How to handle API rate limits in bulk operations?
Začal/a David Park · pred 3 mesiacmi
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Elena Rodriguez
pred 4 mesiacmi
Great solutions above. I'd also add: cache API responses locally so you don't re-fetch unchanged data on retry.
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Sarah Lee
pred 4 mesiacmi
Use exponential backoff with jitter. Here's a decorator pattern:\n\n```python\nimport time, random\n\ndef backoff(max_retries=5, base_delay=1.0):\n def decorator(func):\n def wrapper(*args, **kwargs):\n for i in range(max_retries):\n try:\n return func(*args, **kwargs)\n except RateLimitError:\n delay = base_delay * (2 ** i) + random.uniform(0, 1)\n time.sleep(delay)\n raise Exception("Max retries exceeded")\n return wrapper\n return decorator\n```
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Anna Kowalski
pred 4 mesiacmi
Also consider using asyncio with semaphores to control concurrency. You can run 10 requests in parallel while respecting the rate limit window.
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David Park
pred 3 mesiacmi
I'm hitting rate limits when processing 1000+ API calls. Currently using time.sleep(1) between calls but it's too slow. Any better patterns?
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