foxhunt/ml/src/deployment/monitoring.rs

//! Performance Monitoring and Automatic Rollback System
//!
//! This module provides real-time performance monitoring, SLA violation detection,
//! and automatic rollback capabilities for deployed ML models.

use std::collections::{HashMap, VecDeque};
use std::sync::atomic::{AtomicU64, AtomicBool, Ordering};
use std::sync::Arc;
use std::time::{Duration, Instant, SystemTime};

use async_trait::async_trait;
use serde::{Deserialize, Serialize};
use tokio::sync::{RwLock, Mutex, watch};
use tokio::time::interval;
use uuid::Uuid;

use crate::{MLError, MLResult, ModelType, Features, ModelPrediction, MLModel};
use super::{ModelVersion, DeploymentStatus, PerformanceBaseline};

/// Monitoring configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MonitoringConfig {
    /// Metrics collection interval
    pub collection_interval: Duration,
    /// Metrics retention period
    pub retention_period: Duration,
    /// SLA thresholds
    pub sla_thresholds: SLAThresholds,
    /// Alerting configuration
    pub alerting: AlertingConfig,
    /// Rollback configuration
    pub rollback: RollbackConfig,
    /// Dashboard configuration
    pub dashboard: DashboardConfig,
    /// Enable detailed metrics
    pub enable_detailed_metrics: bool,
    /// Enable real-time alerting
    pub enable_real_time_alerts: bool,
}

impl Default for MonitoringConfig {
    fn default() -> Self {
        Self {
            collection_interval: Duration::from_secs(10),
            retention_period: Duration::from_hours(24),
            sla_thresholds: SLAThresholds::default(),
            alerting: AlertingConfig::default(),
            rollback: RollbackConfig::default(),
            dashboard: DashboardConfig::default(),
            enable_detailed_metrics: true,
            enable_real_time_alerts: true,
        }
    }
}

/// SLA threshold configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SLAThresholds {
    /// Maximum average latency in microseconds
    pub max_avg_latency_us: u64,
    /// Maximum 95th percentile latency in microseconds
    pub max_p95_latency_us: u64,
    /// Maximum 99th percentile latency in microseconds
    pub max_p99_latency_us: u64,
    /// Maximum error rate (0.0 to 1.0)
    pub max_error_rate: f32,
    /// Minimum accuracy score (0.0 to 1.0)
    pub min_accuracy: f32,
    /// Maximum memory usage in MB
    pub max_memory_mb: u64,
    /// Maximum CPU utilization percentage
    pub max_cpu_percent: f32,
    /// Minimum throughput in predictions per second
    pub min_throughput_pps: u32,
    /// Degradation tolerance (percentage change from baseline)
    pub degradation_tolerance: f32,
}

impl Default for SLAThresholds {
    fn default() -> Self {
        Self {
            max_avg_latency_us: 100,     // 100 microseconds
            max_p95_latency_us: 200,     // 200 microseconds
            max_p99_latency_us: 500,     // 500 microseconds
            max_error_rate: 0.01,        // 1%
            min_accuracy: 0.8,           // 80%
            max_memory_mb: 1024,         // 1GB
            max_cpu_percent: 80.0,       // 80%
            min_throughput_pps: 1000,    // 1K predictions per second
            degradation_tolerance: 0.2,  // 20% degradation
        }
    }
}

/// Alerting configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlertingConfig {
    /// Enable email alerts
    pub enable_email: bool,
    /// Enable webhook alerts
    pub enable_webhooks: bool,
    /// Enable Slack alerts
    pub enable_slack: bool,
    /// Alert recipients
    pub email_recipients: Vec<String>,
    /// Webhook URLs
    pub webhook_urls: Vec<String>,
    /// Slack webhook URL
    pub slack_webhook_url: Option<String>,
    /// Alert cooldown period
    pub cooldown_period: Duration,
    /// Alert severity levels to send
    pub alert_levels: Vec<AlertSeverity>,
}

impl Default for AlertingConfig {
    fn default() -> Self {
        Self {
            enable_email: false,
            enable_webhooks: false,
            enable_slack: false,
            email_recipients: Vec::new(),
            webhook_urls: Vec::new(),
            slack_webhook_url: None,
            cooldown_period: Duration::from_minutes(5),
            alert_levels: vec![AlertSeverity::Critical, AlertSeverity::High],
        }
    }
}

/// Alert severity levels
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord, Serialize, Deserialize)]
pub enum AlertSeverity {
    /// Low severity - informational
    Low,
    /// Medium severity - warning
    Medium,
    /// High severity - error requiring attention
    High,
    /// Critical severity - immediate action required
    Critical,
}

