Hive 查询在 spark 中失败，但在直线 Hive 中有效

Posted 2023-04-17

【中文标题】Hive 查询在 spark 中失败，但在直线 Hive 中有效

【英文标题】：Hive query fails in spark, but works in beeline Hive

【发布时间】：2017-07-19 10:02:51

【问题描述】：

我正在尝试使用 HiveContext 从 spark 创建一个配置单元表，如下所示

hiveContext.sql("create table db1.table1 as select A.* from db1.table 2 A left join db2.table1 B on A.col1 = B.col1 and A.col2 = B.col2 where B.col9 = 1")

但我收到以下错误消息

17/07/19 10:27:25 INFO PerfLogger: <PERFLOG method=OrcGetSplits from=org.apache.hadoop.hive.ql.io.orc.ReaderImpl>
17/07/19 10:27:25 INFO deprecation: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir
17/07/19 10:27:25 INFO OrcInputFormat: FooterCacheHitRatio: 0/0
17/07/19 10:27:25 INFO PerfLogger: </PERFLOG method=OrcGetSplits start=1500452845775 end=1500452845815 duration=40 from=org.apache.hadoop.hive.ql.io.orc.ReaderImpl>
17/07/19 10:27:25 INFO PerfLogger: <PERFLOG method=OrcGetSplits from=org.apache.hadoop.hive.ql.io.orc.ReaderImpl>
Exception in thread "main" org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree:
TungstenExchange hashpartitioning(col1#36#36,col2#37#37,200), None
+- Scan OrcRelation[col1#36,col2#37,col3#38,col4#39,col5#40,col6#41,col7#42,col8#43,col9#44,col10#45,col11#46] InputPaths: hdfs://path/to/hdfs

    at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49)
    at org.apache.spark.sql.execution.Exchange.doExecute(Exchange.scala:247)
    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.Sort.doExecute(Sort.scala:64)
    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.joins.SortMergeOuterJoin.doExecute(SortMergeOuterJoin.scala:107)
    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.Filter.doExecute(basicOperators.scala:70)
    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.Project.doExecute(basicOperators.scala:46)
    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.ConvertToSafe.doExecute(rowFormatConverters.scala:56)
    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.hive.execution.InsertIntoHiveTable.sideEffectResult$lzycompute(InsertIntoHiveTable.scala:201)
    at org.apache.spark.sql.hive.execution.InsertIntoHiveTable.sideEffectResult(InsertIntoHiveTable.scala:127)
    at org.apache.spark.sql.hive.execution.InsertIntoHiveTable.doExecute(InsertIntoHiveTable.scala:276)
    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.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
    at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
    at org.apache.spark.sql.hive.execution.CreateTableAsSelect.run(CreateTableAsSelect.scala:89)
    at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:58)
    at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:56)
    at org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:70)
    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.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
    at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
    at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:145)
    at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:130)
    at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)
    at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:817)
    at mcc.analytics.compute.jobs.Horeca_East$delayedInit$body.apply(Horeca_East.scala:75)
    at scala.Function0$class.apply$mcV$sp(Function0.scala:40)
    at scala.runtime.AbstractFunction0.apply$mcV$sp(AbstractFunction0.scala:12)
    at scala.App$$anonfun$main$1.apply(App.scala:71)
    at scala.App$$anonfun$main$1.apply(App.scala:71)
    at scala.collection.immutable.List.foreach(List.scala:318)
    at scala.collection.generic.TraversableForwarder$class.foreach(TraversableForwarder.scala:32)
    at scala.App$class.main(App.scala:71)
    at mcc.analytics.compute.jobs.Horeca_East$.main(Horeca_East.scala:17)
    at mcc.analytics.compute.jobs.Horeca_East.main(Horeca_East.scala)
    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 org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
 Caused by: java.lang.RuntimeException: serious problem
    at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:1021)
    at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getSplits(OrcInputFormat.java:1048)
    at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:199)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:242)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:240)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:240)
    at org.apache.spark.rdd.HadoopRDD$HadoopMapPartitionsWithSplitRDD.getPartitions(HadoopRDD.scala:381)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:242)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:240)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:240)
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:242)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:240)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:240)
    at org.apache.spark.ShuffleDependency.<init>(Dependency.scala:91)
    at org.apache.spark.sql.execution.Exchange.prepareShuffleDependency(Exchange.scala:220)
    at org.apache.spark.sql.execution.Exchange$$anonfun$doExecute$1.apply(Exchange.scala:254)
    at org.apache.spark.sql.execution.Exchange$$anonfun$doExecute$1.apply(Exchange.scala:248)
    at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:48)
    ... 72 more
Caused by: java.lang.NullPointerException
    at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$BISplitStrategy.getSplits(OrcInputFormat.java:560)
    at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:1010)
    ... 93 more

相同的查询在直线中成功运行。我无法找出为什么查询在 hive 中按预期工作但不是通过 spark sql 工作。 你能告诉我出了什么问题吗？

【问题讨论】：

同样的问题，等待解答

【参考方案1】：

您可以尝试将 spark.sql.hive.convertMetastoreOrc 设置为 true ：

hiveContext.sql("set spark.sql.hive.convertMetastoreOrc=true") 

似乎是0字节文件的存在导致了这个bug，上面的参数有助于Spark更好地使用ORC文件（source）。