Cloudflare Workers Performance Optimization

Techniques for maximizing Worker performance and minimizing latency.

Quick Wins

// 1. Avoid unnecessary cloning
// ❌ Bad: Clones entire request
const body = await request.clone().json();

// ✅ Good: Parse directly when not re-using body
const body = await request.json();

// 2. Use streaming instead of buffering
// ❌ Bad: Buffers entire response
const text = await response.text();
return new Response(transform(text));

// ✅ Good: Stream transformation
return new Response(response.body.pipeThrough(new TransformStream({
  transform(chunk, controller) {
    controller.enqueue(process(chunk));
  }
})));

// 3. Cache expensive operations
const cache = caches.default;
const cached = await cache.match(request);
if (cached) return cached;

Critical Rules

1. Stay under CPU limits - 10ms (free), 30ms (paid), 50ms (unbound)
2. Minimize cold starts - Keep bundles < 1MB, avoid dynamic imports
3. Use Cache API - Cache responses at the edge
4. Stream large payloads - Don't buffer entire responses
5. Batch operations - Combine multiple KV/D1 calls

Top 10 Performance Errors

Error	Symptom	Fix
CPU limit exceeded	Worker terminated	Optimize hot paths, use streaming
Cold start latency	First request slow	Reduce bundle size, avoid top-level await
Memory pressure	Slow GC, timeouts	Stream data, avoid large arrays
KV latency	Slow reads	Use Cache API, batch reads
D1 slow queries	High latency	Add indexes, optimize SQL
Large bundles	Slow cold starts	Tree-shake, code split
Blocking operations	Request timeouts	Use Promise.all, streaming
Unnecessary cloning	Memory spike	Only clone when needed
Missing cache	Repeated computation	Implement caching layer
Sync operations	CPU spikes	Use async alternatives

CPU Optimization

Profile Hot Paths

async function profiledHandler(request: Request): Promise<Response> {
  const timing: Record<string, number> = {};

  const time = async <T>(name: string, fn: () => Promise<T>): Promise<T> => {
    const start = Date.now();
    const result = await fn();
    timing[name] = Date.now() - start;
    return result;
  };

  const data = await time('fetch', () => fetchData());
  const processed = await time('process', () => processData(data));
  const response = await time('serialize', () => serialize(processed));

  console.log('Timing:', timing);
  return new Response(response);
}

Optimize JSON Operations

// For large JSON, use streaming parser
import { JSONParser } from '@streamparser/json';

async function parseStreamingJSON(stream: ReadableStream): Promise<unknown[]> {
  const parser = new JSONParser();
  const results: unknown[] = [];

  parser.onValue = (value) => results.push(value);

  for await (const chunk of stream) {
    parser.write(chunk);
  }

  return results;
}

Memory Optimization

Avoid Large Arrays

// ❌ Bad: Loads all into memory
const items = await db.prepare('SELECT * FROM items').all();
const processed = items.results.map(transform);

// ✅ Good: Process in batches
async function* batchProcess(db: D1Database, batchSize = 100) {
  let offset = 0;
  while (true) {
    const { results } = await db
      .prepare('SELECT * FROM items LIMIT ? OFFSET ?')
      .bind(batchSize, offset)
      .all();

    if (results.length === 0) break;

    for (const item of results) {
      yield transform(item);
    }
    offset += batchSize;
  }
}

Caching Strategies

Multi-Layer Cache

interface CacheLayer {
  get(key: string): Promise<unknown | null>;
  set(key: string, value: unknown, ttl?: number): Promise<void>;
}

// Layer 1: In-memory (request-scoped)
const memoryCache = new Map<string, unknown>();

// Layer 2: Cache API (edge-local)
const edgeCache: CacheLayer = {
  async get(key) {
    const response = await caches.default.match(new Request(`https://cache/${key}`));
    return response ? response.json() : null;
  },
  async set(key, value, ttl = 60) {
    await caches.default.put(
      new Request(`https://cache/${key}`),
      new Response(JSON.stringify(value), {
        headers: { 'Cache-Control': `max-age=${ttl}` }
      })
    );
  }
};

// Layer 3: KV (global)
// Use env.KV.get/put

Bundle Optimization

// 1. Tree-shake imports
// ❌ Bad
import * as lodash from 'lodash';

// ✅ Good
import { debounce } from 'lodash-es';

// 2. Lazy load heavy dependencies
let heavyLib: typeof import('heavy-lib') | undefined;

async function getHeavyLib() {
  if (!heavyLib) {
    heavyLib = await import('heavy-lib');
  }
  return heavyLib;
}

When to Load References

Load specific references based on the task:

• Optimizing CPU usage? → Load references/cpu-optimization.md
• Memory issues? → Load references/memory-optimization.md
• Implementing caching? → Load references/caching-strategies.md
• Reducing bundle size? → Load references/bundle-optimization.md
• Cold start problems? → Load references/cold-starts.md

Templates

Template	Purpose	Use When
templates/performance-middleware.ts	Performance monitoring	Adding timing/profiling
templates/caching-layer.ts	Multi-layer caching	Implementing cache
templates/optimized-worker.ts	Performance patterns	Starting optimized worker

Scripts

Script	Purpose	Command
scripts/benchmark.sh	Load testing	./benchmark.sh <url>
scripts/profile-worker.sh	CPU profiling	./profile-worker.sh

Resources

• Performance: https://developers.cloudflare.com/workers/platform/performance/
• Limits: https://developers.cloudflare.com/workers/platform/limits/
• Caching: https://developers.cloudflare.com/workers/runtime-apis/cache/