2nd/10_Wiki/Topics/Architecture/Technical-Architecture.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-technical-architecture	Technical Architecture	10_Wiki/Topics	verified	self	System Architecture Tech Architecture 기술 아키텍처	none	A	0.9	applied	architecture system-design structure		2026-05-10	pending	language framework agnostic C4/arc42

Technical Architecture

매 한 줄

"매 system 의 high-level structure + 매 design decision 의 rationale". 매 component, 매 boundary, 매 data flow, 매 quality attribute (performance, security, scalability) 의 결정. 매 2026 modern stack 은 C4 model + ADR + arc42 의 combination 으로 documentation.

매 핵심

매 4+1 view (Kruchten 1995)

• Logical: 매 functional decomposition (class, module).
• Process: 매 runtime concurrency (thread, service).
• Development: 매 source code organization (package, repo).
• Physical: 매 deployment topology (node, network).
• Scenarios: 매 use case 의 cross-cutting validation.

매 C4 model (Brown 2018)

• L1 Context: 매 system + 매 external actors.
• L2 Container: 매 deployable unit (web app, DB, queue).
• L3 Component: 매 container 의 internal module.
• L4 Code: 매 class diagram (rarely needed).

매 quality attributes (ISO 25010)

• Performance · Scalability · Availability · Security · Maintainability · Testability · Observability.
• 매 trade-off 의 명시 — 매 "all of them" 은 fantasy.

매 응용

1. Greenfield project 시 C4 L1+L2 먼저, ADR 로 매 decision 기록.
2. Legacy reverse engineering — 매 dependency graph 추출 후 component view.
3. Architecture review — quality attribute scenario 의 validation.

💻 패턴

C4 diagram (PlantUML)

@startuml
!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Container.puml

Person(user, "Customer")
System_Boundary(shop, "E-commerce") {
  Container(web, "Web App", "Next.js 15")
  Container(api, "API", "Node.js / Fastify")
  ContainerDb(db, "Database", "Postgres 16")
  Container(queue, "Queue", "Redis Streams")
}
System_Ext(stripe, "Stripe")

Rel(user, web, "Browses", "HTTPS")
Rel(web, api, "API calls", "JSON/HTTPS")
Rel(api, db, "Reads/Writes", "SQL")
Rel(api, queue, "Publishes events")
Rel(api, stripe, "Charges", "REST")
@enduml

ADR template

# ADR 0007: Choose Postgres over MongoDB

## Status: Accepted (2026-05-10)

## Context
Need primary store for order data. Strong consistency required.
Team has 5 years Postgres experience.

## Decision
Postgres 16 with JSONB for flexible product attributes.

## Consequences
+ ACID transactions for orders.
+ Mature ecosystem (Prisma, pgvector for AI features).
+ Single skill set for ops.
- Less flexible schema evolution.
- Manual sharding if scale > single node.

## Alternatives considered
- MongoDB: rejected — eventual consistency unsuitable for orders.
- DynamoDB: rejected — vendor lock-in, query flexibility.

Hexagonal architecture (ports & adapters)

// Domain (port)
interface OrderRepository {
  save(order: Order): Promise<void>;
  findById(id: string): Promise<Order | null>;
}

// Application
class PlaceOrderUseCase {
  constructor(private repo: OrderRepository, private payments: PaymentGateway) {}
  async execute(cmd: PlaceOrderCommand) {
    const order = Order.create(cmd);
    await this.payments.charge(order.total);
    await this.repo.save(order);
  }
}

// Infrastructure (adapter)
class PostgresOrderRepository implements OrderRepository {
  async save(order: Order) { /* SQL */ }
  async findById(id: string) { /* SQL */ }
}

Layered architecture

┌─ Presentation (controllers, DTOs)
├─ Application (use cases)
├─ Domain (entities, value objects, services)
└─ Infrastructure (DB, HTTP, queue adapters)

Event-driven boundary

// Publisher (order service)
await events.publish('OrderPlaced', {
  orderId: order.id,
  customerId: order.customerId,
  total: order.total.amount,
  ts: Date.now(),
});

// Subscriber (notification service — independent deploy)
events.subscribe('OrderPlaced', async (e) => {
  await emailClient.send(e.customerId, 'order-confirmation', e);
});

Quality attribute scenario

attribute: Performance
source: 1000 concurrent users
stimulus: place order
artifact: API
environment: peak load
response: order accepted
measure: p95 latency < 500ms, error rate < 0.1%

매 결정 기준

상황	Approach
Small team, single domain	Layered monolith
Multiple teams, bounded contexts	Microservices
Heavy I/O, async workflow	Event-driven
Domain-rich, complex rules	Hexagonal + DDD
Read-heavy, eventual consistency OK	CQRS + event sourcing

기본값: 매 modular monolith 부터 시작 — 매 microservice 의 premature split 의 regret.

🔗 Graph

• 부모: Software_Architecture · System Design
• 변형: Microservices · Hexagonal Architecture · Event-Driven Architecture
• 응용: C4 Model · Architecture Decision Record
• Adjacent: Domain-Driven Design · Testability_Architecture · Technical_Debt

🤖 LLM 활용

언제: ADR drafting, C4 generation, quality attribute analysis, architecture review. 언제 X: 매 production 의 actual capacity planning — 매 real load test 필요.

❌ 안티패턴

• Big design up front: 매 waterfall 의 회귀.
• No documentation: 매 6개월 후 nobody knows why.
• Microservices for 3 devs: distributed monolith 의 distributed pain.
• Cargo cult architecture: Netflix scale 의 mimicry without justification.

🧪 검증 / 중복

• Verified (Kruchten 4+1 1995; Brown C4 2018; arc42 2024).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — 4+1 + C4 + hexagonal patterns