sgarland

I should check to see if SimCity runs on Windows 11.

And yet somehow, Windows 11 remains a steaming pile of incoherent garbage.

> to the speed, reliability, and assurance requirements your market segment expects.

I suspect that the average Cisco customer expects a bit more stability than $RANDOM_SAAS.

I posted it as well before scrolling down and seeing yours. Deleted mine, as your post goes into much more detail.

I don’t know how people who’ve worked with Python and been frustrated with its performance don’t know about Numba.

Hack (Meta) is presumably contributing somewhat to PHP’s continued existence.

Thank you for raising this, as it predates TFA by a year, and also correctly cites their source.

Not who you’re replying to, but Rust lacks things I assumed were basics in any modern language, like random number generation, JSON handling, and regex. I understood the reasoning once I looked into why, but it was still startling at first.

In contrast (I know this is apples to oranges), Python’s stdlib is incredibly broad. colorsys lets you convert between color spaces. plistlib lets you read and write Apple’s property list files. graphlib has a topological sorter, which can lead to hilariously short interview questions when the interviewer isn’t familiar with Python’s stdlib.

Postgres can absolutely saturate an NVMe drive on its own.

> Additionally, think resource constrained devices like mobile phones.

SQLite will saturate the phone’s I/O before you saturate SQLite.

> Or an aggressive cost reduction mentality where each cpu cycle shouldn't be unnecessarily wasted without feeling guilty.

> Our database Dip stores raw binary as well as json as first class citizens.

These two statements are in conflict. If you want to minimize CPU cycles, parsing JSON is a terrible idea, and you should be pushing everything into bitfields, operating on it with bitwise manipulations.

> If you come from a systems engineering background like that, you wouldn't be much impressed with many traditional databases at all. I am generally not much interested in SQL databases anyway.

I would love to see a Jepsen analysis of your DB. Then a benchmark of it vs. Postgres, or MySQL, or SQLite.

I have operated a BDR Postgres cluster spread across five global regions with hundreds of TB of data, with multiple reader nodes attached to each primary, each on instances with node-local NVMe drives; the entire cluster saw over 1 million QPS. I will grant you that there are much larger examples of scale, but they typically exist on DBs designed for that, like Spanner, Vitess, etc.

I am not asking for hand-holding, I’m asking for one single publicly-available example of this being done, because I cannot fathom it being necessary or beneficial, and you have not provided concrete proof.

The insertion hit isn’t as bad as it’s made out to be; also, the majority of applications are heavily read-centric, something like a 98/2 split, so it’s usually a net gain.

You should, of course, test it on your own data before deciding.

You also now have to manage your own ACID guarantees, and what amounts to cache invalidation. Not sure how this is “a simpler mental model.”

Same with locks / contention: non-locking reads don’t cause contention, or at least, none that wouldn’t also occur with a separate table acting as the index.

Do you have any examples of this being used at scale, preferably with an accompanying blog post or other form of documentation?

I do not see how this could possibly be faster. Now you have an additional indirection.