refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-choreography
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title: Choreography
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Event Choreography, Saga Choreography]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [microservices, distributed-systems, events, saga]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: typescript
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framework: kafka-temporal
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---
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# Choreography
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## 매 한 줄
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> **"매 service 가 자기 part 의 dance step 을 알고 events 으로 react — 매 central conductor 없음."**. Choreography는 distributed workflow를 events 의 emit/subscribe 로 구현하는 pattern. Orchestration 의 반대 — 매 decoupling 매 high, 매 visibility 매 low. 2026년 event-driven microservices, EDA, Saga pattern 의 핵심 선택지.
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## 매 핵심
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### 매 Choreography vs Orchestration
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| 측면 | Choreography | Orchestration |
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|---|---|---|
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| Control | Distributed | Centralized (orchestrator) |
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| Coupling | Low (events) | Higher (orchestrator knows all) |
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| Visibility | Hard (trace tools 필요) | Easy (one workflow definition) |
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| Add new step | Just subscribe | Edit orchestrator |
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| Failure handling | Each service handles own | Orchestrator decides |
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| Examples | Kafka events, NATS | Temporal, AWS Step Functions |
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### 매 패턴 종류
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- **Event-carried state transfer**: event 가 모든 필요 data 포함.
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- **Event notification**: event 는 trigger only, state 는 query.
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- **Saga choreography**: distributed transaction 의 compensating events.
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### 매 응용
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1. E-commerce order flow (OrderCreated → Payment → Shipping events).
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2. User signup pipeline (UserRegistered → emails → analytics).
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3. IoT event processing (sensor → multiple consumers).
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4. Domain events (DDD bounded context integration).
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## 💻 패턴
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### Saga choreography (TypeScript + Kafka)
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```typescript
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// Order service
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async function createOrder(req: CreateOrderRequest) {
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const order = await db.orders.insert({ ...req, status: 'pending' });
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await kafka.produce('order.created', {
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orderId: order.id,
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userId: order.userId,
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amount: order.total,
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});
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return order;
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}
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// Order service also listens for compensations
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kafka.consume('payment.failed', async (evt) => {
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await db.orders.update(evt.orderId, { status: 'cancelled' });
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await kafka.produce('order.cancelled', { orderId: evt.orderId });
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});
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```
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### Payment service reacts independently
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```typescript
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kafka.consume('order.created', async (evt) => {
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try {
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const charge = await stripe.charges.create({
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amount: evt.amount * 100,
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customer: evt.userId,
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});
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await kafka.produce('payment.succeeded', {
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orderId: evt.orderId,
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chargeId: charge.id,
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});
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} catch (err) {
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await kafka.produce('payment.failed', {
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orderId: evt.orderId,
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reason: String(err),
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});
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}
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});
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```
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### Shipping service
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```typescript
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kafka.consume('payment.succeeded', async (evt) => {
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const shipment = await shippingApi.create({ orderId: evt.orderId });
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await kafka.produce('shipment.created', { ...shipment });
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});
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```
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### Event schema (Avro / JSON Schema)
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```json
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{
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"type": "record",
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"name": "OrderCreated",
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"fields": [
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{ "name": "orderId", "type": "string" },
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{ "name": "userId", "type": "string" },
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{ "name": "amount", "type": "double" },
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{ "name": "createdAt", "type": "string" }
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]
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}
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```
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### Idempotent consumer
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```typescript
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async function handleOrderCreated(evt: OrderCreated) {
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// Dedup by event id
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const seen = await redis.set(
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`processed:${evt.eventId}`,
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'1',
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{ NX: true, EX: 86400 },
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);
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if (!seen) return; // already processed
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await processOrder(evt);
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}
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```
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### Distributed tracing (OTel) 매 visibility 회복
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```typescript
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import { trace, context, propagation } from '@opentelemetry/api';
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async function publishWithTrace(topic: string, evt: any) {
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const span = trace.getActiveSpan();
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const carrier: Record<string, string> = {};
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propagation.inject(context.active(), carrier);
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await kafka.produce(topic, {
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...evt,
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_trace: carrier,
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});
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}
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Simple linear workflow | Orchestration (Temporal) |
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| Many independent reactors | Choreography |
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| Strong consistency 필요 | Orchestration + 2PC 또는 Temporal |
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| Fan-out events | Choreography |
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| Need visual workflow + retry | Orchestration |
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| Event-driven domain (DDD) | Choreography |
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**기본값**: 매 simple 2-3 step domain workflow → orchestration (Temporal). 매 broad fan-out + autonomous teams → choreography.
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## 🔗 Graph
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- 부모: [[Event-Driven Architecture]] · [[Microservices]]
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- 변형: [[Pub-Sub]]
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- 응용: [[Broker Topology]] · [[Event Sourcing]]
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- Adjacent: [[Orchestration]] · [[CQRS]]
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## 🤖 LLM 활용
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**언제**: event-driven microservice design, decoupling teams, fan-out workflows, autonomous services.
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**언제 X**: short transactional flow needing visibility, strict ordering 필요한 critical financial workflows (orchestrator + Temporal 가 better).
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## ❌ 안티패턴
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- **Distributed monolith via events**: events 가 actually RPC 호출 — coupling 그대로.
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- **No event versioning**: schema 변경 시 모든 consumer 깨짐 — Schema Registry 필수.
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- **Lost trace**: span 이 event boundary 에서 끊김 — propagation injection 필수.
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- **Implicit ordering assumption**: 매 services 가 임의 order 로 react — explicit ordering 필요 시 partition key.
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## 🧪 검증 / 중복
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- Verified (Hohpe "Enterprise Integration Patterns" / Microservices.io / Confluent docs 2026).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Saga choreography + Kafka + OTel 패턴 |
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