Spark has a variety of aggregate functions to group, cube, and rollup DataFrames.

This post will explain how to use aggregate functions with Spark.

Check out Beautiful Spark Code for a detailed overview of how to structure and test aggregations in production applications.

groupBy()

Let’s create a DataFrame with two famous soccer players and the number of goals they scored in some games.

val goalsDF = Seq( ("messi", 2), ("messi", 1), ("pele", 3), ("pele", 1) ).toDF("name", "goals")

Let’s inspect the contents of the DataFrame:

goalsDF.show() +-----+-----+ | name|goals| +-----+-----+ |messi| 2| |messi| 1| | pele| 3| | pele| 1| +-----+-----+

Let’s use groupBy() to calculate the total number of goals scored by each player.

import org.apache.spark.sql.functions._ goalsDF .groupBy("name") .agg(sum("goals")) .show()

+-----+----------+ | name|sum(goals)| +-----+----------+ | pele| 4| |messi| 3| +-----+----------+

We need to import org.apache.spark.sql.functions._ to access the sum() method in agg(sum("goals") . There are a ton of aggregate functions defined in the functions object.

The groupBy method is defined in the Dataset class. groupBy returns a RelationalGroupedDataset object where the agg() method is defined.

Spark makes great use of object oriented programming!

The RelationalGroupedDataset class also defines a sum() method that can be used to get the same result with less code.

goalsDF .groupBy("name") .sum() .show()

+-----+----------+ | name|sum(goals)| +-----+----------+ | pele| 4| |messi| 3| +-----+----------+

Testing Spark Applications teaches you how to package this aggregation in a custom transformation and write a unit test. You should read the book if you want to fast-track you Spark career and become an expert quickly.

groupBy() with two arguments

Let’s create another DataFrame with information on students, their country, and their continent.

val studentsDF = Seq( ("mario", "italy", "europe"), ("stefano", "italy", "europe"), ("victor", "spain", "europe"), ("li", "china", "asia"), ("yuki", "japan", "asia"), ("vito", "italy", "europe") ).toDF("name", "country", "continent")

Let’s get a count of the number of students in each continent / country.

studentsDF .groupBy("continent", "country") .agg(count("*")) .show()

+---------+-------+--------+ |continent|country|count(1)| +---------+-------+--------+ | europe| italy| 3| | asia| japan| 1| | europe| spain| 1| | asia| china| 1| +---------+-------+--------+

We can also leverage the RelationalGroupedDataset#count() method to get the same result:

studentsDF .groupBy("continent", "country") .count() .show()

+---------+-------+-----+ |continent|country|count| +---------+-------+-----+ | europe| italy| 3| | asia| japan| 1| | europe| spain| 1| | asia| china| 1| +---------+-------+-----+

groupBy() with filters

Let’s create another DataFrame with the number of goals and assists for two hockey players during a few seasons:

val hockeyPlayersDF = Seq( ("gretzky", 40, 102, 1990), ("gretzky", 41, 122, 1991), ("gretzky", 31, 90, 1992), ("messier", 33, 61, 1989), ("messier", 45, 84, 1991), ("messier", 35, 72, 1992), ("messier", 25, 66, 1993) ).toDF("name", "goals", "assists", "season")

Let’s calculate the average number of goals and assists for each player in the 1991 and 1992 seasons.

hockeyPlayersDF .where($"season".isin("1991", "1992")) .groupBy("name") .agg(avg("goals"), avg("assists")) .show()

+-------+----------+------------+ | name|avg(goals)|avg(assists)| +-------+----------+------------+ |messier| 40.0| 78.0| |gretzky| 36.0| 106.0| +-------+----------+------------+

Now let’s calculate the average number of goals and assists for each player with more than 100 assists on average.

hockeyPlayersDF .groupBy("name") .agg(avg("goals"), avg("assists").as("average_assists")) .where($"average_assists" >= 100) .show()

+-------+------------------+------------------+ | name| avg(goals)| average_assists| +-------+------------------+------------------+ |gretzky|37.333333333333336|104.66666666666667| +-------+------------------+------------------+

Many SQL implementations use the HAVING keyword for filtering after aggregations. The same Spark where() clause works when filtering both before and after aggregations.

cube()

cube isn’t used too frequently, so feel free to skip this section.

Let’s create another sample dataset and replicate the cube() examples in this Stackoverflow answer.

val df = Seq( ("bar", 2L), ("bar", 2L), ("foo", 1L), ("foo", 2L) ).toDF("word", "num")

The cube function “takes a list of columns and applies aggregate expressions to all possible combinations of the grouping columns”.

df .cube($"word", $"num") .count() .sort(asc("word"), asc("num")) .show()

+----+----+-----+ |word| num|count| +----+----+-----+ |null|null| 4| Total rows in df |null| 1| 1| Count where num equals 1 |null| 2| 3| Count where num equals 2 | bar|null| 2| Where word equals bar | bar| 2| 2| Where word equals bar and num equals 2 | foo|null| 2| Where word equals foo | foo| 1| 1| Where word equals foo and num equals 1 | foo| 2| 1| Where word equals foo and num equals 2 +----+----+-----+

The order of the arguments passed to the cube() function don’t matter, so cube($"word", $"num") will return the same results as cube($"num", $"word") .

rollup()

rollup is a subset of cube that “computes hierarchical subtotals from left to right”.

df .rollup($"word", $"num") .count() .sort(asc("word"), asc("num")) .show()

+----+----+-----+ |word| num|count| +----+----+-----+ |null|null| 4| Count of all rows | bar|null| 2| Count when word is bar | bar| 2| 2| Count when num is 2 | foo|null| 2| Count when word is foo | foo| 1| 1| When word is foo and num is 1 | foo| 2| 1| When word is foo and num is 2 +----+----+-----+

rollup() returns a subset of the rows returned by cube() . rollup returns 6 rows whereas cube returns 8 rows. Here are the missing rows.

+----+----+-----+ |word| num|count| +----+----+-----+ |null| 1| 1| Word is null and num is 1 |null| 2| 3| Word is null and num is 2 +----+----+-----+

rollup($"word", $"num") doesn’t return the counts when only word is null .

Let’s switch around the order of the arguments passed to rollup and view the difference in the results.

df .rollup($"num", $"word") .count() .sort(asc("word"), asc("num")) .select("word", "num", "count") .show()

+----+----+-----+ |word| num|count| +----+----+-----+ |null|null| 4| |null| 1| 1| |null| 2| 3| | bar| 2| 2| | foo| 1| 1| | foo| 2| 1| +----+----+-----+

Here are the rows missing from rollup($"num", $"word") compared to cube($"word", $"num") .

+----+----+-----+ |word| num|count| +----+----+-----+ | bar|null| 2| Word equals bar and num is null | foo|null| 2| Word equals foo and num is null +----+----+-----+

rollup($"num", $"word") doesn’t return the counts when only num is null .

Next steps

Spark makes it easy to run aggregations at scale.

In production applications, you’ll often want to do much more than run a simple aggregation. You’ll want to verify the correctness of your code with tests and incrementally update aggregations. Make sure you learn how to test your aggregation functions!

If you’re still struggling with the Spark basics, make sure to read a good book to grasp the fundamentals.