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Caching

Caching is a technique used to store frequently accessed data in fast memory to improve system performance and reduce load on primary data sources.

Types of Caching​

Application Cache​

  • In-memory cache within application
  • Fast access
  • Limited by application memory
  • Examples: Guava Cache, Caffeine

Distributed Cache​

  • Shared cache across multiple servers
  • Scalable
  • Network overhead
  • Examples: Redis, Memcached

CDN (Content Delivery Network)​

  • Geographic distribution
  • Static content
  • Edge caching
  • Examples: Cloudflare, Akamai, AWS CloudFront

Cache Strategies​

Cache-Aside (Lazy Loading)​

  • Application checks cache first
  • Loads from source if not found
  • Updates cache after loading
  • Pros: Simple, flexible
  • Cons: Cache miss penalty

Write-Through​

  • Writes to cache and source simultaneously
  • Consistent data
  • Higher write latency
  • Pros: Data consistency
  • Cons: Write performance

Write-Behind​

  • Writes to cache first
  • Asynchronously updates source
  • Better write performance
  • Pros: Fast writes
  • Cons: Potential data loss

Implementation Examples​

Redis Cache​

import redis

# Connect to Redis
r = redis.Redis(host='localhost', port=6379, db=0)

# Set cache
r.set('key', 'value')

# Get cache
value = r.get('key')

Memcached​

// Create client
MemcachedClient client = new MemcachedClient(
    new InetSocketAddress("localhost", 11211)
);

// Set cache
client.set("key", 3600, "value");

// Get cache
Object value = client.get("key");

Best Practices​

  1. Cache Invalidation

    • Time-based expiration
    • Event-based invalidation
    • Version-based keys

  2. Cache Size Management

    • Memory limits
    • Eviction policies
    • Monitoring usage

  3. Performance Optimization

    • Key design
    • Serialization
    • Compression

Common Challenges​

  • Cache consistency
  • Cache invalidation
  • Memory management
  • Network latency

Further Reading​