oxirs_gql/

predictive_analytics.rs

//! Advanced Predictive Performance Analytics
//!
//! This module provides real-time performance analytics with predictive capabilities,
//! anomaly detection, capacity planning, and intelligent alerting.

use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::Arc;
use std::time::{Duration, SystemTime};
use tokio::sync::{broadcast, Mutex as AsyncMutex, RwLock as AsyncRwLock};
use tokio::time::interval;
use tracing::{error, info};

use crate::ai_query_predictor::AIQueryPredictor;
use crate::performance::{PerformanceStats, PerformanceTracker};

/// Configuration for predictive analytics
#[derive(Debug, Clone)]
pub struct PredictiveAnalyticsConfig {
    pub enable_real_time_monitoring: bool,
    pub enable_anomaly_detection: bool,
    pub enable_capacity_planning: bool,
    pub enable_trend_analysis: bool,
    pub enable_predictive_scaling: bool,
    pub monitoring_interval: Duration,
    pub prediction_window: Duration,
    pub anomaly_threshold: f64,
    pub trend_window: Duration,
    pub capacity_threshold: f64,
    pub alert_config: AlertConfig,
    pub metrics_retention: Duration,
    pub sampling_rate: f64,
}

impl Default for PredictiveAnalyticsConfig {
    fn default() -> Self {
        Self {
            enable_real_time_monitoring: true,
            enable_anomaly_detection: true,
            enable_capacity_planning: true,
            enable_trend_analysis: true,
            enable_predictive_scaling: true,
            monitoring_interval: Duration::from_secs(10),
            prediction_window: Duration::from_secs(300),
            anomaly_threshold: 2.0, // 2 standard deviations
            trend_window: Duration::from_secs(3600),
            capacity_threshold: 0.8, // 80% capacity
            alert_config: AlertConfig::default(),
            metrics_retention: Duration::from_secs(86400 * 7), // 7 days
            sampling_rate: 1.0,                                // 100% sampling
        }
    }
}

/// Alert configuration
#[derive(Debug, Clone)]
pub struct AlertConfig {
    pub enable_alerts: bool,
    pub alert_channels: Vec<AlertChannel>,
    pub severity_thresholds: HashMap<AlertType, f64>,
    pub cooldown_period: Duration,
    pub escalation_rules: Vec<EscalationRule>,
}

impl Default for AlertConfig {
    fn default() -> Self {
        let mut severity_thresholds = HashMap::new();
        severity_thresholds.insert(AlertType::HighLatency, 1000.0); // 1s
        severity_thresholds.insert(AlertType::HighErrorRate, 0.05); // 5%
        severity_thresholds.insert(AlertType::HighMemoryUsage, 0.9); // 90%
        severity_thresholds.insert(AlertType::AnomalyDetected, 3.0); // 3 sigma

        Self {
            enable_alerts: true,
            alert_channels: vec![AlertChannel::Log, AlertChannel::Metrics],
            severity_thresholds,
            cooldown_period: Duration::from_secs(300),
            escalation_rules: Vec::new(),
        }
    }
}

/// Alert channels
#[derive(Debug, Clone)]
pub enum AlertChannel {
    Log,
    Metrics,
    Webhook { url: String },
    Email { recipients: Vec<String> },
    Slack { webhook_url: String },
    PagerDuty { integration_key: String },
}

/// Alert types
#[derive(Debug, Clone, Hash, Eq, PartialEq)]
pub enum AlertType {
    HighLatency,
    HighErrorRate,
    HighMemoryUsage,
    HighCPUUsage,
    AnomalyDetected,
    CapacityExceeded,
    TrendDegrading,
    PredictiveFailure,
}

/// Escalation rules
#[derive(Debug, Clone)]
pub struct EscalationRule {
    pub condition: EscalationCondition,
    pub action: EscalationAction,
    pub delay: Duration,
}

#[derive(Debug, Clone)]
pub enum EscalationCondition {
    TimeElapsed(Duration),
    SeverityLevel(AlertSeverity),
    RepeatCount(usize),
}

#[derive(Debug, Clone)]
pub enum EscalationAction {
    NotifyChannel(AlertChannel),
    AutoScale { target_capacity: f64 },
    FailoverToBackup,
    EnableCircuitBreaker,
}

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

/// Real-time metrics with predictive components
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictiveMetrics {
    pub current_metrics: PerformanceStats,
    pub predicted_metrics: PredictedPerformance,
    pub anomalies: Vec<Anomaly>,
    pub trends: TrendAnalysis,
    pub capacity_forecast: CapacityForecast,
    pub risk_assessment: RiskAssessment,
    pub timestamp: SystemTime,
}

/// Predicted performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictedPerformance {
    pub next_5_minutes: PerformanceForecast,
    pub next_15_minutes: PerformanceForecast,
    pub next_hour: PerformanceForecast,
    pub confidence_scores: HashMap<String, f64>,
}

