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    distributed with this work for additional information
    regarding copyright ownership.  The ASF licenses this file
    to you under the Apache License, Version 2.0 (the
    "License"); you may not use this file except in compliance
    with the License.  You may obtain a copy of the License at

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    software distributed under the License is distributed on an
    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    KIND, either express or implied.  See the License for the
    specific language governing permissions and limitations
    under the License.


Plugins
=======

Airflow has a simple plugin manager built-in that can integrate external
features to its core by simply dropping files in your
``$AIRFLOW_HOME/plugins`` folder.

The python modules in the ``plugins`` folder get imported, and **macros** and web **views**
get integrated to Airflow's main collections and become available for use.

To troubleshoot issue with plugins, you can use ``airflow plugins`` command.
This command dumps information about loaded plugins.

.. versionchanged:: 2.0
    Importing operators, sensors, hooks added in plugins via
   ``airflow.{operators,sensors,hooks}.<plugin_name>`` is no longer supported, and these extensions should
   just be imported as regular python modules. For more information, see: :doc:`/modules_management` and
   :doc:`/howto/custom-operator`

What for?
---------

Airflow offers a generic toolbox for working with data. Different
organizations have different stacks and different needs. Using Airflow
plugins can be a way for companies to customize their Airflow installation
to reflect their ecosystem.

Plugins can be used as an easy way to write, share and activate new sets of
features.

There's also a need for a set of more complex applications to interact with
different flavors of data and metadata.

Examples:

* A set of tools to parse Hive logs and expose Hive metadata (CPU /IO / phases/ skew /...)
* An anomaly detection framework, allowing people to collect metrics, set thresholds and alerts
* An auditing tool, helping understand who accesses what
* A config-driven SLA monitoring tool, allowing you to set monitored tables and at what time
  they should land, alert people, and expose visualizations of outages
* ...

Why build on top of Airflow?
----------------------------

Airflow has many components that can be reused when building an application:

* A web server you can use to render your views
* A metadata database to store your models
* Access to your databases, and knowledge of how to connect to them
* An array of workers that your application can push workload to
* Airflow is deployed, you can just piggy back on its deployment logistics
* Basic charting capabilities, underlying libraries and abstractions

.. _plugins:loading:

When are plugins (re)loaded?
----------------------------

Plugins are by default lazily loaded and once loaded, they are never reloaded (except the UI plugins are
automatically loaded in Webserver). To load them at the
start of each Airflow process, set ``[core] lazy_load_plugins = False`` in ``airflow.cfg``.

This means that if you make any changes to plugins and you want the webserver or scheduler to use that new
code you will need to restart those processes.

By default, task execution will use forking to avoid the slow down of having to create a whole new python
interpreter and re-parse all of the Airflow code and start up routines -- this is a big benefit for shorter
running tasks. This does mean that if you use plugins in your tasks, and want them to update you will either
need to restart the worker (if using CeleryExecutor) or scheduler (Local or Sequential executors). The other
option is you can accept the speed hit at start up set the ``core.execute_tasks_new_python_interpreter``
config setting to True, resulting in launching a whole new python interpreter for tasks.

(Modules only imported by DAG files on the other hand do not suffer this problem, as DAG files are not
loaded/parsed in any long-running Airflow process.)

Interface
---------

To create a plugin you will need to derive the
``airflow.plugins_manager.AirflowPlugin`` class and reference the objects
you want to plug into Airflow. Here's what the class you need to derive
looks like:


.. code-block:: python

    class AirflowPlugin:
        # The name of your plugin (str)
        name = None
        # A list of class(es) derived from BaseHook
        hooks = []
        # A list of references to inject into the macros namespace
        macros = []
        # A list of Blueprint object created from flask.Blueprint. For use with the flask_appbuilder based GUI
        flask_blueprints = []
        # A list of dictionaries containing FlaskAppBuilder BaseView object and some metadata. See example below
        appbuilder_views = []
        # A list of dictionaries containing kwargs for FlaskAppBuilder add_link. See example below
        appbuilder_menu_items = []
        # A callback to perform actions when airflow starts and the plugin is loaded.
        # NOTE: Ensure your plugin has *args, and **kwargs in the method definition
        #   to protect against extra parameters injected into the on_load(...)
        #   function in future changes
        def on_load(*args, **kwargs):
            # ... perform Plugin boot actions
            pass

        # A list of global operator extra links that can redirect users to
        # external systems. These extra links will be available on the
        # task page in the form of buttons.
        #
        # Note: the global operator extra link can be overridden at each
        # operator level.
        global_operator_extra_links = []

        # A list of operator extra links to override or add operator links
        # to existing Airflow Operators.
        # These extra links will be available on the task page in form of
        # buttons.
        operator_extra_links = []

        # A list of timetable classes to register so they can be used in DAGs.
        timetables = []

You can derive it by inheritance (please refer to the example below). In the example, all options have been
defined as class attributes, but you can also define them as properties if you need to perform
additional initialization. Please note ``name`` inside this class must be specified.

Make sure you restart the webserver and scheduler after making changes to plugins so that they take effect.


.. _plugin-example:

Example
-------

The code below defines a plugin that injects a set of dummy object
definitions in Airflow.

