class StageInfoRecorderListener extends SparkListener
StageInfoRecorderListener: this listener gathers metrics with Stage execution granularity It is based on the Spark Listener interface Stage metrics are stored in memory and use to produce a report that aggregates resource consumption they can also be consumed "raw" (transformed into a DataFrame and/or saved to a file) See StageMetrics
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- new StageInfoRecorderListener()
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- val StageIdtoJobGroup: HashMap[Int, String]
- val StageIdtoJobId: HashMap[Int, Int]
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def
onApplicationEnd(applicationEnd: SparkListenerApplicationEnd): Unit
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onBlockManagerAdded(blockManagerAdded: SparkListenerBlockManagerAdded): Unit
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onBlockManagerRemoved(blockManagerRemoved: SparkListenerBlockManagerRemoved): Unit
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onBlockUpdated(blockUpdated: SparkListenerBlockUpdated): Unit
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onEnvironmentUpdate(environmentUpdate: SparkListenerEnvironmentUpdate): Unit
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onExecutorAdded(executorAdded: SparkListenerExecutorAdded): Unit
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onExecutorBlacklisted(executorBlacklisted: SparkListenerExecutorBlacklisted): Unit
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onExecutorBlacklistedForStage(executorBlacklistedForStage: SparkListenerExecutorBlacklistedForStage): Unit
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onExecutorMetricsUpdate(executorMetricsUpdate: SparkListenerExecutorMetricsUpdate): Unit
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onExecutorRemoved(executorRemoved: SparkListenerExecutorRemoved): Unit
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onExecutorUnblacklisted(executorUnblacklisted: SparkListenerExecutorUnblacklisted): Unit
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onJobEnd(jobEnd: SparkListenerJobEnd): Unit
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onJobStart(jobStart: SparkListenerJobStart): Unit
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- StageInfoRecorderListener → SparkListener → SparkListenerInterface
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def
onNodeBlacklisted(nodeBlacklisted: SparkListenerNodeBlacklisted): Unit
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onNodeBlacklistedForStage(nodeBlacklistedForStage: SparkListenerNodeBlacklistedForStage): Unit
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onNodeUnblacklisted(nodeUnblacklisted: SparkListenerNodeUnblacklisted): Unit
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onOtherEvent(event: SparkListenerEvent): Unit
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def
onSpeculativeTaskSubmitted(speculativeTask: SparkListenerSpeculativeTaskSubmitted): Unit
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onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit
This methods fires at the end of the stage and collects metrics flattened into the stageMetricsData ListBuffer Note all reported times are in ms, cpu time and shuffle write time are originally in nanoseconds, thus in the code are divided by 1 million to normalize them to milliseconds
This methods fires at the end of the stage and collects metrics flattened into the stageMetricsData ListBuffer Note all reported times are in ms, cpu time and shuffle write time are originally in nanoseconds, thus in the code are divided by 1 million to normalize them to milliseconds
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- StageInfoRecorderListener → SparkListener → SparkListenerInterface
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def
onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted): Unit
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onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit
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onTaskGettingResult(taskGettingResult: SparkListenerTaskGettingResult): Unit
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onTaskStart(taskStart: SparkListenerTaskStart): Unit
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def
onUnpersistRDD(unpersistRDD: SparkListenerUnpersistRDD): Unit
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- val stageMetricsData: ListBuffer[StageVals]
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