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
• Baseline Performance
• Load Test Results
• Response Time Targets
• Throughput Targets
• Resource Utilization
• Cache Performance
• Database Performance
• Autoscaling Behavior
• Before vs After Optimization
• 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:

Single User Performance

Metrics collected with 1 active user:

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

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

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

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

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

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

By Service

Resource Utilization

At Different Load Levels

CPU Utilization

Memory Utilization

Network I/O

Disk I/O

Cache Performance

L1 Cache (In-Memory)

L2 Cache (Redis)

RAG Cache (Vector/Semantic)

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

Connection Pool

Slow Queries

Queries exceeding 100ms threshold:

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

Scaling Events Timeline

0-100 Users (Ramp-up Phase)

100-200 Users (Growth Phase)

200-500 Users (Peak Phase)

VPA Recommendations

VPA observed resource usage and made the following recommendations:

Before Optimization

After Tuning

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)

Cost Implications

Performance SLOs

Production SLOs (100-200 Users)

Performance Budget

Maximum acceptable degradation:

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