Programmatically author, schedule and monitor data pipelines
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
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.
Table of contents
- Getting started
- Installing from PyPI
- Building customized production images
- Beyond the Horizon
- User Interface
- Using hooks and Operators from "master" in Airflow 1.10
- Who uses Airflow?
- Who Maintains Apache Airflow?
- Can I use the Apache Airflow logo in my presentation?
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 at least Python 3.5.3 when using Python 3
Documentation of GitHub master (latest development branch): ReadTheDocs Documentation
For further information, please visit the Airflow Wiki.
Official container (Docker) images for Apache Airflow are described in 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
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.
- Installing just airflow:
pip install apache-airflow==1.10.12 \ --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-1.10.12/constraints-3.7.txt"
- Installing with extras (for example postgres,gcp)
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/airflowwill pull latest stable version of the image with default python version (currently 3.6)
masterbranch of Airflow and in
v1-10-stablebranch 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 if you want to build it using
docker buildcommand or in BREEZE.rst 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 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.
- 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.
DAGs: Overview of all DAGs in your environment.
Tree View: Tree representation of a DAG that spans across time.
Graph View: Visualization of a DAG's dependencies and their current status for a specific run.
Task Duration: Total time spent on different tasks over time.
Gantt View: Duration and overlap of a DAG.
Code View: Quick way to view source code of a DAG.
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. 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
Dependencies between packages are stored in
Want to help build Apache Airflow? Check out our contributing documentation.
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.
- Refer to Committers
Currently officially using Airflow:
- AdBOOST [AdBOOST]
- Agari [@r39132]
- Airbnb [@mistercrunch, @artwr]
- Airtel [@harishbisht]
- Alan [@charles-go]
- allegro.pl [@kretes]
- AltX [@pedromduarte]
- Apigee [@btallman]
- ARGO Labs [California Data Collaborative]
- Astronomer [@schnie, @andscoop, @tedmiston, @benjamingregory]
- Auth0 [@sicarul]
- Away [@trunsky]
- Azri Solutions [@userimack]
- BalanceHero [@swalloow]
- Banco de Formaturas [@guiligan]
- BandwidthX [@dineshdsharma]
- BelugaDB [@fabio-nukui & @joao-sallaberry & @lucianoviola & @tmatuki]
- BlaBlaCar [@puckel & @wmorin]
- Bloc [@dpaola2]
- Blue Yonder [@blue-yonder]
- BlueApron [@jasonjho & @matthewdavidhauser]
- Bluecore [@JLDLaughlin]
- Boda Telecom Suite - CE [@erssebaggala, @bodastage]
- Bodastage Solutions [@erssebaggala, @bodastage]
- Bonnier Broadcasting [@wileeam]
- BounceX [@JoshFerge, @hudsonrio, @ronniekritou]
- California Data Collaborative powered by ARGO Labs
- Carbonite [@ajbosco]
- Celect [@superdosh & @chadcelect]
- Change.org [@change, @vijaykramesh]
- Checkr [@tongboh]
- Children's Hospital of Philadelphia Division of Genomic Diagnostics [@genomics-geek]
- Cinimex DataLab [@kdubovikov]
- City of San Diego [@MrMaksimize, @andrell81 & @arnaudvedy]
- Clairvoyant @shekharv
- Clover Health [@gwax & @vansivallab]
- Chartboost [@cgelman & @dclubb]
- ContaAzul [@bern4rdelli, @renanleme & @sabino]
- Cotap [@maraca & @richardchew]
