Java的内存回收机制

时间:2022-10-26 07:05:52
原文出处: cnblogs-小学徒V

在Java中,它的内存管理包括两方面:内存分配(创建Java对象的时候)和内存回收,这两方面工作都是由JVM自动完成的,降低了Java程序员的学习难度,避免了像C/C++直接操作内存的危险。但是,也正因为内存管理完全由JVM负责,所以也使Java很多程序员不再关心内存分配,导致很多程序低效,耗内存。因此就有了Java程序员到最后应该去了解JVM,才能写出更高效,充分利用有限的内存的程序。

1.Java在内存中的状态

首先我们先写一个代码为例子:

Person.java

Java的内存回收机制
 1 package test;
2
3 import java.io.Serializable;
4
5 public class Person implements Serializable {
6
7 static final long serialVersionUID = 1L;
8
9 String name; // 姓名
10
11 Person friend; //朋友
12
13 public Person() {}
14
15 public Person(String name) {
16 super();
17 this.name = name;
18 }
19 }
Java的内存回收机制

Test.java

Java的内存回收机制
 1 package test;
2
3
4 public class Test{
5
6 public static void main(String[] args) {
7 Person p1 = new Person("Kevin");
8 Person p2 = new Person("Rain");
9 Person p3 = new Person("Sunny");
10
11 p1.friend = p2;
12 p3 = p2;
13 p2 = null;
14 }
15 }
Java的内存回收机制

把上面Test.java中main方面里面的对象引用画成一个从main方法开始的对象引用图的话就是这样的(顶点是对象和引用,有向边是引用关系):

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" alt="" />

当程序运行起来之后,把它在内存中的状态看成是有向图后,可以分为三种:

1)可达状态:在一个对象创建后,有一个以上的引用变量引用它。在有向图中可以从起始顶点导航到该对象,那它就处于可达状态。

2)可恢复状态:如果程序中某个对象不再有任何的引用变量引用它,它将先进入可恢复状态,此时从有向图的起始顶点不能再导航到该对象。在这个状态下,系统的垃圾回收机制准备回收该对象的所占用的内存,在回收之前,系统会调用finalize()方法进行资源清理,如果资源整理后重新让一个以上引用变量引用该对象,则这个对象会再次变为可达状态;否则就会进入不可达状态。

3)不可达状态:当对象的所有关联都被切断,且系统调用finalize()方法进行资源清理后依旧没有使该对象变为可达状态,则这个对象将永久性失去引用并且变成不可达状态,系统才会真正的去回收该对象所占用的资源。

上述三种状态的转换图如下:

aaarticlea/png;base64,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" 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2.Java对对象的4种引用

1)强引用 :创建一个对象并把这个对象直接赋给一个变量,eg :Person person = new Person("sunny"); 不管系统资源有么的紧张,强引用的对象都绝对不会被回收,即使他以后不会再用到

2)软引用 :通过SoftReference类实现,eg : SoftReference<Person> p = new SoftReference<Person>(new Person("Rain"));,内存非常紧张的时候会被回收,其他时候不会被回收,所以在使用之前要判断是否为null从而判断他是否已经被回收了。

3)弱引用 :通过WeakReference类实现,eg : WeakReference<Person> p = new WeakReference<Person>(new Person("Rain"));不管内存是否足够,系统垃圾回收时必定会回收。

4)虚引用 :不能单独使用,主要是用于追踪对象被垃圾回收的状态。通过PhantomReference类和引用队列ReferenceQueue类联合使用实现,eg :

Java的内存回收机制
 1 package test;
2
3 import java.lang.ref.PhantomReference;
4 import java.lang.ref.ReferenceQueue;
5
6
7 public class Test{
8
9 public static void main(String[] args) {
10 //创建一个对象
11 Person person = new Person("Sunny");
12 //创建一个引用队列
13 ReferenceQueue<Person> rq = new ReferenceQueue<Person>();
14 //创建一个虚引用,让此虚引用引用到person对象
15 PhantomReference<Person> pr = new PhantomReference<Person>(person, rq);
16 //切断person引用变量和对象的引用
17 person = null;
18 //试图取出虚引用所引用的对象
19 //发现程序并不能通过虚引用访问被引用对象,所以此处输出为null
20 System.out.println(pr.get());
21 //强制垃圾回收
22 System.gc();
23 System.runFinalization();
24 //因为一旦虚引用中的对象被回收后,该虚引用就会进入引用队列中
25 //所以用队列中最先进入队列中引用与pr进行比较,输出true
26 System.out.println(rq.poll() == pr);
27 }
28 }
Java的内存回收机制

运行结果:aaarticlea/png;base64,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" 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3.Java垃圾回收机制

其实Java垃圾回收主要做的是两件事:1)内存回收 2)碎片整理

3.1垃圾回收算法

1)串行回收(只用一个CPU)和并行回收(多个CPU才有用):串行回收是不管系统有多少个CPU,始终只用一个CPU来执行垃圾回收操作,而并行回收就是把整个回收工作拆分成多个部分,每个部分由一个CPU负责,从而让多个CPU并行回收。并行回收的执行效率很高,但复杂度增加,另外也有一些副作用,如内存碎片增加。

2)并发执行和应用程序停止 :应用程序停止(Stop-the-world)顾名思义,其垃圾回收方式在执行垃圾回收的同时会导致应用程序的暂停。并发执行的垃圾回收虽然不会导致应用程序的暂停,但由于并发执行垃圾需要解决和应用程序的执行冲突(应用程序可能在垃圾回收的过程修改对象),因此并发执行垃圾回收的系统开销比Stop-the-world高,而且执行时需要更多的堆内存。

3)压缩和不压缩和复制 :

①支持压缩的垃圾回收器(标记-压缩 = 标记清除+压缩)会把所有的可达对象搬迁到一端,然后直接清理掉端边界以外的内存,减少了内存碎片。

②不压缩的垃圾回收器(标记-清除)要遍历两次,第一次先从跟开始访问所有可达对象,并将他们标记为可达状态,第二次便利整个内存区域,对未标记可达状态的对象进行回收处理。这种回收方式不压缩,不需要额外内存,但要两次遍历,会产生碎片

复制式的垃圾回收器:将堆内存分成两个相同空间,从根(类似于前面的有向图起始顶点)开始访问每一个关联的可达对象,将空间A的全部可达对象复制到空间B,然后一次性回收空间A。对于该算法而言,因为只需访问所有的可达对象,将所有的可达对象复制走之后就直接回收整个空间,完全不用理会不可达对象,所以遍历空间的成本较小,但需要巨大的复制成本和较多的内存。

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" 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3.2堆内存的分代回收

1)分代回收的依据:

①对象生存时间的长短:大部分对象在Young期间就被回收

②不同代采取不同的垃圾回收策略:新(生存时间短)老(生存时间长)对象之间很少存在引用

2) 堆内存的分代:

①Young代 :

Ⅰ回收机制 :因为对象数量少,所以采用复制回收。

Ⅱ组成区域 :由1个Eden区和2个Survivor区构成,同一时间的两个Survivor区,一个用来保存对象,另一个是空的;每次进行Young代垃圾回收的时候,就把Eden,From中的可达对象复制到To区域中,一些生存时间长的就复制到了老年代,接着清除Eden,From空间,最后原来的To空间变为From空间,原来的From空间变为To空间。

