Technical Addendum: Extended Performance and Scaling Analysis
1. Detailed Performance Metrics
A. System Component Performance
Vector Processing Performance Matrix
| Operation Type | Small Text (<1KB) | Medium Text (<10KB) | Large Text (<100KB) |
|------------------|-------------------|--------------------|--------------------|
| Embedding Gen. | 2ms | 15ms | 100ms |
| Chunk Creation | 1ms | 8ms | 50ms |
| Vector Storage | 5ms | 5ms | 5ms |
| Relationship Map | 3ms | 20ms | 150ms |
| Total Process | 11ms | 48ms | 305ms |
Concurrent Processing Capability:
- Small Text: 90 docs/second/worker
- Medium Text: 20 docs/second/worker
- Large Text: 3 docs/second/worker
Database Operation Performance
PostgreSQL Performance (per instance):
| Operation Type | 100GB Dataset | 500GB Dataset | 1TB Dataset |
|--------------------|---------------|---------------|-------------|
| Index Scan | 5ms | 15ms | 35ms |
| Sequential Scan | 100ms | 500ms | 1200ms |
| Bulk Insert (1000) | 200ms | 250ms | 300ms |
| Vector Search | 20ms | 45ms | 80ms |
| Join Operation | 30ms | 75ms | 150ms |
Buffer Cache Hit Ratios:
- Working Set: 99.5%
- Full Dataset: 95%
- Cold Start: 60%
Cache Performance Metrics
Redis Performance:
| Operation Type | 1KB Object | 10KB Object | 100KB Object |
|------------------|------------|-------------|--------------|
| GET (cached) | 0.1ms | 0.2ms | 1ms |
| SET (with index) | 0.2ms | 0.5ms | 2ms |
| EXPIRE check | 0.1ms | 0.1ms | 0.1ms |
| Memory Usage | 2KB | 12KB | 105KB |
Cache Hit Rates by Operation:
- Document Metadata: 95%
- Search Results: 80%
- User Sessions: 99%
- Vector Queries: 70%
Network Performance Requirements
Internal Network Requirements:
| Component Link | Bandwidth | Latency | Packet Loss |
|----------------------|-----------|----------|-------------|
| App → Database | 1 Gbps | <1ms | <0.001% |
| App → Cache | 1 Gbps | <0.5ms | <0.001% |
| App → Vector Store | 10 Gbps | <1ms | <0.001% |
| Worker → Queue | 1 Gbps | <1ms | <0.001% |
External API Performance:
| Endpoint Type | Bandwidth/Req | Max Latency | Timeout |
|-----------------|---------------|-------------|---------|
| Document Upload | 10 MB | 30s | 60s |
| Search Query | 10 KB | 500ms | 2s |
| Metric Query | 50 KB | 200ms | 1s |
| Health Check | 1 KB | 100ms | 500ms |
Context
The current situation requires a decision because:
- Requirement 1
- Constraint 2
- Need 3
Status
Accepted | YYYY-MM-DD
2. Extended Compatibility Matrices
A. Framework Version Compatibility
| Component | Python 3.9 | 3.10 | 3.11 | Node 16 | 18 | 20 |
|---------------------|------------|------|------|---------|----|----|
| FastAPI | ✓ | ✓ | ✓ | - | - | - |
| Pydantic | ✓ (v1) | ✓ | ✓ | - | - | - |
| SQLAlchemy | ✓ | ✓ | ✓ | - | - | - |
| asyncpg | ✓ | ✓ | ✓ | - | - | - |
| React | - | - | - | ✓ | ✓ | ✓ |
| TypeScript | - | - | - | ✓ | ✓ | ✓ |
| Webpack | - | - | - | ✓ | ✓ | ✓ |
| Vite | - | - | - | ✓ | ✓ | ✓ |
B. Infrastructure Compatibility
| Service Type | AWS | GCP | Azure | DO |
|----------------|--------------|--------------|--------------|--------------|
| Kubernetes | EKS 1.27 | GKE 1.27 | AKS 1.27 | DOKS 1.27 |
| PostgreSQL | RDS 15 | Cloud SQL | Flexible | Managed DB |
| Redis | ElastiCache | MemoryStore | Cache | Managed Redis|
| Object Storage | S3 | GCS | Blob | Spaces |
| Registry | ECR | GCR | ACR | Container Reg|
| Monitoring | CloudWatch | StackDriver | Monitor | Monitoring |
| Load Balancer | ALB/NLB | GLB | ALB | LB |
C. Monitoring Integration Compatibility
| Component | Prometheus | Grafana | CloudWatch | DataDog |
|----------------|------------|---------|------------|---------|
| System Metrics | ✓ | ✓ | ✓ | ✓ |
| Traces | ✓ | ✓ | ✓ | ✓ |
| Logs | ✓ | ✓ | ✓ | ✓ |
| Dashboards | ✓ | ✓ | ✓ | ✓ |
| Alerts | ✓ | ✓ | ✓ | ✓ |
