Show HN: Bringing multithreading to Python's async event loop

[nbsande]

This project explores the integration of multithreading into the asyncio event loop in Python.

While this was initially built with enhancing CPU utilization for FastAPI servers in mind, the approach can be used with more general async programs too.

If you’re interested in diving deeper into the details, I’ve written a blog post about it here: https://www.neilbotelho.com/blog/multithreaded-async.html

[noident]

> It got me wondering if it was actually possible to make Python’s async event loop work with multiple threads.

There is built-in support for this. Take a look at loop.run_in_executor. You can await something scheduled in a separate Thread/ProcessPoolExecutor.

Granted, this is different than making the async library end-to-end multi-threaded as you seem to be trying to do, but it does seem worth mentioning in this context. You _can_ have async and multiple threads at the same time!

[nbsande]

run_in_executor is pretty powerful for running sync code in async code, but my use case was more making async code utlize the cpu better. I think, just using run_in_executor would add a lot of complication and changes to how you use async await. But great point none the less!

[game_the0ry]

I am fairly confident I will get some down votes for this, but here goes...

When I am trying to solve a technical problem, the problem is going to dictate my choice of tooling.

If I am doing some fast scripting or I need to write some glue code, python is my go-to. But if I have a need for resource efficiency, multi threading, non-blocking async i/o, and/or hi performance, I would not consider python - I would probably use JVM over the best python option.

Don't get me wrong, I think its a worthwhile effort to explore this effort, and I certainly do not think its a wasted effort (quite the opposite, this gets my up vote) I just don't think I would ever use it if I had use case for perf and resource efficiency.

[Myrmornis]

Surely your comment only really makes sense from the point of view of green field or hobbyist projects? If you were working for an organization with hundreds of thousands of lines of Python already doing something important, then your last sentence doesn't hold, right?

[mywittyname]

Sure, this works sometimes. But sometimes you have mountains of code and infrastructure dedicated to one platform, and it's worth the effort to round off the occasional square peg in the interest of operational simplicity/consistency.

I've been using ThreadPoolExecutors in Python for a while now. They seem to work pretty well for my use cases. Granted, my use cases don't require things like shared memory segments; I use as_* functions under concurrent.futures to recombine the data as needed. Honestly, I prefer the futures functions as I don't need to think about deadlocks.

[trashtester]

The main object is the in-between-land, where you need 10x-50x the performance of Python, not 500x the performance on some parallelizable workload.

And in many teams, just having to worry about python makes it easier to keep team members productive if they're not expected to handle several different languages productively.

[gloryjulio]

The problem is that your example is not most of the companies that uses python are facing, which is the majority of the python code. They want some kind of performance uplift without rewriting the whole python code base. It's cheaper if python keeps getting some kind of upgrade

An example is facebook's php to hack compiler

[nbsande]

Hard agree. If you want resource efficiency and high performance you're probably better off looking to lower level languages most of the time. In my experience FastAPI usually gets used by teams that need a server done quickly and simply or are constrained by a lack of experience in low level languages. That being said, I do think its worthwhile trying to improve efficiency slightly even for these cases.

[m11a]

Perhaps a silly question, but should an event loop actually be multithreaded?

My understanding was that tasks in an event loop should yield after they dispatch IO tasks, which means the event loop should be CPU-bound right? If so, multithreading should not help much in theory?

[spiffytech]

If your workload is actually CPU-bound after you've deferred IO to the background, that's exactly when multithreading can help.

I've seen code that spends disproportionate CPU time spent on e.g., JSON (de)serializing large objects, or converting Postgres result sets into native data structures, but sometimes it's just plain ol' business logic. And with enough traffic, any app gets too busy for one core.

Single-threaded langs get around this by deploying multiple copies of the app on each server to use up the cores. But that's less efficient than a single, parallel runtime, and eliminates some architectural options.

[limaoscarjuliet]

You are correct, async-io is cooperative. This seems an attempt to enhance these async-io cooperative threads work more like goroutines in golang. Golang can start threads if it "thinks" the workload needs more CPU.

[bastawhiz]

This was exactly my question. Why do you even need an event loop? If awaits are just thread joins then what is the event loop actually doing? IO can just block, since other coroutines are on other threads and are unaffected.

Which is to say, why even bother with async if you want your code to be fully threaded? Async is an abstraction designed specifically to address the case where you're dealing with blocking IO on a single thread. If you're fully threaded, the problems async addresses don't exist anymore. So why bother?

[quotemstr]

At that point, why bother with asyncio? What we really want is something like Java virtual threads, something that doesn't have a code color.

[btown]

gevent is exactly this! http://www.gevent.org/

My startup has been using it in production for years. It excels at I/O bound workflows where you have highly concurrent real-time usage of slow/unpredictable partner APIs. You just write normal (non-async) Python code and the patched system internals create yields to the event loop whenever you’d be waiting for I/O, giving you essentially unlimited concurrency (as long as all pending requests and their context fit in RAM).

https://github.com/gfmio/asyncio-gevent does exist to let you use asyncio code in a gevent context, but it’s far less battle-tested and we’ve avoided using it so far.

[drowsspa]

Not for those of us stuck in Java 8... Is it stable already?

[nbsande]

Hmmm. That would indeed be better. Seems like an interesting experiment to try and implement virtual threads for python!