Cloudflare Edge Patterns: Building Lightning-Fast Applications with Global Distribution

The edge computing revolution has fundamentally changed how we think about application performance. By moving computation closer to users, we can turn latency from a technical constraint into a competitive advantage. Cloudflare's edge platform, with its 300+ data centers worldwide, provides the infrastructure to build applications that feel instant regardless of where your users are located.

After building and optimizing applications on Cloudflare's edge for multiple projects, I've learned that success isn't just about deploying code to the edge—it's about understanding the unique characteristics of edge computing and designing systems that leverage its strengths while working around its limitations.

This comprehensive guide distills years of experience into actionable patterns that reliably improve performance, reduce costs, and enhance user experience. Whether you're building a simple static site or a complex distributed application, these patterns will help you make the most of Cloudflare's edge platform.

The Edge Computing Paradigm

Edge computing represents a fundamental shift from centralized to distributed computation. Instead of processing requests in a few large data centers, edge computing distributes processing power across hundreds of smaller locations closer to end users.

Why Edge Computing Matters

Latency Reduction: The speed of light is a hard limit. By reducing the physical distance between users and computation, we can dramatically improve response times.

Resilience: Distributed systems are inherently more resilient than centralized ones. If one edge location fails, traffic automatically routes to the nearest available location.

Cost Optimization: Edge computing can reduce bandwidth costs by processing requests locally and only sending necessary data back to origin servers.

Scalability: Edge platforms can handle traffic spikes by distributing load across multiple locations rather than scaling up individual servers.

Cloudflare's Edge Advantage

Cloudflare's edge platform offers several unique advantages:

Global Network: 300+ data centers in 100+ countries provide comprehensive global coverage.

Integrated Services: Edge computing, CDN, security, and DNS services work together seamlessly.

Developer Experience: Cloudflare Workers provides a familiar JavaScript runtime for edge computing.

Performance: Sub-50ms response times in most locations worldwide.

Understanding Edge Computing Constraints

Before diving into patterns, it's crucial to understand the constraints and characteristics of edge computing:

Runtime Limitations

CPU Time: Edge functions typically have strict CPU time limits (10-50ms for most operations).

Memory: Limited memory per request (128MB for Cloudflare Workers).

Execution Time: Total execution time is capped (30 seconds for HTTP requests).

Cold Starts: While faster than traditional serverless, there can still be initialization overhead.

Network Characteristics

Geographic Distribution: Requests are processed in the location closest to the user.

Origin Distance: Edge locations may be far from your origin servers.

Bandwidth: Limited bandwidth for communication with origin servers.

Latency: Network latency between edge locations and origins can vary significantly.

Data Access Patterns

Local Storage: Limited local storage options (KV, Durable Objects, R2).

Database Access: Direct database connections from edge are often impractical.

Caching: Edge caching is essential for performance and cost optimization.

State Management: Stateless design is preferred, but some stateful patterns are possible.

Core Edge Patterns

Pattern 1: Smart Caching Strategy

The Problem: Traditional caching strategies don't account for the unique characteristics of edge computing.

The Solution: Implement a multi-tier caching strategy that leverages edge locations for maximum performance.

Implementation:

// Edge-first caching with origin fallback
export default {
  async fetch(request, env) {
    const url = new URL(request.url);
    const cacheKey = new Request(url, request);
    
    // Try edge cache first
    let response = await caches.default.match(cacheKey);
    
    if (!response) {
      // Try KV store for longer-term caching
      const kvValue = await env.CACHE_KV.get(url.pathname);
      
      if (kvValue) {
        response = new Response(kvValue, {
          headers: {
            'Content-Type': 'application/json',
            'Cache-Control': 'public, max-age=3600',
            'X-Cache': 'HIT-KV'
          }
        });
      } else {
        // Fall back to origin
        response = await fetch(request);
        
        // Cache successful responses
        if (response.ok) {
          const responseClone = response.clone();
          const body = await responseClone.text();
          
