PostgreSQL 9.5.10 Documentation | |||
---|---|---|---|

Prev | Up | Chapter 2. The SQL Language | Next |

Like most other relational database products,
PostgreSQL supports
*aggregate functions*.
An aggregate function computes a single result from multiple input rows.
For example, there are aggregates to compute the
`count`

, `sum`

,
`avg`

(average), `max`

(maximum) and
`min`

(minimum) over a set of rows.

As an example, we can find the highest low-temperature reading anywhere with:

SELECT max(temp_lo) FROM weather;

max ----- 46 (1 row)

If we wanted to know what city (or cities) that reading occurred in, we might try:

SELECT city FROM weather WHERE temp_lo = max(temp_lo);WRONG

but this will not work since the aggregate
`max`

cannot be used in the
`WHERE` clause. (This restriction exists because
the `WHERE` clause determines which rows will be
included in the aggregate calculation; so obviously it has to be evaluated
before aggregate functions are computed.)
However, as is often the case
the query can be restated to accomplish the desired result, here
by using a *subquery*:

SELECT city FROM weather WHERE temp_lo = (SELECT max(temp_lo) FROM weather);

city --------------- San Francisco (1 row)

This is OK because the subquery is an independent computation that computes its own aggregate separately from what is happening in the outer query.

Aggregates are also very useful in combination with `GROUP
BY` clauses. For example, we can get the maximum low
temperature observed in each city with:

SELECT city, max(temp_lo) FROM weather GROUP BY city;

city | max ---------------+----- Hayward | 37 San Francisco | 46 (2 rows)

which gives us one output row per city. Each aggregate result is
computed over the table rows matching that city.
We can filter these grouped
rows using `HAVING`:

SELECT city, max(temp_lo) FROM weather GROUP BY city HAVING max(temp_lo) < 40;

city | max ---------+----- Hayward | 37 (1 row)

which gives us the same results for only the cities that have all
`temp_lo` values below 40. Finally, if we only care about
cities whose
names begin with "`S`", we might do:

SELECT city, max(temp_lo) FROM weather WHERE city LIKE 'S%'(1)GROUP BY city HAVING max(temp_lo) < 40;

**(1)**- The
`LIKE`operator does pattern matching and is explained in Section 9.7.

It is important to understand the interaction between aggregates and
SQL's `WHERE` and `HAVING` clauses.
The fundamental difference between `WHERE` and
`HAVING` is this: `WHERE` selects
input rows before groups and aggregates are computed (thus, it controls
which rows go into the aggregate computation), whereas
`HAVING` selects group rows after groups and
aggregates are computed. Thus, the
`WHERE` clause must not contain aggregate functions;
it makes no sense to try to use an aggregate to determine which rows
will be inputs to the aggregates. On the other hand, the
`HAVING` clause always contains aggregate functions.
(Strictly speaking, you are allowed to write a `HAVING`
clause that doesn't use aggregates, but it's seldom useful. The same
condition could be used more efficiently at the `WHERE`
stage.)

In the previous example, we can apply the city name restriction in
`WHERE`, since it needs no aggregate. This is
more efficient than adding the restriction to `HAVING`,
because we avoid doing the grouping and aggregate calculations
for all rows that fail the `WHERE` check.