Databricks

Supported OS Linux Windows Mac OS

Data Jobs Monitoring helps you observe, troubleshoot, and cost-optimize your Databricks jobs and clusters.

This page is limited to documentation for ingesting Databricks model serving metrics, cluster utilization data, and reference tables.

Databricks default dashboard

Overview

Datadog offers several Databricks monitoring capabilities.

Data Jobs Monitoring provides monitoring for your Databricks jobs and clusters. You can detect problematic Databricks jobs and workflows anywhere in your data pipelines, remediate failed and long-running-jobs faster, and optimize cluster resources to reduce costs.

Cloud Cost Management gives you a view to analyze all your Databricks DBU costs alongside the associated cloud spend.

Log Management enables you to aggregate and analyze logs from your Databricks jobs & clusters. You can collect these logs as part of Data Jobs Monitoring.

Infrastructure Monitoring gives you a limited subset of the Data Jobs Monitoring functionality - visibility into the resource utilization of your Databricks clusters and Apache Spark performance metrics.

Reference Tables allow you to import metadata from your Databricks workspace into Datadog. These tables enrich your Datadog telemetry with critical context like workspace names, job definitions, cluster configurations, and user roles.

Model serving metrics provide insights into how your Databricks model serving infrastructure is performing. With these metrics, you can detect endpoints that have high error rate, high latency, are over/under provisioned, and more.

Setup

Installation

Gain insight into the health of your model serving infrastructure by following the Model Serving Configuration instructions.

Monitor Databricks Spark applications with the Datadog Spark integration. Install the Datadog Agent on your clusters following the configuration instructions for your appropriate cluster. Refer to Spark Configuration instructions.

Configuration

Model Serving Configuration

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 an access token for a service principal.
In Datadog, open the Databricks integration tile.
On the Configure tab, click Add Databricks Workspace.
Enter a workspace name, your Databricks workspace URL, and the Databricks token you generated.
In the Select resources to set up collection section, make sure Metrics - Model Serving is Enabled.

Reference Table Configuration

Configure a workspace in Datadog’s Databricks integration tile.
In the accounts detail panel, click Reference Tables.
In the Reference Tables tab, click Add New Reference Table.
Provide the Reference table name, Databricks table name, and Primary key of your Databricks view or table.

For optimal results, create a view in Databricks that includes only the specific data you want to send to Datadog. This means generating a dedicated table that reflects the exact scope needed for your use case.

Click Save.

Spark Configuration

Configure the Spark integration to monitor your Apache Spark Cluster on Databricks and collect system and Spark metrics.

Each script described below can be modified to suits your needs. For instance, you can:

Add specific tags to your instances.
Modify the Spark integration configuration.

For security reasons, it's not recommended to define the `DD_API_KEY` environment variable in plain text directly in the UI. Instead, use Databricks secrets.

With a global init script

A global init script runs on every cluster created in your workspace. Global init scripts are useful when you want to enforce organization-wide library configurations or security screens.

Only workspace admins can manage global init scripts.

Global init scripts only run on clusters configured with single user or legacy no-isolation shared access mode. Therefore, Databricks recommends configuring all init scripts as cluster-scoped and managing them across your workspace using cluster policies.

Use the Databricks UI to edit the global init scripts:

Choose one of the following scripts to install the Agent on the driver or on the driver and worker nodes of the cluster.
Modify the script to suit your needs. For example, you can add tags or define a specific configuration for the integration.
Go to the Admin Settings and click the Global Init Scripts tab.
Click on the + Add button.
Name the script, for example Datadog init script and then paste it in the Script field.
Click on the Enabled toggle to enable it.
Click on the Add button.

After these steps, any new cluster uses the script automatically. More information on global init scripts can be found in the Databricks official documentation.

You can define several init scripts and specify their order in the UI.

Install the Datadog Agent on driver

Install the Datadog Agent on the driver node of the cluster.

You need to define the value of the `DD_API_KEY` variable inside the script.

