Explore this snippet with some demo data here.
The act of unpivoting (or melting, if you're a pandas
user) is to convert columns to rows. Snowflake has added the UNPIVOT query construct to help.
The query template is
SELECT
<columns_to_keep>,
metric,
value
FROM <table> UNPIVOT(value FOR metric IN (<columns_to_unpivot>))
order by metric, value
where
columns_to_keep
- all the columns to be preservedcolumns_to_unpivot
- all the columns to be converted to rowstable
- the table to pull the columns from
(The output column names metric and value can also be changed.)
In the examples below, we use a table with columns A
, B
, C
. We take the columns B
and C
, and turn their values into columns called metric
(containing either the string 'B' or 'C') and value
(the value from either the column B
or C
). The column A
is preserved.
-- Define some dummy data
with a as (
select * from(
select 'a' as A, 1 as B, 2 as C union all
select 'b' as A, 3 as B, 4 as C union all
select 'c' as A, 5 as B, 6 as C
)
)
SELECT
A,
metric,
value
FROM a UNPIVOT(value FOR metric IN (B, C))
order by metric, value
A | metric | value |
---|---|---|
a | B | 1 |
b | B | 3 |
c | B | 5 |
a | C | 2 |
b | C | 4 |
c | C | 6 |