PIVOT, klauzula
Dotyczy: Databricks SQL
Databricks Runtime
Przekształca wiersze table_reference , obracając unikatowe wartości określonej listy kolumn na oddzielne kolumny.
Składnia
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] } [, ...] ) }
Parametry
-
Identyfikuje temat
PIVOT
operacji. -
Wyrażenie dowolnego typu, w którym wszystkie odwołania do kolumn są argumentami
table_reference
funkcji agregujących. -
Opcjonalny alias wyniku agregacji. Jeśli alias nie zostanie określony,
PIVOT
wygeneruje alias naaggregate_expression
podstawie elementu . column_list
Zestaw kolumn, które mają być obracane.
-
Kolumna z
table_reference
.
-
expression_list
Mapuje wartości z
column_list
do aliasów kolumn.-
Wyrażenie literału z typem, które ma najmniej wspólny typ z odpowiednim
column_name
typem .Liczba wyrażeń w każdej krotki musi być zgodna z liczbą w elem
column_names
column_list
. -
Opcjonalny alias określający nazwę wygenerowanej kolumny. Jeśli nie określono
PIVOT
aliasu, zostanie wygenerowany alias na podstawie parametruexpression
s.
-
Result
Tymczasowa tabela następującego formularza:
Wszystkie kolumny z pośredniego
table_reference
zestawu wyników, które nie zostały określone w żadnejaggregate_expression
lubcolumn_list
.Te kolumny grupują kolumny.
Dla każdej
expression
krotki iaggregate_expression
kombinacjiPIVOT
generuje jedną kolumnę. Typ jest typemaggregate_expression
.Jeśli istnieje tylko jedna
aggregate_expression
kolumna o nazwie przy użyciu poleceniacolumn_alias
. W przeciwnym razie nazwa tocolumn_alias_agg_column_alias
.Wartość w każdej komórce jest wynikiem
aggregation_expression
użycia obiektuFILTER ( WHERE column_list IN (expression, ...)
.
Przykłady
-- 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) = (1, 'east')) AS q1_east,
sum(sales) FILTER(WHERE (quarter, region) = (1, 'west')) AS q1_west,
sum(sales) FILTER(WHERE (quarter, region) = (2, 'east')) AS q2_east,
sum(sales) FILTER(WHERE (quarter, region) = (2, 'west')) AS q2_west,
sum(sales) FILTER(WHERE (quarter, region) = (3, 'east')) AS q3_east,
sum(sales) FILTER(WHERE (quarter, region) = (3, 'west')) AS q3_west,
sum(sales) FILTER(WHERE (quarter, region) = (4, 'east')) AS q4_east,
sum(sales) FILTER(WHERE (quarter, region) = (4, 'west')) AS q4_west
FROM sales
GROUP BY year, region;
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;
> CREATE TEMP VIEW person (id, name, age, class, address) AS
VALUES (100, 'John', 30, 1, 'Street 1'),
(200, 'Mary', NULL, 1, 'Street 2'),
(300, 'Mike', 80, 3, 'Street 3'),
(400, 'Dan', 50, 4, 'Street 4');
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
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