Zig Memory Management: Small Binaries, Big Impact

2025-02-18

Zig's memory management enables exceptionally small binaries while maintaining safety. Here's how its allocator system outperforms Rust in Docker and embedded contexts.

Core Allocators

GeneralPurposeAllocator (GPA)

var gpa = std.heap.GeneralPurposeAllocator(.{}){};
const allocator = gpa.allocator();
defer _ = gpa.deinit();

// 300KB static binary vs Rust's 2.8MB
const list = std.ArrayList(u8).init(allocator);

ArenaAllocator

var arena = std.heap.ArenaAllocator.init(allocator);
defer arena.deinit();

// Bulk allocations with zero tracking overhead
const json = try std.json.parseFromTokens(
    Value,
    &parser,
    arena.allocator()
);

FixedBufferAllocator

var buffer: [1024]u8 = undefined;
var fba = std.heap.FixedBufferAllocator.init(&buffer);

// Zero runtime, perfect for embedded
const small_str = try fba.allocator().alloc(u8, 5);

PageAllocator

const page = std.heap.page_allocator;
const big_buffer = try page.alloc(u8, 1024 * 1024);
defer page.free(big_buffer);

Docker Impact

Production Binary Sizes:

• Basic HTTP Server: 300KB (Zig) vs 2.8MB (Rust)
• JSON + SQLite: 850KB (Zig) vs 4.2MB (Rust)
• Full REST API: 1.2MB (Zig) vs 6.8MB (Rust)

Minimal Dockerfile

FROM alpine:3.18 as builder
COPY . /app
WORKDIR /app
RUN wget https://ziglang.org/download/0.11.0/zig-linux-x86_64-0.11.0.tar.xz && \
    tar -xf zig-linux-x86_64-0.11.0.tar.xz
RUN ./zig-linux-x86_64-0.11.0/zig build -Doptimize=ReleaseSmall

FROM scratch
COPY --from=builder /app/zig-out/bin/server /server
ENTRYPOINT ["/server"]

Key Benefits

• Zero Runtime: No mandatory safety checks
• Explicit Control: Choose exactly what overhead you need
• True Static Linking: Complete musl integration

Memory Usage Under Load

# Idle

zig-server   1.2MB
rust-server  8.4MB

# 1000 req/s

zig-server   2.8MB
rust-server  12.6MB

The difference comes from Zig's allocator philosophy: pay only for what you use. Each allocator serves a specific need, from GPA's safety to FixedBuffer's embedded efficiency, enabling precise optimization for your use case.

Recommended Courses

Based on this article's content, here are some courses that might interest you:

1. AWS Advanced AI Engineering (1 week)
   Production LLM architecture patterns using Rust, AWS, and Bedrock.

2. CLI Automation with AWS Cloud Shell and Amazon Q: Building Modern DevOps Workflows (4 weeks)
   Master CLI automation and DevOps workflows using AWS Cloud Shell and Amazon Q, with Docker and CDK integration

3. Enterprise AI Operations with AWS (2 weeks)
   Master enterprise AI operations with AWS services

4. Natural Language AI with Bedrock (1 week)
   Get started with Natural Language Processing using Amazon Bedrock in this introductory course focused on building basic NLP applications. Learn the fundamentals of text processing pipelines and how to leverage Bedrock's core features while following AWS best practices.

5. Natural Language Processing with Amazon Bedrock (2 weeks)
   Build production NLP systems with Amazon Bedrock

Learn more at Pragmatic AI Labs