We’re joined today by our resident development expert, Anand Naidu, who has deep proficiency across the full stack. Rust is celebrated for its incredible runtime speed and safety, yet developers often find themselves grappling with a different kind of slowness: compile times that can turn a development sprint into a crawl. Today, we’ll dive into this paradox with Anand, exploring practical strategies that every Rust developer can use. We’ll discuss the critical decisions around toolchain management, clever techniques for avoiding unnecessary work, and architectural patterns like workspaces for taming large projects. Anand will also walk us through his diagnostic process for pinpointing performance bottlenecks and share some platform-specific tricks to squeeze out every last drop of speed.
Developers often face a choice between always using the latest Rust compiler or pinning a project to a specific version. What are the key trade-offs here, and what factors should a team consider when deciding whether to prioritize the latest optimizations or guarantee project stability?
That’s really the foundational question every Rust team has to answer. On one hand, staying on the cutting edge by running rustup update regularly is incredibly tempting. You’re not just getting updates to rustc; you’re also inheriting all the ongoing optimizations from the LLVM framework it’s built on. The compiler team is relentless, and these updates often bring tangible improvements to compile times without you having to change a single line of code. However, for a team, predictability is paramount. Introducing a new compiler version can subtly change behavior or requirements. That’s why pinning the toolchain to a specific version, say channel = "1.85", offers a stable, reproducible build environment for everyone, which is non-negotiable for CI/CD and large-scale collaboration. The decision ultimately hinges on the project’s phase: a rapidly evolving internal project might benefit from the latest features, while a production system values stability above all else.
To avoid full recompilations, a developer might use cargo check, a caching tool like sccache, or dynamic linking. For each approach, could you describe an ideal use case and outline the specific performance trade-offs, such as the impact of dynamic linking on runtime performance?
These are all part of the “work smarter, not harder” toolkit, and each shines in a different scenario. cargo check is my go-to for the rapid feedback loop during active coding. It performs most of the critical checks without firing up the slower, LLVM-driven build process, which feels liberating. You’re just asking, “Is my logic sound?” not “Build me the entire universe.” For sccache, the magic really happens at the team level. Using it on a solo machine gives you minimal gains, maybe some benefit if you’re reusing crate versions across projects. But when you hook it up to a shared file system for a whole team, it becomes a powerhouse, preventing multiple developers from building the same dependency over and over. Then there’s dynamic linking with a tool like cargo add-dynamic. This is a powerful move for projects with massive dependencies. By wrapping them as “dynlibs,” you slash the linking time dramatically. The trade-off is that you lose some static compilation benefits like inlining code from those crates, but the runtime cost is usually so minimal you’d never notice it.
For large projects, two “divide and conquer” strategies are using Cargo workspaces and enabling parallel compilation. Could you detail the process for refactoring a monolithic crate into a workspace and explain how a developer should test and measure the actual benefit of enabling parallel compilation flags?
“Divide and conquer” is the key to sanity on large codebases. Refactoring a monolith into a Cargo workspace is a deliberate, architectural process. There’s no magic button; you have to manually identify the clean boundaries in your code—like separating your models, views, and controllers in an MVC design—and carve them out into their own subcrates. The payoff is immense: when you only change the view logic, you only recompile that small part of the project. It’s perfect for projects large enough to justify that initial partitioning effort. As for parallel compilation, it’s becoming more standard, but you shouldn’t just flip the switch and hope for the best. To truly validate it, you need to measure. I’d recommend starting with a completely clean build. Then, on an 8-core machine, for example, run the build with the flag -Z threads=8 and time it. Then, run another clean build with -Z threads=4. Comparing those times will give you a clear, demonstrable answer on whether throwing more cores at the problem is actually providing a benefit for your specific project.
Imagine a project’s build time has suddenly doubled. What is your step-by-step diagnostic process using tools like cargo build --timings and compiler flags like -Z time-passes? How do you distinguish a slow dependency from a problematic procedural macro or a linker bottleneck?
When a build time explodes, you need to become a detective. My first and most crucial step is to run cargo build --timings. This command is an absolute gift; it generates a detailed HTML report that visually breaks down the compilation time per crate. It immediately points you to the biggest offenders. If a specific crate is the culprit, the next question is why. It could be a procedural macro gone wild, generating an enormous amount of code. For that, on a nightly compiler, I’d use -Zmacro-stats to see exactly how much code is being expanded. But you can’t forget the final step: linking. I always use -Z time-passes to see a granular breakdown of every compilation step, and it’s shocking how often the linker is the hidden bottleneck. If I see the linker taking up a huge chunk of time, I immediately start experimenting with alternatives like lld or mold to see if that speeds things up.
On Windows, developers can use a Dev Drive, while on Linux, an in-memory filesystem can be used for caching. Could you explain the technical reasons these improve compilation speed and share any metrics or anecdotes on the real-world performance gains you’ve observed from these platform-specific setups?
These platform-specific optimizations are fantastic because they tackle the physical I/O, which is a fundamental constraint. On Windows, the Dev Drive is a game-changer. It uses the ReFS file system, which is designed for performance scenarios. It’s more efficient and has proactive repair mechanisms, but the real magic is how it signals to Windows Defender to use a less aggressive scanning mode. Antivirus scans are a notorious source of I/O drag during compilation, and the Dev Drive effectively gets it out of the way. For Linux, the approach is more direct: set up a temporary filesystem in RAM. Because you’re reading and writing build artifacts directly from memory instead of a physical disk, the speed is phenomenal. I’ve seen teams reclaim significant portions of their build time this way. The obvious downside is that everything in that cache vanishes on a reboot, but if you have long uptimes on your workstation, you only feel that pain once after a restart.
What is your forecast for the future of Rust compilation performance? Do you foresee a point where compiler improvements make most manual speed-up techniques obsolete, or will developers always need to be proactive about optimizing large-scale projects?
The Rust compiler team is doing incredible work, and I have no doubt that baseline compilation times will continue to improve. We’re already seeing features like parallel compilation become more integrated and require less manual intervention. However, I don’t believe we’ll ever reach a point where manual optimization becomes obsolete, especially for large, complex software. The compiler can only optimize what it can see; it can’t understand the unique architecture of your project or make decisions like splitting a monolith into a workspace. There will always be a place for a proactive developer who analyzes their build timings, understands their dependency graph, and makes smart architectural choices. The compiler provides the power, but it will always be up to us to wield it effectively to keep our development workflows fast and fluid.
