Categories:: Query syntax

PIVOT¶

Rotates a table by turning the unique values from one column in the input expression into multiple columns and aggregating results where required on any remaining column values. In a query, it is specified in the FROM clause after the table name or subquery.

The operator supports the built-in aggregate functions AVG, COUNT, MAX, MIN, and SUM.

PIVOT can be used to transform a narrow table (for example, empid, month, sales) into a wider table (for example, empid, jan_sales, feb_sales, mar_sales).

See also:: UNPIVOT

Syntax¶

SELECT ...
FROM ...
   PIVOT ( <aggregate_function> ( <pivot_column> )
            FOR <value_column> IN (
              <pivot_value_1> [ , <pivot_value_2> ... ]
              | ANY [ ORDER BY ... ]
              | <subquery>
            )
            [ DEFAULT ON NULL (<value>) ]
         )

[ ... ]

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Parameters¶

aggregate_function: The aggregate function for combining the grouped values from pivot_column.
pivot_column: The column from the source table or subquery that will be aggregated.
value_column: The column from the source table or subquery that contains the values from which column names will be generated.
pivot_value_N: A list of values for the pivot column to pivot into headings in the query results.
ANY [ ORDER BY ... ]: Pivot on all distinct values of the pivot column. To control the order of the pivot columns in the output, specify an ORDER BY clause after the ANY keyword. If the pivot column contains NULLs, then NULL is also treated as a pivot value.
subquery: Pivot on all values found in the subquery. The DISTINCT keyword is required if the subquery includes an ORDER BY clause. The subquery must be an uncorrelated subquery that returns a single column. Pivoting is performed on all distinct values returned by the subquery. For information about uncorrelated subqueries, see Working with Subqueries.
DEFAULT ON NULL (value): Replace all NULL values in the pivot result with the specified default value. The default value can be any scalar expression that does not depend on the pivot and aggregation column.

Usage notes¶

Snowflake supports dynamic pivot. A dynamic pivot query uses the ANY keyword or a subquery in the PIVOT subclause instead of specifying the pivot values explicitly.
When dynamic pivot is used in a view definition, queries on the view might fail if the underlying data changes so that the pivot output columns are changed.
Dynamic pivot isn’t supported in the body of a stored procedure or user-defined function (UDF).
A pivot query that doesn’t use dynamic pivot can return output with duplicate columns. We recommend avoiding output with duplicate columns. A dynamic pivot query deduplicates duplicate columns.
A pivot query that doesn’t use dynamic pivot might fail if it attempts to CAST a VARIANT column to a different data type. Dynamic pivot queries don’t have this limitation.
Currently, the PIVOT semantic doesn’t allow multiple aggregations, but you can achieve similar results by using PIVOT with the UNION operator. For an example, see Dynamic pivot with multiple aggregations using UNION.

Examples¶

The PIVOT examples use the following quarterly_sales table:

CREATE OR REPLACE TABLE quarterly_sales(
  empid INT,
  amount INT,
  quarter TEXT)
  AS SELECT * FROM VALUES
    (1, 10000, '2023_Q1'),
    (1, 400, '2023_Q1'),
    (2, 4500, '2023_Q1'),
    (2, 35000, '2023_Q1'),
    (1, 5000, '2023_Q2'),
    (1, 3000, '2023_Q2'),
    (2, 200, '2023_Q2'),
    (2, 90500, '2023_Q2'),
    (1, 6000, '2023_Q3'),
    (1, 5000, '2023_Q3'),
    (2, 2500, '2023_Q3'),
    (2, 9500, '2023_Q3'),
    (3, 2700, '2023_Q3'),
    (1, 8000, '2023_Q4'),
    (1, 10000, '2023_Q4'),
    (2, 800, '2023_Q4'),
    (2, 4500, '2023_Q4'),
    (3, 2700, '2023_Q4'),
    (3, 16000, '2023_Q4'),
    (3, 10200, '2023_Q4');

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The following examples use PIVOT:

Dynamic pivot on all distinct column values automatically
Dynamic pivot on column values using a subquery
Dynamic pivot with multiple aggregations using UNION
Dynamic pivot with a join query
Pivot on a specified list of column values for the pivot column
Pivot with a default value for NULL values
Pivot examples that involve multiple columns

Dynamic pivot on all distinct column values automatically¶

Given the table quarterly_sales, pivot on the amount column using the ANY keyword to sum the total sales per employee for all of the distinct quarters, and specify ORDER BY so that the pivot columns are in order:

