OSCL-LXR spark-3.0.0/​docs/​monitoring.md	Source navigation Diff markup Identifier search General search
	Version: spark-3.0.0 hadoop-3.2.0-src hadoop-3.3.0-src spark-2.4.7 linux-4.15.13 Revert   Change

 ---
 layout: global
 title: Monitoring and Instrumentation
 description: Monitoring, metrics, and instrumentation guide for Spark SPARK_VERSION_SHORT
 license: |
   Licensed to the Apache Software Foundation (ASF) under one or more
   contributor license agreements.  See the NOTICE file distributed with
   this work for additional information regarding copyright ownership.
   The ASF licenses this file to You under the Apache License, Version 2.0
   (the "License"); you may not use this file except in compliance with
   the License.  You may obtain a copy of the License at
  
      http://www.apache.org/licenses/LICENSE-2.0
  
   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
 ---
 
 There are several ways to monitor Spark applications: web UIs, metrics, and external instrumentation.
 
 # Web Interfaces
 
 Every SparkContext launches a [Web UI](web-ui.html), 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 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
 
 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:
 
 ### Environment Variables
 
 <table class="table">
   <tr><th style="width:21%">Environment Variable</th><th>Meaning</th></tr>
   <tr>
     <td><code>SPARK_DAEMON_MEMORY</code></td>
     <td>Memory to allocate to the history server (default: 1g).</td>
   </tr>
   <tr>
     <td><code>SPARK_DAEMON_JAVA_OPTS</code></td>
     <td>JVM options for the history server (default: none).</td>
   </tr>
   <tr>
     <td><code>SPARK_DAEMON_CLASSPATH</code></td>
     <td>Classpath for the history server (default: none).</td>
   </tr>
   <tr>
     <td><code>SPARK_PUBLIC_DNS</code></td>
     <td>
       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).
     </td>
   </tr>
   <tr>
     <td><code>SPARK_HISTORY_OPTS</code></td>
     <td>
       <code>spark.history.*</code> configuration options for the history server (default: none).
     </td>
   </tr>
 </table>
 
 ### Applying compaction on rolling event log files
 
 A long-running application (e.g. streaming) can bring a huge single event log file which may cost a lot to maintain and
 also requires a bunch of resource to replay per each update in Spark History Server.
 
 Enabling <code>spark.eventLog.rolling.enabled</code> and <code>spark.eventLog.rolling.maxFileSize</code> would
 let you have rolling event log files instead of single huge event log file which may help some scenarios on its own,
 but it still doesn't help you reducing the overall size of logs.
 
 Spark History Server can apply compaction on the rolling event log files to reduce the overall size of
 logs, via setting the configuration <code>spark.history.fs.eventLog.rolling.maxFilesToRetain</code> on the
 Spark History Server.
 
 Details will be described below, but please note in prior that compaction is LOSSY operation.
 Compaction will discard some events which will be no longer seen on UI - you may want to check which events will be discarded
 before enabling the option.
 
 When the compaction happens, the History Server lists all the available event log files for the application, and considers
 the event log files having less index than the file with smallest index which will be retained as target of compaction.
 For example, if the application A has 5 event log files and <code>spark.history.fs.eventLog.rolling.maxFilesToRetain</code> is set to 2, then first 3 log files will be selected to be compacted.
 
 Once it selects the target, it analyzes them to figure out which events can be excluded, and rewrites them
 into one compact file with discarding events which are decided to exclude.
 
 The compaction tries to exclude the events which point to the outdated data. As of now, below describes the candidates of events to be excluded:
 
 * Events for the job which is finished, and related stage/tasks events
 * Events for the executor which is terminated
 * Events for the SQL execution which is finished, and related job/stage/tasks events
 
 Once rewriting is done, original log files will be deleted, via best-effort manner. The History Server may not be able to delete
 the original log files, but it will not affect the operation of the History Server.
 
 Please note that Spark History Server may not compact the old event log files if figures out not a lot of space
 would be reduced during compaction. For streaming query we normally expect compaction
 will run as each micro-batch will trigger one or more jobs which will be finished shortly, but compaction won't run
 in many cases for batch query.
 
 Please also note that this is a new feature introduced in Spark 3.0, and may not be completely stable. Under some circumstances,
 the compaction may exclude more events than you expect, leading some UI issues on History Server for the application.
 Use it with caution.
 
 ### Spark History Server Configuration Options
 
 Security options for the Spark History Server are covered more detail in the
 [Security](security.html#web-ui) page.
 
