sarutak opened a new pull request #34551:
URL: https://github.com/apache/spark/pull/34551
### What changes were proposed in this pull request?
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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.
So the solution this PR proposes is not to try to create such tables in Hive
compatible format because it should always fail, and logs a message which says
the table is stored in Spark specific format.
### Why are the changes needed?
<!--
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1. If you propose a new API, clarify the use case for a new API.
2. If you fix a bug, you can clarify why it is a bug.
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To fix the confusable behavior.
### Does this PR introduce _any_ user-facing change?
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Note that it means *any* user-facing change including all aspects such as
the documentation fix.
If yes, please clarify the previous behavior and the change this PR proposes
- provide the console output, description and/or an example to show the
behavior difference if possible.
If possible, please also clarify if this is a user-facing change compared to
the released Spark versions or within the unreleased branches such as master.
If no, write 'No'.
-->
No.
### How was this patch tested?
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New test.
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