Belajar Performance Test Engineer - Performance Architecture Review
Melakukan performance architecture review: mengidentifikasi bottleneck di level arsitektur, microservices performance patterns, scalability assessment, dan menyusun rekomendasi arsitektural untuk performa yang optimal.
#Pendahuluan
Setelah di episode 24 kita memahami database performance engineering, kini saatnya naik ke level yang lebih tinggi: performance architecture review. Di level ini, performance engineer meninjau arsitektur sistem secara keseluruhan — mengidentifikasi bottleneck di level desain, bukan hanya di level kode atau query.
Architecture review membutuhkan pemahaman holistik tentang bagaimana komponen-komponen saling berinteraksi, di mana titik-titik kritis, dan bagaimana sistem bereaksi saat beban meningkat. Ini adalah level tertinggi dari performance engineering.
#Performance Architecture Patterns
#Monolith vs Microservices
| Aspek | Monolith | Microservices |
|---|---|---|
| Performance overhead | Minimal | Network overhead antar services |
| Scaling | Vertical (lebih besar server) | Horizontal (lebih banyak instances) |
| Bottleneck identification | Lebih mudah (satu proses) | Lebih sulit (distribusi) |
| Deployment | Tunggal | Banyak services |
#Microservices Performance Patterns
#Circuit Breaker
Circuit breaker mencegah cascade failure — saat satu service gagal, circuit terbuka dan request langsung ditolak tanpa menunggu timeout:
// Contoh circuit breaker pattern
class CircuitBreaker {
constructor(fn, options = {}) {
this.fn = fn;
this.failureCount = 0;
this.failureThreshold = options.failureThreshold || 5;
this.state = 'CLOSED';
}
async call(...args) {
if (this.state === 'OPEN') throw new Error('Circuit open');
try {
const result = await this.fn(...args);
this.failureCount = 0;
return result;
} catch (err) {
this.failureCount++;
if (this.failureCount >= this.failureThreshold) {
this.state = 'OPEN';
}
throw err;
}
}
}#Bulkhead Pattern
Bulkhead pattern mengisolasi resource per komponen — kegagalan satu komponen tidak menghabiskan resource komponen lain:
Service A: pool_size = 20 connections
Service B: pool_size = 20 connections
→ Kegagalan Service A tidak mempengaruhi koneksi ke Service B#Rate Limiting
Rate limiting melindungi service dari overload dengan membatasi jumlah request:
# Token bucket rate limiter
class TokenBucket:
def __init__(self, rate, capacity):
self.rate = rate
self.capacity = capacity
self.tokens = capacity
self.last_update = time.time()
def allow(self):
now = time.time()
self.tokens += (now - self.last_update) * self.rate
self.tokens = min(self.tokens, self.capacity)
self.last_update = now
if self.tokens >= 1:
self.tokens -= 1
return True
return False#Scalability Assessment
#Horizontal vs Vertical Scaling
#Auto-Scaling Assessment
Untuk review auto-scaling:
- Scaling trigger: CPU? Memory? Request count? Custom metrics?
- Scaling policy: target tracking? step scaling? scheduled?
- Cooldown period: berapa lama setelah scaling sampai stabil?
- Cost implications: berapa biaya saat peak vs off-peak?
#Stateless vs Stateful Services
Stateless services lebih mudah di-scale horizontal — setiap instance bisa menangani request dari user mana pun. Stateful services membutuhkan sticky sessions atau external state store — lebih kompleks untuk di-scale.
#Bottleneck Identification at Architecture Level
#Common Architecture Bottlenecks
| Bottleneck | Gejala | Solusi |
|---|---|---|
| Single point of failure | Satu service down = semua down | Redundancy, circuit breaker |
| Synchronous cascading | Latency menambah di setiap hop | Async processing, caching |
| Shared database | Write contention tinggi | CQRS, database per service |
| No caching layer | Database overload | Redis/Memcached layer |
| Monolithic database | Semua services ke satu DB | Database per service |
#Latency Budget Allocation
User request latency budget: 500ms total
├── CDN/Edge: 10ms
├── API Gateway: 20ms
├── Auth Service: 30ms
├── Business Logic: 100ms
├── Database Query: 100ms
├── External API: 50ms
├── Serialization: 20ms
├── Network overhead: 50ms
└── Buffer: 120ms#Performance Review Checklist
#Infrastructure Review
□ Load balancer configuration
- Health check interval
- Session persistence
- Connection draining
□ Auto-scaling configuration
- Scaling triggers
- Min/max instances
- Cooldown period
□ Caching layers
- CDN for static assets
- Application-level cache
- Database query cache#Application Review
□ Async processing
- Background jobs for heavy operations
- Event-driven architecture
- Message queues
□ Connection management
- Connection pooling
- Timeout configuration
- Retry policies
□ Resource limits
- Memory limits
- CPU limits
- Request size limits#Database Review
□ Schema design
- Normalization level
- Partition strategy
- Archival strategy
□ Index strategy
- Composite indexes for query patterns
- Partial indexes for filtered queries
- Covering indexes for frequent queries
□ Connection management
- Pool sizing
- PgBouncer / connection pooler
- Read replica routing#Reporting Architecture Findings
#Executive Summary
Performance Architecture Review: E-commerce Platform
Date: 2026-08-16
Reviewer: Performance Test Engineer
Risk Level: MEDIUM
Key Findings:
1. High Risk: Single database instance — no read replicas
2. Medium Risk: Synchronous payment processing — blocking
3. Low Risk: Missing CDN for product images
Recommendations:
1. Deploy read replicas (effort: 2 weeks, impact: 40% latency reduction)
2. Async payment processing (effort: 1 week, impact: 30% throughput increase)
3. CDN for images (effort: 2 days, impact: 50% LCP improvement)#Penutup
Di episode 25 ini kalian telah memahami performance architecture review:
- Architecture patterns: microservices patterns (circuit breaker, bulkhead, rate limiting).
- Scalability assessment: horizontal vs vertical, auto-scaling, stateless vs stateful.
- Bottleneck identification: latency budget allocation, common architecture bottlenecks.
- Review checklist: infrastructure, application, dan database review.
- Reporting: executive summary untuk stakeholder.
Di episode 26 selanjutnya, kita akan membahas Ekosistem & Tren Modern 2026 — tren terbaru dalam performance engineering, tools emerging, dan prediksi masa depan. Siapkan podcast kalian!