Enable Data Jobs Monitoring for Apache Airflow

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Data Jobs Monitoring provides visibility into the performance and reliability of workflows run by Apache Airflow DAGs.

Requirements

Apache Airflow 2.9.0 or later
apache-airflow-providers-openlineage 1.11.0 or later

Setup

Data Jobs Monitoring supports Apache Airflow deployments with apache-airflow-providers-openlineage installed.

To get started, follow the instructions below.

Install openlineage provider by adding the following into your requirements.txt file or wherever your Airflow depedencies are managed:
```
apache-airflow-providers-openlineage>=1.11.0
```
Configure openlineage provider. The simplest option is to set the following environment variables and make them available to pods where you run Airflow schedulers and Airflow workers:
```
OPENLINEAGE_URL=<DD_DATA_OBSERVABILITY_INTAKE>
OPENLINEAGE_API_KEY=<DD_API_KEY>
```
- Install and configure openlineage provider for both Airflow schedulers and Airflow workers.
- Replace <DD_DATA_OBSERVABILITY_INTAKE> with https://data-obs-intake..
- Replace <DD_API_KEY> with your valid Datadog API key.
Optional:
- Set AIRFLOW__OPENLINEAGE__NAMESPACE with a unique name for your Airflow deployment to allow jobs from different Airflow deployments logically separated.
- Set OPENLINEAGE_CLIENT_LOGGING to DEBUG for OpenLineage client and its child modules. This can be useful in troubleshooting during the configuration of openlineage provider.
Check official documentation configuration-openlineage for other supported configurations of the openlineage provider.
Trigger an update to your Airflow pods and wait for them to finish.

Requirements

Apache Airflow 2.9.0 or later
apache-airflow-providers-openlineage 1.11.0 or later

Setup

Data Jobs Monitoring is supported for Apache Airflow deployment with apache-airflow-providers-openlineage installed.

To get started, follow the instructions below.

Install openlineage provider by adding the following into your requirements.txt file:
```
apache-airflow-providers-openlineage>=1.11.0
```
Configure openlineage provider. The simplest option is to set the following environment variables in your Amazon MWAA start script:
```
#!/bin/sh

export OPENLINEAGE_URL=<DD_DATA_OBSERVABILITY_INTAKE>
export OPENLINEAGE_API_KEY=<DD_API_KEY>
```
- Replace <DD_DATA_OBSERVABILITY_INTAKE> fully with https://data-obs-intake..
- Replace <DD_API_KEY> fully with your valid Datadog API key.
Optional:
- Set AIRFLOW__OPENLINEAGE__NAMESPACE with a unique name for your Airflow deployment to allow jobs from different Airflow deployments logically separated.
- Set OPENLINEAGE_CLIENT_LOGGING to DEBUG for OpenLineage client and its child modules. This can be useful in troubleshooting during the configuration of openlineage provider.
Check official documentation configuration-openlineage for other supported configurations of openlineage provider.
Deploy your updated requirements.txt and Amazon MWAA start script to your Amazon S3 folder configured for your Amazon MWAA Environment.
Ensure your Execution role configured for your Amazon MWAA Environment has the right permissions to the requirements.txt and Amazon MWAA start script. This is required if you are managing your own Execution role and it’s the first time you are adding those supporting files. See official guide Amazon MWAA execution role for details if needed.

Validation

In Datadog, view the Data Jobs Monitoring page to see a list of your Airflow job runs after the setup.

Advanced Configuration

Link your Spark jobs with Airflow task

You can troubleshoot Airflow tasks that run Spark jobs more efficiently by connecting the Spark job run info and telemetry with the respective Airflow task.

Prerequisites: your Spark jobs are currently monitored through Data Jobs Monitoring and are submitted through SparkSubmitOperators from your Airflow jobs.

To see the link between Airflow task and the the Spark application it submitted, follow these steps:

Configure Airflow to turn off lazy loading of Airflow plugins by setting lazy_load_plugins config to False in your airflow.cfg or exporting the following environment variable where your Airflow schedulers and Airflow workers run:
```
export AIRFLOW__CORE__LAZY_LOAD_PLUGINS='False' 
```

Update your Airflow job’s DAG file by adding the following Spark configurations to your SparkSubmitOperator where you submit your Spark Application:

  SparkSubmitOperator(
    conf={
      "spark.openlineage.parentJobNamespace": "{{ macros.OpenLineageProviderPlugin.lineage_job_namespace() }}",
      "spark.openlineage.parentJobName": "{{ macros.OpenLineageProviderPlugin.lineage_job_name(task_instance) }}",
      "spark.openlineage.parentRunId": "{{ macros.OpenLineageProviderPlugin.lineage_run_id(task_instance) }}",
    },
  )

See Lineage job & run macros for the definitions of referenced macros.

Once you have re-deployed your Airflow environment with the updated lazy_load_plugins config and the updated DAG file, and your Airflow DAG as been re-run, go to Data Jobs Monitoring page. You can then find your latest Airflow job run and see a SpanLink in the Airflow Job Run trace to the trace of the launched Spark Application. This makes it possible to debug issues in Airflow or Spark all in one place.