Apache Spark integration for Grafana Cloud

Apache Spark™ is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.

This integration includes 1 pre-built dashboard to help monitor and visualize Apache Spark metrics.

Before you begin

This integration monitors a Spark cluster based on the built-in prometheus plugin, available from version 3.0 upwards, which should be enabled following the official documentation. This tutorial by dzlab might be helpful as well.

Install Apache Spark integration for Grafana Cloud

In your Grafana Cloud stack, click Connections in the left-hand menu.
Find Apache Spark and click its tile to open the integration.
Review the prerequisites in the Configuration Details tab and set up Grafana Agent to send Apache Spark metrics to your Grafana Cloud instance.
Click Install to add this integration’s pre-built dashboard to your Grafana Cloud instance, and you can start monitoring your Apache Spark setup.

Configuration snippets for Grafana Alloy

Advanced mode

The following snippets provide examples to guide you through the configuration process.

To instruct Grafana Alloy to scrape your Apache Spark instances, manually copy and append the snippets to your alloy configuration file, then follow subsequent instructions.

Advanced metrics snippets

discovery.relabel "metrics_integrations_integrations_spark_master" {
	targets = [{
		__address__ = "spark-master:8080",
	}]

	rule {
		target_label = "instance"
		replacement  = constants.hostname
	}

	rule {
		target_label = "instance_type"
		replacement  = "master"
	}

	rule {
		target_label = "spark_cluster"
		replacement  = "<your-cluster-name>"
	}
}

discovery.relabel "metrics_integrations_integrations_spark_worker" {
	targets = [{
		__address__ = "spark-worker:8081",
	}]

	rule {
		target_label = "instance"
		replacement  = constants.hostname
	}

	rule {
		target_label = "instance_type"
		replacement  = "worker"
	}

	rule {
		target_label = "spark_cluster"
		replacement  = "<your-cluster-name>"
	}
}

discovery.relabel "metrics_integrations_integrations_spark_driver" {
	targets = [{
		__address__ = "spark-driver:4040",
	}]

	rule {
		target_label = "instance"
		replacement  = constants.hostname
	}

	rule {
		target_label = "instance_type"
		replacement  = "driver"
	}

	rule {
		target_label = "spark_cluster"
		replacement  = "<your-cluster-name>"
	}
}

prometheus.scrape "metrics_integrations_integrations_spark_master" {
	targets      = discovery.relabel.metrics_integrations_integrations_spark_master.output
	forward_to   = [prometheus.remote_write.metrics_service.receiver]
	job_name     = "integrations/spark-master"
	metrics_path = "/metrics/master/prometheus"
}

prometheus.scrape "metrics_integrations_integrations_spark_worker" {
	targets      = discovery.relabel.metrics_integrations_integrations_spark_worker.output
	forward_to   = [prometheus.remote_write.metrics_service.receiver]
	job_name     = "integrations/spark-worker"
	metrics_path = "/metrics/prometheus"
}

prometheus.scrape "metrics_integrations_integrations_spark_driver" {
	targets      = discovery.relabel.metrics_integrations_integrations_spark_driver.output
	forward_to   = [prometheus.remote_write.metrics_service.receiver]
	job_name     = "integrations/spark-driver"
	metrics_path = "/metrics/prometheus/"
}

To monitor your Apache Spark instance, you must use a discovery.relabel component to discover your Apache Spark Prometheus endpoint and apply appropriate labels, followed by a prometheus.scrape component to scrape it.

Configure the following properties within each discovery.relabel component:

__address__: The address to your Apache Spark Prometheus metrics endpoint.
instance label: constants.hostname sets the instance label to your Grafana Alloy server hostname. If that is not suitable, change it to a value uniquely identifies this Apache Spark instance.
spark_cluster: The spark_cluster label to group your Apache Spark instances within a cluster. Set the same value for all nodes within your cluster.

If you have multiple Apache Spark servers to scrape, configure one discovery.relabel for each and scrape them by including each under targets within the prometheus.scrape component.

Grafana Agent static configuration (deprecated)

The following section shows configuration for running Grafana Agent in static mode which is deprecated. You should use Grafana Alloy for all new deployments.

