From Python to Zig: A System's Perspective
An analysis of the transition from efficient scripting (Python) to high-performance systems programming (Zig). Includes performance benchmarks and a comparison with Rust.
Python works exceptionally well for high-level orchestration, data science, and web APIs. However, for systems requiring predictable latency, direct memory control, or zero-dependency binaries, interpreted languages reach their architectural limits. This post documents the technical reasoning behind adopting Zig for performance-critical workloads.
The Performance Ceiling
While optimizing a high-throughput event processor, I encountered the fundamental limitations of the Python Global Interpreter Lock (GIL) and dynamic type system. Profiling revealed CPU bottlenecks not in IO, but in the interpreter overhead itself.
Benchmark: Event Loop Throughput
- Python (asyncio): ~50k ops/sec
- Zig (std.event.Loop): ~17M ops/sec
The magnitude of this difference (340x) shifted the engineering constraints entirely.
Why Zig?
1. Explicit Memory Layout
Python abstracts memory management completely. Zig forces interaction with an Allocator.
const payload = try allocator.alloc(u8, 1024);
defer allocator.free(payload);This explicitness eliminates hidden allocations—a primary source of latency spikes in garbage-collected languages.
2. Comptime: Logic at Compilation
Zig’s comptime feature allows executing arbitrary code during compilation. This enables powerful metaprogramming, such as generating optimized serialization logic for structs without runtime reflection overhead.
3. C Integration
Zig treats C libraries as native citizens. It can import .h files directly and link against them without complex FFI (Foreign Function Interface) bindings. This makes Zig an excellent “glue” language for existing C infrastructure.
Zig vs. Rust
In the systems capabilities space, Rust is the dominant alternative.
- Rust prioritizes correctness. Its borrow checker enforces memory safety at compile time. This leads to complex architectural patterns (fighting the borrower checker) when implementing shared mutable state.
- Zig prioritizes simplicity. It accepts that the programmer is responsible for memory safety, providing runtime checks (Debug builds) and standard library tooling (Leak Detector) instead of compile-time proofs.
For my use cases—often involving rapid iteration on low-level tools—Zig’s reduced cognitive load and faster compile times were decisive.
Conclusion
I continue to use Python for ML training and high-level scripting. However, the “backend of the backend”—the infrastructure that must run reliably and efficiently—is now written in Zig. It offers the control of C with the ergonomics of a modern toolchain.