 <table class="table">
   <tr><th>Property Name</th><th>Default</th><th>Meaning</th><th>Since Version</th></tr>
   <tr>
     <td>spark.history.provider</td>
     <td><code>org.apache.spark.deploy.history.FsHistoryProvider</code></td>
     <td>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.</td>
     <td>1.1.0</td>
   </tr>
   <tr>
     <td>spark.history.fs.logDirectory</td>
     <td>file:/tmp/spark-events</td>
     <td>
     For the filesystem history provider, the URL to the directory containing application event
     logs to load. This can be a local <code>file://</code> path,
     an HDFS path <code>hdfs://namenode/shared/spark-logs</code>
     or that of an alternative filesystem supported by the Hadoop APIs.
     </td>
     <td>1.1.0</td>
   </tr>
   <tr>
     <td>spark.history.fs.update.interval</td>
     <td>10s</td>
     <td>
       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.
     </td>
     <td>1.4.0</td>
   </tr>
   <tr>
     <td>spark.history.retainedApplications</td>
     <td>50</td>
     <td>
       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 it is accessed from the UI.
     </td>
     <td>1.0.0</td>
   </tr>
   <tr>
     <td>spark.history.ui.maxApplications</td>
     <td>Int.MaxValue</td>
     <td>
       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.
     </td>
     <td>2.0.1</td>
   </tr>
   <tr>
     <td>spark.history.ui.port</td>
     <td>18080</td>
     <td>
       The port to which the web interface of the history server binds.
     </td>
     <td>1.0.0</td>
   </tr>
   <tr>
     <td>spark.history.kerberos.enabled</td>
     <td>false</td>
     <td>
       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.
     </td>
     <td>1.0.1</td>
   </tr>
   <tr>
     <td>spark.history.kerberos.principal</td>
     <td>(none)</td>
     <td>
       When <code>spark.history.kerberos.enabled=true</code>, specifies kerberos principal name for the History Server.
     </td>
     <td>1.0.1</td>
   </tr>
   <tr>
     <td>spark.history.kerberos.keytab</td>
     <td>(none)</td>
     <td>
       When <code>spark.history.kerberos.enabled=true</code>, specifies location of the kerberos keytab file for the History Server.
     </td>
     <td>1.0.1</td>
   </tr>
   <tr>
     <td>spark.history.fs.cleaner.enabled</td>
     <td>false</td>
     <td>
       Specifies whether the History Server should periodically clean up event logs from storage.
     </td>
     <td>1.4.0</td>
   </tr>
   <tr>
     <td>spark.history.fs.cleaner.interval</td>
     <td>1d</td>
     <td>
       When <code>spark.history.fs.cleaner.enabled=true</code>, specifies how often the filesystem job history cleaner checks for files to delete.
       Files are deleted if at least one of two conditions holds.
       First, they're deleted if they're older than <code>spark.history.fs.cleaner.maxAge</code>.
       They are also deleted if the number of files is more than
       <code>spark.history.fs.cleaner.maxNum</code>, Spark tries to clean up the completed attempts
       from the applications based on the order of their oldest attempt time.
     </td>
     <td>1.4.0</td>
   </tr>
   <tr>
     <td>spark.history.fs.cleaner.maxAge</td>
     <td>7d</td>
     <td>
       When <code>spark.history.fs.cleaner.enabled=true</code>, job history files older than this will be deleted when the filesystem history cleaner runs.
     </td>
     <td>1.4.0</td>
   </tr>
   <tr>
     <td>spark.history.fs.cleaner.maxNum</td>
     <td>Int.MaxValue</td>
     <td>
       When <code>spark.history.fs.cleaner.enabled=true</code>, specifies the maximum number of files in the event log directory.
       Spark tries to clean up the completed attempt logs to maintain the log directory under this limit.
       This should be smaller than the underlying file system limit like
       `dfs.namenode.fs-limits.max-directory-items` in HDFS.
     </td>
     <td>3.0.0</td>
   </tr>
   <tr>
     <td>spark.history.fs.endEventReparseChunkSize</td>
     <td>1m</td>
     <td>
       How many bytes to parse at the end of log files looking for the end event. 
       This is used to speed up generation of application listings by skipping unnecessary
       parts of event log files. It can be disabled by setting this config to 0.
     </td>
     <td>2.4.0</td>
   </tr>
   <tr>
     <td>spark.history.fs.inProgressOptimization.enabled</td>
     <td>true</td>
     <td>
       Enable optimized handling of in-progress logs. This option may leave finished
       applications that fail to rename their event logs listed as in-progress.
     </td>
     <td>2.4.0</td>
   </tr>
   <tr>
     <td>spark.history.fs.driverlog.cleaner.enabled</td>
     <td><code>spark.history.fs.cleaner.enabled</code></td>
     <td>
       Specifies whether the History Server should periodically clean up driver logs from storage.
     </td>
     <td>3.0.0</td>
   </tr>
   <tr>
     <td>spark.history.fs.driverlog.cleaner.interval</td>
     <td><code>spark.history.fs.cleaner.interval</code></td>
     <td>
       When <code>spark.history.fs.driverlog.cleaner.enabled=true</code>, specifies how often the filesystem driver log cleaner checks for files to delete.
       Files are only deleted if they are older than <code>spark.history.fs.driverlog.cleaner.maxAge</code>
     </td>
     <td>3.0.0</td>
   </tr>
   <tr>
     <td>spark.history.fs.driverlog.cleaner.maxAge</td>
     <td><code>spark.history.fs.cleaner.maxAge</code></td>
     <td>
       When <code>spark.history.fs.driverlog.cleaner.enabled=true</code>, driver log files older than this will be deleted when the driver log cleaner runs.
     </td>
     <td>3.0.0</td>
   </tr>
   <tr>
     <td>spark.history.fs.numReplayThreads</td>
     <td>25% of available cores</td>
     <td>
       Number of threads that will be used by history server to process event logs.
     </td>
     <td>2.0.0</td>
   </tr>
   <tr>
     <td>spark.history.store.maxDiskUsage</td>
     <td>10g</td>
     <td>
       Maximum disk usage for the local directory where the cache application history information
       are stored.
     </td>
     <td>2.3.0</td>
   </tr>
   <tr>
     <td>spark.history.store.path</td>
     <td>(none)</td>
     <td>
         Local directory where to cache application history data. If set, the history
         server will store application data on disk instead of keeping it in memory. The data
         written to disk will be re-used in the event of a history server restart.
     </td>
     <td>2.3.0</td>
   </tr>
   <tr>
     <td>spark.history.custom.executor.log.url</td>
     <td>(none)</td>
     <td>
         Specifies custom spark executor log URL for supporting external log service instead of using cluster
         managers' application log URLs in the history server. Spark will support some path variables via patterns
         which can vary on cluster manager. Please check the documentation for your cluster manager to
         see which patterns are supported, if any. This configuration has no effect on a live application, it only
         affects the history server.
         <p/>
         For now, only YARN mode supports this configuration
     </td>
     <td>3.0.0</td>
   </tr>
   <tr>
     <td>spark.history.custom.executor.log.url.applyIncompleteApplication</td>
     <td>false</td>
     <td>
         Specifies whether to apply custom spark executor log URL to incomplete applications as well.
         If executor logs for running applications should be provided as origin log URLs, set this to `false`.
         Please note that incomplete applications may include applications which didn't shutdown gracefully.
         Even this is set to `true`, this configuration has no effect on a live application, it only affects the history server.
     </td>
     <td>3.0.0</td>
   </tr>
   <tr>
     <td>spark.history.fs.eventLog.rolling.maxFilesToRetain</td>
     <td>Int.MaxValue</td>
     <td>
       The maximum number of event log files which will be retained as non-compacted. By default,
       all event log files will be retained. The lowest value is 1 for technical reason.<br/>
       Please read the section of "Applying compaction of old event log files" for more details.
     </td>
     <td>3.0.0</td>
   </tr>
 </table>
 
