Python’s asyncio promises smooth concurrent programming, but it’s been lying to you about shared state.
The popular async framework has a dirty secret: its primitives can lose updates when multiple coroutines modify shared data. Think of it like having multiple cashiers updating the same inventory count without talking to each other. Chaos ensues.
Inngest’s engineering team dug deep into this problem. They found that asyncio’s built-in tools for managing shared state—locks, queues, and conditions—don’t prevent the dreaded “lost update” problem. When two coroutines read, modify, and write back data simultaneously, one change gets silently overwritten.
The Modern Web’s Async Problem
This matters more than you think. Modern web apps are async-heavy. Real-time features, background tasks, API calls—they all rely on concurrent programming. If your Python backend loses data updates, users see stale information. Cart items vanish. Messages disappear. Chaos.
The issue isn’t just Python. It’s how we think about shared state in concurrent systems. Most developers assume that async primitives handle data integrity automatically. They don’t. You need explicit coordination mechanisms—like database transactions or message queues—to prevent lost updates.
Python developers have been band-aiding this with external tools. Redis for shared state. PostgreSQL for coordination. Message brokers for reliable updates. But the core language primitives remain flawed.
**OFFART Insight:** This is why Vercel, Netlify, and other platforms push serverless functions so hard—stateless by design means no shared state problems. When every request is isolated, you can’t lose updates that never existed in the first place.
The solution isn’t avoiding asyncio. It’s understanding its limits. Use proper database transactions for critical updates. Implement optimistic locking for user interfaces. Design your data flow to minimize shared state.
Bottom line: Async doesn’t automatically mean safe. Build accordingly.