SELECT *
  FROM quarterly_sales
    PIVOT(SUM(amount) FOR quarter IN (ANY ORDER BY quarter))
  ORDER BY empid;

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+-------+-----------+-----------+-----------+-----------+
| EMPID | '2023_Q1' | '2023_Q2' | '2023_Q3' | '2023_Q4' |
|-------+-----------+-----------+-----------+-----------|
|     1 |     10400 |      8000 |     11000 |     18000 |
|     2 |     39500 |     90700 |     12000 |      5300 |
|     3 |      NULL |      NULL |      2700 |     28900 |
+-------+-----------+-----------+-----------+-----------+

Dynamic pivot on column values using a subquery¶

Assume that in addition to the quarterly_sales table, an ad_campaign_types_by_quarter table tracks the types of advertisements run during particular quarters. This table has the following structure and data:

CREATE OR REPLACE TABLE ad_campaign_types_by_quarter(
  quarter VARCHAR,
  television BOOLEAN,
  radio BOOLEAN,
  print BOOLEAN)
  AS SELECT * FROM VALUES
    ('2023_Q1', TRUE, FALSE, FALSE),
    ('2023_Q2', FALSE, TRUE, TRUE),
    ('2023_Q3', FALSE, TRUE, FALSE),
    ('2023_Q4', TRUE, FALSE, TRUE);

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You can use a subquery in a pivot query to determine the sum of the sales in the quarters that had specific ad campaigns. For example, the following pivot query returns data only for quarters with television ad campaigns:

SELECT *
  FROM quarterly_sales
    PIVOT(SUM(amount) FOR quarter IN (
      SELECT DISTINCT quarter
        FROM ad_campaign_types_by_quarter
        WHERE television = TRUE
        ORDER BY quarter))
  ORDER BY empid;

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+-------+-----------+-----------+
| EMPID | '2023_Q1' | '2023_Q4' |
|-------+-----------+-----------|
|     1 |     10400 |     18000 |
|     2 |     39500 |      5300 |
|     3 |      NULL |     28900 |
+-------+-----------+-----------+

Dynamic pivot with multiple aggregations using UNION¶

You can use the UNION operator to show multiple aggregations in a single result set. This example uses dynamic pivot and the UNION operator to show the following information for each employee in each quarter:

The average amount of a sale, using the AVG function.
The sale with the highest value, using the MAX function.
The sale with the lowest value, using the MIN function.
The number of sales, using the COUNT function.
The total amount for all sales, using the SUM function.

Run the query:

SELECT 'Average sale amount' AS aggregate, *
  FROM quarterly_sales
    PIVOT(AVG(amount) FOR quarter IN (ANY ORDER BY quarter))
UNION
SELECT 'Highest value sale' AS aggregate, *
  FROM quarterly_sales
    PIVOT(MAX(amount) FOR quarter IN (ANY ORDER BY quarter))
UNION
SELECT 'Lowest value sale' AS aggregate, *
  FROM quarterly_sales
    PIVOT(MIN(amount) FOR quarter IN (ANY ORDER BY quarter))
UNION
SELECT 'Number of sales' AS aggregate, *
  FROM quarterly_sales
    PIVOT(COUNT(amount) FOR quarter IN (ANY ORDER BY quarter))
UNION
SELECT 'Total amount' AS aggregate, *
  FROM quarterly_sales
    PIVOT(SUM(amount) FOR quarter IN (ANY ORDER BY quarter))
ORDER BY aggregate, empid;

