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.. contents:: :local:

Local Virtual Environment (virtualenv)
======================================

Use the local virtualenv development option in combination with the `Breeze
<BREEZE.rst#using-local-virtualenv-environment-in-your-host-ide>`_ development environment. This option helps
you benefit from the infrastructure provided
by your IDE (for example, IntelliJ PyCharm/IntelliJ Idea) and work in the
environment where all necessary dependencies and tests are available and set up
within Docker images.

But you can also use the local virtualenv as a standalone development option if you
develop Airflow functionality that does not incur large external dependencies and
CI test coverage.

These are examples of the development options available with the local virtualenv in your IDE:

* local debugging;
* Airflow source view;
* auto-completion;
* documentation support;
* unit tests.

This document describes minimum requirements and instructions for using a standalone version of the local virtualenv.

Prerequisites
=============

Required Software Packages
--------------------------

Use system-level package managers like yum, apt-get for Linux, or
Homebrew for macOS to install required software packages:

* Python (One of: 3.7, 3.8, 3.9, 3.10, 3.11)
* MySQL 5.7+
* libxml

Refer to the `Dockerfile.ci <Dockerfile.ci>`__ for a comprehensive list
of required packages.

Extra Packages
--------------

.. note::

   Only ``pip`` installation is currently officially supported.

   While there are some successes with using other tools like `poetry <https://python-poetry.org/>`_ or
   `pip-tools <https://pypi.org/project/pip-tools/>`_, they do not share the same workflow as
   ``pip`` - especially when it comes to constraint vs. requirements management.
   Installing via ``Poetry`` or ``pip-tools`` is not currently supported.

   There are known issues with ``bazel`` that might lead to circular dependencies when using it to install
   Airflow. Please switch to ``pip`` if you encounter such problems. ``Bazel`` community works on fixing
   the problem in `this PR <https://github.com/bazelbuild/rules_python/pull/1166>`_ so it might be that
  n ewer versions of ``bazel`` will handle it.

   If you wish to install airflow using those tools you should use the constraint files and convert
   them to appropriate format and workflow that your tool requires.


You can also install extra packages (like ``[ssh]``, etc) via
``pip install -e [EXTRA1,EXTRA2 ...]``. However, some of them may
have additional install and setup requirements for your local system.

For example, if you have a trouble installing the mysql client on macOS and get
an error as follows:

.. code:: text

    ld: library not found for -lssl

you should set LIBRARY\_PATH before running ``pip install``:

.. code:: bash

    export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/opt/openssl/lib/

You are STRONGLY encouraged to also install and use `pre-commit hooks <STATIC_CODE_CHECKS.rst#pre-commit-hooks>`_
for your local virtualenv development environment. Pre-commit hooks can speed up your
development cycle a lot.

The full list of extras is available in `<setup.py>`_.

Creating a Local virtualenv
===========================

To use your IDE for Airflow development and testing, you need to configure a virtual
environment. Ideally you should set up virtualenv for all Python versions that Airflow
supports (3.7, 3.8, 3.9, 3.10, 3.11).

To create and initialize the local virtualenv:

1. Create an environment with one of the two options:

   - Option 1: consider using one of the following utilities to create virtual environments and easily switch between them with the ``workon`` command:

    - `pyenv <https://github.com/pyenv/pyenv>`_
    - `pyenv-virtualenv <https://github.com/pyenv/pyenv-virtualenv>`_
    - `virtualenvwrapper <https://virtualenvwrapper.readthedocs.io/en/latest/>`_

    ``mkvirtualenv <ENV_NAME> --python=python<VERSION>``

   - Option 2: create a local virtualenv with Conda

    - install `miniconda3 <https://docs.conda.io/en/latest/miniconda.html>`_

    .. code-block:: bash

      conda create -n airflow python=3.7  # or 3.8, 3.9, 3.10, 3.11
      conda activate airflow

2. Install Python PIP requirements:

.. note::

   Only ``pip`` installation is currently officially supported.

   While they are some successes with using other tools like `poetry <https://python-poetry.org/>`_ or
   `pip-tools <https://pypi.org/project/pip-tools/>`_, they do not share the same workflow as
   ``pip`` - especially when it comes to constraint vs. requirements management.
   Installing via ``Poetry`` or ``pip-tools`` is not currently supported.

   There are known issues with ``bazel`` that might lead to circular dependencies when using it to install
   Airflow. Please switch to ``pip`` if you encounter such problems. ``Bazel`` community works on fixing
   the problem in `this PR <https://github.com/bazelbuild/rules_python/pull/1166>`_ so it might be that
   newer versions of ``bazel`` will handle it.

   If you wish to install airflow using those tools you should use the constraint files and convert
   them to appropriate format and workflow that your tool requires.


