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https://issues.apache.org/jira/browse/CARBONDATA-1985?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Geetika Gupta closed CARBONDATA-1985.
-------------------------------------
Resolution: Fixed
> Insert into failed for multi partitioned table for static partition
> -------------------------------------------------------------------
>
> Key: CARBONDATA-1985
> URL: https://issues.apache.org/jira/browse/CARBONDATA-1985
> Project: CarbonData
> Issue Type: Bug
> Components: data-query
> Affects Versions: 1.3.0
> Environment: spark2.1
> Reporter: Geetika Gupta
> Priority: Major
> Fix For: 1.3.0
>
> Attachments: 2000_UniqData.csv
>
>
> I created a table using:
> CREATE TABLE uniqdata_int_string(ACTIVE_EMUI_VERSION string, DOB timestamp,
> DOJ timestamp, BIGINT_COLUMN1 bigint,BIGINT_COLUMN2 bigint,DECIMAL_COLUMN1
> decimal(30,10),
> DECIMAL_COLUMN2 decimal(36,10),Double_COLUMN1 double, Double_COLUMN2 double,
> INTEGER_COLUMN1 int) Partitioned by (cust_id int, cust_name string) STORED BY
> 'org.apache.carbondata.format' TBLPROPERTIES ("TABLE_BLOCKSIZE"= "256 MB")
> Hive create and load table command:
> CREATE TABLE uniqdata_hive (CUST_ID int,CUST_NAME String,ACTIVE_EMUI_VERSION
> string, DOB timestamp,
> DOJ timestamp, BIGINT_COLUMN1 bigint,BIGINT_COLUMN2 bigint,DECIMAL_COLUMN1
> decimal(30,10),
> DECIMAL_COLUMN2 decimal(36,10),Double_COLUMN1 double, Double_COLUMN2 double,
> INTEGER_COLUMN1 int)ROW FORMAT DELIMITED
> FIELDS TERMINATED BY ',';
> LOAD DATA LOCAL INPATH 'file:///home/geetika/Downloads/2000_UniqData.csv'
> into table UNIQDATA_HIVE;
> Insert into table command:
> insert into uniqdata_int_string
> partition(cust_id='1',cust_name='CUST_NAME_00002') select * from
> uniqdata_hive limit 10;
> Output:
> Error: java.lang.IndexOutOfBoundsException: Index: 4, Size: 4 (state=,code=0)
> Here are the logs:
> 18/01/04 16:24:45 ERROR CarbonLoadDataCommand: pool-23-thread-6
> org.apache.spark.sql.AnalysisException: Cannot insert into table
> `28dec`.`uniqdata_int_string` because the number of columns are different:
> need 10 columns, but query has 12 columns.;
> at
> org.apache.spark.sql.execution.datasources.PreprocessTableInsertion.org$apache$spark$sql$execution$datasources$PreprocessTableInsertion$$preprocess(rules.scala:222)
> at
> org.apache.spark.sql.execution.datasources.PreprocessTableInsertion$$anonfun$apply$3.applyOrElse(rules.scala:280)
> at
> org.apache.spark.sql.execution.datasources.PreprocessTableInsertion$$anonfun$apply$3.applyOrElse(rules.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:288)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:288)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:287)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:277)
> at
> org.apache.spark.sql.execution.datasources.PreprocessTableInsertion.apply(rules.scala:272)
> at
> org.apache.spark.sql.execution.datasources.PreprocessTableInsertion.apply(rules.scala:207)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor$$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$$anonfun$execute$1.apply(RuleExecutor.scala:74)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
> at
> org.apache.spark.sql.hive.CarbonAnalyzer.execute(CarbonSessionState.scala:242)
> at
> org.apache.spark.sql.hive.CarbonAnalyzer.execute(CarbonSessionState.scala:237)
> at
> org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:64)
> at
> org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:62)
> at
> org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:50)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:63)
> at
> org.apache.spark.sql.execution.command.management.CarbonLoadDataCommand.loadDataWithPartition(CarbonLoadDataCommand.scala:641)
> at
> org.apache.spark.sql.execution.command.management.CarbonLoadDataCommand.loadData(CarbonLoadDataCommand.scala:431)
> at
> org.apache.spark.sql.execution.command.management.CarbonLoadDataCommand.processData(CarbonLoadDataCommand.scala:223)
> at
> org.apache.spark.sql.execution.command.DataCommand.run(package.scala:71)
> at
> org.apache.spark.sql.execution.command.management.CarbonInsertIntoCommand.processData(CarbonInsertIntoCommand.scala:48)
> at
> org.apache.spark.sql.execution.command.DataCommand.run(package.scala:71)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:135)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:132)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:113)
> at
> org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:87)
> at
> org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:87)
> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:185)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)
> at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:592)
> at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:699)
> at
> org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.org$apache$spark$sql$hive$thriftserver$SparkExecuteStatementOperation$$execute(SparkExecuteStatementOperation.scala:220)
> at
> org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1$$anon$2.run(SparkExecuteStatementOperation.scala:163)
> at
> org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1$$anon$2.run(SparkExecuteStatementOperation.scala:160)
> at java.security.AccessController.doPrivileged(Native Method)
> at javax.security.auth.Subject.doAs(Subject.java:422)
> at
> org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1698)
> at
> org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1.run(SparkExecuteStatementOperation.scala:173)
> at
> java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
> at java.util.concurrent.FutureTask.run(FutureTask.java:266)
> 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)
> P.S: Load command using static partition was successful
> LOAD DATA INPATH 'hdfs://localhost:54311/Files/2000_UniqData.csv' into table
> uniqdata_int_string partition(cust_id='1', cust_name='CUST_NAME_00002')
> OPTIONS ('FILEHEADER'='CUST_ID,CUST_NAME ,ACTIVE_EMUI_VERSION,DOB,DOJ,
> BIGINT_COLUMN1,BIGINT_COLUMN2,DECIMAL_COLUMN1,DECIMAL_COLUMN2,Double_COLUMN1,
> Double_COLUMN2,INTEGER_COLUMN1','BAD_RECORDS_ACTION'='FORCE');
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