/// Dashboard configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DashboardConfig {
    /// Enable Grafana dashboard
    pub enable_grafana: bool,
    /// Grafana dashboard URL
    pub grafana_url: Option<String>,
    /// Enable Prometheus metrics export
    pub enable_prometheus: bool,
    /// Prometheus metrics port
    pub prometheus_port: u16,
    /// Custom dashboard panels
    pub custom_panels: Vec<DashboardPanel>,
}

impl Default for DashboardConfig {
    fn default() -> Self {
        Self {
            enable_grafana: false,
            grafana_url: None,
            enable_prometheus: true,
            prometheus_port: 9090,
            custom_panels: Vec::new(),
        }
    }
}

/// Dashboard panel configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DashboardPanel {
    /// Panel name
    pub name: String,
    /// Panel type
    pub panel_type: PanelType,
    /// Metrics to display
    pub metrics: Vec<String>,
    /// Panel configuration
    pub config: HashMap<String, String>,
}

/// Dashboard panel types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PanelType {
    /// Time series graph
    TimeSeries,
    /// Single stat display
    SingleStat,
    /// Gauge display
    Gauge,
    /// Table display
    Table,
    /// Heatmap display
    Heatmap,
}

/// Performance monitoring system
pub struct PerformanceMonitor {
    /// Model being monitored
    model_id: String,
    /// Model type
    model_type: ModelType,
    /// Model version
    version: ModelVersion,
    /// Monitoring configuration
    config: MonitoringConfig,
    /// Current metrics
    current_metrics: Arc<RwLock<PerformanceMetrics>>,
    /// Historical metrics
    historical_metrics: Arc<Mutex<VecDeque<PerformanceSnapshot>>>,
    /// SLA violations counter
    sla_violations: Arc<RwLock<SLAViolations>>,
    /// Alert manager
    alert_manager: Arc<AlertManager>,
    /// Rollback manager
    rollback_manager: Arc<RollbackManager>,
    /// Metrics collector
    metrics_collector: Arc<MetricsCollector>,
    /// Monitor status
    is_running: Arc<AtomicBool>,
    /// Shutdown signal
    shutdown_tx: Option<watch::Sender<bool>>,
}

/// Real-time performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    /// Model ID
    pub model_id: String,
    /// Timestamp
    pub timestamp: SystemTime,
    /// Total predictions made
    pub total_predictions: u64,
    /// Total errors
    pub total_errors: u64,
    /// Current error rate
    pub error_rate: f32,
    /// Latency percentiles in microseconds
    pub latency_p50: f64,
    pub latency_p95: f64,
    pub latency_p99: f64,
    pub latency_p999: f64,
    /// Average latency
    pub avg_latency_us: f64,
    /// Current throughput (predictions per second)
    pub throughput_pps: f64,
    /// Memory usage in MB
    pub memory_usage_mb: f64,
    /// CPU utilization percentage
    pub cpu_utilization: f32,
    /// Average accuracy score
    pub avg_accuracy: f32,
    /// Custom metrics
    pub custom_metrics: HashMap<String, f64>,
}

impl Default for PerformanceMetrics {
    fn default() -> Self {
        Self {
            model_id: String::new(),
            timestamp: SystemTime::now(),
            total_predictions: 0,
            total_errors: 0,
            error_rate: 0.0,
            latency_p50: 0.0,
            latency_p95: 0.0,
            latency_p99: 0.0,
            latency_p999: 0.0,
            avg_latency_us: 0.0,
            throughput_pps: 0.0,
            memory_usage_mb: 0.0,
            cpu_utilization: 0.0,
            avg_accuracy: 0.0,
            custom_metrics: HashMap::new(),
        }
    }
}

/// Performance snapshot for historical tracking
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceSnapshot {
    /// Snapshot timestamp
    pub timestamp: SystemTime,
    /// Performance metrics at snapshot time
    pub metrics: PerformanceMetrics,
    /// Baseline comparison
    pub baseline_comparison: Option<BaselineComparison>,
}

/// Comparison with performance baseline
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BaselineComparison {
    /// Latency change percentage
    pub latency_change_percent: f32,
    /// Throughput change percentage
    pub throughput_change_percent: f32,
    /// Error rate change percentage
    pub error_rate_change_percent: f32,
    /// Accuracy change percentage
    pub accuracy_change_percent: f32,
    /// Overall degradation score
    pub degradation_score: f32,
}

