Problem Statement

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Web Cache - Problem Statement

Overview

Design a distributed web caching system similar to Varnish, Squid, or CloudFlare that can cache web content (HTML, CSS, JavaScript, images, API responses) to reduce latency, decrease backend load, and improve user experience. The system should handle millions of requests per second with intelligent cache invalidation and content delivery.

Functional Requirements

Core Caching Features

  • Cache Storage: Store web content with configurable TTL (time-to-live)
  • Cache Lookup: Fast retrieval of cached content by URL/key
  • Cache Invalidation: Purge specific URLs, patterns, or entire cache
  • Cache Warming: Pre-populate cache with frequently accessed content
  • Conditional Requests: Support If-Modified-Since, If-None-Match headers
  • Partial Content: Support HTTP range requests for large files

Cache Policies

  • Eviction Policies: LRU, LFU, FIFO, TTL-based eviction
  • Cache-Control Headers: Respect HTTP cache directives
  • Vary Header Support: Cache multiple versions based on headers
  • Stale-While-Revalidate: Serve stale content while refreshing
  • Cache Bypass: Selective bypass for dynamic content
  • Cache Key Customization: Custom cache keys based on URL, headers, cookies

Content Types

  • Static Assets: Images (JPEG, PNG, GIF, WebP), CSS, JavaScript
  • HTML Pages: Full page caching with personalization
  • API Responses: JSON/XML API response caching
  • Video Streaming: Video segments and manifests
  • Binary Files: PDFs, documents, executables
  • Compressed Content: Gzip, Brotli compressed responses

Cache Management

  • Cache Statistics: Hit rate, miss rate, eviction rate, storage usage
  • Cache Monitoring: Real-time metrics and alerting
  • Cache Purging: Purge by URL, tag, pattern, or wildcard
  • Cache Tagging: Tag-based invalidation for related content
  • Cache Locking: Prevent cache stampede with request coalescing
  • Cache Versioning: Version-based cache invalidation

Non-Functional Requirements

Performance Requirements

  • Cache Hit Latency: <1ms for in-memory cache hits
  • Cache Miss Latency: <50ms for origin fetch + cache store
  • Throughput: 1M+ requests per second per cache node
  • Hit Rate: >90% cache hit rate for static content
  • Origin Offload: Reduce origin traffic by 80-95%
  • Connection Handling: 100K+ concurrent connections per node

Scalability Requirements

  • Horizontal Scaling: Add cache nodes without downtime
  • Storage Capacity: Support petabytes of cached content
  • Geographic Distribution: Deploy cache nodes globally
  • Auto-Scaling: Scale based on traffic patterns
  • Multi-Tier Caching: L1 (memory), L2 (SSD), L3 (origin)
  • Sharding: Distribute cache across multiple nodes

Reliability Requirements

  • System Uptime: 99.99% availability
  • Cache Consistency: Eventually consistent across nodes
  • Graceful Degradation: Serve stale content on origin failure
  • Automatic Failover: Route to healthy nodes on failure
  • Data Durability: Persistent cache survives restarts
  • Health Checks: Continuous monitoring of cache and origin health

Consistency Requirements

  • Cache Coherence: Consistent cache state across distributed nodes
  • Invalidation Propagation: Invalidations reach all nodes within seconds
  • Version Consistency: Serve consistent content versions
  • Conditional Request Handling: Proper ETag and Last-Modified support
  • Stale Content Handling: Configurable stale content policies

Real-time Constraints

Latency Requirements

  • Memory Cache Hit: <500μs
  • SSD Cache Hit: <5ms
  • Cache Miss (Origin Fetch): <100ms
  • Cache Invalidation: <1 second propagation
  • Cache Warming: <10 seconds for critical content
  • Health Check: <100ms response time

Throughput Requirements

  • Read Throughput: 1M requests/sec per node
  • Write Throughput: 100K cache updates/sec per node
  • Invalidation Rate: 10K invalidations/sec cluster-wide
  • Origin Requests: <10% of total traffic (90%+ hit rate)
  • Bandwidth: 10Gbps+ per cache node

Edge Cases and Constraints

Cache Stampede Prevention

  • Request Coalescing: Merge duplicate origin requests
  • Stale-While-Revalidate: Serve stale during refresh
  • Cache Locking: Single request fetches, others wait
  • Probabilistic Early Expiration: Refresh before TTL expires

Large Object Handling

  • Chunked Transfer: Stream large files without full buffering
  • Range Request Support: Partial content delivery
  • Progressive Caching: Cache while streaming to client
  • Size Limits: Configurable max object size (default 10MB)

Dynamic Content Challenges

  • Personalized Content: Cache with user-specific keys
  • Cookie-Based Caching: Vary cache by cookie values
  • Query Parameter Handling: Include/exclude from cache key
  • POST Request Caching: Cache POST responses when safe

Origin Failure Handling

  • Stale Content Serving: Serve expired cache on origin down
  • Grace Period: Extended TTL during origin outage
  • Circuit Breaker: Stop origin requests after failures
  • Fallback Content: Serve default content on errors

Success Metrics

Performance Metrics

  • Cache Hit Rate: >90% for static, >70% for dynamic
  • P50 Latency: <1ms for cache hits
  • P99 Latency: <10ms for cache hits
  • Origin Offload: >85% traffic served from cache
  • Bandwidth Savings: >80% reduction in origin bandwidth

Reliability Metrics

  • Availability: 99.99% uptime
  • Error Rate: <0.01% of requests
  • Invalidation Success: 99.9% successful invalidations
  • Failover Time: <5 seconds to detect and route around failures

Business Metrics

  • Cost Savings: 70% reduction in origin infrastructure costs
  • User Experience: 50% improvement in page load times
  • Origin Load: 90% reduction in origin requests
  • Scalability: Handle 10x traffic spikes without degradation

This problem statement establishes the foundation for designing a production-grade distributed web caching system that can serve as a critical component in modern web infrastructure.