[jira] [Created] (SPARK-20093) Exception when Joining dataframe with another dataframe generated by applying groupBy transformation on original one

["Hosur Narahari (JIRA)" <ji...@apache.org>]

Hosur Narahari created SPARK-20093:
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             Summary: Exception when Joining dataframe with another dataframe generated by applying groupBy transformation on original one
                 Key: SPARK-20093
                 URL: https://issues.apache.org/jira/browse/SPARK-20093
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 2.1.0, 2.0.2, 2.0.1, 2.0.0, 2.2.0
            Reporter: Hosur Narahari


When we generate a dataframe by doing grouping, and perform join on original dataframe with aggregate column, we get AnalysisException. Below I've attached a piece of code and resulting exception to reproduce.

Code:

import org.apache.spark.sql.SparkSession


object App {

  lazy val spark = SparkSession.builder.appName("Test").master("local").getOrCreate

  def main(args: Array[String]): Unit = {
    test1
  }

  private def test1 {
    import org.apache.spark.sql.functions._
    val df = spark.createDataFrame(Seq(("M",172,60), ("M", 170, 60), ("F", 155, 56), ("M", 160, 55), ("F", 150, 53))).toDF("gender", "height", "weight")
    val groupDF = df.groupBy("gender").agg(min("height").as("height"))
    groupDF.show()
    val out = groupDF.join(df, groupDF("height") <=> df("height")).select(df("gender"), df("height"), df("weight"))
    out.show
  }
}

When I ran above code, I got below exception:

Exception in thread "main" org.apache.spark.sql.AnalysisException: resolved attribute(s) height#8 missing from height#19,height#30,gender#29,weight#31,gender#7 in operator !Join Inner, (height#19 <=> height#8);;
!Join Inner, (height#19 <=> height#8)
:- Aggregate [gender#7], [gender#7, min(height#8) AS height#19]
:  +- Project [_1#0 AS gender#7, _2#1 AS height#8, _3#2 AS weight#9]
:     +- LocalRelation [_1#0, _2#1, _3#2]
+- Project [_1#0 AS gender#29, _2#1 AS height#30, _3#2 AS weight#31]
   +- LocalRelation [_1#0, _2#1, _3#2]

	at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:39)
	at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:90)
	at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:342)
	at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:78)
	at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
	at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:78)
	at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:90)
	at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:53)
	at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:67)
	at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2831)
	at org.apache.spark.sql.Dataset.join(Dataset.scala:843)
	at org.apache.spark.sql.Dataset.join(Dataset.scala:807)
	at App$.test1(App.scala:17)
	at App$.main(App.scala:9)
	at App.main(App.scala)

Please someone look into it.



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