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3:T5fa5,
# VoiceAssist Performance Benchmarks

## Overview

This document provides comprehensive performance benchmarks for VoiceAssist Phase 10, including baseline metrics, load test results, and performance targets. Use these benchmarks to:

- Evaluate system performance under various load conditions
- Identify performance regressions
- Set realistic SLOs (Service Level Objectives)
- Plan capacity and scaling strategies

## Table of Contents

- [Testing Environment](#testing-environment)
- [Baseline Performance](#baseline-performance)
- [Load Test Results](#load-test-results)
- [Response Time Targets](#response-time-targets)
- [Throughput Targets](#throughput-targets)
- [Resource Utilization](#resource-utilization)
- [Cache Performance](#cache-performance)
- [Database Performance](#database-performance)
- [Autoscaling Behavior](#autoscaling-behavior)
- [Before vs After Optimization](#before-vs-after-optimization)
- [Performance SLOs](#performance-slos)

---

## Testing Environment

### Infrastructure

- **Kubernetes Version**: 1.28+
- **Node Configuration**:
  - 3 worker nodes
  - 4 vCPU, 16GB RAM per node
  - SSD storage
- **Database**: PostgreSQL 15
  - 2 vCPU, 8GB RAM
  - Connection pool: 20-50 connections
- **Cache**: Redis 7
  - 2 vCPU, 4GB RAM
  - Max memory: 2GB

### Application Configuration

- **API Gateway**: 2-10 replicas (HPA enabled)
- **Worker Service**: 2-8 replicas (HPA enabled)
- **Resource Limits**:
  - CPU: 500m-2000m
  - Memory: 512Mi-2Gi
- **HPA Thresholds**:
  - CPU: 70%
  - Memory: 80%
  - Custom: 50 req/s per pod

---

## Baseline Performance

### No Load Conditions

Metrics collected with zero active users:

| Metric                    | Value            | Notes                 |
| ------------------------- | ---------------- | --------------------- |
| **Idle CPU Usage**        | 5-10%            | Background tasks only |
| **Idle Memory Usage**     | 200-300 MB       | Per pod               |
| **Pod Count**             | 2 (min replicas) | API Gateway + Worker  |
| **DB Connections**        | 5-10 active      | Connection pool idle  |
| **Cache Memory**          | 50-100 MB        | Warm cache            |
| **Health Check Response** | 10-20ms          | P95                   |

### Single User Performance

Metrics collected with 1 active user:

| Endpoint                  | P50 (ms) | P95 (ms) | P99 (ms) | Notes                   |
| ------------------------- | -------- | -------- | -------- | ----------------------- |
| **/health**               | 5        | 10       | 15       | Basic health check      |
| **/api/auth/login**       | 50       | 80       | 100      | Includes password hash  |
| **/api/chat (simple)**    | 150      | 250      | 350      | Simple query, cache hit |
| **/api/chat (complex)**   | 800      | 1200     | 1500     | Complex query, RAG      |
| **/api/documents/upload** | 500      | 800      | 1200     | 1MB document            |
| **/api/admin/dashboard**  | 100      | 180      | 250      | Dashboard metrics       |

---

## Load Test Results

### Test Methodology

- **Tool**: Locust (primary), k6 (validation)
- **User Distribution**:
  - 70% Regular Users (simple queries)
  - 20% Power Users (complex queries)
  - 10% Admin Users (document management)
- **Ramp-up**: Linear, 10 users/minute
- **Duration**: 30 minutes steady state
- **Think Time**: 3-10 seconds between requests

### 50 Virtual Users

**Target**: Baseline performance validation

| Metric                   | Value    | Target    | Status |
| ------------------------ | -------- | --------- | ------ |
| **Throughput**           | 45 req/s | 40+ req/s | PASS   |
| **P50 Response Time**    | 120ms    | <200ms    | PASS   |
| **P95 Response Time**    | 380ms    | <500ms    | PASS   |
| **P99 Response Time**    | 650ms    | <1000ms   | PASS   |
| **Error Rate**           | 0.1%     | <1%       | PASS   |
| **CPU Utilization**      | 35-45%   | <60%      | PASS   |
| **Memory Utilization**   | 40-50%   | <70%      | PASS   |
| **Pod Count**            | 2-3      | -         | -      |
| **DB Connections**       | 15-20    | <40       | PASS   |
| **Cache Hit Rate (L1)**  | 85%      | >80%      | PASS   |
| **Cache Hit Rate (L2)**  | 70%      | >60%      | PASS   |
| **Cache Hit Rate (RAG)** | 55%      | >50%      | PASS   |

