Most extensive test suites are exactly production scars: every time you have a bug or a regression, you write a test that confirms correct behaviour.
SQLite is a good example to bring up because its extensive closed-source tests are what’s often cited as being what keeps people from forking it. (Turso did it, though, but it takes a company to deliver some guarantee of equivalent diligence.)
And yes, years and years of running.
Getting an extensive test suite passing is certainly orders of magnitude better than having no test suite at all, but it still doesn't tell you as much as you need to know. I would absolutely never trust an LLM Postgres rewrite (in any language) in production based on "only" Postgres's test suite passing.
There are reasonable arguments against language ports, but this is not one. You're making an argument against code changing at all ever.
This space of things is astronomically larger than the space of things expressly covered by any test suite.
"Program testing can be used to show the presence of bugs, but never to show their absence." -Edsger W. Dijkstra
Fable's napkin estimate of the effort required to produce a passable reference semantics for Postgres, which would involve novel discoveries in denotational semantics of concurrent transactions and so on, might be in the ballpark of 30–60 years of PhD level work.
So realistically I think the only way to validate a Postgres implementation involves differential testing, fuzzing, acceptance test suites, etc. And still you'll have bugs that need to be hammered out the good old fashioned way.
I think this is also where the real work is. A rewrite is one thing, that you can show off with a flashy blogpost. The maintenance, for years to come, won't be of that nature yet it still requires as much work.
While not an exact fit of an analogy, those tests patch what was a problem with Postgres in the wild. What it doesn't cover are the things that worked in Postgres without tests, but may fail in port and go undetected.
If you can be 100% guaranteed that there indeed is a test for every occurred bug. Sometimes maintainers are not so strict about it.
And some programmers are so good that some issues are self-explanatory and they write good code to note a thing but don't write a test, because implementing the test is more expensive.
They aren't the bugs you get when you write it in Rust.
The kind of bugs you get are usually a function of the problem, language, implementation approach.
But, there are also levels, in terms of quality and human code review, when dealing with rewrites. New bugs can be introduced or there can be style issues, that can take time to fully reveal themselves, and particularly if the person or people involved are not familiar with the other language.
If a Rust rewrite of any of your software becomes available and you aren't installing it immediately and without reservation, then you are simply not giving security the priority it both demands and deserves, and that makes you disastrously insecure. This is a serious issue that should be given all priority. There is no room for debate. Your only policies should be security before all else and compliance with those policies must be absolute and without deviation, or all is lost.
> every time you have a bug or a regression, you write a test that confirms correct behaviour.
What I fail to see in these rewrites however is - what about new bugs introduced by virtue of this rewrite? I mean it'll have to go through its own challenges in real-world scenarios, right?
The problem however is non-covered success cases. A visualisation of the problem: let's say universe of interaction for DB consists of 10.000 SQL queries. Over 10 years various regressions were found and 2.000 SQL queries are guarded by tests. In reference implementation remaining 8.000 never surfaced over this time and it's unclear if they will work.
And, thinking of how many various SQL queries PostgreSQL users around the world are using vs the test cases covered it's obvious that feature space isn't covered in 1% of the success ratio cases.
Now the new, test-based implementation, has to prove it can handle remaining 99%.
There's another way to validate the rewrite though. Just run both pgrust and postgres and compare the output. Know of an edge case? Run it too. Doesn't know? Use a fuzzer or some automated tool to find interesting inputs. Found an inconsistency? The input/output pair becomes a test case now
Not sure if there's tooling for that though. If there is, just give it to Claude so they will incorporate it in their development loop
The biggest lie of software engineering is that everything can be testable with tests. That a 100% test coverage is an indicator of quality software.
Every bug report, code change as a result, PR / commit message, PR comment that steers preferences, etc. is solid signal to generate future tests.
Software like a Database should have an extensive test bench with concurrency tests, all corner cases etc.
I'm not here running the new version on production to tell the maintainer/devs that my 'production unit tests failed'.
What is this even for logic?
