问题描述
我正在和一位朋友通过语言进行比较，他提到Java的自动化内存管理优于Python，因为Java不是压缩，而Python不是，因此对于长时间运行的服务器，Python是一个糟糕的选择。没有进入哪个更好或更糟的是，他的声明是否正确 - CPython的垃圾收集器是否不紧凑内存，因此，长时间运行的Python进程会越来越分散随着时间的推移？
我知道运行CPython的垃圾收集器是可选的。大多数情况下，它使用自动引用计数来实现自动化内存管理，只要引用计数达到零，就释放该对象 - 因此，释放对象所需的唯一一项CPython垃圾收集器就是检测循环，对象在根集中有一个引用。但是我不知道它是否会进行压缩的细节。
如果没有，那么长时间运行的CPython进程如何处理内存碎片问题？
我不确定，但CPython使用及其对象将内存地址用作，所以我会说它不会压缩...并根据，[C] Python不使用内存压缩... [W] Python使用内存压缩，实现C扩展将会使
更多单调乏味且容易出错，并且会减少这样的
扩展 - 限制使用Python的领域。
I was talking with a friend, comparing languages, and he mentioned that Java's automated memory management is superior to Python's as Java's does compaction, while Python's does not - and hence for long-running servers, Python is a poor choice.
Without getting into which is better or worse, is his claim true - does CPython's garbage collector not compact memory and, thus, long-running Python processes get more and more fragmented over time?
I know that running CPython's garbage collector is optional. Mostly it uses automated reference counting for automated memory management, and as soon as a reference count hits zero, the object is freed - thus the only thing that CPython's garbage collector is needed for, in terms of freeing objects, is to detect cycles which no object in the root set has a reference to. But I don't know the details of whether it does any compaction in addition to that.
If it does not, then how do long-running CPython processes address the memory fragmentation issue?
I don't know for sure, but CPython uses reference counting and its objects use memory addresses as ids so I would say it does not do compaction... And according to this, "[C]Python does not use memory compaction ... [w]ould Python use memory compaction, implementing C extensions would be much more tedious and error prone and there would be less such extensions - limiting the domain where Python is used."
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