incubator-airflow/docs/plugins.rst

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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 **hooks**, **operators**, **sensors**, **macros**, **executors** and web **views**
get integrated to Airflow's main collections and become available for use.

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


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:: python

    class AirflowPlugin:
        # The name of your plugin (str)
        name = None
        # A list of class(es) derived from BaseOperator
        operators = []
        # A list of class(es) derived from BaseSensorOperator
        sensors = []
        # A list of class(es) derived from BaseHook
        hooks = []
        # A list of class(es) derived from BaseExecutor
        executors = []
        # 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 FlaskAppBuilder BaseView object and some metadata. See example below
        appbuilder_menu_items = []
        # A function that validate the statsd stat name, apply changes to the stat name if necessary and
        # return the transformed stat name.
        #
        # The function should have the following signature:
        # def func_name(stat_name: str) -> str:
        stat_name_handler = None
        # 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 = []



You can derive it by inheritance (please refer to the example below).
Please note ``name`` inside this class must be specified.

After the plugin is imported into Airflow,
you can invoke it using statement like


.. code:: python

    from airflow.{type, like "operators", "sensors"}.{name specified inside the plugin class} import *


When you write your own plugins, make sure you understand them well.
There are some essential properties for each type of plugin.
For example,

* For ``Operator`` plugin, an ``execute`` method is compulsory.
* For ``Sensor`` plugin, a ``poke`` method returning a Boolean value is compulsory.

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:: 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_hook import BaseHook
    from airflow.models import BaseOperator
    from airflow.models.baseoperator import BaseOperatorLink
    from airflow.sensors.base_sensor_operator import BaseSensorOperator
    from airflow.executors.base_executor import BaseExecutor

    # Will show up under airflow.hooks.test_plugin.PluginHook
    class PluginHook(BaseHook):
        pass

    # Will show up under airflow.operators.test_plugin.PluginOperator
    class PluginOperator(BaseOperator):
        pass

    # Will show up under airflow.sensors.test_plugin.PluginSensorOperator
    class PluginSensorOperator(BaseSensorOperator):
        pass

    # Will show up under airflow.executors.test_plugin.PluginExecutor
    class PluginExecutor(BaseExecutor):
        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("test_plugin/test.html", content="Hello galaxy!")

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

    # Creating a flask appbuilder Menu Item
    appbuilder_mitem = {"name": "Google",
                        "category": "Search",
                        "category_icon": "fa-th",
                        "href": "https://www.google.com"}

    # Validate the statsd stat name
    def stat_name_dummy_handler(stat_name):
        return stat_name

    # A global operator extra link that redirect you to
    # task logs stored in S3
    class S3LogLink(BaseOperatorLink):
        name = 'S3'

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


    # Defining the plugin class
    class AirflowTestPlugin(AirflowPlugin):
        name = "test_plugin"
        operators = [PluginOperator]
        sensors = [PluginSensorOperator]
        hooks = [PluginHook]
        executors = [PluginExecutor]
        macros = [plugin_macro]
        flask_blueprints = [bp]
        appbuilder_views = [v_appbuilder_package]
        appbuilder_menu_items = [appbuilder_mitem]
        stat_name_handler = staticmethod(stat_name_dummy_handler)
        global_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.


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. The structure is determined by
    `airflow.plugins_manager.AirflowPlugin.name` and the class name of the plugin
    component with the pattern `airflow.{component}.{name}.{component_class_name}`.

.. code-block:: python

    # my_package/my_plugin.py
    from airflow.plugins_manager import AirflowPlugin
    from airflow.models import BaseOperator
    from airflow.hooks.base_hook import BaseHook

    class MyOperator(BaseOperator):
      pass

    class MyHook(BaseHook):
      pass

    class MyAirflowPlugin(AirflowPlugin):
      name = 'my_namespace'
      operators = [MyOperator]
      hooks = [MyHook]


.. code-block:: python

    from setuptools import setup

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


This will create a hook, and an operator accessible at:
 - `airflow.hooks.my_namespace.MyHook`
 - `airflow.operators.my_namespace.MyOperator`