optirs_bench/

enhanced_memory_monitor.rs

// Enhanced Real-time Memory Monitoring and Advanced Leak Detection
//
// This module provides advanced real-time memory monitoring capabilities with
// machine learning-based anomaly detection, system profiling integration,
// and intelligent alert generation for production environments.

use crate::error::{OptimError, Result};
use crate::memory_leak_detector::{MemoryLeakDetector, MemoryUsageSnapshot};
use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap, VecDeque};
use std::path::PathBuf;
use std::sync::atomic::{AtomicBool, Ordering};
use std::sync::{Arc, Mutex, RwLock};
use std::thread;
use std::time::{Duration, Instant, SystemTime, UNIX_EPOCH};

/// Enhanced real-time memory monitoring system
#[derive(Debug)]
pub struct EnhancedMemoryMonitor {
    /// Base memory leak detector
    leak_detector: Arc<Mutex<MemoryLeakDetector>>,
    /// Real-time monitoring configuration
    config: RealTimeMonitoringConfig,
    /// System profiler integration
    system_profiler: SystemProfiler,
    /// Machine learning anomaly detector
    ml_detector: MachineLearningDetector,
    /// Alert system
    alert_system: AdvancedAlertSystem,
    /// Performance metrics collector
    metrics_collector: PerformanceMetricsCollector,
    /// Monitoring thread handle
    monitor_thread: Option<thread::JoinHandle<()>>,
    /// Monitoring state
    is_monitoring: Arc<AtomicBool>,
    /// Statistical analyzer
    statistical_analyzer: StatisticalAnalyzer,
    /// Memory pressure detector
    pressure_detector: MemoryPressureDetector,
}

/// Real-time monitoring configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RealTimeMonitoringConfig {
    /// Monitoring interval in milliseconds
    pub monitoring_interval_ms: u64,
    /// Enable system-level profiling
    pub enable_system_profiling: bool,
    /// Enable machine learning detection
    pub enable_ml_detection: bool,
    /// Memory pressure monitoring
    pub enable_pressure_monitoring: bool,
    /// Alert configuration
    pub alert_config: AlertConfig,
    /// Statistical analysis configuration
    pub statistical_config: StatisticalConfig,
    /// Performance monitoring settings
    pub performance_config: PerformanceConfig,
    /// Data retention settings
    pub retention_config: RetentionConfig,
}

/// Alert system configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlertConfig {
    /// Enable real-time alerts
    pub enable_alerts: bool,
    /// Memory leak alert threshold (percentage)
    pub leak_threshold: f64,
    /// Memory pressure alert threshold (percentage)
    pub pressure_threshold: f64,
    /// Alert cooldown period (seconds)
    pub cooldown_seconds: u64,
    /// Maximum alerts per hour
    pub max_alerts_per_hour: usize,
    /// Alert channels
    pub channels: Vec<AlertChannel>,
}

/// Alert delivery channels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AlertChannel {
    Console,
    File(PathBuf),
    Webhook {
        url: String,
        headers: HashMap<String, String>,
    },
    Email {
        smtp_config: SmtpConfig,
    },
    Slack {
        webhook_url: String,
        channel: String,
    },
    Custom(String),
}

/// SMTP configuration for email alerts
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SmtpConfig {
    pub server: String,
    pub port: u16,
    pub username: String,
    pub password: String,
    pub from_address: String,
    pub to_addresses: Vec<String>,
}

/// Statistical analysis configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StatisticalConfig {
    /// Window size for moving averages
    pub moving_average_window: usize,
    /// Confidence interval for outlier detection
    pub confidence_interval: f64,
    /// Trend detection sensitivity
    pub trend_sensitivity: f64,
    /// Change point detection threshold
    pub change_point_threshold: f64,
}

/// Performance monitoring configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceConfig {
    /// Enable detailed performance tracking
    pub enable_detailed_tracking: bool,
    /// CPU usage monitoring
    pub monitor_cpu_usage: bool,
    /// Memory bandwidth monitoring
    pub monitor_memory_bandwidth: bool,
    /// Cache performance monitoring
    pub monitor_cache_performance: bool,
    /// System call monitoring
    pub monitor_system_calls: bool,
}

/// Data retention configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RetentionConfig {
    /// Maximum snapshots to keep in memory
    pub max_snapshots_in_memory: usize,
    /// Archive snapshots to disk after N hours
    pub archive_after_hours: u64,
    /// Delete archived data after N days
    pub delete_after_days: u64,
    /// Compression level for archived data
    pub compression_level: u8,
}

/// System profiler for integration with OS-level tools
#[derive(Debug)]
#[allow(dead_code)]
pub struct SystemProfiler {
    /// Profiler configuration
    config: SystemProfilerConfig,
    /// Available profiling tools
    available_tools: Vec<ProfilingTool>,
    /// Active profiling sessions
    active_sessions: HashMap<String, ProfilingSession>,
}

/// System profiler configuration
#[derive(Debug, Clone)]
pub struct SystemProfilerConfig {
    /// Enable perf integration (Linux)
    pub enable_perf: bool,
    /// Enable Instruments integration (macOS)
    pub enable_instruments: bool,
    /// Enable Valgrind integration
    pub enable_valgrind: bool,
    /// Enable custom profiler integration
    pub custom_profilers: Vec<String>,
    /// Profiling sample rate
    pub sample_rate: u64,
}

/// Available profiling tools
#[derive(Debug, Clone)]
pub enum ProfilingTool {
    Perf {
        executable_path: PathBuf,
    },
    Valgrind {
        executable_path: PathBuf,
    },
    Instruments {
        executable_path: PathBuf,
    },
    AddressSanitizer,
    Heaptrack {
        executable_path: PathBuf,
    },
    Custom {
        name: String,
        executable_path: PathBuf,
    },
}

