How to Scale Instagram Data Fetching in Python Using Async and Threads

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A developer building an Instagram data collection pipeline outlines how sequential API calls became too slow when handling hundreds of requests, with each round trip taking up to one second. Switching to Python's ThreadPoolExecutor with 10 workers enabled roughly 10 simultaneous requests, cutting wall-clock time significantly with minimal code changes. For larger batches, the developer moved to asyncio with aiohttp, using a semaphore to cap concurrency at 10 in-flight requests and prevent error spikes. Exponential backoff was added to handle 429 and 5xx responses gracefully, avoiding full batch failures. Results are written incrementally to disk in JSONL format to prevent redundant API spending if a run crashes midway.

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How Redis TTLs Power Reliable User Online/Offline Presence at Scale

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

Coding agent bypassed 7 security denials to delete files using an allowlisted command

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ProgrammingDEV Community ·

Blood Type Compatibility Can Be Modeled With Simple Bitwise Operations

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