I mean there is balance when i write tests for my production software, but my software is used by me. If i would have a library, i would test everything.
And there was some blog post about another database system were they even virtualized the File access to test cases like when the disk controller stops working.
This is not true, even in principle, even for Postgres itself. You'd be right to say that it'd be hard to pass the test suite and not be robust at all to some extent. But even in Postgres, I bet that you can quite easily introduce a change that will pass the whole test suite but reduce robustness compared to the latest release (for a somewhat silly example, add a call to `exit()` on a timer that's longer than the longest duration test in the suite - that will significantly reduce robustness while still passing the entire test suite).
So no, I wouldn't judge a rewrite as being equal just because it passes the tests. That said, I don't think that means you shouldn't do it. You just have to be pragmatic about it.
Even a 100% test coversge is far away from verifying all behaviour.
"Program testing can be used to show the presence of bugs, but never to show their absence!"
To concisely give an overview of the project, I've been experimenting with using LLMs to build a better version of Postgres. Postgres is 30 years old and we've learned a lot about databases since hten. A lot of the techniques that work for doing a rewrite are also useful for doing a rearchitecture.
I'm now working on a new, not yet published version of pgrust that incorporates a lot of techniques. Currently the new version:
- Passes 100% of Postgres regression suite
- Implements a thread per connection model instead of the process per connection model Postgres does
- Is 50% faster than Postgres on transaction workloads
- Is ~300x faster than Postgres on analytical workloads. Right now it's 2x slower than Clickhouse on clickbench and I think it's possible to get faster than Clickhouse
How does your thread-per-connection model compare to Heikki's proposal[0][1] from back in 2023?
[0]: https://www.postgresql.org/message-id/31cc6df9-53fe-3cd9-af5... [1]: https://www.youtube.com/watch?v=xLLakMmVtbY
- Use thread local variables
- Move everything from shared memory to process memory
- Use threads instead of processes
That sounds like you are storing the data in a columnar format? Or do you do both row and columnar?
In a somewhat similar (yet also quite different) effort, I've been working on δx, a Postgres extension that compresses the data in a columnar format stored in normal Postgres tables (so replication, crash recovery, pg_dump, etc. still work normally). https://github.com/xataio/deltax
It is currently about 30-40% slower than ClickHouse (single node, ofc). The PR to add it to clickbench was just accepted, so you can see the comparison here: https://benchmark.clickhouse.com/#system=+liH|_etx|gQ|saB&ty...
[0] https://github.com/Percona-Lab/sysbench-tpccI know you say it's not production ready and not optimized yet, but in the same breath - in your comment here - you say it's already faster.
One of the things I'd typically do is peek at the commit history. Seeing what people worked on and how they did it tends to say a lot about a project. But with LLMs generating 7101 commits in less than a month that isn't feasible. Even looking at a single day is way too much [1]. It probably also doesn't make sense since the commits content won't tell you much anyway.
ps. How do you easily get to the first commit in a repo on GitHub? Browsing commit history feels rather tedious
[1] - https://github.com/malisper/pgrust/commits/main/?since=2026-...
https://cli.github.com/manual/gh_search_commits
here's the docs with more syntax using the "before x date"
https://docs.github.com/en/search-github/searching-on-github...
there's also an advanced search page, but it does not support commits when filtering with dates
https://github.com/search/advanced
or you can bisect the date in the search widget, this is the first day with a commit
https://github.com/malisper/pgrust/commits/main/?since=2026-...
first commit:
https://github.com/malisper/pgrust/commit/22113dc36b02973060...
Maybe I'm just being a little grumpy. If I really need to look into a repository, I clone it and use vanilla git command line tools to have a look.
It's just annoying that the modern web UI from GitHub takes >1s second to load a page with 34 commits
You can use the syntax github.com/user/repo/commits/?after=last_commit_hash+number_of_commits-2 (-1 for the latest and -1 for the last)
ex : https://github.com/malisper/pgrust/commits/?after=3646a73515...