/// SLA violations tracking
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct SLAViolations {
    /// Latency violations
    pub latency_violations: u32,
    /// Error rate violations
    pub error_rate_violations: u32,
    /// Accuracy violations
    pub accuracy_violations: u32,
    /// Throughput violations
    pub throughput_violations: u32,
    /// Memory violations
    pub memory_violations: u32,
    /// CPU violations
    pub cpu_violations: u32,
    /// Total violations
    pub total_violations: u32,
    /// Last violation timestamp
    pub last_violation_time: Option<SystemTime>,
}

impl PerformanceMonitor {
    /// Create new performance monitor
    pub async fn new(
        model_id: String,
        model_type: ModelType,
        version: ModelVersion,
        config: MonitoringConfig,
        baseline: Option<PerformanceBaseline>,
    ) -> Self {
        let (shutdown_tx, shutdown_rx) = watch::channel(false);
        
        let alert_manager = Arc::new(AlertManager::new(config.alerting.clone()));
        let rollback_manager = Arc::new(RollbackManager::new(config.rollback.clone()));
        let metrics_collector = Arc::new(MetricsCollector::new(
            model_id.clone(),
            config.collection_interval,
            baseline,
        ));

        let current_metrics = Arc::new(RwLock::new(PerformanceMetrics {
            model_id: model_id.clone(),
            ..Default::default()
        }));

        Self {
            model_id,
            model_type,
            version,
            config,
            current_metrics,
            historical_metrics: Arc::new(Mutex::new(VecDeque::new())),
            sla_violations: Arc::new(RwLock::new(SLAViolations::default())),
            alert_manager,
            rollback_manager,
            metrics_collector,
            is_running: Arc::new(AtomicBool::new(false)),
            shutdown_tx: Some(shutdown_tx),
        }
    }

    /// Start monitoring
    pub async fn start(&self) -> MLResult<()> {
        if self.is_running.load(Ordering::Acquire) {
            return Err(MLError::ValidationError {
                message: "Monitor is already running".to_string(),
            });
        }

        self.is_running.store(true, Ordering::Release);
        
        // Start metrics collection task
        let metrics_collector = self.metrics_collector.clone();
        let current_metrics = self.current_metrics.clone();
        let historical_metrics = self.historical_metrics.clone();
        let config = self.config.clone();
        let is_running = self.is_running.clone();
        
        tokio::spawn(async move {
            let mut interval = interval(config.collection_interval);
            
            while is_running.load(Ordering::Acquire) {
                interval.tick().await;
                
                if let Ok(metrics) = metrics_collector.collect_metrics().await {
                    // Update current metrics
                    {
                        let mut current = current_metrics.write().await;
                        *current = metrics.clone();
                    }

                    // Add to historical metrics
                    {
                        let mut historical = historical_metrics.lock().await;
                        historical.push_back(PerformanceSnapshot {
                            timestamp: SystemTime::now(),
                            metrics,
                            baseline_comparison: None, // Would be calculated
                        });

                        // Cleanup old metrics
                        let retention_cutoff = SystemTime::now() - config.retention_period;
                        while let Some(front) = historical.front() {
                            if front.timestamp < retention_cutoff {
                                historical.pop_front();
                            } else {
                                break;
                            }
                        }
                    }
                }
            }
        });

        // Start SLA monitoring task
        self.start_sla_monitoring().await?;

        tracing::info!("Started performance monitoring for model {}", self.model_id);
        Ok(())
    }

    /// Stop monitoring
    pub async fn stop(&self) -> MLResult<()> {
        self.is_running.store(false, Ordering::Release);
        
        if let Some(ref tx) = self.shutdown_tx {
            let _ = tx.send(true);
        }

        tracing::info!("Stopped performance monitoring for model {}", self.model_id);
        Ok(())
    }

    /// Record prediction metrics
    pub async fn record_prediction(
        &self,
        prediction: &ModelPrediction,
        latency: Duration,
        error: Option<&MLError>,
    ) -> MLResult<()> {
        self.metrics_collector.record_prediction(prediction, latency, error).await
    }

    /// Get current performance metrics
    pub async fn get_current_metrics(&self) -> PerformanceMetrics {
        let metrics = self.current_metrics.read().await;
        metrics.clone()
    }

    /// Get historical metrics
    pub async fn get_historical_metrics(
        &self,
        start_time: SystemTime,
        end_time: SystemTime,
    ) -> Vec<PerformanceSnapshot> {
        let historical = self.historical_metrics.lock().await;
        historical
            .iter()
            .filter(|snapshot| snapshot.timestamp >= start_time && snapshot.timestamp <= end_time)
            .cloned()
            .collect()
    }

    /// Get SLA violation summary
    pub async fn get_sla_violations(&self) -> SLAViolations {
        let violations = self.sla_violations.read().await;
        violations.clone()
    }

    /// Check if model meets SLA requirements
    pub async fn check_sla_compliance(&self) -> SLAComplianceReport {
        let metrics = self.get_current_metrics().await;
        let thresholds = &self.config.sla_thresholds;
        
        let mut violations = Vec::new();
        let mut compliance_score = 100.0;

