ROLLUP and CUBE are simple extensions to the SELECT statement's GROUP BY clause.
ROLLUP creates subtotals at any level of aggregation needed, from the most detailed up to a grand total.
CUBE is an extension similar to ROLLUP, enabling a single statement to calculate all possible combinations of subtotals. CUBE can generate the information needed in cross-tab reports with a single query.
This article gives an overview of the functionality available for
aggregation in data warehouses, focusing specifically on the information
required for theOracle Database SQL Expert (1Z0-047) exam.
Setup
The examples in this article will be run against the following simple dimension table.
DROP TABLE dimension_tab;
CREATE TABLE dimension_tab (
fact_1_id NUMBER NOT NULL,
fact_2_id NUMBER NOT NULL,
fact_3_id NUMBER NOT NULL,
fact_4_id NUMBER NOT NULL,
sales_value NUMBER(10,2) NOT NULL
);
INSERT INTO dimension_tab
SELECT TRUNC(DBMS_RANDOM.value(low => 1, high => 3)) AS fact_1_id,
TRUNC(DBMS_RANDOM.value(low => 1, high => 6)) AS fact_2_id,
TRUNC(DBMS_RANDOM.value(low => 1, high => 11)) AS fact_3_id,
TRUNC(DBMS_RANDOM.value(low => 1, high => 11)) AS fact_4_id,
ROUND(DBMS_RANDOM.value(low => 1, high => 100), 2) AS sales_value
FROM dual
CONNECT BY level <= 1000;
COMMIT;
To keep the queries and their output simple I am going to ignore the
fact tables and also limit the number of distinct values in the columns
of the dimension table.
GROUP BY
Let's start be reminding ourselves how the GROUP BY clause
works. An aggregate function takes multiple rows of data returned by a
query and aggregates them into a single result row.
SELECT SUM(sales_value) AS sales_value
FROM dimension_tab;
SALES_VALUE
-----------
50528.39
1 row selected.
SQL>
Including the GROUP BY clause limits the window of data
processed by the aggregate function. This way we get an aggregated value
for each distinct combination of values present in the columns listed
in theGROUP BY clause. The number of rows we expect can be
calculated by multiplying the number of distinct values of each column
listed in the GROUP BY clause. In this case, if the rows
were loaded randomly we would expect the number of distinct values for
the first three columns in the table to be 2, 5 and 10 respectively. So
using the fact_1_id column in the GROUP BY clause should give us 2 rows.
SELECT fact_1_id,
COUNT(*) AS num_rows,
SUM(sales_value) AS sales_value
FROM dimension_tab
GROUP BY fact_1_id
ORDER BY fact_1_id;
FACT_1_ID NUM_ROWS SALES_VALUE
---------- ---------- -----------
1 478 24291.35
2 522 26237.04
2 rows selected.
SQL>
Including the first two columns in the GROUP BY clause should give us 10 rows (2*5), each with its aggregated values.
SELECT fact_1_id,
fact_2_id,
COUNT(*) AS num_rows,
SUM(sales_value) AS sales_value
FROM dimension_tab
GROUP BY fact_1_id, fact_2_id
ORDER BY fact_1_id, fact_2_id;
FACT_1_ID FACT_2_ID NUM_ROWS SALES_VALUE
---------- ---------- ---------- -----------
1 1 83 4363.55
1 2 96 4794.76
1 3 93 4718.25
1 4 105 5387.45
1 5 101 5027.34
2 1 109 5652.84
2 2 96 4583.02
2 3 110 5555.77
2 4 113 5936.67
2 5 94 4508.74
10 rows selected.
SQL>
Including the first three columns in the GROUP BY clause should give us 100 rows (2*5*10).
SELECT fact_1_id,
fact_2_id,
fact_3_id,
COUNT(*) AS num_rows,
SUM(sales_value) AS sales_value
FROM dimension_tab
GROUP BY fact_1_id, fact_2_id, fact_3_id
ORDER BY fact_1_id, fact_2_id, fact_3_id;
FACT_1_ID FACT_2_ID FACT_3_ID NUM_ROWS SALES_VALUE
---------- ---------- ---------- ---------- -----------
1 1 1 10 381.61
1 1 2 6 235.29
1 1 3 7 270.7
1 1 4 13 634.05
1 1 5 10 602.36
1 1 6 7 538.41
1 1 7 5 245.87
1 1 8 8 435.54
1 1 9 8 506.59
1 1 10 9 513.13
...