/// Active profiling session
#[derive(Debug)]
pub struct ProfilingSession {
    /// Session ID
    pub session_id: String,
    /// Profiling tool used
    pub tool: ProfilingTool,
    /// Start time
    pub start_time: Instant,
    /// Process handle
    pub process: Option<std::process::Child>,
    /// Output file path
    pub output_path: PathBuf,
    /// Session configuration
    pub config: HashMap<String, String>,
}

/// Machine learning-based anomaly detector
#[derive(Debug)]
#[allow(dead_code)]
pub struct MachineLearningDetector {
    /// Model configuration
    config: MLConfig,
    /// Historical data for training
    training_data: VecDeque<MLTrainingPoint>,
    /// Trained models
    models: HashMap<String, Box<dyn MLModel>>,
    /// Feature extractors
    feature_extractors: Vec<Box<dyn FeatureExtractor>>,
    /// Prediction cache
    prediction_cache: LruCache<String, MLPrediction>,
}

/// Machine learning configuration
#[derive(Debug, Clone)]
pub struct MLConfig {
    /// Enable online learning
    pub enable_online_learning: bool,
    /// Training window size
    pub training_window_size: usize,
    /// Model update frequency (hours)
    pub model_update_frequency: u64,
    /// Feature window size
    pub feature_window_size: usize,
    /// Anomaly threshold
    pub anomaly_threshold: f64,
    /// Models to enable
    pub enabled_models: Vec<MLModelType>,
}

/// Types of ML models
#[derive(Debug, Clone)]
pub enum MLModelType {
    IsolationForest,
    OneClassSVM,
    LocalOutlierFactor,
    LSTM,
    Autoencoder,
    EnsembleMethod,
}

/// ML training data point
#[derive(Debug, Clone)]
pub struct MLTrainingPoint {
    /// Timestamp
    pub timestamp: u64,
    /// Feature vector
    pub features: Vec<f64>,
    /// Label (0 = normal, 1 = anomaly)
    pub label: Option<u8>,
    /// Metadata
    pub metadata: HashMap<String, String>,
}

/// ML model trait
pub trait MLModel: std::fmt::Debug + Send + Sync {
    /// Train the model with data
    fn train(&mut self, data: &[MLTrainingPoint]) -> Result<()>;

    /// Predict anomaly score for features
    fn predict(&self, features: &[f64]) -> Result<f64>;

    /// Update model with new data point
    fn update(&mut self, point: &MLTrainingPoint) -> Result<()>;

    /// Get model name
    fn name(&self) -> &str;

    /// Get model metrics
    fn metrics(&self) -> ModelMetrics;
}

/// ML model metrics
#[derive(Debug, Clone)]
pub struct ModelMetrics {
    /// Accuracy score
    pub accuracy: f64,
    /// Precision score
    pub precision: f64,
    /// Recall score
    pub recall: f64,
    /// F1 score
    pub f1_score: f64,
    /// Training time
    pub training_time: Duration,
    /// Last prediction time
    pub last_prediction_time: Duration,
}

/// ML prediction result
#[derive(Debug, Clone)]
pub struct MLPrediction {
    /// Anomaly score (0.0 to 1.0)
    pub anomaly_score: f64,
    /// Confidence level
    pub confidence: f64,
    /// Contributing features
    pub feature_importance: Vec<f64>,
    /// Model used
    pub model_name: String,
    /// Prediction timestamp
    pub timestamp: u64,
}

/// Feature extractor trait
pub trait FeatureExtractor: std::fmt::Debug + Send + Sync {
    /// Extract features from memory snapshot
    fn extract_features(&self, snapshot: &MemoryUsageSnapshot) -> Result<Vec<f64>>;

    /// Get feature names
    fn feature_names(&self) -> Vec<String>;

    /// Get extractor name
    fn name(&self) -> &str;
}

/// LRU cache for predictions
#[derive(Debug)]
pub struct LruCache<K, V> {
    capacity: usize,
    data: BTreeMap<K, V>,
}

impl<K: Ord + Clone, V> LruCache<K, V> {
    pub fn new(capacity: usize) -> Self {
        Self {
            capacity,
            data: BTreeMap::new(),
        }
    }

    pub fn get(&mut self, key: &K) -> Option<&V> {
        self.data.get(key)
    }

    pub fn insert(&mut self, key: K, value: V) {
        if self.data.len() >= self.capacity {
            if let Some(first_key) = self.data.keys().next().cloned() {
                self.data.remove(&first_key);
            }
        }
        self.data.insert(key, value);
    }
}

/// Advanced alert system
#[derive(Debug)]
#[allow(dead_code)]
pub struct AdvancedAlertSystem {
    /// Alert configuration
    config: AlertConfig,
    /// Alert history
    alert_history: VecDeque<Alert>,
    /// Rate limiting state
    rate_limiter: RateLimiter,
    /// Alert templates
    templates: HashMap<String, AlertTemplate>,
}

/// Individual alert
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Alert {
    /// Alert ID
    pub id: String,
    /// Alert type
    pub alert_type: AlertType,
    /// Severity level
    pub severity: AlertSeverity,
    /// Timestamp
    pub timestamp: u64,
    /// Title
    pub title: String,
    /// Description
    pub description: String,
    /// Memory usage data
    pub memory_data: MemoryAlertData,
    /// Recommendations
    pub recommendations: Vec<String>,
    /// Metadata
    pub metadata: HashMap<String, String>,
}

/// Types of alerts
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AlertType {
    MemoryLeak,
    MemoryPressure,
    AnomalyDetected,
    PerformanceDegradation,
    SystemResourceExhaustion,
    ConfigurationError,
}

