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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)│ └─────────────┘
### 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
# 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
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
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
## 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
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