[jira] [Updated] (SPARK-17975) EMLDAOptimizer fails with ClassCastException on YARN

2017-01-04 Thread Jeff Stein (JIRA) 

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

Jeff Stein updated SPARK-17975:
---
Attachment: docs.txt

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.run(ThreadPoolExecutor.java:615)
>   at java.lang.Thread.run(Thread.java:722)
> {noformat}



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[jira] [Updated] (SPARK-17975) EMLDAOptimizer fails with ClassCastException on YARN

2016-10-17 Thread Jeff Stein (JIRA) 

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

Jeff Stein updated SPARK-17975:
---
Description: 
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}

  was:
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}


> EMLDAOptimizer fail