Caching Strategies

Overview

Caching reduces latency and backend load by storing frequently accessed data closer to application consumers.

Common Cache Patterns

• Cache-aside: application loads and writes cache explicitly
• Read-through: cache fetches from source on miss
• Write-through: writes update cache and source together
• Write-behind: cache writes asynchronously to source

sequenceDiagram
    participant App
    participant Cache
    participant Database
    App->>Cache: GET product:42
    alt Cache hit
      Cache-->>App: Return cached value
    else Cache miss
      App->>Database: Query product 42
      Database-->>App: Return row
      App->>Cache: SET product:42
    end

Practical Example

var key = $"product:{productId}";
var cached = await redis.StringGetAsync(key);

if (cached.IsNullOrEmpty)
{
  var product = await repository.GetProductAsync(productId);
  await redis.StringSetAsync(key, JsonSerializer.Serialize(product), TimeSpan.FromMinutes(5));
  return product;
}

return JsonSerializer.Deserialize<Product>(cached!)!;

Operational Guidance

• Define TTL based on data freshness requirements
• Monitor hit ratio and eviction rates
• Use key namespacing to avoid collisions
• Invalidate cache after critical data updates

Common Pitfalls

• Unbounded key growth
• Stale data due to weak invalidation
• Caching sensitive data without encryption controls

Key Takeaways

• Cache pattern choice depends on consistency needs.
• TTL and invalidation strategy are central design decisions.
• Metrics are required to validate cache effectiveness.