The queries in Example C1 through Example C4 use a subset of the rows in the SALES tables based on the predicate 'WEEK(SALES_DATE) = 13'.
SELECT WEEK(SALES_DATE) AS WEEK,
DAYOFWEEK(SALES_DATE) AS DAY_WEEK,
SALES_PERSON, SALES AS UNITS_SOLD
FROM SALES
WHERE WEEK(SALES_DATE) = 13
which results in:
WEEK DAY_WEEK SALES_PERSON UNITS_SOLD
----------- ----------- --------------- -----------
13 6 LUCCHESSI 3
13 6 LUCCHESSI 1
13 6 LEE 2
13 6 LEE 2
13 6 LEE 3
13 6 LEE 5
13 6 GOUNOT 3
13 6 GOUNOT 1
13 6 GOUNOT 7
13 7 LUCCHESSI 1
13 7 LUCCHESSI 2
13 7 LUCCHESSI 1
13 7 LEE 7
13 7 LEE 3
13 7 LEE 7
13 7 LEE 4
13 7 GOUNOT 2
13 7 GOUNOT 18
13 7 GOUNOT 1
Example C1: Here is a query with a basic GROUP BY clause over 3 columns:
SELECT WEEK(SALES_DATE) AS WEEK,
DAYOFWEEK(SALES_DATE) AS DAY_WEEK,
SALES_PERSON, SUM(SALES) AS UNITS_SOLD
FROM SALES
WHERE WEEK(SALES_DATE) = 13
GROUP BY WEEK(SALES_DATE), DAYOFWEEK(SALES_DATE), SALES_PERSON
ORDER BY WEEK, DAY_WEEK, SALES_PERSON
This results in:
WEEK DAY_WEEK SALES_PERSON UNITS_SOLD
----------- ----------- --------------- -----------
13 6 GOUNOT 11
13 6 LEE 12
13 6 LUCCHESSI 4
13 7 GOUNOT 21
13 7 LEE 21
13 7 LUCCHESSI 4
Example C2: Produce the result based on two different grouping sets of rows from the SALES table.
SELECT WEEK(SALES_DATE) AS WEEK,
DAYOFWEEK(SALES_DATE) AS DAY_WEEK,
SALES_PERSON, SUM(SALES) AS UNITS_SOLD
FROM SALES
WHERE WEEK(SALES_DATE) = 13
GROUP BY GROUPING SETS ( (WEEK(SALES_DATE), SALES_PERSON),
(DAYOFWEEK(SALES_DATE), SALES_PERSON))
ORDER BY WEEK, DAY_WEEK, SALES_PERSON
This results in:
WEEK DAY_WEEK SALES_PERSON UNITS_SOLD
----------- ----------- --------------- -----------
13 - GOUNOT 32
13 - LEE 33
13 - LUCCHESSI 8
- 6 GOUNOT 11
- 6 LEE 12
- 6 LUCCHESSI 4
- 7 GOUNOT 21
- 7 LEE 21
- 7 LUCCHESSI 4
The rows with WEEK 13 are from the first grouping set and the other rows are from the second grouping set.
Example C3: If you use the 3 distinct columns involved in the grouping sets of Example C2 and perform a ROLLUP, you can see grouping sets for (WEEK,DAY_WEEK,SALES_PERSON), (WEEK, DAY_WEEK), (WEEK) and grand total.
SELECT WEEK(SALES_DATE) AS WEEK,
DAYOFWEEK(SALES_DATE) AS DAY_WEEK,
SALES_PERSON, SUM(SALES) AS UNITS_SOLD
FROM SALES
WHERE WEEK(SALES_DATE) = 13
GROUP BY ROLLUP ( WEEK(SALES_DATE), DAYOFWEEK(SALES_DATE), SALES_PERSON )
ORDER BY WEEK, DAY_WEEK, SALES_PERSON
This results in:
WEEK DAY_WEEK SALES_PERSON UNITS_SOLD
----------- ----------- --------------- -----------
13 6 GOUNOT 11
13 6 LEE 12
13 6 LUCCHESSI 4
13 6 - 27
13 7 GOUNOT 21
13 7 LEE 21
13 7 LUCCHESSI 4
13 7 - 46
13 - - 73
- - - 73
Example C4: If you run the same query as Example C3 only replace ROLLUP with CUBE, you can see additional grouping sets for (WEEK,SALES_PERSON), (DAY_WEEK,SALES_PERSON), (DAY_WEEK), (SALES_PERSON) in the result.