 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://<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.
 
 <table class="table">
   <tr><th>Endpoint</th><th>Meaning</th></tr>
   <tr>
     <td><code>/applications</code></td>
     <td>A list of all applications.
     <br>
     <code>?status=[completed|running]</code> list only applications in the chosen state.
     <br>
     <code>?minDate=[date]</code> earliest start date/time to list.
     <br>
     <code>?maxDate=[date]</code> latest start date/time to list.
     <br>
     <code>?minEndDate=[date]</code> earliest end date/time to list.
     <br>
     <code>?maxEndDate=[date]</code> latest end date/time to list.
     <br>
     <code>?limit=[limit]</code> limits the number of applications listed.
     <br>Examples:
     <br><code>?minDate=2015-02-10</code>
     <br><code>?minDate=2015-02-03T16:42:40.000GMT</code>
     <br><code>?maxDate=2015-02-11T20:41:30.000GMT</code>
     <br><code>?minEndDate=2015-02-12</code>
     <br><code>?minEndDate=2015-02-12T09:15:10.000GMT</code>
     <br><code>?maxEndDate=2015-02-14T16:30:45.000GMT</code>
     <br><code>?limit=10</code></td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/jobs</code></td>
     <td>
       A list of all jobs for a given application.
       <br><code>?status=[running|succeeded|failed|unknown]</code> list only jobs in the specific state.
     </td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/jobs/[job-id]</code></td>
     <td>Details for the given job.</td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/stages</code></td>
     <td>
       A list of all stages for a given application.
       <br><code>?status=[active|complete|pending|failed]</code> list only stages in the state.
     </td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/stages/[stage-id]</code></td>
     <td>
       A list of all attempts for the given stage.
     </td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]</code></td>
     <td>Details for the given stage attempt.</td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]/taskSummary</code></td>
     <td>
       Summary metrics of all tasks in the given stage attempt.
       <br><code>?quantiles</code> summarize the metrics with the given quantiles.
       <br>Example: <code>?quantiles=0.01,0.5,0.99</code>
     </td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]/taskList</code></td>
     <td>
        A list of all tasks for the given stage attempt.
       <br><code>?offset=[offset]&amp;length=[len]</code> list tasks in the given range.
       <br><code>?sortBy=[runtime|-runtime]</code> sort the tasks.
       <br>Example: <code>?offset=10&amp;length=50&amp;sortBy=runtime</code>
     </td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/executors</code></td>
     <td>A list of all active executors for the given application.</td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/executors/[executor-id]/threads</code></td>
     <td>
       Stack traces of all the threads running within the given active executor.
       Not available via the history server.
     </td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/allexecutors</code></td>
     <td>A list of all(active and dead) executors for the given application.</td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/storage/rdd</code></td>
     <td>A list of stored RDDs for the given application.</td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/storage/rdd/[rdd-id]</code></td>
     <td>Details for the storage status of a given RDD.</td>
   </tr>
   <tr>
     <td><code>/applications/[base-app-id]/logs</code></td>
     <td>Download the event logs for all attempts of the given application as files within
     a zip file.
     </td>
   </tr>
   <tr>
     <td><code>/applications/[base-app-id]/[attempt-id]/logs</code></td>
     <td>Download the event logs for a specific application attempt as a zip file.</td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/streaming/statistics</code></td>
     <td>Statistics for the streaming context.</td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/streaming/receivers</code></td>
     <td>A list of all streaming receivers.</td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/streaming/receivers/[stream-id]</code></td>
     <td>Details of the given receiver.</td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/streaming/batches</code></td>
     <td>A list of all retained batches.</td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/streaming/batches/[batch-id]</code></td>
     <td>Details of the given batch.</td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/streaming/batches/[batch-id]/operations</code></td>
     <td>A list of all output operations of the given batch.</td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/streaming/batches/[batch-id]/operations/[outputOp-id]</code></td>
     <td>Details of the given operation and given batch.</td>
   </tr>
   <tr>
     <td><code>/applications/[app-id]/environment</code></td>
     <td>Environment details of the given application.</td>
   </tr>
   <tr>
     <td><code>/version</code></td>
     <td>Get the current spark version.</td>
   </tr>
 </table>
 
 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.
 
