《深入理解java虚拟机》第三章 垃圾收集器与内存分配策略

第三章 垃圾收集器与内存分配策略

3.1 概述

1. 哪些内存需要回收
2. 何时回收
3. 如何回收

程序计数器、虚拟机栈、本地方法栈3个区域随线程而生灭。 java堆和方法区的内存需要回收。

 

3.2 对象已死吗

  什么时候回收内存？

 

3.2.1 引用计数法

给对象中添加一个引用计数器，有地方引用时，计数器加1；当引用失效时，计数器减1。任何时刻计数器为0时的对象就是不可能再被使用的了。

存在问题：对象间的循环引用。  虚拟机不是通过这种方法判断对象是否存活。

 

3.2.2 可达性分析算法

通过一系列"GC Roots"对象作为起始点，从这些节点向下搜索，走过的路径称为引用链，当一个对象到GC Roots没有任何引用链相连（用图论的话来说，从GC Roots到这个对象不可达），证明此对象不可用。

java中可以作为GC Roots的对象包含：

1. 虚拟机栈（栈帧中的本地变量表）引用的对象
2. 方法区中类静态属性引用的对象
3. 方法区中常量引用的对象
4. 本地方法栈中JNI（即一般说的native方法）引用的对象

3.2.3 再谈引用

JDK 1.2之后，引用扩充为

强引用：程序代码中普遍存在的，类似“Object obj = new Object()”这类的引用。只要强引用还在，垃圾收集器永远不会回收掉被引用的对象。

软引用：还有用但非必需的对象。对于软引用关联着的对象，在系统将要发生内存溢出之前，将会把这些对象列进回收范围之中进行第二次回收。

弱引用: 只能存活到下一次垃圾收集发生之前。

虚引用：唯一目的就是能在这个对象被收集器回收时收到一个系统通知。

 

3.2.4 生存还是死亡

宣告一个对象的死亡，至少要经历两次标记过程：如果对象与GC Roots没有引用链，它会被第一次标记并进行筛选，筛选的条件是此对象是否有必要执行finalize()方法。如果对象没有覆盖finalize()方法或者虚拟机已经执行过finalize()方法，虚拟机将都视为“没有必要执行”。如果对象在被判断有必要执行finalize()方法，则会被加入一个F-Queue的对队列中，稍后由一个由虚拟机创建的、低优先级的Finalizer线程去执行队列中的对象的finalize()方法。 对象可以在finalize()中拯救自己，关联一个对象。否则就真被清除了。注意：一个对象的finalize()方法只能被系统自动调用一次。尽量避免使用finalize()方法。

 

3.2.5 回收方法区

永久代的垃圾回收主要有两部分：废弃常量和无用的类。

无用的类要满足以下条件，就“可以“回收

1. 该类所有的实例都已经回收，也就是java堆中不存在该类的实例。
2. 加载该类的ClassLoader已经被回收
3. 该类对应的java.lang.Class对象没有在任何地方被引用，无法再任何地方通过反射访问该类的方法。

 

 

