group-by-clause
The GROUP BY clause specifies an intermediate result table that consists of a grouping of the rows of R. R is the result of the previous clause of the subselect.
In its simplest form, a GROUP BY clause contains a grouping-expression. A grouping-expression is an expression that defines the grouping of R. The following restrictions apply to grouping-expression.
- Each column name included in grouping-expression must unambiguously identify a column of R.
- The result of grouping-expression cannot be a DataLink or XML data type or a distinct type that is based on a DataLink or XML.
- grouping-expression cannot include any
of the following items:
- A correlated column
- A variable
- An aggregate function
- Any function that is non-deterministic
More complex forms of the GROUP BY clause include grouping-sets and super-groups. For a description of these forms, see grouping-sets and super-groups, respectively.
The result of the GROUP BY clause is a set of groups of rows. In each group of more than one row, all values of each grouping-expression are equal, and all rows with the same set of values of the grouping-expressions are in the same group. For grouping, all null values for a grouping-expression are considered equal.
Because every row of a group contains the same value of any grouping-expression, grouping-expressions can be used in a search condition in a HAVING clause, in the SELECT clause, or in a sort-key-expression of an ORDER BY clause (see order-by-clause for details). In each case, the reference specifies only one value for each group. The grouping-expression specified in these clauses must exactly match the grouping-expression in the GROUP BY clause, except that blanks are not significant. For example, a grouping-expression of
SALARY*.10will match the expression in a having-clause of
HAVING SALARY*.10but will not match
HAVING .10 *SALARY
or
HAVING (SALARY*.10)+100If the grouping-expression contains varying-length strings with trailing blanks, the values in the group can differ in the number of trailing blanks and may not all have the same length. In that case, a reference to the grouping-expression still specifies only one value for each group, but the value for a group is chosen arbitrarily from the available set of values. Thus, the actual length of the result value is unpredictable.
Column access controls do not affect the operation
of the GROUP BY clause. The grouping is performed using the original
column values.
If a collating sequence other than *HEX is in effect when the statement that contains the GROUP BY clause is executed, and the grouping-expressions are SBCS data, mixed data, or Unicode data, then the rows are placed into groups using the weighted values. The weighted values are derived by applying the collating sequence to the grouping-expressions. In that case, a reference to the grouping-expression still specifies only one value for each group, but the value for a group is chosen arbitrarily from the available set of values. Thus, the actual value of the result is unpredictable.
grouping-sets
A grouping-sets specification allows multiple grouping clauses to be specified in a single statement. This can be thought of as the union of two or more groups of rows into a single result set. It is logically equivalent to the union of multiple subselects with the group by clause in each subselect corresponding to one grouping set. A grouping set can be a single element or can be a list of elements delimited by parentheses, where an element is either a grouping-expression or a super-group. Using grouping sets allows the groups to be computed with a single pass over the base table.
The grouping-sets specification allows either a simple grouping-expression to be used, or the more complex forms of super-groups. For a description of super-groups, see super-groups.
Note that grouping sets are the fundamental building blocks for GROUP BY operations. A simple GROUP BY with a single column can be considered a grouping set with one element. For example:
GROUP BY ais the same as
GROUP BY GROUPING SETS( (a) )and
GROUP BY a, b, c is the same as
GROUP BY GROUPING SETS( (a,b,c) )Non-aggregation columns from the select list of the subselect that are excluded from a grouping set will return a null for such columns for each row generated for that grouping set. This reflects the fact that aggregation was done without considering the values for those columns.
If a table-reference in the previous clauses of the query identifies a distributed table or a table that has a read trigger; a grouping-sets specification is not allowed.
Example C2 through Example C7 illustrate the use of grouping sets.
super-groups
- ROLLUP ( grouping-expression-list )
- A ROLLUP grouping is an extension to the GROUP BY clause that produces a result set containing sub-total rows in addition to the "regular" grouped rows. Sub-total rows are "super-aggregate" rows that contain further aggregates whose values are derived by applying the same aggregate functions that were used to obtain the grouped rows. These rows are called sub-total rows because that is their most common use; however, any aggregate function can be used for the aggregation. For instance, MAX and AVG are used in example C8.
