> I paid ~$800 to rent a 2x H100 SXM node from Lambda for ~95 hours, and ~$400 in OpenAI API fees to generate the Astra trajectory demonstrations.
> a tiny 4B model went from not being able to understand the harness it was wrapped in, to achieving a 1.81x geometric mean speedup and a summed latency decrease of 44.7% across a workload of join-heavy SQL queries
I can’t find it in the article (may have skimmed it too much), but I suspect they didn’t include those ~95 hours in the benchmark numbers.
I think all database vendors know their query optimizers could do much better if they could afford to spend lots of time to derive query plans.
⇒ 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 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.
I wouldn't be very excited about adding a 4B param model to my database deployment, but using this kind of approach while testing an app to identify query plans where Postgres is leaving performance on the table seems valuable without much risk.
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 seem 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.
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).
> I paid ~$800 to rent a 2x H100 SXM node from Lambda for ~95 hours, and ~$400 in OpenAI API fees to generate the Astra trajectory demonstrations.
> a tiny 4B model went from not being able to understand the harness it was wrapped in, to achieving a 1.81x geometric mean speedup and a summed latency decrease of 44.7% across a workload of join-heavy SQL queries
I can’t find it in the article (may have skimmed it too much), but I suspect they didn’t include those ~95 hours in the benchmark numbers.
I think all database vendors know their query optimizers could do much better if they could afford to spend lots of time to derive query plans.
⇒ 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?
> Frontier intelligence is extremely powerful; the distillation I did off Astra trajectories is proof enough that large models are not going anywhere
Wouldn't admitting this invite trouble due to accusations of distillation flying around between closed and open models.
But how will you know that the query plan actually does what your query asked for?
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.
I wouldn't be very excited about adding a 4B param model to my database deployment, but using this kind of approach while testing an app to identify query plans where Postgres is leaving performance on the table seems valuable without much risk.
`pg_hint_plan` has a debug log so you can verify Postgres actually used the hint or not! Used this during evaluations
I imagine you can perform operations on query plans to transform them and determine equivalence?
"... make no mistakes" :)
Aren't optimizations suppose to be deterministic?
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 seem 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!
They are not. Choice among several query plans depends on various summary statistics about the data, which might not be the most recent.
Including the input parameters.
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).