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Training a 4B model to produce 81% faster query plans than Postgres

702 points · 144 comments · polyphilz

  1. hamilyon2 · · focus · HN ↗
    Optimal plan construction is math-heavy, algorithm-heavy and vary even by workload. There are options like creating just-in-time indexes, so solution space grows even faster than article presents. Sometimes it is the query planner which is the slow part of total execution time.

    LLM is kind of blunt weapon to use here. I am waiting rather for alphago style neural net heuristic.

    1. topaz0 · · focus · HN ↗
      I also wondered why an LLM would be the right starting point. Why would Balzac or billions of lines of rwir code or reddit be relevant to mapping this smallish, well-defined language (SQL) to this other tiny constrained specification language (the query plan suggestions)? You could make a (relatively) tiny network and then actually pass it some relevant features of the actual data, like as numbers, not just as text returned from a tool call.
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