Package: apache-airflow-zack
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 InterfaceContributingWho uses Apache Airflow?Who Maintains Apache Airflow?Can I use the Apache Airflow logo in my presentation?Airflow merchandiseLinksProject 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:Master version (2.1.0dev)Stable version (1.10.15)Python3.6, 3.7, 3.82.7, 3.5, 3.6, 3.7, 3.8PostgreSQL9.6, 10, 11, 12, 139.6, 10, 11, 12, 13MySQL5.7, 85.6, 5.7SQLitelatest stablelatest stableKubernetes1.16.9, 1.17.5, 1.18.61.16.9, 1.17.5, 1.18.6Note:MariaDB and MySQL 5.x are unable to or have limitations with
running multiple schedulers -- please see the "Scheduler" docs.Note:SQLite is used in Airflow tests. Do not use it in production.Additional notes on Python version requirementsStable versionrequiresat least Python 3.5.3 when using Python 3Getting 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 master branch (latest development branch): you can find it ons.apache.org/airflow-docs.For more information on Airflow's Roadmap or Airflow Improvement Proposals (AIPs), visit theAirflow Wiki.Official Docker (container) images for Apache Airflow are described inIMAGES.rst.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, introduced inAirflow 1.10.10and updated inAirflow 1.10.12we also keep a set of "known-to-be-working" constraint files in the
orphanconstraints-masterandconstraints-1-10branches. 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!!!On November 2020, new version of PIP (20.3) has been released with a new, 2020 resolver. This resolver
does not yet work with Apache Airflow and might leads to errors in installation - depends on your choice
of extras. In order to install Airflow you need to either downgrade pip to version 20.2.4pip upgrade --pip==20.2.4or, in case you use Pip 20.3, you need to add option--use-deprecated legacy-resolverto your pip install command.pipinstallapache-airflow==1.10.15\--constraint"https://raw.githubusercontent.com/apache/airflow/constraints-1.10.15/constraints-3.7.txt"NOTE!!!On 30th of November 2020, new version of PIP (20.3) has been released with a new, 2020 resolver.
This resolver does not yet work with Apache Airflow and might leads to errors in installation -
depends on your choice of extras. In order to install Airflow you need to either downgrade
pip to version 20.2.4pip upgrade --pip==20.2.4or, in case you use Pip 20.3, you need to add option--use-deprecated legacy-resolverto your pip install command.pipinstallapache-airflow[postgres,google]==1.10.15\--constraint"https://raw.githubusercontent.com/apache/airflow/constraints-1.10.15/constraints-3.7.txt"For information on installing backport providers check [/docs/backport-providers.rst][/docs/backport-providers.rst].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 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.ContributingWant to help build Apache Airflow? Check out ourcontributing documentation.Who uses Apache Airflow?More than 350 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.LinksDocumentationChat