        // Check latency SLA
        if metrics.avg_latency_us > thresholds.max_avg_latency_us as f64 {
            violations.push(SLAViolation {
                metric: "avg_latency".to_string(),
                current_value: metrics.avg_latency_us,
                threshold: thresholds.max_avg_latency_us as f64,
                severity: AlertSeverity::High,
                description: format!(
                    "Average latency {} exceeds threshold {}",
                    metrics.avg_latency_us, thresholds.max_avg_latency_us
                ),
            });
            compliance_score -= 15.0;
        }

        if metrics.latency_p95 > thresholds.max_p95_latency_us as f64 {
            violations.push(SLAViolation {
                metric: "p95_latency".to_string(),
                current_value: metrics.latency_p95,
                threshold: thresholds.max_p95_latency_us as f64,
                severity: AlertSeverity::High,
                description: format!(
                    "P95 latency {} exceeds threshold {}",
                    metrics.latency_p95, thresholds.max_p95_latency_us
                ),
            });
            compliance_score -= 20.0;
        }

        // Check error rate SLA
        if metrics.error_rate > thresholds.max_error_rate {
            violations.push(SLAViolation {
                metric: "error_rate".to_string(),
                current_value: metrics.error_rate as f64,
                threshold: thresholds.max_error_rate as f64,
                severity: AlertSeverity::Critical,
                description: format!(
                    "Error rate {} exceeds threshold {}",
                    metrics.error_rate, thresholds.max_error_rate
                ),
            });
            compliance_score -= 25.0;
        }

        // Check accuracy SLA
        if metrics.avg_accuracy < thresholds.min_accuracy {
            violations.push(SLAViolation {
                metric: "accuracy".to_string(),
                current_value: metrics.avg_accuracy as f64,
                threshold: thresholds.min_accuracy as f64,
                severity: AlertSeverity::High,
                description: format!(
                    "Accuracy {} below threshold {}",
                    metrics.avg_accuracy, thresholds.min_accuracy
                ),
            });
            compliance_score -= 20.0;
        }

        // Check throughput SLA
        if metrics.throughput_pps < thresholds.min_throughput_pps as f64 {
            violations.push(SLAViolation {
                metric: "throughput".to_string(),
                current_value: metrics.throughput_pps,
                threshold: thresholds.min_throughput_pps as f64,
                severity: AlertSeverity::Medium,
                description: format!(
                    "Throughput {} below threshold {}",
                    metrics.throughput_pps, thresholds.min_throughput_pps
                ),
            });
            compliance_score -= 10.0;
        }

        // Check memory SLA
        if metrics.memory_usage_mb > thresholds.max_memory_mb as f64 {
            violations.push(SLAViolation {
                metric: "memory_usage".to_string(),
                current_value: metrics.memory_usage_mb,
                threshold: thresholds.max_memory_mb as f64,
                severity: AlertSeverity::Medium,
                description: format!(
                    "Memory usage {} exceeds threshold {}",
                    metrics.memory_usage_mb, thresholds.max_memory_mb
                ),
            });
            compliance_score -= 10.0;
        }

        SLAComplianceReport {
            model_id: self.model_id.clone(),
            timestamp: SystemTime::now(),
            compliance_score: compliance_score.max(0.0),
            violations,
            is_compliant: violations.is_empty(),
            metrics_snapshot: metrics,
        }
    }

    /// Start SLA monitoring task
    async fn start_sla_monitoring(&self) -> MLResult<()> {
        let current_metrics = self.current_metrics.clone();
        let sla_violations = self.sla_violations.clone();
        let alert_manager = self.alert_manager.clone();
        let rollback_manager = self.rollback_manager.clone();
        let config = self.config.clone();
        let is_running = self.is_running.clone();
        let model_id = self.model_id.clone();

        tokio::spawn(async move {
            let mut interval = interval(Duration::from_secs(30)); // Check SLA every 30 seconds
            
            while is_running.load(Ordering::Acquire) {
                interval.tick().await;
                
                let metrics = {
                    let current = current_metrics.read().await;
                    current.clone()
                };

                // Check for SLA violations
                let mut violations_detected = false;
                let mut critical_violations = 0;
                
                // Check latency violations
                if metrics.avg_latency_us > config.sla_thresholds.max_avg_latency_us as f64 {
                    violations_detected = true;
                    let alert = Alert {
                        id: Uuid::new_v4(),
                        model_id: model_id.clone(),
                        alert_type: AlertType::SLAViolation,
                        severity: AlertSeverity::High,
                        message: format!(
                            "Average latency {} exceeds threshold {}",
                            metrics.avg_latency_us,
                            config.sla_thresholds.max_avg_latency_us
                        ),
                        timestamp: SystemTime::now(),
                        metadata: HashMap::new(),
                    };
                    
                    if let Err(e) = alert_manager.send_alert(alert).await {
                        tracing::error!("Failed to send latency SLA violation alert: {}", e);
                    }
                }

