@link ExecutorService} that executes each submitted task using one of possibly several pooled threads, normally configured using {@link Executors} factory methods. <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. <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: Core and maximum pool sizes 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. On-demand construction 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. Creating new threads Keep-alive times 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. Queuing Any BlockingQueue may be used to transfer and hold submitted tasks. The use of this queue interacts with pool sizing: If fewer than corePoolSize threads are running, the Executor always prefers adding a new thread rather than queuing. If corePoolSize or more threads are running, the Executor always prefers queuing a request rather than adding a new thread . If a request cannot be queued, a new thread is created unless this would exceed maximumPoolSize, in which case , the task will be rejected. There are three general strategies for queuing: 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. 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. Rejected tasks 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: In the default ThreadPoolExecutor.AbortPolicy, the handler throws a runtime RejectedExecutionException upon rejection. 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. In ThreadPoolExecutor.DiscardPolicy, a task that cannot be executed is simply dropped. 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.) 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. Hook methods 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. If hook or callback methods throw exceptions, internal worker threads may in turn fail and abruptly terminate. Queue maintenance 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. Finalization 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 ) .
+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
@@ -748,6 +786,22 @@
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) decrementWorkerCount(); final ReentrantLock mainLock = this .mainLock; mainLock.lock(); try { completedTaskCount += w.completedTasks; workers.remove(w); } finally { mainLock.unlock(); } tryTerminate(); int c = ctl.get(); if (runStateLessThan(c, STOP)) { if (!completedAbruptly) { int min = allowCoreThreadTimeOut ? 0 : corePoolSize; if (min == 0 && ! workQueue.isEmpty()) min = 1 ; if (workerCountOf(c) >= min) return ; } addWorker(null , false ); } }
这里主要讲了线程 Worker 运行的逻辑
+]]>
+
+ Java
+
+
+ Java
+
+
+
+ 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 (); int c = ctl.get(); if (workerCountOf(c) < corePoolSize) { if (addWorker(command, true )) return ; c = ctl.get(); } if (isRunning(c) && workQueue.offer(command)) { int recheck = ctl.get(); if (! isRunning(recheck) && remove(command)) reject(command); else if (workerCountOf(recheck) == 0 ) addWorker(null , false ); } 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); 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(); if (runStateOf(c) != rs) continue retry; } } 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 { int rs = runStateOf(ctl.get()); if (rs < SHUTDOWN || (rs == SHUTDOWN && firstTask == null )) { if (t.isAlive()) 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
@@ -887,26 +941,6 @@
二叉树
-
- Headscale渐入佳境补充篇-自定义中转derper使用反向代理
- /2024/10/20/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%E4%BD%BF%E7%94%A8%E5%8F%8D%E5%90%91%E4%BB%A3%E7%90%86/
- 之前在使用headscale的自建derper中转的时候,因为使用了acme管理的证书,虽然acme会自动续期,但是由于证书要做转换,没办法很方便的自动更新derper中映射的证书,因为最近在尝试迁移服务器,就在寻找是否有新的方法,正好就结合前面使用的caddy,只需要做好域名解析,caddy 就会自动加上https,那么在docker层面就可以不用额外增加证书啥的,各干各的事 docker还是一样的方式,差别就在于不用映射证书目录了
-docker run --restart always --name derper -p 12345:12345 -p 3478:3478/udp -v /run/tailscale/tailscaled.sock:/var/run/tailscale/tailscaled.sock -e DERP_ADDR=:12345 -e DERP_DOMAIN=derper.domain.com -e DERP_VERIFY_CLIENTS=true -d dockerproxy.cn/yangchuansheng/derper:latest
-derper.domain.com 需要换成自己的域名,端口也可以自己按需,只是还要注意下 /run/tailscale/tailscaled.sock 这个映射是为了让derper能够识别到这是同一个headscale下的客户端链接,防止被滥用,
-curl -fsSL https://tailscale.com/install.sh | sh
-本机通过这个安装下,然后再登录headscale
-tailscale up --login-server=http://headscaleip:headscaleport --accept-routes=true --accept-dns=false
-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 { 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
-
-
Leetcode 105 从前序与中序遍历序列构造二叉树(Construct Binary Tree from Preorder and Inorder Traversal) 题解分析
/2020/12/13/Leetcode-105-%E4%BB%8E%E5%89%8D%E5%BA%8F%E4%B8%8E%E4%B8%AD%E5%BA%8F%E9%81%8D%E5%8E%86%E5%BA%8F%E5%88%97%E6%9E%84%E9%80%A0%E4%BA%8C%E5%8F%89%E6%A0%91-Construct-Binary-Tree-from-Preorder-and-Inorder-Traversal-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/
@@ -998,6 +1032,36 @@
DP
+
+ 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 ; } int left = maxSumNew(root.left); int right = maxSumNew(root.right); int currentSum = Math.max(Math.max(root.val + left , root.val + right), root.val); 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
+]]>
+
+ leetcode
+ Java
+ Binary Tree
+ java
+ Binary Tree
+
+
+ leetcode
+ java
+ 题解
+ Binary Tree
+ 二叉树
+
+
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/
@@ -1039,36 +1103,6 @@
Shift 2D Grid
-
- 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 ; } int left = maxSumNew(root.left); int right = maxSumNew(root.right); int currentSum = Math.max(Math.max(root.val + left , root.val + right), root.val); 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
-]]>
-
- leetcode
- Java
- Binary Tree
- java
- Binary Tree
-
-
- leetcode
- java
- 题解
- Binary Tree
- 二叉树
-
-
Leetcode 155 最小栈(Min Stack) 题解分析
/2020/12/06/Leetcode-155-%E6%9C%80%E5%B0%8F%E6%A0%88-Min-Stack-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/
@@ -1304,19 +1338,46 @@
- 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 (); int c = ctl.get(); if (workerCountOf(c) < corePoolSize) { if (addWorker(command, true )) return ; c = ctl.get(); } if (isRunning(c) && workQueue.offer(command)) { int recheck = ctl.get(); if (! isRunning(recheck) && remove(command)) reject(command); else if (workerCountOf(recheck) == 0 ) addWorker(null , false ); } 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); 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(); if (runStateOf(c) != rs) continue retry; } } 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 { int rs = runStateOf(ctl.get()); if (rs < SHUTDOWN || (rs == SHUTDOWN && firstTask == null )) { if (t.isAlive()) throw new IllegalThreadStateException (); workers.add(w); int s = workers.size(); if (s > largestPoolSize) largestPoolSize = s; workerAdded = true ; } } finally { mainLock.unlock(); } if (workerAdded) { t.start(); workerStarted = true ; } } } finally { if (! workerStarted) addWorkerFailed(w); } return workerStarted; }
-后面会继续讲下线程如何执行队列里的任务
+ Leetcode 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 ) { 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) { head = reverse(null , tempHead, tempEnd); isHeadSet = !isHeadSet; } else { reverse(lastHead, tempHead, tempEnd); } if (tempHead.next != null ) { lastHead = tempHead; tempEnd = tempHead.next; tempHead = tempHead.next; } else { break ; } } else { break ; } } return head; } public ListNode reverse (ListNode lastHead, ListNode tempHead, ListNode tempEnd) { ListNode tail = null ; while (tail != tempEnd) { tail = tempHead.next; tempHead.next = tempEnd.next; tempEnd.next = tempHead; tempHead = tail; } if (lastHead !=null ) { lastHead.next = tail; } return tail; }
]]>
+
+ Java
+ leetcode
+
+
+ leetcode
+ java
+ 题解
+
+
+
+ Leetcode 25 Reverse Nodes in k-Group 题解分析-再解分析
+ /2024/01/21/Leetcode-25-Reverse-Nodes-in-k-Group-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/
+ 上一次主要是给了一个解题方案,没有具体讲解,这次又做到了就来看下几种方案,链表转置一直是我比较疑惑的问题,特别是边界处理,而这个问题要把难度加大了 我先讲一下我一开始的思路和解题方法,首先就是写一个转置方法,就处理 k 个一组的内部转置,然后外部循环处理分组以及前后连接等问题,但是这里就涉及到一个问题,就是前后连接的话对于整个链表头,也就是第一个 k 元素组来说,头是个空的,就需要额外处理,一开始就是用判空做额外处理,然后在前后连接的时候也有一些困扰,看了自己的代码库发现其实之前也做过,而且发现这个思路跟以前做的还是一样的,只不过在处理 k 个元素内部的转置的时候有了点差异 一开始的思路是这样的,想了下其实还是比较有问题,从处理难度上来说,在 k 组内处理的时候一开始 A 节点会是悬空的,然后得处理到组内最后一个元素的时候再接上,逻辑会更复杂,而另一种思路就是直接把 A 先挪到 C 后面,这样每次只需要移动一个,可能思路上会更清晰一点 也就是这样的,这种方案就是之前发过的题解代码,上一篇
+而最后这种则代码会简单一些,但是需要一定的理解成本,比较重要的一点是我们加了个虚拟的头结点,第一步会先获取下链表的总长度,然后在内循环里处理转置,重点就是分四步, 也就是图里的,第一步先把 cur 的下一个节点设置成 next 的 next 节点,这里就是图里 A 连接到 C,第二步是把 next 的 next 节点设置成虚拟头的 next节点,第三步是把 pre 的next 节点设置成 next,第四步是 next 往后移动
+cur.next = next.next; next.next = pre.next; pre.next = next; next = cur.next;
+
+ 在备注下代码
]]>
Java
+ leetcode
- Java
+ leetcode
+ java
+ 题解
@@ -1378,21 +1439,30 @@
- 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. <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. <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: Core and maximum pool sizes 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. On-demand construction 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. Creating new threads Keep-alive times 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. Queuing Any BlockingQueue may be used to transfer and hold submitted tasks. The use of this queue interacts with pool sizing: If fewer than corePoolSize threads are running, the Executor always prefers adding a new thread rather than queuing. If corePoolSize or more threads are running, the Executor always prefers queuing a request rather than adding a new thread . If a request cannot be queued, a new thread is created unless this would exceed maximumPoolSize, in which case , the task will be rejected. There are three general strategies for queuing: 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. 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. Rejected tasks 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: In the default ThreadPoolExecutor.AbortPolicy, the handler throws a runtime RejectedExecutionException upon rejection. 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. In ThreadPoolExecutor.DiscardPolicy, a task that cannot be executed is simply dropped. 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.) 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. Hook methods 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. If hook or callback methods throw exceptions, internal worker threads may in turn fail and abruptly terminate. Queue maintenance 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. Finalization 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 ) .
-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
+ 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
+
+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
+
+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
+
+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
+
+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
+
+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 ; while (i >= 0 && nums[i] >= nums[i + 1 ]) --i; if (i >= 0 ) { while (nums[j] <= nums[i]) --j; int temp = nums[i]; nums[i] = nums[j]; nums[j] = temp; } Arrays.sort(nums, i+1 , nums.length); }
+
]]>
Java
+ leetcode
- Java
+ leetcode
+ java
+ 题解
@@ -1476,49 +1546,6 @@
Leetcode 42
-
- 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 ) { 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) { head = reverse(null , tempHead, tempEnd); isHeadSet = !isHeadSet; } else { reverse(lastHead, tempHead, tempEnd); } if (tempHead.next != null ) { lastHead = tempHead; tempEnd = tempHead.next; tempHead = tempHead.next; } else { break ; } } else { break ; } } return head; } public ListNode reverse (ListNode lastHead, ListNode tempHead, ListNode tempEnd) { ListNode tail = null ; while (tail != tempEnd) { tail = tempHead.next; tempHead.next = tempEnd.next; tempEnd.next = tempHead; tempHead = tail; } if (lastHead !=null ) { lastHead.next = tail; } return tail; }
]]>
-
- Java
- leetcode
-
-
- leetcode
- java
- 题解
-
-
-
- Leetcode 25 Reverse Nodes in k-Group 题解分析-再解分析
- /2024/01/21/Leetcode-25-Reverse-Nodes-in-k-Group-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/
- 上一次主要是给了一个解题方案,没有具体讲解,这次又做到了就来看下几种方案,链表转置一直是我比较疑惑的问题,特别是边界处理,而这个问题要把难度加大了 我先讲一下我一开始的思路和解题方法,首先就是写一个转置方法,就处理 k 个一组的内部转置,然后外部循环处理分组以及前后连接等问题,但是这里就涉及到一个问题,就是前后连接的话对于整个链表头,也就是第一个 k 元素组来说,头是个空的,就需要额外处理,一开始就是用判空做额外处理,然后在前后连接的时候也有一些困扰,看了自己的代码库发现其实之前也做过,而且发现这个思路跟以前做的还是一样的,只不过在处理 k 个元素内部的转置的时候有了点差异 一开始的思路是这样的,想了下其实还是比较有问题,从处理难度上来说,在 k 组内处理的时候一开始 A 节点会是悬空的,然后得处理到组内最后一个元素的时候再接上,逻辑会更复杂,而另一种思路就是直接把 A 先挪到 C 后面,这样每次只需要移动一个,可能思路上会更清晰一点 也就是这样的,这种方案就是之前发过的题解代码,上一篇
-而最后这种则代码会简单一些,但是需要一定的理解成本,比较重要的一点是我们加了个虚拟的头结点,第一步会先获取下链表的总长度,然后在内循环里处理转置,重点就是分四步, 也就是图里的,第一步先把 cur 的下一个节点设置成 next 的 next 节点,这里就是图里 A 连接到 C,第二步是把 next 的 next 节点设置成虚拟头的 next节点,第三步是把 pre 的next 节点设置成 next,第四步是 next 往后移动
-cur.next = next.next; next.next = pre.next; pre.next = next; next = cur.next;
-
- 在备注下代码
-]]>
-
- Java
- leetcode
-
-
- leetcode
- java
- 题解
-
-
Leetcode 48 旋转图像(Rotate Image) 题解分析
/2021/05/01/Leetcode-48-%E6%97%8B%E8%BD%AC%E5%9B%BE%E5%83%8F-Rotate-Image-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/
@@ -1542,24 +1569,20 @@
- Leetcode 747 至少是其他数字两倍的最大数 ( Largest Number At Least Twice of Others *Easy* ) 题解分析
- /2022/10/02/Leetcode-747-%E8%87%B3%E5%B0%91%E6%98%AF%E5%85%B6%E4%BB%96%E6%95%B0%E5%AD%97%E4%B8%A4%E5%80%8D%E7%9A%84%E6%9C%80%E5%A4%A7%E6%95%B0-Largest-Number-At-Least-Twice-of-Others-Easy-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/
- 题目介绍You are given an integer array nums where the largest integer is unique .
-Determine whether the largest element in the array is at least twice as much as every other number in the array. If it is, return the index of the largest element, or return -1 otherwise. 确认在数组中的最大数是否是其余任意数的两倍大及以上,如果是返回索引,如果不是返回-1
-示例 Example 1:
-Input: nums = [3,6,1,0]Output: 1Explanation: 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.
+ 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
-Example 2:
-Input: nums = [1,2,3,4]Output: -1Explanation: 4 is less than twice the value of 3, so we return -1.
+示例 2
+
+Input: root = [3,4,5,1,2,null,null,null,null,0], subRoot = [4,1,2] Output: false
-提示:
-2 <= nums.length <= 50
-0 <= nums[i] <= 100
-The largest element in nums is unique.
-
-简要解析 这个题easy是题意也比较简单,找最大值,并且最大值是其他任意值的两倍及以上,其实就是找最大值跟次大值,比较下就好了
-代码 public int dominantIndex (int [] nums) { int largest = Integer.MIN_VALUE; int second = Integer.MIN_VALUE; int largestIndex = -1 ; for (int i = 0 ; i < nums.length; i++) { if (nums[i] > largest) { second = largest; largest = nums[i]; largestIndex = i; } else if (nums[i] > second) { second = nums[i]; } } 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
+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) { if (isSameTree(root.left, subRoot.left) && isSameTree(root.right, subRoot.right)) { return true ; } } return isSubtree(root.left, subRoot) || isSubtree(root.right, subRoot); } public boolean isSameTree (TreeNode root, TreeNode subTree) { if (root == null && subTree == null ) { return true ; } if (root == null ) { return false ; } if (subTree == null ) { return false ; } if (root.val != subTree.val) { return false ; } return isSameTree(root.left, subTree.left) && isSameTree(root.right, subTree.right); }
]]>
Java
@@ -1603,6 +1626,36 @@
java
+
+ Leetcode 747 至少是其他数字两倍的最大数 ( Largest Number At Least Twice of Others *Easy* ) 题解分析
+ /2022/10/02/Leetcode-747-%E8%87%B3%E5%B0%91%E6%98%AF%E5%85%B6%E4%BB%96%E6%95%B0%E5%AD%97%E4%B8%A4%E5%80%8D%E7%9A%84%E6%9C%80%E5%A4%A7%E6%95%B0-Largest-Number-At-Least-Twice-of-Others-Easy-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/
+ 题目介绍You are given an integer array nums where the largest integer is unique .
+Determine whether the largest element in the array is at least twice as much as every other number in the array. If it is, return the index of the largest element, or return -1 otherwise. 确认在数组中的最大数是否是其余任意数的两倍大及以上,如果是返回索引,如果不是返回-1
+示例 Example 1:
+Input: nums = [3,6,1,0]Output: 1Explanation: 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: -1Explanation: 4 is less than twice the value of 3, so we return -1.
+
+提示:
+2 <= nums.length <= 50
+0 <= nums[i] <= 100
+The largest element in nums is unique.
+
+简要解析 这个题easy是题意也比较简单,找最大值,并且最大值是其他任意值的两倍及以上,其实就是找最大值跟次大值,比较下就好了
+代码 public int dominantIndex (int [] nums) { int largest = Integer.MIN_VALUE; int second = Integer.MIN_VALUE; int largestIndex = -1 ; for (int i = 0 ; i < nums.length; i++) { if (nums[i] > largest) { second = largest; largest = nums[i]; largestIndex = i; } else if (nums[i] > second) { second = nums[i]; } } if (largest >= 2 * second) { return largestIndex; } else { return -1 ; } }
+通过图 第一次错了是把第二大的情况只考虑第一种,也有可能最大值完全没经过替换就变成最大值了
+]]>
+
+ Java
+ leetcode
+
+
+ leetcode
+ java
+ 题解
+
+
Leetcode 83 删除排序链表中的重复元素 ( Remove Duplicates from Sorted List *Easy* ) 题解分析
/2022/03/13/Leetcode-83-%E5%88%A0%E9%99%A4%E6%8E%92%E5%BA%8F%E9%93%BE%E8%A1%A8%E4%B8%AD%E7%9A%84%E9%87%8D%E5%A4%8D%E5%85%83%E7%B4%A0-Remove-Duplicates-from-Sorted-List-Easy-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/
@@ -1636,17 +1689,34 @@
- Leetcode 885 螺旋矩阵 III ( Spiral Matrix III *Medium* ) 题解分析
- /2022/08/23/Leetcode-885-%E8%9E%BA%E6%97%8B%E7%9F%A9%E9%98%B5-III-Spiral-Matrix-III-Medium-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/
- 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 = 0Output: [[0,0],[0,1],[0,2],[0,3]]
-
-Example 2:
-Input: rows = 5, cols = 6, rStart = 1, cStart = 4Output: [[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]]
-
+ 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++
+
+
+
+ Leetcode 885 螺旋矩阵 III ( Spiral Matrix III *Medium* ) 题解分析
+ /2022/08/23/Leetcode-885-%E8%9E%BA%E6%97%8B%E7%9F%A9%E9%98%B5-III-Spiral-Matrix-III-Medium-%E9%A2%98%E8%A7%A3%E5%88%86%E6%9E%90/
+ 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 = 0Output: [[0,0],[0,1],[0,2],[0,3]]
+
+Example 2:
+Input: rows = 5, cols = 6, rStart = 1, cStart = 4Output: [[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
@@ -1668,23 +1738,6 @@
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/
@@ -1768,6 +1821,18 @@
Maven
+
+ 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
+
+
Number of 1 Bits
/2015/03/11/Number-Of-1-Bits/
@@ -1875,40 +1940,6 @@
c++
-
- 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); addServletContextInitializerBeans(beanFactory); 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); addAsRegistrationBean(beanFactory, Servlet.class, new ServletRegistrationBeanAdapter (multipartConfig)); 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)) { 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
- SpringBoot
- Tomcat
-
-
- Java
- Tomcat
- SpringBoot
-
-
Tomcat 系列篇一-介绍下 connector
/2023/09/10/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E4%B8%80-%E4%BB%8B%E7%BB%8D%E4%B8%8B-connector/
@@ -1956,6 +1987,40 @@
正常请求回到这else if (!processSocket(socketWrapper, SocketEvent.OPEN_READ, true)) 然后调用processSocket 进行处理,
public boolean processSocket (SocketWrapperBase<S> socketWrapper, SocketEvent event, boolean dispatch) { try { if (socketWrapper == null ) { return false ; } SocketProcessorBase<S> sc = null ; if (processorCache != null ) { sc = processorCache.pop(); } if (sc == null ) { sc = createSocketProcessor(socketWrapper, event); } else { sc.reset(socketWrapper, event); } Executor executor = getExecutor(); if (dispatch && executor != null ) { executor.execute(sc); } else { sc.run(); } } catch (RejectedExecutionException ree) { getLog().warn(sm.getString("endpoint.executor.fail" , socketWrapper) , ree); return false ; } catch (Throwable t) { ExceptionUtils.handleThrowable(t); getLog().error(sm.getString("endpoint.process.fail" ), t); return false ; } return true ; }
这里就会调用 createSocketProcessor 进行处理了,不过这是下一篇的内容了。
+]]>
+
+ Java
+ SpringBoot
+ Tomcat
+
+
+ Java
+ Tomcat
+ SpringBoot
+
+
+
+ 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); addServletContextInitializerBeans(beanFactory); 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); addAsRegistrationBean(beanFactory, Servlet.class, new ServletRegistrationBeanAdapter (multipartConfig)); 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)) { 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
@@ -2084,6 +2149,44 @@
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 系列篇十-介绍下 Tomcat 里的 Mapper 作用
+ /2023/11/26/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%8D%81-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E9%87%8C%E7%9A%84-Mapper-%E4%BD%9C%E7%94%A8/
+ Mapper 顾名思义是作一个映射作用,在 Tomcat 中会根据域名找到 host 组件,再根据 uri 可以找到对应的 context 和 wrapper 组件,但是对于当前这个环境 (Springboot) 会有一点小区别 之前说到 请求会经过 coyote 适配器进行进一步处理,org.apache.coyote.http11.Http11Processor#service
+if (getErrorState().isIoAllowed()) { try { rp.setStage(org.apache.coyote.Constants.STAGE_SERVICE); getAdapter().service(request, response);
+然后到 coyoteAdapter 的 serviceorg.apache.catalina.connector.CoyoteAdapter#service
+try { postParseSuccess = postParseRequest(req, request, res, response); if (postParseSuccess) { request.setAsyncSupported( connector.getService().getContainer().getPipeline().isAsyncSupported()); connector.getService().getContainer().getPipeline().getFirst().invoke( request, response); }
+主要先看到 postParseRequest 在 postParseRequest 的代码里会调用 Mapper 的 map 方法
+while (mapRequired) { connector.getService().getMapper().map(serverName, decodedURI, version, request.getMappingData()); if (request.getContext() == null ) {
+而后面的 context 就是在 map 方法里处理塞进去的 往里看就是 org.apache.catalina.mapper.Mapper#internalMap 第一步找的是 host
+ MappedHost[] hosts = this .hosts; MappedHost mappedHost = exactFindIgnoreCase(hosts, host);
+而在后续代码里继续设置 context
+if (contextVersion == null ) { contextVersion = contextVersions[versionCount - 1 ]; } mappingData.context = contextVersion.object;
+然后是 wrapper
+if (!contextVersion.isPaused()) { internalMapWrapper(contextVersion, uri, mappingData); }
+在这个方法 org.apache.catalina.mapper.Mapper#internalMapWrapper
+if (mappingData.wrapper == null && !checkJspWelcomeFiles) { if (contextVersion.defaultWrapper != null ) { mappingData.wrapper = contextVersion.defaultWrapper.object; mappingData.requestPath.setChars (path.getBuffer(), path.getStart(), path.getLength()); mappingData.wrapperPath.setChars (path.getBuffer(), path.getStart(), path.getLength()); mappingData.matchType = MappingMatch.DEFAULT;
+这里就设置了 wrapper ,因为我们是在 Springboot 中,所以只有默认的 dispatchServlet 上面主要是在请求处理过程中的查找映射过程,一开始的注册是从 MapperListener 开始的 MapperListener 继承了 LifecycleMbeanBase,也就是有了 Lifecycle 状态变化那一套
+@Override public void startInternal () throws LifecycleException { setState(LifecycleState.STARTING); Engine engine = service.getContainer(); if (engine == null ) { return ; } findDefaultHost(); addListeners(engine); Container[] conHosts = engine.findChildren(); for (Container conHost : conHosts) { Host host = (Host) conHost; if (!LifecycleState.NEW.equals(host.getState())) { registerHost(host); } } }
+在启动过程中就会去把 engine 的子容器 host 找出来进行注册,就是调用 registerHost 方法
+private void registerHost (Host host) { String[] aliases = host.findAliases(); mapper.addHost(host.getName(), aliases, host); for (Container container : host.findChildren()) { if (container.getState().isAvailable()) { registerContext((Context) container); } } findDefaultHost(); if (log.isDebugEnabled()) { log.debug(sm.getString("mapperListener.registerHost" , host.getName(), domain, service)); } }
+在这里面会添加 host 组件,注册 context 等,注册context 里还会处理 wrapper 的添加记录
+private void registerContext (Context context) { String contextPath = context.getPath(); if ("/" .equals(contextPath)) { contextPath = "" ; } Host host = (Host)context.getParent(); WebResourceRoot resources = context.getResources(); String[] welcomeFiles = context.findWelcomeFiles(); List<WrapperMappingInfo> wrappers = new ArrayList <>(); for (Container container : context.findChildren()) { prepareWrapperMappingInfo(context, (Wrapper) container, wrappers); if (log.isDebugEnabled()) { log.debug(sm.getString("mapperListener.registerWrapper" , container.getName(), contextPath, service)); } } mapper.addContextVersion(host.getName(), host, contextPath, context.getWebappVersion(), context, welcomeFiles, resources, wrappers); if (log.isDebugEnabled()) { log.debug(sm.getString("mapperListener.registerContext" , contextPath, service)); } }
+就是在 org.apache.catalina.mapper.Mapper#addContextVersion 方法中
+public void addContextVersion (String hostName, Host host, String path, String version, Context context, String[] welcomeResources, WebResourceRoot resources, Collection<WrapperMappingInfo> wrappers) { hostName = renameWildcardHost(hostName); MappedHost mappedHost = exactFind(hosts, hostName); if (mappedHost == null ) { addHost(hostName, new String [0 ], host); mappedHost = exactFind(hosts, hostName); if (mappedHost == null ) { log.error(sm.getString("mapper.addContext.noHost" , hostName)); return ; } } if (mappedHost.isAlias()) { log.error(sm.getString("mapper.addContext.hostIsAlias" , hostName)); return ; } int slashCount = slashCount(path); synchronized (mappedHost) { ContextVersion newContextVersion = new ContextVersion (version, path, slashCount, context, resources, welcomeResources); if (wrappers != null ) { addWrappers(newContextVersion, wrappers); }
+大致介绍了下 Mapper 的逻辑
]]>
Java
@@ -2125,44 +2228,6 @@
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)) { stop(); } else if (!state.equals(LifecycleState.STARTING)) { invalidTransition(Lifecycle.AFTER_START_EVENT); } else { setStateInternal(LifecycleState.STARTED, null , false ); } } catch (Throwable t) { handleSubClassException(t, "lifecycleBase.startFail" , toString()); } }
stop 跟 destroy 也类似的
-]]>
-
- Java
- SpringBoot
- Tomcat
-
-
- Java
- Tomcat
- SpringBoot
-
-
-
- Tomcat 系列篇十-介绍下 Tomcat 里的 Mapper 作用
- /2023/11/26/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%8D%81-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Tomcat-%E9%87%8C%E7%9A%84-Mapper-%E4%BD%9C%E7%94%A8/
- Mapper 顾名思义是作一个映射作用,在 Tomcat 中会根据域名找到 host 组件,再根据 uri 可以找到对应的 context 和 wrapper 组件,但是对于当前这个环境 (Springboot) 会有一点小区别 之前说到 请求会经过 coyote 适配器进行进一步处理,org.apache.coyote.http11.Http11Processor#service
-if (getErrorState().isIoAllowed()) { try { rp.setStage(org.apache.coyote.Constants.STAGE_SERVICE); getAdapter().service(request, response);
-然后到 coyoteAdapter 的 serviceorg.apache.catalina.connector.CoyoteAdapter#service
-try { postParseSuccess = postParseRequest(req, request, res, response); if (postParseSuccess) { request.setAsyncSupported( connector.getService().getContainer().getPipeline().isAsyncSupported()); connector.getService().getContainer().getPipeline().getFirst().invoke( request, response); }
-主要先看到 postParseRequest 在 postParseRequest 的代码里会调用 Mapper 的 map 方法
-while (mapRequired) { connector.getService().getMapper().map(serverName, decodedURI, version, request.getMappingData()); if (request.getContext() == null ) {
-而后面的 context 就是在 map 方法里处理塞进去的 往里看就是 org.apache.catalina.mapper.Mapper#internalMap 第一步找的是 host
- MappedHost[] hosts = this .hosts; MappedHost mappedHost = exactFindIgnoreCase(hosts, host);
-而在后续代码里继续设置 context
-if (contextVersion == null ) { contextVersion = contextVersions[versionCount - 1 ]; } mappingData.context = contextVersion.object;
-然后是 wrapper
-if (!contextVersion.isPaused()) { internalMapWrapper(contextVersion, uri, mappingData); }
-在这个方法 org.apache.catalina.mapper.Mapper#internalMapWrapper
-if (mappingData.wrapper == null && !checkJspWelcomeFiles) { if (contextVersion.defaultWrapper != null ) { mappingData.wrapper = contextVersion.defaultWrapper.object; mappingData.requestPath.setChars (path.getBuffer(), path.getStart(), path.getLength()); mappingData.wrapperPath.setChars (path.getBuffer(), path.getStart(), path.getLength()); mappingData.matchType = MappingMatch.DEFAULT;
-这里就设置了 wrapper ,因为我们是在 Springboot 中,所以只有默认的 dispatchServlet 上面主要是在请求处理过程中的查找映射过程,一开始的注册是从 MapperListener 开始的 MapperListener 继承了 LifecycleMbeanBase,也就是有了 Lifecycle 状态变化那一套
-@Override public void startInternal () throws LifecycleException { setState(LifecycleState.STARTING); Engine engine = service.getContainer(); if (engine == null ) { return ; } findDefaultHost(); addListeners(engine); Container[] conHosts = engine.findChildren(); for (Container conHost : conHosts) { Host host = (Host) conHost; if (!LifecycleState.NEW.equals(host.getState())) { registerHost(host); } } }
-在启动过程中就会去把 engine 的子容器 host 找出来进行注册,就是调用 registerHost 方法
-private void registerHost (Host host) { String[] aliases = host.findAliases(); mapper.addHost(host.getName(), aliases, host); for (Container container : host.findChildren()) { if (container.getState().isAvailable()) { registerContext((Context) container); } } findDefaultHost(); if (log.isDebugEnabled()) { log.debug(sm.getString("mapperListener.registerHost" , host.getName(), domain, service)); } }
-在这里面会添加 host 组件,注册 context 等,注册context 里还会处理 wrapper 的添加记录
-private void registerContext (Context context) { String contextPath = context.getPath(); if ("/" .equals(contextPath)) { contextPath = "" ; } Host host = (Host)context.getParent(); WebResourceRoot resources = context.getResources(); String[] welcomeFiles = context.findWelcomeFiles(); List<WrapperMappingInfo> wrappers = new ArrayList <>(); for (Container container : context.findChildren()) { prepareWrapperMappingInfo(context, (Wrapper) container, wrappers); if (log.isDebugEnabled()) { log.debug(sm.getString("mapperListener.registerWrapper" , container.getName(), contextPath, service)); } } mapper.addContextVersion(host.getName(), host, contextPath, context.getWebappVersion(), context, welcomeFiles, resources, wrappers); if (log.isDebugEnabled()) { log.debug(sm.getString("mapperListener.registerContext" , contextPath, service)); } }
-就是在 org.apache.catalina.mapper.Mapper#addContextVersion 方法中
-public void addContextVersion (String hostName, Host host, String path, String version, Context context, String[] welcomeResources, WebResourceRoot resources, Collection<WrapperMappingInfo> wrappers) { hostName = renameWildcardHost(hostName); MappedHost mappedHost = exactFind(hosts, hostName); if (mappedHost == null ) { addHost(hostName, new String [0 ], host); mappedHost = exactFind(hosts, hostName); if (mappedHost == null ) { log.error(sm.getString("mapper.addContext.noHost" , hostName)); return ; } } if (mappedHost.isAlias()) { log.error(sm.getString("mapper.addContext.hostIsAlias" , hostName)); return ; } int slashCount = slashCount(path); synchronized (mappedHost) { ContextVersion newContextVersion = new ContextVersion (version, path, slashCount, context, resources, welcomeResources); if (wrappers != null ) { addWrappers(newContextVersion, wrappers); }
-大致介绍了下 Mapper 的逻辑
]]>
Java
@@ -2264,55 +2329,37 @@
- 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) { if (isSameTree(root.left, subRoot.left) && isSameTree(root.right, subRoot.right)) { return true ; } } return isSubtree(root.left, subRoot) || isSubtree(root.right, subRoot); } public boolean isSameTree (TreeNode root, TreeNode subTree) { if (root == null && subTree == null ) { return true ; } if (root == null ) { return false ; } if (subTree == null ) { return false ; } if (root.val != subTree.val) { return false ; } return isSameTree(root.left, subTree.left) && isSameTree(root.right, subTree.right); }
+ Tomcat 系列篇四-介绍下 Valve 架构
+ /2023/10/01/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%9B%9B-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Valve-%E6%9E%B6%E6%9E%84/
+ valve 是 Tomcat 架构中比较重要的一个组成部分, 之前说到
+connector.getService().getContainer().getPipeline().getFirst().invoke( request, response);
+这段代码是通过 CoyoteAdapter 将请求处理往 container 传,这里就有个 pipeline 机制,这个 pipeline 可以看一下接口
+public interface Pipeline extends Contained { public Valve getBasic () ; public void setBasic (Valve valve) ; public void addValve (Valve valve) ; public Valve[] getValves(); public void removeValve (Valve valve) ; public Valve getFirst () ; public boolean isAsyncSupported () ; public void findNonAsyncValves (Set<String> result) ; }
+这里可以往 pipeline 里添加 valve,然后看下 valve 的接口
+public interface Valve { public Valve getNext () ; public void setNext (Valve valve) ; public void backgroundProcess () ; public void invoke (Request request, Response response) throws IOException, ServletException; public boolean isAsyncSupported () ; }
+这里主要看的就是 getNext 跟 setNext,就变成了一个有序的 pipeline,然后就是 invoke 方法,其实 pipeline 是由两部分组成,valve 在其中起到了前后衔接的重要作用,而且可以再 invoke 中进一步串联调用 图中我们可以看到,对于 container 这个 pipeline,是没设置 first 的,只有 basic,basic 就是个兜底的 valve,在 StandardPipeline 中的 getFirst 实现
+@Override public Valve getFirst () { if (first != null ) { return first; } return basic; }
+取不到 first 就降级到 basic,也就是这里的 StandardEngineValve, 它的 invoke 我们来看下
+@Override public final void invoke (Request request, Response response) throws IOException, ServletException { Host host = request.getHost(); if (host == null ) { if (!response.isError()) { response.sendError(404 ); } return ; } if (request.isAsyncSupported()) { request.setAsyncSupported(host.getPipeline().isAsyncSupported()); } host.getPipeline().getFirst().invoke(request, response); }
+比较简单,就是调用 host 中的 pipeline 里的第一个 valve 来处理 第一个是 org.apache.catalina.valves.ErrorReportValve, 这里处理的其实是先调用了 next
+@Override public void invoke (Request request, Response response) throws IOException, ServletException { getNext().invoke(request, response); if (response.isCommitted()) { if (response.setErrorReported()) { AtomicBoolean ioAllowed = new AtomicBoolean (true ); response.getCoyoteResponse().action(ActionCode.IS_IO_ALLOWED, ioAllowed); if (ioAllowed.get()) { try { response.flushBuffer(); } catch (Throwable t) { ExceptionUtils.handleThrowable(t); } response.getCoyoteResponse().action(ActionCode.CLOSE_NOW, request.getAttribute(RequestDispatcher.ERROR_EXCEPTION)); } } return ; } Throwable throwable = (Throwable) request.getAttribute(RequestDispatcher.ERROR_EXCEPTION); if (request.isAsync() && !request.isAsyncCompleting()) { return ; } if (throwable != null && !response.isError()) { response.reset(); response.sendError(HttpServletResponse.SC_INTERNAL_SERVER_ERROR); } response.setSuspended(false ); try { report(request, response, throwable); } catch (Throwable tt) { ExceptionUtils.handleThrowable(tt); } }
+虽然是放在 first,实际是先调用 next 的 invoke,也就是 org.apache.catalina.core.StandardHostValve 的invoke 方法
+@Override public final void invoke (Request request, Response response) throws IOException, ServletException { Context context = request.getContext(); if (context == null ) { if (!response.isError()) { response.sendError(404 ); } return ; } if (request.isAsyncSupported()) { request.setAsyncSupported(context.getPipeline().isAsyncSupported()); } boolean asyncAtStart = request.isAsync(); try { context.bind(Globals.IS_SECURITY_ENABLED, MY_CLASSLOADER); if (!asyncAtStart && !context.fireRequestInitEvent(request.getRequest())) { return ; } try { if (!response.isErrorReportRequired()) { context.getPipeline().getFirst().invoke(request, response); } } catch (Throwable t) { ExceptionUtils.handleThrowable(t); container.getLogger().error("Exception Processing " + request.getRequestURI(), t); if (!response.isErrorReportRequired()) { request.setAttribute(RequestDispatcher.ERROR_EXCEPTION, t); throwable(request, response, t); } } response.setSuspended(false ); Throwable t = (Throwable) request.getAttribute(RequestDispatcher.ERROR_EXCEPTION); if (!context.getState().isAvailable()) { return ; } if (response.isErrorReportRequired()) { AtomicBoolean result = new AtomicBoolean (false ); response.getCoyoteResponse().action(ActionCode.IS_IO_ALLOWED, result); if (result.get()) { if (t != null ) { throwable(request, response, t); } else { status(request, response); } } } if (!request.isAsync() && !asyncAtStart) { context.fireRequestDestroyEvent(request.getRequest()); } } finally { if (ACCESS_SESSION) { request.getSession(false ); } context.unbind(Globals.IS_SECURITY_ENABLED, MY_CLASSLOADER); } }
+这里的往下就是调用 context 的 pipeline 去处理请求了, StandardContext 的 pipeline 里的 first 是 org.apache.catalina.authenticator.NonLoginAuthenticator,处理认证相关的,然后 basic 就是 org.apache.catalina.core.StandardContextValve, 这里的来看下
+@Override public final void invoke (Request request, Response response) throws IOException, ServletException { MessageBytes requestPathMB = request.getRequestPathMB(); if ((requestPathMB.startsWithIgnoreCase("/META-INF/" , 0 )) || (requestPathMB.equalsIgnoreCase("/META-INF" )) || (requestPathMB.startsWithIgnoreCase("/WEB-INF/" , 0 )) || (requestPathMB.equalsIgnoreCase("/WEB-INF" ))) { response.sendError(HttpServletResponse.SC_NOT_FOUND); return ; } Wrapper wrapper = request.getWrapper(); if (wrapper == null || wrapper.isUnavailable()) { response.sendError(HttpServletResponse.SC_NOT_FOUND); return ; } try { response.sendAcknowledgement(ContinueResponseTiming.IMMEDIATELY); } catch (IOException ioe) { container.getLogger().error(sm.getString( "standardContextValve.acknowledgeException" ), ioe); request.setAttribute(RequestDispatcher.ERROR_EXCEPTION, ioe); response.sendError(HttpServletResponse.SC_INTERNAL_SERVER_ERROR); return ; } if (request.isAsyncSupported()) { request.setAsyncSupported(wrapper.getPipeline().isAsyncSupported()); } wrapper.getPipeline().getFirst().invoke(request, response); }
+会调用 wrapper 的 pipeline 去处理请求,这里也只有一个 org.apache.catalina.core.StandardWrapperValve 这部分的逻辑比较长,因为要串联后面的 filter 流程
+@Override public final void invoke (Request request, Response response) throws IOException, ServletException { boolean unavailable = false ; Throwable throwable = null ; long t1=System.currentTimeMillis(); requestCount.incrementAndGet(); StandardWrapper wrapper = (StandardWrapper) getContainer(); Servlet servlet = null ; Context context = (Context) wrapper.getParent(); if (!context.getState().isAvailable()) { response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE, sm.getString("standardContext.isUnavailable" )); unavailable = true ; } if (!unavailable && wrapper.isUnavailable()) { container.getLogger().info(sm.getString("standardWrapper.isUnavailable" , wrapper.getName())); long available = wrapper.getAvailable(); if ((available > 0L ) && (available < Long.MAX_VALUE)) { response.setDateHeader("Retry-After" , available); response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE, sm.getString("standardWrapper.isUnavailable" , wrapper.getName())); } else if (available == Long.MAX_VALUE) { response.sendError(HttpServletResponse.SC_NOT_FOUND, sm.getString("standardWrapper.notFound" , wrapper.getName())); } unavailable = true ; } try { if (!unavailable) { servlet = wrapper.allocate(); } } catch (UnavailableException e) { container.getLogger().error( sm.getString("standardWrapper.allocateException" , wrapper.getName()), e); long available = wrapper.getAvailable(); if ((available > 0L ) && (available < Long.MAX_VALUE)) { response.setDateHeader("Retry-After" , available); response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE, sm.getString("standardWrapper.isUnavailable" , wrapper.getName())); } else if (available == Long.MAX_VALUE) { response.sendError(HttpServletResponse.SC_NOT_FOUND, sm.getString("standardWrapper.notFound" , wrapper.getName())); } } catch (ServletException e) { container.getLogger().error(sm.getString("standardWrapper.allocateException" , wrapper.getName()), StandardWrapper.getRootCause(e)); throwable = e; exception(request, response, e); } catch (Throwable e) { ExceptionUtils.handleThrowable(e); container.getLogger().error(sm.getString("standardWrapper.allocateException" , wrapper.getName()), e); throwable = e; exception(request, response, e); servlet = null ; } MessageBytes requestPathMB = request.getRequestPathMB(); DispatcherType dispatcherType = DispatcherType.REQUEST; if (request.getDispatcherType()==DispatcherType.ASYNC) { dispatcherType = DispatcherType.ASYNC; } request.setAttribute(Globals.DISPATCHER_TYPE_ATTR,dispatcherType); request.setAttribute(Globals.DISPATCHER_REQUEST_PATH_ATTR, requestPathMB); ApplicationFilterChain filterChain = ApplicationFilterFactory.createFilterChain(request, wrapper, servlet); Container container = this .container; try { if ((servlet != null ) && (filterChain != null )) { if (context.getSwallowOutput()) { try { SystemLogHandler.startCapture(); if (request.isAsyncDispatching()) { request.getAsyncContextInternal().doInternalDispatch(); } else { filterChain.doFilter(request.getRequest(), response.getResponse()); } } finally { String log = SystemLogHandler.stopCapture(); if (log != null && log.length() > 0 ) { context.getLogger().info(log); } } } else { if (request.isAsyncDispatching()) { request.getAsyncContextInternal().doInternalDispatch(); } else { filterChain.doFilter (request.getRequest(), response.getResponse()); } } } } catch (ClientAbortException | CloseNowException e) { if (container.getLogger().isDebugEnabled()) { container.getLogger().debug(sm.getString( "standardWrapper.serviceException" , wrapper.getName(), context.getName()), e); } throwable = e; exception(request, response, e); } catch (IOException e) { container.getLogger().error(sm.getString( "standardWrapper.serviceException" , wrapper.getName(), context.getName()), e); throwable = e; exception(request, response, e); } catch (UnavailableException e) { container.getLogger().error(sm.getString( "standardWrapper.serviceException" , wrapper.getName(), context.getName()), e); wrapper.unavailable(e); long available = wrapper.getAvailable(); if ((available > 0L ) && (available < Long.MAX_VALUE)) { response.setDateHeader("Retry-After" , available); response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE, sm.getString("standardWrapper.isUnavailable" , wrapper.getName())); } else if (available == Long.MAX_VALUE) { response.sendError(HttpServletResponse.SC_NOT_FOUND, sm.getString("standardWrapper.notFound" , wrapper.getName())); } } catch (ServletException e) { Throwable rootCause = StandardWrapper.getRootCause(e); if (!(rootCause instanceof ClientAbortException)) { container.getLogger().error(sm.getString( "standardWrapper.serviceExceptionRoot" , wrapper.getName(), context.getName(), e.getMessage()), rootCause); } throwable = e; exception(request, response, e); } catch (Throwable e) { ExceptionUtils.handleThrowable(e); container.getLogger().error(sm.getString( "standardWrapper.serviceException" , wrapper.getName(), context.getName()), e); throwable = e; exception(request, response, e); } finally { if (filterChain != null ) { filterChain.release(); } try { if (servlet != null ) { wrapper.deallocate(servlet); } } catch (Throwable e) { ExceptionUtils.handleThrowable(e); container.getLogger().error(sm.getString("standardWrapper.deallocateException" , wrapper.getName()), e); if (throwable == null ) { throwable = e; exception(request, response, e); } } try { if ((servlet != null ) && (wrapper.getAvailable() == Long.MAX_VALUE)) { wrapper.unload(); } } catch (Throwable e) { ExceptionUtils.handleThrowable(e); container.getLogger().error(sm.getString("standardWrapper.unloadException" , wrapper.getName()), e); if (throwable == null ) { exception(request, response, e); } } long t2=System.currentTimeMillis(); long time=t2-t1; processingTime += time; if ( time > maxTime) { maxTime=time; } if ( time < minTime) { minTime=time; } } }
+这里就会创建 ApplicationFilterChain 然后进行filterChain.doFilter(request.getRequest(), response.getResponse()); doFilter 处理
]]>
Java
- leetcode
+ SpringBoot
+ Tomcat
- leetcode
- java
- 题解
-
-
-
- Tomcat 系列篇五-介绍下 Service 启动过程
- /2023/10/07/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%9B%9B-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Service-%E5%90%AF%E5%8A%A8%E8%BF%87%E7%A8%8B/
- 这里开始介绍下 Service 的启动过程,Tomcat 的启动过程中
-public void start () throws LifecycleException { getServer(); server.start(); }
-getServer之前讲到过
-public Server getServer () { if (server != null ) { return server; } System.setProperty("catalina.useNaming" , "false" ); server = new StandardServer (); initBaseDir(); ConfigFileLoader.setSource(new CatalinaBaseConfigurationSource (new File (basedir), null )); server.setPort( -1 ); Service service = new StandardService (); service.setName("Tomcat" ); server.addService(service); return server; }
-会 new 一个StandardService,添加到 server 里,然后进行启动 而这个外面的 server.start 其实调用的是 org.apache.catalina.Lifecycle#start, 里面是一个 Lifecycle 的接口,这个接口被很多 Tomcat 的组件实现,其实都共用了 Lifecycle 的机制 然后 Lifecycle 里面会根据状态,调用实际的实现层的 startInternal 方法
-@Override protected void startInternal () throws LifecycleException { if (log.isInfoEnabled()) { log.info(sm.getString("standardService.start.name" , this .name)); } setState(LifecycleState.STARTING); if (engine != null ) { synchronized (engine) { engine.start(); } } synchronized (executors) { for (Executor executor: executors) { executor.start(); } } mapperListener.start(); synchronized (connectorsLock) { for (Connector connector: connectors) { if (connector.getState() != LifecycleState.FAILED) { connector.start(); } } } }
-这里会先设置状态,这个状态也有点东西,可以后面再讲,因为里面有个状态事件的触发 然后是启动 Engine,也就是 container,在这里就是 StandardEngine,先不深入去讲里面做了啥,后面讲 StandardEngine 的启动过程会讲,然后是启动线程池了,对于 StandardService 是没有线程池要启动的,或者是 springboot 集成的这个 Tomcat 中不需要,接着就是 mapperListener 的启动,这里其实是给 container 这种添加 listener,用来监听事件,做处理 然后就是启动 connector,connector 的启动就是之前说的,里面会启动 protocolHandler,这里还是一样的通过 Lifecycle 的接口,再通过 Lifecycle 的模板方法调用实际的 connector 实现 startInternal 方法,这也是 Tomcat 的一大特点,关于 Tomcat 也是个很大的课题,后面可能还会调整下组织结构,对新同学更友好一点。 值得注意的还有两个 第一个是添加 connector,先是锁一下,设置 connector 的 Service,然后 connectors 是个数组,这里进行了重新申请一个数组,然后进行拷贝,再把新添加的放到数组最后,
-@Override public void addConnector (Connector connector) { synchronized (connectorsLock) { connector.setService(this ); Connector results[] = new Connector [connectors.length + 1 ]; System.arraycopy(connectors, 0 , results, 0 , connectors.length); results[connectors.length] = connector; connectors = results; } try { if (getState().isAvailable()) { connector.start(); } } catch (LifecycleException e) { throw new IllegalArgumentException ( sm.getString("standardService.connector.startFailed" , connector), e); } support.firePropertyChange("connector" , null , connector); }
-然后是判断当前的状态是否可用,如果可用就启动 connector, 最后触发下 connector 的变更事件 还有一个是设置Engine,
-@Override public void setContainer (Engine engine) { Engine oldEngine = this .engine; if (oldEngine != null ) { oldEngine.setService(null ); } this .engine = engine; if (this .engine != null ) { this .engine.setService(this ); } if (getState().isAvailable()) { if (this .engine != null ) { try { this .engine.start(); } catch (LifecycleException e) { log.error(sm.getString("standardService.engine.startFailed" ), e); } } try { mapperListener.stop(); } catch (LifecycleException e) { log.error(sm.getString("standardService.mapperListener.stopFailed" ), e); } try { mapperListener.start(); } catch (LifecycleException e) { log.error(sm.getString("standardService.mapperListener.startFailed" ), e); } if (oldEngine != null ) { try { oldEngine.stop(); } catch (LifecycleException e) { log.error(sm.getString("standardService.engine.stopFailed" ), e); } } } support.firePropertyChange("container" , oldEngine, this .engine); }
-前面两步是把第一个的 engine 的 service 设置为 null,然后第二个 engine 也就是新的这个的 service 设置成当前的 service,然后也是判断状态,启动 engine,接着是重启 mapperListener,先关闭再启动,最后是关闭老的 engine,最后的最后就是触发 container 变更事件。
-]]>
-
- Java
- SpringBoot
- Tomcat
-
-
- Java
- Tomcat
- SpringBoot
+ Java
+ Tomcat
+ SpringBoot
@@ -2335,40 +2382,6 @@
c++
-
- Tomcat 系列篇四-介绍下 Valve 架构
- /2023/10/01/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%9B%9B-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Valve-%E6%9E%B6%E6%9E%84/
- valve 是 Tomcat 架构中比较重要的一个组成部分, 之前说到
-connector.getService().getContainer().getPipeline().getFirst().invoke( request, response);
-这段代码是通过 CoyoteAdapter 将请求处理往 container 传,这里就有个 pipeline 机制,这个 pipeline 可以看一下接口
-public interface Pipeline extends Contained { public Valve getBasic () ; public void setBasic (Valve valve) ; public void addValve (Valve valve) ; public Valve[] getValves(); public void removeValve (Valve valve) ; public Valve getFirst () ; public boolean isAsyncSupported () ; public void findNonAsyncValves (Set<String> result) ; }
-这里可以往 pipeline 里添加 valve,然后看下 valve 的接口
-public interface Valve { public Valve getNext () ; public void setNext (Valve valve) ; public void backgroundProcess () ; public void invoke (Request request, Response response) throws IOException, ServletException; public boolean isAsyncSupported () ; }
-这里主要看的就是 getNext 跟 setNext,就变成了一个有序的 pipeline,然后就是 invoke 方法,其实 pipeline 是由两部分组成,valve 在其中起到了前后衔接的重要作用,而且可以再 invoke 中进一步串联调用 图中我们可以看到,对于 container 这个 pipeline,是没设置 first 的,只有 basic,basic 就是个兜底的 valve,在 StandardPipeline 中的 getFirst 实现
-@Override public Valve getFirst () { if (first != null ) { return first; } return basic; }
-取不到 first 就降级到 basic,也就是这里的 StandardEngineValve, 它的 invoke 我们来看下
-@Override public final void invoke (Request request, Response response) throws IOException, ServletException { Host host = request.getHost(); if (host == null ) { if (!response.isError()) { response.sendError(404 ); } return ; } if (request.isAsyncSupported()) { request.setAsyncSupported(host.getPipeline().isAsyncSupported()); } host.getPipeline().getFirst().invoke(request, response); }
-比较简单,就是调用 host 中的 pipeline 里的第一个 valve 来处理 第一个是 org.apache.catalina.valves.ErrorReportValve, 这里处理的其实是先调用了 next
-@Override public void invoke (Request request, Response response) throws IOException, ServletException { getNext().invoke(request, response); if (response.isCommitted()) { if (response.setErrorReported()) { AtomicBoolean ioAllowed = new AtomicBoolean (true ); response.getCoyoteResponse().action(ActionCode.IS_IO_ALLOWED, ioAllowed); if (ioAllowed.get()) { try { response.flushBuffer(); } catch (Throwable t) { ExceptionUtils.handleThrowable(t); } response.getCoyoteResponse().action(ActionCode.CLOSE_NOW, request.getAttribute(RequestDispatcher.ERROR_EXCEPTION)); } } return ; } Throwable throwable = (Throwable) request.getAttribute(RequestDispatcher.ERROR_EXCEPTION); if (request.isAsync() && !request.isAsyncCompleting()) { return ; } if (throwable != null && !response.isError()) { response.reset(); response.sendError(HttpServletResponse.SC_INTERNAL_SERVER_ERROR); } response.setSuspended(false ); try { report(request, response, throwable); } catch (Throwable tt) { ExceptionUtils.handleThrowable(tt); } }
-虽然是放在 first,实际是先调用 next 的 invoke,也就是 org.apache.catalina.core.StandardHostValve 的invoke 方法
-@Override public final void invoke (Request request, Response response) throws IOException, ServletException { Context context = request.getContext(); if (context == null ) { if (!response.isError()) { response.sendError(404 ); } return ; } if (request.isAsyncSupported()) { request.setAsyncSupported(context.getPipeline().isAsyncSupported()); } boolean asyncAtStart = request.isAsync(); try { context.bind(Globals.IS_SECURITY_ENABLED, MY_CLASSLOADER); if (!asyncAtStart && !context.fireRequestInitEvent(request.getRequest())) { return ; } try { if (!response.isErrorReportRequired()) { context.getPipeline().getFirst().invoke(request, response); } } catch (Throwable t) { ExceptionUtils.handleThrowable(t); container.getLogger().error("Exception Processing " + request.getRequestURI(), t); if (!response.isErrorReportRequired()) { request.setAttribute(RequestDispatcher.ERROR_EXCEPTION, t); throwable(request, response, t); } } response.setSuspended(false ); Throwable t = (Throwable) request.getAttribute(RequestDispatcher.ERROR_EXCEPTION); if (!context.getState().isAvailable()) { return ; } if (response.isErrorReportRequired()) { AtomicBoolean result = new AtomicBoolean (false ); response.getCoyoteResponse().action(ActionCode.IS_IO_ALLOWED, result); if (result.get()) { if (t != null ) { throwable(request, response, t); } else { status(request, response); } } } if (!request.isAsync() && !asyncAtStart) { context.fireRequestDestroyEvent(request.getRequest()); } } finally { if (ACCESS_SESSION) { request.getSession(false ); } context.unbind(Globals.IS_SECURITY_ENABLED, MY_CLASSLOADER); } }
-这里的往下就是调用 context 的 pipeline 去处理请求了, StandardContext 的 pipeline 里的 first 是 org.apache.catalina.authenticator.NonLoginAuthenticator,处理认证相关的,然后 basic 就是 org.apache.catalina.core.StandardContextValve, 这里的来看下
-@Override public final void invoke (Request request, Response response) throws IOException, ServletException { MessageBytes requestPathMB = request.getRequestPathMB(); if ((requestPathMB.startsWithIgnoreCase("/META-INF/" , 0 )) || (requestPathMB.equalsIgnoreCase("/META-INF" )) || (requestPathMB.startsWithIgnoreCase("/WEB-INF/" , 0 )) || (requestPathMB.equalsIgnoreCase("/WEB-INF" ))) { response.sendError(HttpServletResponse.SC_NOT_FOUND); return ; } Wrapper wrapper = request.getWrapper(); if (wrapper == null || wrapper.isUnavailable()) { response.sendError(HttpServletResponse.SC_NOT_FOUND); return ; } try { response.sendAcknowledgement(ContinueResponseTiming.IMMEDIATELY); } catch (IOException ioe) { container.getLogger().error(sm.getString( "standardContextValve.acknowledgeException" ), ioe); request.setAttribute(RequestDispatcher.ERROR_EXCEPTION, ioe); response.sendError(HttpServletResponse.SC_INTERNAL_SERVER_ERROR); return ; } if (request.isAsyncSupported()) { request.setAsyncSupported(wrapper.getPipeline().isAsyncSupported()); } wrapper.getPipeline().getFirst().invoke(request, response); }
-会调用 wrapper 的 pipeline 去处理请求,这里也只有一个 org.apache.catalina.core.StandardWrapperValve 这部分的逻辑比较长,因为要串联后面的 filter 流程
-@Override public final void invoke (Request request, Response response) throws IOException, ServletException { boolean unavailable = false ; Throwable throwable = null ; long t1=System.currentTimeMillis(); requestCount.incrementAndGet(); StandardWrapper wrapper = (StandardWrapper) getContainer(); Servlet servlet = null ; Context context = (Context) wrapper.getParent(); if (!context.getState().isAvailable()) { response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE, sm.getString("standardContext.isUnavailable" )); unavailable = true ; } if (!unavailable && wrapper.isUnavailable()) { container.getLogger().info(sm.getString("standardWrapper.isUnavailable" , wrapper.getName())); long available = wrapper.getAvailable(); if ((available > 0L ) && (available < Long.MAX_VALUE)) { response.setDateHeader("Retry-After" , available); response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE, sm.getString("standardWrapper.isUnavailable" , wrapper.getName())); } else if (available == Long.MAX_VALUE) { response.sendError(HttpServletResponse.SC_NOT_FOUND, sm.getString("standardWrapper.notFound" , wrapper.getName())); } unavailable = true ; } try { if (!unavailable) { servlet = wrapper.allocate(); } } catch (UnavailableException e) { container.getLogger().error( sm.getString("standardWrapper.allocateException" , wrapper.getName()), e); long available = wrapper.getAvailable(); if ((available > 0L ) && (available < Long.MAX_VALUE)) { response.setDateHeader("Retry-After" , available); response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE, sm.getString("standardWrapper.isUnavailable" , wrapper.getName())); } else if (available == Long.MAX_VALUE) { response.sendError(HttpServletResponse.SC_NOT_FOUND, sm.getString("standardWrapper.notFound" , wrapper.getName())); } } catch (ServletException e) { container.getLogger().error(sm.getString("standardWrapper.allocateException" , wrapper.getName()), StandardWrapper.getRootCause(e)); throwable = e; exception(request, response, e); } catch (Throwable e) { ExceptionUtils.handleThrowable(e); container.getLogger().error(sm.getString("standardWrapper.allocateException" , wrapper.getName()), e); throwable = e; exception(request, response, e); servlet = null ; } MessageBytes requestPathMB = request.getRequestPathMB(); DispatcherType dispatcherType = DispatcherType.REQUEST; if (request.getDispatcherType()==DispatcherType.ASYNC) { dispatcherType = DispatcherType.ASYNC; } request.setAttribute(Globals.DISPATCHER_TYPE_ATTR,dispatcherType); request.setAttribute(Globals.DISPATCHER_REQUEST_PATH_ATTR, requestPathMB); ApplicationFilterChain filterChain = ApplicationFilterFactory.createFilterChain(request, wrapper, servlet); Container container = this .container; try { if ((servlet != null ) && (filterChain != null )) { if (context.getSwallowOutput()) { try { SystemLogHandler.startCapture(); if (request.isAsyncDispatching()) { request.getAsyncContextInternal().doInternalDispatch(); } else { filterChain.doFilter(request.getRequest(), response.getResponse()); } } finally { String log = SystemLogHandler.stopCapture(); if (log != null && log.length() > 0 ) { context.getLogger().info(log); } } } else { if (request.isAsyncDispatching()) { request.getAsyncContextInternal().doInternalDispatch(); } else { filterChain.doFilter (request.getRequest(), response.getResponse()); } } } } catch (ClientAbortException | CloseNowException e) { if (container.getLogger().isDebugEnabled()) { container.getLogger().debug(sm.getString( "standardWrapper.serviceException" , wrapper.getName(), context.getName()), e); } throwable = e; exception(request, response, e); } catch (IOException e) { container.getLogger().error(sm.getString( "standardWrapper.serviceException" , wrapper.getName(), context.getName()), e); throwable = e; exception(request, response, e); } catch (UnavailableException e) { container.getLogger().error(sm.getString( "standardWrapper.serviceException" , wrapper.getName(), context.getName()), e); wrapper.unavailable(e); long available = wrapper.getAvailable(); if ((available > 0L ) && (available < Long.MAX_VALUE)) { response.setDateHeader("Retry-After" , available); response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE, sm.getString("standardWrapper.isUnavailable" , wrapper.getName())); } else if (available == Long.MAX_VALUE) { response.sendError(HttpServletResponse.SC_NOT_FOUND, sm.getString("standardWrapper.notFound" , wrapper.getName())); } } catch (ServletException e) { Throwable rootCause = StandardWrapper.getRootCause(e); if (!(rootCause instanceof ClientAbortException)) { container.getLogger().error(sm.getString( "standardWrapper.serviceExceptionRoot" , wrapper.getName(), context.getName(), e.getMessage()), rootCause); } throwable = e; exception(request, response, e); } catch (Throwable e) { ExceptionUtils.handleThrowable(e); container.getLogger().error(sm.getString( "standardWrapper.serviceException" , wrapper.getName(), context.getName()), e); throwable = e; exception(request, response, e); } finally { if (filterChain != null ) { filterChain.release(); } try { if (servlet != null ) { wrapper.deallocate(servlet); } } catch (Throwable e) { ExceptionUtils.handleThrowable(e); container.getLogger().error(sm.getString("standardWrapper.deallocateException" , wrapper.getName()), e); if (throwable == null ) { throwable = e; exception(request, response, e); } } try { if ((servlet != null ) && (wrapper.getAvailable() == Long.MAX_VALUE)) { wrapper.unload(); } } catch (Throwable e) { ExceptionUtils.handleThrowable(e); container.getLogger().error(sm.getString("standardWrapper.unloadException" , wrapper.getName()), e); if (throwable == null ) { exception(request, response, e); } } long t2=System.currentTimeMillis(); long time=t2-t1; processingTime += time; if ( time > maxTime) { maxTime=time; } if ( time < minTime) { minTime=time; } } }
-这里就会创建 ApplicationFilterChain 然后进行filterChain.doFilter(request.getRequest(), response.getResponse()); doFilter 处理
-]]>
-
- Java
- SpringBoot
- Tomcat
-
-
- Java
- Tomcat
- SpringBoot
-
-
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/
@@ -2424,6 +2437,48 @@
c++
+
+ 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
+
+
+
+ Tomcat 系列篇五-介绍下 Service 启动过程
+ /2023/10/07/Tomcat-%E7%B3%BB%E5%88%97%E7%AF%87%E5%9B%9B-%E4%BB%8B%E7%BB%8D%E4%B8%8B-Service-%E5%90%AF%E5%8A%A8%E8%BF%87%E7%A8%8B/
+ 这里开始介绍下 Service 的启动过程,Tomcat 的启动过程中
+public void start () throws LifecycleException { getServer(); server.start(); }
+getServer之前讲到过
+public Server getServer () { if (server != null ) { return server; } System.setProperty("catalina.useNaming" , "false" ); server = new StandardServer (); initBaseDir(); ConfigFileLoader.setSource(new CatalinaBaseConfigurationSource (new File (basedir), null )); server.setPort( -1 ); Service service = new StandardService (); service.setName("Tomcat" ); server.addService(service); return server; }
+会 new 一个StandardService,添加到 server 里,然后进行启动 而这个外面的 server.start 其实调用的是 org.apache.catalina.Lifecycle#start, 里面是一个 Lifecycle 的接口,这个接口被很多 Tomcat 的组件实现,其实都共用了 Lifecycle 的机制 然后 Lifecycle 里面会根据状态,调用实际的实现层的 startInternal 方法
+@Override protected void startInternal () throws LifecycleException { if (log.isInfoEnabled()) { log.info(sm.getString("standardService.start.name" , this .name)); } setState(LifecycleState.STARTING); if (engine != null ) { synchronized (engine) { engine.start(); } } synchronized (executors) { for (Executor executor: executors) { executor.start(); } } mapperListener.start(); synchronized (connectorsLock) { for (Connector connector: connectors) { if (connector.getState() != LifecycleState.FAILED) { connector.start(); } } } }
+这里会先设置状态,这个状态也有点东西,可以后面再讲,因为里面有个状态事件的触发 然后是启动 Engine,也就是 container,在这里就是 StandardEngine,先不深入去讲里面做了啥,后面讲 StandardEngine 的启动过程会讲,然后是启动线程池了,对于 StandardService 是没有线程池要启动的,或者是 springboot 集成的这个 Tomcat 中不需要,接着就是 mapperListener 的启动,这里其实是给 container 这种添加 listener,用来监听事件,做处理 然后就是启动 connector,connector 的启动就是之前说的,里面会启动 protocolHandler,这里还是一样的通过 Lifecycle 的接口,再通过 Lifecycle 的模板方法调用实际的 connector 实现 startInternal 方法,这也是 Tomcat 的一大特点,关于 Tomcat 也是个很大的课题,后面可能还会调整下组织结构,对新同学更友好一点。 值得注意的还有两个 第一个是添加 connector,先是锁一下,设置 connector 的 Service,然后 connectors 是个数组,这里进行了重新申请一个数组,然后进行拷贝,再把新添加的放到数组最后,
+@Override public void addConnector (Connector connector) { synchronized (connectorsLock) { connector.setService(this ); Connector results[] = new Connector [connectors.length + 1 ]; System.arraycopy(connectors, 0 , results, 0 , connectors.length); results[connectors.length] = connector; connectors = results; } try { if (getState().isAvailable()) { connector.start(); } } catch (LifecycleException e) { throw new IllegalArgumentException ( sm.getString("standardService.connector.startFailed" , connector), e); } support.firePropertyChange("connector" , null , connector); }
+然后是判断当前的状态是否可用,如果可用就启动 connector, 最后触发下 connector 的变更事件 还有一个是设置Engine,
+@Override public void setContainer (Engine engine) { Engine oldEngine = this .engine; if (oldEngine != null ) { oldEngine.setService(null ); } this .engine = engine; if (this .engine != null ) { this .engine.setService(this ); } if (getState().isAvailable()) { if (this .engine != null ) { try { this .engine.start(); } catch (LifecycleException e) { log.error(sm.getString("standardService.engine.startFailed" ), e); } } try { mapperListener.stop(); } catch (LifecycleException e) { log.error(sm.getString("standardService.mapperListener.stopFailed" ), e); } try { mapperListener.start(); } catch (LifecycleException e) { log.error(sm.getString("standardService.mapperListener.startFailed" ), e); } if (oldEngine != null ) { try { oldEngine.stop(); } catch (LifecycleException e) { log.error(sm.getString("standardService.engine.stopFailed" ), e); } } } support.firePropertyChange("container" , oldEngine, this .engine); }
+前面两步是把第一个的 engine 的 service 设置为 null,然后第二个 engine 也就是新的这个的 service 设置成当前的 service,然后也是判断状态,启动 engine,接着是重启 mapperListener,先关闭再启动,最后是关闭老的 engine,最后的最后就是触发 container 变更事件。
+]]>
+
+ Java
+ SpringBoot
+ Tomcat
+
+
+ Java
+ Tomcat
+ SpringBoot
+
+
docker-mysql-cluster
/2016/08/14/docker-mysql-cluster/
@@ -2443,22 +2498,6 @@
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
/2020/03/08/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/
@@ -2571,29 +2610,6 @@
cgroup
-
- docker使用中发现的echo命令的一个小技巧及其他
- /2020/03/29/echo%E5%91%BD%E4%BB%A4%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E6%8A%80%E5%B7%A7/
- echo 实操技巧最近做 docker 系列,会经常需要进到 docker 内部,如上一篇介绍的,这些镜像一般都有用 ubuntu 或者alpine 这样的 Linux 系统作为底包,如果构建镜像的时候没有替换源的话,因为特殊的网络原因,在内部想编辑下东西要安装个类似于 vim 这样的编辑器就会很慢很慢,像视频里 two thousand years later~ 而且如果在容器内部想改源配置的话也要编辑器,就陷入了一个鸡生蛋,跟蛋生鸡的死锁问题中,对于 linux 大神来说应该有一万种方法解决这个问题,对于我这个渣渣来说可能只想到了这个土方法,先 cp backup 一下 sources.list, 再 echo “xxx” > sources.list, 这里就碰到了一个问题,这个 sources.list 一般不止一行,直接 echo 的话就解析不了了,不过 echo 可以支持”\n”转义,就是加-e看一下解释和示例,我这里使用了 tldr ,可以用 npm install -g tldr 安装,也可以直接用man, 或者–help 来查看使用方式
-查看镜像底包 还有一点也是在这个时候要安装 vim 之类的,得知道是什么镜像底包,如果是用 uname 指令,其实看到的是宿主机的系统,得用cat /etc/issue
- 这里稍稍记一下
-寻找系统镜像源 目前国内系统源用得比较多的是阿里云源,不过这里也推荐清华源 , 中科大源 , 浙大源 这里不要脸的推荐下母校的源,不过还不是很完善,尽情期待下。
-]]>
-
- Linux
- Docker
- 命令
- echo
- 发行版本
-
-
- linux
- Docker
- echo
- uname
- 发行版
-
-
docker比一般多一点的初学者介绍四
/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/
@@ -2642,6 +2658,29 @@
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
+
+
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/
@@ -2669,42 +2708,50 @@
- 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
-
-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
-
-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
-
-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
-
-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
-
-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 ; while (i >= 0 && nums[i] >= nums[i + 1 ]) --i; if (i >= 0 ) { while (nums[j] <= nums[i]) --j; int temp = nums[i]; nums[i] = nums[j]; nums[j] = temp; } Arrays.sort(nums, i+1 , nums.length); }
-
+ 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
]]>
- Java
- leetcode
+ git
+ 技巧
- leetcode
- java
- 题解
+ git
- 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
+ git小技巧之git stash
+ /2024/10/27/git%E5%B0%8F%E6%8A%80%E5%B7%A7%E4%B9%8Bgit-stash/
+ 我们日常开发包括我自用的小工具也在用git管理 在使用git的过程中经常有个场景是我在A分支上开发了一部分,临时需要切换到B分支,又不想先把这部分代码提交,因为还没开发完, 这是用git stash命令就能很好的解决这个问题,但是这里在使用git stash的时候我认为有两个阶段 第一阶段就是简单使用 比如我有这个进行中的变更,在有未提交的代码的时候可以先用
+
+添加到暂存区,但不提交,然后使用
+
+保存工作进度,git stash之后再用 git status 就看不到刚才的更改了 然后就是弹出刚才stash的变更了 可以使用 git stash pop 就可以把刚才藏起来的临时变更就弹出来了 这里其实在真正使用时我们如果是深度使用就可能会出现四个问题或者是诉求
+问题一 我不止stash了一次变更,怎么找到stash的多少次呢,因为直接用 pop 就是把栈顶的那次隐藏的变更弹出来了 这个问题就可以通过 git stash list 来解决
+问题二 如果使用的pop我们这个stash相当于栈顶弹出,已经pop出来的就没有了,如果我只是想看一下改了哪些,当然我们可以先pop出来,在stash回去, 但更好的办法就是用git stash apply,这样是不会清栈的
+问题三 如何指定弹出,比如我在list里能看到多次stash,那么我想弹出其中某一次 可以用git stash pop stash@{1} 注意这个 stash@{1} 是指stash list 的第二个,因为是从0开始的 弹出倒数第二次stash的
+问题四 我想知道我这个stash里内容是啥,在不用pop和apply的情况下有没有办法 也是有的 可以使用 git stash show 1 或者 git stash show stash@{1} 来查看这次变更了哪些文件 甚至我们可以用 git stash show 1 -p 来查看具体变更的内容
]]>
- openwrt
+ git
+ 技巧
- openwrt
+ git
+
+
+
+ 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
@@ -2741,50 +2788,40 @@
- 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
+ grep命令小技巧-统计行数
+ /2024/12/15/grep%E5%91%BD%E4%BB%A4%E5%B0%8F%E6%8A%80%E5%B7%A7-%E7%BB%9F%E8%AE%A1%E8%A1%8C%E6%95%B0/
+ 之前在使用grep来匹配内容,想看下匹配的结果数量都是用管道,然后用wc命令的 最近看了下似乎没必要这么麻烦 grep命令自带了这个功能 原来在用grep的时候还有个额外的功能就是看匹配行的前后几行, 使用
+
+是匹配行的前4行,可以用before来记
+
+是匹配行的后4行,可以用after来记 然后
+
+是看匹配行的前后4行 而这次的主角是小写的c
+grep -c 'something' filename.txt
+
+ 类似于这个效果,这又是个可以少打几个字符的偷懒技巧
]]>
- git
技巧
- git
+ grep
- 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
+ grep小技巧之匹配到二进制文件
+ /2024/11/24/grep%E5%B0%8F%E6%8A%80%E5%B7%A7%E4%B9%8B%E5%8C%B9%E9%85%8D%E5%88%B0%E4%BA%8C%E8%BF%9B%E5%88%B6%E6%96%87%E4%BB%B6/
+ 我们在日常使用grep这个强大的命令行工具时有时候会碰到一个问题,就是grep在识别文件的时候在碰到 ‘\000 NUL’的时候会认为文件是二进制文件,就不进行识别了,碰到这种情况我们可以使用 grep -a 或者 grep --text 比如我们做个小实验
+<?php $content = file_get_contents ('input.txt' );$binary = "\0" . $content ; file_put_contents ('output.bin' , $binary );
+然后我们就有了 output.bin 这个二进制文件 本身 input.txt 的内容也很简单
+
+我们尝试用 grep 查找其中的内容, 就会出现这个问题 当我们加上了 -a 或者 --text 就是指定 grep 需要在包含二进制文件格式中或者将文件当做是文本文件来识别匹配
]]>
- git
技巧
- git
-
-
-
- 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 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
+ grep
@@ -2805,6 +2842,27 @@
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 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/
@@ -2850,61 +2908,22 @@
- grep小技巧之匹配到二进制文件
- /2024/11/24/grep%E5%B0%8F%E6%8A%80%E5%B7%A7%E4%B9%8B%E5%8C%B9%E9%85%8D%E5%88%B0%E4%BA%8C%E8%BF%9B%E5%88%B6%E6%96%87%E4%BB%B6/
- 我们在日常使用grep这个强大的命令行工具时有时候会碰到一个问题,就是grep在识别文件的时候在碰到 ‘\000 NUL’的时候会认为文件是二进制文件,就不进行识别了,碰到这种情况我们可以使用 grep -a 或者 grep --text 比如我们做个小实验
-<?php $content = file_get_contents ('input.txt' );$binary = "\0" . $content ; file_put_contents ('output.bin' , $binary );
-然后我们就有了 output.bin 这个二进制文件 本身 input.txt 的内容也很简单
-
-我们尝试用 grep 查找其中的内容, 就会出现这个问题 当我们加上了 -a 或者 --text 就是指定 grep 需要在包含二进制文件格式中或者将文件当做是文本文件来识别匹配
-]]>
-
- 技巧
-
-
- grep
-
-
-
- git小技巧之git stash
- /2024/10/27/git%E5%B0%8F%E6%8A%80%E5%B7%A7%E4%B9%8Bgit-stash/
- 我们日常开发包括我自用的小工具也在用git管理 在使用git的过程中经常有个场景是我在A分支上开发了一部分,临时需要切换到B分支,又不想先把这部分代码提交,因为还没开发完, 这是用git stash命令就能很好的解决这个问题,但是这里在使用git stash的时候我认为有两个阶段 第一阶段就是简单使用 比如我有这个进行中的变更,在有未提交的代码的时候可以先用
-
-添加到暂存区,但不提交,然后使用
-
-保存工作进度,git stash之后再用 git status 就看不到刚才的更改了 然后就是弹出刚才stash的变更了 可以使用 git stash pop 就可以把刚才藏起来的临时变更就弹出来了 这里其实在真正使用时我们如果是深度使用就可能会出现四个问题或者是诉求
-问题一 我不止stash了一次变更,怎么找到stash的多少次呢,因为直接用 pop 就是把栈顶的那次隐藏的变更弹出来了 这个问题就可以通过 git stash list 来解决
-问题二 如果使用的pop我们这个stash相当于栈顶弹出,已经pop出来的就没有了,如果我只是想看一下改了哪些,当然我们可以先pop出来,在stash回去, 但更好的办法就是用git stash apply,这样是不会清栈的
-问题三 如何指定弹出,比如我在list里能看到多次stash,那么我想弹出其中某一次 可以用git stash pop stash@{1} 注意这个 stash@{1} 是指stash list 的第二个,因为是从0开始的 弹出倒数第二次stash的
-问题四 我想知道我这个stash里内容是啥,在不用pop和apply的情况下有没有办法 也是有的 可以使用 git stash show 1 或者 git stash show stash@{1} 来查看这次变更了哪些文件 甚至我们可以用 git stash show 1 -p 来查看具体变更的内容
-]]>
-
- git
- 技巧
-
-
- git
-
-
-
- grep命令小技巧-统计行数
- /2024/12/15/grep%E5%91%BD%E4%BB%A4%E5%B0%8F%E6%8A%80%E5%B7%A7-%E7%BB%9F%E8%AE%A1%E8%A1%8C%E6%95%B0/
- 之前在使用grep来匹配内容,想看下匹配的结果数量都是用管道,然后用wc命令的 最近看了下似乎没必要这么麻烦 grep命令自带了这个功能 原来在用grep的时候还有个额外的功能就是看匹配行的前后几行, 使用
-
-是匹配行的前4行,可以用before来记
-
-是匹配行的后4行,可以用after来记 然后
-
-是看匹配行的前后4行 而这次的主角是小写的c
-grep -c 'something' filename.txt
-
- 类似于这个效果,这又是个可以少打几个字符的偷懒技巧
+ java中arraylist的sort方法的 ConcurrentModificationException 问题
+ /2025/05/18/java%E4%B8%ADarraylist%E7%9A%84sort%E6%96%B9%E6%B3%95%E7%9A%84%E9%97%AE%E9%A2%98/
+ 最近写代码的时候碰到个问题, 比如我们有一个对象列表,对象中有个排序字段,比如时间或者索性就有个序号
+public class Demo { private int order = 0 ; public int getOrder () { return order; } public void setOrder (int order) { this .order = order; } }
+比如这样,有一个前提,就是这个列表其实是有序的,只是为了防止万一 所以对这个列表进行了排序,就直接使用了ArrayList自带的sort方法,由于是不小心用在了多线程环境 就有出现了一个并发变更报错 也就是这个异常 java.util.ConcurrentModificationException#ConcurrentModificationException() 看下注释
+This exception may be thrown by methods that have detected concurrent modification of an object when such modification is not permissible. For example, it is not generally permissible for one thread to modify a Collection while another thread is iterating over it. In general, the results of the iteration are undefined under these circumstances. Some Iterator implementations (including those of all the general purpose collection implementations provided by the JRE) may choose to throw this exception if this behavior is detected. Iterators that do this are known as fail-fast iterators, as they fail quickly and cleanly, rather that risking arbitrary, non-deterministic behavior at an undetermined time in the future.
