alt Hacker NewsOptimal 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.
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It’s ultimately based on a lot of hand-written heuristics. Google has some non-LLM based machine learning technique to guide optimization heuristics in LLVM; that would be closer to what you are looking for.
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.
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.