A DAG that waits for another DAG to complete and so on. You might want to have a DAG that triggers one or multiple DAGs. Lost with all of your DAG dependencies? Hard to remember which DAG waits for which DAG? Not anymore!Ĭreating DAG dependencies is a really common use case. If there are still running dag runs (and no failed dag run) for that day, the day will be shown as 'running'.If all dag runs have succeeded, the day will be shown as 'succeeded'.If there is at least one failure for a day, the color will be a mix of green and red according to the ratio of failed runs over the number of runs for that day.It becomes particularly useful to monitor large backfilling processes.Ĭlick on your DAG and select Calendar ViewĮach day is displayed with a color (heatmap of the ratio of successful and failed runs) according to the DAG runs' states for that day : You can monitor thousands of DAG Runs at once in easy and concise way. The Calendar View introduces an aggregated view of your DAG Run states in a calendar which is way more convenient. While the Tree view is great to have an overview of the task's status for each DAG Run, it becomes hard to read if you have a lot of DAG Runs. Monitor the state of thousands of DAG runs at once with the Calendar View!ĭAGs generate a lot of DAG Runs. With DAG('task_group_dag', start_date=datetime(2021, 1 ,1)) as dag: The decorator gives an even more convenient way of grouping your tasks.įrom corators import task, task_group Whereas SubDAGs are complex and will be deprecated, TaskGroups do the same job in a faster and easier way. TaskGroup is the new of way of grouping your tasks in your DAGs. Group your tasks with the TaskGroup decorator Order_data_dict = json.loads(data_string) Import json # You must make your imports within the functionĭata_string = '' Your tasks run in a python virtual environment so that they can have their own dependencies without altering the 'host' system. Practitioners using the scheduler with Postgres 9.6+ or MySQL 8+ can also profit from the option to run more than one scheduler instance at a time, which adds resiliency to any setup and makes the scheduler high availability compatible.Running tasks in a python virtual environment with the PythonVirtualenvDecoratorĮasiest way of instantiating the PythonVirtualenvOperator. Switching from an executor_config dictionary to the pod_override parameter, “which takes a Kubernetes V1Pod object for a1:1 setting override”, helped to reduce the codebase of the executor and improved execution speed.Īnother focal point on the way to Airflow 2.0 was an initiative to improve the performance of the scheduler, which seems to have reduced the time the component needs to start tasks.

In the spirit of speeding things up and making them easier, the KubernetesExecutor has been re-architected so users can easily access the Kubernetes API to create their.yaml pod_template_file without the need to specify parameters in the Airflow configuration. In order to improve the experience when grouping tasks via the UI, the Airflow team has come up with the concept of task groups, which can be used instead of SubDAGs but comes without drawbacks such as limited parallelism. The so-called TaskFlow API is meant to help handle dependencies and promises to make declaring PythonOperator a tad easier. The latter was proposed in February and adds an API to explicitly declare messages passed between tasks in a DAG. Amongst other things the platform for programmatically authoring, scheduling, and monitoring workflows now comes with a stabilised REST API and introduces a new paradigm to write directed acyclic graphs (DAG). Just before going on their end of year break, the team behind Airbnb-bred open source project Apache Airflow has pushed its second major out of the door.