[jira] [Assigned] (SPARK-37283) Don't try to store a V1 table which contains ANSI intervals in Hive compatible format
[
https://issues.apache.org/jira/browse/SPARK-37283?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-37283:
Assignee: Apache Spark (was: Kousuke Saruta)
> Don't try to store a V1 table which contains ANSI intervals in Hive
> compatible format
> -
>
> Key: SPARK-37283
> URL: https://issues.apache.org/jira/browse/SPARK-37283
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 3.2.0
>Reporter: Kousuke Saruta
>Assignee: Apache Spark
>Priority: Major
>
> If, a table being created contains a column of ANSI interval types and the
> underlying file format has a corresponding Hive SerDe (e.g. Parquet),
> `HiveExternalcatalog` tries to store the table in Hive compatible format.
> But, as ANSI interval types in Spark and interval type in Hive are not
> compatible (Hive only supports interval_year_month and interval_day_time),
> the following warning with stack trace will be logged.
> {code}
> spark-sql> CREATE TABLE tbl1(a INTERVAL YEAR TO MONTH) USING Parquet;
> 21/11/11 14:39:29 WARN SessionState: METASTORE_FILTER_HOOK will be ignored,
> since hive.security.authorization.manager is set to instance of
> HiveAuthorizerFactory.
> 21/11/11 14:39:29 WARN HiveExternalCatalog: Could not persist
> `default`.`tbl1` in a Hive compatible way. Persisting it into Hive metastore
> in Spark SQL specific format.
> org.apache.hadoop.hive.ql.metadata.HiveException:
> java.lang.IllegalArgumentException: Error: type expected at the position 0 of
> 'interval year to month' but 'interval year to month' is found.
> at org.apache.hadoop.hive.ql.metadata.Hive.createTable(Hive.java:869)
> at org.apache.hadoop.hive.ql.metadata.Hive.createTable(Hive.java:874)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$createTable$1(HiveClientImpl.scala:553)
> at
> scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$withHiveState$1(HiveClientImpl.scala:303)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:234)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:233)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:283)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.createTable(HiveClientImpl.scala:551)
> at
> org.apache.spark.sql.hive.HiveExternalCatalog.saveTableIntoHive(HiveExternalCatalog.scala:499)
> at
> org.apache.spark.sql.hive.HiveExternalCatalog.createDataSourceTable(HiveExternalCatalog.scala:397)
> at
> org.apache.spark.sql.hive.HiveExternalCatalog.$anonfun$createTable$1(HiveExternalCatalog.scala:274)
> at
> scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
> at
> org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:102)
> at
> org.apache.spark.sql.hive.HiveExternalCatalog.createTable(HiveExternalCatalog.scala:245)
> at
> org.apache.spark.sql.catalyst.catalog.ExternalCatalogWithListener.createTable(ExternalCatalogWithListener.scala:94)
> at
> org.apache.spark.sql.catalyst.catalog.SessionCatalog.createTable(SessionCatalog.scala:376)
> at
> org.apache.spark.sql.execution.command.CreateDataSourceTableCommand.run(createDataSourceTables.scala:120)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:75)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:73)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:84)
> at
> org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.$anonfun$applyOrElse$1(QueryExecution.scala:97)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
> at
> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
> at
> org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:97)
> at
> org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:93)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.$anon
[jira] [Assigned] (SPARK-37283) Don't try to store a V1 table which contains ANSI intervals in Hive compatible format
[
https://issues.apache.org/jira/browse/SPARK-37283?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-37283:
Assignee: Kousuke Saruta (was: Apache Spark)
> Don't try to store a V1 table which contains ANSI intervals in Hive
> compatible format
> -
>
> Key: SPARK-37283
> URL: https://issues.apache.org/jira/browse/SPARK-37283
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 3.2.0
>Reporter: Kousuke Saruta
>Assignee: Kousuke Saruta
>Priority: Major
>
> If, a table being created contains a column of ANSI interval types and the
> underlying file format has a corresponding Hive SerDe (e.g. Parquet),
> `HiveExternalcatalog` tries to store the table in Hive compatible format.
> But, as ANSI interval types in Spark and interval type in Hive are not
> compatible (Hive only supports interval_year_month and interval_day_time),
> the following warning with stack trace will be logged.
> {code}
> spark-sql> CREATE TABLE tbl1(a INTERVAL YEAR TO MONTH) USING Parquet;
> 21/11/11 14:39:29 WARN SessionState: METASTORE_FILTER_HOOK will be ignored,
> since hive.security.authorization.manager is set to instance of
> HiveAuthorizerFactory.
> 21/11/11 14:39:29 WARN HiveExternalCatalog: Could not persist
> `default`.`tbl1` in a Hive compatible way. Persisting it into Hive metastore
> in Spark SQL specific format.
> org.apache.hadoop.hive.ql.metadata.HiveException:
> java.lang.IllegalArgumentException: Error: type expected at the position 0 of
> 'interval year to month' but 'interval year to month' is found.
> at org.apache.hadoop.hive.ql.metadata.Hive.createTable(Hive.java:869)
> at org.apache.hadoop.hive.ql.metadata.Hive.createTable(Hive.java:874)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$createTable$1(HiveClientImpl.scala:553)
> at
> scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$withHiveState$1(HiveClientImpl.scala:303)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:234)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:233)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:283)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.createTable(HiveClientImpl.scala:551)
> at
> org.apache.spark.sql.hive.HiveExternalCatalog.saveTableIntoHive(HiveExternalCatalog.scala:499)
> at
> org.apache.spark.sql.hive.HiveExternalCatalog.createDataSourceTable(HiveExternalCatalog.scala:397)
> at
> org.apache.spark.sql.hive.HiveExternalCatalog.$anonfun$createTable$1(HiveExternalCatalog.scala:274)
> at
> scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
> at
> org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:102)
> at
> org.apache.spark.sql.hive.HiveExternalCatalog.createTable(HiveExternalCatalog.scala:245)
> at
> org.apache.spark.sql.catalyst.catalog.ExternalCatalogWithListener.createTable(ExternalCatalogWithListener.scala:94)
> at
> org.apache.spark.sql.catalyst.catalog.SessionCatalog.createTable(SessionCatalog.scala:376)
> at
> org.apache.spark.sql.execution.command.CreateDataSourceTableCommand.run(createDataSourceTables.scala:120)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:75)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:73)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:84)
> at
> org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.$anonfun$applyOrElse$1(QueryExecution.scala:97)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
> at
> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
> at
> org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:97)
> at
> org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:93)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.$an