+一般情况就是并发环境下进行了变更, 只是这里比较特殊,这个列表没有进行增删,同时是有序的,理论上应该是不会出现变更才对 但是它的确出现了,就得看下这个产生的原因 主要还是来看下这个java.util.ArrayList#sort的实现跟这个异常的抛出原理
+public void sort (Comparator<? super E> c) { final int expectedModCount = modCount; Arrays.sort((E[]) elementData, 0 , size, c); if (modCount != expectedModCount) throw new ConcurrentModificationException (); modCount++; }
+可以看到在排序前会有个 modCount 的赋值,如果在进行排序后,这个初始的 modCount 跟当前的不等的话就会抛出这个异常 那么什么时候会出现这两者不相等呢 简单推演下这个并发排序下的情况,线程1和线程2同时进入这个方法,拿到了一样的 modCount , 然后进行排序,第一个线程比较快,sort玩之后发现两者相等 就到了 modCount++ , 这时候第二个线程刚排序完,发现 modCount 已经比 expectedModCount 大了 这个时候就出现了这个异常的抛出时机 如果这么理解这个异常的确是应该会出现 而不是我们理解的,如果不变更元素,不调整顺序,就不会触发这个并发变更的异常 还是有很多值得学习的点
]]>
+ java
技巧
- grep
+ java
@@ -2919,31 +2938,6 @@
分别是定义的类,方法名,文件名,以及代码行数 我们就能看到这样的结果
java.lang.Thread.getStackTrace(Thread.java:1564 ) StackTrace.StackTrace.method1(StackTrace.java:14 ) StackTrace.StackTrace.main(StackTrace.java:11 )
因为是栈,所以就是最上面的是最近的方法,而第二行其实是我的目标方法,就是前面描述的逻辑其实应该找到第3个元素,因为第1个是调用 getStackTrace 方法, 第2个是目标方法,第3个才是我们想要找的调用方方法,以及所在的行数
-]]>
-
- java
- 技巧
-
-
- java
-
-
-
- java的agent初体验
- /2024/12/22/java%E7%9A%84agent%E5%88%9D%E4%BD%93%E9%AA%8C/
- 之前在用到arthas就想到过可以研究下java的agent,这里算是个初入门 首先我们有个应用,需要挂上agent来探测一些事情 比如就是简单的主方法
-public class Main { public static void main (String[] args) { System.out.println("Hello world!" ); Demo.start(); Demo.start2(); } }
-然后有个Demo类
-public class Demo { public static void start () { try { System.out.println("Demo starting ..." ); Thread.sleep(123 ); } catch (InterruptedException ignore) { } } public static void start2 () { try { System.out.println("Demo starting 2..." ); Thread.sleep(567 ); } catch (InterruptedException ignore) { } } }
-然后我们想要像切面一样在start方法前后织入一些前后置的逻辑 我们需要将一个maven的module 然后agent代码是这样, premain 方法会在我们指定挂载agent执行,并且就是入口方法
-public class DemoAgent { public static void premain (String arg, Instrumentation instrumentation) { System.out.println("agent start" ); System.out.println("arg is " + arg); instrumentation.addTransformer(new DemoTransformer ()); } }
-然后这个 transformer 就是我要怎么在目标类里如何织入代码的逻辑
-public class DemoTransformer implements ClassFileTransformer { private static final String INJECTED_CLASS = "org.example.Demo" ; @Override public byte [] transform(ClassLoader loader, String className, Class<?> classBeingRedefined, ProtectionDomain protectionDomain, byte [] classfileBuffer) throws IllegalClassFormatException { String realClassName = className.replace("/" , "." ); if (realClassName.equals(INJECTED_CLASS)) { System.out.println("injected class :" + realClassName); CtClass ctClass; try { ClassPool classPool = ClassPool.getDefault(); ctClass = classPool.get(realClassName); CtMethod[] declaredMethods = ctClass.getDeclaredMethods(); for (CtMethod declaredMethod : declaredMethods) { System.out.println(declaredMethod.getName() + "method be inject" ); declaredMethod.addLocalVariable("time" , CtClass.longType); declaredMethod.insertBefore("System.out.println(\"--- start ---\");" ); declaredMethod.insertBefore("time = System.currentTimeMillis();" ); declaredMethod.insertAfter("System.out.println(\"--- end ---\");" ); declaredMethod.insertAfter("System.out.println(\"cost: \" + (System.currentTimeMillis() - time));" ); } return ctClass.toBytecode(); } catch (Throwable ignore) { } } return classfileBuffer; } }
-接下去要在pom.xml里配置如何打包agent的命令
-<build > <plugins > <plugin > <groupId > org.apache.maven.plugins</groupId > <artifactId > maven-compiler-plugin</artifactId > <version > 3.5.1</version > <configuration > <source > 8</source > <target > 8</target > </configuration > </plugin > <plugin > <groupId > org.apache.maven.plugins</groupId > <artifactId > maven-jar-plugin</artifactId > <version > 3.2.0</version > <configuration > <archive > <manifest > <addClasspath > true</addClasspath > </manifest > <manifestEntries > <Menifest-Version > 1.0</Menifest-Version > <Premain-Class > org.example.DemoAgent</Premain-Class > <Can-Redefine-Classes > true</Can-Redefine-Classes > <Can-Retransform-Classes > true</Can-Retransform-Classes > </manifestEntries > </archive > </configuration > </plugin > </plugins > </build >
-需要指定 Premain-Class 来指定入口方法所在的类 然后用 mvn package 进行打包 打包后在我们的目标代码的启动vm参数里加上
--javaagent:/{path}/demo-agent-1.0-SNAPSHOT.jar
-{path} 需要替换成agent的jar包所在路径 然后执行目标main方法 就可以看到结果了,这样我们第一个agent的demo就跑起来了
]]>
java
@@ -2975,15 +2969,21 @@
- java中arraylist的sort方法的 ConcurrentModificationException 问题
- /2025/05/18/java%E4%B8%ADarraylist%E7%9A%84sort%E6%96%B9%E6%B3%95%E7%9A%84%E9%97%AE%E9%A2%98/
- 最近写代码的时候碰到个问题, 比如我们有一个对象列表,对象中有个排序字段,比如时间或者索性就有个序号
-public class Demo { private int order = 0 ; public int getOrder () { return order; } public void setOrder (int order) { this .order = order; } }
-比如这样,有一个前提,就是这个列表其实是有序的,只是为了防止万一 所以对这个列表进行了排序,就直接使用了ArrayList自带的sort方法,由于是不小心用在了多线程环境 就有出现了一个并发变更报错 也就是这个异常 java.util.ConcurrentModificationException#ConcurrentModificationException() 看下注释
-This exception may be thrown by methods that have detected concurrent modification of an object when such modification is not permissible. For example, it is not generally permissible for one thread to modify a Collection while another thread is iterating over it. In general, the results of the iteration are undefined under these circumstances. Some Iterator implementations (including those of all the general purpose collection implementations provided by the JRE) may choose to throw this exception if this behavior is detected. Iterators that do this are known as fail-fast iterators, as they fail quickly and cleanly, rather that risking arbitrary, non-deterministic behavior at an undetermined time in the future.
-一般情况就是并发环境下进行了变更, 只是这里比较特殊,这个列表没有进行增删,同时是有序的,理论上应该是不会出现变更才对 但是它的确出现了,就得看下这个产生的原因 主要还是来看下这个java.util.ArrayList#sort的实现跟这个异常的抛出原理
-public void sort (Comparator<? super E> c) { final int expectedModCount = modCount; Arrays.sort((E[]) elementData, 0 , size, c); if (modCount != expectedModCount) throw new ConcurrentModificationException (); modCount++; }
-可以看到在排序前会有个 modCount 的赋值,如果在进行排序后,这个初始的 modCount 跟当前的不等的话就会抛出这个异常 那么什么时候会出现这两者不相等呢 简单推演下这个并发排序下的情况,线程1和线程2同时进入这个方法,拿到了一样的 modCount , 然后进行排序,第一个线程比较快,sort玩之后发现两者相等 就到了 modCount++ , 这时候第二个线程刚排序完,发现 modCount 已经比 expectedModCount 大了 这个时候就出现了这个异常的抛出时机 如果这么理解这个异常的确是应该会出现 而不是我们理解的,如果不变更元素,不调整顺序,就不会触发这个并发变更的异常 还是有很多值得学习的点
+ java的agent初体验
+ /2024/12/22/java%E7%9A%84agent%E5%88%9D%E4%BD%93%E9%AA%8C/
+ 之前在用到arthas就想到过可以研究下java的agent,这里算是个初入门 首先我们有个应用,需要挂上agent来探测一些事情 比如就是简单的主方法
+public class Main { public static void main (String[] args) { System.out.println("Hello world!" ); Demo.start(); Demo.start2(); } }
+然后有个Demo类
+public class Demo { public static void start () { try { System.out.println("Demo starting ..." ); Thread.sleep(123 ); } catch (InterruptedException ignore) { } } public static void start2 () { try { System.out.println("Demo starting 2..." ); Thread.sleep(567 ); } catch (InterruptedException ignore) { } } }
+然后我们想要像切面一样在start方法前后织入一些前后置的逻辑 我们需要将一个maven的module 然后agent代码是这样, premain 方法会在我们指定挂载agent执行,并且就是入口方法
+public class DemoAgent { public static void premain (String arg, Instrumentation instrumentation) { System.out.println("agent start" ); System.out.println("arg is " + arg); instrumentation.addTransformer(new DemoTransformer ()); } }
+然后这个 transformer 就是我要怎么在目标类里如何织入代码的逻辑
+public class DemoTransformer implements ClassFileTransformer { private static final String INJECTED_CLASS = "org.example.Demo" ; @Override public byte [] transform(ClassLoader loader, String className, Class<?> classBeingRedefined, ProtectionDomain protectionDomain, byte [] classfileBuffer) throws IllegalClassFormatException { String realClassName = className.replace("/" , "." ); if (realClassName.equals(INJECTED_CLASS)) { System.out.println("injected class :" + realClassName); CtClass ctClass; try { ClassPool classPool = ClassPool.getDefault(); ctClass = classPool.get(realClassName); CtMethod[] declaredMethods = ctClass.getDeclaredMethods(); for (CtMethod declaredMethod : declaredMethods) { System.out.println(declaredMethod.getName() + "method be inject" ); declaredMethod.addLocalVariable("time" , CtClass.longType); declaredMethod.insertBefore("System.out.println(\"--- start ---\");" ); declaredMethod.insertBefore("time = System.currentTimeMillis();" ); declaredMethod.insertAfter("System.out.println(\"--- end ---\");" ); declaredMethod.insertAfter("System.out.println(\"cost: \" + (System.currentTimeMillis() - time));" ); } return ctClass.toBytecode(); } catch (Throwable ignore) { } } return classfileBuffer; } }
+接下去要在pom.xml里配置如何打包agent的命令
+<build > <plugins > <plugin > <groupId > org.apache.maven.plugins</groupId > <artifactId > maven-compiler-plugin</artifactId > <version > 3.5.1</version > <configuration > <source > 8</source > <target > 8</target > </configuration > </plugin > <plugin > <groupId > org.apache.maven.plugins</groupId > <artifactId > maven-jar-plugin</artifactId > <version > 3.2.0</version > <configuration > <archive > <manifest > <addClasspath > true</addClasspath > </manifest > <manifestEntries > <Menifest-Version > 1.0</Menifest-Version > <Premain-Class > org.example.DemoAgent</Premain-Class > <Can-Redefine-Classes > true</Can-Redefine-Classes > <Can-Retransform-Classes > true</Can-Retransform-Classes > </manifestEntries > </archive > </configuration > </plugin > </plugins > </build >
+需要指定 Premain-Class 来指定入口方法所在的类 然后用 mvn package 进行打包 打包后在我们的目标代码的启动vm参数里加上
+-javaagent:/{path}/demo-agent-1.0-SNAPSHOT.jar
+{path} 需要替换成agent的jar包所在路径 然后执行目标main方法 就可以看到结果了,这样我们第一个agent的demo就跑起来了
]]>
java
@@ -3003,6 +3003,32 @@
这边就是个string类型,我们就能够获取到 input 这个变量名,然后我们就可以在这里插入一段代码来打印这个参数
declaredMethod.insertAfter("System.out.println( " + attribute.variableName(0 )+ " );" );
这样我们就能做到类似于java中切面的技术
+]]>
+
+ java
+ 技巧
+
+
+ java
+
+
+
+ java的字节码工具-javassist体验三
+ /2025/01/19/java%E7%9A%84%E5%AD%97%E8%8A%82%E7%A0%81%E5%B7%A5%E5%85%B7-javassist%E4%BD%93%E9%AA%8C%E4%B8%89/
+ 这篇还是javassist的一些使用小技巧,我们可以用javassist来读取java的注解信息 首先我们有这样一个注解
+public @interface Author { String name () ; int year () ; }
+注解可以打在类上,那么我们建一个point类
+@Author(name = "nick", year = 2024) public class Point { int x, y; }
+name 是 nick,24年的,
+然后我想通过javassist来获取这个类上的注解信息 我们先从对象池中获取这个类
+CtClass cc = ClassPool.getDefault().get("org.example.Point" );
+然后获取类上的注解
+Object[] all = cc.getAnnotations(); Author a = (Author)all[0 ];
+将第一个注解强转成Author,因为这里我们知道是只有这个注解,否则可以做的更通用一点 然后把这个主机上的值获取出来
+String name = a.name();int year = a.year();System.out.println("name: " + name + ", year: " + year);
+可以获得结果,非常简单
+name: nick, year: 2024 Process finished with exit code 0
+为什么讲这么个简单的例子呢,因为这个刚好可以作为一种实现aop的途径,我们可以获取类的信息,包括注解上的,这样我们就可以从源头去理解这些aop啥的可以通过什么方法来实现
]]>
java
@@ -3041,6 +3067,46 @@
java
+
+ java的字节码工具-javassist初体验
+ /2025/01/05/java%E7%9A%84%E5%AD%97%E8%8A%82%E7%A0%81%E5%B7%A5%E5%85%B7-javassist%E5%88%9D%E4%BD%93%E9%AA%8C/
+ 前面那篇在讲agent的时候用到了javassist,我们就来简单讲个demo 我想用javassist来创建一个类
+public static void createEntity () throws Exception { ClassPool pool = ClassPool.getDefault(); CtClass cl = pool.makeClass("Entity" ); CtConstructor cons = new CtConstructor (new CtClass []{}, cl); cl.addConstructor(cons); cl.writeFile(); }
+这个代码可以给我们创建一个Entity类, 带有一个无参的构造函数,默认在项目根目录下会生成Entity类的class文件 但是其实这个也是不必要的,默认会给生成一个无参的构造函数 然后我们可以给这个类加个字段, 加在 writeFile 方法调用前
+CtField param = new CtField (pool.get("java.lang.String" ), "name" , cl);param.setModifiers(Modifier.PRIVATE); cl.addField(param, CtField.Initializer.constant("ent1" ));
+ 我们就生成了这样一个类 javassist也已经提供好了
+cl.addMethod(CtNewMethod.setter("setName" , param)); cl.addMethod(CtNewMethod.getter("getName" , param));
+我们生成的类就变成这样了 接下去我们可以再加一个默认赋值的无参构造函数
+CtConstructor con = new CtConstructor (new CtClass []{}, cl);con.setBody("{name = \"nick\";}" ); cl.addConstructor(con);
+ 然后我们就有了这样的类,带有一个默认赋值的构造函数,接下去可以来个带参数的构造函数
+con = new CtConstructor (new CtClass []{pool.get("java.lang.String" )}, cl); con.setBody("{$0.name = $1;}" ); cl.addConstructor(con);
+ 最后我们可以再加个打印该字段的方法
+CtMethod ctMethod = new CtMethod (CtClass.voidType, "printName" , new CtClass []{}, cl);ctMethod.setModifiers(Modifier.PUBLIC); ctMethod.setBody("{System.out.println(name);}" ); cl.addMethod(ctMethod);
+这样我们就获得了这样一个类 这只是javassist的简单使用,它还有很多强大的功能可以在官方文档 和网上的资料进行学习
+]]>
+
+ java
+ 技巧
+
+
+ java
+
+
+
+ mac os 14.x 出现 'xxx 已损坏,无法打开。 你应该将它移到废纸篓。' 解决方法
+ /2025/02/16/mac-os-14-x-%E5%87%BA%E7%8E%B0-xxx-%E5%B7%B2%E6%8D%9F%E5%9D%8F%EF%BC%8C%E6%97%A0%E6%B3%95%E6%89%93%E5%BC%80%E3%80%82-%E4%BD%A0%E5%BA%94%E8%AF%A5%E5%B0%86%E5%AE%83%E7%A7%BB%E5%88%B0%E5%BA%9F%E7%BA%B8%E7%AF%93%E3%80%82-%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95/
+ 在运行一个python写的小工具的时候碰到了这个问题 ““Python.framework”已损坏,无法打开。 你应该将它移到废纸篓。” 一开始以为是常规问题,但是可能随着系统更新,也出现了比较不同的情况 首先进去”系统设置”->”隐私和安全性” 一开始是长这样的 发现没有任意来源了, 于是搜索了下,可以直接在当前环境下开启,这个是区别于之前的经验的,因为之前关闭 spctl 好像是需要进恢复模式的 我试了下在 mac os 14.3 中目前是可以直接把这个关闭掉
+sudo spctl --master-disable
+关闭后重新打开系统设置 就能看到任意来源了 捎带解释下这个 spctl 的意思是 System Policy Control 用于管理系统策略控制。 禁用Gatekeeper的主开关,允许运行任何来源的应用。 此命令会降低Mac的安全性,因为它允许运行未经验证的应用。 启用后,需小心下载和运行应用。 所以还是需要小心关闭,一般在确认来源可靠才能关闭
+]]>
+
+ mac
+ 技巧
+
+
+ mac
+
+
minimum-size-subarray-sum-209
/2016/10/11/minimum-size-subarray-sum-209/
@@ -3057,6 +3123,19 @@
c++
+
+ C++ 指针使用中的一个小问题
+ /2014/12/23/my-new-post/
+ 在工作中碰到的一点C++指针上的一点小问题
+在C++中,应该是从C语言就开始了,除了void 型指针之外都是需要有分配对应的内存才可以使用,同时malloc 与free 成对使用,new 与delete 成对使用,否则造成内存泄漏。
+]]>
+
+ C++
+
+
+ 博客,文章
+
+
mybatis 的 foreach 使用的注意点
/2022/07/09/mybatis-%E7%9A%84-foreach-%E4%BD%BF%E7%94%A8%E7%9A%84%E6%B3%A8%E6%84%8F%E7%82%B9/
@@ -3149,19 +3228,6 @@
缓存
-
- 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系列-mybatis是如何初始化mapper的
/2022/12/04/mybatis%E6%98%AF%E5%A6%82%E4%BD%95%E5%88%9D%E5%A7%8B%E5%8C%96mapper%E7%9A%84/
@@ -3713,26 +3779,58 @@
- php-abstract-class-and-interface
- /2016/11/10/php-abstract-class-and-interface/
- PHP抽象类和接口
-抽象类与接口
-抽象类内可以包含非抽象函数,即可实现函数
-抽象类内必须包含至少一个抽象方法,抽象类和接口均不能实例化
-抽象类可以设置访问级别,接口默认都是public
-类可以实现多个接口但不能继承多个抽象类
-类必须实现抽象类和接口里的抽象方法,不一定要实现抽象类的非抽象方法
-接口内不能定义变量,但是可以定义常量
-
-示例代码 <?php interface int1{ const INTER1 = 111; function inter1(); } interface int2{ const INTER1 = 222; function inter2(); } abstract class abst1{ public function abstr1(){ echo 1111; } abstract function abstra1(){ echo 'ahahahha'; } } abstract class abst2{ public function abstr2(){ echo 1111; } abstract function abstra2(); } class normal1 extends abst1{ protected function abstr2(){ echo 222; } }
+ pcre-intro-and-a-simple-package
+ /2015/01/16/pcre-intro-and-a-simple-package/
+ Pcre
+Perl Compatible Regular Expressions (PCRE) is a regular expression C library inspired by the regular expression capabilities in the Perl programming language, written by Philip Hazel, starting in summer 1997.
+
+因为最近工作内容的一部分需要做字符串的识别处理,所以就顺便用上了之前在PHP中用过的正则,在C/C++中本身不包含正则库,这里使用的pcre,对MFC开发,在这里 提供了静态链接库,在引入lib跟.h文件后即可使用。
+
-result PHP Fatal error: Abstract function abst1::abstra1() cannot contain body in new.php on line 17 Fatal error: Abstract function abst1::abstra1() cannot contain body in php on line 17
-]]>
-
- php
-
-
- php
+Regular Expression Syntax 然后是一些正则语法 ,官方的语法文档比较科学严谨,特别是对类似于贪婪匹配等细节的说明,当然一般的使用可以在网上找到很多匹配语法,例如这个 。
+PCRE函数介绍
+pcre_compile 原型:
+
+#include <pcre.h> pcre *pcre_compile (const char *pattern, int options, const char **errptr, int *erroffset, const unsigned char *tableptr) ;
+功能:将一个正则表达式编译成一个内部表示,在匹配多个字符串时,可以加速匹配。其同pcre_compile2功能一样只是缺少一个参数errorcodeptr。 参数:pattern 正则表达式options 为0,或者其他参数选项errptr 出错消息erroffset 出错位置tableptr 指向一个字符数组的指针,可以设置为空NULL
+
+pcre_exec 原型:
+
+#include <pcre.h> int pcre_exec (const pcre *code, const pcre_extra *extra, const char *subject, int length, int startoffset, int options, int *ovector, int ovecsize)
+功能:使用编译好的模式进行匹配,采用与Perl相似的算法,返回匹配串的偏移位置。 参数:code 编译好的模式extra 指向一个pcre_extra结构体,可以为NULLsubject 需要匹配的字符串length 匹配的字符串长度(Byte)startoffset 匹配的开始位置options 选项位ovector 指向一个结果的整型数组ovecsize 数组大小。
+这里是两个最常用的函数的简单说明,pcre的静态库提供了一系列的函数以供使用,可以参考这个博客 说明,另外对于以上函数的具体参数详细说明可以参考官网此处
+一个丑陋的封装 void COcxDemoDlg::pcre_exec_all (const pcre * re, PCRE_SPTR src, vector<pair<int , int >> &vc) { int rc; int ovector[30 ]; int i = 0 ; pair<int , int > pr; rc = pcre_exec (re, NULL , src, strlen (src), i, 0 , ovector, 30 ); for (; rc > 0 ;) { i = ovector[1 ]; pr.first = ovector[2 ]; pr.second = ovector[3 ]; vc.push_back (pr); rc = pcre_exec (re, NULL , src, strlen (src), i, 0 , ovector, 30 ); } }
+vector中是全文匹配后的索引对,只是简单地用下。
+]]>
+
+ C++
+
+
+ c++
+ mfc
+
+
+
+ php-abstract-class-and-interface
+ /2016/11/10/php-abstract-class-and-interface/
+ PHP抽象类和接口
+抽象类与接口
+抽象类内可以包含非抽象函数,即可实现函数
+抽象类内必须包含至少一个抽象方法,抽象类和接口均不能实例化
+抽象类可以设置访问级别,接口默认都是public
+类可以实现多个接口但不能继承多个抽象类
+类必须实现抽象类和接口里的抽象方法,不一定要实现抽象类的非抽象方法
+接口内不能定义变量,但是可以定义常量
+
+示例代码 <?php interface int1{ const INTER1 = 111; function inter1(); } interface int2{ const INTER1 = 222; function inter2(); } abstract class abst1{ public function abstr1(){ echo 1111; } abstract function abstra1(){ echo 'ahahahha'; } } abstract class abst2{ public function abstr2(){ echo 1111; } abstract function abstra2(); } class normal1 extends abst1{ protected function abstr2(){ echo 222; } }
+
+result PHP Fatal error: Abstract function abst1::abstra1() cannot contain body in new.php on line 17 Fatal error: Abstract function abst1::abstra1() cannot contain body in php on line 17
+]]>
+
+ php
+
+
+ php
@@ -3824,38 +3922,6 @@
powershell
-
- pcre-intro-and-a-simple-package
- /2015/01/16/pcre-intro-and-a-simple-package/
- Pcre
-Perl Compatible Regular Expressions (PCRE) is a regular expression C library inspired by the regular expression capabilities in the Perl programming language, written by Philip Hazel, starting in summer 1997.
-
-因为最近工作内容的一部分需要做字符串的识别处理,所以就顺便用上了之前在PHP中用过的正则,在C/C++中本身不包含正则库,这里使用的pcre,对MFC开发,在这里 提供了静态链接库,在引入lib跟.h文件后即可使用。
-
-
-Regular Expression Syntax 然后是一些正则语法 ,官方的语法文档比较科学严谨,特别是对类似于贪婪匹配等细节的说明,当然一般的使用可以在网上找到很多匹配语法,例如这个 。
-PCRE函数介绍
-pcre_compile 原型:
-
-#include <pcre.h> pcre *pcre_compile (const char *pattern, int options, const char **errptr, int *erroffset, const unsigned char *tableptr) ;
-功能:将一个正则表达式编译成一个内部表示,在匹配多个字符串时,可以加速匹配。其同pcre_compile2功能一样只是缺少一个参数errorcodeptr。 参数:pattern 正则表达式options 为0,或者其他参数选项errptr 出错消息erroffset 出错位置tableptr 指向一个字符数组的指针,可以设置为空NULL
-
-pcre_exec 原型:
-
-#include <pcre.h> int pcre_exec (const pcre *code, const pcre_extra *extra, const char *subject, int length, int startoffset, int options, int *ovector, int ovecsize)
-功能:使用编译好的模式进行匹配,采用与Perl相似的算法,返回匹配串的偏移位置。 参数:code 编译好的模式extra 指向一个pcre_extra结构体,可以为NULLsubject 需要匹配的字符串length 匹配的字符串长度(Byte)startoffset 匹配的开始位置options 选项位ovector 指向一个结果的整型数组ovecsize 数组大小。
-这里是两个最常用的函数的简单说明,pcre的静态库提供了一系列的函数以供使用,可以参考这个博客 说明,另外对于以上函数的具体参数详细说明可以参考官网此处
-一个丑陋的封装 void COcxDemoDlg::pcre_exec_all (const pcre * re, PCRE_SPTR src, vector<pair<int , int >> &vc) { int rc; int ovector[30 ]; int i = 0 ; pair<int , int > pr; rc = pcre_exec (re, NULL , src, strlen (src), i, 0 , ovector, 30 ); for (; rc > 0 ;) { i = ovector[1 ]; pr.first = ovector[2 ]; pr.second = ovector[3 ]; vc.push_back (pr); rc = pcre_exec (re, NULL , src, strlen (src), i, 0 , ovector, 30 ); } }
-vector中是全文匹配后的索引对,只是简单地用下。
-]]>
-
- C++
-
-
- c++
- mfc
-
-
powershell 初体验二
/2022/11/20/powershell-%E5%88%9D%E4%BD%93%E9%AA%8C%E4%BA%8C/
@@ -3881,6 +3947,28 @@
powershell
+
+ rabbitmq-tips
+ /2017/04/25/rabbitmq-tips/
+ 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
+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,
+rabbit2$ rabbitmqctl stop_app #后者是停止了rabbitmq和erlang节点 Stopping node rabbit@rabbit2 ...done. rabbit2$ rabbitmqctl join_cluster rabbit@rabbit1 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
+]]>
+
+ php
+
+
+ php
+ mq
+ im
+
+
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/
@@ -3923,28 +4011,6 @@
源码
-
- rabbitmq-tips
- /2017/04/25/rabbitmq-tips/
- 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
-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,
-rabbit2$ rabbitmqctl stop_app #后者是停止了rabbitmq和erlang节点 Stopping node rabbit@rabbit2 ...done. rabbit2$ rabbitmqctl join_cluster rabbit@rabbit1 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
-]]>
-
- php
-
-
- php
- mq
- im
-
-
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/
@@ -3971,18 +4037,13 @@
- redis数据结构介绍五-第五部分 对象
- /2020/01/20/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%BA%94/
- 前面说了这么些数据结构,其实大家对于 redis 最初的印象应该就是个 key-value 的缓存,类似于 memcache,redis 其实也是个 key-value,key 还是一样的字符串,或者说就是用 redis 自己的动态字符串实现,但是 value 其实就是前面说的那些数据结构,差不多快说完了,还有个 quicklist 后面还有一篇,这里先介绍下 redis 对于这些不同类型的 value 是怎么实现的,首先看下 redisObject 的源码头文件
-#define OBJ_STRING 0 #define OBJ_LIST 1 #define OBJ_SET 2 #define OBJ_ZSET 3 #define OBJ_HASH 4 #define OBJ_ENCODING_RAW 0 #define OBJ_ENCODING_INT 1 #define OBJ_ENCODING_HT 2 #define OBJ_ENCODING_ZIPMAP 3 #define OBJ_ENCODING_LINKEDLIST 4 #define OBJ_ENCODING_ZIPLIST 5 #define OBJ_ENCODING_INTSET 6 #define OBJ_ENCODING_SKIPLIST 7 #define OBJ_ENCODING_EMBSTR 8 #define OBJ_ENCODING_QUICKLIST 9 #define OBJ_ENCODING_STREAM 10 #define LRU_BITS 24 #define LRU_CLOCK_MAX ((1<<LRU_BITS)-1) /* Max value of obj-> lru */ #define LRU_CLOCK_RESOLUTION 1000 #define OBJ_SHARED_REFCOUNT INT_MAX typedef struct redisObject { unsigned type:4 ; unsigned encoding:4 ; unsigned lru:LRU_BITS; int refcount; void *ptr; } robj;
-主体结构就是这个 redisObject,
-
-type: 字段表示对象的类型,它对应的就是 redis 的对外暴露的,或者说用户可以使用的五种类型,OBJ_STRING, OBJ_LIST, OBJ_SET, OBJ_ZSET, OBJ_HASH
-encoding: 字段表示这个对象在 redis 内部的编码方式,由OBJ_ENCODING_开头的 11 种
-lru: 做LRU替换算法用,占24个bit
-refcount: 引用计数。它允许robj对象在某些情况下被共享。
-ptr: 指向底层实现数据结构的指针 当 type 是 OBJ_STRING 时,表示类型是个 string,它的编码方式 encoding 可能有 OBJ_ENCODING_RAW,OBJ_ENCODING_INT,OBJ_ENCODING_EMBSTR 三种 当 type 是 OBJ_LIST 时,表示类型是 list,它的编码方式 encoding 是 OBJ_ENCODING_QUICKLIST,对于早一些的版本,2.2这种可能还会使用 OBJ_ENCODING_ZIPLIST,OBJ_ENCODING_LINKEDLIST 当 type 是 OBJ_SET 时,是个集合,但是得看具体元素的类型,有可能使用整数集合,OBJ_ENCODING_INTSET, 如果元素不全是整型或者数量超过一定限制,那么编码就是 OBJ_ENCODING_HT hash table 了 当 type 是 OBJ_ZSET 时,是个有序集合,它底层有可能使用的是 OBJ_ENCODING_ZIPLIST 或者 OBJ_ENCODING_SKIPLIST 当 type 是 OBJ_HASH 时,一开始也是 OBJ_ENCODING_ZIPLIST,然后当数据量大于 hash_max_ziplist_entries 时会转成 OBJ_ENCODING_HT
-
+ redis数据结构介绍二-第二部分 跳表
+ /2020/01/04/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%BA%8C/
+ 跳表 skiplist跳表是个在我们日常的代码中不太常用到的数据结构,相对来讲就没有像数组,链表,字典,散列,树等结构那么熟悉,所以就从头开始分析下,首先是链表,跳表跟链表都有个表字(太硬扯了我🤦♀️),注意这是个有序链表 如上图,在这个链表里如果我要找到 23,是不是我需要从3,5,9开始一直往后找直到找到 23,也就是说时间复杂度是 O(N),N 的一次幂复杂度,那么我们来看看第二个 这个结构跟原先有点不一样,它给链表中偶数位的节点又加了一个指针把它们链接起来,这样子当我们要寻找 23 的时候就可以从原来的一个个往下找变成跳着找,先找到 5,然后是 10,接着是 19,然后是 28,这时候发现 28 比 23 大了,那我在退回到 19,然后从下一层原来的链表往前找, 这里毛估估是不是前面的节点我就少找了一半,有那么点二分法的意思。 前面的其实是跳表的引子,真正的跳表其实不是这样,因为上面的其实有个比较大的问题,就是插入一个元素后需要调整每个元素的指针,在 redis 中的跳表其实是做了个随机层数的优化,因为沿着前面的例子,其实当数据量很大的时候,是不是层数越多,其查询效率越高,但是随着层数变多,要保持这种严格的层数规则其实也会增大处理复杂度,所以 redis 插入每个元素的时候都是使用随机的方式,看一眼代码
+typedef struct zskiplistNode { sds ele; double score; struct zskiplistNode *backward ; struct zskiplistLevel { struct zskiplistNode *forward ; unsigned long span; } level[]; } zskiplistNode; typedef struct zskiplist { struct zskiplistNode *header , *tail ; unsigned long length; int level; } zskiplist; typedef struct zset { dict *dict; zskiplist *zsl; } zset;
+忘了说了,redis 是把 skiplist 跳表用在 zset 里,zset 是个有序的集合,可以看到 zskiplist 就是个跳表的结构,里面用 header 保存跳表的表头,tail 保存表尾,还有长度和最大层级,具体的跳表节点元素使用 zskiplistNode 表示,里面包含了 sds 类型的元素值,double 类型的分值,用来排序,一个 backward 后向指针和一个 zskiplistLevel 数组,每个 level 包含了一个前向指针,和一个 span,span 表示的是跳表前向指针的跨度,这里再补充一点,前面说了为了灵活这个跳表的新增修改,redis 使用了随机层高的方式插入新节点,但是如果所有节点都随机到很高的层级或者所有都很低的话,跳表的效率优势就会减小,所以 redis 使用了个小技巧,贴下代码
+#define ZSKIPLIST_P 0.25 int zslRandomLevel (void ) { int level = 1 ; while ((random()&0xFFFF ) < (ZSKIPLIST_P * 0xFFFF )) level += 1 ; return (level<ZSKIPLIST_MAXLEVEL) ? level : ZSKIPLIST_MAXLEVEL; }
+当随机值跟0xFFFF进行与操作小于ZSKIPLIST_P * 0xFFFF时才会增大 level 的值,因此保持了一个相对递减的概率 可以简单分析下,当 random() 的值小于 0xFFFF 的 1/4,才会 level + 1,就意味着当有 1 - 1/4也就是3/4的概率是直接跳出,所以一层的概率是3/4,也就是 1-P,二层的概率是 P*(1-P),三层的概率是 P² * (1-P) 依次递推。
]]>
Redis
@@ -3998,20 +4059,18 @@
- 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数据结构介绍五-第五部分 对象
+ /2020/01/20/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%BA%94/
+ 前面说了这么些数据结构,其实大家对于 redis 最初的印象应该就是个 key-value 的缓存,类似于 memcache,redis 其实也是个 key-value,key 还是一样的字符串,或者说就是用 redis 自己的动态字符串实现,但是 value 其实就是前面说的那些数据结构,差不多快说完了,还有个 quicklist 后面还有一篇,这里先介绍下 redis 对于这些不同类型的 value 是怎么实现的,首先看下 redisObject 的源码头文件
+#define OBJ_STRING 0 #define OBJ_LIST 1 #define OBJ_SET 2 #define OBJ_ZSET 3 #define OBJ_HASH 4 #define OBJ_ENCODING_RAW 0 #define OBJ_ENCODING_INT 1 #define OBJ_ENCODING_HT 2 #define OBJ_ENCODING_ZIPMAP 3 #define OBJ_ENCODING_LINKEDLIST 4 #define OBJ_ENCODING_ZIPLIST 5 #define OBJ_ENCODING_INTSET 6 #define OBJ_ENCODING_SKIPLIST 7 #define OBJ_ENCODING_EMBSTR 8 #define OBJ_ENCODING_QUICKLIST 9 #define OBJ_ENCODING_STREAM 10 #define LRU_BITS 24 #define LRU_CLOCK_MAX ((1<<LRU_BITS)-1) /* Max value of obj-> lru */ #define LRU_CLOCK_RESOLUTION 1000 #define OBJ_SHARED_REFCOUNT INT_MAX typedef struct redisObject { unsigned type:4 ; unsigned encoding:4 ; unsigned lru:LRU_BITS; int refcount; void *ptr; } robj;
+主体结构就是这个 redisObject,
+
+type: 字段表示对象的类型,它对应的就是 redis 的对外暴露的,或者说用户可以使用的五种类型,OBJ_STRING, OBJ_LIST, OBJ_SET, OBJ_ZSET, OBJ_HASH
+encoding: 字段表示这个对象在 redis 内部的编码方式,由OBJ_ENCODING_开头的 11 种
+lru: 做LRU替换算法用,占24个bit
+refcount: 引用计数。它允许robj对象在某些情况下被共享。
+ptr: 指向底层实现数据结构的指针 当 type 是 OBJ_STRING 时,表示类型是个 string,它的编码方式 encoding 可能有 OBJ_ENCODING_RAW,OBJ_ENCODING_INT,OBJ_ENCODING_EMBSTR 三种 当 type 是 OBJ_LIST 时,表示类型是 list,它的编码方式 encoding 是 OBJ_ENCODING_QUICKLIST,对于早一些的版本,2.2这种可能还会使用 OBJ_ENCODING_ZIPLIST,OBJ_ENCODING_LINKEDLIST 当 type 是 OBJ_SET 时,是个集合,但是得看具体元素的类型,有可能使用整数集合,OBJ_ENCODING_INTSET, 如果元素不全是整型或者数量超过一定限制,那么编码就是 OBJ_ENCODING_HT hash table 了 当 type 是 OBJ_ZSET 时,是个有序集合,它底层有可能使用的是 OBJ_ENCODING_ZIPLIST 或者 OBJ_ENCODING_SKIPLIST 当 type 是 OBJ_HASH 时,一开始也是 OBJ_ENCODING_ZIPLIST,然后当数据量大于 hash_max_ziplist_entries 时会转成 OBJ_ENCODING_HT
+
]]>
Redis
@@ -4068,13 +4127,20 @@
- redis数据结构介绍二-第二部分 跳表
- /2020/01/04/redis%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%BB%8B%E7%BB%8D%E4%BA%8C/
- 跳表 skiplist跳表是个在我们日常的代码中不太常用到的数据结构,相对来讲就没有像数组,链表,字典,散列,树等结构那么熟悉,所以就从头开始分析下,首先是链表,跳表跟链表都有个表字(太硬扯了我🤦♀️),注意这是个有序链表 如上图,在这个链表里如果我要找到 23,是不是我需要从3,5,9开始一直往后找直到找到 23,也就是说时间复杂度是 O(N),N 的一次幂复杂度,那么我们来看看第二个 这个结构跟原先有点不一样,它给链表中偶数位的节点又加了一个指针把它们链接起来,这样子当我们要寻找 23 的时候就可以从原来的一个个往下找变成跳着找,先找到 5,然后是 10,接着是 19,然后是 28,这时候发现 28 比 23 大了,那我在退回到 19,然后从下一层原来的链表往前找, 这里毛估估是不是前面的节点我就少找了一半,有那么点二分法的意思。 前面的其实是跳表的引子,真正的跳表其实不是这样,因为上面的其实有个比较大的问题,就是插入一个元素后需要调整每个元素的指针,在 redis 中的跳表其实是做了个随机层数的优化,因为沿着前面的例子,其实当数据量很大的时候,是不是层数越多,其查询效率越高,但是随着层数变多,要保持这种严格的层数规则其实也会增大处理复杂度,所以 redis 插入每个元素的时候都是使用随机的方式,看一眼代码
-typedef struct zskiplistNode { sds ele; double score; struct zskiplistNode *backward ; struct zskiplistLevel { struct zskiplistNode *forward ; unsigned long span; } level[]; } zskiplistNode; typedef struct zskiplist { struct zskiplistNode *header , *tail ; unsigned long length; int level; } zskiplist; typedef struct zset { dict *dict; zskiplist *zsl; } zset;
-忘了说了,redis 是把 skiplist 跳表用在 zset 里,zset 是个有序的集合,可以看到 zskiplist 就是个跳表的结构,里面用 header 保存跳表的表头,tail 保存表尾,还有长度和最大层级,具体的跳表节点元素使用 zskiplistNode 表示,里面包含了 sds 类型的元素值,double 类型的分值,用来排序,一个 backward 后向指针和一个 zskiplistLevel 数组,每个 level 包含了一个前向指针,和一个 span,span 表示的是跳表前向指针的跨度,这里再补充一点,前面说了为了灵活这个跳表的新增修改,redis 使用了随机层高的方式插入新节点,但是如果所有节点都随机到很高的层级或者所有都很低的话,跳表的效率优势就会减小,所以 redis 使用了个小技巧,贴下代码
-#define ZSKIPLIST_P 0.25 int zslRandomLevel (void ) { int level = 1 ; while ((random()&0xFFFF ) < (ZSKIPLIST_P * 0xFFFF )) level += 1 ; return (level<ZSKIPLIST_MAXLEVEL) ? level : ZSKIPLIST_MAXLEVEL; }
-当随机值跟0xFFFF进行与操作小于ZSKIPLIST_P * 0xFFFF时才会增大 level 的值,因此保持了一个相对递减的概率 可以简单分析下,当 random() 的值小于 0xFFFF 的 1/4,才会 level + 1,就意味着当有 1 - 1/4也就是3/4的概率是直接跳出,所以一层的概率是3/4,也就是 1-P,二层的概率是 P*(1-P),三层的概率是 P² * (1-P) 依次递推。
+ redis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
@@ -4145,6 +4211,33 @@
Evict
+
+ redis系列介绍七-过期策略
+ /2020/04/12/redis%E7%B3%BB%E5%88%97%E4%BB%8B%E7%BB%8D%E4%B8%83/
+ 这一篇不再是数据结构介绍了,大致的数据结构基本都介绍了,这一篇主要是查漏补缺,或者说讲一些重要且基本的概念,也可能是经常被忽略的,很多讲 redis 的系列文章可能都会忽略,学习 redis 的时候也会,因为觉得源码学习就是讲主要的数据结构和“算法”学习了就好了。 redis 的主要应用就是拿来作为高性能的缓存,那么缓存一般有些啥需要注意的,首先是访问速度,如果取得跟数据库一样快,那就没什么存在的意义,第二个是缓存的字面意思,我只是为了让数据读取快一些,通常大部分的场景这个是需要更新过期的,这里就把我要讲的第一点引出来了(真累,
+redis过期策略 redis 是如何过期缓存的,可以猜测下,最无脑的就是每个设置了过期时间的 key 都设个定时器,过期了就删除,这种显然消耗太大,清理地最及时,还有的就是 redis 正在采用的懒汉清理策略和定期清理 懒汉策略就是在使用的时候去检查缓存是否过期,比如 get 操作时,先判断下这个 key 是否已经过期了,如果过期了就删掉,并且返回空,如果没过期则正常返回 主要代码是
+int expireIfNeeded (redisDb *db, robj *key) { if (!keyIsExpired(db,key)) return 0 ; if (server.masterhost != NULL ) return 1 ; server.stat_expiredkeys++; propagateExpire(db,key,server.lazyfree_lazy_expire); notifyKeyspaceEvent(NOTIFY_EXPIRED, "expired" ,key,db->id); return server.lazyfree_lazy_expire ? dbAsyncDelete(db,key) : dbSyncDelete(db,key); } int keyIsExpired (redisDb *db, robj *key) { mstime_t when = getExpire(db,key); mstime_t now; if (when < 0 ) return 0 ; if (server.loading) return 0 ; if (server.lua_caller) { now = server.lua_time_start; } else if (server.fixed_time_expire > 0 ) { now = server.mstime; } else { now = mstime(); } return now > when; } long long getExpire (redisDb *db, robj *key) { dictEntry *de; if (dictSize(db->expires) == 0 || (de = dictFind(db->expires,key->ptr)) == NULL ) return -1 ; serverAssertWithInfo(NULL ,key,dictFind(db->dict,key->ptr) != NULL ); return dictGetSignedIntegerVal(de); }
+这里有几点要注意的,第一是当惰性删除时会根据lazyfree_lazy_expire这个参数去判断是执行同步删除还是异步删除,另外一点是对于 slave,是不需要执行的,因为会在 master 过期时向 slave 发送 del 指令。 光采用这个策略会有什么问题呢,假如一些key 一直未被访问,那这些 key 就不会过期了,导致一直被占用着内存,所以 redis 采取了懒汉式过期加定期过期策略,定期策略是怎么执行的呢
+void databasesCron (void ) { if (server.active_expire_enabled) { if (server.masterhost == NULL ) { activeExpireCycle(ACTIVE_EXPIRE_CYCLE_SLOW); } else { expireSlaveKeys(); } } activeDefragCycle(); if (!hasActiveChildProcess()) { static unsigned int resize_db = 0 ; static unsigned int rehash_db = 0 ; int dbs_per_call = CRON_DBS_PER_CALL; int j; if (dbs_per_call > server.dbnum) dbs_per_call = server.dbnum; for (j = 0 ; j < dbs_per_call; j++) { tryResizeHashTables(resize_db % server.dbnum); resize_db++; } if (server.activerehashing) { for (j = 0 ; j < dbs_per_call; j++) { int work_done = incrementallyRehash(rehash_db); if (work_done) { break ; } else { rehash_db++; rehash_db %= server.dbnum; } } } } } #define ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP 20 #define ACTIVE_EXPIRE_CYCLE_FAST_DURATION 1000 #define ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC 25 #define ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE 10 void activeExpireCycle (int type) { unsigned long effort = server.active_expire_effort-1 , config_keys_per_loop = ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP + ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP/4 *effort, config_cycle_fast_duration = ACTIVE_EXPIRE_CYCLE_FAST_DURATION + ACTIVE_EXPIRE_CYCLE_FAST_DURATION/4 *effort, config_cycle_slow_time_perc = ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC + 2 *effort, config_cycle_acceptable_stale = ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE- effort; static unsigned int current_db = 0 ; static int timelimit_exit = 0 ; static long long last_fast_cycle = 0 ; int j, iteration = 0 ; int dbs_per_call = CRON_DBS_PER_CALL; long long start = ustime(), timelimit, elapsed; if (clientsArePaused()) return ; if (type == ACTIVE_EXPIRE_CYCLE_FAST) { if (!timelimit_exit && server.stat_expired_stale_perc < config_cycle_acceptable_stale) return ; if (start < last_fast_cycle + (long long )config_cycle_fast_duration*2 ) return ; last_fast_cycle = start; } if (dbs_per_call > server.dbnum || timelimit_exit) dbs_per_call = server.dbnum; timelimit = config_cycle_slow_time_perc*1000000 /server.hz/100 ; timelimit_exit = 0 ; if (timelimit <= 0 ) timelimit = 1 ; if (type == ACTIVE_EXPIRE_CYCLE_FAST) timelimit = config_cycle_fast_duration; long total_sampled = 0 ; long total_expired = 0 ; for (j = 0 ; j < dbs_per_call && timelimit_exit == 0 ; j++) { unsigned long expired, sampled; redisDb *db = server.db+(current_db % server.dbnum); current_db++; do { unsigned long num, slots; long long now, ttl_sum; int ttl_samples; iteration++; if ((num = dictSize(db->expires)) == 0 ) { db->avg_ttl = 0 ; break ; } slots = dictSlots(db->expires); now = mstime(); if (num && slots > DICT_HT_INITIAL_SIZE && (num*100 /slots < 1 )) break ; expired = 0 ; sampled = 0 ; ttl_sum = 0 ; ttl_samples = 0 ; if (num > config_keys_per_loop) num = config_keys_per_loop; long max_buckets = num*20 ; long checked_buckets = 0 ; while (sampled < num && checked_buckets < max_buckets) { for (int table = 0 ; table < 2 ; table++) { if (table == 1 && !dictIsRehashing(db->expires)) break ; unsigned long idx = db->expires_cursor; idx &= db->expires->ht[table].sizemask; dictEntry *de = db->expires->ht[table].table[idx]; long long ttl; checked_buckets++; while (de) { dictEntry *e = de; de = de->next; ttl = dictGetSignedIntegerVal(e)-now; if (activeExpireCycleTryExpire(db,e,now)) expired++; if (ttl > 0 ) { ttl_sum += ttl; ttl_samples++; } sampled++; } } db->expires_cursor++; } total_expired += expired; total_sampled += sampled; if (ttl_samples) { long long avg_ttl = ttl_sum/ttl_samples; if (db->avg_ttl == 0 ) db->avg_ttl = avg_ttl; db->avg_ttl = (db->avg_ttl/50 )*49 + (avg_ttl/50 ); } if ((iteration & 0xf ) == 0 ) { elapsed = ustime()-start; if (elapsed > timelimit) { timelimit_exit = 1 ; server.stat_expired_time_cap_reached_count++; break ; } } } while ((expired*100 /sampled) > config_cycle_acceptable_stale); } elapsed = ustime()-start; server.stat_expire_cycle_time_used += elapsed; latencyAddSampleIfNeeded("expire-cycle" ,elapsed/1000 ); double current_perc; if (total_sampled) { current_perc = (double )total_expired/total_sampled; } else current_perc = 0 ; server.stat_expired_stale_perc = (current_perc*0.05 )+ (server.stat_expired_stale_perc*0.95 ); }
+执行定期清除分成两种类型,快和慢,分别由beforeSleep和databasesCron调用,快版有两个限制,一个是执行时长由ACTIVE_EXPIRE_CYCLE_FAST_DURATION限制,另一个是执行间隔是 2 倍的ACTIVE_EXPIRE_CYCLE_FAST_DURATION,另外这还可以由配置的server.active_expire_effort参数来控制,默认是 1,最大是 10
+onfig_cycle_fast_duration = ACTIVE_EXPIRE_CYCLE_FAST_DURATION + ACTIVE_EXPIRE_CYCLE_FAST_DURATION/4 *effort
+然后会从一定数量的 db 中找出一定数量的带过期时间的 key(保存在 expires中),这里的数量是由
+config_keys_per_loop = ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP + ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP/4 *effort ``` 控制,慢速的执行时长是 ```C config_cycle_slow_time_perc = ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC + 2 *effort timelimit = config_cycle_slow_time_perc*1000000 /server.hz/100 ;
+这里还有一个额外的退出条件,如果当前数据库的抽样结果已经达到我们所允许的过期 key 百分比,则下次不再处理当前 db,继续处理下个 db
+]]>
+
+ Redis
+ 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/
@@ -4194,30 +4287,26 @@
- redis系列介绍七-过期策略
- /2020/04/12/redis%E7%B3%BB%E5%88%97%E4%BB%8B%E7%BB%8D%E4%B8%83/
- 这一篇不再是数据结构介绍了,大致的数据结构基本都介绍了,这一篇主要是查漏补缺,或者说讲一些重要且基本的概念,也可能是经常被忽略的,很多讲 redis 的系列文章可能都会忽略,学习 redis 的时候也会,因为觉得源码学习就是讲主要的数据结构和“算法”学习了就好了。 redis 的主要应用就是拿来作为高性能的缓存,那么缓存一般有些啥需要注意的,首先是访问速度,如果取得跟数据库一样快,那就没什么存在的意义,第二个是缓存的字面意思,我只是为了让数据读取快一些,通常大部分的场景这个是需要更新过期的,这里就把我要讲的第一点引出来了(真累,
-redis过期策略 redis 是如何过期缓存的,可以猜测下,最无脑的就是每个设置了过期时间的 key 都设个定时器,过期了就删除,这种显然消耗太大,清理地最及时,还有的就是 redis 正在采用的懒汉清理策略和定期清理 懒汉策略就是在使用的时候去检查缓存是否过期,比如 get 操作时,先判断下这个 key 是否已经过期了,如果过期了就删掉,并且返回空,如果没过期则正常返回 主要代码是
-int expireIfNeeded (redisDb *db, robj *key) { if (!keyIsExpired(db,key)) return 0 ; if (server.masterhost != NULL ) return 1 ; server.stat_expiredkeys++; propagateExpire(db,key,server.lazyfree_lazy_expire); notifyKeyspaceEvent(NOTIFY_EXPIRED, "expired" ,key,db->id); return server.lazyfree_lazy_expire ? dbAsyncDelete(db,key) : dbSyncDelete(db,key); } int keyIsExpired (redisDb *db, robj *key) { mstime_t when = getExpire(db,key); mstime_t now; if (when < 0 ) return 0 ; if (server.loading) return 0 ; if (server.lua_caller) { now = server.lua_time_start; } else if (server.fixed_time_expire > 0 ) { now = server.mstime; } else { now = mstime(); } return now > when; } long long getExpire (redisDb *db, robj *key) { dictEntry *de; if (dictSize(db->expires) == 0 || (de = dictFind(db->expires,key->ptr)) == NULL ) return -1 ; serverAssertWithInfo(NULL ,key,dictFind(db->dict,key->ptr) != NULL ); return dictGetSignedIntegerVal(de); }
-这里有几点要注意的,第一是当惰性删除时会根据lazyfree_lazy_expire这个参数去判断是执行同步删除还是异步删除,另外一点是对于 slave,是不需要执行的,因为会在 master 过期时向 slave 发送 del 指令。 光采用这个策略会有什么问题呢,假如一些key 一直未被访问,那这些 key 就不会过期了,导致一直被占用着内存,所以 redis 采取了懒汉式过期加定期过期策略,定期策略是怎么执行的呢
-void databasesCron (void ) { if (server.active_expire_enabled) { if (server.masterhost == NULL ) { activeExpireCycle(ACTIVE_EXPIRE_CYCLE_SLOW); } else { expireSlaveKeys(); } } activeDefragCycle(); if (!hasActiveChildProcess()) { static unsigned int resize_db = 0 ; static unsigned int rehash_db = 0 ; int dbs_per_call = CRON_DBS_PER_CALL; int j; if (dbs_per_call > server.dbnum) dbs_per_call = server.dbnum; for (j = 0 ; j < dbs_per_call; j++) { tryResizeHashTables(resize_db % server.dbnum); resize_db++; } if (server.activerehashing) { for (j = 0 ; j < dbs_per_call; j++) { int work_done = incrementallyRehash(rehash_db); if (work_done) { break ; } else { rehash_db++; rehash_db %= server.dbnum; } } } } } #define ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP 20 #define ACTIVE_EXPIRE_CYCLE_FAST_DURATION 1000 #define ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC 25 #define ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE 10 void activeExpireCycle (int type) { unsigned long effort = server.active_expire_effort-1 , config_keys_per_loop = ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP + ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP/4 *effort, config_cycle_fast_duration = ACTIVE_EXPIRE_CYCLE_FAST_DURATION + ACTIVE_EXPIRE_CYCLE_FAST_DURATION/4 *effort, config_cycle_slow_time_perc = ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC + 2 *effort, config_cycle_acceptable_stale = ACTIVE_EXPIRE_CYCLE_ACCEPTABLE_STALE- effort; static unsigned int current_db = 0 ; static int timelimit_exit = 0 ; static long long last_fast_cycle = 0 ; int j, iteration = 0 ; int dbs_per_call = CRON_DBS_PER_CALL; long long start = ustime(), timelimit, elapsed; if (clientsArePaused()) return ; if (type == ACTIVE_EXPIRE_CYCLE_FAST) { if (!timelimit_exit && server.stat_expired_stale_perc < config_cycle_acceptable_stale) return ; if (start < last_fast_cycle + (long long )config_cycle_fast_duration*2 ) return ; last_fast_cycle = start; } if (dbs_per_call > server.dbnum || timelimit_exit) dbs_per_call = server.dbnum; timelimit = config_cycle_slow_time_perc*1000000 /server.hz/100 ; timelimit_exit = 0 ; if (timelimit <= 0 ) timelimit = 1 ; if (type == ACTIVE_EXPIRE_CYCLE_FAST) timelimit = config_cycle_fast_duration; long total_sampled = 0 ; long total_expired = 0 ; for (j = 0 ; j < dbs_per_call && timelimit_exit == 0 ; j++) { unsigned long expired, sampled; redisDb *db = server.db+(current_db % server.dbnum); current_db++; do { unsigned long num, slots; long long now, ttl_sum; int ttl_samples; iteration++; if ((num = dictSize(db->expires)) == 0 ) { db->avg_ttl = 0 ; break ; } slots = dictSlots(db->expires); now = mstime(); if (num && slots > DICT_HT_INITIAL_SIZE && (num*100 /slots < 1 )) break ; expired = 0 ; sampled = 0 ; ttl_sum = 0 ; ttl_samples = 0 ; if (num > config_keys_per_loop) num = config_keys_per_loop; long max_buckets = num*20 ; long checked_buckets = 0 ; while (sampled < num && checked_buckets < max_buckets) { for (int table = 0 ; table < 2 ; table++) { if (table == 1 && !dictIsRehashing(db->expires)) break ; unsigned long idx = db->expires_cursor; idx &= db->expires->ht[table].sizemask; dictEntry *de = db->expires->ht[table].table[idx]; long long ttl; checked_buckets++; while (de) { dictEntry *e = de; de = de->next; ttl = dictGetSignedIntegerVal(e)-now; if (activeExpireCycleTryExpire(db,e,now)) expired++; if (ttl > 0 ) { ttl_sum += ttl; ttl_samples++; } sampled++; } } db->expires_cursor++; } total_expired += expired; total_sampled += sampled; if (ttl_samples) { long long avg_ttl = ttl_sum/ttl_samples; if (db->avg_ttl == 0 ) db->avg_ttl = avg_ttl; db->avg_ttl = (db->avg_ttl/50 )*49 + (avg_ttl/50 ); } if ((iteration & 0xf ) == 0 ) { elapsed = ustime()-start; if (elapsed > timelimit) { timelimit_exit = 1 ; server.stat_expired_time_cap_reached_count++; break ; } } } while ((expired*100 /sampled) > config_cycle_acceptable_stale); } elapsed = ustime()-start; server.stat_expire_cycle_time_used += elapsed; latencyAddSampleIfNeeded("expire-cycle" ,elapsed/1000 ); double current_perc; if (total_sampled) { current_perc = (double )total_expired/total_sampled; } else current_perc = 0 ; server.stat_expired_stale_perc = (current_perc*0.05 )+ (server.stat_expired_stale_perc*0.95 ); }
-执行定期清除分成两种类型,快和慢,分别由beforeSleep和databasesCron调用,快版有两个限制,一个是执行时长由ACTIVE_EXPIRE_CYCLE_FAST_DURATION限制,另一个是执行间隔是 2 倍的ACTIVE_EXPIRE_CYCLE_FAST_DURATION,另外这还可以由配置的server.active_expire_effort参数来控制,默认是 1,最大是 10
-onfig_cycle_fast_duration = ACTIVE_EXPIRE_CYCLE_FAST_DURATION + ACTIVE_EXPIRE_CYCLE_FAST_DURATION/4 *effort
-然后会从一定数量的 db 中找出一定数量的带过期时间的 key(保存在 expires中),这里的数量是由
-config_keys_per_loop = ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP + ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP/4 *effort ``` 控制,慢速的执行时长是 ```C config_cycle_slow_time_perc = ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC + 2 *effort timelimit = config_cycle_slow_time_perc*1000000 /server.hz/100 ;
-这里还有一个额外的退出条件,如果当前数据库的抽样结果已经达到我们所允许的过期 key 百分比,则下次不再处理当前 db,继续处理下个 db
+ redis过期策略复习
+ /2021/07/25/redis%E8%BF%87%E6%9C%9F%E7%AD%96%E7%95%A5%E5%A4%8D%E4%B9%A0/
+ redis过期策略复习之前其实写过redis的过期的一些原理,这次主要是记录下,一些使用上的概念,主要是redis使用的过期策略是懒过期和定时清除,懒过期的其实比较简单,即是在key被访问的时候会顺带着判断下这个key是否已过期了,如果已经过期了,就不返回了,但是这种策略有个漏洞是如果有些key之后一直不会被访问了,就等于沉在池底了,所以需要有一个定时的清理机制,去从设置了过期的key池子(expires)里随机地捞key,具体的策略我们看下官网的解释
+
+Test 20 random keys from the set of keys with an associated expire.