**Key Findings**:

- System handles 50 users comfortably with minimal scaling
- Response times well within targets
- Cache performing as expected
- No database bottlenecks

### 100 Virtual Users

**Target**: Production load simulation

| Metric                   | Value    | Target    | Status |
| ------------------------ | -------- | --------- | ------ |
| **Throughput**           | 90 req/s | 80+ req/s | PASS   |
| **P50 Response Time**    | 180ms    | <250ms    | PASS   |
| **P95 Response Time**    | 520ms    | <800ms    | PASS   |
| **P99 Response Time**    | 950ms    | <1500ms   | PASS   |
| **Error Rate**           | 0.3%     | <1%       | PASS   |
| **CPU Utilization**      | 55-65%   | <70%      | PASS   |
| **Memory Utilization**   | 55-65%   | <75%      | PASS   |
| **Pod Count**            | 4-5      | -         | -      |
| **DB Connections**       | 25-35    | <45       | PASS   |
| **Cache Hit Rate (L1)**  | 83%      | >75%      | PASS   |
| **Cache Hit Rate (L2)**  | 68%      | >55%      | PASS   |
| **Cache Hit Rate (RAG)** | 52%      | >45%      | PASS   |

**Key Findings**:

- HPA triggered at ~70 users (CPU threshold)
- Scaled to 4-5 pods
- Response times increased but within targets
- Cache efficiency remains high
- DB connection pool sufficient

### 200 Virtual Users

**Target**: Peak load handling

| Metric                   | Value     | Target     | Status |
| ------------------------ | --------- | ---------- | ------ |
| **Throughput**           | 175 req/s | 150+ req/s | PASS   |
| **P50 Response Time**    | 280ms     | <400ms     | PASS   |
| **P95 Response Time**    | 850ms     | <1200ms    | PASS   |
| **P99 Response Time**    | 1450ms    | <2000ms    | PASS   |
| **Error Rate**           | 0.8%      | <2%        | PASS   |
| **CPU Utilization**      | 68-78%    | <80%       | PASS   |
| **Memory Utilization**   | 65-75%    | <80%       | PASS   |
| **Pod Count**            | 7-8       | -          | -      |
| **DB Connections**       | 35-45     | <50        | PASS   |
| **Cache Hit Rate (L1)**  | 80%       | >70%       | PASS   |
| **Cache Hit Rate (L2)**  | 65%       | >50%       | PASS   |
| **Cache Hit Rate (RAG)** | 48%       | >40%       | PASS   |

**Key Findings**:

- Aggressive scaling to 7-8 pods
- Response times degrading but acceptable
- CPU approaching threshold
- DB connection pool near capacity
- Cache still providing value

### 500 Virtual Users

**Target**: Stress test / Breaking point

| Metric                   | Value     | Target     | Status   |
| ------------------------ | --------- | ---------- | -------- |
| **Throughput**           | 380 req/s | 300+ req/s | PASS     |
| **P50 Response Time**    | 520ms     | <800ms     | PASS     |
| **P95 Response Time**    | 1850ms    | <3000ms    | PASS     |
| **P99 Response Time**    | 3200ms    | <5000ms    | PASS     |
| **Error Rate**           | 2.5%      | <5%        | PASS     |
| **CPU Utilization**      | 75-85%    | <90%       | PASS     |
| **Memory Utilization**   | 70-80%    | <85%       | PASS     |
| **Pod Count**            | 10 (max)  | -          | -        |
| **DB Connections**       | 45-50     | <50        | MARGINAL |
| **Cache Hit Rate (L1)**  | 75%       | >65%       | PASS     |
| **Cache Hit Rate (L2)**  | 60%       | >45%       | PASS     |
| **Cache Hit Rate (RAG)** | 42%       | >35%       | PASS     |

**Key Findings**:

- System at maximum capacity (10 pods)
- Response times significantly degraded
- DB connection pool saturated
- Error rate increasing but acceptable
- Cache hit rates dropping due to churn
- **Recommendation**: 500 users is operational limit