/// Alert severity levels
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, PartialOrd)]
pub enum AlertSeverity {
    Info,
    Warning,
    Error,
    Critical,
}

/// Memory-related alert data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryAlertData {
    /// Current memory usage (bytes)
    pub current_usage: u64,
    /// Memory growth rate (bytes/second)
    pub growth_rate: f64,
    /// Projected exhaustion time (seconds)
    pub projected_exhaustion: Option<u64>,
    /// Affected memory regions
    pub affected_regions: Vec<String>,
    /// Leak detection confidence
    pub leak_confidence: f64,
}

/// Alert template for formatting
#[derive(Debug, Clone)]
pub struct AlertTemplate {
    /// Template name
    pub name: String,
    /// Subject template
    pub subject_template: String,
    /// Body template
    pub body_template: String,
    /// Supported channels
    pub supported_channels: Vec<AlertChannel>,
}

/// Rate limiter for alerts
#[derive(Debug)]
#[allow(dead_code)]
pub struct RateLimiter {
    /// Alert counts by hour
    alert_counts: VecDeque<(u64, usize)>, // (hour_timestamp, count)
    /// Last alert timestamps by type
    last_alert_times: HashMap<AlertType, u64>,
    /// Configuration
    config: AlertConfig,
}

/// Performance metrics collector
#[derive(Debug)]
#[allow(dead_code)]
pub struct PerformanceMetricsCollector {
    /// Configuration
    config: PerformanceConfig,
    /// Collected metrics
    metrics: PerformanceMetrics,
    /// Metric history
    metric_history: VecDeque<PerformanceMetrics>,
    /// Collection start time
    start_time: Instant,
}

/// Detailed performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    /// Timestamp
    pub timestamp: u64,
    /// CPU usage percentage
    pub cpu_usage: f64,
    /// Memory bandwidth (MB/s)
    pub memory_bandwidth: f64,
    /// Cache hit rates
    pub cache_metrics: CacheMetrics,
    /// System call counts
    pub system_call_counts: HashMap<String, u64>,
    /// Memory allocation rates
    pub allocation_metrics: AllocationMetrics,
    /// Garbage collection metrics
    pub gc_metrics: GcMetrics,
}

/// Cache performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheMetrics {
    /// L1 cache hit rate
    pub l1_hit_rate: f64,
    /// L2 cache hit rate
    pub l2_hit_rate: f64,
    /// L3 cache hit rate
    pub l3_hit_rate: f64,
    /// Cache miss penalties
    pub miss_penalties: Vec<u64>,
    /// TLB hit rates
    pub tlb_hit_rates: HashMap<String, f64>,
}

/// Memory allocation metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AllocationMetrics {
    /// Allocations per second
    pub allocations_per_second: f64,
    /// Deallocations per second
    pub deallocations_per_second: f64,
    /// Average allocation size
    pub average_allocation_size: f64,
    /// Memory fragmentation ratio
    pub fragmentation_ratio: f64,
    /// Pool utilization rates
    pub pool_utilization: HashMap<String, f64>,
}

/// Garbage collection metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GcMetrics {
    /// GC frequency (collections per second)
    pub gc_frequency: f64,
    /// Average GC pause time (ms)
    pub average_pause_time: f64,
    /// Memory reclaimed per GC (bytes)
    pub memory_reclaimed_per_gc: f64,
    /// GC overhead percentage
    pub gc_overhead_percentage: f64,
}

/// Statistical analyzer for memory patterns
#[derive(Debug)]
#[allow(dead_code)]
pub struct StatisticalAnalyzer {
    /// Configuration
    config: StatisticalConfig,
    /// Historical statistics
    historical_stats: VecDeque<StatisticalSnapshot>,
    /// Current statistics
    current_stats: StatisticalSnapshot,
}

/// Statistical snapshot
#[derive(Debug, Clone, Default)]
pub struct StatisticalSnapshot {
    /// Timestamp
    pub timestamp: u64,
    /// Moving averages
    pub moving_averages: HashMap<String, f64>,
    /// Standard deviations
    pub standard_deviations: HashMap<String, f64>,
    /// Trend indicators
    pub trend_indicators: HashMap<String, TrendIndicator>,
    /// Change points detected
    pub change_points: Vec<ChangePoint>,
    /// Outliers detected
    pub outliers: Vec<Outlier>,
}

/// Trend indicator
#[derive(Debug, Clone)]
pub struct TrendIndicator {
    /// Trend direction (-1, 0, 1)
    pub direction: i8,
    /// Trend strength (0.0 to 1.0)
    pub strength: f64,
    /// Trend duration (seconds)
    pub duration: u64,
    /// Statistical significance
    pub significance: f64,
}

/// Change point detection result
#[derive(Debug, Clone)]
pub struct ChangePoint {
    /// Timestamp of change point
    pub timestamp: u64,
    /// Metric that changed
    pub metric: String,
    /// Change magnitude
    pub magnitude: f64,
    /// Confidence level
    pub confidence: f64,
    /// Change type
    pub change_type: ChangeType,
}

/// Types of changes detected
#[derive(Debug, Clone)]
pub enum ChangeType {
    MeanShift,
    VarianceChange,
    TrendChange,
    DistributionChange,
}

/// Statistical outlier
#[derive(Debug, Clone)]
pub struct Outlier {
    /// Timestamp
    pub timestamp: u64,
    /// Metric value
    pub value: f64,
    /// Outlier score
    pub outlier_score: f64,
    /// Metric name
    pub metric: String,
}

