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CUBE and ROLLUP: Two Apache Pig Functions That Every Data Scientist Should Know

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I recently found two incredible functions in Apache Pig called CUBE and ROLLUP that every data scientist should know. These functions can be used to compute multi-level aggregations of a data set. I found the documentation for these functions to be confusing, so I will work through a simple example to explain how they work.

Aggregating in Pig Using the GROUP Operator

Before we get into CUBE and ROLLUP, I will describe how to do simple aggregations using the GROUP BY operator in pig. If this is familiar to you, feel free to skip ahead to the next section.

Imagine a simple data set of people where for each person we know their name, the country they live in, their gender, the sport they play, and their height.

$ cat people.csv
Steve,US,M,football,6.5
Alex,US,M,football,5.5
Ted,UK,M,football,6.0
Mary,UK,F,baseball,5.5
Ellen,UK,F,football,5.0

A common task in analytics is to compute the average value (or some other aggregate quantity like median or maximum) for people in different groups. For example, we might be interested in computing the average height of people in a given country.

We can do this using Pig fairly easily. First, we load in the data:

people = LOAD 'people.csv' USING PigStorage(',') 
    AS (name:chararray, country:chararray, 
    gender:chararray, sport:chararray, height:float);

You can follow along by running pig in local mode with with the command:

pig -x local

First, we should DUMP the people table to make sure we loaded it correctly:

(Steve,US,M,football,6.5)
(Alex,US,M,football,5.5)
(Ted,UK,M,football,6.0)
(Mary,UK,F,baseball,5.5)
(Ellen,UK,F,football,5.0)

In order to compute the average height for people of a given gender, we have to first group the data by gender:

grouped = GROUP people BY gender;

The GROUP operator returns, for each gender, a bag containing all of the rows for people of that gender. For our example, the grouped table is:

(F,{(Mary,UK,F,baseball,5.5),(Ellen,UK,F,football,5.0)})
(M,{(Steve,US,M,football,6.5),(Alex,US,M,football,5.5),(Ted,UK,M,football,6.0)})

To compute the average height, we can then use the FOREACH and AVG command. While we are at it, we can compute the number of people with the COUNT command:

heights = FOREACH grouped GENERATE
    group AS gender,
    COUNT(people) AS num_people,
    AVG(people.height) AS avg_height;

The heights table is:

(F,2,5.25)
(M,3,6.0)

This shows that there are 3 men with an average height of 6.0 and two women with an average height of 5.25.

If we wanted to compute the average height for people of a particular gender who played a particular sport, we could again expand upon the GROUP BY command from above:

grouped = GROUP people BY (gender,sport);
heights = FOREACH grouped GENERATE
    FLATTEN(group) AS (gender,sport),
    COUNT(people) AS num_people,
    AVG(people.height) AS avg_height;

The heights table now contains:

(F,baseball,1,5.5)
(F,football,1,5.0)
(M,football,3,6.0)

This says, for example, that there are three men who play football with an average height of 6.0.

Multi-Level Aggregations in Pig the Hard Way

What if both aggregations were important. Suppose we wanted to know both the average height by gender as well as the average height by gender and by sport. One way to solve this is to simply create two tables, one for each aggregation. But this is cumbersome, especially if there were many interesting dimensions to aggregate over.

Another thing we could do is a weighted average of the average by height and gender to compute the average by gender. But this is inelegant, will slow down any future queries on the data set by requiring an additional aggregation, and will not work for more sophisticated aggregate quantities like median or quantiles.

Another option would be to create a duplicate set of rows in our people table where the gender is hard coded to a default value like a star. Then, when we computed the average height by gender and by sport we would automatically also compute the average height by just gender (where sport is equal to a star).

To do this, we can create a new table and UNION it with the original table:

agg_people = FOREACH people GENERATE
    name, 
    country, 
    gender, 
    '*' AS sport, 
    height;

unioned_people = UNION people, agg_people;

grouped = GROUP unioned_people BY (gender,sport);
heights = FOREACH grouped GENERATE
    FLATTEN(group) AS (gender,sport),
    COUNT(unioned_people) AS num_people,
    AVG(unioned_people.height) AS avg_height;

The heights table now contains

(F,*,2,5.25)
(F,baseball,1,5.5)
(F,football,1,5.0)
(M,*,3,6.0)
(M,football,3,6.0)

As you can see, this table nicely combines both data sets from above. The average height by gender for people of any sport can be found by looking for rows where sport equals ‘*’.

