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Posted to issues@spark.apache.org by "Marco Gaido (JIRA)" <ji...@apache.org> on 2018/08/06 09:50:00 UTC
[jira] [Commented] (SPARK-25012) dataframe creation results in
matcherror
[ https://issues.apache.org/jira/browse/SPARK-25012?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16569960#comment-16569960 ]
Marco Gaido commented on SPARK-25012:
-------------------------------------
Seems the same as SPARK-24366. Seems anyway a problem in you schema definition/column mappings.
> 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}
>
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