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UNPIVOT clause

Applies to: check marked yes Databricks SQL check marked yes Databricks Runtime 12.2 LTS and above.

Transforms the rows of the preceding table_reference by rotating groups of columns into rows and collapsing the listed columns: A first new column holds the original column group names (or alias there-of) as values, this column is followed for a group of columns with the values of each column group.

Syntax

UNPIVOT [ { INCLUDE NULLS | EXCLUDE NULLS } ]
{ single_value | multi_value }
( value_column
FOR unpivot_column IN ( { column_name [ column_alias ] } [, ...] ) )
[ table_alias ]

single_value
( value_column
FOR unpivot_column IN ( { column_name [ column_alias ] } [, ...] ) )

multi_value
( ( value_column [, ...] )
FOR unpivot_column IN ( { ( column_name [, ...] ) [ column_alias ] } [, ...] ) )

Parameters

  • INCLUDE NULLS or EXCLUDE NULLS

    Whether, or not to filter out rows with NULL in the value_column. The default is EXCLUDE NULLS.

  • value_column

    An unqualified column alias. This column will hold the values. The type of ech value_column is the least common type of the corresponding column_name column types.

  • unpivot_column

    An unqualified column alias. This column will hold the names of the rotated column_names or their column_aliass. The type of unpivot_column is STRING.

    In case of a multi value UNPIVOT the value will be the concatenation of the '_' separated column_names, if there is no column_alias.

  • column_name

    Identifies a column in relation which will be un-pivoted. The name may be qualified. All column_names must share a least-common type.

  • column_alias

    An optional name used in unpivot_column.

  • table_alias

    Optionally specifies a label for the resulting table. If the table_alias includes column_identifiers their number must match the number of columns produced by UNPIVOT.

Result

A temporary table of the following form:

  • All the columns from the table_reference except those named as column_names.
  • The unpivot_column of type STRING.
  • The value_columns of the least common types of their matching column_names.

Examples

SQL
- A single column UNPIVOT
> CREATE OR REPLACE TEMPORARY VIEW sales(location, year, q1, q2, q3, q4) AS
VALUES ('Toronto' , 2020, 100 , 80 , 70, 150),
('San Francisco', 2020, NULL, 20 , 50, 60),
('Toronto' , 2021, 110 , 90 , 80, 170),
('San Francisco', 2021, 70 , 120, 85, 105);

> SELECT *
FROM sales UNPIVOT INCLUDE NULLS
(sales FOR quarter IN (q1 AS `Jan-Mar`,
q2 AS `Apr-Jun`,
q3 AS `Jul-Sep`,
sales.q4 AS `Oct-Dec`));
location year quarter sales
------------ —--- —------ —-----
Toronto 2020 Jan-Mar 100
Toronto 2020 Apr-Jun 80
Toronto 2020 Jul-Sep 70
Toronto 2020 Oct-Dec 150
San Francisco 2020 Jan-Mar null
San Francisco 2020 Apr-Jun 20
San Francisco 2020 Jul-Sep 50
San Francisco 2020 Oct-Dec 60
Toronto 2021 Jan-Mar 110
Toronto 2021 Apr-Jun 90
Toronto 2021 Jul-Sep 80
Toronto 2021 Oct-Dec 170
San Francisco 2021 Jan-Mar 70
San Francisco 2021 Apr-Jun 120
San Francisco 2021 Jul-Sep 85
San Francisco 2021 Oct-Dec 105

-- This is equivalent to:
> SELECT location, year,
inline(arrays_zip(array('Jan-Mar', 'Apr-Jun', 'Jul-Sep', 'Oct-Dec'),
array(q1 , q2 , q3 , q4)))
AS (quarter, sales)
FROM sales;

- A multi column UNPIVOT
> CREATE OR REPLACE TEMPORARY VIEW oncall
(year, week, area , name1 , email1 , phone1 , name2 , email2 , phone2) AS
VALUES (2022, 1 , 'frontend', 'Freddy', 'fred@alwaysup.org' , 15551234567, 'Fanny' , 'fanny@lwaysup.org' , 15552345678),
(2022, 1 , 'backend' , 'Boris' , 'boris@alwaysup.org', 15553456789, 'Boomer', 'boomer@lwaysup.org', 15554567890),
(2022, 2 , 'frontend', 'Franky', 'frank@lwaysup.org' , 15555678901, 'Fin' , 'fin@alwaysup.org' , 15556789012),
(2022, 2 , 'backend' , 'Bonny' , 'bonny@alwaysup.org', 15557890123, 'Bea' , 'bea@alwaysup.org' , 15558901234);

> SELECT *
FROM oncall UNPIVOT ((name, email, phone) FOR precedence IN ((name1, email1, phone1) AS primary,
(name2, email2, phone2) AS secondary));
year week area precedence name email phone
---- ---- -------- ---------- ------ ------------------ -----------
2022 1 frontend primary Freddy fred@alwaysup.org 15551234567
2022 1 frontend secondary Fanny fanny@lwaysup.org 15552345678
2022 1 backend primary Boris boris@alwaysup.org 15553456789
2022 1 backend secondary Boomer boomer@lwaysup.org 15554567890
2022 2 frontend primary Franky frank@lwaysup.org 15555678901
2022 2 frontend secondary Fin fin@alwaysup.org 15556789012
2022 2 backend primary Bonny bonny@alwaysup.org 15557890123
2022 2 backend secondary Bea bea@alwaysup.org 15558901234

-- This is equivalent to:
> SELECT year, week, area,
inline(arrays_zip(array('primary', 'secondary'),
array(name1, name2),
array(email1, email2),
array(phone1, phone2)))
AS (precedence, name, email, phone)
FROM oncall;