1. 🌍 User Assumptions
- Total Users:
- Daily Active Users (DAU):
- Peak Concurrent Users:
- Average Session Duration:
- Actions per User per Day:
2. 🔁 Traffic Estimation
- Requests per User per Day:
- Total Daily Requests:
- Average RPS:
- Peak RPS Estimate:
- Avg Payload Size (KB):
- Bandwidth In/Out per Day:
3. 💾 Storage Estimation
- Data per User per Day:
- Daily Total Data Written:
- Retention Period:
- Total Storage Needed:
- Index Overhead Estimate:
- Replication Factor:
- Final Storage with Replication:
4. 📚 Read/Write Patterns
- Read QPS:
- Write QPS:
- Read:Write Ratio:
- Type of Reads:
- Write Frequency / Criticality:
5. ⚙️ Backend Compute Estimation
- Estimated CPU per Request:
- Memory per Request:
- # of Instances Needed:
- Batch/Async Job Load:
- Throughput of Worker Jobs:
6. 🕓 Latency Targets
- P95 Target Latency:
- P99 Target Latency:
- Timeout Budgets:
- Cold Start/Init Time:
7. 📦 Caching Strategy
- Cache Types:
- Avg Cache Hit Ratio:
- Origin Load (uncached reqs):
- Eviction Policy / TTLs:
8. 🔐 Security and Limits
- Auth Type:
- Rate Limit per User/IP:
- Token Size and Auth Storage:
- Data Sensitivity:
9. 🔄 Replication & Availability
- Replication Strategy:
- Failover Time Goal:
- Consistency Model:
- Expected Downtime Tolerance:
10. 📊 Monitoring & Metrics
- Throughput per Component:
- Error Rate Budget:
- CPU / Mem / Disk usage monitoring:
- Alerting Rules & Thresholds:
🧪 Summary Table
| Area | Metric | Estimation/Notes |
|---|---|---|
| Users | DAU, MAU | |
| Traffic | RPS, Peak RPS, Payload Size | |
| Storage | Per User, Retention, Replicas | |
| Read/Write | QPS, Ratio | |
| Compute | CPU/Req, Instance Count | |
| Latency | P95, P99, Timeout | |
| Cache | Hit Ratio, TTL | |
| Availability | RF, Downtime Tolerance | |
| Monitoring | Errors, Alerts, Saturation |