12/7/2023 0 Comments Airflow dag with one taskIn that case, you can either mark the first task as successful or add a branch to check whether Airflow is executing the DAG for the first time or not.Īnother solution, sharing the problem of the first execution though, could consist on using ExternalTaskSensor. It will work but you will encounter a problem for the first execution. So, a simple check could be a DAG like this: In the example, we're starting the DAG by generating partition information. To solve the problem, you can use sensors. Even though one specific task will be blocked, meantime you waste your budget on not used resources. Depending on your DAG construction, you may end up with such bad situation when, despite the failure of the previous DAG execution, your cloud resource is up and running, and it's unable to stop because of broken task dependencies: When the 3 properties are not enoughĪs you can see, sometimes using these 3 properties won't be enough. Why it's only an impression? Let's take an example of an ephemeral EMR cluster that starts, executes a job and destroys the resource. Alongside the 2 previously presented properties, when max_active_runs is set to 1, it gives an impression of sequentiality: max_active_runs - this DAG-level property enforces that at given moment there will be only one task instance executed.In other words, it means that if all tasks (task1) -> (task2) -> (task3) are waiting for downstream, the second execution of task2 will wait for the first execution of task1, the second execution of task3 will wait for the first execution of task2 and so on.Īfter adding this property to default arguments of tested DAG (depends_on_past will be automatically set to True), the execution graph looks like: wait_for_downstream - this property is quite similar to depends_on_past unless it refers to the downstream tasks. The following screenshot shows how the DAG behaves when it's running: Sounds unclear? Let's take a sample DAG and see how depends_on_past looks on the job executions diagram: depends_on_past - this task-level property defines whether given task execution depends on the result of the past task execution.Only the next section will present an improved solution that should work for most of the use cases. By the end of this section, you will see that using them is not enough to guarantee the sequential character of the pipelines. This post starts by describing 3 properties that you can use to control the concurrency of your Apache Airflow workloads.
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