[SPARK-33841][CORE] Fix issue with jobs disappearing intermittently from the SHS under high load
### What changes were proposed in this pull request? Mark SHS event log entries that were `processing` at the beginning of the `checkForLogs` run as not stale and check for this mark before deleting an event log. This fixes the issue when a particular job was displayed in the SHS and disappeared after some time, but then, in several minutes showed up again. ### Why are the changes needed? The issue is caused by [SPARK-29043](https://issues.apache.org/jira/browse/SPARK-29043), which is designated to improve the concurrent performance of the History Server. The [change](https://github.com/apache/spark/pull/25797/files#) breaks the ["app deletion" logic](https://github.com/apache/spark/pull/25797/files#diff-128a6af0d78f4a6180774faedb335d6168dfc4defff58f5aa3021fc1bd767bc0R563) because of missing proper synchronization for `processing` event log entries. Since SHS now [filters out](https://github.com/apache/spark/pull/25797/files#diff-128a6af0d78f4a6180774faedb335d6168dfc4defff58f5aa3021fc1bd767bc0R462) all `processing` event log entries, such entries do not have a chance to be [updated with the new `lastProcessed`](https://github.com/apache/spark/pull/25797/files#diff-128a6af0d78f4a6180774faedb335d6168dfc4defff58f5aa3021fc1bd767bc0R472) time and thus any entity that completes processing right after [filtering](https://github.com/apache/spark/pull/25797/files#diff-128a6af0d78f4a6180774faedb335d6168dfc4defff58f5aa3021fc1bd767bc0R462) and before [the check for stale entities](https://github.com/apache/spark/pull/25797/files#diff-128a6af0d78f4a6180774faedb335d6168dfc4defff58f5aa3021fc1bd767bc0R560) will be identified as stale and will be deleted from the UI until the next `checkForLogs` run. This is because [updated `lastProcessed` time is used as criteria](https://github.com/apache/spark/pull/25797/files#diff-128a6af0d78f4a6180774faedb335d6168dfc4defff58f5aa3021fc1bd767bc0R557), and event log entries that missed to be updated with a new time, will match that criteria. The issue can be reproduced by generating a big number of event logs and uploading them to the SHS event log directory on S3. Essentially, around 236(26.7 MB) copies of an event log directory were created using [shs-monitor](https://github.com/vladhlinsky/shs-monitor/tree/spark-master) script. Strange behavior of SHS counting the total number of applications was noticed - at first, the number was increasing as expected, but with the next page refresh, the total number of applications decreased. No errors were logged by SHS. 58 entities are displayed at `17:35:35`: ![1-58-entries-at-17-35](https://user-images.githubusercontent.com/61428392/102648949-1129e400-4171-11eb-9463-ed1454a8f6b2.png) 25 entities are displayed at `17:36:40`: ![2-25-entries-at-17-36](https://user-images.githubusercontent.com/61428392/102648974-1c7d0f80-4171-11eb-95d8-78c2bb37a168.png) ### Does this PR introduce _any_ user-facing change? Yes, SHS users won't face the behavior when the number of displayed applications decreases periodically. ### How was this patch tested? Tested using [shs-monitor](https://github.com/vladhlinsky/shs-monitor/tree/spark-master) script: * Build SHS with the proposed change * Download Hadoop AWS and AWS Java SDK * Prepare S3 bucket and user for programmatic access, grant required roles to the user. Get access key and secret key * Configure SHS to read event logs from S3 * Start [monitor](https://github.com/vladhlinsky/shs-monitor/blob/spark-master/monitor.sh) script to query SHS API * Run 5 [producers](https://github.com/vladhlinsky/shs-monitor/blob/spark-master/producer.sh) for ~5 mins, create 125(14.2 MB) event log directory copies * Wait for SHS to load all the applications * Verify that the number of loaded applications increases continuously over time For more details, please refer to the [shs-monitor](https://github.com/vladhlinsky/shs-monitor/tree/spark-master) repository. > This version of the reproduction uses event log directories instead of single files, since recent optimization > [SPARK-33790](https://issues.apache.org/jira/browse/SPARK-33790) makes it hard to reproduce the issue with single event log files. Closes #30845 from vladhlinsky/SPARK-33841. Authored-by: Vlad Glinsky <vladhlinsky@gmail.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
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@ -474,9 +474,21 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
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val newLastScanTime = clock.getTimeMillis()
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logDebug(s"Scanning $logDir with lastScanTime==$lastScanTime")
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// Mark entries that are processing as not stale. Such entries do not have a chance to be
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// updated with the new 'lastProcessed' time and thus any entity that completes processing
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// right after this check and before the check for stale entities will be identified as stale
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// and will be deleted from the UI until the next 'checkForLogs' run.
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val notStale = mutable.HashSet[String]()
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val updated = Option(fs.listStatus(new Path(logDir))).map(_.toSeq).getOrElse(Nil)
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.filter { entry => isAccessible(entry.getPath) }
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.filter { entry => !isProcessing(entry.getPath) }
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.filter { entry =>
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if (isProcessing(entry.getPath)) {
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notStale.add(entry.getPath.toString())
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false
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} else {
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true
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}
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}
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.flatMap { entry => EventLogFileReader(fs, entry) }
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.filter { reader =>
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try {
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@ -576,12 +588,14 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
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.last(newLastScanTime - 1)
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.asScala
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.toList
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stale.filterNot(isProcessing).foreach { log =>
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log.appId.foreach { appId =>
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cleanAppData(appId, log.attemptId, log.logPath)
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listing.delete(classOf[LogInfo], log.logPath)
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stale.filterNot(isProcessing)
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.filterNot(info => notStale.contains(info.logPath))
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.foreach { log =>
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log.appId.foreach { appId =>
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cleanAppData(appId, log.attemptId, log.logPath)
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listing.delete(classOf[LogInfo], log.logPath)
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}
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}
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}
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lastScanTime.set(newLastScanTime)
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} catch {
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