Apache Airflow (incubating) Documentation¶
Disclaimer: Apache Airflow is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
Airflow is a platform to programmatically author, schedule and monitor workflows.
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.
When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- 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. Airflow is ready to scale to infinity.
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 or Storm space, it is more comparable to Oozie or Azkaban.
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.
- Quick Start
- How-to Guides
- Setting Configuration Options
- Initializing a Database Backend
- Managing Connections
- Securing Connections
- Writing Logs
- Scaling Out with Celery
- Scaling Out with Dask
- Scaling Out with Mesos (community contributed)
- Running Airflow with systemd
- Running Airflow with upstart
- Using the Test Mode Configuration
- UI / Screenshots
- Core Ideas
- Additional Functionality
- Packaged dags
- Data Profiling
- Command Line Interface
- Positional Arguments
- Scheduling & Triggers
- Web Authentication
- OAuth Authentication
- Time zones
- Experimental Rest API
- Reverse Proxy
- Azure: Microsoft Azure
- AWS: Amazon Web Services
- AWS EMR
- AWS S3
- AWS EC2 Container Service
- AWS Batch Service
- AWS RedShift
- GCP: Google Cloud Platform
- Cloud DataFlow
- Cloud DataProc
- Cloud Datastore
- Cloud ML Engine
- Cloud Storage
- Google Kubernetes Engine
- Why isn’t my task getting scheduled?
- How do I trigger tasks based on another task’s failure?
- Why are connection passwords still not encrypted in the metadata db after I installed airflow[crypto]?
- What’s the deal with
- How can I create DAGs dynamically?
- What are all the
airflow runcommands in my process list?
- How can my airflow dag run faster?
- How can we reduce the airflow UI page load time?
- How to fix Exception: Global variable explicit_defaults_for_timestamp needs to be on (1)?
- How to reduce airflow dag scheduling latency in production?
- API Reference