[ https://issues.apache.org/jira/browse/SPARK-32894?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Thomas Graves updated SPARK-32894: ---------------------------------- Summary: Timestamp cast in exernal orc table (was: Timestamp cast in exernal ocr table) > Timestamp cast in exernal orc table > ----------------------------------- > > Key: SPARK-32894 > URL: https://issues.apache.org/jira/browse/SPARK-32894 > Project: Spark > Issue Type: Bug > Components: Java API > Affects Versions: 3.0.0 > Environment: Spark 3.0.0 > Java 1.8 > Hadoop 3.3.0 > Hive 3.1.2 > Python 3.7 (from pyspark) > Reporter: Grigory Skvortsov > Priority: Major > > I have the external hive table stored as orc. I want to work with timestamp > column in my table using pyspark. > For example, I try this: > spark.sql('select id, time_ from mydb.table1`).show() > > Py4JJavaError: An error occurred while calling o2877.showString. > : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 > in stage 4.0 failed 4 times, most recent failure: Lost task 0.3 in stage 4.0 > (TID 19, 172.29.14.241, executor 1): java.lang.ClassCastException: > org.apache.spark.unsafe.types.UTF8String cannot be cast to java.lang.Long > at scala.runtime.BoxesRunTime.unboxToLong(BoxesRunTime.java:107) > at > org.apache.spark.sql.catalyst.expressions.MutableLong.update(SpecificInternalRow.scala:148) > at > org.apache.spark.sql.catalyst.expressions.SpecificInternalRow.update(SpecificInternalRow.scala:228) > at > org.apache.spark.sql.hive.HiveInspectors.$anonfun$unwrapperFor$53(HiveInspectors.scala:730) > at > org.apache.spark.sql.hive.HiveInspectors.$anonfun$unwrapperFor$53$adapted(HiveInspectors.scala:730) > at > org.apache.spark.sql.hive.orc.OrcFileFormat$.$anonfun$unwrapOrcStructs$4(OrcFileFormat.scala:351) > at scala.collection.Iterator$$anon$10.next(Iterator.scala:459) > at scala.collection.Iterator$$anon$10.next(Iterator.scala:459) > at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.next(FileScanRDD.scala:96) > at scala.collection.Iterator$$anon$10.next(Iterator.scala:459) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown > Source) > at > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > at > org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729) > at > org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340) > at > org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872) > at > org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:313) > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) > at org.apache.spark.scheduler.Task.run(Task.scala:127) > at > org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444) > at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) > at java.lang.Thread.run(Thread.java:748) > Driver stacktrace: > at > org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2023) > at > org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:1972) > at > org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:1971) > at > scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62) > at > scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55) > at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49) > at > org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1971) > at > org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:950) > at > org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:950) > at scala.Option.foreach(Option.scala:407) > at > org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:950) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2203) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2152) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2141) > at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) > at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:752) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:2093) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:2114) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:2133) > at > org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:467) > at > org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:420) > at > org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47) > at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3625) > at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2695) > at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3616) > at > org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100) > at > org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160) > at > org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87) > at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763) > at > org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64) > at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3614) > at org.apache.spark.sql.Dataset.head(Dataset.scala:2695) > at org.apache.spark.sql.Dataset.take(Dataset.scala:2902) > at org.apache.spark.sql.Dataset.getRows(Dataset.scala:300) > at org.apache.spark.sql.Dataset.showString(Dataset.scala:337) > at sun.reflect.GeneratedMethodAccessor80.invoke(Unknown Source) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:498) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:282) > at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) > at py4j.commands.CallCommand.execute(CallCommand.java:79) > at py4j.GatewayConnection.run(GatewayConnection.java:238) > at java.lang.Thread.run(Thread.java:748) > Caused by: java.lang.ClassCastException: > org.apache.spark.unsafe.types.UTF8String cannot be cast to java.lang.Long > at scala.runtime.BoxesRunTime.unboxToLong(BoxesRunTime.java:107) > at > org.apache.spark.sql.catalyst.expressions.MutableLong.update(SpecificInternalRow.scala:148) > at > org.apache.spark.sql.catalyst.expressions.SpecificInternalRow.update(SpecificInternalRow.scala:228) > at > org.apache.spark.sql.hive.HiveInspectors.$anonfun$unwrapperFor$53(HiveInspectors.scala:730) > at > org.apache.spark.sql.hive.HiveInspectors.$anonfun$unwrapperFor$53$adapted(HiveInspectors.scala:730) > at > org.apache.spark.sql.hive.orc.OrcFileFormat$.$anonfun$unwrapOrcStructs$4(OrcFileFormat.scala:351) > at scala.collection.Iterator$$anon$10.next(Iterator.scala:459) > at scala.collection.Iterator$$anon$10.next(Iterator.scala:459) > at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.next(FileScanRDD.scala:96) > at scala.collection.Iterator$$anon$10.next(Iterator.scala:459) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown > Source) > at > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > at > org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729) > at > org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340) > at > org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872) > at > org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:313) > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) > at org.apache.spark.scheduler.Task.run(Task.scala:127) > at > org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444) > at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) > ... 