The rollup operation takes n number of columns and produces n + 1 aggregates.
For example, given a rollup on columns A
, B
, & C
, it computes the
requested aggregates for the following combinations of columns:
{ABC}
- unique A B C
triplets{AB }
- unique A B
pairs{A }
- unique A
values{ }
- all valuesThe rollup operation is an aggregate function that can be invoked natively in
the options
map of the /aggregate/groupby endpoint. It is also
available via the SQL ROLLUP function. Be mindful
that rollup is an expensive operation.
If using the rollup operation with a column that contains
null values, it may not be apparent which aggregations pertain to
the column's null values and which pertain to all the column's values, as both
will be represented by null
in the result set. To avoid this confusion,
it's recommended to use the GROUPING()
function to make a distinction between null values in the data and null
grouping values generated by the rollup operation. There will be an example
of this below.
The following example uses the Python API to perform a rollup operation. For SQL examples, see the ROLLUP section in SQL Support.
The following request will aggregate the average opening stock price for these groups:
h_db.aggregate_group_by(
table_name = "stocks",
column_names = [
"Sector",
"Symbol",
"IF(Grouping(Sector) = 0, Sector, '<ALL SECTORS>') as SectorGroup",
"IF(Grouping(Symbol) = 0, Symbol, '<ALL SYMBOLS>') as SymbolGroup",
"AVG(Open) as AvgOpen"
],
options = {"rollup": "(Sector, Symbol)"}
)
column_names
parameter