#!/bin/bash
cat <<EOF > /tmp/start_datadog.sh
#!/bin/bash

date -u +"%Y-%m-%d %H:%M:%S UTC"
echo "Running on the driver? \$DB_IS_DRIVER"
echo "Driver ip: \$DB_DRIVER_IP"

DB_CLUSTER_NAME=$(echo "$DB_CLUSTER_NAME" | sed -e 's/ /_/g' -e "s/'/_/g")
DD_API_KEY='<YOUR_API_KEY>'

if [[ \${DB_IS_DRIVER} = "TRUE" ]]; then
  echo "Installing Datadog Agent on the driver..."

  # CONFIGURE HOST TAGS FOR DRIVER
  DD_TAGS="environment:\${DD_ENV}","databricks_cluster_id:\${DB_CLUSTER_ID}","databricks_cluster_name:\${DB_CLUSTER_NAME}","spark_host_ip:\${DB_DRIVER_IP}","spark_node:driver","databricks_instance_type:\${DB_INSTANCE_TYPE}","databricks_is_job_cluster:\${DB_IS_JOB_CLUSTER}"

  # INSTALL THE LATEST DATADOG AGENT 7 ON DRIVER AND WORKER NODES
  DD_INSTALL_ONLY=true \
    DD_API_KEY=\$DD_API_KEY \
    DD_HOST_TAGS=\$DD_TAGS \
    DD_HOSTNAME="\$(hostname | xargs)" \
    DD_SITE="\${DD_SITE:-datadoghq.com}" \
    bash -c "\$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script_agent7.sh)"

  # Avoid conflicts on port 6062
  echo "process_config.expvar_port: 6063" >> /etc/datadog-agent/datadog.yaml

  echo "Datadog Agent is installed"

  while [ -z \$DB_DRIVER_PORT ]; do
    if [ -e "/tmp/driver-env.sh" ]; then
      DB_DRIVER_PORT="\$(grep -i "CONF_UI_PORT" /tmp/driver-env.sh | cut -d'=' -f2)"
    fi
    echo "Waiting 2 seconds for DB_DRIVER_PORT"
    sleep 2
  done

  echo "DB_DRIVER_PORT=\$DB_DRIVER_PORT"

  # WRITING CONFIG FILE FOR SPARK INTEGRATION WITH STRUCTURED STREAMING METRICS ENABLED
  # MODIFY TO INCLUDE OTHER OPTIONS IN spark.d/conf.yaml.example
  echo "init_config:
instances:
    - spark_url: http://\${DB_DRIVER_IP}:\${DB_DRIVER_PORT}
      spark_cluster_mode: spark_driver_mode
      cluster_name: \${DB_CLUSTER_NAME}
      streaming_metrics: true
      executor_level_metrics: true
logs:
    - type: file
      path: /databricks/driver/logs/*.log
      source: spark
      service: databricks
      log_processing_rules:
        - type: multi_line
          name: new_log_start_with_date
          pattern: \d{2,4}[\-\/]\d{2,4}[\-\/]\d{2,4}.*" > /etc/datadog-agent/conf.d/spark.d/spark.yaml

  echo "Spark integration configured"

  # ENABLE LOGS IN datadog.yaml TO COLLECT DRIVER LOGS
  sed -i '/.*logs_enabled:.*/a logs_enabled: true' /etc/datadog-agent/datadog.yaml
fi

echo "Restart the agent"
sudo service datadog-agent restart
EOF

chmod a+x /tmp/start_datadog.sh
/tmp/start_datadog.sh >> /tmp/datadog_start.log 2>&1 & disown

Install the Datadog Agent on driver and worker nodes

Install the Datadog Agent on the driver and worker nodes of the cluster.

You will need to define the value of the `DD_API_KEY` variable inside the script.

#!/bin/bash
cat <<EOF > /tmp/start_datadog.sh
#!/bin/bash

date -u +"%Y-%m-%d %H:%M:%S UTC"
echo "Running on the driver? \$DB_IS_DRIVER"
echo "Driver ip: \$DB_DRIVER_IP"

DB_CLUSTER_NAME=$(echo "$DB_CLUSTER_NAME" | sed -e 's/ /_/g' -e "s/'/_/g")
DD_API_KEY='<YOUR_API_KEY>'

if [[ \${DB_IS_DRIVER} = "TRUE" ]]; then
  echo "Installing Datadog Agent on the driver (master node)."