These rewrites are just test-driven development taken to the absolute extreme. Created under the hope that the existing tests are exhaustive and cover every relevant use case, such that if they all pass, the rewrite must be at least as good as the original. So just go with the vibes and burn tokens until they pass, and your job is done.
In practice, this is never true for any codebase above a certain level of complexity, especially not one as mature and widely used as Postgres. But reality doesn't seem to be an obstacle for vibe coders.
Went straight into my vault of brilliant quotes!
That's a wrong question. The right question is "why would one go about rewriting a piece of code in X". Once and if you find a good answer to that question, you will see the answer to your's.
All these "rewritten in rust" projects only reinforce the idea that a significant part of the rust community consists of software talibans and not of engineers who must deliver something that works and is reliable over time.
Cue some story here on a bank or airline somewhere still relying on cobol backend servers.
These LLM conversions really seem to make modernization of large parts software layers possible!
Consider: You have a big mainframe running your tier 1 bank. Assume that you can see all the code on it, and you can feed all that to an LLM if you like. Getting it to spit out a Rust version is not what you actually want - you now have a modern language but it's still a singleton instance, so where do you run it? Most hardware doesn't give you enough uptime for what you need here, because what you actually needed was a re-architecture for distribution / failover / whatever, and while you could ask your LLM to do that you aren't going to run your bank on the result.
Why not?
I feel like we're entering a new era of prejudice against not a category of humans, but against non-human intelligences.
The design patterns for distributed and fault-tolerant systems are well-known and established in the industry. Both humans and AIs are familiar with them!
So if you sketch a design for the AI to follow, establish the rules in AGENTS.md, have a robust test suite, use a frontier model dialed up to eleven, etc... why not rely on the LLM output?
At the end of the day, humans are not without fault either.
I've been wading through some legacy "pre-AI" code recently and it has more bugs than a rainforest! Static fields used incorrectly, causing data races. Floating point types used for money amounts. JavaScript and SQL injection up the wazoo. Wildly unsafe password handling. So on, and so forth. This is the norm for most human-written software, not the exception.
As a proof-of-concept, I tried an AI rewrite of one such legacy app[1], and it is not bug free, but it notably has fewer bugs than the original. Different bugs, sure, and I'll have to iron them out after a round or two of UAT, but I'm honestly more confident with what I got from the chatbot than the code inherited from humans.
[1] Deals with money, but admittedly at a much lower level of risk and consequence than a banking app running on a mainframe.
Sure you could keep vibe coding it but I wouldn't bet my data on that. A database needs to be rock solid.
There's existing money and expertise in those environments to rewrite the whole thing, yet they don't. You may loan them free engineers/experts and they might still not rewrite anything.
The existing system works. Yes, it costs a lot to maintain, and you could definitely reduce that if you moved to a more modern system. So now you're talking payback periods. Cost of development / maintenance cost savings per year = number of years before you pay back the project.
Problem is, that the cost of the development is often unclear, and the maintenance cost savings, while definitely above zero, and often unclear, and approximated the numbers usually come to a payback period in decades.
And that's without the usual tech caveats; We can't promise there won't be bugs. We can't promise deadlines will be met. We can't promise the project will succeed at all. We can't promise existing functionality will be faithfully reproduced in the new system. The normal risks around any software dev project.
All in all, it looks really expensive and really risky compared to just doing nothing and running the same old system for another five years.
Source: I helped do some of the maths on this for a Y2K project.
In 2026, not sure if it was satire. Do some people truly believe that all their software stack has to be single tech, from device drivers to end user apps? Does that extend to remotely accessed services?
I will note that, very funny
Is managing memory safely important? YES
Is managing memory safely the solution to most of the problems? Absolutely not.
Advocating the language ignoring everything else (having as first and only argument that the code was rewritten in rust fully qualify for this case) is dogma and not engineering.
We have a problem with software religious fundamentalists in our organisation and it's an apt description.
We went down the earlier Udi Dahan and DDD crap.