3.3 垃圾收集算法 

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F/Enk8LLLIjaqklkdI6knWJHuXKq1URzcr1rhcFovQV1op5oo556lmpPUzOlczOdROhrc70REJdZVRUVHLVTOxHE1XuX2FjgiosP2aKeJEwyKsqI2Juaki4LQg2enkprZE2dMTvzLKm571Vyp8yceeri6RwAAZVWXBda8WWJUyx1RI9U9bY3KnzLioSZaaVIFak2ouoruhHY2Z9WSovOYYqevT/AOSmSZ+PN4Vr/wAGrn3FwuJYV1XqiObse1d6dKHejWmzE8WZslVFG7wdU6thvDIVdNDVSuVJVRNrm7dVW5/Zxgm099mmobJOsMaLcHNR6I5cMyxXbN/Qe0dokfWRVNdWPqnQI5ImrG1iN1kwqrjpXBGptG1p30DVr5309BIrqeJWNRETCojXL0rhF2Kai6aI9PpHcH2uO6TUtO2kWo8E9Gvcrmt8IrFfu3bPafVxunhrjHE+jjlghuMMDJHOVFSRWqrlTG7KfipYUtkgp7G+1Pe6WF7ZGucqIiqj1VV/M8o9H42UNFTOqZXrTVKVLpXImtK/KqufbkkROl+RNtbc3lDfpnwU0fgI0rX1jqaWLWXDNVVVzt+NVEX3oea6Q1aUKXbk8PitX4wrl8Lqa2rr7vXjcT4rNDFfJrmj3K6RmEiVE1Wu6HOT1qiIi+wjN0da2NKTlkvi1JPCJS6qfta2rrdOrnq92SxfxXR7W+51lddK6FYYWU1JOsOvrKrn/ZRUXHQnSfVVI+ovlNQtkcyKONamXVVUV23DW53Zyq+xD3obc2imrZGyOdyqfwyoqJ9lVREwn4HnU000d3pq6BmumqsEzUXbqquUcmdy5z6lEeF/nyU80HSmevhp6BaKVkevWxMfrK5M5cmE2dW/1Ho65XGaoqYaGGmctGjUmdK5yI96t1tVuOjZjau8nXW2pc6RsXhXRSRysmjkaiLqvauUXC9PsIklmlWomnpq+SnfUtalRqxtVHqiY1kz+iuP5EtOq6JlBWxXa1Q1bEc2OePWxnCpnpTKe/aeFjmkkopYZpFkkpZ306vVdrkauxV9eMEqnpo6ChjpqeNVihZqsYi7VRE/M8LPSy0tE506I2onkdPK1Nuqrlzj3JhPca8WfBn6qNfC3SukWSWOnqlbJGtXJF/d6jf0MORqKir0dZO0cZBWtdc4ElbA5dWGOSple9E6F10c5URc/wDu4yhyax1q3KWoY6iljWdZ42TpIqNcrUTaiLhVTGxeolUFurae6S1craFvh0Tw3gUkRXKnQuFXGfX04MUxazVU3et1dJLVUVDG90aVD1dK5q4XUamVRF6srhPYqk+ojfLAscczolXCa7URVRPVkh3KnmdNSVlOxZJKaRcxoqIr2OTDkTPX0L7iXUsmkgc2CbwMi9D9RHY9ymmWWpK6sfobQuWpkWeqqkp3Tq7Lka6VUVc78Jg9Z4qumuFda6KSokhWCGoSNZ1WRE11a9rXuXKZRExt6c9BLpNG1htDrZNXSSwtcj4npG1jono7W1kVOnaT6K2uppZ6maodPVzNRrpXMRuGtzqojU2Im1V9aqSyzMKCKtdBIlJUy1NPTRzrJqK50s2MZbFluVXreu1cNwirtNSixVtHlj1dFMzY+N2MoqdKKn5latj1IadaerkiqYnOcs6tRyyK/wDSVyLsyvyJ0EDbdbWQU8b5GwRarGIqazsJ0ZXrUvhqeOjxsdTLUWxqTv15oZHwSP8A2lY5W59+MlkQLRRyUduZHNjw73Oll1ejXe5XLj3rj3HrX1rLfQzVUiKrY25RqdLl6EanrVVRE9pr1RV6P7LNHGn6MUs0Tf3WSvanyRC0IdqpX0Vrp6eRUWVrMyKnQr12uX8VUmHlni6RwAARQAAec0MdRA+GViPjemHNXrQhsrprW1Iq9ss1MmxtW1uvhP8AxETai/8AUiYXpXBYA1TVNKTF3Ke5UNUxHwVlPK1ehWSIp7cog87H8SFdUWq3Vb1fU2+kmcvS6SBjlX3qh4837L2Pb+Fj7p03sM4yt/Dwedj+JByiDzsfxIVHN+y9j2/hY+6Ob9l7Ht/Cx90u9gxlb8og87H8SDlEHnY/iQqOb9l7Ht/Cx90c37L2Pb+Fj7o3sGMrflEHnY/iQcog87H8SFRzfsvY9v4WPujm/Zex7fwsfdG9gxlb8og87H8SDlEHnY/iQqOb9l7Ht/Cx90c37L2Pb+Fj7o3sGMrflEHnY/iQ4tTTomVmiRN6vQqeb9l7Ht/Cx906lgszVy20W9FTrSlj+g3sGMvaa+0aPWGkctbUouPBU2HKn7y/otT2qnqyKaml8M6rq3NdUvTVRGfoRN/Zb/Nev1JsJMcbIY0jjY1jE6GtRERPYiH0YqrmVimwADm0AADznhjqIHwysR0b01XNXr+hDZXzWtEir2yzUybG1bWq7Cf+IibUX/qRMda4LAGqappSYu5T3KhqmI+nrKeVi9CskRf5ntyiDzsfxIV9RardVvV9TQUkzl6VkgY5fxVDw5v2Xse38LH3TpvYZxlb8og87H8SDlEHnY/iQqOb9l7Ht/Cx90c37L2Pb+Fj7pd7BjK35RB52P4kHKIPOx/EhUc37L2Pb+Fj7o5v2Xse38LH3RvYMZW/KIPOx/Eg5RB52P4kKjm/Zex7fwsfdHN+y9j2/hY+6N7BjK35RB52P4kHKIPOx/EhUc37L2Pb+Fj7o5v2Xse38LH3RvYMZW/KIPOx/Eg5RB52P4kKjm/Zex7fwsfdHN+y9j2/hY+6N7BjK35RB52P4kHh4POx/EhUc37L2Pb+Fj7o5v2Xse38LH3RvYMZW/KIPOx/Eg5RB52P4kKjm/Zex7fwsfdHN+y9j2/hY+6N7BjK35RB52P4kHKIPOx/EhUc37L2Pb+Fj7o5v2Xse38LH3RvYMZW/KIPOx/Eg5RB52P4kKjm/Zex7fwsfdHN+y9j2/hY+6N7BjK35RB52P4kHKIPOx/EhUc37L2Pb+Fj7o5v2Xse38LH3RvYMZS6i9W2lVGyVkSyO/RijXXe72NblV/Ajo2ouckctVE6CmjVHR07lTWc7qc/Cqmzqbn1ruJNPSU1I1W01PDAi9KRRoz8kPYxVtL8FikABzaAAAVEVMLtRSuY+ezJqNilqKBF+ykaa0kCbsdLmJ1YyqdGFLEGqaphJi7zprtbqtM09bTv24VEemUXcqdKL6lJPKIPOx/EhBqbfRVq5qqOnnXo/vYmv/NCNzesvY9v4WPunTewzjK35RB52P4kHKIPOx/EhUc37L2Pb+Fj7o5v2Xse38LH3S72DGVvyiDzsfxIOUQedj+JCo5v2Xse38LH3RzfsvY9v4WPujewYyt+UQedj+JByiDzsfxIVHN+y9j2/hY+6Ob9l7Ht/Cx90b2DGVvyiDzsfxIOUQedj+JCo5v2Xse38LH3RzfsvY9v4WPujewYyt+UQedj+JByiDzsfxIVHN+y9j2/hY+6Ob9l7Ht/Cx90b2DGVvyiDzsfxIPDwedj+JCo5v2Xse38LH3RzfsvY9v4WPujewYyt+UQedj+JByiDzsfxIVHN+y9j2/hY+6Ob9l7Ht/Cx90b2DGVvyiDzsfxIOUQedj+JCo5v2Xse38LH3RzfsvY9v4WPujewYyt+UQedj+JByiDzsfxIVHN+y9j2/hY+6Ob1l7Ht/Cx90b2DGUuovdupneDWrjkmX9GGH+8kd7GtypHZHUXCdlTWRLDFGutDTOVFVF/afhVTO5EyidO3qlU9LT0rFZTQRQtXpbGxGov4HqYq2l+CxSAA5tAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAO49aIFbhelDrOv2HelF2quzrQ1EaI+cL07MDVXqx+J9M6M5+YcmzYuS2ixd8omUzkKmERcnWqmN3vC9HUvvJaLF3yfWquTi7Nh9Im3o6hEEy+VaqDCn0/9FDjur2CYsRLitVOk4fTur2HySeKwAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAIqodyu9TgKO5XecyqrnIAA6qqpwADuV3nAB1XKvScAAAAg//2Q==" alt="" />

