Redis Caching

Redis Caching

Implement Redis as a production support layer, not as a second database.

When to Use

• Add cache-aside or write-through caching around Prisma or API reads.
• Protect expensive routes with rate limiting.
• Store short-lived sessions, verification state, locks, or counters.
• Add pub/sub or stream-backed event fanout.
• Review Redis key design, TTLs, invalidation, or connection handling.

For BullMQ job queue architecture, use nestjs-queue-architect instead of duplicating queue patterns here.

First Decisions

1. Choose the runtime.
   • Long-lived Node.js or NestJS process: use ioredis.
   • Serverless or edge runtime: prefer the Upstash Redis SDK or REST API.
   • Background jobs: use BullMQ guidance from nestjs-queue-architect.
2. Define the cache contract before coding.
   • Source of truth: Prisma/database, upstream API, computed result, or session authority.
   • Freshness: hard TTL, stale-while-revalidate, explicit invalidation, or write-through.
   • Failure mode: fail open for cache reads, fail closed for auth/session/rate-limit writes.
3. Design keys and invalidation together.
   • Namespace every key: app:env:entity:id:variant.
   • Keep keys stable and inspectable.
   • Add TTLs to every cache, lock, session, and rate-limit key.

Installation

bun add ioredis

# Optional serverless Redis
bun add @upstash/redis @upstash/ratelimit

Use one Redis client per process (a module-level singleton guarded against hot-reload duplication). Do not create a new TCP client per request. Wire error, connect, and reconnecting events into the app logger or APM. Avoid logging credentials from connection URLs.

See references/examples.md (§ Redis Client Singleton) for the full setup.

Cache-Aside With Prisma

Use cache-aside for reads where brief staleness is acceptable: check the cache, on miss call the loader, write back with a jittered TTL (baseTtl + random(0, jitter)) so hot keys don't all expire in the same instant.

See references/examples.md (§ Cache-Aside With Prisma) for the getCached helper and a Prisma-backed usage example.

Stampede Protection

Protect hot keys with a short SET key val EX 10 NX lock. Only the holder (matched by a random token) releases it; losers wait briefly and re-check the cache before falling through to a direct load.

See references/examples.md (§ Stampede Protection) for the full lock-and-release implementation.

Invalidation

Prefer direct invalidation for known keys. Use tag sets (a Redis set per tag, mapping to the keys written under it) when one mutation affects many keys — write through a pipeline, invalidate by reading the tag's key set and UNLINK-ing them. Use SCAN instead of KEYS for emergency pattern cleanup. Do not put pattern invalidation on hot request paths.

See references/examples.md (§ Tag-Based Invalidation) for the cacheSetWithTags / invalidateTag implementation.

Rate Limiting

Use sorted sets for sliding-window limits when exactness matters: trim expired entries, add the current request, count members, and set the key's expiry — all in one MULTI. For public traffic, set response headers: X-RateLimit-Limit, X-RateLimit-Remaining, and Retry-After.

See references/examples.md (§ Sliding-Window Rate Limiting) for the full slidingWindowRateLimit implementation.

Sessions And Verification State

• Store opaque session IDs or token hashes, not raw JWTs or long-lived secrets.
• Set TTL on every session key.
• Regenerate sessions after privilege changes.
• Delete sessions on logout, account deletion, and password reset.
• Treat Redis write failure as auth failure for login, logout, MFA, and password reset flows.

See references/examples.md (§ Session Storage) for a createSession example.

Pub/Sub And Streams

• Use pub/sub for ephemeral fanout where missed messages are acceptable.
• Use streams when consumers need replay, backpressure, or durable delivery.
• Keep payloads small; store large objects elsewhere and publish IDs.
• Add consumer observability: lag, dead letters, retries, and handler errors.

Production Checklist

• All cache, lock, session, and rate-limit keys have TTLs.
• Cache reads fail open where possible; auth and rate-limit writes fail closed.
• Hot keys use TTL jitter or stampede protection.
• No request path uses KEYS, unbounded SMEMBERS, or large HGETALL.
• Invalidation is tested alongside the write path.
• Redis metrics cover latency, hit rate, memory, evictions, blocked clients, and reconnects.
• Local tests cover cache hit, cache miss, Redis down, invalidation, and rate-limit exceeded.