By default, downstream tasks are also skipped. In this case, the mapped task is marked skipped, and downstream tasks are run according to the trigger rules you set. For example, when your upstream task that generates the mapping values returns an empty list. You can have a mapped task that results in no task instances.You can use the results of a mapped task as input to a downstream mapped task.If you're using traditional operators and not decorated tasks), the mapping values must be stored in XComs. The upstream task must return a value in a dict or list form. You can use the results of an upstream task as the input to a mapped task.When you work with mapped tasks, keep the following in mind: The partial function specifies a value for y that remains constant in each task. This expand function creates three mapped add tasks, one for each entry in the x input list. expand(), to dynamically generate three task runs: In the following example, the task uses both. This type of mapping uses the function expand_kwargs() instead of expand(). partial(): This function passes any parameters that remain constant across all mapped tasks which are generated by expand().Īirflow 2.4 allowed the mapping of multiple keyword argument sets.A separate parallel task is created for each input. expand(): This function passes the parameters that you want to map.For the task you want to map, all operator parameters must be passed through one of the following functions. In practice, this means that your DAG can create an arbitrary number of parallel tasks at runtime based on some input parameter (the map), and then if needed, have a single task downstream of your parallel mapped tasks that depends on their output (the reduce).Īirflow tasks have two new functions available to implement the map portion of dynamic task mapping. The reduce procedure, which is optional, allows a task to operate on the collected output of a mapped task. Dynamic task mapping creates a single task for each input. The Airflow dynamic task mapping feature is based on the MapReduce programming model.
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Prior to Airflow 2.3, tasks could only be generated dynamically at the time that the DAG was parsed, meaning you had to change your DAG code if you needed to adjust tasks based on some external factor. This feature, known as dynamic task mapping, is a paradigm shift for DAG design in Airflow. With the release of Airflow 2.3, you can write DAGs that dynamically generate parallel tasks at runtime.
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