CUBE And ROLLUP For Multi-Level Aggregations

This UNION approach above works, but it is not very elegant. It suffers from the limitations that it requires a lot of extra code, does not generalize easily to performing aggregations over multiple dimensions, and is not well optimized by Pig.

Fortunately, as of version 0.11, Apache Pig provides the CUBE and ROLLUP function which can perform this kind of calculation much more efficiently and elegantly.

For the same people table from before, we can compute the different possible aggregations by gender and sport using the code:

cubed = CUBE people BY CUBE(gender, sport);

The cubed table is:

((F,baseball),{(F,baseball,Mary,UK,5.5)})
((F,football),{(F,football,Ellen,UK,5.0)})
((F,),{(F,,Mary,UK,5.5),(F,,Ellen,UK,5.0)})
((M,football),{(M,football,Alex,US,5.5),(M,football,Ted,UK,6.0),(M,football,Steve,US,6.5)})
((M,),{(M,,Alex,US,5.5),(M,,Steve,US,6.5),(M,,Ted,UK,6.0)})
((,baseball),{(,baseball,Mary,UK,5.5)})
((,football),{(,football,Ted,UK,6.0),(,football,Steve,US,6.5),(,football,Alex,US,5.5),(,football,Ellen,UK,5.0)})
((,),{(,,Mary,UK,5.5),(,,Alex,US,5.5),(,,Steve,US,6.5),(,,Ellen,UK,5.0),(,,Ted,UK,6.0)})

Although this is hard to look at, you will see that the first column contains the possible values for cubed columns. The second column contains bags of all the rows matching that aggregation. This is almost identical to the way GROUP BY works except that some of the rows now contain NULL values (corresponding to aggregations over that column.

To compute the average height as above, we can again use the FOREACH and AVG operators:

heights = FOREACH cubed GENERATE 
    FLATTEN(group) AS (gender, sport), 
    COUNT_STAR(cube) AS num_people,
    AVG(cube.height) AS avg_height;  

The heights table now contains:

(F,baseball,1,5.5)
(F,football,1,5.0)
(F,,2,5.25)
(M,football,3,6.0)
(M,,3,6.0)
(,baseball,1,5.5)
(,football,4,5.75)
(,,5,5.7)

This table is the same as above, but also computes the average per sport for all genders and the average over all genders and all sports.

Note that in this example we changed from using the COUNT to COUNT_STAR function. The reason is that the COUNT function in Pig will not count rows with NULL values in it whereas COUNT_STAR includes those rows.

Unlike the example above, CUBE sets the aggregate value to NULL instead of a star. I find this annoying and prefer to use the star symbol. We can fix this in Pig:

heights  = FOREACH heights GENERATE
    (gender is not NULL ? gender : '*') as gender, 
    (sport is not NULL ? sport : '*') as sport,
    num_people,
    avg_height;

The heights table finally contains:

(F,baseball,1,5.5)
(F,football,1,5.0)
(F,*,2,5.25)
(M,football,3,6.0)
(M,*,3,6.0)
(*,baseball,1,5.5)
(*,football,4,5.75)
(*,*,5,5.7)

CUBEing Over Multiple Dimensions

What is really great about CUBE is the ability to cube over multiple dimensions. It isn’t much harder using CUBE to compute all possible aggregations of country, gender, and sport:

cubed = CUBE people BY CUBE(country,gender,sport);

heights = FOREACH cubed GENERATE 
    FLATTEN(group) AS (country,gender,sport),
    COUNT_STAR(cube) As num_people,
    AVG(cube.height) AS avg_height;  

heights = FOREACH heights GENERATE
    (country is not NULL ? country : '*') as country, 
    (gender is not NULL ? gender : '*') as gender, 
    (sport is not NULL ? sport : '*') as sport,
    num_people,
    avg_height;

The heights table now contains the average height for all possible aggregations of the three columns:

(UK,F,baseball,1,5.5)
(UK,F,football,1,5.0)
(UK,F,*,2,5.25)
(UK,M,football,1,6.0)
(UK,M,*,1,6.0)
(UK,*,baseball,1,5.5)
(UK,*,football,2,5.5)
(UK,*,*,3,5.5)
(US,M,football,2,6.0)
(US,M,*,2,6.0)
(US,*,football,2,6.0)
(US,*,*,2,6.0)
(*,F,baseball,1,5.5)
(*,F,football,1,5.0)
(*,F,*,2,5.25)
(*,M,football,3,6.0)
(*,M,*,3,6.0)
(*,*,baseball,1,5.5)
(*,*,football,4,5.75)
(*,*,*,5,5.7)

Here CUBE computed all aggregations for a particular country, gender, and sport. But it also computed all aggregations for a particular country and gender (but any sport), country and sport (but any gender), and gender and sport (but any country). It also computed all aggregations for a particular country (but any gender and sport), a particular gender (but any country and sport), and for a particular sport (but any gender and country) And finally, it computed the aggregation over all people.

