предложение PIVOT
Область применения: Databricks SQL Databricks Runtime
Преобразует строки table_reference путем поворота уникальных значений указанного списка столбцов в отдельные столбцы.
Синтаксис
table_reference PIVOT ( { aggregate_expression [ [ AS ] agg_column_alias ] } [, ...]
FOR column_list IN ( expression_list ) )
column_list
{ column_name |
( column_name [, ...] ) }
expression_list
{ expression [ AS ] [ column_alias ] |
{ ( expression [, ...] ) [ AS ] [ column_alias] } [, ...] ) }
Параметры
-
Определяет тему
PIVOT
операции. -
Выражение любого типа, где все ссылки на столбцы
table_reference
являются аргументами для агрегатных функций. -
Необязательный псевдоним для результата статистической обработки. Если псевдоним не указан,
PIVOT
создает псевдоним на основеaggregate_expression
. column_list
Набор столбцов, которые необходимо повернуть.
-
Столбец из
table_reference
.
-
expression_list
Сопоставляет значения от
column_list
с псевдонимами столбцов.-
Литеральное выражение с типом, который делит наименьший общий тип с соответствующим
column_name
.Количество выражений в каждом кортеже должно совпадать с количеством
column_names
вcolumn_list
. -
Необязательный псевдоним, указывающий имя созданного столбца. Если псевдоним не указан,
PIVOT
создает псевдоним на основеexpression
.
-
Результат
Временная таблица следующей формы:
Все столбцы из промежуточного результирующего набора
table_reference
, которые не были указаны в каких-либоaggregate_expression
илиcolumn_list
.Эти столбцы являются группированием столбцов.
Для каждого кортежа
expression
и сочетанияaggregate_expression
создается один столбецPIVOT
. Тип является типомaggregate_expression
.Если есть только один
aggregate_expression
столбец называется с помощьюcolumn_alias
. В противном случае он называетсяcolumn_alias_agg_column_alias
.Значение в каждой ячейке является результатом
aggregation_expression
с использованиемFILTER ( WHERE column_list IN (expression, ...)
.
Примеры
-- A very basic PIVOT
-- Given a table with sales by quarter, return a table that returns sales across quarters per year.
> CREATE TEMP VIEW sales(year, quarter, region, sales) AS
VALUES (2018, 1, 'east', 100),
(2018, 2, 'east', 20),
(2018, 3, 'east', 40),
(2018, 4, 'east', 40),
(2019, 1, 'east', 120),
(2019, 2, 'east', 110),
(2019, 3, 'east', 80),
(2019, 4, 'east', 60),
(2018, 1, 'west', 105),
(2018, 2, 'west', 25),
(2018, 3, 'west', 45),
(2018, 4, 'west', 45),
(2019, 1, 'west', 125),
(2019, 2, 'west', 115),
(2019, 3, 'west', 85),
(2019, 4, 'west', 65);
> SELECT year, region, q1, q2, q3, q4
FROM sales
PIVOT (sum(sales) AS sales
FOR quarter
IN (1 AS q1, 2 AS q2, 3 AS q3, 4 AS q4));
2018 east 100 20 40 40
2019 east 120 110 80 60
2018 west 105 25 45 45
2019 west 125 115 85 65
-- The same query written without PIVOT
> SELECT year, region,
sum(sales) FILTER(WHERE quarter = 1) AS q1,
sum(sales) FILTER(WHERE quarter = 2) AS q2,
sum(sales) FILTER(WHERE quarter = 3) AS q2,
sum(sales) FILTER(WHERE quarter = 4) AS q4
FROM sales
GROUP BY year, region;
2018 east 100 20 40 40
2019 east 120 110 80 60
2018 west 105 25 45 45
2019 west 125 115 85 65
-- Also PIVOT on region
> SELECT year, q1_east, q1_west, q2_east, q2_west, q3_east, q3_west, q4_east, q4_west
FROM sales
PIVOT (sum(sales) AS sales
FOR (quarter, region)
IN ((1, 'east') AS q1_east, (1, 'west') AS q1_west, (2, 'east') AS q2_east, (2, 'west') AS q2_west,
(3, 'east') AS q3_east, (3, 'west') AS q3_west, (4, 'east') AS q4_east, (4, 'west') AS q4_west));
2018 100 105 20 25 40 45 40 45
2019 120 125 110 115 80 85 60 65
-- The same query written without PIVOT
> SELECT year,
sum(sales) FILTER(WHERE (quarter, region) IN ((1, 'east'))) AS q1_east,
sum(sales) FILTER(WHERE (quarter, region) IN ((1, 'west'))) AS q1_west,
sum(sales) FILTER(WHERE (quarter, region) IN ((2, 'east'))) AS q2_east,
sum(sales) FILTER(WHERE (quarter, region) IN ((2, 'west'))) AS q2_west,
sum(sales) FILTER(WHERE (quarter, region) IN ((3, 'east'))) AS q3_east,
sum(sales) FILTER(WHERE (quarter, region) IN ((3, 'west'))) AS q3_west,
sum(sales) FILTER(WHERE (quarter, region) IN ((4, 'east'))) AS q4_east,
sum(sales) FILTER(WHERE (quarter, region) IN ((4, 'west'))) AS q4_west
FROM sales
GROUP BY year;
2018 100 105 20 25 40 45 40 45
2019 120 125 110 115 80 85 60 65
-- To aggregate across regions the column must be removed from the input.
> SELECT year, q1, q2, q3, q4
FROM (SELECT year, quarter, sales FROM sales) AS s
PIVOT (sum(sales) AS sales
FOR quarter
IN (1 AS q1, 2 AS q2, 3 AS q3, 4 AS q4));
2018 205 45 85 85
2019 245 225 165 125
-- The same query without PIVOT
> SELECT year,
sum(sales) FILTER(WHERE quarter = 1) AS q1,
sum(sales) FILTER(WHERE quarter = 2) AS q2,
sum(sales) FILTER(WHERE quarter = 3) AS q3,
sum(sales) FILTER(WHERE quarter = 4) AS q4
FROM sales
GROUP BY year;
-- A PIVOT with multiple aggregations
> SELECT year, q1_total, q1_avg, q2_total, q2_avg, q3_total, q3_avg, q4_total, q4_avg
FROM (SELECT year, quarter, sales FROM sales) AS s
PIVOT (sum(sales) AS total, avg(sales) AS avg
FOR quarter
IN (1 AS q1, 2 AS q2, 3 AS q3, 4 AS q4));
2018 205 102.5 45 22.5 85 42.5 85 42.5
2019 245 122.5 225 112.5 165 82.5 125 62.5
-- The same query without PIVOT
> SELECT year,
sum(sales) FILTER(WHERE quarter = 1) AS q1_total,
avg(sales) FILTER(WHERE quarter = 1) AS q1_avg,
sum(sales) FILTER(WHERE quarter = 2) AS q2_total,
avg(sales) FILTER(WHERE quarter = 2) AS q2_avg,
sum(sales) FILTER(WHERE quarter = 3) AS q3_total,
avg(sales) FILTER(WHERE quarter = 3) AS q3_avg,
sum(sales) FILTER(WHERE quarter = 4) AS q4_total,
avg(sales) FILTER(WHERE quarter = 4) AS q4_avg
FROM sales
GROUP BY year;
2018 205 102.5 45 22.5 85 42.5 85 42.5
2019 245 122.5 225 112.5 165 82.5 125 62.5