django/asgiref

asgiref

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ASGI is a standard for Python asynchronous web apps and servers to communicate with each other, and positioned as an asynchronous successor to WSGI. You can read more at https://asgi.readthedocs.io/en/latest/

This package includes ASGI base libraries, such as:

• Sync-to-async and async-to-sync function wrappers, asgiref.sync
• Server base classes, asgiref.server
• A WSGI-to-ASGI adapter, in asgiref.wsgi

Function wrappers

These allow you to wrap or decorate async or sync functions to call them from the other style (so you can call async functions from a synchronous thread, or vice-versa).

In particular:

• AsyncToSync lets a synchronous subthread stop and wait while the async function is called on the main thread's event loop, and then control is returned to the thread when the async function is finished.

• SyncToAsync lets async code call a synchronous function, which is run in a threadpool and control returned to the async coroutine when the synchronous function completes.

The idea is to make it easier to call synchronous APIs from async code and asynchronous APIs from synchronous code so it's easier to transition code from one style to the other. In the case of Channels, we wrap the (synchronous) Django view system with SyncToAsync to allow it to run inside the (asynchronous) ASGI server.

Note that exactly what threads things run in is very specific, and aimed to keep maximum compatibility with old synchronous code. See "Synchronous code & Threads" below for a full explanation. By default, sync_to_async will run all synchronous code in the program in the same thread for safety reasons; you can disable this for more performance with @sync_to_async(thread_sensitive=False), but make sure that your code does not rely on anything bound to threads (like database connections) when you do.

Thread-sensitive contexts

ThreadSensitiveContext gives thread-sensitive sync_to_async calls a shared, single-worker executor for an async block. This allows you to group thread-sensitive calls, for example, per-request in a web application, rather than per-process::

class ASGIHandler(...):
    ...

    async def __call__(...):
        ...

        async with ThreadSensitiveContext():
            await self.handle(...)

By default, only the outermost ThreadSensitiveContext sets the context. Nested calls are a no-op and reuse it.

Nested contexts can be created by passing the explicit executor keyword argument.

• The executor must run one call at a time on one thread, such as ThreadPoolExecutor(max_workers=1). Recall, it is used for sync_to_async(…, thread_sensitive=True) calls.

• The calling code is responsible for the executor lifetime: the context does not shut it down. You may use it again for a later block, for example in a pool.

• Do not use one executor for two blocks at the same time.

By providing an executor, nested work can be run on a separate thread from the parent context's, including when the block is reached through async_to_sync::

from concurrent.futures import ThreadPoolExecutor

from asgiref.sync import ThreadSensitiveContext, sync_to_async

worker = ThreadPoolExecutor(max_workers=1)

async with ThreadSensitiveContext():
    await sync_to_async(parent_work)()
    async with ThreadSensitiveContext(executor=worker):
        await sync_to_async(child_work)()
        await sync_to_async(more_child_work)()
    await sync_to_async(parent_work)()

Both child calls run on the worker's thread. After the child block exits, calls use the parent thread again. Nested sync_to_async / async_to_sync calls within the child block also retain its thread affinity.

Tasks created inside a block inherit its context. Wait for those tasks before leaving the context. A thread-sensitive call made by such a task after the context has exited raises RuntimeError. To isolate concurrent tasks, each must enter its own context with its own executor.

A context with an executor can be entered only once: create a new one for each block.

Executor contexts separate thread-critical Local storage but do not change ContextVar or plain Local propagation. thread_sensitive=False calls are not affected.

Threadlocal replacement

This is a drop-in replacement for threading.local that works with both threads and asyncio Tasks. Even better, it will proxy values through from a task-local context to a thread-local context when you use sync_to_async to run things in a threadpool, and vice-versa for async_to_sync.

If you instead want true thread- and task-safety, you can set thread_critical on the Local object to ensure this instead.

Server base classes

Includes a StatelessServer class which provides all the hard work of writing a stateless server (as in, does not handle direct incoming sockets but instead consumes external streams or sockets to work out what is happening).

An example of such a server would be a chatbot server that connects out to a central chat server and provides a "connection scope" per user chatting to it. There's only one actual connection, but the server has to separate things into several scopes for easier writing of the code.