+Delete all the keys found expired.
+If more than 25% of keys were expired, start again from step 1.
+
+从池子里随机获取20个key,将其中过期的key删掉,如果这其中有超过25%的key已经过期了,那就再来一次,以此保持过期的key不超过25%(左右),并且这个定时策略可以在redis的配置文件
+# Redis calls an internal function to perform many background tasks, like # closing connections of clients in timeout , purging expired keys that are # never requested, and so forth. # # tasks to perform according to the specified "hz" value. # # Redis is idle, but at the same time will make Redis more responsive when # there are many keys expiring at the same time, and timeouts may be # handled with more precision. # # a good idea. Most users should use the default of 10 and raise this up to # 100 only in environments where very low latency is required. hz 10
+
+可以配置这个hz的值,代表的含义是每秒的执行次数,默认是10,其实也用了hz的普遍含义。有兴趣可以看看之前写的一篇文章redis系列介绍七-过期策略
]]>
- Redis
- 数据结构
- C
- 源码
- Redis
+ redis
redis
- 数据结构
- 源码
+ 应用
+ 过期策略
@@ -4296,55 +4385,6 @@
不可变引用
-
- redis过期策略复习
- /2021/07/25/redis%E8%BF%87%E6%9C%9F%E7%AD%96%E7%95%A5%E5%A4%8D%E4%B9%A0/
- redis过期策略复习之前其实写过redis的过期的一些原理,这次主要是记录下,一些使用上的概念,主要是redis使用的过期策略是懒过期和定时清除,懒过期的其实比较简单,即是在key被访问的时候会顺带着判断下这个key是否已过期了,如果已经过期了,就不返回了,但是这种策略有个漏洞是如果有些key之后一直不会被访问了,就等于沉在池底了,所以需要有一个定时的清理机制,去从设置了过期的key池子(expires)里随机地捞key,具体的策略我们看下官网的解释
-
-Test 20 random keys from the set of keys with an associated expire.
-Delete all the keys found expired.
-If more than 25% of keys were expired, start again from step 1.
-
-从池子里随机获取20个key,将其中过期的key删掉,如果这其中有超过25%的key已经过期了,那就再来一次,以此保持过期的key不超过25%(左右),并且这个定时策略可以在redis的配置文件
-# Redis calls an internal function to perform many background tasks, like # closing connections of clients in timeout , purging expired keys that are # never requested, and so forth. # # tasks to perform according to the specified "hz" value. # # Redis is idle, but at the same time will make Redis more responsive when # there are many keys expiring at the same time, and timeouts may be # handled with more precision. # # a good idea. Most users should use the default of 10 and raise this up to # 100 only in environments where very low latency is required. hz 10
-
-可以配置这个hz的值,代表的含义是每秒的执行次数,默认是10,其实也用了hz的普遍含义。有兴趣可以看看之前写的一篇文章redis系列介绍七-过期策略
-]]>
-
- redis
-
-
- redis
- 应用
- 过期策略
-
-
-
- java的字节码工具-javassist体验三
- /2025/01/19/java%E7%9A%84%E5%AD%97%E8%8A%82%E7%A0%81%E5%B7%A5%E5%85%B7-javassist%E4%BD%93%E9%AA%8C%E4%B8%89/
- 这篇还是javassist的一些使用小技巧,我们可以用javassist来读取java的注解信息 首先我们有这样一个注解
-public @interface Author { String name () ; int year () ; }
-注解可以打在类上,那么我们建一个point类
-@Author(name = "nick", year = 2024) public class Point { int x, y; }
-name 是 nick,24年的,
-然后我想通过javassist来获取这个类上的注解信息 我们先从对象池中获取这个类
-CtClass cc = ClassPool.getDefault().get("org.example.Point" );
-然后获取类上的注解
-Object[] all = cc.getAnnotations(); Author a = (Author)all[0 ];
-将第一个注解强转成Author,因为这里我们知道是只有这个注解,否则可以做的更通用一点 然后把这个主机上的值获取出来
-String name = a.name();int year = a.year();System.out.println("name: " + name + ", year: " + year);
-可以获得结果,非常简单
-name: nick, year: 2024 Process finished with exit code 0
-为什么讲这么个简单的例子呢,因为这个刚好可以作为一种实现aop的途径,我们可以获取类的信息,包括注解上的,这样我们就可以从源头去理解这些aop啥的可以通过什么方法来实现
-]]>
-
- java
- 技巧
-
-
- java
-
-
rust学习笔记-所有权一
/2021/04/18/rust%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/
@@ -4500,6 +4540,32 @@
再调用里面的初始化方法,org.springframework.boot.web.embedded.tomcat.TomcatWebServer#initialize
private void initialize () throws WebServerException { logger.info("Tomcat initialized with port(s): " + getPortsDescription(false )); synchronized (this .monitor) { try { addInstanceIdToEngineName(); Context context = findContext(); context.addLifecycleListener((event) -> { if (context.equals(event.getSource()) && Lifecycle.START_EVENT.equals(event.getType())) { removeServiceConnectors(); } }); this .tomcat.start(); rethrowDeferredStartupExceptions(); try { ContextBindings.bindClassLoader(context, context.getNamingToken(), getClass().getClassLoader()); } catch (NamingException ex) { } startDaemonAwaitThread(); } catch (Exception ex) { stopSilently(); destroySilently(); throw new WebServerException ("Unable to start embedded Tomcat" , ex); } } } ``` 这里就是继续调用 `org.apache.catalina.startup.Tomcat#start` ```java public void start () throws LifecycleException { this .getServer(); this .server.start(); } ``` 获取server, `org.apache.catalina.startup.Tomcat#getServer` ```java public Server getServer () { if (this .server != null ) { return this .server; } else { System.setProperty("catalina.useNaming" , "false" ); this .server = new StandardServer (); this .initBaseDir(); ConfigFileLoader.setSource(new CatalinaBaseConfigurationSource (new File (this .basedir), (String)null )); this .server.setPort(-1 ); Service service = new StandardService (); service.setName("Tomcat" ); this .server.addService(service); return this .server; } }
然后就是启动 server,后面可以继续看这个启动 TomcatServer 内部的逻辑
+]]>
+
+ Java
+ SpringBoot
+
+
+ Java
+ SpringBoot
+
+
+
+ springboot 处理请求的小分支-跳转 & cookie
+ /2023/09/03/springboot-%E5%A4%84%E7%90%86%E8%AF%B7%E6%B1%82%E7%9A%84%E5%B0%8F%E5%88%86%E6%94%AF-%E8%B7%B3%E8%BD%AC-cookie/
+ 首先是跳转,应该说设置状态,其中跳转是其中一个状态,
+@RequestMapping(value = "request1", method = RequestMethod.GET) public void request1 (HttpServletRequest request, HttpServletResponse response) { response.setStatus(HttpServletResponse.SC_OK); }
+200 状态就很简单,如果想做跳转可以使用 302 并设置 location
+@RequestMapping(value = "request2", method = RequestMethod.GET) public void request2 (HttpServletRequest request, HttpServletResponse response) { response.setHeader("Location" , "https://baidu.com" ); response.setStatus(HttpServletResponse.SC_MOVED_TEMPORARILY); }
+这里如果设置了 302,但没设置跳转 location,就是 302 found
+int SC_MOVED_TEMPORARILY = 302 ;int SC_FOUND = 302 ;
+
+而如果是 301 跳转,代表原地址不可用了,永久跳转
+@RequestMapping(value = "request3", method = RequestMethod.GET) public void request3 (HttpServletRequest request, HttpServletResponse response) { response.setHeader("Location" , "https://baidu.com" ); response.setStatus(HttpServletResponse.SC_MOVED_PERMANENTLY); }
+另一个问题是设置 cookie @RequestMapping(value = "request4", method = RequestMethod.GET) public void request4 (HttpServletRequest request, HttpServletResponse response) { Cookie cookie = new Cookie ("a" , "b" ); response.addCookie(cookie); response.setStatus(HttpServletResponse.SC_OK); }
+这样的设置就能成功设定 cookie,而随着目前的浏览器 cookie 策略,如果要跳转后设置的话,估计是会越来越难,包括
+response.addHeader("Set-Cookie" , "a=b; domain=baidu.com; SameSite=none;Secure" );
+这样的方式也没法实现。
]]>
Java
@@ -4574,32 +4640,6 @@
@Nullable public Object invokeForRequest (NativeWebRequest request, @Nullable ModelAndViewContainer mavContainer, Object... providedArgs) throws Exception { Object[] args = getMethodArgumentValues(request, mavContainer, providedArgs); if (logger.isTraceEnabled()) { logger.trace("Arguments: " + Arrays.toString(args)); } return doInvoke(args); }
来到了最核心处 org.springframework.web.method.support.InvocableHandlerMethod#doInvoke
@Nullable protected Object doInvoke (Object... args) throws Exception { Method method = getBridgedMethod(); ReflectionUtils.makeAccessible(method); try { if (KotlinDetector.isSuspendingFunction(method)) { return CoroutinesUtils.invokeSuspendingFunction(method, getBean(), args); } return method.invoke(getBean(), args); }
-]]>
-
- Java
- SpringBoot
-
-
- Java
- SpringBoot
-
-
-
- springboot 处理请求的小分支-跳转 & cookie
- /2023/09/03/springboot-%E5%A4%84%E7%90%86%E8%AF%B7%E6%B1%82%E7%9A%84%E5%B0%8F%E5%88%86%E6%94%AF-%E8%B7%B3%E8%BD%AC-cookie/
- 首先是跳转,应该说设置状态,其中跳转是其中一个状态,
-@RequestMapping(value = "request1", method = RequestMethod.GET) public void request1 (HttpServletRequest request, HttpServletResponse response) { response.setStatus(HttpServletResponse.SC_OK); }
-200 状态就很简单,如果想做跳转可以使用 302 并设置 location
-@RequestMapping(value = "request2", method = RequestMethod.GET) public void request2 (HttpServletRequest request, HttpServletResponse response) { response.setHeader("Location" , "https://baidu.com" ); response.setStatus(HttpServletResponse.SC_MOVED_TEMPORARILY); }
-这里如果设置了 302,但没设置跳转 location,就是 302 found
-int SC_MOVED_TEMPORARILY = 302 ;int SC_FOUND = 302 ;
-
-而如果是 301 跳转,代表原地址不可用了,永久跳转
-@RequestMapping(value = "request3", method = RequestMethod.GET) public void request3 (HttpServletRequest request, HttpServletResponse response) { response.setHeader("Location" , "https://baidu.com" ); response.setStatus(HttpServletResponse.SC_MOVED_PERMANENTLY); }
-另一个问题是设置 cookie @RequestMapping(value = "request4", method = RequestMethod.GET) public void request4 (HttpServletRequest request, HttpServletResponse response) { Cookie cookie = new Cookie ("a" , "b" ); response.addCookie(cookie); response.setStatus(HttpServletResponse.SC_OK); }
-这样的设置就能成功设定 cookie,而随着目前的浏览器 cookie 策略,如果要跳转后设置的话,估计是会越来越难,包括
-response.addHeader("Set-Cookie" , "a=b; domain=baidu.com; SameSite=none;Secure" );
-这样的方式也没法实现。
]]>
Java
@@ -4649,21 +4689,6 @@
c++
-
- mac os 14.x 出现 'xxx 已损坏,无法打开。 你应该将它移到废纸篓。' 解决方法
- /2025/02/16/mac-os-14-x-%E5%87%BA%E7%8E%B0-xxx-%E5%B7%B2%E6%8D%9F%E5%9D%8F%EF%BC%8C%E6%97%A0%E6%B3%95%E6%89%93%E5%BC%80%E3%80%82-%E4%BD%A0%E5%BA%94%E8%AF%A5%E5%B0%86%E5%AE%83%E7%A7%BB%E5%88%B0%E5%BA%9F%E7%BA%B8%E7%AF%93%E3%80%82-%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95/
- 在运行一个python写的小工具的时候碰到了这个问题 ““Python.framework”已损坏,无法打开。 你应该将它移到废纸篓。” 一开始以为是常规问题,但是可能随着系统更新,也出现了比较不同的情况 首先进去”系统设置”->”隐私和安全性” 一开始是长这样的 发现没有任意来源了, 于是搜索了下,可以直接在当前环境下开启,这个是区别于之前的经验的,因为之前关闭 spctl 好像是需要进恢复模式的 我试了下在 mac os 14.3 中目前是可以直接把这个关闭掉
-sudo spctl --master-disable
-关闭后重新打开系统设置 就能看到任意来源了 捎带解释下这个 spctl 的意思是 System Policy Control 用于管理系统策略控制。 禁用Gatekeeper的主开关,允许运行任何来源的应用。 此命令会降低Mac的安全性,因为它允许运行未经验证的应用。 启用后,需小心下载和运行应用。 所以还是需要小心关闭,一般在确认来源可靠才能关闭
-]]>
-
- mac
- 技巧
-
-
- mac
-
-
swoole-websocket-test
/2016/07/13/swoole-websocket-test/
@@ -4703,6 +4728,53 @@ user3:
swoole
+
+ systemtap学习记录一
+ /2025/01/26/systemtap%E5%AD%A6%E4%B9%A0%E8%AE%B0%E5%BD%95%E4%B8%80/
+ 前两天拜读了章亦春大佬的关于Dynamic Tracing的文章,觉得对现在碰到的一些问题有了一些新的思考,为了能有所产出就先写一点简单的学习记录 首先这个systemtap类似于一个linux系统层面的探针工具,可以让用户去监控系统的各种活动 以阿里云的 ubuntu 22.04 为例, 首先我们需要安装 systemtap-sdt-dev 这个包, 然后因为通过c的方式比较常规,我是想研究下针对像 php 这种怎么去监控 我们需要一个编译参数带上--enable-dtrace 的php版本 然后在编译完后我们写一个简单的测试脚本
+<?php function a ($b , $c ) { return $b + $c ; } sleep (1 );echo a (1 , 2 );
+这其实主要是为了触发系统调用 然后重点就是systemtap的脚本,它有点类似于c的代码,结构主要是 “事件” - “处理器” 比如我监听到了php发起了一个系统调用,那这个时候我想把它记录下来,打印日志这种
+probe syscall.* { if (execname() == "php") { printf ("%s(%d) open, call %s \n", execname(), pid(), name) } }
+这里就是针对所有的系统调用,如果是由php发起调用,那就打印出来pid和系统调用的名字(name就是这里的系统调用名字)
+事件 syscall.system_call 只是其中一类事件 还有对虚拟文件系统的操作 vfs.file_operation 内核的方法调用 kernel.function("function") 比如 probe kernel.function("*@net/socket.c") { } 还有可以是内核的一些追踪点kernel.trace("tracepoint") 比如 kernel.trace("kfree_skb") 这个表示每次当内核中的network buffer被释放的时候触发 还有的是时间时间timer events 比如
+probe timer.s(4) { printf("hello world\n") }
+每4秒打印一个 hello world 还有这些
+timer.ms(milliseconds) timer.us(microseconds) timer.ns(nanoseconds) timer.hz(hertz) timer.jiffies(jiffies)
+处理器 最常规的一类就是打印信息了printf ( ) Statements 这个跟c语言也很像,可以用%s 跟 %d 作为占位符来填充字符串和数字 然后就是一些定义好的取值方法execname() (a string with the executable name) 就比如刚才的php, 线程id
+tid() The ID of the current thread.
+用户id
+uid() The ID of the current user.
+比如系统调用就直接用 name 还有很多可以研究,第一篇学习就先到这里
+]]>
+
+ 系统
+
+
+ trace
+
+
+
+ tail命令小技巧
+ /2024/12/08/tail%E5%91%BD%E4%BB%A4%E5%B0%8F%E6%8A%80%E5%B7%A7/
+ 我们日常在服务器上查看日志的时候用的很多的就是tail命令,使用tail最基本的就是
+
+ 这种,可以看到日志文件的最后10行 我们一般会使用
+
+可以用 -f来看到后续新生成的日志,可以用来观察是否有新增的异常日志或者是否有没有按预期打印新的日志 然后对于我们对输出的日志行数有具体要求的
+tail -f -n 100 filename.log
+可以用 -n 参数,可以指定显示行数,比如想要显示100行 而有一个技巧就是之前看到别的同学用过
+
+可以和上面产生一样的效果 这样可以少输几个字符,也算是一个偷懒小技巧了,如果对于经常需要登录服务器看日志的也算是个效率小窍门 只是这样子对于比如我只想看新增的特定内容的日志来说,好像还不行 这里我们就可以使用组合技,结合 tail 和 grep 命令
+tail -f filename.log | grep something
+这样就可以关注 something 的新增了
+]]>
+
+ 技巧
+
+
+ linux
+
+
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/
@@ -4753,31 +4825,6 @@ user3:
jvm
-
- java的字节码工具-javassist初体验
- /2025/01/05/java%E7%9A%84%E5%AD%97%E8%8A%82%E7%A0%81%E5%B7%A5%E5%85%B7-javassist%E5%88%9D%E4%BD%93%E9%AA%8C/
- 前面那篇在讲agent的时候用到了javassist,我们就来简单讲个demo 我想用javassist来创建一个类
-public static void createEntity () throws Exception { ClassPool pool = ClassPool.getDefault(); CtClass cl = pool.makeClass("Entity" ); CtConstructor cons = new CtConstructor (new CtClass []{}, cl); cl.addConstructor(cons); cl.writeFile(); }
-这个代码可以给我们创建一个Entity类, 带有一个无参的构造函数,默认在项目根目录下会生成Entity类的class文件 但是其实这个也是不必要的,默认会给生成一个无参的构造函数 然后我们可以给这个类加个字段, 加在 writeFile 方法调用前
-CtField param = new CtField (pool.get("java.lang.String" ), "name" , cl);param.setModifiers(Modifier.PRIVATE); cl.addField(param, CtField.Initializer.constant("ent1" ));
- 我们就生成了这样一个类 javassist也已经提供好了
-cl.addMethod(CtNewMethod.setter("setName" , param)); cl.addMethod(CtNewMethod.getter("getName" , param));
-我们生成的类就变成这样了 接下去我们可以再加一个默认赋值的无参构造函数
-CtConstructor con = new CtConstructor (new CtClass []{}, cl);con.setBody("{name = \"nick\";}" ); cl.addConstructor(con);
- 然后我们就有了这样的类,带有一个默认赋值的构造函数,接下去可以来个带参数的构造函数
-con = new CtConstructor (new CtClass []{pool.get("java.lang.String" )}, cl); con.setBody("{$0.name = $1;}" ); cl.addConstructor(con);
- 最后我们可以再加个打印该字段的方法
-CtMethod ctMethod = new CtMethod (CtClass.voidType, "printName" , new CtClass []{}, cl);ctMethod.setModifiers(Modifier.PUBLIC); ctMethod.setBody("{System.out.println(name);}" ); cl.addMethod(ctMethod);
-这样我们就获得了这样一个类 这只是javassist的简单使用,它还有很多强大的功能可以在官方文档 和网上的资料进行学习
-]]>
-
- java
- 技巧
-
-
- java
-
-
《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/
@@ -4801,8 +4848,53 @@ user3:
生活
- 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/
+ 上周在处理一个 nginx 配置的时候,发现了一个之前不理解的小点,说一个场景,就是我们一般的处理方式就是一个 ip 端口只能配置一个域名的服务,比如 https://nicksxs.me 对应配置到 127.0.0.1:443,如果我想要把 https://nicksxs.com 也解析到这个服务器,并转发到不同的下游,这里就需要借助所谓的 SNI 的功能
+Server Name Indication A more generic solution for running several HTTPS servers on a single IP address is TLS Server Name Indication extension (SNI, RFC 6066), which allows a browser to pass a requested server name during the SSL handshake and, therefore, the server will know which certificate it should use for the connection. SNI is currently supported by most modern browsers, though may not be used by some old or special clients.来源 机翻一下:在单个 IP 地址上运行多个 HTTPS 服务器的更通用的解决方案是 TLS 服务器名称指示扩展(SNI,RFC 6066),它允许浏览器在 SSL 握手期间传递请求的服务器名称,因此,服务器将知道哪个 它应该用于连接的证书。 目前大多数现代浏览器都支持 SNI,但某些旧的或特殊的客户端可能不使用 SNI。
+首先我们需要确认 sni 已被支持 在实际的配置中就可以这样
+stream { map $ssl_preread_server_name $stream_map { nicksxs.me nme; nicksxs.com ncom; } upstream nme { server 127.0.0.1:8000 ; } upstream ncom { server 127.0.0.1:8001 ; } server { listen 443 reuseport; proxy_pass $stream_map ; ssl_preread on ; } }
+类似这样,但是这个理解是非常肤浅和不完善的,只是简单记忆下,后续再进行补充完整
+还有一点就是我们在配置的时候经常配置就是 server_name,但是会看到直接在使用 ssl_server_name, 其实在listen 标识了 ssl, 对应的 ssl_server_name 就等于 server_name,不需要额外处理了。
+]]>
+
+ nginx
+
+
+ nginx
+
+
+
+ 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
+
+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
+
+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
+]]>
+
+ Java
+
+
+ Java
@@ -4828,58 +4920,36 @@ user3:
- 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/
- 上周在处理一个 nginx 配置的时候,发现了一个之前不理解的小点,说一个场景,就是我们一般的处理方式就是一个 ip 端口只能配置一个域名的服务,比如 https://nicksxs.me 对应配置到 127.0.0.1:443,如果我想要把 https://nicksxs.com 也解析到这个服务器,并转发到不同的下游,这里就需要借助所谓的 SNI 的功能
-Server Name Indication A more generic solution for running several HTTPS servers on a single IP address is TLS Server Name Indication extension (SNI, RFC 6066), which allows a browser to pass a requested server name during the SSL handshake and, therefore, the server will know which certificate it should use for the connection. SNI is currently supported by most modern browsers, though may not be used by some old or special clients.来源 机翻一下:在单个 IP 地址上运行多个 HTTPS 服务器的更通用的解决方案是 TLS 服务器名称指示扩展(SNI,RFC 6066),它允许浏览器在 SSL 握手期间传递请求的服务器名称,因此,服务器将知道哪个 它应该用于连接的证书。 目前大多数现代浏览器都支持 SNI,但某些旧的或特殊的客户端可能不使用 SNI。
-首先我们需要确认 sni 已被支持 在实际的配置中就可以这样
-stream { map $ssl_preread_server_name $stream_map { nicksxs.me nme; nicksxs.com ncom; } upstream nme { server 127.0.0.1:8000 ; } upstream ncom { server 127.0.0.1:8001 ; } server { listen 443 reuseport; proxy_pass $stream_map ; ssl_preread on ; } }
-类似这样,但是这个理解是非常肤浅和不完善的,只是简单记忆下,后续再进行补充完整
-还有一点就是我们在配置的时候经常配置就是 server_name,但是会看到直接在使用 ssl_server_name, 其实在listen 标识了 ssl, 对应的 ssl_server_name 就等于 server_name,不需要额外处理了。
+ 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。
]]>
- nginx
+ 生活
+ 运动
- nginx
+ 生活
- systemtap学习记录一
- /2025/01/26/systemtap%E5%AD%A6%E4%B9%A0%E8%AE%B0%E5%BD%95%E4%B8%80/
- 前两天拜读了章亦春大佬的关于Dynamic Tracing的文章,觉得对现在碰到的一些问题有了一些新的思考,为了能有所产出就先写一点简单的学习记录 首先这个systemtap类似于一个linux系统层面的探针工具,可以让用户去监控系统的各种活动 以阿里云的 ubuntu 22.04 为例, 首先我们需要安装 systemtap-sdt-dev 这个包, 然后因为通过c的方式比较常规,我是想研究下针对像 php 这种怎么去监控 我们需要一个编译参数带上--enable-dtrace 的php版本 然后在编译完后我们写一个简单的测试脚本
-<?php function a ($b , $c ) { return $b + $c ; } sleep (1 );echo a (1 , 2 );
-这其实主要是为了触发系统调用 然后重点就是systemtap的脚本,它有点类似于c的代码,结构主要是 “事件” - “处理器” 比如我监听到了php发起了一个系统调用,那这个时候我想把它记录下来,打印日志这种
-probe syscall.* { if (execname() == "php") { printf ("%s(%d) open, call %s \n", execname(), pid(), name) } }
-这里就是针对所有的系统调用,如果是由php发起调用,那就打印出来pid和系统调用的名字(name就是这里的系统调用名字)
-事件 syscall.system_call 只是其中一类事件 还有对虚拟文件系统的操作 vfs.file_operation 内核的方法调用 kernel.function("function") 比如 probe kernel.function("*@net/socket.c") { } 还有可以是内核的一些追踪点kernel.trace("tracepoint") 比如 kernel.trace("kfree_skb") 这个表示每次当内核中的network buffer被释放的时候触发 还有的是时间时间timer events 比如
-probe timer.s(4) { printf("hello world\n") }
-每4秒打印一个 hello world 还有这些
-timer.ms(milliseconds) timer.us(microseconds) timer.ns(nanoseconds) timer.hz(hertz) timer.jiffies(jiffies)
-处理器 最常规的一类就是打印信息了printf ( ) Statements 这个跟c语言也很像,可以用%s 跟 %d 作为占位符来填充字符串和数字 然后就是一些定义好的取值方法execname() (a string with the executable name) 就比如刚才的php, 线程id
-tid() The ID of the current thread.
-用户id
-uid() The ID of the current user.
-比如系统调用就直接用 name 还有很多可以研究,第一篇学习就先到这里
-]]>
-
- 系统
-
-
- trace
-
-
-
- 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。
+ 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
]]>
- 生活
- 运动
+ Docker
- 生活
+ Docker
@@ -4951,48 +5021,6 @@ user3:
中间件
-
- tail命令小技巧
- /2024/12/08/tail%E5%91%BD%E4%BB%A4%E5%B0%8F%E6%8A%80%E5%B7%A7/
- 我们日常在服务器上查看日志的时候用的很多的就是tail命令,使用tail最基本的就是
-
- 这种,可以看到日志文件的最后10行 我们一般会使用
-
-可以用 -f来看到后续新生成的日志,可以用来观察是否有新增的异常日志或者是否有没有按预期打印新的日志 然后对于我们对输出的日志行数有具体要求的
-tail -f -n 100 filename.log
-可以用 -n 参数,可以指定显示行数,比如想要显示100行 而有一个技巧就是之前看到别的同学用过
-
-可以和上面产生一样的效果 这样可以少输几个字符,也算是一个偷懒小技巧了,如果对于经常需要登录服务器看日志的也算是个效率小窍门 只是这样子对于比如我只想看新增的特定内容的日志来说,好像还不行 这里我们就可以使用组合技,结合 tail 和 grep 命令
-tail -f filename.log | grep something
-这样就可以关注 something 的新增了
-]]>
-
- 技巧
-
-
- linux
-
-
-
- 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/
@@ -5090,54 +5118,6 @@ user3:
算法
-
- 介绍下最近比较实用的端口转发
- /2021/11/14/%E4%BB%8B%E7%BB%8D%E4%B8%8B%E6%9C%80%E8%BF%91%E6%AF%94%E8%BE%83%E5%AE%9E%E7%94%A8%E7%9A%84%E7%AB%AF%E5%8F%A3%E8%BD%AC%E5%8F%91/
- vscode 扩展转发在日常使用云服务器的时候,如果要访问上面自建的 mysql,一般要不直接开对应的端口,然后需要对本地 ip 进行授权,但是这个方案会有比较多的限制,比如本地 ip 变了,比如是非固定出口 ip 的家用宽带,或者要在家里跟公司都要访问,如果对所有 ip 都授权的话会不安全,这个时候其实是用 ssh 端口转发是个比较安全方便的方式。 原来在这个之前其实对这块内容不太了解,后面是听朋友说的,vscode 的 Remote - SSH 扩展可以很方便的使用端口转发,在使用该扩展的时候,会在控制台位置里都出现一个”端口” tab 如图中所示,我就是将一个服务器上的 mysql 的 3306 端口转发到本地的 3307 端口,至于为什么不用 3306 是因为本地我也有个 mysql 已经使用了 3306 端口,这个方法是使用的 vscode 的这个扩展,
-ssh 命令转发 还有个方式是直接使用 ssh 命令 命令可以如此
-ssh -CfNg -L 3307:127.0.0.1:3306 user1@199.199.199.199
-简单介绍下这个命令-C 表示的是压缩数据包-f 表示后台执行命令-N 是表示不执行具体命令只用于端口转发-g 表示允许远程主机连接本地转发端口-L 则是具体端口转发的映射配置 上面的命令就是将远程主机的 127.0.0.1:3306 对应转发到本地 3307 而后面的用户则就是登录主机的用户名user1和ip地址199.199.199.199,当然这个配置也不是唯一的
-ssh config 配置转发 还可以在ssh 的 config 配置中加对应的配置
-Host host1 HostName 199.199.199.199 User user1 IdentityFile /Users/user1/.ssh/id_rsa ServerAliveInterval 60 LocalForward 3310 127.0.0.1:3306
-然后通过 ssh host1 连接服务器的时候就能顺带做端口转发
-]]>
-
- ssh
- 技巧
-
-
- ssh
- 端口转发
-
-
-
- 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
-
-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
-
-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
-]]>
-
- Java
-
-
- Java
-
-
介绍一下Java的BiFunction
/2024/06/30/%E4%BB%8B%E7%BB%8D%E4%B8%80%E4%B8%8BJava%E7%9A%84BiFunction/
@@ -5174,55 +5154,35 @@ user3:
- 从丁仲礼被美国制裁聊点啥
- /2020/12/20/%E4%BB%8E%E4%B8%81%E4%BB%B2%E7%A4%BC%E8%A2%AB%E7%BE%8E%E5%9B%BD%E5%88%B6%E8%A3%81%E8%81%8A%E7%82%B9%E5%95%A5/
- 几年前看了柴静的《穹顶之下》觉得这个记者调查得很深入,挺有水平,然后再看到了她跟丁仲礼的采访,其实没看完整,也没试着去理解,就觉得环境问题挺严重的,为啥柴静这个对面的这位好像对这个很不屑的样子,最近因为丁仲礼上了美国制裁名单,B 站又有人把这个视频发了出来,就完整看了下,就觉得自己挺惭愧的,就抱着对柴静的好感而没来由的否定了丁老的看法和说法,所以人也需要不断地学习,改正之前错误的观点,当然不是说我现在说的就是百分百正确,只是个人的一些浅显的见解
-先聊聊这个事情,整体看下来我的一些理解,IPCC给中国的方案其实是个很大的陷阱,它里面有几个隐藏的点是容易被我们外行忽略的,第一点是基数,首先发达国家目前(指2010年采访或者IPCC方案时间)的人均碳排放量已经是远高于发展中国家的了,这也就导致了所谓的发达国家承诺减排80%是个非常有诚意的承诺其实就是忽悠;第二点是碳排放是个累计过程,从1900年开始到2050年,或者说到2010年,发达国家已经排的量是远超过发展中国家的,这是非常不公平的;第三点其实是通过前两点推导出来的,也就是即使发达国家这么有诚意地说减排80%,扒开这层虚伪的外衣,其实是给他们11亿人分走了48%的碳排放量,留给发展中国家55亿人口的只剩下了52%;第四点,人是否因为国家的发达与否而应受到不平等待遇,如果按国家维度,丁老说的,摩纳哥要跟中国分同样的排放量么,中国人还算不算人;第五点,这点算是我自己想的,也可能是柴静屁股决定脑袋想不到的点,她作为一个物质生活条件已经足够好了,那么对于那些生活在物质条件平均线以下的,他们是否能像城里人那样有空调地暖,洗澡有热水器浴霸,上下班能开车,这些其实都直接或者间接地导致了碳排放;他们有没有改善物质生活条件地权利呢,并且再说回来,其实丁老也给了我们觉得合理地方案,我们保证不管发达国家不管减排多少,我们都不会超过他们的80%,我觉得这是真正的诚意,而不是接着减排80%的噱头来忽悠人,也是像丁老这样的专家才能看破这个陷阱,碳排放权其实就是发展权,就是人权,中国人就不是人了么,或者说站在贫困线以下的人民是否有改善物质条件的权利,而不是说像柴静这样,只是管她自己,可能觉得小孩因为空气污染导致身体不好,所以做了穹顶之下这个纪录片,想去改善这个事情,空气污染不是说对的,只是每个国家都有这个过程,如果不发展,哪里有资源去让人活得好,活得好了是前提,然后再去各方面都改善。
-对于这个问题其实更想说的是人的认知偏差,之前总觉得美帝是更自由民主,公平啥的,或者说觉得美帝啥都好,有种无脑愤青的感觉,外国的月亮比较圆,但是经历了像川普当选美国总统以来的各种魔幻操作,还有对于疫情的种种不可思议的美国民众的反应,其实更让人明白第一是外国的月亮没比较圆,第二是事情总是没那么简单粗暴非黑即白,美国不像原先设想地那么领先优秀,但是的确有很多方面是全球领先的,天朝也有体制所带来的优势,不可妄自菲薄,也不能忙不自大,还是要多学习知识,提升认知水平。
-]]>
-
- 生活
- 吐槽
- 疫情
- 美国
-
-
- 生活
- 吐槽
- 疫情
- 美国
-
-
-
- 从清华美院学姐聊聊我们身边的恶人
- /2020/11/29/%E4%BB%8E%E6%B8%85%E5%8D%8E%E7%BE%8E%E9%99%A2%E5%AD%A6%E5%A7%90%E8%81%8A%E8%81%8A%E6%88%91%E4%BB%AC%E8%BA%AB%E8%BE%B9%E7%9A%84%E6%81%B6%E4%BA%BA/
- 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
+ 介绍下laravel herd工具
+ /2025/07/13/%E4%BB%8B%E7%BB%8D%E4%B8%8Blaravel-herd%E5%B7%A5%E5%85%B7/
+ herd是在使用过了valet之后,又一款laravel出品的好用的工具,之前使用valet可以把laravel应用的本地域名解析和https加密等都处理好,是个非常不错的工具。 但是在我的使用场景里还有一个比较头疼的问题就是多版本php的切换,虽然php在大型网站,非常复杂的业务系统使用会有一些不足,弱类型会导致复杂逻辑的后续维护重构变得比较困难 只是对于个人使用来说,仍然是个非常不错的工具语言 同时laravel也是个很不错(除了有些过度封装)的框架,但是因为之前我自己写的工具大部分是基于php7.4的,laravel是比较古老的laravel 5.6版本,目前很多新的包都要求php版本是8.x,laravel也要10+了,一方面是我原先依赖的工具包没有对应的更新,另一方面是php7.4切换其他版本会有icu4c依赖版本的问题,挺麻烦 这次研究了下herd,它是可以做php版本切换的, 像这边我装了7.4和8.2版本,并且最难的是它吧依赖也一并打包了,所以就不用管icu4c这种依赖版本的问题,省心省力 还可以针对site来设置不同的php版本,这个是非常方便的 只是它也不是完全没有问题的,在使用的时候发现一个比较奇葩的问题,就是默认运行时会有这个报错preg_match(): Allocation of JIT memory failed 而它需要的解决方式在herd环境中变得无效,因为这里给出的错误提示是要把php的配置中的 pcre.jit 设置成 0 我们可以通过在版本号上右键,看到对应版本php的ini配置文件所在位置 但是打开后发现这个设置已经存在 后来搜了一圈才发现了一个很奇葩的环境变量 比如是php7.4就需要设置这个HERD_PHP_74_INI_SCAN_DIR="/Users/dasun/Library/Application Support/Herd/config/php/74/" 来源是在这个issue 里,还是个挺麻烦的问题 另外就是herd其实是卖收费版本的,相对来说存在一些限制,比如php的extension是不支持便捷的自定义安装的,如果有需要可能需要一些奇技淫巧,参考这里 ,但是至少还是帮我解决了问题了
]]>
- 生活
- 吐槽
- 疫情
- 口罩
+ php
- 生活
- 吐槽
- 疫情
- 公交车
- 口罩
- 杀人诛心
+ php
- 介绍下laravel herd工具
- /2025/07/13/%E4%BB%8B%E7%BB%8D%E4%B8%8Blaravel-herd%E5%B7%A5%E5%85%B7/
- herd是在使用过了valet之后,又一款laravel出品的好用的工具,之前使用valet可以把laravel应用的本地域名解析和https加密等都处理好,是个非常不错的工具。 但是在我的使用场景里还有一个比较头疼的问题就是多版本php的切换,虽然php在大型网站,非常复杂的业务系统使用会有一些不足,弱类型会导致复杂逻辑的后续维护重构变得比较困难 只是对于个人使用来说,仍然是个非常不错的工具语言 同时laravel也是个很不错(除了有些过度封装)的框架,但是因为之前我自己写的工具大部分是基于php7.4的,laravel是比较古老的laravel 5.6版本,目前很多新的包都要求php版本是8.x,laravel也要10+了,一方面是我原先依赖的工具包没有对应的更新,另一方面是php7.4切换其他版本会有icu4c依赖版本的问题,挺麻烦 这次研究了下herd,它是可以做php版本切换的, 像这边我装了7.4和8.2版本,并且最难的是它吧依赖也一并打包了,所以就不用管icu4c这种依赖版本的问题,省心省力 还可以针对site来设置不同的php版本,这个是非常方便的 只是它也不是完全没有问题的,在使用的时候发现一个比较奇葩的问题,就是默认运行时会有这个报错preg_match(): Allocation of JIT memory failed 而它需要的解决方式在herd环境中变得无效,因为这里给出的错误提示是要把php的配置中的 pcre.jit 设置成 0 我们可以通过在版本号上右键,看到对应版本php的ini配置文件所在位置 但是打开后发现这个设置已经存在 后来搜了一圈才发现了一个很奇葩的环境变量 比如是php7.4就需要设置这个HERD_PHP_74_INI_SCAN_DIR="/Users/dasun/Library/Application Support/Herd/config/php/74/" 来源是在这个issue 里,还是个挺麻烦的问题 另外就是herd其实是卖收费版本的,相对来说存在一些限制,比如php的extension是不支持便捷的自定义安装的,如果有需要可能需要一些奇技淫巧,参考这里 ,但是至少还是帮我解决了问题了
+ 介绍下最近比较实用的端口转发
+ /2021/11/14/%E4%BB%8B%E7%BB%8D%E4%B8%8B%E6%9C%80%E8%BF%91%E6%AF%94%E8%BE%83%E5%AE%9E%E7%94%A8%E7%9A%84%E7%AB%AF%E5%8F%A3%E8%BD%AC%E5%8F%91/
+ vscode 扩展转发在日常使用云服务器的时候,如果要访问上面自建的 mysql,一般要不直接开对应的端口,然后需要对本地 ip 进行授权,但是这个方案会有比较多的限制,比如本地 ip 变了,比如是非固定出口 ip 的家用宽带,或者要在家里跟公司都要访问,如果对所有 ip 都授权的话会不安全,这个时候其实是用 ssh 端口转发是个比较安全方便的方式。 原来在这个之前其实对这块内容不太了解,后面是听朋友说的,vscode 的 Remote - SSH 扩展可以很方便的使用端口转发,在使用该扩展的时候,会在控制台位置里都出现一个”端口” tab 如图中所示,我就是将一个服务器上的 mysql 的 3306 端口转发到本地的 3307 端口,至于为什么不用 3306 是因为本地我也有个 mysql 已经使用了 3306 端口,这个方法是使用的 vscode 的这个扩展,
+ssh 命令转发 还有个方式是直接使用 ssh 命令 命令可以如此
+ssh -CfNg -L 3307:127.0.0.1:3306 user1@199.199.199.199
+简单介绍下这个命令-C 表示的是压缩数据包-f 表示后台执行命令-N 是表示不执行具体命令只用于端口转发-g 表示允许远程主机连接本地转发端口-L 则是具体端口转发的映射配置 上面的命令就是将远程主机的 127.0.0.1:3306 对应转发到本地 3307 而后面的用户则就是登录主机的用户名user1和ip地址199.199.199.199,当然这个配置也不是唯一的
+ssh config 配置转发 还可以在ssh 的 config 配置中加对应的配置
+Host host1 HostName 199.199.199.199 User user1 IdentityFile /Users/user1/.ssh/id_rsa ServerAliveInterval 60 LocalForward 3310 127.0.0.1:3306
+然后通过 ssh host1 连接服务器的时候就能顺带做端口转发
]]>
- php
+ ssh
+ 技巧
- php
+ ssh
+ 端口转发
@@ -5265,6 +5225,73 @@ user3:
java
+
+ 介绍个非常好用的工具 pakeplus
+ /2025/05/25/%E4%BB%8B%E7%BB%8D%E4%B8%AA%E9%9D%9E%E5%B8%B8%E5%A5%BD%E7%94%A8%E7%9A%84%E5%B7%A5%E5%85%B7-pakeplus/
+ 程序员中例如我这样的主要是服务端的其实要开发一款app会面临很多问题,相对便捷的可能是做一个网页版的,套用下组件啥的,网页版的要做成app的话之前比较可行的是通过 Electron ,但是也是学习成本比较大的一件事,现在要介绍的这个pakeplus是基于rust tauri开发的一个傻瓜式地把网页转换成app的开源软件 首先它的github地址是 https://github.com/Sjj1024/PakePlus?tab=readme-ov-file
+可以在 https://github.com/Sjj1024/PakePlus/releases 下载对应的版本 下载后打开长这样 点击➕号就能开始创建一个app 然后就是填入网页的地址,比如我这边想把我常用的一个crontab工具打包成一个app, 那就填入地址,以及相关的软件描述,还可以控制是否开启调试等 保存后就可以开始点击预览 这样一个简单的网页就打包成了一个mac上的app了,这里可能更多的场景是比如我就写了一个网页版的小工具,想打包成一个app来方便随时打开使用 比如本地的json工具等等,有些是自己定制的更趁手,还是非常不错的,记得之前这个开发者是可能做的命令行版的 这次是更彻底的界面化的比较傻瓜式的打包工具 方便很多不了解开发,命令行的同学进行使用
+]]>
+
+ 工具
+
+
+ 工具
+
+
+
+ 从丁仲礼被美国制裁聊点啥
+ /2020/12/20/%E4%BB%8E%E4%B8%81%E4%BB%B2%E7%A4%BC%E8%A2%AB%E7%BE%8E%E5%9B%BD%E5%88%B6%E8%A3%81%E8%81%8A%E7%82%B9%E5%95%A5/
+ 几年前看了柴静的《穹顶之下》觉得这个记者调查得很深入,挺有水平,然后再看到了她跟丁仲礼的采访,其实没看完整,也没试着去理解,就觉得环境问题挺严重的,为啥柴静这个对面的这位好像对这个很不屑的样子,最近因为丁仲礼上了美国制裁名单,B 站又有人把这个视频发了出来,就完整看了下,就觉得自己挺惭愧的,就抱着对柴静的好感而没来由的否定了丁老的看法和说法,所以人也需要不断地学习,改正之前错误的观点,当然不是说我现在说的就是百分百正确,只是个人的一些浅显的见解
+先聊聊这个事情,整体看下来我的一些理解,IPCC给中国的方案其实是个很大的陷阱,它里面有几个隐藏的点是容易被我们外行忽略的,第一点是基数,首先发达国家目前(指2010年采访或者IPCC方案时间)的人均碳排放量已经是远高于发展中国家的了,这也就导致了所谓的发达国家承诺减排80%是个非常有诚意的承诺其实就是忽悠;第二点是碳排放是个累计过程,从1900年开始到2050年,或者说到2010年,发达国家已经排的量是远超过发展中国家的,这是非常不公平的;第三点其实是通过前两点推导出来的,也就是即使发达国家这么有诚意地说减排80%,扒开这层虚伪的外衣,其实是给他们11亿人分走了48%的碳排放量,留给发展中国家55亿人口的只剩下了52%;第四点,人是否因为国家的发达与否而应受到不平等待遇,如果按国家维度,丁老说的,摩纳哥要跟中国分同样的排放量么,中国人还算不算人;第五点,这点算是我自己想的,也可能是柴静屁股决定脑袋想不到的点,她作为一个物质生活条件已经足够好了,那么对于那些生活在物质条件平均线以下的,他们是否能像城里人那样有空调地暖,洗澡有热水器浴霸,上下班能开车,这些其实都直接或者间接地导致了碳排放;他们有没有改善物质生活条件地权利呢,并且再说回来,其实丁老也给了我们觉得合理地方案,我们保证不管发达国家不管减排多少,我们都不会超过他们的80%,我觉得这是真正的诚意,而不是接着减排80%的噱头来忽悠人,也是像丁老这样的专家才能看破这个陷阱,碳排放权其实就是发展权,就是人权,中国人就不是人了么,或者说站在贫困线以下的人民是否有改善物质条件的权利,而不是说像柴静这样,只是管她自己,可能觉得小孩因为空气污染导致身体不好,所以做了穹顶之下这个纪录片,想去改善这个事情,空气污染不是说对的,只是每个国家都有这个过程,如果不发展,哪里有资源去让人活得好,活得好了是前提,然后再去各方面都改善。
+对于这个问题其实更想说的是人的认知偏差,之前总觉得美帝是更自由民主,公平啥的,或者说觉得美帝啥都好,有种无脑愤青的感觉,外国的月亮比较圆,但是经历了像川普当选美国总统以来的各种魔幻操作,还有对于疫情的种种不可思议的美国民众的反应,其实更让人明白第一是外国的月亮没比较圆,第二是事情总是没那么简单粗暴非黑即白,美国不像原先设想地那么领先优秀,但是的确有很多方面是全球领先的,天朝也有体制所带来的优势,不可妄自菲薄,也不能忙不自大,还是要多学习知识,提升认知水平。
+]]>
+
+ 生活
+ 吐槽
+ 疫情
+ 美国
+
+
+ 生活
+ 吐槽
+ 疫情
+ 美国
+
+
+
+ 从清华美院学姐聊聊我们身边的恶人
+ /2020/11/29/%E4%BB%8E%E6%B8%85%E5%8D%8E%E7%BE%8E%E9%99%A2%E5%AD%A6%E5%A7%90%E8%81%8A%E8%81%8A%E6%88%91%E4%BB%AC%E8%BA%AB%E8%BE%B9%E7%9A%84%E6%81%B6%E4%BA%BA/
+ 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
+]]>
+
+ 生活
+ 吐槽
+ 疫情
+ 口罩
+
+
+ 生活
+ 吐槽
+ 疫情
+ 公交车
+ 口罩
+ 杀人诛心
+
+
+
+ 体验Roo Code代码生成插件
+ /2025/10/26/%E4%BD%93%E9%AA%8CRoo-Code%E4%BB%A3%E7%A0%81%E7%94%9F%E6%88%90%E6%8F%92%E4%BB%B6/
+ 之前在GLM网站看到他们家有适配各种代码生成插件,就先来体验下这个Roo Code 这是个在vscode插件市场里可以安装的插件, 安装完在这就有个图标了 然后在API Provider里选择 Z AI Z AI Entrypoint 选择 China Coding Plan Z AI API KEY 就是他们家的API KEY 然后选择Model,可以选择最新的glm-4.6 这样就配置好了 还是以相同任务来做下测验 他会先逐个求问,来帮忙细化我前面比较粗略的prompt 然后会生成这样的架构计划 这让我觉得有点惊喜,在开发前已经在做系分和架构设计了
+但是在N步之后,还是有出了问题12:23:12 AM [vite] (client) Pre-transform error: [postcss] It looks like you're trying to use tailwindcssdirectly as a PostCSS plugin. The PostCSS plugin has moved to a separate package, so to continue using Tailwind CSS with PostCSS you'll need to install@tailwindcss/postcss and update your PostCSS configuration. Plugin: vite:css
+不过它识别到了这个错误 但是其实这个修复还是有问题,后续又因为token超限了,这样的一个简单应用,它用了83K的token,虽然可能设想很好,实现的还是问题比较多 整体看下来Roo Code应该是想解决比较大型的项目的架构设计和代码实现,但是可能因为模型能力不够,也可能是在prompt的设计中出现了问题,比如这里看下来跟tailwind的版本和具体的使用方式有关系,这一点可能跟glm模型的训练有关 即使我用了copilot基于claude-sonnet-4.5模型调试了挺久还是没有解决问题 倒不是特别悲观,只是可能很多项目都有复杂的历史包袱,要在非常核心的项目(特别是历史悠久)中使用,目前来看还需要一些时间来进步
+]]>
+
+ LLM
+
+
+ LLM
+
+
体验openai开源的gpt-oss模型
/2025/08/30/%E4%BD%93%E9%AA%8Copenai%E5%BC%80%E6%BA%90%E7%9A%84gpt-oss%E6%A8%A1%E5%9E%8B/
@@ -5714,19 +5741,31 @@ user3:
- 体验Roo Code代码生成插件
- /2025/10/26/%E4%BD%93%E9%AA%8CRoo-Code%E4%BB%A3%E7%A0%81%E7%94%9F%E6%88%90%E6%8F%92%E4%BB%B6/
- 之前在GLM网站看到他们家有适配各种代码生成插件,就先来体验下这个Roo Code 这是个在vscode插件市场里可以安装的插件, 安装完在这就有个图标了 然后在API Provider里选择 Z AI Z AI Entrypoint 选择 China Coding Plan Z AI API KEY 就是他们家的API KEY 然后选择Model,可以选择最新的glm-4.6 这样就配置好了 还是以相同任务来做下测验 他会先逐个求问,来帮忙细化我前面比较粗略的prompt 然后会生成这样的架构计划 这让我觉得有点惊喜,在开发前已经在做系分和架构设计了
-但是在N步之后,还是有出了问题12:23:12 AM [vite] (client) Pre-transform error: [postcss] It looks like you're trying to use tailwindcssdirectly as a PostCSS plugin. The PostCSS plugin has moved to a separate package, so to continue using Tailwind CSS with PostCSS you'll need to install@tailwindcss/postcss and update your PostCSS configuration. Plugin: vite:css
-不过它识别到了这个错误 但是其实这个修复还是有问题,后续又因为token超限了,这样的一个简单应用,它用了83K的token,虽然可能设想很好,实现的还是问题比较多 整体看下来Roo Code应该是想解决比较大型的项目的架构设计和代码实现,但是可能因为模型能力不够,也可能是在prompt的设计中出现了问题,比如这里看下来跟tailwind的版本和具体的使用方式有关系,这一点可能跟glm模型的训练有关 即使我用了copilot基于claude-sonnet-4.5模型调试了挺久还是没有解决问题 倒不是特别悲观,只是可能很多项目都有复杂的历史包袱,要在非常核心的项目(特别是历史悠久)中使用,目前来看还需要一些时间来进步
-]]>
-
- LLM
-
-
- LLM
-
-
+ 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 { 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(() -> { 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, false ); Objects.requireNonNull(task); 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 ; 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
+
+
体验心流助手iflow
/2025/11/02/%E4%BD%93%E9%AA%8C%E5%BF%83%E6%B5%81%E5%8A%A9%E6%89%8Biflow/
@@ -5742,42 +5781,35 @@ user3:
- 介绍个非常好用的工具 pakeplus
- /2025/05/25/%E4%BB%8B%E7%BB%8D%E4%B8%AA%E9%9D%9E%E5%B8%B8%E5%A5%BD%E7%94%A8%E7%9A%84%E5%B7%A5%E5%85%B7-pakeplus/
- 程序员中例如我这样的主要是服务端的其实要开发一款app会面临很多问题,相对便捷的可能是做一个网页版的,套用下组件啥的,网页版的要做成app的话之前比较可行的是通过 Electron ,但是也是学习成本比较大的一件事,现在要介绍的这个pakeplus是基于rust tauri开发的一个傻瓜式地把网页转换成app的开源软件 首先它的github地址是 https://github.com/Sjj1024/PakePlus?tab=readme-ov-file
-可以在 https://github.com/Sjj1024/PakePlus/releases 下载对应的版本 下载后打开长这样 点击➕号就能开始创建一个app 然后就是填入网页的地址,比如我这边想把我常用的一个crontab工具打包成一个app, 那就填入地址,以及相关的软件描述,还可以控制是否开启调试等 保存后就可以开始点击预览 这样一个简单的网页就打包成了一个mac上的app了,这里可能更多的场景是比如我就写了一个网页版的小工具,想打包成一个app来方便随时打开使用 比如本地的json工具等等,有些是自己定制的更趁手,还是非常不错的,记得之前这个开发者是可能做的命令行版的 这次是更彻底的界面化的比较傻瓜式的打包工具 方便很多不了解开发,命令行的同学进行使用
+ 体验最新的gemini cli工具来实现一个简单的网页小程序
+ /2025/06/29/%E4%BD%93%E9%AA%8C%E6%9C%80%E6%96%B0%E7%9A%84gemini-cli%E5%B7%A5%E5%85%B7%E6%9D%A5%E5%AE%9E%E7%8E%B0%E4%B8%80%E4%B8%AA%E7%AE%80%E5%8D%95%E7%9A%84%E7%BD%91%E9%A1%B5%E5%B0%8F%E7%A8%8B%E5%BA%8F/
+ 谷歌新出了个对标claude code的命令行工具,叫gemini-cli,还是开源的,所以赶紧来体验下,就是前提是有科学上网,要去google的ai studio申请gemini的api key 然后在有科学网络的情况下用node 18及以上的版本安装
+npm install -g @google/gemini-cli
+然后在环境变量里设置gemini key
+export GEMINI_API_KEY="xxxxxxxx"
+接着运行gemini就可以啦 这里我是刚好有个需求,因为娃最近有点不太爱喝奶,经常剩下,想要记录统计下,看看每天的总的奶量 那我就建了个空的目录,并在这个目录下运行了 gemini ,然后输入了我的需求, 原始的诉求是这样我想要写个网页版小程序,可以按时间记录宝宝每次的奶量,包括泡了多少,剩了多少,能按天统计每天的奶量,以及查看趋势 然后gemini就开始运行
+✦ 好的,这个需求很明确,我们可以创建一个名为“奶量备忘” (Milk Memo) 的网页应用来实现它。 这是一个很好的项目,完全可以通过前端技术实现,将数据存储在浏览器中,无需复杂的后端。 这是我的计划: 应用名称:奶量备忘 (Milk Memo) 核心功能: 1. 记录喂奶量:一个简洁的表单,用于输入“冲泡量”和“剩余量”。提交后,应用会自动计算“实际饮用量”并记录当前时间。 2. 每日记录列表:清晰地展示当天的所有喂奶记录,包括时间、冲泡量、剩余量和实际饮用量。 3. 今日统计:在页面顶部或底部显眼地展示当天累计的总饮用量。 4. 历史趋势:一个图表页面,用柱状图或折线图展示最近7天或30天的每日总饮用量,帮助您直观地了解宝宝的饮奶趋势。 技术选型: * 前端框架: React - 用于构建用户界面,组件化的思想很适合这个应用。 * 样式与布局: Bootstrap - 一个流行的CSS框架,可以快速构建出简洁、美观且移动端优先的响应式布局。 * 图表: Chart.js - 一个强大的图表库,用于实现“历史趋势”功能。 * 数据存储: 浏览器本地存储 (Local Storage) - 无需后端和数据库,所有数据将安全地存储在您自己的浏览器里,简单快捷。 用户体验与设计: 我将采用现代化、简洁的设计风格,确保在手机浏览器上也能获得良好的操作体验。界面会以卡片的形式展示数据,按钮和输入框都会清晰易用。 这个计划听起来如何?如果同意,我将开始着手创建这个应用。
+然后我输入了同意他就开始帮我实现了 它会请求我的同意来运行比如npm命令,安装依赖等等 然后就自己运行了npm start 来演示运行这个应用 不算非常有设计感,但是基本的功能是已经实现了 接着我又让它帮我增加了时间选项,以及趋势图查看功能, 接着它就又生成了一版
+✦ 功能已经更新完毕。 现在应用具备了以下功能: * 自定义时间:您可以在添加记录时,精确选择到分钟。 * 视图切换:通过顶部的按钮,可以在“记录列表”和“历史趋势”图表之间自由切换。 * 趋势图表:在“历史趋势”视图中,您可以看到一个折线图,展示了每天的总饮奶量。 * 数据排序:所有记录现在会按时间从近到远排序,方便查看最近的条目。
+ 感觉完成了所需的95%的功能,除了在设计上还能再美观些,以及增加一些服务端存储数据的功能,基本是个比较完善的,我自己都完全没介入,的确ai的发展真的一日千里,另外我还是比较看好谷歌的,没有什么大新闻,就一直努力追赶,现在基本已经是第一梯队了,很好很强大
]]>
- 工具
+ LLM
- 工具
+ LLM
- 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 { 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(() -> { 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, false ); Objects.requireNonNull(task); 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 ; 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
+ 使用 chatbox 来连接火山引擎等api服务来使用Deepseek-R1 全尺寸大模型
+ /2025/03/09/%E4%BD%BF%E7%94%A8-chatbox-%E6%9D%A5%E8%BF%9E%E6%8E%A5%E7%81%AB%E5%B1%B1%E5%BC%95%E6%93%8E%E7%AD%89api%E6%9C%8D%E5%8A%A1%E6%9D%A5%E4%BD%BF%E7%94%A8Deepseek-R1-%E5%85%A8%E5%B0%BA%E5%AF%B8%E5%A4%A7%E6%A8%A1%E5%9E%8B/
+ chatbox 是个可以链接大模型api服务的客户端工具,之前我们说的都是使用自己部署的蒸馏模型,如果现在本地使用全尺寸大模型的话,就可以使用像 chatbox 这样的客户端工具 首先我们需要下载 chatbox 这个软件,注意到现在他们家的网站需要科学上网才能打开,所以需要处理下网络问题下载完成后我们需要去注册并开通大模型的api服务 目前有像火山引擎,硅基流动等等在提供支持deepseek-r1全尺寸模型的api服务,可以自由选择,这里以火山引擎为例 首先是注册火山引擎,地址是这 https://www.volcengine.com/experience/ark 然后不介意的话,可以填下我的邀请码,我们都可以获得比较可观数量的token额度,我的邀请码是 7R4RJOO8 接下去就是开通api服务, 这里就要使用我们自定义的模型提供上,这里的api域名是参考 的url进行配置,以及确定模型名是 deepseek-r1-250120 接下去我们就可以在chatbox中使用我们的deepseek-r1大模型了, 比如我让它生成一个网页版的贪吃蛇 还是能够很流畅的响应,毕竟火山背靠头条 这边也贴一下我的火山邀请码 另外还有硅基流动的邀请码 idw8RreO 同样我们都能获得一定数量的token
]]>
- java
+ LLM
- java
+ LLM
@@ -5793,9 +5825,9 @@ user3:
- 使用 chatbox 来连接火山引擎等api服务来使用Deepseek-R1 全尺寸大模型
- /2025/03/09/%E4%BD%BF%E7%94%A8-chatbox-%E6%9D%A5%E8%BF%9E%E6%8E%A5%E7%81%AB%E5%B1%B1%E5%BC%95%E6%93%8E%E7%AD%89api%E6%9C%8D%E5%8A%A1%E6%9D%A5%E4%BD%BF%E7%94%A8Deepseek-R1-%E5%85%A8%E5%B0%BA%E5%AF%B8%E5%A4%A7%E6%A8%A1%E5%9E%8B/
- chatbox 是个可以链接大模型api服务的客户端工具,之前我们说的都是使用自己部署的蒸馏模型,如果现在本地使用全尺寸大模型的话,就可以使用像 chatbox 这样的客户端工具 首先我们需要下载 chatbox 这个软件,注意到现在他们家的网站需要科学上网才能打开,所以需要处理下网络问题下载完成后我们需要去注册并开通大模型的api服务 目前有像火山引擎,硅基流动等等在提供支持deepseek-r1全尺寸模型的api服务,可以自由选择,这里以火山引擎为例 首先是注册火山引擎,地址是这 https://www.volcengine.com/experience/ark 然后不介意的话,可以填下我的邀请码,我们都可以获得比较可观数量的token额度,我的邀请码是 7R4RJOO8 接下去就是开通api服务, 这里就要使用我们自定义的模型提供上,这里的api域名是参考 的url进行配置,以及确定模型名是 deepseek-r1-250120 接下去我们就可以在chatbox中使用我们的deepseek-r1大模型了, 比如我让它生成一个网页版的贪吃蛇 还是能够很流畅的响应,毕竟火山背靠头条 这边也贴一下我的火山邀请码 另外还有硅基流动的邀请码 idw8RreO 同样我们都能获得一定数量的token
+ 使用 LM Studio 在本地部署 Deepseek-R1 的蒸馏版大模型
+ /2025/02/23/%E4%BD%BF%E7%94%A8-LM-Studio-%E5%9C%A8%E6%9C%AC%E5%9C%B0%E9%83%A8%E7%BD%B2-Deepseek-%E5%A4%A7%E6%A8%A1%E5%9E%8B/
+ deepseek-v3 和 deepseek-r1 是由深度求索公司推出的大语言模型,并且在生成效果上基本已经到达国内甚至全球的顶级水准,并且是免费使用,并且是开源的 连着用三个并且不是我文字能力差,而是真的佩服deepseek这家公司和创始人梁文峰,作为校友感觉是真的忝列门墙 早先了解的幻方量化就是一家小众低调但又多金的公司,奈何都是招的全栈工程师,可能不是太适合,当前对于deepseek来说那就更遥远了,从公开的不一定可靠的信息来看,目前在deepseek的都是像清北的博士这样的顶级人才,而且在人工智能这块,只是浅显地涉及过一些机器学习相关的工作,对于最前沿的大模型这块着实还有比较大的差距,当然也是在学习中。 虽然不是完全专业,但是对比大部分跟这个领域完全不相关的人来说,可能还是可以给出一些半专业的见解 首先例如 deepseek 只使用了很少的显卡来训练这一点也只是相对的,比如最新出的 grok 3 的20万卡,的确省了比较多资源,使用了2048张H800显卡,说实话这个也不是任何公司可以负担得起的,训练的总成本估计是560万美元,H800的价格预计在20万-40万元,后期因为禁售等可能涨价至后者,即使以20万一张的价格,光显卡的价格就需要4亿的资金,这不是随便一个公司就负担得起的,也得亏之前幻方就是靠机器学习做量化交易,原先就有显卡储备,并且盈利能力很强。 第二点是对于现在市面上乱七八糟的 deepseek 服务,因为实在太火了,导致 deepseek 的官方服务一直处于限流状态,然后就出现了各种说上线了 deepseek 大模型,以真实情况来讲除了少数几家本身就在做这个的,浑水摸鱼的存在比较多,例如用了 deepseek 的 api 接口的,或者是类似于我后面要讲的,只是部署了个蒸馏模型,因为全尺寸模型,再不用什么垃圾佬的省显卡黑科技的情况,671B的大小我通过 grok-3 的推理计算大概也需要 2000张H800显卡才能支持约100qps,这个可能也不是随便一家公司能负担得起的,并且还有这个带来的巨大能耗费用,只是把它跑起来因为模型文件有接近700G,那么至少需要 80G 现存的 H800 显卡 9张,为了推理效率更高,那要求就更高了。 而对于我们本地想使用这个模型的话,普通情况下也只能使用小尺寸的蒸馏后模型,这个效果肯定是跟deepseek官方和比如腾讯等大厂提供的全尺寸是没法比的,所以很多网上的视频和文章说教你本地部署 deepseek-r1 的绝大多数是这种蒸馏模型,因为普通人哪怕是最新的5090显卡也只有32G的显存,不可能载入全尺寸模型,按多卡算得 22 张 5090,我相信普通人很难搞到这么多显卡,并且常规的 U 有PCIE通道数量上线,单机不太可能能承载22张 5090。 而全尺寸的671B的模型跟常规家用电脑能跑起来的例如 7B 跟 14B 的差异类似于视频,480P的视频跟4K的视频,虽然都能看,但是效果来说还是差挺多,所以有些同学觉得官方的 deepseek 经常繁忙,而听信网上一堆文章说能直接在自己电脑上跑 deepseek-r1 的,可能还是以忽悠为主。本地自己的家用电脑部署主要是能够方便的使用例如 deepseek-r1 的小参数量蒸馏版本,来提供一些非高标准的要求,以及一些学习目的,比如我们可以在本地部署一个 7b 或者 14b 版本的模型,然后再基于这个模型加上 RAG 功能,这样就能把我们平常躺在那的笔记跟知识库变成一个能够更高效的提供知识检索的高级知识库。 这里我们使用LM Studio 在Windows 环境下部署一个 7b的蒸馏模型,比如 lmstudio-community/DeepSeek-R1-Distill-Qwen-7B-GGUF/DeepSeek-R1-Distill-Qwen-7B-Q4_K_M.gguf 这个模型, 首先这个模型是基于 Qwen2.5-Math-7B 通过监督微调(SFT)从 DeepSeek-R1 生成的数据集上训练得到的。 所以基础模型来说它是有目的性的,主要为数学方向做的训练,在此基础上会学习到 deepseek-r1 的推理能力 此前我们写过使用 ollama 来在本地跑谷歌开源的 Gemma 模型,因为 deepseek-r1 的这些蒸馏模型在ollama的支持没那么快,所以我是先用lm studio来跑了Lm Studio 相比 ollama 来说是带有了 UI 界面的一个大模型运行软件,这样就更方便的可以开箱即用 从官网下载完安装完对应平台的 LM Studio 以后差不多是这样子, 我以Win10系统为例 默认还没有任何模型,我们可以点击左侧的搜索样图标 在这里可以进行模型的搜索,但是由于众所周知的原因,这个搜索模型的并不像ollama一样能够很顺畅地下载,一般国内网络是打不开的,或者说只有模型列表,但是无法打开详情页面,我们需要一些小操作 首先我们找到LM Studio的安装目录,例如我这样的 D:\Program Files\LM Studio 我的是自己修改过的,常规的方式可以在桌面 LM Studio 图标上右键,然后点击 “打开文件所在的位置” 就可以找到LM Studio 的安装目录了,然后继续打开安装目录中的 resources 文件夹,再打开其中的 app 目录,就可以看到一个 .webpack 文件夹,建议用 vscode 或者sublime 打开目录,然后全局搜索 huggingface.co 替换成镜像地址 hf-mirror.com,然后重新打开 LM Studio 就可以搜索到上面提到的模型了,可能是由于这个镜像站没那么充足的资源,一般是白天的下载速度会快一点,晚上一般速度不太行,有条件的可以上全局代理,不过幸好 LM Studio 是支持断点续传的,只是它的下载很容易超时后自动异常,并不会自动重试,,这是个比较头疼的地方。 下载完成后就可以在顶部选择模型 这里会有一些配置,比如显卡的超载情况,是否将模型完整载入内容,上下文长度,显卡我这边是笔记本上的 3060,只有6g的显存,所以用不了很大的模型,内存倒可以载入,但是除非只用cpu来运行,否则内存载入只是将从硬盘载入显存的速度加快了,因为普通的PC并不像Mac使用的是统一内存,当然目前好像有清华的团队已经搞出来使用kTransformer通过一张4090显卡加384G内存运行全尺寸的 deepseek-r1 ,这类暂时不考虑在内,如果只是一些简单的使用场景,可以把上下文的大小调小一些,这样可以加快生成速度 问一个比较简单的问题,也是我比较讨厌的问题9.11跟9.08 哪个大 果然是以数学模型蒸馏出来的,其实很多用来话题讨论度比较高的用来比较大模型能力水平的都是比较没意义的,因为这种问题不依赖于大模型,甚至都不用搜索就能知道,会让这么大成本训练出来的大模型显得更加弱智,本身就不是为了这个而生的,唠叨的比较多,后面可以继续讲