          // Cache in both edge cache and KV
          caches.default.put(cacheKey, response.clone());
          await env.CACHE_KV.put(url.pathname, body, {
            expirationTtl: 3600
          });
        }
      }
    } else {
      response.headers.set('X-Cache', 'HIT-EDGE');
    }
    
    return response;
  }
};

Best Practices:

Use appropriate TTL values based on data freshness requirements
Implement cache invalidation strategies
Monitor cache hit rates and adjust strategies accordingly
Use different caching strategies for different types of content

Pattern 2: Request Routing and Load Balancing

The Problem: Distributing requests efficiently across multiple origin servers while maintaining performance.

The Solution: Implement intelligent routing at the edge that considers server health, geographic proximity, and load.

Implementation:

export default {
  async fetch(request, env) {
    const origins = [
      'https://origin1.example.com',
      'https://origin2.example.com',
      'https://origin3.example.com'
    ];
    
    // Get user's country for geographic routing
    const country = request.cf.country;
    
    // Select origin based on geographic proximity
    let selectedOrigin = origins[0]; // Default
    
    if (country === 'US') {
      selectedOrigin = origins[0];
    } else if (country === 'EU') {
      selectedOrigin = origins[1];
    } else {
      selectedOrigin = origins[2];
    }
    
    // Try primary origin with fallback
    try {
      const response = await fetch(selectedOrigin + request.url, {
        method: request.method,
        headers: request.headers,
        body: request.body
      });
      
      if (response.ok) {
        return response;
      }
    } catch (error) {
      console.log('Primary origin failed, trying fallback');
    }
    
    // Fallback to other origins
    for (const origin of origins) {
      if (origin !== selectedOrigin) {
        try {
          const response = await fetch(origin + request.url, {
            method: request.method,
            headers: request.headers,
            body: request.body
          });
          
          if (response.ok) {
            return response;
          }
        } catch (error) {
          continue;
        }
      }
    }
    
    return new Response('Service unavailable', { status: 503 });
  }
};

Best Practices:

Implement health checks for origin servers
Use geographic routing when appropriate
Implement circuit breakers for failing origins
Monitor and log routing decisions

Pattern 3: Security and Authentication

The Problem: Implementing security measures at the edge while maintaining performance.

The Solution: Use edge computing for lightweight security checks and authentication validation.

Implementation:

export default {
  async fetch(request, env) {
    // Rate limiting
    const clientIP = request.headers.get('CF-Connecting-IP');
    const rateLimitKey = `rate_limit:${clientIP}`;
    
    const rateLimit = await env.RATE_LIMIT_KV.get(rateLimitKey);
    if (rateLimit && parseInt(rateLimit) > 100) {
      return new Response('Rate limit exceeded', { status: 429 });
    }
    
    // Increment rate limit counter
    await env.RATE_LIMIT_KV.put(rateLimitKey, 
      (parseInt(rateLimit) || 0) + 1, 
      { expirationTtl: 3600 }
    );
    
    // JWT validation
    const authHeader = request.headers.get('Authorization');
    if (authHeader && authHeader.startsWith('Bearer ')) {
      const token = authHeader.substring(7);
      
      try {
        const payload = await verifyJWT(token, env.JWT_SECRET);
        
        // Add user info to request headers
        const modifiedRequest = new Request(request, {
          headers: {
            ...request.headers,
            'X-User-ID': payload.userId,
            'X-User-Role': payload.role
          }
        });
        
        return fetch(modifiedRequest);
      } catch (error) {
        return new Response('Invalid token', { status: 401 });
      }
    }
    
    // For public endpoints, continue without authentication
    return fetch(request);
  }
};

async function verifyJWT(token, secret) {
  // Simplified JWT verification
  // In production, use a proper JWT library
  const parts = token.split('.');
  if (parts.length !== 3) {
    throw new Error('Invalid token format');
  }
  
  const payload = JSON.parse(atob(parts[1]));
  const now = Math.floor(Date.now() / 1000);
  
  if (payload.exp < now) {
    throw new Error('Token expired');
  }
  
  return payload;
}

Best Practices:

Implement rate limiting to prevent abuse
Use JWT for stateless authentication
Validate tokens at the edge when possible
Implement proper error handling and logging

Pattern 4: Dynamic Content Generation

The Problem: Generating personalized content at the edge while maintaining performance.

