Major Description
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.
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
In such case, `HiveExternalCatalog` fallbacks to store the table in Spark specific format but the stack trace is surprising and confusable.
