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https://issues.apache.org/jira/browse/CARBONDATA-4273?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Akash R Nilugal resolved CARBONDATA-4273.
-----------------------------------------
Fix Version/s: 2.3.0
Assignee: Indhumathi Muthumurugesh
Resolution: Fixed
> Cannot create table with partitions in Spark in EMR
> ---------------------------------------------------
>
> Key: CARBONDATA-4273
> URL: https://issues.apache.org/jira/browse/CARBONDATA-4273
> Project: CarbonData
> Issue Type: Bug
> Components: spark-integration
> Affects Versions: 2.2.0
> Environment: Release label:emr-5.24.1
> Hadoop distribution:Amazon 2.8.5
> Applications:
> Hive 2.3.4, Pig 0.17.0, Hue 4.4.0, Flink 1.8.0, Spark 2.4.2, Presto 0.219,
> JupyterHub 0.9.6
> Jar complied with:
> apache-carbondata:2.2.0
> spark:2.4.5
> hadoop:2.8.3
> Reporter: Bigicecream
> Assignee: Indhumathi Muthumurugesh
> Priority: Critical
> Labels: EMR, spark
> Fix For: 2.3.0
>
> Time Spent: 2h 10m
> Remaining Estimate: 0h
>
>
> When trying to create a table like this:
> {code:sql}
> CREATE TABLE IF NOT EXISTS will_not_work(
> timestamp string,
> name string
> )
> PARTITIONED BY (dt string, hr string)
> STORED AS carbondata
> LOCATION 's3a://my-bucket/CarbonDataTests/will_not_work
> {code}
> The folder 's3a://my-bucket/CarbonDataTests/will_not_work' is a not existing
> folder
> I get the following error:
> {noformat}
> org.apache.carbondata.common.exceptions.sql.MalformedCarbonCommandException:
> Partition is not supported for external table
> at
> org.apache.spark.sql.parser.CarbonSparkSqlParserUtil$.buildTableInfoFromCatalogTable(CarbonSparkSqlParserUtil.scala:219)
> at
> org.apache.spark.sql.CarbonSource$.createTableInfo(CarbonSource.scala:235)
> at
> org.apache.spark.sql.CarbonSource$.createTableMeta(CarbonSource.scala:394)
> at
> org.apache.spark.sql.execution.command.table.CarbonCreateDataSourceTableCommand.processMetadata(CarbonCreateDataSourceTableCommand.scala:69)
> at
> org.apache.spark.sql.execution.command.MetadataCommand$$anonfun$run$1.apply(package.scala:137)
> at
> org.apache.spark.sql.execution.command.MetadataCommand$$anonfun$run$1.apply(package.scala:137)
> at
> org.apache.spark.sql.execution.command.Auditable$class.runWithAudit(package.scala:118)
> at
> org.apache.spark.sql.execution.command.MetadataCommand.runWithAudit(package.scala:134)
> at
> org.apache.spark.sql.execution.command.MetadataCommand.run(package.scala:137)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:79)
> at org.apache.spark.sql.Dataset$$anonfun$6.apply(Dataset.scala:194)
> at org.apache.spark.sql.Dataset$$anonfun$6.apply(Dataset.scala:194)
> at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3364)
> at
> org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
> at
> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3363)
> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:194)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:79)
> at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:643)
> ... 64 elided
> {noformat}
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