[
https://issues.apache.org/jira/browse/MAPREDUCE-7508?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=18015923#comment-18015923
]
ASF GitHub Bot commented on MAPREDUCE-7508:
-------------------------------------------
hadoop-yetus commented on PR #7859:
URL: https://github.com/apache/hadoop/pull/7859#issuecomment-3219428761
:broken_heart: **-1 overall**
| Vote | Subsystem | Runtime | Logfile | Comment |
|:----:|----------:|--------:|:--------:|:-------:|
| +0 :ok: | reexec | 21m 18s | | Docker mode activated. |
|||| _ Prechecks _ |
| +1 :green_heart: | dupname | 0m 0s | | No case conflicting files
found. |
| +0 :ok: | codespell | 0m 0s | | codespell was not available. |
| +0 :ok: | detsecrets | 0m 0s | | detect-secrets was not available.
|
| +1 :green_heart: | @author | 0m 0s | | The patch does not contain
any @author tags. |
| -1 :x: | test4tests | 0m 0s | | The patch doesn't appear to include
any new or modified tests. Please justify why no new tests are needed for this
patch. Also please list what manual steps were performed to verify this patch.
|
|||| _ trunk Compile Tests _ |
| +1 :green_heart: | mvninstall | 43m 31s | | trunk passed |
| +1 :green_heart: | compile | 0m 44s | | trunk passed with JDK
Ubuntu-11.0.27+6-post-Ubuntu-0ubuntu120.04 |
| +1 :green_heart: | compile | 0m 38s | | trunk passed with JDK
Private Build-1.8.0_452-8u452-ga~us1-0ubuntu1~20.04-b09 |
| +1 :green_heart: | checkstyle | 0m 44s | | trunk passed |
| +1 :green_heart: | mvnsite | 0m 44s | | trunk passed |
| +1 :green_heart: | javadoc | 0m 34s | | trunk passed with JDK
Ubuntu-11.0.27+6-post-Ubuntu-0ubuntu120.04 |
| +1 :green_heart: | javadoc | 0m 28s | | trunk passed with JDK
Private Build-1.8.0_452-8u452-ga~us1-0ubuntu1~20.04-b09 |
| +1 :green_heart: | spotbugs | 1m 24s | | trunk passed |
| +1 :green_heart: | shadedclient | 41m 59s | | branch has no errors
when building and testing our client artifacts. |
|||| _ Patch Compile Tests _ |
| +1 :green_heart: | mvninstall | 0m 31s | | the patch passed |
| +1 :green_heart: | compile | 0m 35s | | the patch passed with JDK
Ubuntu-11.0.27+6-post-Ubuntu-0ubuntu120.04 |
| +1 :green_heart: | javac | 0m 35s | | the patch passed |
| +1 :green_heart: | compile | 0m 31s | | the patch passed with JDK
Private Build-1.8.0_452-8u452-ga~us1-0ubuntu1~20.04-b09 |
| +1 :green_heart: | javac | 0m 31s | | the patch passed |
| +1 :green_heart: | blanks | 0m 0s | | The patch has no blanks
issues. |
| -0 :warning: | checkstyle | 0m 31s |
[/results-checkstyle-hadoop-mapreduce-project_hadoop-mapreduce-client_hadoop-mapreduce-client-core.txt](https://ci-hadoop.apache.org/job/hadoop-multibranch/job/PR-7859/3/artifact/out/results-checkstyle-hadoop-mapreduce-project_hadoop-mapreduce-client_hadoop-mapreduce-client-core.txt)
|
hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core:
The patch generated 3 new + 73 unchanged - 3 fixed = 76 total (was 76) |
| +1 :green_heart: | mvnsite | 0m 35s | | the patch passed |
| +1 :green_heart: | javadoc | 0m 21s | | the patch passed with JDK
Ubuntu-11.0.27+6-post-Ubuntu-0ubuntu120.04 |
| +1 :green_heart: | javadoc | 0m 20s | | the patch passed with JDK
Private Build-1.8.0_452-8u452-ga~us1-0ubuntu1~20.04-b09 |
| +1 :green_heart: | spotbugs | 1m 22s | | the patch passed |
| +1 :green_heart: | shadedclient | 41m 0s | | patch has no errors
when building and testing our client artifacts. |
|||| _ Other Tests _ |
| +1 :green_heart: | unit | 8m 53s | | hadoop-mapreduce-client-core in
the patch passed. |
| +1 :green_heart: | asflicense | 0m 38s | | The patch does not