]]>
LLM
@@ -5805,23 +5837,37 @@ user3:
- 体验最新的gemini cli工具来实现一个简单的网页小程序
- /2025/06/29/%E4%BD%93%E9%AA%8C%E6%9C%80%E6%96%B0%E7%9A%84gemini-cli%E5%B7%A5%E5%85%B7%E6%9D%A5%E5%AE%9E%E7%8E%B0%E4%B8%80%E4%B8%AA%E7%AE%80%E5%8D%95%E7%9A%84%E7%BD%91%E9%A1%B5%E5%B0%8F%E7%A8%8B%E5%BA%8F/
- 谷歌新出了个对标claude code的命令行工具,叫gemini-cli,还是开源的,所以赶紧来体验下,就是前提是有科学上网,要去google的ai studio申请gemini的api key 然后在有科学网络的情况下用node 18及以上的版本安装
-npm install -g @google/gemini-cli
-然后在环境变量里设置gemini key
-export GEMINI_API_KEY="xxxxxxxx"
-接着运行gemini就可以啦 这里我是刚好有个需求,因为娃最近有点不太爱喝奶,经常剩下,想要记录统计下,看看每天的总的奶量 那我就建了个空的目录,并在这个目录下运行了 gemini ,然后输入了我的需求, 原始的诉求是这样我想要写个网页版小程序,可以按时间记录宝宝每次的奶量,包括泡了多少,剩了多少,能按天统计每天的奶量,以及查看趋势 然后gemini就开始运行
-✦ 好的,这个需求很明确,我们可以创建一个名为“奶量备忘” (Milk Memo) 的网页应用来实现它。 这是一个很好的项目,完全可以通过前端技术实现,将数据存储在浏览器中,无需复杂的后端。 这是我的计划: 应用名称:奶量备忘 (Milk Memo) 核心功能: 1. 记录喂奶量:一个简洁的表单,用于输入“冲泡量”和“剩余量”。提交后,应用会自动计算“实际饮用量”并记录当前时间。 2. 每日记录列表:清晰地展示当天的所有喂奶记录,包括时间、冲泡量、剩余量和实际饮用量。 3. 今日统计:在页面顶部或底部显眼地展示当天累计的总饮用量。 4. 历史趋势:一个图表页面,用柱状图或折线图展示最近7天或30天的每日总饮用量,帮助您直观地了解宝宝的饮奶趋势。 技术选型: * 前端框架: React - 用于构建用户界面,组件化的思想很适合这个应用。 * 样式与布局: Bootstrap - 一个流行的CSS框架,可以快速构建出简洁、美观且移动端优先的响应式布局。 * 图表: Chart.js - 一个强大的图表库,用于实现“历史趋势”功能。 * 数据存储: 浏览器本地存储 (Local Storage) - 无需后端和数据库,所有数据将安全地存储在您自己的浏览器里,简单快捷。 用户体验与设计: 我将采用现代化、简洁的设计风格,确保在手机浏览器上也能获得良好的操作体验。界面会以卡片的形式展示数据,按钮和输入框都会清晰易用。 这个计划听起来如何?如果同意,我将开始着手创建这个应用。
-然后我输入了同意他就开始帮我实现了 它会请求我的同意来运行比如npm命令,安装依赖等等 然后就自己运行了npm start 来演示运行这个应用 不算非常有设计感,但是基本的功能是已经实现了 接着我又让它帮我增加了时间选项,以及趋势图查看功能, 接着它就又生成了一版
-✦ 功能已经更新完毕。 现在应用具备了以下功能: * 自定义时间:您可以在添加记录时,精确选择到分钟。 * 视图切换:通过顶部的按钮,可以在“记录列表”和“历史趋势”图表之间自由切换。 * 趋势图表:在“历史趋势”视图中,您可以看到一个折线图,展示了每天的总饮奶量。 * 数据排序:所有记录现在会按时间从近到远排序,方便查看最近的条目。
- 感觉完成了所需的95%的功能,除了在设计上还能再美观些,以及增加一些服务端存储数据的功能,基本是个比较完善的,我自己都完全没介入,的确ai的发展真的一日千里,另外我还是比较看好谷歌的,没有什么大新闻,就一直努力追赶,现在基本已经是第一梯队了,很好很强大
+ 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, opsimport towheeimport osfrom pymilvus import MilvusClientimport json 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) 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' ) 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
]]>
- LLM
+ 算法
- LLM
+ 算法
+
+
+
+ 使用rclone来当临时图床
+ /2025/05/11/%E4%BD%BF%E7%94%A8rclone%E6%9D%A5%E5%BD%93%E4%B8%B4%E6%97%B6%E5%9B%BE%E5%BA%8A/
+ 鉴于用upic配置r2存储的图床失败后,目前的方式退化了一下,直接使用rclone作为临时替代 但是也才了个小坑,因为rclone在做copy的时候是不管bucket是否存在的,直接会调用 CreateBucket 命令 同时我们的accessKey一般是只有对bucket内部文件的读写权限,所以会导致一个比较尴尬的问题 直接用copy就是报
+2025/05/11 21:16:04 NOTICE: Failed to copy: failed to prepare upload: operation error S3: CreateBucket, https response error StatusCode: 403, RequestID: , HostID: , api error AccessDenied: Access Denied
+这个403的错误,结果谷歌搜索了下 可以通过这个选项进行屏蔽
+
+这样我就能用
+./rclone --s3-no-check-bucket copy ~/Downloads/r2/test_for_rclone.png r2_new:blog
+来上传了,只是这里相比uPic麻烦了两步,第一截图后需要对文件进行命名,不然就是qq截图或者微信截图的文件名, 第二上传后需要自己拼图片的链接,当然希望是能够使用uPic的,但目前的问题是uPic似乎对于s3协议的支持不那么好 噢,对了,我用的是直接基于开源代码的自己打包版本,个人感觉还有点不太习惯从免费用成付费软件,主要是它是开源的 打算空了再折腾打包试试,或者自己调试下,实在不行就付费了
+]]>
+
+ 体验
+
+
+ 恶意盗刷
@@ -5843,31 +5889,45 @@ user3:
- 使用milvus和towhee实现简陋版的以图搜图
- /2024/08/04/%E4%BD%BF%E7%94%A8milvus%E5%92%8Ctowhee%E5%AE%9E%E7%8E%B0%E7%AE%80%E9%99%8B%E7%89%88%E7%9A%84%E4%BB%A5%E5%9B%BE%E6%90%9C%E5%9B%BE/
- 上次我们尝试用 towhee 跟 milvus 实现了图片的向量化,那么顺势我们就能在这个基础上实现以图搜图 首先我们找一些图片, 然后我们先把他们都向量化,存储在milvus里
-from towhee import AutoPipes, AutoConfig, opsimport towheeimport osfrom pymilvus import MilvusClientimport json 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) 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' ) 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
+ 关于arthas的一个比较有用的使用方式
+ /2025/04/13/%E5%85%B3%E4%BA%8Earthas%E7%9A%84%E4%B8%80%E4%B8%AA%E6%AF%94%E8%BE%83%E6%9C%89%E7%94%A8%E7%9A%84%E4%BD%BF%E7%94%A8%E6%96%B9%E5%BC%8F/
+ arthas是阿里开源的一个非常好用的java诊断工具,提供了很多很好用的命令,这里讲一个最近使用到的 就是将arthas挂载上我们的springboot应用,然后调用其中的方法,这样能够在如果没加日志已经看不到函数返回时更方便的排查问题 首先举个例子,我们有个Controller 它的一个query方法是这样的
+@RequestMapping(value = "/query", method = RequestMethod.GET) @ResponseBody public String query () { return demoService.queryName("1" ); }
+而在这个demoService中它的实现是这样
+public String queryName (String no) { if ("1" .equals(no)) { return "no1" ; } else { return "no2" ; } }
+假如现在Controller这的这个方法有点问题,那么我想确认下是不是demoService这个方法的实现有问题,或者说确定下它的返回值是否符合预期 那么我们就可以在应用启动后,运行arthas,找到这个应用的进程,进行挂载 然后执行
+vmtool --action getInstances --className com.nicksxs.spbdemo.service.DemoServiceImpl --express 'instances[0].queryName("1")'
+先介绍下这个vmtool命令 主要来说 vmtool 可以利用JVMTI接口,实现查询内存对象,强制 GC 等功能。 例如官方示例里的,我想把内存里的string对象捞一些出来看看存的是啥
+vmtool --action getInstances --className java.lang.String --limit 10
+就可以这样,首先这个action就是指定要做的操作,支持的action 还包括
+
+等,那么对于 getInstances 就是从内存里捞出这个类的对象,然后是后面一部分--express 就是执行表达式,这里的表达式,instances[0].queryName("1") 其中 instances 就是前面从内存中获取的对象数组,因为这些是对象的非静态方法,那就需要从其中取一个来执行我们的方法 另外假如我们的场景里需要对比如返回结果做个json序列化 我们可以这样
+vmtool --action getInstances --className com.nicksxs.spbdemo.service.DemoServiceImpl --express '@com.alibaba.fastjson.JSON@toJSONString(instances[0].queryName("1"))'
+这里为什么类开头跟方法开头要用 @, 是因为对于类和静态方法的调用规则是这样,还有如果代码比较多,有可能默认的类加载器中没有加载这个JSON类,那么就需要在参数中加上指定的classloader, 可以用sc命令来查找我们的目标类的类加载器,一般来说如果目标类是我们核心业务的,大概率也会有JSON这个类
+sc -d com.nicksxs.spbdemo.service.DemoServiceImpl
+然后在上面命令中加上sc结果中的 classLoaderHash 的值,
+vmtool --action getInstances -c 18b4aac2 --className com.nicksxs.spbdemo.service.DemoServiceImpl --express '@com.alibaba.fastjson.JSON@toJSONString(instances[0].queryName("1"))'
+这样就能正常执行了
]]>
- 算法
+ java
- 算法
+ java
- 使用 LM Studio 在本地部署 Deepseek-R1 的蒸馏版大模型
- /2025/02/23/%E4%BD%BF%E7%94%A8-LM-Studio-%E5%9C%A8%E6%9C%AC%E5%9C%B0%E9%83%A8%E7%BD%B2-Deepseek-%E5%A4%A7%E6%A8%A1%E5%9E%8B/
- deepseek-v3 和 deepseek-r1 是由深度求索公司推出的大语言模型,并且在生成效果上基本已经到达国内甚至全球的顶级水准,并且是免费使用,并且是开源的 连着用三个并且不是我文字能力差,而是真的佩服deepseek这家公司和创始人梁文峰,作为校友感觉是真的忝列门墙 早先了解的幻方量化就是一家小众低调但又多金的公司,奈何都是招的全栈工程师,可能不是太适合,当前对于deepseek来说那就更遥远了,从公开的不一定可靠的信息来看,目前在deepseek的都是像清北的博士这样的顶级人才,而且在人工智能这块,只是浅显地涉及过一些机器学习相关的工作,对于最前沿的大模型这块着实还有比较大的差距,当然也是在学习中。 虽然不是完全专业,但是对比大部分跟这个领域完全不相关的人来说,可能还是可以给出一些半专业的见解 首先例如 deepseek 只使用了很少的显卡来训练这一点也只是相对的,比如最新出的 grok 3 的20万卡,的确省了比较多资源,使用了2048张H800显卡,说实话这个也不是任何公司可以负担得起的,训练的总成本估计是560万美元,H800的价格预计在20万-40万元,后期因为禁售等可能涨价至后者,即使以20万一张的价格,光显卡的价格就需要4亿的资金,这不是随便一个公司就负担得起的,也得亏之前幻方就是靠机器学习做量化交易,原先就有显卡储备,并且盈利能力很强。 第二点是对于现在市面上乱七八糟的 deepseek 服务,因为实在太火了,导致 deepseek 的官方服务一直处于限流状态,然后就出现了各种说上线了 deepseek 大模型,以真实情况来讲除了少数几家本身就在做这个的,浑水摸鱼的存在比较多,例如用了 deepseek 的 api 接口的,或者是类似于我后面要讲的,只是部署了个蒸馏模型,因为全尺寸模型,再不用什么垃圾佬的省显卡黑科技的情况,671B的大小我通过 grok-3 的推理计算大概也需要 2000张H800显卡才能支持约100qps,这个可能也不是随便一家公司能负担得起的,并且还有这个带来的巨大能耗费用,只是把它跑起来因为模型文件有接近700G,那么至少需要 80G 现存的 H800 显卡 9张,为了推理效率更高,那要求就更高了。 而对于我们本地想使用这个模型的话,普通情况下也只能使用小尺寸的蒸馏后模型,这个效果肯定是跟deepseek官方和比如腾讯等大厂提供的全尺寸是没法比的,所以很多网上的视频和文章说教你本地部署 deepseek-r1 的绝大多数是这种蒸馏模型,因为普通人哪怕是最新的5090显卡也只有32G的显存,不可能载入全尺寸模型,按多卡算得 22 张 5090,我相信普通人很难搞到这么多显卡,并且常规的 U 有PCIE通道数量上线,单机不太可能能承载22张 5090。 而全尺寸的671B的模型跟常规家用电脑能跑起来的例如 7B 跟 14B 的差异类似于视频,480P的视频跟4K的视频,虽然都能看,但是效果来说还是差挺多,所以有些同学觉得官方的 deepseek 经常繁忙,而听信网上一堆文章说能直接在自己电脑上跑 deepseek-r1 的,可能还是以忽悠为主。本地自己的家用电脑部署主要是能够方便的使用例如 deepseek-r1 的小参数量蒸馏版本,来提供一些非高标准的要求,以及一些学习目的,比如我们可以在本地部署一个 7b 或者 14b 版本的模型,然后再基于这个模型加上 RAG 功能,这样就能把我们平常躺在那的笔记跟知识库变成一个能够更高效的提供知识检索的高级知识库。 这里我们使用LM Studio 在Windows 环境下部署一个 7b的蒸馏模型,比如 lmstudio-community/DeepSeek-R1-Distill-Qwen-7B-GGUF/DeepSeek-R1-Distill-Qwen-7B-Q4_K_M.gguf 这个模型, 首先这个模型是基于 Qwen2.5-Math-7B 通过监督微调(SFT)从 DeepSeek-R1 生成的数据集上训练得到的。 所以基础模型来说它是有目的性的,主要为数学方向做的训练,在此基础上会学习到 deepseek-r1 的推理能力 此前我们写过使用 ollama 来在本地跑谷歌开源的 Gemma 模型,因为 deepseek-r1 的这些蒸馏模型在ollama的支持没那么快,所以我是先用lm studio来跑了Lm Studio 相比 ollama 来说是带有了 UI 界面的一个大模型运行软件,这样就更方便的可以开箱即用 从官网下载完安装完对应平台的 LM Studio 以后差不多是这样子, 我以Win10系统为例 默认还没有任何模型,我们可以点击左侧的搜索样图标 在这里可以进行模型的搜索,但是由于众所周知的原因,这个搜索模型的并不像ollama一样能够很顺畅地下载,一般国内网络是打不开的,或者说只有模型列表,但是无法打开详情页面,我们需要一些小操作 首先我们找到LM Studio的安装目录,例如我这样的 D:\Program Files\LM Studio 我的是自己修改过的,常规的方式可以在桌面 LM Studio 图标上右键,然后点击 “打开文件所在的位置” 就可以找到LM Studio 的安装目录了,然后继续打开安装目录中的 resources 文件夹,再打开其中的 app 目录,就可以看到一个 .webpack 文件夹,建议用 vscode 或者sublime 打开目录,然后全局搜索 huggingface.co 替换成镜像地址 hf-mirror.com,然后重新打开 LM Studio 就可以搜索到上面提到的模型了,可能是由于这个镜像站没那么充足的资源,一般是白天的下载速度会快一点,晚上一般速度不太行,有条件的可以上全局代理,不过幸好 LM Studio 是支持断点续传的,只是它的下载很容易超时后自动异常,并不会自动重试,,这是个比较头疼的地方。 下载完成后就可以在顶部选择模型 这里会有一些配置,比如显卡的超载情况,是否将模型完整载入内容,上下文长度,显卡我这边是笔记本上的 3060,只有6g的显存,所以用不了很大的模型,内存倒可以载入,但是除非只用cpu来运行,否则内存载入只是将从硬盘载入显存的速度加快了,因为普通的PC并不像Mac使用的是统一内存,当然目前好像有清华的团队已经搞出来使用kTransformer通过一张4090显卡加384G内存运行全尺寸的 deepseek-r1 ,这类暂时不考虑在内,如果只是一些简单的使用场景,可以把上下文的大小调小一些,这样可以加快生成速度 问一个比较简单的问题,也是我比较讨厌的问题9.11跟9.08 哪个大 果然是以数学模型蒸馏出来的,其实很多用来话题讨论度比较高的用来比较大模型能力水平的都是比较没意义的,因为这种问题不依赖于大模型,甚至都不用搜索就能知道,会让这么大成本训练出来的大模型显得更加弱智,本身就不是为了这个而生的,唠叨的比较多,后面可以继续讲
+ 关于Termux中的shell命令的历史记录问题
+ /2025/04/06/%E5%85%B3%E4%BA%8ETermux%E4%B8%AD%E7%9A%84shell%E5%91%BD%E4%BB%A4%E7%9A%84%E5%8E%86%E5%8F%B2%E8%AE%B0%E5%BD%95%E9%97%AE%E9%A2%98/
+ 之前发现Termux在安卓手机中是个比较厉害的神器,就相当于一个随身携带的小型服务器,伴随着现在手机性能的逐渐强大,有些手机可能已经比很多个人用的云服务器还要强大很多,只是有着散热和电量的限制,但是充当一下临时的使用还是很不错的 记得之前提过,在用rustdesk的时候,如果被连端是Mac,并且被连端进入锁屏了,可能需要ssh连上去执行个唤醒命令,正好最近有需求要偶尔从手机连上家里的Mac,从而引发的一个问题是比如我经常用Termux来执行ssh命令,那么理论上我直接从shell的history就可以找到执行过的ssh命令,也就不用经常记对应的ip啥的,但是试用了下发现不行,一开始搜了下以为是没有设置历史存储的大小,可能默认是0,那么需要通过配置来设置
+HISTSIZE=1000 HISTFILESIZE=2000
+比如设置1000个命令大小,只是在Termux里不是这个原因,而是在于shell去保存历史的时机,安卓手机在退出应用或者切换应用程序都没法记录这个执行历史,而是需要在Termux中手动地用exit命令退出,才会记录下history历史, 当然也有人在Termux的issue上提到了,能不能捕捉安卓的事件来执行这个exit,否则这个exit有点难记得,相对来说也不方便了很多,容易遗忘,只是这个issue 也被关掉了,看看后面会不会优化吧
]]>
- LLM
+ 小技巧
- LLM
+ 小技巧
@@ -5892,14 +5952,27 @@ user3:
健康码
+
+ 分享一次折腾老旧笔记本的体验-续篇
+ /2023/02/12/%E5%88%86%E4%BA%AB%E4%B8%80%E6%AC%A1%E6%8A%98%E8%85%BE%E8%80%81%E6%97%A7%E7%AC%94%E8%AE%B0%E6%9C%AC%E7%9A%84%E4%BD%93%E9%AA%8C-%E7%BB%AD%E7%AF%87/
+ 接着上一篇的折腾记,因为这周又尝试了一些新的措施和方法,想继续记录分享下,上周的整体情况大概是 Ubuntu 系统能进去了,但是 Windows 进不去,PE 也进不去,Windows 启动盘也进不去,因为我的机器加过一个 msata 的固态,Windows 是装在 msata 固态硬盘里的,Ubuntu 是装在机械硬盘里的,所以有了一种猜测就是可能这个固态硬盘有点问题,还有就是还是怀疑内存的问题,正好家里还有个msata 的固态硬盘,是以前想给LD 的旧笔记本换上的,因为买回来放在那没有及时装,后面会又找不到,直到很后面才找到,LD 也不怎么用那个笔记本了,所以就一直放着了,这次我就想拿来换上。 周末回家我就开始尝试了,换上了新的固态硬盘后,插上 Windows 启动 U 盘,这次一开始看起来有点顺利,在 BIOS 选择 U 盘启动,进入了 Windows 安装界面,但是装到一半,后面重启了之后就一直说硬盘有问题,让重启,但是重启并没有解决问题,变成了一直无效地重复重启,再想进 U 盘启动,就又进不去了,这时候怎么说呢,感觉硬盘不是没有问题,但是呢,问题应该不完全出在这,所以按着总体的逻辑来讲,主板带着cpu 跟显卡,都换掉了,硬盘也换掉了,剩下的就是内存了,可是内存我也尝试过把后面加的那条金士顿拔掉,可还是一样,也尝试过用橡皮擦金手指,这里感觉也很奇怪了,找了一圈了都感觉没啥明确的原因,比如其实我的猜测,主板电池的问题,一些电阻坏掉了,但是主板是换过的,那如果内存有问题,照理我用原装的那条应该会没问题,也有一种非常小的可能,就是两条内存都坏了,或者说这也是一种不太可能的可能,所以最后的办法就打算试试把两条内存都换掉,不过现在网上都找不到这个内存的确切信息了,只能根据大致的型号去买来试试,就怕买来的还是坏的,其实也怕是这个买来的主板因为也是别的拆机下来的,不一定保证完全没问题,要是有类似的问题或者也有别的问题导致开不起来就很尴尬,也没有很多专业的仪器可以排查原因,比如主板有没有什么短路的,对了还有一个就是电源问题,但是电源的问题也就可能是从充电器插的口子到主板的连线,因为 LD 的电源跟我的口子一样,也试过,但是结果还是一样,顺着正常逻辑排查,目前也没有剩下很明确的方向了,只能再尝试下看看。
+]]>
+
+ Windows
+ 小技巧
+
+
+ Windows
+
+
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!
]]>
- 生活
读后感
+ 生活
白岩松
幸福了吗
@@ -5912,9 +5985,9 @@ user3:
- 分享一次折腾老旧笔记本的体验-续篇
- /2023/02/12/%E5%88%86%E4%BA%AB%E4%B8%80%E6%AC%A1%E6%8A%98%E8%85%BE%E8%80%81%E6%97%A7%E7%AC%94%E8%AE%B0%E6%9C%AC%E7%9A%84%E4%BD%93%E9%AA%8C-%E7%BB%AD%E7%AF%87/
- 接着上一篇的折腾记,因为这周又尝试了一些新的措施和方法,想继续记录分享下,上周的整体情况大概是 Ubuntu 系统能进去了,但是 Windows 进不去,PE 也进不去,Windows 启动盘也进不去,因为我的机器加过一个 msata 的固态,Windows 是装在 msata 固态硬盘里的,Ubuntu 是装在机械硬盘里的,所以有了一种猜测就是可能这个固态硬盘有点问题,还有就是还是怀疑内存的问题,正好家里还有个msata 的固态硬盘,是以前想给LD 的旧笔记本换上的,因为买回来放在那没有及时装,后面会又找不到,直到很后面才找到,LD 也不怎么用那个笔记本了,所以就一直放着了,这次我就想拿来换上。 周末回家我就开始尝试了,换上了新的固态硬盘后,插上 Windows 启动 U 盘,这次一开始看起来有点顺利,在 BIOS 选择 U 盘启动,进入了 Windows 安装界面,但是装到一半,后面重启了之后就一直说硬盘有问题,让重启,但是重启并没有解决问题,变成了一直无效地重复重启,再想进 U 盘启动,就又进不去了,这时候怎么说呢,感觉硬盘不是没有问题,但是呢,问题应该不完全出在这,所以按着总体的逻辑来讲,主板带着cpu 跟显卡,都换掉了,硬盘也换掉了,剩下的就是内存了,可是内存我也尝试过把后面加的那条金士顿拔掉,可还是一样,也尝试过用橡皮擦金手指,这里感觉也很奇怪了,找了一圈了都感觉没啥明确的原因,比如其实我的猜测,主板电池的问题,一些电阻坏掉了,但是主板是换过的,那如果内存有问题,照理我用原装的那条应该会没问题,也有一种非常小的可能,就是两条内存都坏了,或者说这也是一种不太可能的可能,所以最后的办法就打算试试把两条内存都换掉,不过现在网上都找不到这个内存的确切信息了,只能根据大致的型号去买来试试,就怕买来的还是坏的,其实也怕是这个买来的主板因为也是别的拆机下来的,不一定保证完全没问题,要是有类似的问题或者也有别的问题导致开不起来就很尴尬,也没有很多专业的仪器可以排查原因,比如主板有没有什么短路的,对了还有一个就是电源问题,但是电源的问题也就可能是从充电器插的口子到主板的连线,因为 LD 的电源跟我的口子一样,也试过,但是结果还是一样,顺着正常逻辑排查,目前也没有剩下很明确的方向了,只能再尝试下看看。
+ 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。
]]>
Windows
@@ -5938,9 +6011,13 @@ 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。
+ 分享一次比较诡异的 Windows 下 U盘无法退出的经历
+ /2023/01/29/%E5%88%86%E4%BA%AB%E4%B8%80%E6%AC%A1%E6%AF%94%E8%BE%83%E8%AF%A1%E5%BC%82%E7%9A%84-Windows-%E4%B8%8B-U%E7%9B%98%E6%97%A0%E6%B3%95%E9%80%80%E5%87%BA%E7%9A%84%E7%BB%8F%E5%8E%86/
+ 作为一个 Windows 的老用户,并且也算是 Windows 系统的半个粉丝,但是秉承一贯的优缺点都应该说的原则,Windows 系统有一点缺点是真的挺难受,相信 Windows 用过比较久的都会经历过,就是 U盘无法退出的问题,在比较远古时代,这个问题似乎能采取的措施不多,关机再拔 U盘的方式是一种比较保险的方式,其他貌似有 360这种可以解除占用的,但是需要安装 360 软件,对于目前的使用环境来说有点得不偿失,也是比较流氓的一类软件了,目前在 Windows 环境我主要就安装了个火绒,或者就用 Windows 自带的 defender。
+第一种 最近这次经历也是有火绒的一定责任,在我尝试推出 U盘的时候提示了我被另一个大流氓软件,XlibabaProtect.exe 占用了,这个流氓软件真的是充分展示了某里的技术实力,试过 N 多种办法都关不掉也删不掉,尝试了很多种办法也没办法删除,但是后面换了种思路,一般这种情况肯定是有进程在占用 U盘里的内容,最新版本的 Powertoys 会在文件的右键菜单里添加一个叫 File Locksmith 的功能,可以用于检查正在使用哪些文件以及由哪些进程使用,但是可能是我的使用姿势不对,没有仔细看文档,它里面有个”以管理员身份重启”,可能会有用。 这算是第一种方式,
+第二种 第二种方式是 Windows 任务管理器中性能 tab 下的”打开资源监视器”, ,假如我的 U 盘的盘符是F: 就可以搜索到占用这个盘符下文件的进程,这里千万小心‼️‼️,不可轻易杀掉这些进程,有些系统进程如果轻易杀掉会导致蓝屏等问题,不可轻易尝试,除非能确认这些进程的作用。 对于前两种方式对我来说都无效,
+第三种 所以尝试了第三种, 就是磁盘脱机的方式,在”计算机”右键管理,点击”磁盘管理”,可以找到 U 盘盘符右键,点击”脱机”,然后再”推出”,这个对我来说也不行
+第四种 这种是唯一对我有效的,在开始菜单搜索”event”,可以搜到”事件查看器”, ,这个可以看到当前最近 Windows 发生的事件,打开这个后就点击U盘推出,因为推不出来也是一种错误事件,点击下刷新就能在这看到具体是因为什么推出不了,具体的进程信息 最后发现是英特尔的驱动管理程序的一个进程,关掉就退出了,虽然前面说的某里的进程是流氓,但这边是真的冤枉它了
]]>
Windows
@@ -5988,38 +6065,41 @@ user3:
- 分享一次比较诡异的 Windows 下 U盘无法退出的经历
- /2023/01/29/%E5%88%86%E4%BA%AB%E4%B8%80%E6%AC%A1%E6%AF%94%E8%BE%83%E8%AF%A1%E5%BC%82%E7%9A%84-Windows-%E4%B8%8B-U%E7%9B%98%E6%97%A0%E6%B3%95%E9%80%80%E5%87%BA%E7%9A%84%E7%BB%8F%E5%8E%86/
- 作为一个 Windows 的老用户,并且也算是 Windows 系统的半个粉丝,但是秉承一贯的优缺点都应该说的原则,Windows 系统有一点缺点是真的挺难受,相信 Windows 用过比较久的都会经历过,就是 U盘无法退出的问题,在比较远古时代,这个问题似乎能采取的措施不多,关机再拔 U盘的方式是一种比较保险的方式,其他貌似有 360这种可以解除占用的,但是需要安装 360 软件,对于目前的使用环境来说有点得不偿失,也是比较流氓的一类软件了,目前在 Windows 环境我主要就安装了个火绒,或者就用 Windows 自带的 defender。
-第一种 最近这次经历也是有火绒的一定责任,在我尝试推出 U盘的时候提示了我被另一个大流氓软件,XlibabaProtect.exe 占用了,这个流氓软件真的是充分展示了某里的技术实力,试过 N 多种办法都关不掉也删不掉,尝试了很多种办法也没办法删除,但是后面换了种思路,一般这种情况肯定是有进程在占用 U盘里的内容,最新版本的 Powertoys 会在文件的右键菜单里添加一个叫 File Locksmith 的功能,可以用于检查正在使用哪些文件以及由哪些进程使用,但是可能是我的使用姿势不对,没有仔细看文档,它里面有个”以管理员身份重启”,可能会有用。 这算是第一种方式,
-第二种 第二种方式是 Windows 任务管理器中性能 tab 下的”打开资源监视器”, ,假如我的 U 盘的盘符是F: 就可以搜索到占用这个盘符下文件的进程,这里千万小心‼️‼️,不可轻易杀掉这些进程,有些系统进程如果轻易杀掉会导致蓝屏等问题,不可轻易尝试,除非能确认这些进程的作用。 对于前两种方式对我来说都无效,
-第三种 所以尝试了第三种, 就是磁盘脱机的方式,在”计算机”右键管理,点击”磁盘管理”,可以找到 U 盘盘符右键,点击”脱机”,然后再”推出”,这个对我来说也不行
-第四种 这种是唯一对我有效的,在开始菜单搜索”event”,可以搜到”事件查看器”, ,这个可以看到当前最近 Windows 发生的事件,打开这个后就点击U盘推出,因为推不出来也是一种错误事件,点击下刷新就能在这看到具体是因为什么推出不了,具体的进程信息 最后发现是英特尔的驱动管理程序的一个进程,关掉就退出了,虽然前面说的某里的进程是流氓,但这边是真的冤枉它了
+ 千万别升级Chrome-您的扩展程序可能无法使用-教你怎么解决
+ /2025/07/20/%E5%8D%83%E4%B8%87%E5%88%AB%E5%8D%87%E7%BA%A7Chrome-%E6%82%A8%E7%9A%84%E6%89%A9%E5%B1%95%E7%A8%8B%E5%BA%8F%E5%8F%AF%E8%83%BD%E6%97%A0%E6%B3%95%E4%BD%BF%E7%94%A8/
+ 允许我小小地标题党一下,后半句是真实情况,最近重启电脑就自动升级了chrome,发现很多必须的扩展程序都不能用了,之前是可以通过设置开启,这次升级到 138.0.7204.158 版本就直接不允许开启了 这个很久之前chrome也做了提示,主要是扩展程序的manifest版本要从v2升级成v3,但是这个升级分两方面说,一方面是升级难度,可以参考这个核对文档 , 需要
+
+迁移到 Service Worker
+更新 API 调用
+替换屏蔽 Web 请求监听器
+提高扩展程序的安全性 这些项的修改,修改升级难度不小,如果使用了很多api的话是比较麻烦的 另一方面是很多扩展都不是自己做的开发,开发者都不一定在维护,但是很多都是非常深度使用的 这边有个例子就是Proxy SwitchyOmega 2 平时会用来做代理切换,原本的仓库已经没人维护了,并且chrome扩展商店也没有更新, 这里介绍两种方法, 第一种是找到这个应用所在目录 首先找到应用的id,然后需要到chrome的扩展目录 mac是在这里 ~/Library/Application Support/Google/Chrome/Default/Extensions windows是在 C:\Users\<用户名>\AppData\Local\Google\Chrome\User Data\Default\Extensions 找到以后就找上面扩展的id就是扩展的文件目录,把它拷出来以后,然后找到扩展目录里的 manifest.json 文件,把版本改成3 然后再通过扩展路径直接打开,比如这个扩展就是 chrome-extension://padekgcemlokbadohgkifijomclgjgif/options.html 这个选项页,为啥要这么打开是因为这个扩展的配置比较麻烦,就是现在可以搜到替代应用,但是要重配一遍也是挺头疼的 第二种是有点釜底抽薪的 可以在chrome的flag配置中修改 网上说的可能已经就删掉了,我这个版本的目前还可以用这个配置项来修改chrome://flags/#allow-legacy-mv2-extensions 可以通过开启这个来让manifest版本是2的扩展能够被启用,但是这个肯定也是个临时方案,大家该把配置导出备份的尽早备份,同时也尽早寻找替代的升级了manifest 3的扩展程序
+
]]>
- Windows
- 小技巧
+ 技巧
- Windows
+ 技巧
- 使用rclone来当临时图床
- /2025/05/11/%E4%BD%BF%E7%94%A8rclone%E6%9D%A5%E5%BD%93%E4%B8%B4%E6%97%B6%E5%9B%BE%E5%BA%8A/
- 鉴于用upic配置r2存储的图床失败后,目前的方式退化了一下,直接使用rclone作为临时替代 但是也才了个小坑,因为rclone在做copy的时候是不管bucket是否存在的,直接会调用 CreateBucket 命令 同时我们的accessKey一般是只有对bucket内部文件的读写权限,所以会导致一个比较尴尬的问题 直接用copy就是报
-2025/05/11 21:16:04 NOTICE: Failed to copy: failed to prepare upload: operation error S3: CreateBucket, https response error StatusCode: 403, RequestID: , HostID: , api error AccessDenied: Access Denied
-这个403的错误,结果谷歌搜索了下 可以通过这个选项进行屏蔽
-
-这样我就能用
-./rclone --s3-no-check-bucket copy ~/Downloads/r2/test_for_rclone.png r2_new:blog
-来上传了,只是这里相比uPic麻烦了两步,第一截图后需要对文件进行命名,不然就是qq截图或者微信截图的文件名, 第二上传后需要自己拼图片的链接,当然希望是能够使用uPic的,但目前的问题是uPic似乎对于s3协议的支持不那么好 噢,对了,我用的是直接基于开源代码的自己打包版本,个人感觉还有点不太习惯从免费用成付费软件,主要是它是开源的 打算空了再折腾打包试试,或者自己调试下,实在不行就付费了
+ 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/
+ 之前做了个简单的铺垫,作为大模型应用技术领域非常重要的一环,向量数据库我们在前面有做一些引导性的介绍,其中的索引技术, 而在众多向量数据库比较有代表性的 Milvus,这边我们来尝试安装 Milvus 初体验一下 因为只是初体验,不作为生产环境使用,所以就用最简单的方式,Docker单机部署的方式 首先需要需要有Docker环境 确认下 docker 命令可执行 然后拉取启动脚本
+wget https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh bash standalone_embed.sh start
+启动过程中需要注意一些问题,这也是有点想吐槽的,这个官方的启动脚本竟然都不是稳定成功的,最开始就是失败的,后面重新删除拉下来的镜像再启动就好了 并且官方提供的demo python版本的示例也是有问题的 因为主要是java的缘故,就用了java的sdk来尝试 只是我们可以先运行一个ui工具
+docker run -p 8000:3000 -e MILVUS_URL={milvus server IP}:19530 zilliz/attu:v2.4
+ 这里就能看到运行情况 然后我们运行个简单的代码
+package MilvusDemo;import com.google.gson.Gson;import com.google.gson.JsonObject;import com.google.gson.JsonParser;import io.milvus.v2.client.ConnectConfig;import io.milvus.v2.client.MilvusClientV2;import io.milvus.v2.common.DataType;import io.milvus.v2.common.IndexParam;import io.milvus.v2.service.collection.request.AddFieldReq;import io.milvus.v2.service.collection.request.CreateCollectionReq;import io.milvus.v2.service.index.request.CreateIndexReq;import io.milvus.v2.service.vector.request.InsertReq;import java.util.ArrayList;import java.util.List;public class Demo { public static void main (String[] args) { ConnectConfig connectConfig = ConnectConfig.builder() .uri("http://127.0.0.1:19530" ) .build(); MilvusClientV2 clientV2 = new MilvusClientV2 (connectConfig); CreateCollectionReq.CollectionSchema collectionSchema = clientV2.createSchema(); Integer dim = 5 ; collectionSchema.addField(AddFieldReq.builder().fieldName("my_id" ).dataType(DataType.Int64).isPrimaryKey(Boolean.TRUE).autoID(Boolean.FALSE).description("id" ).build()); collectionSchema.addField(AddFieldReq.builder().fieldName("my_vector" ).dataType(DataType.FloatVector).dimension(dim).build()); CreateCollectionReq req = CreateCollectionReq .builder() .collectionSchema(collectionSchema) .collectionName("quick_setup" ) .dimension(dim).build(); clientV2.createCollection(req); IndexParam indexParam = IndexParam.builder() .fieldName("my_id" ) .build(); IndexParam vertorIndexParam = IndexParam.builder() .fieldName("my_vector" ) .indexType(IndexParam.IndexType.AUTOINDEX) .metricType(IndexParam.MetricType.IP) .build(); List<IndexParam> indexParamList = new ArrayList <>(); indexParamList.add(indexParam); indexParamList.add(vertorIndexParam); clientV2.createIndex(CreateIndexReq.builder() .collectionName("quick_setup" ) .indexParams(indexParamList) .build()); JsonObject jsonObject = JsonParser.parseString("{\"my_id\": 0, \"my_vector\": [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], \"color\": \"pink_8682\"}" ).getAsJsonObject(); List<JsonObject> jsonData = new ArrayList <>(); jsonData.add(jsonObject); clientV2.insert(InsertReq.builder().collectionName("quick_setup" ) .data(jsonData).build()); clientV2.loadCollection(LoadCollectionReq.builder().collectionName("quick_setup" ).build()); SearchResp searchResp = clientV2.search(SearchReq.builder() .collectionName("quick_setup" ) .data(Collections.singletonList(new FloatVec (new float []{0.3580376395471989F , -0.6023495712049978F , 0.18414012509913835F , -0.26286205330961354F , 0.9029438446296592F }))) .topK(10 ) .outputFields(Collections.singletonList("*" )) .build() ); for (List<SearchResp.SearchResult> searchResult : searchResp.getSearchResults()) { System.out.println(searchResult); } } }
+我们用同样的向量进行搜索
+[SearchResp.SearchResult(entity={my_id=0 , $meta={"color" :"pink_8682" }, my_vector=[0.35803765 , -0.6023496 , 0.18414013 , -0.26286206 , 0.90294385 ]}, score=1.4093276 , id=0 )]
+得出的score就是很高的,只是做了下尝试,官方的示例代码是少得可怜,也不全,很难想象是目前比较热门的向量数据库
]]>
- 体验
+ 算法
- 恶意盗刷
+ 算法
@@ -6070,37 +6150,75 @@ user3:
- 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/
- 之前做了个简单的铺垫,作为大模型应用技术领域非常重要的一环,向量数据库我们在前面有做一些引导性的介绍,其中的索引技术, 而在众多向量数据库比较有代表性的 Milvus,这边我们来尝试安装 Milvus 初体验一下 因为只是初体验,不作为生产环境使用,所以就用最简单的方式,Docker单机部署的方式 首先需要需要有Docker环境 确认下 docker 命令可执行 然后拉取启动脚本
-wget https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh bash standalone_embed.sh start
-启动过程中需要注意一些问题,这也是有点想吐槽的,这个官方的启动脚本竟然都不是稳定成功的,最开始就是失败的,后面重新删除拉下来的镜像再启动就好了 并且官方提供的demo python版本的示例也是有问题的 因为主要是java的缘故,就用了java的sdk来尝试 只是我们可以先运行一个ui工具
-docker run -p 8000:3000 -e MILVUS_URL={milvus server IP}:19530 zilliz/attu:v2.4
- 这里就能看到运行情况 然后我们运行个简单的代码
-package MilvusDemo;import com.google.gson.Gson;import com.google.gson.JsonObject;import com.google.gson.JsonParser;import io.milvus.v2.client.ConnectConfig;import io.milvus.v2.client.MilvusClientV2;import io.milvus.v2.common.DataType;import io.milvus.v2.common.IndexParam;import io.milvus.v2.service.collection.request.AddFieldReq;import io.milvus.v2.service.collection.request.CreateCollectionReq;import io.milvus.v2.service.index.request.CreateIndexReq;import io.milvus.v2.service.vector.request.InsertReq;import java.util.ArrayList;import java.util.List;public class Demo { public static void main (String[] args) { ConnectConfig connectConfig = ConnectConfig.builder() .uri("http://127.0.0.1:19530" ) .build(); MilvusClientV2 clientV2 = new MilvusClientV2 (connectConfig); CreateCollectionReq.CollectionSchema collectionSchema = clientV2.createSchema(); Integer dim = 5 ; collectionSchema.addField(AddFieldReq.builder().fieldName("my_id" ).dataType(DataType.Int64).isPrimaryKey(Boolean.TRUE).autoID(Boolean.FALSE).description("id" ).build()); collectionSchema.addField(AddFieldReq.builder().fieldName("my_vector" ).dataType(DataType.FloatVector).dimension(dim).build()); CreateCollectionReq req = CreateCollectionReq .builder() .collectionSchema(collectionSchema) .collectionName("quick_setup" ) .dimension(dim).build(); clientV2.createCollection(req); IndexParam indexParam = IndexParam.builder() .fieldName("my_id" ) .build(); IndexParam vertorIndexParam = IndexParam.builder() .fieldName("my_vector" ) .indexType(IndexParam.IndexType.AUTOINDEX) .metricType(IndexParam.MetricType.IP) .build(); List<IndexParam> indexParamList = new ArrayList <>(); indexParamList.add(indexParam); indexParamList.add(vertorIndexParam); clientV2.createIndex(CreateIndexReq.builder() .collectionName("quick_setup" ) .indexParams(indexParamList) .build()); JsonObject jsonObject = JsonParser.parseString("{\"my_id\": 0, \"my_vector\": [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], \"color\": \"pink_8682\"}" ).getAsJsonObject(); List<JsonObject> jsonData = new ArrayList <>(); jsonData.add(jsonObject); clientV2.insert(InsertReq.builder().collectionName("quick_setup" ) .data(jsonData).build()); clientV2.loadCollection(LoadCollectionReq.builder().collectionName("quick_setup" ).build()); SearchResp searchResp = clientV2.search(SearchReq.builder() .collectionName("quick_setup" ) .data(Collections.singletonList(new FloatVec (new float []{0.3580376395471989F , -0.6023495712049978F , 0.18414012509913835F , -0.26286205330961354F , 0.9029438446296592F }))) .topK(10 ) .outputFields(Collections.singletonList("*" )) .build() ); for (List<SearchResp.SearchResult> searchResult : searchResp.getSearchResults()) { System.out.println(searchResult); } } }
-我们用同样的向量进行搜索
-[SearchResp.SearchResult(entity={my_id=0 , $meta={"color" :"pink_8682" }, my_vector=[0.35803765 , -0.6023496 , 0.18414013 , -0.26286206 , 0.90294385 ]}, score=1.4093276 , id=0 )]
-得出的score就是很高的,只是做了下尝试,官方的示例代码是少得可怜,也不全,很难想象是目前比较热门的向量数据库
-]]>
-
- 算法
-
-
- 算法
-
-
-
- 关于Termux中的shell命令的历史记录问题
- /2025/04/06/%E5%85%B3%E4%BA%8ETermux%E4%B8%AD%E7%9A%84shell%E5%91%BD%E4%BB%A4%E7%9A%84%E5%8E%86%E5%8F%B2%E8%AE%B0%E5%BD%95%E9%97%AE%E9%A2%98/
- 之前发现Termux在安卓手机中是个比较厉害的神器,就相当于一个随身携带的小型服务器,伴随着现在手机性能的逐渐强大,有些手机可能已经比很多个人用的云服务器还要强大很多,只是有着散热和电量的限制,但是充当一下临时的使用还是很不错的 记得之前提过,在用rustdesk的时候,如果被连端是Mac,并且被连端进入锁屏了,可能需要ssh连上去执行个唤醒命令,正好最近有需求要偶尔从手机连上家里的Mac,从而引发的一个问题是比如我经常用Termux来执行ssh命令,那么理论上我直接从shell的history就可以找到执行过的ssh命令,也就不用经常记对应的ip啥的,但是试用了下发现不行,一开始搜了下以为是没有设置历史存储的大小,可能默认是0,那么需要通过配置来设置
-HISTSIZE=1000 HISTFILESIZE=2000
-比如设置1000个命令大小,只是在Termux里不是这个原因,而是在于shell去保存历史的时机,安卓手机在退出应用或者切换应用程序都没法记录这个执行历史,而是需要在Termux中手动地用exit命令退出,才会记录下history历史, 当然也有人在Termux的issue上提到了,能不能捕捉安卓的事件来执行这个exit,否则这个exit有点难记得,相对来说也不方便了很多,容易遗忘,只是这个issue 也被关掉了,看看后面会不会优化吧
+ 回归本源理解下mysql的 select for update 锁
+ /2025/06/01/%E5%9B%9E%E5%BD%92%E6%9C%AC%E6%BA%90%E7%90%86%E8%A7%A3%E4%B8%8Bmysql%E7%9A%84for-update%E9%94%81/
+ 很多的业务场景我们会用到锁,包括各种炫酷的分布式锁,但是其实很多情况由于db的可靠性是相对比较高的,所以也可以在适当的情况下使用db来作为锁 这里就介绍下比较常用了 select for update 锁
+什么是 SELECT FOR UPDATE SELECT FOR UPDATE 是 MySQL 中一种特殊的查询语句,它在执行查询的同时对选中的行加上排他锁(Exclusive Lock),确保在当前事务结束之前,其他事务无法修改这些数据。这种机制主要用于解决并发环境下的数据一致性问题。
+基本语法 SELECT column1, column2, ... FROM table_name WHERE condition FOR UPDATE ;
+
+工作原理 当执行 SELECT FOR UPDATE 时,MySQL 会:
+
+加排他锁 :对查询结果中的每一行数据加上排他锁(X锁)
+阻塞其他事务 :其他事务如果要修改被锁定的行,必须等待当前事务结束
+事务结束释放 :当前事务提交(COMMIT)或回滚(ROLLBACK)时,锁会自动释放
+
+使用场景 1. 防止超卖问题 电商系统中最常见的应用场景:
+START TRANSACTION;SELECT stock FROM products WHERE id = 1001 FOR UPDATE ;UPDATE products SET stock = stock - 3 WHERE id = 1001 ;COMMIT ;
+这里也可以直接使用start; 开启事务,
+2. 生成唯一序列号 确保序列号的唯一性:
+START TRANSACTION;SELECT max_seq FROM sequence_table WHERE table_name = 'orders' FOR UPDATE ;UPDATE sequence_table SET max_seq = max_seq + 1 WHERE table_name = 'orders' ;COMMIT ;
+
+3. 账户余额操作 银行转账等涉及账户余额的操作:
+START TRANSACTION;SELECT balance FROM accounts WHERE account_id = 'A001' FOR UPDATE ;SELECT balance FROM accounts WHERE account_id = 'B001' FOR UPDATE ;UPDATE accounts SET balance = balance - 1000 WHERE account_id = 'A001' ;UPDATE accounts SET balance = balance + 1000 WHERE account_id = 'B001' ;COMMIT ;
+
+锁的范围和类型 1. 行级锁 vs 表级锁
+InnoDB 存储引擎 :使用行级锁,只锁定符合条件的行
+MyISAM 存储引擎 :使用表级锁,锁定整个表
+
+2. 索引对锁范围的影响 SELECT * FROM users WHERE id = 100 FOR UPDATE ; SELECT * FROM users WHERE age = 25 FOR UPDATE ; SELECT * FROM users WHERE name = 'John' FOR UPDATE ;
+
+死锁问题与预防 死锁产生的原因 START TRANSACTION;SELECT * FROM table1 WHERE id = 1 FOR UPDATE ;SELECT * FROM table2 WHERE id = 2 FOR UPDATE ; START TRANSACTION;SELECT * FROM table2 WHERE id = 2 FOR UPDATE ;SELECT * FROM table1 WHERE id = 1 FOR UPDATE ;
+这样就会造成死锁等待,注意一点死锁不一定是两个事务相互等待,也可以是循环等待,要达到的是竞态等待
+START TRANSACTION;SELECT * FROM table1 WHERE id = 1 FOR UPDATE ;SELECT * FROM table2 WHERE id = 2 FOR UPDATE ; START TRANSACTION;SELECT * FROM table2 WHERE id = 2 FOR UPDATE ;SELECT * FROM table1 WHERE id = 3 FOR UPDATE ; START TRANSACTION;SELECT * FROM table2 WHERE id = 3 FOR UPDATE ;SELECT * FROM table1 WHERE id = 1 FOR UPDATE ;
+
+这样也会造成死锁,所以不要把死锁就单纯理解为两个线程互相等待
+预防死锁的策略
+统一加锁顺序 :所有事务按相同顺序获取锁
+减少锁持有时间 :尽快提交或回滚事务
+使用较低的隔离级别 :在业务允许的情况下
+合理设计索引 :避免锁定过多不必要的行
+
+性能优化建议 1. 合理使用索引 SELECT * FROM orders WHERE status = 'pending' FOR UPDATE ;ALTER TABLE orders ADD INDEX idx_status (status);
+
+2. 缩小锁定范围 SELECT * FROM products WHERE category = 'electronics' FOR UPDATE ;SELECT id, stock FROM products WHERE category = 'electronics' FOR UPDATE ;
+
+3. 合理的事务边界 START TRANSACTION;SELECT * FROM table1 FOR UPDATE ;UPDATE table1 SET ...;COMMIT ;START TRANSACTION;SELECT * FROM table1 FOR UPDATE ;UPDATE table1 SET ...;COMMIT ;
+
+注意事项和限制 1. 必须在事务中使用 SELECT * FROM users WHERE id = 1 FOR UPDATE ;START TRANSACTION;SELECT * FROM users WHERE id = 1 FOR UPDATE ;COMMIT ;
+
+2. 自动提交模式的影响 SHOW VARIABLES LIKE 'autocommit' ;SET autocommit = 0 ; START TRANSACTION;
+
+3. 锁等待超时 SET innodb_lock_wait_timeout = 60 ;
+
+替代方案 1. 乐观锁 UPDATE products SET stock = stock - 1 , version = version + 1 WHERE id = 1001 AND version = @old_version ;
+
+2. 原子性操作 UPDATE products SET stock = stock - 1 WHERE id = 1001 AND stock >= 1 ;
+
+总结 SELECT FOR UPDATE 是 MySQL 中解决并发问题的重要工具,但使用时需要注意:
+
+合理设计事务边界,避免长时间持有锁
+正确使用索引,减少锁定范围
+统一加锁顺序,预防死锁
+根据业务场景选择合适的并发控制策略
+
+正确使用 SELECT FOR UPDATE 可以有效保证数据一致性,但也要权衡其对系统性能的影响,在实际应用中需要根据具体业务场景做出合理的选择。
]]>
- 小技巧
+ msyql
+ 技巧
- 小技巧
+ mysql
@@ -6132,31 +6250,19 @@ user3:
- 关于arthas的一个比较有用的使用方式
- /2025/04/13/%E5%85%B3%E4%BA%8Earthas%E7%9A%84%E4%B8%80%E4%B8%AA%E6%AF%94%E8%BE%83%E6%9C%89%E7%94%A8%E7%9A%84%E4%BD%BF%E7%94%A8%E6%96%B9%E5%BC%8F/
- arthas是阿里开源的一个非常好用的java诊断工具,提供了很多很好用的命令,这里讲一个最近使用到的 就是将arthas挂载上我们的springboot应用,然后调用其中的方法,这样能够在如果没加日志已经看不到函数返回时更方便的排查问题 首先举个例子,我们有个Controller 它的一个query方法是这样的
-@RequestMapping(value = "/query", method = RequestMethod.GET) @ResponseBody public String query () { return demoService.queryName("1" ); }
-而在这个demoService中它的实现是这样
-public String queryName (String no) { if ("1" .equals(no)) { return "no1" ; } else { return "no2" ; } }
-假如现在Controller这的这个方法有点问题,那么我想确认下是不是demoService这个方法的实现有问题,或者说确定下它的返回值是否符合预期 那么我们就可以在应用启动后,运行arthas,找到这个应用的进程,进行挂载 然后执行
-vmtool --action getInstances --className com.nicksxs.spbdemo.service.DemoServiceImpl --express 'instances[0].queryName("1")'
-先介绍下这个vmtool命令 主要来说 vmtool 可以利用JVMTI接口,实现查询内存对象,强制 GC 等功能。 例如官方示例里的,我想把内存里的string对象捞一些出来看看存的是啥
-vmtool --action getInstances --className java.lang.String --limit 10
-就可以这样,首先这个action就是指定要做的操作,支持的action 还包括
-
-等,那么对于 getInstances 就是从内存里捞出这个类的对象,然后是后面一部分--express 就是执行表达式,这里的表达式,instances[0].queryName("1") 其中 instances 就是前面从内存中获取的对象数组,因为这些是对象的非静态方法,那就需要从其中取一个来执行我们的方法 另外假如我们的场景里需要对比如返回结果做个json序列化 我们可以这样
-vmtool --action getInstances --className com.nicksxs.spbdemo.service.DemoServiceImpl --express '@com.alibaba.fastjson.JSON@toJSONString(instances[0].queryName("1"))'
-这里为什么类开头跟方法开头要用 @, 是因为对于类和静态方法的调用规则是这样,还有如果代码比较多,有可能默认的类加载器中没有加载这个JSON类,那么就需要在参数中加上指定的classloader, 可以用sc命令来查找我们的目标类的类加载器,一般来说如果目标类是我们核心业务的,大概率也会有JSON这个类
-sc -d com.nicksxs.spbdemo.service.DemoServiceImpl
-然后在上面命令中加上sc结果中的 classLoaderHash 的值,
-vmtool --action getInstances -c 18b4aac2 --className com.nicksxs.spbdemo.service.DemoServiceImpl --express '@com.alibaba.fastjson.JSON@toJSONString(instances[0].queryName("1"))'
-这样就能正常执行了
+ 在claude code中体验下GLM最新模型
+ /2025/10/18/%E5%9C%A8claude-code%E4%B8%AD%E4%BD%93%E9%AA%8C%E4%B8%8BGLM%E6%9C%80%E6%96%B0%E6%A8%A1%E5%9E%8B/
+ 在很久之前最开始体验国产模型的时候就体验了ChatGLM,那个时候属于国内基本没几家有出大语言模型,但是说实话体验效果的确比较一般 但是目前GLM也是国内比较头部的大语言模型了,特别是最新出的GLM4.6,看大佬的评测好像比Deepseek V3.2还好一些 我们就基于claude code来做一下体验,还是老规矩,用最简单的prompt让它实现个todo应用 前置工作我们先去 bigmodel 申请下apikey 申请完了就在我们体验的项目目录下
+export ANTHROPIC_BASE_URL=``https://open.bigmodel.cn/api/anthropic export ANTHROPIC_AUTH_TOKEN="刚才复制的 API Key"
+一开始运行还发现模型是不对的,用的是claude的模型 需要手动指定
+export ANTHROPIC_DEFAULT_SONNET_MODEL=GLM-4.5
+这里为什么用4.5呢,因为没付费,不给用4.6 所以我们先测一下4.5,待会再看下4.6 4.5模型的测了好几次都是中途卡住 生成的效果也比较一般 接下来我充了值,再试下4.6模型,果然动用了钞能力就是不一样,一遍完成,并且样式也美化了很多 过程中的任务编排也是比较合理,并且逐条完成了,4.5感觉还是完成度不高,4.6给我的感觉可能跟deepseek r1接近, 没有特别明显的优势,可能需要更加复杂的任务才能测出来差别,总之国产模型还是有在进步的
]]>
- java
+ LLM
- java
+ LLM
@@ -6180,9 +6286,9 @@ user3:
- 在老丈人家的小工记四
- /2020/09/26/%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E7%9A%84%E5%B0%8F%E5%B7%A5%E8%AE%B0%E5%9B%9B/
- 小工记四第四周去的时候让我们去了现在在住的房子里,去三楼整理东西了,蛮多的东西需要收拾整理,有些需要丢一下,以前往往是把不太要用的东西就放三楼了,但是后面就不会再去收拾整理,LD 跟丈母娘负责收拾,我不太知道哪些还要的,哪些不要了,而且本来也不擅长这种收拾🤦♂️,然后就变成她们收拾出来废纸箱,我负责拆掉,压平,这时候终于觉得体重还算是有点作用,总体来说这个事情我其实也不擅长,不擅长的主要是捆起来,可能我总是小题大做,因为纸箱大小不一,如果不做一下分类,然后把大的折小一些的话,直接绑起来,容易拎起来就散掉了,而且一些鞋盒子这种小件的纸盒会比较薄,冰箱这种大件的比较厚,厚的比较不容易变形,需要大力踩踏,而且扎的时候需要用体重压住捆实了之后那样子才是真的捆实的,不然待会又是松松垮垮容易滑出来散架,因为压住了捆好后,下来了之后箱子就会弹开了把绳子崩紧实,感觉又是掌握到生活小技巧了😃,我这里其实比较单调无聊,然后 LD 那可以说非常厉害了,一共理出来 11 把旧电扇,还有好多没用过全新的不锈钢脸盆大大小小的,感觉比店里在卖的还多,还有是有比较多小时候的东西,特别多小时候的衣服,其实这种对我来说最难了,可能需要读一下断舍离,蛮多东西都舍不得扔,但是其实是没啥用了,然后还占地方,这天应该算是比较轻松的一天了,上午主要是把收拾出来要的和不要的搬下楼,然后下午要去把纸板给卖掉。中午还是去小快餐店吃的,在住的家里理东西还有个好处就是中午吃完饭可以小憩一下,因为我个人是非常依赖午休的,不然下午完全没精神,而且心态也会比较烦躁,一方面是客观的的确比较疲惫,另一方面应该主观心理作用也有点影响,就像上班的时候也是觉得不午睡就会很难受,心理作用也有一点,不过总之能睡还是睡一会,真的没办法就心态好点,吃完午饭之后我们就推着小平板车去收废品的地方卖掉了上午我收拾捆起来的纸板,好像卖了一百多,都是直接过地磅了,不用一捆一捆地称,不过有个小插曲,那里另外一个大爷在倒他的三轮车的时候撞了我一下,还好车速慢,屁股上肉垫后,接下来就比较麻烦了,是LD 她们两姐妹从小到大的书,也要去卖掉,小平板车就载不下了,而且着实也不太好推,轮子不太平,导致推着很累,书有好多箱,本来是想去亲戚家借电动三轮车,因为不会开摩托的那种,摩托的那种 LD 邻居家就有,可是到了那发现那个也是很大,而且刹车是用脚踩的那种,俺爹不太放心,就说第二天周日他有空会帮忙去载了卖掉的,然后比较搞笑的来了,丈母娘看错了时间,以为已经快五点了,就让我们顺便在车里带点东西去在修的房子,放到那边三楼去,到了那还跟老丈人说已经这么迟了要赶紧去菜场买菜了,结果我们回来以后才发现看错了一个小时🤦♂️。 前面可以没提,前三周去的我们一般就周六去一天,然后周日因为要早点回杭州,而且可能想让我们周日能休息下,但是这周就因为周日的时候我爸要去帮忙载书,然后 LD 姐姐也会过来收拾东西,我们周日就又去整理收拾了,周日由于俺爹去的很早,我过去的时候书已经木有了,主要是去收拾东西了,把一些有用没用的继续整理,基本上三楼的就处理完毕了,舒了一大口气,毕竟让丈母娘一个人收拾实在是太累了,但是要扔掉的衣服比较棘手,附近知道的青蛙回收桶被推倒了,其他地方也不知道哪里有,我们就先载了一些东西去在修的房子那,然后去找青蛙桶,结果一个小区可以进,但是已经满了,另一个不让进,后来只能让 LD 姐姐带去她们小区扔了,塞了满满一车。因为要赶回杭州的车就没有等我爸一起回来,他还在那帮忙搞卫生间的墙缝。 虽然这两天不太热,活也不算很吃力,不过我这个体重和易出汗的体质,还是让短袖不知道湿透了多少次,灌了好多水和冰红茶(下午能提提神),回来周一早上称体重也比较喜人,差一点就达到阶段目标,可以想想去哪里吃之前想好的烤肉跟火锅了(估计吃完立马回到解放前)。
+ 在老丈人家的小工记五
+ /2020/10/18/%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E7%9A%84%E5%B0%8F%E5%B7%A5%E8%AE%B0%E4%BA%94/
+ 终于回忆起来了,年纪大了写这种东西真的要立马写,不然很容易想不起来,那天应该是 9 月 12 日,也就是上周六,因为我爸也去了,而且娘亲(丈母娘,LD 这么叫,我也就随了她这么叫,当然是背后,当面就叫妈)也在那,早上一到那二爹就给我爸指挥了活,要挖一条院子的出水道,自己想出来的词,因为觉得下水道是竖的,在那稍稍帮了一会会忙,然后我还是比较惯例的跟着 LD 还有娘亲去住的家里,主要是老丈人可能也不太想让我干太累的活,因为上次已经差不多把三楼都整理干净了,然后就是二楼了,二楼说实话我也帮不上什么忙,主要是衣服被子什么的,正好是有张以前小孩子睡过的那种摇篮床,看上去虽然有一些破损,整体还是不错的,所以打算拿过去,我就负责把它拆掉了,比较简单的是只要拧螺丝就行了,但是其实是用了好多好多工具才搞定的,一开始只要螺丝刀就行了,但是因为年代久了,后面的螺帽也有点锈住或者本身就会串着会一起动,所以拿来了个扳手,大部分的其实都被这两个工具给搞定了,但是后期大概还剩下四分之一的时候,有一颗完全锈住,并且螺纹跟之前那些都不一样,但是这个已经是最大的螺丝刀了,也没办法换个大的了,所以又去找来个一字的,因为十字的不是也可以用一字的拧嘛,结果可能是我买的工具箱里的一字螺丝刀太新了,口子那很锋利,直接把螺丝花纹给划掉了,大的小的都划掉,然后真的变成凹进去一个圆柱体了,然后就想能不能隔一层布去拧,然而因为的确是已经变成圆柱体了,布也不太给力,不放弃的我又去找来了个老虎钳,妄图把划掉的螺丝用老虎钳钳住,另一端用扳手拧开螺帽,但是这个螺丝跟螺帽真的是生锈的太严重了,外加上钳不太牢,完全是两边一起转,实在是没办法了,在征得同意之后,直接掰断了,火死了,一颗螺丝折腾得比我拆一张床还久,那天因为早上去的也比较晚了,然后就快吃午饭了,正好想着带一点东西过去,就把一些脸盆,泡脚桶啥的拿过去了,先是去吃了饭,还是在那家快餐店,菜的口味还是依然不错,就是人比较多,我爸旁边都是素菜,都没怎么吃远一点的荤菜,下次要早点去,把荤菜放我爸旁边😄(PS:他们家饭还是依然尴尬,需要等),吃完就开到在修的房子那把东西拿了出来,我爸已经动作很快的打了一小半的地沟了,说实话那玩意真的是很重,我之前把它从三楼拿下来,就觉得这个太重了,这次还要用起来,感觉我的手会分分钟废掉,不过一开始我还是跟着LD去了住的家里,惯例睡了午觉,那天睡得比较踏实,竟然睡了一个小时,醒了想了下,其实LD她们收拾也用不上我(没啥力气活),我还是去帮我爸他们,跟LD说了下就去了在修的老房子那,两位老爹在一起钻地,看着就很累,我连忙上去想换一会他们,因为刚好是钻到混凝土地线,特别难,力道不够就会滑开,用蛮力就是钻进去拔不出来,原理是因为本身浇的时候就是很紧实的,需要边钻边动,那家伙实在是太重了,真的是汗如雨下,基本是三个人轮流来,我是个添乱的,经常卡住,然后把地线,其实就是一条混凝土横梁,里面还有14跟18的钢筋,需要割断,这个割断也是很有技巧,钢筋本身在里面是受到挤压的,直接用切割的,到快断掉的时候就会崩一下,非常危险,还是老丈人比较有经验,要留一点点,然后直接用榔头敲断就好了,本来以为这个是最难的了,结果下面是一块非常大的青基石,而且也是石头跟石头挤一块,边上一点点打钻有点杯水车薪,后来是用那种螺旋的钻,钻四个洞,相对位置大概是个长方形,这样子把中间这个长方形钻出来就比较容易地能拿出来了,后面的也容易搞出来了,后面的其实难度不是特别大了,主要是地沟打好之后得看看高低是不是符合要求的,不能本来是往外排水的反而外面高,这个怎么看就又很有技巧了,一般在地上的只要侧着看一下就好了,考究点就用下水平尺,但是在地下的,不用水平尺,其实可以借助于地沟里正要放进去的水管,放点水进去,看水往哪流就行了,铺好水管后,就剩填埋的活了,不是太麻烦了,那天真的是累到了,打那个混凝土的时候我真的是把我整个人压上去了,不过也挺爽的,有点把平时无处发泄的蛮力发泄出去了。
]]>
生活
@@ -6200,9 +6306,9 @@ user3:
- 在老丈人家的小工记五
- /2020/10/18/%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E7%9A%84%E5%B0%8F%E5%B7%A5%E8%AE%B0%E4%BA%94/
