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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)

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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