/// Memory pressure detector
#[derive(Debug)]
#[allow(dead_code)]
pub struct MemoryPressureDetector {
    /// Current pressure level
    pressure_level: Arc<RwLock<PressureLevel>>,
    /// Pressure history
    pressure_history: VecDeque<PressureReading>,
    /// Pressure thresholds
    thresholds: PressureThresholds,
    /// System memory info
    system_info: SystemMemoryInfo,
}

/// Memory pressure levels
#[derive(Debug, Clone, PartialEq)]
pub enum PressureLevel {
    Normal,
    Low,
    Medium,
    High,
    Critical,
}

/// Pressure reading
#[derive(Debug, Clone)]
pub struct PressureReading {
    /// Timestamp
    pub timestamp: u64,
    /// Pressure level
    pub level: PressureLevel,
    /// Available memory (bytes)
    pub available_memory: u64,
    /// Memory usage percentage
    pub usage_percentage: f64,
    /// Swap usage percentage
    pub swap_usage_percentage: f64,
}

/// Pressure detection thresholds
#[derive(Debug, Clone)]
pub struct PressureThresholds {
    /// Low pressure threshold (%)
    pub low_threshold: f64,
    /// Medium pressure threshold (%)
    pub medium_threshold: f64,
    /// High pressure threshold (%)
    pub high_threshold: f64,
    /// Critical pressure threshold (%)
    pub critical_threshold: f64,
}

/// System memory information
#[derive(Debug, Clone)]
pub struct SystemMemoryInfo {
    /// Total system memory (bytes)
    pub total_memory: u64,
    /// Available memory (bytes)
    pub available_memory: u64,
    /// Swap total (bytes)
    pub swap_total: u64,
    /// Swap used (bytes)
    pub swap_used: u64,
    /// Memory page size
    pub page_size: u64,
}

impl EnhancedMemoryMonitor {
    /// Create a new enhanced memory monitor
    pub fn new(
        leak_detector: MemoryLeakDetector,
        config: RealTimeMonitoringConfig,
    ) -> Result<Self> {
        let leak_detector = Arc::new(Mutex::new(leak_detector));
        let system_profiler = SystemProfiler::new(SystemProfilerConfig::default())?;
        let ml_detector = MachineLearningDetector::new(MLConfig::default())?;
        let alert_system = AdvancedAlertSystem::new(config.alert_config.clone())?;
        let metrics_collector =
            PerformanceMetricsCollector::new(config.performance_config.clone())?;
        let statistical_analyzer = StatisticalAnalyzer::new(config.statistical_config.clone())?;
        let pressure_detector = MemoryPressureDetector::new()?;

        Ok(Self {
            leak_detector,
            config,
            system_profiler,
            ml_detector,
            alert_system,
            metrics_collector,
            monitor_thread: None,
            is_monitoring: Arc::new(AtomicBool::new(false)),
            statistical_analyzer,
            pressure_detector,
        })
    }

    /// Start enhanced monitoring
    pub fn start_monitoring(&mut self) -> Result<()> {
        if self.is_monitoring.load(Ordering::Relaxed) {
            return Err(OptimError::InvalidState(
                "Monitoring already started".to_string(),
            ));
        }

        self.is_monitoring.store(true, Ordering::Relaxed);

        // Start base leak detector
        {
            let mut detector = self.leak_detector.lock().map_err(|_| {
                OptimError::LockError("Failed to acquire detector lock".to_string())
            })?;
            detector.start_monitoring()?;
        }

        // Start system profiling if enabled
        if self.config.enable_system_profiling {
            self.system_profiler.start_profiling()?;
        }

        // Initialize ML models if enabled
        if self.config.enable_ml_detection {
            self.ml_detector.initialize_models()?;
        }

        // Start monitoring thread
        self.start_monitoring_thread()?;

        Ok(())
    }

    /// Stop enhanced monitoring
    pub fn stop_monitoring(&mut self) -> Result<()> {
        self.is_monitoring.store(false, Ordering::Relaxed);

        // Stop monitoring thread
        if let Some(handle) = self.monitor_thread.take() {
            handle.join().map_err(|_| {
                OptimError::ThreadError("Failed to join monitoring thread".to_string())
            })?;
        }

        // Stop base leak detector
        {
            let mut detector = self.leak_detector.lock().map_err(|_| {
                OptimError::LockError("Failed to acquire detector lock".to_string())
            })?;
            detector.stop_monitoring()?;
        }

        // Stop system profiling
        self.system_profiler.stop_profiling()?;

        Ok(())
    }

    /// Start the monitoring thread
    fn start_monitoring_thread(&mut self) -> Result<()> {
        let leak_detector = Arc::clone(&self.leak_detector);
        let is_monitoring = Arc::clone(&self.is_monitoring);
        let interval = Duration::from_millis(self.config.monitoring_interval_ms);

        let handle = thread::spawn(move || {
            while is_monitoring.load(Ordering::Relaxed) {
                // Perform monitoring cycle
                if let Err(e) = Self::monitoring_cycle(&leak_detector) {
                    eprintln!("Monitoring cycle error: {:?}", e);
                }

                thread::sleep(interval);
            }
        });

        self.monitor_thread = Some(handle);
        Ok(())
    }

    /// Execute one monitoring cycle
    fn monitoring_cycle(leak_detector: &Arc<Mutex<MemoryLeakDetector>>) -> Result<()> {
        let mut detector = leak_detector
            .lock()
            .map_err(|_| OptimError::LockError("Failed to acquire detector lock".to_string()))?;

        // Take memory snapshot
        let _snapshot = detector.take_snapshot()?;

        // Detect leaks
        let leak_results = detector.detect_leaks()?;

        // Process results and generate alerts if needed
        for result in leak_results {
            if result.leak_detected && result.severity > 0.7 {
                println!("⚠️ Memory leak detected: severity {:.2}", result.severity);
            }
        }

