The article discusses the challenges of optimizing I/O-bound tasks in Python, particularly when using a ThreadPoolExecutor. Users may notice decreased performance gains despite increasing threads, due to CPU constraints. A better solution put forth is a custom executor that seamlessly distributes work across threads and processes, improving efficiency. Simulated clients, connection management, and a timing context manager are detailed as part of the implementation. The article suggests that with proper handling, substantial performance improvements can be achieved for workloads that lack clear batch splitting.
After increasing the number of threads in a ThreadPoolExecutor, you might hit a limit where CPU utilization maxes out but speed does not improve.
If I/O tasks become bound and stop scaling well, one potential solution is to use a ProcessPoolExecutor which can run code across separate processes.
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