That’s a very valid question. We’ve done zero optimization on the encoding side so far, and improving that is definitely on our roadmap. Technically, once we learn the best expressions, they can be reused — data is often very similar across row groups — which opens the door to caching and amortizing the cost.

For very wide tables, expression detection only needs to happen once. Beyond that, we’re also exploring techniques like grouping columns into smaller sets or applying more aggressive heuristics to prune irrelevant columns. These are areas we’re actively investigating, and we plan to support them in future versions of FastLanes.