rusqlite VS rust

"Rusqlite is an ergonomic wrapper for using SQLite from Rust." - Crates.io

If you're less concerned about the "structure" of your data (e.g., serializing into rust types) and just need tabular data that can be queried (e.g., how much did we bet on X date, who placed a bet on Y team, etc.) I would definitely lean more towards a SQLite database for that kind of work. rusqlite can get you a functional database fairly quickly with a little reading of the documentation (be sure to use the "bundled" feature).

Create a connection per task. WAL is probably a good idea.

Even using SERIALIZED mode, sqlite has multiple APIs which are completely broken if two clients touch the same connection (https://github.com/rusqlite/rusqlite/issues/342#issuecomment...).

Don't bother, just don't share connections between threads and use the regular multi-thread mode (do use that though).

It fails your "ship with a crate" requirement, but when it comes to "csv but too small for a database" it's always worth having a think about SQLite. Of note, the rusqlite crate with the bundled feature will download, compile, and link against sqlite.

You may want to read the Readme of Rusqlite, especially the Optional Features.

As far as I know, the only option for an embedded SQL database is SQLite. The most actively maintained one, for rust, seems to be rusqlite (https://github.com/rusqlite/rusqlite).

Yes, but it's readonly. Also they did not merge loadable extensions support, which I need - https://github.com/rusqlite/rusqlite/pull/910

rusqlite 0.27.0, which looks like it's still the latest version

SQLite is written in C, while workers is based on V8 isolates, so it mainly runs JavaScript. Fortunately, it also supports running WASM through initialising and calling WASM modules via JavaScript. Emscripten can be used to build WASM from C, but I'd rather use it through Rust (using rusqlite), so this is what I focus on right away. Workers can also be written entirely in Rust using worker-rs.

Almost fixed the compiler: https://github.com/rust-lang/rust/pull/123325

Rust: A secure, efficient, and modern programming language (omitting ten thousand words). You can simply follow the installation instructions provided on the official website.

Recently did something similar in Rust but for generating SVGs. We've adopted it for snapshot testing of cargo and rustc's output. Don't have a good PR handy for showing Github's rendering of changes in the SVG (text, side-by-side, swiping) but https://github.com/rust-lang/rust/pull/121877/files has newly added SVGs.

To see what is supported, see the screenshot in the docs: https://docs.rs/anstyle-svg/latest/anstyle_svg/

We strongly believe in Rust as a powerful language for building production-grade software, especially for systems like ours that run alongside Kubernetes.

The above Assert<{N % 2 == 1}> requires #![feature(generic_const_exprs)] and the nightly toolchain. See https://github.com/rust-lang/rust/issues/76560 for more info.

Another week, another dive into Rust. This time, we're delving into structs. Structs bear resemblance to interfaces in TypeScript, enabling the grouping of intricate data sets within an object, much like TypeScript/JavaScript. Rust also accommodates functions within these structs, offering a semblance of classes, albeit with distinctions. Let's delve into this topic.

There’s also other reasons. For example, take binary search:

* prefetch + cmov. These should be part of the STL but languages and compilers struggle to emit the cmov properly (Rust’s been broken for 6 years: https://github.com/rust-lang/rust/issues/53823). Prefetch is an interesting one because while you do optimize the binary search in a micro benchmark, you’re potentially putting extra pressure on the cache with “garbage” data which means it’s a greedy optimization that might hurt surrounding code. Probably should have separate implementations as binary search isn’t necessarily always in the hot path.

* Eytzinger layout has additional limitations that are often not discussed when pointing out “hey this is faster”. Adding elements is non-trivial since you first have to add + sort (as you would for binary search) and then rebuild a new parallel eytzinger layout from scratch (i.e. you’d have it be an index of pointers rather than the values themselves which adds memory overhead + indirection for the comparisons). You can’t find the “insertion” position for non-existent elements which means it can’t be used for std::lower_bound (i.e. if the element doesn’t exist, you just get None back instead of Err(position where it can be slotted in to maintain order).

Basically, optimizations can sometimes rely on changing the problem domain so that you can trade off features of the algorithm against the runtime. These kinds of algorithms can be a bad fit for a standard library which aims to be a toolbox of “good enough” algorithms and data structures for problems that appear very very frequently. Or they could be part of the standard library toolkit just under a different name but you also have to balance that against maintenance concerns.