Is there a way to join two spark dataframes with custom join for each row

Question

I have two dataframes df and df2 as below

+------+---+----+
|  name|age|city|
+------+---+----+
|  John| 25|  LA|
|  Jane| 26|  LA|
|Joseph| 28|  SA|
+------+---+----+

+---+----+------+
|age|city|salary|
+---+----+------+
| 25|  LA| 40000|
| 26|    | 50000|
|   |  SF| 60000|
+---+----+------+

I want my result dataframe as below

+------+---+----+------+
|  name|age|city|salary|
+------+---+----+------+
|  John| 25|  LA| 40000|
|  Jane| 26|  LA| 50000|
|Joseph| 28|  SF| 60000|
+------+---+----+------+

Basically here I need to join using age, city as join columns but if any one of the column is empty in df2 then I need to join only with the other non null column. The solution I am looking for should be applicable even if there are around 5 columns to join only non null column should participate in the join for each row.

Answer 1

You could give more conditions when you join those dataframes and then select, groupBy would be needed.

df1.join(df2, 
    ($"age" === $"age2" || $"age2".isNull) &&
    ($"city" === $"city2" || $"city2".isNull), "left")
   .show

The result will be:

+------+---+----+----+-----+-------+
|  name|age|city|age2|city2|salary2|
+------+---+----+----+-----+-------+
|  John| 25|  LA|  25|   LA|  40000|
|  Jane| 26|  LA|  26| null|  50000|
|Joseph| 28|  SF|null|   SF|  60000|
+------+---+----+----+-----+-------+

But when you have more columns or the second dataframe has more null values, the result will be more complex.

Answer 2

df1.join(df2,df1.col("age")===df2.col("age") || df1.col("city")===df2.col("city")).select(df1.col("name"),df1.col("age"),df1.col("city"),df2.col("salary")).show
+----+---+----+------+
|name|age|city|salary|
+----+---+----+------+
|john| 25|  LA| 40000|
|Jane| 26|  LA| 40000|
|Jane| 26|  LA| 50000|
+----+---+----+------+```