97 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Wei Zhang
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3e83ccc5d8
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[SPARK-31516][DOC] Fix non-existed metric hiveClientCalls.count of CodeGenerator in DOC
### What changes were proposed in this pull request? This PR proposes to remove the non-existed `hiveClientCalls.count` metric documentation of `CodeGenerator` of the Spark metrics system in the monitoring guide. There is a duplicated `hiveClientCalls.count` metric in both `namespace=HiveExternalCatalog` and `namespace=CodeGenerator` bullet lists, but there is only one defined inside object `HiveCatalogMetrics`. Closes #28292 from wezhang/monitoringdoc. Authored-by: Wei Zhang <wezhang@outlook.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org> |
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Luca Canali
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aa98ac52db
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[SPARK-30775][DOC] Improve the description of executor metrics in the monitoring documentation
### What changes were proposed in this pull request? This PR (SPARK-30775) aims to improve the description of the executor metrics in the monitoring documentation. ### Why are the changes needed? Improve and clarify monitoring documentation by: - adding reference to the Prometheus end point, as implemented in [SPARK-29064] - extending the list and descripion of executor metrics, following up from [SPARK-27157] ### Does this PR introduce any user-facing change? Documentation update. ### How was this patch tested? n.a. Closes #27526 from LucaCanali/docPrometheusMetricsFollowupSpark29064. Authored-by: Luca Canali <luca.canali@cern.ch> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org> |
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HyukjinKwon
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9628aca68b
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[MINOR][DOCS] Fix [[...]] to ... and <code>...</code> in documentation
### What changes were proposed in this pull request? Before: -  -  -  After: -  -  -  ### Why are the changes needed? To render the code block properly in the documentation ### Does this PR introduce any user-facing change? Yes, code rendering in documentation. ### How was this patch tested? Manually built the doc via `SKIP_API=1 jekyll build`. Closes #27899 from HyukjinKwon/minor-docss. Authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org> |
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beliefer
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c63366a693 |
[SPARK-30891][CORE][DOC] Add version information to the configuration of History
### What changes were proposed in this pull request? 1.Add version information to the configuration of `History`. 2.Update the docs of `History`. I sorted out some information show below. Item name | Since version | JIRA ID | Commit ID | Note -- | -- | -- | -- | -- spark.history.fs.logDirectory | 1.1.0 | SPARK-1768 | 21ddd7d1e9f8e2a726427f32422c31706a20ba3f#diff-a7befb99e7bd7e3ab5c46c2568aa5b3e | spark.history.fs.safemodeCheck.interval | 1.6.0 | SPARK-11020 | cf04fdfe71abc395163a625cc1f99ec5e54cc07e#diff-a7befb99e7bd7e3ab5c46c2568aa5b3e | spark.history.fs.update.interval | 1.4.0 | SPARK-6046 | 4527761bcd6501c362baf2780905a0018b9a74ba#diff-a7befb99e7bd7e3ab5c46c2568aa5b3e | spark.history.fs.cleaner.enabled | 1.3.0 | SPARK-3562 | 8942b522d8a3269a2a357e3a274ed4b3e66ebdde#diff-a7befb99e7bd7e3ab5c46c2568aa5b3e | Branch branch-1.3 does not exist, exists in branch-1.4, but it is 1.3.0-SNAPSHOT in pom.xml spark.history.fs.cleaner.interval | 1.4.0 | SPARK-5933 | 1991337336596f94698e79c2366f065c374128ab#diff-a7befb99e7bd7e3ab5c46c2568aa5b3e | spark.history.fs.cleaner.maxAge | 1.4.0 | SPARK-5933 | 1991337336596f94698e79c2366f065c374128ab#diff-a7befb99e7bd7e3ab5c46c2568aa5b3e | spark.history.fs.cleaner.maxNum | 3.0.0 | SPARK-28294 | bbc2be4f425c4c26450e1bf21db407e81046ce21#diff-6bddeb5e25239974fc13db66266b167b | spark.history.store.path | 2.3.0 | SPARK-20642 | 74daf622de4e534d5a5929b424a6e836850eefad#diff-19f35f981fdc5b0a46f070b879a9a9fc | spark.history.store.maxDiskUsage | 2.3.0 | SPARK-20654 | 8b497046c647a21bbed1bdfbdcb176745a1d5cd5#diff-19f35f981fdc5b0a46f070b879a9a9fc | spark.history.ui.port | 1.0.0 | SPARK-1276 | 9ae80bf9bd3e4da7443af97b41fe26aa5d35d70b#diff-b49b5b9c31ddb36a9061004b5b723058 | spark.history.fs.inProgressOptimization.enabled | 2.4.0 | SPARK-6951 | 653fe02415a537299e15f92b56045569864b6183#diff-19f35f981fdc5b0a46f070b879a9a9fc | spark.history.fs.endEventReparseChunkSize | 2.4.0 | SPARK-6951 | 653fe02415a537299e15f92b56045569864b6183#diff-19f35f981fdc5b0a46f070b879a9a9fc | spark.history.fs.eventLog.rolling.maxFilesToRetain | 3.0.0 | SPARK-30481 | a2fe73b83c0e7c61d1c83b236565a71e3d005a71#diff-6bddeb5e25239974fc13db66266b167b | spark.history.fs.eventLog.rolling.compaction.score.threshold | 3.0.0 | SPARK-30481 | a2fe73b83c0e7c61d1c83b236565a71e3d005a71#diff-6bddeb5e25239974fc13db66266b167b | spark.history.fs.driverlog.cleaner.enabled | 3.0.0 | SPARK-25118 | 5f11e8c4cb9a5db037ac239b8fcc97f3a746e772#diff-6bddeb5e25239974fc13db66266b167b | spark.history.fs.driverlog.cleaner.interval | 3.0.0 | SPARK-25118 | 5f11e8c4cb9a5db037ac239b8fcc97f3a746e772#diff-6bddeb5e25239974fc13db66266b167b | spark.history.fs.driverlog.cleaner.maxAge | 3.0.0 | SPARK-25118 | 5f11e8c4cb9a5db037ac239b8fcc97f3a746e772#diff-6bddeb5e25239974fc13db66266b167b | spark.history.ui.acls.enable | 1.0.1 | Spark 1489 | c8dd13221215275948b1a6913192d40e0c8cbadd#diff-b49b5b9c31ddb36a9061004b5b723058 | spark.history.ui.admin.acls | 2.1.1 | SPARK-19033 | 4ca1788805e4a0131ba8f0ccb7499ee0e0242837#diff-a7befb99e7bd7e3ab5c46c2568aa5b3e | spark.history.ui.admin.acls.groups | 2.1.1 | SPARK-19033 | 4ca1788805e4a0131ba8f0ccb7499ee0e0242837#diff-a7befb99e7bd7e3ab5c46c2568aa5b3e | spark.history.fs.numReplayThreads | 2.0.0 | SPARK-13988 | 6fdd0e32a6c3fdce1f3f7e1f8d252af05c419f7b#diff-a7befb99e7bd7e3ab5c46c2568aa5b3e | spark.history.retainedApplications | 1.0.0 | SPARK-1276 | 9ae80bf9bd3e4da7443af97b41fe26aa5d35d70b#diff-b49b5b9c31ddb36a9061004b5b723058 | spark.history.provider | 1.1.0 | SPARK-1768 | 21ddd7d1e9f8e2a726427f32422c31706a20ba3f#diff-a7befb99e7bd7e3ab5c46c2568aa5b3e | spark.history.kerberos.enabled | 1.0.1 | Spark-1490 | 866b03ef4d27b2160563b58d577de29ba6eb4442#diff-b49b5b9c31ddb36a9061004b5b723058 | spark.history.kerberos.principal | 1.0.1 | Spark-1490 | 866b03ef4d27b2160563b58d577de29ba6eb4442#diff-b49b5b9c31ddb36a9061004b5b723058 | spark.history.kerberos.keytab | 1.0.1 | Spark-1490 | 866b03ef4d27b2160563b58d577de29ba6eb4442#diff-b49b5b9c31ddb36a9061004b5b723058 | spark.history.custom.executor.log.url | 3.0.0 | SPARK-26311 | ae5b2a6a92be4986ef5b8062d7fb59318cff6430#diff-6bddeb5e25239974fc13db66266b167b | spark.history.custom.executor.log.url.applyIncompleteApplication | 3.0.0 | SPARK-26311 | ae5b2a6a92be4986ef5b8062d7fb59318cff6430#diff-6bddeb5e25239974fc13db66266b167b | ### Why are the changes needed? Supplemental configuration version information. ### Does this PR introduce any user-facing change? No ### How was this patch tested? Exists UT Closes #27751 from beliefer/add-version-to-history-config. Authored-by: beliefer <beliefer@163.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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Jungtaek Lim (HeartSaVioR)
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02f8165343 |
[SPARK-30481][DOCS][FOLLOWUP] Document event log compaction into new section of monitoring.md
### What changes were proposed in this pull request? This is a FOLLOW-UP PR for review comment on #27208 : https://github.com/apache/spark/pull/27208#pullrequestreview-347451714 This PR documents a new feature `Eventlog Compaction` into the new section of `monitoring.md`, as it only has one configuration on the SHS side and it's hard to explain everything on the description on the single configuration. ### Why are the changes needed? Event log compaction lacks the documentation for what it is and how it helps. This PR will explain it. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? Built docs via jekyll. > change on the new section <img width="951" alt="Screen Shot 2020-02-16 at 2 23 18 PM" src="https://user-images.githubusercontent.com/1317309/74599587-eb9efa80-50c7-11ea-942c-f7744268e40b.png"> > change on the table <img width="1126" alt="Screen Shot 2020-01-30 at 5 08 12 PM" src="https://user-images.githubusercontent.com/1317309/73431190-2e9c6680-4383-11ea-8ce0-815f10917ddd.png"> Closes #27398 from HeartSaVioR/SPARK-30481-FOLLOWUP-document-new-feature. Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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yi.wu
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68d7edf949 |
[SPARK-30812][SQL][CORE] Revise boolean config name to comply with new config naming policy
### What changes were proposed in this pull request? Revise below config names to comply with [new config naming policy](http://apache-spark-developers-list.1001551.n3.nabble.com/DISCUSS-naming-policy-of-Spark-configs-td28875.html): SQL: * spark.sql.execution.subquery.reuse.enabled / [SPARK-27083](https://issues.apache.org/jira/browse/SPARK-27083) * spark.sql.legacy.allowNegativeScaleOfDecimal.enabled / [SPARK-30252](https://issues.apache.org/jira/browse/SPARK-30252) * spark.sql.adaptive.optimizeSkewedJoin.enabled / [SPARK-29544](https://issues.apache.org/jira/browse/SPARK-29544) * spark.sql.legacy.property.nonReserved / [SPARK-30183](https://issues.apache.org/jira/browse/SPARK-30183) * spark.sql.streaming.forceDeleteTempCheckpointLocation.enabled / [SPARK-26389](https://issues.apache.org/jira/browse/SPARK-26389) * spark.sql.analyzer.failAmbiguousSelfJoin.enabled / [SPARK-28344](https://issues.apache.org/jira/browse/SPARK-28344) * spark.sql.adaptive.shuffle.reducePostShufflePartitions.enabled / [SPARK-30074](https://issues.apache.org/jira/browse/SPARK-30074) * spark.sql.execution.pandas.arrowSafeTypeConversion / [SPARK-25811](https://issues.apache.org/jira/browse/SPARK-25811) * spark.sql.legacy.looseUpcast / [SPARK-24586](https://issues.apache.org/jira/browse/SPARK-24586) * spark.sql.legacy.arrayExistsFollowsThreeValuedLogic / [SPARK-28052](https://issues.apache.org/jira/browse/SPARK-28052) * spark.sql.sources.ignoreDataLocality.enabled / [SPARK-29189](https://issues.apache.org/jira/browse/SPARK-29189) * spark.sql.adaptive.shuffle.fetchShuffleBlocksInBatch.enabled / [SPARK-9853](https://issues.apache.org/jira/browse/SPARK-9853) CORE: * spark.eventLog.erasureCoding.enabled / [SPARK-25855](https://issues.apache.org/jira/browse/SPARK-25855) * spark.shuffle.readHostLocalDisk.enabled / [SPARK-30235](https://issues.apache.org/jira/browse/SPARK-30235) * spark.scheduler.listenerbus.logSlowEvent.enabled / [SPARK-29001](https://issues.apache.org/jira/browse/SPARK-29001) * spark.resources.coordinate.enable / [SPARK-27371](https://issues.apache.org/jira/browse/SPARK-27371) * spark.eventLog.logStageExecutorMetrics.enabled / [SPARK-23429](https://issues.apache.org/jira/browse/SPARK-23429) ### Why are the changes needed? To comply with the config naming policy. ### Does this PR introduce any user-facing change? No. Configurations listed above are all newly added in Spark 3.0. ### How was this patch tested? Pass Jenkins. Closes #27563 from Ngone51/revise_boolean_conf_name. Authored-by: yi.wu <yi.wu@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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Jungtaek Lim (HeartSaVioR)
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5445fe9288 |