  # CONFIGURE HOST TAGS FOR DRIVER
  DD_TAGS="environment:\${DD_ENV}","databricks_cluster_id:\${DB_CLUSTER_ID}","databricks_cluster_name:\${DB_CLUSTER_NAME}","spark_host_ip:\${DB_DRIVER_IP}","spark_node:driver","databricks_instance_type:\${DB_INSTANCE_TYPE}","databricks_is_job_cluster:\${DB_IS_JOB_CLUSTER}"

  # INSTALL THE LATEST DATADOG AGENT 7 ON DRIVER AND WORKER NODES
  DD_INSTALL_ONLY=true \
    DD_API_KEY=\$DD_API_KEY \
    DD_HOST_TAGS=\$DD_TAGS \
    DD_HOSTNAME="\$(hostname | xargs)" \
    DD_SITE="\${DD_SITE:-datadoghq.com}" \
    bash -c "\$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script_agent7.sh)"

  echo "Datadog Agent is installed"

  while [ -z \$DB_DRIVER_PORT ]; do
    if [ -e "/tmp/driver-env.sh" ]; then
      DB_DRIVER_PORT="\$(grep -i "CONF_UI_PORT" /tmp/driver-env.sh | cut -d'=' -f2)"
    fi
    echo "Waiting 2 seconds for DB_DRIVER_PORT"
    sleep 2
  done

  echo "DB_DRIVER_PORT=\$DB_DRIVER_PORT"

  # WRITING CONFIG FILE FOR SPARK INTEGRATION WITH STRUCTURED STREAMING METRICS ENABLED
  # MODIFY TO INCLUDE OTHER OPTIONS IN spark.d/conf.yaml.example
  echo "init_config:
instances:
    - spark_url: http://\${DB_DRIVER_IP}:\${DB_DRIVER_PORT}
      spark_cluster_mode: spark_driver_mode
      cluster_name: \${DB_CLUSTER_NAME}
      streaming_metrics: true
      executor_level_metrics: true
logs:
    - type: file
      path: /databricks/driver/logs/*.log
      source: spark
      service: databricks
      log_processing_rules:
        - type: multi_line
          name: new_log_start_with_date
          pattern: \d{2,4}[\-\/]\d{2,4}[\-\/]\d{2,4}.*" > /etc/datadog-agent/conf.d/spark.d/spark.yaml

  echo "Spark integration configured"

  # ENABLE LOGS IN datadog.yaml TO COLLECT DRIVER LOGS
  sed -i '/.*logs_enabled:.*/a logs_enabled: true' /etc/datadog-agent/datadog.yaml
else
  echo "Installing Datadog Agent on the worker."

  # CONFIGURE HOST TAGS FOR WORKERS
  DD_TAGS="environment:\${DD_ENV}","databricks_cluster_id:\${DB_CLUSTER_ID}","databricks_cluster_name:\${DB_CLUSTER_NAME}","spark_host_ip:\${SPARK_LOCAL_IP}","spark_node:worker","databricks_instance_type:\${DB_INSTANCE_TYPE}","databricks_is_job_cluster:\${DB_IS_JOB_CLUSTER}"

  # INSTALL THE LATEST DATADOG AGENT 7 ON DRIVER AND WORKER NODES
  # CONFIGURE HOSTNAME EXPLICITLY IN datadog.yaml TO PREVENT AGENT FROM FAILING ON VERSION 7.40+
  # SEE https://github.com/DataDog/datadog-agent/issues/14152 FOR CHANGE
  DD_INSTALL_ONLY=true DD_API_KEY=\$DD_API_KEY DD_HOST_TAGS=\$DD_TAGS DD_HOSTNAME="\$(hostname | xargs)" bash -c "\$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script_agent7.sh)"

  echo "Datadog Agent is installed"
fi

# Avoid conflicts on port 6062
echo "process_config.expvar_port: 6063" >> /etc/datadog-agent/datadog.yaml

echo "Restart the agent"
sudo service datadog-agent restart
EOF

chmod a+x /tmp/start_datadog.sh
/tmp/start_datadog.sh >> /tmp/datadog_start.log 2>&1 & disown

With a cluster-scoped init script

Cluster-scoped init scripts are init scripts defined in a cluster configuration. Cluster-scoped init scripts apply to both clusters you create and those created to run jobs. Databricks supports configuration and storage of init scripts through:

