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Posted to issues@carbondata.apache.org by "Prasanna Ravichandran (JIRA)" <ji...@apache.org> on 2018/08/01 12:04:00 UTC
[jira] [Updated] (CARBONDATA-2576) MV Datamap - MV is not working
fine if there is more than 3 aggregate function in the same datamap.
[ https://issues.apache.org/jira/browse/CARBONDATA-2576?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Prasanna Ravichandran updated CARBONDATA-2576:
----------------------------------------------
Description:
MV is not working fine if there is more than 3 aggregate function in the same datamap. It is working fine upto 3 aggregate functions on the same MV. Please see the attached document for more details.
Test queries:
scala> carbon.sql("create datamap datamap_comp_maxsumminavg using 'mv' as select empno,max(projectenddate),sum(salary),min(projectjoindate),avg(attendance) from originTable group by empno").show(200,false)
++
++
++
rebuild data
scala> carbon.sql("rebuild datamap datamap_comp_maxsumminavg").show(200,false)
++
++
++
scala> carbon.sql("explain select empno,max(projectenddate),sum(salary),min(projectjoindate),avg(attendance) from originTable group by empno").show(200,false)
org.apache.spark.sql.AnalysisException: expression 'datamap_comp_maxsumminavg_table.`avg_attendance`' is neither present in the group by, nor is it an aggregate function. Add to group by or wrap in first() (or first_value) if you don't care which value you get.;;
Aggregate [origintable_empno#2925|#2925], [origintable_empno#2925 AS empno#3002, max(max_projectenddate#2926) AS max(projectenddate)#3003, sum(sum_salary#2927L) AS sum(salary)#3004L, min(min_projectjoindate#2928) AS min(projectjoindate)#3005, avg_attendance#2929 AS avg(attendance)#3006|#2925 AS empno#3002, max(max_projectenddate#2926) AS max(projectenddate)#3003, sum(sum_salary#2927L) AS sum(salary)#3004L, min(min_projectjoindate#2928) AS min(projectjoindate)#3005, avg_attendance#2929 AS avg(attendance)#3006]
+- SubqueryAlias datamap_comp_maxsumminavg_table
+- Relation[origintable_empno#2925,max_projectenddate#2926,sum_salary#2927L,min_projectjoindate#2928,avg_attendance#2929|#2925,max_projectenddate#2926,sum_salary#2927L,min_projectjoindate#2928,avg_attendance#2929] CarbonDatasourceHadoopRelation [ Database name :default, Table name :datamap_comp_maxsumminavg_table, Schema :Some(StructType(StructField(origintable_empno,IntegerType,true), StructField(max_projectenddate,TimestampType,true), StructField(sum_salary,LongType,true), StructField(min_projectjoindate,TimestampType,true), StructField(avg_attendance,DoubleType,true))) ]
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:39)
at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:91)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.org$apache$spark$sql$catalyst$analysis$CheckAnalysis$class$$anonfun$$checkValidAggregateExpression$1(CheckAnalysis.scala:247)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$org$apache$spark$sql$catalyst$analysis$CheckAnalysis$class$$anonfun$$checkValidAggregateExpression$1$5.apply(CheckAnalysis.scala:253)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$org$apache$spark$sql$catalyst$analysis$CheckAnalysis$class$$anonfun$$checkValidAggregateExpression$1$5.apply(CheckAnalysis.scala:253)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.org$apache$spark$sql$catalyst$analysis$CheckAnalysis$class$$anonfun$$checkValidAggregateExpression$1(CheckAnalysis.scala:253)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$9.apply(CheckAnalysis.scala:280)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$9.apply(CheckAnalysis.scala:280)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:280)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:78)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:78)
at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:91)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:52)
at org.apache.spark.sql.CarbonSession.withProfiler(CarbonSession.scala:148)