Before you begin

Install Apache Spark integration for Grafana Cloud

In your Grafana Cloud stack, click Connections in the left-hand menu.
Find Apache Spark and click its tile to open the integration.
Review the prerequisites in the Configuration Details tab and set up Grafana Agent to send Apache Spark metrics to your Grafana Cloud instance.
Click Install to add this integration’s pre-built dashboard to your Grafana Cloud instance, and you can start monitoring your Apache Spark setup.

Post-install configuration for the Apache Spark integration

After enabling the metrics generation, you should instruct Grafana Agent to scrape your Spark nodes.

Master nodes exposes a /metrics/master/prometheus endpoint, while Worker and Driver nodes expose a /metrics/prometheus/ endpoint. To scrape these, add the snippets above to your agent configuration file.

You must configure two custom labels for this integration:

instance_type, which identifies the node type, and must be one of master, worker, application or driver
spark_cluster, which must be given a value that identifies the spark cluster. If you are monitoring different clusters, give each a unique name, identifying all composing instances with the same value.

Make sure to change targets in the snippet according to your environment.

Configuration snippets for Grafana Agent

Below metrics.configs.scrape_configs, insert the following lines and change the URLs according to your environment:

    - job_name: 'integrations/spark-master'
      metrics_path: '/metrics/master/prometheus'
      relabel_configs:
        - replacement: '<your-instance-name>'
          target_label: instance
        - replacement: master
          target_label: instance_type
        - replacement: '<your-cluster-name>'
          target_label: spark_cluster
      static_configs:
        - targets: ['spark-master:8080']
    - job_name: 'integrations/spark-worker'
      metrics_path: '/metrics/prometheus'
      relabel_configs:
        - replacement: '<your-instance-name>'
          target_label: instance
        - replacement: worker
          target_label: instance_type
        - replacement: '<your-cluster-name>'
          target_label: spark_cluster
      static_configs:
        - targets: ['spark-worker:8081']
    - job_name: 'integrations/spark-driver'
      metrics_path: '/metrics/prometheus/'
      relabel_configs:
        - replacement: '<your-instance-name>'
          target_label: instance
        - replacement: driver
          target_label: instance_type
        - replacement: '<your-cluster-name>'
          target_label: spark_cluster
      static_configs:
        - targets: ['spark-driver:4040']

Full example configuration for Grafana Agent

Refer to the following Grafana Agent configuration for a complete example that contains all the snippets used for the Apache Spark integration. This example also includes metrics that are sent to monitor your Grafana Agent instance.

integrations:
  prometheus_remote_write:
  - basic_auth:
      password: <your_prom_pass>
      username: <your_prom_user>
    url: <your_prom_url>
  agent:
    enabled: true
    relabel_configs:
    - action: replace
      source_labels:
      - agent_hostname
      target_label: instance
    - action: replace
      target_label: job
      replacement: "integrations/agent-check"
    metric_relabel_configs:
    - action: keep
      regex: (prometheus_target_sync_length_seconds_sum|prometheus_target_scrapes_.*|prometheus_target_interval.*|prometheus_sd_discovered_targets|agent_build.*|agent_wal_samples_appended_total|process_start_time_seconds)
      source_labels:
      - __name__
  # Add here any snippet that belongs to the `integrations` section.
  # For a correct indentation, paste snippets copied from Grafana Cloud at the beginning of the line.
logs:
  configs:
  - clients:
    - basic_auth:
        password: <your_loki_pass>
        username: <your_loki_user>
      url: <your_loki_url>
    name: integrations
    positions:
      filename: /tmp/positions.yaml
    scrape_configs:
      # Add here any snippet that belongs to the `logs.configs.scrape_configs` section.
      # For a correct indentation, paste snippets copied from Grafana Cloud at the beginning of the line.
metrics:
  configs:
  - name: integrations
    remote_write:
    - basic_auth:
        password: <your_prom_pass>
        username: <your_prom_user>
      url: <your_prom_url>
    scrape_configs:
      # Add here any snippet that belongs to the `metrics.configs.scrape_configs` section.
      # For a correct indentation, paste snippets copied from Grafana Cloud at the beginning of the line.
    - job_name: 'integrations/spark-master'
      metrics_path: '/metrics/master/prometheus'
      relabel_configs:
        - replacement: '<your-instance-name>'
          target_label: instance
        - replacement: master
          target_label: instance_type
        - replacement: '<your-cluster-name>'
          target_label: spark_cluster
      static_configs:
        - targets: ['spark-master:8080']
    - job_name: 'integrations/spark-worker'
      metrics_path: '/metrics/prometheus'
      relabel_configs:
        - replacement: '<your-instance-name>'
          target_label: instance
        - replacement: worker
          target_label: instance_type
        - replacement: '<your-cluster-name>'
          target_label: spark_cluster
      static_configs:
        - targets: ['spark-worker:8081']
    - job_name: 'integrations/spark-driver'
      metrics_path: '/metrics/prometheus/'
      relabel_configs:
        - replacement: '<your-instance-name>'
          target_label: instance
        - replacement: driver
          target_label: instance_type
        - replacement: '<your-cluster-name>'
          target_label: spark_cluster
      static_configs:
        - targets: ['spark-driver:4040']
  global:
    scrape_interval: 60s
  wal_directory: /tmp/grafana-agent-wal

Dashboards

The Apache Spark integration installs the following dashboards in your Grafana Cloud instance to help monitor your system.

Apache Spark Metrics

Apache Spark Dashboard

Metrics

The most important metrics provided by the Apache Spark integration, which are used on the pre-built dashboard, are as follows:

metrics_master_workers_Number
metrics_spark_app_driver_BlockManager_disk_diskSpaceUsed_MB_Number
metrics_spark_app_driver_BlockManager_memory_maxMem_MB_Number
metrics_spark_app_driver_BlockManager_memory_maxOffHeapMem_MB_Number
metrics_spark_app_driver_BlockManager_memory_maxOnHeapMem_MB_Number
metrics_spark_app_driver_DAGScheduler_job_activeJobs_Number
metrics_spark_app_driver_DAGScheduler_job_allJobs_Number
metrics_spark_app_driver_DAGScheduler_messageProcessingTime_Max
metrics_spark_app_driver_DAGScheduler_messageProcessingTime_Mean
metrics_spark_app_driver_DAGScheduler_messageProcessingTime_Min
metrics_spark_app_driver_DAGScheduler_messageProcessingTime_StdDev
metrics_spark_app_driver_DAGScheduler_stage_failedStages_Number
metrics_spark_app_driver_DAGScheduler_stage_runningStages_Number
metrics_spark_app_driver_DAGScheduler_stage_waitingStages_Number
metrics_spark_app_driver_LiveListenerBus_listenerProcessingTime_org_apache_spark_HeartbeatReceiver_Count
metrics_spark_app_driver_LiveListenerBus_listenerProcessingTime_org_apache_spark_HeartbeatReceiver_Max
metrics_spark_app_driver_LiveListenerBus_listenerProcessingTime_org_apache_spark_HeartbeatReceiver_Mean
metrics_spark_app_driver_LiveListenerBus_listenerProcessingTime_org_apache_spark_HeartbeatReceiver_Min
metrics_worker_coresFree_Number
metrics_worker_coresUsed_Number
metrics_worker_memFree_MB_Number
metrics_worker_memUsed_MB_Number
up

Changelog

# 0.0.5 - November 2023

* Replaced Angular dashboard panels with React panels

# 0.0.4 - September 2023

* New Filter Metrics option for configuring the Grafana Agent, which saves on metrics cost by dropping any metric not used by this integration. Beware that anything custom built using metrics that are not on the snippet will stop working.
* New hostname relabel option, which applies the instance name you write on the text box to the Grafana Agent configuration snippets, making it easier and less error prone to configure this mandatory label.

# 0.0.3 - March 2023

* Updated to the latest mixin version

# 0.0.2 - May 2022

* Updated to the last mixin version:
  - fix panels that did not have the /integrations prefix on the queries.

# 0.0.1 - December 2021

* Initial release

Cost

By connecting your Apache Spark instance to Grafana Cloud, you might incur charges. To view information on the number of active series that your Grafana Cloud account uses for metrics included in each Cloud tier, see Active series and dpm usage and Cloud tier pricing.