The Solution: Use edge computing to generate dynamic content based on user context and cached data.

Implementation:

export default {
  async fetch(request, env) {
    const url = new URL(request.url);
    
    // Get user context
    const country = request.cf.country;
    const city = request.cf.city;
    const userAgent = request.headers.get('User-Agent');
    
    // Generate personalized content
    const personalizedContent = await generatePersonalizedContent({
      country,
      city,
      userAgent,
      env
    });
    
    return new Response(JSON.stringify(personalizedContent), {
      headers: {
        'Content-Type': 'application/json',
        'Cache-Control': 'public, max-age=300', // 5 minutes
        'X-Generated-At': new Date().toISOString()
      }
    });
  }
};

async function generatePersonalizedContent(context) {
  const { country, city, userAgent, env } = context;
  
  // Get localized content from KV
  const localizedContent = await env.CONTENT_KV.get(`content:${country}`);
  const baseContent = JSON.parse(localizedContent || '{}');
  
  // Detect device type
  const isMobile = /Mobile|Android|iPhone/i.test(userAgent);
  
  // Generate personalized recommendations
  const recommendations = await generateRecommendations({
    location: { country, city },
    device: isMobile ? 'mobile' : 'desktop',
    env
  });
  
  return {
    ...baseContent,
    recommendations,
    device: isMobile ? 'mobile' : 'desktop',
    location: { country, city },
    timestamp: new Date().toISOString()
  };
}

async function generateRecommendations(context) {
  // Simplified recommendation logic
  // In production, this would use ML models or complex algorithms
  const { location, device, env } = context;
  
  const popularItems = await env.POPULAR_KV.get(`popular:${location.country}`);
  const items = JSON.parse(popularItems || '[]');
  
  return items.slice(0, device === 'mobile' ? 3 : 5);
}

Best Practices:

Cache generated content appropriately
Use user context for personalization
Implement fallbacks for missing data
Monitor performance of dynamic generation

Pattern 5: API Gateway and Request Transformation

The Problem: Managing multiple APIs and transforming requests/responses at the edge.

The Solution: Implement an API gateway pattern at the edge for request routing, transformation, and aggregation.

Implementation:

export default {
  async fetch(request, env) {
    const url = new URL(request.url);
    const path = url.pathname;
    
    // Route to appropriate service
    if (path.startsWith('/api/users')) {
      return handleUserAPI(request, env);
    } else if (path.startsWith('/api/products')) {
      return handleProductAPI(request, env);
    } else if (path.startsWith('/api/orders')) {
      return handleOrderAPI(request, env);
    }
    
    // Default response
    return new Response('Not found', { status: 404 });
  }
};

async function handleUserAPI(request, env) {
  const url = new URL(request.url);
  const path = url.pathname.replace('/api/users', '');
  
  // Transform request for user service
  const userServiceRequest = new Request(
    `https://user-service.example.com${path}`,
    {
      method: request.method,
      headers: {
        ...request.headers,
        'X-Service': 'user-service',
        'X-Request-ID': crypto.randomUUID()
      },
      body: request.body
    }
  );
  
  const response = await fetch(userServiceRequest);
  
  // Transform response
  if (response.ok) {
    const data = await response.json();
    
    // Add additional fields or transform data
    const transformedData = {
      ...data,
      timestamp: new Date().toISOString(),
      source: 'edge-gateway'
    };
    
    return new Response(JSON.stringify(transformedData), {
      status: response.status,
      headers: {
        'Content-Type': 'application/json',
        'X-Transformed': 'true'
      }
    });
  }
  
  return response;
}

async function handleProductAPI(request, env) {
  // Similar implementation for product API
  // ...
}

async function handleOrderAPI(request, env) {
  // Similar implementation for order API
  // ...
}

Best Practices:

Implement proper request/response transformation
Add monitoring and logging
Handle errors gracefully
Use consistent error formats

Advanced Edge Patterns

Pattern 6: Real-time Data Synchronization

The Problem: Keeping data synchronized across edge locations in real-time.

