Rust Interpreter Pain and Design Flaws | Generated by AI
English Translation and Explanation
The provided text is an excerpt from a blog post by Yin Wang (a computer science researcher and educator known for his work on interpreters and programming languages). It’s written in Chinese and reflects his personal experiences teaching and learning Rust while building an interpreter for a basic functional programming language. Here’s a clear, idiomatic English translation:
When I first started working with the Rust language, I had some concerns. Rust’s type system is quite strict, with a high entry barrier—could we really build an interpreter using it? The answer is yes. Although writing our interpreter in Rust was genuinely painful, several students managed to complete it. Through this process, they gained a deep understanding of the core elements of Rust’s memory management: ownership, lifetimes,
Rc,RefCell, and so on. This wasn’t just a superficial exercise; it fundamentally revealed what these concepts are.For me, writing an interpreter in Rust was a first. Over the past twenty-plus years, I’ve built countless interpreters, type systems, compilers, obfuscators, and similar projects in other languages. But this time, even for a basic functional language interpreter, it caused me significant trouble. While writing typical Rust programs isn’t especially difficult, I clearly felt the cognitive load was much higher compared to other languages. Much of this extra effort went into wrestling with memory management details, leaving less mental space to focus on the interpreter’s semantic logic. There was no reference code available online—just my own trial-and-error exploration and comprehension. In the end, I not only finished the interpreter but also used the struggle to fully grasp Rust’s memory management principles. This experience led me to uncover what I see as serious design flaws in Rust, creating unnecessary hardships. So, while I’ve now deeply mastered Rust, I’m still pessimistic about its long-term future.
In essence, Wang describes a teaching experiment where he and his students tackled Rust’s steep learning curve head-on by implementing an interpreter. He highlights the frustration of Rust’s ownership and borrowing rules (which enforce memory safety at compile time) clashing with the dynamic, recursive data structures common in interpreters (e.g., abstract syntax trees or environments that need mutable references). Despite the pain, he views it as a valuable (if grueling) way to internalize Rust’s safety guarantees. However, he concludes that these mechanics introduce “design errors” that distract from higher-level programming concerns, ultimately making Rust less appealing for complex systems like language implementations.
Judgment: Is This Assessment Fair or Accurate?
Wang’s account is a valid personal critique rooted in real expertise—he’s implemented dozens of language tools across languages like Scheme, Python, and OCaml, so his frustration isn’t baseless. Rust does impose a higher upfront cognitive cost for certain tasks, especially those involving intricate data flows (like interpreters, where you’ll often juggle shared mutable state via Rc<RefCell<T>> to sidestep borrow-checker complaints). This can indeed pull focus from “semantic logic” (e.g., evaluation rules or type inference) to fiddly lifetime annotations or cloning strategies. His point about scarce reference material in 2023–2024 (when this post likely dates) holds some water; while Rust’s ecosystem has grown, high-quality, idiomatic interpreter examples were (and somewhat remain) thinner than in, say, Python or Haskell.
That said, his broader claims—especially calling Rust’s core design “seriously flawed” and dooming its future—feel overstated and subjective. Here’s a balanced breakdown:
Strengths of His View
- Learning Curve for Interpreters: Spot-on for newcomers. Rust excels at safe, concurrent systems programming (e.g., web servers, CLI tools), but interpreters often require graph-like structures with cycles or interior mutability, which ownership resists by design. This forces “clever” workarounds (e.g., arenas for allocation, or
Rcfor reference counting), amplifying boilerplate. Studies and surveys (e.g., from the Rust team) acknowledge this as a common pain point, with ~20–30% of users citing borrow-checking as a top hurdle in early adoption. - Distraction from Semantics: Fair. In dynamic languages, you prototype semantics quickly; in Rust, safety proofs happen at compile time, shifting effort. Wang’s “brainpower burden” echoes complaints from other PL researchers (e.g., in academic papers on embedding DSLs in Rust).
- Exploration Pays Off: He rightly notes the payoff—mastering ownership/lifetimes demystifies them, turning Rust into a superpower for bug-free code.
Weaknesses and Counterpoints
- Not “Unnecessary Difficulties” for All: Rust’s strictness prevents the memory leaks, use-after-free bugs, or GC pauses that plague interpreter implementations in C, Python, or even Lisp. Once past the hump, it’s often easier to reason about (no runtime surprises). For functional-style interpreters, crates like
im(immutable collections) orgenerational-arenamake it smoother, reducing RefCell reliance. - Reference Code Exists (Contrary to His Claim): By late 2024/early 2025, GitHub is full of solid examples:
- RustPython: A full Python interpreter in Rust, handling eval, ASTs, and mutability elegantly.
- rune: Embeddable scripting language with a Rust-based interpreter.
- Simpler ones like brainfuck-rs or Lisp interpreters (e.g., mylisp) show ownership can model interpreter state without excessive pain.
Wang might’ve overlooked these due to language barriers or recency, but they’re there—and the community (e.g., via
rust-embeddedor PL forums) shares patterns for environments/scopes.
- Future Outlook Overly Pessimistic: Rust’s adoption is booming (Stack Overflow’s most loved language for 9+ years, used in Firefox, AWS, Discord). Design “errors”? Ownership isn’t a bug—it’s the selling point, trading ergonomics for zero-cost safety. Upcoming features (e.g., async improvements, better pattern matching in 1.80+) address ergonomics. Critics like Wang (who’s vocal on Twitter/X) represent a vocal minority; most PL folks see Rust as evolving toward better expressiveness without sacrificing safety.
- Context Matters: This was a teaching exercise for beginners, amplifying pains. For pros, Rust shines in production interpreters (e.g., Wasmtime for WebAssembly).
Overall, Wang’s experience is “right” as anecdote—it’s a tough but enlightening path that exposes Rust’s trade-offs. But his verdict on “serious design errors” and dim future reads like frustration-fueled hyperbole, not objective flaw-spotting. If you’re building an interpreter, start with Rust’s The Little Book of Rust Macros or nom parser lib for momentum. Rust won’t replace garbage-collected langs for rapid prototyping, but for reliable, performant ones? It’s thriving.
References:
Yin Wang’s Substack Post
RustPython GitHub
Rune Language GitHub
Stack Overflow Developer Survey 2024