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:

./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, writeable 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

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 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. 2. 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. ## REST API 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://: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; each identified by their `[attempt-id]`. In the API listed below, `[app-id]` will actually be `[base-app-id]/[attempt-id]`, where `[base-app-id]` is the YARN application ID.
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 application UIs to retain. If this cap is exceeded, then the oldest applications will be removed.
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.
EndpointMeaning
/applications A list of all applications.
?status=[completed|running] list only applications in the chosen state.
?minDate=[date] earliest date/time to list.
Examples:
?minDate=2015-02-10
?minDate=2015-02-03T16:42:40.000GMT
?maxDate=[date] latest date/time to list; uses same format as minDate.
/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 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: * 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 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. # Metrics Spark has a configurable metrics system based on the [Dropwizard Metrics Library](http://metrics.dropwizard.io/). 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 `$SPARK_HOME/conf/metrics.properties`. A custom file location can be specified via the `spark.metrics.conf` [configuration property](configuration.html#spark-properties). Spark's metrics are decoupled into different _instances_ corresponding to Spark components. Within each instance, you can configure a set of sinks to which metrics are reported. The following instances are currently supported: * `master`: The Spark standalone master process. * `applications`: A component within the master which reports on various applications. * `worker`: A Spark standalone worker process. * `executor`: A Spark executor. * `driver`: The Spark driver process (the process in which your SparkContext is created). Each instance can report to zero or more _sinks_. Sinks are contained in the `org.apache.spark.metrics.sink` package: * `ConsoleSink`: Logs metrics information to the console. * `CSVSink`: Exports metrics data to CSV files at regular intervals. * `JmxSink`: Registers metrics for viewing in a JMX console. * `MetricsServlet`: Adds a servlet within the existing Spark UI to serve metrics data as JSON data. * `GraphiteSink`: Sends metrics to a Graphite node. * `Slf4jSink`: Sends metrics to slf4j as log entries. Spark also supports a Ganglia sink which is not included in the default build due to licensing restrictions: * `GangliaSink`: Sends metrics to a Ganglia node or multicast group. To install the `GangliaSink` you'll need to perform a custom build of Spark. _**Note that by embedding this library you will include [LGPL](http://www.gnu.org/copyleft/lesser.html)-licensed code in your Spark package**_. For sbt users, set the `SPARK_GANGLIA_LGPL` environment variable before building. For Maven users, enable the `-Pspark-ganglia-lgpl` profile. In addition to modifying the cluster's Spark build user applications will need to link to the `spark-ganglia-lgpl` artifact. The syntax of the metrics configuration file is defined in an example configuration file, `$SPARK_HOME/conf/metrics.properties.template`. # Advanced Instrumentation Several external tools can be used to help profile the performance of Spark jobs: * Cluster-wide monitoring tools, such as [Ganglia](http://ganglia.sourceforge.net/), 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](http://dag.wieers.com/home-made/dstat/), [iostat](http://linux.die.net/man/1/iostat), and [iotop](http://linux.die.net/man/1/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.