at org.apache.spark.sql.CarbonSession.sql(CarbonSession.scala:95)
at org.apache.carbondata.mv.datamap.MVAnalyzerRule.apply(MVAnalyzerRule.scala:72)
at org.apache.carbondata.mv.datamap.MVAnalyzerRule.apply(MVAnalyzerRule.scala:38)
at org.apache.spark.sql.hive.CarbonAnalyzer.execute(CarbonAnalyzer.scala:46)
at org.apache.spark.sql.hive.CarbonAnalyzer.execute(CarbonAnalyzer.scala:27)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:69)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:67)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:50)
at org.apache.spark.sql.execution.QueryExecution.withCachedData$lzycompute(QueryExecution.scala:73)
at org.apache.spark.sql.execution.QueryExecution.withCachedData(QueryExecution.scala:72)
at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:78)
at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:78)
at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:84)
at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:80)
at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:89)
at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:89)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
at org.apache.spark.sql.execution.command.table.CarbonExplainCommand.collectProfiler(CarbonExplainCommand.scala:54)
at org.apache.spark.sql.execution.command.table.CarbonExplainCommand.processMetadata(CarbonExplainCommand.scala:45)
at org.apache.spark.sql.execution.command.MetadataCommand.run(package.scala:68)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:67)
at org.apache.spark.sql.Dataset.<init>(Dataset.scala:183)
at org.apache.spark.sql.CarbonSession$$anonfun$sql$1.apply(CarbonSession.scala:108)
at org.apache.spark.sql.CarbonSession$$anonfun$sql$1.apply(CarbonSession.scala:97)
at org.apache.spark.sql.CarbonSession.withProfiler(CarbonSession.scala:155)
at org.apache.spark.sql.CarbonSession.sql(CarbonSession.scala:95)
... 48 elided
was:
MV is not working fine if there is more than 3 aggregate function in the same datamap. It is working fine upto 3 aggregate functions on the same MV. Please see the attached document for more details.
Test queries:
scala> carbon.sql("create datamap datamap_comp_maxsumminavg using 'mv' as select empno,max(projectenddate),sum(salary),min(projectjoindate),avg(attendance) from originTable group by empno").show(200,false)
++
++
++
scala> carbon.sql("rebuild datamap datamap_comp_maxsumminavg").show(200,false)
++
++
++
scala> carbon.sql("explain select empno,max(projectenddate),sum(salary),min(projectjoindate),avg(attendance) from originTable group by empno").show(200,false)
org.apache.spark.sql.AnalysisException: expression 'datamap_comp_maxsumminavg_table.`avg_attendance`' is neither present in the group by, nor is it an aggregate function. Add to group by or wrap in first() (or first_value) if you don't care which value you get.;;
Aggregate [origintable_empno#2925|#2925], [origintable_empno#2925 AS empno#3002, max(max_projectenddate#2926) AS max(projectenddate)#3003, sum(sum_salary#2927L) AS sum(salary)#3004L, min(min_projectjoindate#2928) AS min(projectjoindate)#3005, avg_attendance#2929 AS avg(attendance)#3006|#2925 AS empno#3002, max(max_projectenddate#2926) AS max(projectenddate)#3003, sum(sum_salary#2927L) AS sum(salary)#3004L, min(min_projectjoindate#2928) AS min(projectjoindate)#3005, avg_attendance#2929 AS avg(attendance)#3006]
+- SubqueryAlias datamap_comp_maxsumminavg_table
+- Relation[origintable_empno#2925,max_projectenddate#2926,sum_salary#2927L,min_projectjoindate#2928,avg_attendance#2929|#2925,max_projectenddate#2926,sum_salary#2927L,min_projectjoindate#2928,avg_attendance#2929] CarbonDatasourceHadoopRelation [ Database name :default, Table name :datamap_comp_maxsumminavg_table, Schema :Some(StructType(StructField(origintable_empno,IntegerType,true), StructField(max_projectenddate,TimestampType,true), StructField(sum_salary,LongType,true), StructField(min_projectjoindate,TimestampType,true), StructField(avg_attendance,DoubleType,true))) ]
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:39)
