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https://issues.apache.org/jira/browse/SPARK-34563?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Michael Kamprath updated SPARK-34563:
-------------------------------------
Affects Version/s: 3.1.1
> Checkpointing a union with another checkpoint fails
> ---------------------------------------------------
>
> Key: SPARK-34563
> URL: https://issues.apache.org/jira/browse/SPARK-34563
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 3.0.2, 3.1.1
> Environment: I am running Spark 3.0.2 in stand alone cluster mode,
> built for Hadoop 2.7, and Scala 2.12.12. I am using QFS 2.2.2 (Quantcast File
> System) as the underlying DFS. The nodes run on Debian Stretch, and Java is
> openjdk version "1.8.0_275".
> Reporter: Michael Kamprath
> Priority: Major
>
> I have some PySpark code that periodically checkpoints a data frame that I
> am building in pieces by union-ing those pieces together as they are
> constructed. (Py)Spark fails on the second checkpoint, which would be a union
> of a new piece of the desired data frame with a previously checkpointed
> piece. Some simplified PySpark code that will trigger this problem is:
>
> {code:java}
> RANGE_STEP = 10000
> PARTITIONS = 5
> COUNT_UNIONS = 20
> df = spark.range(1, RANGE_STEP+1, numPartitions=PARTITIONS)
> for i in range(1, COUNT_UNIONS+1):
> print('Processing i = {0}'.format(i))
> new_df = spark.range(RANGE_STEP*i + 1, RANGE_STEP*(i+1) + 1,
> numPartitions=PARTITIONS)
> df = df.union(new_df).checkpoint()
> df.count()
> {code}
> When this code gets to the checkpoint on the second loop iteration (i=2) the
> job fails with an error:
>
> {code:java}
> Py4JJavaError: An error occurred while calling o119.checkpoint.
> : org.apache.spark.SparkException: Job aborted due to stage failure: Task 9
> in stage 10.0 failed 4 times, most recent failure: Lost task 9.3 in stage
> 10.0 (TID 264, 10.20.30.13, executor 0):
> com.esotericsoftware.kryo.KryoException: Encountered unregistered class ID:
> 9062
> at
> com.esotericsoftware.kryo.util.DefaultClassResolver.readClass(DefaultClassResolver.java:137)
> at com.esotericsoftware.kryo.Kryo.readClass(Kryo.java:693)
> at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:804)
> at
> org.apache.spark.serializer.KryoDeserializationStream.readObject(KryoSerializer.scala:296)
> at
> org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:168)
> at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729)
> at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
> at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1804)
> at org.apache.spark.rdd.RDD.$anonfun$count$1(RDD.scala:1227)
> at org.apache.spark.rdd.RDD.$anonfun$count$1$adapted(RDD.scala:1227)
> at
> org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2154)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:127)
> at
> org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:462)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:465)
> 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)
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2059)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2008)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2007)
> at
> scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
> at
> scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2007)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:973)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:973)
> at scala.Option.foreach(Option.scala:407)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:973)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2239)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2188)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2177)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:775)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2114)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2135)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2154)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2179)
> at org.apache.spark.rdd.RDD.count(RDD.scala:1227)
> at org.apache.spark.sql.Dataset.$anonfun$checkpoint$1(Dataset.scala:696)
> at
> org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3618)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
> at
> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3616)
> at org.apache.spark.sql.Dataset.checkpoint(Dataset.scala:687)
> at org.apache.spark.sql.Dataset.checkpoint(Dataset.scala:650)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
> at py4j.Gateway.invoke(Gateway.java:282)
> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.GatewayConnection.run(GatewayConnection.java:238)
> at java.lang.Thread.run(Thread.java:748)
> Caused by: com.esotericsoftware.kryo.KryoException: Encountered unregistered
> class ID: 9062
> at
> com.esotericsoftware.kryo.util.DefaultClassResolver.readClass(DefaultClassResolver.java:137)
> at com.esotericsoftware.kryo.Kryo.readClass(Kryo.java:693)
> at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:804)
> at
> org.apache.spark.serializer.KryoDeserializationStream.readObject(KryoSerializer.scala:296)
> at
> org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:168)
> at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729)
> at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
> at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1804)
> at org.apache.spark.rdd.RDD.$anonfun$count$1(RDD.scala:1227)
> at org.apache.spark.rdd.RDD.$anonfun$count$1$adapted(RDD.scala:1227)
> at
> org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2154)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:127)
> at
> org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:462)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:465)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> ... 1 more
> {code}
>
> Note that the checkpoint directory is set, as the first checkpoint does
> succeed. Also, if the checkpoint method is removed, the sample code succeeds
> as expected, so the problems isolated to the use of the checkpoint.
>
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