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I'm working on a real-time data processing application in Python where I need to efficiently process a continuous stream of data from multiple sources concurrently. The data comes in various formats, and I want to ensure that I'm making the most efficient use of available CPU cores.

import threading

def process_data(data):
    # Process data from a single source
    # ...

def main():
    data_sources = ['source1', 'source2', 'source3']  # List of data sources
    threads = []

    for source in data_sources:
        thread = threading.Thread(target=process_data, args=(source,))
        threads.append(thread)
        thread.start()

    for thread in threads:
        thread.join()

if __name__ == "__main__":
    main()

I'm looking for guidance on how to optimize this code for concurrency and ensure efficient utilization of CPU cores. Additionally, I'd like to handle errors gracefully and implement proper synchronization where needed.

Any insights, best practices, or code examples for achieving efficient concurrency in this scenario would be highly appreciated.

prabu naresh
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    This question is far too broad. We don't know the type of sources, the amount of data, the type of processing, the type of synchronization, your desired form of error handling (what exactly should happen?). All I can tell you is that if you want to utilize multiple cores, you probably want to read [What is the global interpreter lock (GIL) in CPython?](https://stackoverflow.com/questions/1294382/what-is-the-global-interpreter-lock-gil-in-cpython) and potentially switch to multiprocessing rather than multithreading. But whether that is necessary depends on the type of processing – Homer512 Sep 01 '23 at 10:17

0 Answers0