Databricks Integration

This integration collects telemetry from Databricks (including Spark on Databricks) and/or Spark telemetry from any Spark deployment. See the Features section for supported telemetry types.

Table of Contents

• Important Notes
• Getting Started
  • On-host Deployment
• Features
• Usage
  • Command Line Options
  • Configuration
    • General configuration
    • Pipeline configuration
    • Log configuration
    • Databricks configuration
    • Spark configuration
  • Authentication
  • Apache Spark Data
  • Consumption & Cost Data
  • Job Run Data
  • Pipeline Update Metrics
  • Pipeline Event Logs
  • Cluster Health
  • Query Metrics
• Building
  • Coding Conventions
  • Local Development
  • Releases
  • Github Workflows
• Appendix

Important Notes

• All references within this document to Databricks documentation reference the Databricks on AWS documentation. Use the cloud switcher menu located in the upper right hand corner of the documentation to select corresponding documentation for a different cloud provider.

Getting Started

To get started with the Databricks Integration, deploy the integration using a supported deployment type, configure the integration using supported configuration mechanisms, and optionally import the sample dashboards included in the examples directory.

On-host

The Databricks Integration can be deployed inside a Databricks cluster on the cluster's driver node (recommended) or outside a Databricks cluster on a supported host platform.

NOTE:

• Apache Spark data for a Databricks cluster cannot be collected remotely. In order to collect Apache Spark data from a Databricks cluster, the Databricks Integration must be deployed inside the cluster.
• The Databricks Integration can be used separately from Databricks to solely collect Apache Spark data from any accessible Spark deployment.

Deploy the integration on the driver node of a Databricks cluster

The Databricks Integration can be deployed on the driver node of a Databricks all-purpose, job, or pipeline cluster using a cluster-scoped init script. The init script uses custom environment variables to specify configuration parameters necessary for the integration configuration.

To install the init script on an all-purpose cluster, perform the following steps.

1. Login to your Databricks account and navigate to the desired workspace.
2. Follow the recommendations for init scripts to store the cluster_init_integration.sh script within your workspace in the recommended manner. For example, if your workspace is enabled for Unity Catalog, you should store the init script in a Unity Catalog volume.
3. Navigate to the Compute tab and select the desired all-purpose compute to open the compute details UI.
4. Click the button labeled Edit to edit the compute's configuration.
5. Follow the steps to use the UI to configure a cluster-scoped init script and point to the location where you stored the init script in step 2 above.
6. If your cluster is not running, click on the button labeled Confirm to save your changes. Then, restart the cluster. If your cluster is already running, click on the button labeled Confirm and restart to save your changes and restart the cluster.

Additionally, follow the steps to set environment variables to add the following environment variables.

• NEW_RELIC_API_KEY - Your New Relic User API Key
• NEW_RELIC_LICENSE_KEY - Your New Relic License Key
• NEW_RELIC_ACCOUNT_ID - Your New Relic Account ID
• NEW_RELIC_REGION - The region of your New Relic account; one of US or EU
• NEW_RELIC_DATABRICKS_WORKSPACE_HOST - The instance name of the target Databricks instance
• NEW_RELIC_DATABRICKS_ACCESS_TOKEN - To authenticate with a personal access token, your personal access token
• NEW_RELIC_DATABRICKS_OAUTH_CLIENT_ID - To use a service principal to authenticate with Databricks (OAuth M2M), the OAuth client ID for the service principal
• NEW_RELIC_DATABRICKS_OAUTH_CLIENT_SECRET - To use a service principal to authenticate with Databricks (OAuth M2M), an OAuth client secret associated with the service principal
• NEW_RELIC_DATABRICKS_USAGE_ENABLED - Set to true to enable collection of consumption and cost data from this cluster node or false to disable collection. Defaults to true.
• NEW_RELIC_DATABRICKS_SQL_WAREHOUSE - The ID of a SQL warehouse where usage queries should be run
• NEW_RELIC_DATABRICKS_JOB_RUNS_ENABLED - Set to true to enable collection of job run data from this cluster node or false to disable collection. Defaults to true.
• NEW_RELIC_DATABRICKS_PIPELINE_EVENT_LOGS_ENABLED - Set to true to enable collection of Databricks Delta Live Tables pipeline event logs from this cluster node or false to disable collection. Defaults to true.
• NEW_RELIC_DATABRICKS_QUERY_METRICS_ENABLED - Set to true to enable collection of Databricks query metrics for this workspace or false to disable collection. Defaults to true.
• NEW_RELIC_INFRASTRUCTURE_ENABLED - Set to true to install the New Relic Infrastructure agent on the driver and worker nodes of the cluster. Defaults to false.
• NEW_RELIC_INFRASTRUCTURE_LOGS_ENABLED - Set to true to enable collection of the Spark driver and executor logs, the Spark driver event log, and the driver and worker init script logs. Logs will be forwarded to New Relic Logs via the New Relic Infrastructure agent and therefore, setting this environment variable to true requires that the NEW_RELIC_INFRASTRUCTURE_ENABLED environment variable also be set to true. Defaults to false.

NOTE:

• The NEW_RELIC_API_KEY and NEW_RELIC_ACCOUNT_ID are currently unused but are required by the new-relic-client-go module used by the integration.
• Only the personal access token or OAuth credentials need to be specified but not both. If both are specified, the OAuth credentials take precedence.
• Sensitive data like credentials and API keys should never be specified directly in custom environment variables. Instead, it is recommended to create a secret using the Databricks CLI and reference the secret in the environment variable. See the appendix for an example of creating a secret and referencing it in a custom environment variable.
• To install the init script on a job cluster, see the section configure compute for jobs in the Databricks documentation.
• To install the init script on a pipeline cluster, see the section configure compute for a DLT pipeline in the Databricks documentation.
• Make sure to restart the cluster following the configuration of the environment variables.

Deploy the integration on a host outside a Databricks cluster

The Databricks Integration provides binaries for the following host platforms.

• Linux amd64
• Windows amd64

To run the Databricks integration on a host outside a Databricks cluster, perform the following steps.

1. Download the appropriate archive for your platform from the latest release.
2. Extract the archive to a new or existing directory.
3. Create a directory named configs in the same directory.
4. Create a file named config.yml in the configs directory and copy the contents of the file configs/config.template.yml in this repository into it.
5. Edit the config.yml file to configure the integration appropriately for your environment.
6. From the directory where the archive was extracted, execute the integration binary using the command ./newrelic-databricks-integration (or .\newrelic-databricks-integration.exe on Windows) with the appropriate Command Line Options.

Features

The Databricks Integration supports the following capabilities.

• Collect Spark telemetry

  When deployed inside a Databricks cluster, the Databricks Integration can collect telemetry from all Spark applications running in the cluster.

  When deployed outside a Databricks cluster, the Databricks Integration can also collect Spark telemetry from all Spark applications on any accessible Spark deployment.

• Collect Databricks consumption and cost data

  The Databricks Integration can collect consumption and cost related data from the Databricks system tables. This data can be used to show Databricks DBU consumption metrics and estimated Databricks costs directly within New Relic.

• Collect Databricks job run telemetry

  The Databricks Integration can collect telemetry about Databricks Job runs, such as job run durations, task run durations, the current state of job and task runs, if a job or a task is a retry, and the number of times a task was retried.

• Collect Databricks Delta Live Tables Pipeline event logs

  The Databricks Integration can collect Databricks Delta Live Tables Pipeline event logs for all Databricks Delta Live Tables Pipelines defined in a workspace. Databricks Delta Live Tables Pipeline event log entries for every pipeline update are collected and sent to New Relic Logs.

Usage

Command Line Options

Option	Description	Default
--config_path	path to the (#configyml) to use	configs/config.yml
--dry_run	flag to enable "dry run" mode	false
--env_prefix	prefix to use for environment variable lookup	''
--verbose	flag to enable "verbose" mode	false
--version	display version information only	N/a

Configuration

The Databricks integration is configured using the config.yml and/or environment variables. For Databricks, authentication related configuration parameters may also be set in a Databricks configuration profile. In all cases, where applicable, environment variables always take precedence.

config.yml

All configuration parameters for the Databricks integration can be set using a YAML file named config.yml. The default location for this file is configs/config.yml relative to the current working directory when the integration binary is executed. The supported configuration parameters are listed below. See config.template.yml for a full configuration example.

General configuration

The parameters in this section are configured at the top level of the config.yml.

licenseKey

Description	Valid Values	Required	Default
New Relic license key	string	Y	N/a

This parameter specifies the New Relic License Key (INGEST) that should be used to send generated metrics.

The license key can also be specified using the NEW_RELIC_LICENSE_KEY environment variable.

region

Description	Valid Values	Required	Default
New Relic region identifier	US / EU	N	US

This parameter specifies which New Relic region that generated metrics should be sent to.

interval

Description	Valid Values	Required	Default
Polling interval (in seconds)	numeric	N	60

This parameter specifies the interval (in seconds) at which the integration should poll for data.

This parameter is only used when runAsService is set to true.

runAsService

Description	Valid Values	Required	Default
Flag to enable running the integration as a "service"	true / false	N	false

The integration can run either as a "service" or as a simple command line utility which runs once and exits when it is complete.

When set to true, the integration process will run continuously and poll the for data at the recurring interval specified by the interval parameter. The process will only exit if it is explicitly stopped or a fatal error or panic occurs.

When set to false, the integration will run once and exit. This is intended for use with an external scheduling mechanism like cron.

pipeline

Description	Valid Values	Required	Default
The root node for the set of pipeline configuration parameters	YAML Mapping	N	N/a

The integration retrieves, processes, and exports data to New Relic using a data pipeline consisting of one or more receivers, a processing chain, and a New Relic exporter. Various aspects of the pipeline are configurable. This element groups together the configuration parameters related to pipeline configuration.

log

Description	Valid Values	Required	Default
The root node for the set of log configuration parameters	YAML Mapping	N	N/a

The integration uses the logrus package for application logging. This element groups together the configuration parameters related to log configuration.

mode

Description	Valid Values	Required	Default
The integration execution mode	databricks	N	databricks

The integration execution mode. Currently, the only supported execution mode is databricks.

Deprecated: As of v2.3.0, this configuration parameter is no longer used. The presence (or not) of the databricks top-level node will be used to enable (or disable) the Databricks collector. Likewise, the presence (or not) of the spark top-level node will be used to enable (or disable) the Spark collector separate from Databricks.

databricks

Description	Valid Values	Required	Default
The root node for the set of Databricks configuration parameters	YAML Mapping	N	N/a

This element groups together the configuration parameters to configure the Databricks collector. If this element is not specified, the Databricks collector will not be run.

Note that this node is not required. It can be used with or without the spark top-level node.

spark

Description	Valid Values	Required	Default
The root node for the set of Spark configuration parameters	YAML Mapping	N	N/a

This element groups together the configuration parameters to configure the Spark collector. If this element is not specified, the Spark collector will not be run.

Note that this node is not required. It can be used with or without the databricks top-level node.

tags

Description	Valid Values	Required	Default
The root node for a set of custom tags to add to all telemetry sent to New Relic	YAML Mapping	N	N/a

This element specifies a group of custom tags that will be added to all telemetry sent to New Relic. The tags are specified as a set of key-value pairs.

Pipeline configuration

receiveBufferSize

Description	Valid Values	Required	Default
Size of the buffer that holds items before processing	number	N	500

This parameter specifies the size of the buffer that holds received items before being flushed through the processing chain and on to the exporters. When this size is reached, the items in the buffer will be flushed automatically.

harvestInterval

Description	Valid Values	Required	Default
Harvest interval (in seconds)	number	N	60

This parameter specifies the interval (in seconds) at which the pipeline should automatically flush received items through the processing chain and on to the exporters. Each time this interval is reached, the pipeline will flush items even if the item buffer has not reached the size specified by the receiveBufferSize parameter.

instances

Description	Valid Values	Required	Default
Number of concurrent pipeline instances to run	number	N	3

The integration retrieves, processes, and exports metrics to New Relic using a data pipeline consisting of one or more receivers, a processing chain, and a New Relic exporter. When runAsService is true, the integration can launch one or more "instances" of this pipeline to receive, process, and export data concurrently. Each "instance" will be configured with the same processing chain and exporters and the receivers will be spread across the available instances in a round-robin fashion.

This parameter specifies the number of pipeline instances to launch.

NOTE: When runAsService is false, only a single pipeline instance is used.

Log configuration

level

Description	Valid Values	Required	Default
Log level	panic / fatal / error / warn / info / debug / trace	N	warn

This parameter specifies the maximum severity of log messages to output with trace being the least severe and panic being the most severe. For example, at the default log level (warn), all log messages with severities warn, error, fatal, and panic will be output but info, debug, and trace will not.

fileName

Description	Valid Values	Required	Default
Path to a file where log output will be written	string	N	stderr

This parameter designates a file path where log output should be written. When no path is specified, log output will be written to the standard error stream (stderr).

Databricks configuration

The Databricks configuration parameters are used to configure the Databricks collector.

workspaceHost

Description	Valid Values	Required	Default
Databricks workspace instance name	string	conditional	N/a

This parameter specifies the instance name of the target Databricks instance for which data should be collected. This is used by the integration when constructing the URLs for API calls. Note that the value of this parameter must not include the https:// prefix, e.g. https://my-databricks-instance-name.cloud.databricks.com.

The workspace host can also be specified using the DATABRICKS_HOST environment variable.

accessToken

Description	Valid Values	Required	Default
Databricks personal access token	string	N	N/a

When set, the integration will use Databricks personal access token authentication to authenticate Databricks API calls with the value of this parameter as the Databricks personal access token.

The personal access token can also be specified using the DATABRICKS_TOKEN environment variable or any other SDK-supported mechanism (e.g. the token field in a Databricks configuration profile).

See the authentication section for more details.

oauthClientId

Description	Valid Values	Required	Default
Databricks OAuth M2M client ID	string	N	N/a

When set, the integration will use a service principal to authenticate with Databricks (OAuth M2M) when making Databricks API calls. The value of this parameter will be used as the OAuth client ID.

The OAuth client ID can also be specified using the DATABRICKS_CLIENT_ID environment variable or any other SDK-supported mechanism (e.g. the client_id field in a Databricks configuration profile).

See the authentication section for more details.

oauthClientSecret

Description	Valid Values	Required	Default
Databricks OAuth M2M client secret	string	N	N/a

When the oauthClientId is set, this parameter can be set to specify the OAuth secret associated with the service principal.

The OAuth client secret can also be specified using the DATABRICKS_CLIENT_SECRET environment variable or any other SDK-supported mechanism (e.g. the client_secret field in a Databricks configuration profile).

See the authentication section for more details.

sqlStatementTimeout

Description	Valid Values	Required	Default
Timeout (in seconds) to use when executing SQL statements on a SQL warehouse	number	N	30

Certain telemetry and data collected by the Databricks collector requires the collector to run Databricks SQL statements on a SQL warehouse. This configuration parameter specifies the number of seconds to wait before timing out a pending or running SQL query.

usage

Description	Valid Values	Required	Default
The root node for the set of Databricks Usage configuration parameters	YAML Mapping	N	N/a

This element groups together the configuration parameters to configure the Databricks collector settings related to the collection of consumption and cost data.

jobs

Description	Valid Values	Required	Default
The root node for the set of Databricks Job configuration parameters	YAML Mapping	N	N/a

This element groups together the configuration parameters to configure the Databricks collector settings related to the collection of job data.

pipelines

Description	Valid Values	Required	Default
The root node for the set of Databricks Pipeline configuration parameters	YAML Mapping	N	N/a

This element groups together the configuration parameters to configure the Databricks collector settings related to the collection of Databricks Delta Live Tables Pipelines telemetry.

queries

Description	Valid Values	Required	Default
The root node for the set of Databricks Query configuration parameters	YAML Mapping	N	N/a

This element groups together the configuration parameters to configure the Databricks collector settings related to the collection of Databricks query telemetry.

