Performance Benchmarks¶

Comprehensive benchmarking framework with benchmark categories, scoring, and automated regression detection.

Benchmark System Overview¶

Location: /Sources/OSClient/Performance/PerformanceBenchmarkSystem.swift

The performance benchmark system provides:

Comprehensive benchmarking across all performance-critical components
0.0-1.0 scoring scale for each category
Automated regression detection (10%+ performance drops)
Real-time metrics collection
Performance data export for analysis

Benchmark Categories¶

1. Cache Performance¶

What it measures:

Hit rate measurement under load
Response time analysis (L1/L2/L3 cache)
Cache statistics and efficiency
Memory usage patterns

Targets:

Hit rate: 80% (target)
Response time: < 1ms (L1 cache)
Eviction rate: < 20% of total entries
Memory efficiency: < 100MB for 10k resources

Pass threshold: 0.8 (80% of target performance)

Why these targets:

80% hit rate = 80% API call reduction
1ms response time = instant from user perspective
Low eviction rate = cache is right-sized
Memory efficiency = runs on modest hardware

2. Search Performance¶

What it measures:

Cross-service search speed
Result relevance scoring accuracy
Query optimization effectiveness
Parallel execution efficiency

Targets:

Average search time: < 500ms
P95 search time: < 1000ms
Timeout rate: < 5%
Result relevance: > 90% accuracy

Pass threshold: 0.8 (80% of target performance)

Why these targets:

500ms = acceptable wait for cross-service search
1000ms P95 = most searches feel fast
< 5% timeout = services are mostly healthy
High relevance = users find what they need

3. Memory Management¶

What it measures:

Allocation success rates
Cleanup efficiency
Memory usage tracking
Pressure handling

Targets:

Memory utilization: < 80% of available
Cleanup success rate: > 95%
Memory leak rate: 0%
Pressure response time: < 100ms

Pass threshold: 0.8 (80% of target performance)

Why these targets:

80% utilization = headroom for spikes
High cleanup success = no memory leaks
Zero leak rate = production-ready
Fast pressure response = prevents OOM

4. System Integration¶

What it measures:

Component interaction efficiency
Resource usage correlation
Overall system responsiveness
End-to-end operation timing

Targets:

API call latency: < 2s (uncached)
UI responsiveness: < 100ms
System health score: > 90%
Integration overhead: < 10%

Pass threshold: 0.8 (80% of target performance)

Why these targets:

2s API latency = acceptable for uncached calls
100ms UI = feels instant to users
90% health = system is reliable
Low overhead = efficient integration

5. Rendering Performance (TUI Mode)¶

What it measures:

Frame rate monitoring
Rendering optimization
UI responsiveness
Screen update efficiency

Targets:

Frame rate: 60 FPS (16.7ms per frame)
UI update latency: < 50ms
Screen refresh rate: consistent
Rendering overhead: < 5%

Pass threshold: 0.8 (80% of target performance)

Why these targets:

60 FPS = smooth animations
50ms update = feels instant
Consistent refresh = no jank
Low overhead = CPU-efficient

Benchmark Scoring¶

Performance Score Calculation¶

// From PerformanceBenchmarkSystem.swift:895
private struct PerformanceTargets {
    let cacheHitRate: Double = 0.8          // 80% cache hit rate
    let cacheResponseTime: TimeInterval = 0.001  // 1ms cache response
    let searchResponseTime: TimeInterval = 0.5   // 500ms search response
    let memoryUtilization: Double = 0.8     // 80% memory utilization max
    let systemHealthScore: Double = 0.9     // 90% system health
}

Score calculation:

1.0 = Exceeds target performance
0.8-1.0 = Meets or exceeds targets (PASS)
0.6-0.8 = Below target but acceptable
< 0.6 = Performance issue (FAIL)

Overall score:

Weighted average across all categories
Cache performance: 30% weight (most impactful)
Search performance: 25% weight
Memory management: 20% weight
System integration: 15% weight
Rendering performance: 10% weight

Regression Detection¶

Automatic alerts trigger when:

Performance drops 10%+ from baseline
Score falls below 0.8 threshold
Timeout rate increases significantly
Memory usage spikes unexpectedly

Baseline establishment:

Run benchmarks on clean install
Average 5 benchmark runs
Establish per-category baselines
Track trends over time

Running Benchmarks¶

Full Benchmark Suite¶

let benchmarkSystem = PerformanceBenchmarkSystem(...)
let report = await benchmarkSystem.runFullBenchmarkSuite()

print("Overall Score: \(report.overallScore)")
print("Cache Performance: \(report.cacheScore)")
print("Search Performance: \(report.searchScore)")

When to run:

After code changes affecting performance
Before releases (regression check)
During production troubleshooting
Weekly for trend analysis

Specific Benchmarks¶

// Run cache benchmark only
let cacheResults = await benchmarkSystem.runBenchmark(.cache)

// Run search benchmark only
let searchResults = await benchmarkSystem.runBenchmark(.search)

// Run memory benchmark only
let memoryResults = await benchmarkSystem.runBenchmark(.memory)

When to run specific benchmarks:

Cache: After changing TTL configurations
Search: After modifying search algorithms
Memory: When investigating memory issues
System: After architectural changes

Benchmark Scheduling¶

Automated benchmark execution:

Cache benchmarks: Every 5 minutes (lightweight)
Memory benchmarks: Every 3 minutes (critical for stability)
Search benchmarks: Every 10 minutes (more expensive)
System integration: Every 15 minutes (comprehensive)
Full suite: On-demand or nightly

Real-Time Metrics¶

getCurrentPerformanceMetrics() API¶

public struct RealTimeMetrics: Sendable {
    public let timestamp: Date
    public let cacheHitRate: Double           // Current cache hit rate
    public let memoryUtilization: Double      // Memory usage percentage
    public let systemHealthScore: Double      // Overall system health (0-1)
    public let averageResponseTime: TimeInterval // Average API response time
    public let renderingFPS: Double           // Current rendering frame rate
    public let totalApiCalls: Int            // Total API calls made
}

Usage:

let metrics = await benchmarkSystem.getCurrentPerformanceMetrics()

print("Cache Hit Rate: \(metrics.cacheHitRate * 100)%")
print("Memory Usage: \(metrics.memoryUtilization * 100)%")
print("Health Score: \(metrics.systemHealthScore * 100)%")

Performance Alerts¶

Automatic alerts trigger when:

Cache hit rate < 60%
Memory utilization > 85%
Average response time > 2 seconds
System health score < 70%
Rendering FPS < 30 (half target)

Benchmark Reports¶

Comprehensive Report Structure¶

public struct BenchmarkReport {
    public let timestamp: Date
    public let overallScore: Double
    public let cacheScore: Double
    public let searchScore: Double
    public let memoryScore: Double
    public let systemScore: Double
    public let renderingScore: Double
    public let detailedMetrics: [String: Double]
    public let recommendations: [String]
    public let regressions: [RegressionAlert]
}

Report includes:

Performance scores: 0.0-1.0 scale for each category
Detailed metrics: Response times, hit rates, memory usage
Recommendations: Specific optimization suggestions
Trend analysis: Performance changes over time
Regression alerts: Automatic performance regression detection

Sample Benchmark Report¶

=== Performance Benchmark Report ===
Timestamp: 2025-10-05 14:23:45

Overall Score: 0.87 (PASS)

Category Scores:
  Cache Performance:     0.92 (EXCELLENT)
  Search Performance:    0.85 (GOOD)
  Memory Management:     0.88 (GOOD)
  System Integration:    0.84 (GOOD)
  Rendering Performance: 0.86 (GOOD)

Detailed Metrics:
  Cache Hit Rate:        89.2%
  Cache Response Time:   0.8ms
  Search Time (avg):     450ms
  Search Time (p95):     890ms
  Memory Utilization:    72%
  System Health:         93%
  Rendering FPS:         58.4

Recommendations:
  - Cache performance excellent (89% hit rate)
  - Search performance within targets
  - Memory usage healthy
  - No regressions detected

Status: All systems nominal

Performance Data Export¶

Export API¶

// Export performance data for analysis
let performanceData = await benchmarkSystem.exportPerformanceData()

// Includes:
// - Trends over time
// - Recent benchmark results
// - Regression detection data
// - Historical baselines

Export formats:

JSON (for programmatic analysis)
CSV (for spreadsheet import)
Structured logs (for monitoring systems)

What's exported:

All benchmark scores (time-series)
Detailed metrics per benchmark
Regression events
Alert history
System configuration at time of benchmark

Interpreting Benchmark Results¶

Excellent Performance (Score: 0.9-1.0)¶

What it means:

System performing above targets
No optimization needed
Celebrate good architecture

Actions:

Document current configuration
Establish as baseline
Monitor for regressions

Good Performance (Score: 0.8-0.9)¶

What it means:

System meeting targets
Minor optimization opportunities
Production-ready

Actions:

Monitor trends
Consider minor tuning
Track for future improvements

Acceptable Performance (Score: 0.6-0.8)¶

What it means:

System below targets
Optimization recommended
Still functional but not optimal

Actions:

Review configuration
Identify bottlenecks
Plan optimization work

Poor Performance (Score: < 0.6)¶

What it means:

System significantly below targets
Performance issues present
Immediate action required

Actions:

Investigate immediately
Check for regressions
Review recent changes
See Troubleshooting Guide

Benchmark Best Practices¶

1. Consistent Environment¶

Run benchmarks on consistent hardware
Minimize background processes
Use same OpenStack environment
Avoid peak usage times

2. Baseline Establishment¶

Run 5+ benchmarks to establish baseline
Average scores for consistency
Document environment configuration
Update baseline quarterly

3. Regular Monitoring¶

Schedule automated benchmarks
Review trends weekly
Investigate anomalies immediately
Track improvements over time

4. Regression Response¶

Investigate 10%+ drops immediately
Compare with recent code changes
Rollback if necessary
Document root cause

See Also:

Performance Overview - Architecture and key components
Performance Tuning - Optimization strategies
Troubleshooting - Performance issue diagnosis

Note: All benchmark targets are based on production testing with 10K+ resources. Adjust targets based on your specific environment and requirements.