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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-25229) ExternalCatalogUtils.prunePartitionsByFilter throw an AnalysisException when partition name contains upper letter
Date Fri, 24 Aug 2018 13:22:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-25229?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-25229:
------------------------------------

    Assignee:     (was: Apache Spark)

> ExternalCatalogUtils.prunePartitionsByFilter  throw an AnalysisException when partition
name contains upper letter
> ------------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-25229
>                 URL: https://issues.apache.org/jira/browse/SPARK-25229
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1
>            Reporter: Xiaochen Ouyang
>            Priority: Blocker
>
> {code:java}
> // code placeholder
> scala> spark.version
> res0: String = 2.3.0
> scala> spark.sql("create table t(id int,name string) partitioned by(aA string)")
> res1: org.apache.spark.sql.DataFrame = []
> scala> spark.sql("insert into table t values(1,'Donahue','US')")
> res2: org.apache.spark.sql.DataFrame = []
> spark.sql("select id,name from t where aA = 'US'").show(1)
> org.apache.spark.sql.AnalysisException: Expected only partition pruning predicates: List(isnotnull(aA#25),
(aA#25 = US));
> at org.apache.spark.sql.catalyst.catalog.ExternalCatalogUtils$.prunePartitionsByFilter(ExternalCatalogUtils.scala:145)
> at org.apache.spark.sql.hive.MetastoreRelation.getHiveQlPartitions(MetastoreRelation.scala:158)
> at org.apache.spark.sql.hive.execution.HiveTableScanExec$$anonfun$10.apply(HiveTableScanExec.scala:151)
> at org.apache.spark.sql.hive.execution.HiveTableScanExec$$anonfun$10.apply(HiveTableScanExec.scala:150)
> at org.apache.spark.util.Utils$.withDummyCallSite(Utils.scala:2393)
> at org.apache.spark.sql.hive.execution.HiveTableScanExec.doExecute(HiveTableScanExec.scala:149)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:115)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:115)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:136)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:133)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:114)
> at org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:240)
> at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:323)
> at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:39)
> at org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2194)
> at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
> at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2547)
> at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2193)
> at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2200)
> at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1936)
> at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1935)
> at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2577)
> at org.apache.spark.sql.Dataset.head(Dataset.scala:1935)
> at org.apache.spark.sql.Dataset.take(Dataset.scala:2150)
> at org.apache.spark.sql.Dataset.showString(Dataset.scala:240)
> at org.apache.spark.sql.Dataset.show(Dataset.scala:527)
> at org.apache.spark.sql.Dataset.show(Dataset.scala:487)
> ... 48 elided
> {code}



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