Python Popen.communicate()内存限制的替代方法? [英] Alternatives to Python Popen.communicate() memory limitations?

问题描述

我有以下Python代码段(运行v2.7)，导致我在处理大型(几个GB)文件时抛出MemoryError异常:

I have the following chunk of Python code (running v2.7) that results in MemoryError exceptions being thrown when I work with large (several GB) files:

myProcess = Popen(myCmd, shell=True, stdout=PIPE, stderr=PIPE)
myStdout, myStderr = myProcess.communicate()
sys.stdout.write(myStdout)
if myStderr:
    sys.stderr.write(myStderr)

在阅读Popen.communicate() 的文档时，似乎进行一些缓冲:

In reading the documentation to Popen.communicate(), there appears to be some buffering going on:

注意:读取的数据缓存在内存中，因此，如果数据大小很大或没有限制，请不要使用此方法.

Note The data read is buffered in memory, so do not use this method if the data size is large or unlimited.

是否有办法禁用此缓冲，或强制在进程运行时定期清除缓存?

Is there a way to disable this buffering, or force the cache to be cleared periodically while the process runs?

我应该在Python中使用哪种替代方法来运行将千兆字节的数据流传输到stdout的命令?

What alternative approach should I use in Python for running a command that streams gigabytes of data to stdout?

我应该注意，我需要处理输出和错误流.

I should note that I need to handle output and error streams.

推荐答案

我想我找到了解决方法:

I think I found a solution:

myProcess = Popen(myCmd, shell=True, stdout=PIPE, stderr=PIPE)
for ln in myProcess.stdout:
    sys.stdout.write(ln)
for ln in myProcess.stderr:
    sys.stderr.write(ln)

这似乎使我的内存使用量降低到足以完成任务.

This seems to get my memory usage down enough to get through the task.

更新

最近，我发现使用

I have recently found a more flexible way of handing data streams in Python, using threads. It's interesting that Python is so poor at something that shell scripts can do so easily!

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