at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:91)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.org$apache$spark$sql$catalyst$analysis$CheckAnalysis$class$$anonfun$$checkValidAggregateExpression$1(CheckAnalysis.scala:247)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$org$apache$spark$sql$catalyst$analysis$CheckAnalysis$class$$anonfun$$checkValidAggregateExpression$1$5.apply(CheckAnalysis.scala:253)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$org$apache$spark$sql$catalyst$analysis$CheckAnalysis$class$$anonfun$$checkValidAggregateExpression$1$5.apply(CheckAnalysis.scala:253)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.org$apache$spark$sql$catalyst$analysis$CheckAnalysis$class$$anonfun$$checkValidAggregateExpression$1(CheckAnalysis.scala:253)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$9.apply(CheckAnalysis.scala:280)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$9.apply(CheckAnalysis.scala:280)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:280)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:78)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:78)
at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:91)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:52)
at org.apache.spark.sql.CarbonSession.withProfiler(CarbonSession.scala:148)
at org.apache.spark.sql.CarbonSession.sql(CarbonSession.scala:95)
at org.apache.carbondata.mv.datamap.MVAnalyzerRule.apply(MVAnalyzerRule.scala:72)
at org.apache.carbondata.mv.datamap.MVAnalyzerRule.apply(MVAnalyzerRule.scala:38)
at org.apache.spark.sql.hive.CarbonAnalyzer.execute(CarbonAnalyzer.scala:46)
at org.apache.spark.sql.hive.CarbonAnalyzer.execute(CarbonAnalyzer.scala:27)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:69)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:67)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:50)
at org.apache.spark.sql.execution.QueryExecution.withCachedData$lzycompute(QueryExecution.scala:73)
at org.apache.spark.sql.execution.QueryExecution.withCachedData(QueryExecution.scala:72)
at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:78)
at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:78)
at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:84)
at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:80)
at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:89)
at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:89)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
at org.apache.spark.sql.execution.command.table.CarbonExplainCommand.collectProfiler(CarbonExplainCommand.scala:54)
at org.apache.spark.sql.execution.command.table.CarbonExplainCommand.processMetadata(CarbonExplainCommand.scala:45)
at org.apache.spark.sql.execution.command.MetadataCommand.run(package.scala:68)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:67)
at org.apache.spark.sql.Dataset.<init>(Dataset.scala:183)
at org.apache.spark.sql.CarbonSession$$anonfun$sql$1.apply(CarbonSession.scala:108)
at org.apache.spark.sql.CarbonSession$$anonfun$sql$1.apply(CarbonSession.scala:97)
at org.apache.spark.sql.CarbonSession.withProfiler(CarbonSession.scala:155)
at org.apache.spark.sql.CarbonSession.sql(CarbonSession.scala:95)
... 48 elided
> MV Datamap - MV is not working fine if there is more than 3 aggregate function in the same datamap.
> ---------------------------------------------------------------------------------------------------
>
> Key: CARBONDATA-2576
> URL: https://issues.apache.org/jira/browse/CARBONDATA-2576
> Project: CarbonData
> Issue Type: Bug
> Components: data-query
> Reporter: Prasanna Ravichandran
> Assignee: Ravindra Pesala
> Priority: Major
> Labels: CARBONDATA., MV, Materialistic_Views
> Fix For: 1.5.0, 1.4.1
>
> Attachments: From 4th aggregate function -error shown.docx, data.csv
>
>
> MV is not working fine if there is more than 3 aggregate function in the same datamap. It is working fine upto 3 aggregate functions on the same MV. Please see the attached document for more details.