2 5 1 14 714.84
2 5 2 13 686.56
2 5 3 13 579.5
2 5 4 10 336.87
2 5 5 5 215.17
2 5 6 4 268.72
2 5 7 14 667.22
2 5 8 7 451.29
2 5 9 8 365.24
2 5 10 6 223.33
100 rows selected.
SQL>
ROLLUP
In addition to the regular aggregation results we expect from the GROUP BY clause, the ROLLUP extension produces group subtotals from right to left and a grand total. If "n" is the number of columns listed in the ROLLUP, there will be n+1 levels of subtotals.
SELECT fact_1_id,
fact_2_id,
SUM(sales_value) AS sales_value
FROM dimension_tab
GROUP BY ROLLUP (fact_1_id, fact_2_id)
ORDER BY fact_1_id, fact_2_id;
FACT_1_ID FACT_2_ID SALES_VALUE
---------- ---------- -----------
1 1 4363.55
1 2 4794.76
1 3 4718.25
1 4 5387.45
1 5 5027.34
1 24291.35
2 1 5652.84
2 2 4583.02
2 3 5555.77
2 4 5936.67
2 5 4508.74
2 26237.04
50528.39
13 rows selected.
SQL>
Looking at the output in a SQL*Plus or a grid output, you can visually
identify the rows containing subtotals as they have null values in the
ROLLUP columns. It may be easier to spot when scanning down the output of the following query shown
here.
Obviously, if the raw data contains null values, using this visual
identification is not an accurate approach, but we will discuss this
issue later.
SELECT fact_1_id,
fact_2_id,
fact_3_id,
SUM(sales_value) AS sales_value
FROM dimension_tab
GROUP BY ROLLUP (fact_1_id, fact_2_id, fact_3_id)
ORDER BY fact_1_id, fact_2_id, fact_3_id;
It is possible to do a partial rollup to reduce the number of subtotals
calculated. The output from the following partial rollup is shown
here.
SELECT fact_1_id,
fact_2_id,
fact_3_id,
SUM(sales_value) AS sales_value
FROM dimension_tab
GROUP BY fact_1_id, ROLLUP (fact_2_id, fact_3_id)
ORDER BY fact_1_id, fact_2_id, fact_3_id;
CUBE
In addition to the subtotals generated by the ROLLUP extension, the CUBE extension
will generate subtotals for all combinations of the dimensions
specified. If "n" is the number of columns listed in the CUBE, there will be 2n subtotal combinations.
SELECT fact_1_id,
fact_2_id,
SUM(sales_value) AS sales_value
FROM dimension_tab
GROUP BY CUBE (fact_1_id, fact_2_id)
ORDER BY fact_1_id, fact_2_id;
FACT_1_ID FACT_2_ID SALES_VALUE
---------- ---------- -----------
1 1 4363.55
1 2 4794.76
1 3 4718.25
1 4 5387.45
1 5 5027.34
1 24291.35
2 1 5652.84
2 2 4583.02
2 3 5555.77
2 4 5936.67
2 5 4508.74
2 26237.04
1 10016.39
2 9377.78
3 10274.02
4 11324.12
5 9536.08
50528.39
18 rows selected.
SQL>
As the number of dimensions increase, so do the combinations of
subtotals that need to be calculated, as shown by the output of the
following query, shown
here.
SELECT fact_1_id,
fact_2_id,
fact_3_id,
SUM(sales_value) AS sales_value
FROM dimension_tab
GROUP BY CUBE (fact_1_id, fact_2_id, fact_3_id)
ORDER BY fact_1_id, fact_2_id, fact_3_id;
It is possible to do a partial cube to reduce the number of subtotals
calculated. The output from the following partial cube is shown
here.
SELECT fact_1_id,
fact_2_id,
fact_3_id,
SUM(sales_value) AS sales_value
FROM dimension_tab
GROUP BY fact_1_id, CUBE (fact_2_id, fact_3_id)
ORDER BY fact_1_id, fact_2_id, fact_3_id;
GROUPING Functions
GROUPING
It can be quite easy to visually identify subtotals generated by rollups
and cubes, but to do it programatically you really need something more
accurate than the presence of null values in the grouping columns. This
is where the GROUPING function comes in. It accepts a
single column as a parameter and returns "1" if the column contains a
null value generated as part of a subtotal by a ROLLUP or CUBE operation or "0" for any other value, including stored null values.
The following query is a repeat of a previous cube, but the GROUPING function has been added for each of the dimensions in the cube.
