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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

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

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