Metadata-Version: 2.1
Name: apache-airflow
Version: 1.10.12
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/1.10.12
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: Programming Language :: Python :: 3.8
Classifier: Topic :: System :: Monitoring
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# Apache Airflow

[![PyPI version](https://badge.fury.io/py/apache-airflow.svg)](https://badge.fury.io/py/apache-airflow)
![Airflow](https://github.com/apache/airflow/workflows/Airflow/badge.svg)
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[![Slack Status](https://img.shields.io/badge/slack-join_chat-white.svg?logo=slack&style=social)](https://s.apache.org/airflow-slack)

_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 -->
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**Table of contents**

- [Requirements](#requirements)
- [Getting started](#getting-started)
- [Installing from PyPI](#installing-from-pypi)
- [Building customized production images](#building-customized-production-images)
- [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, 3.8
* Postgres DB: 9.6, 10
* MySQL DB: 5.7
* Sqlite - latest stable (it is used mainly for development purpose)
* Kubernetes - 1.16.2, 1.17.0

### Stable version (1.10.11)

* Python versions: 2.7, 3.5, 3.6, 3.7, 3.8
* Postgres DB: 9.6, 10
* MySQL DB: 5.6, 5.7
* Sqlite - latest stable (it is used mainly for development purpose)
* Kubernetes - 1.16.2, 1.17.0

### Additional notes on Python version requirements

* Stable version [requires](https://github.com/apache/airflow/issues/8162) at least Python 3.5.3 when using Python 3

## 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).

## Installing from PyPI

Airflow is published as `apache-airflow` package in PyPI. Installing it however might be sometimes tricky
because Airflow is a bit of both a library and application. Libraries usually keep their dependencies open and
applications usually pin them, but we should do neither and both at the same time. We decided to keep
our dependencies as open as possible (in `setup.py`) so users can install different versions of libraries
if needed. This means that from time to time plain `pip install apache-airflow` will not work or will
produce unusable Airflow installation.

In order to have repeatable installation, however, introduced in **Airflow 1.10.10** and updated in
**Airflow 1.10.12** we also keep a set of "known-to-be-working" constraint files in the
orphan `constraints-master` and `constraints-1-10` branches. We keep those "known-to-be-working"
constraints files separately per major/minor python version.
You can use them as constraint files when installing Airflow from PyPI. Note that you have to specify
correct Airflow tag/version/branch and python versions in the URL.

1. Installing just airflow:

```bash
pip install apache-airflow==1.10.12 \
 --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-1.10.12/constraints-3.7.txt"
```

2. Installing with extras (for example postgres,gcp)
```bash
pip install apache-airflow[postgres,gcp]==1.10.11 \
 --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-1.10.12/constraints-3.7.txt"
```

## Building customized production images

In order to use Airflow in Docker Compose or Kubernetes, you might need to use or build production images
of Apache Airflow. The community provides two types of support for the production images:

* We provide pre-build relesed version of production image in PyPI build from released
  sources of Apache Airflow - shortly after release. Those images are available in the DockerHub.
  You can pull those images via `docker pull apache/airflow:<VERSION>-pythonX.Y` - version is the
  version number (for example 1.10.11). Additionally `docker pull apache/airflow` will pull latest
  stable version of the image with default python version (currently 3.6)

* In `master` branch of Airflow and in `v1-10-stable` branch we provide Dockerfiles and accompanying
  files that allow to build your own customized version of the Airflow Production image. The instructions
  on how to build your own image with additional dependencies (if needed) are provided in the
  [IMAGES.rst](IMAGES.rst#production-images) if you want to build it using `docker build` command or in
  [BREEZE.rst](BREEZE.rst#building-production-images) to use Breeze tool which easier interface,
  auto-complete, and accompanying screencast video. Note, that while it is possible to use master
  branch to build images for released Airflow versions, it might at times get broken so you should
  rather rely on building your own images from the v1-10-stable branch.

## 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)

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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://s.apache.org/airflow-slack)
- [More](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Links)