> Test queries:
>
> scala> carbon.sql("create datamap datamap_comp_maxsumminavg using 'mv' as select empno,max(projectenddate),sum(salary),min(projectjoindate),avg(attendance) from originTable group by empno").show(200,false)
> ++
>
> ++
> ++
>
> rebuild data
> scala> carbon.sql("rebuild datamap datamap_comp_maxsumminavg").show(200,false)
> ++
>
> ++
> ++
>
>
> scala> carbon.sql("explain select empno,max(projectenddate),sum(salary),min(projectjoindate),avg(attendance) from originTable group by empno").show(200,false)
> org.apache.spark.sql.AnalysisException: expression 'datamap_comp_maxsumminavg_table.`avg_attendance`' is neither present in the group by, nor is it an aggregate function. Add to group by or wrap in first() (or first_value) if you don't care which value you get.;;
> Aggregate [origintable_empno#2925|#2925], [origintable_empno#2925 AS empno#3002, max(max_projectenddate#2926) AS max(projectenddate)#3003, sum(sum_salary#2927L) AS sum(salary)#3004L, min(min_projectjoindate#2928) AS min(projectjoindate)#3005, avg_attendance#2929 AS avg(attendance)#3006|#2925 AS empno#3002, max(max_projectenddate#2926) AS max(projectenddate)#3003, sum(sum_salary#2927L) AS sum(salary)#3004L, min(min_projectjoindate#2928) AS min(projectjoindate)#3005, avg_attendance#2929 AS avg(attendance)#3006]
> +- SubqueryAlias datamap_comp_maxsumminavg_table
> +- Relation[origintable_empno#2925,max_projectenddate#2926,sum_salary#2927L,min_projectjoindate#2928,avg_attendance#2929|#2925,max_projectenddate#2926,sum_salary#2927L,min_projectjoindate#2928,avg_attendance#2929] CarbonDatasourceHadoopRelation [ Database name :default, Table name :datamap_comp_maxsumminavg_table, Schema :Some(StructType(StructField(origintable_empno,IntegerType,true), StructField(max_projectenddate,TimestampType,true), StructField(sum_salary,LongType,true), StructField(min_projectjoindate,TimestampType,true), StructField(avg_attendance,DoubleType,true))) ]
>
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:39)
> at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:91)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.org$apache$spark$sql$catalyst$analysis$CheckAnalysis$class$$anonfun$$checkValidAggregateExpression$1(CheckAnalysis.scala:247)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$org$apache$spark$sql$catalyst$analysis$CheckAnalysis$class$$anonfun$$checkValidAggregateExpression$1$5.apply(CheckAnalysis.scala:253)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$org$apache$spark$sql$catalyst$analysis$CheckAnalysis$class$$anonfun$$checkValidAggregateExpression$1$5.apply(CheckAnalysis.scala:253)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.org$apache$spark$sql$catalyst$analysis$CheckAnalysis$class$$anonfun$$checkValidAggregateExpression$1(CheckAnalysis.scala:253)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$9.apply(CheckAnalysis.scala:280)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$9.apply(CheckAnalysis.scala:280)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:280)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:78)
> at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:78)
> at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:91)
> at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:52)
> at org.apache.spark.sql.CarbonSession.withProfiler(CarbonSession.scala:148)
> at org.apache.spark.sql.CarbonSession.sql(CarbonSession.scala:95)
> at org.apache.carbondata.mv.datamap.MVAnalyzerRule.apply(MVAnalyzerRule.scala:72)
> at org.apache.carbondata.mv.datamap.MVAnalyzerRule.apply(MVAnalyzerRule.scala:38)
> at org.apache.spark.sql.hive.CarbonAnalyzer.execute(CarbonAnalyzer.scala:46)
> at org.apache.spark.sql.hive.CarbonAnalyzer.execute(CarbonAnalyzer.scala:27)
> at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:69)
> at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:67)
> at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:50)
> at org.apache.spark.sql.execution.QueryExecution.withCachedData$lzycompute(QueryExecution.scala:73)
> at org.apache.spark.sql.execution.QueryExecution.withCachedData(QueryExecution.scala:72)
> at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:78)
> at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:78)
> at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:84)
> at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:80)
> at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:89)
> at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:89)
> at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
> at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
> at org.apache.spark.sql.execution.command.table.CarbonExplainCommand.collectProfiler(CarbonExplainCommand.scala:54)
> at org.apache.spark.sql.execution.command.table.CarbonExplainCommand.processMetadata(CarbonExplainCommand.scala:45)
> at org.apache.spark.sql.execution.command.MetadataCommand.run(package.scala:68)
> at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
> at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
> at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:67)
> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:183)
> at org.apache.spark.sql.CarbonSession$$anonfun$sql$1.apply(CarbonSession.scala:108)
> at org.apache.spark.sql.CarbonSession$$anonfun$sql$1.apply(CarbonSession.scala:97)
> at org.apache.spark.sql.CarbonSession.withProfiler(CarbonSession.scala:155)
> at org.apache.spark.sql.CarbonSession.sql(CarbonSession.scala:95)
> ... 48 elided
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