[SPARK-14226] Caching a table with 1,100 columns and a few million rows fails

• Type: Bug
• Status: Closed
• Priority: Critical
• Resolution: Duplicate
• Affects Version/s: 2.0.0
• Component/s: SQL
• Target Version/s: 2.0.0
• Assignee: Unassigned
• Reporter: Hossein Falaki
• Created: 29/Mar/16 01:27
• Updated: 03/May/16 17:22
• Resolved: 03/May/16 17:22

Description

I created a DataFrame from the 1000 genomes data set using csv data source. I register it as a table and tried to cache it. I get the following error:

val vcfData = sqlContext.read.format("csv").options(Map(
  "comment" -> "#", "header" -> "false", "delimiter" -> "\t"
)).load("/1000genomes/")

val colNames = sc.textFile("/hossein/1000genomes/").take(100).filter(_.startsWith("#CHROM")).head.split("\t")
val data = vcfData.toDF(colNames: _*).registerTempTable("genomeTable)

%sql cache table genomeTable
org.apache.spark.SparkException: Job aborted due to stage failure: Total size of serialized results of 2086 tasks (4.0 GB) is bigger than spark.driver.maxResultSize (4.0 GB)
	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1457)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1445)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1444)
	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1444)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:809)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:809)
	at scala.Option.foreach(Option.scala:236)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:809)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1666)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1625)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1614)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1765)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1778)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1791)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1805)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:881)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:357)
	at org.apache.spark.rdd.RDD.collect(RDD.scala:880)
	at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:276)
	at org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:1979)
	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:53)
	at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2242)
	at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:1978)
	at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:1985)
	at org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:1995)
	at org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:1994)
	at org.apache.spark.sql.Dataset.withCallback(Dataset.scala:2255)
	at org.apache.spark.sql.Dataset.count(Dataset.scala:1994)
	at org.apache.spark.sql.execution.command.CacheTableCommand.run(commands.scala:270)
	at org.apache.spark.sql.execution.command.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:61)
	at org.apache.spark.sql.execution.command.ExecutedCommand.sideEffectResult(commands.scala:59)
	at org.apache.spark.sql.execution.command.ExecutedCommand.doExecute(commands.scala:73)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:118)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:118)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:137)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:134)
	at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:117)
	at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:60)
	at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:60)
	at org.apache.spark.sql.Dataset.<init>(Dataset.scala:179)
	at org.apache.spark.sql.Dataset.<init>(Dataset.scala:164)
	at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:59)
	at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:748)

cc yhuai and rxin

Issue Links

• is duplicated by: SPARK-12837 Spark driver requires large memory space for serialized results even there are no data collected to the driver