PIVOT 子句

發行項
03/01/2024

適用于： 核取標示為是 Databricks SQL 檢查標示為是 Databricks Runtime

將指定資料行清單的唯一值旋轉成個別的資料行，以轉換 table_reference 的資料列。

語法

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] } [, ...] ) }

參數

table_reference

識別作業的主 PIVOT 旨。
aggregate_expression

任何類型的運算式，其中所有資料行參考 table_reference 都是彙總函式的引數。
agg_column_alias

匯總結果的選擇性別名。如果未指定別名， PIVOT 則根據 aggregate_expression 產生別名。
column_list

要旋轉的資料行集。
- column_name
  
  來自 table_reference 的資料行。
expression_list

將值從 column_list 對應至資料行別名。
- 表達
  
  具有型別的常值運算式，其與各自的 column_name 共用最不通用型別。
  
  每個 Tuple 中的運算式數目必須符合中的 column_list 數目 column_names 。
- column_alias
  
  指定所產生資料行名稱的選擇性別名。如果未指定 PIVOT 別名，則會根據 expression s 產生別名。

結果

下列格式的臨時表：

任何 aggregate_expression 或 column_list 中未指定之之中繼結果集中 table_reference 的所有資料行。

這些資料行會分組資料行。
針對每個 expression Tuple 和 aggregate_expression 組合， PIVOT 產生一個資料行。此類型是的型別 aggregate_expression 。

如果只有一個 aggregate_expression 資料行使用來命名 column_alias 。否則，它會命名為 column_alias_agg_column_alias 。

每個資料格中的值都是使用 FILTER ( WHERE column_list IN (expression, ...) 的結果 aggregation_expression 。

例子

-- 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

PIVOT 子句

語法

參數

結果

例子

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其他資源