divide data in date chunks and fetch concurrently #32
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liorz-cf
suggested changes
Oct 30, 2024
| for i in range(0, len(requests_params), max_requests): | ||
| req_params = requests_params[i: i + max_requests] | ||
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| with concurrent.futures.ThreadPoolExecutor( |
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If we need true parallelism for CPU-bound tasks, the concurrent.futures.ProcessPoolExecutor is often a better choice. It uses separate processes rather than threads, allowing each to have its own Python interpreter and thus bypassing the GIL.
However, for I/O-bound tasks (like network requests, file I/O, or waiting on APIs), ThreadPoolExecutor can still be quite effective than the previous implementation.
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