为何添加if __name__ == '__main__'可修复Python读取Feather文件时的SIGEMT错误？

Great question—this is a super common pitfall with Python's multiprocessing behavior, and it ties directly into how Feather handles parallel file reading under the hood. Let's break it down step by step:

First, what does if __name__ == '__main__': actually do? #

In Python, when you run a script directly (like python your_script.py), the interpreter sets the special __name__ variable to '__main__' for that module. But if another module imports your script (which is exactly what happens when Python spawns child processes), __name__ gets set to the script's filename instead.

This check acts as a guard: it ensures the code inside the block only runs when the script is executed directly, not when it's imported by another process or module.

How does this relate to Feather and your SIGEMT error? #

You’re exactly right—Feather (specifically the pyarrow backend pandas uses for read_feather) leverages multiprocessing to speed up reading large files. When you called read_feather without wrapping your main code in that guard clause:

1. Your script runs the top-level code, including the read_feather call.
2. Feather spawns child processes to handle parallel reading.
3. Each child process needs to import your script’s module to access necessary code/functions.
4. Since your read_feather call was in the top-level (not guarded), every child process re-executes that call—leading to multiple processes trying to read the same Feather file simultaneously, or conflicting with the parent process’s operations.

This chaos causes low-level resource conflicts that manifest as the confusing SIGEMT error. SIGEMT typically signals an invalid hardware instruction, but in this case, it’s a side effect of the multiprocessing misconfiguration rather than a true hardware issue.

Why adding the guard clause fixes everything #

When you wrap your main code (including the read_feather call) inside if __name__ == '__main__'::

• The parent process runs the guarded code as expected.
• When child processes spawn and import your module, __name__ isn’t '__main__', so they skip executing the guarded code entirely.
• This lets Feather’s multiprocessing work as intended—child processes handle their part of the file reading without re-triggering the entire workflow, eliminating the resource conflicts and the resulting SIGEMT error.

It’s a classic case of protecting your script’s entry point from unintended execution during multiprocessing module imports—a best practice for any Python script that uses multiprocessing, even indirectly like through Feather.

内容的提问来源于stack exchange，提问作者Adrian Keister