Distributed Cache Invalidation and Multi-Level Caching on AWS
Quick summary: Cache-aside without an invalidation story ships stale pricing to 2% of users—the hardest 2% to debug. This guide layers CloudFront, ElastiCache, and DAX with TTL, event-driven purge, and when write-through beats cache-aside.
Key Takeaways
- Cache-aside without an invalidation story ships stale pricing to 2% of users—the hardest 2% to debug
- This guide layers CloudFront, ElastiCache, and DAX with TTL, event-driven purge, and when write-through beats cache-aside
- June 2026: CloudFront invalidation costs and limits make wildcard purge a financial event; ElastiCache has no global invalidation API—you design key versioning or TTL discipline
- Benchmark pattern — Product catalog API: cache-aside only, 90 s TTL, stale price incidents ~1
- 8% of checkouts after admin updates
Table of Contents
June 2026: CloudFront invalidation costs and limits make wildcard purge a financial event; ElastiCache has no global invalidation API—you design key versioning or TTL discipline.
Symptom → mechanism → AWS control
| Production symptom | Mechanism | AWS control |
|---|---|---|
| Stale data after update | L1 not invalidated on write | Redis pub/sub invalidation channel, versioned cache keys |
| Inconsistent L1 across pods | Per-instance cache without coordination | Short L1 TTL (30–60s) + centralized L2 ElastiCache |
| Invalidation storm | Broadcast to all nodes on bulk update | SQS fan-out with batched invalidation keys |
Opinionated take: Two-tier cache (L1 in-process + L2 ElastiCache) with pub/sub invalidation beats a single Redis layer for read-heavy APIs—keep L1 TTL under 60 seconds.
Benchmark pattern — Product catalog API: cache-aside only, 90 s TTL, stale price incidents ~1.8% of checkouts after admin updates. EventBridge → Lambda invalidation +
Cache-Control: max-age=30on CloudFront dropped stale reads to <0.05% with $40/mo invalidation spend vs $12k mis-priced orders estimate. Baseline strategies: ElastiCache production guide.
Patterns
| Pattern | Pros | Cons |
|---|---|---|
| Cache-aside | Simple | Stale reads; stampede risk |
| Write-through | Fresher cache | Write latency |
| Write-behind | Fast writes | Loss window |
| Read-through DAX | DynamoDB accelerator | DynamoDB only |
Multi-level architecture
- CloudFront — public GET, short TTL, signed URLs for semi-private content.
- ElastiCache/Valkey — computed aggregates, rate limit counters, session.
- DAX — microsecond DynamoDB reads when item model fits.
Eventual consistency challenge: each layer has different TTL; version stamps in origin responses let all layers align.
AWS services map
| Need | Service | Skip when |
|---|---|---|
| L2 shared cache | ElastiCache Redis | Single-instance app with no horizontal scale |
| Invalidation bus | ElastiCache pub/sub or SNS | Immutable content with long TTL |
| Edge caching | CloudFront + cache-control headers | Personalized per-user responses |
When this advice breaks
- Strong consistency required — skip cache on ledger reads; use DynamoDB consistent read.
- Personalized HTML — CloudFront cache key must include variant; or do not cache at edge.
What to do this week
- List top 10 cached keys and their invalidation trigger (TTL only vs event).
- Add
X-Cache-Versionheader tied to domain event sequence. - Wire EventBridge rule on
ProductUpdated→ invalidation Lambda. - Review CloudFront cache hit ratio vs origin load—aim >80% on static assets.
More in This Track
Part of the Engineering Guides library (June 2026).
- Previous: Part 1
- Next: Part 3
- Browse tracks: Engineering Guides hub
What this guide doesn’t cover
Bloom filters and HyperLogLog—part 3 of this track.
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