[jira] [Created] (SPARK-25229) ExternalCatalogUtils.prunePartitionsByFilter throw an AnalysisException when partition name contains upper letter

["Xiaochen Ouyang (JIRA)" <ji...@apache.org>]

Xiaochen Ouyang created SPARK-25229:
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             Summary: 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: 2.3.1, 2.3.0, 2.2.2, 2.2.1, 2.2.0
            Reporter: Xiaochen Ouyang


{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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