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 == ) {
// 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 <= ;
}

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() + ) * 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 * , used / M, used_threshold / M); return used >= used_threshold;
} bool ZDirector::is_warm() const {
return ZStatCycle::ncycles() >= ;
} // 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 <= ;
} bool ZDirector::is_first() const {
return ZStatCycle::ncycles() == ;
}

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 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 <= ;
}

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

ZGC gc策略及回收过程-源码分析的更多相关文章

  1. (3.10)mysql基础深入——mysqld 服务器与客户端连接过程 源码分析【待写】

    (3.10)mysql基础深入——mysqld 服务器与客户端连接过程 源码分析[待写]

  2. Netty源码分析 (七)----- read过程 源码分析

    在上一篇文章中,我们分析了processSelectedKey这个方法中的accept过程,本文将分析一下work线程中的read过程. private static void processSele ...

  3. 设计模式(二十三)——策略模式(Arrays源码分析)

    1 编写鸭子项目,具体要求如下: 1) 有各种鸭子(比如 野鸭.北京鸭.水鸭等, 鸭子有各种行为,比如 叫.飞行等) 2) 显示鸭子的信息 2 传统方案解决鸭子问题的分析和代码实现 1) 传统的设计方 ...

  4. YARN(MapReduce 2)运行MapReduce的过程-源码分析

    这是我的分析,当然查阅书籍和网络.如有什么不对的,请各位批评指正.以下的类有的并不完全,只列出重要的方法. 如要转载,请注上作者以及出处. 一.源码阅读环境 需要安装jdk1.7.0版本及其以上版本, ...

  5. Flink中TaskManager端执行用户逻辑过程(源码分析)

    TaskManager接收到来自JobManager的jobGraph转换得到的TDD对象,启动了任务,在StreamInputProcessor类的processInput()方法中 通过一个whi ...

  6. Netty源码分析 (八)----- write过程 源码分析

    上一篇文章主要讲了netty的read过程,本文主要分析一下write和writeAndFlush. 主要内容 本文分以下几个部分阐述一个java对象最后是如何转变成字节流,写到socket缓冲区中去 ...

  7. HDFS dfsclient写文件过程 源码分析

    HDFS写入文件的重要概念 HDFS一个文件由多个block构成.HDFS在进行block读写的时候是以packet(默认每个packet为64K)为单位进行的.每一个packet由若干个chunk( ...

  8. spring启动component-scan类扫描加载过程---源码分析

    http://blog.csdn.net/xieyuooo/article/details/9089441#comments

  9. elasticsearch 5.5 query 过程 源码分析

    (1)请求 transfer to  任意node 节点 标记为coordinate node server入口函数 transportSearchAction doExecute方法 coordin ...

随机推荐

  1. 【转载】pandas中的循环

    原始文章链接: https://towardsdatascience.com/how-to-make-your-pandas-loop-71-803-times-faster-805030df4f06 ...

  2. Winform中设置ZedGraph曲线图的字体样式是避免出现边框

    场景 Winforn中设置ZedGraph曲线图的属性.坐标轴属性.刻度属性: https://blog.csdn.net/BADAO_LIUMANG_QIZHI/article/details/10 ...

  3. Centos7上配置nginx的负载均衡

    前言 在配置nginx负载均衡前.我们需要明白几个名词的概念 注: 如果不小心忘了tomcat和nginx的启动,关闭命令,可参考写在文章最后的命令 一 重要的概念理解 1 什么是nginx呢? Ng ...

  4. 一个vue练手的小项目

    编程路上的菜鸟一枚 : 最近接触了vue 然后写了一个练手的项目 使用vue-cli脚手架来搭建了的项目 技术: vue2  + vue-router  + ES6 + axios 框架有 mint- ...

  5. C++ std::thread概念介绍

    C++ 11新标准中,正式的为该语言引入了多线程概念.新标准提供了一个线程库thread,通过创建一个thread对象来管理C++程序中的多线程. 本文简单聊一下C++多线程相关的一些概念及threa ...

  6. python小基础

    1.计算机基础知识 中央处理器 CPU 人的大脑 内存 缓存数据 临时记忆 硬盘 储存数据 永久记忆 什么是操作系统 ? 控制计算机工作的流程 什么是应用程序? 安装在操作系统之上的软件 2.pyth ...

  7. Hadoop入门 之 Hadoop常识

    1.Hadoop是什么? 答:Hadoop是开源的分布式存储和分布式计算平台. 2.Hadoop的组成是什么? 答:Hadoop由HDFS和MapReduce这两个核心部分组成. HDFS(Hadoo ...

  8. [LeetCode] 面试题之犄角旮旯 第叁章

    题库:LeetCode题库 - 中等难度 习题:网友收集 - zhizhiyu 此处应为一个简单的核心总结,以及练习笔记. 查找一个数“在不在”?桶排序理论上貌似不错. 回文问题 ----> [ ...

  9. mongodb 获取自增数

    mongodb db.getCollection('user').findAndModify({update:{$inc:{'level':1}},query:{"name":&q ...

  10. 针对媒体不实报道误导大众--抹黑C#工资垫底

    最近注意到一些媒体故意抹黑C# 工资垫底,参见 https://www.toutiao.com/i6741889572931633668/: 通过搜索引擎搜索<编程语言薪酬排行:Python薪资 ...