Caching Patterns: Cache-Aside, Write-Through, Write-Behind, and Refresh Strategies
Understand the major application-level caching strategies and when to choose each one.
Inside this chapter
- Why Caching Strategy Matters
- Cache-Aside
- Write-Through and Write-Behind
- Refresh and Invalidation
- Business Example
Series navigation
Study the chapters in order for the clearest path from Redis basics to advanced cache architecture, operations, and distributed-system design. Use the navigation at the bottom to move smoothly through the full tutorial series.
Why Caching Strategy Matters
Using Redis is not enough by itself. Teams still need to decide when data enters the cache, when it is refreshed, how staleness is handled, and how the cache interacts with the source of truth such as a database.
Cache-Aside
In cache-aside, the application checks Redis first. If the value is missing, it reads from the database, stores the result in Redis, and returns it.
1. Check Redis
2. On miss, query database
3. Store result in Redis with TTL
4. Return responseWrite-Through and Write-Behind
Write-through updates the cache when writes happen so reads stay fresh. Write-behind delays source-of-truth persistence and is more complex and risk-sensitive. Teams should choose carefully based on durability and consistency needs.
Refresh and Invalidation
Invalidation is often one of the hardest parts of caching. TTL helps, but many systems also need event-based or write-triggered invalidation for correctness.
Business Example
An e-commerce product page may use cache-aside for catalog lookups so repeated views avoid heavy database reads. When product details are updated, the related Redis keys may be invalidated immediately to prevent stale display data.