Apache spark 在EMR上使用Spark SQL查询粘合表时获取NullPointerException（名称为null）

我用Spark和齐柏林飞艇建立了一个AWS电子病历，并用AWS胶水目录作为Hive的元存储。我使用了以下说明：

看来EMR集群和齐柏林飞艇正在运作

当我运行以下段落时：

%sql
show databases

它工作并输出我在Glue中设置的数据库

但是，当我尝试查询任何表时，以下方式：

%sql
select * from `fus-bear-parquet-db-prod`.fus_bear_parquet_prod limit 10

我得到以下例外情况：

java.lang.NullPointerException: Name is null
at java.lang.Enum.valueOf(Enum.java:236)
at org.apache.hadoop.hive.ql.metadata.Table.getTableType(Table.java:401)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$getTableOption$1$$anonfun$apply$11.apply(HiveClientImpl.scala:394)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$getTableOption$1$$anonfun$apply$11.apply(HiveClientImpl.scala:373)
at scala.Option.map(Option.scala:146)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$getTableOption$1.apply(HiveClientImpl.scala:373)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$getTableOption$1.apply(HiveClientImpl.scala:371)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$withHiveState$1.apply(HiveClientImpl.scala:290)
at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:231)
at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:230)
at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:273)
at org.apache.spark.sql.hive.client.HiveClientImpl.getTableOption(HiveClientImpl.scala:371)
at org.apache.spark.sql.hive.client.HiveClient$class.getTable(HiveClient.scala:75)
at org.apache.spark.sql.hive.client.HiveClientImpl.getTable(HiveClientImpl.scala:79)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$getRawTable$1.apply(HiveExternalCatalog.scala:118)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$getRawTable$1.apply(HiveExternalCatalog.scala:118)
at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:97)
at org.apache.spark.sql.hive.HiveExternalCatalog.getRawTable(HiveExternalCatalog.scala:117)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$getTable$1.apply(HiveExternalCatalog.scala:675)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$getTable$1.apply(HiveExternalCatalog.scala:675)
at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:97)
at org.apache.spark.sql.hive.HiveExternalCatalog.getTable(HiveExternalCatalog.scala:674)
at org.apache.spark.sql.catalyst.catalog.SessionCatalog.lookupRelation(SessionCatalog.scala:667)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveRelations$$lookupTableFromCatalog(Analyzer.scala:646)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.resolveRelation(Analyzer.scala:601)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$8.applyOrElse(Analyzer.scala:631)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$8.applyOrElse(Analyzer.scala:624)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:62)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:62)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:61)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:624)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:570)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82)
at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:69)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:67)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:50)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:67)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:632)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:691)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.zeppelin.spark.SparkSqlInterpreter.interpret(SparkSqlInterpreter.java:116)
at org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:97)
at org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:498)
at org.apache.zeppelin.scheduler.Job.run(Job.java:175)
at org.apache.zeppelin.scheduler.FIFOScheduler$1.run(FIFOScheduler.java:139)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
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)

感谢您的帮助。

我将重新表述@Andrey Cheptsov的评论作为回答，因为这是解决问题的方法，对于其他面临同样问题的人来说，这种方式将更加突出。谢谢你，安德烈

问题是由于没有为表设置（外部_表、虚拟_视图等）属性造成的。从雅典娜创建表格时会自动设置，但在使用Cloudformation或Terraform时会被忘记

---

要解决此问题，请确保将

TableType

属性添加到表中，然后重试查询。

如果您使用的是CDK，我可以使用

表类型来解决此问题

aws_glue.CfnTable.TableInputProperty(description="My Description",name="my_table_name",table_type="EXTERNAL_TABLE",...,)

仅使用云信息：

"GlueTable": {
      "Type": "AWS::Glue::Table",
      "Properties": {
        "CatalogId": {
          "Ref": "AWS::AccountId"
        },
        "DatabaseName": "my_data_base_name",
        "TableInput": {
          "Description": "My Description",
          "Name": "my_table_name",
          ...
          "TableType": "EXTERNAL_TABLE"
        }
      }
    }

我也有同样的问题。你找到解决办法了吗？谢谢，DrewI做了一个实验，修改了CloudFormation脚本并添加了“TableType:EXTERNAL_TABLE”。这修复了“java.lang.NullPointerException:Name为null”的问题。Athena不需要这个属性，当它不存在时，使用外部表作为默认值。我相信EMR也能做到。非常感谢Andrey。你能确切地告诉我你把TableType:EXTERNAL_TABLE放在哪里吗？我在PySpark中得到了这个确切的错误。我在声明表的CloudFormation模板中丢失了它。检查您是如何创建表的。显然，它没有指定表类型。我们能够根据您的反馈解决此问题！非常感谢您抽出时间@Andrey