I seriously don't get it though. Rust is a nice language, but so is X. However we don't see X people brigading existing projects with constant bombardment with "rewritten in X". What is that about Rust that prompts this behavior?
People see the safety as a moral superiority so it attracts obnoxious zealots.
Other languages' features and syntax aren't nearly so easy for zealots to form behind. The perception of absolute safety it puts in some people makes them crazy.
If it _is_ 50% faster, then that's the reason
Obviously like any new database it's very risky to use so probably only used for niche use cases at first, but if it turns out to be just as reliable as postgres and faster then why not?
You're right to talk about the trend though, because what it shows is how the cost of re-writing well covered project has completely crashed, so that in itself is a learning.
But most of my issues were related to concurrency and data sanification, especially when the other end of communication fails with unexpected behavior. These bugs are nastier than memory.
So, I have pointers, and I am not afraid to use them.
If there's particular unsafes that you think are egregious, let me know.
So how much AI usage does it make it an “AI rewrite”?
Who wants to contribute to an unmaintainable code base?
It’s mostly grunt work and LLMs are well suited for translation tasks (iirc transformers arch was originally invented for translation)
Such crude takes only cause unnecessary friction. If you have a black box that spits out code, and you are unable to distinguish the quality between a top tier dev and an AI inside the black box, then the distinction is unnecessary. Most of the code on the internet is already a black box to you. What percentage of code running on your machines have you vetted by who wrote it and code quality?
AI coding isn't going anywhere and will likely end up generating most code going forward so instead of rejecting it outright or arbitrarily categorizing it we need to focus on solid quantitative and qualitative measures of code and functionality regardless of who wrote it.
If the dev doesn't vet the code, it doesn't matter how good quality a dev they would be if they wrote the code - they didn't. Sure, the dev would probably drive the initial architecture discussion better and some people are using AI in small batches with tests and vetting everything, but some previously great devs are throwing in PRs that touch hundreds of files at once with one commit.
A lot of people I previously considered great developers have become people I would not recommend for a job in the past 2-3 years.
> If you have a black box that spits out code, and you are unable to distinguish the quality between a top tier dev and an AI inside the black box, then the distinction is unnecessary.
Sure, but this is just begging the question. If nobody could tell, the term 'slop' wouldn't have become so popular.
100% there is slop created by humans and really solid code bases generated by AI driven by a meticulous developer. You are making the exact error I was addressing, which is bucketing all AI code as the same.
> I never claimed that there wasn't AI slop
No, but you implied that a top tier dev doesn't produce slop when using AI.
> If you have a black box that spits out code, and you are unable to distinguish the quality between a top tier dev and an AI inside the black box
My point was that "if" is doing a lot of heavy lifting here and you're coming very close to begging the question.
> bucketing all AI code as the same.
Most people are not "top tier devs" and over time this will probably become more true. Even if I accepted your premise that "top tier devs" only generate solid code bases with AI, the ease of entry and the ease of spitting out thousands of lines of code means the ratio of bad AI to good AI will not go in a good direction unless it becomes too expensive for non "top tier devs" to use. Given this, I think it's fair to assume AI code is low quality until proven otherwise.
I also did not claim that all "top tier devs" would always produce better code with AI, but the qualification for a "top tier dev" in this case would be someone who verifies code multiple ways to make sure it is correct. I've seen amazing code come from bad interns that was reviewed mercilessly by season devs, and there's absolutely no reason it would not be the same with AI generated code.
You do realize that you can review the entire architecture and code line for line even if it's AI generated right? My black box comment did not mean you couldn't see the code, it meant you don't know whether a machine wrote it or not.
However, Jarred still describes a lot of unsafe, and usage of Miri in continuous integration.
Funnily enough, RAII is cited as a major benefit of rewriting from Zig to Rust, while C++ already has RAII. I wonder if C++ and Rust are more suited to larger programs than Zig, unless the architecture in Zig is handled carefully.
