What is Caching Strategies?

Caching strategies store frequently accessed data in fast-access locations, eliminating expensive database queries and network requests. Effective caching dramatically improves performance and scalability by trading storage space for reduced computation.

Types of Caches

CPU Caches

Processors cache recently accessed memory. Cache performance is transparent to applications but affects overall system performance.

Database Query Caching

Databases cache query results:

  • Bitmap indices cache matching results
  • Query result caches store results of frequently executed queries
  • Connection pooling caches database connections

Application-Level Caching

Applications cache data in memory:

  • In-process caches store data in application memory
  • Distributed caches (Redis, Memcached) store data in dedicated servers
  • Content delivery networks cache content globally

HTTP Caching

Web servers and browsers cache HTTP responses:

  • Cache-Control headers guide caching behaviour
  • ETags enable conditional requests
  • Expires headers indicate cache validity

Caching Policies

Time-Based Expiration

Cached data expires after specified duration:

// Cache for 5 minutes
cache.set('user-123', userData, {ttl: 300000});

Appropriate TTL depends on data change frequency.

Event-Based Invalidation

Cached data is invalidated when related events occur:

// Invalidate when user updates profile
user.onUpdate(() => {
cache.delete(`user-${user.id}`);
});

LRU (Least Recently Used) Eviction

When cache is full, least recently used items are evicted. This strategy keeps frequently accessed data cached.

LFU (Least Frequently Used) Eviction

When cache is full, least frequently accessed items are evicted. This strategy optimises for access frequency rather than recency.

Caching Layers

Client-Side Caching

Browsers cache frequently accessed resources:

  • HTML files
  • CSS stylesheets
  • JavaScript files
  • Images

Browser caching reduces server load and improves page load times.

Application-Level Caching

Applications cache computed results and database queries:

  • User profile data
  • Product catalogues
  • Authentication tokens
  • Computed results

Database-Level Caching

Databases cache query results and indices:

  • Query result caching
  • Connection pooling
  • Index caching

CDN Caching

Content delivery networks cache content geographically:

  • Static assets cached worldwide
  • Content served from nearest location
  • Reduced latency for global users

Caching Challenges

Cache Invalidation

Keeping cached data synchronized with source data is notoriously difficult. Invalidation strategies:

  • Time-based expiration - Simple but may serve stale data
  • Event-based invalidation - Accurate but complex to implement
  • Manual invalidation - Reliable but requires discipline

Cache Warming

Popular data should be pre-cached reducing initial requests:

// Warm cache on startup
async function warmCache() {
const popularProducts = await fetchPopularProducts();
popularProducts.forEach(product => {
cache.set(`product-${product.id}`, product);
});
}

Thundering Herd

When cached data expires, many requests may simultaneously recompute:

  • Staggered expiration prevents simultaneous recomputation
  • Lock-based recomputation ensures single recomputation
  • Probabilistic early expiration spreads load

Cache Stampede

When popular data fails, overwhelming requests hit source system:

  • Circuit breakers prevent cascading failures
  • Return stale data if source is unavailable
  • Graceful degradation prevents system collapse

Caching at PixelForce

PixelForce leverages caching strategically. Application caching reduces database load. CDN caching accelerates global content delivery. Proper cache invalidation ensures data freshness.

Caching Tools

  • Redis - In-memory data store enabling distributed caching
  • Memcached - Simple in-memory cache
  • Varnish - HTTP caching proxy
  • CloudFront - AWS content delivery network
  • Local Storage - Browser-based client-side caching

Caching Metrics

Cache Hit Rate

Percentage of requests served from cache. Higher hit rates indicate effective caching.

Cache Miss Rate

Percentage of requests requiring source data retrieval. Lower miss rates indicate better caching strategy.

Cache Size

How much memory caching consumes. Larger caches provide better hit rates but consume more resources.

Caching Best Practices

  • Cache strategically - Cache expensive operations, not trivial ones
  • Invalidate carefully - Keep cached data synchronized with source
  • Warm important data - Pre-cache popular data on startup
  • Monitor effectiveness - Track hit rates guiding cache optimisation
  • Handle failures - Gracefully degrade when cache is unavailable
  • Respect privacy - Do not cache sensitive user data

Effective caching dramatically improves application performance and scalability. Strategic caching decisions yield significant performance benefits with minimal complexity.