Asynchronous frameworks like aiohttp in Python enable building highly concurrent applications by efficiently handling thousands of open connections. However, there are practical limits on how many open connections aiohttp can handle simultaneously. Finding the right balance for your application architecture is key.
The Basics
The
connector = aiohttp.TCPConnector(limit=100)Each client session will use this connector to manage connections. The limit controls the number of concurrent connections, not total connections. So a single session can handle 100 requests simultaneously to a host, then reuse connections as requests complete.
Why You Need Limits
There are downsides to allowing unlimited connections:
That's why aiohttp enforces a reasonable default limit per host. But when should you adjust it?
Identifying Bottlenecks
Watch for symptoms indicating you need more connections:
Tools like
Increasing Limits
If needed, incrementally raise the connection limit. Measure again until latency stabilizes under peak load.
connector = aiohttp.TCPConnector(limit=400) How high you can go depends on the server and application architecture. Adding more processes and hosts also scales capacity.
Finding the Optimal Value
There are no fixed rules for the ideal limit, it requires testing under production-like load. Too few hurts concurrency, too many wastes resources.
Aim for the lowest setting that provides maximum throughput and low latency during traffic spikes. Monitor resource usage so you provision enough capacity.
Other Options
If adjusting limits doesn't help, there are other ways to scale:
Conclusion
aiohttp provides configurable connection limits to prevent runaway resource usage. Tuning this setting for your particular architecture and traffic levels is key to building a robust, high-throughput async system. Stress test under load and monitor metrics to find the optimal balance.