[
https://issues.apache.org/jira/browse/SPARK-34790?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
hezuojiao updated SPARK-34790:
------------------------------
Description:
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}
was:
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.
> 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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