Caching Patterns and Strategies

Introduction

Different caching strategies suit different use cases. Understanding patterns helps you choose the right approach.

Key Concepts

Cache-Aside: Application manages cache reads/writes.

Write-Through: Updates cache on every write.

TTL: Time-to-live for cache entries.

Deep Dive

Cache-Aside Pattern

python
def get_article(slug):
    cache_key = f'article:{slug}'
    article = cache.get(cache_key)
    
    if article is None:
        article = Article.objects.get(slug=slug)
        cache.set(cache_key, article, timeout=3600)
    
    return article

Write-Through Pattern

python
class Article(models.Model):
    def save(self, *args, **kwargs):
        super().save(*args, **kwargs)
        cache.set(f'article:{self.slug}', self, timeout=3600)
    
    def delete(self, *args, **kwargs):
        cache.delete(f'article:{self.slug}')
        super().delete(*args, **kwargs)

Stale-While-Revalidate

python
import threading

def get_with_stale(key, compute_fn, timeout=3600):
    value = cache.get(key)
    stale_key = f'{key}:stale'
    
    if value is None:
        # Check for stale value
        value = cache.get(stale_key)
        
        # Recompute in background
        thread = threading.Thread(
            target=lambda: cache.set(key, compute_fn(), timeout)
        )
        thread.start()
        
        if value is None:
            value = compute_fn()
            cache.set(key, value, timeout)
    
    # Always update stale cache
    cache.set(stale_key, value, timeout * 2)
    return value

Best Practices

1. Use meaningful cache keys: Include version for invalidation.
2. Set appropriate TTLs: Balance freshness vs performance.
3. Handle cache failures gracefully: Fall back to database.

Summary

Cache-aside is simplest for read-heavy workloads. Write-through ensures consistency. Stale-while-revalidate provides best user experience for expensive computations.