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Fri, 24 Aug 2018 13:22:00 +0000 (UTC) Date: Fri, 24 Aug 2018 13:22:00 +0000 (UTC) From: "Apache Spark (JIRA)" To: issues@spark.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Assigned] (SPARK-25229) ExternalCatalogUtils.prunePartitionsByFilter throw an AnalysisException when partition name contains upper letter MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit X-JIRA-FingerPrint: 30527f35849b9dde25b450d4833f0394 [ 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} -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org For additional commands, e-mail: issues-help@spark.apache.org