Aggregate Functions
Aggregate functions compute a single result from a set of input rows. They are typically used with the GROUP BY clause in SELECT statements, but can also be used without GROUP BY to aggregate over all rows. When used in a SELECT with non-aggregate columns and no GROUP BY, the result is a single row.
All standard aggregate functions ignore NULL values (except count(*)). If every input value is NULL, the aggregate returns NULL, with the exception of count() (which returns 0) and total() (which returns 0.0).
Function Reference
| Function | Return Type | Description |
|---|
avg(X) | REAL | Average of all non-NULL values of X |
count(X) | INTEGER | Count of rows where X is not NULL |
count(*) | INTEGER | Count of all rows in the group |
group_concat(X) | TEXT | Concatenation of all non-NULL values of X, separated by commas |
group_concat(X, Y) | TEXT | Concatenation of all non-NULL values of X, separated by Y |
string_agg(X, Y) | TEXT | Alias for group_concat(X, Y) |
max(X) | same as X | Maximum non-NULL value of X |
min(X) | same as X | Minimum non-NULL value of X |
sum(X) | INTEGER or REAL | Sum of all non-NULL values of X. Returns NULL if all values are NULL |
total(X) | REAL | Sum of all non-NULL values of X. Always returns REAL, 0.0 if all values are NULL |
Detailed Descriptions and Examples
The examples below use the following table:
CREATE TABLE sales (
id INTEGER PRIMARY KEY,
region TEXT,
product TEXT,
amount REAL,
quantity INTEGER
);
INSERT INTO sales VALUES
(1, 'North', 'Widget', 100.00, 5),
(2, 'North', 'Gadget', 250.00, 2),
(3, 'South', 'Widget', 150.00, 8),
(4, 'South', 'Gadget', NULL, 3),
(5, 'North', 'Widget', 200.00, NULL),
(6, 'South', 'Widget', 175.00, 6);
avg(X)
Returns the average of all non-NULL values of X as a REAL (floating-point) number. Returns NULL if all values are NULL.
| Parameter | Type | Description |
|---|
| X | any numeric | The expression to average |
SELECT avg(amount) FROM sales;
-- 175.0 (sum of non-NULL amounts / count of non-NULL amounts = 875.0 / 5)
SELECT region, avg(amount) AS avg_amount
FROM sales
GROUP BY region;
| region | avg_amount |
|---|
| North | 183.333333333333 |
| South | 162.5 |
avg(X) ignores NULL values in both the sum and the count. In the example above, the South region has three non-NULL amounts (150 + 175 = 325, but also the NULL row is excluded), so the average is computed over the non-NULL values only.
count(X) and count(*)
count(X) returns the number of rows where X is not NULL. count(*) returns the total number of rows in the group, including rows with NULL values.
| Parameter | Type | Description |
|---|
| X | any | The expression to count non-NULL values for |
* | special | Counts all rows regardless of NULL |
SELECT count(*) FROM sales;
-- 6 (all rows)
SELECT count(amount) FROM sales;
-- 5 (excludes the row where amount is NULL)
SELECT region, count(*) AS total_rows, count(amount) AS rows_with_amount
FROM sales
GROUP BY region;
| region | total_rows | rows_with_amount |
|---|
| North | 3 | 3 |
| South | 3 | 2 |
group_concat(X) and group_concat(X, Y)
group_concat(X)
group_concat(X, Y)
,). When Y is provided, it is used as the separator instead.
| Parameter | Type | Description |
|---|
| X | any | The expression whose values to concatenate |
| Y | TEXT | Separator string (default: ",") |
SELECT group_concat(product) FROM sales;
-- 'Widget,Gadget,Widget,Gadget,Widget,Widget'
SELECT group_concat(DISTINCT product) FROM sales;
-- 'Widget,Gadget'
SELECT region, group_concat(product, ' | ') AS products
FROM sales
GROUP BY region;
| region | products |
|---|
| North | Widget | Gadget | Widget |
| South | Widget | Gadget | Widget |
string_agg(X, Y)
Alias for group_concat(X, Y). Provided for compatibility with PostgreSQL.
| Parameter | Type | Description |
|---|
| X | any | The expression whose values to concatenate |
| Y | TEXT | Separator string |
SELECT region, string_agg(product, ', ') AS products
FROM sales
GROUP BY region;
| region | products |
|---|
| North | Widget, Gadget, Widget |
| South | Widget, Gadget, Widget |
max(X) and min(X)
max(X) returns the maximum non-NULL value of X. min(X) returns the minimum non-NULL value of X. Values are compared using the standard SQLite comparison rules. Returns NULL if all values are NULL.
| Parameter | Type | Description |
|---|
| X | any | The expression to find the maximum or minimum value of |
SELECT max(amount), min(amount) FROM sales;
-- max: 250.0, min: 100.0
SELECT region, max(amount) AS highest, min(amount) AS lowest
FROM sales
GROUP BY region;
| region | highest | lowest |
|---|
| North | 250.0 | 100.0 |
| South | 175.0 | 150.0 |
When max(X) or min(X) is called with a single argument in an aggregate context, it acts as an aggregate function. When called with two or more arguments (e.g., max(a, b, c)), it acts as a scalar function and returns the largest argument. sum(X) and total(X)
Both functions return the sum of all non-NULL values of X. They differ in return type and behavior when all values are NULL.
| Parameter | Type | Description |
|---|
| X | any numeric | The expression to sum |
sum(X): INTEGER if all non-NULL inputs are integers and no overflow occurs, otherwise REAL. Returns NULL if all values are NULL.total(X): Always REAL. Returns 0.0 if all values are NULL.
