[jira] [Commented] (MAPREDUCE-7435) ManifestCommitter OOM on azure job

["ASF GitHub Bot (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MAPREDUCE-7435?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17728411#comment-17728411 ] 

ASF GitHub Bot commented on MAPREDUCE-7435:
-------------------------------------------

steveloughran commented on code in PR #5519:
URL: https://github.com/apache/hadoop/pull/5519#discussion_r1213371187


##########
hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/lib/output/committer/manifest/impl/ManifestCommitterSupport.java:
##########
@@ -224,6 +231,23 @@ public static ManifestSuccessData createManifestOutcome(
     return outcome;
   }
 
+  /**
+   * Add heap information to IOStatisticSetters gauges, with a stage in front of every key.
+   * @param ioStatisticsSetters map to update
+   * @param stage stage
+   */
+  public static void addHeapInformation(IOStatisticsSetters ioStatisticsSetters,
+      String stage) {
+    // force a gc. bit of bad form but it makes for better numbers
+    System.gc();

Review Comment:
   GC pulled out of production code & only invoked in test code





> ManifestCommitter OOM on azure job
> ----------------------------------
>
>                 Key: MAPREDUCE-7435
>                 URL: https://issues.apache.org/jira/browse/MAPREDUCE-7435
>             Project: Hadoop Map/Reduce
>          Issue Type: Bug
>          Components: client
>    Affects Versions: 3.3.5
>            Reporter: Steve Loughran
>            Assignee: Steve Loughran
>            Priority: Major
>              Labels: pull-request-available
>
> I've got some reports of spark jobs OOM if the manifest committer is used through abfs.
> either the manifests are using too much memory, or something is not working with azure stream memory use (or both).
> before proposing a solution, first step should be to write a test to load many, many manifests, each with lots of dirs and files to see what breaks.
> note: we did have OOM issues with the s3a committer, on teragen but those structures have to include every etag of every block, so the manifest size is O(blocks); the new committer is O(files + dirs).
> {code}
> java.lang.OutOfMemoryError: Java heap space
> at org.apache.hadoop.fs.azurebfs.services.AbfsInputStream.readOneBlock(AbfsInputStream.java:314)
> at org.apache.hadoop.fs.azurebfs.services.AbfsInputStream.read(AbfsInputStream.java:267)
> at java.io.DataInputStream.read(DataInputStream.java:149)
> at com.fasterxml.jackson.core.json.ByteSourceJsonBootstrapper.ensureLoaded(ByteSourceJsonBootstrapper.java:539)
> at com.fasterxml.jackson.core.json.ByteSourceJsonBootstrapper.detectEncoding(ByteSourceJsonBootstrapper.java:133)
> at com.fasterxml.jackson.core.json.ByteSourceJsonBootstrapper.constructParser(ByteSourceJsonBootstrapper.java:256)
> at com.fasterxml.jackson.core.JsonFactory._createParser(JsonFactory.java:1656)
> at com.fasterxml.jackson.core.JsonFactory.createParser(JsonFactory.java:1085)
> at com.fasterxml.jackson.databind.ObjectMapper.readValue(ObjectMapper.java:3585)
> at org.apache.hadoop.util.JsonSerialization.fromJsonStream(JsonSerialization.java:164)
> at org.apache.hadoop.util.JsonSerialization.load(JsonSerialization.java:279)
> at org.apache.hadoop.mapreduce.lib.output.committer.manifest.files.TaskManifest.load(TaskManifest.java:361)
> at org.apache.hadoop.mapreduce.lib.output.committer.manifest.impl.ManifestStoreOperationsThroughFileSystem.loadTaskManifest(ManifestStoreOperationsThroughFileSystem.java:133)
> at org.apache.hadoop.mapreduce.lib.output.committer.manifest.stages.AbstractJobOrTaskStage.lambda$loadManifest$6(AbstractJobOrTaskStage.java:493)
> at org.apache.hadoop.mapreduce.lib.output.committer.manifest.stages.AbstractJobOrTaskStage$$Lambda$231/1813048085.apply(Unknown Source)
> at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.invokeTrackingDuration(IOStatisticsBinding.java:543)
> at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.lambda$trackDurationOfOperation$5(IOStatisticsBinding.java:524)
> at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding$$Lambda$217/489150849.apply(Unknown Source)
> at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.trackDuration(IOStatisticsBinding.java:445)
> at org.apache.hadoop.mapreduce.lib.output.committer.manifest.stages.AbstractJobOrTaskStage.loadManifest(AbstractJobOrTaskStage.java:492)
> at org.apache.hadoop.mapreduce.lib.output.committer.manifest.stages.LoadManifestsStage.fetchTaskManifest(LoadManifestsStage.java:170)
> at org.apache.hadoop.mapreduce.lib.output.committer.manifest.stages.LoadManifestsStage.processOneManifest(LoadManifestsStage.java:138)
> at org.apache.hadoop.mapreduce.lib.output.committer.manifest.stages.LoadManifestsStage$$Lambda$229/137752948.run(Unknown Source)
> at org.apache.hadoop.util.functional.TaskPool$Builder.lambda$runParallel$0(TaskPool.java:410)
> at org.apache.hadoop.util.functional.TaskPool$Builder$$Lambda$230/467893357.run(Unknown Source)
> at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
> at java.util.concurrent.FutureTask.run(FutureTask.java:266)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:750)
> {code}



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