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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2016/04/18 10:53:25 UTC
[jira] [Updated] (SPARK-14226) Caching a table with 1,100 columns
and a few million rows fails
[ https://issues.apache.org/jira/browse/SPARK-14226?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen updated SPARK-14226:
------------------------------
Target Version/s: (was: 2.0.0)
> 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
> Affects Versions: 2.0.0
> Reporter: Hossein Falaki
>
> 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]
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