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Contributing

Contributions are welcome and are greatly appreciated! Every little bit helps, and credit will always be given.

Table of Contents

Types of Contributions

Report Bugs

Report bugs through Apache Jira

Please report relevant information and preferably code that exhibits the problem.

Fix Bugs

Look through the Jira issues for bugs. Anything is open to whoever wants to implement it.

Implement Features

Look through the Apache Jira for features. Any unassigned "Improvement" issue is open to whoever wants to implement it.

We've created the operators, hooks, macros and executors we needed, but we made sure that this part of Airflow is extensible. New operators, hooks, macros and executors are very welcomed!

Improve Documentation

Airflow could always use better documentation, whether as part of the official Airflow docs, in docstrings, docs/*.rst or even on the web as blog posts or articles.

Submit Feedback

The best way to send feedback is to open an issue on Apache Jira

If you are proposing a feature:

  • Explain in detail how it would work.
  • Keep the scope as narrow as possible, to make it easier to implement.
  • Remember that this is a volunteer-driven project, and that contributions are welcome :)

Documentation

The latest API documentation is usually available here. To generate a local version, you need to have set up an Airflow development environemnt (see below). Also install the doc extra.

pip install -e .[doc]

Generate the documentation by running:

cd docs && ./build.sh

Only a subset of the API reference documentation builds. Install additional extras to build the full API reference.

Development and Testing

Set up a development env using Docker

Go to your Airflow directory and start a new docker container. You can choose between Python 2 or 3, whatever you prefer.

# Start docker in your Airflow directory
docker run -t -i -v `pwd`:/airflow/ python:2 bash

# Go to the Airflow directory
cd /airflow/

# Install Airflow with all the required dependencies,
# including the devel which will provide the development tools
pip install -e ".[hdfs,hive,druid,devel]"

# Init the database
airflow initdb

nosetests -v tests/hooks/test_druid_hook.py

  test_get_first_record (tests.hooks.test_druid_hook.TestDruidDbApiHook) ... ok
  test_get_records (tests.hooks.test_druid_hook.TestDruidDbApiHook) ... ok
  test_get_uri (tests.hooks.test_druid_hook.TestDruidDbApiHook) ... ok
  test_get_conn_url (tests.hooks.test_druid_hook.TestDruidHook) ... ok
  test_submit_gone_wrong (tests.hooks.test_druid_hook.TestDruidHook) ... ok
  test_submit_ok (tests.hooks.test_druid_hook.TestDruidHook) ... ok
  test_submit_timeout (tests.hooks.test_druid_hook.TestDruidHook) ... ok
  test_submit_unknown_response (tests.hooks.test_druid_hook.TestDruidHook) ... ok

  ----------------------------------------------------------------------
  Ran 8 tests in 3.036s

  OK

The Airflow code is mounted inside of the Docker container, so if you change something using your favorite IDE, you can directly test is in the container.

Set up a development env using Virtualenv

Please install python(2.7.x or 3.4.x), mysql, and libxml by using system-level package managers like yum, apt-get for Linux, or homebrew for Mac OS at first. It is usually best to work in a virtualenv and tox. Install development requirements:

cd $AIRFLOW_HOME
virtualenv env
source env/bin/activate
pip install -e .[devel]
tox

Feel free to customize based on the extras available in setup.py

Pull Request Guidelines

Before you submit a pull request from your forked repo, check that it meets these guidelines:

  1. The pull request should include tests, either as doctests, unit tests, or both. The airflow repo uses Travis CI to run the tests and codecov to track coverage. You can set up both for free on your fork. It will help you making sure you do not break the build with your PR and that you help increase coverage.
  2. Please rebase your fork, squash commits, and resolve all conflicts.
  3. Every pull request should have an associated JIRA. The JIRA link should also be contained in the PR description.
  4. Preface your commit's subject & PR's title with [AIRFLOW-XXX] where XXX is the JIRA number. We compose release notes (i.e. for Airflow releases) from all commit titles in a release. By placing the JIRA number in the commit title and hence in the release notes, Airflow users can look into JIRA and Github PRs for more details about a particular change.
  5. Add an Apache License header to all new files
  6. If the pull request adds functionality, the docs should be updated as part of the same PR. Doc string are often sufficient. Make sure to follow the Sphinx compatible standards.
  7. The pull request should work for Python 2.7 and 3.4. If you need help writing code that works in both Python 2 and 3, see the documentation at the Python-Future project (the future package is an Airflow requirement and should be used where possible).
  8. As Airflow grows as a project, we try to enforce a more consistent style and try to follow the Python community guidelines. We track this using landscape.io, which you can setup on your fork as well to check before you submit your PR. We currently enforce most PEP8 and a few other linting rules. It is usually a good idea to lint locally as well using flake8 using flake8 airflow tests. git diff upstream/master -u -- "*.py" | flake8 --diff will return any changed files in your branch that require linting.
  9. Please read this excellent article on commit messages and adhere to them. It makes the lives of those who come after you a lot easier.

Testing locally

TL;DR

Tests can then be run with (see also the Running unit tests section below):

./run_unit_tests.sh

Individual test files can be run with:

nosetests [path to file]

Running unit tests

We highly recommend setting up Travis CI on your repo to automate this. It is free for open source projects. If for some reason you cannot, you can use the steps below to run tests.

Here are loose guidelines on how to get your environment to run the unit tests. We do understand that no one out there can run the full test suite since Airflow is meant to connect to virtually any external system and that you most likely have only a subset of these in your environment. You should run the CoreTests and tests related to things you touched in your PR.

To set up a unit test environment, first take a look at run_unit_tests.sh and understand that your AIRFLOW_CONFIG points to an alternate config file while running the tests. You shouldn't have to alter this config file but you may if need be.

From that point, you can actually export these same environment variables in your shell, start an Airflow webserver airflow webserver -d and go and configure your connection. Default connections that are used in the tests should already have been created, you just need to point them to the systems where you want your tests to run.

Once your unit test environment is setup, you should be able to simply run ./run_unit_tests.sh at will.

For example, in order to just execute the "core" unit tests, run the following:

./run_unit_tests.sh tests.core:CoreTest -s --logging-level=DEBUG

or a single test method:

./run_unit_tests.sh tests.core:CoreTest.test_check_operators -s --logging-level=DEBUG

For more information on how to run a subset of the tests, take a look at the nosetests docs.

See also the list of test classes and methods in tests/core.py.

Changing the Metadata Database

When developing features the need may arise to persist information to the the metadata database. Airflow has Alembic built-in to handle all schema changes. Alembic must be installed on your development machine before continuing.

# starting at the root of the project
$ pwd
~/airflow
# change to the airflow directory
$ cd airflow
$ alembic revision -m "add new field to db"
  Generating
~/airflow/airflow/migrations/versions/12341123_add_new_field_to_db.py