There are several ways to monitor Spark applications: web UIs, metrics, and external instrumentation.
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.
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:
./sbin/start-history-server.sh
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:
Security options for the Spark History Server are covered more detail in the Security page.
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
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.
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.
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.
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.
The number of jobs and stages which can be 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.
The REST API exposes the values of the Task Metrics collected by Spark executors with the granularity of task execution. The metrics can be used for performance troubleshooting and workload characterization. A list of the available metrics, with a short description:
These endpoints have been strongly versioned to make it easier to develop applications on top. In particular, Spark guarantees:
api/v2). New versions are not required to be backwards compatible.Note that even when examining the UI of 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/metrics.properties. 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 spark.app.id. 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. spark.app.id) 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 ${spark.app.name}. 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 spark.app.id, 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:
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).shuffleService: The Spark shuffle service.applicationMaster: The Spark ApplicationMaster when running on YARN.Each instance can report to zero or more sinks. Sinks are contained in the org.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.StatsdSink: Sends metrics to a StatsD node.Spark also supports a Ganglia sink which is not included in the default build due to licensing restrictions:
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 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/metrics.properties.template.
Several external tools can be used to help profile the performance of Spark jobs:
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.