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https://issues.apache.org/jira/browse/MAPREDUCE-7435?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17714198#comment-17714198
]
ASF GitHub Bot commented on MAPREDUCE-7435:
-------------------------------------------
steveloughran commented on PR #5519:
URL: https://github.com/apache/hadoop/pull/5519#issuecomment-1515035989
tested azure cardiff, which is a slow test run today (no network). Move the
LoadManifests test into the parallel bit of the test run
```
----
[INFO] Tests run: 2, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 5.937
s - in org.apache.hadoop.fs.azurebfs.services.ITestReadBufferManager
[INFO] Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed:
67.344 s - in org.apache.hadoop.fs.azurebfs.commit.ITestAbfsLoadManifestsStage
[INFO] Tests run: 7, Failures: 0, Errors: 0, Skipped: 0, Time elapsed:
137.539 s - in org.apache.hadoop.fs.azurebfs.commit.ITestAbfsTerasort
[INFO] Tests run: 8, Failures: 0, Errors: 0, Skipped: 0, Time elapsed:
90.628 s - in org.apache.hadoop.fs.azurebfs.ITestAzureBlobFileSystemListStatus
[INFO] Tests run: 13, Failures: 0, Errors: 0, Skipped: 0, Time elapsed:
140.388 s - in
org.apache.hadoop.fs.azurebfs.contract.ITestAbfsFileSystemContractDistCp
----
> 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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