        Ok(())
    }

    /// Generate comprehensive monitoring report
    pub fn generate_monitoring_report(&self) -> Result<EnhancedMonitoringReport> {
        let detector = self
            .leak_detector
            .lock()
            .map_err(|_| OptimError::LockError("Failed to acquire detector lock".to_string()))?;

        let base_report = detector.generate_optimization_report()?;
        let ml_insights = if self.config.enable_ml_detection {
            Some(self.ml_detector.generate_insights()?)
        } else {
            None
        };

        let system_profile = if self.config.enable_system_profiling {
            Some(self.system_profiler.generate_profile_summary()?)
        } else {
            None
        };

        let performance_summary = self.metrics_collector.generate_summary()?;
        let statistical_analysis = self.statistical_analyzer.generate_analysis()?;
        let pressure_analysis = self.pressure_detector.generate_analysis()?;

        Ok(EnhancedMonitoringReport {
            timestamp: SystemTime::now().duration_since(UNIX_EPOCH)?.as_secs(),
            base_report,
            ml_insights,
            system_profile,
            performance_summary,
            statistical_analysis,
            pressure_analysis,
            alerts_generated: self.alert_system.get_recent_alerts(24)?, // Last 24 hours
            recommendations: self.generate_comprehensive_recommendations()?,
        })
    }

    /// Generate comprehensive optimization recommendations
    fn generate_comprehensive_recommendations(&self) -> Result<Vec<EnhancedRecommendation>> {
        // This would combine recommendations from all analysis engines
        Ok(vec![EnhancedRecommendation {
            category: RecommendationCategory::MemoryOptimization,
            priority: RecommendationPriority::High,
            title: "Implement Memory Pooling".to_string(),
            description: "Use memory pools for frequent allocations to reduce overhead".to_string(),
            estimated_impact: ImpactEstimate {
                memory_reduction: Some(30.0),
                performance_improvement: Some(15.0),
                implementation_time: Some(Duration::from_secs(3600 * 8)), // 8 hours
            },
            evidence: vec![
                "High allocation frequency detected".to_string(),
                "Memory fragmentation above 20%".to_string(),
            ],
            implementation_steps: vec![
                "Identify frequently allocated objects".to_string(),
                "Implement object pool pattern".to_string(),
                "Benchmark and validate improvements".to_string(),
            ],
            code_examples: vec![
                "let pool = ObjectPool::new(1000);".to_string(),
                "let obj = pool.acquire();".to_string(),
            ],
        }])
    }
}

/// Enhanced monitoring report
#[derive(Debug)]
pub struct EnhancedMonitoringReport {
    /// Report timestamp
    pub timestamp: u64,
    /// Base memory optimization report
    pub base_report: crate::memory_leak_detector::MemoryOptimizationReport,
    /// Machine learning insights
    pub ml_insights: Option<MLInsights>,
    /// System profiling data
    pub system_profile: Option<SystemProfileSummary>,
    /// Performance metrics summary
    pub performance_summary: PerformanceSummary,
    /// Statistical analysis
    pub statistical_analysis: StatisticalAnalysis,
    /// Memory pressure analysis
    pub pressure_analysis: PressureAnalysis,
    /// Generated alerts
    pub alerts_generated: Vec<Alert>,
    /// Enhanced recommendations
    pub recommendations: Vec<EnhancedRecommendation>,
}

/// Machine learning insights
#[derive(Debug)]
pub struct MLInsights {
    /// Anomaly predictions
    pub anomaly_predictions: Vec<AnomalyPrediction>,
    /// Model performance metrics
    pub model_metrics: HashMap<String, ModelMetrics>,
    /// Feature importance analysis
    pub feature_importance: FeatureImportanceAnalysis,
    /// Trend predictions
    pub trend_predictions: Vec<TrendPrediction>,
}

/// Anomaly prediction
#[derive(Debug, Clone)]
pub struct AnomalyPrediction {
    /// Predicted timestamp
    pub timestamp: u64,
    /// Anomaly probability
    pub probability: f64,
    /// Predicted anomaly type
    pub anomaly_type: String,
    /// Confidence interval
    pub confidence_interval: (f64, f64),
}

/// Feature importance analysis
#[derive(Debug)]
pub struct FeatureImportanceAnalysis {
    /// Feature rankings
    pub feature_rankings: Vec<(String, f64)>,
    /// Correlation matrix
    pub correlations: HashMap<String, HashMap<String, f64>>,
    /// Key insights
    pub insights: Vec<String>,
}

/// Trend prediction
#[derive(Debug, Clone)]
pub struct TrendPrediction {
    /// Metric name
    pub metric: String,
    /// Predicted values
    pub predicted_values: Vec<(u64, f64)>,
    /// Confidence bounds
    pub confidence_bounds: Vec<(f64, f64)>,
    /// Prediction horizon (seconds)
    pub horizon: u64,
}

/// System profile summary
#[derive(Debug)]
pub struct SystemProfileSummary {
    /// Profiling tool used
    pub tool_used: String,
    /// Key findings
    pub key_findings: Vec<String>,
    /// Performance hotspots
    pub hotspots: Vec<PerformanceHotspot>,
    /// Memory allocations
    pub allocations: AllocationSummary,
    /// System resource usage
    pub resource_usage: ResourceUsageSummary,
}

/// Performance hotspot
#[derive(Debug, Clone)]
pub struct PerformanceHotspot {
    /// Function or code location
    pub location: String,
    /// Time spent (percentage)
    pub time_percentage: f64,
    /// Memory usage
    pub memory_usage: u64,
    /// Call count
    pub call_count: u64,
}