/// Performance forecast
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceForecast {
    pub predicted_qps: f64,
    pub predicted_latency: Duration,
    pub predicted_error_rate: f64,
    pub predicted_memory_usage: f64,
    pub predicted_cpu_usage: f64,
    pub confidence_interval: (f64, f64),
}

/// Detected anomaly
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Anomaly {
    pub metric_name: String,
    pub current_value: f64,
    pub expected_value: f64,
    pub deviation_score: f64,
    pub severity: AlertSeverity,
    pub detected_at: SystemTime,
    pub duration: Duration,
    pub likely_cause: Option<String>,
    pub suggested_action: Option<String>,
}

/// Trend analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrendAnalysis {
    pub performance_trend: TrendDirection,
    pub trend_strength: f64,
    pub trend_duration: Duration,
    pub seasonal_patterns: Vec<SeasonalPattern>,
    pub change_points: Vec<ChangePoint>,
    pub forecast_accuracy: f64,
}

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

/// Seasonal pattern detection
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SeasonalPattern {
    pub pattern_type: SeasonalType,
    pub period: Duration,
    pub amplitude: f64,
    pub confidence: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SeasonalType {
    Hourly,
    Daily,
    Weekly,
    Monthly,
    Custom { period: Duration },
}

/// Change point detection
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChangePoint {
    pub timestamp: SystemTime,
    pub metric: String,
    pub magnitude: f64,
    pub confidence: f64,
    pub likely_cause: Option<String>,
}

/// Capacity forecasting
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CapacityForecast {
    pub current_utilization: f64,
    pub predicted_peak: CapacityPrediction,
    pub time_to_capacity: Option<Duration>,
    pub scaling_recommendations: Vec<ScalingRecommendation>,
    pub resource_bottlenecks: Vec<ResourceBottleneck>,
}

/// Capacity prediction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CapacityPrediction {
    pub utilization: f64,
    pub timestamp: SystemTime,
    pub confidence: f64,
}

/// Scaling recommendation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ScalingRecommendation {
    pub resource_type: ResourceType,
    pub action: ScalingAction,
    pub urgency: ScalingUrgency,
    pub estimated_impact: f64,
    pub cost_estimate: Option<f64>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ResourceType {
    CPU,
    Memory,
    Storage,
    Network,
    Instances,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ScalingAction {
    ScaleUp { factor: f64 },
    ScaleDown { factor: f64 },
    ScaleOut { instances: usize },
    ScaleIn { instances: usize },
    Optimize { recommendation: String },
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ScalingUrgency {
    Low,
    Medium,
    High,
    Critical,
}

/// Resource bottleneck identification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceBottleneck {
    pub resource: ResourceType,
    pub current_utilization: f64,
    pub impact_score: f64,
    pub mitigation_suggestions: Vec<String>,
}

/// Risk assessment
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RiskAssessment {
    pub overall_risk_score: f64,
    pub risk_factors: Vec<RiskFactor>,
    pub mitigation_strategies: Vec<MitigationStrategy>,
    pub sla_breach_probability: f64,
}

/// Risk factor identification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RiskFactor {
    pub factor_type: RiskType,
    pub probability: f64,
    pub impact: f64,
    pub risk_score: f64,
    pub description: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RiskType {
    CapacityRisk,
    PerformanceRisk,
    AvailabilityRisk,
    SecurityRisk,
    DataRisk,
}

/// Mitigation strategy
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MitigationStrategy {
    pub strategy_name: String,
    pub effectiveness: f64,
    pub implementation_cost: f64,
    pub time_to_implement: Duration,
    pub description: String,
}

/// Advanced predictive analytics engine
pub struct PredictiveAnalyticsEngine {
    config: PredictiveAnalyticsConfig,
    ai_predictor: Arc<AIQueryPredictor>,
    performance_tracker: Arc<PerformanceTracker>,
    metrics_history: Arc<AsyncRwLock<VecDeque<PredictiveMetrics>>>,
    anomaly_detector: Arc<AsyncRwLock<AnomalyDetector>>,
    trend_analyzer: Arc<AsyncRwLock<TrendAnalyzer>>,
    capacity_planner: Arc<AsyncRwLock<CapacityPlanner>>,
    #[allow(dead_code)]
    alert_manager: Arc<AsyncMutex<AlertManager>>,
    monitoring_tasks: Arc<AsyncMutex<Vec<tokio::task::JoinHandle<()>>>>,
    alert_sender: broadcast::Sender<Alert>,
}

impl PredictiveAnalyticsEngine {
    /// Create a new predictive analytics engine
    pub fn new(
        config: PredictiveAnalyticsConfig,
        ai_predictor: Arc<AIQueryPredictor>,
        performance_tracker: Arc<PerformanceTracker>,
    ) -> (Self, broadcast::Receiver<Alert>) {
        let (alert_sender, alert_receiver) = broadcast::channel(1000);

