Similar to SQL “GROUP BY” clause, Spark groupBy() function is used to collect the identical data into groups on DataFrame/Dataset and perform aggregate functions on the grouped data. In this article, I will explain several groupBy() examples with the Scala language.

The same approach can be used with the Pyspark (Spark with Python).

Syntax:

groupBy(col1 : scala.Predef.String, cols : scala.Predef.String*) : org.apache.spark.sql.RelationalGroupedDataset

When we perform groupBy() on Spark Dataframe, it returns RelationalGroupedDataset object which contains below aggregate functions.

count() - Returns the count of rows for each group.

mean() - Returns the mean of values for each group.

max() - Returns the maximum of values for each group.

min() - Returns the minimum of values for each group.

sum() - Returns the total for values for each group.

avg() - Returns the average for values for each group.

agg() - Using agg() function, we can calculate more than one aggregate at a time.

pivot() - This function is used to Pivot the DataFrame which I will not be covered in this article as I already have a dedicated article for Pivot & Unvot DataFrame.

Preparing Data & DataFrame

Before we start, let’s create the DataFrame from a sequence of the data to work with. This DataFrame contains columns “ employee_name ”, “ department ”, “ state “, “ salary ”, “ age ” and “ bonus ” columns.

We will use this Spark DataFrame to run groupBy() on “department” columns and calculate aggregates like minimum, maximum, average, total salary for each group using min(), max() and sum() aggregate functions respectively. and finally, we will also see how to do group and aggregate on multiple columns.

import spark.implicits._ val simpleData = Seq(("James","Sales","NY",90000,34,10000), ("Michael","Sales","NY",86000,56,20000), ("Robert","Sales","CA",81000,30,23000), ("Maria","Finance","CA",90000,24,23000), ("Raman","Finance","CA",99000,40,24000), ("Scott","Finance","NY",83000,36,19000), ("Jen","Finance","NY",79000,53,15000), ("Jeff","Marketing","CA",80000,25,18000), ("Kumar","Marketing","NY",91000,50,21000) ) val df = simpleData.toDF("employee_name","department","state","salary","age","bonus") df.show()

Yields below output.

+-------------+----------+-----+------+---+-----+ |employee_name|department|state|salary|age|bonus| +-------------+----------+-----+------+---+-----+ | James| Sales| NY| 90000| 34|10000| | Michael| Sales| NY| 86000| 56|20000| | Robert| Sales| CA| 81000| 30|23000| | Maria| Finance| CA| 90000| 24|23000| | Raman| Finance| CA| 99000| 40|24000| | Scott| Finance| NY| 83000| 36|19000| | Jen| Finance| NY| 79000| 53|15000| | Jeff| Marketing| CA| 80000| 25|18000| | Kumar| Marketing| NY| 91000| 50|21000| +-------------+----------+-----+------+---+-----+

groupBy and aggregate on DataFrame columns

Let’s do the groupBy() on department column of DataFrame and then find the sum of salary for each department using sum() aggregate function.

df.groupBy("department").sum("salary").show(false) +----------+-----------+ |department|sum(salary)| +----------+-----------+ |Sales |257000 | |Finance |351000 | |Marketing |171000 | +----------+-----------+

Similarly, we can calculate the number of employee in each department using count()

df.groupBy("department").count()

Calculate the minimum salary of each department using min()

df.groupBy("department").min("salary")

Calculate the maximin salary of each department using max()

df.groupBy("department").max("salary")

Calculate the average salary of each department using avg()

df.groupBy("department").avg( "salary")

Calculate the mean salary of each department using mean()

df.groupBy("department").mean( "salary")

groupBy and aggregate on multiple DataFrame columns

Similarly, we can also run groupBy and aggregate on two or more DataFrame columns, below example does group by on department , state and does sum() on salary and bonus columns.

//GroupBy on multiple columns df.groupBy("department","state") .sum("salary","bonus") .show(false)

This yields the below output.

+----------+-----+-----------+----------+ |department|state|sum(salary)|sum(bonus)| +----------+-----+-----------+----------+ |Finance |NY |162000 |34000 | |Marketing |NY |91000 |21000 | |Sales |CA |81000 |23000 | |Marketing |CA |80000 |18000 | |Finance |CA |189000 |47000 | |Sales |NY |176000 |30000 | +----------+-----+-----------+----------+

similarly, we can run group by and aggregate on tow or more columns for other aggregate functions, please refer below source code for example.

