Saleem Ansari created SPARK-18531:
-------------------------------------

             Summary: Apache Spark FPGrowth algorithm implementation fails with 
java.lang.StackOverflowError
                 Key: SPARK-18531
                 URL: https://issues.apache.org/jira/browse/SPARK-18531
             Project: Spark
          Issue Type: Bug
          Components: MLlib
    Affects Versions: 1.6.1
            Reporter: Saleem Ansari



More details can be found here: 
https://gist.github.com/tuxdna/37a69b53e6f9a9442fa3b1d5e53c2acb



Spark FPGrowth algorithm croaks with a small dataset as show below. 

$ spark-shell --master "local[*]" --driver-memory 5g
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 1.6.1
      /_/

Using Scala version 2.10.5 (OpenJDK 64-Bit Server VM, Java 1.8.0_102)
Spark context available as sc.
SQL context available as sqlContext.

scala> import org.apache.spark.mllib.fpm.FPGrowth
import org.apache.spark.mllib.fpm.FPGrowth

scala> import org.apache.spark.rdd.RDD
import org.apache.spark.rdd.RDD

scala> import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.SQLContext

scala> import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.{SparkConf, SparkContext}

scala> val data = sc.textFile("bug.data")
data: org.apache.spark.rdd.RDD[String] = bug.data MapPartitionsRDD[1] at 
textFile at <console>:31

scala> val transactions: RDD[Array[String]] = data.map(l => 
l.split(",").distinct)
transactions: org.apache.spark.rdd.RDD[Array[String]] = MapPartitionsRDD[2] at 
map at <console>:33

scala> transactions.cache()
res0: transactions.type = MapPartitionsRDD[2] at map at <console>:33

scala> val fpg = new FPGrowth().setMinSupport(0.05).setNumPartitions(10)
fpg: org.apache.spark.mllib.fpm.FPGrowth = 
org.apache.spark.mllib.fpm.FPGrowth@66d62c59

scala> val model = fpg.run(transactions)
model: org.apache.spark.mllib.fpm.FPGrowthModel[String] = 
org.apache.spark.mllib.fpm.FPGrowthModel@6e92f150

scala> model.freqItemsets.take(1).foreach { i => i.items.mkString("[", ",", 
"]") + ", " + i.freq }
[Stage 3:>                                                          (0 + 2) / 
2]16/11/21 23:56:14 ERROR Executor: Managed memory leak detected; size = 
18068980 bytes, TID = 14
16/11/21 23:56:14 ERROR Executor: Exception in task 0.0 in stage 3.0 (TID 14)
java.lang.StackOverflowError
    at org.xerial.snappy.Snappy.arrayCopy(Snappy.java:84)
    at 
org.xerial.snappy.SnappyOutputStream.rawWrite(SnappyOutputStream.java:273)
    at org.xerial.snappy.SnappyOutputStream.write(SnappyOutputStream.java:115)
    at 
org.apache.spark.io.SnappyOutputStreamWrapper.write(CompressionCodec.scala:202)
    at 
java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
    at 
java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786)
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1495)
    at 
java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
    at 
java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)





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