HeapDump性能社区

性能工具
登录注册
性能文章>美团海量连接服务端jvm参数调优分享>

美团海量连接服务端jvm参数调优分享原创

闪电侠
3年前
3585115

应用：shark-新美大移动端网络优化(每日接受移动端请求约150亿)

应用特点：

  1. qps比较高，新生代增长飞快
  2. 用户的连接需要维持一段时间
  3. 单机需要维持海量连接，几十万的级别

以上三个特点导致有大量小对象聚集在old区，高峰期old区域增长非常快，对象在一段时间内肯定会消亡

初始的线上gc的情况如下

对应的jvm参数为

-Xms10g -Xmx10g -Xss512k -XX:PermSize=384m -XX:MaxPermSize=384m -XX:NewSize=7g -XX:MaxNewSize=7g
-XX:SurvivorRatio=8 -XX:MaxDirectMemorySize=4g -XX:+UseParNewGC -XX:ParallelGCThreads=4
-XX:MaxTenuringThreshold=9 -XX:+UseConcMarkSweepGC -XX:+DisableExplicitGC -XX:+UseCMSInitiatingOccupancyOnly
-XX:+ScavengeBeforeFullGC -XX:+UseCMSCompactAtFullCollection -XX:+CMSParallelRemarkEnabled
-XX:CMSFullGCsBeforeCompaction=9 -XX:CMSInitiatingOccupancyFraction=70

可以看到新生代为7G（其中Survivor为2*700M），老年代为3G，对象年龄居然只有9（导致new 区域进入到old区域的速度太快，old区域很快被撑满，频繁old gc），考虑到我这边的对象在一段时间内（几分钟）肯定会消亡，于是先进行一次调优尝试

第一次调优

将年龄调成无穷，调大young区

对应的jvm参数为

-Xms14g -Xmx14g -Xss512k -XX:PermSize=384m -XX:MaxPermSize=384m -XX:NewSize=12g -XX:MaxNewSize=12g 
-XX:SurvivorRatio=18 -XX:MaxDirectMemorySize=2g -XX:+UseParNewGC -XX:ParallelGCThreads=4 
-XX:MaxTenuringThreshold=30 -XX:+UseConcMarkSweepGC -XX:+DisableExplicitGC -XX:+UseCMSInitiatingOccupancyOnly 
-XX:+ScavengeBeforeFullGC -XX:+UseCMSCompactAtFullCollection -XX:+CMSParallelRemarkEnabled 
-XX:CMSFullGCsBeforeCompaction=9 -XX:CMSInitiatingOccupancyFraction=70 -XX:+CMSClassUnloadingEnabled 
-XX:SoftRefLRUPolicyMSPerMB=0 -XX:-ReduceInitialCardMarks -XX:+CMSPermGenSweepingEnabled 
-XX:CMSInitiatingPermOccupancyFraction=70

结果old区域一直为空，但是yong gc时间拉长许多，平均每次都要0.2s的时间，比之前的new gc多了整整三倍，是无法接受的：

 

 
2017.09.22更新 关于设置-XX:MaxTenuringThreshold大于15，在jdk1.7某个版本之前表示是无穷大，之后不管设置成多少，都是15，jdk1.8之后大于15直接报错

见 http://mail.openjdk.java.net/pipermail/hotspot-gc-dev/2008-May/000309.html

第二次调优

将XX:MaxTenuringThreshold调整回来，调整成最大的值15（大于15即对象长命百岁），由于之前cms 在old gc花的时间比较多，所以这里尝试的serial old

对应的jvm参数为

-Xms14g -Xmx14g -Xss512k -XX:PermSize=384m -XX:MaxPermSize=384m -XX:NewSize=12g -XX:MaxNewSize=12g 
-XX:SurvivorRatio=18 -XX:MaxDirectMemorySize=2g -XX:+UseParNewGC -XX:ParallelGCThreads=4 
-XX:MaxTenuringThreshold=15