SELECT fact_1_id,
fact_2_id,
SUM(sales_value) AS sales_value,
GROUPING(fact_1_id) AS f1g,
GROUPING(fact_2_id) AS f2g
FROM dimension_tab
GROUP BY CUBE (fact_1_id, fact_2_id)
ORDER BY fact_1_id, fact_2_id;
FACT_1_ID FACT_2_ID SALES_VALUE F1G F2G
---------- ---------- ----------- ---------- ----------
1 1 4363.55 0 0
1 2 4794.76 0 0
1 3 4718.25 0 0
1 4 5387.45 0 0
1 5 5027.34 0 0
1 24291.35 0 1
2 1 5652.84 0 0
2 2 4583.02 0 0
2 3 5555.77 0 0
2 4 5936.67 0 0
2 5 4508.74 0 0
2 26237.04 0 1
1 10016.39 1 0
2 9377.78 1 0
3 10274.02 1 0
4 11324.12 1 0
5 9536.08 1 0
50528.39 1 1
18 rows selected.
SQL>
From this we can see:
- F1G=0,F2G=0 : Represents a row containing regular subtotal we would expect from a
GROUP BY operation.
- F1G=0,F2G=1 : Represents a row containing a subtotal for a distinct value of the
FACT_1_ID column, as generated by ROLLUP and CUBE operations.
- F1G=1,F2G=0 : Represents a row containing a subtotal for a distinct value of the
FACT_2_ID column, which we would only see in a CUBE operation.
- F1G=1,F2G=1 : Represents a row containing a grand total for the query, as generated by
ROLLUP and CUBE operations.
It would now be easy to write a program to accurately process the data.
The GROUPING columns can used for ordering or filtering results.
SELECT fact_1_id,
fact_2_id,
SUM(sales_value) AS sales_value,
GROUPING(fact_1_id) AS f1g,
GROUPING(fact_2_id) AS f2g
FROM dimension_tab
GROUP BY CUBE (fact_1_id, fact_2_id)
HAVING GROUPING(fact_1_id) = 1 OR GROUPING(fact_2_id) = 1
ORDER BY GROUPING(fact_1_id), GROUPING(fact_2_id);
FACT_1_ID FACT_2_ID SALES_VALUE F1G F2G
---------- ---------- ----------- ---------- ----------
1 24291.35 0 1
2 26237.04 0 1
4 11324.12 1 0
3 10274.02 1 0
2 9377.78 1 0
1 10016.39 1 0
5 9536.08 1 0
50528.39 1 1
8 rows selected.
SQL>
GROUPING_ID
The GROUPING_ID function provides an alternate and more
compact way to identify subtotal rows. Passing the dimension columns as
arguments, it returns a number indicating the GROUP BY level.
SELECT fact_1_id,
fact_2_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_1_id, fact_2_id) AS grouping_id
FROM dimension_tab
GROUP BY CUBE (fact_1_id, fact_2_id)
ORDER BY fact_1_id, fact_2_id;
FACT_1_ID FACT_2_ID SALES_VALUE GROUPING_ID
---------- ---------- ----------- -----------
1 1 4363.55 0
1 2 4794.76 0
1 3 4718.25 0
1 4 5387.45 0
1 5 5027.34 0
1 24291.35 1
2 1 5652.84 0
2 2 4583.02 0
2 3 5555.77 0
2 4 5936.67 0
2 5 4508.74 0
2 26237.04 1
1 10016.39 2
2 9377.78 2
3 10274.02 2
4 11324.12 2
5 9536.08 2
50528.39 3
18 rows selected.
SQL>
GROUP_ID
It's possible to write queries that return the duplicate subtotals, which can be a little confusing. The GROUP_ID function
assigns the value "0" to the first set, and all subsequent sets get
assigned a higher number. The following query forces duplicates to show
the GROUP_ID function in action.
SELECT fact_1_id,
fact_2_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_1_id, fact_2_id) AS grouping_id,
GROUP_ID() AS group_id
FROM dimension_tab
GROUP BY GROUPING SETS(fact_1_id, CUBE (fact_1_id, fact_2_id))
ORDER BY fact_1_id, fact_2_id;
FACT_1_ID FACT_2_ID SALES_VALUE GROUPING_ID GROUP_ID
---------- ---------- ----------- ----------- ----------
1 1 4363.55 0 0
1 2 4794.76 0 0
1 3 4718.25 0 0
1 4 5387.45 0 0
1 5 5027.34 0 0
1 24291.35 1 1
1 24291.35 1 0
2 1 5652.84 0 0
2 2 4583.02 0 0
2 3 5555.77 0 0
2 4 5936.67 0 0
2 5 4508.74 0 0
2 26237.04 1 1
2 26237.04 1 0
1 10016.39 2 0
2 9377.78 2 0
3 10274.02 2 0
4 11324.12 2 0
5 9536.08 2 0
50528.39 3 0
20 rows selected.
