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# RFC-042: Implement Read Replicas for Analytics **Status:** Draft | In Review | Accepted | Rejected | Implemented **Author:** Alice (alice@example.com) **Reviewers:** Bob, Charlie, David **Created:** 2024-01-15 **Updated:** 2024-01-20 **Target Date:** Q1 2024 ## Summary Add PostgreSQL read replicas to separate analytical queries from transactional workload, improving database performance and enabling new analytics features. ## Problem Statement ### Current Situation Our PostgreSQL database serves both transactional (OLTP) and analytical (OLAP) workloads: - 1000 writes/min (checkout, orders, inventory) - 5000 reads/min (user browsing, search) - 500 analytics queries/min (dashboards, reports) ### Issues 1. **Performance degradation**: Analytics queries slow down transactions 2. **Resource contention**: Complex reports consume CPU/memory 3. **Blocking features**: Can't add more dashboards without impacting users 4. **Peak hour problems**: Analytics scheduled during business hours ### Impact - Checkout p95 latency: 800ms (target: <300ms) - Database CPU: 75% average, 95% peak - Customer complaints about slow pages - Product team blocked on analytics features ### Success Criteria - Checkout latency <300ms p95 - Database CPU <50% - Support 2x more analytics queries - Zero impact on transactional performance ## Proposed Solution ### High-Level Design
┌─────────────┐ │ Primary │────────────────┐ │ (Write) │ │ └─────────────┘ │ ▼ ┌─────────────┐ │ Replica 1 │ │ (Read) │ └─────────────┘ ▼ ┌─────────────┐ │ Replica 2 │ │ (Analytics)│ └─────────────┘
code
### Architecture
1. **Primary database**: Handles all writes and critical reads
2. **Read Replica 1**: Serves user-facing read queries
3. **Read Replica 2**: Dedicated to analytics/reporting
### Routing Strategy
```typescript
const db = {
primary: primaryConnection,
read: replicaConnection,
analytics: analyticsConnection,
};
// Write
await db.primary.users.create(data);
// Critical read (always fresh)
await db.primary.users.findById(id);
// Non-critical read (can be slightly stale)
await db.read.products.search(query);
// Analytics
await db.analytics.orders.aggregate(pipeline);
Replication
- •Type: Streaming replication
- •Lag: <1 second for read replica, <5 seconds acceptable for analytics
- •Monitoring: Alert if lag >5 seconds
Detailed Design
Database Configuration
yaml
# Primary max_connections: 200 shared_buffers: 4GB work_mem: 16MB # Read Replica max_connections: 100 shared_buffers: 8GB work_mem: 32MB # Analytics Replica max_connections: 50 shared_buffers: 16GB work_mem: 64MB
Connection Pooling
typescript
const pools = {
primary: new Pool({ max: 20, min: 5 }),
read: new Pool({ max: 50, min: 10 }),
analytics: new Pool({ max: 10, min: 2 }),
};
Query Classification
typescript
enum QueryType {
WRITE = "primary",
CRITICAL_READ = "primary",
READ = "read",
ANALYTICS = "analytics",
}
function route(queryType: QueryType) {
return pools[queryType];
}
Alternatives Considered
Alternative 1: Vertical Scaling
Approach: Upgrade to larger database instance
- •Pros: Simple, no code changes
- •Cons: Expensive ($500 → $2000/month), doesn't separate workloads, still hits limits
- •Verdict: Rejected - doesn't solve isolation problem
Alternative 2: Separate Analytics Database
Approach: Copy data to dedicated analytics DB (e.g., ClickHouse)
- •Pros: Optimal for analytics, no impact on primary
- •Cons: Complex ETL pipeline, eventual consistency, high maintenance
- •Verdict: Defer - consider for future if replicas insufficient
Alternative 3: Materialized Views
Approach: Pre-compute analytics results
- •Pros: Fast queries, no replicas needed
- •Cons: Limited to known queries, maintenance overhead
- •Verdict: Complement to replicas, not replacement
Tradeoffs
What We're Optimizing For
- •Performance isolation
- •Cost efficiency
- •Quick implementation
- •Operational simplicity
What We're Sacrificing
- •Slight data staleness (acceptable for analytics)
- •Additional infrastructure complexity
- •Higher operational costs
Risks & Mitigations
Risk 1: Replication Lag
Impact: Analytics sees stale data Probability: Medium Mitigation:
- •Monitor lag continuously
- •Alert if >5 seconds
- •Document expected lag for users
Risk 2: Configuration Complexity
Impact: Routing errors, performance issues Probability: Low Mitigation:
- •Comprehensive testing
- •Gradual rollout
- •Easy rollback mechanism
Risk 3: Cost Overrun
Impact: Budget exceeded Probability: Low Mitigation:
- •Use smaller instance for analytics ($300/month)
- •Monitor usage
- •Right-size after 1 month
Rollout Plan
Phase 1: Setup (Week 1-2)
- • Provision read replica 1
- • Provision analytics replica 2
- • Configure replication
- • Verify lag <1 second
- • Load testing
Phase 2: Read Replica (Week 3)
- • Deploy routing logic
- • Route 10% search queries to replica
- • Monitor errors and latency
- • Ramp to 100%
Phase 3: Analytics Migration (Week 4-5)
- • Identify analytics queries
- • Update dashboard queries to analytics replica
- • Test reports
- • Migrate all analytics
Phase 4: Validation (Week 6)
- • Measure checkout latency improvement
- • Verify CPU reduction
- • User acceptance testing
- • Mark as complete
Success Metrics
Primary Goals
- •✅ Checkout latency <300ms p95 (currently 800ms)
- •✅ Primary DB CPU <50% (currently 75%)
- •✅ Zero errors from replication lag
Secondary Goals
- •Support 2x analytics queries
- •Enable new dashboard features
- •Team satisfaction survey >8/10
Cost Analysis
| Component | Current | Proposed | Delta |
|---|---|---|---|
| Primary DB | $500/mo | $500/mo | $0 |
| Read Replica | - | $500/mo | +$500 |
| Analytics Replica | - | $300/mo | +$300 |
| Total | $500/mo | $1,300/mo | +$800/mo |
ROI: Better performance enables revenue growth; analytics unlocks product insights
Open Questions
- •What's acceptable replication lag for analytics? (Proposed: <5 sec)
- •How do we handle replica failure? (Proposed: Fallback to primary)
- •Should we add more replicas later? (Proposed: Monitor and decide in Q2)
Timeline
- •Week 1-2: Provisioning and setup
- •Week 3: Read replica migration
- •Week 4-5: Analytics migration
- •Week 6: Validation
- •Total: 6 weeks
Appendix
References
Review History
- •2024-01-15: Initial draft (Alice)
- •2024-01-17: Added cost analysis (Bob)
- •2024-01-20: Addressed review comments
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## RFC Process ### 1. Draft (1 week) - Author writes RFC - Include problem, solution, alternatives - Share with team for early feedback ### 2. Review (1-2 weeks) - Distribute to reviewers - Collect comments - Address feedback - Iterate on design ### 3. Approval (1 week) - Present to architecture review - Resolve remaining concerns - Vote: Accept/Reject - Update status ### 4. Implementation - Track progress - Update RFC with learnings - Mark as implemented ## Best Practices 1. **Clear problem**: Start with why 2. **Concrete solution**: Be specific 3. **Consider alternatives**: Show you explored options 4. **Honest tradeoffs**: Every choice has costs 5. **Measurable success**: Define done 6. **Risk mitigation**: Plan for failure 7. **Iterative**: Update based on feedback ## Output Checklist - [ ] Problem statement - [ ] Proposed solution with architecture - [ ] 2+ alternatives considered - [ ] Tradeoffs documented - [ ] Risks with mitigations - [ ] Rollout plan with phases - [ ] Success metrics defined - [ ] Cost analysis - [ ] Timeline estimated - [ ] Reviewers assigned