pandas 和numpy线程安全 [英] pandas and numpy thread safety

问题描述

我正在Web服务器(apache + modwsgi + django)上使用pandas，并且有一个难以复制的错误，现在我发现是由于熊猫不是线程安全的.

I'm using pandas on a web server (apache + modwsgi + django) and have an hard-to-reproduce bug which now I discovered is caused by pandas not being thread-safe.

经过大量的代码减少，我终于找到了一个简短的独立程序，可以用来重现该问题.您可以在下面看到它.

After a lot of code reduction I finally found a short standalone program which can be used to reproduce the problem. You can see it below.

重点是:与此问题的答案相反，该示例表明即使不修改数据框的非常简单的操作，熊猫也可能崩溃.我无法想象这个简单的代码片段在线程中可能是不安全的...

The point is: contrary to the answer of this question this example shows that pandas can crash even with very simple operations which do not modify a dataframe. I'm not able to imagine how this simple code snippet could possibly be unsafe with threads...

问题是有关在Web服务器中使用pandas和numpy的问题.是否有可能?我应该如何使用熊猫修复代码? (使用锁的示例会有所帮助)

The question is about using pandas and numpy in a web server. Is it possible? How am I supposed to fix my code using pandas? (an example of lock usage would be helpful)

以下是导致细分错误的代码:

Here is the code which causes a Segmentation Fault:

import threading
import pandas as pd
import numpy as np

def let_crash(crash=True):
    t = 0.02 * np.arange(100000) # ok con 10000                                                                               
    data = pd.DataFrame({'t': t})
    if crash:
        data['t'] * 1.5  # CRASH
    else:
        data['t'].values * 1.5  # THIS IS OK!

if __name__ == '__main__':
        threads = []
        for i in range(100):
            if True:  # asynchronous                                                                                          
                t = threading.Thread(target=let_crash, args = ())
                t.daemon = True
                t.start()
                threads.append(t)
            else:  # synchronous                                                                                              
                let_crash()
        for t in threads:
            t.join()

我的环境:python 2.7.3，numpy 1.8.0，pandas 0.13.1

My environment: python 2.7.3, numpy 1.8.0, pandas 0.13.1

推荐答案

请参阅以下文档中的警告:

see caveat in the docs here: http://pandas.pydata.org/pandas-docs/dev/gotchas.html#thread-safety

pandas不是线程安全的，因为底层的复制机制不是.我相信Numpy可以执行原子复制操作，但是pandas可以在其上面进行操作.

pandas is not thread safe because the underlying copy mechanism is not. Numpy I believe has an atomic copy operation, but pandas has a layer above this.

复制是熊猫操作的基础(因为大多数操作都会生成一个新对象以返回给用户)

Copy is the basis of pandas operations (as most operations generate a new object to return to the user)

解决此问题并非易事，并且会带来相当高的性能成本，因此需要一些工作才能正确处理此问题.

It is not trivial to fix this and would come with a pretty heavy perf cost so would need a bit of work to deal with this properly.

最简单的方法就是不跨线程共享对象或锁定对象使用权限.

Easiest is simply not to share objects across threads or lock them on usage.

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