发现效果确实比较明显，new gc的时间缩小了两倍左右，并且old gc的时间也从原来的15000ms 缩小到1500ms

 

 

第三次调优

仔细研究了一下第二种调优方式的组合，yong区域使用parNew 的方式，old区域使用serial old 的方式，如果在其他条件都相同的条件下使用parNew+cms的方式，old gc的时间会不会大幅度缩短？毕竟cms还是比较先进的收集器，于是分析了一下cms的几个阶段，有两个阶段是stop the world的，一个是初始化标记（intial mark），一个是重复标记（cms remark)，重复标记的原因是从cms old gc开始的那一刻到开始清除可能很多对象的状态都产生了变化，所以这个时候需要暂停用户所有的线程，来一次标记再清除

查看gc日志，发现remark的时间比较长，日志上下文如下

{Heap before GC invocations=23679 (full 18):
par new generation   total 6606080K, used 5902124K [0x0000000568000000, 0x0000000728000000, 0x0000000728000000)
eden space 5872128K,  99% used [0x0000000568000000, 0x00000006ce54f988, 0x00000006ce680000)
from space 733952K,   4% used [0x00000006fb340000, 0x00000006fd1bb758, 0x0000000728000000)
to   space 733952K,   0% used [0x00000006ce680000, 0x00000006ce680000, 0x00000006fb340000)
concurrent mark-sweep generation total 3145728K, used 2200878K [0x0000000728000000, 0x00000007e8000000, 0x00000007e8000000)
concurrent-mark-sweep perm gen total 393216K, used 37361K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
2016-08-27T17:26:00.058+0800: 261980.413: [GC2016-08-27T17:26:00.058+0800: 261980.413: [ParNew: 5902124K->27858K(6606080K), 0.0464930 secs] 8103002K->2230601K(9751808K), 0.0468010 secs] [Times: user=0.18 sys=0.00, real=0.05 secs]
Heap after GC invocations=23680 (full 18):
par new generation   total 6606080K, used 27858K [0x0000000568000000, 0x0000000728000000, 0x0000000728000000)
eden space 5872128K,   0% used [0x0000000568000000, 0x0000000568000000, 0x00000006ce680000)
from space 733952K,   3% used [0x00000006ce680000, 0x00000006d01b4910, 0x00000006fb340000)
to   space 733952K,   0% used [0x00000006fb340000, 0x00000006fb340000, 0x0000000728000000)
concurrent mark-sweep generation total 3145728K, used 2202742K [0x0000000728000000, 0x00000007e8000000, 0x00000007e8000000)
concurrent-mark-sweep perm gen total 393216K, used 37361K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
}
2016-08-27T17:26:00.107+0800: 261980.462: Application time: 0.0014750 seconds
2016-08-27T17:26:00.108+0800: 261980.463: [GC [1 CMS-initial-mark: 2202742K(3145728K)] 2235571K(9751808K), 0.0561400 secs] [Times: user=0.05 sys=0.00, real=0.05 secs]
2016-08-27T17:26:00.165+0800: 261980.520: [CMS-concurrent-mark-start]
2016-08-27T17:26:00.918+0800: 261981.273: [CMS-concurrent-mark: 0.753/0.754 secs] [Times: user=1.27 sys=0.12, real=0.76 secs]
2016-08-27T17:26:00.918+0800: 261981.273: [CMS-concurrent-preclean-start]
2016-08-27T17:26:00.949+0800: 261981.304: [CMS-concurrent-preclean: 0.028/0.031 secs] [Times: user=0.04 sys=0.01, real=0.03 secs]
2016-08-27T17:26:00.949+0800: 261981.304: [CMS-concurrent-abortable-preclean-start]
