[ https://issues.apache.org/jira/browse/SPARK-14226?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Davies Liu closed SPARK-14226. ------------------------------ Resolution: Duplicate > Caching a table with 1,100 columns and a few million rows fails > --------------------------------------------------------------- > > Key: SPARK-14226 > URL: https://issues.apache.org/jira/browse/SPARK-14226 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 2.0.0 > Reporter: Hossein Falaki > Priority: Critical > > 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: > {code} > 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) > {code} > cc [~yhuai] and [~rxin] -- This message was 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