MONOPOLY — Scale Benchmarks & Estimation Formulas
Quick Estimation Formulas
User → RPS Conversion
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)
Storage Estimation
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
Bandwidth Estimation
Inbound bandwidth = avg_request_size × RPS
Outbound bandwidth = avg_response_size × RPS
Convert: 1 Gbps = 125 MB/s
10 Gbps = 1.25 GB/s
Known Scale Limits of Common Technologies
Databases
| Technology | Single Node Writes | Reads (with replicas) | Recommended Shard/Cluster Trigger |
|---|
| PostgreSQL | ~5K–20K writes/s | ~50K–200K reads/s | >5TB data or >20K writes/s |
| MySQL | ~10K–25K writes/s | ~60K–250K reads/s | >5TB or >25K writes/s |
| MongoDB | ~20K–50K writes/s | ~50K–100K reads/s | >100GB or >50K writes/s |
| Cassandra | ~200K–1M writes/s | ~200K–500K reads/s | Almost never needs explicit sharding |
| DynamoDB | Unlimited (managed) | Unlimited (managed) | Use provisioned capacity mode |
| Redis | ~500K–1M ops/s | Same | >50GB data or cluster needed |
| Elasticsearch | ~10K–50K docs/s | ~1K–10K queries/s | >100M documents per index |
Queues / Streams
| Technology | Max Throughput | Max Consumers | Retention |
|---|
| Kafka | 1M+ msgs/s per cluster | Unlimited consumer groups | Configurable (days–forever) |
| RabbitMQ | ~50K–100K msgs/s | Limited by connections | Until consumed |
| SQS Standard | Unlimited (AWS-managed) | Unlimited | 14 days |
| SQS FIFO | 3K msgs/s per queue | Per group | 14 days |
| Redis Pub/Sub | ~1M msgs/s | Limited by subscribers | None (fire-and-forget) |
Caching
| Technology | Max Memory (single) | Max Throughput | Latency |
|---|
| Redis | ~1TB RAM | ~1M ops/s | <1ms |
| Memcached | ~64GB RAM | ~1M ops/s | <1ms |
| In-process (Caffeine/Guava) | JVM heap | Unlimited (local) | <0.1ms |
Capacity Planning by User Scale
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+
Common SLO Targets
| Tier | Availability | Monthly Downtime Allowed |
|---|
| 99% | Basic | 7.2 hours/month |
| 99.9% (three nines) | Standard production | 43.8 minutes/month |
| 99.95% | Important services | 21.9 minutes/month |
| 99.99% (four nines) | Critical services | 4.38 minutes/month |
| 99.999% (five nines) | Telecom / payments | 26 seconds/month |
Achieving four nines requires: Multi-AZ deployment, automated failover, zero-downtime deploys, chaos engineering, 24/7 on-call.
Latency Budget Guidelines
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)
Limitations
- This is a reference document and may not cover all edge cases. Always verify architectures before production.