- 终于回忆起来了,年纪大了写这种东西真的要立马写,不然很容易想不起来,那天应该是 9 月 12 日,也就是上周六,因为我爸也去了,而且娘亲(丈母娘,LD 这么叫,我也就随了她这么叫,当然是背后,当面就叫妈)也在那,早上一到那二爹就给我爸指挥了活,要挖一条院子的出水道,自己想出来的词,因为觉得下水道是竖的,在那稍稍帮了一会会忙,然后我还是比较惯例的跟着 LD 还有娘亲去住的家里,主要是老丈人可能也不太想让我干太累的活,因为上次已经差不多把三楼都整理干净了,然后就是二楼了,二楼说实话我也帮不上什么忙,主要是衣服被子什么的,正好是有张以前小孩子睡过的那种摇篮床,看上去虽然有一些破损,整体还是不错的,所以打算拿过去,我就负责把它拆掉了,比较简单的是只要拧螺丝就行了,但是其实是用了好多好多工具才搞定的,一开始只要螺丝刀就行了,但是因为年代久了,后面的螺帽也有点锈住或者本身就会串着会一起动,所以拿来了个扳手,大部分的其实都被这两个工具给搞定了,但是后期大概还剩下四分之一的时候,有一颗完全锈住,并且螺纹跟之前那些都不一样,但是这个已经是最大的螺丝刀了,也没办法换个大的了,所以又去找来个一字的,因为十字的不是也可以用一字的拧嘛,结果可能是我买的工具箱里的一字螺丝刀太新了,口子那很锋利,直接把螺丝花纹给划掉了,大的小的都划掉,然后真的变成凹进去一个圆柱体了,然后就想能不能隔一层布去拧,然而因为的确是已经变成圆柱体了,布也不太给力,不放弃的我又去找来了个老虎钳,妄图把划掉的螺丝用老虎钳钳住,另一端用扳手拧开螺帽,但是这个螺丝跟螺帽真的是生锈的太严重了,外加上钳不太牢,完全是两边一起转,实在是没办法了,在征得同意之后,直接掰断了,火死了,一颗螺丝折腾得比我拆一张床还久,那天因为早上去的也比较晚了,然后就快吃午饭了,正好想着带一点东西过去,就把一些脸盆,泡脚桶啥的拿过去了,先是去吃了饭,还是在那家快餐店,菜的口味还是依然不错,就是人比较多,我爸旁边都是素菜,都没怎么吃远一点的荤菜,下次要早点去,把荤菜放我爸旁边😄(PS:他们家饭还是依然尴尬,需要等),吃完就开到在修的房子那把东西拿了出来,我爸已经动作很快的打了一小半的地沟了,说实话那玩意真的是很重,我之前把它从三楼拿下来,就觉得这个太重了,这次还要用起来,感觉我的手会分分钟废掉,不过一开始我还是跟着LD去了住的家里,惯例睡了午觉,那天睡得比较踏实,竟然睡了一个小时,醒了想了下,其实LD她们收拾也用不上我(没啥力气活),我还是去帮我爸他们,跟LD说了下就去了在修的老房子那,两位老爹在一起钻地,看着就很累,我连忙上去想换一会他们,因为刚好是钻到混凝土地线,特别难,力道不够就会滑开,用蛮力就是钻进去拔不出来,原理是因为本身浇的时候就是很紧实的,需要边钻边动,那家伙实在是太重了,真的是汗如雨下,基本是三个人轮流来,我是个添乱的,经常卡住,然后把地线,其实就是一条混凝土横梁,里面还有14跟18的钢筋,需要割断,这个割断也是很有技巧,钢筋本身在里面是受到挤压的,直接用切割的,到快断掉的时候就会崩一下,非常危险,还是老丈人比较有经验,要留一点点,然后直接用榔头敲断就好了,本来以为这个是最难的了,结果下面是一块非常大的青基石,而且也是石头跟石头挤一块,边上一点点打钻有点杯水车薪,后来是用那种螺旋的钻,钻四个洞,相对位置大概是个长方形,这样子把中间这个长方形钻出来就比较容易地能拿出来了,后面的也容易搞出来了,后面的其实难度不是特别大了,主要是地沟打好之后得看看高低是不是符合要求的,不能本来是往外排水的反而外面高,这个怎么看就又很有技巧了,一般在地上的只要侧着看一下就好了,考究点就用下水平尺,但是在地下的,不用水平尺,其实可以借助于地沟里正要放进去的水管,放点水进去,看水往哪流就行了,铺好水管后,就剩填埋的活了,不是太麻烦了,那天真的是累到了,打那个混凝土的时候我真的是把我整个人压上去了,不过也挺爽的,有点把平时无处发泄的蛮力发泄出去了。
+ 在老丈人家的小工记四
+ /2020/09/26/%E5%9C%A8%E8%80%81%E4%B8%88%E4%BA%BA%E5%AE%B6%E7%9A%84%E5%B0%8F%E5%B7%A5%E8%AE%B0%E5%9B%9B/
+ 小工记四第四周去的时候让我们去了现在在住的房子里,去三楼整理东西了,蛮多的东西需要收拾整理,有些需要丢一下,以前往往是把不太要用的东西就放三楼了,但是后面就不会再去收拾整理,LD 跟丈母娘负责收拾,我不太知道哪些还要的,哪些不要了,而且本来也不擅长这种收拾🤦♂️,然后就变成她们收拾出来废纸箱,我负责拆掉,压平,这时候终于觉得体重还算是有点作用,总体来说这个事情我其实也不擅长,不擅长的主要是捆起来,可能我总是小题大做,因为纸箱大小不一,如果不做一下分类,然后把大的折小一些的话,直接绑起来,容易拎起来就散掉了,而且一些鞋盒子这种小件的纸盒会比较薄,冰箱这种大件的比较厚,厚的比较不容易变形,需要大力踩踏,而且扎的时候需要用体重压住捆实了之后那样子才是真的捆实的,不然待会又是松松垮垮容易滑出来散架,因为压住了捆好后,下来了之后箱子就会弹开了把绳子崩紧实,感觉又是掌握到生活小技巧了😃,我这里其实比较单调无聊,然后 LD 那可以说非常厉害了,一共理出来 11 把旧电扇,还有好多没用过全新的不锈钢脸盆大大小小的,感觉比店里在卖的还多,还有是有比较多小时候的东西,特别多小时候的衣服,其实这种对我来说最难了,可能需要读一下断舍离,蛮多东西都舍不得扔,但是其实是没啥用了,然后还占地方,这天应该算是比较轻松的一天了,上午主要是把收拾出来要的和不要的搬下楼,然后下午要去把纸板给卖掉。中午还是去小快餐店吃的,在住的家里理东西还有个好处就是中午吃完饭可以小憩一下,因为我个人是非常依赖午休的,不然下午完全没精神,而且心态也会比较烦躁,一方面是客观的的确比较疲惫,另一方面应该主观心理作用也有点影响,就像上班的时候也是觉得不午睡就会很难受,心理作用也有一点,不过总之能睡还是睡一会,真的没办法就心态好点,吃完午饭之后我们就推着小平板车去收废品的地方卖掉了上午我收拾捆起来的纸板,好像卖了一百多,都是直接过地磅了,不用一捆一捆地称,不过有个小插曲,那里另外一个大爷在倒他的三轮车的时候撞了我一下,还好车速慢,屁股上肉垫后,接下来就比较麻烦了,是LD 她们两姐妹从小到大的书,也要去卖掉,小平板车就载不下了,而且着实也不太好推,轮子不太平,导致推着很累,书有好多箱,本来是想去亲戚家借电动三轮车,因为不会开摩托的那种,摩托的那种 LD 邻居家就有,可是到了那发现那个也是很大,而且刹车是用脚踩的那种,俺爹不太放心,就说第二天周日他有空会帮忙去载了卖掉的,然后比较搞笑的来了,丈母娘看错了时间,以为已经快五点了,就让我们顺便在车里带点东西去在修的房子,放到那边三楼去,到了那还跟老丈人说已经这么迟了要赶紧去菜场买菜了,结果我们回来以后才发现看错了一个小时🤦♂️。 前面可以没提,前三周去的我们一般就周六去一天,然后周日因为要早点回杭州,而且可能想让我们周日能休息下,但是这周就因为周日的时候我爸要去帮忙载书,然后 LD 姐姐也会过来收拾东西,我们周日就又去整理收拾了,周日由于俺爹去的很早,我过去的时候书已经木有了,主要是去收拾东西了,把一些有用没用的继续整理,基本上三楼的就处理完毕了,舒了一大口气,毕竟让丈母娘一个人收拾实在是太累了,但是要扔掉的衣服比较棘手,附近知道的青蛙回收桶被推倒了,其他地方也不知道哪里有,我们就先载了一些东西去在修的房子那,然后去找青蛙桶,结果一个小区可以进,但是已经满了,另一个不让进,后来只能让 LD 姐姐带去她们小区扔了,塞了满满一车。因为要赶回杭州的车就没有等我爸一起回来,他还在那帮忙搞卫生间的墙缝。 虽然这两天不太热,活也不算很吃力,不过我这个体重和易出汗的体质,还是让短袖不知道湿透了多少次,灌了好多水和冰红茶(下午能提提神),回来周一早上称体重也比较喜人,差一点就达到阶段目标,可以想想去哪里吃之前想好的烤肉跟火锅了(估计吃完立马回到解放前)。
]]>
生活
@@ -6219,111 +6325,6 @@ user3:
干活
-
- 在claude code中体验下GLM最新模型
- /2025/10/18/%E5%9C%A8claude-code%E4%B8%AD%E4%BD%93%E9%AA%8C%E4%B8%8BGLM%E6%9C%80%E6%96%B0%E6%A8%A1%E5%9E%8B/
- 在很久之前最开始体验国产模型的时候就体验了ChatGLM,那个时候属于国内基本没几家有出大语言模型,但是说实话体验效果的确比较一般 但是目前GLM也是国内比较头部的大语言模型了,特别是最新出的GLM4.6,看大佬的评测好像比Deepseek V3.2还好一些 我们就基于claude code来做一下体验,还是老规矩,用最简单的prompt让它实现个todo应用 前置工作我们先去 bigmodel 申请下apikey 申请完了就在我们体验的项目目录下
-export ANTHROPIC_BASE_URL=``https://open.bigmodel.cn/api/anthropic export ANTHROPIC_AUTH_TOKEN="刚才复制的 API Key"
-一开始运行还发现模型是不对的,用的是claude的模型 需要手动指定
-export ANTHROPIC_DEFAULT_SONNET_MODEL=GLM-4.5
-这里为什么用4.5呢,因为没付费,不给用4.6 所以我们先测一下4.5,待会再看下4.6 4.5模型的测了好几次都是中途卡住 生成的效果也比较一般 接下来我充了值,再试下4.6模型,果然动用了钞能力就是不一样,一遍完成,并且样式也美化了很多 过程中的任务编排也是比较合理,并且逐条完成了,4.5感觉还是完成度不高,4.6给我的感觉可能跟deepseek r1接近, 没有特别明显的优势,可能需要更加复杂的任务才能测出来差别,总之国产模型还是有在进步的
-]]>
-
- LLM
-
-
- LLM
-
-
-
- 基于iflow的kimi模型来体验代码生成
- /2025/11/16/%E5%9F%BA%E4%BA%8Eiflow%E7%9A%84kimi%E6%A8%A1%E5%9E%8B%E6%9D%A5%E4%BD%93%E9%AA%8C%E4%BB%A3%E7%A0%81%E7%94%9F%E6%88%90/
- 首先是生成plan
-完整生成完后还是无法运行,多次把问题提给kimi也没有解决
-Compiled with warnings. [eslint] src/components/TodoItem.tsx Line 4:19: 'FiX' is defined but never used @typescript-eslint/no-unused-vars Line 4:108: 'FiClock' is defined but never used @typescript-eslint/no-unused-vars Line 213:7: 'ActionBar' is assigned a value but never used @typescript-eslint/no-unused-vars Line 222:7: 'ActionButtons' is assigned a value but never used @typescript-eslint/no-unused-vars Line 334:7: 'Timestamp' is assigned a value but never used @typescript-eslint/no-unused-vars Search for the keywords to learn more about each warning. To ignore, add // eslint-disable-next-line to the line before. WARNING in [eslint] src/components/TodoItem.tsx Line 4:19: 'FiX' is defined but never used @typescript-eslint/no-unused-vars Line 4:108: 'FiClock' is defined but never used @typescript-eslint/no-unused-vars Line 213:7: 'ActionBar' is assigned a value but never used @typescript-eslint/no-unused-vars Line 222:7: 'ActionButtons' is assigned a value but never used @typescript-eslint/no-unused-vars Line 334:7: 'Timestamp' is assigned a value but never used @typescript-eslint/no-unused-vars webpack compiled with 1 warning Files successfully emitted, waiting for typecheck results... Issues checking in progress... ERROR in src/components/Header.tsx:270:16 TS2786: 'FiSearch' cannot be used as a JSX component. Its return type 'ReactNode' is not a valid JSX element. Type 'undefined' is not assignable to type 'Element | null'. 268 | <SearchContainer> 269 | <SearchIcon> > 270 | <FiSearch {...({ size: 16 } as any)} /> | ^^^^^^^^ 271 | </SearchIcon> 272 | <SearchInput 273 | type="text" ERROR in src/components/Header.tsx:300:18 TS2786: 'FiHome' cannot be used as a JSX component. Its return type 'ReactNode' is not a valid JSX element. 298 | <NavIcons> 299 | <NavIcon> > 300 | <FiHome {...({ size: 24 } as any)} /> | ^^^^^^ 301 | </NavIcon> 302 | <NavIcon> 303 | <FiCompass {...({ size: 24 } as any)} /> ERROR in src/components/TodoItem.tsx:453:43 TS2786: 'FiTag' cannot be used as a JSX component. Its return type 'ReactNode' is not a valid JSX element. 451 | {todo.priority === 'high' ? '🔴 高' : todo.priority === 'medium' ? '🟡 中' : '🟢 低'} 452 | </PriorityBadge> > 453 | {todo.category && <CategoryTag><FiTag size={10} /> #{todo.category}</CategoryTag>} | ^^^^^ 454 | 455 | <div style={{ position: 'relative' }}> 456 | <MoreButton onClick={() => setShowDropdown(!showDropdown)}>
-没办法,后面我好好思考了下,前面因为响应一直很慢,我退出了几次又继续使用同一个prompt,但是实际的模型是需要读取原先写到一半的代码,这样其实每次的生成上下文就被搞混乱了,所以重新清理了代码,再给kimi一次机会,这次就慢慢等了 首先也是列出了todo list 完成后主体的代码也不多
-import React , { useState, useEffect } from 'react' ;import styled from '@emotion/styled' ;import { motion, AnimatePresence } from 'framer-motion' ;import { Plus } from 'lucide-react' ;import TodoItem from './TodoItem' ;import TodoForm from './TodoForm' ;export interface Todo { id : string ; text : string ; completed : boolean ; createdAt : Date ; priority : 'low' | 'medium' | 'high' ; } const Container = styled (motion.div )` background: rgba(255, 255, 255, 0.95); backdrop-filter: blur(20px); border-radius: 24px; box-shadow: 0 25px 50px -12px rgba(0, 0, 0, 0.25); width: 100%; max-width: 500px; min-height: 600px; padding: 30px; position: relative; overflow: hidden; ` ;const Header = styled.div ` text-align: center; margin-bottom: 30px; ` ;const Title = styled.h1 ` font-size: 2.5rem; font-weight: 700; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; margin: 0; letter-spacing: -0.02em; ` ;const Subtitle = styled.p ` color: #6b7280; font-size: 1rem; margin: 8px 0 0 0; font-weight: 500; ` ;const TodoList = styled.div ` margin-top: 30px; max-height: 400px; overflow-y: auto; padding-right: 10px; &::-webkit-scrollbar { width: 6px; } &::-webkit-scrollbar-track { background: #f1f5f9; border-radius: 3px; } &::-webkit-scrollbar-thumb { background: #cbd5e1; border-radius: 3px; } &::-webkit-scrollbar-thumb:hover { background: #94a3b8; } ` ;const EmptyState = styled (motion.div )` text-align: center; padding: 60px 20px; color: #9ca3af; ` ;const EmptyIcon = styled.div ` font-size: 4rem; margin-bottom: 16px; opacity: 0.5; ` ;const EmptyText = styled.p ` font-size: 1.1rem; margin: 0; font-weight: 500; ` ;const Stats = styled.div ` display: flex; justify-content: space-between; align-items: center; margin-top: 20px; padding-top: 20px; border-top: 1px solid #e5e7eb; ` ;const StatItem = styled.div ` text-align: center; ` ;const StatNumber = styled.div ` font-size: 1.5rem; font-weight: 700; color: #667eea; ` ;const StatLabel = styled.div ` font-size: 0.875rem; color: #6b7280; margin-top: 4px; ` ;const TodoApp : React .FC = () => { const [todos, setTodos] = useState<Todo []>([]); const [showForm, setShowForm] = useState (false ); const [editingTodo, setEditingTodo] = useState<Todo | null >(null ); useEffect (() => { const savedTodos = localStorage .getItem ('todos' ); if (savedTodos) { setTodos (JSON .parse (savedTodos).map ((todo: any ) => ({ ...todo, createdAt : new Date (todo.createdAt ) }))); } }, []); useEffect (() => { localStorage .setItem ('todos' , JSON .stringify (todos)); }, [todos]); const addTodo = (text: string , priority: 'low' | 'medium' | 'high' ) => { const newTodo : Todo = { id : Date .now ().toString (), text, completed : false , createdAt : new Date (), priority }; setTodos ([newTodo, ...todos]); setShowForm (false ); }; const toggleTodo = (id: string ) => { setTodos (todos.map (todo => todo.id === id ? { ...todo, completed : !todo.completed } : todo )); }; const deleteTodo = (id: string ) => { setTodos (todos.filter (todo => todo.id !== id)); }; const editTodo = (id: string , newText: string , newPriority: 'low' | 'medium' | 'high' ) => { setTodos (todos.map (todo => todo.id === id ? { ...todo, text : newText, priority : newPriority } : todo )); setEditingTodo (null ); }; const completedCount = todos.filter (todo => todo.completed ).length ; const totalCount = todos.length ; return ( <Container initial ={{ opacity: 0 , y: 20 }} animate ={{ opacity: 1 , y: 0 }} transition ={{ duration: 0.6 , ease: "easeOut " }} > <Header > <Title > My Tasks</Title > <Subtitle > Stay organized and productive</Subtitle > </Header > {showForm && ( <TodoForm onSubmit ={addTodo} onCancel ={() => setShowForm(false)} /> )} {editingTodo && ( <TodoForm todo ={editingTodo} onSubmit ={(text, priority ) => editTodo(editingTodo.id, text, priority)} onCancel={() => setEditingTodo(null)} /> )} <TodoList > <AnimatePresence > {todos.length === 0 ? ( <EmptyState initial ={{ opacity: 0 }} animate ={{ opacity: 1 }} exit ={{ opacity: 0 }} > <EmptyIcon > 📝</EmptyIcon > <EmptyText > No tasks yet. Add one to get started!</EmptyText > </EmptyState > ) : ( todos.map((todo) => ( <TodoItem key ={todo.id} todo ={todo} onToggle ={toggleTodo} onDelete ={deleteTodo} onEdit ={setEditingTodo} /> )) )} </AnimatePresence > </TodoList > {todos.length > 0 && ( <Stats > <StatItem > <StatNumber > {totalCount}</StatNumber > <StatLabel > Total</StatLabel > </StatItem > <StatItem > <StatNumber > {completedCount}</StatNumber > <StatLabel > Completed</StatLabel > </StatItem > <StatItem > <StatNumber > {totalCount - completedCount}</StatNumber > <StatLabel > Remaining</StatLabel > </StatItem > </Stats > )} {!showForm && !editingTodo && ( <motion.button style ={{ position: 'absolute ', bottom: '30px ', right: '30px ', width: '60px ', height: '60px ', borderRadius: '50 %', background: 'linear-gradient (135deg , #667eea 0 %, #764ba2 100 %)', border: 'none ', color: 'white ', fontSize: '24px ', cursor: 'pointer ', boxShadow: '0 10px 25px rgba (102 , 126 , 234 , 0.4 )', display: 'flex ', alignItems: 'center ', justifyContent: 'center ' }} whileHover ={{ scale: 1.1 }} whileTap ={{ scale: 0.95 }} onClick ={() => setShowForm(true)} > <Plus size ={24} /> </motion.button > )} </Container > ); }; export default TodoApp ;
-一开始运行出现了个问题,新增的时候报错 Component selectors can only be used in conjunction with @emotion/babel-plugin, the swc Emotion plugin, or another Emotion-aware compiler transform. 就继续让kimi来解决这个问题,结果一次就改好了, 整体看起来也还行,可能的确是我一开始中断了几次导致上下文太复杂,只是不确定是模型消耗资源比较多还是什么原因响应有时候会很慢,可能免费的资源池是共用的,高峰期就比较慢了
-]]>
-
- LLM
-
-
- LLM
-
-
-
- 回归本源理解下mysql的 select for update 锁
- /2025/06/01/%E5%9B%9E%E5%BD%92%E6%9C%AC%E6%BA%90%E7%90%86%E8%A7%A3%E4%B8%8Bmysql%E7%9A%84for-update%E9%94%81/
- 很多的业务场景我们会用到锁,包括各种炫酷的分布式锁,但是其实很多情况由于db的可靠性是相对比较高的,所以也可以在适当的情况下使用db来作为锁 这里就介绍下比较常用了 select for update 锁
-什么是 SELECT FOR UPDATE SELECT FOR UPDATE 是 MySQL 中一种特殊的查询语句,它在执行查询的同时对选中的行加上排他锁(Exclusive Lock),确保在当前事务结束之前,其他事务无法修改这些数据。这种机制主要用于解决并发环境下的数据一致性问题。
-基本语法 SELECT column1, column2, ... FROM table_name WHERE condition FOR UPDATE ;
-
-工作原理 当执行 SELECT FOR UPDATE 时,MySQL 会:
-
-加排他锁 :对查询结果中的每一行数据加上排他锁(X锁)
-阻塞其他事务 :其他事务如果要修改被锁定的行,必须等待当前事务结束
-事务结束释放 :当前事务提交(COMMIT)或回滚(ROLLBACK)时,锁会自动释放
-
-使用场景 1. 防止超卖问题 电商系统中最常见的应用场景:
-START TRANSACTION;SELECT stock FROM products WHERE id = 1001 FOR UPDATE ;UPDATE products SET stock = stock - 3 WHERE id = 1001 ;COMMIT ;
-这里也可以直接使用start; 开启事务,
-2. 生成唯一序列号 确保序列号的唯一性:
-START TRANSACTION;SELECT max_seq FROM sequence_table WHERE table_name = 'orders' FOR UPDATE ;UPDATE sequence_table SET max_seq = max_seq + 1 WHERE table_name = 'orders' ;COMMIT ;
-
-3. 账户余额操作 银行转账等涉及账户余额的操作:
-START TRANSACTION;SELECT balance FROM accounts WHERE account_id = 'A001' FOR UPDATE ;SELECT balance FROM accounts WHERE account_id = 'B001' FOR UPDATE ;UPDATE accounts SET balance = balance - 1000 WHERE account_id = 'A001' ;UPDATE accounts SET balance = balance + 1000 WHERE account_id = 'B001' ;COMMIT ;
-
-锁的范围和类型 1. 行级锁 vs 表级锁
-InnoDB 存储引擎 :使用行级锁,只锁定符合条件的行
-MyISAM 存储引擎 :使用表级锁,锁定整个表
-
-2. 索引对锁范围的影响 SELECT * FROM users WHERE id = 100 FOR UPDATE ; SELECT * FROM users WHERE age = 25 FOR UPDATE ; SELECT * FROM users WHERE name = 'John' FOR UPDATE ;
-
-死锁问题与预防 死锁产生的原因 START TRANSACTION;SELECT * FROM table1 WHERE id = 1 FOR UPDATE ;SELECT * FROM table2 WHERE id = 2 FOR UPDATE ; START TRANSACTION;SELECT * FROM table2 WHERE id = 2 FOR UPDATE ;SELECT * FROM table1 WHERE id = 1 FOR UPDATE ;
-这样就会造成死锁等待,注意一点死锁不一定是两个事务相互等待,也可以是循环等待,要达到的是竞态等待
-START TRANSACTION;SELECT * FROM table1 WHERE id = 1 FOR UPDATE ;SELECT * FROM table2 WHERE id = 2 FOR UPDATE ; START TRANSACTION;SELECT * FROM table2 WHERE id = 2 FOR UPDATE ;SELECT * FROM table1 WHERE id = 3 FOR UPDATE ; START TRANSACTION;SELECT * FROM table2 WHERE id = 3 FOR UPDATE ;SELECT * FROM table1 WHERE id = 1 FOR UPDATE ;
-
-这样也会造成死锁,所以不要把死锁就单纯理解为两个线程互相等待
-预防死锁的策略
-统一加锁顺序 :所有事务按相同顺序获取锁
-减少锁持有时间 :尽快提交或回滚事务
-使用较低的隔离级别 :在业务允许的情况下
-合理设计索引 :避免锁定过多不必要的行
-
-性能优化建议 1. 合理使用索引 SELECT * FROM orders WHERE status = 'pending' FOR UPDATE ;ALTER TABLE orders ADD INDEX idx_status (status);
-
-2. 缩小锁定范围 SELECT * FROM products WHERE category = 'electronics' FOR UPDATE ;SELECT id, stock FROM products WHERE category = 'electronics' FOR UPDATE ;
-
-3. 合理的事务边界 START TRANSACTION;SELECT * FROM table1 FOR UPDATE ;UPDATE table1 SET ...;COMMIT ;START TRANSACTION;SELECT * FROM table1 FOR UPDATE ;UPDATE table1 SET ...;COMMIT ;
-
-注意事项和限制 1. 必须在事务中使用 SELECT * FROM users WHERE id = 1 FOR UPDATE ;START TRANSACTION;SELECT * FROM users WHERE id = 1 FOR UPDATE ;COMMIT ;
-
-2. 自动提交模式的影响 SHOW VARIABLES LIKE 'autocommit' ;SET autocommit = 0 ; START TRANSACTION;
-
-3. 锁等待超时 SET innodb_lock_wait_timeout = 60 ;
-
-替代方案 1. 乐观锁 UPDATE products SET stock = stock - 1 , version = version + 1 WHERE id = 1001 AND version = @old_version ;
-
-2. 原子性操作 UPDATE products SET stock = stock - 1 WHERE id = 1001 AND stock >= 1 ;
-
-总结 SELECT FOR UPDATE 是 MySQL 中解决并发问题的重要工具,但使用时需要注意:
-
-合理设计事务边界,避免长时间持有锁
-正确使用索引,减少锁定范围
-统一加锁顺序,预防死锁
-根据业务场景选择合适的并发控制策略
-
-正确使用 SELECT FOR UPDATE 可以有效保证数据一致性,但也要权衡其对系统性能的影响,在实际应用中需要根据具体业务场景做出合理的选择。
-]]>
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- msyql
- 技巧
-
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- mysql
-
-
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/
@@ -6353,36 +6354,20 @@ user3:
- 千万别升级Chrome-您的扩展程序可能无法使用-教你怎么解决
- /2025/07/20/%E5%8D%83%E4%B8%87%E5%88%AB%E5%8D%87%E7%BA%A7Chrome-%E6%82%A8%E7%9A%84%E6%89%A9%E5%B1%95%E7%A8%8B%E5%BA%8F%E5%8F%AF%E8%83%BD%E6%97%A0%E6%B3%95%E4%BD%BF%E7%94%A8/
- 允许我小小地标题党一下,后半句是真实情况,最近重启电脑就自动升级了chrome,发现很多必须的扩展程序都不能用了,之前是可以通过设置开启,这次升级到 138.0.7204.158 版本就直接不允许开启了 这个很久之前chrome也做了提示,主要是扩展程序的manifest版本要从v2升级成v3,但是这个升级分两方面说,一方面是升级难度,可以参考这个核对文档 , 需要
-
-迁移到 Service Worker
-更新 API 调用
-替换屏蔽 Web 请求监听器
-提高扩展程序的安全性 这些项的修改,修改升级难度不小,如果使用了很多api的话是比较麻烦的 另一方面是很多扩展都不是自己做的开发,开发者都不一定在维护,但是很多都是非常深度使用的 这边有个例子就是Proxy SwitchyOmega 2 平时会用来做代理切换,原本的仓库已经没人维护了,并且chrome扩展商店也没有更新, 这里介绍两种方法, 第一种是找到这个应用所在目录 首先找到应用的id,然后需要到chrome的扩展目录 mac是在这里 ~/Library/Application Support/Google/Chrome/Default/Extensions windows是在 C:\Users\<用户名>\AppData\Local\Google\Chrome\User Data\Default\Extensions 找到以后就找上面扩展的id就是扩展的文件目录,把它拷出来以后,然后找到扩展目录里的 manifest.json 文件,把版本改成3 然后再通过扩展路径直接打开,比如这个扩展就是 chrome-extension://padekgcemlokbadohgkifijomclgjgif/options.html 这个选项页,为啥要这么打开是因为这个扩展的配置比较麻烦,就是现在可以搜到替代应用,但是要重配一遍也是挺头疼的 第二种是有点釜底抽薪的 可以在chrome的flag配置中修改 网上说的可能已经就删掉了,我这个版本的目前还可以用这个配置项来修改chrome://flags/#allow-legacy-mv2-extensions 可以通过开启这个来让manifest版本是2的扩展能够被启用,但是这个肯定也是个临时方案,大家该把配置导出备份的尽早备份,同时也尽早寻找替代的升级了manifest 3的扩展程序
-
-]]>
-
- 技巧
-
-
- 技巧
-
-
-
- 复习下ER图和它的使用方式
- /2025/07/06/%E5%A4%8D%E4%B9%A0%E4%B8%8BER%E5%9B%BE%E5%92%8C%E5%AE%83%E7%9A%84%E4%BD%BF%E7%94%A8%E6%96%B9%E5%BC%8F/
- er图在我们学习数据库的时候是有比较着重的介绍和学习过的,在实际工作中,如果有想对规范的研发流程,那么在技术方案分析设计阶段也需要对系统中涉及到的数据模型用ER图来表示以方便大家理解数据模型的设计和它们之间的关系 从维基百科上摘的一个说明和介绍
-
-ER模型,全称为实体联系模型、实体关系模型或实体联系模式图(ERM)(英语:Entity-relationship model)由美籍华人计算机科学家陈品山发明,是概念数据模型的高层描述所使用的数据模型或模式图。 常规上的理解ER图分为三个部分, 第一个是实体,也就是ER图的E,表示Entity 第二个是属性,表示Entity中带有的属性,比如以学生表为例,它有姓名,性别,年龄,年纪等等属性 第三个核心的是关系,也就是ER图例的R,表示Relationship,比如学生表跟班级表是怎么样的关系,一般情况下一个学生一定属于一个班级,那么学生和班级的关系就是多对一的关系 对于画ER图,比较推荐使用Draw.io来画,开源免费且功能强大 它左边菜单栏就自带了ER图的组件 我们就可以直接拖动组件来画出ER图 这样就能画出简单的ER图了,并且连1对1,1对多这种关系都有现成的连线组件了,真的是治愈强迫症,如果自己画这个N对N的线,老是会觉得画得不一致而感觉画的不好看 而且还有一个很强大的功能,比如我们在画ER图的时候已经把表结构设计好了,那么就能直接从表结构生成ER图 可以在这里导入表结构sql,就能生成对应的实体图了,如果有外键关联还会生成对应的连接,当然手动加一下也很方便了
-
+ 基于iflow的kimi模型来体验代码生成
+ /2025/11/16/%E5%9F%BA%E4%BA%8Eiflow%E7%9A%84kimi%E6%A8%A1%E5%9E%8B%E6%9D%A5%E4%BD%93%E9%AA%8C%E4%BB%A3%E7%A0%81%E7%94%9F%E6%88%90/
+ 首先是生成plan
+完整生成完后还是无法运行,多次把问题提给kimi也没有解决
+Compiled with warnings. [eslint] src/components/TodoItem.tsx Line 4:19: 'FiX' is defined but never used @typescript-eslint/no-unused-vars Line 4:108: 'FiClock' is defined but never used @typescript-eslint/no-unused-vars Line 213:7: 'ActionBar' is assigned a value but never used @typescript-eslint/no-unused-vars Line 222:7: 'ActionButtons' is assigned a value but never used @typescript-eslint/no-unused-vars Line 334:7: 'Timestamp' is assigned a value but never used @typescript-eslint/no-unused-vars Search for the keywords to learn more about each warning. To ignore, add // eslint-disable-next-line to the line before. WARNING in [eslint] src/components/TodoItem.tsx Line 4:19: 'FiX' is defined but never used @typescript-eslint/no-unused-vars Line 4:108: 'FiClock' is defined but never used @typescript-eslint/no-unused-vars Line 213:7: 'ActionBar' is assigned a value but never used @typescript-eslint/no-unused-vars Line 222:7: 'ActionButtons' is assigned a value but never used @typescript-eslint/no-unused-vars Line 334:7: 'Timestamp' is assigned a value but never used @typescript-eslint/no-unused-vars webpack compiled with 1 warning Files successfully emitted, waiting for typecheck results... Issues checking in progress... ERROR in src/components/Header.tsx:270:16 TS2786: 'FiSearch' cannot be used as a JSX component. Its return type 'ReactNode' is not a valid JSX element. Type 'undefined' is not assignable to type 'Element | null'. 268 | <SearchContainer> 269 | <SearchIcon> > 270 | <FiSearch {...({ size: 16 } as any)} /> | ^^^^^^^^ 271 | </SearchIcon> 272 | <SearchInput 273 | type="text" ERROR in src/components/Header.tsx:300:18 TS2786: 'FiHome' cannot be used as a JSX component. Its return type 'ReactNode' is not a valid JSX element. 298 | <NavIcons> 299 | <NavIcon> > 300 | <FiHome {...({ size: 24 } as any)} /> | ^^^^^^ 301 | </NavIcon> 302 | <NavIcon> 303 | <FiCompass {...({ size: 24 } as any)} /> ERROR in src/components/TodoItem.tsx:453:43 TS2786: 'FiTag' cannot be used as a JSX component. Its return type 'ReactNode' is not a valid JSX element. 451 | {todo.priority === 'high' ? '🔴 高' : todo.priority === 'medium' ? '🟡 中' : '🟢 低'} 452 | </PriorityBadge> > 453 | {todo.category && <CategoryTag><FiTag size={10} /> #{todo.category}</CategoryTag>} | ^^^^^ 454 | 455 | <div style={{ position: 'relative' }}> 456 | <MoreButton onClick={() => setShowDropdown(!showDropdown)}>
+没办法,后面我好好思考了下,前面因为响应一直很慢,我退出了几次又继续使用同一个prompt,但是实际的模型是需要读取原先写到一半的代码,这样其实每次的生成上下文就被搞混乱了,所以重新清理了代码,再给kimi一次机会,这次就慢慢等了 首先也是列出了todo list 完成后主体的代码也不多
+import React , { useState, useEffect } from 'react' ;import styled from '@emotion/styled' ;import { motion, AnimatePresence } from 'framer-motion' ;import { Plus } from 'lucide-react' ;import TodoItem from './TodoItem' ;import TodoForm from './TodoForm' ;export interface Todo { id : string ; text : string ; completed : boolean ; createdAt : Date ; priority : 'low' | 'medium' | 'high' ; } const Container = styled (motion.div )` background: rgba(255, 255, 255, 0.95); backdrop-filter: blur(20px); border-radius: 24px; box-shadow: 0 25px 50px -12px rgba(0, 0, 0, 0.25); width: 100%; max-width: 500px; min-height: 600px; padding: 30px; position: relative; overflow: hidden; ` ;const Header = styled.div ` text-align: center; margin-bottom: 30px; ` ;const Title = styled.h1 ` font-size: 2.5rem; font-weight: 700; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; margin: 0; letter-spacing: -0.02em; ` ;const Subtitle = styled.p ` color: #6b7280; font-size: 1rem; margin: 8px 0 0 0; font-weight: 500; ` ;const TodoList = styled.div ` margin-top: 30px; max-height: 400px; overflow-y: auto; padding-right: 10px; &::-webkit-scrollbar { width: 6px; } &::-webkit-scrollbar-track { background: #f1f5f9; border-radius: 3px; } &::-webkit-scrollbar-thumb { background: #cbd5e1; border-radius: 3px; } &::-webkit-scrollbar-thumb:hover { background: #94a3b8; } ` ;const EmptyState = styled (motion.div )` text-align: center; padding: 60px 20px; color: #9ca3af; ` ;const EmptyIcon = styled.div ` font-size: 4rem; margin-bottom: 16px; opacity: 0.5; ` ;const EmptyText = styled.p ` font-size: 1.1rem; margin: 0; font-weight: 500; ` ;const Stats = styled.div ` display: flex; justify-content: space-between; align-items: center; margin-top: 20px; padding-top: 20px; border-top: 1px solid #e5e7eb; ` ;const StatItem = styled.div ` text-align: center; ` ;const StatNumber = styled.div ` font-size: 1.5rem; font-weight: 700; color: #667eea; ` ;const StatLabel = styled.div ` font-size: 0.875rem; color: #6b7280; margin-top: 4px; ` ;const TodoApp : React .FC = () => { const [todos, setTodos] = useState<Todo []>([]); const [showForm, setShowForm] = useState (false ); const [editingTodo, setEditingTodo] = useState<Todo | null >(null ); useEffect (() => { const savedTodos = localStorage .getItem ('todos' ); if (savedTodos) { setTodos (JSON .parse (savedTodos).map ((todo: any ) => ({ ...todo, createdAt : new Date (todo.createdAt ) }))); } }, []); useEffect (() => { localStorage .setItem ('todos' , JSON .stringify (todos)); }, [todos]); const addTodo = (text: string , priority: 'low' | 'medium' | 'high' ) => { const newTodo : Todo = { id : Date .now ().toString (), text, completed : false , createdAt : new Date (), priority }; setTodos ([newTodo, ...todos]); setShowForm (false ); }; const toggleTodo = (id: string ) => { setTodos (todos.map (todo => todo.id === id ? { ...todo, completed : !todo.completed } : todo )); }; const deleteTodo = (id: string ) => { setTodos (todos.filter (todo => todo.id !== id)); }; const editTodo = (id: string , newText: string , newPriority: 'low' | 'medium' | 'high' ) => { setTodos (todos.map (todo => todo.id === id ? { ...todo, text : newText, priority : newPriority } : todo )); setEditingTodo (null ); }; const completedCount = todos.filter (todo => todo.completed ).length ; const totalCount = todos.length ; return ( <Container initial ={{ opacity: 0 , y: 20 }} animate ={{ opacity: 1 , y: 0 }} transition ={{ duration: 0.6 , ease: "easeOut " }} > <Header > <Title > My Tasks</Title > <Subtitle > Stay organized and productive</Subtitle > </Header > {showForm && ( <TodoForm onSubmit ={addTodo} onCancel ={() => setShowForm(false)} /> )} {editingTodo && ( <TodoForm todo ={editingTodo} onSubmit ={(text, priority ) => editTodo(editingTodo.id, text, priority)} onCancel={() => setEditingTodo(null)} /> )} <TodoList > <AnimatePresence > {todos.length === 0 ? ( <EmptyState initial ={{ opacity: 0 }} animate ={{ opacity: 1 }} exit ={{ opacity: 0 }} > <EmptyIcon > 📝</EmptyIcon > <EmptyText > No tasks yet. Add one to get started!</EmptyText > </EmptyState > ) : ( todos.map((todo) => ( <TodoItem key ={todo.id} todo ={todo} onToggle ={toggleTodo} onDelete ={deleteTodo} onEdit ={setEditingTodo} /> )) )} </AnimatePresence > </TodoList > {todos.length > 0 && ( <Stats > <StatItem > <StatNumber > {totalCount}</StatNumber > <StatLabel > Total</StatLabel > </StatItem > <StatItem > <StatNumber > {completedCount}</StatNumber > <StatLabel > Completed</StatLabel > </StatItem > <StatItem > <StatNumber > {totalCount - completedCount}</StatNumber > <StatLabel > Remaining</StatLabel > </StatItem > </Stats > )} {!showForm && !editingTodo && ( <motion.button style ={{ position: 'absolute ', bottom: '30px ', right: '30px ', width: '60px ', height: '60px ', borderRadius: '50 %', background: 'linear-gradient (135deg , #667eea 0 %, #764ba2 100 %)', border: 'none ', color: 'white ', fontSize: '24px ', cursor: 'pointer ', boxShadow: '0 10px 25px rgba (102 , 126 , 234 , 0.4 )', display: 'flex ', alignItems: 'center ', justifyContent: 'center ' }} whileHover ={{ scale: 1.1 }} whileTap ={{ scale: 0.95 }} onClick ={() => setShowForm(true)} > <Plus size ={24} /> </motion.button > )} </Container > ); }; export default TodoApp ;
+一开始运行出现了个问题,新增的时候报错 Component selectors can only be used in conjunction with @emotion/babel-plugin, the swc Emotion plugin, or another Emotion-aware compiler transform. 就继续让kimi来解决这个问题,结果一次就改好了, 整体看起来也还行,可能的确是我一开始中断了几次导致上下文太复杂,只是不确定是模型消耗资源比较多还是什么原因响应有时候会很慢,可能免费的资源池是共用的,高峰期就比较慢了
]]>
- 技巧
+ LLM
- 技巧
+ LLM
@@ -6402,11 +6387,26 @@ 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
-
+
+ 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
@@ -6421,87 +6421,6 @@ user3:
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 Settingsfrom llama_index.embeddings.huggingface import HuggingFaceEmbeddingSettings.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。
-]]>
-
- LLM
-
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- LLM
-
-
-
- 寄生虫观后感
- /2020/03/01/%E5%AF%84%E7%94%9F%E8%99%AB%E8%A7%82%E5%90%8E%E6%84%9F/
- 寄生虫这部电影在获得奥斯卡之前就有关注了,豆瓣评分很高,一开始看到这个片名以为是像《铁线虫入侵》那种灾难片,后来看到男主,宋康昊,也是老面孔了,从高中时候在学校操场组织看的《汉江怪物》,有点二的感觉,后来在大学寝室电脑上重新看的时候,室友跟我说是韩国国宝级演员,真人不可貌相,感觉是个呆子的形象。
-但是你说这不是个灾难片,而是个反映社会问题的,就业比较容易往这个方向猜,只是剧情会是怎么样的,一时也没啥头绪,后来不知道哪里看了下一个剧情透露,是一个穷人给富人做家教,然后把自己一家都带进富人家,如果是这样的话可能会把这个怎么带进去作为一个主线,不过事实告诉我,这没那么重要,从第一步朋友的介绍,就显得无比顺利,要去当家教了,作为一个穷成这样的人,瞬间转变成一个衣着得体,言行举止都没让富人家看出破绽的延世大学学生,这真的挺难让人理解,所谓江山易改,本性难移,还有就是这人也正好有那么好能力去辅导,并且诡异的是,多惠也是瞬间就喜欢上了男主,多惠跟将男主介绍给她做家教,也就是多惠原来的家教敏赫,应该也有不少的相处时间,这变了有点大了吧,当然这里也可能因为时长需要,如果说这一点是因为时长,那可能我所有的槽点都是因为这个吧,因为我理解的应该是把家里的人如何一步步地带进富人家,这应该是整个剧情的一个需要更多铺垫去克服这个矛盾点,有时候也想过如果我去当导演,是能拍出个啥,没这个机会,可能有也会是很扯淡的,当然这也不能阻拦我谈谈对这个点的一些看法,毕竟评价一台电冰箱不是说我必须得自己会制冷对吧,这大概是我觉得这个电影的第一个槽点,接下去接二连三的,就是我说的这个最核心的矛盾点,不知道谁说过,这种影视剧应该是源自于生活又高于生活,越是好的作品,越要接近生活,这样子才更能有感同身受。
-接下去的点是金基宇介绍金基婷去给多颂当美术家教,这一步又是我理解的败笔吧,就怎么说呢,没什么铺垫,突然从一个社会底层的穷姑娘,转变成一个气场爆表,把富人家太太唬得一愣一愣的,如果说富太太是比较简单无脑的,那富人自己应该是比较有见识而且是做 IT 的,给自己儿子女儿做家教的,查查底细也很正常吧,但是啥都没有,然后呢,她又开始耍司机的心机了,真的是莫名其妙了,司机真的很惨,窈窕淑女君子好逑,而且这个操作也让我摸不着头脑,这是多腹黑并且有经验才会这么操作,脱内裤真的是让我看得一愣愣的,更看得我一愣一愣的,富人竟然也完全按着这个思路去想了,完全没有别的可能呢,甚至可以去查下行车记录仪或者怎样的,或者有没有毛发体液啥的去检验下,毕竟金基婷也乘坐过这辆车,但是最最让我不懂的还是脱内裤这个操作,究竟是什么样的人才会的呢,值得思考。
-金基泽和忠淑的点也是比较奇怪,首先是金基泽,引起最后那个杀人事件的一个由头,大部分观点都是人为朴社长在之前跟老婆啪啪啪的时候说金基泽的身上有股乘地铁的人的味道,简而言之就是穷人的味道,还有去雯光丈夫身下拿钥匙是对金基泽和雯光丈夫身上的味道的鄙夷,可是这个原因真的站不住脚,即使是同样经济水平,如果身上有比较重的异味,背后讨论下,或者闻到了比较重的味道,有不适的表情和动作很正常吧,像雯光丈夫,在地下室里呆了那么久,身上有异味并且比较重太正常了,就跟在厕所呆久了不会觉得味道大,但是从没味道的地方一进有点味道的厕所就会觉得异样,略尴尬的理由;再说忠淑呢,感觉是太厉害了,能胜任这么一家有钱人的各种挑剔的饮食口味要求的保姆职位,也是让人看懵逼了,看到了不禁想到一个问题,这家人开头是那么地穷,不堪,突然转变成这么地像骗子家族,如果有这么好的骗人能力,应该不会到这种地步吧,如果真的是那么穷,没能力,没志气,又怎么会突然变成这么厉害呢,一家人各司其职,把富人家唬得团团转,而这个前提是,这些人的确能胜任这四个位置,这就是我非常不能理解的点。
-然后说回这个标题,寄生虫,不知道是不是翻译过来不准确,如果真的是叫寄生虫的话,这个寄生虫智商未免也太低了,没有像新冠那样机制,致死率低一点,传染能力强一点,潜伏期也能传染,这个寄生虫第一次受到免疫系统的攻击就自爆了;还有呢,作为一个社会比较低层的打工者,乡下人,对这个审题也是不太审的清,是指这一家人是社会的寄生虫,不思进取,并且死的应该,富人是傻白甜,又有钱又善良,这是给有钱人洗地了还是啥,这个奥斯卡真不知道是怎么得的,总觉得奥斯卡,甚至低一点,豆瓣,得奖的,评分高的都是被一群“精英党”把持的,有黑人主角的,得分高;有同性恋的,得分高;结局惨的,得分高;看不懂的,得分高;就像肖申克的救赎,真不知道是哪里好了,最近看了关于明朝那些事的三杨,杨溥的经历应该比这个厉害吧,可是外国人看不懂,就像外国人不懂中国为什么有反分裂国家法,经历了鸦片战争,八国联军,抗日战争等等,其实跟外国对于黑人的权益的问题,因为有南北战争,所以极度重视这个问题,相应的中国也有自己的历史,请理解。
-简而言之我对寄生虫的评分大概 5~6 分吧。
-]]>
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- 生活
- 影评
- 2020
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- 生活
- 影评
- 寄生虫
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- 学习下用Google的agent开发工具Agent Development Kit
- /2025/10/04/%E5%AD%A6%E4%B9%A0%E4%B8%8B%E7%94%A8Google%E7%9A%84agent%E5%BC%80%E5%8F%91%E5%B7%A5%E5%85%B7Agent-Development-Kit/
- 学习下用Google的agent开发工具Agent Development Kit简称 ADK,看着名字就不一般,冲着JDK的地位去的哈哈 首先我们安装下pom包
-<dependencies > <dependency > <groupId > com.google.adk</groupId > <artifactId > google-adk</artifactId > <version > 0.2.0</version > </dependency > <dependency > <groupId > com.google.adk</groupId > <artifactId > google-adk-dev</artifactId > <version > 0.2.0</version > </dependency > </dependencies > <build > <plugins > <plugin > <groupId > org.apache.maven.plugins</groupId > <artifactId > maven-compiler-plugin</artifactId > <version > 3.14.0</version > <configuration > <compilerArgs > <arg > -parameters</arg > </compilerArgs > </configuration > </plugin > </plugins > </build >
-
-这个版本也说明了它还不是个完全体 接下去可以生成一个简单的项目结构
-project_folder/ ├── pom.xml (or build.gradle) ├── src/ ├── └── main/ │ └── java/ │ └── agents/ │ └── multitool/ └── test/
-
-然后再multitool中创建个java文件,可以叫 MultiToolAgent.java 注意这里需要使用jdk 17及以上版本 示例代码如下
-package agents.multitool;import com.google.adk.agents.BaseAgent;import com.google.adk.agents.LlmAgent;import com.google.adk.events.Event;import com.google.adk.runner.InMemoryRunner;import com.google.adk.sessions.Session;import com.google.adk.tools.Annotations.Schema;import com.google.adk.tools.FunctionTool;import com.google.genai.types.Content;import com.google.genai.types.Part;import io.reactivex.rxjava3.core.Flowable;import java.nio.charset.StandardCharsets;import java.text.Normalizer;import java.time.ZoneId;import java.time.ZonedDateTime;import java.time.format.DateTimeFormatter;import java.util.Map;import java.util.Scanner;public class MultiToolAgent { private static String USER_ID = "student" ; private static String NAME = "multi_tool_agent" ; public static final BaseAgent ROOT_AGENT = initAgent(); public static BaseAgent initAgent () { return LlmAgent.builder() .name(NAME) .model("gemini-2.0-flash" ) .description("Agent to answer questions about the time and weather in a city." ) .instruction( "You are a helpful agent who can answer user questions about the time and weather" + " in a city." ) .tools( FunctionTool.create(MultiToolAgent.class, "getCurrentTime" ), FunctionTool.create(MultiToolAgent.class, "getWeather" )) .build(); } public static Map<String, String> getCurrentTime ( @Schema(name = "city", description = "The name of the city for which to retrieve the current time") String city) { String normalizedCity = Normalizer.normalize(city, Normalizer.Form.NFD) .trim() .toLowerCase() .replaceAll("(\\p{IsM}+|\\p{IsP}+)" , "" ) .replaceAll("\\s+" , "_" ); return ZoneId.getAvailableZoneIds().stream() .filter(zid -> zid.toLowerCase().endsWith("/" + normalizedCity)) .findFirst() .map( zid -> Map.of( "status" , "success" , "report" , "The current time in " + city + " is " + ZonedDateTime.now(ZoneId.of(zid)) .format(DateTimeFormatter.ofPattern("HH:mm" )) + "." )) .orElse( Map.of( "status" , "error" , "report" , "Sorry, I don't have timezone information for " + city + "." )); } public static Map<String, String> getWeather ( @Schema(name = "city", description = "The name of the city for which to retrieve the weather report") String city) { if (city.toLowerCase().equals("new york" )) { return Map.of( "status" , "success" , "report" , "The weather in New York is sunny with a temperature of 25 degrees Celsius (77 degrees" + " Fahrenheit)." ); } else { return Map.of( "status" , "error" , "report" , "Weather information for " + city + " is not available." ); } } public static void main (String[] args) throws Exception { InMemoryRunner runner = new InMemoryRunner (ROOT_AGENT); Session session = runner .sessionService() .createSession(NAME, USER_ID) .blockingGet(); try (Scanner scanner = new Scanner (System.in, StandardCharsets.UTF_8)) { while (true ) { System.out.print("\nYou > " ); String userInput = scanner.nextLine(); if ("quit" .equalsIgnoreCase(userInput)) { break ; } Content userMsg = Content.fromParts(Part.fromText(userInput)); Flowable<Event> events = runner.runAsync(USER_ID, session.id(), userMsg); System.out.print("\nAgent > " ); events.blockingForEach(event -> System.out.println(event.stringifyContent())); } } } }
-
-这个示例非常简单,就是初始化模型,提供基本的提示词,接下去就是两个简单的工具可供使用,提供城市的时间和纽约的天气 要运行起来需要申请api key,然后写到环境变量里
-export GOOGLE_GENAI_USE_VERTEXAI=FALSE export GOOGLE_API_KEY=PASTE_YOUR_ACTUAL_API_KEY_HERE
-
-在这里替换成自己的api key就行 然后运行
-mvn exec:java \ -Dexec.mainClass="com.google.adk.web.AdkWebServer" \ -Dexec.args="--adk.agents.source-dir=src/main/java" \ -Dexec.classpathScope="compile"
-
-就可以看到一个简单的运行界面 初使用后发现还是比较容易上手,只是后续更深入的还需要好好研究学习
-]]>
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- LLM
-
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- LLM
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- 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
-]]>
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- 生活
-
-
- 生活
- 小技巧
- 运动
- 减肥
- 跑步
- 干活
-
-
-
- 将claude用的更有效一些的小窍门
- /2025/10/12/%E5%B0%86claude%E7%94%A8%E7%9A%84%E6%9B%B4%E6%9C%89%E6%95%88%E4%B8%80%E4%BA%9B%E7%9A%84%E5%B0%8F%E7%AA%8D%E9%97%A8/
- claude的模型现在在写代码这块还是比较权威的,只是也别小瞧了本身它的通用能力 我们打开类似于gpt,claude经常是以文本形式的提问,再得到文本形式的回答,但是对于我们程序员来说,还有一些用途其实是可以更有效的 比如我们可以让claude帮我们画图 像我们的uml图,可以用文本化的plantuml或者mermaid等这些工具来画 比如我可以让claude给我画个深度学习路径图
