Frankensqlite a Rust reimplementation of SQLite with concurrent writers
frankensqlite.com44 points by rahimnathwani 3 days ago
44 points by rahimnathwani 3 days ago
> TCL test harness. C SQLite's test suite is driven by ~90,000+ lines of TCL scripts deeply intertwined with the C API. These cannot be meaningfully ported. Instead, FrankenSQLite uses native Rust #[test] modules, proptest for property-based testing, a conformance harness comparing SQL output against C SQLite golden files, and asupersync's lab reactor for deterministic concurrency tests.
If you're not running against the SQLite test suite, then you haven't written a viable SQLite replacement.
I thought I read somewhere that their full test suite is not publicly available?
The TH3 test suite is proprietary, but the TCL test suite that they refer to is public domain.
I'm not sure where they get their 90k CLOC count though, that seems like it might be an LLM induced hallucination given the rest of the project. The public domain TCL test suite is ~27k CLOC, and the proprietary suite is 1055k CLOC.
This and this needs Jepsen testing.
The value of SQLite is how robust it is and that’s because of the rigorous test suite.
Isn't that test suite private though?
The TH3 test suite is proprietary, but the TCL test suite that they refer to is public domain.
I'm not sure where they get their 90k CLOC count though, that seems like it might be an LLM induced hallucination given the rest of the project. The public domain TCL test suite is ~27k CLOC, and the proprietary suite is 1055k CLOC.
Thanks for the clarification, I appreciate it.
> and the proprietary suite is 1055k CLOC.
Why is the code size of the proprietary test suite even public though?
You can buy access to it.
Any serious SQLite re-implementation should buy it and test against it.
If this wasn't ambitious enough, the author is also porting glibc to rust. As I understand it, all of it is agentic coded using custom harnesses.
It doesn't read ambitious so much as naive.
It entirely depends on how much the author reads the result of the agentic coding.
It's worth scrolling down to the current implementation status part:
https://github.com/Dicklesworthstone/frankensqlite#current-i...
Although I will admit that even after reading it, I'm not exactly sure what the current implementation status is.
It's fake. It doesn't exist. It never happened. The whole thing is an LLM hallucination. You can notice that it's all half implemented if you read the code: https://github.com/Taufiqkemall2/frankensqlite/blob/main/cra...
Clean room implementation yea sure buddy
The author seems obsessed with RaptorQ[1], this is not a good place for it.
RS over GF256 is more than adequate. Or just plain LDPC.
This kind of slop spewing into Github feels like the modern equivalent of toxic plumes coming from smoke stacks.
Utterly unmaintainable by any human, likely never to be completed or used, but now deposited into the atmosphere for future trained AI models and humans alike to stumble across and ingest, degrading the environment for everyone around it.
It's kinda like when the web first started taking off then there were WYSIWYGs. Everyone and their mom was creating static HTML websites.
But nobody shows off static HTML sites on HN.
Looks mildly interesting, but what's up with the license?
MIT plus a condition that designates OpenAI and Anthropic as restricted parties that are not permitted to use or else?
We need to ban this kind of AI slop yesterday.
Says on top it's called monster but then it speaks of frankensql. Confusing website imho for a nice project
While I don't think the website is particularly well-designed, "monster" can be used as an adjective.
There's a limit to what Claude can do without a competent human helping …
I was looking at this repo the other day. Time travel queries look really useful.
Impressive piece of work from the AIs here.
Even though it looks like LLM slop, we are starting to see big projects being translated/refactored with LLMs. It reminds me of the 2023 AI video era. If the pattern follows, we will start to see way fewer errors until it is economically viable.
Was it vibe coded?
Extremely. Repo is littered with one-off Python scripts, among many other indicators.
Yeah, “rewrite in rust” strikes again, this time equipped with a AI slop generator.
Is the implementation untouched by generative AI? Seems a bit ignorant/dishonest to claim “clean-room” in such a case
AGENTS.md and COMPREHENSIVE_SPEC_FOR_FRANKENSQLITE_V1_CODEX.md in the root folder, and ugly AI slop image on the home page and README.
A better question is if the implementation was touched by anything other than generative AI.
Love the "race" demo on the site, but very curious about how you approached building this. Appreciated the markdown docs for the insight on the prompt, spec, etc
If you can't tell this is LLM slop then I don't really know what to tell you. What gave it away for me was the RaptorQ nonsense & conformance w/ standard sqlite file format. If you actually read the code you'll notice all sorts of half complete implementations of whatever is promised in the marketing materials: https://github.com/Taufiqkemall2/frankensqlite/blob/main/cra...
If you bothered to do any research at all you’d know the author as an extreme, frontier, avant-garde, eccentric LLM user and I say it as an LLM enthusiast.
Thanks. Next time I'll do more research on what counts for LLM code artwork before commenting on an incomplete implementation w/ all sorts of logically inconsistent requirements. All I can really do at this point is humbly ask for your & their avant-garde forgiveness b/c I won't make the same mistake again & that's a real promise you can take to the crypto bank.
Great! But note I haven’t said that you should be doing the research. This was more of a warning about today, but it also was a different kind of warning about the next 12-18 months once models catch up to what this guy wants to do with them.
Thank you for your wisdom. I'll make a note & make sure to follow up on this later b/c you obviously know much more about the future than a humble plebeian like myself.