---
id: wiki-2026-0508-eventual-consistency
title: Eventual Consistency
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Eventual Consistency, BASE, AP System]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [distributed-systems, database, consistency, cap]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: sql
  framework: cassandra
---

# Eventual Consistency

## 매 한 줄
> **"매 update 후 충분한 시간이 지나면 매 모든 replica 가 같은 값에 수렴한다"**. 매 Eventual Consistency는 CAP theorem 의 AP 선택 — strong consistency 의 latency/availability cost 대신 매 staleness 허용. DynamoDB, Cassandra, Riak 의 default model. 2026 globally-distributed system 의 매 default trade-off.

## 매 핵심

### 매 CAP & PACELC
- **CAP**: partition 시 Consistency vs Availability 택 1
- **PACELC** (Abadi): partition 없을 때도 Latency vs Consistency
- 매 Eventual = AP + EL (Else Latency)
- 매 strong consistency 는 quorum write/read latency cost

### 매 BASE vs ACID
- **B**asically **A**vailable: 매 partial failure 시 partial response
- **S**oft state: 매 state 는 변할 수 있음 (replica sync)
- **E**ventual consistency: 매 시간이 지나면 수렴
- 매 ACID와 trade-off — choose per use case

### 매 응용
1. DNS (TTL-based eventual).
2. CDN cache invalidation.
3. Social media feed (read-your-writes는 보장).
4. Shopping cart (Amazon Dynamo paper).

## 💻 패턴

### Cassandra tunable consistency
```python
from cassandra.cluster import Cluster
from cassandra import ConsistencyLevel
from cassandra.query import SimpleStatement

cluster = Cluster(['node1', 'node2', 'node3'])
session = cluster.connect('myapp')

# 매 write: ONE = fast, eventual; QUORUM = stronger
write = SimpleStatement(
    "INSERT INTO users (id, name) VALUES (%s, %s)",
    consistency_level=ConsistencyLevel.QUORUM
)
session.execute(write, (user_id, name))

# 매 R + W > N → strong consistency
# 매 R=1, W=1, N=3 → eventual
```

### CRDT (G-Counter, conflict-free)
```python
class GCounter:
    def __init__(self, node_id: str):
        self.node_id = node_id
        self.counts = {node_id: 0}
    
    def increment(self):
        self.counts[self.node_id] += 1
    
    def value(self) -> int:
        return sum(self.counts.values())
    
    def merge(self, other: 'GCounter'):
        # 매 idempotent + commutative + associative
        for nid, count in other.counts.items():
            self.counts[nid] = max(self.counts.get(nid, 0), count)
```

### Vector Clock (causal ordering)
```python
class VectorClock:
    def __init__(self, node_id):
        self.node_id = node_id
        self.clock = {}
    
    def tick(self):
        self.clock[self.node_id] = self.clock.get(self.node_id, 0) + 1
    
    def update(self, other_clock):
        for nid, ts in other_clock.items():
            self.clock[nid] = max(self.clock.get(nid, 0), ts)
        self.tick()
    
    def happens_before(self, other) -> bool:
        return all(self.clock.get(k, 0) <= other.clock.get(k, 0) 
                   for k in self.clock) and self.clock != other.clock
```

### Read-your-writes (sticky session)
```nginx
upstream backend {
    ip_hash;  # 매 same client → same backend → reads see own writes
    server backend1;
    server backend2;
    server backend3;
}
```

### Last-Write-Wins (DynamoDB style)
```python
def lww_merge(local: dict, remote: dict) -> dict:
    if remote['updated_at'] > local['updated_at']:
        return remote
    elif remote['updated_at'] < local['updated_at']:
        return local
    else:
        # 매 tie-break by node_id
        return remote if remote['node_id'] > local['node_id'] else local
```

### Hinted Handoff (Cassandra)
```yaml
# cassandra.yaml
hinted_handoff_enabled: true
max_hint_window_in_ms: 10800000  # 매 3 hours
# 매 down node 회복 시 hint replay → eventual consistency 보장
```

### Anti-Entropy (Merkle tree sync)
```python
def merkle_sync(local_tree, remote_tree, path=""):
    if local_tree.hash == remote_tree.hash:
        return  # 매 subtree identical, skip
    if local_tree.is_leaf:
        sync_data(path)
        return
    for i, (l, r) in enumerate(zip(local_tree.children, remote_tree.children)):
        merkle_sync(l, r, path + f"/{i}")
```

## 매 결정 기준
| 상황 | Consistency |
|---|---|
| Bank transfer | Strong (linearizable) |
| Social feed | Eventual |
| Shopping cart | Eventual + LWW |
| Counter (likes, views) | Eventual + CRDT |
| Configuration / leader election | Strong (Raft, etcd) |
| User profile | Read-your-writes |

**기본값**: 매 eventual + CRDT (counter, set, register). 매 money / lock / unique-id 는 strong.

## 🔗 Graph
- 부모: [[Distributed Systems]] · [[CAP Theorem]]
- 변형: [[Strong Consistency]] · [[Read-Your-Writes]]
- 응용: [[Cassandra]] · [[CRDT]]
- Adjacent: [[Vector Clock]] · [[Quorum]]

## 🤖 LLM 활용
**언제**: 매 system design interview, distributed DB 선택, conflict resolution strategy.
**언제 X**: 매 financial transaction, inventory deduction — strong consistency 필요.

## ❌ 안티패턴
- **모든 곳에 eventual**: 매 money/lock 도 eventual → 매 double-spend, race.
- **Conflict ignore**: 매 LWW만 쓰고 user-visible conflict 무시 → 매 silent data loss.
- **No bounded staleness**: 매 sync 영원히 안 됨 → "eventual" 의미 무.
- **Vector clock 무한 성장**: 매 GC/pruning 없음 → 매 metadata explosion.

## 🧪 검증 / 중복
- Verified (DeCandia et al., "Dynamo: Amazon's Highly Available Key-value Store", 2007).
- Verified (Brewer, CAP Theorem, PODC 2000).
- Verified (Vogels, "Eventually Consistent", CACM 2009).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — CAP/BASE + CRDT + Dynamo patterns |