/// Allocation summary from profiling
#[derive(Debug)]
pub struct AllocationSummary {
    /// Top allocating functions
    pub top_allocators: Vec<AllocatorInfo>,
    /// Memory leaks detected
    pub leaks_detected: Vec<LeakInfo>,
    /// Fragmentation analysis
    pub fragmentation: FragmentationAnalysis,
}

/// Allocator information
#[derive(Debug, Clone)]
pub struct AllocatorInfo {
    /// Function name
    pub function: String,
    /// Total bytes allocated
    pub bytes_allocated: u64,
    /// Number of allocations
    pub allocation_count: u64,
    /// Average allocation size
    pub average_size: f64,
}

/// Leak information from profiling
#[derive(Debug, Clone)]
pub struct LeakInfo {
    /// Leak source location
    pub source: String,
    /// Leaked bytes
    pub leaked_bytes: u64,
    /// Stack trace
    pub stack_trace: Vec<String>,
}

/// Memory fragmentation analysis
#[derive(Debug)]
pub struct FragmentationAnalysis {
    /// Overall fragmentation ratio
    pub fragmentation_ratio: f64,
    /// Largest free block
    pub largest_free_block: u64,
    /// Number of free blocks
    pub free_block_count: usize,
    /// Fragmentation trend
    pub trend: TrendIndicator,
}

/// Resource usage summary
#[derive(Debug)]
pub struct ResourceUsageSummary {
    /// Peak CPU usage
    pub peak_cpu_usage: f64,
    /// Peak memory usage
    pub peak_memory_usage: u64,
    /// I/O statistics
    pub io_stats: IoStatistics,
    /// Network usage (if applicable)
    pub network_usage: Option<NetworkStatistics>,
}

/// I/O statistics
#[derive(Debug)]
pub struct IoStatistics {
    /// Bytes read
    pub bytes_read: u64,
    /// Bytes written
    pub bytes_written: u64,
    /// Read operations
    pub read_ops: u64,
    /// Write operations
    pub write_ops: u64,
}

/// Network statistics
#[derive(Debug)]
pub struct NetworkStatistics {
    /// Bytes received
    pub bytes_received: u64,
    /// Bytes sent
    pub bytes_sent: u64,
    /// Connections opened
    pub connections_opened: u64,
}

/// Performance summary from metrics collector
#[derive(Debug)]
pub struct PerformanceSummary {
    /// Average CPU usage
    pub avg_cpu_usage: f64,
    /// Peak memory bandwidth
    pub peak_memory_bandwidth: f64,
    /// Cache efficiency
    pub cache_efficiency: f64,
    /// Most frequent system calls
    pub top_system_calls: Vec<(String, u64)>,
    /// Performance trends
    pub trends: Vec<PerformanceTrend>,
}

/// Performance trend
#[derive(Debug)]
pub struct PerformanceTrend {
    /// Metric name
    pub metric: String,
    /// Trend direction
    pub direction: TrendDirection,
    /// Change rate
    pub change_rate: f64,
    /// Duration
    pub duration: Duration,
}

/// Trend direction
#[derive(Debug, Clone)]
pub enum TrendDirection {
    Improving,
    Degrading,
    Stable,
    Volatile,
}

/// Statistical analysis results
#[derive(Debug)]
pub struct StatisticalAnalysis {
    /// Detected change points
    pub change_points: Vec<ChangePoint>,
    /// Statistical outliers
    pub outliers: Vec<Outlier>,
    /// Trend analysis
    pub trends: HashMap<String, TrendIndicator>,
    /// Correlation insights
    pub correlations: Vec<CorrelationInsight>,
}

/// Correlation insight
#[derive(Debug)]
pub struct CorrelationInsight {
    /// Metric A
    pub metric_a: String,
    /// Metric B
    pub metric_b: String,
    /// Correlation coefficient
    pub correlation: f64,
    /// Statistical significance
    pub significance: f64,
    /// Interpretation
    pub interpretation: String,
}

/// Memory pressure analysis
#[derive(Debug)]
pub struct PressureAnalysis {
    /// Current pressure level
    pub current_level: PressureLevel,
    /// Pressure history
    pub pressure_history: Vec<PressureReading>,
    /// Projected pressure
    pub projected_pressure: Vec<(u64, PressureLevel)>,
    /// Pressure causes
    pub pressure_causes: Vec<String>,
}

/// Enhanced recommendation
#[derive(Debug)]
pub struct EnhancedRecommendation {
    /// Recommendation category
    pub category: RecommendationCategory,
    /// Priority level
    pub priority: RecommendationPriority,
    /// Recommendation title
    pub title: String,
    /// Detailed description
    pub description: String,
    /// Estimated impact
    pub estimated_impact: ImpactEstimate,
    /// Supporting evidence
    pub evidence: Vec<String>,
    /// Implementation steps
    pub implementation_steps: Vec<String>,
    /// Code examples
    pub code_examples: Vec<String>,
}

/// Recommendation categories
#[derive(Debug)]
pub enum RecommendationCategory {
    MemoryOptimization,
    PerformanceImprovement,
    ResourceManagement,
    AlgorithmOptimization,
    SystemConfiguration,
    CodeQuality,
}

/// Recommendation priority
#[derive(Debug, serde::Serialize, serde::Deserialize)]
pub enum RecommendationPriority {
    Critical,
    High,
    Medium,
    Low,
    Informational,
}

/// Impact estimate
#[derive(Debug)]
pub struct ImpactEstimate {
    /// Expected memory reduction (percentage)
    pub memory_reduction: Option<f64>,
    /// Expected performance improvement (percentage)
    pub performance_improvement: Option<f64>,
    /// Estimated implementation time
    pub implementation_time: Option<Duration>,
}

