Mathias STRASSER
b7dc27f2a5
Les enseignants ont besoin de moyennes à jour immédiatement après la publication ou modification des notes, sans attendre un batch nocturne. Le système recalcule via Domain Events synchrones : statistiques d'évaluation (min/max/moyenne/médiane), moyennes matières pondérées (normalisation /20), et moyenne générale par élève. Les résultats sont stockés dans des tables dénormalisées avec cache Redis (TTL 5 min). Trois endpoints API exposent les données avec contrôle d'accès par rôle. Une commande console permet le backfill des données historiques au déploiement.
89 lines
2.0 KiB
Markdown
89 lines
2.0 KiB
Markdown
---
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name: 'step-04d-subagent-scalability'
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description: 'Subagent: Scalability NFR assessment'
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subagent: true
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outputFile: '/tmp/tea-nfr-scalability-{{timestamp}}.json'
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---
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# Subagent 4D: Scalability NFR Assessment
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## SUBAGENT CONTEXT
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This is an **isolated subagent** running in parallel with other NFR domain assessments.
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**Your task:** Assess SCALABILITY NFR domain only.
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---
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## SUBAGENT TASK
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### 1. Scalability Assessment Categories
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**A) Horizontal Scaling:**
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- Stateless architecture
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- Load balancer configuration
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- Container orchestration (K8s)
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- Auto-scaling policies
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**B) Vertical Scaling:**
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- Resource allocation
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- Database size limits
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- Memory management
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- CPU optimization
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**C) Data Scaling:**
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- Database partitioning/sharding
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- Read replicas
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- Caching layers
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- Data archival strategy
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**D) Traffic Handling:**
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- CDN for static assets
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- Rate limiting
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- Queue systems for async work
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- WebSocket scaling
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---
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## OUTPUT FORMAT
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```json
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{
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"domain": "scalability",
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"risk_level": "MEDIUM",
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"findings": [
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{
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"category": "Horizontal Scaling",
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"status": "PASS",
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"description": "Stateless architecture with container orchestration",
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"evidence": ["Docker + Kubernetes setup", "Auto-scaling configured"],
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"recommendations": []
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},
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{
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"category": "Data Scaling",
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"status": "CONCERN",
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"description": "No database sharding strategy for large data growth",
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"evidence": ["Single database instance", "No partitioning"],
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"recommendations": ["Plan database sharding strategy", "Implement read replicas", "Consider database clustering"]
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}
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],
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"compliance": {
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"1M_users": "PASS",
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"10M_users": "CONCERN",
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"100M_users": "FAIL"
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},
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"priority_actions": ["Design database sharding strategy for future growth", "Implement read replicas for read-heavy workloads"],
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"summary": "Scalability is good up to 1M users, concerns for 10M+ users"
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}
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```
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---
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## EXIT CONDITION
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Subagent completes when JSON output written to temp file.
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