What is Apache Airflow DAG?
DAG stands for Directed Acyclic Graph. DAGs can be used to schedule and monitor airflow tasks. It is a collection of all the tasks we want to run, organized in a way that reflects their relationships and dependencies.
How to work with DAGs?
Some key principles to remember when working with DAGs:
- Tasks in a DAG must have a unique task_id.
- Tasks can have dependencies on other tasks, but these dependencies cannot create a cycle.
- You can specify the order in which tasks should be run by setting the depends_on_past parameter to True for tasks that should only run after the previous task has completed successfully.
- You can also specify an optional start_date for your DAG, which is the earliest date that any task in the DAG can run.
- By default, DAGs are run on a schedule. You can specify a schedule_interval when you create a DAG, or you can use the @once decorator to specify that a DAG should only run once.
- Finally, you can specify an end_date for your DAG, which is the latest date that any task in the DAG can run.
What are some of Apache Airflow DAG pitfalls?
There are several potential problems with an Apache Airflow DAG. One is that it can be challenging to understand the relationships between all of the tasks in a DAG. Another potential problem is that if one task in a DAG fails, it can cause the entire DAG to fail.
Some common problems with Apache Airflow DAGs and their solution:
1. Tasks can fail if their dependencies are not met.
There are a few ways to make sure that dependencies are met for each task in an Apache Airflow DAG:
- You can use the depends_on_past parameter to make sure that a task only runs after the previous task has completed successfully.
- You can also use the trigger_rule parameter to specify how a task should be triggered.
- Finally, you can use the resources parameter to specify the resources that a task needs in order to run.
2. Tasks can also fail if they are not configured correctly.
Some important configurations to verify before running a DAG are:
- You can use the validate_dag parameter to ensure that a DAG is valid before running.
- You can also use the catchup parameter to ensure that a DAG catches up on missed runs.
- Finally, you can use the max_active_runs parameter to limit the number of active runs for a DAG.
3. Tasks can fail if the resources they need are unavailable.
There are a few ways to make sure that the resources a DAG needs are available:
- You can use the queue parameter to specify the queue that a DAG should run in.
- You can also use the pool parameter to specify the pool that a DAG should run in.
- Finally, you can use the SLA parameter to specify the SLA for a DAG.