The Solution: Use Durable Objects for stateful edge computing and real-time synchronization.

Implementation:

// Durable Object for real-time synchronization
export class RealtimeSync {
  constructor(state, env) {
    this.state = state;
    this.env = env;
  }
  
  async fetch(request) {
    const url = new URL(request.url);
    
    if (request.method === 'POST') {
      // Update data
      const data = await request.json();
      await this.state.storage.put('data', data);
      
      // Notify all connected clients
      const clients = this.state.getWebSockets();
      for (const client of clients) {
        client.send(JSON.stringify({
          type: 'update',
          data: data,
          timestamp: Date.now()
        }));
      }
      
      return new Response('OK');
    } else if (request.method === 'GET') {
      // Get current data
      const data = await this.state.storage.get('data');
      return new Response(JSON.stringify(data || {}));
    }
    
    return new Response('Method not allowed', { status: 405 });
  }
  
  async webSocket(ws) {
    // Handle WebSocket connections for real-time updates
    ws.accept();
    
    // Send current data to new client
    const data = await this.state.storage.get('data');
    ws.send(JSON.stringify({
      type: 'init',
      data: data || {},
      timestamp: Date.now()
    }));
  }
}

// Worker that uses the Durable Object
export default {
  async fetch(request, env) {
    const url = new URL(request.url);
    const id = env.REALTIME_SYNC.idFromName('global');
    const obj = env.REALTIME_SYNC.get(id);
    
    return obj.fetch(request);
  }
};

Pattern 7: Edge-Side Includes (ESI)

The Problem: Combining static and dynamic content efficiently at the edge.

The Solution: Use edge-side includes to compose pages from multiple sources.

Implementation:

export default {
  async fetch(request, env) {
    const url = new URL(request.url);
    
    if (url.pathname === '/dashboard') {
      // Compose dashboard from multiple sources
      const [header, content, footer] = await Promise.all([
        fetch('https://api.example.com/header'),
        fetch('https://api.example.com/dashboard-content'),
        fetch('https://api.example.com/footer')
      ]);
      
      const headerHtml = await header.text();
      const contentHtml = await content.text();
      const footerHtml = await footer.text();
      
      const composedHtml = `
        <!DOCTYPE html>
        <html>
          <head>
            <title>Dashboard</title>
            <style>
              /* Inline critical CSS */
              body { font-family: Arial, sans-serif; margin: 0; }
              .header { background: #333; color: white; padding: 1rem; }
              .content { padding: 2rem; }
              .footer { background: #f5f5f5; padding: 1rem; text-align: center; }
            </style>
          </head>
          <body>
            <div class="header">${headerHtml}</div>
            <div class="content">${contentHtml}</div>
            <div class="footer">${footerHtml}</div>
          </body>
        </html>
      `;
      
      return new Response(composedHtml, {
        headers: {
          'Content-Type': 'text/html',
          'Cache-Control': 'public, max-age=300'
        }
      });
    }
    
    return fetch(request);
  }
};

Pattern 8: A/B Testing at the Edge

The Problem: Implementing A/B tests without impacting performance.

The Solution: Use edge computing to determine test variants and serve appropriate content.

Implementation:

export default {
  async fetch(request, env) {
    const url = new URL(request.url);
    
    // Determine A/B test variant
    const variant = await determineABTestVariant(request, env);
    
    // Route to appropriate content based on variant
    if (variant === 'A') {
      return serveVariantA(request, env);
    } else {
      return serveVariantB(request, env);
    }
  }
};

async function determineABTestVariant(request, env) {
  const clientIP = request.headers.get('CF-Connecting-IP');
  const userAgent = request.headers.get('User-Agent');
  
  // Create consistent hash for user
  const userHash = await crypto.subtle.digest(
    'SHA-256',
    new TextEncoder().encode(clientIP + userAgent)
  );
  
  const hashValue = new Uint8Array(userHash)[0];
  
  // 50/50 split
  return hashValue < 128 ? 'A' : 'B';
}

async function serveVariantA(request, env) {
  // Serve variant A content
  const content = await env.CONTENT_KV.get('variant-a');
  return new Response(content, {
    headers: {
      'Content-Type': 'text/html',
      'X-AB-Test': 'A'
    }
  });
}

async function serveVariantB(request, env) {
  // Serve variant B content
  const content = await env.CONTENT_KV.get('variant-b');
  return new Response(content, {
    headers: {
      'Content-Type': 'text/html',
      'X-AB-Test': 'B'
    }
  });
}

Performance Optimization Strategies

Caching Optimization

Cache Key Design: Design cache keys that maximize hit rates while maintaining data freshness.

function generateCacheKey(request, context) {
  const url = new URL(request.url);
  const country = context.country;
  const userAgent = request.headers.get('User-Agent');
  const isMobile = /Mobile|Android|iPhone/i.test(userAgent);
  
  // Include relevant factors in cache key
  return `${url.pathname}:${country}:${isMobile ? 'mobile' : 'desktop'}`;
}

Cache Invalidation: Implement smart cache invalidation strategies.

async function invalidateCache(pattern, env) {
  // Invalidate edge cache
  await caches.default.delete(pattern);
  
  // Invalidate KV store
  const keys = await env.CACHE_KV.list({ prefix: pattern });
  for (const key of keys.keys) {
    await env.CACHE_KV.delete(key.name);
  }
}

Request Optimization

Request Deduplication: Prevent duplicate requests to origin servers.

const pendingRequests = new Map();

export default {
  async fetch(request, env) {
    const cacheKey = request.url;
    
    if (pendingRequests.has(cacheKey)) {
      // Wait for existing request
      return pendingRequests.get(cacheKey);
    }
    
    const promise = fetch(request);
    pendingRequests.set(cacheKey, promise);
    
    try {
      const response = await promise;
      return response;
    } finally {
      pendingRequests.delete(cacheKey);
    }
  }
};

Request Batching: Batch multiple requests to reduce origin load.

export default {
  async fetch(request, env) {
    const url = new URL(request.url);
    
    if (url.pathname === '/batch') {
      const requests = await request.json();
      
      const responses = await Promise.all(
        requests.map(req => fetch(req.url, req.options))
      );
      
      return new Response(JSON.stringify(responses), {
        headers: { 'Content-Type': 'application/json' }
      });
    }
    
    return fetch(request);
  }
};

Monitoring and Observability

Performance Monitoring

Response Time Tracking: Monitor response times across different edge locations.

export default {
  async fetch(request, env) {
    const startTime = Date.now();
    
    try {
      const response = await fetch(request);
      
      const responseTime = Date.now() - startTime;
      
      // Log performance metrics
      await env.ANALYTICS_KV.put(
        `perf:${Date.now()}`,
        JSON.stringify({
          url: request.url,
          responseTime,
          status: response.status,
          location: request.cf.colo
        }),
        { expirationTtl: 86400 }
      );
      
      return response;
    } catch (error) {
      // Log errors
      await env.ERRORS_KV.put(
        `error:${Date.now()}`,
        JSON.stringify({
          url: request.url,
          error: error.message,
          location: request.cf.colo
        }),
        { expirationTtl: 86400 }
      );
      
      throw error;
    }
  }
};

Cache Hit Rate Monitoring: Track cache performance.

