How to Use right join in Spark Scala

How to Use right join in Spark Scala with Examples

The join function in Apache Spark is used to combine two DataFrames based on common columns or keys. This operation is essential for data integration and analysis tasks. In this tutorial, we will cover how to perform right join in Spark using Scala with practical examples.

1. Creating Sample DataFrames

We will start by creating two sample DataFrames to demonstrate the join operations. Here's the data we'll use:

Employee Table

id name age
1 John 28
2 Alice 34
4 Bob 23

Salary Table

id salary
1 50000
2 60000 

//please use below line else toDF will show error
import sparkSession.implicits._

val employeeData = Seq(
   (1, "rahul", 28),
   (2, "anil", 34),
   (4, "Raju", 23)
  )
  
  val salaryData = Seq(
    (1, 50000),
    (2, 60000)
  )

val employeeDF = employeeData.toDF("id", "name", "age")
val salaryDF = salaryData.toDF("id", "salary")

2. Importing Necessary Libraries

Before we can use right join, we need to import the necessary libraries:

import org.apache.spark.sql.{SparkSession}

3. right join : If Joining column name is same in both DataFrames

Now that we have our DataFrames, we can combine them using the join function: 

val leftJoinDF1 = employeeDF.join(salaryDF,Seq("id"),"right")
leftJoinDF1.show()

3.right Join: Multiple column name are same in both DataFrames

This approach is particularly useful for controlling column selection when dealing with multiple common columns across both DataFrames.

    val leftJoinDF2 = employeeDF.as("emp").join(salaryDF.as("sal"),
    employeeDF.col("id")===salaryDF.col("id"),"right")
    .select("emp.id","emp.name","emp.age","sal.salary")
  leftJoinDF2.show()

Complete Code

  import org.apache.spark.sql.SparkSession

  object RightJoinsInSpark {
    def main(args: Array[String]): Unit = {
      val sparkSession = SparkSession
        .builder()
        .appName("Use union in scala spark")
        .master("local")
        .getOrCreate()
  
      //please use below line else toDF will show error
      import sparkSession.implicits._
  
      val employeeData = Seq(
        (1, "rahul", 28),
        (2, "anil", 34),
        (4, "Raju", 23)
      )
  
      val salaryData = Seq(
        (1, 50000),
        (2, 60000)
      )
      val employeeDF = employeeData.toDF("id", "name", "age")
      val salaryDF = salaryData.toDF("id", "salary")
  
      //style 1 use this if only joining column name is same
      val innerJoinDF1 = employeeDF.join(salaryDF,Seq("id"),"right")
      innerJoinDF1.show()
  
      //style 2 - use this if column names are same other then joining column
      val innerJoinDF2 = employeeDF.as("emp").join(salaryDF.as("sal"),
        employeeDF.col("id")===salaryDF.col("id"),"right")
        .select("emp.id","emp.name","emp.age","sal.salary")
      innerJoinDF2.show()
  
    }
  
  }

4. Output

Alps