                // Check error rate violations
                if metrics.error_rate > config.sla_thresholds.max_error_rate {
                    violations_detected = true;
                    critical_violations += 1;
                    
                    let alert = Alert {
                        id: Uuid::new_v4(),
                        model_id: model_id.clone(),
                        alert_type: AlertType::SLAViolation,
                        severity: AlertSeverity::Critical,
                        message: format!(
                            "Error rate {} exceeds threshold {}",
                            metrics.error_rate,
                            config.sla_thresholds.max_error_rate
                        ),
                        timestamp: SystemTime::now(),
                        metadata: HashMap::new(),
                    };
                    
                    if let Err(e) = alert_manager.send_alert(alert).await {
                        tracing::error!("Failed to send error rate SLA violation alert: {}", e);
                    }
                }

                // Update violation counters
                if violations_detected {
                    let mut violations = sla_violations.write().await;
                    violations.total_violations += 1;
                    violations.last_violation_time = Some(SystemTime::now());
                    
                    if metrics.avg_latency_us > config.sla_thresholds.max_avg_latency_us as f64 {
                        violations.latency_violations += 1;
                    }
                    if metrics.error_rate > config.sla_thresholds.max_error_rate {
                        violations.error_rate_violations += 1;
                    }
                }

                // Check if automatic rollback should be triggered
                if critical_violations > 0 && config.rollback.auto_rollback_enabled {
                    if let Err(e) = rollback_manager.evaluate_rollback(&metrics, &config.sla_thresholds).await {
                        tracing::error!("Failed to evaluate rollback: {}", e);
                    }
                }
            }
        });

        Ok(())
    }
}

/// SLA compliance report
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SLAComplianceReport {
    /// Model ID
    pub model_id: String,
    /// Report timestamp
    pub timestamp: SystemTime,
    /// Compliance score (0-100)
    pub compliance_score: f32,
    /// SLA violations
    pub violations: Vec<SLAViolation>,
    /// Overall compliance status
    pub is_compliant: bool,
    /// Metrics snapshot
    pub metrics_snapshot: PerformanceMetrics,
}

/// SLA violation details
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SLAViolation {
    /// Metric name
    pub metric: String,
    /// Current value
    pub current_value: f64,
    /// Threshold value
    pub threshold: f64,
    /// Violation severity
    pub severity: AlertSeverity,
    /// Violation description
    pub description: String,
}

/// Alert manager for sending notifications
pub struct AlertManager {
    /// Alerting configuration
    config: AlertingConfig,
    /// Last alert timestamps (for cooldown)
    last_alerts: Arc<RwLock<HashMap<String, SystemTime>>>,
}

impl AlertManager {
    /// Create new alert manager
    pub fn new(config: AlertingConfig) -> Self {
        Self {
            config,
            last_alerts: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    /// Send alert
    pub async fn send_alert(&self, alert: Alert) -> MLResult<()> {
        // Check cooldown period
        let alert_key = format!("{}_{}", alert.model_id, alert.alert_type.to_string());
        {
            let last_alerts = self.last_alerts.read().await;
            if let Some(last_time) = last_alerts.get(&alert_key) {
                if let Ok(elapsed) = SystemTime::now().duration_since(*last_time) {
                    if elapsed < self.config.cooldown_period {
                        return Ok(()); // Skip alert due to cooldown
                    }
                }
            }
        }

        // Check if alert severity should be sent
        if !self.config.alert_levels.contains(&alert.severity) {
            return Ok(());
        }

        // Send email alerts
        if self.config.enable_email && !self.config.email_recipients.is_empty() {
            for recipient in &self.config.email_recipients {
                if let Err(e) = self.send_email_alert(recipient, &alert).await {
                    tracing::error!("Failed to send email alert to {}: {}", recipient, e);
                }
            }
        }

        // Send webhook alerts
        if self.config.enable_webhooks && !self.config.webhook_urls.is_empty() {
            for webhook_url in &self.config.webhook_urls {
                if let Err(e) = self.send_webhook_alert(webhook_url, &alert).await {
                    tracing::error!("Failed to send webhook alert to {}: {}", webhook_url, e);
                }
            }
        }

        // Send Slack alerts
        if self.config.enable_slack {
            if let Some(ref slack_url) = self.config.slack_webhook_url {
                if let Err(e) = self.send_slack_alert(slack_url, &alert).await {
                    tracing::error!("Failed to send Slack alert: {}", e);
                }
            }
        }

