海量连接服务端jvm参数调优杂记

稳酱51332
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原文链接： mp.weixin.qq.com

应用：shark-美团点评移动端长连代理，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，对象在一段时间内必然消亡

初始的线上gc的情况如下

对应的jvm参数为

  1. -Xms10g -Xmx10g -Xss512k -XX:PermSize=384m -XX:MaxPermSize=384m -XX:NewSize=7g -XX:MaxNewSize=7g
  2. -XX:SurvivorRatio=8 -XX:MaxDirectMemorySize=4g -XX:+UseParNewGC -XX:ParallelGCThreads=4
  3. -XX:MaxTenuringThreshold=9 -XX:+UseConcMarkSweepGC -XX:+DisableExplicitGC -XX:+UseCMSInitiatingOccupancyOnly
  4. -XX:+ScavengeBeforeFullGC -XX:+UseCMSCompactAtFullCollection -XX:+CMSParallelRemarkEnabled
  5. -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

  1. -Xms14g -Xmx14g -Xss512k -XX:PermSize=384m -XX:MaxPermSize=384m -XX:NewSize=12g -XX:MaxNewSize=12g 
  2. -XX:SurvivorRatio=18 -XX:MaxDirectMemorySize=2g -XX:+UseParNewGC -XX:ParallelGCThreads=4 
  3. -XX:MaxTenuringThreshold=30 -XX:+UseConcMarkSweepGC -XX:+DisableExplicitGC -XX:+UseCMSInitiatingOccupancyOnly 
  4. -XX:+ScavengeBeforeFullGC -XX:+UseCMSCompactAtFullCollection -XX:+CMSParallelRemarkEnabled 
  5. -XX:CMSFullGCsBeforeCompaction=9 -XX:CMSInitiatingOccupancyFraction=70 -XX:+CMSClassUnloadingEnabled 
  6. -XX:SoftRefLRUPolicyMSPerMB=0 -XX:-ReduceInitialCardMarks -XX:+CMSPermGenSweepingEnabled 
  7. -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

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

  1. -Xms14g -Xmx14g -Xss512k -XX:PermSize=384m -XX:MaxPermSize=384m -XX:NewSize=12g -XX:MaxNewSize=12g 
  2. -XX:SurvivorRatio=18 -XX:MaxDirectMemorySize=2g -XX:+UseParNewGC -XX:ParallelGCThreads=4 
  3. -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

  1. {Heap before GC invocations=23679 (full 18):
  2. par new generation   total 6606080K, used 5902124K [0x0000000568000000, 0x0000000728000000, 0x0000000728000000)
  3. eden space 5872128K,  99% used [0x0000000568000000, 0x00000006ce54f988, 0x00000006ce680000)
  4. from space 733952K,   4% used [0x00000006fb340000, 0x00000006fd1bb758, 0x0000000728000000)
  5. to   space 733952K,   0% used [0x00000006ce680000, 0x00000006ce680000, 0x00000006fb340000)
  6. concurrent mark-sweep generation total 3145728K, used 2200878K [0x0000000728000000, 0x00000007e8000000, 0x00000007e8000000)
  7. concurrent-mark-sweep perm gen total 393216K, used 37361K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
  8. 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]
  9. Heap after GC invocations=23680 (full 18):
  10. par new generation   total 6606080K, used 27858K [0x0000000568000000, 0x0000000728000000, 0x0000000728000000)
  11. eden space 5872128K,   0% used [0x0000000568000000, 0x0000000568000000, 0x00000006ce680000)
  12. from space 733952K,   3% used [0x00000006ce680000, 0x00000006d01b4910, 0x00000006fb340000)
  13. to   space 733952K,   0% used [0x00000006fb340000, 0x00000006fb340000, 0x0000000728000000)
  14. concurrent mark-sweep generation total 3145728K, used 2202742K [0x0000000728000000, 0x00000007e8000000, 0x00000007e8000000)
  15. concurrent-mark-sweep perm gen total 393216K, used 37361K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
  16. }
  17. 2016-08-27T17:26:00.107+0800: 261980.462: Application time: 0.0014750 seconds
  18. 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]
  19. 2016-08-27T17:26:00.165+0800: 261980.520: [CMS-concurrent-mark-start]
  20. 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]
  21. 2016-08-27T17:26:00.918+0800: 261981.273: [CMS-concurrent-preclean-start]
  22. 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]
  23. 2016-08-27T17:26:00.949+0800: 261981.304: [CMS-concurrent-abortable-preclean-start]
  24. 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]
  25. 2016-08-27T17:26:06.160+0800: 261986.515: Application time: 5.9951640 seconds
  26. 2016-08-27T17:26:06.161+0800: 261986.516: [GC[YG occupancy: 4756927 K (6606080 K)]
  27. 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
  28. 2016-08-27T17:26:24.323+0800: 262004.678: [class unloading, 0.0069760 secs
  29. 2016-08-27T17:26:24.330+0800: 262004.685: [scrub symbol table, 0.0040020 secs
  30. 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]
  31. 2016-08-27T17:26:24.338+08002016-08-27T17:26:24.338+0800: : 262004.693: Application time: 0.0002080 seconds
  32. 262004.693: [CMS-concurrent-sweep-start]
  33. 2016-08-27T17:26:24.347+0800: 262004.702: Application time: 0.0079820 seconds
  34. 2016-08-27T17:26:24.868+0800: 262005.223: Application time: 0.5186580 seconds
  35. {Heap before GC invocations=23680 (full 19):
  36. par new generation   total 6606080K, used 5899299K [0x0000000568000000, 0x0000000728000000, 0x0000000728000000)
  37. eden space 5872128K,  99% used [0x0000000568000000, 0x00000006ce5d44b8, 0x00000006ce680000)
  38. from space 733952K,   3% used [0x00000006ce680000, 0x00000006d01b4910, 0x00000006fb340000)
  39. to   space 733952K,   0% used [0x00000006fb340000, 0x00000006fb340000, 0x0000000728000000)
  40. concurrent mark-sweep generation total 3145728K, used 1891716K [0x0000000728000000, 0x00000007e8000000, 0x00000007e8000000)
  41. concurrent-mark-sweep perm gen total 393216K, used 37361K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
  42. 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]
  43. Heap after GC invocations=23681 (full 19):
  44. par new generation   total 6606080K, used 56108K [0x0000000568000000, 0x0000000728000000, 0x0000000728000000)
  45. eden space 5872128K,   0% used [0x0000000568000000, 0x0000000568000000, 0x00000006ce680000)
  46. from space 733952K,   7% used [0x00000006fb340000, 0x00000006fea0b1f8, 0x0000000728000000)
  47. to   space 733952K,   0% used [0x00000006ce680000, 0x00000006ce680000, 0x00000006fb340000)
  48. concurrent mark-sweep generation total 3145728K, used 1893599K [0x0000000728000000, 0x00000007e8000000, 0x00000007e8000000)
  49. concurrent-mark-sweep perm gen total 393216K, used 37361K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
  50. }

