[ https://issues.apache.org/jira/browse/SPARK-25012?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-25012. ---------------------------------- Resolution: Duplicate > dataframe creation results in matcherror > ---------------------------------------- > > Key: SPARK-25012 > URL: https://issues.apache.org/jira/browse/SPARK-25012 > Project: Spark > Issue Type: Bug > Components: Input/Output > Affects Versions: 2.3.1 > Environment: spark 2.3.1 > mac > scala 2.11.12 > > Reporter: uwe > Priority: Major > > hi, > > running the attached code results in a > > {code:java} > scala.MatchError: 2017-02-09 00:09:27.0 (of class java.sql.Timestamp) > {code} > # i do think this is wrong (at least i do not see the issue in my code) > # the error is the ein 90% of the cases (it sometimes passes). that makes me > think something weird is going on > > > {code:java} > package misc > import java.sql.Timestamp > import java.time.LocalDateTime > import java.time.format.DateTimeFormatter > import org.apache.spark.rdd.RDD > import org.apache.spark.sql.sources._ > import org.apache.spark.sql.types.{StringType, StructField, StructType, > TimestampType} > import org.apache.spark.sql.{Row, SQLContext, SparkSession} > case class LogRecord(application:String, dateTime: Timestamp, component: > String, level: String, message: String) > class LogRelation(val sqlContext: SQLContext, val path: String) extends > BaseRelation with PrunedFilteredScan { > override def schema: StructType = StructType(Seq( > StructField("application", StringType, false), > StructField("dateTime", TimestampType, false), > StructField("component", StringType, false), > StructField("level", StringType, false), > StructField("message", StringType, false))) > override def buildScan(requiredColumns: Array[String], filters: > Array[Filter]): RDD[Row] = { > val str = "2017-02-09T00:09:27" > val ts =Timestamp.valueOf(LocalDateTime.parse(str, > DateTimeFormatter.ISO_LOCAL_DATE_TIME)) > val > data=List(Row("app",ts,"comp","level","mess"),Row("app",ts,"comp","level","mess")) > sqlContext.sparkContext.parallelize(data) > } > } > class LogDataSource extends DataSourceRegister with RelationProvider { > override def shortName(): String = "log" > override def createRelation(sqlContext: SQLContext, parameters: Map[String, > String]): BaseRelation = > new LogRelation(sqlContext, parameters("path")) > } > object f0 extends App { > lazy val spark: SparkSession = > SparkSession.builder().master("local").appName("spark session").getOrCreate() > val df = spark.read.format("log").load("hdfs:///logs") > df.show() > } > > {code} > > results in the following stacktrace > > {noformat} > 11:20:06 [task-result-getter-0] ERROR o.a.spark.scheduler.TaskSetManager - > Task 0 in stage 0.0 failed 1 times; aborting job > Exception in thread "main" org.apache.spark.SparkException: Job aborted due > to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: > Lost task 0.0 in stage 0.0 (TID 0, localhost, executor driver): > scala.MatchError: 2017-02-09 00:09:27.0 (of class java.sql.Timestamp) > at > org.apache.spark.sql.catalyst.CatalystTypeConverters$StringConverter$.toCatalystImpl(CatalystTypeConverters.scala:276) > at > org.apache.spark.sql.catalyst.CatalystTypeConverters$StringConverter$.toCatalystImpl(CatalystTypeConverters.scala:275) > at > org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:103) > at > org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:379) > at > org.apache.spark.sql.execution.RDDConversions$$anonfun$rowToRowRdd$1$$anonfun$apply$3.apply(ExistingRDD.scala:60) > at > org.apache.spark.sql.execution.RDDConversions$$anonfun$rowToRowRdd$1$$anonfun$apply$3.apply(ExistingRDD.scala:57) > at scala.collection.Iterator$$anon$11.next(Iterator.scala:410) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown > Source) > at > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > at > org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:253) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:830) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:830) > at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) > at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) > at org.apache.spark.scheduler.Task.run(Task.scala:109) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) > at java.lang.Thread.run(Thread.java:745) > Driver stacktrace: > at > org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1602) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1590) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1589) > at > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) > at > org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1589) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831) > at scala.Option.foreach(Option.scala:257) > at > org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1823) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1772) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1761) > at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) > at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:2055) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:2074) > at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:363) > at > org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38) > at > org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3273) > at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484) > at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484) > at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3254) > at > org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77) > at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3253) > at org.apache.spark.sql.Dataset.head(Dataset.scala:2484) > at org.apache.spark.sql.Dataset.take(Dataset.scala:2698) > at org.apache.spark.sql.Dataset.showString(Dataset.scala:254) > at org.apache.spark.sql.Dataset.show(Dataset.scala:723) > at org.apache.spark.sql.Dataset.show(Dataset.scala:682) > at org.apache.spark.sql.Dataset.show(Dataset.scala:691) > at > com.cadence.uwes.mock.bughunting.misc.f0$.delayedEndpoint$com$cadence$uwes$mock$bughunting$misc$f0$1(f1.scala:42) > at > com.cadence.uwes.mock.bughunting.misc.f0$delayedInit$body.apply(f1.scala:38) > at scala.Function0$class.apply$mcV$sp(Function0.scala:34) > at scala.runtime.AbstractFunction0.apply$mcV$sp(AbstractFunction0.scala:12) > at scala.App$$anonfun$main$1.apply(App.scala:76) > at scala.App$$anonfun$main$1.apply(App.scala:76) > at scala.collection.immutable.List.foreach(List.scala:392) > at > scala.collection.generic.TraversableForwarder$class.foreach(TraversableForwarder.scala:35) > at scala.App$class.main(App.scala:76) > at com.cadence.uwes.mock.bughunting.misc.f0$.main(f1.scala:38) > at com.cadence.uwes.mock.bughunting.misc.f0.main(f1.scala) > Caused by: scala.MatchError: 2017-02-09 00:09:27.0 (of class > java.sql.Timestamp) > at > org.apache.spark.sql.catalyst.CatalystTypeConverters$StringConverter$.toCatalystImpl(CatalystTypeConverters.scala:276) > at > org.apache.spark.sql.catalyst.CatalystTypeConverters$StringConverter$.toCatalystImpl(CatalystTypeConverters.scala:275) > at > org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:103) > at > org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:379) > at > org.apache.spark.sql.execution.RDDConversions$$anonfun$rowToRowRdd$1$$anonfun$apply$3.apply(ExistingRDD.scala:60) > at > org.apache.spark.sql.execution.RDDConversions$$anonfun$rowToRowRdd$1$$anonfun$apply$3.apply(ExistingRDD.scala:57) > at scala.collection.Iterator$$anon$11.next(Iterator.scala:410) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown > Source) > at > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > at > org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:253) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:830) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:830) > at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) > at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) > at org.apache.spark.scheduler.Task.run(Task.scala:109) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) > at java.lang.Thread.run(Thread.java:745) > Process finished with exit code 1 > {noformat} > -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org