If N is sufficiently larger than k, it might actually be faster to do something like

  let count = query("SELECT COUNT() FROM table")
  let indices = computeIndices(count, k)
  for index in indices {
    results.append(query("SELECT * FROM table LIMIT 1 OFFSET ?", index))
  }

Yeah, you're doing a bunch of queries, but you're removing all of copying all the data from all the rows.

OFFSET j is O(j) in most RDMSes. You made an o(N/2 * k) algorithm given j is a random number in [0,N).

Depending on the size of k compared to N, and how much data is in each row, it's still probably better than iterating over the entire contents of the table.

Also, do you have any citation for OFFSET j being O(j)? I can believe that's the case, but it also seems fairly obvious to optimize OFFSET for the case of a non-filtered unordered SELECT (or one ordered in a way that matches an index).

Assuming that the DB is backed by some kind of tree, it would need to maintain extra metadata in the branches in order to achieve even O(log n) offsetting. For this reason I could understand it being a linear operation.