With Airflow, you author workflows as Directed Acyclic Graphs (DAGs) of tasks written in Python. The notifications framework allows you to send messages to external systems when a task instance/DAG run changes state. According to the documentation, Apache Airflow is an open-source platform to author, schedule, and monitor workflows programmatically. See the cluster policy docs for more details. By allowing multiple hooks to be defined, it makes it easier for more than one team to run hooks in a single Airflow instance. dag_policy), can now come from Airflow plugins in addition to Airflow local settings. Cluster Policy hooks can come from pluginsĬluster policy hooks (e.g. When workflows are defined as code, they become. With a simpler implementation than the outgoing code handling these tasks, tasks stuck in queued will no longer slip through the cracks and stay stuck.įor more details, see the Unsticking Airflow: Stuck Queued Tasks are No More in 2.6.0 Medium post. Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. Consolidation of handling stuck queued tasksĪirflow now has a single configuration, task_queued_timeout, to handle tasks that get stuck in queued for too long. If you choose to filter downstream, this is the result:Ī user-friendly form is now shown to users triggering runs for DAGs with DAG level params. For example, in the screenshot above, describe_integrity is the selected task. You can also filter upstream and downstream from a single task. This offers a more integrated graph representation of the DAG, where choosing a task in either the grid or graph will highlight the same task in both views. Most notably, there is now a graph tab in the grid view. The grid view has received a number of minor improvements in this release. They appear right alongside the rest of the logs from your task.Īdding this feature required changes across the entire Airflow logging stack, so be sure to update your providers if you are using remote logging. In this project I created a pipeline using Airflow and Docker to carryout the following tasks: Download the ETF and stock datasets from the primary dataset available at Setup a data structure to retain all data from ETFs and stocks with the following columns. Trigger logs have now been added to task logs. Monitor the performance of your Airflow DAGs, Snowflake, BigQuery, and DBT tasks using Datadog and the data consistency job. Trigger logs can now be viewed in webserver Follow the deployment steps mentioned in the previous sections of this document to sync the DAGs to a Google Cloud Storage bucket and configure the Airflow instance to use the GCS bucket as its DAG folder. □ Docker Image: “docker pull apache/airflow:2.6.0”Īs the changelog is quite large, the following are some notable new features that shipped in this release. I am excited to announce that Apache Airflow 2.6.0 has been released, bringing many minor features and improvements to the community.Īpache Airflow 2.6.0 contains over 500 commits, which include 42 new features, 58 improvements, 38 bug fixes, and 17 documentation changes.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |