源码文件:/src/hotspot/share/gc/z/zDirector.cpp
一、回收策略
main入口函数:
void ZDirector::run_service() { // Main loop while (_metronome.wait_for_tick()) { sample_allocation_rate(); const GCCause::Cause cause = make_gc_decision(); if (cause != GCCause::_no_gc) { ZCollectedHeap::heap()->collect(cause); } } }
ZMetronome::wait_for_tick 是zgc定义的一个循环时钟函数,sample_allocation_rate函数则用于rule_allocation_rate策略估算可能oom的时间。重点关注:make_gc_decision函数,在判断从make_gc_decision函数返回的结果不是no_gc后,zgc将进行一次gc。
make_gc_decision函数:
GCCause::Cause ZDirector::make_gc_decision() const { // Rule 0: Timer if (rule_timer()) { return GCCause::_z_timer; } // Rule 1: Warmup if (rule_warmup()) { return GCCause::_z_warmup; } // Rule 2: Allocation rate if (rule_allocation_rate()) { return GCCause::_z_allocation_rate; } // Rule 3: Proactive if (rule_proactive()) { return GCCause::_z_proactive; } // No GC return GCCause::_no_gc; }
make_gc_decision一共提供了4种被动gc策略:
rule 1:固定间隔时间
通过配置ZCollectionInterval参数,可以控制zgc在一个固定的时间间隔进行gc,默认值为0,表示不采用该策略,否则则判断从上次gc到现在的时间间隔是否大于ZCollectionInterval秒,是则gc。源码如下:
bool ZDirector::rule_timer() const { if (ZCollectionInterval == 0) { // Rule disabled return false; } // Perform GC if timer has expired. const double time_since_last_gc = ZStatCycle::time_since_last(); const double time_until_gc = ZCollectionInterval - time_since_last_gc; log_debug(gc, director)("Rule: Timer, Interval: %us, TimeUntilGC: %.3lfs", ZCollectionInterval, time_until_gc); return time_until_gc <= 0; }
rule 2:预热规则
is_warm函数判断gc次数是否已超过3次,是则不使用该策略。
注释说的很清楚,当gc次数少于3时,判断堆使用率达到10%/20%/30%时,使用该策略
bool ZDirector::rule_warmup() const { if (is_warm()) { // Rule disabled return false; } // Perform GC if heap usage passes 10/20/30% and no other GC has been // performed yet. This allows us to get some early samples of the GC // duration, which is needed by the other rules. const size_t max_capacity = ZHeap::heap()->current_max_capacity(); const size_t used = ZHeap::heap()->used(); const double used_threshold_percent = (ZStatCycle::ncycles() + 1) * 0.1; const size_t used_threshold = max_capacity * used_threshold_percent; log_debug(gc, director)("Rule: Warmup %.0f%%, Used: " SIZE_FORMAT "MB, UsedThreshold: " SIZE_FORMAT "MB", used_threshold_percent * 100, used / M, used_threshold / M); return used >= used_threshold; } bool ZDirector::is_warm() const { return ZStatCycle::ncycles() >= 3; } // 位置:ZStat.cpp uint64_t ZStatCycle::ncycles() { return _ncycles; // gc次数 }
rule 3:分配速率预估
is_first函数判断如果是首次gc,则直接返回false。
ZAllocationSpikeTolerance默认值为2,分配速率策略采用正态分布模型预测内存分配速率,加上ZAllocationSpikeTolerance修正因子,可以覆盖超过99.9%的内存分配速率的可能性
bool ZDirector::rule_allocation_rate() const { if (is_first()) { // Rule disabled return false; } // Perform GC if the estimated max allocation rate indicates that we // will run out of memory. The estimated max allocation rate is based // on the moving average of the sampled allocation rate plus a safety // margin based on variations in the allocation rate and unforeseen // allocation spikes. // Calculate amount of free memory available to Java threads. Note that // the heap reserve is not available to Java threads and is therefore not // considered part of the free memory. const size_t max_capacity = ZHeap::heap()->current_max_capacity(); const size_t max_reserve = ZHeap::heap()->max_reserve(); const size_t used = ZHeap::heap()->used(); const size_t free_with_reserve = max_capacity - used; const size_t free = free_with_reserve - MIN2(free_with_reserve, max_reserve); // Calculate time until OOM given the max allocation rate and the amount // of free memory. The allocation rate is a moving average and we multiply // that with an allocation spike tolerance factor to guard against unforeseen // phase changes in the allocate rate. We then add ~3.3 sigma to account for // the allocation rate variance, which means the probability is 1 in 1000 // that a sample is outside of the confidence interval. const double max_alloc_rate = (ZStatAllocRate::avg() * ZAllocationSpikeTolerance) + (ZStatAllocRate::avg_sd() * one_in_1000); const double time_until_oom = free / (max_alloc_rate + 1.0); // Plus 1.0B/s to avoid division by zero // Calculate max duration of a GC cycle. The duration of GC is a moving // average, we add ~3.3 sigma to account for the GC duration variance. const AbsSeq& duration_of_gc = ZStatCycle::normalized_duration(); const double max_duration_of_gc = duration_of_gc.davg() + (duration_of_gc.dsd() * one_in_1000); // Calculate time until GC given the time until OOM and max duration of GC. // We also deduct the sample interval, so that we don‘t overshoot the target // time and end up starting the GC too late in the next interval. const double sample_interval = 1.0 / ZStatAllocRate::sample_hz; const double time_until_gc = time_until_oom - max_duration_of_gc - sample_interval; log_debug(gc, director)("Rule: Allocation Rate, MaxAllocRate: %.3lfMB/s, Free: " SIZE_FORMAT "MB, MaxDurationOfGC: %.3lfs, TimeUntilGC: %.3lfs", max_alloc_rate / M, free / M, max_duration_of_gc, time_until_gc); return time_until_gc <= 0; } bool ZDirector::is_first() const { return ZStatCycle::ncycles() == 0; }
