PIVOT, klauzula

Dotyczy: Databricks SQL Databricks Runtime

Przekształca wiersze poprzedniej table_reference, obracając unikatowe wartości określonej listy kolumn na oddzielne kolumny.

Składnia

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

• aggregate_expression

  Wyrażenie dowolnego typu, w którym wszystkie odwołania do kolumny table_reference są argumentami funkcji agregujących.

• agg_column_alias

  Opcjonalny alias wyniku agregacji. Jeśli alias nie zostanie określony, PIVOT wygeneruje alias na aggregate_expressionpodstawie elementu .

• column_list

  Zestaw kolumn, które mają być obracane.

  • column_name

    Kolumna z table_reference.

• expression_list

  Przypisuje wartości z column_list do aliasów kolumn.

  • wyrażenie

    Wyrażenie literału z typem, który ma najmniej wspólny typ z odpowiednimi column_name. Jeśli wyrażenie nie jest literałem, Azure Databricks zgłasza NON_LITERAL_PIVOT_VALUES. Jeśli typ nie jest zgodny, Azure Databricks zgłasza PIVOT_VALUE_DATA_TYPE_MISMATCH.

    Liczba wyrażeń w każdej krotki musi być zgodna z liczbą w elem column_namescolumn_list.

  • column_alias

    Opcjonalny alias określający nazwę wygenerowanej kolumny. Jeśli nie określono PIVOT aliasu, zostanie wygenerowany alias na podstawie parametru expressions.

Result

Tymczasowa tabela o następującej formie:

• Wszystkie kolumny z pośredniego zestawu wyników table_reference, które nie zostały uwzględnione w żadnej aggregate_expression lub column_list.

  Te kolumny są kolumnami grupującymi.

• Dla każdej kombinacji krotki expression i aggregate_expressionPIVOT generuje jedną kolumnę. Typ jest typem aggregate_expression.

  Jeśli istnieje tylko jedna aggregate_expression kolumna ma nazwę przy użyciu column_alias. W przeciwnym razie nazwa to column_alias_agg_column_alias.

  Wartość w każdej komórce jest wynikiem aggregation_expression użycia obiektu FILTER ( WHERE column_list IN (expression, ...).

Typowe warunki błędów

• INCOMPARABLE_PIVOT_COLUMN
• NON_LITERAL_PIVOT_VALUES
• PIVOT_VALUE_DATA_TYPE_MISMATCH

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));
 year  region  q1   q2   q3  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;
 year  region  q1   q2   q3  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

-- 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));
 year  q1_east  q1_west  q2_east  q2_west  q3_east  q3_west  q4_east  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;
 year  q1_east  q1_west  q2_east  q2_west  q3_east  q3_west  q4_east  q4_west
 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));
  year   q1   q2   q3   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;
  year   q1   q2   q3   q4
  2018  205   45   85   85
  2019  245  225  165  125

-- 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));
 year  q1_total  q1_avg  q2_total  q2_avg  q3_total  q3_avg  q4_total  q4_avg
 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;
 year  q1_total  q1_avg  q2_total  q2_avg  q3_total  q3_avg  q4_total  q4_avg
 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 values must be literals.
> SELECT * FROM sales
  PIVOT (sum(sales) FOR quarter IN (1 + 0 AS q1));
  Error: NON_LITERAL_PIVOT_VALUES

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