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Query Syntax

GROUP BY CUBE

GROUP BY CUBE is an extension of the GROUP BY clause similar to GROUP BY ROLLUP. In addition to producing all the rows of a GROUP BY ROLLUP, GROUP BY CUBE adds all the “cross-tabulations” rows. Sub-total rows are rows that further aggregate whose values are derived by computing the same aggregate functions that were used to produce the grouped rows.

A CUBE grouping is equivalent to a series of grouping sets and is essentially a shorter specification. The N elements of a CUBE specification correspond to 2^N GROUPING SETS.

See also:

GROUP BY GROUPING SETS , GROUP BY ROLLUP

Syntax

SELECT ...
FROM ...
[ ... ]
GROUP BY CUBE ( groupCube [ , groupCube [ , ... ] ] )
[ ... ]

Where:

groupCube ::= { <column_alias> | <position> | <expr> }
column_alias

Column alias appearing in the query block’s SELECT list.

position

Position of an expression in the SELECT list.

expr

Any expression on tables in the current scope.

Usage Notes

  • Snowflake allows up to 7 elements (equivalent to 128 grouping sets) in each cube.

Examples

Start by creating and loading a table with information about sales from a chain store that has branches in different cities and states/territories.

-- Create some tables and insert some rows.
CREATE TABLE products (product_ID INTEGER, wholesale_price REAL);
INSERT INTO products (product_ID, wholesale_price) VALUES 
    (1, 1.00),
    (2, 2.00);

CREATE TABLE sales (product_ID INTEGER, retail_price REAL, 
    quantity INTEGER, city VARCHAR, state VARCHAR);
INSERT INTO sales (product_id, retail_price, quantity, city, state) VALUES 
    (1, 2.00,  1, 'SF', 'CA'),
    (1, 2.00,  2, 'SJ', 'CA'),
    (2, 5.00,  4, 'SF', 'CA'),
    (2, 5.00,  8, 'SJ', 'CA'),
    (2, 5.00, 16, 'Miami', 'FL'),
    (2, 5.00, 32, 'Orlando', 'FL'),
    (2, 5.00, 64, 'SJ', 'PR');

Run a cube query that shows profit by city, state, and total across all states. The example below shows a query that has three “levels”:

  • Each city.

  • Each state.

  • All revenue combined.

This example uses ORDER BY state, city NULLS LAST to ensure that each state’s rollup comes immediately after all of the cities in that state, and that the final rollup appears at the end of the output.

SELECT state, city, SUM((s.retail_price - p.wholesale_price) * s.quantity) AS profit 
 FROM products AS p, sales AS s
 WHERE s.product_ID = p.product_ID
 GROUP BY CUBE (state, city)
 ORDER BY state, city NULLS LAST
 ;
+-------+---------+--------+
| STATE | CITY    | PROFIT |
|-------+---------+--------|
| CA    | SF      |     13 |
| CA    | SJ      |     26 |
| CA    | NULL    |     39 |
| FL    | Miami   |     48 |
| FL    | Orlando |     96 |
| FL    | NULL    |    144 |
| PR    | SJ      |    192 |
| PR    | NULL    |    192 |
| NULL  | Miami   |     48 |
| NULL  | Orlando |     96 |
| NULL  | SF      |     13 |
| NULL  | SJ      |    218 |
| NULL  | NULL    |    375 |
+-------+---------+--------+

Some rollup rows contain NULL values. For example, the last row in the table contains a NULL value for the city and a NULL value for the state because the data is for all cities and states, not a specific city and state.

Both GROUP BY CUBE and GROUP BY ROLLUP produce one row for each city/state pair, and both GROUP BY clauses also produce rollup rows for each individual state and for all states combined. The difference between the two GROUP BY clauses is that GROUP BY CUBE also produces an output row for each city name (‘Miami’, ‘SJ’, etc.).

Be careful using GROUP BY CUBE on hierarchical data. In this example, the row for “SJ” contains totals for both the city named “SJ” in the state of “CA” and the city named “SJ” in the territory of “PR”, even though the only relationship between those cities is that they have the same name. In general, use GROUP BY ROLLUP to analyze hierarchical data, and GROUP BY CUBE to analyze data across independent axes.

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