Python内存泄漏？

Question

我正在写一个似乎在泄漏内存的python扩展。我正在尝试使用valgrind找出问题的原因。

但是，它似乎是python本身泄漏内存根据valgrind。使用下面的简单脚本：

hello.py

print "Hello World!" 

，做

> 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. 

有谁有这个strage行为的解释？ python解释器真的在泄漏内存吗？

python开发人员用什么工具调试他们的内存泄漏？

Answer 1

有在Python源，解释各种警告尝试使用Valgrind的使用Python整体README.valgrind：

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. 

Answer 2

泄漏很可能来自您自己的扩展，而不是来自Python。大型系统通常会在内存仍然分配的情况下退出，只是因为如果进程即将结束，显式释放内存并不值得。