Apache Airflow

When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows (Dags) that orchestrate 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. Table of contents Project Focus Principles Requirements Getting started Installing from PyPI Installation Official source code Convenience packages User Interface Semantic versioning Version Life Cycle Support for Python and Kubernetes versions Base OS support for reference Airflow images Approach to dependencies of Airflow Contributing Community standards Agent-assisted contribution (apache-magpie) Voting Policy Who uses Apache Airflow? Who maintains Apache Airflow? What goes into the next release? Can I use the Apache Airflow logo in my presentation? Links Sponsors Airflow works best with workflows that are mostly static and slowly changing. When the Dag structure is similar from one run to the next, it clarifies the unit of work and continuity. Other similar projects include Luigi, Oozie and Azkaban. Airflow is commonly used to process data, but has the opinion that tasks should ideally be idempotent (i.e., results of the task will be the same, and will not create duplicated data in a destination system), and should not pass large quantities of data from one task to the next (though tasks can pass metadata using Airflow's XCom feature). For high-volume, data-intensive tasks, a best practice is to delegate to external services specializing in that type of work. Visit the official Airflow website documentation (latest stable release) for help with installing Airflow, getting started, or walking through a more complete tutorial. Note: If you're looking for documentation for the main branch (latest development branch): you can find it on s.apache.org/airflow-docs. For more information on Airflow Improvement Proposals (AIPs), visit the Airflow Wiki. Documentation for dependent projects like provider distributions, Docker image, Helm Chart, you'll find it in the documentation index. We publish Apache Airflow 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 simultaneously. We decided to keep our dependencies as open as possible (in pyproject.toml ) so users can install different versions of libraries if needed. This means that pip install apache-airflow will not work from time to time or will produce unusable Airflow installation. To have repeatable installation, however, we keep a set of "known-to-be-working" constraint files in the orphan constraints-main and constraints-2-0 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. If you wish to install Airflow using those tools, you should use the constraint files and convert them to the appropriate format and workflow that your tool requires. For comprehensive instructions on setting up your local development environment and installing Apache Airflow, please refer to the INSTALLING.md file. Following the ASF rules, the source packages released must be sufficient for a user to build and test the release provided they have access to the appropriate platform and tools. There are other ways of installing and using Airflow. Those are "convenience" methods - they are not "official releases" as stated by the ASF Release Policy , but they can be used by the use