Aren't you making a strawman argument ? AFAIK this project is not made by an official PostgreSQL core developer, so the entire premise of your argument is invalid.
It was trained on all code the code that could be found.
Not just code written by genius programmers like Carmack and Bellard.
Given that it's average, I'd prefer a human coder above average :)
Then, by giving them context or by post-training, you can make them sample non-average parts of the distribution they learned.
How do you derive that something is "below average" or "average" or "above average"?
In the case of real world LLMs and post-training, what is above average is defined roughly as: labeled good by expert humans, and scoring high on RL environments related to coding like debugging, passing tests, or running efficiently and verifiably correctly.
How do you? I mean, that was your point basis.
One technique is RLHF: have an human expert assess it.
Like a short example is easier to grade, but not in the same ballpark as a whole codebase.
I've been programming a long time and considered myself among the top in my domain and AI agents using like GPT 5.5 etc. are much better than me.
Ex falso quodlibet
> I've been programming a long time and considered myself among the top in my domain
I am not trying to attack you, but you considered yourself that... I don't know whether you actually were and frankly I don't care.
I like the AGPL and think it's the best truly free open source license, but I worry if this is compatible. Ie, if this is rewritten from the original source, should the original apply? (Yes.) There has been a trend to rewrite open source software with a more restrictive license (like coretools in Rust). This looks considerably more ethical by choosing the AGPL - I just wonder, safer with no change at all?
[0] https://www.postgresql.org/about/licence/
[1] https://github.com/malisper/pgrust?tab=AGPL-3.0-1-ov-file
On one hand, they give an LLM a short feedback loop to correct itself, and iterate fast when writing code. A human also uses it as a feedback loop, but we don't iterate as fast and don't handle big walls of conditions, so its effect is not as big.
On the other hand, LLM's ability to handle a big wall of if-conditions can backfire if it starts taking shortcuts and taking the tests-as-a-spec too literally, overfitting the solution, overly focusing on the given datapoints (conditions checked by tests) and missing the overall behavior shape that the tests intend to pin down. For humans, this is less of a concern because we are bad at big walls of if-conditions, and we'd rather try to see the original shape that the tests are pinning down than monkey-patch the solution to fit the individual points.
It's interesting to see how one balanced these two. In this case particularly. Maybe you could play around with separating the data you give an LLM into "training set" and "validation set", training set can be seen fully, but validation set is hidden and is only queried when the solution is deemed ready. Say, training set = original source code + half of the tests; LLM uses that for quick feedback loop. And validation set = the remaining half of the tests; test code is not shown to the LLM and run only when the LLM says it's done to catch potential overfitting of the resulting solution over training set.
To me, the credibility of a solution like that would depend on what methodology the authors used. If they just let the LLM see all tests, I'd be skeptical (albeit unable to point out specific bugs due to the volume of work and LLM's ability to make bad things look trustworthy). The good thing is, real-life use will add new, unseen before datapoints for testing — so validation set will build up with time. Really curious to see how it will work.
Everyone is standing on the shoulders of those which came before. If LLMs allow us to combine the incredible decades of effort and knowledge and experiences that's gone into building something as great as Postgres, and take that and combine the experience and philosophy that has led to the creation of a language that potentially provides tangible benefits, and for far less human time and effort that it would have otherwise taken...surely something that should be celebrated as absolutely incredible?
That's understandable but it's still a bit of a negative emotion that probably isn't very productive. Or very rational. This thread is full of people trying to argue that this can't be any good, shouldn't be any good, and is clearly going to end in tears. And obviously this thing passing tens of thousands of carefully curated tests that accumulated over decades suggests otherwise. It's hard to argue against that.
This probably is going to have some new issues. But it's an impressive achievement.
https://github.com/malisper/pgrust/blob/3646a73515a5e4ac7d0b...
https://github.com/malisper/pgrust/blob/3646a73515a5e4ac7d0b...
https://github.com/postgres/postgres/blob/2e6578292a9184dcaa...