Workspace Files
Unity Catalog Volumes
Cloud Object Storage

Use the Databricks UI to edit the cluster to run the init script:

Choose one of the following scripts to install the Agent on the driver or on the driver and worker nodes of the cluster.
Modify the script to suit your needs. For example, you can add tags or define a specific configuration for the integration.
Save the script into your workspace with the Workspace menu on the left. If using Unity Catalog Volume, save the script in your Volume with the Catalog menu on the left.
On the cluster configuration page, click the Advanced options toggle.
In the Environment variables, specify the DD_API_KEY environment variable and, optionally, the DD_ENV and the DD_SITE environment variables.
Go to the Init Scripts tab.
In the Destination dropdown, select the Workspace destination type. If using Unity Catalog Volume, in the Destination dropdown, select the Volume destination type.
Specify a path to the init script.
Click on the Add button.

If you stored your datadog_init_script.sh directly in the Shared workspace, you can access the file at the following path: /Shared/datadog_init_script.sh.

If you stored your datadog_init_script.sh directly in a user workspace, you can access the file at the following path: /Users/$EMAIL_ADDRESS/datadog_init_script.sh.

If you stored your datadog_init_script.sh directly in a Unity Catalog Volume, you can access the file at the following path: /Volumes/$VOLUME_PATH/datadog_init_script.sh.

More information on cluster init scripts can be found in the Databricks official documentation.

Install the Datadog Agent on Driver

Install the Datadog Agent on the driver node of the cluster.

#!/bin/bash
cat <<EOF > /tmp/start_datadog.sh
#!/bin/bash

date -u +"%Y-%m-%d %H:%M:%S UTC"
echo "Running on the driver? \$DB_IS_DRIVER"
echo "Driver ip: \$DB_DRIVER_IP"

DB_CLUSTER_NAME=$(echo "$DB_CLUSTER_NAME" | sed -e 's/ /_/g' -e "s/'/_/g")

if [[ \${DB_IS_DRIVER} = "TRUE" ]]; then
  echo "Installing Datadog Agent on the driver..."

  # CONFIGURE HOST TAGS FOR DRIVER
  DD_TAGS="environment:\${DD_ENV}","databricks_cluster_id:\${DB_CLUSTER_ID}","databricks_cluster_name:\${DB_CLUSTER_NAME}","spark_host_ip:\${DB_DRIVER_IP}","spark_node:driver","databricks_instance_type:\${DB_INSTANCE_TYPE}","databricks_is_job_cluster:\${DB_IS_JOB_CLUSTER}"

  # INSTALL THE LATEST DATADOG AGENT 7 ON DRIVER AND WORKER NODES
  DD_INSTALL_ONLY=true \
    DD_API_KEY=\$DD_API_KEY \
    DD_HOST_TAGS=\$DD_TAGS \
    DD_HOSTNAME="\$(hostname | xargs)" \
    DD_SITE="\${DD_SITE:-datadoghq.com}" \
    bash -c "\$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script_agent7.sh)"

  # Avoid conflicts on port 6062
  echo "process_config.expvar_port: 6063" >> /etc/datadog-agent/datadog.yaml

  echo "Datadog Agent is installed"

  while [ -z \$DB_DRIVER_PORT ]; do
    if [ -e "/tmp/driver-env.sh" ]; then
      DB_DRIVER_PORT="\$(grep -i "CONF_UI_PORT" /tmp/driver-env.sh | cut -d'=' -f2)"
    fi
    echo "Waiting 2 seconds for DB_DRIVER_PORT"
    sleep 2
  done

  echo "DB_DRIVER_PORT=\$DB_DRIVER_PORT"

  # WRITING CONFIG FILE FOR SPARK INTEGRATION WITH STRUCTURED STREAMING METRICS ENABLED
  # MODIFY TO INCLUDE OTHER OPTIONS IN spark.d/conf.yaml.example
  echo "init_config:
instances:
    - spark_url: http://\${DB_DRIVER_IP}:\${DB_DRIVER_PORT}
      spark_cluster_mode: spark_driver_mode
      cluster_name: \${DB_CLUSTER_NAME}
      streaming_metrics: true
      executor_level_metrics: true
logs:
    - type: file
      path: /databricks/driver/logs/*.log
      source: spark
      service: databricks
      log_processing_rules:
        - type: multi_line
          name: new_log_start_with_date
          pattern: \d{2,4}[\-\/]\d{2,4}[\-\/]\d{2,4}.*" > /etc/datadog-agent/conf.d/spark.d/spark.yaml

  echo "Spark integration configured"