From this table, we see that the average height of all football players is 5.75, the average height of all people in the US is 6.0, and the average height of all football players in the US is 6.0.

The ROLLUP Operator

The ROLLUP operator is similar to CUBE, but will only perform hierarchical aggregations. For our example, ROLLUP of country, gender, and sport will perform all aggregations for a particular country, gender, and sport. Then it will perform all aggregations for a particular country and gender (but any sport). Then it will perform all aggregations for a particular country (but for any gender and sport). And finally it will compute an aggregation for all people.

To ROLLUP our example from above:

rolledup = CUBE people BY ROLLUP(country,gender,sport);

heights = FOREACH rolledup GENERATE 
    FLATTEN(group) AS (country,gender,sport),
    COUNT_STAR(cube) AS num_people,
    AVG(cube.height) AS avg_height;  

heights = FOREACH heights GENERATE
    (country is not NULL ? country : '*') as country, 
    (gender is not NULL ? gender : '*') as gender, 
    (sport is not NULL ? sport : '*') as sport,
    num_people,
    avg_height;

The heights table now contains:

(UK,F,baseball,1,5.5)
(UK,F,football,1,5.0)
(UK,F,*,2,5.25)
(UK,M,football,1,6.0)
(UK,M,*,1,6.0)
(UK,*,*,3,5.5)
(US,M,football,2,6.0)
(US,M,*,2,6.0)
(US,*,*,2,6.0)
(*,*,*,5,5.7)

Although this might seem artificial, it is very useful if the columns in the data set are hierarchical. For example, this would be useful if the data had the columns continent, country, and city.

Not Aggregating Over Certain Columns

The last situation is where you may not want to aggregation over a certain column. This may be useful if one of the columns cannot logically be aggregated over.

For our example data set above, supposed that we wanted to cube over gender and sport. But what if we wanted this quantity only for a particular country, not aggregated over all countries as before.

We can do this with CUBE followed by FLATTEN:

cubed = CUBE people BY CUBE(gender, sport);

flattened_bags = FOREACH cubed GENERATE 
    FLATTEN(cube);

flattened_bags = FOREACH flattened_bags GENERATE
    name as name,
    country as country,
    (gender is not NULL ? gender : '*') as gender, 
    (sport is not NULL ? sport : '*') as sport,
    height as height;

The flattened_bags table contains all of the rows from the bags created by CUBE:

(Mary,UK,F,baseball,5.5)
(Ellen,UK,F,football,5.0)
(Mary,UK,F,*,5.5)
(Ellen,UK,F,*,5.0)
(Alex,US,M,football,5.5)
(Ted,UK,M,football,6.0)
(Steve,US,M,football,6.5)
(Alex,US,M,*,5.5)
(Steve,US,M,*,6.5)
(Ted,UK,M,*,6.0)
(Mary,UK,*,baseball,5.5)
(Ted,UK,*,football,6.0)
(Steve,US,*,football,6.5)
(Alex,US,*,football,5.5)
(Ellen,UK,*,football,5.0)
(Mary,UK,*,*,5.5)
(Alex,US,*,*,5.5)
(Steve,US,*,*,6.5)
(Ellen,UK,*,*,5.0)
(Ted,UK,*,*,6.0)

Finally, we can group the flattened bags by any columns we want to aggregate over:

grouped = GROUP flattened_bags BY (country,gender,sport);

heights = FOREACH grouped GENERATE
    FLATTEN(group) AS (country,gender,sport),
    COUNT(flattened_bags) AS num_people,
    AVG(flattened_bags.height) AS avg_height;

The heights table now contains:

(UK,*,*,3,5.5)
(UK,*,baseball,1,5.5)
(UK,*,football,2,5.5)
(UK,F,*,2,5.25)
(UK,F,baseball,1,5.5)
(UK,F,football,1,5.0)
(UK,M,*,1,6.0)
(UK,M,football,1,6.0)
(US,*,*,2,6.0)
(US,*,football,2,6.0)
(US,M,*,2,6.0)
(US,M,football,2,6.0)

This table has all possible combinations of aggregations over gender and sport, but does not aggregate over country.

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