Pushing Asyncio to the Limit: Understanding Concurrency Limits

The asyncio module in Python enables concurrent execution of code by running tasks asynchronously. But how many tasks can it actually handle at once? The short answer is: it depends.

There are a few key factors that impact how much concurrency asyncio can handle:

Number of Threads

Asyncio is single-threaded but runs tasks concurrently by switching context quickly between tasks. More CPU cores = more threads = more tasks asyncio can interleave.

import multiprocessing
num_cpus = multiprocessing.cpu_count() 

Nature of Tasks

CPU-bound tasks limit concurrency as they monopolize the thread while running. I/O-bound tasks like network calls allow more concurrency as asyncio can switch between tasks while waiting for I/O.

Settings

The asyncio event loop has configurable limits on number of tasks:

import asyncio
asyncio.get_event_loop().set_task_factory(
    lambda loop, coro: asyncio.Task(coro, loop=loop)
)

You can tune other settings like max_queue_size and max_workers to allow more tasks.

Diminishing Returns

Pushing asyncio too hard can have diminishing returns. Context switching has a cost, so at some point more tasks slows things down.

Best Practice: Start with number of CPU cores as a guide for number of concurrent tasks. Profile, benchmark, and tune limits based on task nature and performance needs. Finding the right balance takes experimentation.

The key is understanding asyncio concurrency models and how parameters impact resource utilization and throughput. Don't be afraid to stress test limits, but optimize for best real-world performance.

Concurrency in asyncio is a complex topic but this should provide a good starting point.