Databricks Usage Configuration

The Databricks usage configuration parameters are used to configure Databricks collector settings related to the collection of Databricks consumption and cost data.

Databricks Usage enabled

Description	Valid Values	Required	Default
Flag to enable automatic collection of consumption and cost data	true / false	N	true

By default, when the Databricks collector is enabled, it will automatically collect consumption and cost data.

This flag can be used to disable the collection of consumption and cost data by the Databricks collector. This may be useful when running multiple instances of the Databricks Integration. In this scenario, Databricks consumption and cost data collection should only be enabled on a single instance. Otherwise, this data will be recorded more than once in New Relic, affecting consumption and cost calculations.

warehouseId

Description	Valid Values	Required	Default
ID of a SQL warehouse on which to run usage-related SQL statements	string	Y	N/a

The ID of a SQL warehouse on which to run the SQL statements used to collect Databricks consumption and cost data.

This parameter is required when the collection of Databricks consumption and cost data is enabled.

includeIdentityMetadata

Description	Valid Values	Required	Default
Flag to enable inclusion of identity related metadata in consumption and cost data	true / false	N	false

When the collection of Databricks consumption and cost data is enabled, the Databricks collector can include several pieces of identifying information along with the consumption and cost data.

By default, when the collection of Databricks consumption and cost data is enabled, the Databricks collector will not collect such data as it may be personally identifiable. This flag can be used to enable the inclusion of the identifying information.

When enabled, the following values are included.

• The identity of the user a serverless billing record is attributed to. This value is included in the identity metadata returned from usage records in the billable usage system table.
• The identity of the cluster creator for each usage record for billable usage attributed to all-purpose and job compute.
• The single user name for each usage record for billable usage attributed to all-purpose and job compute configured for single-user access mode.
• The identity of the warehouse creator for each usage record for billable usage attributed to SQL warehouse compute.
• The identity of the user or service principal used to run jobs for each query result collected by job cost data queries.

runTime

Description	Valid Values	Required	Default
Time of day (as HH:mm:ss) at which to run usage data collection	string with format HH:mm:ss	N	02:00:00

This parameter specifies the time of day at which the collection of consumption and cost data occur. The value must be of the form HH:mm:ss where HH is the 0-padded 24-hour clock hour (00 - 23), mm is the 0-padded minute (00 - 59) and ss is the 0-padded second (00 - 59). For example, 09:00:00 is the time 9:00 AM and 23:30:00 is the time 11:30 PM.

The time will always be interpreted according to the UTC time zone. The time zone can not be configured. For example, to specify that the integration should be run at 2:00 AM EST (-0500), the value 07:00:00 should be specified.

optionalQueries

Description	Valid Values	Required	Default
The root node for the set of flags used to selectively enable or disable optional usage queries	YAML Mapping	N	N/a

When the collection of Databricks consumption and cost data is enabled, the Databricks collector will always collect billable usage data and list pricing data on every run. In addition, by default, the Databricks collector will also run all job cost queries on every run. However, the latter behavior can be configured using a set of flags specified with this configuration property to selectively enable or disable the job cost queries. Each flag is specified using a property with the query ID as the name of the property and true or false as the value of the property.The following flags are supported.

• jobs_cost_list_cost_per_job_run
• jobs_cost_list_cost_per_job
• jobs_cost_frequent_failures
• jobs_cost_most_retries

For example, to enable the list cost per job run query and the list cost per job query but disable the list cost of failed job runs for jobs with frequent failures query and the list cost of repaired job runs for jobs with frequent repairs query, the following configuration would be specified.

  optionalQueries:
    jobs_cost_list_cost_per_job_run: true
    jobs_cost_list_cost_per_job: true
    jobs_cost_frequent_failures: false
    jobs_cost_most_retries: false

Databricks Job configuration

runs

Description	Valid Values	Required	Default
The root node for the set of Databricks Job Run configuration parameters	YAML Mapping	N	N/a

This element groups together the configuration parameters to configure the Databricks collector settings related to the collection of job run data.

Databricks job run configuration

The Databricks job run configuration parameters are used to configure Databricks collector settings related to the collection of Databricks job run data.

Databricks job runs enabled

Description	Valid Values	Required	Default
Flag to enable automatic collection of job run data	true / false	N	true

By default, when the Databricks collector is enabled, it will automatically collect job run data.

This flag can be used to disable the collection of job run data by the Databricks collector. This may be useful when running multiple instances of the Databricks Integration against the same Databricks workspace. In this scenario, Databricks job run data collection should only be enabled on a single instance of the integration. Otherwise, this data will be recorded more than once in New Relic, affecting product features that use job run metrics (e.g. dashboards and alerts).

Databricks job run metricPrefix

Description	Valid Values	Required	Default
A prefix to prepend to Databricks job run metric names	string	N	N/a

This parameter specifies a prefix that will be prepended to each Databricks job run metric name when the metric is exported to New Relic.

For example, if this parameter is set to databricks., then the full name of the metric representing the duration of a job run (job.run.duration) will be databricks.job.run.duration.

Note that it is not recommended to leave this value empty as the metric names without a prefix may be ambiguous.

Databricks job run includeRunId

Description	Valid Values	Required	Default
Flag to enable inclusion of the job run ID in the databricksJobRunId attribute on all job run metrics	true / false	N	false

By default, the Databricks collector will not include job run IDs on any of the job run metrics in order to avoid possible violations of metric cardinality limits due to the fact that job run IDs have high cardinality because they are unique across all jobs and job runs.

This flag can be used to enable the inclusion of the job run ID in the databricksJobRunId attribute on all job metrics.

When enabled, use the Limits UI and/or create a dashboard in order to monitor your limit status. Additionally, set alerts on resource metrics to provide updates on limits changes.

startOffset

Description	Valid Values	Required	Default
Offset (in seconds) from the current time to use for calculating the earliest job run start time to match when listing job runs	number	N	86400 (1 day)

This parameter specifies an offset, in seconds that can be used to tune the collector's performance by limiting the number of job runs to return by constraining the start time of job runs to match to be greather than a particular time in the past calculated as an offset from the current time.

See the section startOffset Configuration for more details.

Databricks Pipeline configuration

metrics

Description	Valid Values	Required	Default
The root node for the set of Databricks Pipeline Update Metrics configuration parameters	YAML Mapping	N	N/a

This element groups together the configuration parameters to configure the Databricks collector settings related to the collection of Databricks Delta Live Tables pipeline update metrics.

logs

Description	Valid Values	Required	Default
The root node for the set of Databricks Pipeline Event Logs configuration parameters	YAML Mapping	N	N/a

This element groups together the configuration parameters to configure the Databricks collector settings related to the collection of Databricks Delta Live Tables pipeline event logs.

Databricks pipeline event logs configuration

The Databricks pipeline event logs configuration parameters are used to configure Databricks collector settings related to the collection of Databricks Delta Live Tables pipeline event logs.

Databricks pipeline event logs enabled

Description	Valid Values	Required	Default
Flag to enable automatic collection of Databricks Delta Live Tables pipeline event logs	true / false	N	true

By default, when the Databricks collector is enabled, it will automatically collect Databricks Delta Live Tables pipeline event logs.

This flag can be used to disable the collection of Databricks Delta Live Tables pipeline event logs by the Databricks collector. This may be useful when running multiple instances of the Databricks Integration against the same Databricks workspace. In this scenario, the collection of Databricks Delta Live Tables pipeline event logs should only be enabled on a single instance of the integration. Otherwise, duplicate New Relic log entries will be created for each Databricks Delta Live Tables pipeline event log entry, making troubleshooting challenging and affecting product features that rely on signal integrity such as anomaly detection.

Databricks pipeline update metrics configuration

The Databricks pipeline update metrics configuration parameters are used to configure Databricks collector settings related to the collection of Databricks Delta Live Tables pipeline update metrics.

Databricks pipeline update metrics enabled

Description	Valid Values	Required	Default
Flag to enable automatic collection of Databricks Delta Live Tables pipeline update metrics	true / false	N	true

By default, when the Databricks collector is enabled, it will automatically collect Databricks Delta Live Tables pipeline update metrics.

This flag can be used to disable the collection of Databricks Delta Live Tables pipeline update metrics by the Databricks collector. This may be useful when running multiple instances of the Databricks Integration against the same Databricks workspace. In this scenario, the collection of Databricks Delta Live Tables pipeline update metrics should only be enabled on a single instance of the integration. Otherwise, Databricks Delta Live Tables pipeline update metrics will be recorded more than once, making troubleshooting challenging and affecting product features that use these metrics (e.g. dashboards and alerts).

Databricks pipeline update metrics metricPrefix

Description	Valid Values	Required	Default
A prefix to prepend to Databricks pipeline update metric names	string	N	N/a

This parameter specifies a prefix that will be prepended to each Databricks pipeline update metric name when the metric is exported to New Relic.

For example, if this parameter is set to databricks., then the full name of the metric representing the duration of a pipeline update (pipeline.update.duration) will be databricks.pipeline.update.duration.

Note that it is not recommended to leave this value empty as the metric names without a prefix may be ambiguous.

Databricks pipeline update metrics includeUpdateId

Description	Valid Values	Required	Default
Flag to enable inclusion of the pipeline update ID in the databricksPipelineUpdateId attribute on all pipeline update metrics	true / false	N	false

By default, the Databricks collector will not include pipeline update IDs on any of the pipeline update metrics in order to avoid possible violations of metric cardinality limits due to the fact that update IDs have high cardinality because they are unique across all pipeline updates.

This flag can be used to enable the inclusion of the pipeline update ID in the databricksPipelineUpdateId attribute on all pipeline update metrics.

When enabled, use the Limits UI and/or create a dashboard in order to monitor your limit status. Additionally, set alerts on resource metrics to provide updates on limits changes.

Databricks pipeline update metrics startOffset

Description	Valid Values	Required	Default
Offset (in seconds) from the current time to use for calculating the earliest pipeline event timestamp to match when listing pipeline events	number	N	86400 (1 day)

This parameter specifies an offset, in seconds, that can be used to tune the collector's performance by limiting the number of pipeline events to return by constraining the timestamp of pipeline events to match to be greather than a particular time in the past calculated as an offset from the current time.

See the section startOffset Configuration for more details.

Databricks pipeline update metrics intervalOffset

Description	Valid Values	Required	Default
Offset (in seconds) to use for delaying the collection of pipeline events to account for lag in the list pipeline events API	number	N	5

This parameter specifies an offset, in seconds, that the collector will use to delay collection of pipeline events in order to account for potential lag in the list pipeline events endpoint of the Databricks ReST API.

See the section intervalOffset Configuration for more details.

Databricks Query configuration

metrics

Description	Valid Values	Required	Default
The root node for the set of Databricks Query Metrics configuration parameters	YAML Mapping	N	N/a

This element groups together the configuration parameters to configure the Databricks collector settings related to the collection of Databricks query metrics.

Databricks query metrics configuration

The Databricks query metrics configuration parameters are used to configure Databricks collector settings related to the collection of Databricks query metrics.

Databricks query metrics enabled

Description	Valid Values	Required	Default
Flag to enable automatic collection of Databricks query metrics	true / false	N	true

By default, when the Databricks collector is enabled, it will automatically collect Databricks query metrics.

This flag can be used to disable the collection of query metrics by the Databricks collector. This may be useful when running multiple instances of the Databricks Integration against the same Databricks workspace. In this scenario, the collection of Databricks query metrics should only be enabled on a single instance of the integration. Otherwise, query metrics will be recorded more than once, making troubleshooting challenging and affecting product features that use these metrics (e.g. dashboards and alerts).

Databricks query metrics includeIdentityMetadata

Description	Valid Values	Required	Default
Flag to enable inclusion of identity related metadata in Databricks query metric data	true / false	N	false

When the collection of Databricks query metrics is enabled, the Databricks collector can include several pieces of identifying information along with the query metric data.

By default, when the collection of Databricks query metrics is enabled, the Databricks collector will not collect such data as it may be personally identifiable. This flag can be used to enable the inclusion of the identifying information.

When enabled, the following values are included.

• The ID of the user that executed the query
• The email address or username of the user that executed the query
• The ID of the user whose credentials were used to execute the query
• The email address or username whose credentials were used to execute the query
• The identity of the warehouse creator of the SQL warehouse where the query was executed

Databricks query metrics startOffset

Description	Valid Values	Required	Default
Offset (in seconds) to use for the start of the time range used on the list query history API call	number	N	600

This parameter specifies an offset, in seconds, that the collector will use to calculate the start time of the time range used to call the list query history API.

See the section Query Metrics startOffset Configuration for more details.

Databricks query metrics intervalOffset

Description	Valid Values	Required	Default
Offset (in seconds) to use for the time window used to check for terminated queries to account for any lag setting the query end time in the list query history API	number	N	5

This parameter specifies an offset, in seconds, that the collector will use to offset the current time window when checking for terminated queries, allowing the query end time additional time to be reflected in the list query history API.

See the section Query Metrics intervalOffset Configuration for more details.

Databricks query metrics maxResults

Description	Valid Values	Required	Default
The maximum number of query results to return per page when calling the list query history API	number	N	100

This parameter specifies the maximum number of query results to return per page when listing queries via the list query history API

NOTE: For performance reasons, since the integration retrieves query metrics for each query using the list query history API, it is recommended to keep the maxResults parameter at its default value in most cases.

Spark configuration

The Spark configuration parameters are used to configure the Spark collector.

webUiUrl

Description	Valid Values	Required	Default
The Web UI URL of an application on the Spark deployment to monitor	string	N	N/a

This parameter can be used to monitor a Spark deployment. It specifies the URL of the Web UI of an application running on the Spark deployment to monitor. The value should be of the form http[s]://<hostname>:<port> where <hostname> is the hostname of the Spark deployment to monitor and <port> is the port number of the Spark application's Web UI (typically 4040 or 4041, 4042, etc if more than one application is running on the same host).

Note that the value must not contain a path. The path of the Spark ReST API endpoints (mounted at /api/v1) will automatically be appended.

metricPrefix

Description	Valid Values	Required	Default
A prefix to prepend to Spark metric names	string	N	N/a

This parameter specifies a prefix that will be prepended to each Spark metric name when the metric is exported to New Relic.

For example, if this parameter is set to spark., then the full name of the metric representing the value of the memory used on application executors (app.executor.memoryUsed) will be spark.app.executor.memoryUsed.

Note that it is not recommended to leave this value empty as the metric names without a prefix may be ambiguous. Additionally, note that this parameter has no effect on Spark telemetry collected by the Databricks collector. In that case, use the sparkMetricPrefix instead.

Authentication

The Databricks integration uses the Databricks SDK for Go to access the Databricks ReST API. The SDK performs authentication on behalf of the integration and provides many options for configuring the authentication type and credentials to be used. See the SDK documentation and the Databricks client unified authentication documentation for details.

For convenience purposes, the following parameters can be used in the Databricks configuration section of the `config.yml file.

• accessToken - When set, the integration will instruct the SDK to explicitly use Databricks personal access token authentication. The SDK will not attempt to try other authentication mechanisms and instead will fail immediately if personal access token authentication fails.
• oauthClientId - When set, the integration will instruct the SDK to explicitly use a service principal to authenticate with Databricks (OAuth M2M). The SDK will not attempt to try other authentication mechanisms and instead will fail immediately if OAuth M2M authentication fails. The OAuth Client secret can be set using the oauthClientSecret configuration parameter or any of the other mechanisms supported by the SDK (e.g. the client_secret field in a Databricks configuration profile or the DATABRICKS_CLIENT_SECRET environment variable).
• oauthClientSecret - The OAuth client secret to use for OAuth M2M authentication. This value is only used when oauthClientId is set in the config.yml. The OAuth client secret can also be set using any of the other mechanisms supported by the SDK (e.g. the client_secret field in a Databricks configuration profile or the DATABRICKS_CLIENT_SECRET environment variable).

