Spark SQL Full Outer Join with Example

Spark SQL Full Outer Join

Spark SQL Full Outer Join (<code>outer, full,fullouter, full_outer) returns all rows from both DataFrame/Datasets, where join expression doesn’t match it returns null on respective columns.

In this Spark article, I will explain how to do Full Outer Join(outer, full,fullouter, full_outer) on two DataFrames with Scala Example.

Before we jump into Spark Full Outer 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 DataFrame Full Outer Join Example

In order to use Full Outer Join on Spark DataFrame, you can use either outer, full, fullouter Join as a join type.


empDF.join(deptDF,empDF("emp_dept_id") ===   deptDF("dept_id"),"outer")
    .show(false)
  empDF.join(deptDF,empDF("emp_dept_id") ===  deptDF("dept_id"),"full")
    .show(false)
  empDF.join(deptDF,empDF("emp_dept_id") ===  deptDF("dept_id"),"fullouter")
    .show(false)

From our emp dataset’s emp_dept_id with value 60 doesn’t have a record on dept hence dept columns have null and dept_id 30 doesn’t have a record in emp hence you see null’s on emp columns. Below is the result of the above Join expression.

Yields below output


+------+--------+---------------+-----------+-----------+------+------+---------+-------+
|emp_id|name    |superior_emp_id|year_joined|emp_dept_id|gender|salary|dept_name|dept_id|
+------+--------+---------------+-----------+-----------+------+------+---------+-------+
|2     |Rose    |1              |2010       |20         |M     |4000  |Marketing|20     |
|5     |Brown   |2              |2010       |40         |      |-1    |IT       |40     |
|1     |Smith   |-1             |2018       |10         |M     |3000  |Finance  |10     |
|3     |Williams|1              |2010       |10         |M     |1000  |Finance  |10     |
|4     |Jones   |2              |2005       |10         |F     |2000  |Finance  |10     |
|6     |Brown   |2              |2010       |50         |      |-1    |null     |null   |
|null  |null    |null           |null       |null       |null  |null  |Sales    |30     |
+------+--------+---------------+-----------+-----------+-----

Using Spark SQL Full Outer Join

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

<pre><code class="language-Scala">
empDF.createOrReplaceTempView("EMP")
deptDF.createOrReplaceTempView("DEPT")

joinDF2 = spark.sql("SELECT e.* FROM EMP e Full OUTER JOIN DEPT d ON e.emp_dept_id == d.dept_id")
  .show(truncate=False)
</code></pre>

This also returns same output as above.

Conclusion

In this Spark article, you have learned Full Outer Join ( outer, full, fullouter, full_outer) returns all rows from both DataFrame/Datasets regardless of match found or not, where join expression doesn’t match it returns null on respective record columns.

Hope you Like it !!

References:

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