Introduction

Let's see different ways to get the distinct values in every column or select multiple columns in a DataFrame.

First, let’s create a DataFrame with some duplicate rows and duplicate values in a few columns. We use this DataFrame to demonstrate how to get distinct multiple columns.

import spark.implicits._

val simpleData = Seq(
  ("James", "Sales", 3000),
  ("Michael", "Sales", 4600),
  ("Robert", "Sales", 4100),
  ("Maria", "Finance", 3000),
  ("James", "Sales", 3000),
  ("Scott", "Finance", 3300),
  ("Jen", "Finance", 3900),
  ("Jeff", "Marketing", 3000),
  ("Kumar", "Marketing", 2000),
  ("Saif", "Sales", 4100)
)

val df = simpleData.toDF("employee_name", "department", "salary")
df.show()

Output:

+-------------+----------+------+
|employee_name|department|salary|
+-------------+----------+------+
|        James|     Sales|  3000|
|      Michael|     Sales|  4600|
|       Robert|     Sales|  4100|
|        Maria|   Finance|  3000|
|        James|     Sales|  3000|
|        Scott|   Finance|  3300|
|          Jen|   Finance|  3900|
|         Jeff| Marketing|  3000|
|        Kumar| Marketing|  2000|
|         Saif|     Sales|  4100|
+-------------+----------+------+

As you notice, we have 2 rows that have duplicate values on all columns, and we have 4 rows that have duplicate values on “department” and “salary” columns.

1. Get Distinct All Columns

In the above DataFrame, we have a total of 10 rows and one row with all values duplicated. Performing distinct on this DataFrame should get us 9, as we have one duplicate.

// Distinct all columns
val distinctDF = df.distinct()
println("Distinct count: " + distinctDF.count())
distinctDF.show(false)

The distinct() function on DataFrame returns a new DataFrame after removing the duplicate records. This example yields the below output.

Output:

Distinct count: 9
+-------------+----------+------+
|employee_name|department|salary|
+-------------+----------+------+
|James        |Sales     |3000  |
|Michael      |Sales     |4600  |
|Maria        |Finance   |3000  |
|Robert       |Sales     |4100  |
|Saif         |Sales     |4100  |
|Scott        |Finance   |3300  |
|Jeff         |Marketing |3000  |
|Jen          |Finance   |3900  |
|Kumar        |Marketing |2000  |
+-------------+----------+------+

Alternatively, you can also run the dropDuplicates() function which returns a new DataFrame with duplicate rows removed.

val df2 = df.dropDuplicates()
println("Distinct count: " + df2.count())
df2.show(false)

2. Distinct of Multiple Columns

Spark doesn’t have a distinct method that takes columns to run distinct on. However, Spark provides another signature of the dropDuplicates() function, which takes multiple columns to eliminate duplicates.

Calling dropDuplicates() on a DataFrame returns a new DataFrame with duplicate rows removed.

// Distinct using dropDuplicates
val dropDisDF = df.dropDuplicates("department", "salary")
println("Distinct count of department & salary: " + dropDisDF.count())
dropDisDF.show(false)

This yields the output below. If you notice the output, it dropped 2 records that are duplicates.

Output:

Distinct count of department & salary: 8
+-------------+----------+------+
|employee_name|department|salary|
+-------------+----------+------+
|Jen          |Finance   |3900  |
|Maria        |Finance   |3000  |
|Scott        |Finance   |3300  |
|Michael      |Sales     |4600  |
|Kumar        |Marketing |2000  |
|Robert       |Sales     |4100  |
|James        |Sales     |3000  |
|Jeff         |Marketing |3000  |
+-------------+----------+------+

The complete example is available at GitHub for reference.