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+---------------------+-------+--------------+--------------+--------------+--------------+
| AGGREGATE           | EMPID |    '2023_Q1' |    '2023_Q2' |    '2023_Q3' |    '2023_Q4' |
|---------------------+-------+--------------+--------------+--------------+--------------|
| Average sale amount |     1 |  5200.000000 |  4000.000000 |  5500.000000 |  9000.000000 |
| Average sale amount |     2 | 19750.000000 | 45350.000000 |  6000.000000 |  2650.000000 |
| Average sale amount |     3 |         NULL |         NULL |  2700.000000 |  9633.333333 |
| Highest value sale  |     1 | 10000.000000 |  5000.000000 |  6000.000000 | 10000.000000 |
| Highest value sale  |     2 | 35000.000000 | 90500.000000 |  9500.000000 |  4500.000000 |
| Highest value sale  |     3 |         NULL |         NULL |  2700.000000 | 16000.000000 |
| Lowest value sale   |     1 |   400.000000 |  3000.000000 |  5000.000000 |  8000.000000 |
| Lowest value sale   |     2 |  4500.000000 |   200.000000 |  2500.000000 |   800.000000 |
| Lowest value sale   |     3 |         NULL |         NULL |  2700.000000 |  2700.000000 |
| Number of sales     |     1 |     2.000000 |     2.000000 |     2.000000 |     2.000000 |
| Number of sales     |     2 |     2.000000 |     2.000000 |     2.000000 |     2.000000 |
| Number of sales     |     3 |     0.000000 |     0.000000 |     1.000000 |     3.000000 |
| Total amount        |     1 | 10400.000000 |  8000.000000 | 11000.000000 | 18000.000000 |
| Total amount        |     2 | 39500.000000 | 90700.000000 | 12000.000000 |  5300.000000 |
| Total amount        |     3 |         NULL |         NULL |  2700.000000 | 28900.000000 |
+---------------------+-------+--------------+--------------+--------------+--------------+

Dynamic pivot with a join query¶

To pivot in a query with a join, you can use a common table expression (CTE) for the pivot query.

For example, assume a simple table maps employees to managers:

CREATE OR REPLACE TABLE emp_manager(
    empid INT,
    managerid INT)
  AS SELECT * FROM VALUES
    (1, 7),
    (2, 8),
    (3, 9);

SELECT * from emp_manager;

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+-------+-----------+
| EMPID | MANAGERID |
|-------+-----------|
|     1 |         7 |
|     2 |         8 |
|     3 |         9 |
+-------+-----------+

Run a query that joins the emp_manager table and the quarterly_sales table and pivots on the amount column in the quarterly_sales table:

WITH
  src AS
  (
    SELECT *
      FROM quarterly_sales
        PIVOT(SUM(amount) FOR quarter IN (ANY ORDER BY quarter))
  )
SELECT em.managerid, src.*
  FROM emp_manager em
  JOIN src ON em.empid = src.empid
  ORDER BY empid;

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+-----------+-------+-----------+-----------+-----------+-----------+
| MANAGERID | EMPID | '2023_Q1' | '2023_Q2' | '2023_Q3' | '2023_Q4' |
|-----------+-------+-----------+-----------+-----------+-----------|
|         7 |     1 |     10400 |      8000 |     11000 |     18000 |
|         8 |     2 |     39500 |     90700 |     12000 |      5300 |
|         9 |     3 |      NULL |      NULL |      2700 |     28900 |
+-----------+-------+-----------+-----------+-----------+-----------+

Pivot on a specified list of column values for the pivot column¶

Given the table quarterly_sales, pivot on the amount column to sum the total sales per employee for the specified quarters:

SELECT *
  FROM quarterly_sales
    PIVOT(SUM(amount) FOR quarter IN (
      '2023_Q1',
      '2023_Q2',
      '2023_Q3'))
  ORDER BY empid;

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+-------+-----------+-----------+-----------+
| EMPID | '2023_Q1' | '2023_Q2' | '2023_Q3' |
|-------+-----------+-----------+-----------|
|     1 |     10400 |      8000 |     11000 |
|     2 |     39500 |     90700 |     12000 |
|     3 |      NULL |      NULL |      2700 |
+-------+-----------+-----------+-----------+

You can pivot on all of the quarters in the amount column by running the following query:

SELECT *
  FROM quarterly_sales
    PIVOT(SUM(amount) FOR quarter IN (
      '2023_Q1',
      '2023_Q2',
      '2023_Q3',
      '2023_Q4'))
  ORDER BY empid;

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+-------+-----------+-----------+-----------+-----------+
| EMPID | '2023_Q1' | '2023_Q2' | '2023_Q3' | '2023_Q4' |
|-------+-----------+-----------+-----------+-----------|
|     1 |     10400 |      8000 |     11000 |     18000 |
|     2 |     39500 |     90700 |     12000 |      5300 |
|     3 |      NULL |      NULL |      2700 |     28900 |
+-------+-----------+-----------+-----------+-----------+

You can modify the column names in the output with the AS clause. For example, to shorten the column names and show them without quotes, run the following query:

SELECT *
  FROM quarterly_sales
    PIVOT(SUM(amount) FOR quarter IN (
      '2023_Q1',
      '2023_Q2',
      '2023_Q3',
      '2023_Q4')) AS p (employee, q1, q2, q3, q4)
  ORDER BY employee;

