cloudflare-workers-multi-lang

name	cloudflare-workers-multi-lang
description	Multi-language Workers development with Rust, Python, and WebAssembly. Use when building Workers in languages other than JavaScript/TypeScript, or when integrating WASM modules for performance-critical code.
metadata	{"version":"1.0.0"}
license	MIT

Multi-Language Workers Development

Build Cloudflare Workers using Rust, Python, or WebAssembly for performance-critical operations.

Language Comparison

Feature	JavaScript/TS	Rust	Python
Startup	Fast	Fastest (WASM)	Moderate
CPU Perf	Good	Excellent	Good
Memory	Higher	Lower	Higher
Bundle Size	Smaller	Medium	Larger
Type Safety	Optional (TS)	Strict	Optional
Best For	General apps	CPU-intensive	Data/ML

Quick Decision

Need maximum performance? → Rust/WASM
Heavy computation (crypto, image processing)? → Rust/WASM
Data processing, ML inference? → Python
General web apps? → JavaScript/TypeScript

Top 10 Multi-Lang Errors

Error	Language	Cause	Solution
WebAssembly.instantiate() failed	Rust	Invalid WASM	Check wasm-pack build output
Module parse failed: Unexpected token	Rust	ESM/CJS mismatch	Use --target bundler
Cannot find module	Python	Missing dep	Add to pyproject.toml
Out of memory	All	Large WASM	Enable streaming instantiation
Exceeded CPU time limit	All	Long computation	Chunk processing
wasm-bindgen version mismatch	Rust	Dep conflict	Align versions in Cargo.toml
RuntimeError: unreachable	Rust	Panic in WASM	Add proper error handling
TypeError: not a function	Rust	Missing export	Add #[wasm_bindgen] attribute
Python worker startup timeout	Python	Slow init	Minimize imports
SharedArrayBuffer not supported	All	Security	Add COOP/COEP headers

Rust Quick Start

# Install tools
cargo install wasm-pack

# Create project
cargo new --lib my-worker
cd my-worker

# Add to Cargo.toml
cat >> Cargo.toml << 'EOF'
[lib]
crate-type = ["cdylib"]

[dependencies]
wasm-bindgen = "0.2"
worker = "0.3"
console_error_panic_hook = "0.1"

[profile.release]
opt-level = "s"
lto = true
EOF

// src/lib.rs
use worker::*;

#[event(fetch)]
async fn fetch(req: Request, env: Env, _ctx: Context) -> Result<Response> {
    console_error_panic_hook::set_once();

    Router::new()
        .get("/", |_, _| Response::ok("Hello from Rust!"))
        .get("/compute", |_, _| {
            // CPU-intensive computation
            let result = heavy_computation();
            Response::ok(format!("Result: {}", result))
        })
        .run(req, env)
        .await
}

fn heavy_computation() -> u64 {
    (1..1_000_000).filter(|n| is_prime(*n)).count() as u64
}

fn is_prime(n: u64) -> bool {
    if n < 2 { return false; }
    (2..=(n as f64).sqrt()  ).(|i| n % i != )
}

Python Quick Start (Workers for Platforms)

# pyproject.toml
[project]
name = "my-worker"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = []

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

# src/entry.py
from js import Response, Headers

async def on_fetch(request, env):
    url = request.url

    if "/compute" in url:
        result = heavy_computation()
        return Response.new(f"Result: {result}")

    return Response.new("Hello from Python!")

def heavy_computation():
    """CPU-intensive computation"""
    return sum(1 for n in range(2, 100000) if is_prime(n))

def is_prime(n):
    if n < 2:
        return False
    return all(n % i != 0 for i in range(2, int(n**0.5) + 1))

WASM Module Integration

// Load and use WASM module in TypeScript Worker
import wasmModule from './pkg/my_lib_bg.wasm';
import { init, process_data } from './pkg/my_lib';

let wasmInstance: WebAssembly.Instance;

export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    // Initialize WASM once
    if (!wasmInstance) {
      wasmInstance = await WebAssembly.instantiate(wasmModule);
      init();
    }

    // Use WASM function
    const result = process_data(inputData);

    return Response.json({ result });
  },
};

When to Load References

Reference	Load When
references/rust-workers.md	Building Workers with Rust/WASM
references/python-workers.md	Using Python on Workers for Platforms
references/wasm-integration.md	Integrating WASM modules in any Worker

Performance Tips

1. WASM Initialization: Cache instance, use streaming
2. Memory: Use typed arrays for data transfer
3. Bundle Size: Enable LTO, strip debug info
4. Cold Starts: Keep WASM modules small
5. Data Transfer: Minimize JS/WASM boundary crossings

See Also

• workers-performance - General optimization techniques
• workers-testing - Testing multi-language Workers
• cloudflare-worker-base - Basic Workers setup