generate ASF License warnings. |
| | | 168m 18s | | |
| Subsystem | Report/Notes |
|----------:|:-------------|
| Docker | ClientAPI=1.51 ServerAPI=1.51 base:
https://ci-hadoop.apache.org/job/hadoop-multibranch/job/PR-7859/3/artifact/out/Dockerfile
|
| GITHUB PR | https://github.com/apache/hadoop/pull/7859 |
| Optional Tests | dupname asflicense compile javac javadoc mvninstall
mvnsite unit shadedclient spotbugs checkstyle codespell detsecrets |
| uname | Linux db1e18bd8a74 5.15.0-144-generic #157-Ubuntu SMP Mon Jun 16
07:33:10 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux |
| Build tool | maven |
| Personality | dev-support/bin/hadoop.sh |
| git revision | trunk / 75ca4e18cd5c83d3df163b0abc229d86cb0f9de0 |
| Default Java | Private Build-1.8.0_452-8u452-ga~us1-0ubuntu1~20.04-b09 |
| Multi-JDK versions |
/usr/lib/jvm/java-11-openjdk-amd64:Ubuntu-11.0.27+6-post-Ubuntu-0ubuntu120.04
/usr/lib/jvm/java-8-openjdk-amd64:Private
Build-1.8.0_452-8u452-ga~us1-0ubuntu1~20.04-b09 |
| Test Results |
https://ci-hadoop.apache.org/job/hadoop-multibranch/job/PR-7859/3/testReport/ |
| Max. process+thread count | 1076 (vs. ulimit of 5500) |
| modules | C:
hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core
U:
hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core |
| Console output |
https://ci-hadoop.apache.org/job/hadoop-multibranch/job/PR-7859/3/console |
| versions | git=2.25.1 maven=3.6.3 spotbugs=4.2.2 |
| Powered by | Apache Yetus 0.14.0 https://yetus.apache.org |
This message was automatically generated.
> FileInputFormat can throw ArrayIndexOutofBoundsException because of some
> concurrent execution.
> ----------------------------------------------------------------------------------------------
>
> Key: MAPREDUCE-7508
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-7508
> Project: Hadoop Map/Reduce
> Issue Type: Bug
> Components: mapreduce-client
> Reporter: liang yu
> Priority: Major
> Labels: pull-request-available
>
> Using Spark Streaming (version 2.4.0), Hadoop Mapreduce-client (version 2.6.5)
> {*}Scenario{*}:
> I am using Spark Streaming to process files stored in HDFS. In my setup, the
> upstream system sometimes starts two identical tasks that attempt to create
> and write to the same HDFS file simultaneously. This can lead to conflicts
> where a file is created and written to twice in quick succession.
> {*}Problem{*}:
> When Spark scans for files, it uses the FileInputFormat.getSplits() method to
> split the file. The first step in getSplits is to retrieve the file's length.
> If the file length is not zero, the next step is to get the block locations
> array for that file. However, if the two upstream programs rapidly create and
> write to the same file (i.e., the file is overwritten or appended to almost
> simultaneously), a race condition may occur:
> The file's length is already non-zero,
> but calling getFileBlockLocations() returns an empty array because the file
> is being overwritten or is not yet fully written.
> When this happens, subsequent logic in getSplits (such as accessing the last
> element of the block locations array) will throw an
> ArrayIndexOutOfBoundsException because the block locations array is
> unexpectedly empty.
> {*}Summary{*}:
> This issue can occur when multiple upstream writers operate on the same HDFS
> file nearly simultaneously. As a result, Spark jobs may intermittently fail
> due to an unhandled empty block locations array in
> FileInputFormat.getSplits() when processing files that are in the process of
> being overwritten or not yet fully written.
