Training a 4B model to produce 81% faster query plans than Postgres
Leis et al. asked this exact question in 2015. Despite an enormous body of research spanning a decade since their original exploration, they found that query optimizers continue to leave much to be desired. I was surprised when I first learned about this.
- ▪Leis et al. asked this exact question in 2015.
- ▪Despite an enormous body of research spanning a decade since their original exploration, they found that query optimizers continue to leave much to be desired.
- ▪I was surprised when I first learned about this.
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| Original publisher | Rohan Bansal |
| Canonical URL | https://rohanbansal.com/qorl |
| Publication time | Wed, 16 Sep 2026 18:50:00 +0000 |
| Retrieval time | 2026-09-16T19:28:41.681Z |
| Last seen | 2026-09-16T19:28:41.681Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Aycku8aXoGV5 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
How good are query optimizers, really? Leis et al. asked this exact question in 2015. Then, they asked it again 10 years later. Despite an enormous body of research spanning a decade since their original exploration, they found that query optimizers continue to leave much to be desired. I was surprised when I first learned about this. A Postgres database should know everything about the stuff that lives in its tables, no? How hard can it be? As it turns out: enormously hard. In fact, one particular task a query optimizer needs to do, join ordering, is known to be NP-hard. So query optimizers are hard. What’s not as hard is verifying whether a query plan an optimizer picks is good or not. Put simply, a good query optimizer produces plans that run fast, and a bad one produces slow plans.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Rohan Bansal.
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