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+----------+-------+-------+-------+-------+
| EMPLOYEE |    Q1 |    Q2 |    Q3 |    Q4 |
|----------+-------+-------+-------+-------|
|        1 | 10400 |  8000 | 11000 | 18000 |
|        2 | 39500 | 90700 | 12000 |  5300 |
|        3 |  NULL |  NULL |  2700 | 28900 |
+----------+-------+-------+-------+-------+

You can also list specific pivot columns in the SELECT list and change the column names:

SELECT empid,
       "'2023_Q1'" AS q1,
       "'2023_Q2'" AS q2,
       "'2023_Q3'" AS q3,
       "'2023_Q4'" AS q4
  FROM quarterly_sales
    PIVOT(sum(amount) FOR quarter IN (
      '2023_Q1',
      '2023_Q2',
      '2023_Q3',
      '2023_Q4'))
  ORDER BY empid;

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+-------+-------+-------+-------+-------+
| EMPID |    Q1 |    Q2 |    Q3 |    Q4 |
|-------+-------+-------+-------+-------|
|     1 | 10400 |  8000 | 11000 | 18000 |
|     2 | 39500 | 90700 | 12000 |  5300 |
|     3 |  NULL |  NULL |  2700 | 28900 |
+-------+-------+-------+-------+-------+

Pivot with a default value for NULL values¶

If the query returns NULL values, you can replace them with a default value by using DEFAULT ON NULL. For example, you can use dynamic pivot and replace the NULL values with a default value of 0 by running the following query:

SELECT *
  FROM quarterly_sales
    PIVOT(SUM(amount) FOR quarter IN (ANY ORDER BY quarter)
      DEFAULT ON NULL (0))
  ORDER BY empid;

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+-------+-----------+-----------+-----------+-----------+
| EMPID | '2023_Q1' | '2023_Q2' | '2023_Q3' | '2023_Q4' |
|-------+-----------+-----------+-----------+-----------|
|     1 |     10400 |      8000 |     11000 |     18000 |
|     2 |     39500 |     90700 |     12000 |      5300 |
|     3 |         0 |         0 |      2700 |     28900 |
+-------+-----------+-----------+-----------+-----------+

You can also use DEFAULT ON NULL with a specified list of columns:

SELECT *
  FROM quarterly_sales
    PIVOT(SUM(amount)
      FOR quarter IN (
        '2023_Q1',
        '2023_Q2')
      DEFAULT ON NULL (0))
  ORDER BY empid;

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+-------+-----------+-----------+
| EMPID | '2023_Q1' | '2023_Q2' |
|-------+-----------+-----------|
|     1 |     10400 |      8000 |
|     2 |     39500 |     90700 |
|     3 |         0 |         0 |
+-------+-----------+-----------+

Pivot examples that involve multiple columns¶

Pivot queries can work with multiple columns. Before running these examples, add a column to the quarterly_sales table and populate the column with random values.

First, add a column that tracks the discount applied to each sale to the quarterly_sales table:

ALTER TABLE quarterly_sales ADD COLUMN discount_percent INT DEFAULT 0;

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Populate the new column with random values between 0 and 5, which specify the discount percentage for each sale:

UPDATE quarterly_sales SET discount_percent = UNIFORM(0, 5, RANDOM());

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Query the table to show the new column with the random values added:

SELECT * FROM quarterly_sales;

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+-------+--------+---------+------------------+
| EMPID | AMOUNT | QUARTER | DISCOUNT_PERCENT |
|-------+--------+---------+------------------|
|     1 |  10000 | 2023_Q1 |                0 |
|     1 |    400 | 2023_Q1 |                1 |
|     2 |   4500 | 2023_Q1 |                4 |
|     2 |  35000 | 2023_Q1 |                2 |
|     1 |   5000 | 2023_Q2 |                2 |
|     1 |   3000 | 2023_Q2 |                1 |
|     2 |    200 | 2023_Q2 |                2 |
|     2 |  90500 | 2023_Q2 |                1 |
|     1 |   6000 | 2023_Q3 |                1 |
|     1 |   5000 | 2023_Q3 |                3 |
|     2 |   2500 | 2023_Q3 |                1 |
|     2 |   9500 | 2023_Q3 |                3 |
|     3 |   2700 | 2023_Q3 |                1 |
|     1 |   8000 | 2023_Q4 |                1 |
|     1 |  10000 | 2023_Q4 |                4 |
|     2 |    800 | 2023_Q4 |                3 |
|     2 |   4500 | 2023_Q4 |                5 |
|     3 |   2700 | 2023_Q4 |                3 |
|     3 |  16000 | 2023_Q4 |                0 |
|     3 |  10200 | 2023_Q4 |                1 |
+-------+--------+---------+------------------+

Now that the new column is added and populated, run the following examples:

Exclude columns from a pivot query with a CTE
Run a multidimensional pivot query

Exclude columns from a pivot query with a CTE¶

You can use a common table expression (CTE) to exclude columns from a pivot query.

