---
id: wiki-2026-0508-event-storming
title: Event Storming
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [EventStorming, DDD discovery workshop]
duplicate_of: none
source_trust_level: A
confidence_score: 0.92
verification_status: applied
tags: [ddd, modeling, workshop, architecture, discovery]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: methodology
  framework: ddd
---

# Event Storming

## 매 한 줄
> **"매 sticky-note 의 도메인 의 explosion"**. Alberto Brandolini 의 2013 invent, 매 domain experts + devs 의 한 방 (혹은 Miro/FigJam) 에 모여 매 orange sticky note (domain event) 의 timeline 의 plot. 매 2026 의 매 distributed workshop tool (Miro AI, FigJam AI) 의 매 LLM-assisted aggregation 의 standard.

## 매 핵심

### 매 sticky note color convention
- 🟧 **Orange** — Domain Event (past tense — "OrderPlaced", "PaymentReceived").
- 🟦 **Blue** — Command (intent — "PlaceOrder", "RefundPayment").
- 🟨 **Yellow** — Actor / Persona.
- 🟪 **Purple** — Policy / Reactive logic ("when X then Y").
- 🟩 **Green** — Read Model / View.
- 🟥 **Red / Pink** — Hotspot / Issue (매 unclear / disagreement).
- ⬜ **White** — Aggregate (매 consistency boundary).
- 🟫 **Brown** — External system.

### 매 3 levels
1. **Big Picture** — 매 entire business — 매 chaos exploration, 매 hours.
2. **Process Level** — 매 한 process flow — 매 commands / policies / read models.
3. **Design Level** — 매 aggregate / bounded context — 매 implementation 의 input.

### 매 step-by-step (Big Picture)
1. **Chaotic exploration** — 매 모두 orange events 의 plaster.
2. **Timeline** — 매 left → right 의 sort.
3. **Pivotal events** — 매 phase boundary 의 mark.
4. **Hotspot identification** — 매 red sticky 의 disagreement.
5. **Bounded context** — 매 swimlane 의 split.

### 매 응용
1. Greenfield DDD design — 매 aggregate / bounded context discovery.
2. Legacy understanding — 매 domain knowledge 의 surface.
3. Microservice decomposition — 매 service boundary 의 inform.

## 💻 패턴

### Pattern 1: Miro-export → JSON event log
```typescript
interface DomainEvent {
  id: string;
  name: string;          // PascalCase past tense
  timestamp: number;     // 매 column index
  aggregate?: string;
  triggeredBy?: string;  // command id
  hotspots: string[];
}

const events: DomainEvent[] = [
  { id: "e1", name: "OrderPlaced", timestamp: 1, aggregate: "Order",
    triggeredBy: "c1", hotspots: [] },
  { id: "e2", name: "PaymentReceived", timestamp: 2, aggregate: "Payment",
    triggeredBy: "c2", hotspots: ["partial-payment-policy"] },
];
```

### Pattern 2: Event → TypeScript event type
```typescript
// 매 sticky 의 code 의 transition
export type OrderEvent =
  | { type: "OrderPlaced"; orderId: string; items: Item[]; placedAt: Date }
  | { type: "OrderPaid"; orderId: string; paymentId: string }
  | { type: "OrderShipped"; orderId: string; trackingNo: string }
  | { type: "OrderCancelled"; orderId: string; reason: string };
```

### Pattern 3: Policy as code
```typescript
// Purple sticky: "When OrderPaid then schedule shipment"
function onOrderPaid(e: Extract<OrderEvent, {type:"OrderPaid"}>) {
  shipmentService.schedule({ orderId: e.orderId });
}
eventBus.on("OrderPaid", onOrderPaid);
```

### Pattern 4: Aggregate boundary check
```typescript
// 매 white sticky 의 invariant
class OrderAggregate {
  private events: OrderEvent[] = [];
  place(items: Item[]) {
    if (items.length === 0) throw new Error("empty order");
    this.events.push({ type: "OrderPlaced", orderId: this.id, items, placedAt: new Date() });
  }
  // 매 모든 mutation 의 매 event 의 emit.
}
```

### Pattern 5: Bounded context map (Mermaid)
```mermaid
flowchart LR
  subgraph Sales
    Order
    Cart
  end
  subgraph Billing
    Payment
    Invoice
  end
  subgraph Logistics
    Shipment
  end
  Order -- "OrderPlaced" --> Payment
  Payment -- "OrderPaid" --> Shipment
```

### Pattern 6: AI-assisted event extraction (2026)
```typescript
// 매 transcript / Miro export → event suggestions
const prompt = `From this user interview, extract domain events (PascalCase past tense),
commands, and hotspots. Output JSON matching: { events:[], commands:[], hotspots:[] }.

Interview: ${transcript}`;
const result = await claude.messages.create({
  model: "claude-opus-4-7",
  max_tokens: 4000,
  messages: [{ role: "user", content: prompt }],
});
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Greenfield complex domain | Big Picture → Process → Design |
| Legacy reverse engineering | Big Picture only |
| Microservice split | Process Level + bounded context |
| Small CRUD app | Skip — overkill |
| Distributed team | Miro / FigJam + AI summarizer |

**기본값**: 매 complex domain 시 Big Picture (4 hours), 매 implementation 직전 Design Level.

## 🔗 Graph
- 응용: [[Bounded Context]] · [[CQRS]]
- Adjacent: [[Event Sourcing]] · [[User-Story-Mapping]] · [[C4 Model (Architecture Documentation)]]

## 🤖 LLM 활용
**언제**: 매 domain discovery, 매 microservice boundary 의 find, 매 onboarding 의 understanding.
**언제 X**: 매 trivial CRUD, 매 well-known domain (e.g., todo app).

## ❌ 안티패턴
- **Tech-first sticky**: 매 "INSERT INTO orders" — 매 domain event 의 X.
- **Present tense**: 매 "PlaceOrder" 의 event 의 X — 매 command.
- **No business expert**: 매 dev-only — 매 EventStorming purpose 의 lost.
- **Skip hotspot**: 매 red sticky 의 ignore — 매 가장 valuable disagreement.
- **Premature aggregate**: 매 Big Picture 에서 white sticky 의 too early.

## 🧪 검증 / 중복
- Verified (Brandolini "Introducing EventStorming" book 2021, DDD Europe talks).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — sticky color + 3 levels + AI-assisted |