CMS: abort preclean due to time 2016-08-27T17:26:06.159+0800: 261986.514: [CMS-concurrent-abortable-preclean: 5.197/5.210 secs] [Times: user=8.31 sys=0.66, real=5.21 secs]
2016-08-27T17:26:06.160+0800: 261986.515: Application time: 5.9951640 seconds
2016-08-27T17:26:06.161+0800: 261986.516: [GC[YG occupancy: 4756927 K (6606080 K)]2016-08-27T17:26:06.161+0800: 261986.516: [Rescan (parallel) , 18.1621480 secs]2016-08-27T17:26:24.323+0800: 262004.678: [weak refs processing, 0.0000750 secs]2016-08-27T17:26:24.323+0800: 262004.678: [class unloading, 0.0069760 secs]2016-08-27T17:26:24.330+0800: 262004.685: [scrub symbol table, 0.0040020 secs]2016-08-27T17:26:24.334+0800: 262004.689: [scrub string table, 0.0009240 secs] [1 CMS-remark: 2202742K(3145728K)] 6959670K(9751808K), 18.1769610 secs] [Times: user=71.37 sys=0.06, real=18.18 secs]
2016-08-27T17:26:24.338+08002016-08-27T17:26:24.338+0800: : 262004.693: Application time: 0.0002080 seconds
262004.693: [CMS-concurrent-sweep-start]
2016-08-27T17:26:24.347+0800: 262004.702: Application time: 0.0079820 seconds
2016-08-27T17:26:24.868+0800: 262005.223: Application time: 0.5186580 seconds
{Heap before GC invocations=23680 (full 19):
par new generation   total 6606080K, used 5899299K [0x0000000568000000, 0x0000000728000000, 0x0000000728000000)
eden space 5872128K,  99% used [0x0000000568000000, 0x00000006ce5d44b8, 0x00000006ce680000)
from space 733952K,   3% used [0x00000006ce680000, 0x00000006d01b4910, 0x00000006fb340000)
to   space 733952K,   0% used [0x00000006fb340000, 0x00000006fb340000, 0x0000000728000000)
concurrent mark-sweep generation total 3145728K, used 1891716K [0x0000000728000000, 0x00000007e8000000, 0x00000007e8000000)
concurrent-mark-sweep perm gen total 393216K, used 37361K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
2016-08-27T17:26:24.870+0800: 262005.225: [GC2016-08-27T17:26:24.870+0800: 262005.225: [ParNew: 5899299K->56108K(6606080K), 0.0580910 secs] 7791015K->1949708K(9751808K), 0.0584000 secs] [Times: user=0.22 sys=0.01, real=0.05 secs]
Heap after GC invocations=23681 (full 19):
par new generation   total 6606080K, used 56108K [0x0000000568000000, 0x0000000728000000, 0x0000000728000000)
eden space 5872128K,   0% used [0x0000000568000000, 0x0000000568000000, 0x00000006ce680000)
from space 733952K,   7% used [0x00000006fb340000, 0x00000006fea0b1f8, 0x0000000728000000)
to   space 733952K,   0% used [0x00000006ce680000, 0x00000006ce680000, 0x00000006fb340000)
concurrent mark-sweep generation total 3145728K, used 1893599K [0x0000000728000000, 0x00000007e8000000, 0x00000007e8000000)
concurrent-mark-sweep perm gen total 393216K, used 37361K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
}

