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SQL server 2012 : T-SQL Enhancements - The GROUPING SETS Operator (part 1) - Rolling Up by Level, Rolling Up All Level Combinations

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The GROUP BY clause has been part of the SELECT statement syntax since the earliest dialects of T-SQL. You use GROUP BY to create queries that collapse multiple rows belonging to the same group into a single summary row and perform aggregate calculations (such as SUM and AVG) across the individual rows of each group. SQL Server 6.5 later extended the GROUP BY clause by adding the WITH CUBE and WITH ROLLUP operators. These operators perform additional grouping and aggregation of data in standard relational queries, similar to what is provided by online analytical processing (OLAP) queries that slice and dice your data into Analysis Services cubes, but without ever leaving the relational database world. SQL Server 2008 added the GROUPING SETS operator that further extends the capabilities of the GROUP BY clause for summarizing and analyzing your data.

In this section, you will examine GROUP BY in many of its variant forms. You’ll start with the basic GROUP BY clause, and then you’ll learn how the WITH CUBE and WITH ROLLUP operators can be used to enhance those summary results. Then you’ll explore the GROUPING SETS operator added in SQL Server 2008.

Start with a simple inventory table that contains quantities for various items in diverse colors that are available at different store locations, as shown in Example 1.

Example 1. Creating the Inventory table.

CREATE TABLE Inventory(
  Store varchar(2),
  Item varchar(20),
  Color varchar(10),
  Quantity decimal)

Next, add some inventory data. There are 13 rows that contain inventory for tables, chairs, and sofas available in blue, red, and green, at NY, NJ, and PA locations, as shown in Example 2.

Example 2. Populating the Inventory table.

INSERT INTO Inventory VALUES('NY', 'Table', 'Blue', 124)
INSERT INTO Inventory VALUES('NJ', 'Table', 'Blue', 100)
INSERT INTO Inventory VALUES('NY', 'Table', 'Red', 29)
INSERT INTO Inventory VALUES('NJ', 'Table', 'Red', 56)
INSERT INTO Inventory VALUES('PA', 'Table', 'Red', 138)
INSERT INTO Inventory VALUES('NY', 'Table', 'Green', 229)
INSERT INTO Inventory VALUES('PA', 'Table', 'Green', 304)
INSERT INTO Inventory VALUES('NY', 'Chair', 'Blue', 101)
INSERT INTO Inventory VALUES('NJ', 'Chair', 'Blue', 22)
INSERT INTO Inventory VALUES('NY', 'Chair', 'Red', 21)
INSERT INTO Inventory VALUES('NJ', 'Chair', 'Red', 10)
INSERT INTO Inventory VALUES('PA', 'Chair', 'Red', 136)
INSERT INTO Inventory VALUES('NJ', 'Sofa', 'Green', 2)

Now use a basic GROUP BY clause to query on this data:

SELECT Item, Color, SUM(Quantity) AS TotalQty, COUNT(Store) AS Stores
 FROM Inventory
 GROUP BY Item, Color
 ORDER BY Item, Color

As implied by its syntax, this query groups all the inventory records by item and then by color within each item. The result set therefore includes one summary row for each unique combination of items and colors. The store location is not included in the grouping, and so the results returned by the query apply to all stores. Each summary row includes a TotalQty column calculated by the SUM aggregate function as the total quantity for all rows of the same item and color across all stores. The last column, Stores, is calculated by the COUNT aggregate function as the number of store locations at which each unique combination of items and colors is available, as shown here:

Item                 Color      TotalQty                       Stores
-------------------- ---------- ------------------------------ ------
Chair                Blue       123                            2
Chair                Red        167                            3
Sofa                 Green      2                              1
Table                Blue       224                            2
Table                Green      533                            2
Table                Red        223                            3

(6 row(s) affected)

These results show that SQL Server grouped the inventory records sharing the same item and color into a single summary row. The store location is not included in the breakdown, because you did not group by it, and so each summary row applies to all stores. For each item, the total quantity is calculated as the sum of the individual quantity values for the item and color combinations in each group, and the store count is calculated as the number of store locations at which each item and color combination is available. With GROUP BY, every column returned by the query must be either one of the columns actually being grouped by (such as the Store, Item, and Color columns) or an aggregate function that operates across all the combined member rows for the group [such as the SUM(Quantity) and COUNT(Store) functions].

1. Rolling Up by Level

This query demonstrates the most basic application of the GROUP BY clause, which simply groups and aggregates. It answers the question “How many items per color are in each store location?” by grouping items and colors. The WITH ROLLUP and WITH CUBE operators (which were introduced in SQL Server 6.5) can be used to answer more questions than that. Each of these operators supplements the results of an ordinary GROUP BY clause with additional summary aggregations on the underlying data. Here is the same query you ran before, only this time using WITH ROLLUP:

SELECT Item, Color, SUM(Quantity) AS TotalQty, COUNT(Store) AS Stores
 FROM Inventory
 GROUP BY Item, Color WITH ROLLUP
 ORDER BY Item, Color
GO

Item                 Color      TotalQty                       Stores
-------------------- ---------- ------------------------------ ------
NULL                 NULL       1272                           13
Chair                NULL       290                            5
Chair                Blue       123                            2
Chair                Red        167                            3
Sofa                 NULL       2                              1
Sofa                 Green      2                              1
Table                NULL       980                            7
Table                Blue       224                            2
Table                Green      533                            2
Table                Red        223                            3

(10 row(s) affected)

This time, you receive the same six grouped results as before, supplemented with four additional rollup rows (the ones with NULL values for Item or Color, highlighted here in bold). Rollup rows contain additional higher-level summary information that essentially “groups the groups” of the query results. Any row with NULL values in it is a rollup row, and the NULL should be thought of as “all values” in this context.