. 新生代(Young Generation)：也有叫做年轻代的，这里使用《深入理解JAVA虚拟机》中的叫法，下同。
其实看名称就能看出一些，一般情况下，新创建的对象都会存放到新生代中(大对象除外)。
新生代中对象的特点是：很快就会被GC回收掉的或者不是特别大的对象。
为了方便垃圾收集，新生代又分出了一个Eden区，两个 Survivor区。
JVM 每次只会使用 Eden区 和其中的一块 Survivor 区域来为对象服务，另一块Survivor区域是空的，用于垃圾回收。
举个例子，第一次回收的时候，虚拟机会将 Eden区+Survivor（from）区域的存活对象复制到Survivor（to）上（存活对象小于Survivor（to）的空间），清空Survivor（from），虚拟机使用Eden区+Survivor（to）；
第二次回收的时候，虚拟机再将Eden区+Survivor（to）存活的对象复制到Survivor（from）。
这三个区域默认情况下是按照8：：1分配，也可以手动配置。
. 老年代（Old Generation）：在新生代每进行一次垃圾收集后，就会给存活的对象“加1岁”，当年龄达到一定数量的时候就会进入老年代（默认是15,可以通过-XX：MaxTenuringThreshold来设置）。
另外，比较大的对象也会进入老年代，可以-XX：PretenureSizeThreshold进行设置。
如-XX：PretenureSizeThreshold3M，那么大于3M的对象就会直接就进入老年代。
因此，老年代中存放的都是一些生命周期较长的对象或者特别大的对象。
. 永久代（Permanent Generation ）：即JVM的方法区。在这里存放着一些被虚拟机加载的类信息（别忘了还有动态生成的类）的静态文件，这就导致了这个区中的东西比老年代和新生代更不容易回收。
永久代大小通过-XX:MaxPermSize=<N>进行设置。
. 元空间（Metaspace）：从JDK 8开始，Java开始使用元空间取代永久代，元空间并不在虚拟机中，而是直接使用本地内存。
那么，默认情况下，元空间的大小仅受本地内存限制。当然，也可以对元空间的大小手动的配置。

