SQL
Aggregate Functions
Like most other relational database products, DuckDB 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:  
```sql
SELECT max(temp_lo)
FROM weather;
```  
| max(temp_lo) |
|-------------:|
| 46           |  
If we wanted to know what city (or cities) that reading occurred in, we might try:  
```sql
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:  
```sql
SELECT city
FROM weather
WHERE temp_lo = (SELECT max(temp_lo) FROM weather);
```  
|     city      |
|---------------|
| San Francisco |  
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:  
```sql
SELECT city, max(temp_lo)
FROM weather
GROUP BY city;
```  
|     city      | max(temp_lo) |
|---------------|--------------|
| San Francisco | 46           |
| Hayward       | 37           |  
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`:  
```sql
SELECT city, max(temp_lo)
FROM weather
GROUP BY city
HAVING max(temp_lo) < 40;
```  
|  city   | max(temp_lo) |
|---------|-------------:|
| Hayward | 37           |  
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 can use the `LIKE` operator:  
```sql
SELECT city, max(temp_lo)
FROM weather
WHERE city LIKE 'S%'            -- (1)
GROUP BY city
HAVING max(temp_lo) < 40;
```  
More information about the `LIKE` operator can be found in the [pattern matching page](#docs:sql:functions:pattern_matching).  
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.  
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.