在 Python 中使用多处理时我应该如何记录?

问题描述

现在我在一个框架中有一个中央模块，它使用 Python 2.6 生成多个进程 multiprocessing 模块.因为它使用multiprocessing，所以有模块级的multiprocessing-aware log，LOG = multiprocessing.get_logger().根据文档，这个记录器有进程共享锁，这样你就不会通过让多个进程同时向 sys.stderr(或任何文件句柄)写入内容，在 sys.stderr(或任何文件句柄)中乱码.

Right now I have a central module in a framework that spawns multiple processes using the Python 2.6 multiprocessing module. Because it uses multiprocessing, there is module-level multiprocessing-aware log, LOG = multiprocessing.get_logger(). Per the docs, this logger has process-shared locks so that you don't garble things up in sys.stderr (or whatever filehandle) by having multiple processes writing to it simultaneously.

我现在遇到的问题是框架中的其他模块不支持多处理.在我看来，我需要使对这个中央模块的所有依赖都使用多处理感知日志记录.这在框架内很烦人，更不用说框架的所有客户端了.有没有我没有想到的替代方案?

The issue I have now is that the other modules in the framework are not multiprocessing-aware. The way I see it, I need to make all dependencies on this central module use multiprocessing-aware logging. That's annoying within the framework, let alone for all clients of the framework. Are there alternatives I'm not thinking of?

推荐答案

非侵入式地处理这个问题的唯一方法是:

The only way to deal with this non-intrusively is to:

1. 生成每个工作进程，使其日志转到不同的文件描述符(到磁盘或到管道).理想情况下，所有日志条目都应带有时间戳.
2. 然后，您的控制器进程可以执行以下一个:
   • 如果使用磁盘文件:在运行结束时合并日志文件，按时间戳排序
   • 如果使用管道(推荐): 将所有管道中的日志条目即时合并到中央日志文件中.(例如，从管道的文件描述符中定期select，对可用的日志条目执行归并排序，并刷新到集中日志.重复.)

1. Spawn each worker process such that its log goes to a different file descriptor (to disk or to pipe.) Ideally, all log entries should be timestamped.
2. Your controller process can then do one of the following:
   • If using disk files: Coalesce the log files at the end of the run, sorted by timestamp
   • If using pipes (recommended): Coalesce log entries on-the-fly from all pipes, into a central log file. (E.g., Periodically select from the pipes' file descriptors, perform merge-sort on the available log entries, and flush to centralized log. Repeat.)

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