-graph TD Start[开始学习神经网络] --> Phase1[第一阶段: 数学基础] Phase1 --> Math1[线性代数] Phase1 --> Math2[微积分] Phase1 --> Math3[概率统计] Math1 --> Math1a["矩阵运算、特征值<br/>📚 3Blue1Brown线性代数系列<br/>🔗 youtube.com/c/3blue1brown"] Math2 --> Math2a["偏导数、梯度、链式法则<br/>📚 MIT 18.01单变量微积分<br/>🔗 ocw.mit.edu"] Math3 --> Math3a["期望、方差、贝叶斯定理<br/>📚 Probabilistic ML书籍<br/>🔗 probml.github.io"] Math1a --> Phase2 Math2a --> Phase2 Math3a --> Phase2 Phase2[第二阶段: 编程基础] --> Prog1[Python编程] Phase2 --> Prog2[NumPy/Pandas] Phase2 --> Prog3[数据可视化] Prog1 --> Prog1a["基础语法、面向对象<br/>📚 Python Crash Course<br/>🔗 nostarch.com/python-crash-course"] Prog2 --> Prog2a["数组操作、数据处理<br/>📚 NumPy官方教程<br/>🔗 numpy.org/doc"] Prog3 --> Prog3a["Matplotlib、Seaborn<br/>📚 Python Data Science Handbook<br/>🔗 jakevdp.github.io"] Prog1a --> Phase3 Prog2a --> Phase3 Prog3a --> Phase3 Phase3[第三阶段: 机器学习基础] --> ML1[监督学习] Phase3 --> ML2[损失函数] Phase3 --> ML3[优化算法] ML1 --> ML1a["线性回归、逻辑回归<br/>📚 Andrew Ng ML课程<br/>🔗 coursera.org/learn/machine-learning"] ML2 --> ML2a["MSE、交叉熵<br/>📚 Deep Learning Book Ch5<br/>🔗 deeplearningbook.org"] ML3 --> ML3a["梯度下降、SGD<br/>📚 Sebastian Ruder优化综述<br/>🔗 ruder.io/optimizing-gradient-descent"] ML1a --> Phase4 ML2a --> Phase4 ML3a --> Phase4 Phase4[第四阶段: 神经网络基础] --> NN1[感知机] Phase4 --> NN2[前馈神经网络] Phase4 --> NN3[反向传播] NN1 --> NN1a["单层感知机、多层感知机<br/>📚 Neural Networks and Deep Learning<br/>🔗 neuralnetworksanddeeplearning.com"] NN2 --> NN2a["全连接层、激活函数<br/>📚 Stanford CS231n Lecture 4<br/>🔗 cs231n.stanford.edu"] NN3 --> NN3a["链式法则、梯度计算<br/>📚 Backprop Calculus详解<br/>🔗 colah.github.io"] NN1a --> Phase5 NN2a --> Phase5 NN3a --> Phase5 Phase5[第五阶段: 深度学习框架] --> FW1[PyTorch] Phase5 --> FW2[TensorFlow/Keras] FW1 --> FW1a["Tensor操作、自动微分<br/>📚 PyTorch官方教程<br/>🔗 pytorch.org/tutorials"] FW2 --> FW2a["模型构建、训练流程<br/>📚 TensorFlow实战<br/>🔗 tensorflow.org/tutorials"] FW1a --> Phase6 FW2a --> Phase6 Phase6[第六阶段: 卷积神经网络CNN] --> CNN1[卷积层原理] Phase6 --> CNN2[经典架构] Phase6 --> CNN3[应用领域] CNN1 --> CNN1a["卷积、池化、感受野<br/>📚 CS231n Convolutional Networks<br/>🔗 cs231n.github.io/convolutional-networks"] CNN2 --> CNN2a["LeNet、AlexNet、VGG<br/>ResNet、Inception、EfficientNet<br/>📚 论文集合: paperswithcode.com<br/>🔗 paperswithcode.com/methods/category/convolutional-neural-networks"] CNN3 --> CNN3a["图像分类、目标检测、分割<br/>📚 Computer Vision: Algorithms and Applications<br/>🔗 szeliski.org/Book"] CNN1a --> Phase7 CNN2a --> Phase7 CNN3a --> Phase7 Phase7[第七阶段: 循环神经网络RNN] --> RNN1[RNN基础] Phase7 --> RNN2[LSTM/GRU] Phase7 --> RNN3[序列建模] RNN1 --> RNN1a["循环结构、时间展开<br/>📚 Understanding LSTM Networks<br/>🔗 colah.github.io/posts/2015-08-Understanding-LSTMs"] RNN2 --> RNN2a["门控机制、长期依赖<br/>📚 Illustrated Guide to LSTM/GRU<br/>🔗 towardsdatascience.com"] RNN3 --> RNN3a["时间序列、语言模型<br/>📚 Sequence Models课程<br/>🔗 coursera.org/learn/nlp-sequence-models"] RNN1a --> Phase8 RNN2a --> Phase8 RNN3a --> Phase8 Phase8[第八阶段: 注意力机制与Transformer] --> ATT1[注意力机制] Phase8 --> ATT2[Transformer架构] Phase8 --> ATT3[预训练模型] ATT1 --> ATT1a["Self-Attention、Multi-Head<br/>📚 Attention Is All You Need<br/>🔗 arxiv.org/abs/1706.03762"] ATT2 --> ATT2a["Encoder-Decoder、位置编码<br/>📚 The Illustrated Transformer<br/>🔗 jalammar.github.io/illustrated-transformer"] ATT3 --> ATT3a["BERT、GPT系列、T5<br/>📚 Hugging Face Course<br/>🔗 huggingface.co/course"] ATT1a --> Phase9 ATT2a --> Phase9 ATT3a --> Phase9 Phase9[第九阶段: 生成模型] --> GEN1[自编码器] Phase9 --> GEN2[生成对抗网络GAN] Phase9 --> GEN3[扩散模型] GEN1 --> GEN1a["AE、VAE、特征学习<br/>📚 Tutorial on VAE<br/>🔗 arxiv.org/abs/1606.05908"] GEN2 --> GEN2a["判别器、生成器、对抗训练<br/>📚 GAN Lab交互式可视化<br/>🔗 poloclub.github.io/ganlab"] GEN3 --> GEN3a["DDPM、Stable Diffusion<br/>📚 Diffusion Models教程<br/>🔗 lilianweng.github.io/posts/2021-07-11-diffusion-models"] GEN1a --> Phase10 GEN2a --> Phase10 GEN3a --> Phase10 Phase10[第十阶段: 高级技术] --> ADV1[正则化技术] Phase10 --> ADV2[优化技巧] Phase10 --> ADV3[模型压缩] ADV1 --> ADV1a["Dropout、BatchNorm、数据增强<br/>📚 CS231n训练技巧<br/>🔗 cs231n.github.io/neural-networks-2"] ADV2 --> ADV2a["Adam、学习率调度、梯度裁剪<br/>📚 An Overview of Optimization<br/>🔗 arxiv.org/abs/1609.04747"] ADV3 --> ADV3a["剪枝、量化、知识蒸馏<br/>📚 Model Compression Survey<br/>🔗 arxiv.org/abs/1710.09282"] ADV1a --> Phase11 ADV2a --> Phase11 ADV3a --> Phase11 Phase11[第十一阶段: 实践项目] --> PROJ1[计算机视觉项目] Phase11 --> PROJ2[自然语言处理项目] Phase11 --> PROJ3[多模态项目] PROJ1 --> PROJ1a["Kaggle图像竞赛<br/>物体检测系统<br/>📚 PyImageSearch教程<br/>🔗 pyimagesearch.com"] PROJ2 --> PROJ2a["文本分类、情感分析<br/>问答系统、对话机器人<br/>📚 实战项目集合<br/>🔗 github.com/dair-ai/ML-Papers-Explained"] PROJ3 --> PROJ3a["图像描述、视觉问答<br/>📚 OpenAI CLIP论文<br/>🔗 arxiv.org/abs/2103.00020"] PROJ1a --> Phase12 PROJ2a --> Phase12 PROJ3a --> Phase12 Phase12[第十二阶段: 前沿研究] --> RES1[大语言模型LLM] Phase12 --> RES2[强化学习] Phase12 --> RES3[神经架构搜索] RES1 --> RES1a["GPT-4、Claude、Llama系列<br/>📚 LLM综述论文<br/>🔗 arxiv.org/abs/2303.18223"] RES2 --> RES2a["DQN、PPO、AlphaGo<br/>📚 Spinning Up in Deep RL<br/>🔗 spinningup.openai.com"] RES3 --> RES3a["AutoML、NAS方法<br/>📚 NAS Survey<br/>🔗 arxiv.org/abs/1808.05377"] RES1a --> End RES2a --> End RES3a --> End End[🎓 持续学习与研究] style Start fill:#e1f5e1 style Phase1 fill:#fff4e6 style Phase2 fill:#fff4e6 style Phase3 fill:#e3f2fd style Phase4 fill:#e3f2fd style Phase5 fill:#f3e5f5 style Phase6 fill:#fce4ec style Phase7 fill:#fce4ec style Phase8 fill:#e0f2f1 style Phase9 fill:#fff9c4 style Phase10 fill:#ffebee style Phase11 fill:#e8f5e9 style Phase12 fill:#e1bee7 style End fill:#c8e6c9
-生成出来的图效果还是挺不错的 另外比如我们想要生成一个电商系统的下单交易流程
-sequenceDiagram actor User as 👤 用户 participant Web as 🌐 Web/App前端 participant Gateway as 🚪 API网关 participant Auth as 🔐 认证服务 participant Product as 📦 商品服务 participant Cart as 🛒 购物车服务 participant Order as 📋 订单服务 participant Inventory as 📊 库存服务 participant Coupon as 🎫 优惠券服务 participant Payment as 💳 支付服务 participant PayGateway as 💰 支付网关 participant MQ as 📨 消息队列 participant WMS as 🏭 仓储系统 participant Logistics as 🚚 物流系统 participant Notify as 📧 通知服务 participant AfterSale as 🔄 售后服务 participant Finance as 💵 财务系统 Note over User,Finance: 📚 参考架构:微服务电商系统<br/>🔗 github.com/macrozheng/mall %% 登录认证阶段 rect rgb(230, 245, 255) Note over User,Auth: 第一阶段:用户认证 User->>Web: 1. 打开电商平台 Web->>Gateway: 2. 请求登录页面 Gateway->>Auth: 3. 验证会话状态 alt 未登录 Auth-->>Web: 4. 返回登录页面 User->>Web: 5. 输入账号密码/手机验证码 Web->>Auth: 6. 提交登录请求 Note right of Auth: 🔐 JWT Token生成<br/>📚 RFC 7519标准<br/>🔗 jwt.io Auth->>Auth: 7. 验证凭证 Auth-->>Web: 8. 返回Token Web->>Web: 9. 存储Token到Cookie/LocalStorage else 已登录 Auth-->>Web: 4. 验证通过 end end %% 商品浏览阶段 rect rgb(245, 255, 230) Note over User,Product: 第二阶段:商品浏览 User->>Web: 10. 搜索/浏览商品 Web->>Gateway: 11. 商品查询请求 Gateway->>Product: 12. 转发查询 Note right of Product: 🔍 Elasticsearch全文搜索<br/>📚 搜索引擎实战<br/>🔗 elastic.co/guide Product->>Product: 13. 从ES/缓存查询 Product-->>Web: 14. 返回商品列表 User->>Web: 15. 点击商品详情 Web->>Gateway: 16. 请求商品详情 Gateway->>Product: 17. 查询商品信息 Gateway->>Inventory: 18. 查询库存信息 Note right of Inventory: 💾 Redis缓存+MySQL<br/>📚 缓存穿透/击穿方案<br/>🔗 redis.io/topics/lru-cache par 并行查询 Product-->>Gateway: 19a. 返回商品详情 and Inventory-->>Gateway: 19b. 返回库存数量 end Gateway-->>Web: 20. 聚合返回 end %% 购物车阶段 rect rgb(255, 245, 230) Note over User,Cart: 第三阶段:加入购物车 User->>Web: 21. 选择规格并加入购物车 Web->>Gateway: 22. 加购请求 Gateway->>Cart: 23. 添加到购物车 Note right of Cart: 🗄️ Redis存储购物车<br/>📚 分布式Session方案<br/>🔗 spring.io/projects/spring-session Cart->>Inventory: 24. 验证库存是否充足 Inventory-->>Cart: 25. 返回库存状态 Cart->>Cart: 26. 计算商品小计 Cart-->>Web: 27. 返回购物车数据 User->>Web: 28. 查看购物车 Web->>Gateway: 29. 获取购物车列表 Gateway->>Cart: 30. 查询购物车 Cart->>Product: 31. 批量查询商品最新价格 Cart->>Coupon: 32. 查询可用优惠券 Note right of Coupon: 🎫 优惠券系统设计<br/>📚 促销引擎架构<br/>🔗 tech.meituan.com par 并行查询 Product-->>Cart: 33a. 返回价格信息 and Coupon-->>Cart: 33b. 返回可用优惠券 end Cart-->>Web: 34. 返回购物车详情 end %% 订单创建阶段 rect rgb(255, 240, 245) Note over User,Order: 第四阶段:订单创建 User->>Web: 35. 点击结算 Web->>Gateway: 36. 进入结算页 Gateway->>Order: 37. 创建预订单 User->>Web: 38. 选择收货地址/优惠券 Web->>Gateway: 39. 提交订单 Gateway->>Order: 40. 创建订单请求 Note right of Order: 🔢 雪花算法生成订单号<br/>📚 分布式ID生成方案<br/>🔗 github.com/twitter/snowflake Order->>Order: 41. 生成订单号 Order->>Coupon: 42. 锁定优惠券 Order->>Inventory: 43. 预扣减库存 Note right of Inventory: ⚠️ 分布式锁防超卖<br/>📚 Redis+Lua脚本<br/>🔗 redisson.org alt 库存充足 Inventory-->>Order: 44. 扣减成功 Coupon-->>Order: 45. 锁定成功 Order->>Order: 46. 订单状态:待支付 Order-->>Web: 47. 返回订单信息 else 库存不足 Inventory-->>Order: 44. 库存不足 Order-->>Web: 47. 返回库存不足提示 end end %% 支付阶段 rect rgb(240, 248, 255) Note over User,PayGateway: 第五阶段:支付处理 User->>Web: 48. 选择支付方式 Web->>Gateway: 49. 发起支付请求 Gateway->>Payment: 50. 创建支付单 Note right of Payment: 💳 聚合支付设计<br/>📚 支付系统架构<br/>🔗 github.com/Exrick/xpay Payment->>Payment: 51. 生成支付单号 Payment->>Order: 52. 关联订单 Payment->>PayGateway: 53. 调用支付渠道 alt 支付宝支付 Note right of PayGateway: 💰 支付宝SDK<br/>📚 开放平台文档<br/>🔗 opendocs.alipay.com PayGateway->>PayGateway: 54. 调用支付宝API else 微信支付 Note right of PayGateway: 💚 微信支付API<br/>📚 支付开发文档<br/>🔗 pay.weixin.qq.com PayGateway->>PayGateway: 54. 调用微信支付API end PayGateway-->>User: 55. 返回支付页面/二维码 User->>User: 56. 完成支付操作 Note over PayGateway,Payment: 🔔 异步回调通知 PayGateway->>Payment: 57. 支付成功回调 Note right of Payment: 📚 幂等性设计<br/>🔗 martinfowler.com/articles/patterns-of-distributed-systems Payment->>Payment: 58. 验证签名并去重 Payment->>Order: 59. 更新订单状态为已支付 Payment->>MQ: 60. 发送支付成功消息 par 异步处理 MQ->>Notify: 61a. 发送通知 Note right of Notify: 📧 消息推送<br/>📚 RabbitMQ/Kafka<br/>🔗 rabbitmq.com Notify->>User: 短信/邮件/Push通知 and MQ->>Order: 61b. 确认订单 Order->>Order: 更新订单:待发货 end end %% 仓储物流阶段 rect rgb(232, 245, 233) Note over Order,Logistics: 第六阶段:仓储配送 Order->>WMS: 62. 推送发货指令 Note right of WMS: 📦 WMS系统设计<br/>📚 仓储管理实践<br/>🔗 github.com/topics/wms WMS->>WMS: 63. 订单拆分(多仓) WMS->>WMS: 64. 波次拣货 WMS->>WMS: 65. 打包称重 WMS->>Logistics: 66. 创建物流订单 Note right of Logistics: 🚚 电子面单生成<br/>📚 菜鸟/快递鸟API<br/>🔗 kdniao.com Logistics->>Logistics: 67. 生成运单号 Logistics->>Logistics: 68. 打印电子面单 Logistics-->>WMS: 69. 返回物流信息 WMS->>Order: 70. 更新订单为已发货 Order->>MQ: 71. 发送发货消息 MQ->>Notify: 72. 通知用户已发货 Notify->>User: 73. 发送物流信息 loop 物流跟踪 Logistics->>Logistics: 74. 更新物流轨迹 Note right of Logistics: 🗺️ 物流轨迹追踪<br/>📚 快递100 API<br/>🔗 kuaidi100.com Logistics->>Order: 75. 同步物流状态 Order->>Notify: 76. 推送物流更新 Notify->>User: 77. 实时物流通知 end Logistics->>Order: 78. 签收成功 Order->>Order: 79. 更新订单:已签收 Order->>MQ: 80. 发送签收消息 end %% 订单完成阶段 rect rgb(255, 243, 224) Note over User,Finance: 第七阶段:订单完成 MQ->>Order: 81. 触发自动确认收货定时器 Note right of Order: ⏰ 7天自动确认<br/>📚 XXL-Job定时任务<br/>🔗 xuxueli.com/xxl-job alt 用户主动确认 User->>Web: 82a. 确认收货 Web->>Order: 83a. 确认收货请求 else 超时自动确认 Order->>Order: 82b. 7天后自动确认 end Order->>Order: 84. 订单状态:已完成 Order->>Finance: 85. 触发结算 Note right of Finance: 💰 T+1结算<br/>📚 财务对账系统<br/>🔗 accounting-system-design.com Finance->>Finance: 86. 计算平台佣金 Finance->>Finance: 87. 生成结算单 User->>Web: 88. 评价商品 Web->>Product: 89. 提交评价 Note right of Product: ⭐ UGC内容审核<br/>📚 评价系统设计<br/>🔗 review-system-architecture.com Product->>Product: 90. 审核并发布评价 end %% 售后阶段 rect rgb(255, 235, 238) Note over User,AfterSale: 第八阶段:售后服务(可选) opt 需要售后 User->>Web: 91. 申请退货/换货 Web->>AfterSale: 92. 创建售后单 Note right of AfterSale: 🔄 售后工单系统<br/>📚 7天无理由退货<br/>🔗 消费者权益保护法 AfterSale->>AfterSale: 93. 售后审核 alt 审核通过 AfterSale-->>User: 94. 审核通过,返回退货地址 User->>Logistics: 95. 寄回商品 Logistics->>WMS: 96. 商品入库 WMS->>AfterSale: 97. 确认收货 AfterSale->>Inventory: 98. 库存回补 AfterSale->>Payment: 99. 发起退款 Note right of Payment: 💵 原路退回<br/>📚 退款流程设计<br/>🔗 refund-process-design.com Payment->>PayGateway: 100. 调用退款接口 PayGateway-->>Payment: 101. 退款成功 Payment->>MQ: 102. 发送退款消息 MQ->>Notify: 103. 通知用户 Notify->>User: 104. 退款到账通知 else 审核拒绝 AfterSale-->>User: 94. 拒绝售后申请 end end end %% 数据分析阶段 rect rgb(227, 242, 253) Note over Order,Finance: 第九阶段:数据统计分析 Note over Order,Finance: 📊 实时数据大屏<br/>📚 Flink流式计算<br/>🔗 flink.apache.org par 数据采集 Order->>MQ: 订单数据埋点 and Payment->>MQ: 支付数据埋点 and Product->>MQ: 商品数据埋点 end MQ->>Finance: 数据聚合分析 Finance->>Finance: 生成报表<br/>(销售额/转化率/ROI等) Note right of Finance: 📈 BI系统<br/>📚 数据仓库建设<br/>🔗 github.com/topics/data-warehouse end Note over User,Finance: ✅ 完整交易流程结束
-
]]>
LLM
@@ -6642,16 +6561,84 @@ user3:
- 屯菜惊魂记
- /2022/04/24/%E5%B1%AF%E8%8F%9C%E6%83%8A%E9%AD%82%E8%AE%B0/
- 因某国际大都市的给力表现,昨儿旁边行政区启动应急响应,同事早上就在群里说要去超市买菜了,到了超市人还特别多,由于来的就是我们经常去的那家超市,一方面为了安全,另一方面是怕已经抢不到了,就去了另一家比较远的超市,开车怕没车位就骑了小电驴,还下着小雨,结果到了超市差不多 12 点多,超市里出来的人都是推着一整车一整车的物资,有些比较像我,整箱的泡面,好几提纸巾,还有各种吃的,都是整箱整箱的,进了超市发现结账包括自助结账的都排很长的队,到了蔬菜货架附近,差点哼起那首歌“空空如也~”,新鲜蔬菜基本已被抢空,只剩下一些卖相不太好的土豆番薯之类的,也算是意料之外情理之中了,本来以为这家超市稍微离封控区远一些会空一点,结果就是所谓的某大都市封控了等物资,杭州市是屯了物资等封控,新鲜蔬菜没了我们也只能买点其他的,神奇的是水果基本都在,可能困难时期水果不算必需品了?还是水果基本人人都已经储备了很多,不太能理解,虽然水果还在,但是称重的地方也还有好多人排队,我们采取了并行策略,LD 在那排队,遥控指挥我去拿其他物资,拿了点碱水面,黑米,那黑米的时候还闹了个乌龙,因为前面就是散装鸡蛋的堆货的地方,结果我们以为是在那后面排队,结果称重那个在那散步了,我们还在那排队,看到后面排队,那几个挑的人也该提醒下吧,几个鸡蛋挑了半天,看看人家大妈,直接拿了四盘,看了下牛奶货架也比较空,不过还有致优跟优倍,不过不算很实惠,本来想买,只是后来赶着去结账,就给忘了,称好了黑米去看了下肉,结果肉也没了,都在买猪蹄,我们也不太爱吃猪蹄,就买了点鸡胸肉,整体看起来我们买的东西真的有点格格不入,不买泡面(因为 LD 不让买了),也不屯啥米和鸡蛋,其实鸡蛋已经买了,米也买了,其他的本身冰箱小也放不下太多东西,我是觉得还可能在屯一点这那的,LD 觉得太多了,基本的米面油有了,其他调味品什么也有了。后面就是排队结账,我去排的时候刚好前面一个小伙子跟大妈在争执,大妈说我们差不多时间来的,你要排前面就前面,小伙子有点不高兴,觉得她就是插队,哈哈,平时一般这种剧情都是发生在我身上的,这会看着前面的吵起来还是很开心的,终于有跟我一样较真的人了,有时候总觉得我是个很纠结,很较真的人,但是我现在慢慢认可了这种较真,如果没有人指出来这种是插队行为,是不对的,就会有越来越多的人觉得是可以随意插队的,正确的事应该要坚持,很多情况大家总是觉得多一事不如少一事,鸡毛蒜皮的没什么好计较的,正是这种想法,那么多人才不管任何规则,反而搞得像遵守规则都是傻 X 似的。回到屯物资,后面结账排到队了也没来得及买原来想买的花生牛奶什么的,毕竟那么多人排着队,回家后因为没有蔬菜,结果就只能吃干菜汤和饭了
+ 学习下用Google的agent开发工具Agent Development Kit
+ /2025/10/04/%E5%AD%A6%E4%B9%A0%E4%B8%8B%E7%94%A8Google%E7%9A%84agent%E5%BC%80%E5%8F%91%E5%B7%A5%E5%85%B7Agent-Development-Kit/
+ 学习下用Google的agent开发工具Agent Development Kit简称 ADK,看着名字就不一般,冲着JDK的地位去的哈哈 首先我们安装下pom包
+<dependencies > <dependency > <groupId > com.google.adk</groupId > <artifactId > google-adk</artifactId > <version > 0.2.0</version > </dependency > <dependency > <groupId > com.google.adk</groupId > <artifactId > google-adk-dev</artifactId > <version > 0.2.0</version > </dependency > </dependencies > <build > <plugins > <plugin > <groupId > org.apache.maven.plugins</groupId > <artifactId > maven-compiler-plugin</artifactId > <version > 3.14.0</version > <configuration > <compilerArgs > <arg > -parameters</arg > </compilerArgs > </configuration > </plugin > </plugins > </build >
+
+这个版本也说明了它还不是个完全体 接下去可以生成一个简单的项目结构
+project_folder/ ├── pom.xml (or build.gradle) ├── src/ ├── └── main/ │ └── java/ │ └── agents/ │ └── multitool/ └── test/
+
+然后再multitool中创建个java文件,可以叫 MultiToolAgent.java 注意这里需要使用jdk 17及以上版本 示例代码如下
+package agents.multitool;import com.google.adk.agents.BaseAgent;import com.google.adk.agents.LlmAgent;import com.google.adk.events.Event;import com.google.adk.runner.InMemoryRunner;import com.google.adk.sessions.Session;import com.google.adk.tools.Annotations.Schema;import com.google.adk.tools.FunctionTool;import com.google.genai.types.Content;import com.google.genai.types.Part;import io.reactivex.rxjava3.core.Flowable;import java.nio.charset.StandardCharsets;import java.text.Normalizer;import java.time.ZoneId;import java.time.ZonedDateTime;import java.time.format.DateTimeFormatter;import java.util.Map;import java.util.Scanner;public class MultiToolAgent { private static String USER_ID = "student" ; private static String NAME = "multi_tool_agent" ; public static final BaseAgent ROOT_AGENT = initAgent(); public static BaseAgent initAgent () { return LlmAgent.builder() .name(NAME) .model("gemini-2.0-flash" ) .description("Agent to answer questions about the time and weather in a city." ) .instruction( "You are a helpful agent who can answer user questions about the time and weather" + " in a city." ) .tools( FunctionTool.create(MultiToolAgent.class, "getCurrentTime" ), FunctionTool.create(MultiToolAgent.class, "getWeather" )) .build(); } public static Map<String, String> getCurrentTime ( @Schema(name = "city", description = "The name of the city for which to retrieve the current time") String city) { String normalizedCity = Normalizer.normalize(city, Normalizer.Form.NFD) .trim() .toLowerCase() .replaceAll("(\\p{IsM}+|\\p{IsP}+)" , "" ) .replaceAll("\\s+" , "_" ); return ZoneId.getAvailableZoneIds().stream() .filter(zid -> zid.toLowerCase().endsWith("/" + normalizedCity)) .findFirst() .map( zid -> Map.of( "status" , "success" , "report" , "The current time in " + city + " is " + ZonedDateTime.now(ZoneId.of(zid)) .format(DateTimeFormatter.ofPattern("HH:mm" )) + "." )) .orElse( Map.of( "status" , "error" , "report" , "Sorry, I don't have timezone information for " + city + "." )); } public static Map<String, String> getWeather ( @Schema(name = "city", description = "The name of the city for which to retrieve the weather report") String city) { if (city.toLowerCase().equals("new york" )) { return Map.of( "status" , "success" , "report" , "The weather in New York is sunny with a temperature of 25 degrees Celsius (77 degrees" + " Fahrenheit)." ); } else { return Map.of( "status" , "error" , "report" , "Weather information for " + city + " is not available." ); } } public static void main (String[] args) throws Exception { InMemoryRunner runner = new InMemoryRunner (ROOT_AGENT); Session session = runner .sessionService() .createSession(NAME, USER_ID) .blockingGet(); try (Scanner scanner = new Scanner (System.in, StandardCharsets.UTF_8)) { while (true ) { System.out.print("\nYou > " ); String userInput = scanner.nextLine(); if ("quit" .equalsIgnoreCase(userInput)) { break ; } Content userMsg = Content.fromParts(Part.fromText(userInput)); Flowable<Event> events = runner.runAsync(USER_ID, session.id(), userMsg); System.out.print("\nAgent > " ); events.blockingForEach(event -> System.out.println(event.stringifyContent())); } } } }
+
+这个示例非常简单,就是初始化模型,提供基本的提示词,接下去就是两个简单的工具可供使用,提供城市的时间和纽约的天气 要运行起来需要申请api key,然后写到环境变量里
+export GOOGLE_GENAI_USE_VERTEXAI=FALSE export GOOGLE_API_KEY=PASTE_YOUR_ACTUAL_API_KEY_HERE
+
+在这里替换成自己的api key就行 然后运行
+mvn exec:java \ -Dexec.mainClass="com.google.adk.web.AdkWebServer" \ -Dexec.args="--adk.agents.source-dir=src/main/java" \ -Dexec.classpathScope="compile"
+
+就可以看到一个简单的运行界面 初使用后发现还是比较容易上手,只是后续更深入的还需要好好研究学习
+]]>
+
+ LLM
+
+
+ LLM
+
+
+
+ 寄生虫观后感
+ /2020/03/01/%E5%AF%84%E7%94%9F%E8%99%AB%E8%A7%82%E5%90%8E%E6%84%9F/
+ 寄生虫这部电影在获得奥斯卡之前就有关注了,豆瓣评分很高,一开始看到这个片名以为是像《铁线虫入侵》那种灾难片,后来看到男主,宋康昊,也是老面孔了,从高中时候在学校操场组织看的《汉江怪物》,有点二的感觉,后来在大学寝室电脑上重新看的时候,室友跟我说是韩国国宝级演员,真人不可貌相,感觉是个呆子的形象。
+但是你说这不是个灾难片,而是个反映社会问题的,就业比较容易往这个方向猜,只是剧情会是怎么样的,一时也没啥头绪,后来不知道哪里看了下一个剧情透露,是一个穷人给富人做家教,然后把自己一家都带进富人家,如果是这样的话可能会把这个怎么带进去作为一个主线,不过事实告诉我,这没那么重要,从第一步朋友的介绍,就显得无比顺利,要去当家教了,作为一个穷成这样的人,瞬间转变成一个衣着得体,言行举止都没让富人家看出破绽的延世大学学生,这真的挺难让人理解,所谓江山易改,本性难移,还有就是这人也正好有那么好能力去辅导,并且诡异的是,多惠也是瞬间就喜欢上了男主,多惠跟将男主介绍给她做家教,也就是多惠原来的家教敏赫,应该也有不少的相处时间,这变了有点大了吧,当然这里也可能因为时长需要,如果说这一点是因为时长,那可能我所有的槽点都是因为这个吧,因为我理解的应该是把家里的人如何一步步地带进富人家,这应该是整个剧情的一个需要更多铺垫去克服这个矛盾点,有时候也想过如果我去当导演,是能拍出个啥,没这个机会,可能有也会是很扯淡的,当然这也不能阻拦我谈谈对这个点的一些看法,毕竟评价一台电冰箱不是说我必须得自己会制冷对吧,这大概是我觉得这个电影的第一个槽点,接下去接二连三的,就是我说的这个最核心的矛盾点,不知道谁说过,这种影视剧应该是源自于生活又高于生活,越是好的作品,越要接近生活,这样子才更能有感同身受。
+接下去的点是金基宇介绍金基婷去给多颂当美术家教,这一步又是我理解的败笔吧,就怎么说呢,没什么铺垫,突然从一个社会底层的穷姑娘,转变成一个气场爆表,把富人家太太唬得一愣一愣的,如果说富太太是比较简单无脑的,那富人自己应该是比较有见识而且是做 IT 的,给自己儿子女儿做家教的,查查底细也很正常吧,但是啥都没有,然后呢,她又开始耍司机的心机了,真的是莫名其妙了,司机真的很惨,窈窕淑女君子好逑,而且这个操作也让我摸不着头脑,这是多腹黑并且有经验才会这么操作,脱内裤真的是让我看得一愣愣的,更看得我一愣一愣的,富人竟然也完全按着这个思路去想了,完全没有别的可能呢,甚至可以去查下行车记录仪或者怎样的,或者有没有毛发体液啥的去检验下,毕竟金基婷也乘坐过这辆车,但是最最让我不懂的还是脱内裤这个操作,究竟是什么样的人才会的呢,值得思考。
+金基泽和忠淑的点也是比较奇怪,首先是金基泽,引起最后那个杀人事件的一个由头,大部分观点都是人为朴社长在之前跟老婆啪啪啪的时候说金基泽的身上有股乘地铁的人的味道,简而言之就是穷人的味道,还有去雯光丈夫身下拿钥匙是对金基泽和雯光丈夫身上的味道的鄙夷,可是这个原因真的站不住脚,即使是同样经济水平,如果身上有比较重的异味,背后讨论下,或者闻到了比较重的味道,有不适的表情和动作很正常吧,像雯光丈夫,在地下室里呆了那么久,身上有异味并且比较重太正常了,就跟在厕所呆久了不会觉得味道大,但是从没味道的地方一进有点味道的厕所就会觉得异样,略尴尬的理由;再说忠淑呢,感觉是太厉害了,能胜任这么一家有钱人的各种挑剔的饮食口味要求的保姆职位,也是让人看懵逼了,看到了不禁想到一个问题,这家人开头是那么地穷,不堪,突然转变成这么地像骗子家族,如果有这么好的骗人能力,应该不会到这种地步吧,如果真的是那么穷,没能力,没志气,又怎么会突然变成这么厉害呢,一家人各司其职,把富人家唬得团团转,而这个前提是,这些人的确能胜任这四个位置,这就是我非常不能理解的点。
+然后说回这个标题,寄生虫,不知道是不是翻译过来不准确,如果真的是叫寄生虫的话,这个寄生虫智商未免也太低了,没有像新冠那样机制,致死率低一点,传染能力强一点,潜伏期也能传染,这个寄生虫第一次受到免疫系统的攻击就自爆了;还有呢,作为一个社会比较低层的打工者,乡下人,对这个审题也是不太审的清,是指这一家人是社会的寄生虫,不思进取,并且死的应该,富人是傻白甜,又有钱又善良,这是给有钱人洗地了还是啥,这个奥斯卡真不知道是怎么得的,总觉得奥斯卡,甚至低一点,豆瓣,得奖的,评分高的都是被一群“精英党”把持的,有黑人主角的,得分高;有同性恋的,得分高;结局惨的,得分高;看不懂的,得分高;就像肖申克的救赎,真不知道是哪里好了,最近看了关于明朝那些事的三杨,杨溥的经历应该比这个厉害吧,可是外国人看不懂,就像外国人不懂中国为什么有反分裂国家法,经历了鸦片战争,八国联军,抗日战争等等,其实跟外国对于黑人的权益的问题,因为有南北战争,所以极度重视这个问题,相应的中国也有自己的历史,请理解。
+简而言之我对寄生虫的评分大概 5~6 分吧。
]]>
生活
+ 影评
+ 2020
生活
- 囤物资
+ 影评
+ 寄生虫
+
+
+
+ 将claude用的更有效一些的小窍门
+ /2025/10/12/%E5%B0%86claude%E7%94%A8%E7%9A%84%E6%9B%B4%E6%9C%89%E6%95%88%E4%B8%80%E4%BA%9B%E7%9A%84%E5%B0%8F%E7%AA%8D%E9%97%A8/
+ claude的模型现在在写代码这块还是比较权威的,只是也别小瞧了本身它的通用能力 我们打开类似于gpt,claude经常是以文本形式的提问,再得到文本形式的回答,但是对于我们程序员来说,还有一些用途其实是可以更有效的 比如我们可以让claude帮我们画图 像我们的uml图,可以用文本化的plantuml或者mermaid等这些工具来画 比如我可以让claude给我画个深度学习路径图
+graph TD Start[开始学习神经网络] --> Phase1[第一阶段: 数学基础] Phase1 --> Math1[线性代数] Phase1 --> Math2[微积分] Phase1 --> Math3[概率统计] Math1 --> Math1a["矩阵运算、特征值<br/>📚 3Blue1Brown线性代数系列<br/>🔗 youtube.com/c/3blue1brown"] Math2 --> Math2a["偏导数、梯度、链式法则<br/>📚 MIT 18.01单变量微积分<br/>🔗 ocw.mit.edu"] Math3 --> Math3a["期望、方差、贝叶斯定理<br/>📚 Probabilistic ML书籍<br/>🔗 probml.github.io"] Math1a --> Phase2 Math2a --> Phase2 Math3a --> Phase2 Phase2[第二阶段: 编程基础] --> Prog1[Python编程] Phase2 --> Prog2[NumPy/Pandas] Phase2 --> Prog3[数据可视化] Prog1 --> Prog1a["基础语法、面向对象<br/>📚 Python Crash Course<br/>🔗 nostarch.com/python-crash-course"] Prog2 --> Prog2a["数组操作、数据处理<br/>📚 NumPy官方教程<br/>🔗 numpy.org/doc"] Prog3 --> Prog3a["Matplotlib、Seaborn<br/>📚 Python Data Science Handbook<br/>🔗 jakevdp.github.io"] Prog1a --> Phase3 Prog2a --> Phase3 Prog3a --> Phase3 Phase3[第三阶段: 机器学习基础] --> ML1[监督学习] Phase3 --> ML2[损失函数] Phase3 --> ML3[优化算法] ML1 --> ML1a["线性回归、逻辑回归<br/>📚 Andrew Ng ML课程<br/>🔗 coursera.org/learn/machine-learning"] ML2 --> ML2a["MSE、交叉熵<br/>📚 Deep Learning Book Ch5<br/>🔗 deeplearningbook.org"] ML3 --> ML3a["梯度下降、SGD<br/>📚 Sebastian Ruder优化综述<br/>🔗 ruder.io/optimizing-gradient-descent"] ML1a --> Phase4 ML2a --> Phase4 ML3a --> Phase4 Phase4[第四阶段: 神经网络基础] --> NN1[感知机] Phase4 --> NN2[前馈神经网络] Phase4 --> NN3[反向传播] NN1 --> NN1a["单层感知机、多层感知机<br/>📚 Neural Networks and Deep Learning<br/>🔗 neuralnetworksanddeeplearning.com"] NN2 --> NN2a["全连接层、激活函数<br/>📚 Stanford CS231n Lecture 4<br/>🔗 cs231n.stanford.edu"] NN3 --> NN3a["链式法则、梯度计算<br/>📚 Backprop Calculus详解<br/>🔗 colah.github.io"] NN1a --> Phase5 NN2a --> Phase5 NN3a --> Phase5 Phase5[第五阶段: 深度学习框架] --> FW1[PyTorch] Phase5 --> FW2[TensorFlow/Keras] FW1 --> FW1a["Tensor操作、自动微分<br/>📚 PyTorch官方教程<br/>🔗 pytorch.org/tutorials"] FW2 --> FW2a["模型构建、训练流程<br/>📚 TensorFlow实战<br/>🔗 tensorflow.org/tutorials"] FW1a --> Phase6 FW2a --> Phase6 Phase6[第六阶段: 卷积神经网络CNN] --> CNN1[卷积层原理] Phase6 --> CNN2[经典架构] Phase6 --> CNN3[应用领域] CNN1 --> CNN1a["卷积、池化、感受野<br/>📚 CS231n Convolutional Networks<br/>🔗 cs231n.github.io/convolutional-networks"] CNN2 --> CNN2a["LeNet、AlexNet、VGG<br/>ResNet、Inception、EfficientNet<br/>📚 论文集合: paperswithcode.com<br/>🔗 paperswithcode.com/methods/category/convolutional-neural-networks"] CNN3 --> CNN3a["图像分类、目标检测、分割<br/>📚 Computer Vision: Algorithms and Applications<br/>🔗 szeliski.org/Book"] CNN1a --> Phase7 CNN2a --> Phase7 CNN3a --> Phase7 Phase7[第七阶段: 循环神经网络RNN] --> RNN1[RNN基础] Phase7 --> RNN2[LSTM/GRU] Phase7 --> RNN3[序列建模] RNN1 --> RNN1a["循环结构、时间展开<br/>📚 Understanding LSTM Networks<br/>🔗 colah.github.io/posts/2015-08-Understanding-LSTMs"] RNN2 --> RNN2a["门控机制、长期依赖<br/>📚 Illustrated Guide to LSTM/GRU<br/>🔗 towardsdatascience.com"] RNN3 --> RNN3a["时间序列、语言模型<br/>📚 Sequence Models课程<br/>🔗 coursera.org/learn/nlp-sequence-models"] RNN1a --> Phase8 RNN2a --> Phase8 RNN3a --> Phase8 Phase8[第八阶段: 注意力机制与Transformer] --> ATT1[注意力机制] Phase8 --> ATT2[Transformer架构] Phase8 --> ATT3[预训练模型] ATT1 --> ATT1a["Self-Attention、Multi-Head<br/>📚 Attention Is All You Need<br/>🔗 arxiv.org/abs/1706.03762"] ATT2 --> ATT2a["Encoder-Decoder、位置编码<br/>📚 The Illustrated Transformer<br/>🔗 jalammar.github.io/illustrated-transformer"] ATT3 --> ATT3a["BERT、GPT系列、T5<br/>📚 Hugging Face Course<br/>🔗 huggingface.co/course"] ATT1a --> Phase9 ATT2a --> Phase9 ATT3a --> Phase9 Phase9[第九阶段: 生成模型] --> GEN1[自编码器] Phase9 --> GEN2[生成对抗网络GAN] Phase9 --> GEN3[扩散模型] GEN1 --> GEN1a["AE、VAE、特征学习<br/>📚 Tutorial on VAE<br/>🔗 arxiv.org/abs/1606.05908"] GEN2 --> GEN2a["判别器、生成器、对抗训练<br/>📚 GAN Lab交互式可视化<br/>🔗 poloclub.github.io/ganlab"] GEN3 --> GEN3a["DDPM、Stable Diffusion<br/>📚 Diffusion Models教程<br/>🔗 lilianweng.github.io/posts/2021-07-11-diffusion-models"] GEN1a --> Phase10 GEN2a --> Phase10 GEN3a --> Phase10 Phase10[第十阶段: 高级技术] --> ADV1[正则化技术] Phase10 --> ADV2[优化技巧] Phase10 --> ADV3[模型压缩] ADV1 --> ADV1a["Dropout、BatchNorm、数据增强<br/>📚 CS231n训练技巧<br/>🔗 cs231n.github.io/neural-networks-2"] ADV2 --> ADV2a["Adam、学习率调度、梯度裁剪<br/>📚 An Overview of Optimization<br/>🔗 arxiv.org/abs/1609.04747"] ADV3 --> ADV3a["剪枝、量化、知识蒸馏<br/>📚 Model Compression Survey<br/>🔗 arxiv.org/abs/1710.09282"] ADV1a --> Phase11 ADV2a --> Phase11 ADV3a --> Phase11 Phase11[第十一阶段: 实践项目] --> PROJ1[计算机视觉项目] Phase11 --> PROJ2[自然语言处理项目] Phase11 --> PROJ3[多模态项目] PROJ1 --> PROJ1a["Kaggle图像竞赛<br/>物体检测系统<br/>📚 PyImageSearch教程<br/>🔗 pyimagesearch.com"] PROJ2 --> PROJ2a["文本分类、情感分析<br/>问答系统、对话机器人<br/>📚 实战项目集合<br/>🔗 github.com/dair-ai/ML-Papers-Explained"] PROJ3 --> PROJ3a["图像描述、视觉问答<br/>📚 OpenAI CLIP论文<br/>🔗 arxiv.org/abs/2103.00020"] PROJ1a --> Phase12 PROJ2a --> Phase12 PROJ3a --> Phase12 Phase12[第十二阶段: 前沿研究] --> RES1[大语言模型LLM] Phase12 --> RES2[强化学习] Phase12 --> RES3[神经架构搜索] RES1 --> RES1a["GPT-4、Claude、Llama系列<br/>📚 LLM综述论文<br/>🔗 arxiv.org/abs/2303.18223"] RES2 --> RES2a["DQN、PPO、AlphaGo<br/>📚 Spinning Up in Deep RL<br/>🔗 spinningup.openai.com"] RES3 --> RES3a["AutoML、NAS方法<br/>📚 NAS Survey<br/>🔗 arxiv.org/abs/1808.05377"] RES1a --> End RES2a --> End RES3a --> End End[🎓 持续学习与研究] style Start fill:#e1f5e1 style Phase1 fill:#fff4e6 style Phase2 fill:#fff4e6 style Phase3 fill:#e3f2fd style Phase4 fill:#e3f2fd style Phase5 fill:#f3e5f5 style Phase6 fill:#fce4ec style Phase7 fill:#fce4ec style Phase8 fill:#e0f2f1 style Phase9 fill:#fff9c4 style Phase10 fill:#ffebee style Phase11 fill:#e8f5e9 style Phase12 fill:#e1bee7 style End fill:#c8e6c9
+生成出来的图效果还是挺不错的 另外比如我们想要生成一个电商系统的下单交易流程
+sequenceDiagram actor User as 👤 用户 participant Web as 🌐 Web/App前端 participant Gateway as 🚪 API网关 participant Auth as 🔐 认证服务 participant Product as 📦 商品服务 participant Cart as 🛒 购物车服务 participant Order as 📋 订单服务 participant Inventory as 📊 库存服务 participant Coupon as 🎫 优惠券服务 participant Payment as 💳 支付服务 participant PayGateway as 💰 支付网关 participant MQ as 📨 消息队列 participant WMS as 🏭 仓储系统 participant Logistics as 🚚 物流系统 participant Notify as 📧 通知服务 participant AfterSale as 🔄 售后服务 participant Finance as 💵 财务系统 Note over User,Finance: 📚 参考架构:微服务电商系统<br/>🔗 github.com/macrozheng/mall %% 登录认证阶段 rect rgb(230, 245, 255) Note over User,Auth: 第一阶段:用户认证 User->>Web: 1. 打开电商平台 Web->>Gateway: 2. 请求登录页面 Gateway->>Auth: 3. 验证会话状态 alt 未登录 Auth-->>Web: 4. 返回登录页面 User->>Web: 5. 输入账号密码/手机验证码 Web->>Auth: 6. 提交登录请求 Note right of Auth: 🔐 JWT Token生成<br/>📚 RFC 7519标准<br/>🔗 jwt.io Auth->>Auth: 7. 验证凭证 Auth-->>Web: 8. 返回Token Web->>Web: 9. 存储Token到Cookie/LocalStorage else 已登录 Auth-->>Web: 4. 验证通过 end end %% 商品浏览阶段 rect rgb(245, 255, 230) Note over User,Product: 第二阶段:商品浏览 User->>Web: 10. 搜索/浏览商品 Web->>Gateway: 11. 商品查询请求 Gateway->>Product: 12. 转发查询 Note right of Product: 🔍 Elasticsearch全文搜索<br/>📚 搜索引擎实战<br/>🔗 elastic.co/guide Product->>Product: 13. 从ES/缓存查询 Product-->>Web: 14. 返回商品列表 User->>Web: 15. 点击商品详情 Web->>Gateway: 16. 请求商品详情 Gateway->>Product: 17. 查询商品信息 Gateway->>Inventory: 18. 查询库存信息 Note right of Inventory: 💾 Redis缓存+MySQL<br/>📚 缓存穿透/击穿方案<br/>🔗 redis.io/topics/lru-cache par 并行查询 Product-->>Gateway: 19a. 返回商品详情 and Inventory-->>Gateway: 19b. 返回库存数量 end Gateway-->>Web: 20. 聚合返回 end %% 购物车阶段 rect rgb(255, 245, 230) Note over User,Cart: 第三阶段:加入购物车 User->>Web: 21. 选择规格并加入购物车 Web->>Gateway: 22. 加购请求 Gateway->>Cart: 23. 添加到购物车 Note right of Cart: 🗄️ Redis存储购物车<br/>📚 分布式Session方案<br/>🔗 spring.io/projects/spring-session Cart->>Inventory: 24. 验证库存是否充足 Inventory-->>Cart: 25. 返回库存状态 Cart->>Cart: 26. 计算商品小计 Cart-->>Web: 27. 返回购物车数据 User->>Web: 28. 查看购物车 Web->>Gateway: 29. 获取购物车列表 Gateway->>Cart: 30. 查询购物车 Cart->>Product: 31. 批量查询商品最新价格 Cart->>Coupon: 32. 查询可用优惠券 Note right of Coupon: 🎫 优惠券系统设计<br/>📚 促销引擎架构<br/>🔗 tech.meituan.com par 并行查询 Product-->>Cart: 33a. 返回价格信息 and Coupon-->>Cart: 33b. 返回可用优惠券 end Cart-->>Web: 34. 返回购物车详情 end %% 订单创建阶段 rect rgb(255, 240, 245) Note over User,Order: 第四阶段:订单创建 User->>Web: 35. 点击结算 Web->>Gateway: 36. 进入结算页 Gateway->>Order: 37. 创建预订单 User->>Web: 38. 选择收货地址/优惠券 Web->>Gateway: 39. 提交订单 Gateway->>Order: 40. 创建订单请求 Note right of Order: 🔢 雪花算法生成订单号<br/>📚 分布式ID生成方案<br/>🔗 github.com/twitter/snowflake Order->>Order: 41. 生成订单号 Order->>Coupon: 42. 锁定优惠券 Order->>Inventory: 43. 预扣减库存 Note right of Inventory: ⚠️ 分布式锁防超卖<br/>📚 Redis+Lua脚本<br/>🔗 redisson.org alt 库存充足 Inventory-->>Order: 44. 扣减成功 Coupon-->>Order: 45. 锁定成功 Order->>Order: 46. 订单状态:待支付 Order-->>Web: 47. 返回订单信息 else 库存不足 Inventory-->>Order: 44. 库存不足 Order-->>Web: 47. 返回库存不足提示 end end %% 支付阶段 rect rgb(240, 248, 255) Note over User,PayGateway: 第五阶段:支付处理 User->>Web: 48. 选择支付方式 Web->>Gateway: 49. 发起支付请求 Gateway->>Payment: 50. 创建支付单 Note right of Payment: 💳 聚合支付设计<br/>📚 支付系统架构<br/>🔗 github.com/Exrick/xpay Payment->>Payment: 51. 生成支付单号 Payment->>Order: 52. 关联订单 Payment->>PayGateway: 53. 调用支付渠道 alt 支付宝支付 Note right of PayGateway: 💰 支付宝SDK<br/>📚 开放平台文档<br/>🔗 opendocs.alipay.com PayGateway->>PayGateway: 54. 调用支付宝API else 微信支付 Note right of PayGateway: 💚 微信支付API<br/>📚 支付开发文档<br/>🔗 pay.weixin.qq.com PayGateway->>PayGateway: 54. 调用微信支付API end PayGateway-->>User: 55. 返回支付页面/二维码 User->>User: 56. 完成支付操作 Note over PayGateway,Payment: 🔔 异步回调通知 PayGateway->>Payment: 57. 支付成功回调 Note right of Payment: 📚 幂等性设计<br/>🔗 martinfowler.com/articles/patterns-of-distributed-systems Payment->>Payment: 58. 验证签名并去重 Payment->>Order: 59. 更新订单状态为已支付 Payment->>MQ: 60. 发送支付成功消息 par 异步处理 MQ->>Notify: 61a. 发送通知 Note right of Notify: 📧 消息推送<br/>📚 RabbitMQ/Kafka<br/>🔗 rabbitmq.com Notify->>User: 短信/邮件/Push通知 and MQ->>Order: 61b. 确认订单 Order->>Order: 更新订单:待发货 end end %% 仓储物流阶段 rect rgb(232, 245, 233) Note over Order,Logistics: 第六阶段:仓储配送 Order->>WMS: 62. 推送发货指令 Note right of WMS: 📦 WMS系统设计<br/>📚 仓储管理实践<br/>🔗 github.com/topics/wms WMS->>WMS: 63. 订单拆分(多仓) WMS->>WMS: 64. 波次拣货 WMS->>WMS: 65. 打包称重 WMS->>Logistics: 66. 创建物流订单 Note right of Logistics: 🚚 电子面单生成<br/>📚 菜鸟/快递鸟API<br/>🔗 kdniao.com Logistics->>Logistics: 67. 生成运单号 Logistics->>Logistics: 68. 打印电子面单 Logistics-->>WMS: 69. 返回物流信息 WMS->>Order: 70. 更新订单为已发货 Order->>MQ: 71. 发送发货消息 MQ->>Notify: 72. 通知用户已发货 Notify->>User: 73. 发送物流信息 loop 物流跟踪 Logistics->>Logistics: 74. 更新物流轨迹 Note right of Logistics: 🗺️ 物流轨迹追踪<br/>📚 快递100 API<br/>🔗 kuaidi100.com Logistics->>Order: 75. 同步物流状态 Order->>Notify: 76. 推送物流更新 Notify->>User: 77. 实时物流通知 end Logistics->>Order: 78. 签收成功 Order->>Order: 79. 更新订单:已签收 Order->>MQ: 80. 发送签收消息 end %% 订单完成阶段 rect rgb(255, 243, 224) Note over User,Finance: 第七阶段:订单完成 MQ->>Order: 81. 触发自动确认收货定时器 Note right of Order: ⏰ 7天自动确认<br/>📚 XXL-Job定时任务<br/>🔗 xuxueli.com/xxl-job alt 用户主动确认 User->>Web: 82a. 确认收货 Web->>Order: 83a. 确认收货请求 else 超时自动确认 Order->>Order: 82b. 7天后自动确认 end Order->>Order: 84. 订单状态:已完成 Order->>Finance: 85. 触发结算 Note right of Finance: 💰 T+1结算<br/>📚 财务对账系统<br/>🔗 accounting-system-design.com Finance->>Finance: 86. 计算平台佣金 Finance->>Finance: 87. 生成结算单 User->>Web: 88. 评价商品 Web->>Product: 89. 提交评价 Note right of Product: ⭐ UGC内容审核<br/>📚 评价系统设计<br/>🔗 review-system-architecture.com Product->>Product: 90. 审核并发布评价 end %% 售后阶段 rect rgb(255, 235, 238) Note over User,AfterSale: 第八阶段:售后服务(可选) opt 需要售后 User->>Web: 91. 申请退货/换货 Web->>AfterSale: 92. 创建售后单 Note right of AfterSale: 🔄 售后工单系统<br/>📚 7天无理由退货<br/>🔗 消费者权益保护法 AfterSale->>AfterSale: 93. 售后审核 alt 审核通过 AfterSale-->>User: 94. 审核通过,返回退货地址 User->>Logistics: 95. 寄回商品 Logistics->>WMS: 96. 商品入库 WMS->>AfterSale: 97. 确认收货 AfterSale->>Inventory: 98. 库存回补 AfterSale->>Payment: 99. 发起退款 Note right of Payment: 💵 原路退回<br/>📚 退款流程设计<br/>🔗 refund-process-design.com Payment->>PayGateway: 100. 调用退款接口 PayGateway-->>Payment: 101. 退款成功 Payment->>MQ: 102. 发送退款消息 MQ->>Notify: 103. 通知用户 Notify->>User: 104. 退款到账通知 else 审核拒绝 AfterSale-->>User: 94. 拒绝售后申请 end end end %% 数据分析阶段 rect rgb(227, 242, 253) Note over Order,Finance: 第九阶段:数据统计分析 Note over Order,Finance: 📊 实时数据大屏<br/>📚 Flink流式计算<br/>🔗 flink.apache.org par 数据采集 Order->>MQ: 订单数据埋点 and Payment->>MQ: 支付数据埋点 and Product->>MQ: 商品数据埋点 end MQ->>Finance: 数据聚合分析 Finance->>Finance: 生成报表<br/>(销售额/转化率/ROI等) Note right of Finance: 📈 BI系统<br/>📚 数据仓库建设<br/>🔗 github.com/topics/data-warehouse end Note over User,Finance: ✅ 完整交易流程结束
+
+]]>
+
+ LLM
+
+
+ LLM
+
+
+
+ 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
@@ -6711,73 +6698,16 @@ user3:
- 我是如何走上跑步这条不归路的
- /2020/07/26/%E6%88%91%E6%98%AF%E5%A6%82%E4%BD%95%E8%B5%B0%E4%B8%8A%E8%B7%91%E6%AD%A5%E8%BF%99%E6%9D%A1%E4%B8%8D%E5%BD%92%E8%B7%AF%E7%9A%84/
- 这周因为没有准备技术方面的内容加之之前有想分享下我和跑步的一些事情,我从小学开始就是个体育渣,因为体重大非常胖,小学的时候要做仰卧起坐,基本是一个都起不来,然后那时候跑步也是要我命那种,跟另外一个比较胖的同学在跑步队尾苟延残喘,只有立定跳远还行。
-时光飞逝,我在初中高中的时候因为爱打篮球,以为自己体质已经有了质的变化,所以在体育课跑步的时候妄图跟一位体育非常好的同学一起跑,结果跟的快断气了,最终还是确认了自己是个体育渣,特别是到了大学的第一次体测跑一千米,跑完直接吐了,一则是大学太宅不运动,二则的确是底子不好。那么怎么会去跑步了呢,其实也没什么特殊的原因,就是工作以后因为运动得更少,体质差,而且越来越胖,所以就想运动下,加之跑步也是我觉得成本最低的运动了,刚好那时候17 年租的地方附近小区周围的路车不太多,一圈刚好一公里多,就觉得开始跑跑看,其实想想以前觉得一千米是非常远的,学校塑胶跑道才 400 米,一千米要两圈半,太难了,但是后来在这个小区周围跑的时候好像跑了一圈以后还能再跑一点,最后跑了两圈,可把自己牛坏了,我都能跑两千米了,哈哈,这是个什么概念呢,大学里最让我绝望的两项体育相关的内容就是一千米和十二分钟跑,一千米把我跑吐了,十二分钟跑及格五圈半也能让我跑完花一周时间恢复以及提前一周心理压力爆炸,虽然我那时候跑的不快,但是已经能跑两千米了,瞬间让自己自信心爆炸,并且跑完步出完汗的感觉是非常棒的,毕竟吃奶茶鸡排都能心安理得了,谁叫我跑步了呢😄,其实现在回去看,那时候跑得还算快的,因为还比较瘦,现在要跑得那么快心跳就太快了,关于心跳什么的后面说,开始建立起自信心之后,对跑步这件事就开始不那么排斥跟害怕了,毕竟我能跑两千米了,然后试试三公里,哇,也可以了呢,三公里是什么概念呢,我大学里跑过最多的一次是十二分钟跑五圈半还是六圈,也就是两公里多,不到三公里,几乎是生涯最长了,一时间产生了一些我可能是个被埋没的运动天才的错觉,其实细想下也能明白,只是速度足够慢了就能跑多一点,毕竟提测一千米是要跑进四分钟才及格,自己跑的时候一千米跑六分多钟已经算不慢了(对我自己来说),但是即使是这样还是对把跑步坚持下去这件事有了很大的正面激励作用,并且由于那时候上下班骑车,整个体重控制的比较理想,导致一时间误会跑步就能非常快的减肥(其实这是我跑步历程中比较大的误区之一),因为会在跑步前后称下体重,如果跑个五公里(后面可以跑五公里了),可能体重就能降 0.5 千克,但实际上这只是这五公里跑步身体流失的水分,喝杯水就回来了,那时候能控制体重主要是骑车跟跑步一起的作用,并且工作压力相对来讲比较小,没有过劳肥。
-后面其实跑步慢慢变得一个比较习惯的运动了,从三公里,到五公里,到七公里,再到十公里,十公里差不多对我来说是个坎,一直还不能比较轻松的跑十公里,可能近一两年好了一些(原谅我只是跟自己比较,跟那些大神比差得不知道多远),其实对我来说每次都是个突破,因为其实与他人比较没有特别大意义,比较顶尖的差得太远,比较普通的也不行,都会打击自信心,比较比我差的就更没意义了,所以其实能挑战自己,能把自己的上限提高才是最有意义的,这也是我看着朋友圈里的一些大神的速度除了佩服赞叹之外没什么其他的惭愧或者说嫌弃自己的感觉(阿 Q 精神😄)。
-一直感性地觉得,跑步能解压,跑完浑身汗,有种把身体的负能量都排出去的感觉,也把吃太多的罪恶感排解掉了🤦♂️,之前朋友有看一本书,书名差不多叫越跑越接近自己,这个也是我觉得挺准确的一句话,当跑到接近极限了,还想再继续再跑一点,再跑一点就能突破自己上一次的最远记录了,再跑一点就能又一次突破自己了,成人以后,毕业以后,进入社会以后,世事总是难以件件顺遂,磕磕绊绊的往前走,总觉得要崩溃了,但是还是得坚持,再熬一下,再拼一下,可能还是失败,但人生呢,运气好的人和事总是小概率的,唯有面对挫折,还是日拱一卒,来日方长,我们再坚持下,没准下一次,没准再跑一会,就能突破自己,达到新的境界。
-另外个人后期对跑步的一些知识和理解也变得深入一些,比如伤膝盖,其实跑步的确伤膝盖,需要做一些准备和防护,最好的是适合自己的跑鞋和比较好的路(最好的是塑胶跑道了),也要注意热身跟跑后的拉伸(虽然我做的很差),还有就是注意心率,每个人有自己的适宜心率,我这就不冒充科普达人了,可以自行搜索关键字,先说到这吧~
+ 屯菜惊魂记
+ /2022/04/24/%E5%B1%AF%E8%8F%9C%E6%83%8A%E9%AD%82%E8%AE%B0/
+ 因某国际大都市的给力表现,昨儿旁边行政区启动应急响应,同事早上就在群里说要去超市买菜了,到了超市人还特别多,由于来的就是我们经常去的那家超市,一方面为了安全,另一方面是怕已经抢不到了,就去了另一家比较远的超市,开车怕没车位就骑了小电驴,还下着小雨,结果到了超市差不多 12 点多,超市里出来的人都是推着一整车一整车的物资,有些比较像我,整箱的泡面,好几提纸巾,还有各种吃的,都是整箱整箱的,进了超市发现结账包括自助结账的都排很长的队,到了蔬菜货架附近,差点哼起那首歌“空空如也~”,新鲜蔬菜基本已被抢空,只剩下一些卖相不太好的土豆番薯之类的,也算是意料之外情理之中了,本来以为这家超市稍微离封控区远一些会空一点,结果就是所谓的某大都市封控了等物资,杭州市是屯了物资等封控,新鲜蔬菜没了我们也只能买点其他的,神奇的是水果基本都在,可能困难时期水果不算必需品了?还是水果基本人人都已经储备了很多,不太能理解,虽然水果还在,但是称重的地方也还有好多人排队,我们采取了并行策略,LD 在那排队,遥控指挥我去拿其他物资,拿了点碱水面,黑米,那黑米的时候还闹了个乌龙,因为前面就是散装鸡蛋的堆货的地方,结果我们以为是在那后面排队,结果称重那个在那散步了,我们还在那排队,看到后面排队,那几个挑的人也该提醒下吧,几个鸡蛋挑了半天,看看人家大妈,直接拿了四盘,看了下牛奶货架也比较空,不过还有致优跟优倍,不过不算很实惠,本来想买,只是后来赶着去结账,就给忘了,称好了黑米去看了下肉,结果肉也没了,都在买猪蹄,我们也不太爱吃猪蹄,就买了点鸡胸肉,整体看起来我们买的东西真的有点格格不入,不买泡面(因为 LD 不让买了),也不屯啥米和鸡蛋,其实鸡蛋已经买了,米也买了,其他的本身冰箱小也放不下太多东西,我是觉得还可能在屯一点这那的,LD 觉得太多了,基本的米面油有了,其他调味品什么也有了。后面就是排队结账,我去排的时候刚好前面一个小伙子跟大妈在争执,大妈说我们差不多时间来的,你要排前面就前面,小伙子有点不高兴,觉得她就是插队,哈哈,平时一般这种剧情都是发生在我身上的,这会看着前面的吵起来还是很开心的,终于有跟我一样较真的人了,有时候总觉得我是个很纠结,很较真的人,但是我现在慢慢认可了这种较真,如果没有人指出来这种是插队行为,是不对的,就会有越来越多的人觉得是可以随意插队的,正确的事应该要坚持,很多情况大家总是觉得多一事不如少一事,鸡毛蒜皮的没什么好计较的,正是这种想法,那么多人才不管任何规则,反而搞得像遵守规则都是傻 X 似的。回到屯物资,后面结账排到队了也没来得及买原来想买的花生牛奶什么的,毕竟那么多人排着队,回家后因为没有蔬菜,结果就只能吃干菜汤和饭了
]]>
生活
- 运动
- 跑步
生活
- 运动
- 减肥
- 跑步
-
-
-
- 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
-
-
-
- 折腾记-给玩客云装上casaos
- /2024/04/28/%E6%8A%98%E8%85%BE%E8%AE%B0-%E7%BB%99%E7%8E%A9%E5%AE%A2%E4%BA%91%E8%A3%85%E4%B8%8Acasaos/
- 玩客云 casaos玩客云这样的小机器现在也有一些自己的生态了,感谢开源大佬们的贡献,这个casaos就是个颜值颇高的轻nas方案,casaos虽然叫做os,但其实一套轻nas组件,并且有一个好看的webui,然后通过docker来作为内部的软件市场安装形式,我们沿着上一篇接下来尝试下安装轻度试用下这个casaos,首先安装
-curl -fsSL https://get.casaos.io | sudo bash
-
-安装会提示空间不够大,可以先忽略,如果想更保险的话就可以通过加一张SD卡或者USB插外接硬盘
-安装中主要是执行了casaos的一些安装shell,以及启动rclone服务,完成后给出了登录地址,默认是80端口,可以在后面UI界面里更改端口
- 也可以设置自动挂载usb
-
-安装完以后打开地址
-
-创建账户
-
-进入系统后的UI长这样
- 还是挺赏心悦目的,默认会展示系统的状态,cpu,内存和存储网络这些,预装了syncthing作为同步工具,还有个App Store 有挺多应用在商店
-只是这里就会发现个问题,只是玩客云是32为的ArmV7系统
-
-有很多组件不支持,包括jellyfin这些,不过它提供了自定义安装功能,可以自定义安装docker镜像
-
-不过这里有个小bug,拷贝镜像名进入第一个输入框以后会提示不能为空,这边需要带上tag一起复制进去
-
-第二个问题是我安装的0.4.8是没办法安装latest以外的tag的,不然会提示 store app not found
-在dockerhub上是有armv7版本的jellyfin的,只是没有latest这个tag的,如果实在想体验下就考虑安装casaos的0.4.7或更早先版本的或者可以自己在Armbian里拉取镜像再安装下Portainer来管理docker,不过玩客云的性能是真的很羸弱,所以不要抱什么期望,低负载的任务可以跑跑,jellyfin显然不是很合适的,只是作为一个示例
-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
+ 囤物资
@@ -6803,20 +6733,153 @@ user3:
- 搬运两个 StackOverflow 上的 Mysql 编码相关的问题解答
- /2022/01/16/%E6%90%AC%E8%BF%90%E4%B8%A4%E4%B8%AA-StackOverflow-%E4%B8%8A%E7%9A%84-Mysql-%E7%BC%96%E7%A0%81%E7%9B%B8%E5%85%B3%E7%9A%84%E9%97%AE%E9%A2%98%E8%A7%A3%E7%AD%94/
- Mysql 字符编码和排序规则这个一直是属于一知半解的状态,知道 utf8 跟 utf8mb4 的区别主要是能不能支持 emoji,但是具体后面配置的排序规则是用来干嘛,或者有什么区别,应该使用哪个,所以在 stackoverflow 上找了下,有两个比较不错的解答,就搬过来并且配合机翻做了点修改
-For those people still arriving at this question in 2020 or later, there are newer options that may be better than both of these. For example, utf8mb4_0900_ai_ci.
-All these collations are for the UTF-8 character encoding. The differences are in how text is sorted and compared.
-_unicode_ci and _general_ci are two different sets of rules for sorting and comparing text according to the way we expect. Newer versions of MySQL introduce new sets of rules, too, such as _0900_ai_ci for equivalent rules based on Unicode 9.0 - and with no equivalent _general_ci variant. People reading this now should probably use one of these newer collations instead of either _unicode_ci or _general_ci. The description of those older collations below is provided for interest only.
-MySQL is currently transitioning away from an older, flawed UTF-8 implementation. For now, you need to use utf8mb4 instead of utf8 for the character encoding part, to ensure you are getting the fixed version. The flawed version remains for backward compatibility, though it is being deprecated.
-Key differences
-utf8mb4_unicode_ci is based on the official Unicode rules for universal sorting and comparison, which sorts accurately in a wide range of languages.
-utf8mb4_general_ci is a simplified set of sorting rules which aims to do as well as it can while taking many short-cuts designed to improve speed. It does not follow the Unicode rules and will result in undesirable sorting or comparison in some situations, such as when using particular languages or characters.
-On modern servers, this performance boost will be all but negligible. It was devised in a time when servers had a tiny fraction of the CPU performance of today’s computers.
-Benefits of utf8mb4_unicode_ci over utf8mb4_general_ci
-utf8mb4_unicode_ci, which uses the Unicode rules for sorting and comparison, employs a fairly complex algorithm for correct sorting in a wide range of languages and when using a wide range of special characters. These rules need to take into account language-specific conventions; not everybody sorts their characters in what we would call ‘alphabetical order’.
