This submodule contains analytical functions that process primarily Arrow columnar data; some functions can process scalar or Arrow-based array inputs. These are intended for use inside query engines, data frame libraries, etc.
Many functions have SQL-like semantics in that they perform elementwise or scalar operations on whole arrays at a time. Other functions are not SQL-like and compute results that may be a different length or whose results depend on the order of the values.
Some basic terminology:
- We use the term "function" to refer to particular general operation that may have many different implementations corresponding to different combinations of types or function behavior options.
- We call a specific implementation of a function a "kernel". When executing a function on inputs, we must first select a suitable kernel (kernel selection is called "dispatching") corresponding to the value types of the inputs
- Functions along with their kernel implementations are collected in a "function registry". Given a function name and argument types, we can look up that function and dispatch to a compatible kernel.
Types of functions
- Scalar functions: elementwise functions that perform scalar operations in a vectorized manner. These functions are generally valid for SQL-like context. These are called "scalar" in that the functions executed consider each value in an array independently, and the output array or arrays have the same length as the input arrays. The result for each array cell is generally independent of its position in the array.
- Vector functions, which produce a result whose output is generally dependent on the entire contents of the input arrays. These functions are generally not valid for SQL-like processing because the output size may be different than the input size, and the result may change based on the order of the values in the array. This includes things like array subselection, sorting, hashing, and more.
- Scalar aggregate functions of which can be used in a SQL-like context