Ⅲ对象来源 :绝大多数对象先分配到Eden区,一些大的对象会直接被分配到Old代中。

Ⅳ回收频率 :因为Young代对象大部分很快进入不可达状态,因此回收频率高且回收速度快

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g4ZtqQmeSXu1VZtrq6ijHWNOm00wSpqG+aFElVGRjjlZWVrcoysjym6M7C/bP3Bq/N+Oa3CUWtCy+Pdjoi805padS0IXXnl7tVWebz+bT+kKWdJkhFfdOkSJrK4Hl+q7KMLI3IlRZdmw8HPm53cHxgbptQ1PUy8vsWFJjpF5/dN21IffkV3aos43leR2WkmCZIS33TpIi6yli8L1d46lyf6H7POuwO4sdbhK4Mi9bGO4WnTjOH1JFf0UwrQ8UpQMMs0Mp4NiwXP3Gsjt5lWPRwi1CXYZHP1Y6fOMwcUkd+RTOtDBWnAA2zQCtjAcnFTwZ999sYFs2ECXUZFvHDrfjJoJlD6sivaKaVoeIUoGEWaGU8HZKL5wZ4p5Vh0XSYUJdhEe+04rkBM4fUkV/RTCtDxSlAwyzQypgflIu9Nt55h2GRJ0Soy7CId97BXpuZQ+rIr2imlaHiFKBhFmhlPBmQix/38eg2w6KJEKEuwyIe3caP+8wcUkd+RTOtDBWnAA1TeZpx71SXjX/wNuGNrDJEXWXM2eTiR/d4RzPDovEQoS7DIt7RjB/dM3NIHfkVzbQyVJwCNEylacb/i+5zFaWZX+vGV4a3Ty5+2MMPNTEsGtsk1GVYxA814Yc9Zg6pI7+imVaGilOAhqkwzbmKUhP/m+7qKuNxr1w828UPNjIsGtkk1GVYxA824tkuM4fUkV/RTCtDxSlAw0y2y5BtCuYqShXfPCzhDdJLSxPfQeLNYQnvLaZ0AEn6tup73n1dbWV0y8WzHD/YwLDIFSTUZVjEDzbgWc7MIXXkVzTjykh/CtAwlaf55j1k4q7uFJWR7F2q3m5XEg+XH6DmbdVfo64yHnXJxTMdvL2BYdFwkFCXYRFvb8AzHWYOqSO/oplWhopTgIaZYppvfu+/3QUkq4y4azruVU3cf8YdpHSAujc8jaGuMh5ycvF0Oz9Qz7AIbRDqMiziB+rxdLuZQ+rIr2imlaHiFKBhppnmXMWblxoqKyNx5/BmPxF/kMIB2a+M2bty8VQbb6tjWDS0QajLsIi31eGpNjOH1JFf0UwrQ8UpQMNMN82EC1xNZbz5ioTjEw6SH5D9yphpl4s9rXx/LcOiwQChLsMivr8We1rNHFJHfkUzrQwVpwANUz5N2bUuf3ERf4tB4armLCWlFotywygfoOpt1WOoq4zpNrl40sr332BYNBAg1GVYxPffwJNWM4fUkV/RTCtDxSlAw1ScZpJ30N592MK97ZVkb5q+tyNk2wg179qtuzKmWuXiiTt8XzXDItsrQl2GRXxfNZ64Y+aQOvIrmmllqDgFaJgF+hfGPVa52N3M37vOsKjvFaEuwyL+3nXsbjZzSB35Fc20MlScAjTMAq2Mydty8XgT33uVYVGPn1CXYRHfexWPN5k5pI78imZaGSpOARpmgVbGBCsXP2jke64wLOryE+oyLOJ7ruAHjWYOqSO/oplWhopTgIZZoJXhviUXjzXw3VUMizg/oS7DIr67Co81mDmkjvyKZloZKk4BGmaBVsb4z3LxaD3fdYlh0V0/oS7DIr7rEh6tN3NIHfkVzbQyVJwCNMwCrYwHP8nFI3V850WGRW1+Ql2GRXznRTxSZ+aQOvIrmmllqDgFaJgFWhljN+ViVw3PXWBY1PKSUJdhEc9dwK4aM4fUkV/RTCtDxSlAwyzQyhitkYvvV/Pc+f9rRk0vxNsvCV0ZFvHceXy/2swhdeRXNNPKUHEK0DALtDJGquXi4Wu+jnP7mvsvPwux64Si9c/xu80OvuMsHr5m2pD68iuaaWWoOAVomAVaGa6rcrHzyip39mhD038mVxvXCUXPPvZb2E5fRzl2XjFtSH35Fc20MlScAjTMAq2M4StyBVT1ovMse+PUge6xm2tiwwtCy8M2T1VN5fOOcgFVmTakvvyKZloZKk4BGmYBVkbwyruS7XvirNqj6PgxeO/8dMPxf9fUfD7srX9BqHjCs3qoocVdd+xVzznR8aM5Q+rOryC6GL74G92VkWyaIB31TpMi6d/5/XntAaHjGEGX9ig5LkYHLvjaTqIrR764fuNAj7vcG6h+Ltb+Soyx3Bv468DMgYa2vh8/X7R+s237QXJcNFvIDPPLFXu/fXXtd7rf+T3ZNEEq6p4mRVJVhiAIa2tr3uaTuPkgGbogV7RXhHq/n2/6yn7prxU/fPVBI/cOixij/KCRO3mp3FZ52Nv4ZbC7XLRXmDBk5vn3iK2fLv10ZHV1VesPWdppgsare5oUSVUZoij6/X4P6o1U/5bYz5HBH+QKA+c2u08vsMdd//us5/zB9tP7W05+ZP02t7ac/OiXU/u7zx0cvvzp/K0vA52nsE05HsWQWcwfb6Tuj5O9TS9evBAEQdOk1UwTNFjd06RIqsqQJCkUCnm93gn2zE7jX4j9rKKCrTzcferXtq+f3jo2e/OLyRufu6uP5NSJ6iPTtX9/0vivtdavQ93fCbZyaeD7ZPFohcxu/pg7tz9+VH90eno6GAyKoqhp0iqnCRpmJtOkSKrKIITs7Oysra25XC7+2p9x80FiO00GyuVKtjO47/R2z3ebnd8GOk68uvvfnBroOBHkvtnqPon7Tku2M4qRqIfMev6d5kOr1/807LD7fL5oNCpJktZhq5wmaICZT5MWaSpDkqRwOLywsGC32ydq/hmpeR9bD4vcMWI7LVfqPyX1nxKNUuo/pRgjtQaHzDy/2HVcaPlbpPYPszc+HejrmZ+fD4VC+n4paZommAuzOE1apKkMQoggCMFgcH5+fmhoqKf5xvy1A+uX39uqLAON8VXVuwtXP+y/VWW3271e78bGRiave2GahTRNKqSvDEKIIAibm5vLy8tut9tms3Ec19bW1tLSYgVyTGtra0dHR19f39jY2OLiYjAYFAQhw00sTJMWuZim8aiqDEKIKIqRSMTv9y8vL3u93qmpqYmJCTeQYzwej9frXVpaWl9f397eztYOFqZJhRxN02DUVgYhRJIkQRCi0Wg4HA4Gg4FA4BWQSwKBwMbGRjgcjkajWf91BNM0mJxO00g0VMYukiSJgFHk+mcLpmkk+dsUu+ipDAAAihaoDAAANACVAQCABqAyAADQAFQGAAAagMoAAEADUBkAAGgAKgMAAA1AZQAAoAGoDAAANACVAQCABqAy8pKSkhL5f8c/mOzg1EeqOR1Q5MCPQl6iWBkkybWtrzLSftvY42pIey4gj4Bx5iXqK0NliaQ9Rvf3AQoMGHn+oeZi3v2U4i/8tLuArFQPFEpBAkPVw1xFaUmJhUt8oLRizpizK1ZGsmpQPDLFd1N8RM2Rmd8xAfICGKo+OEtJXEdwlsQGySG7jaDmfoGOytC0cUjxIfRFoQJz1UncRsPoLYamFyapv5Zov87V7CygLwoYGK1u3mw0ErYYcxWlu7/4Ex58uwl5e3zscc7y+vi42on/NnGfSnZBatplkCSXfbI7HfKNTLIXPqnDAIUBTFc/cxWlJaUWy9stRsItDs5SklgNb74ssTJ26yDupU7cf8btYFJcqCkqQ99lL39cZTvALqPggdFmQmyDEF8S8Xc0dj9OvctQPHx3x5F4DCEkG7sMkpvKgHsZxQDMNSMSWiJblZFkl7GL+pcD+nYK+r5WfZEBeQ0MNSNyVxnyOyK7qL96dVeGyusfbmoUITDUjEhsibjbF4l/DBt3WPxrmSSVwVlS/wFM2n2B8ZUBFA8w8oyQ/YWMuA1CwmW/+7iFe1sUyXYZe//AJO29jD2fiq+MZLc/U3y3TJoFKHhg5KZj718tldVS6gs1vhrS3p5M2xep/wREXknJSBEYyC9glqZjzx1P2V9OT/+7XeUlCq8+AB3AT4MJSf26BABoApUBAIAGoDIAANAAVAYAABqAygAAQANQGQAAaOD/AbjOpdwnclaVAAAAAElFTkSuQmCC" 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" alt="" />