-As far as Latin (ie “European”) languages go, there is not much difference between the Unicode sorting and the simplified utf8mb4_general_cisorting in MySQL, but there are still a few differences:
+ 怎么给wsl占用的硬盘空间做下瘦身
+ /2025/09/27/%E6%80%8E%E4%B9%88%E7%BB%99wsl%E5%8D%A0%E7%94%A8%E7%9A%84%E7%A1%AC%E7%9B%98%E7%A9%BA%E9%97%B4%E5%81%9A%E4%B8%8B%E7%98%A6%E8%BA%AB/
+ wsl出来这些年感觉在windows里使用linux,或者ubuntu真的是方便了很多,不用搞虚拟机这些 但是也有一些明显的问题,首先就是默认它是在C盘中安装,长久使用占用的空间就会越来越大,而且对于一些临时使用占用空间后,即使删除了文件,windows并不会对已经扩容的虚拟磁盘文件进行缩容 所以就介绍下怎么手动来给它释放空间 首先可以看下安装的系统有哪些
+
+比如我这就是一个ubuntu 20.04
+ NAME STATE VERSION * Ubuntu-20.04 Running 2
+然后我们需要寻找下虚拟磁盘的所在位置 我的是在C:\Users\Administrator\AppData\Local\Packages\CanonicalGroupLimited.Ubuntu20.04onWindows_79rhkp1fndgsc\LocalState\ext4.vhdx 可以按文件名 ext4.vhdx 进行查找 当然在操作之前我们还是要做好备份工作,并且还有特殊功效
+# 首先进行关机 wsl --shutdown # 然后做打包备份到制定路径,放心这里打包的大小就是实际大小 wsl --export Ubuntu-20.04 X:\Ubuntu-20.04.tar
+然后我们运行 diskpart 命令,这是windows用来管理磁盘文件的工具 运行后会进入到 diskpart 工具的命令状态下
+select vdisk file="C:\Users\Administrator\AppData\Local\Packages\CanonicalGroupLimited.Ubuntu20.04onWindows_79rhkp1fndgsc\LocalState\ext4.vhdx"
+接着我们再运行select vdisk命令来选中这个文件 选中以后会提示DiskPart 已成功选择虚拟磁盘文件。 接着运行压缩命令
+
+运行时会有个压缩进度展示,压缩完成后会显示以下内容
+ 100 百分比已完成 DiskPart 已成功压缩虚拟磁盘文件。
+最终我们运行
+
+分离命令就可以了 这样子我们的wsl因为扩容后无法自动收回的空间就被我们压缩出来了
+]]>
+
+ wsl
+
+
+ wsl
+
+
+
+ 怎么让claude code用国内的模型
+ /2025/09/13/%E6%80%8E%E4%B9%88%E8%AE%A9claude-code%E7%94%A8%E5%9B%BD%E5%86%85%E7%9A%84%E6%A8%A1%E5%9E%8B/
+ 之前体验过gemini code这个工具,谷歌还算比较友好,有token,有网络就可以用,claude code目前是个很严格,而且这家公司也对国内用户很不友好 所以就研究了下怎么把它对接上国内的模型 幸好之前deepseek发布v3.1的时候天然给了支持
+# export ANTHROPIC_BASE_URL=https://api.deepseek.com/anthropic # export ANTHROPIC_AUTH_TOKEN={token} # export ANTHROPIC_MODEL=deepseek-chat # export ANTHROPIC_SMALL_FAST_MODEL=deepseek-chat
+
+如果需要设置乘推理型的 可以把模型改成
+export ANTHROPIC_MODEL=deepseek-reasoner
+当然这个如果要长久有效得放在 .zshrc ,根据所用的shell工具来设置 然后安装claude需要node版本在18及以上
+nvm use v18 npm install -g @anthropic-ai/claude-code
+安装以后到想要开发的项目 运行
+
+接下来我们同样以todo应用作为测试应用
+帮我写一个todo应用,使用react,ui需要尽可能美观
+只是加了个美观的要求
+⏺ Update Todos ⎿ ☐ Set up React project structure and dependencies ☐ Create main Todo component with state management ☐ Design and implement beautiful UI with CSS/styling ☐ Add todo functionality (add, delete, toggle complete) ☐ Test the application ⏺ Let me start by checking the current directory structure and then create the React todo application.
+首先它分了五步 然后逐步运行对应的动作,生成文件等 生成的还是比较美观可用的,说明国内模型在简单的编码任务上也具有比较不错的能力了
+]]>
+
+ LLM
+
+
+ LLM
+
+
+
+ 我是如何走上跑步这条不归路的
+ /2020/07/26/%E6%88%91%E6%98%AF%E5%A6%82%E4%BD%95%E8%B5%B0%E4%B8%8A%E8%B7%91%E6%AD%A5%E8%BF%99%E6%9D%A1%E4%B8%8D%E5%BD%92%E8%B7%AF%E7%9A%84/
+ 这周因为没有准备技术方面的内容加之之前有想分享下我和跑步的一些事情,我从小学开始就是个体育渣,因为体重大非常胖,小学的时候要做仰卧起坐,基本是一个都起不来,然后那时候跑步也是要我命那种,跟另外一个比较胖的同学在跑步队尾苟延残喘,只有立定跳远还行。
+时光飞逝,我在初中高中的时候因为爱打篮球,以为自己体质已经有了质的变化,所以在体育课跑步的时候妄图跟一位体育非常好的同学一起跑,结果跟的快断气了,最终还是确认了自己是个体育渣,特别是到了大学的第一次体测跑一千米,跑完直接吐了,一则是大学太宅不运动,二则的确是底子不好。那么怎么会去跑步了呢,其实也没什么特殊的原因,就是工作以后因为运动得更少,体质差,而且越来越胖,所以就想运动下,加之跑步也是我觉得成本最低的运动了,刚好那时候17 年租的地方附近小区周围的路车不太多,一圈刚好一公里多,就觉得开始跑跑看,其实想想以前觉得一千米是非常远的,学校塑胶跑道才 400 米,一千米要两圈半,太难了,但是后来在这个小区周围跑的时候好像跑了一圈以后还能再跑一点,最后跑了两圈,可把自己牛坏了,我都能跑两千米了,哈哈,这是个什么概念呢,大学里最让我绝望的两项体育相关的内容就是一千米和十二分钟跑,一千米把我跑吐了,十二分钟跑及格五圈半也能让我跑完花一周时间恢复以及提前一周心理压力爆炸,虽然我那时候跑的不快,但是已经能跑两千米了,瞬间让自己自信心爆炸,并且跑完步出完汗的感觉是非常棒的,毕竟吃奶茶鸡排都能心安理得了,谁叫我跑步了呢😄,其实现在回去看,那时候跑得还算快的,因为还比较瘦,现在要跑得那么快心跳就太快了,关于心跳什么的后面说,开始建立起自信心之后,对跑步这件事就开始不那么排斥跟害怕了,毕竟我能跑两千米了,然后试试三公里,哇,也可以了呢,三公里是什么概念呢,我大学里跑过最多的一次是十二分钟跑五圈半还是六圈,也就是两公里多,不到三公里,几乎是生涯最长了,一时间产生了一些我可能是个被埋没的运动天才的错觉,其实细想下也能明白,只是速度足够慢了就能跑多一点,毕竟提测一千米是要跑进四分钟才及格,自己跑的时候一千米跑六分多钟已经算不慢了(对我自己来说),但是即使是这样还是对把跑步坚持下去这件事有了很大的正面激励作用,并且由于那时候上下班骑车,整个体重控制的比较理想,导致一时间误会跑步就能非常快的减肥(其实这是我跑步历程中比较大的误区之一),因为会在跑步前后称下体重,如果跑个五公里(后面可以跑五公里了),可能体重就能降 0.5 千克,但实际上这只是这五公里跑步身体流失的水分,喝杯水就回来了,那时候能控制体重主要是骑车跟跑步一起的作用,并且工作压力相对来讲比较小,没有过劳肥。
+后面其实跑步慢慢变得一个比较习惯的运动了,从三公里,到五公里,到七公里,再到十公里,十公里差不多对我来说是个坎,一直还不能比较轻松的跑十公里,可能近一两年好了一些(原谅我只是跟自己比较,跟那些大神比差得不知道多远),其实对我来说每次都是个突破,因为其实与他人比较没有特别大意义,比较顶尖的差得太远,比较普通的也不行,都会打击自信心,比较比我差的就更没意义了,所以其实能挑战自己,能把自己的上限提高才是最有意义的,这也是我看着朋友圈里的一些大神的速度除了佩服赞叹之外没什么其他的惭愧或者说嫌弃自己的感觉(阿 Q 精神😄)。
+一直感性地觉得,跑步能解压,跑完浑身汗,有种把身体的负能量都排出去的感觉,也把吃太多的罪恶感排解掉了🤦♂️,之前朋友有看一本书,书名差不多叫越跑越接近自己,这个也是我觉得挺准确的一句话,当跑到接近极限了,还想再继续再跑一点,再跑一点就能突破自己上一次的最远记录了,再跑一点就能又一次突破自己了,成人以后,毕业以后,进入社会以后,世事总是难以件件顺遂,磕磕绊绊的往前走,总觉得要崩溃了,但是还是得坚持,再熬一下,再拼一下,可能还是失败,但人生呢,运气好的人和事总是小概率的,唯有面对挫折,还是日拱一卒,来日方长,我们再坚持下,没准下一次,没准再跑一会,就能突破自己,达到新的境界。
+另外个人后期对跑步的一些知识和理解也变得深入一些,比如伤膝盖,其实跑步的确伤膝盖,需要做一些准备和防护,最好的是适合自己的跑鞋和比较好的路(最好的是塑胶跑道了),也要注意热身跟跑后的拉伸(虽然我做的很差),还有就是注意心率,每个人有自己的适宜心率,我这就不冒充科普达人了,可以自行搜索关键字,先说到这吧~
+]]>
+
+ 生活
+ 运动
+ 跑步
+
+
+ 生活
+ 运动
+ 减肥
+ 跑步
+
+
+
+ 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
+
+
+
+ 折腾记-给玩客云装上casaos
+ /2024/04/28/%E6%8A%98%E8%85%BE%E8%AE%B0-%E7%BB%99%E7%8E%A9%E5%AE%A2%E4%BA%91%E8%A3%85%E4%B8%8Acasaos/
+ 玩客云 casaos玩客云这样的小机器现在也有一些自己的生态了,感谢开源大佬们的贡献,这个casaos就是个颜值颇高的轻nas方案,casaos虽然叫做os,但其实一套轻nas组件,并且有一个好看的webui,然后通过docker来作为内部的软件市场安装形式,我们沿着上一篇接下来尝试下安装轻度试用下这个casaos,首先安装
+curl -fsSL https://get.casaos.io | sudo bash
+
+安装会提示空间不够大,可以先忽略,如果想更保险的话就可以通过加一张SD卡或者USB插外接硬盘
+安装中主要是执行了casaos的一些安装shell,以及启动rclone服务,完成后给出了登录地址,默认是80端口,可以在后面UI界面里更改端口
+ 也可以设置自动挂载usb
+
+安装完以后打开地址
+
+创建账户
+
+进入系统后的UI长这样
+ 还是挺赏心悦目的,默认会展示系统的状态,cpu,内存和存储网络这些,预装了syncthing作为同步工具,还有个App Store 有挺多应用在商店
+只是这里就会发现个问题,只是玩客云是32为的ArmV7系统
+
+有很多组件不支持,包括jellyfin这些,不过它提供了自定义安装功能,可以自定义安装docker镜像
+
+不过这里有个小bug,拷贝镜像名进入第一个输入框以后会提示不能为空,这边需要带上tag一起复制进去
+
+第二个问题是我安装的0.4.8是没办法安装latest以外的tag的,不然会提示 store app not found
+在dockerhub上是有armv7版本的jellyfin的,只是没有latest这个tag的,如果实在想体验下就考虑安装casaos的0.4.7或更早先版本的或者可以自己在Armbian里拉取镜像再安装下Portainer来管理docker,不过玩客云的性能是真的很羸弱,所以不要抱什么期望,低负载的任务可以跑跑,jellyfin显然不是很合适的,只是作为一个示例
+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
+
+
+
+ 折腾记-讲一下iptables的一个小问题
+ /2024/05/05/%E6%8A%98%E8%85%BE%E8%AE%B0-%E8%AE%B2%E4%B8%80%E4%B8%8Biptables%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E9%97%AE%E9%A2%98/
+ 很早之前就有用过iptables,那时候是早期版本的Ubuntu系统,配置防火墙的时候,是挺复杂的,就是在reject之前添加accept规则,并且当时很多网上资料都是在后续添加就行,这个其实在当时是有问题的,所以比较印象深刻。 这次碰到的问题是对iptables的概念不熟悉有关,因为iptables的逻辑其实也有一定问题我觉得,比如最基本的命令,iptables -L 这里help显示的是会展示所有chain的rules 但是这样完全把table概念给忽略了,如果不注明理论上应该把所有table的展示出来,或者把包含这个参数并且是默认给出filter这个table的写清楚 所以这次要补一下这部分概念 iptables里首先就是有四种table,分别是 Filter, NAT, Mangle, Raw四种内建表 并且层次结构是 iptables -> Tables -> Chains -> Rules. filter: This is the default table (if no -t option is passed). It contains the built-in chains INPUT (for packets destined to local sockets), FORWARD (for packets being routed through the box), and OUTPUT (for locally-generated packets). 包含 INPUT,FORWORD和OUPUT三个chain,默认就是展示这个,所以就会导致用iptables -L 显示不了mangle table的规则 nat: This table is consulted when a packet that creates a new connection is encountered. It consists of three built-ins: PREROUTING (for altering packets as soon as they come in), OUTPUT (for altering locally-generated packets before routing), and POSTROUTING (for altering packets as they are about to go out). nat包含 PREROUTING,OUTPUT,POSTROUTING三个chain mangle: This table is used for specialized packet alteration. Until kernel 2.4.17 it had two built-in chains: PREROUTING (for altering incoming packets before routing) and OUTPUT (for altering locally-generated packets before routing). Since kernel 2.4.18, three other built-in chains are also supported: INPUT (for packets coming into the box itself), FORWARD (for altering packets being routed through the box), and POSTROUTING (for altering packets as they are about to go out). mangle主要用于专门的数据包更改,包含 PREROUTING,INPUT,OUTPUT,FORWARD,POSTROUTING 五个chain,注意有内核版本区别,只是现在基本没那么老的内核了 raw: This table is used mainly for configuring exemptions from connection tracking in combination with the NOTRACK target. It registers at the netfilter hooks with higher priority and is thus called before ip_conntrack, or any other IP tables. It provides the following built-in chains: PREROUTING (for packets arriving via any network interface) OUTPUT (for packets generated by local processes) 包含 PREROUTING和OUTPUT,主要用来配置例外 chain内部会有实际的rule规则,这个具体后面可以介绍
+]]>
+
+ 折腾
+
+
+ 折腾
+
+
+
+ 搬运两个 StackOverflow 上的 Mysql 编码相关的问题解答
+ /2022/01/16/%E6%90%AC%E8%BF%90%E4%B8%A4%E4%B8%AA-StackOverflow-%E4%B8%8A%E7%9A%84-Mysql-%E7%BC%96%E7%A0%81%E7%9B%B8%E5%85%B3%E7%9A%84%E9%97%AE%E9%A2%98%E8%A7%A3%E7%AD%94/
+ Mysql 字符编码和排序规则这个一直是属于一知半解的状态,知道 utf8 跟 utf8mb4 的区别主要是能不能支持 emoji,但是具体后面配置的排序规则是用来干嘛,或者有什么区别,应该使用哪个,所以在 stackoverflow 上找了下,有两个比较不错的解答,就搬过来并且配合机翻做了点修改
+For those people still arriving at this question in 2020 or later, there are newer options that may be better than both of these. For example, utf8mb4_0900_ai_ci.
+All these collations are for the UTF-8 character encoding. The differences are in how text is sorted and compared.
+_unicode_ci and _general_ci are two different sets of rules for sorting and comparing text according to the way we expect. Newer versions of MySQL introduce new sets of rules, too, such as _0900_ai_ci for equivalent rules based on Unicode 9.0 - and with no equivalent _general_ci variant. People reading this now should probably use one of these newer collations instead of either _unicode_ci or _general_ci. The description of those older collations below is provided for interest only.
+MySQL is currently transitioning away from an older, flawed UTF-8 implementation. For now, you need to use utf8mb4 instead of utf8 for the character encoding part, to ensure you are getting the fixed version. The flawed version remains for backward compatibility, though it is being deprecated.
+Key differences
+utf8mb4_unicode_ci is based on the official Unicode rules for universal sorting and comparison, which sorts accurately in a wide range of languages.
+utf8mb4_general_ci is a simplified set of sorting rules which aims to do as well as it can while taking many short-cuts designed to improve speed. It does not follow the Unicode rules and will result in undesirable sorting or comparison in some situations, such as when using particular languages or characters.
+On modern servers, this performance boost will be all but negligible. It was devised in a time when servers had a tiny fraction of the CPU performance of today’s computers.
+Benefits of utf8mb4_unicode_ci over utf8mb4_general_ci
+utf8mb4_unicode_ci, which uses the Unicode rules for sorting and comparison, employs a fairly complex algorithm for correct sorting in a wide range of languages and when using a wide range of special characters. These rules need to take into account language-specific conventions; not everybody sorts their characters in what we would call ‘alphabetical order’.
+As far as Latin (ie “European”) languages go, there is not much difference between the Unicode sorting and the simplified utf8mb4_general_cisorting in MySQL, but there are still a few differences:
For examples, the Unicode collation sorts “ß” like “ss”, and “Œ” like “OE” as people using those characters would normally want, whereas utf8mb4_general_cisorts them as single characters (presumably like “s” and “e” respectively).
Some Unicode characters are defined as ignorable, which means they shouldn’t count toward the sort order and the comparison should move on to the next character instead. utf8mb4_unicode_cihandles these properly.
In non-latin languages, such as Asian languages or languages with different alphabets, there may be a lot more differences between Unicode sorting and the simplified utf8mb4_general_cisorting. The suitability of utf8mb4_general_ciwill depend heavily on the language used. For some languages, it’ll be quite inadequate.
@@ -6891,41 +6954,15 @@ user3:
- 折腾记-讲一下iptables的一个小问题
- /2024/05/05/%E6%8A%98%E8%85%BE%E8%AE%B0-%E8%AE%B2%E4%B8%80%E4%B8%8Biptables%E7%9A%84%E4%B8%80%E4%B8%AA%E5%B0%8F%E9%97%AE%E9%A2%98/
- 很早之前就有用过iptables,那时候是早期版本的Ubuntu系统,配置防火墙的时候,是挺复杂的,就是在reject之前添加accept规则,并且当时很多网上资料都是在后续添加就行,这个其实在当时是有问题的,所以比较印象深刻。 这次碰到的问题是对iptables的概念不熟悉有关,因为iptables的逻辑其实也有一定问题我觉得,比如最基本的命令,iptables -L 这里help显示的是会展示所有chain的rules 但是这样完全把table概念给忽略了,如果不注明理论上应该把所有table的展示出来,或者把包含这个参数并且是默认给出filter这个table的写清楚 所以这次要补一下这部分概念 iptables里首先就是有四种table,分别是 Filter, NAT, Mangle, Raw四种内建表 并且层次结构是 iptables -> Tables -> Chains -> Rules. filter: This is the default table (if no -t option is passed). It contains the built-in chains INPUT (for packets destined to local sockets), FORWARD (for packets being routed through the box), and OUTPUT (for locally-generated packets). 包含 INPUT,FORWORD和OUPUT三个chain,默认就是展示这个,所以就会导致用iptables -L 显示不了mangle table的规则 nat: This table is consulted when a packet that creates a new connection is encountered. It consists of three built-ins: PREROUTING (for altering packets as soon as they come in), OUTPUT (for altering locally-generated packets before routing), and POSTROUTING (for altering packets as they are about to go out). nat包含 PREROUTING,OUTPUT,POSTROUTING三个chain mangle: This table is used for specialized packet alteration. Until kernel 2.4.17 it had two built-in chains: PREROUTING (for altering incoming packets before routing) and OUTPUT (for altering locally-generated packets before routing). Since kernel 2.4.18, three other built-in chains are also supported: INPUT (for packets coming into the box itself), FORWARD (for altering packets being routed through the box), and POSTROUTING (for altering packets as they are about to go out). mangle主要用于专门的数据包更改,包含 PREROUTING,INPUT,OUTPUT,FORWARD,POSTROUTING 五个chain,注意有内核版本区别,只是现在基本没那么老的内核了 raw: This table is used mainly for configuring exemptions from connection tracking in combination with the NOTRACK target. It registers at the netfilter hooks with higher priority and is thus called before ip_conntrack, or any other IP tables. It provides the following built-in chains: PREROUTING (for packets arriving via any network interface) OUTPUT (for packets generated by local processes) 包含 PREROUTING和OUTPUT,主要用来配置例外 chain内部会有实际的rule规则,这个具体后面可以介绍
-]]>
-
- 折腾
-
-
- 折腾
-
-
-
- 怎么给wsl占用的硬盘空间做下瘦身
- /2025/09/27/%E6%80%8E%E4%B9%88%E7%BB%99wsl%E5%8D%A0%E7%94%A8%E7%9A%84%E7%A1%AC%E7%9B%98%E7%A9%BA%E9%97%B4%E5%81%9A%E4%B8%8B%E7%98%A6%E8%BA%AB/
- wsl出来这些年感觉在windows里使用linux,或者ubuntu真的是方便了很多,不用搞虚拟机这些 但是也有一些明显的问题,首先就是默认它是在C盘中安装,长久使用占用的空间就会越来越大,而且对于一些临时使用占用空间后,即使删除了文件,windows并不会对已经扩容的虚拟磁盘文件进行缩容 所以就介绍下怎么手动来给它释放空间 首先可以看下安装的系统有哪些
-
-比如我这就是一个ubuntu 20.04
- NAME STATE VERSION * Ubuntu-20.04 Running 2
-然后我们需要寻找下虚拟磁盘的所在位置 我的是在C:\Users\Administrator\AppData\Local\Packages\CanonicalGroupLimited.Ubuntu20.04onWindows_79rhkp1fndgsc\LocalState\ext4.vhdx 可以按文件名 ext4.vhdx 进行查找 当然在操作之前我们还是要做好备份工作,并且还有特殊功效
-# 首先进行关机 wsl --shutdown # 然后做打包备份到制定路径,放心这里打包的大小就是实际大小 wsl --export Ubuntu-20.04 X:\Ubuntu-20.04.tar
-然后我们运行 diskpart 命令,这是windows用来管理磁盘文件的工具 运行后会进入到 diskpart 工具的命令状态下
-select vdisk file="C:\Users\Administrator\AppData\Local\Packages\CanonicalGroupLimited.Ubuntu20.04onWindows_79rhkp1fndgsc\LocalState\ext4.vhdx"
-接着我们再运行select vdisk命令来选中这个文件 选中以后会提示DiskPart 已成功选择虚拟磁盘文件。 接着运行压缩命令
-
-运行时会有个压缩进度展示,压缩完成后会显示以下内容
- 100 百分比已完成 DiskPart 已成功压缩虚拟磁盘文件。
-最终我们运行
-
-分离命令就可以了 这样子我们的wsl因为扩容后无法自动收回的空间就被我们压缩出来了
+ 新奇体验-记我的博客图片被恶意盗刷
+ /2025/03/23/%E6%96%B0%E5%A5%87%E4%BD%93%E9%AA%8C-%E8%AE%B0%E6%88%91%E7%9A%84%E5%8D%9A%E5%AE%A2%E5%9B%BE%E7%89%87%E8%A2%AB%E6%81%B6%E6%84%8F%E7%9B%97%E5%88%B7/
+ 上周四,突然接到腾讯云的电话,当然不是叫我去面试的,是说我账户欠费了,而且一下欠了二十多,因为这个账户我除了包年包月的我都会及时续费,剩下的就是一个放我博客图片的cos对象存储,本来可能一天就几分钱,一年充个十块钱都足够用了,突然一天就欠了二十多还是引起了我的警觉,去看了下说是什么外网下行流量费,我想着怎么又有这么个收费名目了,腾讯云给我的感觉就是一直巧立名目,或者增加收费项,后面也问了客服,说是我开的是公有读私有写,而且允许空refer访问(这个我记得是改过的,不知道为啥现在变成了空refer),也挺奇怪的,这种page托管的博客,不都是只能公有读么,私有读的话我还放啥图片,除非全都走服务端中转。 唯一一个优点是总算有个日志,结果看了下是江苏泰顺一哥们一直在换着ip刷,用的还是固定的okhttp客户端,这里想放个图,但是这个优点也被后面的缺点给淹没了,因为腾讯云不支持按访问客户端和ip段设置黑名单,简直离谱,都立了这个名目收费了,对应的功能还不行,都能怀疑是故意漏个口子让刷 可能暂时就不能放出来图片了,主要是也没精力搞什么服务端加密,打算迁到r2或者其他方式了,如果有大佬对这方面有经验又碰巧看到了这篇文章,希望能给下指导。 还有这位用了江苏泰顺ip(说不定是肉鸡)的哥们,是有啥想不开的呢
]]>
- wsl
+ 体验
- wsl
+ 恶意盗刷
@@ -6969,6 +7006,25 @@ user3:
LLM
+
+ 来介绍个源码阅读理解神器-deepwiki
+ /2025/04/27/%E6%9D%A5%E4%BB%8B%E7%BB%8D%E4%B8%AA%E6%BA%90%E7%A0%81%E9%98%85%E8%AF%BB%E7%90%86%E8%A7%A3%E7%A5%9E%E5%99%A8-deepwiki/
+ 上次简单介绍了openmanus的使用,但是它究竟是怎么个原理还是一知半解的,如果想要能比较深入的理解,最直接粗暴的就是阅读源码了,然而对于很多人包括我来说阅读源码不是件简单的事情,有时候会陷入局部细节,不得要领 正好这次我发现了有个理解项目的神器,这次不加双引号是因为这个真的好 比如我们就拿openmanus举例,首先python的语法就没那么熟,以及对整体结构的理解 那么我们就可以打开 openmanus 的仓库地址https://github.com/mannaandpoem/OpenManus 然后把 github 替换成 deepwiki, 页面变成了这样 左边是结构大纲,中间我们可以看到项目的主体介绍,包括有核心的架构图,agent的继承关系,tool的生态系统,LLM的集成 这里就展示了核心架构,通过这个方式我们如果想对一个项目有个初始的认识,就变得非常简单,因为很多项目的当前的代码都是非常复杂的,没有足够的时间精力是没办法一下子学习到项目的整体结构,因为除非我们是要真正投入到一个项目的开发贡献中,我们大概率都是从了解整体的概况,再分模块的去学习,而这个正是这个deepwiki做得非常牛的地方,相当于给每个项目都加了一个更详细具体的wiki,更牛的还有我们可以通过对话的形式进行提问题,比如我们想自己开发个工具,让openmanus集成进去进行调用 它就给出了一个非常详尽的回答, 首先
+
+创建自定义工具类,创建一个基于 BaseTool 的新工具类
+将工具添加到Manus代理,修改Manus类的available_tools定义,将您的工具添加到默认工具列表
+使用MCP协议集成远程工具(可选)
+工具执行流程 一旦您的工具被集成,Manus代理会在执行过程中使用它:
+
+
+代理的think()方法会向LLM发送请求,包含所有可用工具的信息
+LLM会决定使用哪个工具(包括您的Excel工具)
+代理的act()方法会执行工具调用
+您的工具的execute()方法会被调用,执行Excel函数并返回结果
+结果会被添加到代理的记忆中,用于后续决策 这样子就让一个项目的理解跟上手变得非常简单,甚至比如我们想要参与这个项目的开源贡献,也能借助这个 deepwiki 来让我们能快速上手。 如果对这个结果不满意还可以开启deep research,能让大模型通过深度思考来给出更加合理的答案,这个deepwiki是目前为止我觉得大模型对程序员最有效的一个工具了。
+
+]]>
+
来看下google最新力作Antigravity的水平如何
/2025/11/23/%E6%9D%A5%E7%9C%8B%E4%B8%8Bgoogle%E6%9C%80%E6%96%B0%E5%8A%9B%E4%BD%9Cgemini-3-pro%E7%9A%84%E6%B0%B4%E5%B9%B3%E5%A6%82%E4%BD%95/
@@ -6996,22 +7052,16 @@ user3:
- 怎么让claude code用国内的模型
- /2025/09/13/%E6%80%8E%E4%B9%88%E8%AE%A9claude-code%E7%94%A8%E5%9B%BD%E5%86%85%E7%9A%84%E6%A8%A1%E5%9E%8B/
- 之前体验过gemini code这个工具,谷歌还算比较友好,有token,有网络就可以用,claude code目前是个很严格,而且这家公司也对国内用户很不友好 所以就研究了下怎么把它对接上国内的模型 幸好之前deepseek发布v3.1的时候天然给了支持
-# export ANTHROPIC_BASE_URL=https://api.deepseek.com/anthropic # export ANTHROPIC_AUTH_TOKEN={token} # export ANTHROPIC_MODEL=deepseek-chat # export ANTHROPIC_SMALL_FAST_MODEL=deepseek-chat
-
-如果需要设置乘推理型的 可以把模型改成
-export ANTHROPIC_MODEL=deepseek-reasoner
-当然这个如果要长久有效得放在 .zshrc ,根据所用的shell工具来设置 然后安装claude需要node版本在18及以上
-nvm use v18 npm install -g @anthropic-ai/claude-code
-安装以后到想要开发的项目 运行
-
-接下来我们同样以todo应用作为测试应用
-帮我写一个todo应用,使用react,ui需要尽可能美观
-只是加了个美观的要求
-⏺ Update Todos ⎿ ☐ Set up React project structure and dependencies ☐ Create main Todo component with state management ☐ Design and implement beautiful UI with CSS/styling ☐ Add todo functionality (add, delete, toggle complete) ☐ Test the application ⏺ Let me start by checking the current directory structure and then create the React todo application.
-首先它分了五步 然后逐步运行对应的动作,生成文件等 生成的还是比较美观可用的,说明国内模型在简单的编码任务上也具有比较不错的能力了
+ 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
+
+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
@@ -7117,18 +7167,6 @@ user3:
LLM
-
- 新奇体验-记我的博客图片被恶意盗刷
- /2025/03/23/%E6%96%B0%E5%A5%87%E4%BD%93%E9%AA%8C-%E8%AE%B0%E6%88%91%E7%9A%84%E5%8D%9A%E5%AE%A2%E5%9B%BE%E7%89%87%E8%A2%AB%E6%81%B6%E6%84%8F%E7%9B%97%E5%88%B7/
- 上周四,突然接到腾讯云的电话,当然不是叫我去面试的,是说我账户欠费了,而且一下欠了二十多,因为这个账户我除了包年包月的我都会及时续费,剩下的就是一个放我博客图片的cos对象存储,本来可能一天就几分钱,一年充个十块钱都足够用了,突然一天就欠了二十多还是引起了我的警觉,去看了下说是什么外网下行流量费,我想着怎么又有这么个收费名目了,腾讯云给我的感觉就是一直巧立名目,或者增加收费项,后面也问了客服,说是我开的是公有读私有写,而且允许空refer访问(这个我记得是改过的,不知道为啥现在变成了空refer),也挺奇怪的,这种page托管的博客,不都是只能公有读么,私有读的话我还放啥图片,除非全都走服务端中转。 唯一一个优点是总算有个日志,结果看了下是江苏泰顺一哥们一直在换着ip刷,用的还是固定的okhttp客户端,这里想放个图,但是这个优点也被后面的缺点给淹没了,因为腾讯云不支持按访问客户端和ip段设置黑名单,简直离谱,都立了这个名目收费了,对应的功能还不行,都能怀疑是故意漏个口子让刷 可能暂时就不能放出来图片了,主要是也没精力搞什么服务端加密,打算迁到r2或者其他方式了,如果有大佬对这方面有经验又碰巧看到了这篇文章,希望能给下指导。 还有这位用了江苏泰顺ip(说不定是肉鸡)的哥们,是有啥想不开的呢
-]]>
-
- 体验
-
-
- 恶意盗刷
-
-
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/
@@ -7152,59 +7190,39 @@ user3:
- 看完了扫黑风暴,聊聊感想
- /2021/10/24/%E7%9C%8B%E5%AE%8C%E4%BA%86%E6%89%AB%E9%BB%91%E9%A3%8E%E6%9A%B4-%E8%81%8A%E8%81%8A%E6%84%9F%E6%83%B3/
- 一直在想这篇怎么写,看了这部剧其实对我的一些观念是有影响的,应该是在 9 月份就看完了,到现在可能才会稍微平静一点,一开始是没有想看这部剧,因为同期有一部差不多同名的电影,被投诉了对湖南埋尸案家属伤害很大,我以为就是投诉的这部电视剧,后来同事跟我说不是,所以就想着看一下,但是没有马上看,因为一直不喜欢追这种比较纠结的剧,当时看人民的名义,就是后面等不了了直接看了小说,所以差不多是等到更完了才看的。
-尝试保持一个比较冷静的状态来聊聊,在看的时候有一点感想就是如果要剧里的坏人排个名,因为明眼看都是孙兴是个穷凶极恶的坏人,干尽了坏事,而且可能是演员表演地好,让人真的恨的牙痒痒,但是更多地还是停留在那些剧情中的表现和他的表情,其实对应的真实案例有更多的,这里尽量不展开,有兴趣可以自行搜索关键字,所以其实我想排个名的话,孙兴的母亲应该是我心目中是造成这个结果的比较大占比的始作俑者,因为是方方面面的,包括对林汉的栽赃迫害,最后串起来是因为他看到了孙兴又出来了,就是那句老话,撒了一个谎以后就要用无数个谎来圆,贺芸为了孙兴,作了第一个恶以后就用了一系列的丧心病狂的操作来保护孙兴,而且这之后所做的事情一件比一件可怕,并且如果不是督导组各种想方设法地去破解谜题,这个事情还可以一直被通过各种操作瞒下去,而孙兴还可以继续地为虎作伥,当然其他的包括高明远以及后面的王政,当然是为了这个操作也提供的各种方式的帮助,甚至是主导了这些操作,但是这里贺芸还是在这个位子上能够通过权力做出非常关键的动作,包括栽赃林汉,并且搞掉了李成阳。其中还有一点是我对剧情设计的质疑,也是我前面提到过一点,因为里面孙兴好像是很爱他的母亲贺芸,似乎想表达的是孙兴作的恶是因为得不到母爱,并且个人感觉如果是一个比较敬爱自己母亲的儿子,似乎应该有所畏惧,对他的行为也会有所限制,不应该变成这样一个无恶不作的恶霸,这也是我一直以来的观点,很多人作恶太多可能是因为没有信仰,不管是信基督耶稣还是信道教佛教,总归有一些制约,当然不是说就绝对不会作恶,只是偏向于有所畏惧敬畏,除了某绿哈。
-而对于其他的人感觉演技都不错,只是最后有一些虎头蛇尾吧,不知道是不是审核的原因,也不细说了怕被请喝茶,还有提一点就是麦佳的这个事情,她其实是里面很惨的一个人,把高明远当成最亲近的人,而其实真相令人感觉不寒而栗,杀父杀母的仇人,对于麦佳这个演员,一直觉得印象深刻,后来才想起来就是在爱情公寓里演被关谷救了要以身相遇的那个女孩,长相其实蛮令人印象深刻的,但好像也一直不温不火,不过也不能说演技很好吧,只是在这里演的任务真的是很可怜了,剧情设计里也应该是个很重要的串联人物,最终被高明远献给了大佬,这里扯开一点,好像有的观点说贺芸之前也是这样的,只是一种推测了。
-看完这部剧其实有很多想说的,但是也为了不被请喝茶,尽量少说了,只想说珍爱生命,还是自己小心吧
+ 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 { EventLoopGroup boss = new NioEventLoopGroup (); EventLoopGroup work = new NioEventLoopGroup (); try { ServerBootstrap bootstrap = new ServerBootstrap (); bootstrap.group(boss, work) .option(ChannelOption.SO_BACKLOG, 1024 ) .handler(new LoggingHandler (LogLevel.DEBUG)) .channel(NioServerSocketChannel.class) .childHandler(new HttpServerInitializer ()); 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(); pipeline.addLast(new HttpServerCodec ()); pipeline.addLast(new HttpContentCompressor ((CompressionOptions[]) null )); pipeline.addLast(new HttpServerExpectContinueHandler ()); 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) { ctx.flush(); } @Override protected void channelRead0 (ChannelHandlerContext channelHandlerContext, HttpObject msg) throws Exception { System.out.println("channel read" ); if (msg instanceof HttpRequest) { HttpRequest httpRequest = (HttpRequest) msg; boolean keepAlive = HttpUtil.isKeepAlive(httpRequest); FullHttpResponse response = new DefaultFullHttpResponse (httpRequest.protocolVersion(), OK, Unpooled.wrappedBuffer(CONTENT)); response.headers() .set(CONTENT_TYPE, TEXT_PLAIN) .setInt(CONTENT_LENGTH, response.content().readableBytes()); if (keepAlive) { if (!httpRequest.protocolVersion().isKeepAliveDefault()) { response.headers().set(CONNECTION, KEEP_ALIVE); } } else { response.headers().set(CONNECTION, CLOSE); } ChannelFuture future = channelHandlerContext.write(response); 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
]]>
- 生活
+ Java
- 生活
- 影评
+ Java
- 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
-
-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
+ 看完了扫黑风暴,聊聊感想
+ /2021/10/24/%E7%9C%8B%E5%AE%8C%E4%BA%86%E6%89%AB%E9%BB%91%E9%A3%8E%E6%9A%B4-%E8%81%8A%E8%81%8A%E6%84%9F%E6%83%B3/
+ 一直在想这篇怎么写,看了这部剧其实对我的一些观念是有影响的,应该是在 9 月份就看完了,到现在可能才会稍微平静一点,一开始是没有想看这部剧,因为同期有一部差不多同名的电影,被投诉了对湖南埋尸案家属伤害很大,我以为就是投诉的这部电视剧,后来同事跟我说不是,所以就想着看一下,但是没有马上看,因为一直不喜欢追这种比较纠结的剧,当时看人民的名义,就是后面等不了了直接看了小说,所以差不多是等到更完了才看的。
+尝试保持一个比较冷静的状态来聊聊,在看的时候有一点感想就是如果要剧里的坏人排个名,因为明眼看都是孙兴是个穷凶极恶的坏人,干尽了坏事,而且可能是演员表演地好,让人真的恨的牙痒痒,但是更多地还是停留在那些剧情中的表现和他的表情,其实对应的真实案例有更多的,这里尽量不展开,有兴趣可以自行搜索关键字,所以其实我想排个名的话,孙兴的母亲应该是我心目中是造成这个结果的比较大占比的始作俑者,因为是方方面面的,包括对林汉的栽赃迫害,最后串起来是因为他看到了孙兴又出来了,就是那句老话,撒了一个谎以后就要用无数个谎来圆,贺芸为了孙兴,作了第一个恶以后就用了一系列的丧心病狂的操作来保护孙兴,而且这之后所做的事情一件比一件可怕,并且如果不是督导组各种想方设法地去破解谜题,这个事情还可以一直被通过各种操作瞒下去,而孙兴还可以继续地为虎作伥,当然其他的包括高明远以及后面的王政,当然是为了这个操作也提供的各种方式的帮助,甚至是主导了这些操作,但是这里贺芸还是在这个位子上能够通过权力做出非常关键的动作,包括栽赃林汉,并且搞掉了李成阳。其中还有一点是我对剧情设计的质疑,也是我前面提到过一点,因为里面孙兴好像是很爱他的母亲贺芸,似乎想表达的是孙兴作的恶是因为得不到母爱,并且个人感觉如果是一个比较敬爱自己母亲的儿子,似乎应该有所畏惧,对他的行为也会有所限制,不应该变成这样一个无恶不作的恶霸,这也是我一直以来的观点,很多人作恶太多可能是因为没有信仰,不管是信基督耶稣还是信道教佛教,总归有一些制约,当然不是说就绝对不会作恶,只是偏向于有所畏惧敬畏,除了某绿哈。
+而对于其他的人感觉演技都不错,只是最后有一些虎头蛇尾吧,不知道是不是审核的原因,也不细说了怕被请喝茶,还有提一点就是麦佳的这个事情,她其实是里面很惨的一个人,把高明远当成最亲近的人,而其实真相令人感觉不寒而栗,杀父杀母的仇人,对于麦佳这个演员,一直觉得印象深刻,后来才想起来就是在爱情公寓里演被关谷救了要以身相遇的那个女孩,长相其实蛮令人印象深刻的,但好像也一直不温不火,不过也不能说演技很好吧,只是在这里演的任务真的是很可怜了,剧情设计里也应该是个很重要的串联人物,最终被高明远献给了大佬,这里扯开一点,好像有的观点说贺芸之前也是这样的,只是一种推测了。
+看完这部剧其实有很多想说的,但是也为了不被请喝茶,尽量少说了,只想说珍爱生命,还是自己小心吧
]]>
- LLM
+ 生活
- LLM
+ 生活
+ 影评
-
- 来介绍个源码阅读理解神器-deepwiki
- /2025/04/27/%E6%9D%A5%E4%BB%8B%E7%BB%8D%E4%B8%AA%E6%BA%90%E7%A0%81%E9%98%85%E8%AF%BB%E7%90%86%E8%A7%A3%E7%A5%9E%E5%99%A8-deepwiki/
- 上次简单介绍了openmanus的使用,但是它究竟是怎么个原理还是一知半解的,如果想要能比较深入的理解,最直接粗暴的就是阅读源码了,然而对于很多人包括我来说阅读源码不是件简单的事情,有时候会陷入局部细节,不得要领 正好这次我发现了有个理解项目的神器,这次不加双引号是因为这个真的好 比如我们就拿openmanus举例,首先python的语法就没那么熟,以及对整体结构的理解 那么我们就可以打开 openmanus 的仓库地址https://github.com/mannaandpoem/OpenManus 然后把 github 替换成 deepwiki, 页面变成了这样 左边是结构大纲,中间我们可以看到项目的主体介绍,包括有核心的架构图,agent的继承关系,tool的生态系统,LLM的集成 这里就展示了核心架构,通过这个方式我们如果想对一个项目有个初始的认识,就变得非常简单,因为很多项目的当前的代码都是非常复杂的,没有足够的时间精力是没办法一下子学习到项目的整体结构,因为除非我们是要真正投入到一个项目的开发贡献中,我们大概率都是从了解整体的概况,再分模块的去学习,而这个正是这个deepwiki做得非常牛的地方,相当于给每个项目都加了一个更详细具体的wiki,更牛的还有我们可以通过对话的形式进行提问题,比如我们想自己开发个工具,让openmanus集成进去进行调用 它就给出了一个非常详尽的回答, 首先
-
-创建自定义工具类,创建一个基于 BaseTool 的新工具类
-将工具添加到Manus代理,修改Manus类的available_tools定义,将您的工具添加到默认工具列表
-使用MCP协议集成远程工具(可选)
-工具执行流程 一旦您的工具被集成,Manus代理会在执行过程中使用它:
-
-
-代理的think()方法会向LLM发送请求,包含所有可用工具的信息
-LLM会决定使用哪个工具(包括您的Excel工具)
-代理的act()方法会执行工具调用
-您的工具的execute()方法会被调用,执行Excel函数并返回结果
-结果会被添加到代理的记忆中,用于后续决策 这样子就让一个项目的理解跟上手变得非常简单,甚至比如我们想要参与这个项目的开源贡献,也能借助这个 deepwiki 来让我们能快速上手。 如果对这个结果不满意还可以开启deep research,能让大模型通过深度思考来给出更加合理的答案,这个deepwiki是目前为止我觉得大模型对程序员最有效的一个工具了。
-
-]]>
-
真正适合spring ai的使用方式
/2025/08/24/%E7%9C%9F%E6%AD%A3%E9%80%82%E5%90%88spring-ai%E7%9A%84%E4%BD%BF%E7%94%A8%E6%96%B9%E5%BC%8F/
@@ -7229,38 +7247,6 @@ user3:
LLM
-
- 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
-
-
-
- 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 { EventLoopGroup boss = new NioEventLoopGroup (); EventLoopGroup work = new NioEventLoopGroup (); try { ServerBootstrap bootstrap = new ServerBootstrap (); bootstrap.group(boss, work) .option(ChannelOption.SO_BACKLOG, 1024 ) .handler(new LoggingHandler (LogLevel.DEBUG)) .channel(NioServerSocketChannel.class) .childHandler(new HttpServerInitializer ()); 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(); pipeline.addLast(new HttpServerCodec ()); pipeline.addLast(new HttpContentCompressor ((CompressionOptions[]) null )); pipeline.addLast(new HttpServerExpectContinueHandler ()); 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) { ctx.flush(); } @Override protected void channelRead0 (ChannelHandlerContext channelHandlerContext, HttpObject msg) throws Exception { System.out.println("channel read" ); if (msg instanceof HttpRequest) { HttpRequest httpRequest = (HttpRequest) msg; boolean keepAlive = HttpUtil.isKeepAlive(httpRequest); FullHttpResponse response = new DefaultFullHttpResponse (httpRequest.protocolVersion(), OK, Unpooled.wrappedBuffer(CONTENT)); response.headers() .set(CONTENT_TYPE, TEXT_PLAIN) .setInt(CONTENT_LENGTH, response.content().readableBytes()); if (keepAlive) { if (!httpRequest.protocolVersion().isKeepAliveDefault()) { response.headers().set(CONNECTION, KEEP_ALIVE); } } else { response.headers().set(CONNECTION, CLOSE); } ChannelFuture future = channelHandlerContext.write(response); 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
-]]>
-
- Java
-
-
- Java
-
-
简单介绍下mcp是什么
/2025/05/04/%E7%AE%80%E5%8D%95%E4%BB%8B%E7%BB%8D%E4%B8%8Bmcp%E6%98%AF%E4%BB%80%E4%B9%88/
@@ -7286,6 +7272,60 @@ user3:
LLM
+
+ 结合本地部署的蒸馏 deepseek 大模型和 Anything LLM 来实现rag功能
+ /2025/03/02/%E7%BB%93%E5%90%88%E6%9C%AC%E5%9C%B0%E9%83%A8%E7%BD%B2%E7%9A%84%E8%92%B8%E9%A6%8Fdeepseek%E6%9D%A5%E5%AE%9E%E7%8E%B0rag%E5%8A%9F%E8%83%BD/
+ 之前我们用LM Studio 本地部署了 deepseek的蒸馏大模型,虽然肯定无法跟满血版比,但是对于本地的一些小应用还是可以尝试的 这边我们就不自己实现了,借助于 Anything LLM来做个尝试 首先可以在 anythingllm 下载对应系统版本的 Anything,注意安装的时候它有两个阶段,会走两次进度条,第二次会慢一些。 安装好后,我们要选择它的大模型供应者 先要在LM Studio中开启http服务, 然后再在 AnythingLLM 中选好以 LM Studio 作为大模型提供商 之后在 Anything 窗口中可以新建一个工作区 新建完之后,我们为了实现rag功能,就用个简单的示例,比如以raid 作为话题,我找到某乎的一篇文章 点击左侧工作区旁边的上传 我们可以这里提交链接,由Anything LLM帮我们把网页保存下来然后embedding到向量数据库里,然后就可以在我们对话的时候根据这个文章来做增项 然后我们问个问题,就可以发现它已经借助于本地文档的信息来增强内容生成 这样就非常方便的实现了rag,还可以提交本地的笔记文稿等,现在这些应用也已经变得越来越成熟了。
+]]>
+
+ LLM
+
+
+ LLM
+
+
+
+ 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
+
+
+
+ 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
+
+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
+
+这样ip配置就生效了 可以通过ip addr show ens33查看
+]]>
+
+ linux
+
+
+ linux
+
+
聊一下 Java 的日志系列一
/2024/01/07/%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%80/
@@ -7430,6 +7470,18 @@ user3:
Java
+
+ 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
+
+
聊一下 RocketMQ 的 DefaultMQPushConsumer 源码
/2020/06/26/%E8%81%8A%E4%B8%80%E4%B8%8B-RocketMQ-%E7%9A%84-Consumer/
@@ -7731,31 +7783,27 @@ user3:
- 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
-
-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
-
-这样ip配置就生效了 可以通过ip addr show ens33查看
+ 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) { 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
]]>
- linux
+ java
- linux
+ java
@@ -7888,91 +7936,166 @@ user3:
- 结合本地部署的蒸馏 deepseek 大模型和 Anything LLM 来实现rag功能
- /2025/03/02/%E7%BB%93%E5%90%88%E6%9C%AC%E5%9C%B0%E9%83%A8%E7%BD%B2%E7%9A%84%E8%92%B8%E9%A6%8Fdeepseek%E6%9D%A5%E5%AE%9E%E7%8E%B0rag%E5%8A%9F%E8%83%BD/
- 之前我们用LM Studio 本地部署了 deepseek的蒸馏大模型,虽然肯定无法跟满血版比,但是对于本地的一些小应用还是可以尝试的 这边我们就不自己实现了,借助于 Anything LLM来做个尝试 首先可以在 anythingllm 下载对应系统版本的 Anything,注意安装的时候它有两个阶段,会走两次进度条,第二次会慢一些。 安装好后,我们要选择它的大模型供应者 先要在LM Studio中开启http服务, 然后再在 AnythingLLM 中选好以 LM Studio 作为大模型提供商 之后在 Anything 窗口中可以新建一个工作区 新建完之后,我们为了实现rag功能,就用个简单的示例,比如以raid 作为话题,我找到某乎的一篇文章 点击左侧工作区旁边的上传 我们可以这里提交链接,由Anything LLM帮我们把网页保存下来然后embedding到向量数据库里,然后就可以在我们对话的时候根据这个文章来做增项 然后我们问个问题,就可以发现它已经借助于本地文档的信息来增强内容生成 这样就非常方便的实现了rag,还可以提交本地的笔记文稿等,现在这些应用也已经变得越来越成熟了。
-]]>
-
- LLM
-
-
- LLM
-
-
-
- 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
-]]>
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- linux
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- linux
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- 聊在东京奥运会闭幕式这天-二
- /2021/08/19/%E8%81%8A%E5%9C%A8%E4%B8%9C%E4%BA%AC%E5%A5%A5%E8%BF%90%E4%BC%9A%E9%97%AD%E5%B9%95%E5%BC%8F%E8%BF%99%E5%A4%A9-%E4%BA%8C/
- 前面主要还是说了乒乓球的,因为整体还是乒乓球的比赛赛程比较长,比较激烈,扣人心弦,记得那会在公司没法看视频直播,就偶尔看看奥运会官网的比分,还几场马龙樊振东,陈梦被赢了一局就吓尿了,已经被混双那场留下了阴影,其实后面去看看16 年的比赛什么的,中国队虽然拿了这么多冠军,但是自改成 11 分制以来,其实都没办法那么完全彻底地碾压,而且像张继科,樊振东,陈梦都多少有些慢热,现在看来是马龙比较全面,不过看过了马龙,刘国梁,许昕等的一些过往经历,都是起起伏伏,即使是张怡宁这样的大魔王,也经历过逢王楠不赢的阶段,心态无法调整好。
-其实最开始是举重项目,侯志慧是女子 49 公斤级的冠军,这场比赛是全场都看,其实看中国队的举重比赛跟跳水有点像,每一轮都需要到最后才能等到中国队,跳水其实每轮都有,举重会按照自己报的试举重量进行排名,重量大的会在后面举,抓举和挺举各三次试举机会,有时候会看着比较焦虑,一直等不来,怕一上来就没试举成功,而且中国队一般试举重量就是很大的,容易一次试举不成功就马上下一次,连着举其实压力会非常大,说实话真的是外行看热闹,每次都是多懂一点点,这次由于实在是比较无聊,所以看的会比较专心点,对于对应的规则知识点也会多了解一点,同时对于举重,没想到我们国家的这些运动员有这么强,最后八块金牌拿了七块,有一块拿到银牌也是有点因为教练的策略问题,这里其实也稍微知道一点,因为报上去的试举重量是谁小谁先举,并且我们国家都是实力非常强的,所以都会报大一些,并且如果这个项目有实力相近的选手,会比竞对多报一公斤,这样子如果前面竞争对手没举成功,我们把握就很大了,最坏的情况即使对手试举成功了,我们还有机会搏一把,比如谌利军这样的,只是说说感想,举重运动员真的是个比较单纯的群体,而且训练是非常痛苦枯燥的,非常容易受伤,像挺举就有点会压迫呼吸通道,看到好几个都是脸憋得通红,甚至直接因为压迫气道而没法完成后面的挺举,像之前 16 年的举重比赛,有个运动员没成功夺冠就非常愧疚地哭着说对不起祖国,没有获得冠军,这是怎么样的一种歉疚,怎么样的一种纯粹的感情呢,相对应地来说,我又要举男足,男篮的例子了,很多人在那嘲笑我这样对男足男篮愤愤不平的人,说可能我这样的人都没交个税(从缴纳个税的数量比例来算有可能),只是这里有两个打脸的事情,我足额缴纳个税,接近 20%的薪资都缴了个税,并且我买的所有东西都缴了增值税,如果让我这样缴纳了个税,缴纳了增值税的有个人的投票权,我一定会投票不让男足男篮使用我缴纳我的税金,用我们的缴纳的税,打出这么烂的表现,想乒乓球混双,拿个亚军都会被喷,那可是世界第二了,而且是就输了那么一场,足球篮球呢,我觉得是一方面成绩差,因为比赛真的有状态跟心态的影响,偶尔有一场失误非常正常,NBA 被黑八的有这么多强队,但是如果像男足男篮,成绩是越来越差,用范志毅的话来说就是脸都不要了,还有就是精气神,要在比赛中打出胜负欲,保持这种争胜心,才有机会再进步,前火箭队主教练鲁迪·汤姆贾诺维奇的话,“永远不要低估冠军的决心”,即使我现在打不过你,我会在下一次,下下次打败你,竞技体育永远要有这种精神,可以接受一时的失败,但是要保持永远争胜的心。
-第一块金牌是杨倩拿下的,中国队拿奥运会首金也是有政治任务的,而恰恰杨倩这个金牌也有点碰巧是对手最后一枪失误了,当然竞技体育,特别是射击,真的是容不得一点点失误,像前面几届的美国神通埃蒙斯,失之毫厘差之千里,但是这个具体评价就比较少,唯一一点让我比较出戏的就是杨倩真的非常像王刚的徒弟漆二娃,哈哈,微博上也有挺多人觉得像,射击还是个比较可以接受年纪稍大的运动员,需要经验和稳定性,相对来说爆发力体力稍好一点,像庞伟这样的,混合团体10米气手枪金牌,36 岁可能其他项目已经是年龄很大了,不过前面说的举重的吕小军军神也是年纪蛮大了,但是非常强,而且在油管上简直就是个神,相对来说射击是关注比较少,杨倩的也只是看了后面拿到冠军这个结果,有些因为时间或者电视上没放,但是成绩还是不错的,没多少喷点。
-第二篇先到这,纯主观,轻喷。
-]]>
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- 生活
- 运动
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- 生活
- 运动
- 东京奥运会
- 举重
- 射击
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- 聊在东京奥运会闭幕式这天
- /2021/08/08/%E8%81%8A%E5%9C%A8%E4%B8%9C%E4%BA%AC%E5%A5%A5%E8%BF%90%E4%BC%9A%E9%97%AD%E5%B9%95%E5%BC%8F%E8%BF%99%E5%A4%A9/
- 这届奥运会有可能是我除了 08 年之外关注度最高的一届奥运会,原因可能是因为最近也没什么电影综艺啥的比较好看,前面看跑男倒还行,不是说多好,也就图一乐,最开始看向往的生活觉得也挺不错的,后面变成了统一来了就看黄磊做饭,然后夸黄磊做饭好吃,然后无聊的说这种生活多么多么美好,单调无聊,差不多弃了,这里面还包括大华不在了,大华其实个人还是有点呱噪的,但是挺能搞气氛,并且也有才华,彭彭跟子枫人是不讨厌,但是撑不起来,所以也导致了前面说的结果,都变成了黄磊彩虹屁现场,虽然偶尔怀疑他是否做得好吃,但是整体还是承认的,可对于一个这么多季了的综艺来说,这样也有点单调了。
-还有奥运会像乒乓球,篮球,跳水这几个都是比较喜欢的项目,篮球🏀是从初中开始就也有在自己在玩的,虽然因为身高啊体质基本没什么天赋,但也算是热爱驱动,差不多到了大学因为比较懒才放下了,初中高中还是有很多时间花在上面,不像别人经常打球跑跑跳跳还能长高,我反而一直都没长个子,也因为这个其实蛮遗憾的,后面想想可能是初中的时候远走他乡去住宿读初中,伙食营养跟不上导致的,可能也是自己的一厢情愿吧,总觉得应该还能再长点个,这一点以后我自己的小孩我应该会特别注意这段时间他/她的营养摄入了;然后像乒乓球🏓的话其实小时候是比较讨厌的,因为家里人,父母都没有这类爱好习惯,我也完全不会,但是小学那会班里的“恶霸”就以公平之名要我们男生每个人都排队打几个,我这种不会的反而又要被嘲笑,这个小时候的阴影让我有了比较不好的印象,对它🏓的改观是在工作以后,前司跟一个同样不会的同事经常在饭点会打打,而且那会因为这个其实身体得到了锻炼,感觉是个不错的健身方式,然后又是中国的优势项目,小时候跟着我爸看孔令辉,那时候完全不懂,印象就觉得老瓦很牛,后面其实也没那么关注,上一届好像看了马龙的比赛;跳水也是中国的优势项目,而且也比较简单,不是说真的很简单,就是我们外行观众看着就看看水花大小图一乐。
-这次的观赛过程其实主要还是在乒乓球上面,现在都有点怪我的乌鸦嘴,混双我一直就不太放心(关我什么事,我也不专业),然后一直觉得混双是不是不太稳,结果那天看的时候也是因为央视一套跟五套都没放,我家的有线电视又是没有五加体育,然后用电脑投屏就很卡,看得也很不爽,同时那天因为看的时候已经是 2:0还是再后面点了,一方面是不懂每队只有一次暂停,另一方面不知道已经用过暂停了,所以就特别怀疑马林是不是只会无脑鼓掌,感觉作为教练,并且是前冠军,应该也能在擦汗间隙,或者局间休息调整的时候多给些战略战术的指导,类似于后面男团小胖打奥恰洛夫,像解说都看出来了,其实奥恰那会的反手特别顺,打得特别凶,那就不能让他能特别顺手的上反手位,这当然是外行比较粗浅的看法,在混双过程中其实除了这个,还有让人很不爽的就是我们的许昕跟刘诗雯有种拿不出破釜沉舟的勇气的感觉,在气势上完全被对面两位日本乒乓球最讨厌的两位对手压制着,我都要输了,我就每一颗都要不让你好过,因为真的不是说没有实力,对面水谷隼也不是多么多么强的,可能上一届男团许昕输给他还留着阴影,但是以许昕 19 年男单世界第一的实力,目前也排在世界前三,输一场不应该成为这种阻力,有一些失误也很可惜,后面孙颖莎真的打得很解气,第二局一度以为又要被翻盘了,结果来了个大逆转,女团的时候也是,感觉在心态上孙颖莎还是很值得肯定的,少年老成这个词很适合,看其他的视频也觉得莎莎萌萌哒,陈梦总感觉还欠一点王者霸气,王曼昱还是可以的,反手很凶,我觉得其实这一届日本女乒就是打得非常凶,即使像平野这种看着很弱的妹子,打的球可一点都不弱,也是这种凶狠的打法,有点要压制中国的感觉,这方面我觉得是需要改善的,打这种要不就是实力上的完全碾压,要不就是我实力虽然比较没强多少,但是你狠我打得比你还狠,越保守越要输,我不太成熟的想法是这样的,还有就是面对逆境,这个就要说到男队的了,樊振东跟马龙在半决赛的时候,特别是男团的第二盘,樊振东打奥恰很好地表现了这个心态,当然樊振东我不是特别了解,据说他是比较善于打相持,比较善于焦灼的情况,不过整体看下来樊振东还是有一些欠缺,就是面对情况的快速转变应对,这一点也是马龙特别强的,虽然看起来马龙真的是年纪大了点,没有 16 年那会满头发胶,油光锃亮的大背头和满脸胶原蛋白的意气风发,大范围运动能力也弱了一点,但是经验和能力的全面性也让他最终能再次站上巅峰,还是非常佩服的,这里提一下张继科,虽然可能天赋上是张继科更强点,但是男乒一直都是有强者出现,能为国家队付出这么多并且一直坚持的可不是人人都可以,即使现在同台竞技马龙打不过张继科我还是更喜欢马龙。再来说说我们的对手,主要分三部分,德国男乒,里面有波尔(我刚听到的时候在想怎么又出来个叫波尔的,是不是像举重的石智勇一样,又来一个同名的,结果是同一个,已经四十岁了),这真是个让人敬佩的对手,实力强,经验丰富,虽然男单有点可惜,但是帮助男团获得银牌,真的是起到了定海神针的作用;奥恰洛夫,以前完全不认识,或者说看过也忘了,这次是真的有点意外,竟然有这么个马龙护法,其实他也坦言非常想赢一次马龙,并且在半决赛也非常接近赢得比赛,是个实力非常强的对手,就是男团半决赛输给张本智和有点可惜,有点被打蒙的感觉,佛朗西斯卡的话也是实力不错的选手,就是可能被奥恰跟波尔的光芒掩盖了,跟波尔在男团第一盘男双的比赛中打败日本那对男双也是非常给力的,说实话,最后打国乒的时候的确是国乒实力更胜一筹,但是即使德国赢了我也是充满尊敬,拼的就是硬实力,就像第二盘奥恰打樊振东,反手是真的很强,反过来看奥恰可能也不是很善于快速调整,樊振东打出来自己的节奏,主攻奥恰的中路,他好像没什么好办法解决。再来说我最讨厌的日本,嗯,小日本,张本智和、水谷隼、伊藤美诚,一一评价下(我是外行,绝对主观评价),张本智和,父母也是中国人,原来叫张智和,改日本籍后加了个本,被微博网友笑称日本尖叫鸡,男单输给了斯洛文尼亚选手,男团里是赢了两场,但是在我看来其实实力上可能比不上全力的奥恰,主要是特别能叫,会干扰对手,如果觉得这种也是种能力我也无话可说,要是有那种吼声能直接把对手震聋的,都不需要打比赛了,我简单记了下,赢一颗球,他要叫八声,用 LD 的话来说烦都烦死了,心态是在面对一些困境顺境的应对调整适应能力,而不是对这种噪音的适应能力,至少我是这么看的,所以我很期待樊振东能好好地虐虐他,因为其他像林昀儒真的是非常优秀的新选手,所谓的国乒克星估计也是小日本自己说说的,国乒其实有很多对手,马龙跟樊振东在男单半决赛碰到的这两个几乎都差点把他们掀翻了,所以还是练好自己的实力再来吹吧,免得打脸;水谷隼的话真的是长相就是特别地讨厌,还搞出那套不打比赛的姿态,男团里被波尔干掉就是很好的例子,波尔虽然真的很强,但毕竟 40 岁了,跟伊藤美诚一起说了吧,伊藤实力说实话是有的,混双中很大一部分的赢面来自于她,刘诗雯做了手术状态不好,许昕失误稍多,但是这种赢球了就感觉我赢了你一辈子一场没输的感觉,还有那种不知道怎么形容的笑,实力强的正常打比赛的我都佩服,像女团决赛里,平野跟石川佳纯的打法其实也很凶狠,但是都是正常的比赛,即使中国队两位实力不济输了也很正常,这种就真的需要像孙颖莎这样的小魔王无视各种魔法攻击,无视你各种花里胡哨的打法的人好好教训一下,混双输了以后了解了下她,感觉实力真的不错,是个大威胁,但是其实我们孙颖莎也是经历了九个月的继续成长,像张怡宁也评价了她,可能后面就没什么空间了,当然如果由张怡宁来打她就更适合了,净整这些有的没的,就打得你没脾气。
-乒乓球的说的有点多,就分篇说了,第一篇先到这。
-]]>
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- 生活
- 运动
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- 生活
- 运动
- 东京奥运会
- 乒乓球
- 跳水
-
-
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- 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) { 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 tlshttps 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:
+
+“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。
+“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。
+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。
+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 。)
+私钥被使用: 服务器对预主密钥进行解密。
+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。
+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。
+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。
+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。
+
+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
+
+
+
+0
+HelloRequest
+
+
+1
+ClientHello
+
+
+2
+ServerHello
+
+
+11
+Certificate
+
+
+12
+ServerKeyExchange
+
+
+13
+CertificateRequest
+
+
+14
+ServerHelloDone
+
+
+15
+CertificateVerify
+
+
+16
+ClientKeyExchange
+
+
+20
+Finished
+
+
+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