SQL>
If necessary, you could then filter the results using the group.
SELECT fact_1_id,
fact_2_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_1_id, fact_2_id) AS grouping_id,
GROUP_ID() AS group_id
FROM dimension_tab
GROUP BY GROUPING SETS(fact_1_id, CUBE (fact_1_id, fact_2_id))
HAVING GROUP_ID() = 0
ORDER BY fact_1_id, fact_2_id;
FACT_1_ID FACT_2_ID SALES_VALUE GROUPING_ID GROUP_ID
---------- ---------- ----------- ----------- ----------
1 1 4363.55 0 0
1 2 4794.76 0 0
1 3 4718.25 0 0
1 4 5387.45 0 0
1 5 5027.34 0 0
1 24291.35 1 0
2 1 5652.84 0 0
2 2 4583.02 0 0
2 3 5555.77 0 0
2 4 5936.67 0 0
2 5 4508.74 0 0
2 26237.04 1 0
1 10016.39 2 0
2 9377.78 2 0
3 10274.02 2 0
4 11324.12 2 0
5 9536.08 2 0
50528.39 3 0
18 rows selected.
SQL>
GROUPING SETS
Calculating all possible subtotals in a cube, especially those with many
dimensions, can be quite an intensive process. If you don't need all
the subtotals, this can represent a considerable amount of wasted
effort. The following cube with three dimensions gives 8 levels of
subtotals (GROUPING_ID: 0-7), shown
here.
SELECT fact_1_id,
fact_2_id,
fact_3_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_1_id, fact_2_id, fact_3_id) AS grouping_id
FROM dimension_tab
GROUP BY CUBE(fact_1_id, fact_2_id, fact_3_id)
ORDER BY fact_1_id, fact_2_id, fact_3_id;
If we only need a few of these levels of subtotaling we can use the GROUPING SETS expression
and specify exactly which ones we need, saving us having to calculate
the whole cube. In the following query we are only interested in
subtotals for the "FACT_1_ID, FACT_2_ID" and "FACT_1_ID, FACT_3_ID" groups.
SELECT fact_1_id,
fact_2_id,
fact_3_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_1_id, fact_2_id, fact_3_id) AS grouping_id
FROM dimension_tab
GROUP BY GROUPING SETS((fact_1_id, fact_2_id), (fact_1_id, fact_3_id))
ORDER BY fact_1_id, fact_2_id, fact_3_id;
FACT_1_ID FACT_2_ID FACT_3_ID SALES_VALUE GROUPING_ID
---------- ---------- ---------- ----------- -----------
1 1 4363.55 1
1 2 4794.76 1
1 3 4718.25 1
1 4 5387.45 1
1 5 5027.34 1
1 1 2737.4 2
1 2 1854.29 2
1 3 2090.96 2
1 4 2605.17 2
1 5 2590.93 2
1 6 2506.9 2
1 7 1839.85 2
1 8 2953.04 2
1 9 2778.75 2
1 10 2334.06 2
2 1 5652.84 1
2 2 4583.02 1
2 3 5555.77 1
2 4 5936.67 1
2 5 4508.74 1
2 1 3512.69 2
2 2 2847.94 2
2 3 2972.5 2
2 4 2534.06 2
2 5 3115.99 2
2 6 2775.85 2
2 7 2208.19 2
2 8 2358.55 2
2 9 1884.11 2
2 10 2027.16 2
30 rows selected.
SQL>
Notice how we have gone from returning 198 rows with 8 subtotal levels in the cube, to just 30 rows with 2 subtotal levels.
Composite Columns
ROLLUP and CUBE consider each column independently when deciding which subtotals must be calculated. For ROLLUP this means stepping back through the list to determine the groupings.
ROLLUP (a, b, c)
(a, b, c)
(a, b)
(a)
()
CUBE creates a grouping for every possible combination of columns.
CUBE (a, b, c)
(a, b, c)
(a, b)
(a, c)
(a)
(b, c)
(b)
(c)
()
Composite columns allow columns to be grouped together with braces so
they are treated as a single unit when determining the necessary
groupings. In the following ROLLUP columns "a" and "b" have
been turned into a composite column by the additional braces. As a
result the group of "a" is not longer calculated as the column "a" is
only present as part of the composite column in the statement.
