How caching improves system performance

· Category: System Design

Short answer

Caching stores copies of frequently accessed data in fast storage, reducing latency and backend load.

Steps

  1. Identify read-heavy data with low update frequency as caching candidates.
  2. Choose a caching pattern: cache-aside, read-through, write-through, or write-behind.
  3. Set TTL values that balance freshness with hit rates.
  4. Implement cache invalidation on data updates to prevent stale responses.
  5. Monitor hit rates, eviction rates, and memory usage.

Tips

  • Use Least Recently Used eviction when memory is constrained.
  • Apply consistent hashing to distribute cache keys across a cluster.
  • Warm caches after restarts to avoid cold-start latency spikes.
  • Compress large cached objects to maximize memory efficiency.

Common issues

  • Cache stampede when many requests simultaneously miss and hit the backend.
  • Stale data served due to missing or delayed invalidation.
  • Thundering herd on expiration without probabilistic early refresh.
  • Memory exhaustion from unbounded cache growth.

Example

# Consistent hashing for service discovery
import hashlib

def get_node(key, nodes):
    hash_val = int(hashlib.md5(key.encode()).hexdigest(), 16)
    return nodes[hash_val % len(nodes)]

node = get_node('user-123', ['node-a', 'node-b', 'node-c'])

This snippet implements consistent hashing to distribute keys across nodes, a foundational technique in scalable distributed systems.