Performance Analysis

/// Performance metrics structure
#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    /// Messages processed per second
    pub throughput: f64,
    /// Average message processing latency
    pub average_latency: Duration,
    /// 95th percentile latency
    pub p95_latency: Duration,
    /// 99th percentile latency
    pub p99_latency: Duration,
    /// Memory usage in bytes
    pub memory_usage: usize,
    /// CPU utilization percentage
    pub cpu_utilization: f64,
    /// Error rate percentage
    pub error_rate: f64,
}

// CPU profiling data structure
#[derive(Debug)]
pub struct CpuProfile {
    pub function_name: String,
    pub total_time: Duration,
    pub self_time: Duration,
    pub call_count: u64,
    pub percentage: f64,
}

// Example profiling results
let cpu_profiles = vec![
    CpuProfile {
        function_name: "MessageBuffer::push".to_string(),
        total_time: Duration::from_millis(1500),
        self_time: Duration::from_millis(800),
        call_count: 1_000_000,
        percentage: 35.2,
    },
    CpuProfile {
        function_name: "TopicRouter::route".to_string(),
        total_time: Duration::from_millis(1200),
        self_time: Duration::from_millis(600),
        call_count: 1_000_000,
        percentage: 28.1,
    },
];

// Memory profiling data structure
#[derive(Debug)]
pub struct MemoryProfile {
    pub allocation_site: String,
    pub total_allocated: usize,
    pub allocation_count: u64,
    pub average_size: usize,
}

// Example memory profiling results
let memory_profiles = vec![
    MemoryProfile {
        allocation_site: "Message::new".to_string(),
        total_allocated: 1024 * 1024 * 100, // 100MB
        allocation_count: 1_000_000,
        average_size: 100, // bytes
    },
    MemoryProfile {
        allocation_site: "HashMap::insert".to_string(),
        total_allocated: 1024 * 1024 * 50, // 50MB
        allocation_count: 500_000,
        average_size: 100, // bytes
    },
];

/// Analysis of message size impact on performance
#[derive(Debug)]
pub struct MessageSizeAnalysis {
    pub message_size: usize,
    pub throughput: f64,
    pub latency: Duration,
    pub memory_usage: usize,
}

let size_analysis = vec![
    MessageSizeAnalysis {
        message_size: 64,     // 64 bytes
        throughput: 1_000_000.0,
        latency: Duration::from_micros(800),
        memory_usage: 64 * 1024, // 64KB buffer
    },
    MessageSizeAnalysis {
        message_size: 1024,   // 1KB
        throughput: 500_000.0,
        latency: Duration::from_micros(1200),
        memory_usage: 1024 * 1024, // 1MB buffer
    },
    MessageSizeAnalysis {
        message_size: 10_240, // 10KB
        throughput: 100_000.0,
        latency: Duration::from_micros(2500),
        memory_usage: 10 * 1024 * 1024, // 10MB buffer
    },
];

/// Analysis of concurrent load impact
#[derive(Debug)]
pub struct ConcurrentLoadAnalysis {
    pub concurrent_tasks: usize,
    pub throughput: f64,
    pub latency: Duration,
    pub cpu_utilization: f64,
}

let load_analysis = vec![
    ConcurrentLoadAnalysis {
        concurrent_tasks: 1,
        throughput: 100_000.0,
        latency: Duration::from_micros(800),
        cpu_utilization: 25.0,
    },
    ConcurrentLoadAnalysis {
        concurrent_tasks: 4,
        throughput: 350_000.0,
        latency: Duration::from_micros(1200),
        cpu_utilization: 65.0,
    },
    ConcurrentLoadAnalysis {
        concurrent_tasks: 16,
        throughput: 800_000.0,
        latency: Duration::from_micros(2500),
        cpu_utilization: 95.0,
    },
];

/// Bottleneck mitigation strategies
pub struct BottleneckMitigation {
    pub bottleneck: String,
    pub impact: f64, // Percentage impact on performance
    pub mitigation: String,
    pub expected_improvement: f64,
}

let mitigations = vec![
    BottleneckMitigation {
        bottleneck: "Memory Allocation".to_string(),
        impact: 25.0,
        mitigation: "Implement memory pooling".to_string(),
        expected_improvement: 20.0,
    },
    BottleneckMitigation {
        bottleneck: "Lock Contention".to_string(),
        impact: 15.0,
        mitigation: "Use lock-free data structures".to_string(),
        expected_improvement: 12.0,
    },
    BottleneckMitigation {
        bottleneck: "Cache Misses".to_string(),
        impact: 10.0,
        mitigation: "Improve data locality".to_string(),
        expected_improvement: 8.0,
    },
];

