[jira] [Created] (SPARK-28926) CLONE - ArrayIndexOutOfBoundsException and Not-stable AUC metrics in ALS for datasets with 12 billion instances

["Qiang Wang (Jira)" <ji...@apache.org>]

Qiang Wang created SPARK-28926:
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             Summary: CLONE - ArrayIndexOutOfBoundsException and Not-stable AUC metrics in ALS for datasets  with 12 billion instances
                 Key: SPARK-28926
                 URL: https://issues.apache.org/jira/browse/SPARK-28926
             Project: Spark
          Issue Type: Bug
          Components: MLlib
    Affects Versions: 2.2.1
            Reporter: Qiang Wang
            Assignee: Xiangrui Meng


The stack trace is below:
{quote}19/08/28 07:00:40 WARN Executor task launch worker for task 325074 BlockManager: Block rdd_10916_493 could not be removed as it was not found on disk or in memory 19/08/28 07:00:41 ERROR Executor task launch worker for task 325074 Executor: Exception in task 3.0 in stage 347.1 (TID 325074) java.lang.ArrayIndexOutOfBoundsException: 6741 at org.apache.spark.dpshade.recommendation.ALS$$anonfun$org$apache$spark$ml$recommendation$ALS$$computeFactors$1.apply(ALS.scala:1460) at org.apache.spark.dpshade.recommendation.ALS$$anonfun$org$apache$spark$ml$recommendation$ALS$$computeFactors$1.apply(ALS.scala:1440) at org.apache.spark.rdd.PairRDDFunctions$$anonfun$mapValues$1$$anonfun$apply$40$$anonfun$apply$41.apply(PairRDDFunctions.scala:760) at org.apache.spark.rdd.PairRDDFunctions$$anonfun$mapValues$1$$anonfun$apply$40$$anonfun$apply$41.apply(PairRDDFunctions.scala:760) at scala.collection.Iterator$$anon$11.next(Iterator.scala:409) at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:216) at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1041) at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1032) at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:972) at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1032) at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:763) at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334) at org.apache.spark.rdd.RDD.iterator(RDD.scala:285) at org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:141) at org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:137) at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733) at scala.collection.immutable.List.foreach(List.scala:381) at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732) at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:137) 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.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.ShuffleMapTask.runTask(ShuffleMapTask.scala:96) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53) at org.apache.spark.scheduler.Task.run(Task.scala:108) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:358) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745)
{quote}
This exception happened sometimes.  And we also found that the AUC metric was not stable when evaluating the inner product of the user factors and the item factors with the same dataset and configuration. AUC varied from 0.60 to 0.67 which was not stable for production environment. 

Dataset capacity: ~12 billion ratings
 Here is the our code:
{code:java}
val hivedata = sc.sql(sqltext).select(id,dpid,score).coalesce(numPartitions)
val predataItem =  hivedata.rdd.map(r=>(r._1._1,(r._1._2,r._2.sum)))
  .groupByKey().zipWithIndex()
  .persist(StorageLevel.MEMORY_AND_DISK_SER)
val predataUser = predataItem.flatMap(r=>r._1._2.map(y=>(y._1,(r._2.toInt,y._2))))
  .aggregateByKey(zeroValueArr,numPartitions)((a,b)=> a += b,(a,b)=>a ++ b).map(r=>(r._1,r._2.toIterable))
  .zipWithIndex().persist(StorageLevel.MEMORY_AND_DISK_SER)
//x._2 is the item_id, y._1 is the user_id, y._2 is the rating
val trainData = predataUser.flatMap(x => x._1._2.map(y => (x._2.toInt, y._1, y._2.toFloat)))
  .setName(trainDataName).persist(StorageLevel.MEMORY_AND_DISK_SER)

case class ALSData(user:Int, item:Int, rating:Float) extends Serializable
val ratingData = trainData.map(x => ALSData(x._1, x._2, x._3)).toDF()
    val als = new ALS
    val paramMap = ParamMap(als.alpha -> 25000).
      put(als.checkpointInterval, 5).
      put(als.implicitPrefs, true).
      put(als.itemCol, "item").
      put(als.maxIter, 60).
      put(als.nonnegative, false).
      put(als.numItemBlocks, 600).
      put(als.numUserBlocks, 600).
      put(als.regParam, 4.5).
      put(als.rank, 25).
      put(als.userCol, "user")
    als.fit(ratingData, paramMap)
{code}



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