swc VS rust

First, we switched the default compiler for new projects from Babel to SWC (Speedy Web Compiler). SWC is dramatically faster than Babel and requires zero configuration. We’ll continue to support Babel in any project currently using it.

SWC

As the reference explains “**SWC** (Speedy Web Compiler) is an extensible Rust-based platform that can be used for both compilation and bundling. Using SWC with Nest CLI is a great and simple way to significantly speed up your development process.”

This is specifically about breaking the myth that performing expensive self-contained operations (e.g, parsing GraphQL) in a native extension (C, Rust, etc.) is always faster than the interpreted language.

The JS ecosystem has the same problem, people think rewriting everything in Rust will be a magic fix. In practice, there's always the problem highlighted in the post (transitioning is expensive, causes optimization bailouts), as well as the cost of actually getting the results back into Node-land. This is why SWC abandoned the JS API for writing plugins - constantly bouncing back and forth while traversing AST nodes was even slower than Babel (e.g https://github.com/swc-project/swc/issues/1392#issuecomment-...)

Why Speedy Web Compiler ?

FTA is a TypeScript static analysis tool built on the speedy foundations of swc. FTA is fast; capable of analyzing more than 150 files per second on typical hardware, it offers a powerful addition to your code quality toolkit.

Very cool! I'm curious, is this intended for dev tooling?

For example, I could see this (or something similar) being useful as the engine for a typescript language server that would be faster than the standard one

But if it's not aimed at 1:1 with tsc, would it be intended more for something like swc[1]?

Or what would you expect people to use this for, besides just being a cool project to learn from?

[1] https://github.com/swc-project/swc

This is... good news, but I still cannot fathom using the default Typescript compiler for regular development. Seriously, leave the type-checking to your IDE and CICD chain, and switch to using tsx (https://www.npmjs.com/package/tsx) or swc (https://swc.rs/) and you will _immediately_ notice the difference in speed and productivity.

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.