I have taken a closer look at the code, and it seems superficially a somewhat faithful rewrite, not quite idiomatic Rust, but closer than I anticipated at first. I know there are non-LLM rewriting tools for C to Rust, and with a test suite to help, a rewrite to Rust might be greatly helped. The new Rust code does have some drawbacks in some ways, and there are topics I am curious about.
1. human code reviews are dead. We don't yet know what's next. Two reasons they are dead: too much code to review, and code reviewing sucks (who wants to spend their days reviewing code?) 2. Not knowing how to review LLM code is a big barrier to adoption, but bigger regression test suites (testability/evals) is almost certainly the direction. 3. There are a lot of projects that haven't moved to more modern infra because it was too hard. Now it's much easier. Sure stuff will go wrong. Sure it all has to be tested. What's new here? 4. Programming languages for LLMs are coming. 5. Projects that don't allow AI coding will be forced to come around or fade.
Separately, bit off topic:
New projects will often have LLMs built in, so non-determinism will be inherent in the project. No amount of code review will be able to eliminate that.
I know it says it is not performance optimized yet, but if this succeeds, will it only bring more "memory safety" or is there a serious performance gain as well?
will it only bring more "memory safety" or is there a serious performance gain as well?
https://knowyourmeme.com/memes/i-dont-want-to-play-with-you-...
This is usually a good example of a test case that the upstream project is not covering and can be contributed back.
Parity should be bidirectional, so definitely it is possible for both parties to benefit from it.
Rewriten in Rust is becoming a meme now.
uh-huh, sure.
you want to show off "look what the LLM can do / look what I burned a bunch of tokens on"?
you want to brag about how your LLM-generated slop is somehow more maintainable than the original because blah blah blah Rust?
here [0] is the version history of Postgres. pick a version from the past. let's say 14.x because it's the most current that's still under active support.
have your LLM implement version parity with 14.x. show off how it passes all the tests blah blah blah.
then have it upgrade your codebase to parity with 15.x, implementing whatever new features and bugfixes that includes.
and have it generate an automated test that demonstrates upgrading an actual database from LLM-14.x to LLM-15.x and verifying there's no data loss or corruption. maybe even multiple such tests, if you're feeling fancy.
then lather, rinse and repeat with 16, 17, and 18.
and show off the diffs of each version. does the LLM rewrite a huge pile of already-working code in the process of each version upgrade? does it introduce new latent bugs in the process - the kind of things the existing test suite didn't think to explicitly test for?
"I took a static snapshot of code and converted it to another static snapshot of code" is meaningless. all you're doing is bragging about having more money than good sense.
the stability and trustworthiness of software like Postgres does not come from a one-time snapshot showing tests passing. it comes from the engineering process that produces the software and its test suite.
oh, and for shits and giggles, because this same test was so illuminating with the Bun "rewrite" into Rust, here is the file with the most unsafe blocks in the codebase:
> rg -c unsafe crates/backend/parser/gram_core/src/convert_ddl.rs
128
> wc -l crates/backend/parser/gram_core/src/convert_ddl.rs
2055 crates/backend/parser/gram_core/src/convert_ddl.rs
why is the parser unsafe at all?!?
let r = unsafe { &*p };
The parser was generated by c2rust. The Postgres parser is generated from yacc/bison itself so I didn't bother making it idiomatic.
I really don't understand why this is needed outside of an opportunity to show how impressive LLMs can be when working within large codebases, but even then people in the comments are finding bizarre implementation choices that a human developer wouldn't make. I'll stick with Postgres and its - gasp - C implementation for now, thanks.
They will ask relevant Claude skill.md
In fact from a porting effort this is the first blog post I would expect. Not that the hey we successfully did it.
Copyleft and the whole software licensing ecosystem only matter when producing that software actually requires serious human effort and dedication.
For my machine translation of SQLite to Go I added this to the README as to licencing:
Most of the code here is machine translated using wasm2go. As such, the original authors retain copyright and the original licenses remain in effect. Everything else is licensed under MIT-0.
