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https://issues.apache.org/jira/browse/SPARK-17975?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15799156#comment-15799156
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Jeff Stein edited comment on SPARK-17975 at 1/4/17 7:47 PM:
Attaching vertical bar delimited documents (one per line).
With my quick fix, I'm seeing a lot more persisted RDDs on the "Storage" tab.
I'm either not cleaning something up or there's another issue related to that.
was (Author: jvstein):
Attaching vertical bar delimited documents (one per line).
> EMLDAOptimizer fails with ClassCastException on YARN
>
>
> Key: SPARK-17975
> URL: https://issues.apache.org/jira/browse/SPARK-17975
> Project: Spark
> Issue Type: Bug
> Components: MLlib
>Affects Versions: 2.0.1
> Environment: Centos 6, CDH 5.7, Java 1.7u80
>Reporter: Jeff Stein
> Attachments: docs.txt
>
>
> I'm able to reproduce the error consistently with a 2000 record text file
> with each record having 1-5 terms and checkpointing enabled. It looks like
> the problem was introduced with the resolution for SPARK-13355.
> The EdgeRDD class seems to be lying about it's type in a way that causes
> RDD.mapPartitionsWithIndex method to be unusable when it's referenced as an
> RDD of Edge elements.
> {code}
> val spark = SparkSession.builder.appName("lda").getOrCreate()
> spark.sparkContext.setCheckpointDir("hdfs:///tmp/checkpoints")
> val data: RDD[(Long, Vector)] = // snip
> data.setName("data").cache()
> val lda = new LDA
> val optimizer = new EMLDAOptimizer
> lda.setOptimizer(optimizer)
> .setK(10)
> .setMaxIterations(400)
> .setAlpha(-1)
> .setBeta(-1)
> .setCheckpointInterval(7)
> val ldaModel = lda.run(data)
> {code}
> {noformat}
> 16/10/16 23:53:54 WARN TaskSetManager: Lost task 3.0 in stage 348.0 (TID
> 1225, server2.domain): java.lang.ClassCastException: scala.Tuple2 cannot be
> cast to org.apache.spark.graphx.Edge
> at
> org.apache.spark.graphx.EdgeRDD$$anonfun$1$$anonfun$apply$1.apply(EdgeRDD.scala:107)
> at scala.collection.Iterator$class.foreach(Iterator.scala:893)
> at
> org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
> at org.apache.spark.graphx.EdgeRDD$$anonfun$1.apply(EdgeRDD.scala:107)
> at org.apache.spark.graphx.EdgeRDD$$anonfun$1.apply(EdgeRDD.scala:105)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$25.apply(RDD.scala:820)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$25.apply(RDD.scala:820)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD$$anonfun$8.apply(RDD.scala:332)
> at org.apache.spark.rdd.RDD$$anonfun$8.apply(RDD.scala:330)
> at
> org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:935)
> at
> org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:926)
> at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:866)
> at
> org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:926)
> at
> org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:670)
> at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:330)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:281)
> at org.apache.spark.graphx.EdgeRDD.compute(EdgeRDD.scala:50)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
> at org.apache.spark.scheduler.Task.run(Task.scala:86)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.r
