How many PC models does a particular supplier produce? How the average price is defined
for computers with the same specifications? The answers to these and other questions
associated with some statistic information may be obtained by means of **summarizing (aggregate) functions**.
The following aggregate functions are assumed as standard:

Function |
Description |

COUNT(*) | Returns the number of rows of the record source. |

COUNT(<column name>) | Returns the number of values in the specified column. |

SUM(<column name>) | Returns the sum of values in the specified column. |

AVG(<column name>) | Returns the average value in the specified column. |

MIN(<column name>) | Returns the minimal value in the specified column. |

MAX(<column name>) | Returns the maximum value in the specified column. |

All these functions return a single value. In so doing, the functions **COUNT, MIN**, and **MAX**
are applicable to any data types, while the functions **SUM** and **AVG** are only used
with numeric fields. The difference between the functions **COUNT(*)** and **COUNT(<column name>)** is that
the second does not calculate NULL-values.

* Example.* Find the minimal and maximal prices for PCs:

SELECT MIN(price) AS Min_price, MAX(price) AS Max_price FROM PC; |

The result is a single row containing the aggregate values:

Min_price |
Max_price |

350.0 | 980.0 |

* Example.* Find the number of available computers produced by the maker :

SELECT COUNT(*) AS Qty FROM PC WHERE model IN (SELECT model FROM Product WHERE maker = 'A'); |

As a result we get:

Qty |

7 |

* Example.* If the number of different models produced by the maker A is needed,
the query may be written as follows (taking into account the fact that each model in the
Product table is shown once):

SELECT COUNT(model) AS Qty_model FROM Product WHERE maker = 'A'; |

The coincidence in the results is fully accidental and is due to the number of computers produced by maker A in database being equal to the number of models produced by this maker:

Qty_model |

7 |

* Example.* Find the number of available different models produced by maker A.
This query is similar to the preceding one for the total number of models produced by
maker A. Now we need to find the number of different models in the PC table
(available for sale).

To use only unique values in calculating the statistic, the **parameter DISTINCT**
with an **aggregate function argument** may be used.
**ALL** is another (default) parameter and assumes that all the column values returned are calculated.
The statement

SELECT COUNT(DISTINCT model) AS Qty FROM PC WHERE model IN (SELECT model FROM Product WHERE maker = 'A'); |

gives the following result:

Qty |

2 |

If we need the number of PC models produced by **each** maker, we will need to use
the **GROUP BY clause**, placed immediately after the **WHERE clause**, if any.

When a

Let us consider a simple example:

SELECT model, COUNT(model) AS Qty_model, AVG(price) AS Avg_price FROM PC GROUP BY model; |

The number of computers and their average price are defined for each PC model in the query. All rows with the same model value are combined in a group with value count and the average price calculated for each group thereafter. Executing this query gives the following table:

model |
Qty_model |
Avg_price |

1121 | 3 | 850.0 |

1232 | 4 | 425.0 |

1233 | 3 | 843.33333333333337 |

1260 | 1 | 350.0 |

Should the SELECT clause include date column these characteristics may be calculated for each date specified. For that, the date should be added as grouping column with the aggregate functions be calculated for each combination of (model−date).

There are some particular **rules for executing aggregate functions**:

- If
**none of the rows was returned**by the query (or none of the rows for the given group), the source data for any aggregate function to be calculated is missing. In such case, the COUNT function returns zero, while other functions return NULL. - The
**argument**of the aggregate function**cannot include aggregate functions itself**(the function of function) i.e. no maximum of average values is obtainable. - The result of the COUNT function is
**integer**. Other aggregate functions inherit the types of processing data. - An
**error**occurs where the result is over the maximal value of the used data type while executing SUM function.

In so doing, if the query does not include **GROUP BY clause**, the ** aggregate functions**
in the **SELECT clause** process all the result rows of this query.
If the query includes the **GROUP BY clause**, each row set with the same value in the column or
the same combination of values in several columns given in the **GROUP BY clause** forms
a group with the **aggregate functions** being calculated for each group separately.

While **WHERE clause** gives predicate for filtering rows, the **HAVING clause**
is applied **after grouping** that gives a similar predicate but filtering groups by
the values of **aggregate functions**. This clause is nessesary for checking the values
that are obtained by means of an **aggregate function** not from separate rows of
record source in the **FROM clause** but from the groups of these rows. Therefore,
this checking is not applicable to the **WHERE clause**.

* Example.* Get the count of PC and the average price for each model providing
average price is less than $800:

SELECT model, COUNT(model) AS Qty_model, AVG(price) AS Avg_price FROM PC GROUP BY model HAVING AVG(price) < 800; |

As a result, we get:

model |
Qty_model |
Avg_price |

1232 | 4 | 425.0 |

1260 | 1 | 350.0 |

Note that the **alias** (Avg_price) for naming values of the **aggregate function**
in the SELECT clause may not be used in the HAVING clause. This is because the **SELECT clause**
forming the query result set is executed last but before the **ORDER BY clause**.

Below is the **execution order of clauses** in the SELECT operator:

- FROM
- WHERE
- GROUP BY
- HAVING
- SELECT
- ORDER BY

This order does not correspond to the syntax order of SELECT operator generally formed as follows:

**SELECT** [**DISTINCT** | **ALL**]{*

| [<column expression> [[**AS**] <alias>]] [,]}

**FROM** <table name> [[**AS**] <alias>] [,]

[**WHERE** <predicate>]

[[**GROUP BY** <colunm list>]

[**HAVING** <condition on aggregate values>] ]

[**ORDER BY** <column list>]

Suggested exercises: 10, 11, 12, 14, 15, 20, 22, 33, 43, 51, 52.

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