3. Detailed Scaling Scenarios
A. Vertical Scaling Limits
Application Servers:
| Resource | Minimum | Optimal | Maximum |
|----------------|---------|---------|---------|
| CPU Cores | 2 | 8 | 64 |
| RAM (GB) | 4 | 32 | 256 |
| Storage (GB) | 20 | 100 | 500 |
| Network (Gbps) | 1 | 10 | 25 |
Database Servers:
| Resource | Minimum | Optimal | Maximum |
|----------------|---------|---------|---------|
| CPU Cores | 4 | 16 | 96 |
| RAM (GB) | 16 | 64 | 512 |
| Storage (GB) | 100 | 1000 | 10000 |
| Network (Gbps) | 1 | 10 | 100 |
B. Horizontal Scaling Thresholds
Application Layer:
| Metric | Add Node When | Remove Node When |
|--------------------|-------------------|-------------------|
| CPU Utilization | >70% for 5min | <30% for 15min |
| Memory Usage | >75% for 5min | <40% for 15min |
| Request Queue | >1000 for 1min | <100 for 15min |
| Active Connections | >2000 per node | <500 per node |
Database Layer:
| Metric | Add Node When | Remove Node When |
|--------------------|-------------------|-------------------|
| CPU Utilization | >60% for 10min | <20% for 30min |
| Memory Usage | >80% for 10min | <40% for 30min |
| IOPS | >80% for 5min | <30% for 30min |
| Replication Lag | >10s for 5min | <1s for 30min |
C. Load Balancing Configurations
| Layer | Algorithm | Health Check | Session Affinity |
|----------------|---------------|--------------|------------------|
| API Gateway | Round Robin | /health | None |
| App Servers | Least Conn | /ready | None |
| Database | Connection # | TCP | True |
| Cache | Consistent Hash| TCP | True |
4. Detailed Resource Calculations
A. Resource Requirements by Scale
Small Scale (100 req/s)
Application Tier:
- 2 x Application Servers
CPU: 4 cores each = 8 cores
RAM: 16GB each = 32GB
Storage: 50GB each = 100GB
Database Tier:
- 1 x Primary DB
CPU: 8 cores
RAM: 32GB
Storage: 500GB
- 1 x Replica DB
CPU: 8 cores
RAM: 32GB
Storage: 500GB
Cache Tier:
- 2 x Redis Nodes
CPU: 2 cores each = 4 cores
RAM: 8GB each = 16GB
Storage: 20GB each = 40GB
Total Resources:
- CPU: 28 cores
- RAM: 112GB
- Storage: 1140GB
Medium Scale (1000 req/s)
Application Tier:
- 5 x Application Servers
CPU: 8 cores each = 40 cores
RAM: 32GB each = 160GB
Storage: 100GB each = 500GB
Database Tier:
- 1 x Primary DB
CPU: 16 cores
RAM: 64GB
Storage: 2TB
- 2 x Replica DB
CPU: 16 cores each = 32 cores
RAM: 64GB each = 128GB
Storage: 2TB each = 4TB
Cache Tier:
- 3 x Redis Nodes
CPU: 4 cores each = 12 cores
RAM: 16GB each = 48GB
Storage: 50GB each = 150GB
Total Resources:
- CPU: 100 cores
- RAM: 400GB
- Storage: 6.65TB
Large Scale (10000 req/s)
Application Tier:
- 12 x Application Servers
CPU: 16 cores each = 192 cores
RAM: 64GB each = 768GB
Storage: 200GB each = 2.4TB
Database Tier:
- 1 x Primary DB
CPU: 32 cores
RAM: 256GB
Storage: 10TB
- 3 x Replica DB
CPU: 32 cores each = 96 cores
RAM: 256GB each = 768GB
Storage: 10TB each = 30TB
Cache Tier:
- 6 x Redis Nodes
CPU: 8 cores each = 48 cores
RAM: 32GB each = 192GB
Storage: 100GB each = 600GB
Total Resources:
- CPU: 368 cores
- RAM: 1984GB
- Storage: 43TB
B. Cost Optimization Strategies
Resource Allocation Rules:
1. CPU Allocation:
- Base: 2 cores per service
- Increment: 2 cores per 100 req/s
- Maximum: 32 cores per instance
2. Memory Allocation:
- Base: 4GB per service
- Increment: 4GB per 100 req/s
- Maximum: 256GB per instance
3. Storage Allocation:
- Base: 20GB per service
- Increment: 100GB per 1M documents
- Maximum: Based on storage type
4. Network Allocation:
- Base: 1Gbps per service
- Increment: 1Gbps per 1000 req/s
- Maximum: Based on provider limits
Would you like me to:
- Add more specific performance scenarios?
- Create more detailed scaling calculations?
- Add component-specific metrics?
- Expand any particular section?