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Posted to issues@carbondata.apache.org by "anubhav tarar (JIRA)" <ji...@apache.org> on 2018/02/02 05:27:00 UTC
[jira] [Commented] (CARBONDATA-2041) Not able to load data into a
partitioned table using insert overwrite
[ https://issues.apache.org/jira/browse/CARBONDATA-2041?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16349810#comment-16349810 ]
anubhav tarar commented on CARBONDATA-2041:
-------------------------------------------
invalid bug partition spec can not be empty please close this bug
> Not able to load data into a partitioned table using insert overwrite
> ----------------------------------------------------------------------
>
> Key: CARBONDATA-2041
> URL: https://issues.apache.org/jira/browse/CARBONDATA-2041
> Project: CarbonData
> Issue Type: Bug
> Components: data-load
> Affects Versions: 1.3.0
> Environment: spark 2.1
> Reporter: Vandana Yadav
> Assignee: anubhav tarar
> Priority: Major
> Attachments: 2000_UniqData.csv
>
>
> Not able to load data into a partitioned table using insert overwrite
> Steps to reproduce:
> 1) Create Hive table and load data in it:
> a) 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 ',';
> b) LOAD DATA LOCAL INPATH '/home/knoldus/Desktop/csv/TestData/Data/uniqdata/2000_UniqData.csv' into table UNIQDATA_HIVE;
> 2) Create a partitioned table(hive and carbon both):
> a) CREATE TABLE uniqdata_string(CUST_ID int,CUST_NAME 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(ACTIVE_EMUI_VERSION string) STORED BY 'org.apache.carbondata.format' TBLPROPERTIES ('TABLE_BLOCKSIZE'= '256 MB');
> b) CREATE TABLE uniqdata_hive_partition (CUST_ID int,CUST_NAME 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 (active_emui_version string)ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';
>
> 3) Load data into the partitioned table using insert overwrite:
> a) load into the hive_partitioned table:
> insert overwrite table uniqdata_hive_partition partition (active_emui_version) select CUST_ID,CUST_NAME, DOB, DOJ, BIGINT_COLUMN1,BIGINT_COLUMN2,DECIMAL_COLUMN1, DECIMAL_COLUMN2,Double_COLUMN1, Double_COLUMN2, INTEGER_COLUMN1,active_emui_version from uniqdata_hive limit 10;
>
> output:
> Data successfully loaded into the table:
> validation Query:
> select count(*) from uniqdata_hive_partition
> output:
> count(1) |
> +-----------+--+
> | 10 |
> +-----------+–+
>
> b) load into carbon partitioned table:
> insert overwrite table uniqdata_string partition(active_emui_version) select CUST_ID, CUST_NAME,DOB,doj, bigint_column1, bigint_column2, decimal_column1, decimal_column2,double_column1, double_column2,integer_column1,active_emui_version from uniqdata_hive limit 10;
>
> Expected Result: Data should be loaded successfully
> Actual Result:
> Error: org.apache.spark.SparkException: Job aborted. (state=,code=0)
>
> logs:
> org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: partition spec is invalid; field active_emui_version does not exist or is empty;
> at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:98)
> at org.apache.spark.sql.hive.HiveExternalCatalog.createPartitions(HiveExternalCatalog.scala:842)
> at org.apache.spark.sql.catalyst.catalog.SessionCatalog.createPartitions(SessionCatalog.scala:679)
> at org.apache.spark.sql.hive.CarbonSessionCatalog.createPartitions(CarbonSessionState.scala:155)
> at org.apache.spark.sql.execution.command.AlterTableAddPartitionCommand.run(ddl.scala:361)
> at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.org$apache$spark$sql$execution$datasources$DataSourceAnalysis$$anonfun$$refreshPartitionsCallback$1(DataSourceStrategy.scala:221)
> at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1$$anonfun$8.apply(DataSourceStrategy.scala:243)
> at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1$$anonfun$8.apply(DataSourceStrategy.scala:243)
> at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply$mcV$sp(FileFormatWriter.scala:143)
> at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:121)
> at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:121)
> at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
> at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:121)
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:101)
> at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$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$$anonfun$executeQuery$1.apply(SparkPlan.scala:135)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:132)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:113)
> at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:87)
> at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:87)
> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:185)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)
> at org.apache.spark.sql.execution.command.management.CarbonLoadDataCommand.overwritePartition(CarbonLoadDataCommand.scala:830)
> at org.apache.spark.sql.execution.command.management.CarbonLoadDataCommand.loadDataWithPartition(CarbonLoadDataCommand.scala:635)
> at org.apache.spark.sql.execution.command.management.CarbonLoadDataCommand.loadData(CarbonLoadDataCommand.scala:447)
> at org.apache.spark.sql.execution.command.management.CarbonLoadDataCommand.processData(CarbonLoadDataCommand.scala:230)
> at org.apache.spark.sql.execution.command.DataCommand.run(package.scala:71)
> at org.apache.spark.sql.execution.command.management.CarbonInsertIntoCommand.processData(CarbonInsertIntoCommand.scala:48)
> at org.apache.spark.sql.execution.command.DataCommand.run(package.scala:71)
> at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
> at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
> at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
> at org.apache.spark.sql.execution.SparkPlan$$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:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
>
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