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https://issues.apache.org/jira/browse/SPARK-10967?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
RaviShankar KS updated SPARK-10967:
-----------------------------------
Description:
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>).;
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.
was:
We notice that the join conditions are not working as expected in the case of
nested columns being compared.
As long as leaf column names are same under nested columns, should order matter
??
Consider below example for two data frames d5 and d5_opp :
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 :
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>).;
> Incorrect Join behavior in filter conditions
> --------------------------------------------
>
> Key: SPARK-10967
> URL: https://issues.apache.org/jira/browse/SPARK-10967
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, SQL
> Affects Versions: 1.4.1
> Environment: RHEL
> Reporter: RaviShankar KS
> Assignee: Josh Rosen
> Labels: sql, union
> Fix For: 1.5.0
>
>
> 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>).;
> 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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