export default {
  async fetch(request, env) {
    const cacheKey = new Request(request.url, request);
    const cachedResponse = await caches.default.match(cacheKey);
    
    if (cachedResponse) {
      // Log cache hit
      await env.CACHE_STATS_KV.put(
        `hit:${Date.now()}`,
        JSON.stringify({
          url: request.url,
          location: request.cf.colo
        }),
        { expirationTtl: 86400 }
      );
      
      return cachedResponse;
    }
    
    // Log cache miss
    await env.CACHE_STATS_KV.put(
      `miss:${Date.now()}`,
      JSON.stringify({
        url: request.url,
        location: request.cf.colo
      }),
      { expirationTtl: 86400 }
    );
    
    const response = await fetch(request);
    
    if (response.ok) {
      caches.default.put(cacheKey, response.clone());
    }
    
    return response;
  }
};

Security Considerations

Input Validation

Sanitization: Validate and sanitize all inputs at the edge.

function sanitizeInput(input) {
  if (typeof input !== 'string') {
    return '';
  }
  
  // Remove potentially dangerous characters
  return input
    .replace(/[<>]/g, '')
    .replace(/javascript:/gi, '')
    .replace(/on\w+=/gi, '')
    .trim();
}

export default {
  async fetch(request, env) {
    const url = new URL(request.url);
    const searchParams = url.searchParams;
    
    // Sanitize query parameters
    const sanitizedParams = new URLSearchParams();
    for (const [key, value] of searchParams) {
      sanitizedParams.set(key, sanitizeInput(value));
    }
    
    // Create new request with sanitized parameters
    const sanitizedUrl = new URL(request.url);
    sanitizedUrl.search = sanitizedParams.toString();
    
    const sanitizedRequest = new Request(sanitizedUrl, request);
    
    return fetch(sanitizedRequest);
  }
};

Rate Limiting

Advanced Rate Limiting: Implement sophisticated rate limiting strategies.

export default {
  async fetch(request, env) {
    const clientIP = request.headers.get('CF-Connecting-IP');
    const userAgent = request.headers.get('User-Agent');
    
    // Different rate limits for different endpoints
    const url = new URL(request.url);
    const endpoint = url.pathname;
    
    let rateLimit = 100; // Default
    if (endpoint.startsWith('/api/')) {
      rateLimit = 50;
    } else if (endpoint.startsWith('/upload/')) {
      rateLimit = 10;
    }
    
    const rateLimitKey = `rate:${clientIP}:${endpoint}`;
    const currentCount = await env.RATE_LIMIT_KV.get(rateLimitKey);
    
    if (currentCount && parseInt(currentCount) >= rateLimit) {
      return new Response('Rate limit exceeded', {
        status: 429,
        headers: {
          'Retry-After': '60',
          'X-RateLimit-Limit': rateLimit.toString(),
          'X-RateLimit-Remaining': '0'
        }
      });
    }
    
    // Increment counter
    await env.RATE_LIMIT_KV.put(
      rateLimitKey,
      (parseInt(currentCount) || 0) + 1,
      { expirationTtl: 3600 }
    );
    
    return fetch(request);
  }
};

Cost Optimization

Bandwidth Optimization

Response Compression: Compress responses to reduce bandwidth usage.

export default {
  async fetch(request, env) {
    const response = await fetch(request);
    
    if (response.ok && response.headers.get('Content-Type')?.includes('text')) {
      const text = await response.text();
      const compressed = new CompressionStream('gzip');
      const writer = compressed.writable.getWriter();
      const reader = compressed.readable.getReader();
      
      writer.write(new TextEncoder().encode(text));
      writer.close();
      
      const chunks = [];
      let done = false;
      while (!done) {
        const { value, done: readerDone } = await reader.read();
        done = readerDone;
        if (value) chunks.push(value);
      }
      
      const compressedData = new Uint8Array(
        chunks.reduce((acc, chunk) => acc + chunk.length, 0)
      );
      let offset = 0;
      for (const chunk of chunks) {
        compressedData.set(chunk, offset);
        offset += chunk.length;
      }
      
      return new Response(compressedData, {
        headers: {
          ...response.headers,
          'Content-Encoding': 'gzip',
          'Content-Length': compressedData.length.toString()
        }
      });
    }
    
    return response;
  }
};