+
+
+
+ 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
+
+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
+]]>
+
+ Java
+
+
+ Java
+ Apollo
+
+
+
+ 聊在东京奥运会闭幕式这天-二
+ /2021/08/19/%E8%81%8A%E5%9C%A8%E4%B8%9C%E4%BA%AC%E5%A5%A5%E8%BF%90%E4%BC%9A%E9%97%AD%E5%B9%95%E5%BC%8F%E8%BF%99%E5%A4%A9-%E4%BA%8C/
+ 前面主要还是说了乒乓球的,因为整体还是乒乓球的比赛赛程比较长,比较激烈,扣人心弦,记得那会在公司没法看视频直播,就偶尔看看奥运会官网的比分,还几场马龙樊振东,陈梦被赢了一局就吓尿了,已经被混双那场留下了阴影,其实后面去看看16 年的比赛什么的,中国队虽然拿了这么多冠军,但是自改成 11 分制以来,其实都没办法那么完全彻底地碾压,而且像张继科,樊振东,陈梦都多少有些慢热,现在看来是马龙比较全面,不过看过了马龙,刘国梁,许昕等的一些过往经历,都是起起伏伏,即使是张怡宁这样的大魔王,也经历过逢王楠不赢的阶段,心态无法调整好。
+其实最开始是举重项目,侯志慧是女子 49 公斤级的冠军,这场比赛是全场都看,其实看中国队的举重比赛跟跳水有点像,每一轮都需要到最后才能等到中国队,跳水其实每轮都有,举重会按照自己报的试举重量进行排名,重量大的会在后面举,抓举和挺举各三次试举机会,有时候会看着比较焦虑,一直等不来,怕一上来就没试举成功,而且中国队一般试举重量就是很大的,容易一次试举不成功就马上下一次,连着举其实压力会非常大,说实话真的是外行看热闹,每次都是多懂一点点,这次由于实在是比较无聊,所以看的会比较专心点,对于对应的规则知识点也会多了解一点,同时对于举重,没想到我们国家的这些运动员有这么强,最后八块金牌拿了七块,有一块拿到银牌也是有点因为教练的策略问题,这里其实也稍微知道一点,因为报上去的试举重量是谁小谁先举,并且我们国家都是实力非常强的,所以都会报大一些,并且如果这个项目有实力相近的选手,会比竞对多报一公斤,这样子如果前面竞争对手没举成功,我们把握就很大了,最坏的情况即使对手试举成功了,我们还有机会搏一把,比如谌利军这样的,只是说说感想,举重运动员真的是个比较单纯的群体,而且训练是非常痛苦枯燥的,非常容易受伤,像挺举就有点会压迫呼吸通道,看到好几个都是脸憋得通红,甚至直接因为压迫气道而没法完成后面的挺举,像之前 16 年的举重比赛,有个运动员没成功夺冠就非常愧疚地哭着说对不起祖国,没有获得冠军,这是怎么样的一种歉疚,怎么样的一种纯粹的感情呢,相对应地来说,我又要举男足,男篮的例子了,很多人在那嘲笑我这样对男足男篮愤愤不平的人,说可能我这样的人都没交个税(从缴纳个税的数量比例来算有可能),只是这里有两个打脸的事情,我足额缴纳个税,接近 20%的薪资都缴了个税,并且我买的所有东西都缴了增值税,如果让我这样缴纳了个税,缴纳了增值税的有个人的投票权,我一定会投票不让男足男篮使用我缴纳我的税金,用我们的缴纳的税,打出这么烂的表现,想乒乓球混双,拿个亚军都会被喷,那可是世界第二了,而且是就输了那么一场,足球篮球呢,我觉得是一方面成绩差,因为比赛真的有状态跟心态的影响,偶尔有一场失误非常正常,NBA 被黑八的有这么多强队,但是如果像男足男篮,成绩是越来越差,用范志毅的话来说就是脸都不要了,还有就是精气神,要在比赛中打出胜负欲,保持这种争胜心,才有机会再进步,前火箭队主教练鲁迪·汤姆贾诺维奇的话,“永远不要低估冠军的决心”,即使我现在打不过你,我会在下一次,下下次打败你,竞技体育永远要有这种精神,可以接受一时的失败,但是要保持永远争胜的心。
+第一块金牌是杨倩拿下的,中国队拿奥运会首金也是有政治任务的,而恰恰杨倩这个金牌也有点碰巧是对手最后一枪失误了,当然竞技体育,特别是射击,真的是容不得一点点失误,像前面几届的美国神通埃蒙斯,失之毫厘差之千里,但是这个具体评价就比较少,唯一一点让我比较出戏的就是杨倩真的非常像王刚的徒弟漆二娃,哈哈,微博上也有挺多人觉得像,射击还是个比较可以接受年纪稍大的运动员,需要经验和稳定性,相对来说爆发力体力稍好一点,像庞伟这样的,混合团体10米气手枪金牌,36 岁可能其他项目已经是年龄很大了,不过前面说的举重的吕小军军神也是年纪蛮大了,但是非常强,而且在油管上简直就是个神,相对来说射击是关注比较少,杨倩的也只是看了后面拿到冠军这个结果,有些因为时间或者电视上没放,但是成绩还是不错的,没多少喷点。
+第二篇先到这,纯主观,轻喷。
+]]>
+
+ 生活
+ 运动
+
+
+ 生活
+ 运动
+ 东京奥运会
+ 举重
+ 射击
+
+
+
+ 聊在东京奥运会闭幕式这天
+ /2021/08/08/%E8%81%8A%E5%9C%A8%E4%B8%9C%E4%BA%AC%E5%A5%A5%E8%BF%90%E4%BC%9A%E9%97%AD%E5%B9%95%E5%BC%8F%E8%BF%99%E5%A4%A9/
+ 这届奥运会有可能是我除了 08 年之外关注度最高的一届奥运会,原因可能是因为最近也没什么电影综艺啥的比较好看,前面看跑男倒还行,不是说多好,也就图一乐,最开始看向往的生活觉得也挺不错的,后面变成了统一来了就看黄磊做饭,然后夸黄磊做饭好吃,然后无聊的说这种生活多么多么美好,单调无聊,差不多弃了,这里面还包括大华不在了,大华其实个人还是有点呱噪的,但是挺能搞气氛,并且也有才华,彭彭跟子枫人是不讨厌,但是撑不起来,所以也导致了前面说的结果,都变成了黄磊彩虹屁现场,虽然偶尔怀疑他是否做得好吃,但是整体还是承认的,可对于一个这么多季了的综艺来说,这样也有点单调了。
+还有奥运会像乒乓球,篮球,跳水这几个都是比较喜欢的项目,篮球🏀是从初中开始就也有在自己在玩的,虽然因为身高啊体质基本没什么天赋,但也算是热爱驱动,差不多到了大学因为比较懒才放下了,初中高中还是有很多时间花在上面,不像别人经常打球跑跑跳跳还能长高,我反而一直都没长个子,也因为这个其实蛮遗憾的,后面想想可能是初中的时候远走他乡去住宿读初中,伙食营养跟不上导致的,可能也是自己的一厢情愿吧,总觉得应该还能再长点个,这一点以后我自己的小孩我应该会特别注意这段时间他/她的营养摄入了;然后像乒乓球🏓的话其实小时候是比较讨厌的,因为家里人,父母都没有这类爱好习惯,我也完全不会,但是小学那会班里的“恶霸”就以公平之名要我们男生每个人都排队打几个,我这种不会的反而又要被嘲笑,这个小时候的阴影让我有了比较不好的印象,对它🏓的改观是在工作以后,前司跟一个同样不会的同事经常在饭点会打打,而且那会因为这个其实身体得到了锻炼,感觉是个不错的健身方式,然后又是中国的优势项目,小时候跟着我爸看孔令辉,那时候完全不懂,印象就觉得老瓦很牛,后面其实也没那么关注,上一届好像看了马龙的比赛;跳水也是中国的优势项目,而且也比较简单,不是说真的很简单,就是我们外行观众看着就看看水花大小图一乐。
+这次的观赛过程其实主要还是在乒乓球上面,现在都有点怪我的乌鸦嘴,混双我一直就不太放心(关我什么事,我也不专业),然后一直觉得混双是不是不太稳,结果那天看的时候也是因为央视一套跟五套都没放,我家的有线电视又是没有五加体育,然后用电脑投屏就很卡,看得也很不爽,同时那天因为看的时候已经是 2:0还是再后面点了,一方面是不懂每队只有一次暂停,另一方面不知道已经用过暂停了,所以就特别怀疑马林是不是只会无脑鼓掌,感觉作为教练,并且是前冠军,应该也能在擦汗间隙,或者局间休息调整的时候多给些战略战术的指导,类似于后面男团小胖打奥恰洛夫,像解说都看出来了,其实奥恰那会的反手特别顺,打得特别凶,那就不能让他能特别顺手的上反手位,这当然是外行比较粗浅的看法,在混双过程中其实除了这个,还有让人很不爽的就是我们的许昕跟刘诗雯有种拿不出破釜沉舟的勇气的感觉,在气势上完全被对面两位日本乒乓球最讨厌的两位对手压制着,我都要输了,我就每一颗都要不让你好过,因为真的不是说没有实力,对面水谷隼也不是多么多么强的,可能上一届男团许昕输给他还留着阴影,但是以许昕 19 年男单世界第一的实力,目前也排在世界前三,输一场不应该成为这种阻力,有一些失误也很可惜,后面孙颖莎真的打得很解气,第二局一度以为又要被翻盘了,结果来了个大逆转,女团的时候也是,感觉在心态上孙颖莎还是很值得肯定的,少年老成这个词很适合,看其他的视频也觉得莎莎萌萌哒,陈梦总感觉还欠一点王者霸气,王曼昱还是可以的,反手很凶,我觉得其实这一届日本女乒就是打得非常凶,即使像平野这种看着很弱的妹子,打的球可一点都不弱,也是这种凶狠的打法,有点要压制中国的感觉,这方面我觉得是需要改善的,打这种要不就是实力上的完全碾压,要不就是我实力虽然比较没强多少,但是你狠我打得比你还狠,越保守越要输,我不太成熟的想法是这样的,还有就是面对逆境,这个就要说到男队的了,樊振东跟马龙在半决赛的时候,特别是男团的第二盘,樊振东打奥恰很好地表现了这个心态,当然樊振东我不是特别了解,据说他是比较善于打相持,比较善于焦灼的情况,不过整体看下来樊振东还是有一些欠缺,就是面对情况的快速转变应对,这一点也是马龙特别强的,虽然看起来马龙真的是年纪大了点,没有 16 年那会满头发胶,油光锃亮的大背头和满脸胶原蛋白的意气风发,大范围运动能力也弱了一点,但是经验和能力的全面性也让他最终能再次站上巅峰,还是非常佩服的,这里提一下张继科,虽然可能天赋上是张继科更强点,但是男乒一直都是有强者出现,能为国家队付出这么多并且一直坚持的可不是人人都可以,即使现在同台竞技马龙打不过张继科我还是更喜欢马龙。再来说说我们的对手,主要分三部分,德国男乒,里面有波尔(我刚听到的时候在想怎么又出来个叫波尔的,是不是像举重的石智勇一样,又来一个同名的,结果是同一个,已经四十岁了),这真是个让人敬佩的对手,实力强,经验丰富,虽然男单有点可惜,但是帮助男团获得银牌,真的是起到了定海神针的作用;奥恰洛夫,以前完全不认识,或者说看过也忘了,这次是真的有点意外,竟然有这么个马龙护法,其实他也坦言非常想赢一次马龙,并且在半决赛也非常接近赢得比赛,是个实力非常强的对手,就是男团半决赛输给张本智和有点可惜,有点被打蒙的感觉,佛朗西斯卡的话也是实力不错的选手,就是可能被奥恰跟波尔的光芒掩盖了,跟波尔在男团第一盘男双的比赛中打败日本那对男双也是非常给力的,说实话,最后打国乒的时候的确是国乒实力更胜一筹,但是即使德国赢了我也是充满尊敬,拼的就是硬实力,就像第二盘奥恰打樊振东,反手是真的很强,反过来看奥恰可能也不是很善于快速调整,樊振东打出来自己的节奏,主攻奥恰的中路,他好像没什么好办法解决。再来说我最讨厌的日本,嗯,小日本,张本智和、水谷隼、伊藤美诚,一一评价下(我是外行,绝对主观评价),张本智和,父母也是中国人,原来叫张智和,改日本籍后加了个本,被微博网友笑称日本尖叫鸡,男单输给了斯洛文尼亚选手,男团里是赢了两场,但是在我看来其实实力上可能比不上全力的奥恰,主要是特别能叫,会干扰对手,如果觉得这种也是种能力我也无话可说,要是有那种吼声能直接把对手震聋的,都不需要打比赛了,我简单记了下,赢一颗球,他要叫八声,用 LD 的话来说烦都烦死了,心态是在面对一些困境顺境的应对调整适应能力,而不是对这种噪音的适应能力,至少我是这么看的,所以我很期待樊振东能好好地虐虐他,因为其他像林昀儒真的是非常优秀的新选手,所谓的国乒克星估计也是小日本自己说说的,国乒其实有很多对手,马龙跟樊振东在男单半决赛碰到的这两个几乎都差点把他们掀翻了,所以还是练好自己的实力再来吹吧,免得打脸;水谷隼的话真的是长相就是特别地讨厌,还搞出那套不打比赛的姿态,男团里被波尔干掉就是很好的例子,波尔虽然真的很强,但毕竟 40 岁了,跟伊藤美诚一起说了吧,伊藤实力说实话是有的,混双中很大一部分的赢面来自于她,刘诗雯做了手术状态不好,许昕失误稍多,但是这种赢球了就感觉我赢了你一辈子一场没输的感觉,还有那种不知道怎么形容的笑,实力强的正常打比赛的我都佩服,像女团决赛里,平野跟石川佳纯的打法其实也很凶狠,但是都是正常的比赛,即使中国队两位实力不济输了也很正常,这种就真的需要像孙颖莎这样的小魔王无视各种魔法攻击,无视你各种花里胡哨的打法的人好好教训一下,混双输了以后了解了下她,感觉实力真的不错,是个大威胁,但是其实我们孙颖莎也是经历了九个月的继续成长,像张怡宁也评价了她,可能后面就没什么空间了,当然如果由张怡宁来打她就更适合了,净整这些有的没的,就打得你没脾气。
+乒乓球的说的有点多,就分篇说了,第一篇先到这。
+]]>
+
+ 生活
+ 运动
+
+
+ 生活
+ 运动
+ 东京奥运会
+ 乒乓球
+ 跳水
@@ -8172,116 +8295,16 @@ user3:
- 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 tlshttps 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:
+ 聊聊 Java 的类加载机制一
+ /2020/11/08/%E8%81%8A%E8%81%8A-Java-%E7%9A%84%E7%B1%BB%E5%8A%A0%E8%BD%BD%E6%9C%BA%E5%88%B6/
+ 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。
+
-“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。
-“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。
-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。
-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 。)
-私钥被使用: 服务器对预主密钥进行解密。
-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。
-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。
-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。
-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。
-
-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
-
-
-
-0
-HelloRequest
-
-
-1
-ClientHello
-
-
-2
-ServerHello
-
-
-11
-Certificate
-
-
-12
-ServerKeyExchange
-
-
-13
-CertificateRequest
-
-
-14
-ServerHelloDone
-
-
-15
-CertificateVerify
-
-
-16
-ClientKeyExchange
-
-
-20
-Finished
-
-
-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 Java 的类加载机制一
- /2020/11/08/%E8%81%8A%E8%81%8A-Java-%E7%9A%84%E7%B1%BB%E5%8A%A0%E8%BD%BD%E6%9C%BA%E5%88%B6/
- 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。
-
-
-通过一个类的全限定名来获取定义此类的二进制字节流
-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
-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
+通过一个类的全限定名来获取定义此类的二进制字节流
+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
+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
@@ -8649,38 +8672,6 @@ user3:
幻读
-
- 聊聊 mysql 索引的一些细节
- /2020/12/27/%E8%81%8A%E8%81%8A-mysql-%E7%B4%A2%E5%BC%95%E7%9A%84%E4%B8%80%E4%BA%9B%E7%BB%86%E8%8A%82/
- 前几天同事问了我个 mysql 索引的问题,虽然大概知道,但是还是想来实践下,就是 is null,is not null 这类查询是否能用索引,可能之前有些网上的文章说都是不能用索引,但是其实不是,我们来看个小试验
-CREATE TABLE `null_index_t` ( `id` int (10 ) unsigned NOT NULL AUTO_INCREMENT, `null_key` varchar (255 ) DEFAULT NULL , `null_key1` varchar (255 ) DEFAULT NULL , `null_key2` varchar (255 ) DEFAULT NULL , PRIMARY KEY (`id`), KEY `idx_1` (`null_key`) USING BTREE, KEY `idx_2` (`null_key1`) USING BTREE, KEY `idx_3` (`null_key2`) USING BTREE ) ENGINE= InnoDB DEFAULT CHARSET= utf8mb4;
-用个存储过程来插入数据
-delimiter $ #以delimiter来标记用$表示存储过程结束 create procedure nullIndex1()begin declare i int ; declare j int ; set i= 1 ;set j= 1 ;while(i<= 100 ) do while(j<= 100 ) do IF (i % 3 = 0 ) THEN INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (null , LEFT (MD5(RAND()), 8 ), LEFT (MD5(RAND()), 8 )); ELSEIF (i % 3 = 1 ) THEN INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT (MD5(RAND()), 8 ), NULL , LEFT (MD5(RAND()), 8 )); ELSE INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT (MD5(RAND()), 8 ), LEFT (MD5(RAND()), 8 ), NULL ); END IF; set j= j+ 1 ; end while; set i= i+ 1 ; set j= 1 ; end while;end $ call nullIndex1();
-然后看下我们的 is null 查询
-EXPLAIN select * from null_index_t WHERE null_key is null ;
- 再来看看另一个
-EXPLAIN select * from null_index_t WHERE null_key is not null ;
- 从这里能看出来啥呢,可以思考下
-从上面可以发现,is null应该是用上了索引了,所以至少不是一刀切不能用,但是看着is not null好像不太行额 我们在做一点小改动,把这个表里的数据改成 9100 条是 null,剩下 900 条是有值的,然后再执行下 然后再来看看执行结果
-EXPLAIN select * from null_index_t WHERE null_key is null ;
-
-EXPLAIN select * from null_index_t WHERE null_key is not null ;
- 是不是不一样了,这里再补充下我试验使用的 mysql 是 5.7 的,不保证在其他版本的一致性, 其实可以看出随着数据量的变化,mysql 会不会使用索引是会变化的,不是说 is not null 一定会使用,也不是一定不会使用,而是优化器会根据查询成本做个预判,这个预判尽可能会减小查询成本,主要包括回表啥的,但是也不一定完全准确。
-]]>
-
- Mysql
- C
- 索引
- Mysql
-
-
- mysql
- 索引
- is null
- is not null
- procedure
-
-
聊聊 mysql 的 MVCC
/2020/04/26/%E8%81%8A%E8%81%8A-mysql-%E7%9A%84-MVCC/
@@ -8721,6 +8712,38 @@ user3:
read view
+
+ 聊聊 mysql 索引的一些细节
+ /2020/12/27/%E8%81%8A%E8%81%8A-mysql-%E7%B4%A2%E5%BC%95%E7%9A%84%E4%B8%80%E4%BA%9B%E7%BB%86%E8%8A%82/
+ 前几天同事问了我个 mysql 索引的问题,虽然大概知道,但是还是想来实践下,就是 is null,is not null 这类查询是否能用索引,可能之前有些网上的文章说都是不能用索引,但是其实不是,我们来看个小试验
+CREATE TABLE `null_index_t` ( `id` int (10 ) unsigned NOT NULL AUTO_INCREMENT, `null_key` varchar (255 ) DEFAULT NULL , `null_key1` varchar (255 ) DEFAULT NULL , `null_key2` varchar (255 ) DEFAULT NULL , PRIMARY KEY (`id`), KEY `idx_1` (`null_key`) USING BTREE, KEY `idx_2` (`null_key1`) USING BTREE, KEY `idx_3` (`null_key2`) USING BTREE ) ENGINE= InnoDB DEFAULT CHARSET= utf8mb4;
+用个存储过程来插入数据
+delimiter $ #以delimiter来标记用$表示存储过程结束 create procedure nullIndex1()begin declare i int ; declare j int ; set i= 1 ;set j= 1 ;while(i<= 100 ) do while(j<= 100 ) do IF (i % 3 = 0 ) THEN INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (null , LEFT (MD5(RAND()), 8 ), LEFT (MD5(RAND()), 8 )); ELSEIF (i % 3 = 1 ) THEN INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT (MD5(RAND()), 8 ), NULL , LEFT (MD5(RAND()), 8 )); ELSE INSERT INTO null_index_t ( `null_key`, `null_key1`, `null_key2` ) VALUES (LEFT (MD5(RAND()), 8 ), LEFT (MD5(RAND()), 8 ), NULL ); END IF; set j= j+ 1 ; end while; set i= i+ 1 ; set j= 1 ; end while;end $ call nullIndex1();
+然后看下我们的 is null 查询
+EXPLAIN select * from null_index_t WHERE null_key is null ;
+ 再来看看另一个
+EXPLAIN select * from null_index_t WHERE null_key is not null ;
+ 从这里能看出来啥呢,可以思考下
+从上面可以发现,is null应该是用上了索引了,所以至少不是一刀切不能用,但是看着is not null好像不太行额 我们在做一点小改动,把这个表里的数据改成 9100 条是 null,剩下 900 条是有值的,然后再执行下 然后再来看看执行结果
+EXPLAIN select * from null_index_t WHERE null_key is null ;
+
+EXPLAIN select * from null_index_t WHERE null_key is not null ;
+ 是不是不一样了,这里再补充下我试验使用的 mysql 是 5.7 的,不保证在其他版本的一致性, 其实可以看出随着数据量的变化,mysql 会不会使用索引是会变化的,不是说 is not null 一定会使用,也不是一定不会使用,而是优化器会根据查询成本做个预判,这个预判尽可能会减小查询成本,主要包括回表啥的,但是也不一定完全准确。
+]]>
+
+ Mysql
+ C
+ 索引
+ Mysql
+
+
+ mysql
+ 索引
+ is null
+ is not null
+ procedure
+
+
聊聊 redis 缓存的应用问题
/2021/01/31/%E8%81%8A%E8%81%8A-redis-%E7%BC%93%E5%AD%98%E7%9A%84%E5%BA%94%E7%94%A8%E9%97%AE%E9%A2%98/
@@ -8813,6 +8836,36 @@ user3:
AutoConfiguration
+
+ 聊聊一次 brew update 引发的血案-202502更新
+ /2025/02/02/%E8%81%8A%E8%81%8A%E4%B8%80%E6%AC%A1-brew-update-%E5%BC%95%E5%8F%91%E7%9A%84%E8%A1%80%E6%A1%88-202502%E6%9B%B4%E6%96%B0/
+ 之前写了这么一篇标题党,只是这个的确是比较头疼的事情,brew更新了下,php就不能用了,这里面主要是 icu4c 这个库的更新导致的,比如最近我又碰到了 , 正好又解决了下 因为后续brew在 m系列芯片的mac上有了更新,所以之前那篇需要有一些改动, 首先是这个目录 $(brew --prefix)/Homebrew/Library/Taps/homebrew/homebrew-core/Formula 目前这个替换符 $(brew --prefix) 还是有效的,不过路径变更为了 /opt/homebrew,但是后面的路径改变了,变成了 /Library/Taps/homebrew/homebrew-core/Formula 中间少了 Homebrew , 这是第一点, 第二点是对于这个文件 icu4c.rb 的重新安装 因为目前我还在使用 php 7.4 版本,依赖的是 icu4c 的 71 版本,所以就切换到类似于 e3317b86c11c644e88c762e03eb7b310c3337587 这个 commit id 这样,
+git checkout -b icu4c-71 e3317b86c11c644e88c762e03eb7b310c3337587
+但是由于目前的brew已经没有 switch 命令了,所以只能把最新版本的卸载掉,再进行安装老版本 先通过
+
+卸载老版本的 icu4c 然后再使用
+brew reinstall ./icu4c.rb
+安装 71 版本的,这样就能解决这个问题 第三个问题会在安装时出现 类似于
+attestation verification failed: Failure while executing; `/usr/bin/env GH_TOKEN=****** /usr/local/bin/gh attestation verify /Users/xxx/Library/Caches/Homebrew/downloads/
+这样的操作,可以通过设置常量的形式来解决
+export HOMEBREW_NO_VERIFY_ATTESTATIONS=true
+这样就不去做这个验证了
+]]>
+
+ Mac
+ PHP
+ Homebrew
+ PHP
+ icu4c
+
+
+ Mac
+ PHP
+ Homebrew
+ icu4c
+ zsh
+
+
聊聊一次 brew update 引发的血案
/2020/06/13/%E8%81%8A%E8%81%8A%E4%B8%80%E6%AC%A1-brew-update-%E5%BC%95%E5%8F%91%E7%9A%84%E8%A1%80%E6%A1%88/
@@ -9171,6 +9224,26 @@ user3:
windows
+
+ 解决wsl2中的ubuntu无法通过ssh连接的问题
+ /2025/06/22/%E8%A7%A3%E5%86%B3wsl2%E4%B8%AD%E7%9A%84ubuntu%E6%97%A0%E6%B3%95%E9%80%9A%E8%BF%87ssh%E8%BF%9E%E6%8E%A5%E7%9A%84%E9%97%AE%E9%A2%98/
+ 之前介绍过wsl2的ssh连接问题,但是最近又碰到无法连接的情况,通过一顿查资料 发现了个比较特别的方法 首先可以通过powershell命令查看目前的端口开放情况
+Get-NetFirewallRule | Where-Object {$_ .LocalPort -eq 22 -or $_ .DisplayName -like "*SSH*" } | Select-Object DisplayName, Enabled, Action, Direction
+ 这个是查看的22端口或者名称带有SSH的 还可以直接看22端口的入站规则 Get-NetFirewallRule -Direction Inbound | Where-Object {$_ .LocalPort -eq 22 } | Select-Object DisplayName, Enabled, Action
确认下是否已有对应规则,没有的话可以通过下面的命令来添加 方法1:允许所有网络的ssh访问
+New-NetFirewallRule -DisplayName "WSL2 SSH Inbound" -Direction Inbound -Protocol TCP -LocalPort 22 -Action Allow
+当然这样不太安全 所以我们最好是指定下来源ip 方法二:只允许局域网ip来访问
+New-NetFirewallRule -DisplayName "WSL2 SSH LAN" -Direction Inbound -Protocol TCP -LocalPort 22 -Action Allow -RemoteAddress 192.168 .0.0 /16 ,10.0 .0.0 /8 ,172.16 .0.0 /12
+后面的网段可以根据所需限定 然后可以在所需机器进行访问验证
+ssh -v user@192.168.xxx.xxx
+可以访问了的话说明我们这个就已经放行了,通过这个我们也能发现其实wsl2的这种方式一方面是让linux更加完整,但是对于所处的windows宿主机,想进行一些连通访问就会变得更加麻烦,也算是种取舍
+]]>
+
+ wsl
+
+
+ wsl
+
+
解决一个比较奇妙的问题 - leancloud 阅读计数不显示
/2025/02/09/%E8%A7%A3%E5%86%B3%E4%B8%80%E4%B8%AA%E6%AF%94%E8%BE%83%E5%A5%87%E5%A6%99%E7%9A%84%E9%97%AE%E9%A2%98/
@@ -9192,26 +9265,21 @@ user3:
- 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
-
-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
+ 解决下idea中maven依赖的cannot download sources问题
+ /2025/06/15/%E8%A7%A3%E5%86%B3%E4%B8%8Bidea%E4%B8%AD%E4%BE%9D%E8%B5%96%E7%9A%84cannot-download-sources%E9%97%AE%E9%A2%98/
+ 发现一个比较奇怪的问题,在看一个依赖的源码的时候没法下载sources,idea点击的时候报”cannot download sources” 可能有点强迫症,看着缩略过的代码比较不舒服,就查了下这个问题的解决方法
+第一种maven命令 首先第一种是直接使用maven命令 在项目目录下执行
+mvn dependency:resolve -Dclassifier=sources
+命令,这样就会去下载这些依赖包的sources源码,等待命令构建完成就可以了 如果idea自动重建索引了就直接生效了,如果没有则需要choose sources
+第二种idea设置 还有一种,找到idea的设置中的build工具,maven,import设置,勾选自动下载sources 可以勾选sources,就会在下载依赖包的时候就自动下载sources 点击idea的maven刷新按钮就会在下载依赖包的时候同代下载sources源码 这些前提是本身打包的时候是带上sources源码的,没有用一些混淆加密手段过
]]>
Java
+ Maven
Java
- Apollo
+ Maven
@@ -9312,22 +9380,6 @@ 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/
@@ -9341,6 +9393,22 @@ user3:
<?php use PHPUnit \Framework \TestCase ;use Nicksxs \PhpRename \Rename ;use function PHPUnit \Framework \assertEquals ;class RenameTest extends TestCase { public function setUp ( ) :void { $myfile = fopen (__DIR__ . DIRECTORY_SEPARATOR . "oldfile.txt" , "w" ) or die ("Unable to open file!" ); $txt = "file test1\n" ; fwrite ($myfile , $txt ); fclose ($myfile ); } public function testRename ( ) { Rename ::renameSingleFile (__DIR__ . DIRECTORY_SEPARATOR . "oldfile.txt" , "newfile" ); assertEquals (is_file (__DIR__ . DIRECTORY_SEPARATOR . "newfile.txt" ), true ); } protected function tearDown ( ): void { unlink (__DIR__ . DIRECTORY_SEPARATOR . "newfile.txt" ); } }
setUp 跟 tearDown 就是初始化跟结束清理的,但是注意如果不指明 __DIR__ ,待会的目录就会在执行 vendor/bin/phpunit 下面, 或者也可以指定在一个 tmp/ 目录下 最后就可以通过vendor/bin/phpunit 来执行测试 执行结果
❯ vendor/bin/phpunit PHPUnit 9.5.25 by Sebastian Bergmann and contributors. . 1 / 1 (100%) Time: 00:00.005, Memory: 6.00 MB OK (1 test, 1 assertion)
+]]>
+
+ php
+
+
+ php
+
+
+
+ 记录下 phpunit 的入门使用方法之setUp和tearDown
+ /2022/10/23/%E8%AE%B0%E5%BD%95%E4%B8%8B-phpunit-%E7%9A%84%E5%85%A5%E9%97%A8%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95%E4%B9%8BsetUp%E5%92%8CtearDown/
+ 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
@@ -9363,6 +9431,33 @@ user3:
redis
+
+ 记录下 zookeeper 集群迁移和易错点
+ /2022/05/29/%E8%AE%B0%E5%BD%95%E4%B8%8B-zookeeper-%E9%9B%86%E7%BE%A4%E8%BF%81%E7%A7%BB/
+ 前阵子做了zk 的集群升级迁移,大概情况是原来是一个三节点的 zk 集群(最小可用 大概是
+zk1 192.168.2.1 zk2 192.168.2.2 zk3 192.168.2.3
+在 zoo.cfg 中的配置就是如下
+server.1=192.168.2.1:2888:3888 server.2=192.168.2.2:2888:3888 server.3=192.168.2.3:2888:3888
+加节点 需要将集群迁移到 192.168.2.4(简称 zk4),192.168.2.5(简称 zk5),192.168.2.6(简称 zk6) 这三台机器上,目前新的这三台机器上是没有 zk 部署的, 我们想要的是数据不丢失,那主要考虑的就是滚动升级,这里我其实犯了几个错误,也特别说明下 首先我们想要新的三台机器加进去,所以我在zk4,zk5,zk6 的配置是这样
+server.1=192.168.2.1:2888:3888 server.2=192.168.2.2:2888:3888 server.3=192.168.2.3:2888:3888 server.4=192.168.2.4:2888:3888 server.5=192.168.2.5:2888:3888 server.6=192.168.2.6:2888:3888
+这样起来发现状态是该节点没起来, PS:查看当前节点状态可以通过 ./zkServer.sh status 来查看 第一个问题是我需要一个myid文件,标识我是哪个节点,里面的内容就写 4或 5 或 6 这样就行了,并且这个文件的路径应该在配置文件中指定的dataDir=数据目录下 第二个问题是困扰我比较久的,我在按上面的配置启动节点后,发现这几个节点都是没起来的,并且有 FastLeaderElection@xxx - Notification time out: 60000 这个报错,一开始以为是网络不通,端口没开这些原因,检查了下都是通的,结果原因其实跟我之前的一个考虑是相关的,当有六个节点的时候,理论上需要有半数以上的节点可用,集群才会是健康的,但是按我这个方式起来,其实我配置了六个节点,但是其中三个都是不可用的(包括自身节点),那么它自然是没办法正常工作,所以这里其实也需要滚动添加,类似于这样 我的 zk4 的配置应该是这样
+server.1=192.168.2.1:2888:3888 server.2=192.168.2.2:2888:3888 server.3=192.168.2.3:2888:3888 server.4=192.168.2.4:2888:3888
+然后 zk5 的配置
+server.1=192.168.2.1:2888:3888 server.2=192.168.2.2:2888:3888 server.3=192.168.2.3:2888:3888 server.4=192.168.2.4:2888:3888 server.5=192.168.2.5:2888:3888
+接着 zk6 的配置就可以是全部了
+server.1=192.168.2.1:2888:3888 server.2=192.168.2.2:2888:3888 server.3=192.168.2.3:2888:3888 server.4=192.168.2.4:2888:3888 server.5=192.168.2.5:2888:3888 server.6=192.168.2.6:2888:3888
+然后为了集群完全更新,就继续在 zk4 和 zk5 加上其他节点,这样我的 6 节点集群就起来了
+下节点 这里我踩了另外一个坑,或者说没搞清楚两种方式的差别,
+第一种 首先说说我没采用的第一种方式,(也是比较合理的)其实上面这个集群有个明显的问题,老集群其实还是各自认了一个三节点的集群,其中 zk3 是主节点,对于 zk1,zk2,zk3 来说它们能看到的就只有这三个节点,对于后三个 zk4,zk5,zk6 节点来说他们能连上其余五个节点,可以认为这是个六节点的集群,那么比较合理的操作应该是在老的三节点上把后面三个也都加进来,即每个节点的配置里 server 都有 6 个,然后我再对老的节点进行下线,这里下线需要注意的比较理想的是下一个节点就要修改配置,挪掉下线的节点后进行一遍重启,比如我知道了集群中的 leader 是在 zk3 上面,那么我先将 zk1 和 zk2 下掉,那么在我将 zk1 下线的之后,我将其他的五个节点都删除 zk1 的配置,然后重启,这样其实不是必须,但相对会可靠些,理论上我也可以在下掉 zk1 和 zk2 之后再修改配置重启其余节点。而当只剩下 zk3,zk4,zk5,zk6 四个节点的集群后,并且每个节点里的配置也只有这四个 server,我再下线 zk3 这个 leader 的时候,就会进行选举,再选出新的 leader,因为刚好是三节点,同样保证了最小可用。
+第二种 这也是我踩坑的一种方式,就是我没有修改原来三节点的配置,并且我一开始以为可以通过下线 zk1,zk2,zk3(进行选举)的方式完成下线,然后再进行重启,但是这种方式就是我上面说的,原来的三节点里我下掉 zk1 还是能够正常运行,但是我下线 zk2 的时候,这个集群就等于是挂了,小于最小可用了,这样三节点都挂了,而且对于新加入的三个节点来说,又回到了最初起不来一样状态,六节点里只有三节点在线,导致整个集群都挂了,所以对于我这样的操作来说,我需要滚动修改启动,在下线 zk1 的时候就需要把 zk4,zk5,zk6 中的 zk1 移除后重启,当然这样唯一的好处就是可以少重启几个,同样继续下线 zk2 的时候,把 zk2 移除掉再重启,其实在移除 zk1 后修改重启后,在下线 zk2 的时候,集群就会重新选举了,因为 zk2 下线的时候,zk3 还是会一起下线。这个是我们需要特别注意的
+]]>
+
+ java
+
+
+ zookeeper
+
+
记录下一次服务器迁移
/2023/11/12/%E8%AE%B0%E5%BD%95%E4%B8%8B%E4%B8%80%E6%AC%A1%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%81%E7%A7%BB/
@@ -9409,53 +9504,6 @@ user3:
干活
-
- 解决wsl2中的ubuntu无法通过ssh连接的问题
- /2025/06/22/%E8%A7%A3%E5%86%B3wsl2%E4%B8%AD%E7%9A%84ubuntu%E6%97%A0%E6%B3%95%E9%80%9A%E8%BF%87ssh%E8%BF%9E%E6%8E%A5%E7%9A%84%E9%97%AE%E9%A2%98/
- 之前介绍过wsl2的ssh连接问题,但是最近又碰到无法连接的情况,通过一顿查资料 发现了个比较特别的方法 首先可以通过powershell命令查看目前的端口开放情况
-Get-NetFirewallRule | Where-Object {$_ .LocalPort -eq 22 -or $_ .DisplayName -like "*SSH*" } | Select-Object DisplayName, Enabled, Action, Direction
- 这个是查看的22端口或者名称带有SSH的 还可以直接看22端口的入站规则 Get-NetFirewallRule -Direction Inbound | Where-Object {$_ .LocalPort -eq 22 } | Select-Object DisplayName, Enabled, Action
确认下是否已有对应规则,没有的话可以通过下面的命令来添加 方法1:允许所有网络的ssh访问
-New-NetFirewallRule -DisplayName "WSL2 SSH Inbound" -Direction Inbound -Protocol TCP -LocalPort 22 -Action Allow
-当然这样不太安全 所以我们最好是指定下来源ip 方法二:只允许局域网ip来访问
-New-NetFirewallRule -DisplayName "WSL2 SSH LAN" -Direction Inbound -Protocol TCP -LocalPort 22 -Action Allow -RemoteAddress 192.168 .0.0 /16 ,10.0 .0.0 /8 ,172.16 .0.0 /12
-后面的网段可以根据所需限定 然后可以在所需机器进行访问验证
-ssh -v user@192.168.xxx.xxx
-可以访问了的话说明我们这个就已经放行了,通过这个我们也能发现其实wsl2的这种方式一方面是让linux更加完整,但是对于所处的windows宿主机,想进行一些连通访问就会变得更加麻烦,也算是种取舍
-]]>
-
- wsl
-
-
- wsl
-
-
-
- 记录下 zookeeper 集群迁移和易错点
- /2022/05/29/%E8%AE%B0%E5%BD%95%E4%B8%8B-zookeeper-%E9%9B%86%E7%BE%A4%E8%BF%81%E7%A7%BB/
- 前阵子做了zk 的集群升级迁移,大概情况是原来是一个三节点的 zk 集群(最小可用 大概是
-zk1 192.168.2.1 zk2 192.168.2.2 zk3 192.168.2.3
-在 zoo.cfg 中的配置就是如下
-server.1=192.168.2.1:2888:3888 server.2=192.168.2.2:2888:3888 server.3=192.168.2.3:2888:3888
-加节点 需要将集群迁移到 192.168.2.4(简称 zk4),192.168.2.5(简称 zk5),192.168.2.6(简称 zk6) 这三台机器上,目前新的这三台机器上是没有 zk 部署的, 我们想要的是数据不丢失,那主要考虑的就是滚动升级,这里我其实犯了几个错误,也特别说明下 首先我们想要新的三台机器加进去,所以我在zk4,zk5,zk6 的配置是这样
-server.1=192.168.2.1:2888:3888 server.2=192.168.2.2:2888:3888 server.3=192.168.2.3:2888:3888 server.4=192.168.2.4:2888:3888 server.5=192.168.2.5:2888:3888 server.6=192.168.2.6:2888:3888
-这样起来发现状态是该节点没起来, PS:查看当前节点状态可以通过 ./zkServer.sh status 来查看 第一个问题是我需要一个myid文件,标识我是哪个节点,里面的内容就写 4或 5 或 6 这样就行了,并且这个文件的路径应该在配置文件中指定的dataDir=数据目录下 第二个问题是困扰我比较久的,我在按上面的配置启动节点后,发现这几个节点都是没起来的,并且有 FastLeaderElection@xxx - Notification time out: 60000 这个报错,一开始以为是网络不通,端口没开这些原因,检查了下都是通的,结果原因其实跟我之前的一个考虑是相关的,当有六个节点的时候,理论上需要有半数以上的节点可用,集群才会是健康的,但是按我这个方式起来,其实我配置了六个节点,但是其中三个都是不可用的(包括自身节点),那么它自然是没办法正常工作,所以这里其实也需要滚动添加,类似于这样 我的 zk4 的配置应该是这样
-server.1=192.168.2.1:2888:3888 server.2=192.168.2.2:2888:3888 server.3=192.168.2.3:2888:3888 server.4=192.168.2.4:2888:3888
-然后 zk5 的配置
-server.1=192.168.2.1:2888:3888 server.2=192.168.2.2:2888:3888 server.3=192.168.2.3:2888:3888 server.4=192.168.2.4:2888:3888 server.5=192.168.2.5:2888:3888
-接着 zk6 的配置就可以是全部了
-server.1=192.168.2.1:2888:3888 server.2=192.168.2.2:2888:3888 server.3=192.168.2.3:2888:3888 server.4=192.168.2.4:2888:3888 server.5=192.168.2.5:2888:3888 server.6=192.168.2.6:2888:3888
-然后为了集群完全更新,就继续在 zk4 和 zk5 加上其他节点,这样我的 6 节点集群就起来了
-下节点 这里我踩了另外一个坑,或者说没搞清楚两种方式的差别,
-第一种 首先说说我没采用的第一种方式,(也是比较合理的)其实上面这个集群有个明显的问题,老集群其实还是各自认了一个三节点的集群,其中 zk3 是主节点,对于 zk1,zk2,zk3 来说它们能看到的就只有这三个节点,对于后三个 zk4,zk5,zk6 节点来说他们能连上其余五个节点,可以认为这是个六节点的集群,那么比较合理的操作应该是在老的三节点上把后面三个也都加进来,即每个节点的配置里 server 都有 6 个,然后我再对老的节点进行下线,这里下线需要注意的比较理想的是下一个节点就要修改配置,挪掉下线的节点后进行一遍重启,比如我知道了集群中的 leader 是在 zk3 上面,那么我先将 zk1 和 zk2 下掉,那么在我将 zk1 下线的之后,我将其他的五个节点都删除 zk1 的配置,然后重启,这样其实不是必须,但相对会可靠些,理论上我也可以在下掉 zk1 和 zk2 之后再修改配置重启其余节点。而当只剩下 zk3,zk4,zk5,zk6 四个节点的集群后,并且每个节点里的配置也只有这四个 server,我再下线 zk3 这个 leader 的时候,就会进行选举,再选出新的 leader,因为刚好是三节点,同样保证了最小可用。
-第二种 这也是我踩坑的一种方式,就是我没有修改原来三节点的配置,并且我一开始以为可以通过下线 zk1,zk2,zk3(进行选举)的方式完成下线,然后再进行重启,但是这种方式就是我上面说的,原来的三节点里我下掉 zk1 还是能够正常运行,但是我下线 zk2 的时候,这个集群就等于是挂了,小于最小可用了,这样三节点都挂了,而且对于新加入的三个节点来说,又回到了最初起不来一样状态,六节点里只有三节点在线,导致整个集群都挂了,所以对于我这样的操作来说,我需要滚动修改启动,在下线 zk1 的时候就需要把 zk4,zk5,zk6 中的 zk1 移除后重启,当然这样唯一的好处就是可以少重启几个,同样继续下线 zk2 的时候,把 zk2 移除掉再重启,其实在移除 zk1 后修改重启后,在下线 zk2 的时候,集群就会重新选举了,因为 zk2 下线的时候,zk3 还是会一起下线。这个是我们需要特别注意的
-]]>
-
- java
-
-
- zookeeper
-
-
远程桌面工具rustdesk的私有化部署-mac锁定问题
/2024/05/19/%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-mac%E9%94%81%E5%AE%9A%E9%97%AE%E9%A2%98/
@@ -9476,51 +9524,95 @@ user3:
- 聊聊一次 brew update 引发的血案-202502更新
- /2025/02/02/%E8%81%8A%E8%81%8A%E4%B8%80%E6%AC%A1-brew-update-%E5%BC%95%E5%8F%91%E7%9A%84%E8%A1%80%E6%A1%88-202502%E6%9B%B4%E6%96%B0/
- 之前写了这么一篇标题党,只是这个的确是比较头疼的事情,brew更新了下,php就不能用了,这里面主要是 icu4c 这个库的更新导致的,比如最近我又碰到了 , 正好又解决了下 因为后续brew在 m系列芯片的mac上有了更新,所以之前那篇需要有一些改动, 首先是这个目录 $(brew --prefix)/Homebrew/Library/Taps/homebrew/homebrew-core/Formula 目前这个替换符 $(brew --prefix) 还是有效的,不过路径变更为了 /opt/homebrew,但是后面的路径改变了,变成了 /Library/Taps/homebrew/homebrew-core/Formula 中间少了 Homebrew , 这是第一点, 第二点是对于这个文件 icu4c.rb 的重新安装 因为目前我还在使用 php 7.4 版本,依赖的是 icu4c 的 71 版本,所以就切换到类似于 e3317b86c11c644e88c762e03eb7b310c3337587 这个 commit id 这样,
-git checkout -b icu4c-71 e3317b86c11c644e88c762e03eb7b310c3337587
-但是由于目前的brew已经没有 switch 命令了,所以只能把最新版本的卸载掉,再进行安装老版本 先通过
-
-卸载老版本的 icu4c 然后再使用
-brew reinstall ./icu4c.rb
-安装 71 版本的,这样就能解决这个问题 第三个问题会在安装时出现 类似于
-attestation verification failed: Failure while executing; `/usr/bin/env GH_TOKEN=****** /usr/local/bin/gh attestation verify /Users/xxx/Library/Caches/Homebrew/downloads/
-这样的操作,可以通过设置常量的形式来解决
-export HOMEBREW_NO_VERIFY_ATTESTATIONS=true
-这样就不去做这个验证了
+ 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:
+
+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
]]>
- Mac
- PHP
- Homebrew
- PHP
- icu4c
+ 工具
- Mac
- PHP
- Homebrew
- icu4c
- zsh
+ 工具
- 解决下idea中maven依赖的cannot download sources问题
- /2025/06/15/%E8%A7%A3%E5%86%B3%E4%B8%8Bidea%E4%B8%AD%E4%BE%9D%E8%B5%96%E7%9A%84cannot-download-sources%E9%97%AE%E9%A2%98/
- 发现一个比较奇怪的问题,在看一个依赖的源码的时候没法下载sources,idea点击的时候报”cannot download sources” 可能有点强迫症,看着缩略过的代码比较不舒服,就查了下这个问题的解决方法
-第一种maven命令 首先第一种是直接使用maven命令 在项目目录下执行
-mvn dependency:resolve -Dclassifier=sources
-命令,这样就会去下载这些依赖包的sources源码,等待命令构建完成就可以了 如果idea自动重建索引了就直接生效了,如果没有则需要choose sources
-第二种idea设置 还有一种,找到idea的设置中的build工具,maven,import设置,勾选自动下载sources 可以勾选sources,就会在下载依赖包的时候就自动下载sources 点击idea的maven刷新按钮就会在下载依赖包的时候同代下载sources源码 这些前提是本身打包的时候是带上sources源码的,没有用一些混淆加密手段过
+ 逐步迁移-用rclone将腾讯云cos迁移到cloudflare的r2
+ /2025/03/30/%E9%80%90%E6%AD%A5%E8%BF%81%E7%A7%BB-%E7%94%A8rclone%E5%B0%86%E8%85%BE%E8%AE%AF%E4%BA%91cos%E8%BF%81%E7%A7%BB%E5%88%B0cloudflare%E7%9A%84r2/
+ 因为上次那个问题,所以打算把图库迁移到靠谱一些的cloudflare上,这里用到了rclone这个很强大的工具
+在开始迁移前,先做一下准备
+
+分别在腾讯云和 Cloudflare 平台申请 Access Key 和 Secret Key
+安装 rclone 工具(可从 rclone 官网 下载)
+
+配置远程存储 配置步骤
+打开终端,输入 rclone config 开始远程存储配置
+按 n 创建新的远程存储
+
+配置腾讯云 COS
+输入远程存储名称,例如 cos
+选择存储类型,选择 Amazon S3 兼容模式(选项编号可能因 rclone 版本而异)
+选择服务提供商为腾讯云 COS
+输入您的 access_key_id 和 secret_access_key(从腾讯云控制台获取)
+选择相应的端点 API(根据您的存储桶所在地区选择北京或上海等)
+配置访问权限:可以选择仅资源所有者拥有完全访问权限,或根据您的安全需求选择其他选项
+选择存储类型(通常选择标准存储)
+确认配置
+
+配置 Cloudflare R2
+再次输入 n 创建新的远程存储
+输入远程存储名称,例如 r2
+同样选择 Amazon S3 兼容模式
+选择服务提供商为 Cloudflare R2
+输入您的 access_key_id 和 secret_access_key(从 Cloudflare 控制台获取)
+输入 R2 的端点 URL(通常格式为 https://<account_id>.r2.cloudflarestorage.com)
+配置访问权限
+选择存储类型
+确认配置
+
+执行迁移操作 完成配置后,使用以下命令执行迁移:
+rclone copy cos:mystore-xxxxxxx/img/ r2:img/ --progress
+
+上述命令将把腾讯云 COS 中 mystore-xxxxxxx 存储桶下的 img 目录中的所有文件复制到 Cloudflare R2 的 img 存储桶中。添加的 --progress 参数可以显示迁移进度。
+高级选项 为了获得更好的迁移体验,您可以考虑以下高级选项:
+
+使用 --transfers=N 参数设置并行传输数量,加快迁移速度
+使用 --checkers=N 参数设置并行检查数量
+使用 --dry-run 参数测试迁移过程而不实际复制文件
+使用 --log-file=FILE 参数将迁移日志保存到文件中
+
+例如:
+rclone copy cos:mystore-xxxxxxx/img/ r2:img/ --progress --transfers=4 --checkers=8 --stats=10s
+
+验证迁移结果 迁移完成后,可以使用以下命令验证两边的文件是否一致:
+rclone check cos:mystore-xxxxxxx/img/ r2:img/ --one-way
+
+结语 通过 rclone 这一强大工具,可以轻松实现云存储服务之间的数据迁移。Cloudflare R2 提供了稳定可靠的对象存储服务,同时其无出站流量费用的特性也使其成为图库存储的理想选择。
]]>
- Java
- Maven
+ 体验
- Java
- Maven
+ 恶意盗刷
+
+
+
+ 通过显卡来给gpt-oss做个加速
+ /2025/09/21/%E9%80%9A%E8%BF%87%E6%98%BE%E5%8D%A1%E6%9D%A5%E7%BB%99gpt-oss%E5%81%9A%E4%B8%AA%E5%8A%A0%E9%80%9F/
+ 之前在我的MacBookPro里使用gpt-oss,因为总的内存只有18g,加上系统本身和各种后台程序的占用,剩下的一般就一半左右,然后这个模型大小就有13g左右,运行起来是很吃力的 正好我这有个服役比较久的windows笔记本,带有3060显卡,只是显存是6g的,想试下看能不能通过显卡来给它加加速,这里我使用了lm studio来运行gpt-oss 20b模型 这个lm studio可以配置有多少层transformer运行在gpu上, 我们可以大致除一下 这个gpt-oss 20b是有24层,官标的显存需求是16g,我们有6g显存,大约可以加载8~9层的样子,并且lm studio也自动算出来是加载8层 通过这个配置我们运行的gpt-oss能够得到勉强可用的程度 能达到4.x个token的生成速度,还是比mac上要把所有程序全关了,清掉很多token来得方便 实测将gpu卸载层数调整到9是勉强还可以运行,再往上就加载不了了,显存小没办法 当然我们还可以继续用 unsloth 进行微调 例如参考 https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-Fine-tuning.ipynb 同时可以通过nvidia-smi来查看显存占用 过程中倒是没看到显存完全占满,可以通过nvitop再进行观察 能够在gpu中加载部分层的确能把大模型的生成速度提升一些,但是还是受限于显存大小,最好是能有显存大于16g的显卡可以尝试测一下
+]]>
+
+ LLM
+
+
+ LLM
@@ -9562,12 +9654,11 @@ user3:
- 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/
- 周末吃完中饭去买菜,没想到碰到这个神(zhi)奇(zhang)大哥带着两个娃,在非机动车道虽然没有像上班高峰车那么多,但是有送外卖,各种叮咚买菜和普通像我这样骑电驴,骑自行车的人,我的情况可能还特殊点,前面说过电驴买了以后本来网上找到过怎么解除限速的,后面看了下,限速 25 虽然慢,但还是对安全很有好处的,我上下班也不赶这个时间,所以就没解除,其他路上的电瓶车包括这位带娃的大哥可能有不少都不符合国标的限速要求或者解除了限速,这些算是铺垫。
-那位大哥,骑电瓶车一前一后带着两个娃,在非机动车道靠右边行驶,肉眼估计是在我右前方大概十几米的距离,不知道是小孩不舒服了还是啥,想下来还是就在跟他爹玩耍,我算是比较谨慎骑车的,看到这种情况已经准备好捏刹车了,但是也没想到这个娃这么神,差不多能并排四五辆电瓶车的非机动车道,直接从他爸的车下来跑到了非机动车道的最左边,前面我铺垫了电瓶车 25 码,换算一下大概 1 秒能前进 7 米,我是直接把刹车捏死了,才勉强避免撞上这个小孩,并且当时的情况本来我左后方有另一个大哥是想从我左边超过去,因为我刹车了他也赶紧刹车。
-现在我们做个假设,假如我刹车不够及时,撞上了这个小孩,会是啥后果呢,小孩人没事还好,即使没事也免不了大吵一架,说我骑车不看前面,然后去医院做检查,负责医药费,如果是有点啥伤了,这事估计是没完了,我是心里一阵后怕。
-说实话是张口快骂人了,“怎么带小孩的”,结果那大哥竟然还是那套话术,“你们骑车不会慢点的啊,说一下就好了啊,用得着这么说吗”,我是真的被这位的逻辑给打败了,还好是想超我车那大哥刹住车了,他要是刹不住呢,把我撞了我怪谁?这不是追尾事件,是 zhizhang 大哥的小孩鬼探头,下个电瓶车就下车,下来就往另一边跑,我们尽力刹车没撞到这小孩,说他没管好小孩这大哥还觉得自己委屈了?结果我倒是想骂脏话了,结果我左后方的的大哥就跟他说“你这么教小孩教得真好,你真厉害”,果然在中国还是不能好好说话,阴阳怪气才是王道,我前面也说了真的是后怕,为什么我从头到尾都没有说这个小孩不对,我是觉得这个年纪的小孩(估摸着也就五六岁或者再大个一两岁)这种安全意识应该是要父母和学校老师一起教育培养的,在路上不能这么随便乱跑,即使别人撞了他,别人有责任,那小孩的生理伤痛和心理伤害,父母也肯定要心疼的吧,另外对我们来说前面也说了,真的撞到了我们也是很难受的,这个社会里真的是自私自利的人太多了,平时让外卖小哥送爬下楼梯送上来外卖都觉得挺抱歉的,每次的接过来都说谢谢,人家也不容易,换在有些人身上大概会觉得自己花了钱就是大爷,给我送上来是必须的。
+ 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
@@ -9577,11 +9668,12 @@ user3:
- 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。
+ 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/
+ 周末吃完中饭去买菜,没想到碰到这个神(zhi)奇(zhang)大哥带着两个娃,在非机动车道虽然没有像上班高峰车那么多,但是有送外卖,各种叮咚买菜和普通像我这样骑电驴,骑自行车的人,我的情况可能还特殊点,前面说过电驴买了以后本来网上找到过怎么解除限速的,后面看了下,限速 25 虽然慢,但还是对安全很有好处的,我上下班也不赶这个时间,所以就没解除,其他路上的电瓶车包括这位带娃的大哥可能有不少都不符合国标的限速要求或者解除了限速,这些算是铺垫。
+那位大哥,骑电瓶车一前一后带着两个娃,在非机动车道靠右边行驶,肉眼估计是在我右前方大概十几米的距离,不知道是小孩不舒服了还是啥,想下来还是就在跟他爹玩耍,我算是比较谨慎骑车的,看到这种情况已经准备好捏刹车了,但是也没想到这个娃这么神,差不多能并排四五辆电瓶车的非机动车道,直接从他爸的车下来跑到了非机动车道的最左边,前面我铺垫了电瓶车 25 码,换算一下大概 1 秒能前进 7 米,我是直接把刹车捏死了,才勉强避免撞上这个小孩,并且当时的情况本来我左后方有另一个大哥是想从我左边超过去,因为我刹车了他也赶紧刹车。
+现在我们做个假设,假如我刹车不够及时,撞上了这个小孩,会是啥后果呢,小孩人没事还好,即使没事也免不了大吵一架,说我骑车不看前面,然后去医院做检查,负责医药费,如果是有点啥伤了,这事估计是没完了,我是心里一阵后怕。
+说实话是张口快骂人了,“怎么带小孩的”,结果那大哥竟然还是那套话术,“你们骑车不会慢点的啊,说一下就好了啊,用得着这么说吗”,我是真的被这位的逻辑给打败了,还好是想超我车那大哥刹住车了,他要是刹不住呢,把我撞了我怪谁?这不是追尾事件,是 zhizhang 大哥的小孩鬼探头,下个电瓶车就下车,下来就往另一边跑,我们尽力刹车没撞到这小孩,说他没管好小孩这大哥还觉得自己委屈了?结果我倒是想骂脏话了,结果我左后方的的大哥就跟他说“你这么教小孩教得真好,你真厉害”,果然在中国还是不能好好说话,阴阳怪气才是王道,我前面也说了真的是后怕,为什么我从头到尾都没有说这个小孩不对,我是觉得这个年纪的小孩(估摸着也就五六岁或者再大个一两岁)这种安全意识应该是要父母和学校老师一起教育培养的,在路上不能这么随便乱跑,即使别人撞了他,别人有责任,那小孩的生理伤痛和心理伤害,父母也肯定要心疼的吧,另外对我们来说前面也说了,真的撞到了我们也是很难受的,这个社会里真的是自私自利的人太多了,平时让外卖小哥送爬下楼梯送上来外卖都觉得挺抱歉的,每次的接过来都说谢谢,人家也不容易,换在有些人身上大概会觉得自己花了钱就是大爷,给我送上来是必须的。
]]>
生活
@@ -9618,96 +9710,4 @@ user3:
大扫除
-
- 通过显卡来给gpt-oss做个加速
- /2025/09/21/%E9%80%9A%E8%BF%87%E6%98%BE%E5%8D%A1%E6%9D%A5%E7%BB%99gpt-oss%E5%81%9A%E4%B8%AA%E5%8A%A0%E9%80%9F/
- 之前在我的MacBookPro里使用gpt-oss,因为总的内存只有18g,加上系统本身和各种后台程序的占用,剩下的一般就一半左右,然后这个模型大小就有13g左右,运行起来是很吃力的 正好我这有个服役比较久的windows笔记本,带有3060显卡,只是显存是6g的,想试下看能不能通过显卡来给它加加速,这里我使用了lm studio来运行gpt-oss 20b模型 这个lm studio可以配置有多少层transformer运行在gpu上, 我们可以大致除一下 这个gpt-oss 20b是有24层,官标的显存需求是16g,我们有6g显存,大约可以加载8~9层的样子,并且lm studio也自动算出来是加载8层 通过这个配置我们运行的gpt-oss能够得到勉强可用的程度 能达到4.x个token的生成速度,还是比mac上要把所有程序全关了,清掉很多token来得方便 实测将gpu卸载层数调整到9是勉强还可以运行,再往上就加载不了了,显存小没办法 当然我们还可以继续用 unsloth 进行微调 例如参考 https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-Fine-tuning.ipynb 同时可以通过nvidia-smi来查看显存占用 过程中倒是没看到显存完全占满,可以通过nvitop再进行观察 能够在gpu中加载部分层的确能把大模型的生成速度提升一些,但是还是受限于显存大小,最好是能有显存大于16g的显卡可以尝试测一下
-]]>
-
- LLM
-
-
- LLM
-
-
-
- 逐步迁移-用rclone将腾讯云cos迁移到cloudflare的r2
- /2025/03/30/%E9%80%90%E6%AD%A5%E8%BF%81%E7%A7%BB-%E7%94%A8rclone%E5%B0%86%E8%85%BE%E8%AE%AF%E4%BA%91cos%E8%BF%81%E7%A7%BB%E5%88%B0cloudflare%E7%9A%84r2/
- 因为上次那个问题,所以打算把图库迁移到靠谱一些的cloudflare上,这里用到了rclone这个很强大的工具
-在开始迁移前,先做一下准备
-
-分别在腾讯云和 Cloudflare 平台申请 Access Key 和 Secret Key
-安装 rclone 工具(可从 rclone 官网 下载)
-
-配置远程存储 配置步骤
-打开终端,输入 rclone config 开始远程存储配置
-按 n 创建新的远程存储
-
-配置腾讯云 COS
-输入远程存储名称,例如 cos
-选择存储类型,选择 Amazon S3 兼容模式(选项编号可能因 rclone 版本而异)
-选择服务提供商为腾讯云 COS
-输入您的 access_key_id 和 secret_access_key(从腾讯云控制台获取)
-选择相应的端点 API(根据您的存储桶所在地区选择北京或上海等)
-配置访问权限:可以选择仅资源所有者拥有完全访问权限,或根据您的安全需求选择其他选项
-选择存储类型(通常选择标准存储)
-确认配置
-
-配置 Cloudflare R2
-再次输入 n 创建新的远程存储
-输入远程存储名称,例如 r2
-同样选择 Amazon S3 兼容模式
-选择服务提供商为 Cloudflare R2
-输入您的 access_key_id 和 secret_access_key(从 Cloudflare 控制台获取)
-输入 R2 的端点 URL(通常格式为 https://<account_id>.r2.cloudflarestorage.com)
-配置访问权限
-选择存储类型
-确认配置
-
-执行迁移操作 完成配置后,使用以下命令执行迁移:
-rclone copy cos:mystore-xxxxxxx/img/ r2:img/ --progress
-
-上述命令将把腾讯云 COS 中 mystore-xxxxxxx 存储桶下的 img 目录中的所有文件复制到 Cloudflare R2 的 img 存储桶中。添加的 --progress 参数可以显示迁移进度。
-高级选项 为了获得更好的迁移体验,您可以考虑以下高级选项:
-
-使用 --transfers=N 参数设置并行传输数量,加快迁移速度
-使用 --checkers=N 参数设置并行检查数量
-使用 --dry-run 参数测试迁移过程而不实际复制文件
-使用 --log-file=FILE 参数将迁移日志保存到文件中
-
-例如:
-rclone copy cos:mystore-xxxxxxx/img/ r2:img/ --progress --transfers=4 --checkers=8 --stats=10s
-
-验证迁移结果 迁移完成后,可以使用以下命令验证两边的文件是否一致:
-rclone check cos:mystore-xxxxxxx/img/ r2:img/ --one-way
-
-结语 通过 rclone 这一强大工具,可以轻松实现云存储服务之间的数据迁移。Cloudflare R2 提供了稳定可靠的对象存储服务,同时其无出站流量费用的特性也使其成为图库存储的理想选择。
-]]>
-
- 体验
-
-
- 恶意盗刷
-
-
-
- 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:
-
-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
-]]>
-
- 工具
-
-
- 工具
-
-
diff --git a/sitemap.xml b/sitemap.xml
index b1adaba201..f2c8aaf5bd 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -146,7 +146,7 @@
- https://nicksxs.me/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/
+ https://nicksxs.me/2022/06/11/dubbo-%E5%AE%A2%E6%88%B7%E7%AB%AF%E9%85%8D%E7%BD%AE%E7%9A%84%E4%B8%80%E4%B8%AA%E9%87%8D%E8%A6%81%E7%9F%A5%E8%AF%86%E7%82%B9/
2025-10-12
@@ -155,7 +155,7 @@
- https://nicksxs.me/2022/06/11/dubbo-%E5%AE%A2%E6%88%B7%E7%AB%AF%E9%85%8D%E7%BD%AE%E7%9A%84%E4%B8%80%E4%B8%AA%E9%87%8D%E8%A6%81%E7%9F%A5%E8%AF%86%E7%82%B9/
+ https://nicksxs.me/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/
2025-10-12
@@ -281,7 +281,7 @@
- https://nicksxs.me/2021/03/28/%E8%81%8A%E8%81%8A-Linux-%E4%B8%8B%E7%9A%84-top-%E5%91%BD%E4%BB%A4/
+ https://nicksxs.me/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/
2025-10-12
@@ -290,7 +290,7 @@
- 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/2021/03/28/%E8%81%8A%E8%81%8A-Linux-%E4%B8%8B%E7%9A%84-top-%E5%91%BD%E4%BB%A4/
2025-10-12
@@ -299,7 +299,7 @@
- https://nicksxs.me/2021/12/05/%E8%81%8A%E8%81%8A%E9%83%A8%E5%88%86%E5%85%AC%E4%BA%A4%E8%BD%A6%E7%9A%84%E8%AE%BE%E8%AE%A1bug/
+ 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/
2025-10-12
@@ -308,7 +308,7 @@
- https://nicksxs.me/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/
+ https://nicksxs.me/2021/12/05/%E8%81%8A%E8%81%8A%E9%83%A8%E5%88%86%E5%85%AC%E4%BA%A4%E8%BD%A6%E7%9A%84%E8%AE%BE%E8%AE%A1bug/
2025-10-12
@@ -569,7 +569,7 @@
- https://nicksxs.me/2024/03/17/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E5%AE%9E%E6%88%98%E7%AF%87/
+ https://nicksxs.me/2024/10/20/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%E4%BD%BF%E7%94%A8%E5%8F%8D%E5%90%91%E4%BB%A3%E7%90%86/
2025-10-12
@@ -578,7 +578,7 @@
- https://nicksxs.me/2024/10/20/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%E4%BD%BF%E7%94%A8%E5%8F%8D%E5%90%91%E4%BB%A3%E7%90%86/
+ https://nicksxs.me/2024/03/17/Java-%E7%BA%BF%E7%A8%8B%E6%B1%A0%E7%B3%BB%E5%88%97-%E5%AE%9E%E6%88%98%E7%AF%87/
2025-10-12
@@ -587,7 +587,7 @@
- https://nicksxs.me/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/
+ https://nicksxs.me/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/
2025-10-12
@@ -596,7 +596,7 @@
- https://nicksxs.me/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/
+ https://nicksxs.me/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/
2025-10-12
@@ -650,7 +650,7 @@
- https://nicksxs.me/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/
+ https://nicksxs.me/2024/11/24/grep%E5%B0%8F%E6%8A%80%E5%B7%A7%E4%B9%8B%E5%8C%B9%E9%85%8D%E5%88%B0%E4%BA%8C%E8%BF%9B%E5%88%B6%E6%96%87%E4%BB%B6/
2025-10-12
@@ -659,7 +659,7 @@
- https://nicksxs.me/2024/11/24/grep%E5%B0%8F%E6%8A%80%E5%B7%A7%E4%B9%8B%E5%8C%B9%E9%85%8D%E5%88%B0%E4%BA%8C%E8%BF%9B%E5%88%B6%E6%96%87%E4%BB%B6/
+ https://nicksxs.me/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/
2025-10-12
@@ -668,7 +668,7 @@
- https://nicksxs.me/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/
+ https://nicksxs.me/2025/02/16/mac-os-14-x-%E5%87%BA%E7%8E%B0-xxx-%E5%B7%B2%E6%8D%9F%E5%9D%8F%EF%BC%8C%E6%97%A0%E6%B3%95%E6%89%93%E5%BC%80%E3%80%82-%E4%BD%A0%E5%BA%94%E8%AF%A5%E5%B0%86%E5%AE%83%E7%A7%BB%E5%88%B0%E5%BA%9F%E7%BA%B8%E7%AF%93%E3%80%82-%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95/
2025-10-12
@@ -677,7 +677,7 @@
- https://nicksxs.me/2021/04/18/rust%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0-%E6%89%80%E6%9C%89%E6%9D%83%E4%BA%8C/
+ https://nicksxs.me/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/
2025-10-12
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