SELECT WEEK(SALES_DATE) AS WEEK,
DAYOFWEEK(SALES_DATE) AS DAY_WEEK,
SALES_PERSON, SUM(SALES) AS UNITS_SOLD
FROM SALES
WHERE WEEK(SALES_DATE) = 13
GROUP BY CUBE ( WEEK(SALES_DATE), DAYOFWEEK(SALES_DATE), SALES_PERSON )
ORDER BY WEEK, DAY_WEEK, SALES_PERSON
This results in:
WEEK DAY_WEEK SALES_PERSON UNITS_SOLD
----------- ----------- --------------- -----------
13 6 GOUNOT 11
13 6 LEE 12
13 6 LUCCHESSI 4
13 6 - 27
13 7 GOUNOT 21
13 7 LEE 21
13 7 LUCCHESSI 4
13 7 - 46
13 - GOUNOT 32
13 - LEE 33
13 - LUCCHESSI 8
13 - - 73
- 6 GOUNOT 11
- 6 LEE 12
- 6 LUCCHESSI 4
- 6 - 27
- 7 GOUNOT 21
- 7 LEE 21
- 7 LUCCHESSI 4
- 7 - 46
- - GOUNOT 32
- - LEE 33
- - LUCCHESSI 8
- - - 73
Example C5: Obtain a result set which includes a grand-total of selected rows from the SALES table together with a group of rows aggregated by SALES_PERSON and MONTH.
SELECT SALES_PERSON,
MONTH(SALES_DATE) AS MONTH,
SUM(SALES) AS UNITS_SOLD
FROM SALES
GROUP BY GROUPING SETS ( (SALES_PERSON, MONTH(SALES_DATE)),
()
)
ORDER BY SALES_PERSON, MONTH
This results in:
SALES_PERSON MONTH UNITS_SOLD --------------- ----------- ----------- GOUNOT 3 35 GOUNOT 4 14 GOUNOT 12 1 LEE 3 60 LEE 4 25 LEE 12 6 LUCCHESSI 3 9 LUCCHESSI 4 4 LUCCHESSI 12 1 - - 155
Example C6: This example shows two simple ROLLUP queries followed by a query which treats the two ROLLUPs as grouping sets in a single result set and specifies row ordering for each column involved in the grouping sets.
Example C6-1:
SELECT WEEK(SALES_DATE) AS WEEK,
DAYOFWEEK(SALES_DATE) AS DAY_WEEK,
SUM(SALES) AS UNITS_SOLD
FROM SALES
GROUP BY ROLLUP ( WEEK(SALES_DATE), DAYOFWEEK(SALES_DATE) )
ORDER BY WEEK, DAY_WEEK
results in:
WEEK DAY_WEEK UNITS_SOLD
----------- ----------- -----------
13 6 27
13 7 46
13 - 73
14 1 31
14 2 43
14 - 74
53 1 8
53 - 8
- - 155
Example C6-2:
SELECT MONTH(SALES_DATE) AS MONTH,
REGION,
SUM(SALES) AS UNITS_SOLD
FROM SALES
GROUP BY ROLLUP ( MONTH(SALES_DATE), REGION );
ORDER BY MONTH, REGION
results in:
MONTH REGION UNITS_SOLD
----------- --------------- -----------
3 Manitoba 22
3 Ontario-North 8
3 Ontario-South 34
3 Quebec 40
3 - 104
4 Manitoba 17
4 Ontario-North 1
4 Ontario-South 14
4 Quebec 11
4 - 43
12 Manitoba 2
12 Ontario-South 4
12 Quebec 2
12 - 8
- - 155
SELECT WEEK(SALES_DATE) AS WEEK,
DAYOFWEEK(SALES_DATE) AS DAY_WEEK,
MONTH(SALES_DATE) AS MONTH,
REGION,
SUM(SALES) AS UNITS_SOLD
FROM SALES
GROUP BY GROUPING SETS ( ROLLUP( WEEK(SALES_DATE), DAYOFWEEK(SALES_DATE) ),
ROLLUP( MONTH(SALES_DATE), REGION ) )
ORDER BY WEEK, DAY_WEEK, MONTH, REGION
results in:
WEEK DAY_WEEK MONTH REGION UNITS_SOLD
----------- ----------- ----------- --------------- -----------
13 6 - - 27
13 7 - - 46
13 - - - 73
14 1 - - 31
14 2 - - 43
14 - - - 74
53 1 - - 8
53 - - - 8
- - 3 Manitoba 22
- - 3 Ontario-North 8
- - 3 Ontario-South 34
- - 3 Quebec 40
- - 3 - 104
- - 4 Manitoba 17
- - 4 Ontario-North 1
- - 4 Ontario-South 14
- - 4 Quebec 11
- - 4 - 43
- - 12 Manitoba 2
- - 12 Ontario-South 4
- - 12 Quebec 2
- - 12 - 8
- - - - 155
- - - - 155
Using the two ROLLUPs as grouping sets causes the result to include duplicate rows. There are even two grand total rows.
