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 the rows that will
go into the aggregation stage; so it has to be evaluated before
aggregate functions are computed.)
However, as is often the case
the query can be restated to accomplish the intended 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 the*PostgreSQL 7.3 User's Guide*.

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,
`HAVING` clauses always contain aggregate functions.
(Strictly speaking, you are allowed to write a `HAVING`
clause that doesn't use aggregates, but it's wasteful: The same condition
could be used more efficiently at the `WHERE` stage.)

Observe that 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.