Development Guidelines¶
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.
Install¶
Clone this repository with git:
git clone git@github.com:dask/distributed.git
cd distributed
Install all dependencies:
On Linux / MacOSX:
source continuous_integration/travis/install.sh
On Windows:
Install anaconda or miniconda
conda create -n dask-distributed -c conda-forge -c defaults python=3.8 tornado=6 conda activate dask-distributed conda env update --file continuous_integration/environment-windows.yml python -m pip install .
To keep a fork in sync with the upstream source:
cd distributed
git remote add upstream git@github.com:dask/distributed.git
git remote -v
git fetch -a upstream
git checkout master
git pull upstream master
git push origin master
Tornado¶
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
TCPServer and IOStream available here:
Dask.distributed wraps a bit of logic around Tornado. See Foundations for more information.
Writing Tests¶
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 master 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
using the @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
The @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 await.
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 client
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
variables 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.
The @gen_cluster tests test particular logic while the client pytest
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.