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

  1.  -Xms14g -Xmx14g -Xss512k -XX:PermSize=384m -XX:MaxPermSize=384m -XX:NewSize=12g -XX:MaxNewSize=12g 
  2.  -XX:SurvivorRatio=18 -XX:MaxDirectMemorySize=2g -XX:+UseParNewGC -XX:ParallelGCThreads=4 
  3.  -XX:MaxTenuringThreshold=15  -XX:+CMSScavengeBeforeRemark -XX:+UseConcMarkSweepGC -XX:+DisableExplicitGC 
  4.  -XX:+UseCMSInitiatingOccupancyOnly -XX:+ScavengeBeforeFullGC -XX:+UseCMSCompactAtFullCollection 
  5.  -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

  1. {Heap before GC invocations=4644 (full 6):
  2.  par new generation   total 11953792K, used 11384636K [0x0000000468000000, 0x0000000768000000, 0x0000000768000000)
  3.   eden space 11324672K,  99% used [0x0000000468000000, 0x000000071b30eb48, 0x000000071b340000)
  4.   from space 629120K,   9% used [0x000000071b340000, 0x000000071ee004e0, 0x00000007419a0000)
  5.   to   space 629120K,   0% used [0x00000007419a0000, 0x00000007419a0000, 0x0000000768000000)
  6.  concurrent mark-sweep generation total 2097152K, used 1464467K [0x0000000768000000, 0x00000007e8000000, 0x00000007e8000000)
  7.  concurrent-mark-sweep perm gen total 393216K, used 37291K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
  8. 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]
  9. Heap after GC invocations=4645 (full 6):
  10.  par new generation   total 11953792K, used 61763K [0x0000000468000000, 0x0000000768000000, 0x0000000768000000)
  11.   eden space 11324672K,   0% used [0x0000000468000000, 0x0000000468000000, 0x000000071b340000)
  12.   from space 629120K,   9% used [0x00000007419a0000, 0x00000007455f0dd8, 0x0000000768000000)
  13.   to   space 629120K,   0% used [0x000000071b340000, 0x000000071b340000, 0x00000007419a0000)
  14.  concurrent mark-sweep generation total 2097152K, used 1466964K [0x0000000768000000, 0x00000007e8000000, 0x00000007e8000000)
  15.  concurrent-mark-sweep perm gen total 393216K, used 37291K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
  16. }
  17. 2016-08-28T10:46:19.590+0800: 68444.431: Application time: 9.6715460 seconds
  18. {Heap before GC invocations=4645 (full 6):
  19.  par new generation   total 11953792K, used 11384705K [0x0000000468000000, 0x0000000768000000, 0x0000000768000000)
  20.   eden space 11324672K,  99% used [0x0000000468000000, 0x000000071b18f768, 0x000000071b340000)
  21.   from space 629120K,   9% used [0x00000007419a0000, 0x00000007455f0dd8, 0x0000000768000000)
  22.   to   space 629120K,   0% used [0x000000071b340000, 0x000000071b340000, 0x00000007419a0000)
  23.  concurrent mark-sweep generation total 2097152K, used 1466964K [0x0000000768000000, 0x00000007e8000000, 0x00000007e8000000)
  24.  concurrent-mark-sweep perm gen total 393216K, used 37291K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
  25. 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]
  26. Heap after GC invocations=4646 (full 6):
  27.  par new generation   total 11953792K, used 69180K [0x0000000468000000, 0x0000000768000000, 0x0000000768000000)
  28.   eden space 11324672K,   0% used [0x0000000468000000, 0x0000000468000000, 0x000000071b340000)
  29.   from space 629120K,  10% used [0x000000071b340000, 0x000000071f6cf378, 0x00000007419a0000)
  30.   to   space 629120K,   0% used [0x00000007419a0000, 0x00000007419a0000, 0x0000000768000000)
  31.  concurrent mark-sweep generation total 2097152K, used 1469519K [0x0000000768000000, 0x00000007e8000000, 0x00000007e8000000)
  32.  concurrent-mark-sweep perm gen total 393216K, used 37291K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