重新标记居然用了18s(这一段[1 CMS-remark: 2202742K(3145728K)] 6959670K(9751808K), 18.1769610 secs])！重新标记的时候，old区域的大小是固定的(这里设置成old区域的70%)，按理说每次remark的时间应该都差不多才对，但是查了很多cms old gc日志，发现高峰期和低峰期remark的时间相差太大，两者的区别也只有elden区域，因为我这里elden的设置是比较大的,高峰期的时候，一次cms old开始，到remark之间这段时间，用户程序会和gc线程同步执行，到remark的时候，eden区很可能已经有大量对象了，如果remark之前能把eden区域的对象都清理一遍，那remark的对象将会少很多很多对吧？google了一把，发现cms有这么个参数-XX:+CMSScavengeBeforeRemark，这玩意的意思是在remark之前，来一次yong gc，满足我们的要求，加了这个参数之后

对应的jvm参数为

 -Xms14g -Xmx14g -Xss512k -XX:PermSize=384m -XX:MaxPermSize=384m -XX:NewSize=12g -XX:MaxNewSize=12g 
 -XX:SurvivorRatio=18 -XX:MaxDirectMemorySize=2g -XX:+UseParNewGC -XX:ParallelGCThreads=4 
 -XX:MaxTenuringThreshold=15  -XX:+CMSScavengeBeforeRemark -XX:+UseConcMarkSweepGC -XX:+DisableExplicitGC 
 -XX:+UseCMSInitiatingOccupancyOnly -XX:+ScavengeBeforeFullGC -XX:+UseCMSCompactAtFullCollection 
 -XX:+CMSParallelRemarkEnabled -XX:CMSFullGCsBeforeCompaction=9 -XX:CMSInitiatingOccupancyFraction=70

效果如下

发现old gc的时间缩小到原来cms的1/100，窃喜。接下来分析gc日志，来验证我的猜想
{Heap before GC invocations=4644 (full 6):
 par new generation   total 11953792K, used 11384636K [0x0000000468000000, 0x0000000768000000, 0x0000000768000000)
  eden space 11324672K,  99% used [0x0000000468000000, 0x000000071b30eb48, 0x000000071b340000)
  from space 629120K,   9% used [0x000000071b340000, 0x000000071ee004e0, 0x00000007419a0000)
  to   space 629120K,   0% used [0x00000007419a0000, 0x00000007419a0000, 0x0000000768000000)
 concurrent mark-sweep generation total 2097152K, used 1464467K [0x0000000768000000, 0x00000007e8000000, 0x00000007e8000000)
 concurrent-mark-sweep perm gen total 393216K, used 37291K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
2016-08-28T10:46:09.846+0800: 68434.688: [GC2016-08-28T10:46:09.846+0800: 68434.688: [ParNew: 11384636K->61763K(11953792K), 0.0716150 secs] 12849103K->1528727K(14050944K), 0.0719060 secs] [Times: user=0.28 sys=0.00, real=0.07 secs]
Heap after GC invocations=4645 (full 6):
 par new generation   total 11953792K, used 61763K [0x0000000468000000, 0x0000000768000000, 0x0000000768000000)
  eden space 11324672K,   0% used [0x0000000468000000, 0x0000000468000000, 0x000000071b340000)
  from space 629120K,   9% used [0x00000007419a0000, 0x00000007455f0dd8, 0x0000000768000000)
  to   space 629120K,   0% used [0x000000071b340000, 0x000000071b340000, 0x00000007419a0000)
 concurrent mark-sweep generation total 2097152K, used 1466964K [0x0000000768000000, 0x00000007e8000000, 0x00000007e8000000)
 concurrent-mark-sweep perm gen total 393216K, used 37291K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
}
2016-08-28T10:46:19.590+0800: 68444.431: Application time: 9.6715460 seconds
{Heap before GC invocations=4645 (full 6):
 par new generation   total 11953792K, used 11384705K [0x0000000468000000, 0x0000000768000000, 0x0000000768000000)
  eden space 11324672K,  99% used [0x0000000468000000, 0x000000071b18f768, 0x000000071b340000)
  from space 629120K,   9% used [0x00000007419a0000, 0x00000007455f0dd8, 0x0000000768000000)
  to   space 629120K,   0% used [0x000000071b340000, 0x000000071b340000, 0x00000007419a0000)
 concurrent mark-sweep generation total 2097152K, used 1466964K [0x0000000768000000, 0x00000007e8000000, 0x00000007e8000000)
 concurrent-mark-sweep perm gen total 393216K, used 37291K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
2016-08-28T10:46:19.591+0800: 68444.433: [GC2016-08-28T10:46:19.591+0800: 68444.433: [ParNew: 11384705K->69180K(11953792K), 0.0728020 secs] 12851669K->1538700K(14050944K), 0.0730170 secs] [Times: user=0.27 sys=0.01, real=0.07 secs]
Heap after GC invocations=4646 (full 6):
 par new generation   total 11953792K, used 69180K [0x0000000468000000, 0x0000000768000000, 0x0000000768000000)
  eden space 11324672K,   0% used [0x0000000468000000, 0x0000000468000000, 0x000000071b340000)
  from space 629120K,  10% used [0x000000071b340000, 0x000000071f6cf378, 0x00000007419a0000)
  to   space 629120K,   0% used [0x00000007419a0000, 0x00000007419a0000, 0x0000000768000000)
 concurrent mark-sweep generation total 2097152K, used 1469519K [0x0000000768000000, 0x00000007e8000000, 0x00000007e8000000)
 concurrent-mark-sweep perm gen total 393216K, used 37291K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
}
2016-08-28T10:46:19.666+0800: 68444.508: Application time: 0.0019110 seconds
2016-08-28T10:46:19.667+0800: 68444.509: [GC [1 CMS-initial-mark: 1469519K(2097152K)] 1545525K(14050944K), 0.0869600 secs] [Times: user=0.09 sys=0.00, real=0.08 secs]
2016-08-28T10:46:19.755+0800: 68444.597: [CMS-concurrent-mark-start]
2016-08-28T10:46:20.418+0800: 68445.260: [CMS-concurrent-mark: 0.663/0.663 secs] [Times: user=1.47 sys=0.24, real=0.66 secs]
2016-08-28T10:46:20.418+0800: 68445.260: [CMS-concurrent-preclean-start]
2016-08-28T10:46:20.438+0800: 68445.280: [CMS-concurrent-preclean: 0.018/0.020 secs] [Times: user=0.04 sys=0.01, real=0.02 secs]
2016-08-28T10:46:20.438+0800: 68445.280: [CMS-concurrent-abortable-preclean-start]
2016-08-28T10:46:24.542+0800: 68449.384: [CMS-concurrent-abortable-preclean: 4.090/4.104 secs] [Times: user=8.60 sys=1.40, real=4.10 secs]
2016-08-28T10:46:24.543+0800: 68449.385: Application time: 4.7880220 seconds

// 这里在remark之前进行一次young gc
＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝yong gc开始＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝
2016-08-28T10:46:24.544+0800: 68449.386: [GC[YG occupancy: 5803756 K (11953792 K)]{Heap before GC invocations=4646 (full 7):
 par new generation   total 11953792K, used 5803756K [0x0000000468000000, 0x0000000768000000, 0x0000000768000000)
  eden space 11324672K,  50% used [0x0000000468000000, 0x00000005c602bd88, 0x000000071b340000)
  from space 629120K,  10% used [0x000000071b340000, 0x000000071f6cf378, 0x00000007419a0000)
  to   space 629120K,   0% used [0x00000007419a0000, 0x00000007419a0000, 0x0000000768000000)
 concurrent mark-sweep generation total 2097152K, used 1469519K [0x0000000768000000, 0x00000007e8000000, 0x00000007e8000000)
 concurrent-mark-sweep perm gen total 393216K, used 37291K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
2016-08-28T10:46:24.544+0800: 68449.386: [GC2016-08-28T10:46:24.544+0800: 68449.386: [ParNew: 5803756K->70533K(11953792K), 0.0668770 secs] 7273276K->1542365K(14050944K), 0.0669610 secs] [Times: user=0.25 sys=0.01, real=0.07 secs]
Heap after GC invocations=4647 (full 7):
 par new generation   total 11953792K, used 70533K [0x0000000468000000, 0x0000000768000000, 0x0000000768000000)
  eden space 11324672K,   0% used [0x0000000468000000, 0x0000000468000000, 0x000000071b340000)
 from space 629120K,  11% used [0x00000007419a0000, 0x0000000745e81738, 0x0000000768000000)
  to   space 629120K,   0% used [0x000000071b340000, 0x000000071b340000, 0x00000007419a0000)
 concurrent mark-sweep generation total 2097152K, used 1471831K [0x0000000768000000, 0x00000007e8000000, 0x00000007e8000000)
 concurrent-mark-sweep perm gen total 393216K, used 37291K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
}
＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝yong gc结束,开始remark＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝

2016-08-28T10:46:24.611+0800: 68449.453: [Rescan (parallel) , 0.0392690 secs]2016-08-28T10:46:24.650+0800: 68449.492: [weak refs processing, 0.0001190 secs]2016-08-28T10:46:24.650+0800: 68449.492: [class unloading, 0.0072200 secs]2016-08-28T10:46:24.658+0800: 68449.500: [scrub symbol table, 0.0083430 secs]2016-08-28T10:46:24.666+0800: 68449.508: [scrub string table, 0.0011760 secs] [1 CMS-remark: 1471831K(2097152K)] 1542365K(14050944K), 0.1264420 secs] [Times: user=0.42 sys=0.01, real=0.13 secs]
2016-08-28T10:46:24.671+0800: 68449.513: [CMS-concurrent-sweep-start]
2016-08-28T10:46:24.672+0800: 68449.514: Application time: 0.0018070 seconds
2016-08-28T10:46:26.388+0800: 68451.230: [CMS-concurrent-sweep: 1.714/1.717 secs] [Times: user=3.70 sys=0.58, real=1.72 secs]
2016-08-28T10:46:26.388+0800: 68451.230: [CMS-concurrent-reset-start]
2016-08-28T10:46:26.396+0800: 68451.238: [CMS-concurrent-reset: 0.007/0.007 secs] [Times: user=0.02 sys=0.00, real=0.01 secs]
2016-08-28T10:46:34.434+0800: 68459.276: Application time: 9.7600810 seconds

可以看到，在remark之前，来一次yong gc，eden区域从50%降到0(总大小为10.8G)，这些空间如果不消除，那么cms将会在这些空间上进行非常耗时的标记，最后再看看remark的时间（[1 CMS-remark: 1471831K(2097152K)] 1542365K(14050944K), 0.1264420 secs]），降到0.1264420s，和原来相比，整整一百倍的提高。

总结：

最后，对于长连接，push一类的海量服务端应用，16G内存8核心，推荐的JVM参数如下
2017.06.28更新
jdk 1.7 14g->13g

-Xms13g -Xmx13g -Xss512k -XX:PermSize=384m -XX:MaxPermSize=384m -XX:NewSize=12g -XX:MaxNewSize=12g 
-XX:SurvivorRatio=18 -XX:MaxDirectMemorySize=2g -XX:+UseParNewGC -XX:ParallelGCThreads=4 
-XX:MaxTenuringThreshold=15 -XX:+CMSParallelRemarkEnabled -XX:+CMSScavengeBeforeRemark -XX:+UseConcMarkSweepGC
-XX:+DisableExplicitGC -XX:+UseCMSInitiatingOccupancyOnly -XX:CMSInitiatingOccupancyFraction=70 
-XX:+ScavengeBeforeFullGC -XX:+UseCMSCompactAtFullCollection -XX:CMSFullGCsBeforeCompaction=9  
-XX:+CMSClassUnloadingEnabled  -XX:CMSInitiatingPermOccupancyFraction=70 -XX:+ExplicitGCInvokesConcurrent  
-XX:+PrintGCDetails -XX:+PrintGCDateStamps -XX:+PrintGCApplicationConcurrentTime -XX:+PrintHeapAtGC 
-Xloggc:/data/applogs/heap_trace.txt -XX:-HeapDumpOnOutOfMemoryError 
-XX:HeapDumpPath=/data/applogs/HeapDumpOnOutOfMemoryError