The translator (wasm2go) has a licence chosen by, and a copyright notice from, me. Makes no sense for the translated code.
And avoid relying on direct LLM output for actual work to make sure I don't accidentally include some regurgitated snippet from an incompatible license.
It helps that LLMs struggle to write good, idiomatic code in my language of choice.
Fixed that for you. Code generated by an LLM is not copyrightable (because copyright only protects human effort), so the codebase is automatically public domain and cannot be licensed at all.
They could theoretically copyright the prompts that they used, but as that's not part of the output, and the output doesn't deterministically arise from those prompts, they'd struggle to use that to back a copyright claim.
Even though I'm sure it won't be easy to convince the Postgres project to switch to Rust, I do think that trying would be time better spent.
Rust:
https://github.com/malisper/pgrust/blob/3646a73515a5e4ac7d0b...
Original:
https://github.com/postgres/postgres/blob/df293aed46e3133df3...
Usage:
https://github.com/malisper/pgrust/blob/3646a73515a5e4ac7d0b...
The return type in the rewrite is both some sort of Error tagged union that supports the Try machinery in Rust; but, it also contains a boolean that apparently must be checked; or something. It seems labyrinthical and possibly broken and terrible.
Though often when applicable, a simple tagged union is used instead when that would document the intention better. Like, the Rust version of search_pg_class_full_form::call() returns a Some for cache hit and None for cache miss as far as I can skim, and that group of methods returning that could arguably have returned a basic enum instead with CacheHit(value) and CacheMiss. Though this is a nitpick on my part.
The error-tagged union is PgResult<bool> - which means it contains bool as the result if things go well. (The other part in the union is of course the error.)
In the original function also, it is returning a boolean: "bool has_subclass".
So anyway you have to check for the boolean as part of the logic. That is what it is doing.
Edit: Looking at the code again, perhaps I was mistaken, since the boolean might not have been for error handling, just the result of the function, and C's limitations regarding error handling led it to using something like elog(), apparently a macro defined in https://github.com/postgres/postgres/blob/master/src/include... .
I wonder how many "unsafe" blocks are in there...
https://github.com/malisper/pgrust/blob/3646a73515a5e4ac7d0b...
https://github.com/malisper/pgrust/blob/3646a73515a5e4ac7d0b...
The boolean being returned is the return value of the function. It's not used to return an error.
An unfortunate aspect is that the code has become a bit more bloated in some regards due to usage of Result, instead of an implicit elog() macro and similar. Passing Result around, in some ways as an alternative to an unwinding exception, is cleaner in some ways, but it also bloats the code somewhat.
The rewrite also could have simpler code in some cases, like
https://github.com/malisper/pgrust/blob/3646a73515a5e4ac7d0b...
could perhaps just be
match syscache_seams::search_pg_class_full_form::call(ctx.mcx(), relationId)? {
Some(form) => Ok(form.relhassubclass),
None => {
Err(ereport(ERROR)
.errmsg(format!("cache lookup failed for relation {relationId}"))
.into_error())
}
}
I see a lot of MemoryContext. I am not sure how much that bloats the code (though the C code is bloated due to C's issues and problems, like re-using collections and such). Does it incur an overhead?
I have privately wondered for years, pre-AI, why Apple hadn’t paid some engineers to go off and write some comprehensive test suites and then port these to Swift. It would shut down entire swaths of memory safety bugs they have been coping with for literally decades. SO MANY of the zeroclick iOS exploits can be traced to a few fragile and vulnerable foss libraries, xkcd 2347 style.
DST systems such as Antithesis can definitely help.
Common pronunciations allow you to stay perfectly ambiguous about where the L goes, which aligns quite well with the name as spelled. If you do it right, nobody can tell if you're saying sequel-ite or sequel-lite or seque-lite on the one hand, or S-Q-L-ite or S-Q-L-lite or S-Q-lite on the other.
AFAIK there is no official word on how the name is intended to be read or said.