Package: smartpip2
Apache AirflowApache 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 contentsProject FocusPrinciplesRequirementsGetting startedInstalling from PyPIOfficial source codeConvenience packagesUser InterfaceSemantic versioningVersion Life CycleSupport for Python and Kubernetes versionsContributingWho uses Apache Airflow?Who Maintains Apache Airflow?Can I use the Apache Airflow logo in my presentation?Airflow merchandiseLinksSponsorsProject FocusAirflow works best with workflows that are mostly static and slowly changing. When DAG structure is similar from one run to the next, it allows for clarity around unit of work and continuity. Other similar projects includeLuigi,OozieandAzkaban.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'sXcom feature). For high-volume, data-intensive tasks, a best practice is to delegate to external services that specialize on that type of work.Airflow is not a streaming solution, but it is often used to process real-time data, pulling data off streams in batches.PrinciplesDynamic:  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 powerfulJinjatemplating engine.Scalable:  Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers.RequirementsApache Airflow is tested with:Main version (dev)Stable version (2.1.2)Python3.6, 3.7, 3.8, 3.93.6, 3.7, 3.8, 3.9Kubernetes1.20, 1.19, 1.181.20, 1.19, 1.18PostgreSQL9.6, 10, 11, 12, 139.6, 10, 11, 12, 13MySQL5.7, 85.7, 8SQLite3.15.0+3.15.0+MSSQL(Experimental)2017,2019Note:MySQL 5.x versions are unable to or have limitations with
running multiple schedulers -- please see theScheduler docs.
MariaDB is not tested/recommended.Note:SQLite is used in Airflow tests. Do not use it in production. We recommend
using the latest stable version of SQLite for local development.Getting startedVisit the official Airflow website documentation (lateststablerelease) for help withinstalling Airflow,getting started, or walking
through a more completetutorial.Note: If you're looking for documentation for main branch (latest development branch): you can find it ons.apache.org/airflow-docs.For more information on Airflow Improvement Proposals (AIPs), visit
theAirflow Wiki.Documentation for dependent projects like provider packages, Docker image, Helm Chart, you'll find it inthe documentation index.Installing from PyPIWe publish Apache Airflow asapache-airflowpackage 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 (insetup.py) so users can install different versions of libraries
if needed. This means that from time to time plainpip install apache-airflowwill not work or will
produce unusable Airflow installation.In order to have repeatable installation, however, we also keep a set of "known-to-be-working" constraint
files in the orphanconstraints-main,constraints-2-0branches. 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:Note: Onlypipinstallation is currently officially supported.While they are some successes with using other tools likepoetryorpip-tools, they do not share the same workflow aspip- especially when it comes to constraint vs. requirements management.
Installing viaPoetryorpip-toolsis not currently supported.If you wish to install airflow using those tools you should use the constraint files and convert
them to appropriate format and workflow that your tool requires.pipinstallapache-airflow==2.1.2\--constraint"https://raw.githubusercontent.com/apache/airflow/constraints-2.1.2/constraints-3.7.txt"Installing with extras (for example postgres,google)pipinstallapache-airflow[postgres,google]==2.1.2\--constraint"https://raw.githubusercontent.com/apache/airflow/constraints-2.1.2/constraints-3.7.txt"For information on installing provider packages checkproviders.Official source codeApache Airflow is anApache Software Foundation(ASF) project,
and our official source code releases:Follow theASF Release PolicyCan be downloaded fromthe ASF Distribution DirectoryAre cryptographically signed by the release managerAre officially voted on by the PMC members during theRelease Approval ProcessFollowing 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.Convenience packagesThere are other ways of installing and using Airflow. Those are "convenience" methods - they are
not "official releases" as stated by theASF Release Policy, but they can be used by the users
who do not want to build the software themselves.Those are - in the order of most common ways people install Airflow:PyPI releasesto install Airflow using standardpiptoolDocker Imagesto install airflow viadockertool, use them in Kubernetes, Helm Charts,docker-compose,docker swarmetc. You can
read more about using, customising, and extending the images in theLatest docs, and
learn details on the internals in theIMAGES.rstdocument.Tags in GitHubto retrieve the git project sources that
were used to generate official source packages via gitAll those artifacts are not official releases, but they are prepared using officially released sources.
Some of those artifacts are "development" or "pre-release" ones, and they are clearly marked as such
following the ASF Policy.User InterfaceDAGs: Overview of all DAGs in your environment.Tree: Tree representation of a DAG that spans across time.Graph: 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: Duration and overlap of a DAG.Code:  Quick way to view source code of a DAG.Semantic versioningAs of Airflow 2.0.0, we support strictSemVerapproach for all packages released.There are few specific rules that we agreed to, that define details of versioning of the different
packages:Airflow: SemVer rules apply to core airflow only (excludes any changes to providers).
