Attila Zsolt Piros created SPARK-39152: ------------------------------------------
Summary: StreamCorruptedException cause job failure for disk persisted RDD Key: SPARK-39152 URL: https://issues.apache.org/jira/browse/SPARK-39152 Project: Spark Issue Type: Improvement Components: Block Manager Affects Versions: 3.4.0 Reporter: Attila Zsolt Piros Assignee: Attila Zsolt Piros In case of a disk corruption a disk persisted RDD block will lead to job failure as the block registration is always leads to the same file. So even when the task is rescheduled on a different executor the job will fail. *Example* First failure (the block is locally available): {noformat} 22/04/25 07:15:28 ERROR executor.Executor: Exception in task 17024.0 in stage 12.0 (TID 51853) java.io.StreamCorruptedException: invalid stream header: 00000000 at java.io.ObjectInputStream.readStreamHeader(ObjectInputStream.java:943) at java.io.ObjectInputStream.<init>(ObjectInputStream.java:401) at org.apache.spark.serializer.JavaDeserializationStream$$anon$1.<init>(JavaSerializer.scala:63) at org.apache.spark.serializer.JavaDeserializationStream.<init>(JavaSerializer.scala:63) at org.apache.spark.serializer.JavaSerializerInstance.deserializeStream(JavaSerializer.scala:122) at org.apache.spark.serializer.SerializerManager.dataDeserializeStream(SerializerManager.scala:209) at org.apache.spark.storage.BlockManager.getLocalValues(BlockManager.scala:617) at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:897) at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:335) at org.apache.spark.rdd.RDD.iterator(RDD.scala:286) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) {noformat} Then the task might be rescheduled on a different executor but as the block is registered to the first blockmanager the error will be the same: {noformat} java.io.StreamCorruptedException: invalid stream header: 00000000 at java.io.ObjectInputStream.readStreamHeader(ObjectInputStream.java:943) at java.io.ObjectInputStream.<init>(ObjectInputStream.java:401) at org.apache.spark.serializer.JavaDeserializationStream$$anon$1.<init>(JavaSerializer.scala:63) at org.apache.spark.serializer.JavaDeserializationStream.<init>(JavaSerializer.scala:63) at org.apache.spark.serializer.JavaSerializerInstance.deserializeStream(JavaSerializer.scala:122) at org.apache.spark.serializer.SerializerManager.dataDeserializeStream(SerializerManager.scala:209) at org.apache.spark.storage.BlockManager$$anonfun$getRemoteValues$1.apply(BlockManager.scala:698) at org.apache.spark.storage.BlockManager$$anonfun$getRemoteValues$1.apply(BlockManager.scala:696) at scala.Option.map(Option.scala:146) at org.apache.spark.storage.BlockManager.getRemoteValues(BlockManager.scala:696) at org.apache.spark.storage.BlockManager.get(BlockManager.scala:831) at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:886) at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:335) {noformat} My idea is to retry the IO operations a few times and when all of them failed deregistering the block and let the following task to recompute it. -- This message was sent by Atlassian Jira (v8.20.7#820007) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org