Routes, error handlers, before request, after request, and teardown
functions can all be coroutine functions if Flask is installed with the
async extra (
pip install flask[async]). It requires Python 3.7+
contextvars.ContextVar is available. This allows views to be
async def and use
@app.route("/get-data") async def get_data(): data = await async_db_query(...) return jsonify(data)
Pluggable class-based views also support handlers that are implemented as
coroutines. This applies to the
method in views that inherit from the
flask.views.View class, as
well as all the HTTP method handlers in views that inherit from the
async on Windows on Python 3.8
Python 3.8 has a bug related to asyncio on Windows. If you encounter
ValueError: set_wakeup_fd only works in main thread,
please upgrade to Python 3.9.
Async functions require an event loop to run. Flask, as a WSGI application, uses one worker to handle one request/response cycle. When a request comes in to an async view, Flask will start an event loop in a thread, run the view function there, then return the result.
Each request still ties up one worker, even for async views. The upside is that you can run async code within a view, for example to make multiple concurrent database queries, HTTP requests to an external API, etc. However, the number of requests your application can handle at one time will remain the same.
Async is not inherently faster than sync code. Async is beneficial when performing concurrent IO-bound tasks, but will probably not improve CPU-bound tasks. Traditional Flask views will still be appropriate for most use cases, but Flask’s async support enables writing and using code that wasn’t possible natively before.
Async functions will run in an event loop until they complete, at
which stage the event loop will stop. This means any additional
spawned tasks that haven’t completed when the async function completes
will be cancelled. Therefore you cannot spawn background tasks, for
If you wish to use background tasks it is best to use a task queue to trigger background work, rather than spawn tasks in a view function. With that in mind you can spawn asyncio tasks by serving Flask with an ASGI server and utilising the asgiref WsgiToAsgi adapter as described in ASGI. This works as the adapter creates an event loop that runs continually.
When to use Quart instead¶
Flask’s async support is less performant than async-first frameworks due to the way it is implemented. If you have a mainly async codebase it would make sense to consider Quart. Quart is a reimplementation of Flask based on the ASGI standard instead of WSGI. This allows it to handle many concurrent requests, long running requests, and websockets without requiring multiple worker processes or threads.
It has also already been possible to run Flask with Gevent or Eventlet
to get many of the benefits of async request handling. These libraries
patch low-level Python functions to accomplish this, whereas
await and ASGI use standard, modern Python capabilities. Deciding
whether you should use Flask, Quart, or something else is ultimately up
to understanding the specific needs of your project.
Flask extensions predating Flask’s async support do not expect async views. If they provide decorators to add functionality to views, those will probably not work with async views because they will not await the function or be awaitable. Other functions they provide will not be awaitable either and will probably be blocking if called within an async view.
Extension authors can support async functions by utilising the
flask.Flask.ensure_sync() method. For example, if the extension
provides a view function decorator add
ensure_sync before calling
the decorated function,
def extension(func): @wraps(func) def wrapper(*args, **kwargs): ... # Extension logic return current_app.ensure_sync(func)(*args, **kwargs) return wrapper
Check the changelog of the extension you want to use to see if they’ve implemented async support, or make a feature request or PR to them.