"... make no mistakes" :)

I imagine you can perform operations on query plans to transform them and determine equivalence?

`pg_hint_plan` has a debug log so you can verify Postgres actually used the hint or not! Used this during evaluations

I would like to think that pg_hint_plan is designed in such a way that any hint it accepts must be a valid plan for the query. I’m quite confident that schemes with this property that can also express high quality plans are possible and not even excessively complicated.

This is not to say that it’s possible to genetically verify that a proposed algorithm does what you want it to — that would be undecidable or NP-hard or co-NP-hard depending on how you formulate the question.

They are not. Choice among several query plans depends on various summary statistics about the data, which might not be the most recent.

I'd like to contribute my amateur hour entry into this thread, although I did administer and develop mssql stuff for awhile.

sure optimizations based on stats, but the stats are the wildcard, in my experience query plans can change suddenly.

Queries are translated into plans according to statistics. However the transforms will be deterministic and should only change one valid plan to another. I could very easily see a neural network manipulate transforms the same way the current programming does, its just that the neural networks are by nature really nicely suitable because the "decisions" are based on training, and this training can be closed world type things like the ai assists that chess engines are now getting. Obviously ai still can't play chess but apparently its very good at ranking board positions just by developing that much statistical info because its training comes not from reading the web, but playing a gazzilian games against itself in a "closed" chess world of its own.

I'm thinking that the ai does "this legal transform of the query plan should be applied to this pattern of data (statistics, cardinality, etc)" simply because the ai encountered it in closed world training, much like the chess thing.

Just a theory tho feel free to correct!

> ⇒ this may be useful for some workloads, but even then, can you afford to spend hours every now and then to update your 4B model to ensure it still picks a good query plan?

I think this would be likely comparable to a scheduled backup, so I think it would be an acceptable maintenance window. However, deterministic algorithms would likely beat re-training (or re-fine-tuning) the model. For example, one could analyze actual distributions or whatever (instead of assuming uniform), and then some plans would automatically be eliminated.

Imo a good thought experiment is to look at places that are hyper-optimized, like compilers. Would LLMs bring anything to the table (architecturally or performance-wise) to a piece of software that has been carefully crafted for decades? (Methinks no.)