 ### Executor Task Metrics
 
 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:
 
 <table class="table">
   <tr><th>Spark Executor Task Metric name</th>
       <th>Short description</th>
   </tr>
   <tr>
     <td>executorRunTime</td>
     <td>Elapsed time the executor spent running this task. This includes time fetching shuffle data.
     The value is expressed in milliseconds.</td>
   </tr>
   <tr>
     <td>executorCpuTime</td>
     <td>CPU time the executor spent running this task. This includes time fetching shuffle data.
     The value is expressed in nanoseconds.</td>
   </tr>
   <tr>
     <td>executorDeserializeTime</td>
     <td>Elapsed time spent to deserialize this task. The value is expressed in milliseconds.</td>
   </tr>
   <tr>
     <td>executorDeserializeCpuTime</td>
     <td>CPU time taken on the executor to deserialize this task. The value is expressed
     in nanoseconds.</td>
   </tr>
   <tr>
     <td>resultSize</td>
     <td>The number of bytes this task transmitted back to the driver as the TaskResult.</td>
   </tr>
   <tr>
     <td>jvmGCTime</td>
     <td>Elapsed time the JVM spent in garbage collection while executing this task.
     The value is expressed in milliseconds.</td>
   </tr>
   <tr>
     <td>resultSerializationTime</td>
     <td>Elapsed time spent serializing the task result. The value is expressed in milliseconds.</td>
   </tr>
   <tr>
     <td>memoryBytesSpilled</td>
     <td>The number of in-memory bytes spilled by this task.</td>
   </tr>
   <tr>
     <td>diskBytesSpilled</td>
     <td>The number of on-disk bytes spilled by this task.</td>
   </tr>
   <tr>
     <td>peakExecutionMemory</td>
     <td>Peak memory used by internal data structures created during shuffles, aggregations and
         joins. The value of this accumulator should be approximately the sum of the peak sizes
         across all such data structures created in this task. For SQL jobs, this only tracks all
          unsafe operators and ExternalSort.</td>
   </tr>
   <tr>
     <td>inputMetrics.*</td>
     <td>Metrics related to reading data from <code>org.apache.spark.rdd.HadoopRDD</code>
     or from persisted data.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.bytesRead</td>
     <td>Total number of bytes read.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.recordsRead</td>
     <td>Total number of records read.</td>
   </tr>
   <tr>
     <td>outputMetrics.*</td>
     <td>Metrics related to writing data externally (e.g. to a distributed filesystem),
     defined only in tasks with output.</td>            
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.bytesWritten</td>
     <td>Total number of bytes written</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.recordsWritten</td>
     <td>Total number of records written</td>
   </tr>
   <tr>
     <td>shuffleReadMetrics.*</td>
     <td>Metrics related to shuffle read operations.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.recordsRead</td>
     <td>Number of records read in shuffle operations</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.remoteBlocksFetched</td>
     <td>Number of remote blocks fetched in shuffle operations</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.localBlocksFetched</td>
     <td>Number of local (as opposed to read from a remote executor) blocks fetched
     in shuffle operations</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.totalBlocksFetched</td>
     <td>Number of blocks fetched in shuffle operations (both local and remote)</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.remoteBytesRead</td>
     <td>Number of remote bytes read in shuffle operations</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.localBytesRead</td>
     <td>Number of bytes read in shuffle operations from local disk (as opposed to
     read from a remote executor)</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.totalBytesRead</td>
     <td>Number of bytes read in shuffle operations (both local and remote)</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.remoteBytesReadToDisk</td>
     <td>Number of remote bytes read to disk in shuffle operations.
     Large blocks are fetched to disk in shuffle read operations, as opposed to 
     being read into memory, which is the default behavior.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.fetchWaitTime</td>
     <td>Time the task spent waiting for remote shuffle blocks. 
         This only includes the time blocking on shuffle input data.
         For instance if block B is being fetched while the task is still not finished 
         processing block A, it is not considered to be blocking on block B.
         The value is expressed in milliseconds.</td>
   </tr>
   <tr>
     <td>shuffleWriteMetrics.*</td>
     <td>Metrics related to operations writing shuffle data.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.bytesWritten</td>
     <td>Number of bytes written in shuffle operations</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.recordsWritten</td>
     <td>Number of records written in shuffle operations</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.writeTime</td>
     <td>Time spent blocking on writes to disk or buffer cache. The value is expressed
      in nanoseconds.</td>
   </tr>
 </table>
 
 ### Executor Metrics
 
 Executor-level metrics are sent from each executor to the driver as part of the Heartbeat to describe the performance metrics of Executor itself like JVM heap memory, GC information.
 Executor metric values and their measured memory peak values per executor are exposed via the REST API in JSON format and in Prometheus format.
 The JSON end point is exposed at: `/applications/[app-id]/executors`, and the Prometheus endpoint at: `/metrics/executors/prometheus`.
 The Prometheus endpoint is experimental and conditional to a configuration parameter: `spark.ui.prometheus.enabled=true` (the default is `false`).
 In addition, aggregated per-stage peak values of the executor memory metrics are written to the event log if
 `spark.eventLog.logStageExecutorMetrics` is true.  
 Executor memory metrics are also exposed via the Spark metrics system based on the Dropwizard metrics library.
 A list of the available metrics, with a short description:
 
