Data Jobs Monitoring gives visibility into the performance and reliability of your Apache Spark and Databricks jobs.

Setup

Follow these steps to enable Data Jobs Monitoring for Databricks.

1. Configure the Datadog-Databricks integration with your Databricks API token.
2. Install the Datadog Agent on your Databricks cluster(s).

Configure the Datadog-Databricks integration

1. In your Databricks workspace, click on your profile in the top right corner and go to Settings. Select Developer in the left side bar. Next to Access tokens, click Manage.

2. Click Generate new token, enter “Datadog Integration” in the Comment field, remove the default value in Lifetime (days), and click Generate. Take note of your token.

   Important:

   • Make sure you delete the default value in Lifetime (days) so that the token doesn’t expire and the integration doesn’t break.
   • Ensure the account generating the token has CAN VIEW access for the Databricks jobs and clusters you want to monitor.

   As an alternative, follow the official Databricks documentation to generate access token for a service principal.

3. In Datadog, open the Databricks integration tile.

4. On the Configure tab, click Add New.

5. Enter a workspace name, your Databricks workspace URL, and the Databricks token you generated.

Install the Datadog Agent on your Databricks cluster(s)

You can choose to install the Agent globally, or on a specific Databricks cluster.

export DD_API_KEY=<YOUR API KEY>
export DD_SITE=<YOUR DATADOG SITE>
export DATABRICKS_WORKSPACE="<YOUR WORKSPACE NAME>"

Validation

In Datadog, view the Data Jobs Monitoring page to see a list of all your Databricks jobs.

Advanced Configuration

Tag spans at runtime

You can set tags on Spark spans at runtime. These tags are applied only to spans that start after the tag is added.

// Add tag for all next Spark computations
sparkContext.setLocalProperty("spark.datadog.tags.key", "value")
spark.read.parquet(...)

To remove a runtime tag:

// Remove tag for all next Spark computations
sparkContext.setLocalProperty("spark.datadog.tags.key", null)

Aggregate cluster metrics from one-time job runs

This configuration is applicable if you want cluster resource utilization data about your jobs and create a new job and cluster for each run via the one-time run API endpoint (common when using orchestration tools outside of Databricks such as Airflow or Azure Data Factory).

If you are submitting Databricks Jobs via the one-time run API endpoint, each job run will have a unique job ID. This can make it difficult to group and analyze cluster metrics for jobs that use ephemeral clusters. To aggregate cluster utilization from the same job and assess performance across multiple runs, you must set the DD_JOB_NAME variable inside the spark_env_vars of every new_cluster to the same value as your request payload’s run_name.

Here’s an example of a one-time job run request body:

{
   "run_name": "Example Job",
   "idempotency_token": "8f018174-4792-40d5-bcbc-3e6a527352c8",
   "tasks": [
      {
         "task_key": "Example Task",
         "description": "Description of task",
         "depends_on": [],
         "notebook_task": {
            "notebook_path": "/Path/to/example/task/notebook",
            "source": "WORKSPACE"
         },
         "new_cluster": {
            "num_workers": 1,
            "spark_version": "13.3.x-scala2.12",
            "node_type_id": "i3.xlarge",
            "spark_env_vars": {
               "DD_JOB_NAME": "Example Job"
            }
         }
      }
   ]
}

Set up Data Jobs Monitoring with Databricks Networking Restrictions

With Databricks Networking Restrictions, Datadog may not have access to your Databricks APIs, which is required to collect traces for Databricks job executions along with tags and other metadata.

If you are controlling Databricks API access through IP access lists, allow-listing Datadog’s specific IP addresses allows your cluster to perform all these interactions with Datadog services. Please see Databricks documentation for more details on how to manage IP access lists in Databricks.

If you are using Databricks Private Connectivity, the steps to configure the connection depend on your cloud provider.

For further assistance, contact the Datadog support team.

Further Reading

Más enlaces, artículos y documentación útiles:

Data Jobs MonitoringDOCUMENTATION