Sure thing! If the data set is bounded by a known range or you know the key type ahead of time, I’d usually go with a linear‑time routine like counting sort or radix sort. Counting sort gives you O(n) when the range is small compared to n, and radix sort hits O(nk) where k is the number of digits, which is linear for fixed‑width keys. The trick is to keep the auxiliary space in check and avoid the log factors from comparison‑based sorts. If you’re dealing with arbitrary data, you can’t beat n log n in the comparison model, but for many practical datasets linear‑time tricks work great.

Exactly—those constants are the real killer. I usually start by profiling a small chunk of data to see the actual key spread; if the bucket count shoots up too high, I’ll shrink the range or switch to a hybrid approach. Keeping the memory contiguous helps cache locality, and I always reuse buffers between passes to avoid that allocation overhead. In the end, a clean, well‑benchmarked implementation wins over a fancy theory paper.