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Python queue to list fast12/29/2023 ![]() The recipient process can then parse the file. Instead of passing data directly, you can write the data to disk, and then pass the path to this file to the subprocess (as an argument) or parent process (as the return value of the function running in the worker process). This will reduce the overhead of pickling somewhat, though it will still exist. In addition, it’s worth noting that Python has a faster version of pickling that is not enabled by default as of 3.11 it might be enabled in a future version. Spending a few seconds on pickling doesn’t really matter if your subsequent computation takes 10 minutes. In particular, if you minimize how much data gets passed and forth between processes, and the computation in each process is significant enough, the cost of copying and serializing data might not significantly impact your program’s runtime. If you’re stuck with using processes, you might just decide to live with the overhead of pickling. ![]() Need to identify the performance and memory bottlenecks in your own Python data processing code? Try the Sciagraph profiler, with support for profiling both in development and production on macOS and Linux, and with built-in Jupyter support. ![]() Note: Whether or not any particular tool or technique will speed things up depends on where the bottlenecks are in your software. So another approach is to use a library like Polars which is designed from the ground-up for parallelism, to the point where you don’t have to think about it at all, it has an internal thread pool. However, anything involving strings, or Python objects in general, will not. Pandas is built on NumPy, so many numeric operations will likely release the GIL as well. In situations where parallelism is possible with Python threads, like using much of NumPy’s API, there’s much less motivation to use processes.įor more details, read this introduction to the GIL. Let’s try an example, comparing a thread pool to a process pool: This serialization and deserialization process involves computation, which can potentially be slow.
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