Major Description
More details can be found here: https://gist.github.com/tuxdna/37a69b53e6f9a9442fa3b1d5e53c2acb
Spark FPGrowth algorithm croaks with a small dataset as shown below
$ spark-shell --master "local[*]" --driver-memory 5g
Welcome to
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/__/ ./_,// //_\ version 1.6.1
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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)
This failure is likely due to the size of baskets which contains over thousands of items.
scala> val maxBasketSize = transactions.map(_.length).max()
maxBasketSize: Int = 1171
scala> transactions.filter(_.length == maxBasketSize).collect()
res3: Array[Array[String]] = Array(Array(3858, 109, 5842, 2184, 2481, 534