Metadata-Version: 2.1
Name: apache-airflow
Version: 1.10.10
Summary: Programmatically author, schedule and monitor data pipelines
Home-page: http://airflow.apache.org/
Author: Apache Software Foundation
Author-email: dev@airflow.apache.org
License: Apache License 2.0
Download-URL: https://dist.apache.org/repos/dist/release/airflow/1.10.10
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        # Apache Airflow
        
        [![PyPI version](https://badge.fury.io/py/apache-airflow.svg)](https://badge.fury.io/py/apache-airflow)
        [![Build Status](https://travis-ci.org/apache/airflow.svg?branch=master)](https://travis-ci.org/apache/airflow)
        [![Coverage Status](https://img.shields.io/codecov/c/github/apache/airflow/master.svg)](https://codecov.io/github/apache/airflow?branch=master)
        [![Documentation Status](https://readthedocs.org/projects/airflow/badge/?version=latest)](https://airflow.readthedocs.io/en/latest/?badge=latest)
        [![License](http://img.shields.io/:license-Apache%202-blue.svg)](http://www.apache.org/licenses/LICENSE-2.0.txt)
        [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/apache-airflow.svg)](https://pypi.org/project/apache-airflow/)
        [![Twitter Follow](https://img.shields.io/twitter/follow/ApacheAirflow.svg?style=social&label=Follow)](https://twitter.com/ApacheAirflow)
        
        _NOTE: The transition from 1.8.0 (or before) to 1.8.1 (or after) requires uninstalling Apache Airflow before installing the new version. The package name was changed from `airflow` to `apache-airflow` as of version 1.8.1._
        
        Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows.
        
        When workflows are defined as code, they become more maintainable,
        versionable, testable, and collaborative.
        
        Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
        
        <!-- START doctoc generated TOC please keep comment here to allow auto update -->
        <!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
        **Table of contents**
        
        - [Requirements](#requirements)
        - [Getting started](#getting-started)
        - [Beyond the Horizon](#beyond-the-horizon)
        - [Principles](#principles)
        - [User Interface](#user-interface)
        - [Using hooks and Operators from "master" in Airflow 1.10](#using-hooks-and-operators-from-master-in-airflow-110)
        - [Contributing](#contributing)
        - [Who uses Airflow?](#who-uses-airflow)
        - [Who Maintains Apache Airflow?](#who-maintains-apache-airflow)
        - [Can I use the Apache Airflow logo in my presentation?](#can-i-use-the-apache-airflow-logo-in-my-presentation)
        - [Links](#links)
        
        <!-- END doctoc generated TOC please keep comment here to allow auto update -->
        
        ## Requirements
        
        Apache Airflow is tested with:
        
        ### Master version (2.0.0dev)
        
        * Python versions: 3.6, 3.7
        * Postgres DB: 9.6, 10
        * MySQL DB: 5.7
        * Sqlite - latest stable (it is used mainly for development purpose)
        
        ### Stable version (1.10.9)
        
        * Python versions: 2.7, 3.5, 3.6, 3.7
        * Postgres DB: 9.6, 10
        * MySQL DB: 5.6, 5.7
        * Sqlite - latest stable (it is used mainly for development purpose)
        
        ## Getting started
        Please visit the Airflow Platform documentation (latest **stable** release) for help with [installing Airflow](https://airflow.apache.org/installation.html), getting a [quick start](https://airflow.apache.org/start.html), or a more complete [tutorial](https://airflow.apache.org/tutorial.html).
        
        Documentation of GitHub master (latest development branch): [ReadTheDocs Documentation](https://airflow.readthedocs.io/en/latest/)
        
        For further information, please visit the [Airflow Wiki](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Home).
        
        Official container (Docker) images for Apache Airflow are described in [IMAGES.rst](IMAGES.rst).
        
        ## Beyond the Horizon
        
        Airflow **is not** a data streaming solution. Tasks do not move data from
        one to the other (though tasks can exchange metadata!). Airflow is not
        in the [Spark Streaming](http://spark.apache.org/streaming/)
        or [Storm](https://storm.apache.org/) space, it is more comparable to
        [Oozie](http://oozie.apache.org/) or
        [Azkaban](https://azkaban.github.io/).
        
        Workflows are expected to be mostly static or slowly changing. You can think
        of the structure of the tasks in your workflow as slightly more dynamic
        than a database structure would be. Airflow workflows are expected to look
        similar from a run to the next, this allows for clarity around
        unit of work and continuity.
        
        ## Principles
        
        - **Dynamic**:  Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically.
        - **Extensible**:  Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.
        - **Elegant**:  Airflow pipelines are lean and explicit. Parameterizing your scripts is built into the core of Airflow using the powerful **Jinja** templating engine.
        - **Scalable**:  Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers.
        
        ## User Interface
        
        - **DAGs**: Overview of all DAGs in your environment.
        ![](/docs/img/dags.png)
        
        - **Tree View**: Tree representation of a DAG that spans across time.
        ![](/docs/img/tree.png)
        
        - **Graph View**: Visualization of a DAG's dependencies and their current status for a specific run.
        ![](/docs/img/graph.png)
        
        - **Task Duration**: Total time spent on different tasks over time.
        ![](/docs/img/duration.png)
        
        - **Gantt View**: Duration and overlap of a DAG.
        ![](/docs/img/gantt.png)
        
        - **Code View**:  Quick way to view source code of a DAG.
        ![](/docs/img/code.png)
        
        
        ## Using hooks and Operators from "master" in Airflow 1.10
        
        Currently stable versions of Apache Airflow are released in 1.10.* series. We are working on the
        future, major version of Airflow from the 2.0.* series. It is going to be released in
        in 2020. However the exact time of release depends on many factors and is yet unknown.
        We have already a lot of changes in the hooks/operators/sensors for many external systems
        and they are not used because they are part of the master/2.0 release.
        
