Cache Aside with Redis

controlplane ~ on ☁️ (us-east-1) ➔ curl localhost:8000/profile/7
{"id": 7, "bio": "Original bio for user 7."}

# Repeat the same request; this may be served from cache
controlplane ~ on ☁️ (us-east-1) ➔ curl localhost:8000/profile/7
{"id": 7, "bio": "Original bio for user 7."}

# Inspect recent application logs
controlplane ~ on ☁️ (us-east-1) ➔ docker compose logs app | tail -2
# (example log lines)
app_1  | INFO  cache: hit for profile 7
app_1  | INFO  response served from cache

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What you’ll observe

• Baseline: requests read directly from the database (no Redis), with typical latency.
• After adding Redis: first request for an item populates the cache (cache miss → DB read); subsequent requests for the same item are served from Redis (cache hit), reducing latency.
• Stale data scenario: if you update the database directly without invalidating the Redis entry, the application may return the old value until the cached key expires or is explicitly refreshed/invalidated.

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Quick commands and examples

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How the cache-aside pattern works (summary)

1. Application receives a request for resource X.
2. Check Redis for key X:
   • If present (cache hit): deserialize and return to client.
   • If absent (cache miss): read X from the database, return to client, and write X into Redis (optionally with a TTL).
3. If the database is modified by another process, the cache must be invalidated or updated to avoid serving stale data. Common strategies are: write-through, write-behind, explicit invalidation, or short TTLs.

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Debugging tips

• Look for log lines indicating cache behavior, e.g. cache: hit for profile 7 or cache: miss for profile 7.
• When debugging stale reads:
  • Verify if the database row actually changed.
  • Check whether the Redis key still exists and contains the old value.
  • Confirm whether your application invalidates cache keys on updates.
• Use Redis CLI to inspect keys: redis-cli GET profile:7 (or the key format your app uses).

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Common invalidation strategies

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References and further reading

• Redis documentation
• Cache-aside pattern overview (Martin Fowler)
• [Designing Data-Intensive Applications (Book) — caching patterns]