---
id: wiki-2026-0508-scalability
title: Scalability
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Scalability, 확장성, scale-out, scale-up]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [architecture, distributed-systems, performance]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: yaml
  framework: kubernetes
---

# Scalability

## 매 한 줄
> **"매 부하가 늘 때 매 graceful하게 capacity를 키울 수 있는 능력"**. Scalability는 매 단일 dimension(traffic, data, compute)이 아니라 매 multi-axis property. 2026년에는 매 K8s HPA + KEDA, 매 serverless auto-scale, 매 LLM token-throughput scaling이 매 일상.

## 매 핵심

### 매 두 축
- **Vertical (scale-up)**: 매 큰 머신 — 매 limit 빨리.
- **Horizontal (scale-out)**: 매 더 많은 머신 — 매 stateless 필요.

### 매 차원
- **Load**: req/sec.
- **Data**: GB → PB.
- **Geographic**: 매 region.
- **User**: 매 동시 user.
- **Functional**: 매 feature 추가가 매 system을 깨지 않음.

### 매 응용
1. 매 web tier — auto-scale group.
2. 매 DB — sharding / read replica.
3. 매 LLM serving — vLLM tensor parallel + KV cache 분산.
4. 매 event pipeline — Kafka partition scale.

## 💻 패턴

### 매 K8s HPA (CPU 기반)
```yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata: { name: api-hpa }
spec:
  scaleTargetRef: { apiVersion: apps/v1, kind: Deployment, name: api }
  minReplicas: 3
  maxReplicas: 50
  metrics:
    - type: Resource
      resource:
        name: cpu
        target: { type: Utilization, averageUtilization: 70 }
```

### 매 KEDA (event-driven scale)
```yaml
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata: { name: kafka-consumer }
spec:
  scaleTargetRef: { name: consumer }
  minReplicaCount: 0
  maxReplicaCount: 100
  triggers:
    - type: kafka
      metadata:
        bootstrapServers: kafka:9092
        consumerGroup: orders
        topic: order-events
        lagThreshold: "100"
```

### 매 Stateless service (scale-out 가능)
```typescript
// 매 session 매 외부화 (Redis)
import express from "express";
import session from "express-session";
import RedisStore from "connect-redis";
import { createClient } from "redis";

const redis = createClient({ url: "redis://redis:6379" });
await redis.connect();

const app = express();
app.use(session({
  store: new RedisStore({ client: redis }),
  secret: process.env.SESSION_SECRET!,
  resave: false, saveUninitialized: false,
}));
// 매 어느 instance든 매 동일 session.
```

### 매 DB sharding (hash-based)
```typescript
function shardFor(userId: string): string {
  const hash = crc32(userId);
  return `db-shard-${hash % 8}`;
}
async function getUser(id: string) {
  const shard = shardFor(id);
  return pool[shard].query("SELECT * FROM users WHERE id=$1", [id]);
}
```

### 매 Read replica
```typescript
const writeDb = postgres({ host: "primary" });
const readDb = postgres({ host: "replica.read" });

async function placeOrder(o: Order) { return writeDb`INSERT INTO orders ...`; }
async function listOrders(uid: string) { return readDb`SELECT * FROM orders WHERE uid=${uid}`; }
```

### 매 LLM tensor-parallel (vLLM 0.7+)
```bash
vllm serve meta-llama/Llama-3.3-70B-Instruct \
  --tensor-parallel-size 4 \
  --gpu-memory-utilization 0.92 \
  --max-num-seqs 256
```

### 매 Cache layer (scale read)
```typescript
async function getProduct(id: string) {
  const cached = await redis.get(`p:${id}`);
  if (cached) return JSON.parse(cached);
  const p = await db.query("SELECT * FROM products WHERE id=$1", [id]);
  await redis.setex(`p:${id}`, 60, JSON.stringify(p));
  return p;
}
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| 매 traffic spike (예측 가능) | HPA + capacity planning. |
| 매 burst (predicate X) | Serverless / KEDA scale-to-zero. |
| 매 data > single node | Sharding. |
| 매 read >> write | Replica. |
| 매 global users | Multi-region + edge cache. |
| 매 LLM serving | vLLM TP + KV-cache routing. |

**기본값**: 매 stateless service + HPA + Redis cache + read replica.

## 🔗 Graph
- 부모: [[Distributed Systems]]
- 응용: [[Microservices]] · [[Serverless_Architecture]] · [[Service Mesh]]
- Adjacent: [[CAP Theorem]] · [[Sharding]] · [[Load Balancer]]

## 🤖 LLM 활용
**언제**: 매 capacity 설계, 매 bottleneck 진단, 매 cost-perf trade-off.
**언제 X**: 매 단일 user 매 internal tool (매 over-engineering).

## ❌ 안티패턴
- **매 premature sharding**: 매 single PG로 매 충분한데 매 split.
- **매 stateful pod scale-out**: 매 session 매 일부 instance 만.
- **매 cache stampede 무시**: 매 expiry 동시에.
- **매 N+1 query에서 scale-out 으로 도망**: 매 query 먼저 고칠 것.
- **매 monolith 만 scale-up**: 매 vertical 한계.

## 🧪 검증 / 중복
- Verified (Designing Data-Intensive Applications, K8s docs, vLLM docs).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — HPA/KEDA/sharding/vLLM patterns |