[SPARK-30827][DOCS] Document direct relationship among configurations in "spark.history.*" namespace
### What changes were proposed in this pull request? This patch adds direct relationship among configurations under "spark.history" namespace. ### Why are the changes needed? Refer the discussion thread: https://lists.apache.org/thread.html/r43c4e57cace116aca1f0f099e8a577cf202859e3671a04077867b84a%40%3Cdev.spark.apache.org%3E ### Does this PR introduce any user-facing change? No. ### How was this patch tested? Locally ran jekyll and confirmed. Screenshots for the modified spots: <img width="1159" alt="Screen Shot 2020-02-15 at 8 20 14 PM" src="https://user-images.githubusercontent.com/1317309/74587003-d5922b00-5030-11ea-954b-ee37fc08470a.png"> <img width="1158" alt="Screen Shot 2020-02-15 at 8 20 44 PM" src="https://user-images.githubusercontent.com/1317309/74587005-d62ac180-5030-11ea-98fc-98b1c9d83ff4.png"> <img width="1149" alt="Screen Shot 2020-02-15 at 8 19 56 PM" src="https://user-images.githubusercontent.com/1317309/74587002-d1660d80-5030-11ea-84b5-dec3d7f5c97c.png"> Closes #27575 from HeartSaVioR/SPARK-30827. Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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Wing Yew Poon
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387ce89a06 |
[SPARK-27324][DOC][CORE] Document configurations related to executor metrics and modify a configuration
### What changes were proposed in this pull request? Add a section to the Configuration page to document configurations for executor metrics. At the same time, rename spark.eventLog.logStageExecutorProcessTreeMetrics.enabled to spark.executor.processTreeMetrics.enabled and make it independent of spark.eventLog.logStageExecutorMetrics.enabled. ### Why are the changes needed? Executor metrics are new in Spark 3.0. They lack documentation. Memory metrics as a whole are always collected, but the ones obtained from the process tree have to be optionally enabled. Making this depend on a single configuration makes for more intuitive behavior. Given this, the configuration property is renamed to better reflect its meaning. ### Does this PR introduce any user-facing change? Yes, only in that the configurations are all new to 3.0. ### How was this patch tested? Not necessary. Closes #27329 from wypoon/SPARK-27324. Authored-by: Wing Yew Poon <wypoon@cloudera.com> Signed-off-by: Imran Rashid <irashid@cloudera.com> |
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Jungtaek Lim (HeartSaVioR)
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a2fe73b83c |
[SPARK-30481][CORE] Integrate event log compactor into Spark History Server
### What changes were proposed in this pull request? This patch addresses remaining functionality on event log compaction: integrate compaction into FsHistoryProvider. This patch is next task of SPARK-30479 (#27164), please refer the description of PR #27085 to see overall rationalization of this patch. ### Why are the changes needed? One of major goal of SPARK-28594 is to prevent the event logs to become too huge, and SPARK-29779 achieves the goal. We've got another approach in prior, but the old approach required models in both KVStore and live entities to guarantee compatibility, while they're not designed to do so. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? Added UT. Closes #27208 from HeartSaVioR/SPARK-30481. Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com> Signed-off-by: Marcelo Vanzin <vanzin@apache.org> |
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Kazuaki Ishizaki
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f31d9a629b |
[MINOR][DOC][SQL][CORE] Fix typo in document and comments
### What changes were proposed in this pull request? Fixed typo in `docs` directory and in other directories 1. Find typo in `docs` and apply fixes to files in all directories 2. Fix `the the` -> `the` ### Why are the changes needed? Better readability of documents ### Does this PR introduce any user-facing change? No ### How was this patch tested? No test needed Closes #26976 from kiszk/typo_20191221. Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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Luca Canali
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729f43f499 |
[SPARK-27189][CORE] Add Executor metrics and memory usage instrumentation to the metrics system
## What changes were proposed in this pull request? This PR proposes to add instrumentation of memory usage via the Spark Dropwizard/Codahale metrics system. Memory usage metrics are available via the Executor metrics, recently implemented as detailed in https://issues.apache.org/jira/browse/SPARK-23206. Additional notes: This takes advantage of the metrics poller introduced in #23767. ## Why are the changes needed? Executor metrics bring have many useful insights on memory usage, in particular on the usage of storage memory and executor memory. This is useful for troubleshooting. Having the information in the metrics systems allows to add those metrics to Spark performance dashboards and study memory usage as a function of time, as in the example graph https://issues.apache.org/jira/secure/attachment/12962810/Example_dashboard_Spark_Memory_Metrics.PNG ## Does this PR introduce any user-facing change? Adds `ExecutorMetrics` source to publish executor metrics via the Dropwizard metrics system. Details of the available metrics in docs/monitoring.md Adds configuration parameter `spark.metrics.executormetrics.source.enabled` ## How was this patch tested? Tested on YARN cluster and with an existing setup for a Spark dashboard based on InfluxDB and Grafana. Closes #24132 from LucaCanali/memoryMetricsSource. Authored-by: Luca Canali <luca.canali@cern.ch> Signed-off-by: Imran Rashid <irashid@cloudera.com> |
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Luca Canali
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60f20e5ea2 |
[SPARK-30060][CORE] Rename metrics enable/disable configs
### What changes were proposed in this pull request? This proposes to introduce a naming convention for Spark metrics configuration parameters used to enable/disable metrics source reporting using the Dropwizard metrics library: `spark.metrics.sourceNameCamelCase.enabled` and update 2 parameters to use this naming convention. ### Why are the changes needed? Currently Spark has a few parameters to enable/disable metrics reporting. Their naming pattern is not uniform and this can create confusion. Currently we have: `spark.metrics.static.sources.enabled` `spark.app.status.metrics.enabled` `spark.sql.streaming.metricsEnabled` ### Does this PR introduce any user-facing change? Update parameters for enabling/disabling metrics reporting new in Spark 3.0: `spark.metrics.static.sources.enabled` -> `spark.metrics.staticSources.enabled`, `spark.app.status.metrics.enabled` -> `spark.metrics.appStatusSource.enabled`. Note: `spark.sql.streaming.metricsEnabled` is left unchanged as it is already in use in Spark 2.x. ### How was this patch tested? Manually tested Closes #26692 from LucaCanali/uniformNamingMetricsEnableParameters. Authored-by: Luca Canali <luca.canali@cern.ch> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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Luca Canali
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2888009d66 |
[SPARK-29654][CORE] Add configuration to allow disabling registration of static sources to the metrics system
### What changes were proposed in this pull request? The Spark metrics system produces many different metrics and not all of them are used at the same time. This proposes to introduce a configuration parameter to allow disabling the registration of metrics in the "static sources" category. ### Why are the changes needed? This allows to reduce the load and clutter on the sink, in the cases when the metrics in question are not needed. The metrics registerd as "static sources" are under the namespaces CodeGenerator and HiveExternalCatalog and can produce a significant amount of data, as they are registered for the driver and executors. ### Does this PR introduce any user-facing change? It introduces a new configuration parameter `spark.metrics.register.static.sources.enabled` ### How was this patch tested? Manually tested. ``` $ cat conf/metrics.properties *.sink.prometheusServlet.class=org.apache.spark.metrics.sink.PrometheusServlet *.sink.prometheusServlet.path=/metrics/prometheus master.sink.prometheusServlet.path=/metrics/master/prometheus applications.sink.prometheusServlet.path=/metrics/applications/prometheus $ bin/spark-shell $ curl -s http://localhost:4040/metrics/prometheus/ | grep Hive metrics_local_1573330115306_driver_HiveExternalCatalog_fileCacheHits_Count 0 metrics_local_1573330115306_driver_HiveExternalCatalog_filesDiscovered_Count 0 metrics_local_1573330115306_driver_HiveExternalCatalog_hiveClientCalls_Count 0 metrics_local_1573330115306_driver_HiveExternalCatalog_parallelListingJobCount_Count 0 metrics_local_1573330115306_driver_HiveExternalCatalog_partitionsFetched_Count 0 $ bin/spark-shell --conf spark.metrics.static.sources.enabled=false $ curl -s http://localhost:4040/metrics/prometheus/ | grep Hive ``` Closes #26320 from LucaCanali/addConfigRegisterStaticMetrics. Authored-by: Luca Canali <luca.canali@cern.ch> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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Marcelo Vanzin
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d51d228048 |
[SPARK-29397][CORE] Extend plugin interface to include the driver
Spark 2.4 added the ability for executor plugins to be loaded into Spark (see SPARK-24918). That feature intentionally skipped the driver to keep changes small, and also because it is possible to load code into the Spark driver using listeners + configuration. But that is a bit awkward, because the listener interface does not provide hooks into a lot of Spark functionality. This change reworks the executor plugin interface to also extend to the driver. - there's a "SparkPlugin" main interface that provides APIs to load driver and executor components. - custom metric support (added in SPARK-28091) can be used by plugins to register metrics both in the driver process and in executors. - a communication channel now exists that allows the plugin's executor components to send messages to the plugin's driver component easily, using the existing Spark RPC system. The latter was a feature intentionally left out of the original plugin design (also because it didn't include a driver component). To avoid polluting the "org.apache.spark" namespace, I added the new interfaces to the "org.apache.spark.api" package, which seems like a better place in any case. The actual implementation is kept in an internal package. The change includes unit tests for the new interface and features, but I've also been running a custom plugin that extends the new API in real applications. Closes #26170 from vanzin/SPARK-29397. Authored-by: Marcelo Vanzin <vanzin@cloudera.com> Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com> |
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Luca Canali
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2b3c3793c9 |
[SPARK-29032][FOLLOWUP][DOCS] Add PrometheusServlet in the monitoring documentation
This adds an entry about PrometheusServlet to the documentation, following SPARK-29032 ### Why are the changes needed? The monitoring documentation lists all the available metrics sinks, this should be added to the list for completeness. Closes #26081 from LucaCanali/FollowupSpark29032. Authored-by: Luca Canali <luca.canali@cern.ch> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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Luca Canali
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cd481773c3 |
[SPARK-28091][CORE] Extend Spark metrics system with user-defined metrics using executor plugins
## What changes were proposed in this pull request? This proposes to improve Spark instrumentation by adding a hook for user-defined metrics, extending Spark’s Dropwizard/Codahale metrics system. The original motivation of this work was to add instrumentation for S3 filesystem access metrics by Spark job. Currently, [[ExecutorSource]] instruments HDFS and local filesystem metrics. Rather than extending the code there, we proposes with this JIRA to add a metrics plugin system which is of more flexible and general use. Context: The Spark metrics system provides a large variety of metrics, see also , useful to monitor and troubleshoot Spark workloads. A typical workflow is to sink the metrics to a storage system and build dashboards on top of that. Highlights: - The metric plugin system makes it easy to implement instrumentation for S3 access by Spark jobs. - The metrics plugin system allows for easy extensions of how Spark collects HDFS-related workload metrics. This is currently done using the Hadoop Filesystem GetAllStatistics method, which is deprecated in recent versions of Hadoop. Recent versions of Hadoop Filesystem recommend using method GetGlobalStorageStatistics, which also provides several additional metrics. GetGlobalStorageStatistics is not available in Hadoop 2.7 (had been introduced in Hadoop 2.8). Using a metric plugin for Spark would allow an easy way to “opt in” using such new API calls for those deploying suitable Hadoop versions. - We also have the use case of adding Hadoop filesystem monitoring for a custom Hadoop compliant filesystem in use in our organization (EOS using the XRootD protocol). The metrics plugin infrastructure makes this easy to do. Others may have similar use cases. - More generally, this method makes it straightforward to plug in Filesystem and other metrics to the Spark monitoring system. Future work on plugin implementation can address extending monitoring to measure usage of external resources (OS, filesystem, network, accelerator cards, etc), that maybe would not normally be considered general enough for inclusion in Apache Spark code, but that can be nevertheless useful for specialized use cases, tests or troubleshooting. Implementation: The proposed implementation extends and modifies the work on Executor Plugin of SPARK-24918. Additionally, this is related to recent work on extending Spark executor metrics, such as SPARK-25228. As discussed during the review, the implementaiton of this feature modifies the Developer API for Executor Plugins, such that the new version is incompatible with the original version in Spark 2.4. ## How was this patch tested? This modifies existing tests for ExecutorPluginSuite to adapt them to the API changes. In addition, the new funtionality for registering pluginMetrics has been manually tested running Spark on YARN and K8S clusters, in particular for monitoring S3 and for extending HDFS instrumentation with the Hadoop Filesystem “GetGlobalStorageStatistics” metrics. Executor metric plugin example and code used for testing are available, for example at: https://github.com/cerndb/SparkExecutorPlugins Closes #24901 from LucaCanali/executorMetricsPlugin. Authored-by: Luca Canali <luca.canali@cern.ch> Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com> |
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zhengruifeng
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ae4edd5489 |
[SPARK-28538][UI] Document SQL page
## What changes were proposed in this pull request? 1, add basic doc for each page; 2, doc SQL page with an exmple; ## How was this patch tested? locally built   Closes #25349 from zhengruifeng/doc_ui_sql. Authored-by: zhengruifeng <ruifengz@foxmail.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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Nick Karpov
|
6d32deeecc |
[SPARK-28475][CORE] Add regex MetricFilter to GraphiteSink
## What changes were proposed in this pull request? Today all registered metric sources are reported to GraphiteSink with no filtering mechanism, although the codahale project does support it. GraphiteReporter (ScheduledReporter) from the codahale project requires you implement and supply the MetricFilter interface (there is only a single implementation by default in the codahale project, MetricFilter.ALL). Propose to add an additional regex config to match and filter metrics to the GraphiteSink ## How was this patch tested? Included a GraphiteSinkSuite that tests: 1. Absence of regex filter (existing default behavior maintained) 2. Presence of `regex=<regexexpr>` correctly filters metric keys Closes #25232 from nkarpov/graphite_regex. Authored-by: Nick Karpov <nick@nickkarpov.com> Signed-off-by: jerryshao <jerryshao@tencent.com> |
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Luca Canali
|
f2a2d980ed |
[SPARK-25285][CORE] Add startedTasks and finishedTasks to the metrics system in the executor instance
## What changes were proposed in this pull request? The motivation for these additional metrics is to help in troubleshooting and monitoring task execution workload when running on a cluster. Currently available metrics include executor threadpool metrics for task completed and for active tasks. The addition of threadpool taskStarted metric will allow for example to collect info on the (approximate) number of failed tasks by computing the difference thread started – (active threads + completed tasks and/or successfully finished tasks). The proposed metric finishedTasks is also intended for this type of troubleshooting. The difference between finshedTasks and threadpool.completeTasks, is that the latter is a (dropwizard library) gauge taken from the threadpool, while the former is a (dropwizard) counter computed in the [[Executor]] class, when a task successfully finishes, together with several other task metrics counters. Note, there are similarities with some of the metrics introduced in SPARK-24398, however there are key differences, coming from the fact that this PR concerns the executor source, therefore providing metric values per executor + metric values do not require to pass through the listerner bus in this case. ## How was this patch tested? Manually tested on a YARN cluster Closes #22290 from LucaCanali/AddMetricExecutorStartedTasks. Lead-authored-by: Luca Canali <luca.canali@cern.ch> Co-authored-by: LucaCanali <luca.canali@cern.ch> Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com> |
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Dongjoon Hyun
|
bbc2be4f42 |
[SPARK-28294][CORE] Support spark.history.fs.cleaner.maxNum configuration
## What changes were proposed in this pull request? Up to now, Apache Spark maintains the given event log directory by **time** policy, `spark.history.fs.cleaner.maxAge`. However, there are two issues. 1. Some file system has a limitation on the maximum number of files in a single directory. For example, HDFS `dfs.namenode.fs-limits.max-directory-items` is 1024 * 1024 by default. https://hadoop.apache.org/docs/r3.2.0/hadoop-project-dist/hadoop-hdfs/hdfs-default.xml 2. Spark is sometimes unable to to clean up some old log files due to permission issues (mainly, security policy). To handle both (1) and (2), this PR aims to support an additional policy configuration for the maximum number of files in the event log directory, `spark.history.fs.cleaner.maxNum`. Spark will try to keep the number of files in the event log directory according to this policy. ## How was this patch tested? Pass the Jenkins with a newly added test case. Closes #25072 from dongjoon-hyun/SPARK-28294. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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|
Luca Canali
|
adf72e26d9 |
[SPARK-27773][FOLLOWUP][DOC] Add numCaughtExceptions metric to monitoring doc
## What changes were proposed in this pull request? SPARK-27773 has introduced a new metric (counter) numCaughtExceptions to the Spark Dropwizard monitoring system. This PR adds an entry in the monitoring documentation to document this. Closes #24790 from LucaCanali/addDocFollowingSPARK27773. Authored-by: Luca Canali <luca.canali@cern.ch> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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Sean Owen
|
754f820035 |
[SPARK-26918][DOCS] All .md should have ASF license header
