2nd/10_Wiki/Topic_Programming/Architecture/Stream-Processing-Architectures.md

---
id: wiki-2026-0508-stream-processing-architectures
title: Stream Processing Architectures
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Stream Processing, Streaming Systems, Real-time Data Processing]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [stream-processing, kafka, flink, architecture]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: java
  framework: kafka-streams-flink
---

# Stream Processing Architectures

## 매 한 줄
> **"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.

## 매 핵심

### 매 Stream vs Batch
- **Batch**: bounded, high throughput, hours latency (Spark, Hadoop).
- **Stream**: unbounded, lower throughput, ms-sec latency (Flink, Kafka Streams).
- **Unified**: 매 single API 의 batch + stream (Flink Table API, Beam).

### 매 Processing semantics
- **At-most-once**: drop on failure (low latency, lossy).
- **At-least-once**: retry (duplicates possible).
- **Exactly-once**: 매 idempotent + transactional (Kafka EOS, Flink checkpoints).

### 매 Time semantics
- **Event time**: 매 sensor emit 시각 (correct but late).
- **Processing time**: 매 system clock 시각 (fast but wrong on lag).
- **Watermark**: 매 event time 의 progress marker — 매 late event 의 cutoff.

### 매 응용
1. Real-time fraud detection (sub-100ms decision).
2. Trading / market data aggregation.
3. CDC pipelines (Debezium → Kafka → Flink → warehouse).
4. IoT telemetry (sensor → MQTT → stream proc).

## 💻 패턴

### Kafka Streams — windowed aggregation
```java
KStream<String, Order> orders = builder.stream("orders");
orders
    .groupBy((k, v) -> v.getCustomerId())
    .windowedBy(TimeWindows.ofSizeWithNoGrace(Duration.ofMinutes(5)))
    .aggregate(
        () -> 0.0,
        (k, order, total) -> total + order.getAmount(),
        Materialized.as("customer-5min-total"))
    .toStream()
    .to("customer-totals");
```

### Flink — event-time + watermark
```java
DataStream<Event> stream = env
    .fromSource(kafkaSource, WatermarkStrategy
        .<Event>forBoundedOutOfOrderness(Duration.ofSeconds(10))
        .withTimestampAssigner((e, ts) -> e.getEventTime()),
      "kafka");

stream
    .keyBy(Event::getUserId)
    .window(TumblingEventTimeWindows.of(Time.minutes(1)))
    .aggregate(new CountAgg())
    .sinkTo(icebergSink);
```

### Flink SQL — streaming join
```sql
SELECT o.order_id, o.amount, u.tier
FROM orders o
JOIN users FOR SYSTEM_TIME AS OF o.proc_time AS u
  ON o.user_id = u.id
WHERE o.amount > 100;
```

### Stateful processing — Flink ProcessFunction
```java
public class FraudDetector extends KeyedProcessFunction<Long, Tx, Alert> {
    private ValueState<Double> lastAmount;

    @Override
    public void open(Configuration cfg) {
        lastAmount = getRuntimeContext().getState(
            new ValueStateDescriptor<>("last", Double.class));
    }

    @Override
    public void processElement(Tx tx, Context ctx, Collector<Alert> out) throws Exception {
        Double prev = lastAmount.value();
        if (prev != null && tx.amount > prev * 10) {
            out.collect(new Alert(tx.id, "spike"));
        }
        lastAmount.update(tx.amount);
    }
}
```

### Exactly-once with Kafka transactions
```java
producer.initTransactions();
try {
    producer.beginTransaction();
    producer.send(record1);
    producer.send(record2);
    producer.sendOffsetsToTransaction(offsets, consumerGroup);
    producer.commitTransaction();
} catch (Exception e) {
    producer.abortTransaction();
}
```

### Backpressure — Flink credit-based flow control
```java
// Flink auto-handles via network buffers + credit
env.setParallelism(8);
env.getConfig().setAutoWatermarkInterval(200);
env.enableCheckpointing(60_000, CheckpointingMode.EXACTLY_ONCE);
```

### Lakehouse streaming sink — Iceberg
```java
FlinkSink.forRowData(stream)
    .table(icebergTable)
    .tableLoader(loader)
    .upsert(true)
    .equalityFieldColumns(List.of("id"))
    .append();
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Simple ETL, Kafka-native | Kafka Streams |
| Complex CEP, large state | Flink |
| Unified batch+stream | Flink / Beam |
| SQL-only team | Flink SQL / ksqlDB |
| Tiny scale | Single consumer + handler |

**기본값**: 매 Kafka + Flink — 매 production-grade exactly-once streaming.

## 🔗 Graph
- 부모: [[Distributed Systems]] · [[Event-Driven Architecture]]
- 변형: [[Apache Flink]]

## 🤖 LLM 활용
**언제**: continuous unbounded data, sub-second latency, stateful aggregation.
**언제 X**: hourly/daily batch (use Spark), tiny volumes (use cron).

## ❌ 안티패턴
- **Processing-time on lagged sources**: 매 watermark/event-time 의 사용.
- **Unbounded state**: 매 TTL 의 set — state 의 무한 grow 의 OOM.
- **Single-partition hot key**: 매 skew 의 partition rebalance.
- **Sync external call in operator**: 매 AsyncIO 의 사용.

## 🧪 검증 / 중복
- Verified (Apache Flink docs, Kafka Streams Developer Guide 2026).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — Kafka Streams + Flink patterns, EOS, watermarks |