Spark SQL Left Anti Join with Example

Spark SQL Left Anti Join

When you join two DataFrame using Left Anti Join (left, left anti, left_anti), it returns only columns from the left DataFrame for non-matched records.

In this Spark article, I will explain how to do Left Anti Join(left, leftanti, left_anti) on two DataFrames with Scala Example.

leftanti join does the exact opposite of the leftsemi join.

Before we jump into Spark Left Anti Join examples, first, let’s create an emp and dept DataFrame’s. here, column emp_id is unique on emp and dept_id is unique on the dept DataFrame and emp_dept_id from emp has a reference to dept_id on dept dataset.


   import org.apache.spark.sql.SparkSession
     val spark = SparkSession.builder
       .appName("sparkbyexamples.com")
       .master("local")
       .getOrCreate()

  val emp = Seq((1,"Smith",-1,"2018","10","M",3000),
    (2,"Rose",1,"2010","20","M",4000),
    (3,"Williams",1,"2010","10","M",1000),
    (4,"Jones",2,"2005","10","F",2000),
    (5,"Brown",2,"2010","40","",-1),
      (6,"Brown",2,"2010","50","",-1)
  )
  val empColumns = Seq("emp_id","name","superior_emp_id","year_joined",
       "emp_dept_id","gender","salary")
  import spark.sqlContext.implicits._
  val empDF = emp.toDF(empColumns:_*)
  empDF.show(false)

  val dept = Seq(("Finance",10),
    ("Marketing",20),
    ("Sales",30),
    ("IT",40)
  )

  val deptColumns = Seq("dept_name","dept_id")
  val deptDF = dept.toDF("deptColumns")
  deptDF.show(false)

This prints emp and dept DataFrame to console.


Emp Dataset
+------+--------+---------------+-----------+-----------+------+------+
|emp_id|name    |superior_emp_id|year_joined|emp_dept_id|gender|salary|
+------+--------+---------------+-----------+-----------+------+------+
|1     |Smith   |-1             |2018       |10         |M     |3000  |
|2     |Rose    |1              |2010       |20         |M     |4000  |
|3     |Williams|1              |2010       |10         |M     |1000  |
|4     |Jones   |2              |2005       |10         |F     |2000  |
|5     |Brown   |2              |2010       |40         |      |-1    |
|6     |Brown   |2              |2010       |50         |      |-1    |
+------+--------+---------------+-----------+-----------+------+------+

Dept Dataset
+---------+-------+
|dept_name|dept_id|
+---------+-------+
|Finance  |10     |
|Marketing|20     |
|Sales    |30     |
|IT       |40     |
+---------+-------+

Spark Left Anti Join (leftanti) Example

In order to use left anti join, you can use either antileftantileft_anti as a join type.

empDF.join(deptDF,empDF(“emp_dept_id”) === deptDF(“dept_id”),”left”) .show(false)

Yields below output


+------+-----+---------------+-----------+-----------+------+------+
|emp_id|name |superior_emp_id|year_joined|emp_dept_id|gender|salary|
+------+-----+---------------+-----------+-----------+------+------+
|6     |Brown|2              |2010       |50         |      |-1    |
+------+-----+---------------+-----------+-----------+------+-

Using Spark SQL Left Anti Join

Let’s see how use Left Anti Join on Spark SQL expression, In order to do so first let’s create a temporary view for EMP and DEPT tables.

empDF.createOrReplaceTempView("EMP")
deptDF.createOrReplaceTempView("DEPT")

joinDF2 = spark.sql("SELECT e.* FROM EMP e LEFT ANTI JOIN DEPT d ON e.emp_dept_id == d.dept_id") 
  .show(truncate=False)

This also returns same output as above.

Conclusion

In this Spark article, you have learned Left Anti Join which is used to get only columns from the left DataFrame for non-matched records.

Hope you Like it !!

References:

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