4563df404c
- congiguration.py now generates the test config and real airflow config with the same method - fix warning logs in local UT execution, related to missing FERNET-KEY in unittest.cfg - fix warning logs in Travis UT exeuction, related to missing FERNET-KEY in airflow_travis.cfg - added one to validate config generation |
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| airflow | ||
| dags | ||
| docs | ||
| scripts/ci | ||
| tests | ||
| .coveralls.yml | ||
| .gitignore | ||
| .travis.yml | ||
| CONTRIBUTING.md | ||
| COPYRIGHT.txt | ||
| LICENSE.txt | ||
| MANIFEST.in | ||
| README.md | ||
| TODO.md | ||
| init.sh | ||
| migrations.sql | ||
| requirements.txt | ||
| run_tox.sh | ||
| run_unit_tests.sh | ||
| setup.cfg | ||
| setup.py | ||
| tox.ini | ||
README.md
Airflow
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.
![img] (http://i.imgur.com/6Gs4hxT.gif)
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.
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.
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. Airflow is ready to scale to infinity.
Who uses Airflow?
As the 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.
Currently officially using Airflow:
- Airbnb [@mistercrunch]
- Agari @r39132
- BlueApron [@jasonjho & @matthewdavidhauser]
- Chartboost [@cgelman & @dclubb]
- Cotap [@maraca & @richardchew]
- Easy Taxi [@caique-lima]
- Handy [@marcintustin / @mtustin-handy]
- Jampp
- LingoChamp [@haitaoyao]
- Lyft
- Sense360 [@kamilmroczek]
- Stripe [@jbalogh]
- WeTransfer [@jochem]
- Wooga
- Xoom [@gepser & @omarvides]
- Yahoo!
Links
- Full documentation on pythonhosted.com
- Airflow Google Group (mailing list / forum)
- Airbnb Blog Post about Airflow
- Hadoop Summit Airflow Video
- Docker Airflow (externally maintained)
- Airflow at Agari Blog Post
- Airflow Chef recipe (community contributed) [github] (https://github.com/bahchis/airflow-cookbook) [chef] (https://supermarket.chef.io/cookbooks/airflow)