2nd/10_Wiki/Topics/Architecture/Event_Storming.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-event-storming	Event Storming	10_Wiki/Topics	verified	self	EventStorming DDD discovery workshop	none	A	0.92	applied	ddd modeling workshop architecture discovery		2026-05-10	pending	language framework methodology 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

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

// 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

// 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

// 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)

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)

// 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