Python内存泄漏？答案

【问题标题】：Python memory leaks?Python内存泄漏？
【发布时间】：2010-11-27 23:47:19
【问题描述】：

我正在编写一个似乎正在泄漏内存的 python 扩展。我正在尝试使用 valgrind 找出问题的根源。

但是，根据 valgrind，python 本身似乎正在泄漏内存。使用以下简单脚本：

hello.py

  print "Hello World!"

做事

> valgrind --tool=memcheck python ./hello.py

(...)
==7937== ERROR SUMMARY: 580 errors from 34 contexts (suppressed: 21 from 1)
==7937== malloc/free: in use at exit: 721,878 bytes in 190 blocks.
==7937== malloc/free: 2,436 allocs, 2,246 frees, 1,863,631 bytes allocated.
==7937== For counts of detected errors, rerun with: -v
==7937== Use --track-origins=yes to see where uninitialised values come from
==7937== searching for pointers to 190 not-freed blocks.
==7937== checked 965,952 bytes.
==7937== 
==7937== LEAK SUMMARY:
==7937==    definitely lost: 0 bytes in 0 blocks.
==7937==      possibly lost: 4,612 bytes in 13 blocks.
==7937==    still reachable: 717,266 bytes in 177 blocks.
==7937==         suppressed: 0 bytes in 0 blocks.
==7937== Rerun with --leak-check=full to see details of leaked memory.

有人对这种诡计行为有解释吗？ python解释器真的会内存泄漏吗？

python 开发人员使用什么工具来调试他们的内存泄漏？

【问题讨论】：

标签： python memory memory-management valgrind

【解决方案1】：

Python 源代码中有一个完整的 README.valgrind，解释了尝试将 Valgrind 与 Python 一起使用的各种注意事项：

http://svn.python.org/projects/python/trunk/Misc/README.valgrind

Python uses its own small-object allocation scheme on top of malloc,
called PyMalloc.

Valgrind may show some unexpected results when PyMalloc is used.
Starting with Python 2.3, PyMalloc is used by default.  You can disable
PyMalloc when configuring python by adding the --without-pymalloc option.
If you disable PyMalloc, most of the information in this document and
the supplied suppressions file will not be useful.  As discussed above,
disabling PyMalloc can catch more problems.

If you use valgrind on a default build of Python,  you will see
many errors like:

        ==6399== Use of uninitialised value of size 4
        ==6399== at 0x4A9BDE7E: PyObject_Free (obmalloc.c:711)
        ==6399== by 0x4A9B8198: dictresize (dictobject.c:477)

These are expected and not a problem.

【讨论】：

【解决方案2】：

泄漏很可能来自您自己的扩展，而不是来自 Python。大型系统通常在内存仍然分配的情况下退出，仅仅是因为如果进程即将结束，显式释放它是不值得的。

【讨论】：