 <table class="table">
   <tr><th>Executor Level Metric name</th>
       <th>Short description</th>
   </tr>
   <tr>
     <td>rddBlocks</td>
     <td>RDD blocks in the block manager of this executor.</td>
   </tr>
   <tr>
     <td>memoryUsed</td>
     <td>Storage memory used by this executor.</td>
   </tr>
   <tr>
     <td>diskUsed</td>
     <td>Disk space used for RDD storage by this executor.</td>
   </tr>
   <tr>
     <td>totalCores</td>
     <td>Number of cores available in this executor.</td>
   </tr>
   <tr>
     <td>maxTasks</td>
     <td>Maximum number of tasks that can run concurrently in this executor.</td>
   </tr>
   <tr>
     <td>activeTasks</td>
     <td>Number of tasks currently executing.</td>
   </tr>
   <tr>
     <td>failedTasks</td>
     <td>Number of tasks that have failed in this executor.</td>
   </tr>
   <tr>
     <td>completedTasks</td>
     <td>Number of tasks that have completed in this executor.</td>
   </tr>
   <tr>
     <td>totalTasks</td>
     <td>Total number of tasks (running, failed and completed) in this executor.</td>
   </tr>
   <tr>
     <td>totalDuration</td>
     <td>Elapsed time the JVM spent executing tasks in this executor.
     The value is expressed in milliseconds.</td>
   </tr>
   <tr>
     <td>totalGCTime</td>
     <td>Elapsed time the JVM spent in garbage collection summed in this executor.
     The value is expressed in milliseconds.</td>
   </tr>
   <tr>
     <td>totalInputBytes</td>
     <td>Total input bytes summed in this executor.</td>
   </tr>
   <tr>
     <td>totalShuffleRead</td>
     <td>Total shuffle read bytes summed in this executor.</td>
   </tr>
   <tr>
     <td>totalShuffleWrite</td>
     <td>Total shuffle write bytes summed in this executor.</td>
   </tr>
   <tr>
     <td>maxMemory</td>
     <td>Total amount of memory available for storage, in bytes.</td>
   </tr>
   <tr>
     <td>memoryMetrics.*</td>
     <td>Current value of memory metrics:</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.usedOnHeapStorageMemory</td>
     <td>Used on heap memory currently for storage, in bytes.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.usedOffHeapStorageMemory</td>
     <td>Used off heap memory currently for storage, in bytes.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.totalOnHeapStorageMemory</td>
     <td>Total available on heap memory for storage, in bytes. This amount can vary over time,  on the MemoryManager implementation.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.totalOffHeapStorageMemory</td>
     <td>Total available off heap memory for storage, in bytes. This amount can vary over time, depending on the MemoryManager implementation.</td>
   </tr>
   <tr>
     <td>peakMemoryMetrics.*</td>
     <td>Peak value of memory (and GC) metrics:</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.JVMHeapMemory</td>
     <td>Peak memory usage of the heap that is used for object allocation.
     The heap consists of one or more memory pools. The used and committed size of the returned memory usage is the sum of those values of all heap memory pools whereas the init and max size of the returned memory usage represents the setting of the heap memory which may not be the sum of those of all heap memory pools.
     The amount of used memory in the returned memory usage is the amount of memory occupied by both live objects and garbage objects that have not been collected, if any.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.JVMOffHeapMemory</td>
     <td>Peak memory usage of non-heap memory that is used by the Java virtual machine. The non-heap memory consists of one or more memory pools. The used and committed size of the returned memory usage is the sum of those values of all non-heap memory pools whereas the init and max size of the returned memory usage represents the setting of the non-heap memory which may not be the sum of those of all non-heap memory pools.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.OnHeapExecutionMemory</td>
     <td>Peak on heap execution memory in use, in bytes.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.OffHeapExecutionMemory</td>
     <td>Peak off heap execution memory in use, in bytes.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.OnHeapStorageMemory</td>
     <td>Peak on heap storage memory in use, in bytes.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.OffHeapStorageMemory</td>
     <td>Peak off heap storage memory in use, in bytes.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.OnHeapUnifiedMemory</td>
     <td>Peak on heap memory (execution and storage).</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.OffHeapUnifiedMemory</td>
     <td>Peak off heap memory (execution and storage).</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.DirectPoolMemory</td>
     <td>Peak memory that the JVM is using for direct buffer pool (<code>java.lang.management.BufferPoolMXBean</code>)</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.MappedPoolMemory</td>
     <td>Peak memory that the JVM is using for mapped buffer pool (<code>java.lang.management.BufferPoolMXBean</code>)</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.ProcessTreeJVMVMemory</td>
     <td>Virtual memory size in bytes. Enabled if spark.executor.processTreeMetrics.enabled is true.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.ProcessTreeJVMRSSMemory</td>
     <td>Resident Set Size: number of pages the process has
       in real memory.  This is just the pages which count
       toward text, data, or stack space.  This does not
       include pages which have not been demand-loaded in,
       or which are swapped out. Enabled if spark.executor.processTreeMetrics.enabled is true.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.ProcessTreePythonVMemory</td>
     <td>Virtual memory size for Python in bytes. Enabled if spark.executor.processTreeMetrics.enabled is true.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.ProcessTreePythonRSSMemory</td>
     <td>Resident Set Size for Python. Enabled if spark.executor.processTreeMetrics.enabled is true.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.ProcessTreeOtherVMemory</td>
     <td>Virtual memory size for other kind of process in bytes. Enabled if spark.executor.processTreeMetrics.enabled is true.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.ProcessTreeOtherRSSMemory</td>
     <td>Resident Set Size for other kind of process. Enabled if spark.executor.processTreeMetrics.enabled is true.</td>
   </tr>
     <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.MinorGCCount</td>
     <td>Total minor GC count. For example, the garbage collector is one of     Copy, PS Scavenge, ParNew, G1 Young Generation and so on.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.MinorGCTime</td>
     <td>Elapsed total minor GC time. 
     The value is expressed in milliseconds.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.MajorGCCount</td>
     <td>Total major GC count. For example, the garbage collector is one of     MarkSweepCompact, PS MarkSweep, ConcurrentMarkSweep, G1 Old Generation and so on.</td>
   </tr>
   <tr>
     <td>&nbsp;&nbsp;&nbsp;&nbsp;.MajorGCTime</td>
     <td>Elapsed total major GC time. 
     The value is expressed in milliseconds.</td>
   </tr>
 </table>
 The computation of RSS and Vmem are based on [proc(5)](http://man7.org/linux/man-pages/man5/proc.5.html)
 
