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---
id: wiki-2026-0508-architecture-refactor
title: Architecture Refactor
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [strangler-fig, big-bang-rewrite, modernization]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [architecture, refactor, strangler-fig, modernization]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
language: polyglot
framework: strangler-fig
---
# Architecture Refactor
## 매 한 줄
> **"매 incremental 의 always wins"**. 매 architecture refactor 의 default approach 의 Martin Fowler 의 Strangler Fig (2004) — 매 old system 의 around 의 new system 의 gradual 의 grow. 매 big-bang rewrite 의 historically failure rate >70% (Ousterhout, Brooks). 매 2026 의 typical pattern: 매 monolith → modular monolith → selective service extraction.
## 매 핵심
### 매 refactor strategies
- **Strangler Fig**: 매 facade 의 routing — 매 traffic 의 incrementally 의 new system 의 redirect.
- **Branch by Abstraction**: 매 abstraction layer 의 introduce — 매 dual 의 implementation — 매 old 의 remove.
- **Parallel Run**: 매 old + new 의 동시 실행 — 매 output 의 compare.
- **Big Bang Rewrite**: 매 last resort — 매 only 매 system 의 small + scope 의 frozen 의 가능.
### 매 refactor 의 prerequisite
- **Test coverage**: 매 characterization tests (Feathers) 의 minimum.
- **Observability**: 매 metrics + traces 의 before/after diff.
- **Feature flag**: 매 reversibility 의 prerequisite.
- **ADR**: 매 decision rationale 의 documented.
### 매 응용
1. Monolith → modular monolith — 매 module boundary 의 enforce (ArchUnit).
2. Service extraction — 매 bounded context 의 따라.
3. Database split — 매 most expensive — 매 last 의 do.
4. Framework upgrade — 매 incremental migration.
## 💻 패턴
### Strangler Fig — Nginx routing facade
```nginx
upstream legacy { server legacy.internal:8080; }
upstream new { server new.internal:8080; }
server {
listen 443 ssl;
# New endpoints — gradual rollout
location /api/v2/orders { proxy_pass http://new; }
location /api/v2/users { proxy_pass http://new; }
# Everything else still legacy
location / { proxy_pass http://legacy; }
}
```
### Branch by Abstraction (Java)
```java
// Step 1 — extract interface
interface PaymentGateway {
Receipt charge(Order o);
}
// Step 2 — wrap legacy
class LegacyStripeGateway implements PaymentGateway { /* ... */ }
// Step 3 — new impl behind flag
class AdyenGateway implements PaymentGateway { /* ... */ }
// Step 4 — feature-flag pick
class GatewayFactory {
PaymentGateway pick(String tenant) {
return flags.isOn("adyen", tenant)
? new AdyenGateway()
: new LegacyStripeGateway();
}
}
// Step 5 — once 100% rollout, delete LegacyStripeGateway
```
### Parallel Run (shadow traffic)
```ts
async function chargeShadow(order: Order): Promise<Receipt> {
const [primary, shadow] = await Promise.allSettled([
legacy.charge(order),
newImpl.charge(order) // never persists, just observes
]);
if (shadow.status === 'fulfilled' && primary.status === 'fulfilled') {
diff.record('charge', primary.value, shadow.value);
}
return primary.status === 'fulfilled' ? primary.value : Promise.reject(primary.reason);
}
```
### Database Strangling — dual write + read switch
```python
# Phase 1: dual write
def save_order(o: Order):
legacy_db.insert(o)
try:
new_db.insert(o)
except Exception as e:
log.warn("shadow write failed", e=e)
# Phase 2: dual read + diff
def get_order(id: str):
a = legacy_db.get(id)
b = new_db.get(id)
if a != b: metrics.increment("order.read.diverge")
return a # legacy still source of truth
# Phase 3: flip read source
def get_order(id: str):
return new_db.get(id)
# Phase 4: stop legacy write, decommission
```
### Module extraction (modular monolith → service)
```bash
# 1. Codify module boundary first (ArchUnit)
# 2. Replace direct calls with in-process interface
# 3. Add feature flag: in-process vs HTTP/gRPC
# 4. Deploy as separate process behind flag
# 5. Remove in-process path
```
### Characterization test (Feathers)
```python
import json, pytest
@pytest.mark.parametrize("fixture", load_fixtures("legacy_outputs/*.json"))
def test_legacy_behavior_preserved(fixture):
inputs = fixture["input"]
expected = fixture["legacy_output"]
actual = new_impl.run(**inputs)
assert actual == expected, f"divergence: {fixture['id']}"
```
### Refactor scoreboard (CI)
```yaml
# refactor-progress.yml — auto-generated dashboard
metrics:
- name: legacy-endpoints-remaining
cmd: grep -r "@legacy" src/ | wc -l
- name: new-coverage
cmd: jacoco --module=new-impl
- name: traffic-on-new
promql: sum(rate(http_requests_total{service="new"}[5m]))
/ sum(rate(http_requests_total[5m]))
```
## 매 결정 기준
| 상황 | Strategy |
|---|---|
| Active legacy + traffic | Strangler Fig |
| Library/abstraction swap | Branch by Abstraction |
| Risk-critical (payment, billing) | Parallel Run |
| DB schema change | Dual-write + flip read |
| Tiny system, frozen scope | Big Bang (rare) |
| Distributed monolith | Reverse first → modular monolith → extract |
**기본값**: Strangler Fig + feature flag + parallel-run on critical paths.
## 🔗 Graph
- 부모: [[Software Architecture]] · [[Refactoring_Best_Practices|Refactoring]]
- 변형: [[Strangler-Fig]] · [[Branch-by-Abstraction]]
- 응용: [[Modular Monolith]] · [[Microservices]]
- Adjacent: [[Architecture Erosion (아키텍처 침식)]] · [[Architectural-Constraint-Enforcement]] · [[Architecture Review (아키텍처 및 설계 리뷰)]]
## 🤖 LLM 활용
**언제**: 매 legacy code → modern equivalent translation, 매 codemod plan generation, 매 ADR draft.
**언제 X**: 매 production cutover decision — 매 human + traffic data 의 필수.
## ❌ 안티패턴
- **Big bang rewrite**: 매 70%+ failure rate — 매 last resort.
- **Refactor without tests**: 매 silent regression 의 guarantee.
- **No flag, no rollback**: 매 forward-only deploy — 매 incident magnification.
- **Premature service extraction**: 매 distributed monolith 의 worst-of-both.
- **Stop midway**: 매 두 system 의 forever maintain — 매 cost 의 doubled.
## 🧪 검증 / 중복
- Verified (Fowler — *StranglerFigApplication*; Feathers — *Working Effectively with Legacy Code*; Newman — *Monolith to Microservices*).
- 신뢰도 A.
## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — Strangler Fig + Branch-by-Abstraction + parallel-run patterns |