Why I Like Rust Better Than Python
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Why I Like Rust Better Than Python
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このコンテンツについて
Technology requires constant reassessment. Six-month deprecation cycle for skills/tools.
Memory Safety Architecture- Compile-time memory validation
- Zero-cost abstractions eliminate GC overhead
- Production metrics: 30% CPU reduction vs Python services
- Default performance matters (electric car vs 1968 Suburban analogy)
- No GIL bottleneck = true parallelism
- Direct hardware access capability
- Deterministic operation timing
- Type system prevents race conditions by design
- Real parallel processing vs Python's IO-bound concurrency
- Async/await with actual hardware utilization
- Compilation = runtime validation
- No 3AM TypeError incidents
- Superior to Python's bolt-on typing (Pydantic)
- IDE integration for systems development
- Cargo: deterministic dependency resolution
- Single source of truth vs Python's fragmented ecosystem (venv/conda/poetry)
- Eliminates "works on my machine" syndrome
- Zero-overhead FFI
- Embedded systems support
- Kernel module development potential
- Native cross-compilation (x86/ARM)
- Minimal runtime footprint
- Docker images: 10MB vs Python's 200MB
- Built-in tooling (rustfmt, clippy)
- First-class documentation
- IDE support for systems development
- AWS Lambda core uses Rust
- Cost optimization through CPU/memory efficiency
- Growing ML/LLM ecosystem
- "Wash the Cup" principle: Build once, maintain forever
- Compiler-driven refactoring
- Technical debt caught at compile-time
- 80% reduction in runtime issues
- Single binary deployment
- Cross-compilation support
- ECR storage reduction: 95%
- Elimination of dependency hell
- Standard library utilities
- Quick scripts without dependencies
- Notebook experimentation
- Not suited for production-scale systems
Production systems demand predictable performance, memory safety, and deployment certainty. Rust delivers these by design.
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