 ### 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 as 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 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 are generated by sources embedded in the Spark code base. They
 provide instrumentation for specific activities and Spark components.
 The metrics system is configured via a configuration file that Spark expects to be present
 at `$SPARK_HOME/conf/metrics.properties`. A custom file location can be specified via the
 `spark.metrics.conf` [configuration property](configuration.html#spark-properties).
 Instead of using the configuration file, a set of configuration parameters with prefix
 `spark.metrics.conf.` can be used.
 By default, the root namespace used for driver or executor metrics is 
 the value of `spark.app.id`. However, often times, users want to be able to track the metrics 
 across apps for driver and executors, which is hard to do with application ID 
 (i.e. `spark.app.id`) since it changes with every invocation of the app. For such use cases,
 a custom namespace can be specified for metrics reporting using `spark.metrics.namespace`
 configuration property. 
 If, say, users wanted to set the metrics namespace to the name of the application, they
 can set the `spark.metrics.namespace` property to a value like `${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.
 * `mesos_cluster`: The Spark cluster scheduler when running on Mesos.
 
 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.
 * `PrometheusServlet`: (Experimental) Adds a servlet within the existing Spark UI to serve metrics data in Prometheus format.
 * `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](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 and the parameters available for each sink are defined
 in an example configuration file,
 `$SPARK_HOME/conf/metrics.properties.template`.
 
 When using Spark configuration parameters instead of the metrics configuration file, the relevant
 parameter names are composed by the prefix `spark.metrics.conf.` followed by the configuration
 details, i.e. the parameters take the following form:
 `spark.metrics.conf.[instance|*].sink.[sink_name].[parameter_name]`.
 This example shows a list of Spark configuration parameters for a Graphite sink:
 ```
 "spark.metrics.conf.*.sink.graphite.class"="org.apache.spark.metrics.sink.GraphiteSink"
 "spark.metrics.conf.*.sink.graphite.host"="graphiteEndPoint_hostName>"
 "spark.metrics.conf.*.sink.graphite.port"=<graphite_listening_port>
 "spark.metrics.conf.*.sink.graphite.period"=10
 "spark.metrics.conf.*.sink.graphite.unit"=seconds
 "spark.metrics.conf.*.sink.graphite.prefix"="optional_prefix"
 "spark.metrics.conf.*.sink.graphite.regex"="optional_regex_to_send_matching_metrics"
 ```
 
 Default values of the Spark metrics configuration are as follows:
 ```
 "*.sink.servlet.class" = "org.apache.spark.metrics.sink.MetricsServlet"
 "*.sink.servlet.path" = "/metrics/json"
 "master.sink.servlet.path" = "/metrics/master/json"
 "applications.sink.servlet.path" = "/metrics/applications/json"
 ```
 
 Additional sources can be configured using the metrics configuration file or the configuration
 parameter `spark.metrics.conf.[component_name].source.jvm.class=[source_name]`. At present the 
 JVM source is the only available optional source. For example the following configuration parameter
 activates the JVM source:
 `"spark.metrics.conf.*.source.jvm.class"="org.apache.spark.metrics.source.JvmSource"`
 
 ## List of available metrics providers 
 
 Metrics used by Spark are of multiple types: gauge, counter, histogram, meter and timer, 
 see [Dropwizard library documentation for details](https://metrics.dropwizard.io/3.1.0/getting-started/).
 The following list of components and metrics reports the name and some details about the available metrics,
 grouped per component instance and source namespace.
 The most common time of metrics used in Spark instrumentation are gauges and counters. 
 Counters can be recognized as they have the `.count` suffix. Timers, meters and histograms are annotated
 in the list, the rest of the list elements are metrics of type gauge.
 The large majority of metrics are active as soon as their parent component instance is configured,
 some metrics require also to be enabled via an additional configuration parameter, the details are
 reported in the list.
 
