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PIVOT
Pivoting is implemented as a combination of SQL query re-writing and a dedicated PhysicalPivot
operator for higher performance.
Each PIVOT
is implemented as set of aggregations into lists and then the dedicated PhysicalPivot
operator converts those lists into column names and values.
Additional pre-processing steps are required if the columns to be created when pivoting are detected dynamically (which occurs when the IN
clause is not in use).
DuckDB, like most SQL engines, requires that all column names and types be known at the start of a query.
In order to automatically detect the columns that should be created as a result of a PIVOT
statement, it must be translated into multiple queries.
ENUM
types are used to find the distinct values that should become columns.
Each ENUM
is then injected into one of the PIVOT
statement's IN
clauses.
After the IN
clauses have been populated with ENUM
s, the query is re-written again into a set of aggregations into lists.
For example:
PIVOT cities
ON year
USING sum(population);
is initially translated into:
CREATE TEMPORARY TYPE __pivot_enum_0_0 AS ENUM (
SELECT DISTINCT
year::VARCHAR
FROM cities
ORDER BY
year
);
PIVOT cities
ON year IN __pivot_enum_0_0
USING sum(population);
and finally translated into:
SELECT country, name, list(year), list(population_sum)
FROM (
SELECT country, name, year, sum(population) AS population_sum
FROM cities
GROUP BY ALL
)
GROUP BY ALL;
This produces the result:
country | name | list("year") | list(population_sum) |
---|---|---|---|
NL | Amsterdam | [2000, 2010, 2020] | [1005, 1065, 1158] |
US | Seattle | [2000, 2010, 2020] | [564, 608, 738] |
US | New York City | [2000, 2010, 2020] | [8015, 8175, 8772] |
The PhysicalPivot
operator converts those lists into column names and values to return this result:
country | name | 2000 | 2010 | 2020 |
---|---|---|---|---|
NL | Amsterdam | 1005 | 1065 | 1158 |
US | Seattle | 564 | 608 | 738 |
US | New York City | 8015 | 8175 | 8772 |
UNPIVOT
Internals
Unpivoting is implemented entirely as rewrites into SQL queries.
Each UNPIVOT
is implemented as set of unnest
functions, operating on a list of the column names and a list of the column values.
If dynamically unpivoting, the COLUMNS
expression is evaluated first to calculate the column list.
For example:
UNPIVOT monthly_sales
ON jan, feb, mar, apr, may, jun
INTO
NAME month
VALUE sales;
is translated into:
SELECT
empid,
dept,
unnest(['jan', 'feb', 'mar', 'apr', 'may', 'jun']) AS month,
unnest(["jan", "feb", "mar", "apr", "may", "jun"]) AS sales
FROM monthly_sales;
Note the single quotes to build a list of text strings to populate month
, and the double quotes to pull the column values for use in sales
.
This produces the same result as the initial example:
empid | dept | month | sales |
---|---|---|---|
1 | electronics | jan | 1 |
1 | electronics | feb | 2 |
1 | electronics | mar | 3 |
1 | electronics | apr | 4 |
1 | electronics | may | 5 |
1 | electronics | jun | 6 |
2 | clothes | jan | 10 |
2 | clothes | feb | 20 |
2 | clothes | mar | 30 |
2 | clothes | apr | 40 |
2 | clothes | may | 50 |
2 | clothes | jun | 60 |
3 | cars | jan | 100 |
3 | cars | feb | 200 |
3 | cars | mar | 300 |
3 | cars | apr | 400 |
3 | cars | may | 500 |
3 | cars | jun | 600 |