**Breaking Point Analysis**:

- At 600+ users: Error rate >5%, P99 >8000ms
- Primary bottleneck: Database connection pool
- Secondary bottleneck: CPU at peak load
- Mitigation: Scale database vertically or add read replicas

---

## Response Time Targets

### SLO Definitions

| Percentile | Target  | Critical Threshold | Notes                  |
| ---------- | ------- | ------------------ | ---------------------- |
| **P50**    | <200ms  | <500ms             | Median user experience |
| **P95**    | <500ms  | <1000ms            | 95% of requests        |
| **P99**    | <1000ms | <2000ms            | Edge cases             |
| **P99.9**  | <2000ms | <5000ms            | Rare outliers          |

### By Endpoint Category

#### Fast Endpoints (<100ms P95)

- Health checks
- Static content
- Cache hits
- Simple queries

#### Medium Endpoints (100-500ms P95)

- Authentication
- Simple chat queries
- Profile operations
- Dashboard views

#### Slow Endpoints (500-1500ms P95)

- Complex chat queries (RAG)
- Document uploads
- Batch operations
- Report generation

#### Acceptable Outliers (>1500ms)

- Large document processing
- Complex analytics
- Historical data exports
- AI model inference (cold start)

---

## Throughput Targets

### Overall System

| Load Level             | Target (req/s) | Measured (req/s) | Status |
| ---------------------- | -------------- | ---------------- | ------ |
| **Light** (50 users)   | 40+            | 45               | PASS   |
| **Normal** (100 users) | 80+            | 90               | PASS   |
| **Heavy** (200 users)  | 150+           | 175              | PASS   |
| **Peak** (500 users)   | 300+           | 380              | PASS   |

### By Service

| Service              | Target (req/s) | Peak (req/s) | Notes                   |
| -------------------- | -------------- | ------------ | ----------------------- |
| **API Gateway**      | 400+           | 380          | Primary entry point     |
| **Auth Service**     | 50+            | 45           | Login/logout operations |
| **Chat Service**     | 300+           | 280          | Main workload           |
| **Document Service** | 20+            | 25           | Upload/download         |
| **Admin Service**    | 10+            | 15           | Management operations   |

---

## Resource Utilization

### At Different Load Levels

#### CPU Utilization

| Load          | Avg CPU | Peak CPU | Pod Count | Notes            |
| ------------- | ------- | -------- | --------- | ---------------- |
| **50 users**  | 40%     | 55%      | 2-3       | Minimal scaling  |
| **100 users** | 60%     | 75%      | 4-5       | Active scaling   |
| **200 users** | 73%     | 85%      | 7-8       | Frequent scaling |
| **500 users** | 80%     | 95%      | 10        | Max capacity     |

#### Memory Utilization

| Load          | Avg Memory | Peak Memory | Pod Count | Notes            |
| ------------- | ---------- | ----------- | --------- | ---------------- |
| **50 users**  | 45%        | 60%         | 2-3       | Stable           |
| **100 users** | 60%        | 72%         | 4-5       | Gradual increase |
| **200 users** | 70%        | 82%         | 7-8       | High utilization |
| **500 users** | 75%        | 88%         | 10        | Near limit       |

#### Network I/O

| Load          | Ingress (MB/s) | Egress (MB/s) | Notes         |
| ------------- | -------------- | ------------- | ------------- |
| **50 users**  | 2.5            | 3.5           | Low bandwidth |
| **100 users** | 5.0            | 7.0           | Moderate      |
| **200 users** | 10.0           | 14.0          | High          |
| **500 users** | 22.0           | 30.0          | Very high     |

#### Disk I/O

| Load          | Read (IOPS) | Write (IOPS) | Notes              |
| ------------- | ----------- | ------------ | ------------------ |
| **50 users**  | 150         | 80           | Minimal disk usage |
| **100 users** | 300         | 150          | Moderate           |
| **200 users** | 550         | 280          | High               |
| **500 users** | 1200        | 600          | Very high          |

---

## Cache Performance

### L1 Cache (In-Memory)

| Metric            | 50 Users | 100 Users | 200 Users | 500 Users | Target |
| ----------------- | -------- | --------- | --------- | --------- | ------ |
| **Hit Rate**      | 85%      | 83%       | 80%       | 75%       | >70%   |
| **Miss Rate**     | 15%      | 17%       | 20%       | 25%       | <30%   |
| **Avg Latency**   | 0.5ms    | 0.6ms     | 0.8ms     | 1.2ms     | <2ms   |
| **P95 Latency**   | 1.0ms    | 1.2ms     | 1.5ms     | 2.5ms     | <5ms   |
| **Eviction Rate** | 2/min    | 5/min     | 12/min    | 35/min    | -      |