 ### Component instance = Driver
 This is the component with the largest amount of instrumented metrics
 
 - namespace=BlockManager
   - disk.diskSpaceUsed_MB
   - memory.maxMem_MB
   - memory.maxOffHeapMem_MB
   - memory.maxOnHeapMem_MB
   - memory.memUsed_MB
   - memory.offHeapMemUsed_MB
   - memory.onHeapMemUsed_MB
   - memory.remainingMem_MB
   - memory.remainingOffHeapMem_MB
   - memory.remainingOnHeapMem_MB
 
 - namespace=HiveExternalCatalog
   - **note:**: these metrics are conditional to a configuration parameter:
     `spark.metrics.staticSources.enabled` (default is true) 
   - fileCacheHits.count
   - filesDiscovered.count
   - hiveClientCalls.count
   - parallelListingJobCount.count
   - partitionsFetched.count
 
 - namespace=CodeGenerator
   - **note:**: these metrics are conditional to a configuration parameter:
     `spark.metrics.staticSources.enabled` (default is true) 
   - compilationTime (histogram)
   - generatedClassSize (histogram)
   - generatedMethodSize (histogram)
   - sourceCodeSize (histogram)
 
 - namespace=DAGScheduler
   - job.activeJobs 
   - job.allJobs
   - messageProcessingTime (timer)
   - stage.failedStages
   - stage.runningStages
   - stage.waitingStages
 
 - namespace=LiveListenerBus
   - listenerProcessingTime.org.apache.spark.HeartbeatReceiver (timer)
   - listenerProcessingTime.org.apache.spark.scheduler.EventLoggingListener (timer)
   - listenerProcessingTime.org.apache.spark.status.AppStatusListener (timer)
   - numEventsPosted.count
   - queue.appStatus.listenerProcessingTime (timer)
   - queue.appStatus.numDroppedEvents.count
   - queue.appStatus.size
   - queue.eventLog.listenerProcessingTime (timer)
   - queue.eventLog.numDroppedEvents.count
   - queue.eventLog.size
   - queue.executorManagement.listenerProcessingTime (timer)
 
 - namespace=appStatus (all metrics of type=counter)
   - **note:** Introduced in Spark 3.0. Conditional to a configuration parameter:  
    `spark.metrics.appStatusSource.enabled` (default is false)
   - stages.failedStages.count
   - stages.skippedStages.count
   - stages.completedStages.count
   - tasks.blackListedExecutors.count
   - tasks.completedTasks.count
   - tasks.failedTasks.count
   - tasks.killedTasks.count
   - tasks.skippedTasks.count
   - tasks.unblackListedExecutors.count
   - jobs.succeededJobs
   - jobs.failedJobs
   - jobDuration
   
 - namespace=AccumulatorSource  
   - **note:** User-configurable sources to attach accumulators to metric system
   - DoubleAccumulatorSource
   - LongAccumulatorSource
 
 - namespace=spark.streaming
   - **note:** This applies to Spark Structured Streaming only. Conditional to a configuration
   parameter: `spark.sql.streaming.metricsEnabled=true` (default is false) 
   - eventTime-watermark
   - inputRate-total
   - latency
   - processingRate-total
   - states-rowsTotal
   - states-usedBytes
 
 - namespace=JVMCPU
   - jvmCpuTime
 
 - namespace=ExecutorMetrics
   - **note:** these metrics are conditional to a configuration parameter:
     `spark.metrics.executorMetricsSource.enabled` (default is true) 
   - This source contains memory-related metrics. A full list of available metrics in this 
     namespace can be found in the corresponding entry for the Executor component instance.
  
 - namespace=plugin.\<Plugin Class Name>
   - Optional namespace(s). Metrics in this namespace are defined by user-supplied code, and
   configured using the Spark plugin API. See "Advanced Instrumentation" below for how to load
   custom plugins into Spark.
 
 ### Component instance = Executor
 These metrics are exposed by Spark executors. Note, currently they are not available
 when running in local mode.
  
 - namespace=executor (metrics are of type counter or gauge)
   - bytesRead.count
   - bytesWritten.count
   - cpuTime.count
   - deserializeCpuTime.count
   - deserializeTime.count
   - diskBytesSpilled.count
   - filesystem.file.largeRead_ops
   - filesystem.file.read_bytes
   - filesystem.file.read_ops
   - filesystem.file.write_bytes
   - filesystem.file.write_ops
   - filesystem.hdfs.largeRead_ops
   - filesystem.hdfs.read_bytes
   - filesystem.hdfs.read_ops
   - filesystem.hdfs.write_bytes
   - filesystem.hdfs.write_ops
   - jvmGCTime.count
   - memoryBytesSpilled.count
   - recordsRead.count
   - recordsWritten.count
   - resultSerializationTime.count
   - resultSize.count
   - runTime.count
   - shuffleBytesWritten.count
   - shuffleFetchWaitTime.count
   - shuffleLocalBlocksFetched.count
   - shuffleLocalBytesRead.count
   - shuffleRecordsRead.count
   - shuffleRecordsWritten.count
   - shuffleRemoteBlocksFetched.count
   - shuffleRemoteBytesRead.count
   - shuffleRemoteBytesReadToDisk.count
   - shuffleTotalBytesRead.count
   - shuffleWriteTime.count
   - succeededTasks.count
   - threadpool.activeTasks
   - threadpool.completeTasks
   - threadpool.currentPool_size
   - threadpool.maxPool_size
   - threadpool.startedTasks
 
