The master Airflow image doesn't allow this kind of low-level control. It's easier to customize our additional dependencies with Airflow by building our own image. Why not just use the official docker-compose file? Local dependencies are added to local-requirements.txt.
This template installs Airflow using the constraints file at $.txt and allows for building a custom Airflow image on top of this constraints file by simply adding additional dependencies to. To combat this, Airflow provides a set of constraints files that are known working versions of Airflow. This means that depending on the day, a simple pip install apache-airflow is not guaranteed to produce a workable version of the Airflow application. Airflow is both, so it doesn't freeze dependencies. Applications freeze their dependencies to ensure stability, while libraries leave their dependencies open for upgrades to take advantage of new features. Logs are viewable in the UI.Īirflow is tricky to install correctly because it is both an application and a library. Logs persist both in flat files and the database, and Airflow can be setup to write remote logs (to S3 for example). See the Airflow documentation for more detail: Īirflow also comes with its own architecture: a database to persist the state of DAGs and connections, a web server that supports the user-interface, and workers that are managed together by the scheduler and database.
#Airflow github examples how to
Originally conceived at Facebook and eventually open-sourced at AirBnB, Airflow allows you to define complex directed acyclic graphs (DAG) by writing simple Python.Īirflow has a number of built-in concepts that make data engineering simple, including DAGs (which describe how to run a workflow) and Operators (which describe what actually gets done). Apache Airflow is the leading orchestration tool for batch workloads.
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