APACHE AIRFLOW PERFORMANCE INSTALL
APACHE AIRFLOW PERFORMANCE CODE
APACHE AIRFLOW PERFORMANCE WINDOWS

This means that pip install apache-airflow will not work from time to time or will Our dependencies as open as possible (in setup.py) so users can install different versions of libraries Libraries usually keep their dependencies open, andĪpplications usually pin them, but we should do neither and both simultaneously. Installing it however might be sometimes trickyīecause Airflow is a bit of both a library and application. We publish Apache Airflow as apache-airflow package in PyPI. Note: If you're looking for documentation for the main branch (latest development branch): you can find it on s./airflow-docs.įor more information on Airflow Improvement Proposals (AIPs), visitĭocumentation for dependent projects like provider packages, Docker image, Helm Chart, you'll find it in the documentation index. Visit the official Airflow website documentation (latest stable release) for help with Is used in the Community managed DockerHub image is The only distro that is used in our CI tests and that You should only use Linux-based distros as 'Production' execution environmentĪs this is the only environment that is supported.

APACHE AIRFLOW PERFORMANCE WINDOWS

The work to add Windows support is tracked via #10388 but On Windows you can run it via WSL2 (Windows Subsystem for Linux 2) or via Linux Containers.

Tested on fairly modern Linux Distros and recent versions of MacOS.

Note: Airflow currently can be run on POSIX-compliant Operating Systems. Using the latest stable version of SQLite for local development. Running multiple schedulers - please see the Scheduler docs. Note: MySQL 5.x versions are unable to or have limitations with

Scalable: Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers.

Parameterizing your scripts is built into the core of Airflow using the powerful Jinja templating engine.

Elegant: Airflow pipelines are lean and explicit.

Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.

APACHE AIRFLOW PERFORMANCE CODE

This allows for writing code that instantiates pipelines dynamically.

Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation.

For high-volume, data-intensive tasks, a best practice is to delegate to external services specializing in that type of work.Īirflow is not a streaming solution, but it is often used to process real-time data, pulling data off streams in batches. Other similar projects include Luigi, Oozie and Azkaban.Īirflow 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's XCom feature). When the DAG structure is similar from one run to the next, it clarifies the unit of work and continuity.

Can I use the Apache Airflow logo in my presentation?Īirflow works best with workflows that are mostly static and slowly changing.

Base OS support for reference Airflow images.

Support for Python and Kubernetes versions.

The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. Rich command line utilities make performing complex surgeries on DAGs a snap.

The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows.

Apache airflow performance