The org.apache.spark.sql.execution.EvaluatePython.takeAndServe exception can happen in a lot of places it might be easier to figure out if you have a code snippet you can share where this is occurring?
On Wed, Apr 13, 2016 at 2:27 PM, AlexModestov <aleksandrmodes...@gmail.com> wrote: > I get this error. > Who knows what does it mean? > > Py4JJavaError: An error occurred while calling > z:org.apache.spark.sql.execution.EvaluatePython.takeAndServe. > : org.apache.spark.SparkException: Job aborted due to stage failure: > Exception while getting task result: > org.apache.spark.storage.BlockFetchException: Failed to fetch block from 1 > locations. Most recent failure cause: > at > org.apache.spark.scheduler.DAGScheduler.org > $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431) > at > > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419) > at > > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418) > at > > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > at > scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > at > org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418) > at > > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799) > at > > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799) > at scala.Option.foreach(Option.scala:236) > at > > org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799) > at > > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640) > at > > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599) > at > > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588) > at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) > at > org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1952) > at org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1025) > at > > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) > at > > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) > at org.apache.spark.rdd.RDD.withScope(RDD.scala:316) > at org.apache.spark.rdd.RDD.reduce(RDD.scala:1007) > at > org.apache.spark.rdd.RDD$$anonfun$takeOrdered$1.apply(RDD.scala:1397) > at > > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) > at > > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) > at org.apache.spark.rdd.RDD.withScope(RDD.scala:316) > at org.apache.spark.rdd.RDD.takeOrdered(RDD.scala:1384) > at > > org.apache.spark.sql.execution.TakeOrderedAndProject.collectData(basicOperators.scala:213) > at > > org.apache.spark.sql.execution.TakeOrderedAndProject.doExecute(basicOperators.scala:223) > at > > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132) > at > > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130) > at > > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) > at > org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130) > at > > org.apache.spark.sql.execution.Union$$anonfun$doExecute$1.apply(basicOperators.scala:144) > at > > org.apache.spark.sql.execution.Union$$anonfun$doExecute$1.apply(basicOperators.scala:144) > at > > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) > at > > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) > at scala.collection.immutable.List.foreach(List.scala:318) > at > scala.collection.TraversableLike$class.map(TraversableLike.scala:244) > at scala.collection.AbstractTraversable.map(Traversable.scala:105) > at > org.apache.spark.sql.execution.Union.doExecute(basicOperators.scala:144) > at > > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132) > at > > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130) > at > > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) > at > org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130) > at > org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:187) > at > > org.apache.spark.sql.execution.EvaluatePython$$anonfun$takeAndServe$1.apply$mcI$sp(python.scala:126) > at > > org.apache.spark.sql.execution.EvaluatePython$$anonfun$takeAndServe$1.apply(python.scala:124) > at > > org.apache.spark.sql.execution.EvaluatePython$$anonfun$takeAndServe$1.apply(python.scala:124) > at > > org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56) > at > org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:2086) > at > > org.apache.spark.sql.execution.EvaluatePython$.takeAndServe(python.scala:124) > at > org.apache.spark.sql.execution.EvaluatePython.takeAndServe(python.scala) > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > at > > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) > at > > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:606) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231) > at > py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381) > at py4j.Gateway.invoke(Gateway.java:259) > at > py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133) > at py4j.commands.CallCommand.execute(CallCommand.java:79) > at py4j.GatewayConnection.run(GatewayConnection.java:209) > at java.lang.Thread.run(Thread.java:745) > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/error-Py4JJavaError-An-error-occurred-while-calling-z-org-apache-spark-sql-execution-EvaluatePython--tp26779.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > > -- Cell : 425-233-8271 Twitter: https://twitter.com/holdenkarau
