The chart below shows the percent of all window function use attributed to the top five most used. Then we counted up uses of each window function in our dataset and identified the most frequently used. We built a dataset of all queries created since Januand used some regex magic to extract window functions. Okay, so we were curious to understand which window functions are most commonly used, so we built a window (pun intended) into our customer’s usage of these types of calculations. Behind the scenes, the window function is able to access more than just the current row of the query result."įor those not familiar with the concept of window functions, I recommend you take a look at the Mode tutorial on this subject before moving on. But unlike regular aggregate functions, use of a window function does not cause rows to become grouped into a single output row - the rows retain their separate identities. This is comparable to the type of calculation that can be done with an aggregate function. "A window function performs a calculation across a set of table rows that are somehow related to the current row. We’re going to borrow PostgreSQL’s definition: So, in that vein, we thought it would be fun to use some SQL to analyze some SQL! That said, we don’t believe in analysis for the sake of analysis, so we did some research to help our readers better understand one of the most powerful tools in SQL-window functions. We code it, write about it, and empower people to use it all over the world.
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