[jira] [Created] (SPARK-10968) Incorrect Join behavior in filter conditions

["RaviShankar KS (JIRA)" <ji...@apache.org>]

RaviShankar KS created SPARK-10968:
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             Summary: Incorrect Join behavior in filter conditions
                 Key: SPARK-10968
                 URL: https://issues.apache.org/jira/browse/SPARK-10968
             Project: Spark
          Issue Type: Bug
          Components: Spark Core, SQL
    Affects Versions: 1.5.1, 1.4.1
         Environment: RHEL, spark-shell
            Reporter: RaviShankar KS


We notice that the join conditions are not working as expected in the case of nested columns being compared.
As long as leaf columns have the same name under a nested column, should order matter ??

Consider below example for two data frames d5 and d5_opp : 
d5 and d5_opp have a nested field 'value', but their inner leaf columns do not have the same ordering. 

--       d5.printSchema
root
 |-- key: integer (nullable = false)
 |-- value: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- col1: string (nullable = true)
 |    |    |-- col2: string (nullable = true)
 |-- value1: struct (nullable = false)
 |    |-- col1: string (nullable = false)
 |    |-- col2: string (nullable = false)

--        d5_opp.printSchema
root
 |-- key: integer (nullable = false)
 |-- value: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- col2: string (nullable = true)
 |    |    |-- col1: string (nullable = true)
 |-- value1: struct (nullable = false)
 |    |-- col2: string (nullable = false)
 |    |-- col1: string (nullable = false)

The below join statement do not work in spark 1.5, and raises exception. In spark 1.4, no exception is raised, but join result is incorrect :

--    d5.as("d5").join( d5_opp.as("d5_opp"),  $"d5.value"  === $"d5_opp.value",  "inner").show
Exception raised is :  
org.apache.spark.sql.AnalysisException: cannot resolve '(value = value)' due to data type mismatch: differing types in '(value = value)' (array<struct<col1:string,col2:string>> and array<struct<col2:string,col1:string>>).;

--    d5.as("d5").join( d5_opp.as("d5_opp"),  $"d5.value1"  === $"d5_opp.value1",  "inner").show
Exception raised is :
org.apache.spark.sql.AnalysisException: cannot resolve '(value1 = value1)' due to data type mismatch: differing types in '(value1 = value1)' (struct<col1:string,col2:string> and struct<col2:string,col1:string>).;

// Code to be used in spark shell to create the data frames is attached.
-------------------------
The only work-around is to explode the conditions for every leaf field. 
In our case, we are generating the conditions and dataframes programmatically, and exploding the conditions for every leaf field is additional overhead, and may not be always possible.



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