Which Spark version are you using? Can you file a JIRA for this issue? On Thu, Jun 25, 2015 at 6:35 AM, Peter Rudenko <petro.rude...@gmail.com> wrote:
> Hi, i have a small but very wide dataset (2000 columns). Trying to > optimize Dataframe pipeline for it, since it behaves very poorly comparing > to rdd operation. > With spark.sql.codegen=true it throws StackOverflow: > > 15/06/25 16:27:16 INFO CacheManager: Partition rdd_12_3 not found, computing > it > 15/06/25 16:27:16 INFO HadoopRDD: Input split: > file:/home/peter/validation.csv:0+337768 > 15/06/25 16:27:16 INFO CacheManager: Partition rdd_12_1 not found, computing > it > 15/06/25 16:27:16 INFO HadoopRDD: Input split: > file:/home/peter/work/train.csv:0+15540706 > 15/06/25 16:27:16 INFO CacheManager: Partition rdd_12_0 not found, computing > it > 15/06/25 16:27:16 INFO HadoopRDD: Input split: > file:/home/peter/holdout.csv:0+336296 > 15/06/25 16:27:16 INFO CacheManager: Partition rdd_12_2 not found, computing > it > 15/06/25 16:27:16 INFO HadoopRDD: Input split: > file:/home/peter/train.csv:15540706+14866642 > 15/06/25 16:27:17 ERROR Executor: Exception in task 1.0 in stage 1.0 (TID 2) > org.spark-project.guava.util.concurrent.ExecutionError: > java.lang.StackOverflowError > at > org.spark-project.guava.cache.LocalCache$Segment.get(LocalCache.java:2261) > at org.spark-project.guava.cache.LocalCache.get(LocalCache.java:4000) > at > org.spark-project.guava.cache.LocalCache.getOrLoad(LocalCache.java:4004) > at > org.spark-project.guava.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4874) > at > org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.generate(CodeGenerator.scala:105) > at > org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.generate(CodeGenerator.scala:102) > at > org.apache.spark.sql.execution.SparkPlan.newMutableProjection(SparkPlan.scala:170) > at > org.apache.spark.sql.execution.Project.buildProjection$lzycompute(basicOperators.scala:38) > at > org.apache.spark.sql.execution.Project.buildProjection(basicOperators.scala:38) > at > org.apache.spark.sql.execution.Project$$anonfun$1.apply(basicOperators.scala:41) > at > org.apache.spark.sql.execution.Project$$anonfun$1.apply(basicOperators.scala:40) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:686) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:686) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) > at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:242) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:70) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) > at org.apache.spark.scheduler.Task.run(Task.scala:70) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.StackOverflowError > at > scala.reflect.internal.Symbols$Symbol.fullNameInternal(Symbols.scala:1042) > at > scala.reflect.internal.Symbols$Symbol.fullNameAsName(Symbols.scala:1047) > at > scala.reflect.internal.Symbols$Symbol.fullNameInternal(Symbols.scala:1044) > at > scala.reflect.internal.Symbols$Symbol.fullNameAsName(Symbols.scala:1047) > at > scala.reflect.internal.Symbols$Symbol.fullNameInternal(Symbols.scala:1044) > at > scala.reflect.internal.Symbols$Symbol.fullNameAsName(Symbols.scala:1047) > at > scala.reflect.internal.Symbols$Symbol.fullNameInternal(Symbols.scala:1044) > at > scala.reflect.internal.Symbols$Symbol.fullNameAsName(Symbols.scala:1047) > at > scala.reflect.internal.Symbols$Symbol.fullNameInternal(Symbols.scala:1044) > at > scala.reflect.internal.Symbols$Symbol.fullNameAsName(Symbols.scala:1047) > at > scala.reflect.internal.Symbols$Symbol.fullNameInternal(Symbols.scala:1044) > at > scala.reflect.internal.Symbols$Symbol.fullNameAsName(Symbols.scala:1047) > at scala.reflect.internal.Symbols$Symbol.fullName(Symbols.scala:1036) > at scala.reflect.internal.Symbols$Symbol.fullName(Symbols.scala:1052) > at scala.reflect.internal.Types$TypeRef.needsPreString(Types.scala:2462) > at scala.reflect.internal.Types$TypeRef.preString(Types.scala:2465) > at scala.reflect.internal.Types$TypeRef.safeToString(Types.scala:2514) > at scala.reflect.internal.Types$class.typeToString(Types.scala:7345) > at > scala.reflect.runtime.JavaUniverse.scala$reflect$runtime$SynchronizedTypes$$super$typeToString(JavaUniverse.scala:12) > at > scala.reflect.runtime.SynchronizedTypes$class.typeToString(SynchronizedTypes.scala:79) > at > scala.reflect.runtime.JavaUniverse.typeToString(JavaUniverse.scala:12) > at scala.reflect.internal.Types$Type.toString(Types.scala:1018) > at > scala.reflect.internal.Printers$TreePrinter.printTree(Printers.scala:398) > at > scala.reflect.internal.Printers$TreePrinter$$anonfun$print$1.apply(Printers.scala:446) > at > scala.reflect.internal.Printers$TreePrinter$$anonfun$print$1.apply(Printers.scala:443) > at > scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) > at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:34) > at scala.reflect.internal.Printers$TreePrinter.print(Printers.scala:443) > at > scala.reflect.internal.Printers$TreePrinter.printOpt(Printers.scala:159) > at > scala.reflect.internal.Printers$TreePrinter.printTree(Printers.scala:218) > at > scala.reflect.internal.Printers$TreePrinter$$anonfun$print$1.apply(Printers.scala:446) > at > scala.reflect.internal.Printers$TreePrinter$$anonfun$print$1.apply(Printers.scala:443) > at > scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) > at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:34) > at scala.reflect.internal.Printers$TreePrinter.print(Printers.scala:443) > at > scala.reflect.internal.Printers$TreePrinter$$anonfun$printColumn$2.apply(Printers.scala:95) > at > scala.reflect.internal.Printers$TreePrinter$$anonfun$printColumn$2.apply(Printers.scala:95) > at > scala.reflect.internal.Printers$TreePrinter.printSeq(Printers.scala:89) > at > scala.reflect.internal.Printers$TreePrinter.printSeq(Printers.scala:89) > at > scala.reflect.internal.Printers$TreePrinter.printSeq(Printers.scala:89) > at > scala.reflect.internal.Printers$TreePrinter.printSeq(Printers.scala:89) > at > scala.reflect.internal.Printers$TreePrinter.printSeq(Printers.scala:89) > at > scala.reflect.internal.Printers$TreePrinter.printSeq(Printers.scala:89) > at > scala.reflect.internal.Printers$TreePrinter.printSeq(Printers.scala:89) > at > scala.reflect.internal.Printers$TreePrinter.printSeq(Printers.scala:89) > > ... > > For thin dataset it works, but fails for wide one. > > Thanks, > Peter Rudenko >
