Talks - Arun Suresh Kumar: Sync vs. Async in Python: Tools, Benchmarks, and ASGI/WSGI Explained

ASGI handles multiple concurrent connections more efficiently than WSGI, allowing better scaling and performance for high-traffic applications

When migrating from sync to async:

• Core functionality should be non-blocking
• Careful consideration needed for library choices as not all support true async
• Memory usage typically increases but handles more concurrent users
• Use native async libraries when possible for best performance

Benchmarking insights:

• WSGI typically handles ~100 concurrent users
• ASGI can handle 1000+ users with higher requests per second (1333.5 RPS)
• Docker/container environments require different benchmarking approaches than bare metal
• Watch for unexpected performance dips from remaining sync code

Key Python async evolution milestones:

• Python 3.4: Native async support introduced
• Python 3.5: async/await keywords added
• Modern frameworks like FastAPI, Django, and Falcon support both WSGI and ASGI

Best practices:

• Use Task Groups for handling multiple concurrent functions
• Consider WebSocket/HTTP/2 for long-polling communications
• Monitor memory and CPU utilization carefully
• Scale horizontally and vertically based on workload
• Use async versions of ORMs and database drivers when available

Common frameworks compatibility:

• Django: Supports both WSGI and ASGI
• FastAPI: ASGI only
• Flask: WSGI
• Falcon: Supports both
• Tornado: ASGI support