Thread

1. hamilyon2 · 1789593005 · 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. yipinwong · 1789595402 · focus · HN ↗

      What if we use a hybrid model of using both query optimizer and LLM? Whichever produces better result, the database can use?

      - a question from someone with lack of DB depth, me.

      1. Sesse__ · 1789595743 · focus · HN ↗

         The immediate problem: How do you know which one is better without running them?

         1. haroldl · 1789604743 · focus · HN ↗

            You create formulas to estimate the cost of running a given query plan. Use statistics collected about the tables (e.g. how many rows) to try to be accurate. The topic is "Cost Based Optimization".

            1. ants_a · 1789722114 · focus · HN ↗

               This is how the built in planner works already. It generates all possible plans and picks the one with the lowest cost. But calculating the cost is based on statistics and models, and these are wrong. Usually useful, but always wrong.

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