  # ENABLE LOGS IN datadog.yaml TO COLLECT DRIVER LOGS
  sed -i '/.*logs_enabled:.*/a logs_enabled: true' /etc/datadog-agent/datadog.yaml
fi


echo "Restart the agent"
sudo service datadog-agent restart
EOF

chmod a+x /tmp/start_datadog.sh
/tmp/start_datadog.sh >> /tmp/datadog_start.log 2>&1 & disown

Install the Datadog Agent on driver and worker nodes

Install the Datadog Agent on the driver and worker nodes of the cluster.

#!/bin/bash
cat <<EOF > /tmp/start_datadog.sh
#!/bin/bash

date -u +"%Y-%m-%d %H:%M:%S UTC"
echo "Running on the driver? \$DB_IS_DRIVER"
echo "Driver ip: \$DB_DRIVER_IP"

DB_CLUSTER_NAME=$(echo "$DB_CLUSTER_NAME" | sed -e 's/ /_/g' -e "s/'/_/g")

if [[ \${DB_IS_DRIVER} = "TRUE" ]]; then
  echo "Installing Datadog Agent on the driver (master node)."

  # CONFIGURE HOST TAGS FOR DRIVER
  DD_TAGS="environment:\${DD_ENV}","databricks_cluster_id:\${DB_CLUSTER_ID}","databricks_cluster_name:\${DB_CLUSTER_NAME}","spark_host_ip:\${DB_DRIVER_IP}","spark_node:driver","databricks_instance_type:\${DB_INSTANCE_TYPE}","databricks_is_job_cluster:\${DB_IS_JOB_CLUSTER}"

  # INSTALL THE LATEST DATADOG AGENT 7 ON DRIVER AND WORKER NODES
  DD_INSTALL_ONLY=true \
    DD_API_KEY=\$DD_API_KEY \
    DD_HOST_TAGS=\$DD_TAGS \
    DD_HOSTNAME="\$(hostname | xargs)" \
    DD_SITE="\${DD_SITE:-datadoghq.com}" \
    bash -c "\$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script_agent7.sh)"

  echo "Datadog Agent is installed"

  while [ -z \$DB_DRIVER_PORT ]; do
    if [ -e "/tmp/driver-env.sh" ]; then
      DB_DRIVER_PORT="\$(grep -i "CONF_UI_PORT" /tmp/driver-env.sh | cut -d'=' -f2)"
    fi
    echo "Waiting 2 seconds for DB_DRIVER_PORT"
    sleep 2
  done

  echo "DB_DRIVER_PORT=\$DB_DRIVER_PORT"

  # WRITING CONFIG FILE FOR SPARK INTEGRATION WITH STRUCTURED STREAMING METRICS ENABLED
  # MODIFY TO INCLUDE OTHER OPTIONS IN spark.d/conf.yaml.example
  echo "init_config:
instances:
    - spark_url: http://\${DB_DRIVER_IP}:\${DB_DRIVER_PORT}
      spark_cluster_mode: spark_driver_mode
      cluster_name: \${DB_CLUSTER_NAME}
      streaming_metrics: true
      executor_level_metrics: true
logs:
    - type: file
      path: /databricks/driver/logs/*.log
      source: spark
      service: databricks
      log_processing_rules:
        - type: multi_line
          name: new_log_start_with_date
          pattern: \d{2,4}[\-\/]\d{2,4}[\-\/]\d{2,4}.*" > /etc/datadog-agent/conf.d/spark.d/spark.yaml

  echo "Spark integration configured"

  # ENABLE LOGS IN datadog.yaml TO COLLECT DRIVER LOGS
  sed -i '/.*logs_enabled:.*/a logs_enabled: true' /etc/datadog-agent/datadog.yaml
else
  echo "Installing Datadog Agent on the worker."

