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Clause PIVOT

S’applique à :case marquée oui Databricks SQL case marquée oui Databricks Runtime

Transforme les lignes de l'table_reference précédente en faisant pivoter des valeurs uniques d’une liste de colonnes spécifiée en colonnes distinctes.

Syntaxe

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] } [, ...] ) }

Paramètres

  • aggregate_expression

    Expression de tout type dans laquelle toutes les références de colonne à table_reference sont des arguments pour les fonctions d’agrégation.

  • agg_column_alias

    Alias facultatif pour le résultat de l’agrégation. Si aucun alias est spécifié, PIVOT génère un alias basé sur aggregate_expression.

  • column_list

    Ensemble de colonnes à faire pivoter.

  • expression_list

    Mappage des valeurs de column_list à des alias de colonne.

    • expression

      Expression littérale avec un type qui partage un type le moins commun avec column_name.

      Le nombre d’expressions dans chaque tuple doit correspondre au nombre de column_names dans column_list.

    • column_alias

      Alias facultatif spécifiant le nom de la colonne générée. Si aucun alias est spécifié, PIVOT génère un alias basé sur les expression.

Résultats

Une table temporaire au format suivant :

  • Toutes les colonnes du jeu de résultats intermédiaire de table_reference qui n’ont pas été spécifiées dans aggregate_expression ou column_list.

    Ces colonnes regroupent des colonnes.

  • Pour chaque tuple expression et combinaison aggregate_expression, PIVOT génère une colonne. Le type représente le type de aggregate_expression.

    S’il n’y a queaggregate_expression, la colonne nommée à l’aide de column_alias. Dans le cas contraire elle est nommée column_alias_agg_column_alias.

    La valeur de chaque cellule est le résultat de aggregation_expression utilisant FILTER ( WHERE column_list IN (expression, ...).

Exemples

-- 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
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