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https://issues.apache.org/jira/browse/SPARK-23351?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16363133#comment-16363133
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Sean Owen commented on SPARK-23351:
-----------------------------------
[~davidahern] for the record, it's usually the opposite. Vendor branches like
Cloudera say "2.2.0" but should be read like "2.2.x". They will as a rule only
vary from the upstream branch in which commits go into a maintenance branch.
This fix could be in a vendor 2.2.x release that isn't in an upstream one (or
vice versa), or appear in a maintenance branch earlier from a vendor. That's in
theory the value proposition; in this particular case I have no idea.
> checkpoint corruption in long running application
> -------------------------------------------------
>
> Key: SPARK-23351
> URL: https://issues.apache.org/jira/browse/SPARK-23351
> Project: Spark
> Issue Type: Bug
> Components: Structured Streaming
> Affects Versions: 2.2.0
> Reporter: David Ahern
> Priority: Major
>
> hi, after leaving my (somewhat high volume) Structured Streaming application
> running for some time, i get the following exception. The same exception
> also repeats when i try to restart the application. The only way to get the
> application back running is to clear the checkpoint directory which is far
> from ideal.
> Maybe a stream is not being flushed/closed properly internally by Spark when
> checkpointing?
>
> User class threw exception:
> org.apache.spark.sql.streaming.StreamingQueryException: Job aborted due to
> stage failure: Task 55 in stage 1.0 failed 4 times, most recent failure: Lost
> task 55.3 in stage 1.0 (TID 240, gbslixaacspa04u.metis.prd, executor 2):
> java.io.EOFException
> at java.io.DataInputStream.readInt(DataInputStream.java:392)
> at
> org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider.org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$readSnapshotFile(HDFSBackedStateStoreProvider.scala:481)
> at
> org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1.apply(HDFSBackedStateStoreProvider.scala:359)
> at
> org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1.apply(HDFSBackedStateStoreProvider.scala:358)
> at scala.Option.getOrElse(Option.scala:121)
> at
> org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider.org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap(HDFSBackedStateStoreProvider.scala:358)
> at
> org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider.getStore(HDFSBackedStateStoreProvider.scala:265)
> at
> org.apache.spark.sql.execution.streaming.state.StateStore$.get(StateStore.scala:200)
> at
> org.apache.spark.sql.execution.streaming.state.StateStoreRDD.compute(StateStoreRDD.scala:61)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:108)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
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