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)

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 infrastucture, which is written entirely in this style.
  4. For complex control and data structures in advanced applications.