- Credit Karma [@preete-dixit-ck & @harish-gaggar-ck & @greg-finley-ck]
- CreditCards.com[@vmAggies & @jay-wallaby]
- Creditas [@dcassiano]
- Custom Ink [@david-dalisay, @dmartin11 & @mpeteuil]
- Data Reply [@kaxil]
- DataFox [@sudowork]
- Digital First Media [@duffn & @mschmo & @seanmuth]
- DocuTAP [@jshvrsn & @lhvphan & @cloneluke]
- Dotmodus [@dannylee12]
- Drivy [@AntoineAugusti]
- Easy Taxi [@caique-lima & @WesleyBatista & @diraol]
- eRevalue [@hamedhsn]
- evo.company [@orhideous]
- FreshBooks [@DinoCow]
- Fundera [@andyxhadji]
- GameWisp [@tjbiii & @theryanwalls]
- Gentner Lab [@neuromusic]
- Glassdoor [@syvineckruyk]
- Global Fashion Group [@GFG]
- GovTech GDS [@chrissng & @datagovsg]
- Grand Rounds [@richddr, @timz1290, @wenever, & @runongirlrunon]
- Groupalia [@jesusfcr]
- Groupon [@stevencasey]
- Gusto [@frankhsu]
- Handshake [@mhickman]
- Handy [@marcintustin / @mtustin-handy]
- HBC Digital [@tmccartan & @dmateusp]
- Healthjump [@miscbits]
- HelloFresh [@tammymendt & @davidsbatista & @iuriinedostup]
- Holimetrix [@thibault-ketterer]
- Hostnfly [@CyrilLeMat & @pierrechopin & @alexisrosuel]
- HotelQuickly [@zinuzoid]
- IFTTT [@apurvajoshi]
- imgix [@dclubb]
- Intercom [@fox & @paulvic]
- Investorise [@svenvarkel]
- JobTeaser [@stefani75 & @knil-sama]
- Kalibrr [@charlesverdad]
- Karmic [@hyw]
- Kiwi.com [@underyx]
- Kogan.com [@geeknam]
- Lemann Foundation [@fernandosjp]
- LendUp [@lendup]
- LetsBonus [@jesusfcr & @OpringaoDoTurno]
- liligo [@tromika]
- LingoChamp [@haitaoyao]
- Lucid [@jbrownlucid & @kkourtchikov]
- Lumos Labs [@rfroetscher & @zzztimbo]
- M4U [@msantino]
- Madrone [@mbreining & @scotthb]
- Markovian [@al-xv, @skogsbaeck, @waltherg]
- Mercadoni [@demorenoc]
- Mercari [@yu-iskw]
- MFG Labs
- MiNODES [@dice89, @diazcelsa]
- Multiply [@nrhvyc]
- mytaxi [@mytaxi]
- New Relic [@marcweil]
- Newzoo [@newzoo-nexus]
- Nextdoor [@SivaPandeti, @zshapiro & @jthomas123]
- OdysseyPrime [@davideberdin]
- OneFineStay [@slangwald]
- Open Knowledge International @vitorbaptista
- Overstock [@mhousley & @mct0006]
- Pandora Media [@Acehaidrey & @wolfier]
- PAYMILL [@paymill & @matthiashuschle]
- PayPal [@r39132 & @jhsenjaliya]
- Pernod-Ricard [@romain-nio]
- Plaid [@plaid, @AustinBGibbons & @jeeyoungk]
- Playbuzz [@clintonboys & @dbn]
- PMC [@andrewm4894]
- Postmates [@syeoryn]
- Pronto Tools [@zkan & @mesodiar]
- PubNub [@jzucker2]
- Qplum [@manti]
- Quantopian [@eronarn]
- Qubole [@msumit]
- Quizlet [@quizlet]
- REA Group
- Reddit [@reddit]
- Robinhood [@vineet-rh]
- Scaleway [@kdeldycke]
- Sense360 [@kamilmroczek]
- Shopkick [@shopkick]
- Sidecar [@getsidecar]
- SimilarWeb [@similarweb]
- SmartNews [@takus]
- SocialCops [@vinayak-mehta & @sharky93]
- Spotahome [@spotahome]
- Spotify [@znichols]
- Stripe [@jbalogh]
- Tails.com [@alanmcruickshank]
- Thinking Machines [@marksteve]
- Thinknear [@d3cay1, @ccson, & @ababian]
- Thumbtack [@natekupp]
- Tile [@ranjanmanish]
- Tokopedia @topedmaria
- Twine Labs [@ivorpeles]
- Twitter [@aoen]
- T2 Systems [@unclaimedpants]
- Ubisoft [@Walkoss]
- United Airlines [@ilopezfr]
- Upsight [@dhuang]
- Vente-Exclusive.com [@alexvanboxel]
- Vevo [@csetiawan & @jerrygillespie]
- Vnomics [@lpalum]
- WePay [@criccomini & @mtagle]
- WeTransfer [@jochem]
- Whistle Labs [@ananya77041]
- Xero [@yan9yu]
- Yieldr [@ggeorgiadis]
- Zapier [@drknexus & @statwonk]
- Zego [@ruimffl]
- Zenly [@cerisier & @jbdalido]
- 99 [@fbenevides, @gustavoamigo & @mmmaia]
Who Maintains Apache Airflow?
Airflow is the work of the community, but the core committers/maintainers 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.
Can I use the Apache Airflow logo in my presentation?
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