In these results, the first row is the top-level rollup, as indicated by NULL values for both Item and Color. This top-level rollup reports a grand total quantity of 1,272 for the entire set (all items in all colors) in all store locations (where the entire set consists of the 13 unique item/color combinations across all locations).

The next result is an item-level rollup for chairs. It reports a total quantity of 290 for chairs in all colors across 5 store locations. The two results that follow are the same summary rows for chairs returned by the first “plain” GROUP BY query and that were just rolled up. They show 123 blue chairs in 2 locations and 167 red chairs in 3 locations. The next result is an item-level rollup for sofas. Only one store location carries sofas, and they’re available only in green. The sofa rollup therefore contains the same values as the one and only summary row for 2 green sofas available in 1 location. The last set of rows report on tables in the same way that the chair and sofa data was returned. This includes an item-level rollup showing 980 tables across 7 locations followed by the summary rows showing 224 blue tables in 2 locations, 533 green tables in 2 locations, and 223 red tables in 3 locations returned.

So by simply adding WITH ROLLUP, you can answer a second question that the first ordinary GROUP BY query couldn’t: “How many chairs, tables, and sofas are in stock, regardless of color?”

2. Rolling Up All Level Combinations

Using WITH CUBE now instead of WITH ROLLUP takes this result set to the next level, as shown here:

SELECT Item, Color, SUM(Quantity) AS TotalQty, COUNT(Store) AS Stores
 FROM Inventory
 GROUP BY Item, Color WITH CUBE
 ORDER BY Item, Color
GO

Item                 Color      TotalQty                       Stores
-------------------- ---------- ------------------------------ ------
NULL                 NULL       1272                           13
NULL                 Blue       347                            4
NULL                 Green      535                            3
NULL                 Red        390                            6
Chair                NULL       290                            5
Chair                Blue       123                            2
Chair                Red        167                            3
Sofa                 NULL       2                              1
Sofa                 Green      2                              1
Table                NULL       980                            7
Table                Blue       224                            2
Table                Green      533                            2
Table                Red        223                            3

(13 row(s) affected)

You now have the same result set returned by WITH ROLLUP, only this time three more rollup rows have been added (again, indicated in bold here). Let’s look at exactly what SQL Server did. By applying WITH CUBE, you instructed the database engine to create a multidimensional representation of the data on the fly, which is loosely referred to as a cube. The number of dimensions in the cube is based on the number of grouping columns. This inventory example has only two dimensions, but a query could have many more dimensions if it specifies more grouping columns. A cube contains rollups for all the possible permutations of dimension values, not just the combinations of one value within another, as per the nesting levels defined by grouping columns listed in the GROUP BY clause.

So WITH CUBE returns the same rollups returned by WITH ROLLUP—which includes all items regardless of color—plus additional rollups for all colors regardless of item. As a result, you can now answer a third question that the earlier GROUP BY queries couldn’t: “How many items of any type in a particular color are in stock?” That means that you can now also see how many blue, green, or red items you have in inventory regardless of whether they are chairs, sofas, or tables.

Because a cube rolls up every possible combination of dimension values independent of the order of levels expressed with GROUP BY, each additional grouping level increases the size of the result set exponentially. For example, if you modify the query to group by store location as well, SQL Server returns 44 rows including rollups for every possible combination of values across the three grouping columns Store, Item, and Color, as follows:

SELECT Store, Item, Color, SUM(Quantity) AS TotalQty
 FROM Inventory
 GROUP BY Store, Item, Color WITH CUBE
 ORDER BY Store, Item, Color
GO

Store Item                 Color      TotalQty
----- -------------------- ---------- ------------------------------
NULL  NULL                 NULL       1272
NULL  NULL                 Blue       347
NULL  NULL                 Green      535
NULL  NULL                 Red        390
NULL  Chair                NULL       290
NULL  Chair                Blue       123
NULL  Chair                Red        167
NULL  Sofa                 NULL       2
NULL  Sofa                 Green      2
NULL  Table                NULL       980
NULL  Table                Blue       224
NULL  Table                Green      533
NULL  Table                Red        223
NJ    NULL                 NULL       190
NJ    NULL                 Blue       122
NJ    NULL                 Green      2
NJ    NULL                 Red        66
NJ    Chair                NULL       32
NJ    Chair                Blue       22
NJ    Chair                Red        10
NJ    Sofa                 NULL       2
NJ    Sofa                 Green      2
NJ    Table                NULL       156
NJ    Table                Blue       100
NJ    Table                Red        56
NY    NULL                 NULL       504
NY    NULL                 Blue       225
NY    NULL                 Green      229
NY    NULL                 Red        50
NY    Chair                NULL       122
NY    Chair                Blue       101
NY    Chair                Red        21
NY    Table                NULL       382
NY    Table                Blue       124
NY    Table                Green      229
NY    Table                Red        29
PA    NULL                 NULL       578
PA    NULL                 Green      304
PA    NULL                 Red        274
PA    Chair                NULL       136
PA    Chair                Red        136
PA    Table                NULL       442
PA    Table                Green      304
PA    Table                Red        138

(44 row(s) affected)

These results can now answer inventory questions for every conceivable combination of grouping levels. For example, across all locations, there are 347 blue items (tables, chairs, and sofas), 290 chairs (all colors), and 533 green tables, whereas in NY specifically, there are 50 red items, 382 tables, and 124 blue tables, and so on. Every permutation of store location, item, and color—and their rollups—are returned by this single query.

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