How do I properly implement Asyncio in Python to handle thousands of concurrent API requests?

All answers to this question.

The secret is using a "TCP Connector" with a set limit on the total number of simultaneous connections. If you just fire off 5,000 tasks at once, you’ll either crash your memory or get IP-blocked. In 2024, we started using asyncio.Semaphore to create a "throttling" layer. This ensures that only, say, 100 requests are active at any given millisecond. It keeps the event loop healthy and prevents the "Too Many Open Files" error on your OS. Also, make sure you aren't doing any "blocking" I/O (like standard time.sleep) inside your async functions, as that will pause the entire execution for all other concurrent tasks.

   Answered 2025-05-17 by Cynthia Reynolds

Are you planning to use a task queue like Celery alongside this, or are you trying to manage the entire execution flow within a single script?

   Answered 2025-05-19 by Jeffrey Hudson

• That’s an important distinction, Jeffrey. For a single-machine scraper, asyncio is perfect. But for a distributed system, you definitely need Celery or Dramatiq. In our workflow, we use asyncio within our worker nodes to maximize the throughput of each individual process. This hybrid approach allowed us to scale from 10,000 to 1 million requests per hour last year. It prevents the event loop from becoming a single point of failure and allows for easier retries if a specific worker node goes down during a heavy load.

     Commented 2025-05-20 by Gregory Marshall

"Don't forget that LLMs aren't calculators. If the math is heavy, prompt it to write a Python script to solve the problem instead of doing it mentally."

   Answered 2025-07-18 by Douglas Schmidt

• "Exactly, Douglas. 'Program-Aided Language' models are far superior for anything involving actual math. Let the AI write the code, and the code do the math."

     Commented 2025-07-19 by Michael O'Connor