The following example uses a CTE to exclude the discount_percent column from a pivot query:

WITH
  sales_without_discount AS
    (SELECT * EXCLUDE(discount_percent) FROM quarterly_sales)
SELECT *
  FROM sales_without_discount
    PIVOT(SUM(amount) FOR quarter IN (ANY ORDER BY quarter))
  ORDER BY empid;

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+-------+-----------+-----------+-----------+-----------+
| EMPID | '2023_Q1' | '2023_Q2' | '2023_Q3' | '2023_Q4' |
|-------+-----------+-----------+-----------+-----------|
|     1 |     10400 |      8000 |     11000 |     18000 |
|     2 |     39500 |     90700 |     12000 |      5300 |
|     3 |      NULL |      NULL |      2700 |     28900 |
+-------+-----------+-----------+-----------+-----------+

You can use a CTE to exclude the amount column and show the average discount that each employee gave in each quarter:

WITH
  sales_without_amount AS
    (SELECT * EXCLUDE(amount) FROM quarterly_sales)
SELECT *
  FROM sales_without_amount
    PIVOT(AVG(discount_percent) FOR quarter IN (ANY ORDER BY quarter))
  ORDER BY empid;

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+-------+-----------+-----------+-----------+-----------+
| EMPID | '2023_Q1' | '2023_Q2' | '2023_Q3' | '2023_Q4' |
|-------+-----------+-----------+-----------+-----------|
|     1 |  0.500000 |  1.500000 |  2.000000 |  2.500000 |
|     2 |  3.000000 |  1.500000 |  2.000000 |  4.000000 |
|     3 |      NULL |      NULL |  1.000000 |  1.333333 |
+-------+-----------+-----------+-----------+-----------+

Run a multidimensional pivot query¶

A multidimensional pivot query pivots on more than one column. This example pivots on the amount column and the discount_percentage column. The query returns the sum of all sales by all employees each quarter and the maximum discount percentage for all sales each quarter.

In the query, the SELECT list uses $col_position parameters to run SUM and MAX functions on the returned columns in order, and to name the returned columns. A subquery in the FROM clause supplies the data for the pivot operations. Because the output shows sales results for all employees, the subquery doesn’t include the empid column.

SELECT SUM($1) AS q1_sales_total,
       SUM($2) AS q2_sales_total,
       SUM($3) AS q3_sales_total,
       SUM($4) AS q4_sales_total,
       MAX($5) AS q1_maximum_discount,
       MAX($6) AS q2_maximum_discount,
       MAX($7) AS q3_maximum_discount,
       MAX($8) AS q4_maximum_discount
  FROM
    (SELECT amount,
            quarter AS quarter_amount,
            quarter AS quarter_discount,
            discount_percent
      FROM quarterly_sales)
  PIVOT (
    SUM(amount)
    FOR quarter_amount IN (
      '2023_Q1',
      '2023_Q2',
      '2023_Q3',
      '2023_Q4'))
  PIVOT (
    MAX(discount_percent)
    FOR quarter_discount IN (
      '2023_Q1',
      '2023_Q2',
      '2023_Q3',
      '2023_Q4'));

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+----------------+----------------+----------------+----------------+---------------------+---------------------+---------------------+---------------------+
| Q1_SALES_TOTAL | Q2_SALES_TOTAL | Q3_SALES_TOTAL | Q4_SALES_TOTAL | Q1_MAXIMUM_DISCOUNT | Q2_MAXIMUM_DISCOUNT | Q3_MAXIMUM_DISCOUNT | Q4_MAXIMUM_DISCOUNT |
|----------------+----------------+----------------+----------------+---------------------+---------------------+---------------------+---------------------|
|          49900 |          98700 |          25700 |          52200 |                   4 |                   2 |                   3 |                   5 |
+----------------+----------------+----------------+----------------+---------------------+---------------------+---------------------+---------------------+