Suppose you have the following sales_view table data:
| sales_view | ||||||
|---|---|---|---|---|---|---|
| PID | cost | sale | margin | state | county | city |
| 1 | 38350 | 50150 | 11800 | CA | Los Angeles | Long Beach |
| 1 | 63375 | 82875 | 19500 | CA | San Diego | San Diego |
| 1 | 46800 | 61200 | 14400 | CA | Los Angeles | Avalon |
| 1 | 40625 | 53125 | 12500 | CA | Los Angeles | Long Beach |
| 2 | 20000 | 24750 | 4750 | CA | Los Angeles | Long Beach |
| 2 | 4800 | 5940 | 1140 | CA | San Diego | Santee |
| 1 | 57600 | 71280 | 13680 | CA | San Diego | San Diego |
You are interested in determining the effect of county and PID on sale price.
The following SELECT statement analyzes the effect of county and PID on sale price using CUBE in its GROUP BY clause:
SELECT pid, county, SUM(sale) FROM sales_view GROUP BY CUBE (pid,county);
The query reports the following data, where the QUESTION MARK character indicates a null:
| PID | County | Sum(sale) |
| --- | ------ | --------- |
| 2 | Los Angeles | 24750 |
| 1 | Los Angeles | 164475 |
| 2 | San Diego | 5940 |
| 1 | San Diego | 154155 |
| 2 | ? | 30690 |
| 1 | ? | 381630 |
| ? | Los Angeles | 189225 |
| ? | San Diego | 160095 |
| ? | ? | 349320 |
This query computes four different levels of grouping over the dimensions of county and PID by:
- county and PID (groups 1 through 4).
- PID only (groups 5 and 6).
- county only (groups 7 and 8).
- Nothing (group 9), which aggregates sales over all counties and PIDs.
Notice that nulls are used to represent the empty set, not missing information. The nulls mean that information is not reported at the grouping level represented, not that the information is missing from the sales_view base table.