NOTE: Authentication applies only to calls made to collect Databricks telemetry via the Databricks SDK for Go. The Spark collector does not support authentication when accessing the Spark ReST API.

Apache Spark Data

The Databricks Integration can collect Apache Spark application metrics for all running applications in a given Spark cluster. Metrics are collected by accessing the monitoring ReST API through the Web UI of a given SparkContext.

This feature can be used for any Spark cluster, not just for Spark running on Databricks.

When the Spark collector is enabled (the top-level spark node is specified), Spark application metrics are collected from the Web UI URL specified in the webUiUrl or from the Web UI URL https://localhost:4040 by default.

Spark Application Metric Data

Spark application telemetry is sent to New Relic as dimensional metrics. The provided metrics and attributes (dimensions) are listed in the sections below.

NOTE: Many of the descriptions below are sourced from the Apache Spark monitoring ReST API documentation.

Spark application metric types

The Apache Spark monitoring ReST API returns two types of metrics: monotonically increasing counters (referred to below simply as "counters") and gauges, with the majority of metrics being counters.

While all gauge metrics returned from the ReST API are created as gauge metrics within New Relic, note that all counter metrics returned from the ReST API are also created as gauge metrics. While the latest(), min(), max(), sum(), count(), and average() NRQL aggregator functions are all therefore available for use with these metrics, only latest() will provide meaningful results (e.g. taking the average of a set of data points that represent a monotonically increasing counter makes no sense).

Therefore, in general, only the latest() aggregator function should be used when visualizing or alerting on metrics listed in the sections below with the metric type counter.

Further to this, even when using the latest() aggregator function, the metrics have no meaning without an identifying FACET.

As an example, to show the total number of completed tasks by executor ID, use the query SELECT latest(app.executor.completedTasks) FROM Metric FACET sparkAppName, sparkAppExecutorId and not SELECT count(app.executor.completedTasks) FROM Metric FACET sparkAppName, sparkAppExecutorId. Using count() on a gauge metric accesses the count field of the metric which in this case is always 1 even though the metric being represented is a counter. The counter value is actually in the latest field of the metric. Further, using the metric without faceting by the executor ID will only return the latest app.executor.completedTasks metric in the selected time window and ignore any other instances of that metric in the same time window.

Common Spark application metric attributes

The following attributes are included on all Spark application metrics.

Attribute Name	Data Type	Description
sparkAppId	string	Spark application ID
sparkAppName	string	Spark application name
databricksClusterId	string	Databricks only Databricks cluster ID
databricksClusterName	string	Databricks only Databricks cluster name

Spark application metrics

The following metrics are included for each Spark application.

Attribute Name	Data Type	Description
app.jobs	string	Spark job counts by job status
app.stages	string	Spark stage counts by stage status

Spark application executor metrics

The following metrics are included for each Spark application executor. Metrics are scoped to a given executor using the sparkAppExecutorId attribute.

Metric Name	Metric Type	Description
app.executor.rddBlocks	gauge	RDD blocks in the block manager of this executor
app.executor.memoryUsed	gauge	Storage memory used by this executor
app.executor.diskUsed	gauge	Disk space used for RDD storage by this executor
app.executor.totalCores	counter	Number of cores available in this executor
app.executor.maxTasks	counter	Maximum number of tasks that can run concurrently in this executor
app.executor.activeTasks	gauge	Number of tasks currently executing
app.executor.failedTasks	counter	Number of tasks that have failed in this executor
app.executor.completedTasks	counter	Number of tasks that have completed in this executor
app.executor.totalTasks	counter	Total number of tasks (running, failed and completed) in this executor
app.executor.totalDuration	counter	Elapsed time the JVM spent executing tasks in this executor. The value is expressed in milliseconds.
app.executor.totalGCTime	counter	Elapsed time the JVM spent in garbage collection summed in this executor. The value is expressed in milliseconds.
app.executor.totalInputBytes	counter	Total input bytes summed in this executor
app.executor.totalShuffleRead	counter	Total shuffle read bytes summed in this executor
app.executor.totalShuffleWrite	counter	Total shuffle write bytes summed in this executor
app.executor.maxMemory	gauge	Total amount of memory available for storage, in bytes
app.executor.memory.usedOnHeapStorage	gauge	Used on heap memory currently for storage, in bytes
app.executor.memory.usedOffHeapStorage	gauge	Used off heap memory currently for storage, in bytes
app.executor.memory.totalOnHeapStorage	gauge	Total available on heap memory for storage, in bytes. This amount can vary over time, on the MemoryManager implementation.
app.executor.memory.totalOffHeapStorage	gauge	Total available off heap memory for storage, in bytes. This amount can vary over time, depending on the MemoryManager implementation.
app.executor.memory.peak.jvmHeap	counter	Peak memory usage of the heap that is used for object allocation by the Java virtual machine
app.executor.memory.peak.jvmOffHeap	counter	Peak memory usage of non-heap memory that is used by the Java virtual machine
app.executor.memory.peak.onHeapExecution	counter	Peak on heap execution memory usage, in bytes
app.executor.memory.peak.offHeapExecution	counter	Peak off heap execution memory usage, in bytes
app.executor.memory.peak.onHeapStorage	counter	Peak on heap storage memory usage, in bytes
app.executor.memory.peak.offHeapStorage	counter	Peak off heap storage memory usage, in bytes
app.executor.memory.peak.onHeapUnified	counter	Peak on heap memory usage (execution and storage)
app.executor.memory.peak.offHeapUnified	counter	Peak off heap memory usage (execution and storage)
app.executor.memory.peak.directPool	counter	Peak JVM memory usage for direct buffer pool (java.lang.management.BufferPoolMXBean)
app.executor.memory.peak.mappedPool	counter	Peak JVM memory usage for mapped buffer pool (java.lang.management.BufferPoolMXBean)
app.executor.memory.peak.nettyDirect	counter	Databricks only
app.executor.memory.peak.jvmDirect	counter	Databricks only
app.executor.memory.peak.sparkDirectMemoryOverLimit	counter	Databricks only
app.executor.memory.peak.totalOffHeap	counter	Databricks only
app.executor.memory.peak.processTreeJvmVirtual	counter	Peak virtual memory size, in bytes
app.executor.memory.peak.processTreeJvmRSS	counter	Peak Resident Set Size (number of pages the process has in real memory)
app.executor.memory.peak.processTreePythonVirtual	counter	Peak virtual memory size for Python, in bytes
app.executor.memory.peak.processTreePythonRSS	counter	Peak resident Set Size for Python
app.executor.memory.peak.processTreeOtherVirtual	counter	Peak virtual memory size for other kinds of processes, in bytes
app.executor.memory.peak.processTreeOtherRSS	counter	Peak resident Set Size for other kinds of processes
app.executor.memory.peak.minorGCCount	counter	Total number of minor GCs that have occurred
app.executor.memory.peak.minorGCTime	counter	Total elapsed time spent doing minor GCs. The value is expressed in milliseconds.
app.executor.memory.peak.majorGCCount	counter	Total number of major GCs that have occurred
app.executor.memory.peak.majorGCTime	counter	Total elapsed time spent doing major GCs. The value is expressed in milliseconds.
app.executor.memory.peak.totalGCTime	counter	Total elapsed time spent doing GC (major + minor). The value is expressed in milliseconds.

Spark application executor attributes

The following attributes are included on all Spark application executor metrics.

Attribute Name	Data Type	Description
sparkAppExecutorId	string	Spark executor ID

Spark application job metrics

The following metrics are included for each Spark job in an application. Metrics are scoped to a given job and status using the sparkAppJobId and sparkAppJobStatus attributes.

Metric Name	Metric Type	Description
app.job.duration	counter	Duration of the job. The value is expressed in milliseconds.
app.job.stages	gauge (for active and complete status) / counter (for skipped and failed status)	Spark stage counts by stage status
app.job.tasks	gauge (for active status) / counter (for complete, skipped, failed, and killed status)	Spark task counts by task status
app.job.indices.completed	counter	This metric is not documented in the Apache Spark monitoring ReST API documentation

Spark application job attributes

The following attributes are included on all Spark application job metrics.

Attribute Name	Data Type	Description
sparkAppJobId	number	Spark job ID
sparkAppJobStatus	string	Spark job status
sparkAppStageStatus	string	Spark stage status. Only on app.job.stages metric.
sparkAppTaskStatus	string	Spark task status. Only on app.job.tasks metric.

Spark application stage metrics

The following metrics are included for each Spark stage in an application. Metrics are scoped to a given stage and status using the sparkAppStageId, sparkAppStageAttemptId, sparkAppStageName, and sparkAppStageStatus attributes.

NOTE: The metrics in this section are not documented in the Apache Spark monitoring ReST API documentation. The descriptions provided below were deduced via source code analysis and are not determinate.

Metric Name	Metric Type	Description
app.stage.tasks	gauge (for active, pending, and complete status) / counter (for skipped and failed status)	Spark task counts by task status
app.stage.tasks.total	counter	Total number of tasks for the named stage
app.stage.duration	counter	Duration of the stage. The value is expressed in milliseconds.
app.stage.indices.completed	counter	This metric is not documented in the Apache Spark monitoring ReST API documentation
app.stage.peakNettyDirectMemory	counter	Databricks only
app.stage.peakJvmDirectMemory	counter	Databricks only
app.stage.peakSparkDirectMemoryOverLimit	counter	Databricks only
app.stage.peakTotalOffHeapMemory	counter	Databricks only
app.stage.executor.deserializeTime	counter	Total elapsed time spent by executors deserializing tasks for the named stage. The value is expressed in milliseconds.
app.stage.executor.deserializeCpuTime	counter	Total CPU time spent by executors to deserialize tasks for the named stage. The value is expressed in nanoseconds.
app.stage.executor.runTime	counter	Total elapsed time spent running tasks on executors for the named stage. The value is expressed in milliseconds.
app.stage.executor.cpuTime	counter	Total CPU time spent running tasks on executors for the named stage. This includes time fetching shuffle data. The value is expressed in nanoseconds.
app.stage.resultSize	counter	The total number of bytes transmitted back to the driver by all tasks for the named stage
app.stage.jvmGcTime	counter	Total elapsed time the JVM spent in garbage collection while executing tasks for the named stage. The value is expressed in milliseconds.
app.stage.resultSerializationTime	gauge	Total elapsed time spent serializing task results for the named stage. The value is expressed in milliseconds.
app.stage.memoryBytesSpilled	counter	Sum of the in-memory bytes spilled by all tasks for the named stage
app.stage.diskBytesSpilled	counter	Sum of the number of on-disk bytes spilled by all tasks for the named stage
app.stage.peakExecutionMemory	counter	Sum of the peak memory used by internal data structures created during shuffles, aggregations and joins by all tasks for the named stage
app.stage.inputBytes	counter	Sum of the number of bytes read from org.apache.spark.rdd.HadoopRDD or from persisted data by all tasks for the named stage
app.stage.inputRecords	counter	Sum of the number of records read from org.apache.spark.rdd.HadoopRDD or from persisted data by all tasks for the named stage
app.stage.outputBytes	counter	Sum of the number of bytes written externally (e.g. to a distributed filesystem) by all tasks with output for the named stage
app.stage.outputRecords	counter	Sum of the number of records written externally (e.g. to a distributed filesystem) by all tasks with output for the named stage
app.stage.shuffle.remoteBlocksFetched	counter	Sum of the number of remote blocks fetched in shuffle operations by all tasks for the named stage
app.stage.shuffle.localBlocksFetched	counter	Sum of the number of local (as opposed to read from a remote executor) blocks fetched in shuffle operations by all tasks for the named stage
app.stage.shuffle.fetchWaitTime	counter	Total time tasks spent waiting for remote shuffle blocks for the named stage
app.stage.shuffle.remoteBytesRead	counter	Sum of the number of remote bytes read in shuffle operations by all task for the named stages
app.stage.shuffle.remoteBytesReadToDisk	counter	Sum of the number of remote bytes read to disk in shuffle operations by all tasks for the named stage
app.stage.shuffle.localBytesRead	counter	Sum of the number of bytes read in shuffle operations from local disk (as opposed to read from a remote executor) by all tasks for the named stage
app.stage.shuffle.readBytes	counter	This metric is not documented in the Apache Spark monitoring ReST API documentation
app.stage.shuffle.readRecords	counter	Sum of the number of records read in shuffle operations by all tasks for the named stage
app.stage.shuffle.corruptMergedClockChunks	counter	Sum of the number of corrupt merged shuffle block chunks encountered by all tasks (remote or local) for the named stage
app.stage.shuffle.mergedFetchFallbackCount	counter	Sum of the number of times tasks had to fallback to fetch original shuffle blocks for a merged shuffle block chunk (remote or local) for the named stage
app.stage.shuffle.mergedRemoteBlocksFetched	counter	Sum of the number of remote merged blocks fetched by all tasks for the named stage
app.stage.shuffle.mergedLocalBlocksFetched	counter	Sum of the number of local merged blocks fetched by all tasks for the named stage
app.stage.shuffle.mergedRemoteChunksFetched	counter	Sum of the number of remote merged chunks fetched by all tasks for the named stage
app.stage.shuffle.mergedLocalChunksFetched	counter	Sum of the number of local merged chunks fetched by all tasks for the named stage
app.stage.shuffle.mergedRemoteBytesRead	counter	Sum of the number of remote merged bytes read by all tasks for the named stage
app.stage.shuffle.mergedLocalBytesRead	counter	Sum of the number of local merged bytes read by all tasks for the named stage
app.stage.shuffle.remoteReqsDuration	counter	Total time tasks took executing remote requests for the named stage
app.stage.shuffle.mergedRemoteReqsDuration	counter	Total time tasks took executing remote merged requests for the named stage
app.stage.shuffle.writeBytes	counter	Sum of the number of bytes written in shuffle operations by all tasks for the named stage
app.stage.shuffle.writeTime	counter	Total time tasks spent blocking on writes to disk or buffer cache for the named stage. The value is expressed in nanoseconds.
app.stage.shuffle.writeRecords	counter	Sum of the number of records written in shuffle operations by all tasks for the named stage
app.stage.executor.memory.peak.jvmHeap	counter	Peak memory usage of the heap that is used for object allocation by the Java virtual machine while executing tasks for the named stage
app.stage.executor.memory.peak.jvmOffHeap	counter	Peak memory usage of non-heap memory by the Java virtual machine while executing tasks for the named stage
app.stage.executor.memory.peak.onHeapExecution	counter	Peak on heap execution memory usage while executing tasks for the named stage, in bytes
app.stage.executor.memory.peak.offHeapExecution	counter	Peak off heap execution memory usage while executing tasks for the named stage, in bytes
app.stage.executor.memory.peak.onHeapStorage	counter	Peak on heap storage memory usage while executing tasks for the named stage, in bytes
app.stage.executor.memory.peak.offHeapStorage	counter	Peak off heap storage memory usage while executing tasks for the named stage, in bytes
app.stage.executor.memory.peak.onHeapUnified	counter	Peak on heap memory usage (execution and storage) while executing tasks for the named stage, in bytes
app.stage.executor.memory.peak.offHeapUnified	counter	Peak off heap memory usage (execution and storage) while executing tasks for the named stage, in bytes
app.stage.executor.memory.peak.directPool	counter	Peak JVM memory usage for the direct buffer pool (java.lang.management.BufferPoolMXBean) while executing tasks for the named stage
app.stage.executor.memory.peak.mappedPool	counter	Peak JVM memory usage for the mapped buffer pool (java.lang.management.BufferPoolMXBean) while executing tasks for the named stage
app.stage.executor.memory.peak.processTreeJvmVirtual	counter	Peak virtual memory size while executing tasks for the named stage, in bytes
app.stage.executor.memory.peak.processTreeJvmRSS	counter	Peak Resident Set Size (number of pages the process has in real memory) while executing tasks for the named stage
app.stage.executor.memory.peak.processTreePythonVirtual	counter	Peak virtual memory size for Python while executing tasks for the named stage, in bytes
app.stage.executor.memory.peak.processTreePythonRSS	counter	Peak Resident Set Size for Python while executing tasks for the named stage
app.stage.executor.memory.peak.processTreeOtherVirtual	counter	Peak virtual memory size for other kinds of processes while executing tasks for the named stage, in bytes
app.stage.executor.memory.peak.processTreeOtherRSS	counter	Peak resident Set Size for other kinds of processes while executing tasks for the named stage
app.stage.executor.memory.peak.minorGCCount	counter	Total number of minor GCs that occurred while executing tasks for the named stage
app.stage.executor.memory.peak.minorGCTime	counter	Total elapsed time spent doing minor GCs while executing tasks for the named stage. The value is expressed in milliseconds.
app.stage.executor.memory.peak.majorGCCount	counter	Total number of major GCs that occurred while executing tasks for the named stage
app.stage.executor.memory.peak.majorGCTime	counter	Total elapsed time spent doing major GCs while executing tasks for the named stage. The value is expressed in milliseconds.
app.stage.executor.memory.peak.totalGCTime	counter	Total elapsed time spent doing GC while executing tasks for the named stage. The value is expressed in milliseconds.

