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

https://github.com/NeilBotelho/turboAsync

by nbsande • 2 years ago

82 46 2 years ago

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

quotemstr2• 2 years ago

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• 2 years ago

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.

sidmitra• 2 years ago

I love gevent, but i never was 100% sure that nothing is secretly breaking or some weird thread safety issue. In a large SaaS app all sorts of 3rd party libs do weird background threading stuff or someone randomly starts doing threading.Local and shared global context. After hitting some weird hanging redis-py client issues, i turned gevent off and it went away. Never really got around to spend time to debug the issue(especially since it happened on prod and hard to replicate on stage/local).

Does your app have a lot of dependencies that do background threads? Like Launchdarkly(feature flags), redis, spyne(rpc) and on and on.

drowsspa3• 2 years ago

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

quotemstr2• 2 years ago

I hear that some Amish sects permit the use of technology that's older and proven not to be too worldly, like washing machines, chainsaws, and Java 11. Have you considered converting?

nbsande6• 2 years ago

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

noident3• 2 years ago

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!

nbsande6• 2 years ago

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!

zo12• 2 years ago

I've had plenty of success using run_in_executor in production. You basically just use the decorator, and it mostly just works.

noident3• 2 years ago

but my use case was more making async code utlize the cpu better

But run_in_executor achieves that as well! If you use no-GIL Python and a thread pool (or GIL Python with a Process pool), you will utilize more CPU cores.

d0mine2• 2 years ago

run_in_executor that can be spelled as asyncio.to_thread() won't help utilizing cpu for pure Python code. It may help with blocking I/O, or cpu-heavy C extensions that release GIL. Otherwise, you have to utilize different processes to take full advantage of cpus (there are also subinterpreters (same process, separate GILs) but that exotic).

m11a2• 2 years ago

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• 2 years ago

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• 2 years ago

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.

bastawhiz2• 2 years ago

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?

svieira3• 2 years ago

Looking at the article he's not implementing `Task` with `Thread` - he's round-robinning processing `Task`s through simple `ThreadPool`. So instead of a single `Thread` making continuous progress on the work in the event loop he instead has a set of `Thread`s making progress _in parallel_ on work in the event loop. This is very much Java 21's approach to virtual threads (as well as in-language task runners like the kind you find in Scala libraries like ZIO, Monix, Cats, and the venerable Scalaz).

nbsande6• 2 years ago

Having too many threads all running at the same time can also cause a performance hit, and I don't mean hitting the OS limit on threads. The more threads you have running in parallel(remember this is considering a GIL-less setup) the more you need to context switch between the. Having fewer threads all running in an event loop allows you to manage more events with only a few threads, for example setting the number of event loop threads to the number of cores on the cpu.

game_the0ry4• 2 years ago

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.

trashtester3• 2 years ago

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.

game_the0ry4• 2 years ago

Fair point. Tho I will push back on...

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.

I think this makes sense for individuals and teams, but for an org or company I think having specialist teams makes sense where teams that require perf use JVM and teams that make business-ware or devops or something not perf would use python.

mywittyname• 2 years ago

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.

game_the0ry4• 2 years ago

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 agree with this, this is a fair trade off, but not the direction I would go as a matter of preference.

Myrmornis• 2 years ago

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?

gloryjulio2• 2 years ago

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

game_the0ry4• 2 years ago

Just playing devil's advocate here.

They want some kind of performance uplift without rewriting the whole python code base.

In order take advantage of mutli-threading and/or async i/o, you would need re-write your code anyway, right? And at the point, wouldn't re-writing in different language be an option?

nbsande6• 2 years ago

The use case I wrote it in mind with is FastAPI. In that case, there wouldn't be any change to the Python code. You'd just use a different ASGI server that would use this sort of multithreaded event loop. So instead of running it with uvicorn main:app, you'd run it with alternateASGI main:app.

I have an example of a very basic ASGI server that does just that towards the end of the blog

nbsande6• 2 years ago

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.

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

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