### L2 Cache (Redis)

| Metric            | 50 Users | 100 Users | 200 Users | 500 Users | Target |
| ----------------- | -------- | --------- | --------- | --------- | ------ |
| **Hit Rate**      | 70%      | 68%       | 65%       | 60%       | >55%   |
| **Miss Rate**     | 30%      | 32%       | 35%       | 40%       | <45%   |
| **Avg Latency**   | 2.5ms    | 3.0ms     | 3.8ms     | 5.5ms     | <10ms  |
| **P95 Latency**   | 5.0ms    | 6.0ms     | 8.0ms     | 12.0ms    | <20ms  |
| **Eviction Rate** | 5/min    | 10/min    | 25/min    | 80/min    | -      |

### RAG Cache (Vector/Semantic)

| Metric            | 50 Users | 100 Users | 200 Users | 500 Users | Target |
| ----------------- | -------- | --------- | --------- | --------- | ------ |
| **Hit Rate**      | 55%      | 52%       | 48%       | 42%       | >40%   |
| **Miss Rate**     | 45%      | 48%       | 52%       | 58%       | <60%   |
| **Avg Latency**   | 15ms     | 18ms      | 22ms      | 35ms      | <50ms  |
| **P95 Latency**   | 35ms     | 42ms      | 55ms      | 85ms      | <100ms |
| **Eviction Rate** | 3/min    | 8/min     | 20/min    | 60/min    | -      |

**Key Findings**:

- L1 cache most effective, even at high load
- L2 cache provides good fallback
- RAG cache hit rate lower but still valuable
- Cache eviction increases with load (expected)
- Overall cache strategy working well

---

## Database Performance

### Query Performance

| Query Type           | P50 (ms) | P95 (ms) | P99 (ms) | Target P95 | Status   |
| -------------------- | -------- | -------- | -------- | ---------- | -------- |
| **Simple SELECT**    | 5        | 12       | 18       | <20ms      | PASS     |
| **JOIN (2 tables)**  | 15       | 35       | 55       | <50ms      | PASS     |
| **JOIN (3+ tables)** | 35       | 85       | 150      | <100ms     | MARGINAL |
| **INSERT**           | 8        | 18       | 28       | <25ms      | PASS     |
| **UPDATE**           | 10       | 22       | 35       | <30ms      | PASS     |
| **DELETE**           | 8        | 20       | 32       | <25ms      | PASS     |
| **Aggregate**        | 25       | 65       | 120      | <80ms      | MARGINAL |
| **Full-text Search** | 45       | 120      | 200      | <150ms     | MARGINAL |

### Connection Pool

| Metric                 | 50 Users | 100 Users | 200 Users | 500 Users | Notes            |
| ---------------------- | -------- | --------- | --------- | --------- | ---------------- |
| **Active Connections** | 15-20    | 25-35     | 35-45     | 45-50     | Max: 50          |
| **Idle Connections**   | 5-10     | 5-10      | 3-5       | 0-2       | -                |
| **Wait Time**          | 0ms      | 0ms       | 0-5ms     | 5-20ms    | Queueing at peak |
| **Checkout Time**      | 0.5ms    | 0.8ms     | 1.2ms     | 2.5ms     | -                |
| **Utilization**        | 35%      | 65%       | 85%       | 98%       | Near capacity    |

### Slow Queries

Queries exceeding 100ms threshold:

| Load          | Slow Queries/min | Most Common           | Notes           |
| ------------- | ---------------- | --------------------- | --------------- |
| **50 users**  | 2-5              | Complex JOINs         | Acceptable      |
| **100 users** | 8-15             | Aggregates, Full-text | Within limits   |
| **200 users** | 25-40            | Unoptimized queries   | Needs attention |
| **500 users** | 80-120           | All complex queries   | Critical        |

**Recommendations**:

- Add indexes for common query patterns
- Optimize 3+ table JOINs
- Consider read replicas for 200+ users
- Review and optimize aggregate queries
- Implement query result caching

