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- Hacker News
- This was a thoroughly enjoyable read, both the writing and presentation. I really liked the level of writing as it's basically introducing a whole lot of advanced topics but at just the right level for a non-AI researcher type of engineer like myself to be able to understand what's going on, and I felt it made some elements of LLMs actually something I could understand rather than wizardry done by maths PhDs. Probably because it's more like applied engineering rather than hard mathematics here. Thank you for a delightful post.by BirdieNZ
- > 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?
by Someone - Unless they’ve been elided, there were no indices other than the PK on any table, and no additional statistics. There are correlated columns here: a given country may have produced more movies in a given range of years, as its movie industry built up; a given country may produce more TV series than movies, etc.
Nearly every time I’ve seen someone resorting to hints for a query, it’s because their statistics are incorrect. Adding hints is papering over the problem, and can backfire later if the data shape changes.
by sgarland - The end game is adaptive query plans.
A big reason the initial plan isn't guaranteed to be optimal, even with all the right indexes, is that table statistics aren't perfect. For example, you might track a column's correlation (how closely the column's logical ordering matches its physical ordering in the heap), but that won't be broken down at a per value level. Postal code X might be very correlated, while postal code Y that is used in your query is completely uncorrelated.
The ideal solution is to pick one plan initially, and then update a temporary query-specific statistic model based on the data you actually read while executing the query. Then periodically re-evaluate if an alternative plan would be faster, switching to it in a way that doesn't throw away the current partial result.
Of course switching plans mid flight is very complicated, but Oracle and SQL server both support this feature, so hopefully it lands in Postgres at some point.
by rand_r - > 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.
by devsda - 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.
by hamilyon2 - Engineer: "HELP, our production DB is frozen on this query that worked fine before!"
Infra: "Hmm, let's check... Well would you look at that, it seems like your LLM query planner usually works and produces fast queries, but this time when you changed a variable name to trigger query rebuild, it happened to hallucinate and miss an index, would you mind re-running the LLM a few times until you get a faster query?"
by 2001zhaozhao - “81% faster query plans than Postgres”…on an 8 GB dataset that fits entirely in memory, with shared_buffers constrained to a fraction of that, queries warmed before measuring, and read-only SELECTs.
I would be cautious about over fitting, it’s tough to say if those query plans would really be more optimal than Postgres heuristics at scale and with a bit more realistic OLTP workloads.
In any case, such is life with profile guided optimization. Many of us appreciate how database workloads can drift over time and with scale.
Kudos to the author for getting their hands dirty and writing up their experiments.