the “ in the code snippet below - to define python tasks. Instead of wrapping the PythonOperator around a function, users will be able to use a decorator – i.e. The functional DAG definition, described in AIP-31*, will reduce the need for boilerplate code when working with the PythonOperator. This major feature would not only allow Airflow to integrate with external components, but we are specifically looking forward to use this also to administer users & connections. REST APIĪirflow 2.0 will feature a complete REST API that follows the Open API 3.0 specifications. running multiple schedulers at once – will allow users to obtain a highly available Airflow setup and reduce the risk of tasks being missed. Horizontal scaling of the scheduler – i.e. The changes in Airflow 2.0 are not only aiming to reduce these delays, but also to make it possible to run multiple schedulers at once. This is especially bothersome when Airflow is processing a lot of small tasks and has been a thorn in the side for many Airflow users & contributors. With the current implementation of the scheduler, users can experience a slight delay in tasks being picked up due to the overhead when switching from tasks. The Airflow scheduler, responsible for picking up and distributing tasks over the various workers, is one of the core components in Airflow. Having Airflow DAGs versioned seems like a great addition to that and a another great step towards consistent and clear flows of data. git) and in rapid progress for data (e.g. Versioning is already well incorporated into the data science world with regards to code (e.g. From the presentation, it was not exactly clear how this would look like, but we are definitely looking forward to this features. DAG versioning will be introduced to overcome this inconsistency. DAG versioningĬurrently, adding tasks to an existing DAG has the side effect of introducing “no-status”-tasks in the historic overview. In Airflow 2.0, DAG parsing & serialization will most likely be done by a separate component: ‘serializer’ or ‘DAG parser’ were two of the suggestions, although the exact name is still to be confirmed.
Note: DAG serialization is already available in the latest version of Airflow, where the scheduler takes up this task. The load on the webserver is reduced as the serialized DAGs are retrieved from the database instead of being parsed from the DAG files.This is simplifying the Airflow setup and the deployment of DAGs. The webserver doesn’t need to be able to access the DAG files.This representation can then be fetched by the webserver to populate the user interface. When enabled, the scheduler will take care of parsing the DAG files and store a representation in the database. json) representation of the DAGs in the database. Both of these Airflow components would then actively read and parse the DAG files.ĭAG serialization refers to the process of storing a serialized (i.e. Previously, both the Airflow webserver and scheduler needed to have access to the DAG files. In a previous post, we compared Airflow and Data Factory.If you're new to Airflow or looking for more information, the Airflow website was recently restyled and should be a great starting point.In this article, we will provide a high level summary of the changes that were discussed, focussing on the changes that are most relevant to the people building, maintaining and following up on DAG runs, less so on the setup of Airflow itself. Being big fans of Airflow at element61, we were curious to find out what changes are to be expected in this long-awaited Airflow version. On, the NYC Apache Airflow Meetup hosted a virtual event entitled “What’s coming in Airflow 2.0”.
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