## What changes were proposed in this pull request? Add AL2 license to metadata of all .md files. This seemed to be the tidiest way as it will get ignored by .md renderers and other tools. Attempts to write them as markdown comments revealed that there is no such standard thing. ## How was this patch tested? Doc build Closes #24243 from srowen/SPARK-26918. Authored-by: Sean Owen <sean.owen@databricks.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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|
Luca Canali
|
4b2b3da766 |
[SPARK-26928][CORE][FOLLOWUP] Fix JVMCPUSource file name and minor updates to doc
## What changes were proposed in this pull request? This applies some minor updates/cleaning following up SPARK-26928, notably renaming JVMCPU.scala to JVMCPUSource.scala. ## How was this patch tested? Manually tested Closes #24201 from LucaCanali/fixupSPARK-26928. Authored-by: Luca Canali <luca.canali@cern.ch> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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hehuiyuan
|
d6a3cbea5d |
[MINOR][DOC] Add "completedStages" metircs for namespace=appStatus
## What changes were proposed in this pull request? Add completedStages metircs for namespace=appStatus for monitoring.md. Closes #24109 from hehuiyuan/hehuiyuan-patch-5. Authored-by: hehuiyuan <hehuiyuan@ZBMAC-C02WD3K5H.local> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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Lantao Jin
|
6a6075ac96 |
[SPARK-27157][DOCS] Add Executor level metrics to monitoring docs
## What changes were proposed in this pull request? A sub-task of [SPARK-23206](https://issues.apache.org/jira/browse/SPARK-23206) Add Executor level metrics to monitoring docs ## How was this patch tested? jekyll Closes #24090 from LantaoJin/SPARK-27157. Authored-by: Lantao Jin <jinlantao@gmail.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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|
Luca Canali
|
25d2850665 |
[SPARK-26928][CORE] Add driver CPU Time to the metrics system
## What changes were proposed in this pull request? This proposes to add instrumentation for the driver's JVM CPU time via the Spark Dropwizard/Codahale metrics system. It follows directly from previous work SPARK-25228 and shares similar motivations: it is intended as an improvement to be used for Spark performance dashboards and monitoring tools/instrumentation. Implementation details: this PR takes the code introduced in SPARK-25228 and moves it to a new separate Source JVMCPUSource, which is then used to register the jvmCpuTime gauge metric for both executor and driver. The registration of the jvmCpuTime metric for the driver is conditional, a new configuration parameter `spark.metrics.cpu.time.driver.enabled` (proposed default: false) is introduced for this purpose. ## How was this patch tested? Manually tested, using local mode and using YARN. Closes #23838 from LucaCanali/addCPUTimeMetricDriver. Authored-by: Luca Canali <luca.canali@cern.ch> Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com> |
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Luca Canali
|
5fd28e8f5c |
[SPARK-26890][DOC] Add list of available Dropwizard metrics in Spark and add additional configuration details to the monitoring documentation
## What changes were proposed in this pull request? This PR proposes to extend the documentation of the Spark metrics system in the monitoring guide. In particular by: - adding a list of the available metrics grouped per component instance - adding information on configuration parameters that can be used to configure the metrics system in alternative to the metrics.properties file - adding information on the configuration parameters needed to enable certain metrics - it also propose to add an example of Graphite sink configuration in metrics.properties.template Closes #23798 from LucaCanali/metricsDocUpdate. Authored-by: Luca Canali <luca.canali@cern.ch> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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SongYadong
|
0fe9c144fd |
[DOC][MINOR] Add metrics instance 'mesos_cluster' to monitoring doc
## What changes were proposed in this pull request? Metrics instance "mesos_cluster" exists in spark, but not mentioned in monitoring.md. This PR add it. ## How was this patch tested? Manually test. Closes #23691 from SongYadong/doc_mesos_metrics_inst. Authored-by: SongYadong <song.yadong1@zte.com.cn> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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Jungtaek Lim (HeartSaVioR)
|
ae5b2a6a92 |
[SPARK-26311][CORE] New feature: apply custom log URL pattern for executor log URLs in SHS
## What changes were proposed in this pull request? This patch proposes adding a new configuration on SHS: custom executor log URL pattern. This will enable end users to replace executor logs to other than RM provide, like external log service, which enables to serve executor logs when NodeManager becomes unavailable in case of YARN. End users can build their own of custom executor log URLs with pre-defined patterns which would be vary on each resource manager. This patch adds some patterns to YARN resource manager. (For others, there's even no executor log url available so cannot define patterns as well.) Please refer the doc change as well as added UTs in this patch to see how to set up the feature. ## How was this patch tested? Added UT, as well as manual test with YARN cluster Closes #23260 from HeartSaVioR/SPARK-26311. Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com> Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com> |
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ankurgupta
|
5f11e8c4cb |
[SPARK-25118][CORE] Persist Driver Logs in Client mode to Hdfs
Currently, we do not have a mechanism to collect driver logs if a user chooses to run their application in client mode. This is a big issue as admin teams need to create their own mechanisms to capture driver logs. This commit adds a logger which, if enabled, adds a local log appender to the root logger and asynchronously syncs it an application specific log file on hdfs (Spark Driver Log Dir). Additionally, this collects spark-shell driver logs at INFO level by default. The change is that instead of setting root logger level to WARN, we will set the consoleAppender threshold to WARN, in case of spark-shell. This ensures that only WARN logs are printed on CONSOLE but other log appenders still capture INFO (or the default log level logs). 1. Verified that logs are written to local and remote dir 2. Added a unit test case 3. Verified this for spark-shell, client mode and pyspark. 4. Verified in both non-kerberos and kerberos environment 5. Verified with following unexpected termination conditions: Ctrl + C, Driver OOM, Large Log Files 6. Ran an application in spark-shell and ensured that driver logs were captured at INFO level 7. Started the application at WARN level, programmatically changed the level to INFO and ensured that logs on console were printed at INFO level Closes #22504 from ankuriitg/ankurgupta/SPARK-25118. Authored-by: ankurgupta <ankur.gupta@cloudera.com> Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com> |
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Shahid
|
71876633f3
|
[SPARK-25583][DOC] Add history-server related configuration in the documentation.
## What changes were proposed in this pull request? Add history-server related configuration in the documentation. Some of the history server related configurations were missing in the documentation.Like, 'spark.history.store.maxDiskUsage', 'spark.ui.liveUpdate.period' etc. ## How was this patch tested?    Closes #22601 from shahidki31/historyConf. Authored-by: Shahid <shahidki31@gmail.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org> |
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|
LucaCanali
|
45c4ebc817 |
[SPARK-25170][DOC] Add list and short description of Spark Executor Task Metrics to the documentation.
## What changes were proposed in this pull request? Add description of Executor Task Metrics to the documentation. Closes #22397 from LucaCanali/docMonitoringTaskMetrics. Authored-by: LucaCanali <luca.canali@cern.ch> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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“attilapiros”
|
d2436a8529 |
[SPARK-24594][YARN] Introducing metrics for YARN
## What changes were proposed in this pull request?
In this PR metrics are introduced for YARN. As up to now there was no metrics in the YARN module a new metric system is created with the name "applicationMaster".
To support both client and cluster mode the metric system lifecycle is bound to the AM.
## How was this patch tested?
Both client and cluster mode was tested manually.
Before the test on one of the YARN node spark-core was removed to cause the allocation failure.
Spark was started as (in case of client mode):
```
spark2-submit \
--class org.apache.spark.examples.SparkPi \
--conf "spark.yarn.blacklist.executor.launch.blacklisting.enabled=true" --conf "spark.blacklist.application.maxFailedExecutorsPerNode=2" --conf "spark.dynamicAllocation.enabled=true" --conf "spark.metrics.conf.*.sink.console.class=org.apache.spark.metrics.sink.ConsoleSink" \
--master yarn \
--deploy-mode client \
original-spark-examples_2.11-2.4.0-SNAPSHOT.jar \
1000
```
In both cases the YARN logs contained the new metrics as:
```
$ yarn logs --applicationId application_1529926424933_0015
...
-- Gauges ----------------------------------------------------------------------
application_1531751594108_0046.applicationMaster.numContainersPendingAllocate
value = 0
application_1531751594108_0046.applicationMaster.numExecutorsFailed
value = 3
application_1531751594108_0046.applicationMaster.numExecutorsRunning
value = 9
application_1531751594108_0046.applicationMaster.numLocalityAwareTasks
value = 0
application_1531751594108_0046.applicationMaster.numReleasedContainers
value = 0
...
```
Author: “attilapiros” <piros.attila.zsolt@gmail.com>
Author: Attila Zsolt Piros <2017933+attilapiros@users.noreply.github.com>
Closes #21635 from attilapiros/SPARK-24594.
|
||
Daniel Sakuma
|
6ade5cbb49 |
[MINOR][DOC] Fix some typos and grammar issues
## What changes were proposed in this pull request? Easy fix in the documentation. ## How was this patch tested? N/A Closes #20948 Author: Daniel Sakuma <dsakuma@gmail.com> Closes #20928 from dsakuma/fix_typo_configuration_docs. |
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|
Marcelo Vanzin
|
b30a7d28b3 |
[SPARK-23572][DOCS] Bring "security.md" up to date.