/// Horizontal scaling analysis
#[derive(Debug)]
pub struct HorizontalScaling {
    pub node_count: usize,
    pub throughput: f64,
    pub efficiency: f64, // Percentage of linear scaling
}

let horizontal_scaling = vec![
    HorizontalScaling {
        node_count: 1,
        throughput: 100_000.0,
        efficiency: 100.0,
    },
    HorizontalScaling {
        node_count: 2,
        throughput: 190_000.0,
        efficiency: 95.0,
    },
    HorizontalScaling {
        node_count: 4,
        throughput: 360_000.0,
        efficiency: 90.0,
    },
    HorizontalScaling {
        node_count: 8,
        throughput: 640_000.0,
        efficiency: 80.0,
    },
];

/// Vertical scaling analysis
#[derive(Debug)]
pub struct VerticalScaling {
    pub cpu_cores: usize,
    pub memory_gb: usize,
    pub throughput: f64,
    pub cost_efficiency: f64,
}

let vertical_scaling = vec![
    VerticalScaling {
        cpu_cores: 2,
        memory_gb: 4,
        throughput: 100_000.0,
        cost_efficiency: 100.0,
    },
    VerticalScaling {
        cpu_cores: 4,
        memory_gb: 8,
        throughput: 180_000.0,
        cost_efficiency: 90.0,
    },
    VerticalScaling {
        cpu_cores: 8,
        memory_gb: 16,
        throughput: 300_000.0,
        cost_efficiency: 75.0,
    },
];

/// CPU utilization analysis
#[derive(Debug)]
pub struct CpuUtilization {
    pub workload: String,
    pub user_cpu: f64,
    pub system_cpu: f64,
    pub idle_cpu: f64,
    pub context_switches: u64,
}

let cpu_utilization = vec![
    CpuUtilization {
        workload: "Light Load".to_string(),
        user_cpu: 15.0,
        system_cpu: 5.0,
        idle_cpu: 80.0,
        context_switches: 1_000,
    },
    CpuUtilization {
        workload: "Medium Load".to_string(),
        user_cpu: 45.0,
        system_cpu: 15.0,
        idle_cpu: 40.0,
        context_switches: 5_000,
    },
    CpuUtilization {
        workload: "Heavy Load".to_string(),
        user_cpu: 75.0,
        system_cpu: 20.0,
        idle_cpu: 5.0,
        context_switches: 20_000,
    },
];

/// Memory utilization analysis
#[derive(Debug)]
pub struct MemoryUtilization {
    pub component: String,
    pub heap_usage: usize,
    pub stack_usage: usize,
    pub cache_usage: usize,
    pub fragmentation: f64,
}

let memory_utilization = vec![
    MemoryUtilization {
        component: "Memory System".to_string(),
        heap_usage: 50 * 1024 * 1024, // 50MB
        stack_usage: 2 * 1024 * 1024, // 2MB
        cache_usage: 10 * 1024 * 1024, // 10MB
        fragmentation: 5.0,
    },
    MemoryUtilization {
        component: "Communication".to_string(),
        heap_usage: 25 * 1024 * 1024, // 25MB
        stack_usage: 1 * 1024 * 1024, // 1MB
        cache_usage: 5 * 1024 * 1024, // 5MB
        fragmentation: 3.0,
    },
];

/// Performance regression analysis
#[derive(Debug)]
pub struct PerformanceRegression {
    pub version: String,
    pub metric: String,
    pub baseline_value: f64,
    pub current_value: f64,
    pub change_percentage: f64,
    pub significance: RegressionSignificance,
}

#[derive(Debug)]
pub enum RegressionSignificance {
    Minor,    // < 5% change
    Moderate, // 5-15% change
    Major,    // > 15% change
}

let regressions = vec![
    PerformanceRegression {
        version: "1.1.0".to_string(),
        metric: "Throughput".to_string(),
        baseline_value: 100_000.0,
        current_value: 95_000.0,
        change_percentage: -5.0,
        significance: RegressionSignificance::Minor,
    },
    PerformanceRegression {
        version: "1.2.0".to_string(),
        metric: "Latency".to_string(),
        baseline_value: 2.0, // ms
        current_value: 2.4,  // ms
        change_percentage: 20.0,
        significance: RegressionSignificance::Major,
    },
];