SQL Window Function Example Interview Question - Aggregation

When you're interviewing for a data scientist or data analyst role, it's highly likely you'll encounter SQL questions in your interview. Additionally, it's likely one or more of those SQL questions will require a window function to be solved. Window functions are a core concept in intermediate/advanced SQL, and mastering them will put you one step closer to landing your analytics role. In this post we'll give a quick overview of what window functions are, and then we'll dive into an example interview question along with our solution.

What are SQL window functions?

A window function defines a frame or ‘window’ of rows with a given length around the current row, and performs a calculation across the set of data in the window.

If you’re struggling with the definition above, don’t worry, it should become more clear as you put it into practice. Below we'll step through an example window function interview question that uses a simple aggregation.

Example question - Aggregation in SQL window function

Supose you're given the following table that shows spend by keyword, advertiser, and unique ad ID:

Table: keyword_summary

keyword ad_id advertiser_id total_spend
bicycle 1243213 1234 95
bike 1243213 1234 71
bike tires 1243213 1234 22
bike brakes 1243213 1234 95
bike accessories 1243213 1234 28
... ... ... ...

Using the table above, write a SQL query that returns each keyword along with the advertiser_id of the top spending advertiser, and total spend on the keyword (agnostic of advertiser). Sort the results in descending order by the total spend on the keyword. You can view/query the data in an interactive SQL fiddle here.


Click here to view this solution in an interactive SQL fiddle.

## First we separate the base query to get (1) the total spend on the 
## keywords and (2) the advertiser spend ranking by keyword. 
## Next, we get the total spend by keyword and join to the subquery 
## that will provide the top spend by advertiser
## Last, we sort by the total spend on the keyword

 base AS (
 # separating the base sub-query to get the baseline calculations that we can  
 # use in our subqueries
        ## calculating the total spend on each keyword
        SUM(total_spend) OVER(PARTITION BY keyword) AS spend_keyword,
        ## ranking the advertisers based on how much they spend on a given keyword
        RANK() OVER (PARTITION BY keyword ORDER BY total_spend DESC) as advertiser_rank
    FROM keyword_summary
    SUM(q1.spend_keyword) AS spend_keyword
# we need to query the "base" table 2 times, 
# 1. to get the total spend on each keyword
# 2. to get the top advertiser spend on each keyword
FROM base AS q1
      FROM base
      WHERE advertiser_rank = 1
    ) AS q2
ON q1.keyword = q2.keyword
# sorting the results by the total spend
ORDER BY spend_keyword DESC


keyword advertiser_id spend_keyword
bike 21781 3608
bicycle 1234 2520
bike brakes 1234 266
bike accessories 3829 196
bike tires 3829 170
mountain bike 3829 59