asyncio and free-threaded Python
********************************

asyncio uses an event loop as a scheduler to enable highly efficient
concurrency by switching between tasks to allow non-blocking I/O
operations. This results in better performance for I/O-bound use
cases. It also allows off-loading CPU-bound work to a thread or
process pool, but that is still limited by the *global interpreter
lock* in CPython.

However, in free-threaded Python, the GIL is disabled and Python can
run true multi-threaded code. This means that asyncio can now take
advantage of multiple CPU cores without the limitations imposed by the
GIL.

Since Python 3.14, asyncio has first-class support for free-threaded
Python, and the implementation of asyncio is safe to use in a multi-
threaded environment.

A single event loop on one core can handle many connections
concurrently, but the Python code that runs to handle each one still
executes serially. Once requests involve a non-trivial amount of per-
request computation, that handling becomes the bottleneck, and a
single core can no longer keep up. Combining asyncio with threads is
most useful here: by running an event loop per thread, the handling of
different requests can run in parallel across multiple CPU cores. It
is also useful when you need to run blocking or CPU-bound code from an
asyncio application.

Zobacz także:

  Scaling asyncio on Free-Threaded Python, a blog post by Kumar Aditya
  which explains the internal changes that make asyncio safe and
  efficient under free-threaded Python, together with benchmarks of
  the resulting improvements.


Thread safety considerations
============================

While asyncio is designed to be thread-safe in a free-threaded Python
environment, there are still some considerations to keep in mind when
using asyncio with threads:

1. **Event loop**: Each thread should have its own event loop which
   should not be shared across threads. This ensures that the event
   loop can manage its own tasks and callbacks without interference
   from other threads.

2. **Task management**: Tasks and futures created in one thread should
   not be awaited or manipulated from another thread.

3. **Thread-safe APIs**: When interacting with asyncio from multiple
   threads, it's important to use thread-safe APIs provided by
   asyncio, such as "asyncio.run_coroutine_threadsafe()" for
   submitting coroutines to an event loop from another thread. If you
   need to call a callback from a different thread, you can use
   "loop.call_soon_threadsafe()" to schedule it safely.

4. **Synchronization**: The synchronization primitives provided by
   asyncio (like "asyncio.Lock" and "asyncio.Event") are not designed
   to be used across threads. If you need to synchronize between
   threads, you should use the synchronization primitives from the
   "threading" module instead.


Using asyncio with threads
==========================

asyncio supports running one event loop per thread, which allows you
to take advantage of multiple CPU cores in a free-threaded Python
environment. Each thread can run its own event loop, and tasks can be
scheduled on those loops independently.

Here's an example of how to use asyncio with threads:

   import asyncio
   import threading

   async def worker(name: str) -> None:
       print(f"Worker {name} starting")
       await asyncio.sleep(1)
       print(f"Worker {name} done")

   def run_loop(name: str) -> None:
       asyncio.run(worker(name))

   threads = [
       threading.Thread(target=run_loop, args=(f"T{i}",))
       for i in range(4)
   ]
   for t in threads:
       t.start()
   for t in threads:
       t.join()

In this example, each thread creates its own event loop with
"asyncio.run()" and runs a coroutine on it. The threads execute
concurrently, and in a free-threaded build they can run on separate
CPU cores in parallel.


Producer/consumer across threads
================================

When a regular (non-asyncio) thread needs to hand work to an asyncio
event loop running in another thread, use a thread-safe primitive such
as "queue.Queue" rather than "asyncio.Queue", which is only safe
within a single event loop.:

   import asyncio
   import queue
   import threading

   def producer(q: queue.Queue[int]) -> None:
       for i in range(5):
           print(f"Producing {i}")
           q.put(i)
       q.shutdown()

   async def consumer(q: queue.Queue[int]) -> None:
       while True:
           try:
               item = q.get_nowait()
           except queue.Empty:
               await asyncio.sleep(0.1)
               continue
           except queue.ShutDown:
               break
           print(f"Consumed {item}")
           await asyncio.sleep(item)

   q: queue.Queue[int] = queue.Queue()
   consumer_thread = threading.Thread(
       target=lambda: asyncio.run(consumer(q))
   )
   consumer_thread.start()
   producer(q)
   consumer_thread.join()

The producer runs on the main thread while the consumer runs inside an
event loop on its own thread, yet they communicate safely through
"queue.Queue". When the queue is empty the consumer sleeps briefly and
tries again. When the producer is done it calls "shutdown()", which
causes subsequent "get_nowait()" calls to raise "queue.ShutDown" so
the consumer can exit cleanly.
