Rust Hype Ignores Real Backend Issues | Generated by AI
Question: Explain the post criticizing the trend of startups claiming to use Rust for backends, arguing that most web backends are IO-bound and performance issues stem from poor engineering rather than language choice.
Answer:
The post expresses strong skepticism about the recent surge in tech companies—especially startups—claiming to build their “backend” in Rust. The author views this as a bandwagon/herd behavior, possibly driven by hype rather than genuine technical need.
Core Argument: Most Web Backends Are IO-Bound, Not CPU-Bound
- In typical web/API/data-management backends (CRUD services, auth, business logic layers), the dominant bottleneck is IO wait time — waiting for:
- Network responses (other services, external APIs)
- Database queries
- Cache lookups
- File system operations
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Actual CPU computation done by the application itself is usually minimal. The heavy lifting (indexing, joins, sorting, aggregation) happens inside the database engine.
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For such workloads, the language’s raw single-threaded speed or zero-cost abstractions matter far less than efficient concurrency handling (being able to juggle thousands of concurrent requests without blocking).
- Modern languages with good async/await or lightweight concurrency primitives (Python+asyncio, Go goroutines, JavaScript/Node, Java virtual threads, Kotlin coroutines, C# async/await, Rust+tokio/async-std) can all achieve very high throughput on IO-bound work, provided the code avoids common anti-patterns.
Real-World Example from the Author
The author shares a personal anecdote:
- Their team blamed Python (and its GIL) for poor performance.
- Detailed profiling showed the real issues were:
- Unnecessary locking
- Repeatedly fetching the same data over the network instead of caching
- O(n²) algorithms
- Other classic algorithmic and architectural mistakes
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After targeted fixes (without changing languages), performance improved 200×+, and resource usage dropped dramatically.
- Conclusion: bad engineering created artificial bottlenecks; the language was scapegoated.
Main Thesis — It’s a People/Design Problem, Not a Language Problem
Even if those same engineers switched to Rust:
- They would likely write equally inefficient code in Rust (wrong data structures, excessive cloning, poor async patterns, unnecessary synchronization, etc.).
- Rust prevents certain classes of bugs (memory safety, data races), but it does not prevent:
- Algorithmic inefficiency
- Bad caching strategy
- Chatty network patterns
- Over-complicated business logic
- Misuse of async (e.g., blocking calls inside async contexts)
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Real high-concurrency design requires understanding OS concepts (scheduling, polling vs threads, epoll/kqueue/IOCP, cancellation, backpressure), usually taught in Operating Systems courses — not automatically gained by learning a new language.
- Anecdote from Microsoft: Even teams with “30+ years of experience” in large-scale concurrent systems wrote C# code with fundamental locking mistakes. Switching languages alone would not have fixed the root cause.
Additional Criticism: Exaggerated Requirements & Industry Fashion
- Many companies overstate their scale (“millions of concurrent users”) when in reality they serve hundreds or thousands of enterprise customers.
- They create “artificial difficulty” to justify trendy technology choices → “有困难要上，没有困难制造困难也要上” (If there is difficulty, overcome it; if no difficulty, create one anyway).
- This reflects a broader perceived decline in the industry: focusing on shiny languages/tools instead of fundamentals (“掌握思想” — mastering ideas/thinking), a lesson the author heard already in 1997.
Balanced Counter-Points (Not in the Post but Worth Noting)
Many recent sources (2024–2025) show real reasons companies adopt Rust for backends:
- Lower memory/CPU usage → meaningful cloud cost savings at very high scale
- Predictable low-tail latency (p99/p999) in high-throughput services
- Memory & thread safety reducing production incidents
- Excellent performance in frameworks like Axum/Actix (frequently top Techempower benchmarks)
- Growing ecosystem and big-tech endorsements (AWS, Discord, Cloudflare, Microsoft parts)
For truly IO-heavy simple CRUD APIs with moderate load, Go, Java, C#, or even Python often remain more pragmatic (faster iteration, larger talent pool). Rust shines more when you hit CPU spikes, need extreme efficiency, or want maximum safety in concurrent code.
The author’s central point remains valid for the majority of cases: hype and language-switching cannot substitute for solid computer science fundamentals, good architecture, and competent profiling/fixing of actual bottlenecks.
References:
- Why Rust is Winning in Backend Systems: A Startup Must-Know
- Why you should use Rust on the backend
- Rust vs Go: When to use Rust and when to use Go
- The 2025 Backend Playbook: Rust vs Java vs Go