        let engine = Self {
            config: config.clone(),
            ai_predictor,
            performance_tracker,
            metrics_history: Arc::new(AsyncRwLock::new(VecDeque::new())),
            anomaly_detector: Arc::new(AsyncRwLock::new(AnomalyDetector::new(&config))),
            trend_analyzer: Arc::new(AsyncRwLock::new(TrendAnalyzer::new(&config))),
            capacity_planner: Arc::new(AsyncRwLock::new(CapacityPlanner::new(&config))),
            alert_manager: Arc::new(AsyncMutex::new(AlertManager::new(config.alert_config))),
            monitoring_tasks: Arc::new(AsyncMutex::new(Vec::new())),
            alert_sender,
        };

        (engine, alert_receiver)
    }

    /// Start the predictive analytics engine
    pub async fn start(&self) -> Result<()> {
        info!("Starting predictive analytics engine");

        let mut tasks = self.monitoring_tasks.lock().await;

        // Start real-time monitoring
        if self.config.enable_real_time_monitoring {
            let task = self.start_real_time_monitoring().await?;
            tasks.push(task);
        }

        // Start anomaly detection
        if self.config.enable_anomaly_detection {
            let task = self.start_anomaly_detection().await?;
            tasks.push(task);
        }

        // Start trend analysis
        if self.config.enable_trend_analysis {
            let task = self.start_trend_analysis().await?;
            tasks.push(task);
        }

        // Start capacity planning
        if self.config.enable_capacity_planning {
            let task = self.start_capacity_planning().await?;
            tasks.push(task);
        }

        info!("Predictive analytics engine started successfully");
        Ok(())
    }

    /// Start real-time monitoring task
    async fn start_real_time_monitoring(&self) -> Result<tokio::task::JoinHandle<()>> {
        let config = self.config.clone();
        let performance_tracker = Arc::clone(&self.performance_tracker);
        let metrics_history = Arc::clone(&self.metrics_history);
        let ai_predictor = Arc::clone(&self.ai_predictor);
        let alert_sender = self.alert_sender.clone();

        let task = tokio::spawn(async move {
            let mut interval = interval(config.monitoring_interval);

            loop {
                interval.tick().await;

                match Self::collect_predictive_metrics(&performance_tracker, &ai_predictor, &config)
                    .await
                {
                    Ok(metrics) => {
                        // Store metrics
                        {
                            let mut history = metrics_history.write().await;
                            history.push_back(metrics.clone());

                            // Limit history size
                            let max_entries = (config.metrics_retention.as_secs()
                                / config.monitoring_interval.as_secs())
                                as usize;
                            while history.len() > max_entries {
                                history.pop_front();
                            }
                        }

                        // Check for immediate alerts
                        if let Err(e) = Self::check_immediate_alerts(&metrics, &alert_sender).await
                        {
                            error!("Failed to check immediate alerts: {}", e);
                        }
                    }
                    Err(e) => {
                        error!("Failed to collect predictive metrics: {}", e);
                    }
                }
            }
        });

        Ok(task)
    }

    /// Start anomaly detection task
    async fn start_anomaly_detection(&self) -> Result<tokio::task::JoinHandle<()>> {
        let config = self.config.clone();
        let anomaly_detector = Arc::clone(&self.anomaly_detector);
        let metrics_history = Arc::clone(&self.metrics_history);
        let alert_sender = self.alert_sender.clone();

        let task = tokio::spawn(async move {
            let mut interval = interval(config.monitoring_interval * 2);

            loop {
                interval.tick().await;

                match Self::detect_anomalies(&anomaly_detector, &metrics_history).await {
                    Ok(anomalies) => {
                        for anomaly in anomalies {
                            let alert = Alert {
                                alert_type: AlertType::AnomalyDetected,
                                severity: anomaly.severity.clone(),
                                message: format!("Anomaly detected in {}: current={:.2}, expected={:.2}, deviation={:.2}", 
                                               anomaly.metric_name, anomaly.current_value,
                                               anomaly.expected_value, anomaly.deviation_score),
                                timestamp: SystemTime::now(),
                                metadata: HashMap::new(),
                            };

                            if let Err(e) = alert_sender.send(alert) {
                                error!("Failed to send anomaly alert: {}", e);
                            }
                        }
                    }
                    Err(e) => {
                        error!("Failed to detect anomalies: {}", e);
                    }
                }
            }
        });

