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

问题描述

我有以下 Python 代码块(运行 v2.7)，导致在处理大型(几 GB)文件时引发 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)

阅读文档到Popen.communicate()，似乎有一些缓冲正在进行:

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

注意 读取的数据是缓存在内存中的，所以如果数据量很大或者没有限制就不要使用这种方法.

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

有没有办法禁用这种缓冲，或者在进程运行时强制定期清除缓存?

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 中使用什么替代方法来运行将千兆字节数据流式传输到 stdout 的命令?

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

我应该注意到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案:

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下降到足以完成任务.

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

更新

我最近发现了一种在 Python 中处理数据流的更灵活的方法，使用 线程.有趣的是，Python 在 shell 脚本可以轻松完成的事情上如此糟糕！

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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