Understanding Persistent Key-Value Storage

There is a dangerous misconception in backend development: "Key-Value stores are just caches."

This leads to architectures where critical data is stored in volatile memory. When the cache works, everything is fine. When it restarts, data is lost, and the system fails.

It is time to embrace persistent key-value storage.

Cache vs. Storage

• Cache (e.g. Memcached, default Redis):
  • Goal: Speed.
  • Trade-off: Durability. If RAM fills up, it evicts old keys. If it restarts, it empties.
• Storage (e.g. BaseKV, DynamoDB):
  • Goal: Durability.
  • Trade-off: Slight latency (disk vs RAM). It never evicts keys unless you tell it to.

The Danger of "Cache-as-Database"

I have seen it happen: A developer uses Redis to store active "Session IDs". Redis fills up. The eviction policy kicks in. It silently deletes 10% of the oldest sessions to make room. Suddenly, thousands of users are logged out. Support tickets flood in. The developer thinks it's a bug in the auth code. usage implies persistence, infrastructure provides cache. Mismatch.

Persistent KV Architecture

A persistent KV store writes to a Write-Ahead Log (WAL) or directly to disk structure (like B-Trees or LSM Trees).

1. Write Request: SET key val
2. Disk Sync: Data is fsync'd to the drive.
3. Ack: Client receives "OK".

This guarantees that if the power goes out at Step 3.1, your data is there when the lights come back on.

When to use Persistence

You need persistence for anything that is state, not optimisation.

• State: User sessions, feature flags, shopping carts, job queues, game leaderboards.
• Optimisation: HTML page cache, API response cache, thumbnail cache.

Conclusion

If losing specific keys would break your user's experience (log them out, empty their cart), you are not using a cache. You are using a database. Treat it like one. Choose a persistent key-value storage solution.

BaseKV is built for persistence first. Secure your data now.