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https://issues.apache.org/jira/browse/SPARK-42805?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-42805:
------------------------------------
Assignee: Apache Spark
> 'Conflicting attributes' exception is thrown when joining checkpointed
> dataframe
> --------------------------------------------------------------------------------
>
> Key: SPARK-42805
> URL: https://issues.apache.org/jira/browse/SPARK-42805
> Project: Spark
> Issue Type: Bug
> Components: Optimizer
> Affects Versions: 3.3.2
> Reporter: Maciej Smolenski
> Assignee: Apache Spark
> Priority: Major
>
> Performing join using checkpointed dataframe leads to error in prepared
> 'execution plan' because columns ids/names in 'execution plan' are not unique.
> This issue can be reproduced with this simple code (fails on 3.3.2, succeeds
> on 3.1.2):
> {code:java}
> import spark.implicits._
> spark.sparkContext.setCheckpointDir("file:///tmp/cdir")
> val df = spark.range(10).toDF("id")
> val cdf = df.checkpoint()
> cdf.join(df) // org.apache.spark.sql.AnalysisException thrown on 3.3.2 {code}
>
> The failure message is:
> {noformat}
> org.apache.spark.sql.AnalysisException:
> Failure when resolving conflicting references in Join:
> 'Join Inner
> :- LogicalRDD [id#2L], false
> +- Project [id#0L AS id#2L]
> +- Range (0, 10, step=1, splits=Some(16))Conflicting attributes: id#2L
> ;
> 'Join Inner
> :- LogicalRDD [id#2L], false
> +- Project [id#0L AS id#2L]
> +- Range (0, 10, step=1, splits=Some(16)) at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis.failAnalysis(CheckAnalysis.scala:57)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis.failAnalysis$(CheckAnalysis.scala:56)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:188)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis.$anonfun$checkAnalysis$1(CheckAnalysis.scala:540)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis.$anonfun$checkAnalysis$1$adapted(CheckAnalysis.scala:102)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:367)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis.checkAnalysis(CheckAnalysis.scala:102)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis.checkAnalysis$(CheckAnalysis.scala:97)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:188)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:214)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:330)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:211)
> at
> org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:76)
> at
> org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
> at
> org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$2(QueryExecution.scala:185)
> at
> org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:510)
> at
> org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:185)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
> at
> org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:184)
> at
> org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:76)
> at
> org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:74)
> at
> org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:66)
> at org.apache.spark.sql.Dataset$.$anonfun$ofRows$1(Dataset.scala:91)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:89)
> at org.apache.spark.sql.Dataset.withPlan(Dataset.scala:3887)
> at org.apache.spark.sql.Dataset.join(Dataset.scala:920)
> ... 49 elided
> {noformat}
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