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-stream-processing-architectures
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title: Stream Processing Architectures
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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: [Stream Processing, Streaming Systems, Real-time Data Processing]
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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: [stream-processing, kafka, flink, architecture]
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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: java
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framework: kafka-streams-flink
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---
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# Stream Processing Architectures
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## 매 한 줄
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> **"매 unbounded data 의 continuous compute"**. 매 batch 의 finite data 의 처리와 달리 매 stream 의 무한 event flow 의 sub-second latency 의 처리. 2026 의 standard stack 의 Kafka + Flink + Iceberg 의 lakehouse streaming.
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## 매 핵심
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### 매 Stream vs Batch
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- **Batch**: bounded, high throughput, hours latency (Spark, Hadoop).
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- **Stream**: unbounded, lower throughput, ms-sec latency (Flink, Kafka Streams).
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- **Unified**: 매 single API 의 batch + stream (Flink Table API, Beam).
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### 매 Processing semantics
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- **At-most-once**: drop on failure (low latency, lossy).
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- **At-least-once**: retry (duplicates possible).
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- **Exactly-once**: 매 idempotent + transactional (Kafka EOS, Flink checkpoints).
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### 매 Time semantics
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- **Event time**: 매 sensor emit 시각 (correct but late).
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- **Processing time**: 매 system clock 시각 (fast but wrong on lag).
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- **Watermark**: 매 event time 의 progress marker — 매 late event 의 cutoff.
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### 매 응용
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1. Real-time fraud detection (sub-100ms decision).
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2. Trading / market data aggregation.
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3. CDC pipelines (Debezium → Kafka → Flink → warehouse).
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4. IoT telemetry (sensor → MQTT → stream proc).
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## 💻 패턴
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### Kafka Streams — windowed aggregation
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```java
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KStream<String, Order> orders = builder.stream("orders");
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orders
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.groupBy((k, v) -> v.getCustomerId())
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.windowedBy(TimeWindows.ofSizeWithNoGrace(Duration.ofMinutes(5)))
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.aggregate(
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() -> 0.0,
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(k, order, total) -> total + order.getAmount(),
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Materialized.as("customer-5min-total"))
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.toStream()
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.to("customer-totals");
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```
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### Flink — event-time + watermark
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```java
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DataStream<Event> stream = env
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.fromSource(kafkaSource, WatermarkStrategy
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.<Event>forBoundedOutOfOrderness(Duration.ofSeconds(10))
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.withTimestampAssigner((e, ts) -> e.getEventTime()),
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"kafka");
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stream
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.keyBy(Event::getUserId)
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.window(TumblingEventTimeWindows.of(Time.minutes(1)))
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.aggregate(new CountAgg())
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.sinkTo(icebergSink);
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```
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### Flink SQL — streaming join
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```sql
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SELECT o.order_id, o.amount, u.tier
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FROM orders o
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JOIN users FOR SYSTEM_TIME AS OF o.proc_time AS u
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ON o.user_id = u.id
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WHERE o.amount > 100;
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```
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### Stateful processing — Flink ProcessFunction
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```java
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public class FraudDetector extends KeyedProcessFunction<Long, Tx, Alert> {
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private ValueState<Double> lastAmount;
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@Override
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public void open(Configuration cfg) {
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lastAmount = getRuntimeContext().getState(
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new ValueStateDescriptor<>("last", Double.class));
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}
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@Override
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public void processElement(Tx tx, Context ctx, Collector<Alert> out) throws Exception {
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Double prev = lastAmount.value();
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if (prev != null && tx.amount > prev * 10) {
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out.collect(new Alert(tx.id, "spike"));
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}
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lastAmount.update(tx.amount);
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}
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}
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```
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### Exactly-once with Kafka transactions
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```java
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producer.initTransactions();
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try {
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producer.beginTransaction();
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producer.send(record1);
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producer.send(record2);
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producer.sendOffsetsToTransaction(offsets, consumerGroup);
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producer.commitTransaction();
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} catch (Exception e) {
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producer.abortTransaction();
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}
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```
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### Backpressure — Flink credit-based flow control
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```java
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// Flink auto-handles via network buffers + credit
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env.setParallelism(8);
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env.getConfig().setAutoWatermarkInterval(200);
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env.enableCheckpointing(60_000, CheckpointingMode.EXACTLY_ONCE);
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```
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### Lakehouse streaming sink — Iceberg
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```java
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FlinkSink.forRowData(stream)
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.table(icebergTable)
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.tableLoader(loader)
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.upsert(true)
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.equalityFieldColumns(List.of("id"))
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.append();
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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 ETL, Kafka-native | Kafka Streams |
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| Complex CEP, large state | Flink |
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| Unified batch+stream | Flink / Beam |
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| SQL-only team | Flink SQL / ksqlDB |
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| Tiny scale | Single consumer + handler |
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**기본값**: 매 Kafka + Flink — 매 production-grade exactly-once streaming.
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## 🔗 Graph
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- 부모: [[Distributed Systems]] · [[Event-Driven Architecture]]
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- 변형: [[Apache Flink]]
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## 🤖 LLM 활용
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**언제**: continuous unbounded data, sub-second latency, stateful aggregation.
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**언제 X**: hourly/daily batch (use Spark), tiny volumes (use cron).
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## ❌ 안티패턴
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- **Processing-time on lagged sources**: 매 watermark/event-time 의 사용.
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- **Unbounded state**: 매 TTL 의 set — state 의 무한 grow 의 OOM.
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- **Single-partition hot key**: 매 skew 의 partition rebalance.
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- **Sync external call in operator**: 매 AsyncIO 의 사용.
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## 🧪 검증 / 중복
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- Verified (Apache Flink docs, Kafka Streams Developer Guide 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 — Kafka Streams + Flink patterns, EOS, watermarks |
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