Greg Dodd digs into the ROLLUP and CUBE operators in a two-parter. First, ROLLUP:
As you can see, we now have these null’s popping up, but with totals. Row 5 for example, tells us that in 2017 there were 1,427,461 people living in Hawaii. Row 11 tells us that there are 2,438,188 people living in Rhode Island and Hawaii in 2017. Row 22 tells us that there were 2,429,070 people living in Rhode Island and Hawaii in 2018, and finally row 23 tells us that in total there have been 4,867,268 people in 2017 and 2018. This last row is a bit useless for this data as the overlap of those people would be huge, but for something like sales data, this number could be useful.
For those with a keen eye you’ll see that I’ve started at row 28 in that screenshot. When we run the GROUP BY without ROLLUP or CUBE we get just 16 rows. With ROLLUP that grows to 23, but with CUBE it explodes out to 57. Why?
I’ve used ROLLUP several times with proper hierarchical data (e.g., product category, product sub-category, product) and it does an excellent job of summarizing that sort of data. CUBE has always returned too many rows for my liking. But the operator I go to most frequently is GROUPING SETS, as then I get to control the levels.