Enable Data Jobs Monitoring for Databricks

Docs > Data Jobs Monitoring > Enable Data Jobs Monitoring for Databricks

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.

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

Configure the Datadog-Databricks integration

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.
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.
In Datadog, open the Databricks integration tile.
On the Configure tab, click Add New.
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.

In Databricks, click your display name (email address) in the upper right corner of the page.
Select Settings and click the Compute tab.
In the All purpose clusters section, next to Global init scripts, click Manage.
Click Add. Name your script. Then, in the Script field, copy and paste the following script, remembering to replace the placeholders with your parameter values.
```
#!/bin/bash

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

# Download and run the latest init script
bash -c "$(curl -L https://dd-data-jobs-monitoring-setup.s3.amazonaws.com/scripts/databricks/databricks_init_latest.sh)" || true
```
The script above sets the required parameters, downloads and runs the latest init script for Data Jobs Monitoring in Databricks. If you want to pin your script to a specific version, you can replace the file name in the URL with databricks_init_1.2.0.sh to use the last stable version.
To enable the script for all new and restarted clusters, toggle Enabled.
Click Add.

Set the required init script parameters

Provide the values for the init script parameters at the beginning of the global init script.

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

Optionally, you can also set other init script parameters and Datadog environment variables here, such as DD_ENV and DD_SERVICE. The script can be configured using the following parameters:

Variable	Description	Default
DD_API_KEY	Your Datadog API key.
DD_SITE	Your Datadog site.
DATABRICKS_WORKSPACE	Name of your Databricks Workspace. It should match the name provided in the Datadog-Databricks integration step. Enclose the name in double quotes if it contains whitespace.
DRIVER_LOGS_ENABLED	Collect spark driver logs in Datadog.	false
WORKER_LOGS_ENABLED	Collect spark workers logs in Datadog.	false
DD_DJM_ADD_LOGS_TO_FAILURE_REPORT	Include init script logs for debugging when reporting a failure back to Datadog.	false

In Databricks, create a init script file in Workspace with the following content. Be sure to make note of the file path.
```
#!/bin/bash

# Download and run the latest init script
bash -c "$(curl -L https://dd-data-jobs-monitoring-setup.s3.amazonaws.com/scripts/databricks/databricks_init_latest.sh)" || true
```
The script above downloads and runs the latest init script for Data Jobs Monitoring in Databricks. If you want to pin your script to a specific version, you can replace the file name in the URL with databricks_init_1.2.0.sh to use the last stable version.
On the cluster configuration page, click the Advanced options toggle.
At the bottom of the page, go to the Init Scripts tab.
- Under the Destination drop-down, select Workspace.
- Under Init script path, enter the path to your init script.
- Click Add.

Set the required init script parameters

In Databricks, on the cluster configuration page, click the Advanced options toggle.
At the bottom of the page, go to the Spark tab.
In the Environment variables textbox, provide the values for the init script parameters.
```
DD_API_KEY=<YOUR API KEY>
DD_SITE=<YOUR DATADOG SITE>
DATABRICKS_WORKSPACE="<YOUR WORKSPACE NAME>"
```
Optionally, you can also set other init script parameters and Datadog environment variables here, such as DD_ENV and DD_SERVICE. The script can be configured using the following parameters:

Variable	Description	Default
DD_API_KEY	Your Datadog API key.
DD_SITE	Your Datadog site.
DATABRICKS_WORKSPACE	Name of your Databricks Workspace. It should match the name provided in the Datadog-Databricks integration step. Enclose the name in double quotes if it contains whitespace.
DRIVER_LOGS_ENABLED	Collect spark driver logs in Datadog.	false
WORKER_LOGS_ENABLED	Collect spark workers logs in Datadog.	false
DD_DJM_ADD_LOGS_TO_FAILURE_REPORT	Include init script logs for debugging when reporting a failure back to Datadog.	false

Click Confirm.

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"
            }
         }
      }
   ]
}