Spark application stage attributes

The following attributes are included on all Spark application stage metrics.

Attribute Name	Data Type	Description
sparkAppStageId	string	Spark stage ID
sparkAppStageAttemptId	number	Spark stage attempt ID
sparkAppStageName	string	Spark stage name
sparkAppStageStatus	string	Spark stage status
sparkAppTaskStatus	string	Spark task status. Only on app.stage.tasks metric.

Spark application stage task metrics

The following metrics are included for each Spark task in an application. Metrics are scoped to a given stage and status using the sparkAppStageId, sparkAppStageAttemptId, sparkAppStageName, and sparkAppStageStatus attributes. They are also scoped to a given task and status using the sparkAppTaskId, sparkAppTaskAttempt, and sparkAppTaskStatus attributes and scoped to the executor of the task using the sparkAppTaskExecutorId attribute.

NOTE: Some of the shuffle read metric descriptions below are sourced from the file ShuffleReadMetrics.scala.

Metric Name	Metric Type	Description
app.stage.task.duration	counter	Duration of the task. The value is expressed in milliseconds.
app.stage.task.executorDeserializeTime	counter	Elapsed time spent to deserialize this task. The value is expressed in milliseconds.
app.stage.task.executorDeserializeCpuTime	counter	CPU time taken on the executor to deserialize this task. The value is expressed in nanoseconds.
app.stage.task.executorRunTime	counter	Elapsed time the executor spent running this task. This includes time fetching shuffle data. The value is expressed in milliseconds.
app.stage.task.executorCpuTime	counter	CPU time the executor spent running this task. This includes time fetching shuffle data. The value is expressed in nanoseconds.
app.stage.task.resultSize	counter	The number of bytes this task transmitted back to the driver as the TaskResult
app.stage.task.jvmGcTime	counter	Elapsed time the JVM spent in garbage collection while executing this task. The value is expressed in milliseconds.
app.stage.task.resultSerializationTime	counter	Elapsed time spent serializing the task result. The value is expressed in milliseconds.
app.stage.task.memoryBytesSpilled	counter	The number of in-memory bytes spilled by this task
app.stage.task.diskBytesSpilled	counter	The number of on-disk bytes spilled by this task
app.stage.task.peakExecutionMemory	counter	Peak memory used by internal data structures created during shuffles, aggregations and joins. The value of this accumulator should be approximately the sum of the peak sizes across all such data structures created in this task. For SQL jobs, this only tracks all unsafe operators and ExternalSort.
app.stage.task.input.bytesRead	counter	Total number of bytes read from org.apache.spark.rdd.HadoopRDD or from persisted data
app.stage.task.input.recordsRead	counter	Total number of records read from org.apache.spark.rdd.HadoopRDD or from persisted data
app.stage.task.output.bytesWritten	counter	Total number of bytes written externally (e.g. to a distributed filesystem). Defined only in tasks with output.
app.stage.task.output.recordsWritten	counter	Total number of records written externally (e.g. to a distributed filesystem). Defined only in tasks with output.
app.stage.task.shuffle.read.remoteBlocksFetched	counter	Number of remote blocks fetched in shuffle operations
app.stage.task.shuffle.read.localBlocksFetched	counter	Number of local (as opposed to read from a remote executor) blocks fetched in shuffle operations
app.stage.task.shuffle.read.totalBlocksFetched	gauge	TODO: Not yet implemented
app.stage.task.shuffle.read.fetchWaitTime	counter	Time the task spent waiting for remote shuffle blocks. This only includes the time blocking on shuffle input data. For instance if block B is being fetched while the task is still not finished processing block A, it is not considered to be blocking on block B. The value is expressed in milliseconds.
app.stage.task.shuffle.read.remoteBytesRead	counter	Number of remote bytes read in shuffle operations
app.stage.task.shuffle.read.remoteBytesReadToDisk	counter	Number of remote bytes read to disk in shuffle operations. Large blocks are fetched to disk in shuffle read operations, as opposed to being read into memory, which is the default behavior
app.stage.task.shuffle.read.localBytesRead	counter	Number of bytes read in shuffle operations from local disk (as opposed to read from a remote executor)
app.stage.task.shuffle.read.totalBytesRead	gauge	TODO: Not yet implemented
app.stage.task.shuffle.read.recordsRead	counter	Number of records read in shuffle operations
app.stage.task.shuffle.read.remoteReqsDuration	counter	Total time taken for remote requests to complete by this task. This doesn't include duration of remote merged requests.
app.stage.task.shuffle.read.push.corruptMergedBlockChunks	counter	Number of corrupt merged shuffle block chunks encountered by this task (remote or local)
app.stage.task.shuffle.read.push.mergedFetchFallbackCount	counter	Number of times the task had to fallback to fetch original shuffle blocks for a merged shuffle block chunk (remote or local)
app.stage.task.shuffle.read.push.remoteMergedBlocksFetched	counter	Number of remote merged blocks fetched
app.stage.task.shuffle.read.push.localMergedBlocksFetched	counter	Number of local merged blocks fetched
app.stage.task.shuffle.read.push.remoteMergedChunksFetched	counter	Number of remote merged chunks fetched
app.stage.task.shuffle.read.push.localMergedChunksFetched	counter	Number of local merged chunks fetched
app.stage.task.shuffle.read.push.remoteMergedBytesRead	counter	Total number of remote merged bytes read
app.stage.task.shuffle.read.push.localMergedBytesRead	counter	Total number of local merged bytes read
app.stage.task.shuffle.read.push.remoteMergedReqsDuration	counter	Total time taken for remote merged requests
app.stage.task.shuffle.write.bytesWritten	counter	Number of bytes written in shuffle operations
app.stage.task.shuffle.write.writeTime	counter	Time spent blocking on writes to disk or buffer cache. The value is expressed in nanoseconds.
app.stage.task.shuffle.write.recordsWritten	counter	Number of records written in shuffle operations
app.stage.task.photon.offHeapMinMemorySize		Databricks only
app.stage.task.photon.offHeapMaxMemorySize		Databricks only
app.stage.task.photon.photonBufferPoolMinMemorySize		Databricks only
app.stage.task.photon.photonBufferPoolMaxMemorySize		Databricks only
app.stage.task.photon.photonizedTaskTimeNs		Databricks only

Spark application stage task attributes

The following attributes are included on all Spark application stage task metrics.

Attribute Name	Data Type	Description
sparkAppStageId	string	Spark stage ID
sparkAppStageAttemptId	number	Spark stage attempt ID
sparkAppStageName	string	Spark stage name
sparkAppStageStatus	string	Spark stage status
sparkAppTaskId	string	Spark task ID
sparkAppTaskAttempt	number	Spark task attempt number
sparkAppTaskStatus	string	Spark task status
sparkAppTaskLocality	string	Locality of the data this task operates on
sparkAppTaskSpeculative	boolean	true if this is a speculative task execution, otherwise false
sparkAppTaskExecutorId	string	Spark executor ID

Spark application RDD metrics

The following metrics are included for each Spark RDD in an application. Metrics are scoped to a given RDD using the sparkAppRDDId and sparkAppRDDName attributes.

NOTE: The metrics in this section are not documented in the Apache Spark monitoring ReST API documentation. The descriptions provided below were deduced via source code analysis and are not determinate.

Metric Name	Metric Type	Description
app.storage.rdd.partitions	gauge	The total number of partitions for this RDD
app.storage.rdd.cachedPartitions	gauge	The total number of partitions that have been persisted (cached) in memory and/or on disk
app.storage.rdd.memory.used	gauge	The total amount of memory used by this RDD across all partitions
app.storage.rdd.disk.used	gauge	The total amount of disk space used by this RDD across all partitions
app.storage.rdd.distribution.memory.used	gauge	Unknown
app.storage.rdd.distribution.memory.remaining	gauge	Unknown
app.storage.rdd.distribution.disk.used	gauge	Unknown
app.storage.rdd.distribution.memory.usedOnHeap	gauge	Unknown
app.storage.rdd.distribution.memory.usedOffHeap	gauge	Unknown
app.storage.rdd.distribution.memory.remainingOnHeap	gauge	Unknown
app.storage.rdd.distribution.memory.remainingOffHeap	gauge	Unknown
app.storage.rdd.partition.memory.used	gauge	The total amount of memory used by this RDD partition
app.storage.rdd.partition.disk.used	gauge	The total amount of disk space used by this RDD partition

Spark application RDD attributes

The following attributes are included on all Spark application RDD metrics.

Attribute Name	Data Type	Description
sparkAppRDDId	number	Spark RDD ID
sparkAppRDDName	string	Spark RDD name
sparkAppRddDistributionIndex	number	Numerical index of the RDD distribution in the list of distributions returned for this RDD by the Spark application RDD endpoint of the ReST API. Only on app.storage.rdd.distribution.* metrics.
sparkAppRddPartitionBlockName	string	Name of the block where the RDD partition is stored. Only on app.storage.rdd.partition.* metrics.

Spark job, stage, and task status

Most of the Spark metrics recorded include at least one of the following attributes that indicate the status of the job, stage, or task for the metric.

• sparkAppJobStatus

  Spark job status. One of the following.

  • running - job is executing
  • lost - job status is unknown
  • succeeded - job completed successfully
  • failed - job failed

• sparkAppStageStatus

  Spark stage status. One of the following.

  • active - stage is executing
  • complete - stage completed successfully
  • skipped - stage was skipped because it did not need to be recomputed
  • failed - stage failed
  • pending- stage is waiting to be executed

• sparkAppTaskStatus

  Spark task status. One of the following.

  • active - task is executing
  • complete - task completed successfully
  • skipped - task was skipped because the stage was skipped
  • failed - task failed
  • killed - task was explicitly killed

Spark job, stage, and task counts

On each run, for each Spark application, the integration records the following counter metrics.

• app.jobs

  The number of Spark jobs for an application by job status. This metric will always include the sparkAppJobStatus attribute, which indicates which job status the metric applies to (for instance, if the metric value is 5 and the sparkAppJobStatus attribute is running, it means that there are 5 running jobs).

  Use the sparkAppId or sparkAppName to target a specific application or to group the values by application.

• app.stages

  The number of Spark stages for an application by stage status. This metric will always include the sparkAppStageStatus attribute, which indicates which stage status the metric applies to (for instance, if the metric value is 5 and the sparkAppStageStatus attribute is complete, it means that there are 5 completed stages).

  Use the sparkAppId or sparkAppName attributes to target a specific application or to group the values by application.

• app.job.stages

  The number of Spark stages for a Spark job by stage status. This metric will always include the sparkAppStageStatus and sparkAppJobStatus attributes, which indicate which stage status and job status the metric applies to, respectively (for instance, if the metric value is 2 and the sparkAppStageStatus attribute is complete and the sparkAppJobStatus is running, it means that the job is running and 2 stages have completed).

  Use the sparkAppId or sparkAppName attributes to target a specific application or to group the values by application and the sparkAppJobId attribute to target a specific job or to group the values by job.

• app.job.tasks

  The number of Spark tasks for a Spark job by task status. This metric will always include the sparkAppTaskStatus and sparkAppJobStatus attributes, which indicate which task status and job status the metric applies to, respectively (for instance, if the metric value is 4 and the sparkAppTaskStatus attribute is complete and the sparkAppJobStatus is running, it means that the job is running and 4 tasks have completed).

  Use the sparkAppId or sparkAppName attributes to target a specific application or to group the values by application and the sparkAppJobId attribute to target a specific job or to group the values by job.

• app.stage.tasks

  The number of Spark tasks for a Spark stage by task status. This metric will always include the sparkAppTaskStatus and sparkAppStageStatus attributes, which indicate which task status and stage status the metric applies to, respectively (for instance, if the metric value is 4 and the sparkAppTaskStatus attribute is complete and the sparkAppStageStatus is active, it means that the stage is running and 4 tasks have completed).

  Use the sparkAppId or sparkAppName attributes to target a specific application or to group the values by application and the sparkAppStageId or sparkAppStageName attribute to target a specific stage or to group the values by stage.

In general, only the latest() aggregator function should be used when visualizing or alerting using these counters.

Example Queries

All examples below assume the spark metricPrefix is spark..

