Apache Spark 🔗
Description 🔗
The Splunk Distribution of OpenTelemetry Collector provides this integration as the Apache Spark monitor by using the SignalFx Smart Agent receiver.
The integration monitors Apache Spark clusters.
Note: This integration does not support fetching metrics from Spark Structured Streaming.
For the following cluster modes, the integration only supports HTTP endpoints:
Standalone
Mesos
Hadoop YARN
You need to select distinct monitor configurations and discovery rules for master and worker processes. For the master configuration, set isMaster
to true
.
When you run Apache Spark on Hadoop YARN, this integration can only report application metrics from the master node.
Benefits 🔗
After you configure the integration, you can access these features:
View metrics. You can create your own custom dashboards, and most monitors provide built-in dashboards as well. For information about dashboards, see View dashboards in Observability Cloud.
View a data-driven visualization of the physical servers, virtual machines, AWS instances, and other resources in your environment that are visible to Infrastructure Monitoring. For information about navigators, see Splunk Infrastructure Monitoring navigators.
Access the Metric Finder and search for metrics sent by the monitor. For information, see Use the Metric Finder.
Installation 🔗
Follow these steps to deploy this integration:
Deploy the Splunk Distribution of OpenTelemetry Collector to your host or container platform:
Configure the monitor, as described in the Configuration section.
Restart the Splunk Distribution of OpenTelemetry Collector.
Configuration 🔗
This monitor type is available in the Smart Agent Receiver, which is part of the Splunk Distribution of OpenTelemetry Collector. You can use existing Smart Agent monitors as OpenTelemetry Collector metric receivers with the Smart Agent Receiver.
This monitor type requires a properly configured environment on your system in which you’ve installed a functional Smart Agent release bundle. The Collector provides this bundle in the installation paths for x86_64/amd64
.
To activate this monitor type in the Collector, add the following lines to your configuration (YAML) file:
To activate this monitor in the Splunk Distribution of OpenTelemetry Collector, add one of the following to your agent configuration:
receivers:
smartagent/collectd_spark_master:
type: collectd/spark
... # Additional config
receivers:
smartagent/collectd_spark_worker:
type: collectd/spark
... # Additional config
To complete the integration, include the monitor in a metrics pipeline. Add the monitor item to the service/pipelines/metrics/receivers
section of your configuration file. For example:
service:
pipelines:
metrics:
receivers: [smartagent/collectd_spark_master]
service:
pipelines:
metrics:
receivers: [smartagent/collectd_spark_worker]
Note: The names of the monitor, collectd_spark_master
and collectd_spark_worker
, are for identification purposes and don’t affect functionality. You can use either name in your configuration, but you need to select distinct monitor configurations and discovery rules for master and worker processes. For the master configuration, see the isMaster
field in the Configuration settings section.
Configuration settings 🔗
The following table shows the configuration options for this monitor:
Option |
Required |
Type |
Description |
---|---|---|---|
|
no |
|
This option specifies the path to a Python binary that executes the Python code. If you don’t set this option, the system uses a built-in runtime. You can also include arguments to the binary. |
|
yes |
|
|
|
yes |
|
|
|
no |
|
Set this option to |
|
yes |
|
Set this option to the type of cluster you’re monitoring. The allowed values are |
|
no |
|
The default is |
|
no |
|
The default is |
Metrics 🔗
These are the metrics available for this integration:
Get help 🔗
If you are not able to see your data in Splunk Observability Cloud, try these tips:
Submit a case in the Splunk Support Portal
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To learn about even more support options, see Splunk Customer Success.