The cube operation takes n number of columns and produces 2n aggregates.

For example, given a cube 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 C} - unique A C pairs
  • { BC} - unique B C pairs
  • {A  } - unique A values
  • { B } - unique B values
  • {  C} - unique C values
  • {   } - all values

The cube 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 CUBE function. Be mindful that cube is an expensive operation.

Grouping and Nulls

If using the cube 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 cube operation. There will be an example of this below.


The following example uses the Python API to perform a cube operation. For SQL examples, see the CUBE section in SQL Support.

The following request will aggregate the average opening stock price for these groups:

  • Each market sector & stock symbol pair
  • Each market sector
  • Each stock symbol
  • All sectors and symbols
    table_name = "demo.stocks",
    column_names = [
        "IF(Grouping(Sector) = 0, Sector, '<ALL SECTORS>') as SectorGroup",
        "IF(Grouping(Symbol) = 0, Symbol, '<ALL SYMBOLS>') as SymbolGroup",
        "AVG(Open) as AvgOpen"
    options = {"cube": "(Sector, Symbol)"}


  • The maximum number of dimensions that can be computed is 256, the maximum number of columns that can be aggregated is 8
  • The column(s) used in the cube operation must be listed as columns in the column_names parameter
  • As the cube operation is executed via /aggregate/groupby, all Aggregation Limitations also apply