②Old代 :

Ⅰ回收机制 :采用标记压缩算法回收。

Ⅱ对象来源 :1.对象大直接进入老年代。

       2.Young代中生存时间长的可达对象

Ⅲ回收频率 :因为很少对象会死掉,所以执行频率不高,而且需要较长时间来完成。

③Permanent代 :

Ⅰ用      途 :用来装载Class,方法等信息,默认为64M,不会被回收

Ⅱ对象来源 :eg:对于像Hibernate,Spring这类喜欢AOP动态生成类的框架,往往会生成大量的动态代理类,因此需要更多的Permanent代内存。所以我们经常在调试Hibernate,Spring的时候经常遇到java.lang.OutOfMemoryError:PermGen space的错误,这就是Permanent代内存耗尽所导致的错误。

Ⅲ回收频率 :不会被回收

3.3常见的垃圾回收器

在此之前,我们先讲一下下面将会涉及到的并发和并行两个词的解释:

1)并行:指多条垃圾收集线程并行工作,但此时用户线程仍然处于等待状态;

2)并发:指用户线程与 垃圾收集线程同时执行(但不一定是并行的,可能会交替执行),用户程序继续执行,而垃圾收集程序运行于另一个CPU上。

好啦,继续讲垃圾回收器:

1)串行回收器(只使用一个CPU):Young代采用串行复制算法;Old代使用串行标记压缩算法(三个阶段:标记mark—清除sweep—压缩compact),回收期间程序会产生暂停,

2)并行回收器:对Young代采用的算法和串行回收器一样,只是增加了多CPU并行处理; 对Old代的处理和串行回收器完全一样,依旧是单线程。

3)并行压缩回收器:对Young代处理采用与并行回收器完全一样的算法;只是对Old代采用了不同的算法,其实就是划分不同的区域,然后进行标记压缩算法:

① 将Old代划分成几个固定区域;

② mark阶段(多线程并行),标记可达对象;

③ summary阶段(串行执行),从最左边开始检验知道找到某个达到数值(可达对象密度小)的区域时,此区域及其右边区域进行压缩回收,其左端为密集区域

④ compact阶段(多线程并行),识别出需要装填的区域,多线程并行的把数据复制到这些区域中。经此过程后,Old代一端密集存在大量活动对象,另一端则存在大块空间。

4)并发标识—清理回收(CMS):对Young代处理采用与并行回收器完全一样的算法;只是对Old代采用了不同的算法,但归根待地还是标记清理算法:

① 初始标识(程序暂停):标记被直接引用的对象(一级对象);

② 并发标识(程序运行):通过一级对象寻找其他可达对象;

③ 再标记(程序暂停):多线程并行的重新标记之前可能因为并发而漏掉的对象(简单的说就是防遗漏)

④ 并发清理(程序运行)

4.内存管理小技巧

1)尽量使用直接量,eg:String javaStr = "小学徒的成长历程";

2)使用StringBuilder和StringBuffer进行字符串连接等操作;

3)尽早释放无用对象;

4)尽量少使用静态变量;

5)缓存常用的对象:可以使用开源的开源缓存实现,eg:OSCache,Ehcache;

6)尽量不使用finalize()方法;

7)在必要的时候可以考虑使用软引用SoftReference。