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2nd/10_Wiki/Topics/Domain_Programming/Architecture/Simple event processing.md
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Antigravity Agent c24165b8bc 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>
2026-07-11 11:05:56 +09:00

4.8 KiB

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-simple-event-processing Simple Event Processing 10_Wiki/Topics verified self
SEP
Direct Event Handling
1:1 Event Processing
none A 0.9 applied
event-driven
architecture
eda
messaging
2026-05-10 pending
language framework
typescript kafka

Simple Event Processing

매 한 줄

"매 1 event → 1 reaction. No correlation, no aggregation, no temporal pattern.". 매 EDA 의 simplest tier — notable event 의 detect 후 매 single downstream action 의 trigger. 매 CEP (Complex Event Processing) / ESP (Event Stream Processing) 와 대비되는 매 baseline pattern.

매 핵심

매 SEP vs ESP vs CEP

  • SEP: 1 event → 1 action. No state, no correlation.
  • ESP: stream 의 windowing, aggregation (Flink, Kafka Streams).
  • CEP: pattern matching across events (Drools Fusion, Esper).

매 properties

  • Stateless (매 event 의 self-contained).
  • Low latency (no buffering / windowing).
  • High throughput (parallelize trivially).
  • Idempotent handlers preferred (at-least-once delivery).

매 응용

  1. Order placed → email confirmation.
  2. User signup → welcome workflow.
  3. Sensor reading → threshold alert.
  4. Payment captured → inventory reserve.
  5. Log line → metric increment.

💻 패턴

Kafka consumer (TypeScript)

import { Kafka } from 'kafkajs';

const kafka = new Kafka({ brokers: ['localhost:9092'] });
const consumer = kafka.consumer({ groupId: 'order-emails' });

await consumer.subscribe({ topic: 'orders.placed' });
await consumer.run({
  eachMessage: async ({ message }) => {
    const order = JSON.parse(message.value!.toString());
    await sendOrderEmail(order.userId, order.id);
  },
});

AWS EventBridge rule

import { EventBridgeClient, PutRuleCommand } from '@aws-sdk/client-eventbridge';

await client.send(new PutRuleCommand({
  Name: 'order-placed-email',
  EventPattern: JSON.stringify({
    source: ['app.orders'],
    'detail-type': ['OrderPlaced'],
  }),
  Targets: [{ Arn: lambdaArn, Id: 'send-email' }],
}));

NATS subject handler

import { connect, StringCodec } from 'nats';

const nc = await connect({ servers: 'nats://localhost:4222' });
const sc = StringCodec();
const sub = nc.subscribe('orders.placed');

for await (const msg of sub) {
  const order = JSON.parse(sc.decode(msg.data));
  await reserveInventory(order);
}

Idempotent handler

async function handleOrderPlaced(event: OrderEvent) {
  const seen = await redis.set(`processed:${event.id}`, '1', 'NX', 'EX', 86400);
  if (!seen) return;  // already handled
  await sendEmail(event);
}

Dead-letter handling

await consumer.run({
  eachMessage: async ({ message }) => {
    try {
      await handle(message);
    } catch (err) {
      await producer.send({
        topic: 'orders.placed.dlq',
        messages: [{ value: message.value, headers: { error: err.message } }],
      });
    }
  },
});

CloudEvents envelope

const cloudEvent = {
  specversion: '1.0',
  type: 'com.example.order.placed',
  source: '/orders',
  id: crypto.randomUUID(),
  time: new Date().toISOString(),
  data: { orderId, userId, amount },
};
await producer.send({ topic: 'orders.placed', messages: [{ value: JSON.stringify(cloudEvent) }] });

Webhook fan-out

app.post('/webhooks/payment', async (req, res) => {
  await eventBus.publish('payment.captured', req.body);
  res.status(202).end();
});

매 결정 기준

상황 Approach
1:1 event → action, stateless SEP
Stream aggregation (window sums) ESP (Flink)
Pattern detect (A then B within 5s) CEP (Esper)
Cross-system fan-out SEP via EventBridge/Kafka

기본값: Kafka or EventBridge + idempotent stateless handlers.

🔗 Graph

🤖 LLM 활용

언제: stateless 1:1 event handling — webhook, notification, simple workflow trigger. 언제 X: pattern correlation 필요 — CEP / ESP 사용.

안티패턴

  • Stateful SEP: 매 cross-event state 가지면 ESP 로 reframe.
  • No idempotency: at-least-once delivery 에서 매 duplicate side-effect.
  • Synchronous webhook chain: 매 cascading failure — async queue 사이로.

🧪 검증 / 중복

  • Verified (Hohpe Enterprise Integration Patterns, Confluent docs, AWS EventBridge guide).
  • 신뢰도 A.

🕓 Changelog

날짜 변경
2026-05-08 Phase 1
2026-05-10 Manual cleanup — SEP vs ESP vs CEP, Kafka/EventBridge/NATS patterns