I'm building around frequent repeated reads and want to lower backend load; recommend a cache approach.

For frequent repeated reads, the usual best fit is a read-through cache with a cache-aside fallback pattern.

Recommended approach

• Put a cache in front of your backend for hot reads.
• Check cache first on every read.
• On a miss, fetch from backend, then store in cache with a TTL.
• Invalidate or update cached entries when the underlying data changes.

Why this works well

• Reduces backend load significantly for repeated requests
• Improves latency for hot data
• Simple to implement and scale
• Lets you control freshness via TTLs

Good cache choices

• In-memory cache: fastest, but only per instance; good for single-node or small-scale use
• Distributed cache like Redis or Memcached: best for multiple app instances and shared cached data

Typical setup

1. Request comes in
2. App checks cache
3. If hit: return cached value
4. If miss: query backend
5. Store result in cache with TTL
6. Return result

Important considerations

• TTL tuning: short TTL for fast-changing data, longer TTL for stable data
• Cache invalidation: invalidate on writes if freshness matters
• Stampede protection: use locks/request coalescing so many misses don’t overwhelm backend
• Eviction policy: LRU/LFU can help keep hot items
• Negative caching: cache “not found” responses briefly if repeated misses are common

If your workload is mostly identical repeated reads

A CDN/edge cache may also help if the data is publicly cacheable and not user-specific.

If you want, I can recommend a specific cache strategy based on:

• read/write ratio
• data freshness requirements
• single server vs distributed deployment
• whether data is user-specific or public