MONOPOLY — 规模基准与估算公式
快速估算公式
用户 → RPS 转换
Requests per second (avg) = DAU × avg_requests_per_user_per_day / 86400
Requests per second (peak) = avg_RPS × peak_multiplier
Peak multipliers by app type:
Social media: 5–10×
E-commerce: 3–5× (higher during sales)
News / media: 10–20× (breaking news spike)
B2B SaaS: 2–3× (business hours spike)
Gaming: 5–15× (event-driven)
存储估算
Storage per day = requests_per_day × avg_payload_size
Storage per year = storage_per_day × 365
With replication = storage_per_year × replication_factor (3× typical)
With CDN/cache = reduce by cache_hit_ratio (80% hit = 20% origin load)
Common payload sizes:
Tweet / short text: 500B
Social post with text: 2KB
Profile data: 5KB
Image (compressed): 200KB–2MB
Video (per minute): 50MB (720p), 150MB (1080p)
API JSON response: 1–20KB
带宽估算
Inbound bandwidth = avg_request_size × RPS
Outbound bandwidth = avg_response_size × RPS
Convert: 1 Gbps = 125 MB/s
10 Gbps = 1.25 GB/s
常用技术的已知规模极限
数据库
| 技术 |
单节点写入 |
读取（含副本） |
建议分片/集群触发条件 |
| PostgreSQL |
~5K–20K 写/s |
~50K–200K 读/s |
>5TB 数据或 >20K 写/s |
| MySQL |
~10K–25K 写/s |
~60K–250K 读/s |
>5TB 或 >25K 写/s |
| MongoDB |
~20K–50K 写/s |
~50K–100K 读/s |
>100GB 或 >50K 写/s |
| Cassandra |
~200K–1M 写/s |
~200K–500K 读/s |
几乎不需要显式分片 |
| DynamoDB |
无限（托管） |
无限（托管） |
使用预配置容量模式 |
| Redis |
~500K–1M 操作/s |
同上 |
>50GB 数据或需要集群 |
| Elasticsearch |
~10K–50K 文档/s |
~1K–10K 查询/s |
每索引 >1 亿文档 |
队列 / 流
| 技术 |
最大吞吐量 |
最大消费者 |
消息保留 |
| Kafka |
每集群 1M+ 消息/s |
无限消费者组 |
可配置（天到永久） |
| RabbitMQ |
~50K–100K 消息/s |
受连接数限制 |
直到被消费 |
| SQS 标准队列 |
无限（AWS 托管） |
无限 |
14 天 |
| SQS FIFO |
每队列 3K 消息/s |
按组 |
14 天 |
| Redis Pub/Sub |
~1M 消息/s |
受订阅者限制 |
无（即发即忘） |
缓存
| 技术 |
单节点最大内存 |
最大吞吐量 |
延迟 |
| Redis |
~1TB 内存 |
~1M 操作/s |
<1ms |
| Memcached |
~64GB 内存 |
~1M 操作/s |
<1ms |
| 进程内（Caffeine/Guava） |
JVM 堆 |
无限（本地） |
<0.1ms |
按用户规模的容量规划
1K DAU
Avg RPS: ~1–5 RPS
Peak RPS: ~10–50 RPS
DB size/year: ~10–50GB
Infra needed: Single server, managed DB (RDS t3.medium), basic CDN
Monthly cost: $50–200
10K DAU
Avg RPS: ~10–50 RPS
Peak RPS: ~100–500 RPS
DB size/year: ~100–500GB
Infra needed: 2–4 app servers, RDS r5.large, Redis t3.medium, CDN
Monthly cost: $300–800
100K DAU
Avg RPS: ~100–500 RPS
Peak RPS: ~1K–5K RPS
DB size/year: ~1–5TB
Infra needed: ASG (5–10 app servers), RDS r5.xlarge + 2 replicas, Redis cluster, CDN, ALB
Monthly cost: $2K–8K
1M DAU
Avg RPS: ~1K–5K RPS
Peak RPS: ~10K–50K RPS
DB size/year: ~10–50TB
Infra needed: ASG (20–50 servers), DB sharding or Aurora, Redis cluster, Kafka, CDN, WAF
Monthly cost: $20K–80K
10M DAU
Avg RPS: ~10K–50K RPS
Peak RPS: ~100K–500K RPS
DB size/year: ~100–500TB
Infra needed: Multi-region, microservices, distributed DB (Cassandra/CockroachDB), full CDN, dedicated SRE
Monthly cost: $200K–2M+
常见 SLO 目标
| 等级 |
可用性 |
每月允许停机时间 |
| 99% |
基础 |
7.2 小时/月 |
| 99.9%（三个九） |
标准生产 |
43.8 分钟/月 |
| 99.95% |
重要服务 |
21.9 分钟/月 |
| 99.99%（四个九） |
关键服务 |
4.38 分钟/月 |
| 99.999%（五个九） |
电信 / 支付 |
26 秒/月 |
达到四个九需要： 多可用区部署、自动故障转移、零停机部署、混沌工程、7×24 值班。
延迟预算指南
User perceived latency targets:
< 100ms → Feels instant
100–300ms → Acceptable for most interactions
300ms–1s → Noticeable; optimize if possible
> 1s → Frustrating; unacceptable for critical paths
Network latency by distance (approximate):
Same datacenter: 0.5ms
Same region (AZ): 1–2ms
Cross-region US: 30–60ms
US to Europe: 80–120ms
US to Asia: 150–250ms
Database query targets:
Simple key-value: < 1ms (cache)
Simple DB query: < 5ms
Complex query: < 50ms
Reporting query: < 500ms (async if > 1s)
局限性
- 本文档为参考性质，可能未覆盖所有边界情况。在生产环境前务必验证架构方案。
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