  # CONFIGURE HOST TAGS FOR WORKERS
  DD_TAGS="environment:\${DD_ENV}","databricks_cluster_id:\${DB_CLUSTER_ID}","databricks_cluster_name:\${DB_CLUSTER_NAME}","spark_host_ip:\${SPARK_LOCAL_IP}","spark_node:worker","databricks_instance_type:\${DB_INSTANCE_TYPE}","databricks_is_job_cluster:\${DB_IS_JOB_CLUSTER}"

  # INSTALL THE LATEST DATADOG AGENT 7 ON DRIVER AND WORKER NODES
  # CONFIGURE HOSTNAME EXPLICITLY IN datadog.yaml TO PREVENT AGENT FROM FAILING ON VERSION 7.40+
  # SEE https://github.com/DataDog/datadog-agent/issues/14152 FOR CHANGE
  DD_INSTALL_ONLY=true DD_API_KEY=\$DD_API_KEY DD_HOST_TAGS=\$DD_TAGS DD_HOSTNAME="\$(hostname | xargs)" bash -c "\$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script_agent7.sh)"

  echo "Datadog Agent is installed"
fi

# Avoid conflicts on port 6062
echo "process_config.expvar_port: 6063" >> /etc/datadog-agent/datadog.yaml

echo "Restart the agent"
sudo service datadog-agent restart
EOF

chmod a+x /tmp/start_datadog.sh
/tmp/start_datadog.sh >> /tmp/datadog_start.log 2>&1 & disown

Data Collected

Metrics

databricks.model_serving.cpu_usage_percentage (gauge)	Average CPU utilization used across all replicas during the last minute Shown as percent
databricks.model_serving.gpu_mem_usage_percentage.avg (gauge)	Average GPU memory usage used across all GPUs during the minute Shown as percent
databricks.model_serving.gpu_mem_usage_percentage.max (gauge)	Maximum GPU memory usage used across all GPUs during the minute Shown as percent
databricks.model_serving.gpu_mem_usage_percentage.min (gauge)	Minimum GPU memory usage used across all GPUs during the minute Shown as percent
databricks.model_serving.gpu_usage_percentage.avg (gauge)	Average GPU utilization used across all GPUs during the minute Shown as percent
databricks.model_serving.gpu_usage_percentage.max (gauge)	Maximum GPU utilization used across all GPUs during the minute Shown as percent
databricks.model_serving.gpu_usage_percentage.min (gauge)	Minimum GPU utilization used across all GPUs during the minute Shown as percent
databricks.model_serving.mem_usage_percentage (gauge)	Average memory utilization used across all replicas during the last minute Shown as percent
databricks.model_serving.provisioned_concurrent_requests_total (gauge)	Number of provisioned concurrency during the last minute Shown as request
databricks.model_serving.request_4xx_count_total (gauge)	Number of 4xx errors during the last minute Shown as request
databricks.model_serving.request_5xx_count_total (gauge)	Number of 5xx errors during the last minute Shown as request
databricks.model_serving.request_count_total (gauge)	Number of requests during the last minute Shown as request
databricks.model_serving.request_latency_ms.75percentile (gauge)	75th percentile request latency in milliseconds during the minute Shown as millisecond
databricks.model_serving.request_latency_ms.90percentile (gauge)	90th percentile request latency in milliseconds during the minute Shown as millisecond
databricks.model_serving.request_latency_ms.95percentile (gauge)	95th percentile request latency in milliseconds during the minute Shown as millisecond
databricks.model_serving.request_latency_ms.99percentile (gauge)	99th percentile request latency in milliseconds during the minute Shown as millisecond

Spark Metrics

See the Spark integration documentation for a list of Spark metrics collected.

Service Checks

See the Spark integration documentation for the list of service checks collected.

Events

The Databricks integration does not include any events.

Troubleshooting

You can troubleshoot issues yourself by enabling the Databricks web terminal or by using a Databricks Notebook. Consult the Agent Troubleshooting documentation for information on useful troubleshooting steps.

Need help? Contact Datadog support.

Databricks

Overview

Setup

Installation

Configuration

Model Serving Configuration

Reference Table Configuration

Spark Configuration

With a global init script

Install the Datadog Agent on driver

Install the Datadog Agent on driver and worker nodes

With a cluster-scoped init script

Install the Datadog Agent on Driver

Install the Datadog Agent on driver and worker nodes

Data Collected

Metrics

Spark Metrics

Service Checks

Events

Troubleshooting

Further Reading