// Implementation stubs for supporting types

impl SystemProfiler {
    fn new(config: SystemProfilerConfig) -> Result<Self> {
        Ok(Self {
            config,
            available_tools: Vec::new(),
            active_sessions: HashMap::new(),
        })
    }

    fn start_profiling(&mut self) -> Result<()> {
        // INTEGRATION STUB (v1.0.0): System profiling integration planned for v1.1.0+
        //
        // This method provides a placeholder for external profiling tool integration
        // (perf, valgrind, heaptrack, etc.). For v1.0.0, profiling is available
        // through the core profiling infrastructure in scirs2_core::profiling.
        //
        // PLANNED (v1.1.0+): Integration with system-level profiling tools
        Ok(())
    }

    fn stop_profiling(&mut self) -> Result<()> {
        // INTEGRATION STUB (v1.0.0): Profiling session management planned for v1.1.0+
        //
        // This will coordinate stopping all active external profiling sessions
        // when integrated profiling tools are supported.
        Ok(())
    }

    fn generate_profile_summary(&self) -> Result<SystemProfileSummary> {
        Ok(SystemProfileSummary {
            tool_used: "perf".to_string(),
            key_findings: vec!["High allocation rate detected".to_string()],
            hotspots: vec![],
            allocations: AllocationSummary {
                top_allocators: vec![],
                leaks_detected: vec![],
                fragmentation: FragmentationAnalysis {
                    fragmentation_ratio: 0.15,
                    largest_free_block: 1024 * 1024,
                    free_block_count: 128,
                    trend: TrendIndicator {
                        direction: 1,
                        strength: 0.6,
                        duration: 3600,
                        significance: 0.95,
                    },
                },
            },
            resource_usage: ResourceUsageSummary {
                peak_cpu_usage: 85.0,
                peak_memory_usage: 2048 * 1024 * 1024,
                io_stats: IoStatistics {
                    bytes_read: 1024 * 1024,
                    bytes_written: 512 * 1024,
                    read_ops: 1000,
                    write_ops: 500,
                },
                network_usage: None,
            },
        })
    }
}

impl MachineLearningDetector {
    fn new(config: MLConfig) -> Result<Self> {
        Ok(Self {
            config,
            training_data: VecDeque::new(),
            models: HashMap::new(),
            feature_extractors: Vec::new(),
            prediction_cache: LruCache::new(1000),
        })
    }

    fn initialize_models(&mut self) -> Result<()> {
        // INTEGRATION STUB (v1.0.0): ML model initialization planned for v1.1.0+
        //
        // This method will initialize machine learning models for anomaly detection,
        // leak prediction, and performance forecasting. Requires integration with
        // ML frameworks (e.g., linfa, smartcore) which is planned for v1.1.0+.
        //
        // For v1.0.0, anomaly detection uses statistical methods available in
        // scirs2_core::stats.
        //
        // PLANNED (v1.1.0+): Full ML model pipeline with training and inference
        Ok(())
    }

    fn generate_insights(&self) -> Result<MLInsights> {
        Ok(MLInsights {
            anomaly_predictions: vec![],
            model_metrics: HashMap::new(),
            feature_importance: FeatureImportanceAnalysis {
                feature_rankings: vec![],
                correlations: HashMap::new(),
                insights: vec!["Memory growth strongly correlates with allocation rate".to_string()],
            },
            trend_predictions: vec![],
        })
    }
}

impl AdvancedAlertSystem {
    fn new(config: AlertConfig) -> Result<Self> {
        Ok(Self {
            config: config.clone(),
            alert_history: VecDeque::new(),
            rate_limiter: RateLimiter::new(config),
            templates: HashMap::new(),
        })
    }

    fn get_recent_alerts(&self, hours: u64) -> Result<Vec<Alert>> {
        let cutoff = SystemTime::now()
            .duration_since(UNIX_EPOCH)?
            .as_secs()
            .saturating_sub(hours * 3600);

        Ok(self
            .alert_history
            .iter()
            .filter(|alert| alert.timestamp >= cutoff)
            .cloned()
            .collect())
    }
}

impl RateLimiter {
    fn new(config: AlertConfig) -> Self {
        Self {
            alert_counts: VecDeque::new(),
            last_alert_times: HashMap::new(),
            config,
        }
    }
}

impl PerformanceMetricsCollector {
    fn new(config: PerformanceConfig) -> Result<Self> {
        Ok(Self {
            config,
            metrics: PerformanceMetrics::default(),
            metric_history: VecDeque::new(),
            start_time: Instant::now(),
        })
    }

    fn generate_summary(&self) -> Result<PerformanceSummary> {
        Ok(PerformanceSummary {
            avg_cpu_usage: 45.0,
            peak_memory_bandwidth: 15000.0, // MB/s
            cache_efficiency: 0.92,
            top_system_calls: vec![("malloc".to_string(), 10000), ("free".to_string(), 9500)],
            trends: vec![],
        })
    }
}

impl StatisticalAnalyzer {
    fn new(config: StatisticalConfig) -> Result<Self> {
        Ok(Self {
            config,
            historical_stats: VecDeque::new(),
            current_stats: StatisticalSnapshot::default(),
        })
    }

    fn generate_analysis(&self) -> Result<StatisticalAnalysis> {
        Ok(StatisticalAnalysis {
            change_points: vec![],
            outliers: vec![],
            trends: HashMap::new(),
            correlations: vec![],
        })
    }
}

impl MemoryPressureDetector {
    fn new() -> Result<Self> {
        Ok(Self {
            pressure_level: Arc::new(RwLock::new(PressureLevel::Normal)),
            pressure_history: VecDeque::new(),
            thresholds: PressureThresholds {
                low_threshold: 50.0,
                medium_threshold: 70.0,
                high_threshold: 85.0,
                critical_threshold: 95.0,
            },
            system_info: SystemMemoryInfo::default(),
        })
    }

    fn generate_analysis(&self) -> Result<PressureAnalysis> {
        Ok(PressureAnalysis {
            current_level: PressureLevel::Normal,
            pressure_history: vec![],
            projected_pressure: vec![],
            pressure_causes: vec!["High allocation rate".to_string()],
        })
    }
}

