PIVOT clause (Databricks SQL)

Transforms the intermediate result set of the FROM clause by rotating unique values of a specified column list into separate columns.

Syntax

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] } [, ...] ) }

Parameters

  • aggregate_expression

    An expression of any type where all column references to the FROM clause are arguments to aggregate functions.

  • agg_column_alias

    An optional alias for the result of the aggregation. If no alias is specified, PIVOT generates an alias based on aggregate_xpression.

  • column_list

    The set of columns to be rotated.

  • expression_list

    Maps values from column_list to column aliases.

    • expression

      A literal expression with a type that shares a least common type with the respective column_name.

      The number of expressions in each tuple must match the number of column_names in column_list.

    • column_alias

      An optional alias specifying the name of the generated column. If no alias is specified PIVOT generates an alias based on the expressions.

Result

A temporary table of the following form:

  • All the columns from the intermediate result set of the FROM clause that have not been specified in any aggregate_expression or column_list.

    These columns are grouping columns.

  • For each expression tuple and aggregate_expression combination, PIVOT generates one column. The type is the type of aggregate_expression.

    If there is only one aggregate_expression the column is named using column_alias. Otherwise it is named column_alias_agg_column_alias.

    The value in each cell is the result of the aggregation_expression using a FILTER ( WHERE column_list IN (expression, ...).

Examples

-- 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 = 1) AS q2_total,
         avg(sales) FILTER(WHERE quarter = 1) AS q2_avg,
         sum(sales) FILTER(WHERE quarter = 1) AS q3_total,
         avg(sales) FILTER(WHERE quarter = 1) AS q3_avg,
         sum(sales) FILTER(WHERE quarter = 1) AS q4_total,
         avg(sales) FILTER(WHERE quarter = 1) 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