This change basically rewrites the security documentation so that it's up to date with new features, more correct, and more complete. Because security is such an important feature, I chose to move all the relevant configuration documentation to the security page, instead of having them peppered all over the place in the configuration page. This allows an almost one-stop shop for security configuration in Spark. The only exceptions are some YARN-specific minor features which I left in the YARN page. I also re-organized the page's topics, since they didn't make a lot of sense. You had kerberos features described inside paragraphs talking about UI access control, and other oddities. It should be easier now to find information about specific Spark security features. I also enabled TOCs for both the Security and YARN pages, since that makes it easier to see what is covered. I removed most of the comments from the SecurityManager javadoc since they just replicated information in the security doc, with different levels of out-of-dateness. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #20742 from vanzin/SPARK-23572. |
||
|
“attilapiros”
|
a5a4b83501 |
[SPARK-23235][CORE] Add executor Threaddump to api
## What changes were proposed in this pull request? Extending api with the executor thread dump data. For this new REST URL is introduced: - GET http://localhost:4040/api/v1/applications/{applicationId}/executors/{executorId}/threads <details> <summary>Example response:</summary> ``` javascript [ { "threadId" : 52, "threadName" : "context-cleaner-periodic-gc", "threadState" : "TIMED_WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)\njava.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1093)\njava.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)\njava.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1385411893})", "holdingLocks" : [ ] }, { "threadId" : 48, "threadName" : "dag-scheduler-event-loop", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingDeque.takeFirst(LinkedBlockingDeque.java:492)\njava.util.concurrent.LinkedBlockingDeque.take(LinkedBlockingDeque.java:680)\norg.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:46)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1138053349})", "holdingLocks" : [ ] }, { "threadId" : 17, "threadName" : "dispatcher-event-loop-0", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1764626380})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker832743930})" ] }, { "threadId" : 18, "threadName" : "dispatcher-event-loop-1", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1764626380})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker834153999})" ] }, { "threadId" : 19, "threadName" : "dispatcher-event-loop-2", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1764626380})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker664836465})" ] }, { "threadId" : 20, "threadName" : "dispatcher-event-loop-3", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1764626380})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker1645557354})" ] }, { "threadId" : 21, "threadName" : "dispatcher-event-loop-4", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1764626380})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker1188871851})" ] }, { "threadId" : 22, "threadName" : "dispatcher-event-loop-5", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1764626380})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker920926249})" ] }, { "threadId" : 23, "threadName" : "dispatcher-event-loop-6", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1764626380})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker355222677})" ] }, { "threadId" : 24, "threadName" : "dispatcher-event-loop-7", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1764626380})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker1589745212})" ] }, { "threadId" : 49, "threadName" : "driver-heartbeater", "threadState" : "TIMED_WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)\njava.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1093)\njava.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)\njava.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1602885835})", "holdingLocks" : [ ] }, { "threadId" : 53, "threadName" : "element-tracking-store-worker", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\njava.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1439439099})", "holdingLocks" : [ ] }, { "threadId" : 3, "threadName" : "Finalizer", "threadState" : "WAITING", "stackTrace" : "java.lang.Object.wait(Native Method)\njava.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:143)\njava.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:164)\njava.lang.ref.Finalizer$FinalizerThread.run(Finalizer.java:209)", 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Monitor(scala.tools.jline_embedded.internal.NonBlockingInputStream46248392})\nscala.tools.jline_embedded.console.ConsoleReader.readCharacter(ConsoleReader.java:2145)\nscala.tools.jline_embedded.console.ConsoleReader.readLine(ConsoleReader.java:2349)\nscala.tools.jline_embedded.console.ConsoleReader.readLine(ConsoleReader.java:2269)\nscala.tools.nsc.interpreter.jline_embedded.InteractiveReader.readOneLine(JLineReader.scala:57)\nscala.tools.nsc.interpreter.InteractiveReader$$anonfun$readLine$2.apply(InteractiveReader.scala:37)\nscala.tools.nsc.interpreter.InteractiveReader$$anonfun$readLine$2.apply(InteractiveReader.scala:37)\nscala.tools.nsc.interpreter.InteractiveReader$.restartSysCalls(InteractiveReader.scala:44)\nscala.tools.nsc.interpreter.InteractiveReader$class.readLine(InteractiveReader.scala:37)\nscala.tools.nsc.interpreter.jline_embedded.InteractiveReader.readLine(JLineReader.scala:28)\nscala.tools.nsc.interpreter.ILoop.readOneLine(ILoop.scala:404)\nscala.tools.nsc.interpreter.ILoop.loop(ILoop.scala:413)\nscala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply$mcZ$sp(ILoop.scala:923)\nscala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:909)\nscala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:909)\nscala.reflect.internal.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:97)\nscala.tools.nsc.interpreter.ILoop.process(ILoop.scala:909)\norg.apache.spark.repl.Main$.doMain(Main.scala:76)\norg.apache.spark.repl.Main$.main(Main.scala:56)\norg.apache.spark.repl.Main.main(Main.scala)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\norg.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)\norg.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:879)\norg.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:197)\norg.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:227)\norg.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:136)\norg.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)", "blockedByLock" : "", "holdingLocks" : [ "Monitor(scala.tools.jline_embedded.internal.NonBlockingInputStream46248392})" ] }, { "threadId" : 26, "threadName" : "map-output-dispatcher-0", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native 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Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.MapOutputTrackerMaster$MessageLoop.run(MapOutputTracker.scala:384)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject350285679})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker1947378744})" ] }, { "threadId" : 28, "threadName" : "map-output-dispatcher-2", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.MapOutputTrackerMaster$MessageLoop.run(MapOutputTracker.scala:384)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject350285679})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker507507251})" ] }, { "threadId" : 29, "threadName" : "map-output-dispatcher-3", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.MapOutputTrackerMaster$MessageLoop.run(MapOutputTracker.scala:384)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject350285679})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker1016408627})" ] }, { "threadId" : 30, "threadName" : "map-output-dispatcher-4", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.MapOutputTrackerMaster$MessageLoop.run(MapOutputTracker.scala:384)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject350285679})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker1879219501})" ] }, { "threadId" : 31, "threadName" : "map-output-dispatcher-5", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.MapOutputTrackerMaster$MessageLoop.run(MapOutputTracker.scala:384)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject350285679})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker290509937})" ] }, { "threadId" : 32, "threadName" : "map-output-dispatcher-6", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.MapOutputTrackerMaster$MessageLoop.run(MapOutputTracker.scala:384)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject350285679})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker1889468930})" ] }, { "threadId" : 33, "threadName" : "map-output-dispatcher-7", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.MapOutputTrackerMaster$MessageLoop.run(MapOutputTracker.scala:384)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject350285679})", "holdingLocks" : [ "Lock(java.util.concurrent.ThreadPoolExecutor$Worker1699637904})" ] }, { "threadId" : 47, "threadName" : "netty-rpc-env-timeout", "threadState" : "TIMED_WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)\njava.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1093)\njava.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)\njava.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject977194847})", "holdingLocks" : [ ] }, { "threadId" : 14, "threadName" : "NonBlockingInputStreamThread", "threadState" : "WAITING", "stackTrace" : "java.lang.Object.wait(Native Method)\nscala.tools.jline_embedded.internal.NonBlockingInputStream.run(NonBlockingInputStream.java:278)\njava.lang.Thread.run(Thread.java:748)", "blockedByThreadId" : 1, "blockedByLock" : "Lock(scala.tools.jline_embedded.internal.NonBlockingInputStream46248392})", "holdingLocks" : [ ] }, { "threadId" : 2, "threadName" : "Reference Handler", "threadState" : "WAITING", "stackTrace" : "java.lang.Object.wait(Native Method)\njava.lang.Object.wait(Object.java:502)\njava.lang.ref.Reference.tryHandlePending(Reference.java:191)\njava.lang.ref.Reference$ReferenceHandler.run(Reference.java:153)", "blockedByLock" : "Lock(java.lang.ref.Reference$Lock1359433302})", "holdingLocks" : [ ] }, { "threadId" : 35, "threadName" : "refresh progress", "threadState" : "TIMED_WAITING", "stackTrace" : "java.lang.Object.wait(Native Method)\njava.util.TimerThread.mainLoop(Timer.java:552)\njava.util.TimerThread.run(Timer.java:505)", "blockedByLock" : "Lock(java.util.TaskQueue44276328})", "holdingLocks" : [ ] }, { "threadId" : 34, "threadName" : "RemoteBlock-temp-file-clean-thread", "threadState" : "TIMED_WAITING", "stackTrace" : "java.lang.Object.wait(Native Method)\njava.