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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$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:93)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:481)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:82)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:481)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:267)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:263)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:457)
> at
> org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:93)
> at
> org.apache.spark.sql.execution.QueryExecution.commandExecuted$lzycompute(QueryExecution.scala:80)
> at
> org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:78)
> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:222)
> at org.apache.spark.sql.Dataset$.$anonfun$ofRows$2(Dataset.scala:102)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:99)
> at
> org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:618)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
> at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:613)
> at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:651)
> at
> org.apache.spark.sql.hive.thriftserver.SparkSQLDriver.run(SparkSQLDriver.scala:67)
> at
> org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.processCmd(SparkSQLCLIDriver.scala:384)
> at
> org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.$anonfun$processLine$1(SparkSQLCLIDriver.scala:504)
> at
> org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.$anonfun$processLine$1$adapted(SparkSQLCLIDriver.scala:498)
> at scala.collection.Iterator.foreach(Iterator.scala:943)
> at scala.collection.Iterator.foreach$(Iterator.scala:943)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1431)
> at scala.collection.IterableLike.foreach(IterableLike.scala:74)
> at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
> at scala.collection.AbstractIterable.foreach(Iterable.scala:56)
> at
> org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.processLine(SparkSQLCLIDriver.scala:498)
> at
> org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver$.main(SparkSQLCLIDriver.scala:287)
> at
> org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.main(SparkSQLCLIDriver.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
> at
> org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:955)
> at
> org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:180)
> at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:203)
> at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:90)
> at
> org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:1043)
> at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:1052)
> at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
> Caused by: 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.serde2.typeinfo.TypeInfoUtils$TypeInfoParser.expect(TypeInfoUtils.java:372)
> at
> org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils$TypeInfoParser.expect(TypeInfoUtils.java:355)
> at
> org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils$TypeInfoParser.parseType(TypeInfoUtils.java:416)
> at
> org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils$TypeInfoParser.parseTypeInfos(TypeInfoUtils.java:329)
> at
> org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils.getTypeInfosFromTypeString(TypeInfoUtils.java:814)
> at
> org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe.initialize(ParquetHiveSerDe.java:110)
> at
> org.apache.hadoop.hive.serde2.AbstractSerDe.initialize(AbstractSerDe.java:54)
> at
> org.apache.hadoop.hive.serde2.SerDeUtils.initializeSerDe(SerDeUtils.java:533)
> at
> org.apache.hadoop.hive.metastore.MetaStoreUtils.getDeserializer(MetaStoreUtils.java:453)
> at
> org.apache.hadoop.hive.metastore.MetaStoreUtils.getDeserializer(MetaStoreUtils.java:440)
> at
> org.apache.hadoop.hive.ql.metadata.Table.getDeserializerFromMetaStore(Table.java:281)
> at
> org.apache.hadoop.hive.ql.metadata.Table.checkValidity(Table.java:199)
> at org.apache.hadoop.hive.ql.metadata.Hive.createTable(Hive.java:842)
> ... 73 more
> 21/11/11 14:39:29 WARN HiveConf: HiveConf of name
> hive.internal.ss.authz.settings.applied.marker does not exist
> 21/11/11 14:39:29 WARN HiveConf: HiveConf of name hive.stats.jdbc.timeout
> does not exist
> 21/11/11 14:39:29 WARN HiveConf: HiveConf of name hive.stats.retries.wait
> does not exist
> {code}
> In such case, `HiveExternalCatalog` fallbacks to store the table in Spark
> specific format but the stack trace is surprising and confusable.
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