rule 4:积极回收策略
通过ZProactive可启用积极回收策略,is_warm函数判断启用该策略必须是在预热之后(gc次数超过3次)
自上一次gc后,堆使用率达到xmx的10%或者已过了5分钟,这个参数是弥补第三个规则中没有覆盖的场景,从上述分析可以得到第三个条件更多的覆盖分配速率比较高的场景。
bool ZDirector::rule_proactive() const { if (!ZProactive || !is_warm()) { // Rule disabled return false; } // Perform GC if the impact of doing so, in terms of application throughput // reduction, is considered acceptable. This rule allows us to keep the heap // size down and allow reference processing to happen even when we have a lot // of free space on the heap. // Only consider doing a proactive GC if the heap usage has grown by at least // 10% of the max capacity since the previous GC, or more than 5 minutes has // passed since the previous GC. This helps avoid superfluous GCs when running // applications with very low allocation rate. const size_t used_after_last_gc = ZStatHeap::used_at_relocate_end(); const size_t used_increase_threshold = ZHeap::heap()->current_max_capacity() * 0.10; // 10% const size_t used_threshold = used_after_last_gc + used_increase_threshold; const size_t used = ZHeap::heap()->used(); const double time_since_last_gc = ZStatCycle::time_since_last(); const double time_since_last_gc_threshold = 5 * 60; // 5 minutes if (used < used_threshold && time_since_last_gc < time_since_last_gc_threshold) { // Don‘t even consider doing a proactive GC log_debug(gc, director)("Rule: Proactive, UsedUntilEnabled: " SIZE_FORMAT "MB, TimeUntilEnabled: %.3lfs", (used_threshold - used) / M, time_since_last_gc_threshold - time_since_last_gc); return false; } const double assumed_throughput_drop_during_gc = 0.50; // 50% const double acceptable_throughput_drop = 0.01; // 1% const AbsSeq& duration_of_gc = ZStatCycle::normalized_duration(); const double max_duration_of_gc = duration_of_gc.davg() + (duration_of_gc.dsd() * one_in_1000); const double acceptable_gc_interval = max_duration_of_gc * ((assumed_throughput_drop_during_gc / acceptable_throughput_drop) - 1.0); const double time_until_gc = acceptable_gc_interval - time_since_last_gc; log_debug(gc, director)("Rule: Proactive, AcceptableGCInterval: %.3lfs, TimeSinceLastGC: %.3lfs, TimeUntilGC: %.3lfs", acceptable_gc_interval, time_since_last_gc, time_until_gc); return time_until_gc <= 0; }
最后,当所有策略都不满足时,返回_no_gc,表示不进行gc
二、回收过程
gc整个周期:
彩色指针示意图:
- (STW)Pause Mark Start,开始标记,这个阶段只会标记(Mark0)由root引用的object,组成Root Set
- Concurrent Mark,并发标记,从Root Set出发,并发遍历Root Set object的引用链并标记(Mark1)
- (STW)Pause Mark End,检查是否已经并发标记完成,如果不是,需要进行多一次Concurrent Mark
- Concurrent Process Non-Strong References,并发处理弱引用
- Concurrent Reset Relocation Set
- Concurrent Destroy Detached Pages
- Concurrent Select Relocation Set,并发选择Relocation Set;
- Concurrent Prepare Relocation Set,并发预处理Relocation Set
- (STW)Pause Relocate Start,开始转移对象,依然是遍历root引用
- Concurrent Relocate,并发转移,将需要回收的Page里的对象转移到Relocation Set,然后回收Page给系统重新利用
run_gc_cycle函数(/src/hotspot/share/gc/z/zDriver.cpp):
void ZDriver::run_gc_cycle(GCCause::Cause cause) { ZDriverCycleScope scope(cause); // Phase 1: Pause Mark Start { ZMarkStartClosure cl; vm_operation(&cl); } // Phase 2: Concurrent Mark { ZStatTimer timer(ZPhaseConcurrentMark); ZHeap::heap()->mark(); } // Phase 3: Pause Mark End { ZMarkEndClosure cl; while (!vm_operation(&cl)) { // Phase 3.5: Concurrent Mark Continue ZStatTimer timer(ZPhaseConcurrentMarkContinue); ZHeap::heap()->mark(); } } // Phase 4: Concurrent Process Non-Strong References { ZStatTimer timer(ZPhaseConcurrentProcessNonStrongReferences); ZHeap::heap()->process_non_strong_references(); } // Phase 5: Concurrent Reset Relocation Set { ZStatTimer timer(ZPhaseConcurrentResetRelocationSet); ZHeap::heap()->reset_relocation_set(); } // Phase 6: Concurrent Destroy Detached Pages { ZStatTimer timer(ZPhaseConcurrentDestroyDetachedPages); ZHeap::heap()->destroy_detached_pages(); } // Phase 7: Concurrent Select Relocation Set { ZStatTimer timer(ZPhaseConcurrentSelectRelocationSet); ZHeap::heap()->select_relocation_set(); } // Phase 8: Concurrent Prepare Relocation Set { ZStatTimer timer(ZPhaseConcurrentPrepareRelocationSet); ZHeap::heap()->prepare_relocation_set(); } // Phase 9: Pause Relocate Start { ZRelocateStartClosure cl; vm_operation(&cl); } // Phase 10: Concurrent Relocate { ZStatTimer timer(ZPhaseConcurrentRelocated); ZHeap::heap()->relocate(); } }
未完待续
原文地址:https://www.cnblogs.com/JunFengChan/p/11707542.html