This repository is part of the Dask projects. General development guidelines including where to ask for help, a layout of repositories, testing practices, and documentation and style standards are available at the Dask developer guidelines in the main documentation.
Clone this repository with git:
git clone email@example.com:dask/distributed.git cd distributed
Install anaconda or miniconda (OS-dependent)
conda env create --file continuous_integration/environment-3.8.yaml conda activate dask-distributed python -m pip install -e .
To keep a fork in sync with the upstream source:
cd distributed git remote add upstream firstname.lastname@example.org:dask/distributed.git git remote -v git fetch -a upstream git checkout main git pull upstream main git push origin main
py.test distributed --verbose
Dask.distributed is a Tornado TCP application. Tornado provides us with both a communication layer on top of sockets, as well as a syntax for writing asynchronous coroutines, similar to asyncio. You can make modest changes to the policies within this library without understanding much about Tornado, however moderate changes will probably require you to understand Tornado IOLoops, coroutines, and a little about non-blocking communication.. The Tornado API documentation is quite good and we recommend that you read the following resources:
Additionally, if you want to interact at a low level with the communication
between workers and scheduler then you should understand the Tornado
IOStream available here:
Dask.distributed wraps a bit of logic around Tornado. See Foundations for more information.
Testing distributed systems is normally quite difficult because it is difficult to inspect the state of all components when something goes wrong. Fortunately, the non-blocking asynchronous model within Tornado allows us to run a scheduler, multiple workers, and multiple clients all within a single thread. This gives us predictable performance, clean shutdowns, and the ability to drop into any point of the code during execution. At the same time, sometimes we want everything to run in different processes in order to simulate a more realistic setting.
The test suite contains three kinds of tests
@gen_cluster: Fully asynchronous tests where all components live in the same event loop in the main thread. These are good for testing complex logic and inspecting the state of the system directly. They are also easier to debug and cause the fewest problems with shutdowns.
def test_foo(client): Tests with multiple processes forked from the main process. These are good for testing the synchronous (normal user) API and when triggering hard failures for resilience tests.
popen: Tests that call out to the command line to start the system. These are rare and mostly for testing the command line interface.
If you are comfortable with the Tornado interface then you will be happiest
@gen_cluster style of test, e.g.
# tests/test_submit.py from distributed.utils_test import gen_cluster, inc from distributed import Client, Future, Scheduler, Worker @gen_cluster(client=True) async def test_submit(c, s, a, b): assert isinstance(c, Client) assert isinstance(s, Scheduler) assert isinstance(a, Worker) assert isinstance(b, Worker) future = c.submit(inc, 1) assert isinstance(future, Future) assert future.key in c.futures # result = future.result() # This synchronous API call would block result = await future assert result == 2 assert future.key in s.tasks assert future.key in a.data or future.key in b.data
@gen_cluster decorator sets up a scheduler, client, and workers for
you and cleans them up after the test. It also allows you to directly inspect
the state of every element of the cluster directly. However, you can not use
the normal synchronous API (doing so will cause the test to wait forever) and
instead you need to use the coroutine API, where all blocking functions are
prepended with an underscore (
_) and awaited with
Beware, it is a common mistake to use the blocking interface within these tests.
If you want to test the normal synchronous API you can use the
pytest fixture style test, which sets up a scheduler and workers for you in
different forked processes:
from distributed.utils_test import client def test_submit(client): future = client.submit(inc, 10) assert future.result() == 11
Additionally, if you want access to the scheduler and worker processes you can
also add the
s, a, b fixtures as well.
from distributed.utils_test import client def test_submit(client, s, a, b): future = client.submit(inc, 10) assert future.result() == 11 # use the synchronous/blocking API here a['proc'].terminate() # kill one of the workers result = future.result() # test that future remains valid assert result == 2
In this style of test you do not have access to the scheduler or workers. The
s, a, b are now dictionaries holding a
multiprocessing.Process object and a port integer. However, you can now
use the normal synchronous API (never use
await in this style of test) and you
can close processes easily by terminating them.
Typically for most user-facing functions you will find both kinds of tests.
@gen_cluster tests test particular logic while the
fixture tests test basic interface and resilience.
You should avoid
popen style tests unless absolutely necessary, such as if
you need to test the command line interface.
Dask.distributed uses several code linters (flake8, black, isort, pyupgrade, mypy),
which are enforced by CI. Developers should run them locally before they submit a PR,
through the single command
pre-commit run --all-files. This makes sure that linter
versions and options are aligned for all developers.
Optionally, you may wish to setup the pre-commit hooks to run automatically when you make a git commit. This can be done by running:
from the root of the distributed repository. Now the code linters will be run each time
you commit changes. You can skip these checks with
git commit --no-verify or with
the short version
git commit -n.