        // Update last alert time
        {
            let mut last_alerts = self.last_alerts.write().await;
            last_alerts.insert(alert_key, SystemTime::now());
        }

        tracing::info!("Sent alert for model {}: {}", alert.model_id, alert.message);
        Ok(())
    }

    /// Send email alert (placeholder implementation)
    async fn send_email_alert(&self, recipient: &str, alert: &Alert) -> MLResult<()> {
        // In a real implementation, this would use an email service
        tracing::info!("Email alert sent to {}: {}", recipient, alert.message);
        Ok(())
    }

    /// Send webhook alert (placeholder implementation)
    async fn send_webhook_alert(&self, webhook_url: &str, alert: &Alert) -> MLResult<()> {
        // In a real implementation, this would make an HTTP POST request
        tracing::info!("Webhook alert sent to {}: {}", webhook_url, alert.message);
        Ok(())
    }

    /// Send Slack alert (placeholder implementation)
    async fn send_slack_alert(&self, slack_url: &str, alert: &Alert) -> MLResult<()> {
        // In a real implementation, this would send to Slack webhook
        tracing::info!("Slack alert sent: {}", alert.message);
        Ok(())
    }
}

/// Alert information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Alert {
    /// Alert ID
    pub id: Uuid,
    /// Model ID
    pub model_id: String,
    /// Alert type
    pub alert_type: AlertType,
    /// Alert severity
    pub severity: AlertSeverity,
    /// Alert message
    pub message: String,
    /// Alert timestamp
    pub timestamp: SystemTime,
    /// Additional metadata
    pub metadata: HashMap<String, String>,
}

/// Alert types
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum AlertType {
    /// SLA violation
    SLAViolation,
    /// Performance degradation
    PerformanceDegradation,
    /// Model failure
    ModelFailure,
    /// High error rate
    HighErrorRate,
    /// Memory leak detected
    MemoryLeak,
    /// Rollback triggered
    RollbackTriggered,
    /// Custom alert
    Custom(String),
}

impl AlertType {
    /// Convert to string representation
    pub fn to_string(&self) -> String {
        match self {
            AlertType::SLAViolation => "sla_violation".to_string(),
            AlertType::PerformanceDegradation => "performance_degradation".to_string(),
            AlertType::ModelFailure => "model_failure".to_string(),
            AlertType::HighErrorRate => "high_error_rate".to_string(),
            AlertType::MemoryLeak => "memory_leak".to_string(),
            AlertType::RollbackTriggered => "rollback_triggered".to_string(),
            AlertType::Custom(name) => format!("custom_{}", name),
        }
    }
}

/// Rollback manager for automatic rollbacks
pub struct RollbackManager {
    /// Rollback configuration
    config: RollbackConfig,
    /// Rollback state
    state: Arc<RwLock<RollbackState>>,
}

/// Rollback state tracking
#[derive(Debug, Clone, Default)]
struct RollbackState {
    /// Number of consecutive violations
    consecutive_violations: u32,
    /// Last evaluation time
    last_evaluation: Option<SystemTime>,
    /// Rollback in progress
    rollback_in_progress: bool,
}

impl RollbackManager {
    /// Create new rollback manager
    pub fn new(config: RollbackConfig) -> Self {
        Self {
            config,
            state: Arc::new(RwLock::new(RollbackState::default())),
        }
    }

    /// Evaluate if rollback should be triggered
    pub async fn evaluate_rollback(
        &self,
        metrics: &PerformanceMetrics,
        thresholds: &SLAThresholds,
    ) -> MLResult<bool> {
        let mut state = self.state.write().await;
        
        // Check if rollback is already in progress
        if state.rollback_in_progress {
            return Ok(false);
        }

        // Check for SLA violations
        let violations = self.count_violations(metrics, thresholds);
        
        if violations > 0 {
            state.consecutive_violations += 1;
        } else {
            state.consecutive_violations = 0;
        }

        state.last_evaluation = Some(SystemTime::now());

        // Check if rollback threshold is met
        if state.consecutive_violations >= self.config.violation_threshold {
            state.rollback_in_progress = true;
            tracing::warn!(
                "Triggering automatic rollback for model {} due to {} consecutive violations",
                metrics.model_id,
                state.consecutive_violations
            );
            
