Posted by polyphilz 16 hours ago
However, it misses the whole point of database query planning. You can't just ignore the planning time itself, as if the database query were a static entity to be optimized once at a leisurely pace.
The real constraint on live query planners is quite different: they must improve the combined time - planning + query - based on live database statistics. You can amortize the planning with prepared statements, but that too is fraught since optimal plans can change quite frequently and based on input parameters. "Live" and "faster than the queries themselves" are the hard requirements to be considered a viable database query planner. This project does neither.
It's not unusual for us to end up with bad query plans because the shape of our data can vary pretty greatly. In many cases, a Foo has 1 Bar. But in some cases, a Foo has a million Bars. That can cause the query optimizer to treat lookups on the bar table as if there are few elements there (causing a scan instead of a seek).
For the general case, the optimizer gets it right. However, the fringe case is one that causes the entire system to crash. It's a bit akin to how an insertion sort can be faster than quick sort when n is small. The optimizer might make a bad assumption about the size of n which makes it pick an expensive n lookup when log(n) is available (but slower for small n).
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!
I believe you are wrong on that. Do you mean large language models can’t play chess?
edit: I think you could provide an AI with a service or skill that asks "is this move legal" but given all the overhead for llms or whatever to call a "legal move" service external to its process, well then you aren't really searching the tree very efficiently lol.
However if you just let a neural network score boards and the neural network is in the same process well then I think thats the working solution for using neural networks in chess. The net does not need to score all boards either, simple value based heuristics can obviously provide a preliminary list of good boards (moves) at a certain depth or ply and then select the move that produces the board that the neural net scores highest. I kinda sorta think thats whats done today but as usual I could be full of it lol
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.