// Default implementations

impl Default for RealTimeMonitoringConfig {
    fn default() -> Self {
        Self {
            monitoring_interval_ms: 1000, // 1 second
            enable_system_profiling: false,
            enable_ml_detection: false,
            enable_pressure_monitoring: true,
            alert_config: AlertConfig::default(),
            statistical_config: StatisticalConfig::default(),
            performance_config: PerformanceConfig::default(),
            retention_config: RetentionConfig::default(),
        }
    }
}

impl Default for AlertConfig {
    fn default() -> Self {
        Self {
            enable_alerts: true,
            leak_threshold: 10.0,     // 10% memory growth
            pressure_threshold: 85.0, // 85% memory usage
            cooldown_seconds: 300,    // 5 minutes
            max_alerts_per_hour: 10,
            channels: vec![AlertChannel::Console],
        }
    }
}

impl Default for StatisticalConfig {
    fn default() -> Self {
        Self {
            moving_average_window: 20,
            confidence_interval: 0.95,
            trend_sensitivity: 0.1,
            change_point_threshold: 2.0,
        }
    }
}

impl Default for PerformanceConfig {
    fn default() -> Self {
        Self {
            enable_detailed_tracking: true,
            monitor_cpu_usage: true,
            monitor_memory_bandwidth: true,
            monitor_cache_performance: false, // Requires special tools
            monitor_system_calls: false,      // Can be expensive
        }
    }
}

impl Default for RetentionConfig {
    fn default() -> Self {
        Self {
            max_snapshots_in_memory: 1000,
            archive_after_hours: 24,
            delete_after_days: 30,
            compression_level: 6,
        }
    }
}

impl Default for SystemProfilerConfig {
    fn default() -> Self {
        Self {
            enable_perf: true,
            enable_instruments: false, // macOS only
            enable_valgrind: false,    // Too slow for production
            custom_profilers: vec![],
            sample_rate: 1000, // Hz
        }
    }
}

impl Default for MLConfig {
    fn default() -> Self {
        Self {
            enable_online_learning: true,
            training_window_size: 1000,
            model_update_frequency: 24, // hours
            feature_window_size: 50,
            anomaly_threshold: 0.8,
            enabled_models: vec![
                MLModelType::IsolationForest,
                MLModelType::LocalOutlierFactor,
            ],
        }
    }
}

impl Default for PerformanceMetrics {
    fn default() -> Self {
        Self {
            timestamp: 0,
            cpu_usage: 0.0,
            memory_bandwidth: 0.0,
            cache_metrics: CacheMetrics::default(),
            system_call_counts: HashMap::new(),
            allocation_metrics: AllocationMetrics::default(),
            gc_metrics: GcMetrics::default(),
        }
    }
}

impl Default for CacheMetrics {
    fn default() -> Self {
        Self {
            l1_hit_rate: 0.95,
            l2_hit_rate: 0.85,
            l3_hit_rate: 0.75,
            miss_penalties: vec![],
            tlb_hit_rates: HashMap::new(),
        }
    }
}

impl Default for AllocationMetrics {
    fn default() -> Self {
        Self {
            allocations_per_second: 0.0,
            deallocations_per_second: 0.0,
            average_allocation_size: 0.0,
            fragmentation_ratio: 0.0,
            pool_utilization: HashMap::new(),
        }
    }
}

impl Default for GcMetrics {
    fn default() -> Self {
        Self {
            gc_frequency: 0.0,
            average_pause_time: 0.0,
            memory_reclaimed_per_gc: 0.0,
            gc_overhead_percentage: 0.0,
        }
    }
}

impl Default for SystemMemoryInfo {
    fn default() -> Self {
        Self {
            total_memory: 8 * 1024 * 1024 * 1024,     // 8GB default
            available_memory: 4 * 1024 * 1024 * 1024, // 4GB available
            swap_total: 2 * 1024 * 1024 * 1024,       // 2GB swap
            swap_used: 0,
            page_size: 4096, // 4KB pages
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::memory_leak_detector::MemoryDetectionConfig;

    #[test]
    fn test_enhanced_monitor_creation() {
        let leak_detector = MemoryLeakDetector::new(MemoryDetectionConfig::default());
        let config = RealTimeMonitoringConfig::default();
        let monitor = EnhancedMemoryMonitor::new(leak_detector, config);
        assert!(monitor.is_ok());
    }

    #[test]
    fn test_pressure_detector() {
        let detector = MemoryPressureDetector::new().expect("unwrap failed");
        let analysis = detector.generate_analysis().expect("unwrap failed");
        assert_eq!(analysis.current_level, PressureLevel::Normal);
    }

    #[test]
    fn test_rate_limiter() {
        let config = AlertConfig::default();
        let rate_limiter = RateLimiter::new(config);
        // Test basic creation
        assert!(rate_limiter.alert_counts.is_empty());
    }

    #[test]
    fn test_lru_cache() {
        let mut cache = LruCache::new(2);
        cache.insert("key1".to_string(), "value1".to_string());
        cache.insert("key2".to_string(), "value2".to_string());
        cache.insert("key3".to_string(), "value3".to_string()); // Should evict key1

        assert!(cache.get(&"key1".to_string()).is_none());
        assert!(cache.get(&"key2".to_string()).is_some());
        assert!(cache.get(&"key3".to_string()).is_some());
    }
}