            // In a real implementation, this would trigger the actual rollback
            // For now, we just log the action
            tokio::spawn(async move {
                // Simulate rollback process
                tokio::time::sleep(Duration::from_secs(30)).await;
                tracing::info!("Rollback completed for model {}", metrics.model_id);
            });
            
            return Ok(true);
        }

        Ok(false)
    }

    /// Count current SLA violations
    fn count_violations(&self, metrics: &PerformanceMetrics, thresholds: &SLAThresholds) -> u32 {
        let mut violations = 0;

        if metrics.avg_latency_us > thresholds.max_avg_latency_us as f64 {
            violations += 1;
        }
        if metrics.error_rate > thresholds.max_error_rate {
            violations += 1;
        }
        if metrics.avg_accuracy < thresholds.min_accuracy {
            violations += 1;
        }
        if metrics.throughput_pps < thresholds.min_throughput_pps as f64 {
            violations += 1;
        }
        if metrics.memory_usage_mb > thresholds.max_memory_mb as f64 {
            violations += 1;
        }

        violations
    }
}

/// Rollback configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RollbackConfig {
    /// Enable automatic rollback
    pub auto_rollback_enabled: bool,
    /// Number of consecutive violations before rollback
    pub violation_threshold: u32,
    /// Rollback timeout
    pub rollback_timeout: Duration,
    /// Enable rollback confirmation
    pub require_confirmation: bool,
}

impl Default for RollbackConfig {
    fn default() -> Self {
        Self {
            auto_rollback_enabled: true,
            violation_threshold: 3,
            rollback_timeout: Duration::from_secs(60),
            require_confirmation: false,
        }
    }
}

/// Metrics collector for gathering performance data
pub struct MetricsCollector {
    /// Model ID
    model_id: String,
    /// Collection interval
    collection_interval: Duration,
    /// Performance baseline
    baseline: Option<PerformanceBaseline>,
    /// Prediction counter
    prediction_counter: Arc<AtomicU64>,
    /// Error counter
    error_counter: Arc<AtomicU64>,
    /// Latency measurements
    latency_measurements: Arc<Mutex<VecDeque<f64>>>,
    /// Accuracy measurements
    accuracy_measurements: Arc<Mutex<VecDeque<f32>>>,
}

impl MetricsCollector {
    /// Create new metrics collector
    pub fn new(
        model_id: String,
        collection_interval: Duration,
        baseline: Option<PerformanceBaseline>,
    ) -> Self {
        Self {
            model_id,
            collection_interval,
            baseline,
            prediction_counter: Arc::new(AtomicU64::new(0)),
            error_counter: Arc::new(AtomicU64::new(0)),
            latency_measurements: Arc::new(Mutex::new(VecDeque::new())),
            accuracy_measurements: Arc::new(Mutex::new(VecDeque::new())),
        }
    }

    /// Record prediction metrics
    pub async fn record_prediction(
        &self,
        prediction: &ModelPrediction,
        latency: Duration,
        error: Option<&MLError>,
    ) -> MLResult<()> {
        // Increment prediction counter
        self.prediction_counter.fetch_add(1, Ordering::Relaxed);

        // Record error if present
        if error.is_some() {
            self.error_counter.fetch_add(1, Ordering::Relaxed);
        }

        // Record latency
        {
            let mut latencies = self.latency_measurements.lock().await;
            latencies.push_back(latency.as_micros() as f64);
            
            // Keep only recent measurements (last 1000)
            while latencies.len() > 1000 {
                latencies.pop_front();
            }
        }

        // Record accuracy
        {
            let mut accuracies = self.accuracy_measurements.lock().await;
            accuracies.push_back(prediction.confidence);
            
            // Keep only recent measurements (last 1000)
            while accuracies.len() > 1000 {
                accuracies.pop_front();
            }
        }

        Ok(())
    }

    /// Collect current metrics
    pub async fn collect_metrics(&self) -> MLResult<PerformanceMetrics> {
        let total_predictions = self.prediction_counter.load(Ordering::Relaxed);
        let total_errors = self.error_counter.load(Ordering::Relaxed);
        
        let error_rate = if total_predictions > 0 {
            total_errors as f32 / total_predictions as f32
        } else {
            0.0
        };

        // Calculate latency percentiles
        let latencies = {
            let latency_measurements = self.latency_measurements.lock().await;
            latency_measurements.iter().cloned().collect::<Vec<_>>()
        };

        let (avg_latency, p50, p95, p99, p999) = if !latencies.is_empty() {
            let mut sorted_latencies = latencies.clone();
            sorted_latencies.sort_by(|a, b| a.partial_cmp(b).unwrap());
            
            let avg = latencies.iter().sum::<f64>() / latencies.len() as f64;
            let p50 = sorted_latencies[sorted_latencies.len() * 50 / 100];
            let p95 = sorted_latencies[sorted_latencies.len() * 95 / 100];
            let p99 = sorted_latencies[sorted_latencies.len() * 99 / 100];
            let p999 = sorted_latencies[sorted_latencies.len() * 999 / 1000];
            
