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.
phoroth