You can see an example of this being used in frequensgi <https://github.com/andrewgodwin/frequensgi>_.

WSGI-to-ASGI adapter

Allows you to wrap a WSGI application so it appears as a valid ASGI application.

Simply wrap it around your WSGI application like so::

asgi_application = WsgiToAsgi(wsgi_application)

The WSGI application will be run in a synchronous threadpool, and the wrapped ASGI application will be one that accepts http class messages.

Please note that not all extended features of WSGI may be supported (such as file handles for incoming POST bodies).

Dependencies

asgiref requires Python 3.11 or higher.

Contributing

Please refer to the main Channels contributing docs <https://github.com/django/channels/blob/master/CONTRIBUTING.rst>_.

Testing '''''''

To run tests, make sure you have installed the tests extra with the package::

cd asgiref/
pip install -e .[tests]
pytest

There is also a micro-benchmark suite for asgiref.local in tests/test_benchmark_local.py. It is skipped unless pytest-benchmark (or pytest-codspeed) is installed, and is not part of the regular CI run. To run it via tox::

tox -e benchmark

Benchmark numbers are only meaningful when compared on the same machine, so the typical workflow is to save a baseline and then compare against it::

tox -e benchmark -- --benchmark-save=before
# ... make changes ...
tox -e benchmark -- --benchmark-compare

Building the documentation ''''''''''''''''''''''''''

The documentation uses Sphinx <http://www.sphinx-doc.org>_::

cd asgiref/docs/
pip install sphinx

To build the docs, you can use the default tools::

sphinx-build -b html . _build/html  # or `make html`, if you've got make set up
cd _build/html
python -m http.server

...or you can use sphinx-autobuild to run a server and rebuild/reload your documentation changes automatically::

pip install sphinx-autobuild
sphinx-autobuild . _build/html

Releasing '''''''''

To release, first add details to CHANGELOG.txt and update the version number in asgiref/__init__.py.

Then, build and push the packages::

python -m build
twine upload dist/*
rm -r asgiref.egg-info dist

Implementation Details

Synchronous code & threads ''''''''''''''''''''''''''

The asgiref.sync module provides two wrappers that let you go between asynchronous and synchronous code at will, while taking care of the rough edges for you.

Unfortunately, the rough edges are numerous, and the code has to work especially hard to keep things in the same thread as much as possible. Notably, the restrictions we are working with are:

• All synchronous code called through SyncToAsync and marked with thread_sensitive should run in the same thread as each other (and if the outer layer of the program is synchronous, the main thread)

• If a thread already has a running async loop, AsyncToSync can't run things on that loop if it's blocked on synchronous code that is above you in the call stack.

The first compromise you get to might be that thread_sensitive code should just run in the same thread and not spawn in a sub-thread, fulfilling the first restriction, but that immediately runs you into the second restriction.

The only real solution is to essentially have a variant of ThreadPoolExecutor that executes any thread_sensitive code on the outermost synchronous thread - either the main thread, or a single spawned subthread.

This means you now have two basic states:

• If the outermost layer of your program is synchronous, then all async code run through AsyncToSync will run in a per-call event loop in arbitrary sub-threads, while all thread_sensitive code will run in the main thread.

• If the outermost layer of your program is asynchronous, then all async code runs on the main thread's event loop, and all thread_sensitive synchronous code will run in a single shared sub-thread.

Crucially, this means that in both cases there is a thread which is a shared resource that all thread_sensitive code must run on, and there is a chance that this thread is currently blocked on its own AsyncToSync call. Thus, AsyncToSync needs to act as an executor for thread code while it's blocking.

The CurrentThreadExecutor class provides this functionality; rather than simply waiting on a Future, you can call its run_until_future method and it will run submitted code until that Future is done. This means that code inside the call can then run code on your thread.

Maintenance and Security

To report security issues, please contact [email protected]. For GPG signatures and more security process information, see https://docs.djangoproject.com/en/dev/internals/security/.

To report bugs or request new features, please open a new GitHub issue.

This repository is part of the Channels project. For the shepherd and maintenance team, please see the main Channels readme <https://github.com/django/channels/blob/master/README.rst>_.