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From "Teodor-Bogdan Barbieru (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-14226) Caching a table with 1,100 columns and a few million rows fails
Date Tue, 03 May 2016 14:18:13 GMT

    [ https://issues.apache.org/jira/browse/SPARK-14226?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15268776#comment-15268776
] 

Teodor-Bogdan Barbieru commented on SPARK-14226:
------------------------------------------------

I'm having a similar issue with a large Spark job in 1.6.1 that has been running fine in production
until the number of partitions increased.
Seems like a strange error that can be replicated with the code in SPARK-12837.

> 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]



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