        In the Airflow 2.0 - following AIP-21 "change in import paths" all the non-core operators/hooks/sensors
        of Apache Airflow have been moved to the "airflow.providers" package. This opened a possibility to
        use the operators from Airflow 2.0 in Airflow 1.10 - with the constraint that those
        packages can only be used in python3.6+ environment.
        
        Therefore we decided to prepare and release backport packages that can be installed
        for older Airflow versions. Those backport packages are released more frequently. Users do not
        have to upgrade their Airflow version to use those packages. There are a number of changes
        between Airflow 2.0 and 1.10.* - documented in [UPDATING.md](UPDATING.md). With backported
        providers package users can migrate their DAGs to the new providers package incrementally
        and once they convert to the new operators/sensors/hooks they can seamlessly migrate their
        environments to Airflow 2.0.
        
        More information about the status and releases of the back-ported packages are available
        at [Backported providers package page](https://cwiki.apache.org/confluence/display/AIRFLOW/Backported+providers+packages+for+Airflow+1.10.*+series)
        
        Dependencies between packages are stored in ``airflow/providers/dependencies.json``. See
        [CONTRIBUTING.rst](https://github.com/apache/airflow/blob/master/CONTRIBUTING.rst#backport-providers-packages)
        
        ## Contributing
        
        Want to help build Apache Airflow? Check out our [contributing documentation](https://github.com/apache/airflow/blob/master/CONTRIBUTING.rst).
        
        
        ## Who uses Airflow?
        
        As the Apache Airflow community grows, we'd like to keep track of who is using
        the platform. Please send a PR with your company name and @githubhandle
        if you may.
        
        Committers:
        
        * Refer to [Committers](https://cwiki.apache.org/confluence/display/AIRFLOW/Committers)
        
        Currently **officially** using Airflow:
        
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        ## Who Maintains Apache Airflow?
        
        Airflow is the work of the [community](https://github.com/apache/airflow/graphs/contributors),
        but the [core committers/maintainers](https://people.apache.org/committers-by-project.html#airflow)
        are responsible for reviewing and merging PRs as well as steering conversation around new feature requests.
        If you would like to become a maintainer, please review the Apache Airflow
        [committer requirements](https://cwiki.apache.org/confluence/display/AIRFLOW/Committers).
        
        ## Can I use the Apache Airflow logo in my presentation?
        
        Yes! Be sure to abide by the Apache Foundation [trademark policies](https://www.apache.org/foundation/marks/#books) and the Apache Airflow [Brandbook](https://cwiki.apache.org/confluence/display/AIRFLOW/Brandbook). The most up to date logos are found in [this repo](/docs/img/logos) and on the Apache Software Foundation [website](https://www.apache.org/logos/about.html).
        
        ## Links
        
        
        - [Documentation](https://airflow.apache.org/)
        - [Chat](https://apache-airflow-slack.herokuapp.com/)
        - [More](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Links)
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: System Administrators
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: System :: Monitoring
Requires-Python: >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*
Description-Content-Type: text/markdown
Provides-Extra: elasticsearch
Provides-Extra: grpc
Provides-Extra: ldap
Provides-Extra: emr
Provides-Extra: rabbitmq
Provides-Extra: crypto
Provides-Extra: gcp_api
Provides-Extra: azure_container_instances
Provides-Extra: github_enterprise
Provides-Extra: google_auth
Provides-Extra: vertica
Provides-Extra: azure_data_lake
Provides-Extra: cassandra
Provides-Extra: devel_hadoop
Provides-Extra: hdfs
Provides-Extra: s3
Provides-Extra: devel_ci
Provides-Extra: ssh
Provides-Extra: async
Provides-Extra: mssql
Provides-Extra: dask
Provides-Extra: winrm
Provides-Extra: password
Provides-Extra: redis
Provides-Extra: pinot
Provides-Extra: azure
Provides-Extra: papermill
Provides-Extra: cloudant
Provides-Extra: aws
Provides-Extra: atlas
Provides-Extra: azure_cosmos
Provides-Extra: hive
Provides-Extra: mongo
Provides-Extra: presto
Provides-Extra: jdbc
Provides-Extra: slack
Provides-Extra: snowflake
Provides-Extra: salesforce
Provides-Extra: devel
Provides-Extra: doc
Provides-Extra: segment
Provides-Extra: devel_azure
Provides-Extra: cgroups
Provides-Extra: datadog
Provides-Extra: kerberos
Provides-Extra: sendgrid
Provides-Extra: all_dbs
Provides-Extra: oracle
Provides-Extra: hashicorp
Provides-Extra: webhdfs
Provides-Extra: postgres
Provides-Extra: druid
Provides-Extra: jira
Provides-Extra: kubernetes
Provides-Extra: mysql
Provides-Extra: azure_blob_storage
Provides-Extra: statsd
Provides-Extra: celery
Provides-Extra: gcp
Provides-Extra: all
Provides-Extra: qds
Provides-Extra: docker
Provides-Extra: samba
Provides-Extra: virtualenv
Provides-Extra: sentry
Provides-Extra: databricks
