![]() Dependencies are handled more clearly and XCom is nicer to useĪ quick teaser of what DAGs can now look like:įrom airflow.utils. It defines four Tasks - A, B, C, and D - and dictates the order in which they have to run, and which tasks depend on what others. Most design discussions will take place on the mailing list. (Known in 2.0.0alphas as Functional DAGs.)ĭAGs are now much much nicer to author especially when using PythonOperator. A DAG (Directed Acyclic Graph) is the core concept of Airflow, collecting Tasks together, organized with dependencies and relationships to say how they should run. In Release: 2.0.0: This document captures the design of REST API for Apache Airflow. 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. The full changelog is about 3,000 lines long (already excluding everything backported to 1.10), so for now I’ll simply share some of the major features in 2.0.0 compared to 1.10.14:Ī new way of writing dags: the TaskFlow API (AIP-31) Fri, Apache Airflow 2.7.0 is here Jed Cunningham Apache Airflow 2.7.0 has been released I’m happy to announce that Apache Airflow 2.7. Airflow is a platform to programmatically author, schedule and monitor workflows.
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