125 lines
4.0 KiB
Markdown
125 lines
4.0 KiB
Markdown
<!-- DISCUSSION -->
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<!-- Title: Example Feature Discussion -->
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<!-- Phase: initial_feedback -->
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<!-- Status: OPEN -->
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<!-- Created: 2025-12-08T00:00:00Z -->
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<!-- Template: feature -->
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<!-- Participants: architect, security, pragmatist -->
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# Example Feature Discussion
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## Context
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This is an example discussion file demonstrating the format and structure
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used by Orchestrated Discussions.
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We want to add a new caching layer to improve API response times. The cache
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should store frequently accessed data and invalidate appropriately when
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the underlying data changes.
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## Requirements
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- [ ] Cache frequently accessed API responses
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- [ ] Support configurable TTL per cache key
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- [ ] Invalidate cache on data mutations
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- [ ] Provide cache statistics and monitoring
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## Open Questions
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- What caching backend should we use? (Redis, Memcached, in-memory)
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- How do we handle cache stampedes?
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- What's the expected cache hit ratio?
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## Constraints
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- Must work in our Kubernetes environment
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- Cannot add more than 100ms latency on cache miss
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- Must support horizontal scaling
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---
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*Discussion begins below. Use `discussions turn example.md @architect` to get feedback.*
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---
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Name: AI-Architect
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Looking at this caching proposal, I have several architectural considerations:
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**Caching Backend**
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For a Kubernetes environment, I'd recommend Redis over in-memory caching. Redis provides:
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- Persistence options for durability
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- Built-in TTL support
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- Cluster mode for horizontal scaling
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- Pub/sub for cache invalidation across instances
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**Cache Stampede Prevention**
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We should implement one of these patterns:
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1. **Lock-based**: Only one request populates cache, others wait
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2. **Probabilistic early expiration**: Refresh before actual TTL
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3. **Background refresh**: Async refresh while serving stale data
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Q: What's our tolerance for serving slightly stale data? That affects which pattern works best.
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**Architecture Concern**
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The requirement to "invalidate on data mutations" needs more specificity. Are we talking about:
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- Same-service mutations (easier, can invalidate directly)
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- Cross-service mutations (harder, needs event-driven invalidation)
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CONCERN: Cross-service cache invalidation can become a distributed systems nightmare if not carefully designed.
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VOTE: CHANGES
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---
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Name: AI-Security
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From a security perspective, I need to raise several concerns:
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**Cache Poisoning**
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If an attacker can poison the cache with malicious data, that data gets served to all users. Mitigations:
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- Validate data before caching
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- Use signed cache keys
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- Implement cache isolation per tenant (if multi-tenant)
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**Sensitive Data**
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Q: Will any cached responses contain sensitive data? If so, we need:
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- Encryption at rest in Redis
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- Secure cache key generation (no user IDs in keys if possible)
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- Appropriate TTLs for sensitive data
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**Access Control**
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Redis should not be exposed to the public internet. Ensure:
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- Redis is in private subnet
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- Authentication enabled (requirepass)
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- TLS for data in transit if crossing network boundaries
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CONCERN: The requirements don't mention data sensitivity classification. We need to know what's being cached.
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VOTE: CHANGES
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---
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Name: AI-Pragmatist
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I appreciate the thorough analysis, but let's not over-engineer this.
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**MVP Proposal**
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For v1, I suggest:
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1. Use Redis (it's already in our stack, right?)
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2. Simple TTL-based expiration (no fancy invalidation)
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3. Cache-aside pattern (app checks cache, falls back to DB)
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4. Start with just the top 3 most accessed endpoints
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**What We Can Defer**
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- Sophisticated invalidation (use short TTLs instead for MVP)
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- Cache stampede handling (monitor first, optimize if needed)
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- Cross-service invalidation (start with single-service scope)
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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.
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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.
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VOTE: READY
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---
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*This example shows a typical discussion with different perspectives. The discussion would continue until consensus is reached.*
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