Observe how the use of ORDER BY has affected the results:
Example C7: In queries that perform multiple ROLLUPs in a single pass (such as Example C6-3) you may want to be able to indicate which grouping set produced each row. The following steps demonstrate how to provide a column (called GROUP) which indicates the origin of each row in the result set. By origin, we mean which one of the two grouping sets produced the row in the result set.
Step 1: Introduce a way of "generating" new data values, using a query which selects from a VALUES clause (which is an alternate form of a fullselect). This query shows how a table can be derived called "X" having 2 columns "R1" and "R2" and 1 row of data.
SELECT R1,R2
FROM (VALUES('GROUP 1','GROUP 2')) AS X(R1,R2);
results in:
R1 R2 ------- ------- GROUP 1 GROUP 2
Step 2: Form the cross product of this table "X" with the SALES table. This add columns "R1" and "R2" to every row.
SELECT R1, R2, WEEK(SALES_DATE) AS WEEK,
DAYOFWEEK(SALES_DATE) AS DAY_WEEK,
MONTH(SALES_DATE) AS MONTH,
REGION,
SALES AS UNITS_SOLD
FROM SALES,(VALUES('GROUP 1','GROUP 2')) AS X(R1,R2)
This add columns "R1" and "R2" to every row.
Step 3: Now we can combine these columns with the grouping sets to include these columns in the rollup analysis.
SELECT R1, R2,
WEEK(SALES_DATE) AS WEEK,
DAYOFWEEK(SALES_DATE) AS DAY_WEEK,
MONTH(SALES_DATE) AS MONTH,
REGION, SUM(SALES) AS UNITS_SOLD
FROM SALES,(VALUES('GROUP 1','GROUP 2')) AS X(R1,R2)
GROUP BY GROUPING SETS ((R1, ROLLUP(WEEK(SALES_DATE), DAYOFWEEK(SALES_DATE))),
(R2,ROLLUP( MONTH(SALES_DATE), REGION ) ) )
ORDER BY WEEK, DAY_WEEK, MONTH, REGION
results in:
R1 R2 WEEK DAY_WEEK MONTH REGION UNITS_SOLD ------- ------- ----------- ----------- ----------- --------------- ----------- GROUP 1 - 13 6 - - 27 GROUP 1 - 13 7 - - 46 GROUP 1 - 13 - - - 73 GROUP 1 - 14 1 - - 31 GROUP 1 - 14 2 - - 43 GROUP 1 - 14 - - - 74 GROUP 1 - 53 1 - - 8 GROUP 1 - 53 - - - 8 - GROUP 2 - - 3 Manitoba 22 - GROUP 2 - - 3 Ontario-North 8 - GROUP 2 - - 3 Ontario-South 34 - GROUP 2 - - 3 Quebec 40 - GROUP 2 - - 3 - 104 - GROUP 2 - - 4 Manitoba 17 - GROUP 2 - - 4 Ontario-North 1 - GROUP 2 - - 4 Ontario-South 14 - GROUP 2 - - 4 Quebec 11 - GROUP 2 - - 4 - 43 - GROUP 2 - - 12 Manitoba 2 - GROUP 2 - - 12 Ontario-South 4 - GROUP 2 - - 12 Quebec 2 - GROUP 2 - - 12 - 8 - GROUP 2 - - - - 155 GROUP 1 - - - - - 155
Step 4: Notice that because R1 and R2 are used in different grouping sets, whenever R1 is non-null in the result, R2 is null and whenever R2 is non-null in the result, R1 is null. That means you can consolidate these columns into a single column using the COALESCE function. You can also use this column in the ORDER BY clause to keep the results of the two grouping sets together.
