📦 deps(thirdparty): update snapshots
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---
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name: scale-benchmarks
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description: Reference document for monopoly scale-benchmarks.
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risk: safe
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reports-to: monopoly
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---
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# MONOPOLY — Scale Benchmarks & Estimation Formulas
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## Quick Estimation Formulas
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### User → RPS Conversion
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```
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Requests per second (avg) = DAU × avg_requests_per_user_per_day / 86400
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Requests per second (peak) = avg_RPS × peak_multiplier
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Peak multipliers by app type:
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Social media: 5–10×
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E-commerce: 3–5× (higher during sales)
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News / media: 10–20× (breaking news spike)
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B2B SaaS: 2–3× (business hours spike)
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Gaming: 5–15× (event-driven)
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```
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### Storage Estimation
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```
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Storage per day = requests_per_day × avg_payload_size
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Storage per year = storage_per_day × 365
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With replication = storage_per_year × replication_factor (3× typical)
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With CDN/cache = reduce by cache_hit_ratio (80% hit = 20% origin load)
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Common payload sizes:
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Tweet / short text: 500B
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Social post with text: 2KB
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Profile data: 5KB
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Image (compressed): 200KB–2MB
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Video (per minute): 50MB (720p), 150MB (1080p)
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API JSON response: 1–20KB
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```
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### Bandwidth Estimation
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```
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Inbound bandwidth = avg_request_size × RPS
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Outbound bandwidth = avg_response_size × RPS
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Convert: 1 Gbps = 125 MB/s
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10 Gbps = 1.25 GB/s
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```
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---
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## Known Scale Limits of Common Technologies
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### Databases
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| Technology | Single Node Writes | Reads (with replicas) | Recommended Shard/Cluster Trigger |
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|------------|-------------------|----------------------|----------------------------------|
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| PostgreSQL | ~5K–20K writes/s | ~50K–200K reads/s | >5TB data or >20K writes/s |
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| MySQL | ~10K–25K writes/s | ~60K–250K reads/s | >5TB or >25K writes/s |
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| MongoDB | ~20K–50K writes/s | ~50K–100K reads/s | >100GB or >50K writes/s |
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| Cassandra | ~200K–1M writes/s | ~200K–500K reads/s | Almost never needs explicit sharding |
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| DynamoDB | Unlimited (managed) | Unlimited (managed) | Use provisioned capacity mode |
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| Redis | ~500K–1M ops/s | Same | >50GB data or cluster needed |
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| Elasticsearch | ~10K–50K docs/s | ~1K–10K queries/s | >100M documents per index |
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### Queues / Streams
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| Technology | Max Throughput | Max Consumers | Retention |
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|------------|----------------|---------------|-----------|
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| Kafka | 1M+ msgs/s per cluster | Unlimited consumer groups | Configurable (days–forever) |
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| RabbitMQ | ~50K–100K msgs/s | Limited by connections | Until consumed |
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| SQS Standard | Unlimited (AWS-managed) | Unlimited | 14 days |
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| SQS FIFO | 3K msgs/s per queue | Per group | 14 days |
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| Redis Pub/Sub | ~1M msgs/s | Limited by subscribers | None (fire-and-forget) |
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### Caching
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| Technology | Max Memory (single) | Max Throughput | Latency |
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|------------|--------------------|--------------|----|
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| Redis | ~1TB RAM | ~1M ops/s | <1ms |
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| Memcached | ~64GB RAM | ~1M ops/s | <1ms |
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| In-process (Caffeine/Guava) | JVM heap | Unlimited (local) | <0.1ms |
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---
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## Capacity Planning by User Scale
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### 1K DAU
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```
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Avg RPS: ~1–5 RPS
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Peak RPS: ~10–50 RPS
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DB size/year: ~10–50GB
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Infra needed: Single server, managed DB (RDS t3.medium), basic CDN
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Monthly cost: $50–200
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```
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### 10K DAU
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```
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Avg RPS: ~10–50 RPS
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Peak RPS: ~100–500 RPS
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DB size/year: ~100–500GB
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Infra needed: 2–4 app servers, RDS r5.large, Redis t3.medium, CDN
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Monthly cost: $300–800
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```
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### 100K DAU
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```
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Avg RPS: ~100–500 RPS
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Peak RPS: ~1K–5K RPS
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DB size/year: ~1–5TB
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Infra needed: ASG (5–10 app servers), RDS r5.xlarge + 2 replicas, Redis cluster, CDN, ALB
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Monthly cost: $2K–8K
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```
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### 1M DAU
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```
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Avg RPS: ~1K–5K RPS
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Peak RPS: ~10K–50K RPS
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DB size/year: ~10–50TB
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Infra needed: ASG (20–50 servers), DB sharding or Aurora, Redis cluster, Kafka, CDN, WAF
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Monthly cost: $20K–80K
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```
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### 10M DAU
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```
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Avg RPS: ~10K–50K RPS
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Peak RPS: ~100K–500K RPS
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DB size/year: ~100–500TB
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Infra needed: Multi-region, microservices, distributed DB (Cassandra/CockroachDB), full CDN, dedicated SRE
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Monthly cost: $200K–2M+
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```
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---
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## Common SLO Targets
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| Tier | Availability | Monthly Downtime Allowed |
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|------|-------------|--------------------------|
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| 99% | Basic | 7.2 hours/month |
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| 99.9% (three nines) | Standard production | 43.8 minutes/month |
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| 99.95% | Important services | 21.9 minutes/month |
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| 99.99% (four nines) | Critical services | 4.38 minutes/month |
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| 99.999% (five nines) | Telecom / payments | 26 seconds/month |
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**Achieving four nines requires:** Multi-AZ deployment, automated failover, zero-downtime deploys, chaos engineering, 24/7 on-call.
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---
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## Latency Budget Guidelines
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```
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User perceived latency targets:
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< 100ms → Feels instant
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100–300ms → Acceptable for most interactions
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300ms–1s → Noticeable; optimize if possible
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> 1s → Frustrating; unacceptable for critical paths
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Network latency by distance (approximate):
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Same datacenter: 0.5ms
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Same region (AZ): 1–2ms
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Cross-region US: 30–60ms
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US to Europe: 80–120ms
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US to Asia: 150–250ms
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Database query targets:
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Simple key-value: < 1ms (cache)
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Simple DB query: < 5ms
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Complex query: < 50ms
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Reporting query: < 500ms (async if > 1s)
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```
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## Limitations
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- This is a reference document and may not cover all edge cases. Always verify architectures before production.
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