Redis Caching Strategy — Reduce Database Load and Response Times by 10x

Cache-aside (lazy loading): The application checks Redis first. On a cache miss, it queries the database, writes the result to Redis with a TTL, and returns the response. This is the most common pattern and works well for read-heavy workloads where stale data is acceptable for the TTL duration. We implement this at the repository or service layer of your application so caching is transparent to the rest of the code.

Write-through: When the application writes to the database, it simultaneously writes to Redis. This ensures the cache is always warm and consistent with the database, at the cost of slightly slower writes. We use this for data that is read frequently and updated occasionally — user profiles, product catalogs, configuration settings.

Cache invalidation: The hardest problem in computer science, as the saying goes. TTL-based expiry is the simplest approach — set a 5-minute TTL and accept that data might be up to 5 minutes stale. Event-based invalidation is more precise — when a record is updated, explicitly delete its cache key. We implement the appropriate strategy per data type: TTL for aggregations and computed values, event-based for user-facing data where staleness is visible.

Request deduplication: When the cache is cold or a popular key expires, multiple concurrent requests all miss the cache and hit the database simultaneously — the "thundering herd" problem. We implement request coalescing (also called "single-flight") where the first request fetches from the database and all concurrent requests wait for that result instead of firing duplicate queries.

Infrastructure: We provision Redis via AWS ElastiCache (or equivalent managed service) with encryption at rest and in transit, automatic failover for production (Multi-AZ with Redis cluster mode), and appropriate instance sizing based on your data volume and throughput requirements. Non-production environments use smaller instances or Redis on the application host to reduce costs. All provisioned via Terraform.

Key Design: Cache keys follow a consistent naming convention: {service}:{entity}:{id}:{version}. This enables targeted invalidation (delete all keys for a specific entity), namespace isolation (different services do not collide), and version-based cache busting (bump the version to invalidate all keys without explicit deletion).

Serialization: We use MessagePack or Protocol Buffers for cache serialization instead of JSON. MessagePack is 30-50% smaller than JSON and faster to serialize/deserialize, which matters when you are reading thousands of cache entries per second. For simple string values, we skip serialization entirely.

Session Storage: If your application uses server-side sessions, we migrate session storage from your database (slow, unnecessary load) to Redis (fast, purpose-built). We configure session TTL, secure session IDs, and session data encryption for compliance requirements.

Rate Limiting: Redis's atomic increment and TTL make it ideal for rate limiting. We implement sliding window rate limiting using sorted sets — more accurate than fixed-window counters and resistant to burst-at-boundary attacks. Rate limits are configurable per endpoint, user tier, and IP address.

Monitoring: We set up dashboards for cache hit ratio, memory usage, eviction rate, connection count, and command latency. Alerts fire when hit ratio drops below threshold (indicating cache effectiveness problems), memory usage exceeds 80% (indicating sizing issues), or eviction rate spikes (indicating the cache is too small for the dataset).

A production Redis caching implementation:

• Redis deployment — ElastiCache or equivalent with encryption, failover, and right-sized instances
• Caching patterns — cache-aside, write-through, or hybrid per data type
• Key design — consistent naming, namespacing, and versioning conventions
• Invalidation strategy — TTL-based and event-based invalidation per data type
• Session storage — server-side sessions migrated from database to Redis
• Rate limiting — sliding window rate limits per endpoint and user tier
• Thundering herd protection — request coalescing for cold cache scenarios
• Monitoring — hit ratio, memory, eviction, and latency dashboards with alerts