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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: 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

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 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

1. What's acceptable replication lag for analytics? (Proposed: <5 sec)
2. How do we handle replica failure? (Proposed: Fallback to primary)
3. 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

• PostgreSQL Replication Docs
• Cost Analysis Spreadsheet
• Load Test Results

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: patricio0312rev
• Source: patricio0312rev/skillset
• License: MIT

Install and usage instructions live in the source repository linked above.