SELECT COALESCE(R1,R2) AS GROUP,
WEEK(SALES_DATE) AS WEEK,
DAYOFWEEK(SALES_DATE) AS DAY_WEEK,
MONTH(SALES_DATE) AS MONTH,
REGION, SUM(SALES) AS UNITS_SOLD
FROM SALES,(VALUES('GROUP 1','GROUP 2')) AS X(R1,R2)
GROUP BY GROUPING SETS ((R1, ROLLUP(WEEK(SALES_DATE), DAYOFWEEK(SALES_DATE))),
(R2,ROLLUP( MONTH(SALES_DATE), REGION ) ) )
ORDER BY GROUP, WEEK, DAY_WEEK, MONTH, REGION;
results in:
GROUP WEEK DAY_WEEK MONTH REGION UNITS_SOLD ------- ----------- ----------- ----------- --------------- ----------- GROUP 1 13 6 - - 27 GROUP 1 13 7 - - 46 GROUP 1 13 - - - 73 GROUP 1 14 1 - - 31 GROUP 1 14 2 - - 43 GROUP 1 14 - - - 74 GROUP 1 53 1 - - 8 GROUP 1 53 - - - 8 GROUP 1 - - - - 155 GROUP 2 - - 3 Manitoba 22 GROUP 2 - - 3 Ontario-North 8 GROUP 2 - - 3 Ontario-South 34 GROUP 2 - - 3 Quebec 40 GROUP 2 - - 3 - 104 GROUP 2 - - 4 Manitoba 17 GROUP 2 - - 4 Ontario-North 1 GROUP 2 - - 4 Ontario-South 14 GROUP 2 - - 4 Quebec 11 GROUP 2 - - 4 - 43 GROUP 2 - - 12 Manitoba 2 GROUP 2 - - 12 Ontario-South 4 GROUP 2 - - 12 Quebec 2 GROUP 2 - - 12 - 8 GROUP 2 - - - - 155
Example C8: The following example illustrates the use of various column functions when performing a CUBE. The example also makes use of cast functions and rounding to produce a decimal result with reasonable precision and scale.
SELECT MONTH(SALES_DATE) AS MONTH,
REGION,
SUM(SALES) AS UNITS_SOLD,
MAX(SALES) AS BEST_SALE,
CAST(ROUND(AVG(DECIMAL(SALES)),2) AS DECIMAL(5,2)) AS AVG_UNITS_SOLD
FROM SALES
GROUP BY CUBE(MONTH(SALES_DATE),REGION)
ORDER BY MONTH, REGION
This results in:
MONTH REGION UNITS_SOLD BEST_SALE AVG_UNITS_SOLD
----------- --------------- ----------- ----------- --------------
3 Manitoba 22 7 3.14
3 Ontario-North 8 3 2.67
3 Ontario-South 34 14 4.25
3 Quebec 40 18 5.00
3 - 104 18 4.00
4 Manitoba 17 9 5.67
4 Ontario-North 1 1 1.00
4 Ontario-South 14 8 4.67
4 Quebec 11 8 5.50
4 - 43 9 4.78
12 Manitoba 2 2 2.00
12 Ontario-South 4 3 2.00
12 Quebec 2 1 1.00
12 - 8 3 1.60
- Manitoba 41 9 3.73
- Ontario-North 9 3 2.25
- Ontario-South 52 14 4.00
- Quebec 53 18 4.42
- - 155 18 3.87