SELECT sum(amount), total(amount) FROM sales;
-- sum: 875.0, total: 875.0
SELECT sum(quantity), total(quantity) FROM sales;
-- sum: 24, total: 24.0
Difference between sum() and total()
The key difference appears when all values in the group are NULL:
CREATE TABLE empty_amounts (val REAL);
INSERT INTO empty_amounts VALUES (NULL), (NULL);
SELECT sum(val) FROM empty_amounts;
-- NULL
SELECT total(val) FROM empty_amounts;
-- 0.0
total() convenient when you need a numeric result even for empty or all-NULL groups:
SELECT
region,
sum(amount) AS sum_amount,
total(amount) AS total_amount
FROM sales
GROUP BY region;
| region | sum_amount | total_amount |
|---|
| North | 550.0 | 550.0 |
| South | 325.0 | 325.0 |
-- total() is useful in arithmetic to avoid NULL propagation
SELECT total(amount) * 1.1 AS with_tax FROM sales;
-- 962.5
-- sum() with all NULLs would produce NULL, making the multiplication NULL too
sum(X) returns an integer result when all inputs are integers and the result fits within a 64-bit signed integer. If the sum overflows, it automatically switches to REAL. Use total(X) when you always want a floating-point result.
Using Aggregates with GROUP BY
The GROUP BY clause partitions rows into groups. Each aggregate function is computed independently for each group.
SELECT
region,
product,
count(*) AS order_count,
sum(amount) AS total_sales,
avg(amount) AS avg_sale,
min(amount) AS min_sale,
max(amount) AS max_sale
FROM sales
GROUP BY region, product;
| region | product | order_count | total_sales | avg_sale | min_sale | max_sale |
|---|
| North | Gadget | 1 | 250.0 | 250.0 | 250.0 | 250.0 |
| North | Widget | 2 | 300.0 | 150.0 | 100.0 | 200.0 |
| South | Gadget | 1 | NULL | NULL | NULL | NULL |
| South | Widget | 2 | 325.0 | 162.5 | 150.0 | 175.0 |
Filtering Groups with HAVING
The HAVING clause filters groups after aggregation. Use WHERE to filter rows before aggregation and HAVING to filter groups after.
SELECT region, sum(amount) AS total_sales
FROM sales
WHERE amount IS NOT NULL
GROUP BY region
HAVING sum(amount) > 400;
| region | total_sales |
|---|
| North | 550.0 |
Aggregates with DISTINCT
The DISTINCT keyword causes the aggregate to consider only unique non-NULL values.
SELECT count(product) FROM sales;
-- 6 (all non-NULL product values)
SELECT count(DISTINCT product) FROM sales;
-- 2 (only 'Widget' and 'Gadget')
SELECT group_concat(DISTINCT product) FROM sales;
-- 'Widget,Gadget'
Aggregates as Window Functions
All standard aggregate functions can be used as window functions. When used with an OVER clause, the function computes a running or partitioned result without collapsing rows.
SELECT
id,
region,
amount,
sum(amount) OVER (PARTITION BY region ORDER BY id) AS running_total,
count(*) OVER (PARTITION BY region) AS region_count
FROM sales
ORDER BY region, id;
| id | region | amount | running_total | region_count |
|---|
| 1 | North | 100.0 | 100.0 | 3 |
| 2 | North | 250.0 | 350.0 | 3 |
| 5 | North | 200.0 | 550.0 | 3 |
| 3 | South | 150.0 | 150.0 | 3 |
| 4 | South | NULL | 150.0 | 3 |
| 6 | South | 175.0 | 325.0 | 3 |
Turso Extension: stddev(X)
stddev(X) is a Turso extension and is not part of standard SQLite. It is available by default in Turso without loading any additional extensions.
| Parameter | Type | Description |
|---|
| X | any numeric | The expression to compute the standard deviation of |
SELECT stddev(amount) FROM sales;
SELECT region, avg(amount) AS mean, stddev(amount) AS std_dev
FROM sales
GROUP BY region;
Extension Aggregate Functions
The following aggregate functions are available through the percentile extension. Load it before use.
These functions require the percentile extension. Load it with SELECT load_extension('./percentile'); or by configuring your connection to auto-load it.
| Parameter | Type | Description |
|---|
| X | any numeric | The expression to find the median of |
SELECT median(amount) FROM sales;
-- 175.0
SELECT region, median(amount) AS median_amount
FROM sales
GROUP BY region;
percentile(Y, P)
Returns the P-th percentile of all non-NULL values of Y. Uses linear interpolation between adjacent values.
| Parameter | Type | Description |
|---|
| Y | any numeric | The values to compute the percentile over |
| P | REAL | The percentile to compute (0.0 to 100.0) |
SELECT percentile(amount, 50) FROM sales; -- 50th percentile (median)
SELECT percentile(amount, 90) FROM sales; -- 90th percentile
SELECT percentile(amount, 25) FROM sales; -- 25th percentile (Q1)
percentile_cont(Y, P) and percentile_disc(Y, P)
percentile_cont(Y, P)
percentile_disc(Y, P)
percentile_cont uses continuous (interpolated) distribution, while percentile_disc returns the nearest discrete input value.
| Parameter | Type | Description |
|---|
| Y | any numeric | The values to compute the percentile over |
| P | REAL | The percentile fraction (0.0 to 1.0) |
-- Continuous percentile (interpolates between values)
SELECT percentile_cont(amount, 0.5) FROM sales;
-- Discrete percentile (returns an actual input value)
SELECT percentile_disc(amount, 0.5) FROM sales;
Note the difference in P range: percentile(Y, P) takes P from 0 to 100, while percentile_cont and percentile_disc take P from 0.0 to 1.0.
See Also
