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chore: parse in threadpool instead of on event loop #802
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[nitpick] Hardcoding
max_workers=4may not scale across environments—consider making this configurable or usingos.cpu_count()to align with available cores.Uh oh!
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That's actually a good suggestion. Good bot.
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The os.cpu_count doesn't make a lot of sense since python is single threaded. This just configures how many documents can be parsed in parallel. But I'll make it configurable.
Uh oh!
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If the parsing is CPU bound why not use a ProcessPoolExecutor instead?
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NIT: python is not single threaded, it uses a lock to keep threads from executing in parallel. If you have IO bound tasks (reading files, network requests), the GIL is release so it gives concurrency benefits.
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Okay so more precisely python wont utilize more than 1 core to execute that parsing, I think the argument still stands. The cloud lambda also runs only with one core so multiprocessing instead of threading is just additional overhead. I think.
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The cloud lambda is configured to have 2560MiB of RAM, which according to this S/O post should correspond to two cores.