jdk1.8

-Xms13g -Xmx13g -Xss512k -XX:MetaspaceSize=384m -XX:MaxMetaspaceSize=384m -XX:NewSize=11g -XX:MaxNewSize=11g -XX:SurvivorRatio=18 -XX:MaxDirectMemorySize=2g -XX:+UseParNewGC -XX:ParallelGCThreads=4 -XX:MaxTenuringThreshold=15 -XX:+UseConcMarkSweepGC -XX:+DisableExplicitGC -XX:+UseCMSInitiatingOccupancyOnly -XX:+ScavengeBeforeFullGC -XX:+CMSScavengeBeforeRemark -XX:+CMSParallelRemarkEnabled -XX:CMSInitiatingOccupancyFraction=70 -XX:+CMSClassUnloadingEnabled -XX:SoftRefLRUPolicyMSPerMB=0 -XX:-ReduceInitialCardMarks -XX:+CMSClassUnloadingEnabled -XX:+ExplicitGCInvokesConcurrent -XX:+PrintGCDetails -XX:+PrintGCDateStamps -XX:+PrintGCApplicationConcurrentTime -XX:+PrintHeapAtGC -Xloggc:/data/applogs/heap_trace.txt -XX:-HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/data/applogs/HeapDumpOnOutOfMemoryError"

这样可以保证大多数对象在new区域就销毁，并且到了old区，remark之前先yong gc，然后再来一次cms old gc，将old gc控制在毫秒级别

如果你觉得看的不过瘾，想系统学习Netty原理，那么你一定不要错过我的Netty源码分析系列视频：https://coding.imooc.com/class/230.html

 

💥看到这里的你，如果对于我写的内容很感兴趣，有任何疑问，欢迎在下面留言📥，会第一次时间给大家解答，谢谢！

点赞收藏
分类：
Java虚拟机
标签：
jvm
gc
长连接
闪电侠

嗯，程序员一枚

请先登录，查看1条精彩评论吧
快去登录吧，你将获得
  • 浏览更多精彩评论
  • 和开发者讨论交流，共同进步

为你推荐

从 Linux 内核角度探秘 JDK MappedByteBuffer

从 Linux 内核角度探秘 JDK MappedByteBuffer

jvm
操作系统
kernel
MappedByteBuffer VS FileChannel：从内核层面对比两者的性能差异

MappedByteBuffer VS FileChannel：从内核层面对比两者的性能差异

jvm
java
jdk
15
1
Lv3
闪电侠

徽章

嗯，程序员一枚

推荐阅读

一次 GDB 源码角度分析 jvm 无响应问题Vue混淆与还原两个例子带你入门 Disruptor13 轻量级锁的重入 以及 线程1获取轻量级锁并释放线程2获取锁 的调试朋友们，就在今天，JDK 21，它终于带着重磅新特性正式发布了！！！为什么 JVM 叫做基于栈的 RISC 虚拟机
站内搜索
Linux
fgc
字节码
堆外内存
火焰图
堆外内存泄漏
java
jvm
gc
linux
内存泄漏
内核

专题推荐

对于刚刚接触 JVM 的同学来说，JVM 就像一个黑盒一样，完全不知道这是一个什么东西。所以对于小白来说，最重要是搞清楚 JVM 到底是干嘛的，以及其常用的知识框架。针对这样的需求，才有了这个专题，从零开始循序渐进地介绍了 JVM，相信是很不错的 JVM 入门教程。
15篇文章36031阅读量
内存泄漏是指无用对象持续占有内存或无用对象的内存得不到及时释放，从而造成内存空间的浪费称为内存泄漏。随着垃圾回收器活动的增加以及内存占用的不断增加，程序性能会逐渐表现出来下降，极端情况下，会引发OutOfMemoryError导致程序崩溃。本期总结了社区所有的内存泄漏实战案例，同样也是干货+实战，帮你攻克内存泄漏问题！
10篇文章24566阅读量

本月精选性能专题

本月精选线上案例

本月精选原创好文

HeapDump

有性能问题，上HeapDump性能社区

Copyright © 2019-2024 heapdump.cn 版权所有 浙ICP备17054444号

联系我们

HeapDump性能社区

网媒渠道

堆堆酱

扫码加堆堆酱好友

友情链接

PerfMa官网SegmentFault思否TesterHomeXPocket开源社区DCloud 问答社区稀土掘金博客园W3CTech