>
> *Exception Stacktrace:*
> {code:java}
> [ERROR] 2025-08-06 15:22:02 org.apache.spark.deploy.yarn.ApplicationMaster:91
> - User class threw exception: java.lang.ArrayIndexOutOfBoundsException: 0
> java.lang.ArrayIndexOutOfBoundsException: 0 at
> org.apache.hadoop.mapreduce.lib.input.FileInputFormat.getSplits(FileInputFormat.java:449)
> at org.apache.spark.rdd.NewHadoopRDD.getPartitions(NewHadoopRDD.scala:130)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at
> org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at
> scala.Option.getOrElse(Option.scala:121) at
> org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at
> org.apache.spark.streaming.dstream.MyFileInputDStream.org$apache$spark$streaming$dstream$MyFileInputDStream$$filesToRDD(MyFileInputDStream.scala:350)
> at
> org.apache.spark.streaming.dstream.MyFileInputDStream.compute(MyFileInputDStream.scala:155)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:342)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:342)
> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58) at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:341)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:341)
> at
> org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:416)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:336)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:334)
> at scala.Option.orElse(Option.scala:289) at
> org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:331) at
> org.apache.spark.streaming.dstream.MappedDStream.compute(MappedDStream.scala:36)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:342)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:342)
> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58) at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:341)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:341)
> at
> org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:416)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:336)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:334)
> at scala.Option.orElse(Option.scala:289) at
> org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:331) at
> org.apache.spark.streaming.dstream.FilteredDStream.compute(FilteredDStream.scala:36)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:342)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:342)
> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58) at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:341)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:341)
> at
> org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:416)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:336)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:334)
> at scala.Option.orElse(Option.scala:289) at
> org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:331) at
> org.apache.spark.streaming.dstream.ForEachDStream.generateJob(ForEachDStream.scala:48)
> at
> org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:122)
> at
> org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:121)
> at
> scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
> at
> scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at
> scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241) at
> scala.collection.AbstractTraversable.flatMap(Traversable.scala:104) at
> org.apache.spark.streaming.DStreamGraph.generateJobs(DStreamGraph.scala:121)
> at
> org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:249)
> at
> org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:247)
> at scala.util.Try$.apply(Try.scala:192) at
> org.apache.spark.streaming.scheduler.JobGenerator.generateJobs(JobGenerator.scala:247)
> at
> org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:183)
> at
> org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:89)
> at
> org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:88)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) [WARN]
> 2025-08-06 15:22:32 org.apache.hadoop.util.ShutdownHookManager:128 -
> ShutdownHook '$anon$2' timeout, java.util.concurrent.TimeoutException
> java.util.concurrent.TimeoutException at
> java.util.concurrent.FutureTask.get(FutureTask.java:205) at
> org.apache.hadoop.util.ShutdownHookManager.executeShutdown(ShutdownHookManager.java:124)
> at
> org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:95)
> [ERROR] 2025-08-06 15:22:32 org.apache.spark.util.Utils:91 - Uncaught
> exception in thread shutdown-hook-0 java.lang.InterruptedException at
> java.lang.Object.wait(Native Method) at
> java.lang.Thread.join(Thread.java:1252) at
> java.lang.Thread.join(Thread.java:1326) at
> org.apache.spark.streaming.util.RecurringTimer.stop(RecurringTimer.scala:86)
> at
> org.apache.spark.streaming.scheduler.JobGenerator.stop(JobGenerator.scala:137)
> at
> org.apache.spark.streaming.scheduler.JobScheduler.stop(JobScheduler.scala:123)
> at
> org.apache.spark.streaming.StreamingContext$$anonfun$stop$1.apply$mcV$sp(StreamingContext.scala:681)
> at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1340) at
> org.apache.spark.streaming.StreamingContext.stop(StreamingContext.scala:680)
> at
> org.apache.spark.streaming.StreamingContext.org$apache$spark$streaming$StreamingContext$$stopOnShutdown(StreamingContext.scala:714)
> at
> org.apache.spark.streaming.StreamingContext$$anonfun$start$1.apply$mcV$sp(StreamingContext.scala:599)
> at
> org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:216) at
> org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ShutdownHookManager.scala:188)
> at
> org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188)
> at
> org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188)
> at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1945) at
> org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply$mcV$sp(ShutdownHookManager.scala:188)
> at
> org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188)
> at
> org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188)
> at scala.util.Try$.apply(Try.scala:192) at
> org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)
> at
> org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178)
> at java.util.concurrent.Executors$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:748){code}
>
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