# Example Feature Discussion ## Context This is an example discussion file demonstrating the format and structure used by Orchestrated Discussions. We want to add a new caching layer to improve API response times. The cache should store frequently accessed data and invalidate appropriately when the underlying data changes. ## Requirements - [ ] Cache frequently accessed API responses - [ ] Support configurable TTL per cache key - [ ] Invalidate cache on data mutations - [ ] Provide cache statistics and monitoring ## Open Questions - What caching backend should we use? (Redis, Memcached, in-memory) - How do we handle cache stampedes? - What's the expected cache hit ratio? ## Constraints - Must work in our Kubernetes environment - Cannot add more than 100ms latency on cache miss - Must support horizontal scaling --- *Discussion begins below. Use `discussions turn example.md @architect` to get feedback.* --- Name: AI-Architect Looking at this caching proposal, I have several architectural considerations: **Caching Backend** For a Kubernetes environment, I'd recommend Redis over in-memory caching. Redis provides: - Persistence options for durability - Built-in TTL support - Cluster mode for horizontal scaling - Pub/sub for cache invalidation across instances **Cache Stampede Prevention** We should implement one of these patterns: 1. **Lock-based**: Only one request populates cache, others wait 2. **Probabilistic early expiration**: Refresh before actual TTL 3. **Background refresh**: Async refresh while serving stale data Q: What's our tolerance for serving slightly stale data? That affects which pattern works best. **Architecture Concern** The requirement to "invalidate on data mutations" needs more specificity. Are we talking about: - Same-service mutations (easier, can invalidate directly) - Cross-service mutations (harder, needs event-driven invalidation) CONCERN: Cross-service cache invalidation can become a distributed systems nightmare if not carefully designed. VOTE: CHANGES --- Name: AI-Security From a security perspective, I need to raise several concerns: **Cache Poisoning** If an attacker can poison the cache with malicious data, that data gets served to all users. Mitigations: - Validate data before caching - Use signed cache keys - Implement cache isolation per tenant (if multi-tenant) **Sensitive Data** Q: Will any cached responses contain sensitive data? If so, we need: - Encryption at rest in Redis - Secure cache key generation (no user IDs in keys if possible) - Appropriate TTLs for sensitive data **Access Control** Redis should not be exposed to the public internet. Ensure: - Redis is in private subnet - Authentication enabled (requirepass) - TLS for data in transit if crossing network boundaries CONCERN: The requirements don't mention data sensitivity classification. We need to know what's being cached. VOTE: CHANGES --- Name: AI-Pragmatist I appreciate the thorough analysis, but let's not over-engineer this. **MVP Proposal** For v1, I suggest: 1. Use Redis (it's already in our stack, right?) 2. Simple TTL-based expiration (no fancy invalidation) 3. Cache-aside pattern (app checks cache, falls back to DB) 4. Start with just the top 3 most accessed endpoints **What We Can Defer** - Sophisticated invalidation (use short TTLs instead for MVP) - Cache stampede handling (monitor first, optimize if needed) - Cross-service invalidation (start with single-service scope) The architect's concerns about cross-service invalidation are valid, but we don't need to solve that for v1. Let's ship something, measure the impact, then iterate. Q: What's the timeline pressure here? If we have 2 weeks, MVP is the way. If we have 2 months, we can be more thorough. VOTE: READY --- *This example shows a typical discussion with different perspectives. The discussion would continue until consensus is reached.*