- CUBE ( grouping-expression-list )
- A CUBE grouping is an extension to the GROUP BY clause that produces a result set that contains all the rows of a ROLLUP aggregation and, in addition, contains "cross-tabulation" rows. Cross-tabulation rows are additional "super-aggregate" rows that are not part of an aggregation with sub-totals. Only 10 expressions are allowed in the grouping-expression-list.
- grouping-expression-list
- A grouping-expression-list is used within a CUBE or ROLLUP grouping to define the number of elements in the CUBE or ROLLUP operation. This is controlled by using parentheses to delimit elements with multiple grouping-expressions.
- grand-total
- Both CUBE and ROLLUP return a row which is the overall (grand total) aggregation. This may be separately specified with empty parentheses within the GROUPING SETS clause. It may also be specified directly in the GROUP BY clause, although there is no effect on the result of the query. Example C4 uses the grand-total syntax.
Combining grouping sets
This can be used to combine any of the types of GROUP BY clauses. When simple grouping-expressions are combined with other groups, they are "appended" to the beginning of the resulting grouping sets. When ROLLUP and CUBE expressions are combined, they operate like "multipliers" on the remaining expression, forming additional grouping set entries according to the definition of either ROLLUP or CUBE.
For instance, combining grouping-expression elements acts as follows
GROUP BY a, ROLLUP (b,c)is equivalent to
GROUP BY GROUPING SETS( (a,b,c),
(a,b),
(a) )Or similarly
GROUP BY a,b, ROLLUP (c,d)is equivalent to
GROUP BY GROUPING SETS( (a,b,c,d),
(a,b,c),
(a,b) )Combining of ROLLUP elements acts as follows:
GROUP BY ROLLUP(a), ROLLUP (b,c)is equivalent to
GROUP BY GROUPING SETS( (a,b,c),
(a,b),
(a),
(b,c),
(b),
() )Similarly,
GROUP BY ROLLUP(a), CUBE (b,c)is equivalent to
GROUP BY GROUPING SETS( (a,b,c),
(a,b),
(a,c),
(a),
(b,c),
(b),
(c),
() )Combining of CUBE and ROLLUP elements acts as follows:
GROUP BY CUBE(a,b), ROLLUP (c,d)is equivalent to
GROUP BY GROUPING SETS( (a,b,c,d),
(a,b,c),
(a,b),
(a,c,d),
(a,c),
(a),
(b,c,d),
(b,c),
(b),
(c,d),
(c),
() )Like a simple grouping-expression, combining grouping sets also eliminates duplicates within each grouping set. For instance,
GROUP BY a, ROLLUP (a,b)is equivalent to
GROUP BY GROUPING SETS( (a,b),
(a) )A more complete example of combining grouping sets is to construct a result set that eliminates certain rows that would be returned for a full CUBE aggregation.
For example, consider the following GROUP BY clause:
GROUP BY Region,
ROLLUP (Sales_Person, WEEK(Sales_Date)),
CUBE (YEAR(Sales_Date), MONTH(Sales_Date))The column listed immediately to the right of GROUP BY is grouped, those within the parentheses following ROLLUP are rolled up, and those within the parentheses following CUBE are cubed. Thus, the above clause results in a cube of MONTH within YEAR which is then rolled up within WEEK within Sales_Person within the Region aggregation. It does not result in any grand total row or any cross-tabulation rows on Region, Sales_Person, or WEEK(Sales_Date) so produces fewer rows than the clause:
GROUP BY ROLLUP (Region, Sales_Person, WEEK(Sales_Date),
YEAR(Sales_Date), MONTH(Sales_Date))Examples of grouping sets, cube, and rollup
The queries in Example C1 through C4 use a subset of the rows in the SALES table 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) = 13which 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 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_PERSONThis 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_PERSONThis 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 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_PERSONThis 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), and (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_PERSONThis 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, MONTHThis 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_WEEKThis 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
- - 155Example 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, REGIONThis 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
- - 155Example C6-3:
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, REGIONThis 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:
- In the first grouped set, week 53 has been repositioned to the end
- In the second grouped set, month 12 has now been positioned to the end and the regions now appear in alphabetic order.
- Null values are sorted high.
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 means 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 called X can be derived that has 2 columns, R1 and R2, and one 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 adds 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)Step 3: Now these columns are combined 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, REGIONThis 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, REGIONThis 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 aggregate 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 SALES,
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, REGIONThis 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