Current number of jobs by application name and job status

FROM Metric
SELECT latest(spark.app.jobs)
FACET sparkAppName, sparkAppJobStatus

Current number of stages by application name and stage status

FROM Metric
SELECT latest(spark.app.stages)
FACET sparkAppName, sparkAppStageStatus

Current number of stages by application name, job ID, and stage status

FROM Metric
SELECT latest(spark.app.job.stages)
FACET sparkAppName, sparkAppJobId, sparkAppStageStatus

Current number of tasks by application name, stage ID, and task status

FROM Metric
SELECT latest(spark.app.stage.tasks)
FACET sparkAppName, sparkAppStageId, sparkAppTaskStatus
LIMIT MAX

Current number of running jobs by application name

FROM Metric
SELECT latest(spark.app.jobs)
WHERE sparkAppJobStatus = 'running'
FACET sparkAppName

Number of completed tasks by application name and job ID

FROM Metric
SELECT latest(spark.app.job.tasks)
WHERE sparkAppTaskStatus = 'complete'
FACET sparkAppName, sparkAppJobId
LIMIT MAX

Number of failed tasks by application name and job ID

FROM Metric
SELECT latest(spark.app.job.tasks)
WHERE sparkAppTaskStatus = 'failed'
FACET sparkAppName, sparkAppJobId
LIMIT MAX

Job duration by application name, job ID, and job status

FROM Metric
SELECT latest(spark.app.job.duration) / 1000 AS Duration
FACET sparkAppName, sparkAppJobId, sparkAppJobStatus
LIMIT MAX

Stage duration by application name, stage ID, and stage name

FROM Metric
SELECT latest(spark.app.stage.duration) / 1000 AS Duration
FACET sparkAppName, sparkAppStageId, sparkAppStageName
LIMIT MAX

Task duration by application name, stage ID, stage name, and task ID

FROM Metric
SELECT latest(spark.app.stage.task.executorRunTime) / 1000 AS Duration
FACET sparkAppName, sparkAppStageId, sparkAppStageName, sparkAppTaskId
LIMIT MAX

Total elapsed stage executor run time (in seconds) by application name, stage ID, and stage name

FROM Metric
SELECT latest(spark.app.stage.executor.runTime) / 1000 AS Duration
FACET sparkAppName, sparkAppStageId, sparkAppStageName
LIMIT MAX

Total elapsed stage JVM GC time (in seconds) by application name, stage ID, and stage name

FROM Metric
SELECT latest(spark.app.stage.jvmGcTime) / 1000 AS Duration
FACET sparkAppName, sparkAppStageId, sparkAppStageName
LIMIT MAX

Average memory used by application name and executor ID over time

FROM Metric
SELECT average(spark.app.executor.memoryUsed)
WHERE spark.app.executor.memoryUsed IS NOT NULL
FACET sparkAppName, sparkAppExecutorId
TIMESERIES

Number of executors (active and dead) by application name

FROM Metric
SELECT uniqueCount(sparkAppExecutorId)
WHERE metricName = 'spark.app.executor.maxMemory'
FACET sparkAppName

Total number of partitions by application name and RDD name

FROM Metric
SELECT latest(spark.app.storage.rdd.partitions)
FACET sparkAppName, sparkAppRDDName

Average RDD memory used by application name and RDD name

FROM Metric
SELECT average(spark.app.storage.rdd.memory.used)
WHERE spark.app.storage.rdd.memory.used IS NOT NULL
FACET sparkAppName, sparkAppRDDId

Average RDD partition memory used by application name, RDD ID, and RDD block name

FROM Metric
SELECT average(spark.app.storage.rdd.partition.memory.used)
WHERE spark.app.storage.rdd.partition.memory.used IS NOT NULL
FACET sparkAppName, sparkAppRDDId, sparkAppRddPartitionBlockName

Example Apache Spark Dashboard

A sample dashboard is included that shows examples of the types of Apache Spark information that can be displayed and the NRQL statements to use to visualize the data.

Consumption & Cost Data

The Databricks Integration can collect Databricks consumption and cost related data from the Databricks system tables. This data can be used to show Databricks DBU consumption metrics and estimated Databricks costs directly within New Relic.

This feature is enabled by setting the Databricks usage enabled flag to true in the integration configuration. When enabled, the Databricks collector will collect consumption and cost related data once a day at the time specified in the runTime configuration parameter. The following information is collected on each run.

• Billable usage records from the system.billing.usage table
• List pricing records from the system.billing.list_prices table
• List cost per job run of jobs run on jobs compute and serverless compute
• List cost per job of jobs run on jobs compute and serverless compute
• List cost of failed job runs for jobs with frequent failures run on jobs compute and serverless compute
• List cost of repair job runs for jobs with frequent repairs run on jobs compute and serverless compute

NOTE: Job cost data from jobs run on workspaces outside the region of the workspace containing the SQL Warehouse used to collect the consumption data (specified in the workspaceHost configuration parameter) will not be returned by the queries used by the Databricks integration to collect Job cost data.

Consumption and Cost Collection Requirements

In order for the Databricks Integration to collect consumption and cost related data from Databricks, there are several requirements.

1. The SQL warehouse ID of a SQL warehouse within the workspace associated with the configured workspace host must be specified. The Databricks SQL queries used to collect consumption and cost related data from the Databricks system tables. will be run against the specified SQL warehouse.
2. As of v2.6.0, the Databricks integration leverages data that is stored in the system.lakeflow.jobs table. As of 10/24/2024, this table is in public preview. To access this table, the lakeflow schema must be enabled in your system catalog. To enable the lakeflow schema, follow the instructions in the Databricks documentation to enable a system schema using the metastore ID of the Unity Catalog metastore attached to the workspace associated with the configured workspace host and the schema name lakeflow.

Billable Usage Data

On each run, billable usage data is collected from the system.billing.usage table for the previous day. For each billable usage record, a corresponding record is created within New Relic as a New Relic event with the event type DatabricksUsage and the following attributes.

NOTE: Not every attribute is included in every event. For example, the cluster_* attributes are only included in events for usage records relevant to all-purpose or job related compute. Similarly, the warehouse_* attributes are only included in events for usage records relevant to SQL warehouse related compute.

NOTE: Descriptions below are sourced from the billable usage system table reference.

Name	Description
account_id	ID of the account this usage record was generated for
workspace_id	ID of the Workspace this usage record was associated with
workspace_url	URL of the Workspace this usage record was associated with
workspace_instance_name	Instance name of the Workspace this usage record was associated with
record_id	Unique ID for this usage record
sku_name	Name of the SKU associated with this usage record
cloud	Name of the Cloud this usage record is relevant for
usage_start_time	The start time relevant to this usage record
usage_end_time	The end time relevant to this usage record
usage_date	Date of this usage record
usage_unit	Unit this usage record is measured in
usage_quantity	Number of units consumed for this usage record
record_type	Whether the usage record is original, a retraction, or a restatement. See the section "Analyze Correction Records" in the Databricks documentation for more details.
ingestion_date	Date the usage record was ingested into the usage table
billing_origin_product	The product that originated this usage reocrd
usage_type	The type of usage attributed to the product or workload for billing purposes
cluster_id	ID of the cluster associated with this usage record
cluster_creator	Creator of the cluster associated with this usage record (only included if includeIdentityMetadata is true)
cluster_single_user_name	Single user name of the cluster associated with this usage record if the access mode of the cluster is single-user access mode (only included if includeIdentityMetadata is true)
cluster_source	Cluster source of the cluster associated with this usage record
cluster_instance_pool_id	Instance pool ID of the cluster associated with this usage record
warehouse_id	ID of the SQL warehouse associated with this usage record
warehouse_name	Name of the SQL warehouse associated with this usage record
warehouse_creator	Creator of the SQL warehouse associated with this usage record (only included if includeIdentityMetadata is true)
instance_pool_id	ID of the instance pool associated with this usage record
node_type	The instance type of the compute resource associated with this usage record
job_id	ID of the job associated with this usage record for serverless compute or jobs compute usage
job_run_id	ID of the job run associated with this usage record for serverless compute or jobs compute usage
job_name	Name of the job associated with this usage record for serverless compute or jobs compute usage. NOTE: This field will only contain a value for jobs run within a workspace in the same cloud region as the workspace containing the SQL warehouse used to collect the consumption and cost data (specified in workspaceHost).
serverless_job_name	User-given name of the job associated with this usage record for jobs run on serverless compute only
notebook_id	ID of the notebook associated with this usage record for serverless compute for notebook usage
notebook_path	Workspace storage path of the notebook associated with this usage for serverless compute for notebook usage
dlt_pipeline_id	ID of the Delta Live Tables pipeline associated with this usage record
dlt_update_id	ID of the Delta Live Tables pipeline update associated with this usage record
dlt_maintenance_id	ID of the Delta Live Tables pipeline maintenance tasks associated with this usage record
run_name	Unique user-facing identifier of the Mosaic AI Model Training fine-tuning run associated with this usage record
endpoint_name	Name of the model serving endpoint or vector search endpoint associated with this usage record
endpoint_id	ID of the model serving endpoint or vector search endpoint associated with this usage record
central_clean_room_id	ID of the central clean room associated with this usage record
run_as	See the section "Analyze Identity Metadata" in the Databricks documentation for more details (only included if includeIdentityMetadata is true)
jobs_tier	Jobs tier product features for this usage record: values include LIGHT, CLASSIC, or null
sql_tier	SQL tier product features for this usage record: values include CLASSIC, PRO, or null
dlt_tier	DLT tier product features for this usage record: values include CORE, PRO, ADVANCED, or null
is_serverless	Flag indicating if this usage record is associated with serverless usage: values include true or false, or null
is_photon	Flag indicating if this usage record is associated with Photon usage: values include true or false, or null
serving_type	Serving type associated with this usage record: values include MODEL, GPU_MODEL, FOUNDATION_MODEL, FEATURE, or null

In addition, all compute resource tags, jobs tags, and budget policy tags applied to this usage are added as event attributes. For jobs referenced in the job_id attribute of JOBS usage records, custom job tags are also included if the job was run within a workspace in the same cloud region as the workspace containing the SQL warehouse used to collect the consumption data (specified in the workspaceHost configuration parameter).

List Pricing Data

On each run, list pricing data is collected from the system.billing.list_prices table and used to populate a New Relic lookup table named DatabricksListPrices. The entire lookup table is updated on each run. For each pricing record, this table will contain a corresponding row with the following columns.

NOTE: Descriptions below are sourced from the pricing system table reference.

Name	Description
account_id	ID of the account this pricing record was generated for
price_start_time	The time the price in this pricing record became effective in UTC
price_end_time	The time the price in this pricing record stopped being effective in UTC
sku_name	Name of the SKU associated with this pricing record
cloud	Name of the Cloud this pricing record is relevant for
currency_code	The currency the price in this pricing record is expressed in
usage_unit	The unit of measurement that is monetized in this pricing record
list_price	A single price that can be used for simple long-term estimates

Note that the list_price field contains a single price suitable for simple long-term estimates. It does not reflect any promotional pricing or custom price plans.

Job Costs

On each run, the Databricks collector runs a set of queries that leverage data in the system.billing.usage table, the system.billing.list_prices table, and the system.lakeflow.jobs table to collect job cost related data for the previous day. The set of queries that are run collect the following data.

• List cost per job run of jobs run on jobs compute and serverless compute
• List cost per job of jobs run on jobs compute and serverless compute
• List cost of failed job runs for jobs with frequent failures run on jobs compute and serverless compute
• List cost of repaired job runs for jobs with frequent repairs run on jobs compute and serverless compute

By default, the Databricks integration runs each query on every run. This behavior can be configured using the optionalQueries configuration parameter to selectively enable or disable queries by query ID.

For each query that is run, a New Relic event with the event type DatabricksJobCost is created for each result row with one attribute for each column of data. In addition, each event includes a query_id attribute that contains the unique ID of the query that produced the data stored in the event. This ID can be used in NRQL queries to scope the query to the appropriate data set.

See the following sub-sections for more details on the data sets collected by each query.

NOTE: Please see the note at the end of the Consumption & Cost Data section regarding job cost data and the last requirement in the Consumption and Cost Requirements section for important considerations for collecting job cost data.

List cost per job run

Job cost data on the list cost per job run is collected using the query with the query id jobs_cost_list_cost_per_job_run. This query produces DatabricksJobCost events with the following attributes.

Name	Description
workspace_id	ID of the Workspace where the job run referenced in the run_id attribute occurred
workspace_name	Name of the Workspace where the job run referenced in the run_id attribute occurred
job_id	The unique job ID of the job associated with this job run
job_name	The user-given name of the job referenced in the job_id attribute
run_id	The unique run ID of this job run
run_as	The ID of the user or service principal used for the job run (only included if includeIdentityMetadata is true)
list_cost	The estimated list cost of this job run
last_seen_date	The last time a billable usage record was seen referencing the job ID referenced in the job_id attribute and the run ID referenced in the run_id attribute, in UTC

List cost per job

Job cost data on the list cost per job is collected using the query with the query id jobs_cost_list_cost_per_job. This query produces DatabricksJobCost events with the following attributes.

Name	Description
workspace_id	ID of the Workspace containing the job referenced in the job_id attribute
workspace_name	Name of the Workspace containing the job referenced in the job_id attribute
job_id	The unique job ID of the job
job_name	The user-given name of the job referenced in the job_id attribute
runs	The number of job runs seen for the day this result was collected for the job referenced in the job_id attribute
run_as	The ID of the user or service principal used to run the job (only included if includeIdentityMetadata is true)
list_cost	The estimated list cost of all runs for the job referenced in the job_id attribute for the day this result was collected
last_seen_date	The last time a billable usage record was seen referencing the job ID referenced in the job_id attribute, in UTC

List cost of failed job runs for jobs with frequent failures

Job cost data on the list cost of failed job runs for jobs with frequent failures is collected using the query with the query id jobs_cost_frequent_failures. This query produces DatabricksJobCost events with the following attributes.

Name	Description
workspace_id	ID of the Workspace containing the job referenced in the job_id attribute
workspace_name	Name of the Workspace containing the job referenced in the job_id attribute
job_id	The unique job ID of the job
job_name	The user-given name of the job referenced in the job_id attribute
runs	The number of job runs seen for the day this result was collected for the job referenced in the job_id attribute
run_as	The ID of the user or service principal used to run the job (only included if includeIdentityMetadata is true)
failures	The number of job runs seen with the result state ERROR, FAILED, or TIMED_OUT for the day this result was collected for the job referenced in the job_id attribute
list_cost	The estimated list cost of all failed job runs seen for the day this result was collected for the job referenced in the job_id attribute
last_seen_date	The last time a billable usage record was seen referencing the job ID referenced in the job_id attribute, in UTC

List cost of repaired job runs for jobs with frequent repairs

Job cost data on the list cost of repaired job runs for jobs with frequent repairs is collected using the query with the query id jobs_cost_most_retries. This query produces DatabricksJobCost events with the following attributes.

Name	Description
workspace_id	ID of the Workspace where the job run referenced in the run_id attribute occurred
workspace_name	Name of the Workspace where the job run referenced in the run_id attribute occurred
job_id	The unique job ID of the job associated with this job run
job_name	The user-given name of the job referenced in the job_id attribute
run_id	The unique run ID of this job run
run_as	The ID of the user or service principal used to run the job (only included if includeIdentityMetadata is true)
repairs	The number of repair runs seen for the day this result was collected for the job run referenced in the run_id attribute where the result of the final repair run was SUCCEEDED
list_cost	The estimated list cost of the repair runs seen for the day this result was collected for the job run referenced in the run_id attribute
repair_time_seconds	The cumulative duration of the repair runs seen for the day this result was collected for the job run referenced in the run_id attribute

Job Run Data

The Databricks Integration can collect telemetry about Databricks Job runs, such as job run durations, task run durations, the current state of job and task runs, if a job or a task is a retry, and the number of times a task was retried. This feature is enabled by default and can be enabled or disabled using the Databricks jobs enabled flag in the integration configuration.

NOTE: Some of the text below is sourced from the Databricks SDK Go module documentation.

startOffset Configuration

On each run, the integration uses the Databricks ReST API to retrieve job run data. By default, the Databricks ReST API endpoint for listing job runs returns a paginated list of all historical job runs sorted in descending order by start time. On systems with many jobs, there may be a large number of job runs to retrieve and process, impacting the performance of the collector. To account for this, the integration provides the startOffset configuration parameter. This parameter is used to tune the performance of the integration by constraining the start time of job runs to match to be greater than a particular time in the past calculated as an offset from the current time and thus, limiting the number of job runs to return.

The effect of this behavior is that only job runs which have a start time at or after the calculated start time will be returned on the API call. For example, using the default startOffset (86400 seconds or 24 hours), only job runs which started within the last 24 hours will be returned. This means that jobs that started more than 24 hours ago will not be returned even if some of those jobs are not yet in a TERMINATED state. Therefore, it is important to carefully select a value for the startOffset parameter that will account for long-running job runs without degrading the performance of the integration.

Job Run Metric Data

Job run data is sent to New Relic as dimensional metrics. The following metrics and attributes (dimensions) are provided.

