14 KiB
14 KiB
Brainstorm: API Performance Optimization Strategies
Problem Statement
What we're solving: API response time has degraded from 200ms (p95) to 800ms (p95) over the past 3 months. Users are experiencing slow page loads and some are timing out.
Decision to make: Which optimization approaches should we prioritize for the next quarter to bring p95 response time back to <300ms?
Context:
- REST API serving 50k requests/day
- PostgreSQL database, 200GB data
- Node.js/Express backend
- Current p95: 800ms, p50: 350ms
- Team: 3 backend engineers, 1 devops
- Quarterly engineering budget: 4 engineer-months
Constraints:
- Cannot break existing API contracts (backwards compatible)
- Must maintain 99.9% uptime during changes
- No more than $2k/month additional infrastructure cost
- Must ship improvements within 3 months
Diverge: Generate Ideas
Target: 40 ideas
Prompt: Generate as many ways as possible to improve API response time. Suspend judgment. All ideas are valid - from quick wins to major architectural changes.
All Ideas
- Add Redis caching layer for frequent queries
- Database query optimization (add indexes)
- Implement database connection pooling
- Use GraphQL to reduce over-fetching
- Add CDN for static assets
- Implement HTTP/2 server push
- Compress API responses with gzip
- Paginate large result sets
- Use database read replicas
- Implement response caching headers (ETag, If-None-Match)
- Migrate to serverless (AWS Lambda)
- Add API gateway for request routing
- Implement request batching
- Use database query result caching
- Optimize N+1 query problems
- Implement lazy loading for related data
- Switch to gRPC from REST
- Add application-level caching (in-memory)
- Optimize JSON serialization
- Implement database partitioning
- Use faster ORM or raw SQL
- Add async processing for slow operations
- Implement API rate limiting to prevent overload
- Optimize Docker container size
- Use database materialized views
- Implement query result streaming
- Add load balancer for horizontal scaling
- Optimize database schema (denormalization)
- Implement incremental/delta responses
- Use WebSockets for real-time data
- Migrate to NoSQL (MongoDB, DynamoDB)
- Implement API response compression (Brotli)
- Add edge caching (Cloudflare Workers)
- Use database archival for old data
- Implement request queuing/throttling
- Optimize API middleware chain
- Use faster JSON parser (simdjson)
- Implement selective field loading
- Add monitoring and alerting for slow queries
- Database vacuum/analyze for query planner
Total generated: 40 ideas
Cluster: Organize Themes
Goal: Group similar ideas into 4-8 distinct categories
Cluster 1: Caching Strategies (9 ideas)
- Add Redis caching layer for frequent queries
- Implement response caching headers (ETag, If-None-Match)
- Use database query result caching
- Add application-level caching (in-memory)
- Add CDN for static assets
- Add edge caching (Cloudflare Workers)
- Implement response caching at API gateway
- Use database materialized views
- Cache computed/aggregated results
Cluster 2: Database Query Optimization (11 ideas)
- Database query optimization (add indexes)
- Optimize N+1 query problems
- Use faster ORM or raw SQL
- Implement selective field loading
- Optimize database schema (denormalization)
- Add monitoring and alerting for slow queries
- Database vacuum/analyze for query planner
- Implement lazy loading for related data
- Use database query result caching (also in caching)
- Database archival for old data
- Database partitioning
Cluster 3: Data Transfer Optimization (7 ideas)
- Compress API responses with gzip/Brotli
- Paginate large result sets
- Implement request batching
- Optimize JSON serialization
- Use faster JSON parser (simdjson)
- Implement incremental/delta responses
- Implement query result streaming
Cluster 4: Infrastructure Scaling (7 ideas)
- Use database read replicas
- Add load balancer for horizontal scaling
- Implement database connection pooling
- Optimize Docker container size
- Migrate to serverless (AWS Lambda)
- Add API gateway for request routing
- Implement request queuing/throttling
Cluster 5: Architectural Changes (4 ideas)
- Use GraphQL to reduce over-fetching
- Switch to gRPC from REST
- Use WebSockets for real-time data
- Migrate to NoSQL (MongoDB, DynamoDB)
Cluster 6: Async & Offloading (2 ideas)
- Add async processing for slow operations
- Implement background job processing for heavy tasks
Total clusters: 6 themes
Converge: Evaluate & Select
Evaluation Criteria:
- Impact on p95 latency (weight: 3x) - How much will this reduce response time?
- Implementation effort (weight: 2x) - Engineering time required (lower = better)
- Infrastructure cost (weight: 1x) - Additional monthly cost (lower = better)
Scoring scale: 1-10 (higher = better)
Scored Ideas
| Idea | Impact (3x) | Effort (2x) | Cost (1x) | Weighted Total |
|---|---|---|---|---|
| Add Redis caching | 9 | 7 | 7 | 9×3 + 7×2 + 7×1 = 48 |
| Optimize N+1 queries | 8 | 8 | 10 | 8×3 + 8×2 + 10×1 = 50 |
| Add database indexes | 7 | 9 | 10 | 7×3 + 9×2 + 10×1 = 49 |
| Response compression (gzip) | 6 | 9 | 10 | 6×3 + 9×2 + 10×1 = 45 |
| Database connection pooling | 6 | 8 | 10 | 6×3 + 8×2 + 10×1 = 44 |
| Paginate large results | 7 | 7 | 10 | 7×3 + 7×2 + 10×1 = 45 |
| DB read replicas | 8 | 5 | 4 | 8×3 + 5×2 + 4×1 = 38 |
| Async processing | 6 | 6 | 8 | 6×3 + 6×2 + 8×1 = 38 |
| GraphQL migration | 7 | 3 | 9 | 7×3 + 3×2 + 9×1 = 36 |
| Serverless migration | 5 | 2 | 5 | 5×3 + 2×2 + 5×1 = 24 |
Scoring notes:
- Impact: Based on estimated latency reduction (9-10 = >400ms, 7-8 = 200-400ms, 5-6 = 100-200ms)
- Effort: Inverse scale (9-10 = <1 week, 7-8 = 1-2 weeks, 5-6 = 3-4 weeks, 3-4 = 1-2 months, 1-2 = 3+ months)
- Cost: Inverse scale (10 = $0, 8-9 = <$200/mo, 6-7 = <$500/mo, 4-5 = <$1k/mo, 1-3 = >$1k/mo)
Top 3 Selections
1. Fix N+1 Query Problems (Score: 50)
Why selected: Highest overall score - high impact, reasonable effort, zero cost
Rationale:
- Impact (8/10): N+1 queries are a common culprit for slow APIs. Profiling shows several endpoints making 50-100 queries per request. Fixing this could reduce p95 by 300-500ms.
- Effort (8/10): Can identify with APM tools (DataDog), fix iteratively. Estimated 2-3 weeks for main endpoints.
- Cost (10/10): Zero additional infrastructure cost - purely code optimization.
Next steps:
- Week 1: Profile top 10 slowest endpoints with APM to identify N+1 patterns
- Week 2-3: Implement eager loading/joins for identified queries
- Week 4: Deploy with feature flags, measure impact
- Expected improvement: Reduce p95 from 800ms to 500-600ms
Measurement:
- Track p95/p99 latency per endpoint before/after
- Monitor database query counts (should decrease significantly)
- Verify no increase in memory usage from eager loading
2. Add Database Indexes (Score: 49)
Why selected: Second highest score - very low effort for solid impact
Rationale:
- Impact (7/10): Database query analysis shows several full table scans. Adding indexes could reduce individual query time by 50-80%.
- Effort (9/10): Quick wins - can identify missing indexes via EXPLAIN ANALYZE, add indexes with minimal risk. Estimated 1 week.
- Cost (10/10): Marginal storage cost for indexes (~5-10GB), no new infrastructure.
Next steps:
- Day 1-2: Run EXPLAIN ANALYZE on slow queries (from slow query log)
- Day 3-4: Create indexes on foreign keys, WHERE clause columns, JOIN columns
- Day 5: Deploy indexes during low-traffic window, monitor impact
- Expected improvement: Reduce p95 by 100-200ms for index-heavy endpoints
Measurement:
- Compare query execution plans before/after (table scan → index scan)
- Track index usage with pg_stat_user_indexes
- Monitor index size growth
Considerations:
- Some writes may slow down slightly (index maintenance)
- Test on staging first to verify no lock contention
3. Implement Redis Caching (Score: 48)
Why selected: Highest impact potential, moderate effort and cost
Rationale:
- Impact (9/10): Caching frequently-accessed data (user profiles, config, lookup tables) could eliminate 60-70% of database queries. Massive impact for cacheable endpoints.
- Effort (7/10): Moderate effort - setup Redis, implement caching layer, handle cache invalidation. Estimated 2-3 weeks.
- Cost (7/10): Redis managed service ~$200-400/month (ElastiCache t3.medium)
Next steps:
- Week 1: Analyze request patterns - identify most-frequent queries for caching
- Week 2: Setup Redis (ElastiCache), implement cache-aside pattern for top 3 endpoints
- Week 3: Implement cache invalidation strategy (TTL + event-based)
- Week 4: Rollout with monitoring
- Expected improvement: Reduce p95 from 800ms to 300-400ms for cached endpoints (cache hit rate target: >80%)
Measurement:
- Track cache hit rate (target >80%)
- Monitor Redis memory usage and eviction rate
- Compare endpoint latency with/without cache
- Track database query reduction
Considerations:
- Cache invalidation complexity (implement carefully to avoid stale data)
- Redis failover strategy (what happens if Redis is down?)
- Cold start performance (first request still slow)
Runner-Ups (For Future Consideration)
Response Compression (gzip) (Score: 45)
- Very quick win (1-2 days to implement)
- Modest impact for large payloads (~20-30% response size reduction → ~100ms latency improvement)
- Recommendation: Implement in parallel with top 3 (low effort, no downside)
Database Connection Pooling (Score: 44)
- Quick to implement if not already in place
- Reduces connection overhead
- Recommendation: Verify current pooling configuration first - may already be optimized
Pagination (Score: 45)
- Essential for endpoints returning large result sets
- Quick to implement (2-3 days)
- Recommendation: Implement in parallel - protect against future growth
Database Read Replicas (Score: 38)
- Good for read-heavy workload scaling
- Higher cost (~$500-800/month)
- Recommendation: Defer to Q2 after quick wins exhausted - consider if traffic grows 2-3x
Next Steps
Immediate Actions (Week 1-2)
Priority 1: N+1 Query Optimization
- Enable APM detailed query tracing
- Profile top 10 slowest endpoints
- Create backlog of N+1 fixes prioritized by impact
- Assign to Engineer A
Priority 2: Database Index Analysis
- Export slow query log (queries >500ms)
- Run EXPLAIN ANALYZE on top 20 slow queries
- Identify missing indexes
- Assign to Engineer B
Priority 3: Redis Caching Planning
- Analyze request patterns to identify cacheable data
- Design cache key strategy
- Document cache invalidation approach
- Get budget approval for Redis ($300/month)
- Assign to Engineer C
Quick Win (parallel):
- Implement gzip compression (Engineer A, 4 hours)
- Verify connection pooling config (Engineer B, 2 hours)
- Add pagination to
/usersand/ordersendpoints (Engineer C, 1 day)
Timeline
Week 1-2: Analysis + quick wins
- N+1 profiling complete
- Index analysis complete
- Redis architecture designed
- Gzip compression live
- Pagination live for 2 endpoints
Week 3-4: N+1 fixes + Indexes
- Top 5 N+1 queries fixed and deployed
- 10-15 database indexes added
- Target: p95 drops to 600ms
Week 5-7: Redis caching
- Redis infrastructure provisioned
- Top 3 endpoints cached
- Cache invalidation tested
- Target: p95 drops to 350ms for cached endpoints
Week 8-9: Measure, iterate, polish
- Monitor metrics
- Fix any regressions
- Extend caching to 5 more endpoints
- Target: Overall p95 <300ms
Week 10-12: Buffer for unknowns
- Address unexpected issues
- Optimize further if needed
- Document learnings
Success Criteria
Primary:
- p95 latency <300ms (currently 800ms)
- p99 latency <600ms (currently 1.5s)
- No increase in error rate
- 99.9% uptime maintained
Secondary:
- Database query count reduced by >40%
- Cache hit rate >80% for cached endpoints
- Additional infrastructure cost <$500/month
Monitoring:
- Daily p95/p99 latency dashboard
- Weekly review of slow query log
- Redis cache hit rate tracking
- Database connection pool utilization
Risks & Mitigation
Risk 1: N+1 fixes increase memory usage
- Mitigation: Profile memory before/after, implement pagination if needed
- Rollback: Revert to lazy loading if memory spikes >20%
Risk 2: Cache invalidation bugs cause stale data
- Mitigation: Start with short TTL (5 min), add event-based invalidation gradually
- Rollback: Disable caching for affected endpoints immediately
Risk 3: Index additions cause write performance degradation
- Mitigation: Test on staging with production-like load, monitor write latency
- Rollback: Drop problematic indexes
Risk 4: Timeline slips due to complexity
- Mitigation: Front-load quick wins (gzip, indexes) to show early progress
- Contingency: Descope Redis to Q2 if needed, focus on N+1 and indexes
Rubric Self-Assessment
Using rubric_brainstorm_diverge_converge.json:
Scores:
- Divergence Quantity: 5/5 (40 ideas - comprehensive exploration)
- Divergence Variety: 4/5 (good variety from quick fixes to major architecture changes)
- Divergence Creativity: 4/5 (includes both practical and ambitious ideas)
- Cluster Quality: 5/5 (6 distinct, well-labeled themes)
- Cluster Coverage: 5/5 (6 clusters covering infrastructure, data, architecture)
- Evaluation Criteria Clarity: 5/5 (impact, effort, cost - specific and weighted)
- Scoring Rigor: 4/5 (systematic scoring with justification)
- Selection Quality: 5/5 (clear top 3 with tradeoff analysis)
- Actionability: 5/5 (detailed timeline, owners, success criteria)
- Process Integrity: 5/5 (clear phase separation, no premature filtering)
Average: 4.7/5 - Excellent (high-stakes technical decision quality)
Assessment: This brainstorm is ready for use in prioritizing engineering work. Strong divergence phase with 40 varied ideas, clear clustering by mechanism, and rigorous convergence with weighted scoring. Actionable plan with timeline and risk mitigation.