ROLLUP ((a, b), c)
(a, b, c)
(a, b)
()
Not considered:
(a)
In a similar way, the possible combinations of the following CUBE are
reduced because references to "a" or "b" individually are not
considered as they are treated as a single column when the groupings are
determined.
CUBE ((a, b), c)
(a, b, c)
(a, b)
(c)
()
Not considered:
(a, c)
(a)
(b, c)
(b)
The impact of this is shown clearly in the follow two statements, whose output is shown
here and
here.
The regular cube returns 198 rows and 8 groups (0-7), while the cube
with the composite column returns only 121 rows with 4 groups (0, 1, 6,
7)
-- Regular Cube.
SELECT fact_1_id,
fact_2_id,
fact_3_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_1_id, fact_2_id, fact_3_id) AS grouping_id
FROM dimension_tab
GROUP BY CUBE(fact_1_id, fact_2_id, fact_3_id)
ORDER BY fact_1_id, fact_2_id, fact_3_id;
-- Cube with composite column.
SELECT fact_1_id,
fact_2_id,
fact_3_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_1_id, fact_2_id, fact_3_id) AS grouping_id
FROM dimension_tab
GROUP BY CUBE((fact_1_id, fact_2_id), fact_3_id)
ORDER BY fact_1_id, fact_2_id, fact_3_id;
Concatenated Groupings
Concatenated groupings are defined by putting together multiple GROUPING SETS, CUBEs or ROLLUPs
separated by commas. The resulting groupings are the cross-product of
all the groups produced by the individual grouping sets. It might be a
little easier to understand what this means by looking at an example.
The following GROUPING SET results in 2 groups of subtotals, one for the fact_1_id column and one for the fact_id_2 column.
SELECT fact_1_id,
fact_2_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_1_id, fact_2_id) AS grouping_id
FROM dimension_tab
GROUP BY GROUPING SETS(fact_1_id, fact_2_id)
ORDER BY fact_1_id, fact_2_id;
FACT_1_ID FACT_2_ID SALES_VALUE GROUPING_ID
---------- ---------- ----------- -----------
1 24291.35 1
2 26237.04 1
1 10016.39 2
2 9377.78 2
3 10274.02 2
4 11324.12 2
5 9536.08 2
7 rows selected.
SQL>
The next GROUPING SET results in another 2 groups of subtotals, one for the fact_3_id column and one for the fact_4_id column.
SELECT fact_3_id,
fact_4_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_3_id, fact_4_id) AS grouping_id
FROM dimension_tab
GROUP BY GROUPING SETS(fact_3_id, fact_4_id)
ORDER BY fact_3_id, fact_4_id;
FACT_3_ID FACT_4_ID SALES_VALUE GROUPING_ID
---------- ---------- ----------- -----------
1 6250.09 1
2 4702.23 1
3 5063.46 1
4 5139.23 1
5 5706.92 1
6 5282.75 1
7 4048.04 1
8 5311.59 1
9 4662.86 1
10 4361.22 1
1 4718.55 2
2 5439.1 2
3 4643.4 2
4 4515.3 2
5 5110.27 2
6 5910.78 2
7 4987.22 2
8 4846.25 2
9 5458.82 2
10 4898.7 2
20 rows selected.
SQL>
If
we combine them together into a concatenated grouping we get 4 groups
of subtotals. The output of the following query is shown here.
SELECT fact_1_id,
fact_2_id,
fact_3_id,
fact_4_id,
SUM(sales_value) AS sales_value,
GROUPING_ID(fact_1_id, fact_2_id, fact_3_id, fact_4_id) AS grouping_id
FROM dimension_tab
GROUP BY GROUPING SETS(fact_1_id, fact_2_id), GROUPING SETS(fact_3_id, fact_4_id)
ORDER BY fact_1_id, fact_2_id, fact_3_id, fact_4_id;
The output from the previous three queries produce the following groupings.
GROUPING SETS(fact_1_id, fact_2_id)
(fact_1_id)
(fact_2_id)
GROUPING SETS(fact_3_id, fact_4_id)
(fact_3_id)
(fact_4_id)
GROUPING SETS(fact_1_id, fact_2_id), GROUPING SETS(fact_3_id, fact_4_id)
(fact_1_id, fact_3_id)
(fact_1_id, fact_4_id)
(fact_2_id, fact_3_id)
(fact_2_id, fact_4_id)
So we can see the final cross-product of the two GROUPING SETS that make up the concatenated grouping. A generic summary would be as follows.
GROUPING SETS(a, b), GROUPING SETS(c, d)
(a, c)
(a, d)
(b, c)
(b, d)
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