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Mehdi Mehdikhani
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After reading about different caching patterns, I realized there’s no one-size-fits-all solution. Cache-aside is probably most common pattern where application manages cache explicitly - it checks cache first, if miss then loads from database and puts result in cache. This gives you full control but means more code complexity. Write-through cache updates both cache and database at same time, which is slower for writes but ensures consistency. Write-back (or write-behind) only updates cache immediately and writes to database later in batches, which is fastest for writes but risky if cache fails before database write happens.

The choice really depends on your read/write patterns and consistency requirements. If you have read-heavy workload with occasional writes, cache-aside works well. If you need strong consistency and can accept slower writes, write-through is safer choice. Write-back is good for high-write scenarios where you can tolerate some data loss risk. In our robots, we use cache-aside for static maps (read often, rarely updated) and write-through for safety-critical sensor data where we cannot afford inconsistencies.

Most important thing I learned is that cache invalidation is the hardest part. You need to decide when to remove stale data and how to handle cache misses gracefully. TTL (time-to-live) expiration works for some use cases, but event-based invalidation is better when you know exactly when data changes. Also, always have fallback plan when cache is down - your system should still work, just slower.

Simple Decision Guide

Use Cache-Aside when:

  • Read-heavy workload (90% reads)
  • You can tolerate slight inconsistency
  • You want full control over caching logic
  • Data changes infrequently

Use Write-Through when:

  • Strong consistency is required
  • You can accept slower write performance
  • Critical data that must stay synchronized
  • Moderate write load

Use Write-Back when:

  • Very high write volume
  • You can tolerate some data loss risk
  • Write performance is critical
  • You have reliable cache infrastructure

The key is matching pattern to your actual workload. Start simple with cache-aside, then optimize based on observed bottlenecks.