---

## Autoscaling Behavior

### HPA Metrics

| Metric               | Configuration | Observed Behavior               |
| -------------------- | ------------- | ------------------------------- |
| **Min Replicas**     | 2             | Maintained during idle          |
| **Max Replicas**     | 10            | Reached at 500 users            |
| **Target CPU**       | 70%           | Triggers scale-up reliably      |
| **Target Memory**    | 80%           | Rarely triggers (CPU first)     |
| **Custom Metric**    | 50 req/s      | Works well for API Gateway      |
| **Scale-up Speed**   | 1 pod/30s     | Conservative, prevents flapping |
| **Scale-down Speed** | 1 pod/5min    | Gradual, allows warmup          |
| **Stabilization**    | 3min          | Prevents rapid oscillation      |

### Scaling Events Timeline

#### 0-100 Users (Ramp-up Phase)

| User Count | Event    | Pod Count | Reason          |
| ---------- | -------- | --------- | --------------- |
| 0          | Start    | 2         | Min replicas    |
| 50         | -        | 2         | Below threshold |
| 70         | Scale up | 3         | CPU >70%        |
| 85         | Scale up | 4         | CPU >70%        |
| 100        | Stable   | 4-5       | Fluctuating     |

#### 100-200 Users (Growth Phase)

| User Count | Event    | Pod Count | Reason      |
| ---------- | -------- | --------- | ----------- |
| 120        | Scale up | 5         | CPU >70%    |
| 140        | Scale up | 6         | CPU >70%    |
| 170        | Scale up | 7         | CPU >70%    |
| 200        | Stable   | 7-8       | Fluctuating |

#### 200-500 Users (Peak Phase)

| User Count | Event    | Pod Count | Reason       |
| ---------- | -------- | --------- | ------------ |
| 250        | Scale up | 8         | CPU >70%     |
| 320        | Scale up | 9         | CPU >70%     |
| 400        | Scale up | 10        | CPU >70%     |
| 500        | Max      | 10        | Max replicas |

### VPA Recommendations

VPA observed resource usage and made the following recommendations:

#### Before Optimization

| Resource   | Requested | Recommended | Actual Usage  | Notes             |
| ---------- | --------- | ----------- | ------------- | ----------------- |
| **CPU**    | 500m      | 800m        | 600-700m avg  | Under-provisioned |
| **Memory** | 512Mi     | 768Mi       | 650-750Mi avg | Under-provisioned |

#### After Tuning

| Resource   | Requested | Recommended | Actual Usage  | Notes            |
| ---------- | --------- | ----------- | ------------- | ---------------- |
| **CPU**    | 1000m     | 1000m       | 700-900m avg  | Well-provisioned |
| **Memory** | 1Gi       | 1Gi         | 700-900Mi avg | Well-provisioned |

**Result**: VPA recommendations now align with actual usage, indicating proper resource allocation.

---

## Before vs After Optimization

### Optimization Focus Areas

1. **Database Query Optimization**
   - Added missing indexes
   - Optimized N+1 queries
   - Implemented query result caching

2. **Cache Strategy Enhancement**
   - Implemented 3-tier cache (L1, L2, RAG)
   - Optimized TTL values
   - Added cache warming

3. **Resource Tuning**
   - Adjusted CPU/Memory limits based on VPA
   - Optimized connection pool sizing
   - Fine-tuned HPA thresholds

4. **Code Optimization**
   - Reduced middleware overhead
   - Optimized serialization
   - Implemented async processing

### Performance Comparison (100 Users)

| Metric                  | Before   | After    | Improvement   |
| ----------------------- | -------- | -------- | ------------- |
| **P50 Response Time**   | 320ms    | 180ms    | 44% faster    |
| **P95 Response Time**   | 980ms    | 520ms    | 47% faster    |
| **P99 Response Time**   | 1850ms   | 950ms    | 49% faster    |
| **Throughput**          | 65 req/s | 90 req/s | 38% increase  |
| **Error Rate**          | 1.2%     | 0.3%     | 75% reduction |
| **CPU Utilization**     | 75%      | 60%      | 20% reduction |
| **Memory Utilization**  | 70%      | 60%      | 14% reduction |
| **DB Queries**          | 150/s    | 90/s     | 40% reduction |
| **Cache Hit Rate (L1)** | 65%      | 83%      | 28% increase  |
| **Pod Count**           | 5-6      | 4-5      | 1 fewer pod   |

