我有配置单元表，并希望使用pyspark对其进行操作。我的其中一列具有timestamp数据类型。我可以从配置单元中选择它，但是在通过pyspark工作时出现错误。
在pyspark中，我尝试执行以下操作：
2020-09-08 13:21:29,142 WARN scheduler.TaskSetManager: Lost task 0.0 in stage 15.0 (TID 249, 172.29.14.241, executor 7): java.lang.ArrayIndexOutOfBoundsException
2020-09-08 13:21:29,265 WARN scheduler.TaskSetManager: Lost task 0.1 in stage 15.0 (TID 250, 172.29.14.238, executor 0): java.lang.ArrayIndexOutOfBoundsException: 1024
at org.apache.orc.impl.TreeReaderFactory$TreeReader.nextVector(TreeReaderFactory.java:292)
at org.apache.orc.impl.TreeReaderFactory$StringDictionaryTreeReader.nextVector(TreeReaderFactory.java:1820)
at org.apache.orc.impl.TreeReaderFactory$StringTreeReader.nextVector(TreeReaderFactory.java:1517)
at org.apache.orc.impl.ConvertTreeReaderFactory$TimestampFromStringGroupTreeReader.nextVector(ConvertTreeReaderFactory.java:1699)
at org.apache.orc.impl.TreeReaderFactory$StructTreeReader.nextBatch(TreeReaderFactory.java:2059)
at org.apache.orc.impl.RecordReaderImpl.nextBatch(RecordReaderImpl.java:1322)
at org.apache.spark.sql.execution.datasources.orc.OrcColumnarBatchReader.nextBatch(OrcColumnarBatchReader.java:196)
at org.apache.spark.sql.execution.datasources.orc.OrcColumnarBatchReader.nextKeyValue(OrcColumnarBatchReader.java:99)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:93)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:173)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:93)
at org.apache.spark.sql.execution.FileSourceScanExec$$anon$1.hasNext(DataSourceScanExec.scala:490)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.columnartorow_nextBatch_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$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)
2020-09-08 13:21:29,494 ERROR scheduler.TaskSetManager: Task 0 in stage 15.0 failed 4 times; aborting job
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/spark/python/pyspark/sql/dataframe.py", line 440, in show
print(self._jdf.showString(n, 20, vertical))
File "/opt/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
File "/opt/spark/python/pyspark/sql/utils.py", line 131, in deco
return f(*a, **kw)
File "/opt/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o357.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 15.0 failed 4 times, most recent failure: Lost task 0.3 in stage 15.0 (TID 252, 172.29.14.238, executor 0): java.lang.ArrayIndexOutOfBoundsException: 1024
at org.apache.orc.impl.TreeReaderFactory$TreeReader.nextVector(TreeReaderFactory.java:292)
at org.apache.orc.impl.TreeReaderFactory$StringDictionaryTreeReader.nextVector(TreeReaderFactory.java:1820)
at org.apache.orc.impl.TreeReaderFactory$StringTreeReader.nextVector(TreeReaderFactory.java:1517)
at org.apache.orc.impl.ConvertTreeReaderFactory$TimestampFromStringGroupTreeReader.nextVector(ConvertTreeReaderFactory.java:1699)
at org.apache.orc.impl.TreeReaderFactory$StructTreeReader.nextBatch(TreeReaderFactory.java:2059)
at org.apache.orc.impl.RecordReaderImpl.nextBatch(RecordReaderImpl.java:1322)
at org.apache.spark.sql.execution.datasources.orc.OrcColumnarBatchReader.nextBatch(OrcColumnarBatchReader.java:196)
at org.apache.spark.sql.execution.datasources.orc.OrcColumnarBatchReader.nextKeyValue(OrcColumnarBatchReader.java:99)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:93)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:173)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:93)
at org.apache.spark.sql.execution.FileSourceScanExec$$anon$1.hasNext(DataSourceScanExec.scala:490)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.columnartorow_nextBatch_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$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.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
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.ArrayIndexOutOfBoundsException: 1024
at org.apache.orc.impl.TreeReaderFactory$TreeReader.nextVector(TreeReaderFactory.java:292)
at org.apache.orc.impl.TreeReaderFactory$StringDictionaryTreeReader.nextVector(TreeReaderFactory.java:1820)
at org.apache.orc.impl.TreeReaderFactory$StringTreeReader.nextVector(TreeReaderFactory.java:1517)
at org.apache.orc.impl.ConvertTreeReaderFactory$TimestampFromStringGroupTreeReader.nextVector(ConvertTreeReaderFactory.java:1699)
at org.apache.orc.impl.TreeReaderFactory$StructTreeReader.nextBatch(TreeReaderFactory.java:2059)
at org.apache.orc.impl.RecordReaderImpl.nextBatch(RecordReaderImpl.java:1322)
at org.apache.spark.sql.execution.datasources.orc.OrcColumnarBatchReader.nextBatch(OrcColumnarBatchReader.java:196)
at org.apache.spark.sql.execution.datasources.orc.OrcColumnarBatchReader.nextKeyValue(OrcColumnarBatchReader.java:99)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:93)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:173)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:93)
at org.apache.spark.sql.execution.FileSourceScanExec$$anon$1.hasNext(DataSourceScanExec.scala:490)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.columnartorow_nextBatch_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$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
在蜂巢cli中，所有设置都是正确的：
hive> select id, host, time_ from payments.transacts limit 20;
OK
15468499243 h1 2019-11-01 14:35:31
15468498828 h1 2019-11-01 14:35:31
15468498912 h2 2019-11-01 14:35:31
15468498995 h2 2019-11-01 14:35:31
15468499077 h1 2019-11-01 14:35:31
15468499162 h1 2019-11-01 14:35:31
15468499163 h1 2019-11-01 14:35:31
15468499245 h1 2019-11-01 14:35:31
15468498833 h2 2019-11-01 14:35:31
15468498834 h2 2019-11-01 14:35:31
15468498916 h2 2019-11-01 14:35:31
15468499002 h2 2019-11-01 14:35:31
15468499003 h2 2019-11-01 14:35:31
15468499084 h1 2019-11-01 14:35:31
15468499169 h1 2019-11-01 14:35:31
15468499252 h2 2019-11-01 14:35:31
15468498838 h1 2019-11-01 14:35:31
15468498921 h2 2019-11-01 14:35:31
15468499007 h1 2019-11-01 14:35:31
15468499008 h1 2019-11-01 14:35:31
如果我尝试在没有time_列的情况下获取它，一切都很好！
+-----------+----+
| id|host|
+-----------+----+
|15468499243| h1|
|15468498828| h1|
|15468498912| h2|
|15468498995| h2|
|15468499077| h1|
|15468499162| h1|
|15468499163| h1|
|15468499245| h1|
|15468498833| h2|
|15468498834| h2|
|15468498916| h2|
|15468499002| h2|
|15468499003| h2|
|15468499084| h1|
|15468499169| h1|
|15468499252| h2|
|15468498838| h1|
|15468498921| h2|
|15468499007| h1|
|15468499008| h1|
+-----------+----+
我应保留哪些更改以正确使用pyspark的时间戳？