القائمة
Reference document for monopoly scale-benchmarks.
Create or audit ECL Agent Harness infrastructure: AGENTS.md, change tracking, repository guidance, lint checks, CI gates, and agent handoff docs.
Reference document for monopoly patterns.
Reference document for monopoly security-checklist.
MONOPOLY is a Senior System Design Engineer skill for architecting, reviewing, and scaling systems. Triggers on requests involving architecture, databases, scaling, microservices, or infrastructure design. Proactively engages to design resilient backend systems.
Reference document for monopoly tech-matrix.
Before accepting an agent's 'done / shipped / fixed' claim, verify it against ground truth (git ancestry + the commit's own diff) using the DOS kernel's `dos verify` and `dos commit-audit` — never the agent's own narration.
| name | scale-benchmarks |
| description | Reference document for monopoly scale-benchmarks. |
| risk | safe |
| reports-to | monopoly |
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 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
Inbound bandwidth = avg_request_size × RPS
Outbound bandwidth = avg_response_size × RPS
Convert: 1 Gbps = 125 MB/s
10 Gbps = 1.25 GB/s
| 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 |
| 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) |
| 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 |
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
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
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
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
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+
| 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.
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)