The main benefit of a columnar representation in
memory is it's more cache friendly for a typical analytical workload. For example, if I have a dataframe:
(A int, B int, C int, D int)
And I write:
A + B
In a columnar representation, all the As are next to each other, and all the Bs are next to each other, so the process of (A and B in memory) => (A and B in CPU registers) => (addition) => (A + B result back to memory) will be a lot more efficient.
In a row-oriented representation like protobuf, all your C and D values are going to get dragged into the CPU registers alongside the A and B values that you actually want.
Column-oriented representation is also more friendly to SIMD CPU instructions. You can still use SIMD with a row-oriented representation, but you have to use gather-scatter operations which makes the whole thing less efficient.
In a row-oriented representation like protobuf, all your C and D values are going to get dragged into the CPU registers alongside the A and B values that you actually want.
Column-oriented representation is also more friendly to SIMD CPU instructions. You can still use SIMD with a row-oriented representation, but you have to use gather-scatter operations which makes the whole thing less efficient.