rfc-generator

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排名: #13437

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RFC Generator

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RFC Template

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 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
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