# 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

1. Clone this repository with git:

       git clone git@github.com:dask/distributed.git
       cd distributed

2. Install anaconda or miniconda (OS-dependent)

3.     conda env create --file continuous_integration/environment-3.11.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 git@github.com:dask/distributed.git
    git remote -v
    git fetch -a upstream
    git checkout main
    git pull upstream main
    git push origin main

## Test

Test using `py.test`:

    py.test distributed --verbose

## 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:

- http://www.tornadoweb.org/en/stable/gen.html

- http://www.tornadoweb.org/en/stable/ioloop.html

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:

- http://www.tornadoweb.org/en/stable/networking.html

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

1. `@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.

2. `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.

3. `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.

## Code Formatting

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:

    pre-commit install

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`.