1 more > I have the external hive table stored as orc. I want to work with timestamp > column in my table using pyspark. > For example, I try this: > > {{spark.sql('select id, time_ from mydb.table1`).show()}} > And get following output: > > {{Py4JJavaError: An error occurred while calling o2877.showString.: > org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in > stage 4.0 failed 4 times, most recent failure: Lost task 0.3 in stage 4.0 > (TID 19, 172.29.14.241, executor 1): java.lang.ClassCastException: > org.apache.spark.unsafe.types.UTF8String cannot be cast to java.lang.Long > at scala.runtime.BoxesRunTime.unboxToLong(BoxesRunTime.java:107) at > org.apache.spark.sql.catalyst.expressions.MutableLong.update(SpecificInternalRow.scala:148) > at > org.apache.spark.sql.catalyst.expressions.SpecificInternalRow.update(SpecificInternalRow.scala:228) > at > org.apache.spark.sql.hive.HiveInspectors.$anonfun$unwrapperFor$53(HiveInspectors.scala:730) > at > org.apache.spark.sql.hive.HiveInspectors.$anonfun$unwrapperFor$53$adapted(HiveInspectors.scala:730) > at > org.apache.spark.sql.hive.orc.OrcFileFormat$.$anonfun$unwrapOrcStructs$4(OrcFileFormat.scala:351) > at scala.collection.Iterator$$anon$10.next(Iterator.scala:459) at > scala.collection.Iterator$$anon$10.next(Iterator.scala:459) at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.next(FileScanRDD.scala:96) > at scala.collection.Iterator$$anon$10.next(Iterator.scala:459) at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown > Source) at > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > at > org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729) > at > org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340) > at > org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872) > at > org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349) at > org.apache.spark.rdd.RDD.iterator(RDD.scala:313) at > org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at > org.apache.spark.scheduler.Task.run(Task.scala:127) at > org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444) > at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) > at java.lang.Thread.run(Thread.java:748)Driver stacktrace: at > org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2023) > at > org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:1972) > at > org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:1971) > at > scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62) > at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55) > at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49) at > org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1971) > at > org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:950) > at > org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:950) > at scala.Option.foreach(Option.scala:407) at > org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:950) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2203) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2152) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2141) > at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at > org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:752) at > org.apache.spark.SparkContext.runJob(SparkContext.scala:2093) at > org.apache.spark.SparkContext.runJob(SparkContext.scala:2114) at > org.apache.spark.SparkContext.runJob(SparkContext.scala:2133) at > org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:467) > at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:420) > at > org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47) > at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3625) at > org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2695) at > org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3616) at > org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100) > at > org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160) > at > org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87) > at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763) > at > org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64) > at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3614) at > org.apache.spark.sql.Dataset.head(Dataset.scala:2695) at > org.apache.spark.sql.Dataset.take(Dataset.scala:2902) at > org.apache.spark.sql.Dataset.getRows(Dataset.scala:300) at > org.apache.spark.sql.Dataset.showString(Dataset.scala:337) at > sun.reflect.GeneratedMethodAccessor80.invoke(Unknown Source) at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:498) at > py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at > py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at > py4j.Gateway.invoke(Gateway.java:282) at > py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at > py4j.commands.CallCommand.execute(CallCommand.java:79) at > py4j.GatewayConnection.run(GatewayConnection.java:238) at > java.lang.Thread.run(Thread.java:748)Caused by: java.lang.ClassCastException: > org.apache.spark.unsafe.types.UTF8String cannot be cast to java.lang.Long > at scala.runtime.BoxesRunTime.unboxToLong(BoxesRunTime.java:107) at > org.apache.spark.sql.catalyst.expressions.MutableLong.update(SpecificInternalRow.scala:148) > at > org.apache.spark.sql.catalyst.expressions.SpecificInternalRow.update(SpecificInternalRow.scala:228) > at > org.apache.spark.sql.hive.HiveInspectors.$anonfun$unwrapperFor$53(HiveInspectors.scala:730) > at > org.apache.spark.sql.hive.HiveInspectors.$anonfun$unwrapperFor$53$adapted(HiveInspectors.scala:730) > at > org.apache.spark.sql.hive.orc.OrcFileFormat$.$anonfun$unwrapOrcStructs$4(OrcFileFormat.scala:351) > at scala.collection.Iterator$$anon$10.next(Iterator.scala:459) at > scala.collection.Iterator$$anon$10.next(Iterator.scala:459) at > org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.next(FileScanRDD.scala:96) > at scala.collection.Iterator$$anon$10.next(Iterator.scala:459) at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown > Source) at > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > at > org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729) > at > org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340) > at > org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872) > at > org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349) at > org.apache.spark.rdd.RDD.iterator(RDD.scala:313) at > org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at > org.apache.spark.scheduler.Task.run(Task.scala:127) at > org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444) > at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)... > 1 more}} > Also I tried to read orc file like > {{spark.read.load('hdfs://host:9000/user/hive/..../part1=*/part2=*/*.orc')}}, > but had the same exception. > I try to cast types, but all my attempts failed. > Also I tried to create atemporary table as select from the original external. > After that I I was able to see and work with {{time_}} table as expected. > What I should with to working with timestamp columns from orc files? > P.S. I can correctly see if I select columns with HiveCli. Also If can create > table (internal) as select * from original& In this situation I can correctly > work with timestamo column in pyspark. > > Maybe it is bug? How to fix it! > > > {{}} -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org