Running more aggregates at a time

Using agg() aggregate function we can calculate many aggregations at a time on a single statement using Spark SQL aggregate functions sum(), avg(), min(), max() mean() e.t.c. In order to use these, we should import "import org.apache.spark.sql.functions._"

import org.apache.spark.sql.functions._ df.groupBy("department") .agg( sum("salary").as("sum_salary"), avg("salary").as("avg_salary"), sum("bonus").as("sum_bonus"), max("bonus").as("max_bonus")) .show(false)

This example does group on department column and calculates sum() and avg() of salary for each department and calculates sum() and max() of bonus for each department.

+----------+----------+-----------------+---------+---------+ |department|sum_salary|avg_salary |sum_bonus|max_bonus| +----------+----------+-----------------+---------+---------+ |Sales |257000 |85666.66666666667|53000 |23000 | |Finance |351000 |87750.0 |81000 |24000 | |Marketing |171000 |85500.0 |39000 |21000 | +----------+----------+-----------------+---------+---------+

Using filter on aggregate data

Similar to SQL “HAVING” clause, On Spark DataFrame we can use either where() or filter() function to filter the rows of aggregated data.

df.groupBy("department") .agg( sum("salary").as("sum_salary"), avg("salary").as("avg_salary"), sum("bonus").as("sum_bonus"), max("bonus").as("max_bonus")) .where(col("sum_bonus") >= 50000) .show(false)

This removes the sum of a bonus that has less than 50000 and yields below output.

+----------+----------+-----------------+---------+---------+ |department|sum_salary|avg_salary |sum_bonus|max_bonus| +----------+----------+-----------------+---------+---------+ |Sales |257000 |85666.66666666667|53000 |23000 | |Finance |351000 |87750.0 |81000 |24000 | +----------+----------+-----------------+---------+---------+

Source code

package com.sparkbyexamples.spark.dataframe import org.apache.spark.sql.SparkSession import org.apache.spark.sql.functions._ object GroupbyExample extends App { val spark: SparkSession = SparkSession.builder() .master("local[1]") .appName("SparkByExamples.com") .getOrCreate() spark.sparkContext.setLogLevel("ERROR") import spark.implicits._ val simpleData = Seq(("James","Sales","NY",90000,34,10000), ("Michael","Sales","NY",86000,56,20000), ("Robert","Sales","CA",81000,30,23000), ("Maria","Finance","CA",90000,24,23000), ("Raman","Finance","CA",99000,40,24000), ("Scott","Finance","NY",83000,36,19000), ("Jen","Finance","NY",79000,53,15000), ("Jeff","Marketing","CA",80000,25,18000), ("Kumar","Marketing","NY",91000,50,21000) ) val df = simpleData.toDF("employee_name","department","state","salary","age","bonus") df.show() //Group By on single column df.groupBy("department").count().show(false) df.groupBy("department").avg("salary").show(false) df.groupBy("department").sum("salary").show(false) df.groupBy("department").min("salary").show(false) df.groupBy("department").max("salary").show(false) df.groupBy("department").mean("salary").show(false) //GroupBy on multiple columns df.groupBy("department","state") .sum("salary","bonus") .show(false) df.groupBy("department","state") .avg("salary","bonus") .show(false) df.groupBy("department","state") .max("salary","bonus") .show(false) df.groupBy("department","state") .min("salary","bonus") .show(false) df.groupBy("department","state") .mean("salary","bonus") .show(false) //Running Filter df.groupBy("department","state") .sum("salary","bonus") .show(false) //using agg function df.groupBy("department") .agg( sum("salary").as("sum_salary"), avg("salary").as("avg_salary"), sum("bonus").as("sum_bonus"), max("bonus").as("max_bonus")) .show(false) df.groupBy("department") .agg( sum("salary").as("sum_salary"), avg("salary").as("avg_salary"), sum("bonus").as("sum_bonus"), stddev("bonus").as("stddev_bonus")) .where(col("sum_bonus") > 50000) .show(false) }

This example is also available at GitHub project for reference.

Conclusion

In this tutorial, you have learned how to use groupBy() and aggregate functions on Spark DataFrame and also learned how to run these on multiple columns and finally filtering data on the aggregated column.

Thanks for reading. If you like it, please do share the article by following the below social links and any comments or suggestions are welcome in the comments sections!

Happy Learning !!