Monitoring and Instrumentation

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

Web Interfaces

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

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 ports beginning 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 the application. This configures Spark to log Spark events that encode the information displayed in the UI to persisted storage.

Viewing After the Fact

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


This creates a web interface at http://<server-url>:18080 by default, listing incomplete and completed applications and attempts.

When using the file-system provider class (see spark.history.provider below), the base logging directory must be supplied in the spark.history.fs.logDirectory configuration option, and should contain sub-directories that each represents an application’s event logs.

The spark jobs themselves must be configured to log events, and to log them to the same shared, writable directory. For example, if the server was configured with a log directory of hdfs://namenode/shared/spark-logs, then the client-side options would be:

spark.eventLog.enabled true
spark.eventLog.dir hdfs://namenode/shared/spark-logs

The history server can be configured as follows:

Environment Variables

Environment VariableMeaning
SPARK_DAEMON_MEMORY Memory to allocate to the history server (default: 1g).
SPARK_DAEMON_JAVA_OPTS JVM options for the history server (default: none).
SPARK_PUBLIC_DNS The public address for the history server. If this is not set, links to application history may use the internal address of the server, resulting in broken links (default: none).
SPARK_HISTORY_OPTS spark.history.* configuration options for the history server (default: none).

Spark configuration options

Property NameDefaultMeaning
spark.history.provider org.apache.spark.deploy.history.FsHistoryProvider Name of the class implementing the application history backend. Currently there is only one implementation, provided by Spark, which looks for application logs stored in the file system.
spark.history.fs.logDirectory file:/tmp/spark-events For the filesystem history provider, the URL to the directory containing application event logs to load. This can be a local file:// path, an HDFS path hdfs://namenode/shared/spark-logs or that of an alternative filesystem supported by the Hadoop APIs.
spark.history.fs.update.interval 10s The period at which the filesystem history provider checks for new or updated logs in the log directory. A shorter interval detects new applications faster, at the expense of more server load re-reading updated applications. As soon as an update has completed, listings of the completed and incomplete applications will reflect the changes.
spark.history.retainedApplications 50 The number of applications to retain UI data for in the cache. If this cap is exceeded, then the oldest applications will be removed from the cache. If an application is not in the cache, it will have to be loaded from disk if its accessed from the UI.
spark.history.ui.maxApplications Int.MaxValue The number of applications to display on the history summary page. Application UIs are still available by accessing their URLs directly even if they are not displayed on the history summary page.
spark.history.ui.port 18080 The port to which the web interface of the history server binds.
spark.history.kerberos.enabled false Indicates whether the history server should use kerberos to login. This is required if the history server is accessing HDFS files on a secure Hadoop cluster. If this is true, it uses the configs spark.history.kerberos.principal and spark.history.kerberos.keytab.
spark.history.kerberos.principal (none) Kerberos principal name for the History Server.
spark.history.kerberos.keytab (none) Location of the kerberos keytab file for the History Server.
spark.history.ui.acls.enable false Specifies whether acls should be checked to authorize users viewing the applications. If enabled, access control checks are made regardless of what the individual application had set for spark.ui.acls.enable when the application was run. The application owner will always have authorization to view their own application and any users specified via spark.ui.view.acls and groups specified via spark.ui.view.acls.groups when the application was run will also have authorization to view that application. If disabled, no access control checks are made.
spark.history.fs.cleaner.enabled false Specifies whether the History Server should periodically clean up event logs from storage.
spark.history.fs.cleaner.interval 1d How often the filesystem job history cleaner checks for files to delete. Files are only deleted if they are older than spark.history.fs.cleaner.maxAge
spark.history.fs.cleaner.maxAge 7d Job history files older than this will be deleted when the filesystem history cleaner runs.
spark.history.fs.numReplayThreads 25% of available cores Number of threads that will be used by history server to process event logs.

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.


  1. The history server displays both completed and incomplete Spark jobs. If an application makes multiple attempts after failures, the failed attempts will be displayed, as well as any ongoing incomplete attempt or the final successful attempt.

