Improvments to documentation
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@ -5,6 +5,11 @@ from airflow.executors.base_executor import BaseExecutor
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from airflow.executors.local_executor import LocalExecutor
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from airflow.executors.sequential_executor import SequentialExecutor
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try:
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from airflow.executors.celery_executor import CeleryExecutor
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except:
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pass
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from airflow.utils import AirflowException
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_EXECUTOR = configuration.get('core', 'EXECUTOR')
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@ -12,7 +17,6 @@ _EXECUTOR = configuration.get('core', 'EXECUTOR')
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if _EXECUTOR == 'LocalExecutor':
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DEFAULT_EXECUTOR = LocalExecutor()
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elif _EXECUTOR == 'CeleryExecutor':
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from airflow.executors.celery_executor import CeleryExecutor
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DEFAULT_EXECUTOR = CeleryExecutor()
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elif _EXECUTOR == 'SequentialExecutor':
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DEFAULT_EXECUTOR = SequentialExecutor()
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@ -145,20 +145,19 @@ Macros are a way to expose objects to your templates and live under the
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A few commonly used libraries and methods are made available.
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================================= ====================================
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Variable Description
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================================= ====================================
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``macros.datetime`` The standard lib's
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``datetime.datetime``
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``macros.timedelta`` The standard lib's
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``datetime.timedelta``
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``macros.dateutil`` A reference to the ``dateutil``
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package
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``macros.datetime`` The standard lib's ``datetime.datetime``
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``macros.timedelta`` The standard lib's ``datetime.timedelta``
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``macros.dateutil`` A reference to the ``dateutil`` package
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``macros.time`` The standard lib's ``time``
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``macros.uuid`` The standard lib's ``uuid``
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``macros.random`` The standard lib's ``random``
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================================= ====================================
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Some airflow specific macros are also defined:
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.. automodule:: airflow.macros
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15
docs/conf.py
15
docs/conf.py
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@ -13,13 +13,16 @@
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# serve to show the default.
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import os
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import sys
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# Hack to allow changing for piece of the code to behave differently while
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# the docs are being built. The main objective was to alter the
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# behavior of the utils.apply_default that was hiding function headers
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os.environ['BUILDING_AIRFLOW_DOCS'] = 'TRUE'
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from airflow import settings
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# If extensions (or modules to document with autodoc) are in another directory,
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# add these directories to sys.path here. If the directory is relative to the
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# documentation root, use os.path.abspath to make it absolute, like shown here.
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sys.path.insert(0, settings.AIRFLOW_HOME + "/src/airflow")
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# -- General configuration ------------------------------------------------
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@ -51,16 +54,16 @@ master_doc = 'index'
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# General information about the project.
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project = u'Airflow'
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copyright = u'2014, Maxime Beauchemin'
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copyright = u'2014, Maxime Beauchemin, Airbnb'
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# The version info for the project you're documenting, acts as replacement for
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# |version| and |release|, also used in various other places throughout the
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# built documents.
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#
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# The short X.Y version.
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version = '1.0.0'
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#version = '1.0.0'
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# The full version, including alpha/beta/rc tags.
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release = '1.0.0'
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#release = '1.0.0'
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# The language for content autogenerated by Sphinx. Refer to documentation
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# for a list of supported languages.
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@ -119,10 +122,10 @@ html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
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# The name for this set of Sphinx documents. If None, it defaults to
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# "<project> v<release> documentation".
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#html_title = None
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html_title = "Airflow Documentation"
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# A shorter title for the navigation bar. Default is the same as html_title.
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#html_short_title = None
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html_short_title = ""
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# The name of an image file (relative to this directory) to place at the top
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# of the sidebar.
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@ -1,23 +1,22 @@
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.. image:: img/pin_large.png
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:width: 70
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Airflow's Documentation
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Airflow Documentation
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================================
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Airflow is a platform to programmatically author, schedule and monitor
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Airflow is a platform to programmatically author, schedule and monitor
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workflows.
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When workflows are defined as code, they become more maintainable,
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versionable, testable, and collaborative.
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Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks.
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The airflow scheduler executes your tasks on an array of workers while
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following the specified dependencies. Rich command line utilities make
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performing complex surgeries on DAGs a snap. The rich user interface
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makes it easy to visualize pipelines running in production,
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Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks.
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The airflow scheduler executes your tasks on an array of workers while
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following the specified dependencies. Rich command line utilities make
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performing complex surgeries on DAGs a snap. The rich user interface
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makes it easy to visualize pipelines running in production,
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monitor progress, and troubleshoot issues when needed.
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When workflows are defined as code, they become more maintainable,
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versionable, testable, and collaborative.
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------------
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.. image:: img/airflow.gif
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