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hive.metastore.schema.verification is not enabled - while selecting hive table from spark

New Contributor

I am using CDH 5.12

I am getting below error, while using spark cli and also from eclipse program

 

 

hive-site.xml

  <property>
    <name>datanucleus.autoCreateSchema</name>
    <value>false</value>
  </property>

  
  
[cloudera@quickstart ~]$ sudo service hive-metastore status
Hive Metastore is running                                  [  OK  ]
[cloudera@quickstart ~]$ sudo service hive-server2 status
Hive Server2 is running                                    [  OK  ]

 

 

scala> import org.apache.spark.sql.hive._
import org.apache.spark.sql.hive._

scala> val hc = new HiveContext(sc)
hc: org.apache.spark.sql.hive.HiveContext = org.apache.spark.sql.hive.HiveContext@41c04a0c

scala> hc.sql("select * from customer1")

17/09/10 23:13:41 WARN metastore.ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 1.1.0-cdh5.12.0
17/09/10 23:13:42 WARN metastore.ObjectStore: Failed to get database default, returning NoSuchObjectException
org.apache.spark.sql.AnalysisException: Table not found: customer1; line 1 pos 14
    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:37)
    at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:37)
    at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:35)
    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:829)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:33)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:38)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:40)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:42)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:44)
    at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:46)
    at $iwC$$iwC$$iwC$$iwC.<init>(<console>:48)
    at $iwC$$iwC$$iwC.<init>(<console>:50)
    at $iwC$$iwC.<init>(<console>:52)
    at $iwC.<init>(<console>:54)
    at <init>(<console>:56)
    at .<init>(<console>:60)
    at .<clinit>(<console>)
    at .<init>(<console>:7)
    at .<clinit>(<console>)
    at $print(<console>)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1045)
    at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1326)
    at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:821)
    at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:852)
    at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:800)
    at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:857)
    at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:902)
    at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:814)
    at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:657)
    at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:665)
    at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$loop(SparkILoop.scala:670)
    at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply$mcZ$sp(SparkILoop.scala:997)
    at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
    at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
    at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
    at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$process(SparkILoop.scala:945)
    at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1064)
    at org.apache.spark.repl.Main$.main(Main.scala:35)
    at org.apache.spark.repl.Main.main(Main.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:730)
    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)

7 REPLIES 7

Explorer

Please provide the schema/database where table is available. It is clearly saying table is not available in default database.

Go to beeline/hive and check show tables. By default it is checking in default database.

 

org.apache.spark.sql.AnalysisException: Table not found: customer1; line 1 pos 14
    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 

 

Thanks,

Manu

New Contributor

Hey Manu,

 

the table exists in default database.

 

 

hive> use default;
OK
Time taken: 0.034 seconds


hive> show tables;
OK
customer1
Time taken: 0.034 seconds, Fetched: 1 row(s)


hive> describe customer1;
OK
id                  	int                 	                    
name                	string              	                    
city                	string              	                    
Time taken: 0.342 seconds, Fetched: 3 row(s)


hive> select count(*) from customer1;
Query ID = cloudera_20170912015757_ed855d4e-fb2e-49f6-91ec-13d15922d407
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1505161189626_0001, Tracking URL = http://quickstart.cloudera:8088/proxy/application_1505161189626_0001/
Kill Command = /usr/lib/hadoop/bin/hadoop job  -kill job_1505161189626_0001
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2017-09-12 01:57:20,277 Stage-1 map = 0%,  reduce = 0%
2017-09-12 01:57:31,879 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.99 sec
2017-09-12 01:57:44,178 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 4.73 sec
MapReduce Total cumulative CPU time: 4 seconds 730 msec
Ended Job = job_1505161189626_0001
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 4.73 sec   HDFS Read: 7132 HDFS Write: 2 SUCCESS
Total MapReduce CPU Time Spent: 4 seconds 730 msec
OK
4
Time taken: 45.104 seconds, Fetched: 1 row(s)

New Contributor
Actually, it's not able to access metastore. So the information about the table is not available to Spark.

On the other hand metastore is running.

Explorer

Hello!  I am seeing this same issue ... was there a resolution to your problem?  Thanks.

New Contributor

I am having the same problem when creating a table in default schema 

 

Is there any solution?

New Contributor

hello, 

did you find a solution for this issue please? 

thanks

Community Manager

@oblamine As this is an older post you would have a better chance of receiving a resolution by starting a new thread. This will also provide the opportunity to provide details specific to your environment that could aid others in providing a more accurate answer to your question. 


Cy Jervis, Manager, Community Program
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