  2. Incomplete applications are only updated intermittently. The time between updates is defined by the interval between checks for changed files (spark.history.fs.update.interval). On larger clusters the update interval may be set to large values. The way to view a running application is actually to view its own web UI.

  3. Applications which exited without registering themselves as completed will be listed as incomplete —even though they are no longer running. This can happen if an application crashes.

  4. 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: construct to handle the Spark Context setup and tear down.


In addition to viewing the metrics in the UI, they are also available as JSON. This gives developers an easy way to create new visualizations and monitoring tools for Spark. The JSON is available for both 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, and for a running application, at http://localhost:4040/api/v1.

In the API, an application is referenced by its application ID, [app-id]. When running on YARN, each application may have multiple attempts, but there are attempt IDs only for applications in cluster mode, not applications in client mode. Applications in YARN cluster mode can be identified by their [attempt-id]. In the API listed below, when running in YARN cluster mode, [app-id] will actually be [base-app-id]/[attempt-id], where [base-app-id] is the YARN application ID.

/applications A list of all applications.
?status=[completed|running] list only applications in the chosen state.
?minDate=[date] earliest date/time to list.
?maxDate=[date] latest date/time to list; uses same format as minDate.
?limit=[limit] limits the number of applications listed.
/applications/[app-id]/jobs A list of all jobs for a given application.
?status=[complete|succeeded|failed] list only jobs in the specific state.
/applications/[app-id]/jobs/[job-id] Details for the given job.
/applications/[app-id]/stages A list of all stages for a given application.
/applications/[app-id]/stages/[stage-id] A list of all attempts for the given stage.
?status=[active|complete|pending|failed] list only stages in the state.
/applications/[app-id]/stages/[stage-id]/[stage-attempt-id] Details for the given stage attempt
/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]/taskSummary Summary metrics of all tasks in the given stage attempt.
?quantiles summarize the metrics with the given quantiles.
Example: ?quantiles=0.01,0.5,0.99
/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]/taskList A list of all tasks for the given stage attempt.
?offset=[offset]&length=[len] list tasks in the given range.
?sortBy=[runtime|-runtime] sort the tasks.
Example: ?offset=10&length=50&sortBy=runtime
/applications/[app-id]/executors A list of all active executors for the given application.
/applications/[app-id]/allexecutors A list of all(active and dead) executors for the given application.
/applications/[app-id]/storage/rdd A list of stored RDDs for the given application.
/applications/[app-id]/storage/rdd/[rdd-id] Details for the storage status of a given RDD.
/applications/[base-app-id]/logs Download the event logs for all attempts of the given application as files within a zip file.
/applications/[base-app-id]/[attempt-id]/logs Download the event logs for a specific application attempt as a zip file.

The number of jobs and stages which can retrieved is constrained by the same retention mechanism of the standalone Spark UI; "spark.ui.retainedJobs" defines the threshold value triggering garbage collection on jobs, and spark.ui.retainedStages that for stages. Note that the garbage collection takes place on playback: it is possible to retrieve more entries by increasing these values and restarting the history server.

API Versioning Policy

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

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


Spark has a configurable metrics system based on the Dropwizard 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/ A custom file location can be specified via the spark.metrics.conf configuration property. By default, the root namespace used for driver or executor metrics is the value of However, often times, users want to be able to track the metrics across apps for driver and executors, which is hard to do with application ID (i.e. since it changes with every invocation of the app. For such use cases, a custom namespace can be specified for metrics reporting using spark.metrics.namespace configuration property. If, say, users wanted to set the metrics namespace to the name of the application, they can set the spark.metrics.namespace property to a value like ${}. This value is then expanded appropriately by Spark and is used as the root namespace of the metrics system. Non driver and executor metrics are never prefixed with, nor does the spark.metrics.namespace property have any such affect on such metrics.

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:

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

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

To install the GangliaSink you’ll need to perform a custom build of Spark. Note that by 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 build user applications will need to link to the spark-ganglia-lgpl artifact.

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

Advanced Instrumentation

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