#Airflow logo code
Operators are pre-built blocks of code that are specialised for certain types of tasks such as running SQL queries or uploading files to a cloud. This makes it a flexible platform suitable for many different workflow use cases.Īnother special feature of Airflow is its concept of "operators", which allow users to define tasks in workflows. Airflow also supports integration with a wide range of sources and tools, including databases, cloud services and various programming languages. This web interface also provides extensive monitoring functionality allowing users to monitor workflow progress, task behaviour and diagnose errors. One of the special features of Apache Airflow is its user-friendly web interface, which allows users to visually plan and manage workflows. It was originally developed by Airbnb and is now an Apache Foundation project. The notifications framework allows you to send messages to external systems when a task instance/DAG run changes state.Apache Airflow is an open source platform for scheduling, managing and monitoring workflows. 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. 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. 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.
#Airflow logo update
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. Trigger logs have now been added to task logs. Trigger logs can now be viewed in webserver 🐳 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.
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