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There are several ways to monitor Spark applications: web UIs, metrics, and external instrumentation.

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Every SparkContext launches a web UI, by default on port 4040, that displays useful information about the application. This includes:

• A list of scheduler stages and tasks
• A summary of RDD sizes and memory usage
• Environmental information.
• Information about the running executors

You can access this interface by simply opening http://<driver-node>:4040 in a web browser.If multiple SparkContexts are running on the same host, they will bind to successive portsbeginning with 4040 (4041, 4042, etc).

Note that this information is only available for the duration of the application by default.To view the web UI after the fact, set spark.eventLog.enabled to true before starting theapplication. This configures Spark to log Spark events that encode the information displayedin the UI to persisted storage.

Viewing After the Fact

Spark’s Standalone Mode cluster manager also has its ownweb UI. If an application has logged events overthe course of its lifetime, then the Standalone master’s web UI will automatically re-render theapplication’s UI after the application has finished.

If Spark is run on Mesos or YARN, it is still possible to reconstruct the UI of a finishedapplication through Spark’s history server, provided that the application’s event logs exist.You can start the history server by executing:

When using the file-system provider class (see spark.history.provider below), the base loggingdirectory must be supplied in the spark.history.fs.logDirectory configuration option,and should contain sub-directories that each represents an application’s event logs. This creates aweb interface at http://<server-url>:18080 by default. The history server can be configured asfollows:

Note that in all of these UIs, the tables are sortable by clicking their headers,making it easy to identify slow tasks, data skew, etc.

Note that the history server only displays completed Spark jobs. One way to signal the completion of a Spark job is to stop the Spark Context explicitly (sc.stop()), or in Python using the with SparkContext() as sc: to handle the Spark Context setup and tear down, and still show the job history on the UI.

REST API

In addition to viewing the metrics in the UI, they are also available as JSON. This gives developersan easy way to create new visualizations and monitoring tools for Spark. The JSON is available forboth running applications, and in the history server. The endpoints are mounted at /api/v1. Eg.,for the history server, they would typically be accessible at http://<server-url>:18080/api/v1, andfor a running application, at http://localhost:4040/api/v1.

When running on Yarn, each application has multiple attempts, so [app-id] is actually[app-id]/[attempt-id] in all cases.

These endpoints have been strongly versioned to make it easier to develop applications on top. In particular, Spark guarantees:

• Endpoints will never be removed from one version
• Individual fields will never be removed for any given endpoint
• New endpoints may be added
• New fields may be added to existing endpoints
• New versions of the api may be added in the future at a separate endpoint (eg., api/v2). New versions are not required to be backwards compatible.
• Api versions may be dropped, but only after at least one minor release of co-existing with a new api version

Note that even when examining the UI of a running applications, the applications/[app-id] portion isstill required, though there is only one application available. Eg. to see the list of jobs for therunning app, you would go to http://localhost:4040/api/v1/applications/[app-id]/jobs. This is tokeep the paths consistent in both modes.

Spark has a configurable metrics system based on the Coda Hale Metrics Library. This allows users to report Spark metrics to a variety of sinks including HTTP, JMX, and CSV files. The metrics system is configured via a configuration file that Spark expects to be present at $SPARK_HOME/conf/metrics.properties. A custom file location can be specified via the spark.metrics.confconfiguration property.Spark’s metrics are decoupled into different instances corresponding to Spark components. Within each instance, you can configure a set of sinks to which metrics are reported. The following instances are currently supported:

• master: The Spark standalone master process.
• applications: A component within the master which reports on various applications.
• worker: A Spark standalone worker process.
• executor: A Spark executor.
• driver: The Spark driver process (the process in which your SparkContext is created).

Each instance can report to zero or more sinks. Sinks are contained in theorg.apache.spark.metrics.sink package:

• ConsoleSink: Logs metrics information to the console.
• CSVSink: Exports metrics data to CSV files at regular intervals.
• JmxSink: Registers metrics for viewing in a JMX console.
• MetricsServlet: Adds a servlet within the existing Spark UI to serve metrics data as JSON data.
• GraphiteSink: Sends metrics to a Graphite node.
• Slf4jSink: Sends metrics to slf4j as log entries.

Spark also supports a Ganglia sink which is not included in the default build due tolicensing restrictions:

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• GangliaSink: Sends metrics to a Ganglia node or multicast group.

To install the GangliaSink you’ll need to perform a custom build of Spark. Note thatby embedding this library you will include LGPL-licensed code in your Spark package. For sbt users, set the SPARK_GANGLIA_LGPL environment variable before building. For Maven users, enable the -Pspark-ganglia-lgpl profile. In addition to modifying the cluster’s Spark builduser applications will need to link to the spark-ganglia-lgpl artifact.

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The syntax of the metrics configuration file is defined in an example configuration file, $SPARK_HOME/conf/metrics.properties.template.

Several external tools can be used to help profile the performance of Spark jobs:

• Cluster-wide monitoring tools, such as Ganglia, can provide insight into overall cluster utilization and resource bottlenecks. For instance, a Ganglia dashboard can quickly reveal whether a particular workload is disk bound, network bound, or CPU bound.
• OS profiling tools such as dstat, iostat, and iotop can provide fine-grained profiling on individual nodes.
• JVM utilities such as jstack for providing stack traces, jmap for creating heap-dumps, jstat for reporting time-series statistics and jconsole for visually exploring various JVM properties are useful for those comfortable with JVM internals.