### Cost Implications

| Metric                     | Before | After | Savings   |
| -------------------------- | ------ | ----- | --------- |
| **Avg Pod Count**          | 5.5    | 4.5   | 18%       |
| **CPU Hours/Day**          | 132    | 108   | 18%       |
| **Memory GB-Hours/Day**    | 132    | 108   | 18%       |
| **Estimated Monthly Cost** | $450   | $370  | $80 (18%) |

---

## Performance SLOs

### Production SLOs (100-200 Users)

| Metric                | Target     | Critical  | Current      | Status |
| --------------------- | ---------- | --------- | ------------ | ------ |
| **Availability**      | 99.9%      | 99.5%     | 99.95%       | PASS   |
| **P50 Response Time** | <250ms     | <500ms    | 180-280ms    | PASS   |
| **P95 Response Time** | <800ms     | <1500ms   | 520-850ms    | PASS   |
| **P99 Response Time** | <1500ms    | <3000ms   | 950-1450ms   | PASS   |
| **Error Rate**        | <1%        | <3%       | 0.3-0.8%     | PASS   |
| **Throughput**        | >100 req/s | >50 req/s | 90-175 req/s | PASS   |

### Performance Budget

Maximum acceptable degradation:

| Metric                | Baseline | Budget | Alert Threshold |
| --------------------- | -------- | ------ | --------------- |
| **P95 Response Time** | 520ms    | +30%   | >675ms          |
| **Throughput**        | 90 req/s | -20%   | <72 req/s       |
| **Error Rate**        | 0.3%     | +200%  | >0.9%           |
| **Cache Hit Rate**    | 83%      | -10%   | <75%            |

### Alerting Rules

**Critical Alerts** (Page oncall):

- P95 response time >1500ms for 5 minutes
- Error rate >5% for 5 minutes
- Availability <99.5% over 1 hour
- Database connection pool >95% for 10 minutes

**Warning Alerts** (Notify team):

- P95 response time >800ms for 10 minutes
- Error rate >1% for 10 minutes
- CPU utilization >80% for 15 minutes
- Memory utilization >85% for 15 minutes
- Cache hit rate <70% for 15 minutes

**Info Alerts** (Log only):

- P95 response time >500ms for 15 minutes
- CPU utilization >70% for 20 minutes
- Autoscaling events

---

## Continuous Monitoring

### Key Metrics to Track

1. **Golden Signals**
   - Latency (P50, P95, P99)
   - Traffic (req/s)
   - Errors (rate, count)
   - Saturation (CPU, memory, DB connections)

2. **Performance Indicators**
   - Cache hit rates (all tiers)
   - Database query performance
   - Autoscaling behavior
   - Resource utilization

3. **Business Metrics**
   - User satisfaction (survey data)
   - Feature usage
   - Peak load patterns
   - Cost per request

### Dashboards

- **Load Testing Overview**: `/dashboards/load-testing-overview.json`
- **Autoscaling Monitoring**: `/dashboards/autoscaling-monitoring.json`
- **System Performance**: `/dashboards/system-performance.json`

### Review Cadence

- **Daily**: Review overnight metrics, check for anomalies
- **Weekly**: Analyze trends, update capacity plans
- **Monthly**: Review SLOs, update benchmarks
- **Quarterly**: Performance audit, optimization sprint

---

## Conclusion

VoiceAssist Phase 10 demonstrates strong performance characteristics:

**Strengths**:

- Handles 100-200 concurrent users comfortably
- Response times well within targets
- Effective caching strategy
- Reliable autoscaling
- Significant improvements post-optimization

**Areas for Improvement**:

- Database connection pool at capacity during peak load (500+ users)
- Some complex queries need optimization
- Cache eviction rate high at extreme load

**Recommendations**:

1. Plan for database scaling (read replicas) before 300+ users
2. Continue query optimization efforts
3. Monitor cache efficiency and adjust TTLs
4. Consider implementing rate limiting for burst traffic
5. Review and update benchmarks quarterly

**Next Steps**:

- See `LOAD_TESTING_GUIDE.md` for testing procedures
- See `PERFORMANCE_TUNING_GUIDE.md` for optimization techniques
- Use Grafana dashboards for ongoing monitoring
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