4.0 KiB
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:
- Lock-based: Only one request populates cache, others wait
- Probabilistic early expiration: Refresh before actual TTL
- 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:
- Use Redis (it's already in our stack, right?)
- Simple TTL-based expiration (no fancy invalidation)
- Cache-aside pattern (app checks cache, falls back to DB)
- 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.