Requirements & constraints:
- Each Python worker has a small local LRU in-memory cache (hot items).
- Redis is a shared larger cache (single source of truth for cached objects).
- Low latency, high throughput, tolerate occasional staleness, minimize cross-worker misses and network overhead.
High-level architecture:
Workers -> local LRU cache -> Redis (clustered, with TTLs) -> backing datastore (DB/FS) for cold fetches.
Consistency models:
- Eventual consistency (default): workers serve from local cache; updates write to Redis then background invalidate/notify others (faster, lower latency, higher throughput). Acceptable when slight staleness is tolerable.
- Strong consistency (optional): use Redis as authoritative store and perform synchronous invalidation/compare-and-set (CAS) or use Redis transactions and blocking reads on invalidation—adds latency and complexity; use only for critical keys.
Caching strategy:
- Cache-aside for reads/writes (recommended for data pipelines): worker checks local LRU -> Redis -> DB. On miss, fetch from DB, write Redis, populate local cache.
- Write-through for writes that must be durable in Redis immediately (use sparingly because of higher write latency).
Eviction coordination & LRU behavior:
- Local caches use approximate LRU with size limits. Redis uses its own eviction policy (volatile-lru / allkeys-lru) and TTLs per key.
- Coordination: rely on Redis as authoritative capacity manager—local caches only store subset. To avoid stampede on eviction, set slightly shorter TTLs in local caches than Redis TTL and use randomized backoff for refetch.
Invalidation / Notification:
- Use Redis Pub/Sub or Redis Keyspace Notifications to broadcast invalidations. When a worker updates or evicts a key in Redis, publish an invalidation message; workers subscribe and remove/refresh local entries.
- For high throughput, batch invalidations or use lightweight versioning (incrementing version numbers stored with value). Workers check version on access and refresh if stale.
Warm-up / rehydration:
- On worker start, prefetch top-N hot keys (based on Redis LFU counters or external popularity metrics) into local cache. Lazy warm-up for others.
- Use background async rehydration threads to prefetch during idle time.
Minimizing cross-worker misses & network overhead:
- Favor cache-aside with local first + Redis; use local TTL << Redis TTL to reduce refills.
- Maintain popularity statistics (Redis HLL or LFU) to proactively replicate hot keys to workers during warm-up.
- Use batching and pipelining for Redis calls; compress large values.
- Implement negative caching for frequent misses.
Trade-offs:
- Eventual consistency yields lower latency but possible stale reads; strong consistency increases latency and reduces availability.
- Pub/Sub invalidation adds network traffic but reduces stale reads; for extreme scale, use versioning and probabilistic invalidation to reduce noise.
Operational notes:
- Monitor hit rates (local and Redis), invalidation lag, and network usage. Tune local cache sizes, TTLs, and prefetch thresholds.
- Design metrics and dashboards; add circuit-breakers and backpressure on Redis to avoid overload during storms.
