When Fast Code Goes Too Far: Debugging a Python asyncio Race Condition

In the pursuit of better performance, I recently rewrote part of a backend service to improve its speed. I replaced some blocking calls with asynchronous functions, introduced concurrency with asyncio, and streamlined several slow areas of the codebase. The goal was simple: make it faster.

And at first, it worked. Execution time dropped significantly. Throughput improved. Everything looked like a success—on paper.

But then something started to feel off.

The Silent Bug

There were no errors, no crashes, and no warnings in the logs. Yet, the output was occasionally inconsistent. Some requests returned unexpected results, while others behaved as expected. It wasn’t reproducible at first, which made it even harder to debug.

Eventually, I discovered that I had unknowingly introduced a race condition.

Two coroutines were accessing and modifying the same in-memory state concurrently. Without any synchronization in place, subtle data inconsistencies began to emerge. This wasn’t a failure of the system—it was a failure of logic under concurrency.

Understanding the Problem

The issue stemmed from how Python's asyncio model handles concurrency. Although asyncio avoids traditional thread-based pitfalls, it doesn't automatically protect shared state between coroutines. When multiple coroutines try to read from or write to the same data structure at the same time, race conditions can—and often do—occur.

In this case, a shared dictionary was being updated by multiple asynchronous tasks without any locks or coordination. This led to unpredictable behavior, depending on the timing of the context switches.

Example: The Race Condition

Here’s a simplified example of the issue:

# Shared state
data = {}

async def update(key, value):
    data[key] = value  # No protection

If multiple update() calls happen at the same time, they may overwrite each other or leave the dictionary in an inconsistent state.

The Fix

To resolve the race condition, I introduced coordination mechanisms:

import asyncio

data = {}
lock = asyncio.Lock()

async def update(key, value):
    async with lock:
        data[key] = value

Alternatively, for scenarios where order matters:

queue = asyncio.Queue()

async def worker():
    while True:
        key, value = await queue.get()
        data[key] = value
        queue.task_done()

And then enqueue tasks:

await queue.put(("username", "chatgpt"))

Lessons Learned

This experience was a reminder that performance optimizations should never come at the expense of correctness. While asynchronous programming can significantly boost speed and responsiveness, it also requires a strong understanding of concurrency models.

Rushing to optimize without accounting for coordination can introduce subtle and difficult-to-diagnose bugs—bugs that can cost more time to fix than the original optimization ever saved.

Conclusion

Fast code that fails under pressure isn't truly fast. In production systems, correctness and reliability always come first. Performance improvements are valuable, but only when the underlying logic remains sound.

If you're working with asyncio in Python, take extra care when dealing with shared mutable state. The gains of asynchronous execution are real, but so are the risks when concurrency is not managed carefully.

TL;DR

Tried to optimize Python backend with asyncio, but introduced a subtle race condition.
Cause: multiple coroutines accessing shared state without synchronization.
Fix: added asyncio.Lock() and used asyncio.Queue where order and timing mattered.
Lesson: performance gains are meaningless if they break correctness. Always coordinate access in async code.