crates.io VS rust

So, be sure to double-check your critical libraries and be sure their alternatives exist in the Rust ecosystem. There’s a good chance the crates you need are available in Rust's crates.io repository.

The previous section was very simple, this section is also very simple but introduces us to cargo which is Rust's package manager, as a JS dev my mind goes straight to NPM.

Now, there has to be a place where all these packages come from. Similar to npmjs registry, where all node packages are registered, stored and retrieved, Rust also has something called crates.io where many helpful packages and dependencies are registered.

Before proceeding, let's check https://crates.io/, the official Rust package registry.

The most thrilling aspect has been the joy of writing the backend. It's like every struct, enum, and method in Rust forms this interconnected Multiverse of code , which you can see in crates.io which is best Documentation experience I Ever Had.

5. Crates.io

When using crates from eg. crates.io, and also things like std and core

Vendoring your packages was very tedious to even remotely get to work with Cargo. I spent a very long time getting Cargo to work together with cargo-local-registry. We vendor crates from crates.io and a custom internal registry.

So this is my question: Which way was the right to find it out? There is no info about this feature on crates.io. I also have no clue what exactly it does and why it is named rwh_05.

Rust does it so much better with https://crates.io . I don't know why Go can't (or won't) do something similar.

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.