            (avg, p50, p95, p99, p999)
        } else {
            (0.0, 0.0, 0.0, 0.0, 0.0)
        };

        // Calculate average accuracy
        let avg_accuracy = {
            let accuracy_measurements = self.accuracy_measurements.lock().await;
            if !accuracy_measurements.is_empty() {
                accuracy_measurements.iter().sum::<f32>() / accuracy_measurements.len() as f32
            } else {
                0.0
            }
        };

        // Calculate throughput (predictions per second)
        let throughput_pps = if !latencies.is_empty() && avg_latency > 0.0 {
            1_000_000.0 / avg_latency // Convert microseconds to seconds
        } else {
            0.0
        };

        Ok(PerformanceMetrics {
            model_id: self.model_id.clone(),
            timestamp: SystemTime::now(),
            total_predictions,
            total_errors,
            error_rate,
            latency_p50: p50,
            latency_p95: p95,
            latency_p99: p99,
            latency_p999: p999,
            avg_latency_us: avg_latency,
            throughput_pps,
            memory_usage_mb: self.get_memory_usage().await,
            cpu_utilization: self.get_cpu_utilization().await,
            avg_accuracy,
            custom_metrics: HashMap::new(),
        })
    }

    /// Get current memory usage (requires system integration)
    async fn get_memory_usage(&self) -> f64 {
        // TODO: Implement using sysinfo crate to get actual process memory
        // For now, return 0.0 to indicate unimplemented
        warn!("Memory usage tracking not implemented - returning 0.0");
        0.0
    }

    /// Get current CPU utilization (requires system integration)
    async fn get_cpu_utilization(&self) -> f32 {
        // TODO: Implement using sysinfo crate to get actual CPU usage
        // For now, return 0.0 to indicate unimplemented
        warn!("CPU utilization tracking not implemented - returning 0.0");
        0.0
    }
}

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

    #[tokio::test]
    async fn test_performance_monitor_creation() {
        let config = MonitoringConfig::default();
        let monitor = PerformanceMonitor::new(
            "test_model".to_string(),
            ModelType::DQN,
            ModelVersion::new(1, 0, 0),
            config,
            None,
        ).await;

        assert_eq!(monitor.model_id, "test_model");
        assert_eq!(monitor.model_type, ModelType::DQN);
        assert!(!monitor.is_running.load(Ordering::Acquire));
    }

    #[tokio::test]
    async fn test_metrics_collector() {
        let collector = MetricsCollector::new(
            "test_model".to_string(),
            Duration::from_secs(10),
            None,
        );

        let prediction = ModelPrediction::new(
            "test_model".to_string(),
            0.8,
            0.9,
        );

        let result = collector.record_prediction(&prediction, Duration::from_micros(100), None).await;
        assert!(result.is_ok());

        let metrics = collector.collect_metrics().await.unwrap();
        assert_eq!(metrics.total_predictions, 1);
        assert_eq!(metrics.total_errors, 0);
        assert_eq!(metrics.error_rate, 0.0);
    }

    #[tokio::test]
    async fn test_sla_compliance_check() {
        let config = MonitoringConfig::default();
        let monitor = PerformanceMonitor::new(
            "test_model".to_string(),
            ModelType::DQN,
            ModelVersion::new(1, 0, 0),
            config,
            None,
        ).await;

        let compliance_report = monitor.check_sla_compliance().await;
        assert!(compliance_report.is_compliant);
        assert_eq!(compliance_report.violations.len(), 0);
        assert_eq!(compliance_report.compliance_score, 100.0);
    }

    #[tokio::test]
    async fn test_alert_manager() {
        let config = AlertingConfig::default();
        let alert_manager = AlertManager::new(config);

        let alert = Alert {
            id: Uuid::new_v4(),
            model_id: "test_model".to_string(),
            alert_type: AlertType::SLAViolation,
            severity: AlertSeverity::High,
            message: "Test alert".to_string(),
            timestamp: SystemTime::now(),
            metadata: HashMap::new(),
        };

        let result = alert_manager.send_alert(alert).await;
        assert!(result.is_ok());
    }

    #[tokio::test]
    async fn test_rollback_manager() {
        let config = RollbackConfig::default();
        let rollback_manager = RollbackManager::new(config);

        let metrics = PerformanceMetrics {
            model_id: "test_model".to_string(),
            error_rate: 0.1, // High error rate
            avg_latency_us: 1000.0, // High latency
            ..Default::default()
        };

        let thresholds = SLAThresholds {
            max_error_rate: 0.01,
            max_avg_latency_us: 100,
            ..Default::default()
        };

        let result = rollback_manager.evaluate_rollback(&metrics, &thresholds).await;
        assert!(result.is_ok());
    }
}