 

3.3.1 标记-清除算法

首先标记所有需要回收的对象，在标记统一完成后回收所有标记的对象。 不足：1 标记和回收的效率都不高 2标记清除后会产生大量不连续的内存碎片，碎片太多可能会导致以后再重新运行时需要分配较大对象后，无法找到足够的连续内存而不得不提前触发另一次垃圾收集动作。

 

3.3.2 复制算法

它将可用的内存按容量分为大小相等的两块，每次只使用其中的一块。当这块内存用完时，就将还存活的对象复制到另外一块上面，然后再把已使用过的内存空间一次清理掉。

一块较大的Eden 和两块较小的Survivir。分配担保。

 

3.3.3标记-整理算法

同标记清除算法，但是在清除时让所有存活的对象都向一端移动，然后清理掉边界以外的内存。

 

3.3.4分代收集算法

根据对象存活周期不同将内存划分为几块，一般是把java堆分为新生代和老年代。在新生代中，每次垃圾收集时都发生大批对象死去，就选用复制算法。在老年代中因为对象存活率高、没有额外空间对它进行分配担保，就必须使用标记-清理 或标记-整理算法。

 

3.4 HotSpot的算法实现

3.4.1 枚举根节点

GC进行时必须停顿所有java执行线程，这件事被称为“Stop the World”。

3.4.2安全点

程序执行时，只有在安全点才能暂停开始GC。以及如何让线程都跑到安全点再停顿。分为抢占式中断和主动式中断。

1.抢占式。现在基本没有使用抢占式。 所有线程停止，如果没到安全点则继续执行到安全点。

2.主动式。设置一个标记，各个线程执行时主动轮询这个标记，发现中断标记为真时就自己中断挂起，轮询标记的地方和安全点是重合的。

3.4.3 安全区域

安全点的扩充。安全区域是指一段代码中，引用关系不会发生改变，区域中任何一点开始GC都是安全的。

 

3.5 垃圾收集器 举例+ CG日志分析

 

3.6 内存分配与回收机制

对象的内存分配，往大了说就是在堆上分配，对象主要分配在新生区Eden区上，如果启动了本地线程分配缓存，将按线程优先在TLAB上分配。少数情况下可能会直接分配到老年代中。

 

3.6.1对象优先在Eden分配

大多数情况下，对象在Eden区中分配，当Eden区中没有足够空间进行分配时，虚拟机会发生一次Minor GC.

新生代GC（Minor GC）:指发生在新生代的垃圾收集动作，因为java对象大多都具备朝生夕灭的特性，所以Minor GC非常频繁，一般回收速度也较快。

老年代GC(Major GC/Full GC):指发生在老年代的GC，出现Major GC,经常会伴随最少一次的minor gc。一般比前者速度慢10倍以上

 

3.6.2 大对象直接进入老年代

所谓大对象是指，需要大量连续内存空间的java对象，最典型大对象就是很长的字符串以及数组。目的：避免大量复制回收内存。有些回收器可以设置大对象最大内存上限标准。

 

3.6.3长期存活的对象将进入老年代

每一个对象定义了一个对象年龄计数器，对象在Eden区域出生并且经历一次minor GC存活，且能被survivor 容纳，则年龄为1，以后每熬过一次minor GC，年龄加一，超过一定值（默认15）会被移至老年代。

 

3.6.4 动态对象年龄判断

如果在survivor空间中相同年龄所有对象大小的总和大于survivor空间的一半，年龄大于等于该年龄的对象就可以直接进入老年代，无需等待MaxTenuringThreshold中的要求。

 

3.6.5分配担保

如果分配担保失败，进行一次Full GC。