 - namespace=ExecutorMetrics
   - **notes:** 
     - These metrics are conditional to a configuration parameter:
     `spark.metrics.executorMetricsSource.enabled` (default value is true) 
     - ExecutorMetrics are updated as part of heartbeat processes scheduled
    for the executors and for the driver at regular intervals: `spark.executor.heartbeatInterval` (default value is 10 seconds)
     - An optional faster polling mechanism is available for executor memory metrics, 
    it can be activated by setting a polling interval (in milliseconds) using the configuration parameter `spark.executor.metrics.pollingInterval`
   - JVMHeapMemory
   - JVMOffHeapMemory
   - OnHeapExecutionMemory
   - OnHeapStorageMemory
   - OnHeapUnifiedMemory
   - OffHeapExecutionMemory
   - OffHeapStorageMemory
   - OffHeapUnifiedMemory
   - DirectPoolMemory
   - MappedPoolMemory
   - MinorGCCount
   - MinorGCTime
   - MajorGCCount
   - MajorGCTime
   - "ProcessTree*" metric counters:
     - ProcessTreeJVMVMemory
     - ProcessTreeJVMRSSMemory
     - ProcessTreePythonVMemory
     - ProcessTreePythonRSSMemory
     - ProcessTreeOtherVMemory
     - ProcessTreeOtherRSSMemory
     - **note:** "ProcessTree*" metrics are collected only under certain conditions.
       The conditions are the logical AND of the following: `/proc` filesystem exists,
       `spark.executor.processTreeMetrics.enabled=true`.
       "ProcessTree*" metrics report 0 when those conditions are not met.
 
 - namespace=JVMCPU
   - jvmCpuTime
 
 - namespace=NettyBlockTransfer
   - shuffle-client.usedDirectMemory
   - shuffle-client.usedHeapMemory
   - shuffle-server.usedDirectMemory
   - shuffle-server.usedHeapMemory
 
 - namespace=HiveExternalCatalog
   - **note:**: these metrics are conditional to a configuration parameter:
     `spark.metrics.staticSources.enabled` (default is true) 
   - fileCacheHits.count
   - filesDiscovered.count
   - hiveClientCalls.count
   - parallelListingJobCount.count
   - partitionsFetched.count
 
 - namespace=CodeGenerator
   - **note:**: these metrics are conditional to a configuration parameter:
     `spark.metrics.staticSources.enabled` (default is true) 
   - compilationTime (histogram)
   - generatedClassSize (histogram)
   - generatedMethodSize (histogram)
   - hiveClientCalls.count
   - sourceCodeSize (histogram)
 
 - namespace=plugin.\<Plugin Class Name>
   - Optional namespace(s). Metrics in this namespace are defined by user-supplied code, and
   configured using the Spark plugin API. See "Advanced Instrumentation" below for how to load
   custom plugins into Spark.
 
 ### Source = JVM Source 
 Notes: 
   - Activate this source by setting the relevant `metrics.properties` file entry or the 
   configuration parameter:`spark.metrics.conf.*.source.jvm.class=org.apache.spark.metrics.source.JvmSource`  
   - This source is available for driver and executor instances and is also available for other instances.  
   - This source provides information on JVM metrics using the 
   [Dropwizard/Codahale Metric Sets for JVM instrumentation](https://metrics.dropwizard.io/3.1.0/manual/jvm/)
    and in particular the metric sets BufferPoolMetricSet, GarbageCollectorMetricSet and MemoryUsageGaugeSet. 
 
 ### Component instance = applicationMaster
 Note: applies when running on YARN
 
 - numContainersPendingAllocate
 - numExecutorsFailed
 - numExecutorsRunning
 - numLocalityAwareTasks
 - numReleasedContainers
 
 ### Component instance = mesos_cluster
 Note: applies when running on mesos
 
 - waitingDrivers
 - launchedDrivers
 - retryDrivers
 
 ### Component instance = master
 Note: applies when running in Spark standalone as master
 
 - workers
 - aliveWorkers
 - apps
 - waitingApps
 
 ### Component instance = ApplicationSource
 Note: applies when running in Spark standalone as master
 
 - status
 - runtime_ms
 - cores
 
 ### Component instance = worker
 Note: applies when running in Spark standalone as worker
 
 - executors
 - coresUsed
 - memUsed_MB
 - coresFree
 - memFree_MB
 
 ### Component instance = shuffleService
 Note: applies to the shuffle service
 
 - blockTransferRateBytes (meter)
 - numActiveConnections.count
 - numRegisteredConnections.count
 - numCaughtExceptions.count
 - openBlockRequestLatencyMillis (histogram)
 - registerExecutorRequestLatencyMillis (histogram)
 - registeredExecutorsSize
 - shuffle-server.usedDirectMemory
 - shuffle-server.usedHeapMemory
 
 # 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.
 
 Spark also provides a plugin API so that custom instrumentation code can be added to Spark
 applications. There are two configuration keys available for loading plugins into Spark:
 
 - <code>spark.plugins</code>
 - <code>spark.plugins.defaultList</code>
 
 Both take a comma-separated list of class names that implement the
 <code>org.apache.spark.api.plugin.SparkPlugin</code> interface. The two names exist so that it's
 possible for one list to be placed in the Spark default config file, allowing users to
 easily add other plugins from the command line without overwriting the config file's list. Duplicate
 plugins are ignored.
 
 Distribution of the jar files containing the plugin code is currently not done by Spark. The user
 or admin should make sure that the jar files are available to Spark applications, for example, by
 including the plugin jar with the Spark distribution. The exception to this rule is the YARN
 backend, where the <code>--jars</code> command line option (or equivalent config entry) can be
 used to make the plugin code available to both executors and cluster-mode drivers.

This page was automatically generated by the 2.1.0 LXR engine. The LXR team