Origin Load Reduction

Smart Caching: Implement intelligent caching to reduce origin server load.

export default {
  async fetch(request, env) {
    const url = new URL(request.url);
    
    // Check if content should be cached
    const shouldCache = shouldCacheRequest(request);
    
    if (shouldCache) {
      const cacheKey = new Request(url, request);
      const cachedResponse = await caches.default.match(cacheKey);
      
      if (cachedResponse) {
        return cachedResponse;
      }
    }
    
    const response = await fetch(request);
    
    if (shouldCache && response.ok) {
      caches.default.put(cacheKey, response.clone());
    }
    
    return response;
  }
};

function shouldCacheRequest(request) {
  const url = new URL(request.url);
  
  // Cache static assets
  if (url.pathname.match(/\.(css|js|png|jpg|gif|svg)$/)) {
    return true;
  }
  
  // Cache API responses for GET requests
  if (request.method === 'GET' && url.pathname.startsWith('/api/')) {
    return true;
  }
  
  return false;
}

Common Pitfalls and Solutions

Pitfall 1: Over-Caching

Problem: Caching too aggressively can lead to stale data and poor user experience.

Solution: Implement appropriate cache invalidation and TTL strategies.

// Smart cache TTL based on content type
function getCacheTTL(request) {
  const url = new URL(request.url);
  
  if (url.pathname.match(/\.(css|js|png|jpg|gif|svg)$/)) {
    return 86400; // 24 hours for static assets
  } else if (url.pathname.startsWith('/api/')) {
    return 300; // 5 minutes for API responses
  } else {
    return 60; // 1 minute for dynamic content
  }
}

Pitfall 2: Cold Start Performance

Problem: Cold starts can impact performance for infrequently accessed functions.

Solution: Implement warming strategies and optimize initialization.

// Optimize cold starts by pre-loading dependencies
const dependencies = {
  // Pre-load commonly used modules
  crypto: globalThis.crypto,
  fetch: globalThis.fetch
};

export default {
  async fetch(request, env) {
    // Use pre-loaded dependencies
    const response = await dependencies.fetch(request);
    return response;
  }
};

Pitfall 3: Memory Leaks

Problem: Edge functions can accumulate memory over time.

Solution: Implement proper cleanup and resource management.

export default {
  async fetch(request, env) {
    const controller = new AbortController();
    const timeoutId = setTimeout(() => controller.abort(), 5000);
    
    try {
      const response = await fetch(request, {
        signal: controller.signal
      });
      
      clearTimeout(timeoutId);
      return response;
    } catch (error) {
      clearTimeout(timeoutId);
      throw error;
    }
  }
};

Best Practices Summary

Development Best Practices

Start Simple: Begin with basic caching and gradually add complexity
Test Locally: Use Wrangler for local development and testing
Monitor Performance: Implement comprehensive monitoring from day one
Version Control: Use proper versioning for edge functions
Documentation: Document edge-specific behavior and constraints

Deployment Best Practices

Gradual Rollout: Use canary deployments for edge functions
Rollback Strategy: Always have a rollback plan
Environment Separation: Use different environments for testing and production
Configuration Management: Externalize configuration and secrets
Health Checks: Implement health checks for edge functions

Operational Best Practices

Monitoring: Set up alerts for performance and error rates
Logging: Implement structured logging for debugging
Metrics: Track key performance indicators
Incident Response: Have procedures for handling edge function failures
Capacity Planning: Monitor usage and plan for scaling

Conclusion

Cloudflare's edge computing platform provides powerful capabilities for building fast, resilient, and cost-effective applications. The patterns outlined in this guide represent proven approaches that have been successfully implemented across various projects and use cases.

The key to success with edge computing lies in understanding its unique characteristics and constraints, then designing systems that leverage its strengths while working around its limitations. By implementing these patterns thoughtfully and monitoring their performance carefully, you can build applications that provide exceptional user experiences while reducing costs and complexity.

