# Asynchronous Operation

Dask can run fully asynchronously and so interoperate with other highly concurrent applications. Internally Dask is built on top of Tornado coroutines but also has a compatibility layer for asyncio (see below).

## Basic Operation

When starting a client provide the `asynchronous=True` keyword to tell Dask that you intend to use this client within an asynchronous context, such as a function defined with `async/await` syntax.

    async def f():
        client = await Client(asynchronous=True)

Operations that used to block now provide Tornado coroutines on which you can `await`.

Fast functions that only submit work remain fast and don’t need to be awaited. This includes all functions that submit work to the cluster, like `submit`, `map`, `compute`, and `persist`.

    future = client.submit(lambda x: x + 1, 10)

You can await futures directly

    result = await future

    >>> print(result)
    11

Or you can use the normal client methods. Any operation that waited until it received information from the scheduler should now be `await`’ed.

    result = await client.gather(future)

If you want to use an asynchronous function with a synchronous `Client` (one made without the `asynchronous=True` keyword) then you can apply the `asynchronous=True` keyword at each method call and use the `Client.sync` function to run the asynchronous function:

    from dask.distributed import Client

    client = Client()  # normal blocking client

    async def f():
        future = client.submit(lambda x: x + 1, 10)
        result = await client.gather(future, asynchronous=True)
        return result

    client.sync(f)

    async with Client(asynchronous=True) as client:
        arr = da.random.random((1000, 1000), chunks=(1000, 100))
        await client.compute(arr.mean())

## Example

This self-contained example starts an asynchronous client, submits a trivial job, waits on the result, and then shuts down the client. You can see implementations for Asyncio and Tornado.

### Python 3 with Tornado or Asyncio

    from dask.distributed import Client

    async def f():
        client = await Client(asynchronous=True)
        future = client.submit(lambda x: x + 1, 10)
        result = await future
        await client.close()
        return result

    # Either use Tornado
    from tornado.ioloop import IOLoop
    IOLoop().run_sync(f)

    # Or use asyncio
    import asyncio
    asyncio.get_event_loop().run_until_complete(f())

## Use Cases

Historically this has been used in a few kinds of applications:

1. To integrate Dask into other asynchronous services (such as web backends), supplying a computational engine similar to Celery, but while still maintaining a high degree of concurrency and not blocking needlessly.

2. For computations that change or update state very rapidly, such as is common in some advanced machine learning workloads.

3. To develop the internals of Dask’s distributed infrastructure, which is written entirely in this style.

4. For complex control and data structures in advanced applications.