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From "Joseph K. Bradley (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-17975) EMLDAOptimizer fails with ClassCastException on YARN
Date Thu, 09 Feb 2017 18:53:42 GMT

     [ https://issues.apache.org/jira/browse/SPARK-17975?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Joseph K. Bradley resolved SPARK-17975.
---------------------------------------
       Resolution: Fixed
    Fix Version/s: 2.2.0
                   2.1.1
                   2.0.3

> 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
>             Fix For: 2.0.3, 2.1.1, 2.2.0
>
>         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.run(ThreadPoolExecutor.java:615)
> 	at java.lang.Thread.run(Thread.java:722)
> {noformat}



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