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技术 2022年11月11日
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可以从不同的的角度去划分垃圾回收算法:

1.按照基本回收策略分

引用计数(Reference Counting:

比较古老的回收算法。原理是此对象有一个引用,即增加一个计数,删除一个引用则减少一个计数。垃圾回收时,只用收集计数为0的对象。此算法最致命的是无法处理循环引用的问题。

标记清除(Mark-Sweep:

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" alt="" />

此算法执行分两阶段。第一阶段从引用根节点开始标记所有被引用的对象,第二阶段遍历整个堆,把未标记的对象清除。此算法需要暂停整个应用,同时,会产生内存碎片。

复制(Copying:

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" alt="" />

此算法把内存空间划为两个相等的区域,每次只使用其中一个区域。垃圾回收时,遍历当前使用区域,把正在使用中的对象复制到另外一个区域中。次算法每次只处理正在使用中的对象,因此复制成本比较小,同时复制过去以后还能进行相应的内存整理,不会出现“碎片”问题。当然,此算法的缺点也是很明显的,就是需要两倍内存空间。

标记整理(Mark-Compact:

<img 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" alt="" />

此算法结合了“标记-清除”和“复制”两个算法的优点。也是分两阶段,第一阶段从根节点开始标记所有被引用对象,第二阶段遍历整个堆,把清除未标记对象并且把存活对象“压缩”到堆的其中一块,按顺序排放。此算法避免了“标记-清除”的碎片问题,同时也避免了“复制”算法的空间问题。

2.按分区对待的方式分

增量收集(Incremental Collecting:实时垃圾回收算法,即:在应用进行的同时进行垃圾回收。不知道什么原因JDK5.0中的收集器没有使用这种算法的。

分代收集(Generational Collecting:基于对对象生命周期分析后得出的垃圾回收算法。把对象分为年青代、年老代、持久代,对不同生命周期的对象使用不同的算法(上述方式中的一个)进行回收。现在的垃圾回收器(从J2SE1.2开始)都是使用此算法的。

3.按系统线程分

串行收集:串行收集使用单线程处理所有垃圾回收工作,因为无需多线程交互,实现容易,而且效率比较高。但是,其局限性也比较明显,即无法使用多处理器的优势,所以此收集适合单处理器机器。当然,此收集器也可以用在小数据量(100M左右)情况下的多处理器机器上。

并行收集:并行收集使用多线程处理垃圾回收工作,因而速度快,效率高。而且理论上CPU数目越多,越能体现出并行收集器的优势。

并发收集:相对于串行收集和并行收集而言,前面两个在进行垃圾回收工作时,需要暂停整个运行环境,而只有垃圾回收程序在运行,因此,系统在垃圾回收时会有明显的暂停,而且暂停时间会因为堆越大而越长。

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