Is Async Python Really Faster? Uncover the Sync vs Async Truth

1. What Do “Sync” and “Async” Mean?

Web applications often handle many requests from different clients simultaneously, requiring concurrency. Synchronous servers achieve this using OS‑level threads or processes, typically with a pool of workers that handle requests. This model works well on multi‑CPU servers but can become a bottleneck when the number of workers is less than the number of concurrent clients, leading to queued requests.

An asynchronous server runs in a single process with an event loop that creates lightweight tasks for each request. Tasks pause when awaiting I/O (e.g., a database response) and the loop schedules other ready tasks, allowing hundreds or thousands of active tasks without blocking the CPU.

Because async tasks share a single process, they must voluntarily yield control frequently; otherwise they can starve other tasks. Async shines when tasks spend most of their time waiting on I/O rather than performing CPU‑bound work.

2. Two Ways to Implement Async in Python

The most common approach uses the asyncio package, which provides coroutine‑based primitives such as async, await, and yield. Popular async web frameworks built on asyncio include aiohttp, Sanic, FastAPI, and Tornado.

The alternative uses the greenlet library (installable via pip). Greenlets also allow functions to pause and resume, but they do not require special syntax, making it possible to run ordinary synchronous code asynchronously, especially with monkey‑patching libraries like gevent, Eventlet, and Meinheld. Flask automatically adapts to a greenlet‑based server, while Django and Bottle can also benefit from greenlet servers with appropriate patches.

3. Is Async Faster Than Sync?

3.1 Context Switching

Both models share CPU time among tasks, but synchronous context switches are handled by the operating system, whereas async switches are managed by the event loop. Optimized loops (e.g., uvloop, Gevent’s C‑based loop) can be more efficient, but noticeable gains appear only under very high concurrency.

3.2 Scalability

Async can handle many more simultaneous requests because each request is a lightweight task rather than a full worker process. When the workload is I/O‑bound and the system is under high load, async keeps the CPU busy and reduces queuing compared to a limited pool of sync workers.

However, if the tasks are CPU‑bound, both models perform similarly, and spawning many synchronous workers quickly becomes resource‑intensive.

High load is required to see async’s advantage.

The workload must be I/O‑bound.

Measure throughput (requests per unit time) rather than single‑request latency.

4. Conclusion

Async Python is beneficial mainly in high‑load, I/O‑heavy scenarios, and greenlet‑based solutions allow traditional frameworks like Flask or Django to gain some async advantages without rewriting code.