Neha Neha - 1 year ago 160
Scala Question

perform join on multiple DataFrame in spark

I have 3dataframes generated from 3 different processes.
Every dataframe is having columns of same name.
My dataframe looks like this

id val1 val2 val3 val4
1 null null null null
2 A2 A21 A31 A41

id val1 val2 val3 val4
1 B1 B21 B31 B41
2 null null null null

id val1 val2 val3 val4
1 C1 C2 C3 C4
2 C11 C12 C13 C14

Out of these 3 dataframes, i want to create two dataframes, (final and consolidated).
For final, order of preferences -
dataFrame 1 > Dataframe 2 > Dataframe 3

If a result is there in dataframe 1(val1 != null), i will store that row in final dataframe.

My final result should be :

id finalVal1 finalVal2 finalVal3 finalVal4
1 B1 B21 B31 B41
2 A2 A21 A31 A41

Consolidated Dataframe will store results from all 3.

How can i do that efficiently?

Answer Source

If I understood you correctly, for each row you want to find out the first non-null values, first by looking into the first table, then the second table, then the third table.

You simply need to join these three tables based on the id and then use the coalesce function to get the first non-null element

import org.apache.spark.sql.functions._

val df1 = sc.parallelize(Seq(
    (2,"A2","A21","A31", "A41"))
  ).toDF("id", "val1", "val2", "val3", "val4")

val df2 = sc.parallelize(Seq(
    (1,"B1","B21","B31", "B41"),
  ).toDF("id", "val1", "val2", "val3", "val4")

val df3 = sc.parallelize(Seq(
    (2,"C11","C12","C13", "C14"))
  ).toDF("id", "val1", "val2", "val3", "val4")

val consolidated = df1.join(df2, "id").join(df3, "id").select(
  coalesce(df1("val1"), df2("val1"), df3("val1")).as("finalVal1"),
  coalesce(df1("val2"), df2("val2"), df3("val2")).as("finalVal2"),
  coalesce(df1("val3"), df2("val3"), df3("val3")).as("finalVal3"),
  coalesce(df1("val4"), df2("val4"), df3("val4")).as("finalVal4")

Which gives you the expected output

| id|val1|val2|val3|val4|
|  1|  B1| B21| B31| B41|
|  2|  A2| A21| A31| A41|
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