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Multi-Language Workers Development

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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.

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Full skill instructions

Multi-Language Workers Development

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

Language Comparison

FeatureJavaScript/​TSRustPython
StartupFastFastest (WASM)Moderate
CPU PerfGoodExcellentGood
MemoryHigherLowerHigher
Bundle SizeSmallerMediumLarger
Type SafetyOptional (TS)StrictOptional
Best ForGeneral appsCPU-intensiveData/​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

ErrorLanguageCauseSolution
WebAssembly.instantiate() failedRustInvalid WASMCheck wasm-pack build output
Module parse failed: Unexpected tokenRustESM/​CJS mismatchUse --target bundler
Cannot find modulePythonMissing depAdd to pyproject.toml
Out of memoryAllLarge WASMEnable streaming instantiation
Exceeded CPU time limitAllLong computationChunk processing
wasm-bindgen version mismatchRustDep conflictAlign versions in Cargo.toml
RuntimeError: unreachableRustPanic in WASMAdd proper error handling
TypeError: not a functionRustMissing exportAdd #[wasm_bindgen] attribute
Python worker startup timeoutPythonSlow initMinimize imports
SharedArrayBuffer not supportedAllSecurityAdd 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() as u64).all(|i| n % i != 0)
}

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

ReferenceLoad When
references/​rust-workers.mdBuilding Workers with Rust/​WASM
references/​python-workers.mdUsing Python on Workers for Platforms
references/​wasm-integration.mdIntegrating 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

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