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Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. It is designed to orchestrate complex computational workflows and data processing pipelines. Airflow allows users to define workflows as Directed Acyclic Graphs (DAGs) of tasks. The platform is highly extensible and supports a wide range of integrations with other tools and services.
The AirflowHighDagFailureRate alert indicates that the failure rate of DAGs is higher than expected. This alert is triggered when the number of failed DAG runs exceeds a predefined threshold over a specific period.
When this alert is triggered, it suggests that there is a systemic issue causing multiple DAGs to fail. This could be due to various reasons such as misconfigurations, resource constraints, or errors in the DAG code itself. Monitoring this alert is crucial as it helps maintain the reliability and efficiency of your workflows.
To resolve the AirflowHighDagFailureRate alert, follow these steps:
Start by reviewing the logs of the failed DAGs to identify any common failure patterns. You can access the logs through the Airflow UI or by checking the log files directly on the server.
airflow logs -d -t -r
Ensure that your Airflow environment has sufficient resources. Check the CPU and memory usage of your Airflow workers and scheduler. Consider scaling up your resources if necessary.
top
or use a monitoring tool like Grafana to visualize resource usage.
Ensure that all DAG configurations are correct. Check for any recent changes that might have introduced errors. Validate environment variables and connection settings.
Test the failing DAGs locally to isolate the issue. Use the following command to test a DAG:
airflow dags test
If your DAGs depend on external systems, ensure that these systems are operational. Check network connectivity and the status of any APIs or databases your DAGs interact with.
By following these steps, you can diagnose and resolve the AirflowHighDagFailureRate alert effectively. Regular monitoring and maintenance of your Airflow environment will help prevent such issues in the future. For more detailed information, refer to the official Apache Airflow documentation.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)