   .. code-block:: bash

    pip install --upgrade -e ".[devel,<OTHER EXTRAS>]" # for example: pip install --upgrade -e ".[devel,google,postgres]"

In case you have problems with installing airflow because of some requirements are not installable, you can
try to install it with the set of working constraints (note that there are different constraint files
for different python versions). For development on current main source:

   .. code-block:: bash

    # use the same version of python as you are working with, 3.7, 3.8, 3.9, 3.10 or 3.11
    pip install -e ".[devel,<OTHER EXTRAS>]" \
        --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-main/constraints-source-providers-3.7.txt"

This will install Airflow in 'editable' mode - where sources of Airflow are taken directly from the source
code rather than moved to the installation directory. During the installation airflow will install - but then
automatically remove all provider packages installed from PyPI - instead it will automatically use the
provider packages available in your local sources.

You can also install Airflow in non-editable mode:

   .. code-block:: bash

    # use the same version of python as you are working with, 3.7, 3.8, 3.9, 3.10 or 3.11
    pip install ".[devel,<OTHER EXTRAS>]" \
        --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-main/constraints-source-providers-3.7.txt"

This will copy the sources to directory where usually python packages are installed. You can see the list
of directories via ``python -m site`` command. In this case the providers are installed from PyPI, not from
sources, unless you set ``INSTALL_PROVIDERS_FROM_SOURCES`` environment variable to ``true``

   .. code-block:: bash

    # use the same version of python as you are working with, 3.7, 3.8, 3.9, 3.10 or 3.11
    INSTALL_PROVIDERS_FROM_SOURCES="true" pip install ".[devel,<OTHER EXTRAS>]" \
        --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-main/constraints-source-providers-3.7.txt"


Note: when you first initialize database (the next step), you may encounter some problems.
This is because airflow by default will try to load in example dags where some of them requires dependencies ``google`` and ``postgres``.
You can solve the problem by:

- installing the extras i.e. ``[devel,google,postgres]`` or
- disable the example dags with environment variable: ``export AIRFLOW__CORE__LOAD_EXAMPLES=False`` or
- simply ignore the error messages and proceed

*In addition to above, you may also encounter problems during database migration.*
*This is a known issue and please see the progress here:* `AIRFLOW-6265 <https://issues.apache.org/jira/browse/AIRFLOW-6265>`_

3. Create the Airflow sqlite database:

   .. code-block:: bash

    # if necessary, start with a clean AIRFLOW_HOME, e.g.
    # rm -rf ~/airflow
    airflow db init

4. Select the virtualenv you created as the project's default virtualenv in your IDE.

Note that if you have the Breeze development environment installed, the ``breeze``
script can automate initializing the created virtualenv (steps 2 and 3).
Activate your virtualenv, e.g. by using ``workon``, and once you are in it, run:

.. code-block:: bash

  ./scripts/tools/initialize_virtualenv.py

By default Breeze installs the ``devel`` extra only. You can optionally control which extras are
Adding extra dependencies as parameter.

.. code-block:: bash

  ./scripts/tools/initialize_virtualenv.py devel,google,postgres


Developing Providers
--------------------

In Airflow 2.0 we introduced split of Apache Airflow into separate packages - there is one main
apache-airflow package with core of Airflow and 70+ packages for all providers (external services
and software Airflow can communicate with).

Developing providers is part of Airflow development, but when you install airflow as editable in your local
development environment, the corresponding provider packages will be also installed from PyPI. However, the
providers will also be present in your "airflow/providers" folder. This might lead to confusion,
which sources of providers are imported during development. It will depend on your
environment's PYTHONPATH setting in general.

In order to avoid the confusion, you can set ``INSTALL_PROVIDERS_FROM_SOURCES`` environment to ``true``
before running ``pip install`` command:

.. code-block:: bash

  INSTALL_PROVIDERS_FROM_SOURCES="true" pip install -U -e ".[devel,<OTHER EXTRAS>]" \
     --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-main/constraints-3.7.txt"

This way no providers packages will be installed and they will always be imported from the "airflow/providers"
folder.


Running Tests
-------------

Running tests is described in `TESTING.rst <TESTING.rst>`_.

While most of the tests are typical unit tests that do not
require external components, there are a number of Integration tests. You can technically use local
virtualenv to run those tests, but it requires to set up a number of
external components (databases/queues/kubernetes and the like). So, it is
much easier to use the `Breeze <BREEZE.rst>`__ development environment
for Integration tests.

Note: Soon we will separate the integration and system tests out via pytest
so that you can clearly know which tests are unit tests and can be run in
the local virtualenv and which should be run using Breeze.

Connecting to database
----------------------

When analyzing the situation, it is helpful to be able to directly query the database. You can do it using
the built-in Airflow command:

.. code:: bash

    airflow db shell