□ And a new " SmoothOperator" - "from airflow.operators. □ A single page to check release notes instead of UPDATING.md on GitHub & Changelog on Airflow website: \_notes.html Run it with "airflow dag-processor" CLI coomand □ DagProcessorManager can be run as standalone process now.Īs it runs user code, separating it from the scheduler process and running it as an independent process in a different host is a good idea. In its own pod on a kubernetes cluster based on the task's queue It provides the capability of running tasks with either LocalExecutor, which runs tasks within the scheduler service, or with KubernetesExecutor, which runs each task This will help reduce time when running DB Migrations (when updating Airflow version) □ The new `airflow db clean` CLI command for purging old records. Includes a "-show-sql-only" to output all the SQL so that you can run it yourself! This involves reading the release notes and any relevant documentation. You can downgrade to a particular Airflow version or a to a specific Alembic revision id. The first step is to understand the changes in the new version of Python or Kubernetes. □ First class support for DB downgrade - `airflow db downgrade` command. For example, if you are developing using Maven and want to use the SDK for Java with the DirectRunner, add the following dependencies to your pom.xml. The Java SDK is available on Maven Central Repository, and the Python SDK is available on PyPI. Deploy Mage to AWS, GCP, Azure, or DigitalOcean with only 2 commands. The easiest way to use Apache Beam is via one of the released versions in a central repository. Paves way for DAG Versioning - to easily show versions, which was impossible to handle in Tree View ! yay! Dont have a large team dedicated to Airflow Mage makes it easy for a. Show runs and tasks but leave dependency lines to the graph view and handles Task Groups better! An article has been added to describe the long awaited new Advanced Fan Control method. □ Dynamic Task Mapping: No longer hacking around dynamic tasks !!Īllows a way for a workflow to create a number of tasks at runtime based upon current data, rather than the DAG author having to know in advance how many tasks would be needed. □ Docker Image: "docker pull apache/airflow:2.3.0" We aim to keep backwards compatibility of providers with all previously released Airflow 2 versions but there will sometimes be breaking changes that might make. □ And a new "SmoothOperator" - This is a surprise ! And a very powerful feature, try it out and let me know what you think about it □ □ Create Connection in native JSON format - no need to figure out the URI format Tip: Subscribe to scikit-learn releases on libraries.io to be notified when new. □ The new `airflow db clean` CLI command for purging old records Release notes for all scikit-learn releases are linked in this page. The following are the biggest & noteworthy changes□□□: ➡️ 700+ commits since 2.2 including 50 new features, 99 improvements, 85 bug fixes ➡️ This is the biggest Apache Airflow release since 2.0.0 Apache Airflow 2.3.0 is out! Soo many things to talk about □□□
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |