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https://issues.apache.org/jira/browse/SPARK-34790?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17304651#comment-17304651
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hezuojiao commented on SPARK-34790:
-----------------------------------

Thanks for reply, I posted the failure message above in more detail. please 
check it.

> Fail in fetch shuffle blocks in batch when i/o encryption is enabled.
> ---------------------------------------------------------------------
>
>                 Key: SPARK-34790
>                 URL: https://issues.apache.org/jira/browse/SPARK-34790
>             Project: Spark
>          Issue Type: Sub-task
>          Components: Spark Core
>    Affects Versions: 3.1.1
>            Reporter: hezuojiao
>            Priority: Major
>
> When set spark.io.encryption.enabled=true, lots of test cases in 
> AdaptiveQueryExecSuite will be failed. Fetching shuffle blocks in batch is 
> incompatible with io encryption.
> For example:
> After set spark.io.encryption.enabled=true, run the following test suite 
> which in AdaptiveQueryExecSuite:
>  
> {code:java}
>   test("SPARK-33494: Do not use local shuffle reader for repartition") {
>     withSQLConf(SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> "true") {
>       val df = spark.table("testData").repartition('key)
>       df.collect()
>       // local shuffle reader breaks partitioning and shouldn't be used for 
> repartition operation
>       // which is specified by users.
>       checkNumLocalShuffleReaders(df.queryExecution.executedPlan, 
> numShufflesWithoutLocalReader = 1)
>     }
>   }
> {code}
>  
> I got the following error message:
> {code:java}
> 14:05:52.638 WARN org.apache.spark.scheduler.TaskSetManager: Lost task 1.0 in 
> stage 2.0 (TID 3) (11.240.37.88 executor driver): 
> FetchFailed(BlockManagerId(driver, 11.240.37.88, 63574, None), shuffleId=0, 
> mapIndex=0, mapId=0, reduceId=2, message=14:05:52.638 WARN 
> org.apache.spark.scheduler.TaskSetManager: Lost task 1.0 in stage 2.0 (TID 3) 
> (11.240.37.88 executor driver): FetchFailed(BlockManagerId(driver, 
> 11.240.37.88, 63574, None), shuffleId=0, mapIndex=0, mapId=0, reduceId=2, 
> message=org.apache.spark.shuffle.FetchFailedException: Stream is corrupted at 
> org.apache.spark.storage.ShuffleBlockFetcherIterator.throwFetchFailedException(ShuffleBlockFetcherIterator.scala:772)
>  at 
> org.apache.spark.storage.BufferReleasingInputStream.read(ShuffleBlockFetcherIterator.scala:845)
>  at java.io.BufferedInputStream.fill(BufferedInputStream.java:246) at 
> java.io.BufferedInputStream.read(BufferedInputStream.java:265) at 
> java.io.DataInputStream.readInt(DataInputStream.java:387) at 
> org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$2$$anon$3.readSize(UnsafeRowSerializer.scala:113)
>  at 
> org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$2$$anon$3.next(UnsafeRowSerializer.scala:129)
>  at 
> org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$2$$anon$3.next(UnsafeRowSerializer.scala:110)
>  at scala.collection.Iterator$$anon$11.next(Iterator.scala:494) at 
> scala.collection.Iterator$$anon$10.next(Iterator.scala:459) at 
> org.apache.spark.util.CompletionIterator.next(CompletionIterator.scala:29) at 
> org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:40) 
> at scala.collection.Iterator$$anon$10.next(Iterator.scala:459) at 
> org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:345)
>  at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898) 
> at 
> org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
>  at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) 
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373) at 
> org.apache.spark.rdd.RDD.iterator(RDD.scala:337) at 
> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at 
> org.apache.spark.scheduler.Task.run(Task.scala:131) at 
> org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:498)
>  at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1437) at 
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:501) at 
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
>  at 
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
>  at java.lang.Thread.run(Thread.java:748)Caused by: java.io.IOException: 
> Stream is corrupted at 
> net.jpountz.lz4.LZ4BlockInputStream.refill(LZ4BlockInputStream.java:200) at 
> net.jpountz.lz4.LZ4BlockInputStream.refill(LZ4BlockInputStream.java:226) at 
> net.jpountz.lz4.LZ4BlockInputStream.read(LZ4BlockInputStream.java:157) at 
> org.apache.spark.storage.BufferReleasingInputStream.read(ShuffleBlockFetcherIterator.scala:841)
>  ... 25 more
> )
> {code}
>  
>  



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