Presto 0.186 Documentation

6.14. Window Functions

6.14. Window Functions

Window functions perform calculations across rows of the query result. They run after the HAVING clause but before the ORDER BY clause. Invoking a window function requires special syntax using the OVER clause to specify the window. A window has three components:

For example, the following query ranks orders for each clerk by price:

SELECT orderkey, clerk, totalprice,
       rank() OVER (PARTITION BY clerk
                    ORDER BY totalprice DESC) AS rnk
FROM orders
ORDER BY clerk, rnk

Aggregate Functions

All Aggregate Functions can be used as window functions by adding the OVER clause. The aggregate function is computed for each row over the rows within the current row’s window frame.

For example, the following query produces a rolling sum of order prices by day for each clerk:

SELECT clerk, orderdate, orderkey, totalprice,
       sum(totalprice) OVER (PARTITION BY clerk
                             ORDER BY orderdate) AS rolling_sum
FROM orders
ORDER BY clerk, orderdate, orderkey

Ranking Functions

cume_dist() → bigint

Returns the cumulative distribution of a value in a group of values. The result is the number of rows preceding or peer with the row in the window ordering of the window partition divided by the total number of rows in the window partition. Thus, any tie values in the ordering will evaluate to the same distribution value.

dense_rank() → bigint

Returns the rank of a value in a group of values. This is similar to rank(), except that tie values do not produce gaps in the sequence.

ntile(n) → bigint

Divides the rows for each window partition into n buckets ranging from 1 to at most n. Bucket values will differ by at most 1. If the number of rows in the partition does not divide evenly into the number of buckets, then the remainder values are distributed one per bucket, starting with the first bucket.

For example, with 6 rows and 4 buckets, the bucket values would be as follows: 1 1 2 2 3 4

percent_rank() → bigint

Returns the percentage ranking of a value in group of values. The result is (r - 1) / (n - 1) where r is the rank() of the row and n is the total number of rows in the window partition.

rank() → bigint

Returns the rank of a value in a group of values. The rank is one plus the number of rows preceding the row that are not peer with the row. Thus, tie values in the ordering will produce gaps in the sequence. The ranking is performed for each window partition.

row_number() → bigint

Returns a unique, sequential number for each row, starting with one, according to the ordering of rows within the window partition.

Value Functions

first_value(x) → [same as input]

Returns the first value of the window.

last_value(x) → [same as input]

Returns the last value of the window.

nth_value(x, offset) → [same as input]

Returns the value at the specified offset from beginning the window. Offsets start at 1. The offset can be any scalar expression. If the offset is null or greater than the number of values in the window, null is returned. It is an error for the offset to be zero or negative.

lead(x[, offset[, default_value]]) → [same as input]

Returns the value at offset rows after the current row in the window. Offsets start at 0, which is the current row. The offset can be any scalar expression. The default offset is 1. If the offset is null or larger than the window, the default_value is returned, or if it is not specified null is returned.

lag(x[, offset[, default_value]]) → [same as input]

Returns the value at offset rows before the current row in the window Offsets start at 0, which is the current row. The offset can be any scalar expression. The default offset is 1. If the offset is null or larger than the window, the default_value is returned, or if it is not specified null is returned.