VVM is column-oriented, which is how pretty much every Dataframe implementation works. Each column is a vector of whatever the user's type represents; Int64 in

VVM is column-oriented, which is how pretty much every Dataframe implementation works. Each column is a vector of whatever the user's type represents; Int64 in Empirical is i64 in VVM and int64_t in C++. VVM has its own statically typed assembly language. You can see examples of it in the regression tests; here's one that sorts a table: https://github.com/empirical-soft/empirical-lang/blob/master/tests/VVM/sort.vvm https://github.com/empirical-soft/empirical-lang/blob/master... Since it's a virtual machine, VVM is pretty low level and really only meant as a compilation target. While it does some of the heavy lifting to match keys or determine the order of indices in a vector, Empirical is needed to coordinate the moving pieces. Empirical takes a very different approach from Julia. Empirical is statically typed (not "gradually" typed), is focused around Dataframes, and compiles to a VM that is then interpreted. As I mention elsewhere, I haven't done much on the performance side of things. I eventually want SIMD and JIT, but my priority for now is getting the Empirical language right.