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Posted to issues@spark.apache.org by "Saleem Ansari (JIRA)" <ji...@apache.org> on 2016/12/05 07:07:58 UTC
[jira] [Commented] (SPARK-18531) Apache Spark FPGrowth algorithm
implementation fails with java.lang.StackOverflowError
[ https://issues.apache.org/jira/browse/SPARK-18531?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15721440#comment-15721440 ]
Saleem Ansari commented on SPARK-18531:
---------------------------------------
[~yuhaoyan] Thanks for your suggestion.
Here is what worked for me:
* Apache Spark 1.6.1 without spark.driver.extraJavaOptions=-Xss10m -- StackOverflowError
* Apache Spark 1.6.1 with spark.driver.extraJavaOptions=-Xss10m -- RUNS FINE
* Apache Spark 1.6.3 without spark.driver.extraJavaOptions=-Xss10m -- RUNS FINE
Apparently there is some config change between Apache Spark 1.6.1 and Apache Spark 1.6.3 that fixes this issue.
> 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 shown 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)
> *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
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