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:143)\norg.apache.spark.storage.BlockManager$RemoteBlockTempFileManager.org$apache$spark$storage$BlockManager$RemoteBlockTempFileManager$$keepCleaning(BlockManager.scala:1630)\norg.apache.spark.storage.BlockManager$RemoteBlockTempFileManager$$anon$1.run(BlockManager.scala:1608)", "blockedByLock" : "Lock(java.lang.ref.ReferenceQueue$Lock391748181})", "holdingLocks" : [ ] }, { "threadId" : 25, "threadName" : "rpc-server-3-1", "threadState" : "RUNNABLE", "stackTrace" : "sun.nio.ch.KQueueArrayWrapper.kevent0(Native Method)\nsun.nio.ch.KQueueArrayWrapper.poll(KQueueArrayWrapper.java:198)\nsun.nio.ch.KQueueSelectorImpl.doSelect(KQueueSelectorImpl.java:117)\nsun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86) => holding Monitor(sun.nio.ch.KQueueSelectorImpl2057702496})\nsun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)\nio.netty.channel.nio.SelectedSelectionKeySetSelector.select(SelectedSelectionKeySetSelector.java:62)\nio.netty.channel.nio.NioEventLoop.select(NioEventLoop.java:753)\nio.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:409)\nio.netty.util.concurrent.SingleThreadEventExecutor$5.run(SingleThreadEventExecutor.java:858)\nio.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:138)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "", "holdingLocks" : [ "Monitor(io.netty.channel.nio.SelectedSelectionKeySet1066929256})", "Monitor(java.util.Collections$UnmodifiableSet561426729})", "Monitor(sun.nio.ch.KQueueSelectorImpl2057702496})" ] }, { "threadId" : 50, "threadName" : "shuffle-server-5-1", "threadState" : "RUNNABLE", "stackTrace" : "sun.nio.ch.KQueueArrayWrapper.kevent0(Native Method)\nsun.nio.ch.KQueueArrayWrapper.poll(KQueueArrayWrapper.java:198)\nsun.nio.ch.KQueueSelectorImpl.doSelect(KQueueSelectorImpl.java:117)\nsun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86) => holding Monitor(sun.nio.ch.KQueueSelectorImpl1401522546})\nsun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)\nio.netty.channel.nio.SelectedSelectionKeySetSelector.select(SelectedSelectionKeySetSelector.java:62)\nio.netty.channel.nio.NioEventLoop.select(NioEventLoop.java:753)\nio.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:409)\nio.netty.util.concurrent.SingleThreadEventExecutor$5.run(SingleThreadEventExecutor.java:858)\nio.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:138)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "", "holdingLocks" : [ "Monitor(io.netty.channel.nio.SelectedSelectionKeySet385972319})", "Monitor(java.util.Collections$UnmodifiableSet477937109})", "Monitor(sun.nio.ch.KQueueSelectorImpl1401522546})" ] }, { "threadId" : 4, "threadName" : "Signal Dispatcher", "threadState" : "RUNNABLE", "stackTrace" : "", "blockedByLock" : "", "holdingLocks" : [ ] }, { "threadId" : 51, "threadName" : "Spark Context Cleaner", "threadState" : "TIMED_WAITING", "stackTrace" : "java.lang.Object.wait(Native Method)\njava.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:143)\norg.apache.spark.ContextCleaner$$anonfun$org$apache$spark$ContextCleaner$$keepCleaning$1.apply$mcV$sp(ContextCleaner.scala:181)\norg.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1319)\norg.apache.spark.ContextCleaner.org$apache$spark$ContextCleaner$$keepCleaning(ContextCleaner.scala:178)\norg.apache.spark.ContextCleaner$$anon$1.run(ContextCleaner.scala:73)", "blockedByLock" : "Lock(java.lang.ref.ReferenceQueue$Lock1739420764})", "holdingLocks" : [ ] }, { "threadId" : 16, "threadName" : "spark-listener-group-appStatus", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.scheduler.AsyncEventQueue$$anonfun$org$apache$spark$scheduler$AsyncEventQueue$$dispatch$1.apply(AsyncEventQueue.scala:94)\nscala.util.DynamicVariable.withValue(DynamicVariable.scala:58)\norg.apache.spark.scheduler.AsyncEventQueue.org$apache$spark$scheduler$AsyncEventQueue$$dispatch(AsyncEventQueue.scala:83)\norg.apache.spark.scheduler.AsyncEventQueue$$anon$1$$anonfun$run$1.apply$mcV$sp(AsyncEventQueue.scala:79)\norg.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1319)\norg.apache.spark.scheduler.AsyncEventQueue$$anon$1.run(AsyncEventQueue.scala:78)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1287190987})", "holdingLocks" : [ ] }, { "threadId" : 44, "threadName" : "spark-listener-group-executorManagement", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native 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54, "threadName" : "spark-listener-group-shared", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\norg.apache.spark.scheduler.AsyncEventQueue$$anonfun$org$apache$spark$scheduler$AsyncEventQueue$$dispatch$1.apply(AsyncEventQueue.scala:94)\nscala.util.DynamicVariable.withValue(DynamicVariable.scala:58)\norg.apache.spark.scheduler.AsyncEventQueue.org$apache$spark$scheduler$AsyncEventQueue$$dispatch(AsyncEventQueue.scala:83)\norg.apache.spark.scheduler.AsyncEventQueue$$anon$1$$anonfun$run$1.apply$mcV$sp(AsyncEventQueue.scala:79)\norg.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1319)\norg.apache.spark.scheduler.AsyncEventQueue$$anon$1.run(AsyncEventQueue.scala:78)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject334604425})", "holdingLocks" : [ ] }, { "threadId" : 37, "threadName" : "SparkUI-37", "threadState" : "TIMED_WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)\norg.spark_project.jetty.util.BlockingArrayQueue.poll(BlockingArrayQueue.java:392)\norg.spark_project.jetty.util.thread.QueuedThreadPool.idleJobPoll(QueuedThreadPool.java:563)\norg.spark_project.jetty.util.thread.QueuedThreadPool.access$800(QueuedThreadPool.java:48)\norg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:626)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1503479572})", "holdingLocks" : [ ] }, { "threadId" : 38, "threadName" : "SparkUI-38", "threadState" : "RUNNABLE", "stackTrace" : "sun.nio.ch.KQueueArrayWrapper.kevent0(Native Method)\nsun.nio.ch.KQueueArrayWrapper.poll(KQueueArrayWrapper.java:198)\nsun.nio.ch.KQueueSelectorImpl.doSelect(KQueueSelectorImpl.java:117)\nsun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86) => holding Monitor(sun.nio.ch.KQueueSelectorImpl841741934})\nsun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)\nsun.nio.ch.SelectorImpl.select(SelectorImpl.java:101)\norg.spark_project.jetty.io.ManagedSelector$SelectorProducer.select(ManagedSelector.java:243)\norg.spark_project.jetty.io.ManagedSelector$SelectorProducer.produce(ManagedSelector.java:191)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.executeProduceConsume(ExecuteProduceConsume.java:249)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.produceConsume(ExecuteProduceConsume.java:148)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.run(ExecuteProduceConsume.java:136)\norg.spark_project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:671)\norg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:589)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "", "holdingLocks" : [ "Monitor(sun.nio.ch.Util$3873523986})", "Monitor(java.util.Collections$UnmodifiableSet1769333189})", "Monitor(sun.nio.ch.KQueueSelectorImpl841741934})" ] }, { "threadId" : 40, "threadName" : "SparkUI-40-acceptor-034929380-Spark3a557b62{HTTP/1.1,[http/1.1]}{0.0.0.0:4040}", "threadState" : "RUNNABLE", "stackTrace" : "sun.nio.ch.ServerSocketChannelImpl.accept0(Native Method)\nsun.nio.ch.ServerSocketChannelImpl.accept(ServerSocketChannelImpl.java:422)\nsun.nio.ch.ServerSocketChannelImpl.accept(ServerSocketChannelImpl.java:250) => holding Monitor(java.lang.Object1134240909})\norg.spark_project.jetty.server.ServerConnector.accept(ServerConnector.java:371)\norg.spark_project.jetty.server.AbstractConnector$Acceptor.run(AbstractConnector.java:601)\norg.spark_project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:671)\norg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:589)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "", "holdingLocks" : [ "Monitor(java.lang.Object1134240909})" ] }, { "threadId" : 43, "threadName" : "SparkUI-43", "threadState" : "RUNNABLE", "stackTrace" : "sun.management.ThreadImpl.dumpThreads0(Native Method)\nsun.management.ThreadImpl.dumpAllThreads(ThreadImpl.java:454)\norg.apache.spark.util.Utils$.getThreadDump(Utils.scala:2170)\norg.apache.spark.SparkContext.getExecutorThreadDump(SparkContext.scala:596)\norg.apache.spark.status.api.v1.AbstractApplicationResource$$anonfun$threadDump$1$$anonfun$apply$1.apply(OneApplicationResource.scala:66)\norg.apache.spark.status.api.v1.AbstractApplicationResource$$anonfun$threadDump$1$$anonfun$apply$1.apply(OneApplicationResource.scala:65)\nscala.Option.flatMap(Option.scala:171)\norg.apache.spark.status.api.v1.AbstractApplicationResource$$anonfun$threadDump$1.apply(OneApplicationResource.scala:65)\norg.apache.spark.status.api.v1.AbstractApplicationResource$$anonfun$threadDump$1.apply(OneApplicationResource.scala:58)\norg.apache.spark.status.api.v1.BaseAppResource$$anonfun$withUI$1.apply(ApiRootResource.scala:139)\norg.apache.spark.status.api.v1.BaseAppResource$$anonfun$withUI$1.apply(ApiRootResource.scala:134)\norg.apache.spark.ui.SparkUI.withSparkUI(SparkUI.scala:106)\norg.apache.spark.status.api.v1.BaseAppResource$class.withUI(ApiRootResource.scala:134)\norg.apache.spark.status.api.v1.AbstractApplicationResource.withUI(OneApplicationResource.scala:32)\norg.apache.spark.status.api.v1.AbstractApplicationResource.threadDump(OneApplicationResource.scala:58)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\norg.glassfish.jersey.server.model.internal.ResourceMethodInvocationHandlerFactory$1.invoke(ResourceMethodInvocationHandlerFactory.java:81)\norg.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher$1.run(AbstractJavaResourceMethodDispatcher.java:144)\norg.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher.invoke(AbstractJavaResourceMethodDispatcher.java:161)\norg.glassfish.jersey.server.model.internal.JavaResourceMethodDispatcherProvider$TypeOutInvoker.doDispatch(JavaResourceMethodDispatcherProvider.java:205)\norg.