Job run metrics

Metric Name	Metric Type	Description
job.runs	gauge	Job run counts per state
job.tasks	gauge	Task run counts per state
job.run.duration	gauge	Duration (in milliseconds) of the job run
job.run.duration.queue	gauge	Duration (in milliseconds) the job run spent in the queue
job.run.duration.execution	gauge	Duration (in milliseconds) the job run was actually executing commands (only available for single-task job runs)
job.run.duration.setup	gauge	Duration (in milliseconds) it took to setup the cluster (only available for single-task job runs)
job.run.duration.cleanup	gauge	Duration (in milliseconds) it took to terminate the cluster and cleanup associated artifacts (only available for single-task job runs)
job.run.task.duration	gauge	Duration (in milliseconds) of the task run
job.run.task.duration.queue	gauge	Duration (in milliseconds) the task run spent in the queue
job.run.task.duration.execution	gauge	Duration (in milliseconds) the task run was actually executing commands
job.run.task.duration.setup	gauge	Duration (in milliseconds) it took to setup the cluster
job.run.task.duration.cleanup	gauge	Duration (in milliseconds) it took to terminate the cluster and cleanup associated artifacts

Job run attributes

Attribute Name	Data Type	Description
databricksJobId	number	The unique numeric ID of the job being run
databricksJobRunId	number	The unique numeric ID of the job run (only included if includeRunId is set to true)
databricksJobRunName	string	The optional name of the job run
databricksJobRunAttemptNumber	number	The sequence number of the run attempt for this job run (0 for the original attempt or if the job has no retry policy, greater than 0 for subsequent attempts for jobs with a retry policy)
databricksJobRunState	string	The state of the job run
databricksJobRunIsRetry	boolean	true if the job run is a retry of an earlier failed attempt, otherwise false
databricksJobRunTerminationCode	string	For terminated jobs, the job run termination code
databricksJobRunTerminationType	string	For terminated jobs, the job run termination type
databricksJobRunTaskName	string	The unique name of the task within it's parent job
databricksJobRunTaskState	string	The state of the task run
databricksJobRunTaskAttemptNumber	number	The sequence number of the run attempt for this task run (0 for the original attempt or if the task has no retry policy, greater than 0 for subsequent attempts for tasks with a retry policy)
databricksJobRunTaskIsRetry	boolean	true if the task run is a retry of an earlier failed attempt, otherwise false
databricksJobRunTaskTerminationCode	string	For terminated tasks, the task run termination code
databricksJobRunTaskTerminationType	string	For terminated tasks, the task run termination type

Job and task run states

Databricks job and task runs can be in one of 6 states.

• BLOCKED - run is blocked on an upstream dependency
• PENDING - run is waiting to be executed while the cluster and execution context are being prepared
• QUEUED - run is queued due to concurrency limits
• RUNNING - run is executing
• TERMINATING - run has completed, and the cluster and execution context are being cleaned up
• TERMINATED - run has completed

The job and run task states are recorded in the databricksJobRunState and databricksJobRunTaskState, respectively. The databricksJobRunState is on every job run metric including the task related metrics. The databricksJobRunTaskState is only on job run metrics related to tasks, e.g. job.run.task.duration.

Job and task run counts

On each run, the integration records the number of job and task runs in each state (with the exception of runs in the TERMINATED state (see below)), in the metrics job.runs and job.tasks, respectively, using the attributes databricksJobRunState and databricksJobRunTaskState, to indicate the state. In general, only the latest() aggregator function should be used when visualizing or alerting on counts including these states. For example, to display the number of jobs by state, use the following NRQL statement.

FROM Metric
SELECT latest(databricks.job.runs)
FACET databricksJobRunState

The count of job and task runs in the TERMINATED state are also recorded but only include job and task runs that have terminated since the last run of the integration. This is done to avoid counting terminated job and task runs more than once, making it straightforward to use aggregator functions to visualize or alert on values such as "number of job runs completed per time period", "average duration of job runs", and "average duration job runs spent in the queue". For example, to show the average time job runs spend in the queue, grouped by job name, use the following NRQL statement.

FROM Metric
SELECT average(databricks.job.run.duration.queue / 1000)
WHERE databricksJobRunState = 'TERMINATED'
FACET databricksJobRunName
LIMIT MAX

NOTE: Make sure to use the condition databricksJobRunState = 'TERMINATED' or databricksJobRunTaskState = 'TERMINATED' when visualizing or alerting on values using these aggregator functions.

Job and task run durations

The Databricks integration stores job and task run durations in the metrics job.run.duration and job.run.task.duration, respectively. The values stored in these metrics are calculated differently, depending on the state of the job or task, as follows.

• While a job or task run is running (not BLOCKED or TERMINATED), the run duration stored in the respective metrics is the "wall clock" time of the job or task run, calculated as the current time when the metric is collected by the integration minus the start_time of the job or task run as returned from the Databricks ReST API. This duration is inclusive of any time spent in the queue and any setup time, execution time, and cleanup time that has been spent up to the time the duration is calculated but does not include any time the job or task run was blocked.

  This duration is also cumulative, meaning that while the job run is running, the value calculated each time the integration runs will include the entire "wall clock" time since the reported start_time. It is essentially a cumulativeCount metric but stored as a gauge. Within NRQL statements, the latest() function can be used to get the most recent duration. For example, to show the most recent duration of job runs that are running grouped by job name, use the following NRQL (assuming the job run metricPrefix is databricks.).

  FROM Metric
  SELECT latest(databricks.job.run.duration)
  WHERE databricksJobRunState != 'TERMINATED'
  FACET databricksJobRunName

• Once a job or task run has been terminated, the run duration stored in the respective metrics are not calculated but instead come directly from the values returned in the listruns API (also see the SDK documentation for BaseRun and RunTask). For both job and task runs, the run durations are determined as follows.

  • For job runs of multi-task jobs, the job.run.duration for a job run is set to the run_duration field of the corresponding job run item in the runs field returned from the listruns API.
  • For job runs of single-task jobs, the job.run.duration for a job run is set to the sum of the setup_duration, the execution_duration and the cleanup_duration fields of the corresponding job run item in the runs field returned from the listruns API.
  • For task runs, the job.run.task.duration for a task run is set to the sum of the setup_duration, the execution_duration and the cleanup_duration fields of the corresponding task run item in the tasks field of the corresponding job run item in the runs field returned from the listruns API.

In addition to the job.run.duration and job.run.task.duration metrics, once a job or task has terminated, the job.run.duration.queue and job.run.task.duration.queue metrics are set to the queue_duration field of the corresponding job run item in the runs field returned from the listruns API for job runs, and the queue_duration field of the corresponding task run item in the tasks field of the corresponding job run item in the runs field from the listruns API for task runs.

Finally, for job runs and task runs of single-task jobs, the following metrics are set.

• The job.run.duration.setup and job.run.task.duration.setup metrics are set to the setup_duration field of the corresponding job run item in the runs field returned from the listruns API for job runs, and the setup_duration field of the corresponding task run item in the tasks field of the corresponding job run item in the runs field from the listruns API for task runs.
• The job.run.duration.execution and job.run.task.duration.execution are set to the execution_duration field of the corresponding job run item in the runs field returned from the listruns API for job runs, and the execution_duration field of the corresponding task run item in the tasks field of the corresponding job run item in the runs field from the listruns API for task runs.
• The job.run.duration.cleanup and job.run.task.duration.cleanup are set to the cleanup_duration field of the corresponding job run item in the runs field returned from the listruns API for job runs, and the cleanup_duration field of the corresponding task run item in the tasks field of the corresponding job run item in the runs field from the listruns API for task runs.

Example Queries

All examples below assume the job run metricPrefix is databricks..

Current job run counts by state

FROM Metric
SELECT latest(databricks.job.runs)
FACET databricksJobRunState

Current task run counts by state

FROM Metric
SELECT latest(databricks.job.tasks)
FACET databricksJobRunTaskState

Job run durations by job name over time

FROM Metric
SELECT latest(databricks.job.run.duration)
FACET databricksJobRunName
TIMESERIES

Average job run duration by job name

FROM Metric
SELECT average(databricks.job.run.duration)
WHERE databricksJobRunState = 'TERMINATED'
FACET databricksJobRunName

NOTE: Make sure to use the condition databricksJobRunState = 'TERMINATED' or databricksJobRunTaskState = 'TERMINATED' when visualizing or alerting on values using aggregator functions other than latest.

Average task run queued duration by task name over time

FROM Metric
SELECT average(databricks.job.run.task.duration.queue)
WHERE databricksJobRunTaskState = 'TERMINATED'
FACET databricksJobRunTaskName
TIMESERIES

NOTE: Make sure to use the condition databricksJobRunState = 'TERMINATED' or databricksJobRunTaskState = 'TERMINATED' when visualizing or alerting on values using aggregator functions other than latest.

Example Job Runs Dashboard

A sample dashboard is included that shows examples of the types of job run information that can be displayed and the NRQL statements to use to visualize the data.

Pipeline Update Metrics

The Databricks Integration can collect telemetry about Databricks Delta Live Tables Pipeline updates, such as flow durations, flow data quality metrics, and update durations. This feature is enabled by default and can be enabled or disabled using the Databricks pipeline update metrics enabled flag in the integration configuration.

NOTE: Some of the text below is sourced from the Databricks Delta Live Tables pipeline event log schema documentation and the Databricks SDK Go module documentation.

Pipeline startOffset Configuration

On each run, the integration uses the Databricks ReST API to retrieve pipeline events for all Delta Live Tables Pipelines. By default, the Databricks ReST API endpoint for listing pipeline events returns a paginated list of all historical pipeline events sorted in descending order by timestamp. On systems with many pipelines or complex pipelines with many flows, there may be a large number of pipeline events to retrieve and process, impacting the performance of the collector. To account for this, the integration provides the startOffset configuration parameter. This parameter is used to tune the performance of the integration by constraining the timestamp of pipeline events to match to be greater than a particular time in the past calculated as an offset from the current time and thus, limiting the number of pipeline events to return.

The effect of this behavior is that only pipeline events which have a timestamp at or after the calculated start time will be returned on the API call. For example, using the default startOffset (86400 seconds or 24 hours), only pipeline events which occurred within the last 24 hours will be returned. This means that pipeline events that occurred more than 24 hours ago will not be returned even if the associated pipeline update is not yet complete. This can lead to missing flow or update metrics. Therefore, it is important to carefully select a value for the startOffset parameter that will account for long-running pipeline updates without degrading the performance of the integration.

Pipeline intervalOffset Configuration

The integration uses the list pipeline events endpoint of the Databricks ReST API to retrieve pipeline log events. There can be a slight lag time between when a pipeline log event is emitted and when it is available in the API. The intervalOffset configuration parameter can be used to delay the processing of pipeline log events in order to account for this lag. The default value for the intervalOffset configuration parameter is 5 seconds.

To help illustrate, here is the processing behavior for the default settings (60 second collection interval and 5 second interval offset):

• on the first minute, the integration will process all pipeline log events from 60 seconds ago until 55 seconds ago
• on the second interval, the integration will process all pipeline log events from 55 seconds ago until 1:55
• on the third interval, the integration will process all pipeline log events from 1:55 until 2:55
• and so on

Pipeline Update Metric Data

Pipeline update metric data is sent to New Relic as dimensional metrics. The following metrics and attributes (dimensions) are provided.

Pipeline update metrics

Metric Name	Metric Type	Description
pipeline.pipelines	gauge	Pipeline counts per state
pipeline.updates	gauge	Pipeline update counts per status
pipeline.flows	gauge	Pipeline flow counts per status
pipeline.update.duration	gauge	Total duration (in milliseconds) of the pipeline update identified by the id in the databricksPipelineUpdateId attribute
pipeline.update.duration.wait	gauge	Duration (in milliseconds) the pipeline update identified by the id in the databricksPipelineUpdateId attribute spent in the WAITING_FOR_RESOURCES state
pipeline.update.duration.runTime	gauge	Duration (in milliseconds) the pipeline update identified by the id in the databricksPipelineUpdateId attribute spent actually running
pipeline.flow.duration	gauge	Total duration (in milliseconds) of the pipeline flow named in the databricksPipelineFlowName attribute
pipeline.flow.duration.queue	gauge	Duration (in milliseconds) the pipeline flow named in the databricksPipelineFlowName attribute spent in the QUEUED state
pipeline.flow.duration.plan	gauge	Duration (in milliseconds) the pipeline flow named in the databricksPipelineFlowName attribute spent in the PLANNING state
pipeline.flow.backlogBytes	gauge	Number of bytes present in the backlog of the flow named in the databricksPipelineFlowName attribute
pipeline.flow.backlogFiles	gauge	Number of files that exist in the backlog of the flow named in the databricksPipelineFlowName attribute
pipeline.flow.rowsWritten	gauge	Number of rows output by the flow named in the databricksPipelineFlowName attribute
pipeline.flow.recordsDropped	gauge	Number of records dropped by the flow named in the databricksPipelineFlowName attribute
pipeline.flow.expectation.recordsPassed	gauge	Number of records that passed the expectation named in the databricksPipelineFlowExpectationName attribute for the dataset named in the databricksPipelineDatasetName attribute
pipeline.flow.expectation.recordsFailed	gauge	Number of records that failed the expectation named in the databricksPipelineFlowExpectationName attribute for the dataset named in the databricksPipelineDatasetName attribute

Pipeline update attributes

Attribute Name	Data Type	Description
databricksPipelineId	string	The unique UUID of the pipeline associated with the update. Included on all pipeline update metrics.
databricksPipelineName	string	The name of the pipeline associated with the update. Included on all pipeline update metrics.
databricksPipelineState	string	The state of the pipeline associated with the update. Only included on the pipeline.pipelines metric.
databricksClusterId	string	The ID of the cluster where the event associated with the metric originated. Included on all pipeline update metrics, when available.
databricksPipelineUpdateId	string	The unique UUID of the pipeline update. Included on all pipeline update metrics if includeUpdateId is set to true
databricksPipelineUpdateStatus	string	The status of the pipeline update. Included on the pipeline.updates and pipeline.update.duration metrics.
databricksPipelineFlowId	string	The unique UUID of the pipeline flow associated with the metric. Included on all pipeline flow metrics, when available.
databricksPipelineFlowName	string	The unique name of the pipeline flow associated with the metric. Included on all pipeline flow metrics.
databricksPipelineFlowStatus	string	The status of the pipeline flow. Included on all pipeline flow metrics.
databricksPipelineFlowExpectationName	string	The name of the data quality expectation associated with the metric. Included on all pipeline flow expectation metrics.
databricksPipelineDatasetName	string	The name of the dataset associated with the metric. Included on all pipeline flow expectation metrics.

Pipeline states

Databricks pipelines can be in one of nine states.

• DELETED
• DEPLOYING
• FAILED
• IDLE
• RECOVERING
• RESETTING
• RUNNING
• STARTING
• STOPPING

The pipeline state is recorded in the databricksPipelineState attribute on the pipeline.pipelines metric only.

Pipeline update status

Databricks pipeline updates can have one of eleven statuses.

• CANCELED
• COMPLETED
• CREATED
• FAILED
• INITIALIZING
• QUEUED
• RESETTING
• RUNNING
• SETTING_UP_TABLES
• STOPPING
• WAITING_FOR_RESOURCES

The pipeline update status is recorded in the databricksPipelineUpdateStatus attribute on the pipeline.updates metric and the pipeline.update.* metrics.

The statuses CANCELED, COMPLETED, and FAILED are considered update termination statuses.

Pipeline flow status

Databricks pipeline flows can have one of ten statuses.

• COMPLETED
• EXCLUDED
• FAILED
• IDLE
• PLANNING
• QUEUED
• RUNNING
• SKIPPED
• STARTING
• STOPPED

The pipeline flow status is recorded in the databricksPipelineFlowStatus attribute on the pipeline.flows metric and the pipeline.flow.* metrics.

The statuses COMPLETED, EXCLUDED, FAILED, SKIPPED, and STOPPED are considered flow termination statuses.

Pipeline, update, and flow counts

On each run, the integration records the following counts.

• The count of all pipelines by pipeline state is recorded in the metric pipeline.pipelines using the attribute databricksPipelineState to indicate the pipeline state.

• The count of all pipeline updates by update status is recorded in the metric pipeline.updates using the attribute databricksPipelineUpdateStatus to indicate the update status.

  Pipeline updates with a termination update status (CANCELED, COMPLETED, FAILED) are only counted if the corresponding event occurred since the last run of the integration. Otherwise, these updates are ignored. This is done to avoid counting terminated updates more than once.

  Pipeline updates with a non-termination update status are always counted.

• The count of all pipeline flows by flow status is recorded in the metric pipeline.flows using the attribute databricksPipelineFlowStatus to indicate the flow status.

  Pipeline flows with a termination flow status (COMPLETED, EXCLUDED, FAILED, SKIPPED, STOPPED) are only counted if the corresponding event occurred since the last run of the integration. Otherwise, these flows are ignored. This is done to avoid counting terminated flows more than once.

  Pipeline flows with a non-termination flow status are always counted.

Pipeline update and flow durations

The Databricks integration records several update and flow durations. The different durations and the ways in which they are calculated are as follows.

• pipeline.update.duration

  The pipeline.update.duration metric represents the total duration of the update from the time it is created until the time it terminates. After the update is created but before it terminates, the duration recorded in this metric is the "wall clock" time, calculated as the current time when the metric is recorded by the integration minus the timestamp of the pipeline log event where the update was created. Once the update terminates, the duration recorded in this metric is calculated by subtracting the timestamp of the pipeline log event where the update was created from the timestamp of the pipeline log event where the update reached the termination status.

• pipeline.update.duration.wait

  The pipeline.update.duration.wait metric represents the amount of time the update spent waiting for resources (that is, while it has the WAITING_FOR_RESOURCES update status). While the update is waiting for resources, the duration recorded in this metric is the "wall clock" time, calculated as the current time when the metric is recorded by the integration minus the timestamp of the pipeline log event where the update reached the WAITING_FOR_RESOURCES status. Once the update reaches the INITIALIZING status or terminates, the duration recorded in this metric is calculated by subtracting the timestamp of the pipeline log event where the update reached the WAITING_FOR_RESOURCES status from the timestamp of the pipeline log event where the update reached the INITIALIZING status or terminated.

• pipeline.update.duration.runTime

  The pipeline.update.duration.runTime metric represents the amount of time the update spent actually running, defined as the time the update reached the INITIALIZING update status until the time the update terminates. After the update has reached the INITIALIZING status but before it terminates, the duration recorded in this metric is the "wall clock" time, calculated as the current time when the metric is recorded by the integration minus the timestamp of the pipeline log event where the update reached the INITIALIZING status. Once the update terminates, the duration recorded in this metric is calculated by subtracting the timestamp of the pipeline log event where the update reached the INITIALIZING status from the timestamp of the pipeline log event where the update terminated.

• pipeline.flow.duration

  The pipeline.flow.duration metric represents the total duration of the flow from the time it starts until the time it terminates. After the flow starts but before it terminates, the duration recorded in this metric is the "wall clock" time, calculated as the current time when the metric is recorded by the integration minus the timestamp of the pipeline log event where the flow terminated. Once the flow terminates, the duration recorded in this metric is calculated by subtracting the timestamp of the pipeline log event where the flow started from the timestamp of the pipeline log event where the flow terminated.

• pipeline.flow.duration.queue

  The pipeline.flow.duration.queue metric represents the time the flow spent in the queue (that is, while it has the QUEUED flow status). While the update in the queue and before it terminates or reaches the PLANNING or STARTING status, the duration recorded in this metric is the "wall clock" time, calculated as the current time when the metric is recorded by the integration minus the timestamp of the pipeline log event where the flow reached the QUEUED status. Once the flow terminates or reaches either the PLANNING status or the STARTING status, the duration recorded in this metric is calculated by subtracting the timestamp of the pipeline log event where the flow reached the QUEUED status from the timestamp of the log event where the flow terminated or reached either the PLANNING status or the STARTING status.

• pipeline.flow.duration.plan

  The pipeline.flow.duration.plan metric represents the time spent to plan the flow (that is, while it has the PLANNING flow status). While the update has the PLANNING status and before it terminates or reaches the STARTING status, the duration recorded in this metric is the "wall clock" time, calculated as the current time when the metric is recorded by the integration minus the timestamp of the pipeline log event where the flow terminated or reached the PLANNING status. Once the terminates or reaches the STARTING status, the duration recorded in this metric is calculated by subtracting the timestamp of the pipeline log event where the flow reached the PLANNING status from the timestamp of the log event where the flow terminated or reached the STARTING status.

NOTE: Wall clock durations are cumulative, meaning that until the update or flow reaches the status at which the true duration is calculated, the value of each successive metric increases monotonically. It is essentially a cumulativeCount metric but stored as a gauge. Within NRQL statements, the latest() function can be used to get the most recent duration. For example, to show the most recent duration of flows that are running grouped by flow name, use the following NRQL (assuming the pipeline update metricPrefix is databricks.).

FROM Metric
SELECT latest(databricks.pipeline.flow.duration)
WHERE databricksPipelineFlowStatus = `RUNNING`
FACET databricksPipelineFlowName

Example Pipeline Update Queries

All examples below assume the pipeline update metricPrefix is databricks..

Current update counts by state

FROM Metric
SELECT latest(databricks.pipeline.pipelines) AS 'Pipelines'
FACET databricksPipelineState

Current update counts by status

FROM Metric
SELECT latest(databricks.pipeline.updates) AS 'Updates'
FACET databricksPipelineUpdateStatus

Update durations by pipeline name and update ID over time

FROM Metric
SELECT latest(databricks.pipeline.update.duration)
FACET databricksPipelineName, substring(databricksPipelineUpdateId, 0, 6)
TIMESERIES

Recent updates

FROM Metric
SELECT databricksPipelineName AS Pipeline, substring(databricksPipelineUpdateId, 0, 6) AS Update, databricksPipelineUpdateStatus as Status, getField(databricks.pipeline.update.duration, latest) / 1000 AS Duration
WHERE databricks.pipeline.update.duration IS NOT NULL
 AND databricksPipelineUpdateStatus IN ('COMPLETED', 'CANCELED', 'FAILED')
LIMIT MAX

Average update duration by pipeline name and update status

FROM Metric
SELECT average(databricks.pipeline.update.duration) / 1000 AS 'seconds'
WHERE databricksPipelineUpdateStatus IN ('COMPLETED', 'CANCELED', 'FAILED')
FACET databricksPipelineName, databricksPipelineUpdateStatus
TIMESERIES

NOTE: Make sure to use the condition databricksPipelineUpdateStatus IN ('COMPLETED', 'CANCELED', 'FAILED') when visualizing or alerting on update duration using aggregator functions other than latest.

Recent flows

FROM Metric
SELECT databricksPipelineName AS Pipeline, substring(databricksPipelineUpdateId, 0, 6) AS Update, databricksPipelineFlowName AS Flow, databricksPipelineFlowStatus AS Status, getField(databricks.pipeline.flow.duration, latest) / 1000 AS Duration
WHERE databricks.pipeline.flow.duration IS NOT NULL
 AND databricksPipelineFlowStatus IN ('COMPLETED', 'STOPPED', 'SKIPPED', 'FAILED', 'EXCLUDED')
LIMIT MAX

Average flow backlog bytes

FROM Metric
SELECT average(databricks.pipeline.flow.backlogBytes)
FACET databricksPipelineName, databricksPipelineFlowName

Flow expectation records passed

FROM Metric
SELECT databricksPipelineName AS Pipeline, substring(databricksPipelineUpdateId, 0, 6) AS Update, databricksPipelineFlowName AS Flow, databricksPipelineFlowExpectationName AS Expectation, getField(databricks.pipeline.flow.expectation.recordsPassed, latest) AS Records
WHERE databricks.pipeline.flow.expectation.recordsPassed IS NOT NULL
LIMIT MAX

Example Pipeline Updates Dashboard

A sample dashboard is included that shows examples of the types of pipeline update information that can be displayed and the NRQL statements to use to visualize the data.

Pipeline Event Logs

The Databricks Integration can collect Databricks Delta Live Tables Pipeline event logs for all Databricks Delta Live Tables Pipelines defined in a workspace. Databricks Delta Live Tables Pipeline event log entries for every pipeline update are collected and sent to New Relic Logs.

NOTE: Some of the text below is sourced from the Databricks Delta Live Tables pipeline event log schema documentation and the Databricks SDK Go module documentation.

Pipeline Event Log Data

Databricks Delta Live Tables Pipeline event logs are sent to New Relic as New Relic Log data. For each Databricks Delta Live Tables Pipeline event log entry, the fields from the event log entry are mapped to attributes on the corresponding New Relic log entry as follows.

NOTE: Due to the way in which the Databricks ReST API returns Databricks Delta Live Tables Pipeline event log entries (in descending order by timestamp), later event log entries may sometimes be visible in the New Relic Logs UI before older event log entries are visible. This does not affect the temporal ordering of the log entries. Once older log entries become visible (usually 30-60s longer than the value of the interval configuration parameter), they are properly ordered by timestamp in the New Relic Logs UI.

Pipeline event log attributes

Pipeline Event Log Field Name	New Relic Log Entry Attribute Name	Data Type in New Relic Log Entry	Description
message	message	string	A human-readable message describing the event
timestamp	timestamp	integer	The time (in milliseconds since the epoch) the event was recorded
id	databricksPipelineEventId	string	A unique identifier for the event log record
event_type	databricksPipelineEventType	string	The event type
level	level, databricksPipelineEventLevel	string	The severity level of the event, for example, INFO, WARN, ERROR, or METRICS
maturity_level	databricksPipelineEventMaturityLevel	string	The stability of the event schema, one of STABLE, NULL, EVOLVING, or DEPRECATED
n/a	databricksPipelineEventError	boolean	true if an error was captured by the event (see below for additional details), otherwise false
origin.batch_id	databricksPipelineFlowBatchId	integer	The id of a batch. Unique within a flow.
origin.cloud	databricksCloud	string	The cloud provider, e.g., AWS or Azure
origin.cluster_id	databricksClusterId	string	The id of the cluster where an execution happens. Unique within a region.
origin.dataset_name	databricksPipelineDatasetName	string	The name of a dataset. Unique within a pipeline.
origin.flow_id	databricksPipelineFlowId	string	The id of the flow. Globally unique.
origin.flow_name	databricksPipelineFlowName	string	The name of the flow. Not unique.
origin.host	databricksPipelineEventHost	string	The optional host name where the event was triggered
origin.maintenance_id	databricksPipelineMaintenanceId	string	The id of a maintenance run. Globally unique.
origin.materialization_name	databricksPipelineMaterializationName	string	Materialization name
origin.org_id	databricksOrgId	integer	The org id of the user. Unique within a cloud.
origin.pipeline_id	databricksPipelineId	string	The id of the pipeline. Globally unique.
origin.pipeline_name	databricksPipelineName	string	The name of the pipeline. Not unique.
origin.region	databricksCloudRegion	string	The cloud region
origin.request_id	databricksPipelineRequestId	string	The id of the request that caused an update
origin.table_id	databricksDeltaTableId	string	The id of a (delta) table. Globally unique.
origin.uc_resource_id	databricksUcResourceId	string	The Unity Catalog id of the MV or ST being updated
origin.update_id	databricksPipelineUpdateId	string	The id of an execution. Globally unique.

Additionally, if the error field is set on the pipeline event log entry, indicating an error was captured by the event, the following attributes are added to the New Relic log entry.

Pipeline Event Log Field Name	New Relic Log Entry Attribute Name	Data Type in New Relic Log Entry	Description
error.fatal	databricksPipelineEventErrorFatal	boolean	Whether the error is considered fatal, that is, unrecoverable
error.exceptions[N].class_name	databricksPipelineEventErrorExceptionNClassName	string	Runtime class of the Nth exception
error.exceptions[N].message	databricksPipelineEventErrorExceptionNMessage	string	Exception message of the Nth exception

Cluster Health

The New Relic Infrastructure agent can be used to collect infrastructure data like CPU and memory usage from the nodes in a Databricks cluster as well as the Spark driver and executor logs, the Spark driver event log, and the logs from all init scripts on the driver and worker nodes.

Installing the New Relic Infrastructure Agent

The New Relic Infrastructure agent will be automatically installed on the driver and worker nodes of the cluster as part of the process of deploying the integration on the driver node of a cluster if the value of the NEW_RELIC_INFRASTRUCTURE_ENABLED environment variable is set to true. In addition, if the value of the environment variable NEW_RELIC_INFRASTRUCTURE_LOGS_ENABLED is set to true, the Spark driver and executor logs, the Spark driver event log, and the logs from all init scripts on the driver and worker nodes will be collected and forwarded to New Relic Logs.

Cluster Health Data

Cluster health infrastructure data

When installed, the New Relic infrastructure agent collects all default infrastructure monitoring data for the driver and worker nodes. Additionally, a host entity will show up for each node in the cluster.

Cluster health logs

When log forwarding is configured, the following logs are collected.

• Spark driver standard out and standard error logs
• Spark driver log4j log
• Spark event log
• Standard out and standard error logs for all init scripts on the driver node
• Spark application executor standard out and standard error logs
• Standard out and standard error logs for all init scripts on the worker nodes

Cluster health attributes

The following attributes are included on all infrastructure data, all Log events collected by the infrastructure agent, and on the host entities for all nodes in the cluster.

Attribute Name	Data Type	Description
databricksWorkspaceHost	string	The instance name of the target Databricks instance
databricksClusterId	string	The ID of the Databricks cluster where the metric, event, or Log originated from. On a host entity, the ID of the cluster the host is a part of.
databricksClusterName	string	The name of the Databricks cluster where the metric, event, or Log originated from. On a host entity, the name of the cluster the host is a part of.
databricksIsDriverNode	string	true if the metric, event, or Log originated from the driver node. false if the metric, event, or Log originated from a worker node. On a host entity, true if the host is the driver node or false if it is a worker node.
databricksIsJobCluster	string	true if the Databricks cluster where the metric, event or Log originated from is a job cluster, otherwise false. On a host entity, true if the cluster the host is a part of is a job cluster, otherwise false.
databricksLogType	string	The type of log that generated the Log event. Only included on cluster health Log events.

Cluster health log types

The databricksLogType attribute can have one of the following values. These values can be used to determine the log which generated the Log event.

• driver-stdout

  Indicates the Log event originated from the driver standard out log.

• driver-stderr

  Indicates the Log event originated from the driver standard error log.

• driver-log4j

  Indicates the Log event originated from the driver log4j log.

• spark-eventlog

  Indicates the Log event originated from the Spark event log.

• driver-init-script-stdout

  Indicates the Log event originated from the standard out log of an init script on the driver node. The specific init script can be determined by examining the filePath attribute.

• driver-init-script-stderr

  Indicates the Log event originated from the standard error log of an init script on the driver node. The specific init script can be determined by examining the filePath attribute.

• executor-stdout

  Indicates the Log event originated from the standard out log of a Spark application executor. The specific Spark application can be determined by examining the filePath attribute.

• executor-stderr

  Indicates the Log event originated from the standard error log of a Spark application executor. The specific Spark application can be determined by examining the filePath attribute.

• worker-init-script-stdout

  Indicates the Log event originated from the standard out log of an init script on a worker node. The specific init script can be determined by examining the filePath attribute. The specific worker node can be determined by examining the hostname or fullHostname attributes.

• worker-init-script-stderr

  Indicates the Log event originated from the standard error log of an init script on a worker node. The specific init script can be determined by examining the filePath attribute. The specific worker node can be determined by examining the hostname or fullHostname attributes.

Example Cluster Health Queries

Driver node average CPU by cluster and entity name

FROM SystemSample
SELECT average(cpuPercent)
WHERE databricksIsDriverNode = 'true'
FACET databricksClusterName, entityName
TIMESERIES

Worker node average CPU by cluster and entity name

FROM SystemSample
SELECT average(cpuPercent)
WHERE databricksIsDriverNode = 'false'
FACET databricksClusterName, entityName
TIMESERIES

Spark driver log4j logs

WITH position(databricksWorkspaceHost, '.') AS firstDot
FROM Log
SELECT substring(databricksWorkspaceHost, 0, firstDot) AS 'Workspace', databricksClusterName AS 'Cluster', hostname AS Hostname, level AS Level, message AS Message
WHERE databricksClusterId IS NOT NULL
 AND databricksLogType = 'driver-log4j'
LIMIT 1000

Spark event logs

WITH position(databricksWorkspaceHost, '.') AS firstDot,
 capture(filePath, r'/databricks/driver/eventlogs/(?P<sparkContextId>[^/]+)/.*') AS sparkContextId
FROM Log
SELECT substring(databricksWorkspaceHost, 0, firstDot) AS 'Workspace', databricksClusterName AS 'Cluster', sparkContextId AS 'Spark Context ID', Event, `App Name`, `Job ID`, `Stage Info.Stage ID` AS 'Stage ID', `Task Info.Task ID` AS 'Task ID'
WHERE databricksClusterId IS NOT NULL
 AND databricksLogType = 'spark-eventlog'
LIMIT 1000

Spark application executor standard out logs

WITH position(databricksWorkspaceHost, '.') AS firstDot,
 capture(filePath, r'/databricks/spark/work/(?P<appName>[^/]+)/.*') AS appName
FROM Log
SELECT substring(databricksWorkspaceHost, 0, firstDot) AS 'Workspace', databricksClusterName AS 'Cluster', hostname AS Hostname, appName AS Application, message AS Message
WHERE databricksClusterId IS NOT NULL
 AND databricksLogType = 'executor-stdout'
LIMIT 1000

Example Cluster Health Dashboard

A sample dashboard is included that shows examples of visualizing cluster health metrics and logs and the NRQL statements to use to visualize the data.

Query Metrics

The Databricks Integration can collect telemetry about queries executed in Databricks SQL warehouses and serverless compute, including execution times, query statuses, and query I/O metrics such as the number of bytes and rows read and the number of bytes spilled to disk and sent over the network. This feature is enabled by default and can be enabled or disabled using the Databricks query metrics enabled flag in the integration configuration.

NOTE:

• Unlike other metrics collected by the integration, query metrics are only recorded once a query has completed execution.
• When serverless compute is enabled, metrics for all SQL and Python queries executed on serverless compute for notebooks and jobs are returned by the list query history API used to collect query metrics.
• Some of the text below is sourced from the documentation on Databricks queries and the Databricks SDK Go module documentation.

Query Metrics startOffset Configuration

Query metrics are only collected once when the query terminates, that is, when it completes successfully, when it fails, or when it is canceled. Furthermore, query metrics are only collected for queries that terminated since the last execution of the integration. This way, query metrics are only collected once.

Query metrics are collected using the query history endpoint. of the Databricks ReST API By default, this endpoint returns queries sorted in descending order by start time. This means that unless the time range used on the list query history API call includes the time when a query started, it will not be returned. Were the integration to only request the query history since the last execution of the integration, telemetry for queries that started prior to the last execution of the integration but completed since the last execution of the integration would not be recorded because they would not be returned.

To account for this, the integration provides the startOffset configuration parameter. This parameter is used to offset the start time of the time range used on the list query history API call. The effect of this behavior is that metrics for queries that completed since the last execution of the integration but started prior to the last execution of the integration (up to startOffset seconds ago) will be recorded.

For example, consider a query that started 3 minutes prior to the current execution of the integration but completed 10 seconds prior to the current execution of the integration. Using the default value of the interval configuration parameter, without a mechanism to effect the start time used to query the query history, the time range used on list query history API would be 60 seconds ago until 0 seconds ago. Because the list query history API returns queries according to the start time of the query, the example query would not be returned even though it completed since the last execution of the integration. Since the query is not returned, the query end time can not be checked against the time window and the query metrics will never be recorded.

In contrast, with the default startOffset (600 seconds or 10 minutes), the example query would be returned even though it started before the last execution of the integration. Since the query is returned, the integration will see that the query completed since the last execution of the integration, and will record the query metrics for the query.

NOTE: It is important to carefully select a value for the startOffset configuration parameter. Collecting query metrics with the list query history API is an expensive operation. A value should be chosen that accounts for as many long running queries as possible without negatively affecting the performance of the integration or the cost of executing the list query history API call.

Query Metrics intervalOffset Configuration

Since query metrics are only collected if the query terminated since the last execution of the integration, if there is any lag setting the query end time, it is possible for some queries to be missed. For example, consider a query that ends one second prior to the current run time of the integration. If the end time of the query takes one second or more to be reflected in the API, the query end time will not be set during the current run of the integration and can not be checked. On the subsequent run of the integration, the query will be returned and it's end time will be set but the end time will fall just outside the last run time of the integration. As such, the metrics for the query will never be recorded.

To account for this, the integration provides the intervalOffset configuration parameter. This parameter is used to slide the time window that the query end time is checked against by some number of seconds, allowing the query end time additional time to be reflected in the API. The default value for the intervalOffset configuration parameter is 5 seconds.

Query Metric Data

Query metric data is sent to New Relic as events. The following event attributes are provided.

Query Event Attributes

NOTE: Much of the text below is sourced from the file model.go.

Attribute Name	Data Type	Description
id	string	The unique ID of the query
type	string	The query statement type (e.g., SELECT, INSERT)
status	string	The status of the query (e.g., FINISHED, FAILED, CANCELED)
warehouseId	string	The ID of the SQL warehouse where the query was executed
warehouseName	string	The name of the SQL warehouse where the query was executed
warehouseCreator	string	The creator of the SQL warehouse where the query was executed (only included if includeIdentityMetadata is true)
workspaceId	int	The ID of the workspace containing the SQL warehouse where the query was executed
workspaceName	string	The name of the workspace containing the SQL warehouse where the query was executed
workspaceUrl	string	The URL of the workspace containing the SQL warehouse where the query was executed
duration	int	Total execution time of the statement in milliseconds (excluding result fetch time)
query	string	The query text, truncated to 4096 characters
plansState	string	Whether plans exist for the execution, or the reason why they are missing
error	bool	Indicates whether the query failed
errorMessage	string	The error message if the query failed
userId	int	The ID of the user who executed the query (only included if includeIdentityMetadata is true)
userName	string	The email address or username of the user who executed the query (only included if includeIdentityMetadata is true)
runAsUserId	int	The ID of the user whose credentials were used to run the query (only included if includeIdentityMetadata is true)
runAsUserName	string	The email address or username of the user whose credentials were used to run the query (only included if includeIdentityMetadata is true)
executionEndTime	int	The timestamp when the execution of the query ended, in milliseconds
startTime	int	The timestamp when the query started, in milliseconds
endTime	int	The timestamp when the query ended, in milliseconds
final	bool	Indicates whether more updates for the query are expected
compilationTime	int	The time spent loading metadata and optimizing the query, in milliseconds
executionTime	int	The time spent executing the query, in milliseconds
fetchTime	int	The time spent fetching the query results after the execution finished, in milliseconds
totalTime	int	The total execution time of the query from the client’s point of view, in milliseconds
totalTaskTime	int	The sum of the execution time for all of the query’s tasks, in milliseconds
totalPhotonTime	int	The total execution time for all individual Photon query engine tasks in the query, in milliseconds
overloadingQueueStartTime	int	The timestamp when the query was enqueued waiting while the warehouse was at max load, in milliseconds. This field will be 0 if the query skipped the overloading queue.
provisioningQueueStartTime	int	The timestamp when the query was enqueued waiting for a cluster to be provisioned for the warehouse, in milliseconds. This field will be 0 if the query skipped the provisioning queue.
compilationStartTime	int	The timestamp when the underlying compute started compilation of the query, in milliseconds
fromCache	bool	Indicates whether the query result was fetched from the cache
bytesRead	int	The total size of data read by the query, in bytes
cacheBytesRead	int	The total size of persistent data read from the cache, in bytes
filesRead	int	The total number of files read after pruning
partitionsRead	int	The total number of partitions read after pruning
remoteBytesRead	int	The size of persistent data read from cloud object storage on your cloud tenant, in bytes
rowsRead	int	The total number of rows read by the query
rowsReturned	int	The total number of rows returned by the query
bytesPruned	int	The total number of bytes in all tables not read due to pruning
filesPruned	int	The total number of files from all tables not read due to pruning
remoteBytesWritten	int	The size of persistent data written to cloud object storage in your cloud tenant, in bytes
diskBytesSpilled	int	The size of data temporarily written to disk while executing the query, in bytes
networkBytesSent	int	The total amount of data sent over the network between executor nodes during shuffle, in bytes

Example Query Metric Queries

Total completed queries

FROM DatabricksQuery
SELECT count(*) AS Queries
WHERE status = 'FINISHED'

Total failed queries

FROM DatabricksQuery
SELECT count(*) AS Queries
WHERE status = 'FAILED'

Rate of successful queries per second

FROM DatabricksQuery
SELECT rate(count(*), 1 second) AS Queries
WHERE status = 'FINISHED'
TIMESERIES

Number of failed queries by query text

FROM DatabricksQuery
SELECT count(*)
WHERE status = 'FAILED'
FACET query

Average bytes spilled to disk by query text

FROM DatabricksQuery
SELECT average(diskBytesSpilled)
WHERE diskBytesSpilled > 0
ORDER BY diskBytesSpilled DESC
FACET query

Total duration of all queries run by warehouse

FROM DatabricksQuery
SELECT sum(duration) / 1000 AS Duration
FACET warehouseName
TIMESERIES

Query history

WITH
 concat(workspaceUrl, '/sql/warehouses/', warehouseId, '/monitoring?queryId=', id) AS queryLink
FROM DatabricksQuery
SELECT
 status as Status,
 substring(query, 0, 100) as Query,
 startTime as Started,
 duration as Duration,
 userName as User,
 queryLink

Queries with data spilling

WITH
 concat(workspaceUrl, '/sql/warehouses/', warehouseId, '/monitoring?queryId=', id) AS queryLink
FROM DatabricksQuery
SELECT
 diskBytesSpilled,
 query,
 queryLink
WHERE diskBytesSpilled > 0
ORDER BY diskBytesSpilled DESC
LIMIT 100

Example Query Metrics Dashboard

A sample dashboard is included that shows examples of the types of query metric information that can be displayed and the NRQL statements to use to visualize the data.

Building

Coding Conventions

Style Guidelines

While not strictly enforced, the basic preferred editor settings are set in the .editorconfig. Other than this, no style guidelines are currently imposed.

Static Analysis

This project uses both go vet and staticcheck to perform static code analysis. These checks are run via precommit on all commits. Though this can be bypassed on local commit, both tasks are also run during the validate workflow and must have no errors in order to be merged.

Commit Messages

Commit messages must follow the conventional commit format. Again, while this can be bypassed on local commit, it is strictly enforced in the validate workflow.

The basic commit message structure is as follows.

<type>[optional scope][!]: <description>

[optional body]

[optional footer(s)]

In addition to providing consistency, the commit message is used by svu during the release workflow. The presence and values of certain elements within the commit message affect auto-versioning. For example, the feat type will bump the minor version. Therefore, it is important to use the guidelines below and carefully consider the content of the commit message.

Please use one of the types below.

• feat (bumps minor version)
• fix (bumps patch version)
• chore
• build
• docs
• test

Any type can be followed by the ! character to indicate a breaking change. Additionally, any commit that has the text BREAKING CHANGE: in the footer will indicate a breaking change.

Local Development

For local development, simply use go build and go run. For example,

go build cmd/databricks/databricks.go

Or

go run cmd/databricks/databricks.go

If you prefer, you can also use goreleaser with the --single-target option to build the binary for the local GOOS and GOARCH only.

goreleaser build --single-target

Releases

Releases are built and packaged using goreleaser. By default, a new release will be built automatically on any push to the main branch. For more details, review the .goreleaser.yaml and the goreleaser documentation.

The svu utility is used to generate the next tag value based on commit messages.

GitHub Workflows

This project utilizes GitHub workflows to perform actions in response to certain GitHub events.

Workflow	Events	Description
validate	push, pull_request to main branch	Runs precommit to perform static analysis and runs commitlint to validate the last commit message
build	push, pull_request	Builds and tests code
release	push to main branch	Generates a new tag using svu and runs goreleaser
repolinter	pull_request	Enforces repository content guidelines

Appendix

Example: Creating and Using a Secret for your New Relic License Key

Sensitive data like credentials and API keys should never be specified directly in custom environment variables. Instead, it is recommended to create a secret using the Databricks CLI and reference the secret in the environment variable.

For example, the init script used to install the Databricks Integration when deploying the integration on the driver node of a cluster requires the New Relic license key to be specified in the environment variable named NEW_RELIC_LICENSE_KEY. The steps below show an example of how to create a secret for the New Relic license key and reference it in a custom environment variable.

1. Follow the steps to install or update the Databricks CLI.

2. Use the Databricks CLI to create a Databricks-backed secret scope with the name newrelic. For example,

   databricks secrets create-scope newrelic

   NOTE: Be sure to take note of the information in the referenced URL about the MANAGE scope permission and use the correct version of the command.

3. Use the Databricks CLI to create a secret for the license key in the new scope with the name licenseKey. For example,

   databricks secrets put-secret --json '{
      "scope": "newrelic",
      "key": "licenseKey",
      "string_value": "[YOUR_LICENSE_KEY]"
   }'

To set the custom environment variable named NEW_RELIC_LICENSE_KEY and reference the value from the secret, follow the steps to configure custom environment variables and add the following line after the last entry in the Environment variables field.

NEW_RELIC_LICENSE_KEY={{secrets/newrelic/licenseKey}}

Support

New Relic has open-sourced this project. This project is provided AS-IS WITHOUT WARRANTY OR DEDICATED SUPPORT. Issues and contributions should be reported to the project here on GitHub.

We encourage you to bring your experiences and questions to the Explorers Hub where our community members collaborate on solutions and new ideas.

Privacy

At New Relic we take your privacy and the security of your information seriously, and are committed to protecting your information. We must emphasize the importance of not sharing personal data in public forums, and ask all users to scrub logs and diagnostic information for sensitive information, whether personal, proprietary, or otherwise.

We define “Personal Data” as any information relating to an identified or identifiable individual, including, for example, your name, phone number, post code or zip code, Device ID, IP address, and email address.

For more information, review New Relic’s General Data Privacy Notice.

Contribute

We encourage your contributions to improve this project! Keep in mind that when you submit your pull request, you'll need to sign the CLA via the click-through using CLA-Assistant. You only have to sign the CLA one time per project.

If you have any questions, or to execute our corporate CLA (which is required if your contribution is on behalf of a company), drop us an email at [email protected].

A note about vulnerabilities

As noted in our security policy, New Relic is committed to the privacy and security of our customers and their data. We believe that providing coordinated disclosure by security researchers and engaging with the security community are important means to achieve our security goals.

If you believe you have found a security vulnerability in this project or any of New Relic's products or websites, we welcome and greatly appreciate you reporting it to New Relic through HackerOne.

If you would like to contribute to this project, review these guidelines.

To all contributors, we thank you! Without your contribution, this project would not be what it is today.

License

The Databricks Integration project is licensed under the Apache 2.0 License.