.. code-block:: python

    # This is the class you derive to create a plugin
    from airflow.plugins_manager import AirflowPlugin

    from flask import Blueprint
    from flask_appbuilder import expose, BaseView as AppBuilderBaseView

    # Importing base classes that we need to derive
    from airflow.hooks.base import BaseHook
    from airflow.models.baseoperator import BaseOperatorLink
    from airflow.providers.amazon.aws.transfers.gcs_to_s3 import GCSToS3Operator

    # Will show up in Connections screen in a future version
    class PluginHook(BaseHook):
        pass


    # Will show up under airflow.macros.test_plugin.plugin_macro
    # and in templates through {{ macros.test_plugin.plugin_macro }}
    def plugin_macro():
        pass


    # Creating a flask blueprint to integrate the templates and static folder
    bp = Blueprint(
        "test_plugin",
        __name__,
        template_folder="templates",  # registers airflow/plugins/templates as a Jinja template folder
        static_folder="static",
        static_url_path="/static/test_plugin",
    )

    # Creating a flask appbuilder BaseView
    class TestAppBuilderBaseView(AppBuilderBaseView):
        default_view = "test"

        @expose("/")
        def test(self):
            return self.render_template("test_plugin/test.html", content="Hello galaxy!")


    # Creating a flask appbuilder BaseView
    class TestAppBuilderBaseNoMenuView(AppBuilderBaseView):
        default_view = "test"

        @expose("/")
        def test(self):
            return self.render_template("test_plugin/test.html", content="Hello galaxy!")


    v_appbuilder_view = TestAppBuilderBaseView()
    v_appbuilder_package = {
        "name": "Test View",
        "category": "Test Plugin",
        "view": v_appbuilder_view,
    }

    v_appbuilder_nomenu_view = TestAppBuilderBaseNoMenuView()
    v_appbuilder_nomenu_package = {"view": v_appbuilder_nomenu_view}

    # Creating flask appbuilder Menu Items
    appbuilder_mitem = {
        "name": "Google",
        "href": "https://www.google.com",
        "category": "Search",
    }
    appbuilder_mitem_toplevel = {
        "name": "Apache",
        "href": "https://www.apache.org/",
    }

    # A global operator extra link that redirect you to
    # task logs stored in S3
    class GoogleLink(BaseOperatorLink):
        name = "Google"

        def get_link(self, operator, dttm):
            return "https://www.google.com"


    # A list of operator extra links to override or add operator links
    # to existing Airflow Operators.
    # These extra links will be available on the task page in form of
    # buttons.
    class S3LogLink(BaseOperatorLink):
        name = "S3"
        operators = [GCSToS3Operator]

        def get_link(self, operator, dttm):
            return "https://s3.amazonaws.com/airflow-logs/{dag_id}/{task_id}/{logical_date}".format(
                dag_id=operator.dag_id,
                task_id=operator.task_id,
                logical_date=dttm,
            )


    # Defining the plugin class
    class AirflowTestPlugin(AirflowPlugin):
        name = "test_plugin"
        hooks = [PluginHook]
        macros = [plugin_macro]
        flask_blueprints = [bp]
        appbuilder_views = [v_appbuilder_package, v_appbuilder_nomenu_package]
        appbuilder_menu_items = [appbuilder_mitem, appbuilder_mitem_toplevel]
        global_operator_extra_links = [
            GoogleLink(),
        ]
        operator_extra_links = [
            S3LogLink(),
        ]


Note on role based views
------------------------

Airflow 1.10 introduced role based views using FlaskAppBuilder. You can configure which UI is used by setting
``rbac = True``. To support plugin views and links for both versions of the UI and maintain backwards compatibility,
the fields ``appbuilder_views`` and ``appbuilder_menu_items`` were added to the ``AirflowTestPlugin`` class.

``appbuilder_views`` supports both views-with-menu and views-without-menu - to add a view with menu link, add a "name"
key in view's package dictionary, otherwise the view is added to flask appbuilder without menu item.

Exclude views from CSRF protection
----------------------------------

We strongly suggest that you should protect all your views with CSRF. But if needed, you can exclude
some views using a decorator.

.. code-block:: python

    from airflow.www.app import csrf


    @csrf.exempt
    def my_handler():
        # ...
        return "ok"

Plugins as Python packages
--------------------------

It is possible to load plugins via `setuptools entrypoint <https://packaging.python.org/guides/creating-and-discovering-plugins/#using-package-metadata>`_ mechanism. To do this link
your plugin using an entrypoint in your package. If the package is installed, airflow
will automatically load the registered plugins from the entrypoint list.

.. note::
    Neither the entrypoint name (eg, ``my_plugin``) nor the name of the
    plugin class will contribute towards the module and class name of the plugin
    itself.

.. code-block:: python

    # my_package/my_plugin.py
    from airflow.plugins_manager import AirflowPlugin
    from flask import Blueprint

    # Creating a flask blueprint to integrate the templates and static folder
    bp = Blueprint(
        "test_plugin",
        __name__,
        template_folder="templates",  # registers airflow/plugins/templates as a Jinja template folder
        static_folder="static",
        static_url_path="/static/test_plugin",
    )


    class MyAirflowPlugin(AirflowPlugin):
        name = "my_namespace"
        flask_blueprints = [bp]

.. code-block:: python

    from setuptools import setup

    setup(
        name="my-package",
        # ...
        entry_points={
            "airflow.plugins": ["my_plugin = my_package.my_plugin:MyAirflowPlugin"]
        },
    )

Automatic reloading webserver
-----------------------------

To enable automatic reloading of the webserver, when changes in a directory with plugins has been detected,
you should set ``reload_on_plugin_change`` option in ``[webserver]`` section to ``True``.

.. note::
    For more information on setting the configuration, see :doc:`/howto/set-config`

.. note::
    See :doc:`modules_management` for details on how Python and Airflow manage modules.

Troubleshooting
---------------

You can use `the Flask CLI <https://flask.palletsprojects.com/en/1.1.x/cli/>`__ to troubleshoot problems. To run this, you need to set the variable :envvar:`FLASK_APP` to ``airflow.www.app:create_app``.

For example, to print all routes, run:

.. code-block:: bash

    FLASK_APP=airflow.www.app:create_app flask routes