Read Through: Letting the Cache Handle Data Loading

Moving Cache Population to the Cache Layer

Read through moves cache population responsibility from the application to the cache layer itself. The application always reads through the cache, which becomes a smart intermediary handling all data retrieval logic. On a cache miss, the cache layer automatically fetches data from the source of truth (the database), populates itself, and returns the result. The application code contains no fallback logic: it simply calls cache.get(key) and receives data regardless of whether it was cached or freshly fetched.

Centralized Logic Benefits

In cache aside, every application server must implement the same fallback logic correctly: check cache, query database on miss, handle errors, populate cache. With 50 microservices using the same data, that is 50 implementations to maintain. In read through, the cache layer owns this logic. Bug fixes happen once, optimizations benefit everyone, and new applications get correct behavior automatically. This centralization enables advanced features difficult to implement consistently across applications: adaptive TTLs based on query frequency, unified observability across all consumers, and automatic retry policies for database failures.

Request Coalescing Prevents Thundering Herds

The killer feature of read through is request coalescing (combining multiple concurrent requests for the same key into a single backend fetch). Consider a hot key serving 10,000 requests per second. When its TTL expires, cache aside generates 10,000 concurrent database queries as every server detects the miss. Read through generates exactly one query while the other 9,999 requests wait (typically 50-200ms) for that single fetch. The database sees 1 query per second instead of 10,000 during TTL boundaries. For hot keys, this is the difference between stable operation and database overload.

Refresh Ahead for Zero Miss Latency

Smart read through caches implement refresh ahead: entries nearing expiry are proactively refreshed in the background before any client experiences a miss. A key with 5 minute TTL might trigger background refresh at 4 minutes 30 seconds, with the cache serving the existing value while fetching the update. Result: frequently accessed keys never expire from the client perspective. Your p95 and p99 latencies stay flat because clients always hit warm cache. This is how CDN edge caches maintain sub-10ms response times globally even when origin refreshes take 200-500ms.

The Infrastructure Trade-off

Read through requires a smart cache layer that understands your data sources. The cache must know database connection details, query patterns, authentication, and error handling. This tight coupling means cache bugs affect all consumers simultaneously, and cache configuration must coordinate with database schema changes. You need robust circuit breakers (mechanisms that stop calling a failing service to let it recover) and backpressure handling because all miss traffic flows through the cache tier.