Java内存区域划分和GC机制
Posted 宏客王子
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Java 内存区域和GC机制
目录
Java GC（Garbage Collection，垃圾收集，垃圾回收）机制，是Java与C++/C的主要区别之一，作为Java开发者，一般不需要专门编写内存回收和垃圾清理代 码，对内存泄露和溢出的问题，也不需要像C程序员那样战战兢兢。这是因为在Java虚拟机中，存在自动内存管理和垃圾清扫机制。概括地说，该机制对 JVM（Java Virtual Machine）中的内存进行标记，并确定哪些内存需要回收，根据一定的回收策略，自动的回收内存，永不停息（Nerver Stop）的保证JVM中的内存空间，放置出现内存泄露和溢出问题。
关于JVM，需要说明一下的是，目前使用最多的Sun公司的JDK中，自从 1999年的JDK1.2开始直至现在仍在广泛使用的JDK6，其中默认的虚拟机都是HotSpot。2009年，Oracle收购Sun，加上之前收购 的EBA公司，Oracle拥有3大虚拟机中的两个：JRockit和HotSpot，Oracle也表明了想要整合两大虚拟机的意图，但是目前在新发布 的JDK7中，默认的虚拟机仍然是HotSpot，因此本文中默认介绍的虚拟机都是HotSpot，相关机制也主要是指HotSpot的GC机制。
Java GC机制主要完成3件事：确定哪些内存需要回收，确定什么时候需要执行GC，如何执行GC。经过这么长时间的发展（事实上，在Java语言出现之前，就有 GC机制的存在，如Lisp语言），Java GC机制已经日臻完善，几乎可以自动的为我们做绝大多数的事情。然而，如果我们从事较大型的应用软件开发，曾经出现过内存优化的需求，就必定要研究 Java GC机制。
学习Java GC机制，可以帮助我们在日常工作中排查各种内存溢出或泄露问题，解决性能瓶颈，达到更高的并发量，写出更高效的程序。
我们将从4个方面学习Java GC机制，1，内存是如何分配的；2，如何保证内存不被错误回收（即：哪些内存需要回收）；3，在什么情况下执行GC以及执行GC的方式；4，如何监控和优化GC机制。
了解Java GC机制，必须先清楚在JVM中内存区域的划分。在Java运行时的数据区里，由JVM管理的内存区域分为下图几个模块：
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