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 ENUMs, 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:

The PhysicalPivot operator converts those lists into column names and values to return this result:

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: