How to Manage Performance Optimization Projects

Performance optimization requires systematic measurement, prioritization based on impact, and careful tracking of improvements. GitScrum's effort tracking, milestone features, and metrics visualization help teams organize optimization work, measure progress against baselines, and communicate wins to stakeholders effectively.

Performance Project Phases

Performance Project Structure

PERFORMANCE OPTIMIZATION EPIC

Epic: API Performance Improvement Q1

Phase 1: Baseline & Analysis
├── Establish performance baseline
│   └── Document current P50, P95, P99 latencies
├── Set up performance monitoring
│   └── APM integration, dashboards
├── Profile production workload
│   └── Identify top 10 slow endpoints
└── Create prioritized improvement backlog

Phase 2: Quick Wins (Week 1-2)
├── Add missing database indexes
├── Optimize N+1 query patterns
├── Enable response compression
├── Add HTTP caching headers
└── Configure connection pooling

Phase 3: Systematic Improvements (Week 3-6)
├── Implement query result caching
├── Optimize slow report queries
├── Add background job processing
├── Implement pagination for large datasets
└── Database query optimization

Phase 4: Monitoring & Prevention
├── Set up performance budgets
├── Add performance regression tests
├── Configure alerting thresholds
└── Document performance best practices

Bottleneck Prioritization

PERFORMANCE IMPROVEMENT PRIORITIZATION

IMPACT × EFFORT MATRIX

              High Impact
                   │
     Quick         │    Strategic
     Wins          │    Investments
     ★★★★★         │    ★★★★
     Do First      │    Plan Carefully
                   │
─────────────────────────────────────
                   │
     Maybe         │    Avoid
     Later         │    (for now)
     ★★            │    ★
                   │
              Low Impact

SCORING EACH IMPROVEMENT:
┌─────────────────────────────────────────────────┐
│  Improvement          Impact  Effort  Priority  │
│  ─────────────────────────────────────────────  │
│  Add order_id index     5       1      5 ★★★★★  │
│  Fix N+1 in users       4       2      4 ★★★★   │
│  Implement caching      5       4      3 ★★★    │
│  Optimize reports       3       5      2 ★★     │
│  Rewrite search         4       5      2 ★★     │
└─────────────────────────────────────────────────┘

Impact Factors:
• Request volume affected
• Current latency (P95)
• User-facing vs internal
• Revenue impact

Task Template for Performance Work

PERFORMANCE IMPROVEMENT TASK

┌─────────────────────────────────────────────────┐
│  Title: Optimize user list API endpoint         │
│  Labels: [performance] [database] [api]         │
│                                                 │
│  CURRENT STATE:                                 │
│  Endpoint: GET /api/users                       │
│  P50: 450ms | P95: 1200ms | P99: 3500ms        │
│  Requests/day: 45,000                           │
│                                                 │
│  ROOT CAUSE:                                    │
│  • N+1 query loading user preferences           │
│  • Missing index on organization_id             │
│  • No pagination, loading all users             │
│                                                 │
│  PROPOSED SOLUTION:                             │
│  1. Add eager loading for preferences           │
│  2. Add index on organization_id                │
│  3. Implement cursor pagination                 │
│                                                 │
│  TARGET STATE:                                  │
│  P50: <100ms | P95: <300ms | P99: <500ms       │
│                                                 │
│  VALIDATION:                                    │
│  • Run load test before/after                   │
│  • Compare production metrics 24h post-deploy   │
│  • Verify no regression in other endpoints      │
│                                                 │
│  RESULT: (filled after completion)              │
│  P50: 85ms | P95: 210ms | P99: 380ms           │
│  Improvement: 82% at P95                        │
└─────────────────────────────────────────────────┘

Performance Budget

PERFORMANCE BUDGET TRACKING

API LATENCY BUDGET:
┌─────────────────────────────────────────────────┐
│  Metric          Budget    Current   Status     │
│  ─────────────────────────────────────────────  │
│  P50 response    100ms     85ms      ✓ Good     │
│  P95 response    500ms     420ms     ✓ Good     │
│  P99 response    1000ms    980ms     ⚠ Warning  │
│  Error rate      0.1%      0.08%     ✓ Good     │
└─────────────────────────────────────────────────┘

FRONTEND BUDGET:
┌─────────────────────────────────────────────────┐
│  Metric              Budget    Current  Status  │
│  ─────────────────────────────────────────────  │
│  LCP                  2.5s      2.1s    ✓ Good  │
│  FID                  100ms     75ms    ✓ Good  │
│  CLS                  0.1       0.08    ✓ Good  │
│  Bundle size          500KB     480KB   ✓ Good  │
│  Time to interactive  3.5s      3.2s    ✓ Good  │
└─────────────────────────────────────────────────┘

ALERTING RULES:
┌─────────────────────────────────────────────────┐
│  Warning: Metric > 80% of budget                │
│  Critical: Metric > 100% of budget              │
│                                                 │
│  Action: Block deployment if critical           │
│          Investigate if warning                 │
└─────────────────────────────────────────────────┘

Best Practices

1. Measure before optimizing with real baselines
2. Profile in production or production-like environment
3. Track before/after for every change
4. Start with quick wins to build momentum
5. Set performance budgets with alerts
6. Add to CI/CD for regression prevention
7. Document improvements for knowledge sharing
8. Celebrate wins with metrics visibility

Anti-Patterns

✗ Optimizing without measuring
✗ Premature optimization of non-bottlenecks
✗ One big optimization vs incremental
✗ No validation after changes
✗ Ignoring P95/P99 (only looking at averages)
✗ No ongoing monitoring post-project

Related Solutions

• Code Quality Tracking
• Managing Technical Debt Systematically
• CI-CD Pipeline Best Practices