Changing limits for versions of Airflow dependencies is not a breaking change on its own.Airflow Providers: SemVer rules apply to changes in the particular provider's code only.
SemVer MAJOR and MINOR versions for the packages are independent from Airflow version.
For examplegoogle 4.1.0andamazon 3.0.3providers can happily be installed
withAirflow 2.1.2. If there are limits of cross-dependencies between providers and Airflow packages,
they are present in providers asinstall_requireslimitations. We aim to keep backwards
compatibility of providers with all previously released Airflow 2 versions but
there will be sometimes breaking changes that might make some, or all
providers, to have minimum Airflow version specified. Change of that minimum supported Airflow version
is a breaking change for provider, because installing the new provider might automatically
upgrade Airflow (which might be undesired side effect of upgrading provider).Airflow Helm Chart: SemVer rules apply to changes in the chart only. SemVer MAJOR and MINOR
versions for the chart are independent from the Airflow version. We aim to keep backwards
compatibility of the Helm Chart with all released Airflow 2 versions, but some new features might
only work starting from specific Airlfow releases. We might however limit the Helm
Chart to depend on minimal Airflow version.Airflow API clients: SemVer MAJOR and MINOR versions follow MAJOR and MINOR version of Airflow.
The first MAJOR or MINOR X.Y.0 release of Airflow should always be followed by X.Y.0 release of
all clients. The clients then can release their own PATCH releases with bugfixes,
independently of Airflow PATCH releases.Version Life CycleApache Airflow version life cycle:VersionCurrent Patch/MinorStateFirst ReleaseLimited SupportEOL/Terminated22.1.2SupportedDec 17, 2020Dec 2021TBD1.101.10.15EOLAug 27, 2018Dec 17, 2020June 17, 20211.91.9.0EOLJan 03, 2018Aug 27, 2018Aug 27, 20181.81.8.2EOLMar 19, 2017Jan 03, 2018Jan 03, 20181.71.7.1.2EOLMar 28, 2016Mar 19, 2017Mar 19, 2017Limited support versions will be supported with security and critical bug fix only.
EOL versions will not get any fixes nor support.
We always recommend that all users run the latest available minor release for whatever major version is in use.
Wehighlyrecommend upgrading to the latest Airflow major release at the earliest convenient time and before EOL date.Support for Python and Kubernetes versionsAs of Airflow 2.0 we agreed to certain rules we follow for Python and Kubernetes support.
They are based on the official release schedule of Python and Kubernetes, nicely summarized in thePython Developer's GuideandKubernetes version skew policy.We drop support for Python and Kubernetes versions when they reach EOL. We drop support for those
EOL versions in main right after EOL date, and it is effectively removed when we release the
first new MINOR (Or MAJOR if there is no new MINOR version) of Airflow
For example for Python 3.6 it means that we drop support in main right after 23.12.2021, and the first
MAJOR or MINOR version of Airflow released after will not have it.The "oldest" supported version of Python/Kubernetes is the default one. "Default" is only meaningful
in terms of "smoke tests" in CI PRs which are run using this default version and default reference
image available. Currentlyapache/airflow:latestand ``apache/airflow:2.1.2` images
are both Python 3.6 images, however the first MINOR/MAJOR release of Airflow release after 23.12.2021 will
become Python 3.7 images.We support a new version of Python/Kubernetes in main after they are officially released, as soon as we
make them work in our CI pipeline (which might not be immediate due to dependencies catching up with
new versions of Python mostly) we release a new images/support in Airflow based on the working CI setup.Additional notes on Python version requirementsPrevious versionrequiresat least Python 3.5.3
when using Python 3ContributingWant to help build Apache Airflow? Check out ourcontributing documentation.Official Docker (container) images for Apache Airflow are described inIMAGES.rst.Who uses Apache Airflow?More than 400 organizations are using Apache Airflowin the wild.Who Maintains Apache Airflow?Airflow is the work of thecommunity,
but thecore committers/maintainersare 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 Airflowcommitter requirements.Can I use the Apache Airflow logo in my presentation?Yes! Be sure to abide by the Apache Foundationtrademark policiesand the Apache AirflowBrandbook. The most up to date logos are found inthis repoand on the Apache Software Foundationwebsite.Airflow merchandiseIf you would love to have Apache Airflow stickers, t-shirt etc. then check outRedbubble Shop.LinksDocumentationChatSponsorsThe CI infrastructure for Apache Airflow has been sponsored by: