CloudFront Cache Key Optimization: Hit Rate, Cost, and Origin Control

Executive Summary

CloudFront cache key optimization is a core component of CDN cost control. By precisely controlling cache key composition and designing reasonable caching strategies, enterprises can significantly improve cache hit rates and reduce origin costs and latency. This article provides a complete cache key optimization framework, including design principles, implementation methods, monitoring metrics, and common problem solutions.

Key Value

• Cost Reduction: Optimization can reduce origin requests by 40-60%
• Performance Improvement: P99 latency reduced by 50-70%
• Bandwidth Savings: Origin bandwidth consumption reduced by 60-80%
• Enhanced Availability: Reduced origin pressure, improved overall availability

Part 1: Cache Key Fundamentals

1.1 Cache Key Components

The CloudFront cache key determines the uniqueness of objects at edge nodes. By default, the cache key includes:

Basic Components

1. Domain Name (Host Header)

   • Distribution domain: d111111abcdef8.cloudfront.net
   • Alternate domain (CNAME): cdn.example.com
   • Impact: Different domains are treated as different objects even with the same path

2. URI Path

   • Full path: /images/product/item-123.jpg
   • Case-sensitive: /Image.jpg and /image.jpg are different objects
   • Encoding handling: URL encoding of spaces and special characters

3. Query String (Optional)

   • Parameter order: ?a=1&b=2 and ?b=2&a=1 may be different objects
   • Parameter selection: Can be configured to include all, some, or ignore
   • Value sensitivity: Case and encoding of parameter values

1.2 Advanced Cache Key Configuration

Request Headers as Cache Keys

Configuration Examples:
- Accept-Language: Cache different versions based on language
- CloudFront-Viewer-Country: Cache based on country
- CloudFront-Is-Mobile-Viewer: Cache based on device type
- Authorization: Personalized content caching (use with caution)

Cookies as Cache Keys

Use Cases:
- Session-related content: User preference settings
- A/B testing: Experiment group identifiers
- Personalized recommendations: User group identifiers

Considerations:
- Cookies significantly reduce cache hit rates
- Only forward necessary cookies
- Consider using query strings as an alternative

1.3 Cache Key Design Principles

Minimization Principle

• Include only parameters that affect content
• Remove tracking parameters (utm_*, fbclid, etc.)
• Standardize parameter order

Layered Design

Static Resource Layer:
/static/* - Ignore all query strings and cookies
/images/* - Retain only version parameter (v=)
/css/* - Retain version and theme parameters

Dynamic Content Layer:
/api/* - Retain all query strings
/user/* - Include authentication cookies
/search/* - Retain search-related parameters

Part 2: Cache Hit Rate Optimization Strategies

2.1 Query String Optimization

Parameter Whitelist Strategy

# CloudFormation configuration example
CacheBehavior:
  QueryStringCacheKeys:
    - Items:
      - "category"
      - "sort"
      - "page"
      # Ignore tracking parameters: utm_source, utm_medium, fbclid, gclid

Parameter Normalization

// Lambda@Edge function example
exports.handler = async (event) => {
    const request = event.Records[0].cf.request;
    const params = new URLSearchParams(request.querystring);
    
    // Remove tracking parameters
    const trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 
                          'fbclid', 'gclid', 'ref'];
    trackingParams.forEach(param => params.delete(param));
    
    // Sort parameters
    const sortedParams = new URLSearchParams(
        [...params.entries()].sort()
    );
    
    request.querystring = sortedParams.toString();
    return request;
};

2.2 Request Header Optimization

Device Detection Optimization

# Use CloudFront device detection headers instead of User-Agent
CacheBehaviors:
  - PathPattern: "/mobile/*"
    Headers:
      - CloudFront-Is-Mobile-Viewer
      - CloudFront-Is-Tablet-Viewer
    # Do not use full User-Agent

Geographic Location Caching

# Country-level caching strategy
def configure_geo_caching():
    return {
        'Headers': {
            'Quantity': 1,
            'Items': ['CloudFront-Viewer-Country']
        },
        'GeoRestriction': {
            'RestrictionType': 'whitelist',
            'Items': ['US', 'CA', 'GB', 'DE', 'JP', 'CN']
        }
    }

2.3 Content Variant Management

Adaptive Image Format

// Lambda@Edge - Viewer Request
exports.handler = async (event) => {
    const request = event.Records[0].cf.request;
    const headers = request.headers;
    
    // Detect WebP support
    const acceptHeader = headers['accept'] ? headers['accept'][0].value : '';
    const supportsWebP = acceptHeader.includes('image/webp');
    
    if (supportsWebP && request.uri.match(/.(jpg|jpeg|png)$/i)) {
        // Add WebP variant identifier
        request.headers['x-image-format'] = [{
            key: 'X-Image-Format',
            value: 'webp'
        }];
    }
    
    return request;
};

Responsive Image Processing

# Origin configuration example (Nginx)
location ~* .(jpg|jpeg|png)$ {
    # Generate different sizes based on request parameters
    if ($arg_w) {
        rewrite ^(.*).([^.]+)$ /resize?file=$1.$2&width=$arg_w last;
    }
    
    # Cache different size variants
    add_header Cache-Control "public, max-age=31536000";
    add_header Vary "Accept, X-Image-Format";
}

Part 3: Cache Strategy Design

3.1 Layered Cache Architecture

Four-Layer Cache Model

Layer1_Static:
  PathPattern: "/static/*"
  TTL:
    MinTTL: 86400      # 1 day
    DefaultTTL: 604800  # 7 days
    MaxTTL: 31536000   # 1 year
  QueryString: false
  Headers: []
  Cookies: none


Layer2_Images:
  PathPattern: "/images/*"
  TTL:
    MinTTL: 3600       # 1 hour
    DefaultTTL: 86400  # 1 day
    MaxTTL: 2592000    # 30 days
  QueryString: 
    - "v"              # Version number
    - "w"              # Width
    - "h"              # Height
  Headers:
    - "CloudFront-Is-Mobile-Viewer"
  
Layer3_API:
  PathPattern: "/api/*"
  TTL:
    MinTTL: 0
    DefaultTTL: 60     # 1 minute
    MaxTTL: 300        # 5 minutes
  QueryString: all
  Headers:
    - "Authorization"
    - "Accept"
    
Layer4_Dynamic:
  PathPattern: "/*"     # Default behavior
  TTL:
    MinTTL: 0
    DefaultTTL: 0
    MaxTTL: 0
  QueryString: all
  Headers: all
  Cookies: all

3.2 Cache Invalidation Strategy

Versioning Strategy

// Generate version number at build time
const webpack = require('webpack');
const buildVersion = Date.now();


module.exports = {
  output: {
    filename: `[name].${buildVersion}.js`,
    publicPath: `/static/js/`
  },
  plugins: [
    new webpack.DefinePlugin({
      'process.env.BUILD_VERSION': JSON.stringify(buildVersion)
    })
  ]
};

Smart Invalidation Management

import boto3
from datetime import datetime
import hashlib


class CacheInvalidator:
    def __init__(self, distribution_id):
        self.client = boto3.client('cloudfront')
        self.distribution_id = distribution_id
        
    def invalidate_smart(self, paths):
        """Smart invalidation: merge paths, batch processing"""
        # Path deduplication and wildcard optimization
        optimized_paths = self._optimize_paths(paths)
        
        # Batch invalidation (max 3000 paths per batch)
        batch_size = 3000
        for i in range(0, len(optimized_paths), batch_size):
            batch = optimized_paths[i:i+batch_size]
            self._create_invalidation(batch)
    
    def _optimize_paths(self, paths):
        """Optimize invalidation path list"""
        path_tree = {}
        
        for path in paths:
            parts = path.split('/')
            current = path_tree
            
            for part in parts[:-1]:
                if part not in current:
                    current[part] = {}
                current = current[part]
            
            # Mark leaf node
            current[parts[-1]] = True
        
        # Generate optimized paths
        optimized = []
        self._build_paths(path_tree, '', optimized)
        
        return optimized
    
    def _build_paths(self, tree, prefix, result):
        """Recursively build optimized paths"""
        if len(tree) > 10:  # Use wildcard for more than 10 sub-items
            result.append(f"{prefix}/*")
        else:
            for key, value in tree.items():
                path = f"{prefix}/{key}" if prefix else key
                if value is True:
                    result.append(path)
                else:
                    self._build_paths(value, path, result)
    
    def _create_invalidation(self, paths):
        """Create invalidation request"""
        caller_reference = f"{datetime.now().isoformat()}-{len(paths)}"
        
        response = self.client.create_invalidation(
            DistributionId=self.distribution_id,
            InvalidationBatch={
                'Paths': {
                    'Quantity': len(paths),
                    'Items': paths
                },
                'CallerReference': caller_reference
            }
        )
        
        return response['Invalidation']['Id']

3.3 Cache Warming Strategy

Critical Resource Warming

import asyncio
import aiohttp
from urllib.parse import urljoin


class CacheWarmer:
    def __init__(self, base_url, concurrency=10):
        self.base_url = base_url
        self.semaphore = asyncio.Semaphore(concurrency)
        
    async def warm_critical_paths(self):
        """Warm critical paths"""
        critical_paths = [
            '/',
            '/index.html',
            '/static/css/main.css',
            '/static/js/app.js',
            '/api/config',
            '/images/logo.png'
        ]
        
        # Add device variants
        device_headers = [
            {},  # Desktop
            {'CloudFront-Is-Mobile-Viewer': 'true'},  # Mobile
            {'CloudFront-Is-Tablet-Viewer': 'true'}   # Tablet
        ]
        
        tasks = []
        for path in critical_paths:
            for headers in device_headers:
                url = urljoin(self.base_url, path)
                tasks.append(self._warm_url(url, headers))
        
        results = await asyncio.gather(*tasks, return_exceptions=True)
        return self._analyze_results(results)
    
    async def _warm_url(self, url, headers=None):
        """Warm a single URL"""
        async with self.semaphore:
            async with aiohttp.ClientSession() as session:
                try:
                    async with session.get(url, headers=headers) as response:
                        return {
                            'url': url,
                            'status': response.status,
                            'cache_status': response.headers.get('X-Cache', 'UNKNOWN'),
                            'headers': dict(response.headers)
                        }
                except Exception as e:
                    return {
                        'url': url,
                        'error': str(e)
                    }
    
    def _analyze_results(self, results):
        """Analyze warming results"""
        summary = {
            'total': len(results),
            'success': 0,
            'cache_hit': 0,
            'cache_miss': 0,
            'errors': []
        }
        
        for result in results:
            if 'error' in result:
                summary['errors'].append(result)
            else:
                summary['success'] += 1
                cache_status = result.get('cache_status', '')
                if 'Hit' in cache_status:
                    summary['cache_hit'] += 1
                elif 'Miss' in cache_status:
                    summary['cache_miss'] += 1
        
        return summary

Part 4: Monitoring and Optimization

4.1 Key Metrics Monitoring

CloudWatch Metrics Configuration

import boto3
from datetime import datetime, timedelta


class CacheMetricsAnalyzer:
    def __init__(self, distribution_id):
        self.cloudwatch = boto3.client('cloudwatch')
        self.distribution_id = distribution_id
        
    def get_cache_hit_rate(self, period_hours=24):
        """Get cache hit rate"""
        end_time = datetime.utcnow()
        start_time = end_time - timedelta(hours=period_hours)
        
        # Get total requests
        total_requests = self._get_metric_sum(
            'Requests', start_time, end_time
        )
        
        # Get cache hits
        cache_hits = self._get_metric_sum(
            'CacheHitRate', start_time, end_time
        )
        
        if total_requests > 0:
            hit_rate = (cache_hits / total_requests) * 100
            return {
                'hit_rate': round(hit_rate, 2),
                'total_requests': total_requests,
                'cache_hits': cache_hits,
                'cache_misses': total_requests - cache_hits
            }
        
        return None
    
    def analyze_cache_behavior(self):
        """Analyze cache behavior patterns"""
        metrics = {
            'by_path': self._analyze_by_path(),
            'by_query_string': self._analyze_by_query(),
            'by_header': self._analyze_by_header(),
            'by_time': self._analyze_by_time()
        }
        
        return self._generate_recommendations(metrics)
    
    def _analyze_by_path(self):
        """Analyze cache efficiency by path"""
        # Analyze using CloudFront access logs
        query = """
        SELECT 
            uri_stem as path,
            COUNT(*) as requests,
            SUM(CASE WHEN x_edge_result_type LIKE '%Hit' THEN 1 ELSE 0 END) as hits,
            AVG(time_taken) as avg_latency
        FROM cloudfront_logs
        WHERE date = today()
        GROUP BY uri_stem
        ORDER BY requests DESC
        LIMIT 100
        """
        
        # Query using Athena
        return self._run_athena_query(query)
    
    def _generate_recommendations(self, metrics):
        """Generate optimization recommendations"""
        recommendations = []
        
        # Analyze low hit rate paths
        for path_data in metrics['by_path']:
            hit_rate = path_data['hits'] / path_data['requests']
            if hit_rate < 0.5 and path_data['requests'] > 1000:
                recommendations.append({
                    'type': 'LOW_HIT_RATE',
                    'path': path_data['path'],
                    'current_hit_rate': hit_rate,
                    'potential_savings': path_data['requests'] * 0.5 * 0.001,  # Estimated savings
                    'action': 'Consider increasing TTL or reducing cache key variables'
                })
        
        # Analyze query string impact
        qs_impact = metrics['by_query_string']
        if qs_impact['with_qs_hit_rate'] < qs_impact['without_qs_hit_rate'] * 0.7:
            recommendations.append({
                'type': 'QUERY_STRING_IMPACT',
                'impact': f"{(1 - qs_impact['with_qs_hit_rate']/qs_impact['without_qs_hit_rate'])*100:.1f}%",
                'action': 'Consider removing unnecessary query string parameters'
            })
        
        return recommendations

4.2 Performance Analysis Tools

Cache Efficiency Analyzer

class CacheEfficiencyAnalyzer:
    def __init__(self, log_bucket, distribution_id):
        self.s3 = boto3.client('s3')
        self.athena = boto3.client('athena')
        self.log_bucket = log_bucket
        self.distribution_id = distribution_id
        
    def analyze_cache_key_impact(self):
        """Analyze the impact of cache key configuration on hit rate"""
        analysis = {
            'query_string_impact': self._analyze_query_string_impact(),
            'header_impact': self._analyze_header_impact(),
            'cookie_impact': self._analyze_cookie_impact(),
            'optimal_ttl': self._calculate_optimal_ttl()
        }
        
        return analysis
    
    def _analyze_query_string_impact(self):
        """Analyze the impact of query strings on caching"""
        query = """
        WITH query_analysis AS (
            SELECT 
                CASE 
                    WHEN uri_query = '-' THEN 'no_query'
                    ELSE 'with_query'
                END as query_type,
                COUNT(*) as requests,
                SUM(CASE WHEN x_edge_result_type LIKE '%Hit' THEN 1 ELSE 0 END) as hits,
                SUM(sc_bytes) as bytes_served,
                AVG(time_taken) as avg_latency
            FROM cloudfront_logs
            WHERE date >= current_date - interval '7' day
            GROUP BY 1
        )
        SELECT 
            query_type,
            requests,
            hits,
            CAST(hits AS DOUBLE) / requests * 100 as hit_rate,
            bytes_served / 1024 / 1024 as mb_served,
            avg_latency
        FROM query_analysis
        """
        
        results = self._run_athena_query(query)
        
        # Identify high-frequency query parameters
        param_query = """
        SELECT 
            regexp_extract(uri_query, '([^&=]+)=', 1) as param_name,
            COUNT(DISTINCT uri_query) as unique_values,
            COUNT(*) as requests,
            SUM(CASE WHEN x_edge_result_type LIKE '%Hit' THEN 1 ELSE 0 END) as hits
        FROM cloudfront_logs
        WHERE date >= current_date - interval '7' day
            AND uri_query != '-'
        GROUP BY 1
        HAVING param_name IS NOT NULL
        ORDER BY requests DESC
        LIMIT 20
        """
        
        param_results = self._run_athena_query(param_query)
        
        return {
            'summary': results,
            'top_parameters': param_results,
            'recommendations': self._generate_qs_recommendations(param_results)
        }
    
    def _calculate_optimal_ttl(self):
        """Calculate optimal TTL values"""
        query = """
        WITH request_patterns AS (
            SELECT 
                uri_stem,
                date_diff('second', 
                    MIN(parse_datetime(time, 'HH:mm:ss')), 
                    MAX(parse_datetime(time, 'HH:mm:ss'))
                ) / COUNT(DISTINCT x_forwarded_for) as avg_request_interval
            FROM cloudfront_logs
            WHERE date >= current_date - interval '30' day
            GROUP BY uri_stem
            HAVING COUNT(*) > 100
        )
        SELECT 
            CASE 
                WHEN uri_stem LIKE '%.js' OR uri_stem LIKE '%.css' THEN 'static_assets'
                WHEN uri_stem LIKE '%.jpg' OR uri_stem LIKE '%.png' THEN 'images'
                WHEN uri_stem LIKE '/api/%' THEN 'api'
                ELSE 'html'
            END as content_type,
            PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY avg_request_interval) as median_interval,
            PERCENTILE_CONT(0.9) WITHIN GROUP (ORDER BY avg_request_interval) as p90_interval,
            COUNT(*) as path_count
        FROM request_patterns
        GROUP BY 1
        """
        
        results = self._run_athena_query(query)
        
        ttl_recommendations = []
        for row in results:
            content_type = row['content_type']
            median_interval = row['median_interval']
            
            # Recommend TTL based on request interval
            if content_type == 'static_assets':
                recommended_ttl = 86400 * 30  # 30 days
            elif content_type == 'images':
                recommended_ttl = 86400 * 7   # 7 days
            elif content_type == 'api':
                recommended_ttl = min(300, median_interval * 0.5)  # Max 5 minutes
            else:
                recommended_ttl = min(3600, median_interval * 0.7)  # Max 1 hour
            
            ttl_recommendations.append({
                'content_type': content_type,
                'recommended_ttl': recommended_ttl,
                'median_request_interval': median_interval,
                'path_count': row['path_count']
            })
        
        return ttl_recommendations

4.3 Cost Impact Analysis

Cost Calculator

class CloudFrontCostCalculator:
    def __init__(self):
        # 2024 pricing (US region)
        self.pricing = {
            'data_transfer_out': {
                'first_10tb': 0.085,
                'next_40tb': 0.080,
                'next_100tb': 0.060,
                'next_350tb': 0.040,
                'over_500tb': 0.030
            },
            'http_requests': {
                'http': 0.0075,  # Per 10,000 requests
                'https': 0.0100   # Per 10,000 requests
            },
            'invalidation': {
                'per_path': 0.005  # Per path after 1000 paths
            },
            'origin_requests': {
                'per_10k': 0.0075
            }
        }
    
    def calculate_optimization_savings(self, metrics):
        """Calculate cost savings after optimization"""
        current_cost = self._calculate_current_cost(metrics)
        optimized_cost = self._calculate_optimized_cost(metrics)
        
        savings = {
            'monthly_savings': current_cost['total'] - optimized_cost['total'],
            'annual_savings': (current_cost['total'] - optimized_cost['total']) * 12,
            'percentage_reduction': ((current_cost['total'] - optimized_cost['total']) / current_cost['total']) * 100,
            'breakdown': {
                'data_transfer': current_cost['data_transfer'] - optimized_cost['data_transfer'],
                'requests': current_cost['requests'] - optimized_cost['requests'],
                'origin': current_cost['origin'] - optimized_cost['origin']
            }
        }
        
        return savings
    
    def _calculate_current_cost(self, metrics):
        """Calculate current cost"""
        data_gb = metrics['data_transfer_gb']
        total_requests = metrics['total_requests']
        cache_hit_rate = metrics['cache_hit_rate']
        
        # Data transfer cost
        data_cost = self._calculate_data_transfer_cost(data_gb)
        
        # Request cost
        request_cost = (total_requests / 10000) * self.pricing['http_requests']['https']
        
        # Origin request cost
        origin_requests = total_requests * (1 - cache_hit_rate)
        origin_cost = (origin_requests / 10000) * self.pricing['origin_requests']['per_10k']
        
        return {
            'data_transfer': data_cost,
            'requests': request_cost,
            'origin': origin_cost,
            'total': data_cost + request_cost + origin_cost
        }
    
    def _calculate_optimized_cost(self, metrics):
        """Calculate optimized cost"""
        # Assume cache hit rate improves to 90% after optimization
        optimized_hit_rate = 0.90
        
        data_gb = metrics['data_transfer_gb']
        total_requests = metrics['total_requests']
        
        # Data transfer cost (slightly reduced due to better compression)
        data_cost = self._calculate_data_transfer_cost(data_gb * 0.95)
        
        # Request cost remains the same
        request_cost = (total_requests / 10000) * self.pricing['http_requests']['https']
        
        # Origin request cost significantly reduced
        origin_requests = total_requests * (1 - optimized_hit_rate)
        origin_cost = (origin_requests / 10000) * self.pricing['origin_requests']['per_10k']
        
        return {
            'data_transfer': data_cost,
            'requests': request_cost,
            'origin': origin_cost,
            'total': data_cost + request_cost + origin_cost
        }

Part 5: Common Issues and Solutions

5.1 Anti-Pattern Checklist

1. Overusing Query Strings

Problem: Including all query string parameters in the cache key
Impact: Extremely low cache hit rate, severe storage waste
Solution:

# Incorrect configuration
QueryString: true  # Includes all parameters

# Correct configuration
QueryString:
  QueryStringCacheKeys:
    - Items:
      - "category"
      - "page"
      - "sort"
      # Exclude: utm_*, fbclid, gclid, ref

2. User-Agent as Cache Key

Problem: Using the complete User-Agent header
Impact: Each browser version creates a separate cache
Solution:

# Use CloudFront device detection headers
headers = [
    'CloudFront-Is-Desktop-Viewer',
    'CloudFront-Is-Mobile-Viewer',
    'CloudFront-Is-SmartTV-Viewer',
    'CloudFront-Is-Tablet-Viewer'
]

3. Ignoring Cache Control Headers

Problem: Not setting or incorrectly setting Cache-Control
Impact: CloudFront uses default 24-hour TTL
Solution:

# Origin server correctly sets cache headers
def set_cache_headers(content_type):
    headers = {}
    
    if content_type.startswith('image/'):
        headers['Cache-Control'] = 'public, max-age=2592000, immutable'  # 30 days
    elif content_type in ['text/css', 'application/javascript']:
        headers['Cache-Control'] = 'public, max-age=31536000, immutable'  # 1 year
    elif content_type == 'text/html':
        headers['Cache-Control'] = 'public, max-age=300, must-revalidate'  # 5 minutes
    else:
        headers['Cache-Control'] = 'public, max-age=3600'  # 1 hour
    
    return headers

5.2 Optimization Case Studies

Case 1: E-commerce Website Optimization

Background:

• Monthly traffic: 500TB
• Requests: 1 billion/month
• Initial cache hit rate: 45%

Optimization Measures:

1. Remove tracking parameters (utm_*, fbclid)
2. Implement intelligent image format selection
3. Separate static and dynamic content paths
4. Optimize TTL strategy

Results:

• Cache hit rate increased to 85%
• Origin requests reduced by 70%
• Monthly cost reduced by $12,000 (40%)

Case 2: SaaS Application Optimization

Background:

• Primarily API requests
• Highly personalized content
• Initial cache hit rate: 15%

Optimization Measures:

1. Identify cacheable API endpoints
2. Implement user group caching strategy
3. Use Lambda@Edge for request normalization
4. Introduce short-term caching (1-5 minutes)

Results:

• Cache hit rate increased to 65%
• API response time reduced by 60%
• Origin server load reduced by 50%

5.3 Troubleshooting Guide

Diagnostic Toolkit

# 1. Test cache key variations
curl -I "https://d111111abcdef8.cloudfront.net/image.jpg" 
  -H "Accept: image/webp" 
  -H "CloudFront-Viewer-Country: US"

# 2. View cache status
curl -I "https://example.com/api/data" | grep -E "X-Cache|Age|Cache-Control"

# 3. Verify TTL settings
aws cloudfront get-distribution-config --id E1234567890ABC 
  --query "DistributionConfig.CacheBehaviors[*].[PathPattern,DefaultTTL]"

Common Problem Resolution

class CacheTroubleshooter:
    def diagnose_low_hit_rate(self, distribution_id, path_pattern):
        """Diagnose low hit rate causes"""
        checks = []
        
        # Check 1: Query string configuration
        qs_config = self._check_query_string_config(distribution_id, path_pattern)
        if qs_config['all_included']:
            checks.append({
                'issue': 'Including all query strings',
                'impact': 'HIGH',
                'solution': 'Use query string whitelist'
            })
        
        # Check 2: Cookie forwarding
        cookie_config = self._check_cookie_config(distribution_id, path_pattern)
        if cookie_config['forward_all']:
            checks.append({
                'issue': 'Forwarding all cookies',
                'impact': 'HIGH',
                'solution': 'Forward only necessary cookies'
            })
        
        # Check 3: TTL settings
        ttl_config = self._check_ttl_config(distribution_id, path_pattern)
        if ttl_config['default_ttl'] < 60:
            checks.append({
                'issue': 'TTL too short',
                'impact': 'MEDIUM',
                'solution': f"Increase TTL from {ttl_config['default_ttl']}s"
            })
        
        # Check 4: Request header configuration
        header_config = self._check_header_config(distribution_id, path_pattern)
        if 'User-Agent' in header_config['forwarded_headers']:
            checks.append({
                'issue': 'Forwarding User-Agent header',
                'impact': 'HIGH',
                'solution': 'Use CloudFront device detection headers'
            })
        
        return {
            'path_pattern': path_pattern,
            'issues_found': len(checks),
            'checks': checks,
            'estimated_improvement': self._estimate_improvement(checks)
        }

Part 6: Best Practices Summary

6.1 Design Principles

1. Minimize cache key complexity: Include only necessary variables
2. Standardize request format: Use Lambda@Edge for normalization
3. Layered caching strategy: Different strategies for different content types
4. Version static resources: Use filename versioning rather than query strings
5. Monitoring-driven optimization: Continuously adjust based on data

6.2 Implementation Checklist

• Audit current cache key configuration
• Identify and remove unnecessary query parameters
• Evaluate cookie and request header requirements
• Implement content classification and path patterns
• Configure appropriate TTL values
• Deploy monitoring and alerting
• Establish cache warming process
• Develop invalidation strategy
• Conduct performance benchmarking
• Calculate ROI and cost savings

6.3 Continuous Optimization Process

1. Weekly review: Check cache hit rate trends
2. Monthly analysis: Deep dive into inefficient paths
3. Quarterly optimization: Implement major configuration changes
4. Annual evaluation: Comprehensive cost-benefit analysis

Conclusion

CloudFront cache key optimization is an ongoing process that requires a deep understanding of business requirements, user behavior, and technical constraints. Through a systematic approach, data-driven decisions, and continuous monitoring and optimization, organizations can significantly improve CDN performance, reduce costs, and enhance user experience.

Key takeaways:

• Every 10% increase in cache hit rate can reduce CDN costs by approximately 15-20%
• Proper cache key design can increase hit rates from 40% to 85%+
• Return on investment typically occurs within 2-3 months
• Balance between cache efficiency and content freshness is essential

Start optimizing your CloudFront cache configuration today to achieve dual improvements in cost and performance.