Remember that edge computing is not a silver bullet—it's a tool that, when used appropriately, can significantly improve your application's performance and user experience. Start with simple patterns, measure their impact, and gradually add complexity as your needs evolve.

The future of web development is distributed, and edge computing is a crucial part of that future. By mastering these patterns now, you'll be well-positioned to build the next generation of web applications that are fast, reliable, and truly global.

Where the Edge Wins
Caching Beyond Assets
State and Coordination
Data at the Edge
Security and Safety
Observability and Tuning
Rollout Strategy
Common Pitfalls

Where the Edge Wins

Latency: Shorter round‑trips for time‑sensitive paths
Availability: Survive regional failures with smart fallbacks
Cost: Offload hot reads from origin infrastructure
Scale: Absorb spikes with cache and lightweight compute

Caching Beyond Assets

Cache HTML for anonymous users; keep TTLs short and revalidate in background
Key design: Include only fields that materially change content
Personalization split: Edge for layout/data shells; hydrate private bits later
Invalidate deliberately: Purge on content updates, not on every request

State and Coordination

Coordination: Use durable coordination for counters, sessions, and queues
Locality: Keep related work in the same location to minimize hops
Idempotency: Make retried edge work safe by design
Back‑pressure: Bound queues and reject early on overload

Data at the Edge

Configs and flags: Use edge‑local KV or config stores
Warm paths: Replicate read‑heavy data close to users
Consistency: Be explicit about eventual vs. strong needs
Fallbacks: Define behavior when data is cold or unavailable

Security and Safety

Zero trust mindset: Validate inputs at the edge; sanitize aggressively
Secrets: Rotate and scope narrowly; avoid leaking via logs
Abuse protection: Rate limit and challenge suspicious traffic
Privacy: Minimize sensitive data at the edge; anonymize where possible

Observability and Tuning

Hit ratios: Track cache hits/misses by route and geography
Tail latency: Watch P95/P99; they define perceived speed
Request sampling: Log representative requests for analysis
Feature flags: Roll out per‑route and per‑region; compare metrics

Rollout Strategy

Pick one hot, cacheable path
Ship behind a flag to a small region
Measure hit ratio, latency, and error budget
Iterate keys/headers until results stabilize
Expand gradually; keep an easy rollback

Common Pitfalls

Cache stampedes: Use locks or SWR to avoid thundering herds
Over‑personalization: Busts cache and hurts latency
Edge‑origin mismatch: Excess round‑trips negate benefits
Under‑observed rollouts: Fly blind and miss regressions

Latency is a feature. Put the right logic near users, measure relentlessly, and let the data guide how far you go.

The Edge Computing Paradigm

Why Edge Computing Matters

Cloudflare's Edge Advantage

Understanding Edge Computing Constraints

Runtime Limitations

Network Characteristics

Data Access Patterns

Core Edge Patterns

Pattern 1: Smart Caching Strategy

Pattern 2: Request Routing and Load Balancing

Pattern 3: Security and Authentication

Pattern 4: Dynamic Content Generation

Pattern 5: API Gateway and Request Transformation

Advanced Edge Patterns

Pattern 6: Real-time Data Synchronization

Pattern 7: Edge-Side Includes (ESI)

Pattern 8: A/B Testing at the Edge

Performance Optimization Strategies

Caching Optimization

Request Optimization

Monitoring and Observability

Performance Monitoring

Security Considerations

Input Validation

Rate Limiting

Cost Optimization

Bandwidth Optimization

Origin Load Reduction

Common Pitfalls and Solutions

Pitfall 1: Over-Caching

Pitfall 2: Cold Start Performance

Pitfall 3: Memory Leaks

Best Practices Summary

Development Best Practices

Deployment Best Practices

Operational Best Practices

Conclusion

Table of Contents

Where the Edge Wins

Caching Beyond Assets

State and Coordination

Data at the Edge

Security and Safety

Observability and Tuning

Rollout Strategy

Common Pitfalls