zeppelin sql报错找不到表 Table not found

最新推荐文章于 2023-04-04 16:11:37 发布

原创最新推荐文章于 2023-04-04 16:11:37 发布 · 3.2k 阅读

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本文介绍了一个常见的Zeppelin使用问题，当尝试通过SQL查询注册的表时出现“找不到表”的错误，并提供了详细的解决方案。文章强调了使用Zeppelin内置SQLContext的重要性。

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zeppelin中使用sql查询，报错：找不到表，如下

org.apache.spark.sql.AnalysisException: Table not found: emobilelog; line 1 pos 21
	at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.getTable(Analyzer.scala:305)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:314)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:309)
	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)
	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)
	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:56)
	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)
	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:281)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
	at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
	at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
	at scala.collection.AbstractIterator.to(Iterator.scala:1157)
	at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
	at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
	at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:321)
	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:54)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:309)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:299)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:83)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:80)
	at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)
	at scala.collection.immutable.List.foldLeft(List.scala:84)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:80)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:72)
	at scala.collection.immutable.List.foreach(List.scala:318)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:72)
	at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:36)
	at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:36)
	at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34)
	at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)
	at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)
	at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:817)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:606)
	at org.apache.zeppelin.spark.SparkSqlInterpreter.interpret(SparkSqlInterpreter.java:115)
	at org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:94)
	at org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:341)
	at org.apache.zeppelin.scheduler.Job.run(Job.java:176)
	at org.apache.zeppelin.scheduler.FIFOScheduler$1.run(FIFOScheduler.java:139)
	at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
	at java.util.concurrent.FutureTask.run(FutureTask.java:262)
	at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
	at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
	at java.lang.Thread.run(Thread.java:745)

原因一般是代码里使用了 new SQLContext()，用这个sqlContext转化的dataFrame所注册的表不能被zeppelin内置的sqlContext找到。

解决办法：

一定要用zeppelin内置的SQLContext 来生成dataFrame

import org.apache.spark.SparkContext
import org.elasticsearch.spark._
import org.apache.spark.sql._
import org.elasticsearch.spark.sql._

val options13 = Map("es.nodes" -> "168.7.1.67,168.7.1.68,168.7.1.69", "es.port" -> "9200")
//val sqlContext = new SQLContext(sc)  不能新建SQLContext,否则后面的%sql 查询会查不到表
//这里一定要使用zeppelin内置的SQLContext，即sqlc
val df: DataFrame = sql.esDF("log4j-emobilelog/emobilelog", "?q=_exists_:oid",options13)
df.registerTempTable("emobilelog")