What Python’s asyncio primitives get wrong about shared state

the title seems unnecessarily clickbaity.

rather than "What Python's asyncio primitives get wrong" this seems more like "why we chose one asyncio primitive (queue) instead of others (event and condition)"

also, halfway through the post, the problem grows a new requirement:

> Instead of waking consumers and asking "is the current state what you want?", buffer every transition into a per-consumer queue. Each consumer drains its own queue and checks each transition individually. The consumer never misses a state.

if buffering every state change is a requirement, then...yeah, you're gonna need a buffer of some kind. the previous proposed solutions (polling, event, condition) would never have worked.

given the full requirements up-front, you can jump straight to "just use a queue" - with the downside that it would make for a less interesting blog post.

also, this is using queues without any size limit, which seems like a memory leak waiting to happen if events ever get enqueued more quickly than they can be consumed. notably, this could not happen with the simpler use cases that could be satisfied by events and conditions.

> A threading.Lock protects the value and queue list.

unless I'm missing something obvious, this seems like it should be an asyncio.Lock?

One thing that trips people up with asyncio is that “single threaded” gets interpreted as “no concurrency hazards”.

But coroutines still interleave execution at every await point, so shared mutable state can become just as fragile as in multithreaded code — the scheduling boundary just moves from OS threads to cooperative yield points.

In practice that tends to push designs toward queues, actors, or message-passing patterns if you want to avoid subtle state corruption.

I think it’s not so much that the asyncio primitives got wrong about shared state, as much as is what the authors got wrong about the usage of those primitives. They are classic concurrency primitives that’s been around for almost half a century. They work as designed, but require some care to use correctly.

They are trying to use condition variable without a mutex and see missed wake ups. That's a textbook error, no? I'm surprised asyncio.Condition even allows that mode of operation.

This is one of those cases where software transactional memory really shines.

You can often take the naive solution and it will be the correct one. Your code will looks like your intent.

TFA's first attempt:

  async def drain_requests():
      while state != "closing":
          await asyncio.sleep(0.1)
      print("draining pending requests")

  let drain_requests = do
          atomically (
              do s <- readTVar state
                 when (s /= "closing")
                     retry )
          print("draining pending requests")

The thing that burned me with asyncio primitives is that calling an async function doesn't even schedule it (by default). The pattern for fire-and-forget is impossible to guess when coming from any other language - although it's called out in the docs. You must also call create_task AND you must maintain a collection of tasks because otherwise the task might not run, because it can be garbage collected before running, AND you must clean up completed tasks from your collection.

The one thing I wish stock python queues had an option for (async or otherwise) was some kind of explicit termination. e.g. be split into producers and consumers, and have consumers indicate iteration complete when all producers have finished (and vice versa - signal producers that all consumers have gone away). You can kind of kludge around it in one direction with stop sentinals but it's a lot more awkward to deal with - especially if your queues are bounded as then you can get into the situation where you block trying to push the stop sentinal onto the queue as it's full.

Reminds me of https://geocar.sdf1.org/fast-servers.html

What about a more general message-passing mailbox approach? This works really well in the Erlang/gen_server/gen_fsm world. (and in plenty of other contexts, but Erlang's OTP is still some of the best, simplest incarnation of these things)

[dead]

I'm sorry but how do you jump from 1. Polling to 2. Asyncio

There's so many solutions in the middle, I have this theory that most people that get into async don't really know what threading is. Maybe they have a world vision where before 2023 python just could not do more than one thing at once, that's what the GIL was right? But now after 3.12 Guido really pulled himself by the bootstraps and removed the GIL and implemented async and now python can do more than one thing at a time so they start learning about async to be able to do more than one thing at a time.

This is a huge disconnect between what python devs are actually building, a different api towards concurrency. And some junior devs that think they are learning bleeding edge stuff when they are actually learning fundamentals through a very contrived lens.

It 100% comes from ex-node devs, I will save the node criticism, but node has a very specific concurrency model, and node devs that try out python sometimes run to asyncio as a way to soften the learning curve of the new language. And that's how they get into this mess.

The python devs are working on these features because they have to work on something, and updates to foundational tech are supposed to have effects in decades, it's very rare that you need to use bleeding edge features. In 95% of the cases, you should be restricting yourself to using features from versions that are 5-10 years old, especially if you come from other languages! You should start old to new, not new to old.

Sorry, for the rant, or if I misjudged, making a broader claim based on multiple perspectives.