  33. }
  34. 2016-08-28T10:46:19.666+0800: 68444.508: Application time: 0.0019110 seconds
  35. 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]
  36. 2016-08-28T10:46:19.755+0800: 68444.597: [CMS-concurrent-mark-start]
  37. 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]
  38. 2016-08-28T10:46:20.418+0800: 68445.260: [CMS-concurrent-preclean-start]
  39. 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]
  40. 2016-08-28T10:46:20.438+0800: 68445.280: [CMS-concurrent-abortable-preclean-start]
  41. 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]
  42. 2016-08-28T10:46:24.543+0800: 68449.385: Application time: 4.7880220 seconds
  44. // 这里在remark之前进行一次young gc
  45. ＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝yong gc开始＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝
  46. 2016-08-28T10:46:24.544+0800: 68449.386: [GC[YG occupancy: 5803756 K (11953792 K)]{Heap before GC invocations=4646 (full 7):
  47.  par new generation   total 11953792K, used 5803756K [0x0000000468000000, 0x0000000768000000, 0x0000000768000000)
  48.   eden space 11324672K,  50% used [0x0000000468000000, 0x00000005c602bd88, 0x000000071b340000)
  49.   from space 629120K,  10% used [0x000000071b340000, 0x000000071f6cf378, 0x00000007419a0000)
  50.   to   space 629120K,   0% used [0x00000007419a0000, 0x00000007419a0000, 0x0000000768000000)
  51.  concurrent mark-sweep generation total 2097152K, used 1469519K [0x0000000768000000, 0x00000007e8000000, 0x00000007e8000000)
  52.  concurrent-mark-sweep perm gen total 393216K, used 37291K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
  53. 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]
  54. Heap after GC invocations=4647 (full 7):
  55.  par new generation   total 11953792K, used 70533K [0x0000000468000000, 0x0000000768000000, 0x0000000768000000)
  56.   eden space 11324672K,   0% used [0x0000000468000000, 0x0000000468000000, 0x000000071b340000)
  57.  from space 629120K,  11% used [0x00000007419a0000, 0x0000000745e81738, 0x0000000768000000)
  58.   to   space 629120K,   0% used [0x000000071b340000, 0x000000071b340000, 0x00000007419a0000)
  59.  concurrent mark-sweep generation total 2097152K, used 1471831K [0x0000000768000000, 0x00000007e8000000, 0x00000007e8000000)
  60.  concurrent-mark-sweep perm gen total 393216K, used 37291K [0x00000007e8000000, 0x0000000800000000, 0x0000000800000000)
  61. }
  62. ＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝yong gc结束,开始remark＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝
  64. 2016-08-28T10:46:24.611+0800: 68449.453: [Rescan (parallel) , 0.0392690 secs]
  65. 2016-08-28T10:46:24.650+0800: 68449.492: [weak refs processing, 0.0001190 secs
  66. 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]
  67. 2016-08-28T10:46:24.671+0800: 68449.513: [CMS-concurrent-sweep-start]
  68. 2016-08-28T10:46:24.672+0800: 68449.514: Application time: 0.0018070 seconds
  69. 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]
  70. 2016-08-28T10:46:26.388+0800: 68451.230: [CMS-concurrent-reset-start]
  71. 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]
  72. 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 

jdk1.7

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

jdk1.8

  1. -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

最后，强烈推荐一个源码阅读公众号，各类源码解析文章应用尽有，绝对是一流的干货，关注就是赚到!

纯 Java 源码分享公众号，目前有「Dubbo」「SpringCloud」「Java 并发」「RocketMQ」「Sharding-JDBC」「MyCAT」「Elastic-Job」「SkyWalking」「Spring」等等。

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