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher.dispatch(AbstractJavaResourceMethodDispatcher.java:99)\norg.glassfish.jersey.server.model.ResourceMethodInvoker.invoke(ResourceMethodInvoker.java:389)\norg.glassfish.jersey.server.model.ResourceMethodInvoker.apply(ResourceMethodInvoker.java:347)\norg.glassfish.jersey.server.model.ResourceMethodInvoker.apply(ResourceMethodInvoker.java:102)\norg.glassfish.jersey.server.ServerRuntime$2.run(ServerRuntime.java:326)\norg.glassfish.jersey.internal.Errors$1.call(Errors.java:271)\norg.glassfish.jersey.internal.Errors$1.call(Errors.java:267)\norg.glassfish.jersey.internal.Errors.process(Errors.java:315)\norg.glassfish.jersey.internal.Errors.process(Errors.java:297)\norg.glassfish.jersey.internal.Errors.process(Errors.java:267)\norg.glassfish.jersey.process.internal.RequestScope.runInScope(RequestScope.java:317)\norg.glassfish.jersey.server.ServerRuntime.process(ServerRuntime.java:305)\norg.glassfish.jersey.server.ApplicationHandler.handle(ApplicationHandler.java:1154)\norg.glassfish.jersey.servlet.WebComponent.serviceImpl(WebComponent.java:473)\norg.glassfish.jersey.servlet.WebComponent.service(WebComponent.java:427)\norg.glassfish.jersey.servlet.ServletContainer.service(ServletContainer.java:388)\norg.glassfish.jersey.servlet.ServletContainer.service(ServletContainer.java:341)\norg.glassfish.jersey.servlet.ServletContainer.service(ServletContainer.java:228)\norg.spark_project.jetty.servlet.ServletHolder.handle(ServletHolder.java:848)\norg.spark_project.jetty.servlet.ServletHandler.doHandle(ServletHandler.java:584)\norg.spark_project.jetty.server.handler.ContextHandler.doHandle(ContextHandler.java:1180)\norg.spark_project.jetty.servlet.ServletHandler.doScope(ServletHandler.java:512)\norg.spark_project.jetty.server.handler.ContextHandler.doScope(ContextHandler.java:1112)\norg.spark_project.jetty.server.handler.ScopedHandler.handle(ScopedHandler.java:141)\norg.spark_project.jetty.server.handler.gzip.GzipHandler.handle(GzipHandler.java:493)\norg.spark_project.jetty.server.handler.ContextHandlerCollection.handle(ContextHandlerCollection.java:213)\norg.spark_project.jetty.server.handler.HandlerWrapper.handle(HandlerWrapper.java:134)\norg.spark_project.jetty.server.Server.handle(Server.java:534)\norg.spark_project.jetty.server.HttpChannel.handle(HttpChannel.java:320)\norg.spark_project.jetty.server.HttpConnection.onFillable(HttpConnection.java:251)\norg.spark_project.jetty.io.AbstractConnection$ReadCallback.succeeded(AbstractConnection.java:283)\norg.spark_project.jetty.io.FillInterest.fillable(FillInterest.java:108)\norg.spark_project.jetty.io.SelectChannelEndPoint$2.run(SelectChannelEndPoint.java:93)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.executeProduceConsume(ExecuteProduceConsume.java:303)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.produceConsume(ExecuteProduceConsume.java:148)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.run(ExecuteProduceConsume.java:136)\norg.spark_project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:671)\norg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:589)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "", "holdingLocks" : [ ] }, { "threadId" : 67, "threadName" : "SparkUI-67", "threadState" : "RUNNABLE", "stackTrace" : "sun.nio.ch.KQueueArrayWrapper.kevent0(Native Method)\nsun.nio.ch.KQueueArrayWrapper.poll(KQueueArrayWrapper.java:198)\nsun.nio.ch.KQueueSelectorImpl.doSelect(KQueueSelectorImpl.java:117)\nsun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86) => holding Monitor(sun.nio.ch.KQueueSelectorImpl1837806480})\nsun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)\nsun.nio.ch.SelectorImpl.select(SelectorImpl.java:101)\norg.spark_project.jetty.io.ManagedSelector$SelectorProducer.select(ManagedSelector.java:243)\norg.spark_project.jetty.io.ManagedSelector$SelectorProducer.produce(ManagedSelector.java:191)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.executeProduceConsume(ExecuteProduceConsume.java:249)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.produceConsume(ExecuteProduceConsume.java:148)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.run(ExecuteProduceConsume.java:136)\norg.spark_project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:671)\norg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:589)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "", "holdingLocks" : [ "Monitor(sun.nio.ch.Util$3881415814})", "Monitor(java.util.Collections$UnmodifiableSet62050480})", "Monitor(sun.nio.ch.KQueueSelectorImpl1837806480})" ] }, { "threadId" : 68, "threadName" : "SparkUI-68", "threadState" : "RUNNABLE", "stackTrace" : "sun.nio.ch.KQueueArrayWrapper.kevent0(Native Method)\nsun.nio.ch.KQueueArrayWrapper.poll(KQueueArrayWrapper.java:198)\nsun.nio.ch.KQueueSelectorImpl.doSelect(KQueueSelectorImpl.java:117)\nsun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86) => holding Monitor(sun.nio.ch.KQueueSelectorImpl223607814})\nsun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)\nsun.nio.ch.SelectorImpl.select(SelectorImpl.java:101)\norg.spark_project.jetty.io.ManagedSelector$SelectorProducer.select(ManagedSelector.java:243)\norg.spark_project.jetty.io.ManagedSelector$SelectorProducer.produce(ManagedSelector.java:191)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.executeProduceConsume(ExecuteProduceConsume.java:249)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.produceConsume(ExecuteProduceConsume.java:148)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.run(ExecuteProduceConsume.java:136)\norg.spark_project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:671)\norg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:589)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "", "holdingLocks" : [ "Monitor(sun.nio.ch.Util$3543145185})", "Monitor(java.util.Collections$UnmodifiableSet897441546})", "Monitor(sun.nio.ch.KQueueSelectorImpl223607814})" ] }, { "threadId" : 71, "threadName" : "SparkUI-71", "threadState" : "TIMED_WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)\norg.spark_project.jetty.util.BlockingArrayQueue.poll(BlockingArrayQueue.java:392)\norg.spark_project.jetty.util.thread.QueuedThreadPool.idleJobPoll(QueuedThreadPool.java:563)\norg.spark_project.jetty.util.thread.QueuedThreadPool.access$800(QueuedThreadPool.java:48)\norg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:626)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1503479572})", "holdingLocks" : [ ] }, { "threadId" : 77, "threadName" : "SparkUI-77", "threadState" : "TIMED_WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)\norg.spark_project.jetty.util.BlockingArrayQueue.poll(BlockingArrayQueue.java:392)\norg.spark_project.jetty.util.thread.QueuedThreadPool.idleJobPoll(QueuedThreadPool.java:563)\norg.spark_project.jetty.util.thread.QueuedThreadPool.access$800(QueuedThreadPool.java:48)\norg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:626)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1503479572})", "holdingLocks" : [ ] }, { "threadId" : 78, "threadName" : "SparkUI-78", "threadState" : "RUNNABLE", "stackTrace" : "sun.nio.ch.KQueueArrayWrapper.kevent0(Native Method)\nsun.nio.ch.KQueueArrayWrapper.poll(KQueueArrayWrapper.java:198)\nsun.nio.ch.KQueueSelectorImpl.doSelect(KQueueSelectorImpl.java:117)\nsun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86) => holding Monitor(sun.nio.ch.KQueueSelectorImpl403077801})\nsun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)\nsun.nio.ch.SelectorImpl.select(SelectorImpl.java:101)\norg.spark_project.jetty.io.ManagedSelector$SelectorProducer.select(ManagedSelector.java:243)\norg.spark_project.jetty.io.ManagedSelector$SelectorProducer.produce(ManagedSelector.java:191)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.executeProduceConsume(ExecuteProduceConsume.java:249)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.produceConsume(ExecuteProduceConsume.java:148)\norg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.run(ExecuteProduceConsume.java:136)\norg.spark_project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:671)\norg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:589)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "", "holdingLocks" : [ "Monitor(sun.nio.ch.Util$3261312406})", "Monitor(java.util.Collections$UnmodifiableSet852901260})", "Monitor(sun.nio.ch.KQueueSelectorImpl403077801})" ] }, { "threadId" : 72, "threadName" : "SparkUI-JettyScheduler", "threadState" : "TIMED_WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)\njava.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1093)\njava.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)\njava.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject1587346642})", "holdingLocks" : [ ] }, { "threadId" : 63, "threadName" : "task-result-getter-0", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\njava.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject537563105})", "holdingLocks" : [ ] }, { "threadId" : 64, "threadName" : "task-result-getter-1", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\njava.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject537563105})", "holdingLocks" : [ ] }, { "threadId" : 65, "threadName" : "task-result-getter-2", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\njava.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject537563105})", "holdingLocks" : [ ] }, { "threadId" : 66, "threadName" : "task-result-getter-3", "threadState" : "WAITING", "stackTrace" : "sun.misc.Unsafe.park(Native Method)\njava.util.concurrent.locks.LockSupport.park(LockSupport.java:175)\njava.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2039)\njava.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)\njava.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1074)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1134)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)", "blockedByLock" : "Lock(java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject537563105})", "holdingLocks" : [ ] }, { "threadId" : 46, "threadName" : "Timer-0", "threadState" : "WAITING", "stackTrace" : "java.lang.Object.wait(Native Method)\njava.lang.Object.wait(Object.java:502)\njava.util.TimerThread.mainLoop(Timer.java:526)\njava.util.TimerThread.run(Timer.java:505)", "blockedByLock" : "Lock(java.util.TaskQueue635634547})", "holdingLocks" : [ ] } ] ``` </details> ## How was this patch tested? It was tested manually. Old executor page with thread dumps: <img width="1632" alt="screen shot 2018-02-01 at 14 31 19" src="https://user-images.githubusercontent.com/2017933/35682124-e2ec5d96-075f-11e8-9713-a502e12d05c2.png"> New api: <img width="1669" alt="screen shot 2018-02-01 at 14 31 56" src="https://user-images.githubusercontent.com/2017933/35682149-f75b80d6-075f-11e8-95b0-c75d048f0b04.png"> Testing error cases. Initial state:  Dead executor: ```bash $ curl -o - -s -w "\n%{http_code}\n" http://localhost:4040/api/v1/applications/app-20180206122543-0000/executors/1/threads Executor is not active. 400 ``` Never existed (but well formatted: number) executor ID: ```bash $ curl -o - -s -w "\n%{http_code}\n" http://localhost:4040/api/v1/applications/app-20180206122543-0000/executors/42/threads Executor does not exist. 404 ``` Not available stacktrace (dead executor but UI has not registered as dead yet): ```bash $ kill -9 <PID of CoarseGrainedExecutorBackend for executor 2> ; curl -o - -s -w "\n%{http_code}\n" http://localhost:4040/api/v1/applications/app-20180206122543-0000/executors/2/threads No thread dump is available. 404 ``` Invalid executor ID format: ```bash $ curl -o - -s -w "\n%{http_code}\n" http://localhost:4040/api/v1/applications/app-20180206122543-0000/executors/something6/threads Invalid executorId: neither 'driver' nor number. 400 ``` Author: “attilapiros” <piros.attila.zsolt@gmail.com> Closes #20474 from attilapiros/SPARK-23235. |
||
Shashwat Anand
|
84a076e0e9 |
[SPARK-23165][DOC] Spelling mistake fix in quick-start doc.
