Clippy VS rust

I couldn't find it in the API guidelines either. From what I understand, the idea is that any trait bounds, which includes generic type parameter bounds and lifetime bound on a type (struct or enum) would be repeated back in the impl block

there is a nice discussion on this issue here: https://github.com/rust-lang/rust-clippy/issues/1689

You should not blindly follow clippy lints. They are sometimes wrong. Another example https://github.com/rust-lang/rust-clippy/issues/9782 .

I often write () = f() to assert that f() is unit. Unfortunately clippy warns on such code ( https://github.com/rust-lang/rust-clippy/issues/9048 ). There are very recent pull requests for this bug, so hopefully this bug will be fixed very soon. But meanwhile I invented this workaround: [()] = [f()] :)

Apart from running clippy on many projects being essential, clippy is also an exceptionally welcoming project, no matter your prior knowledge.

clippy is a great place to get started :) though it isn't exactly new.

clippy is also pretty compiler-adjacent and unlike rust-analyzer uses rustc's internal APIs. Don't let the size of the code base scare you off! It's actually feasible for a newcomer to contribute even such a substantial change as a new lint, and we have issues labeled as "good first issue" that come with mentorship, so you don't need to go it alone.

Yes, you could use it to write a lint. Although you might find it easier to just fork Clippy and add your own lints to their existing framework.

Check out the readme for more information.

They are two of my favorite Rust tools. If you haven’t tried them yet, I highly recommend giving them a try. Clippy can detect various lints in your code and guide you towards writing more idiomatic code. To install Clippy, simply run rustup component add clippy, and to run it within your workspace, execute cargo clippy. For more details, visit Clippy’s GitHub repository.

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.