## What changes were proposed in this pull request? Fix spelling in quick-start doc. ## How was this patch tested? Doc only. Author: Shashwat Anand <me@shashwat.me> Closes #20336 from ashashwat/SPARK-23165. |
||
guoxiaolong
|
fe93c0bf61 |
[DOC] update the API doc and modify the stage API description
## What changes were proposed in this pull request? **1.stage api modify the description format** <td>A list of all stages for a given application.</td> <br><code>?status=[active|complete|pending|failed]</code> list only stages in the state. content should be included in <td> </ td> fix before:  fix after:  **2.add version api doc '/api/v1/version' in monitoring.md** fix after:  ## How was this patch tested? manual tests Please review http://spark.apache.org/contributing.html before opening a pull request. Author: guoxiaolong <guo.xiaolong1@zte.com.cn> Closes #19532 from guoxiaolongzte/SPARK-22311. |
||
|
Marcelo Vanzin
|
74daf622de |
[SPARK-20642][CORE] Store FsHistoryProvider listing data in a KVStore.
The application listing is still generated from event logs, but is now stored in a KVStore instance. By default an in-memory store is used, but a new config allows setting a local disk path to store the data, in which case a LevelDB store will be created. The provider stores things internally using the public REST API types; I believe this is better going forward since it will make it easier to get rid of the internal history server API which is mostly redundant at this point. I also added a finalizer to LevelDBIterator, to make sure that resources are eventually released. This helps when code iterates but does not exhaust the iterator, thus not triggering the auto-close code. HistoryServerSuite was modified to not re-start the history server unnecessarily; this makes the json validation tests run more quickly. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #18887 from vanzin/SPARK-20642. |
||
Xiaofeng Lin
|
cd5d0f3379 |
[SPARK-11574][CORE] Add metrics StatsD sink
This patch adds statsd sink to the current metrics system in spark core. Author: Xiaofeng Lin <xlin@twilio.com> Closes #9518 from xflin/statsd. Change-Id: Ib8720e86223d4a650df53f51ceb963cd95b49a44 |
||
pgandhi
|
24e6c187fb |
[SPARK-21798] No config to replace deprecated SPARK_CLASSPATH config for launching daemons like History Server
History Server Launch uses SparkClassCommandBuilder for launching the server. It is observed that SPARK_CLASSPATH has been removed and deprecated. For spark-submit this takes a different route and spark.driver.extraClasspath takes care of specifying additional jars in the classpath that were previously specified in the SPARK_CLASSPATH. Right now the only way specify the additional jars for launching daemons such as history server is using SPARK_DIST_CLASSPATH (https://spark.apache.org/docs/latest/hadoop-provided.html) but this I presume is a distribution classpath. It would be nice to have a similar config like spark.driver.extraClasspath for launching daemons similar to history server. Added new environment variable SPARK_DAEMON_CLASSPATH to set classpath for launching daemons. Tested and verified for History Server and Standalone Mode. ## How was this patch tested? Initially, history server start script would fail for the reason being that it could not find the required jars for launching the server in the java classpath. Same was true for running Master and Worker in standalone mode. By adding the environment variable SPARK_DAEMON_CLASSPATH to the java classpath, both the daemons(History Server, Standalone daemons) are starting up and running. Author: pgandhi <pgandhi@yahoo-inc.com> Author: pgandhi999 <parthkgandhi9@gmail.com> Closes #19047 from pgandhi999/master. |
||
Hervé
|
34767997e0 |
Small rewording about history server use case
Hello PR #10991 removed the built-in history view from Spark Standalone, so the history server is no longer useful to Yarn or Mesos only. Author: Hervé <dud225@users.noreply.github.com> Closes #17709 from dud225/patch-1. |
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郭小龙 10207633
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9e0893b53d |
[SPARK-20218][DOC][APP-ID] applications//stages' in REST API,add description.
## What changes were proposed in this pull request?
1. '/applications/[app-id]/stages' in rest api.status should add description '?status=[active|complete|pending|failed] list only stages in the state.'
Now the lack of this description, resulting in the use of this api do not know the use of the status through the brush stage list.
2.'/applications/[app-id]/stages/[stage-id]' in REST API,remove redundant description ‘?status=[active|complete|pending|failed] list only stages in the state.’.
Because only one stage is determined based on stage-id.
code:
GET
def stageList(QueryParam("status") statuses: JList[StageStatus]): Seq[StageData] = {
val listener = ui.jobProgressListener
val stageAndStatus = AllStagesResource.stagesAndStatus(ui)
val adjStatuses = {
if (statuses.isEmpty()) {
Arrays.asList(StageStatus.values(): _*)
} else {
statuses
}
};
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: 郭小龙 10207633 <guo.xiaolong1@zte.com.cn>
Closes #17534 from guoxiaolongzte/SPARK-20218.
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guoxiaolongzte
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c95fbea68e |
[SPARK-20190][APP-ID] applications//jobs' in rest api,status should be [running|s…
…ucceeded|failed|unknown]
## What changes were proposed in this pull request?
'/applications/[app-id]/jobs' in rest api.status should be'[running|succeeded|failed|unknown]'.
now status is '[complete|succeeded|failed]'.
but '/applications/[app-id]/jobs?status=complete' the server return 'HTTP ERROR 404'.
Added '?status=running' and '?status=unknown'.
code ：
public enum JobExecutionStatus {
RUNNING,
SUCCEEDED,
FAILED,
UNKNOWN;
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: guoxiaolongzte <guo.xiaolong1@zte.com.cn>
Closes #17507 from guoxiaolongzte/SPARK-20190.
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hyukjinkwon
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364b0db753 |
[MINOR][DOCS] Replace non-breaking space to normal spaces that breaks rendering markdown
# What changes were proposed in this pull request? It seems there are several non-breaking spaces were inserted into several `.md`s and they look breaking rendering markdown files. These are different. For example, this can be checked via `python` as below: ```python >>> " " '\xc2\xa0' >>> " " ' ' ``` _Note that it seems this PR description automatically replaces non-breaking spaces into normal spaces. Please open a `vi` and copy and paste it into `python` to verify this (do not copy the characters here)._ I checked the output below in Sapari and Chrome on Mac OS and, Internal Explorer on Windows 10. **Before**   **After**   ## How was this patch tested? Manually checking. These instances were found via ``` grep --include=*.scala --include=*.python --include=*.java --include=*.r --include=*.R --include=*.md --include=*.r -r -I " " . ``` in Mac OS. It seems there are several instances more as below: ``` ./docs/sql-programming-guide.md: │ ├── ... ./docs/sql-programming-guide.md: │ │ ./docs/sql-programming-guide.md: │ ├── country=US ./docs/sql-programming-guide.md: │ │ └── data.parquet ./docs/sql-programming-guide.md: │ ├── country=CN ./docs/sql-programming-guide.md: │ │ └── data.parquet ./docs/sql-programming-guide.md: │ └── ... ./docs/sql-programming-guide.md: ├── ... ./docs/sql-programming-guide.md: │ ./docs/sql-programming-guide.md: ├── country=US ./docs/sql-programming-guide.md: │ └── data.parquet ./docs/sql-programming-guide.md: ├── country=CN ./docs/sql-programming-guide.md: │ └── data.parquet ./docs/sql-programming-guide.md: └── ... ./sql/core/src/test/README.md:│ ├── *.avdl # Testing Avro IDL(s) ./sql/core/src/test/README.md:│ └── *.avpr # !! NO TOUCH !! Protocol files generated from Avro IDL(s) ./sql/core/src/test/README.md:│ ├── gen-avro.sh # Script used to generate Java code for Avro ./sql/core/src/test/README.md:│ └── gen-thrift.sh # Script used to generate Java code for Thrift ``` These seems generated via `tree` command which inserts non-breaking spaces. They do not look causing any problem for rendering within code blocks and I did not fix it to reduce the overhead to manually replace it when it is overwritten via `tree` command in the future. Author: hyukjinkwon <gurwls223@gmail.com> Closes #17517 from HyukjinKwon/non-breaking-space. |
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uncleGen
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d027624574 |
[SPARK-16122][DOCS] application environment rest api
## What changes were proposed in this pull request? follow up pr of #16949. ## How was this patch tested? jenkins Author: uncleGen <hustyugm@gmail.com> Closes #17033 from uncleGen/doc-restapi-environment. |
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Parag Chaudhari
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0ff67a1cf9 |
[SPARK-14049][CORE] Add functionality in spark history sever API to query applications by end time
## What changes were proposed in this pull request? Currently, spark history server REST API provides functionality to query applications by application start time range based on minDate and maxDate query parameters, but it lacks support to query applications by their end time. In this pull request we are proposing optional minEndDate and maxEndDate query parameters and filtering capability based on these parameters to spark history server REST API. This functionality can be used for following queries, 1. Applications finished in last 'x' minutes 2. Applications finished before 'y' time 3. Applications finished between 'x' time to 'y' time 4. Applications started from 'x' time and finished before 'y' time. For backward compatibility, we can keep existing minDate and maxDate query parameters as they are and they can continue support filtering based on start time range. ## How was this patch tested? Existing unit tests and 4 new unit tests. Author: Parag Chaudhari <paragpc@amazon.com> Closes #11867 from paragpc/master-SHS-query-by-endtime_2. |
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jerryshao
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4a4c3dc9ca |
[SPARK-19033][CORE] Add admin acls for history server
## What changes were proposed in this pull request? Current HistoryServer's ACLs is derived from application event-log, which means the newly changed ACLs cannot be applied to the old data, this will become a problem where newly added admin cannot access the old application history UI, only the new application can be affected. So here propose to add admin ACLs for history server, any configured user/group could have the view access to all the applications, while the view ACLs derived from application run-time still take effect. ## How was this patch tested? Unit test added. Author: jerryshao <sshao@hortonworks.com> Closes #16470 from jerryshao/SPARK-19033. |
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uncleGen
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6873430cb5 |
[SPARK-19009][DOC] Add streaming rest api doc
## What changes were proposed in this pull request? add streaming rest api doc related to pr #16253 cc saturday-shi srowen ## How was this patch tested? Author: uncleGen <hustyugm@gmail.com> Closes #16414 from uncleGen/SPARK-19009. |
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Jacek Laskowski
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b162cc0c28
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[MINOR][CORE][SQL][DOCS] Typo fixes
## What changes were proposed in this pull request? Typo fixes ## How was this patch tested? Local build. Awaiting the official build. Author: Jacek Laskowski <jacek@japila.pl> Closes #16144 from jaceklaskowski/typo-fixes. |