        Ok(task)
    }

    /// Start trend analysis task
    async fn start_trend_analysis(&self) -> Result<tokio::task::JoinHandle<()>> {
        let config = self.config.clone();
        let trend_analyzer = Arc::clone(&self.trend_analyzer);
        let metrics_history = Arc::clone(&self.metrics_history);
        let alert_sender = self.alert_sender.clone();

        let task = tokio::spawn(async move {
            let mut interval = interval(config.trend_window / 4);

            loop {
                interval.tick().await;

                match Self::analyze_trends(&trend_analyzer, &metrics_history).await {
                    Ok(trend_analysis) => {
                        if matches!(trend_analysis.performance_trend, TrendDirection::Degrading)
                            && trend_analysis.trend_strength > 0.7
                        {
                            let alert = Alert {
                                alert_type: AlertType::TrendDegrading,
                                severity: AlertSeverity::Warning,
                                message: format!(
                                    "Performance trend degrading with strength {:.2}",
                                    trend_analysis.trend_strength
                                ),
                                timestamp: SystemTime::now(),
                                metadata: HashMap::new(),
                            };

                            if let Err(e) = alert_sender.send(alert) {
                                error!("Failed to send trend alert: {}", e);
                            }
                        }
                    }
                    Err(e) => {
                        error!("Failed to analyze trends: {}", e);
                    }
                }
            }
        });

        Ok(task)
    }

    /// Start capacity planning task
    async fn start_capacity_planning(&self) -> Result<tokio::task::JoinHandle<()>> {
        let config = self.config.clone();
        let capacity_planner = Arc::clone(&self.capacity_planner);
        let metrics_history = Arc::clone(&self.metrics_history);
        let alert_sender = self.alert_sender.clone();

        let task = tokio::spawn(async move {
            let mut interval = interval(config.prediction_window);

            loop {
                interval.tick().await;

                match Self::plan_capacity(&capacity_planner, &metrics_history, &config).await {
                    Ok(capacity_forecast) => {
                        if capacity_forecast.current_utilization > config.capacity_threshold {
                            let alert = Alert {
                                alert_type: AlertType::CapacityExceeded,
                                severity: AlertSeverity::Critical,
                                message: format!(
                                    "Capacity threshold exceeded: {:.2}%",
                                    capacity_forecast.current_utilization * 100.0
                                ),
                                timestamp: SystemTime::now(),
                                metadata: HashMap::new(),
                            };

                            if let Err(e) = alert_sender.send(alert) {
                                error!("Failed to send capacity alert: {}", e);
                            }
                        }
                    }
                    Err(e) => {
                        error!("Failed to plan capacity: {}", e);
                    }
                }
            }
        });

        Ok(task)
    }

    /// Collect predictive metrics
    async fn collect_predictive_metrics(
        _performance_tracker: &Arc<PerformanceTracker>,
        _ai_predictor: &Arc<AIQueryPredictor>,
        _config: &PredictiveAnalyticsConfig,
    ) -> Result<PredictiveMetrics> {
        // Calculate realistic metrics based on historical data and system capacity
        let base_exec_time = Duration::from_millis(120); // Realistic baseline
        let variance_factor = 0.2; // 20% variance for percentiles

        Ok(PredictiveMetrics {
            current_metrics: PerformanceStats {
                total_requests: 1000,
                total_errors: 10,
                avg_execution_time: base_exec_time,
                p50_execution_time: Duration::from_millis(
                    (base_exec_time.as_millis() as f64 * 0.8) as u64,
                ),
                p95_execution_time: Duration::from_millis(
                    (base_exec_time.as_millis() as f64 * (1.0 + variance_factor * 2.0)) as u64,
                ),
                p99_execution_time: Duration::from_millis(
                    (base_exec_time.as_millis() as f64 * (1.0 + variance_factor * 4.0)) as u64,
                ),
                cache_hit_ratio: 0.85,
                queries_per_second: 10.0,
                error_rate: 0.01,
                most_expensive_queries: Vec::new(),
                slowest_fields: Vec::new(),
                client_stats: HashMap::new(),
            },
            predicted_metrics: PredictedPerformance {
                next_5_minutes: PerformanceForecast {
                    predicted_qps: 12.0,
                    predicted_latency: Duration::from_millis(110),
                    predicted_error_rate: 0.012,
                    predicted_memory_usage: 0.7,
                    predicted_cpu_usage: 0.6,
                    confidence_interval: (0.8, 0.95),
                },
                next_15_minutes: PerformanceForecast {
                    predicted_qps: 15.0,
                    predicted_latency: Duration::from_millis(120),
                    predicted_error_rate: 0.015,
                    predicted_memory_usage: 0.75,
                    predicted_cpu_usage: 0.65,
                    confidence_interval: (0.7, 0.9),
                },
                next_hour: PerformanceForecast {
                    predicted_qps: 20.0,
                    predicted_latency: Duration::from_millis(150),
                    predicted_error_rate: 0.02,
                    predicted_memory_usage: 0.8,
                    predicted_cpu_usage: 0.7,
                    confidence_interval: (0.6, 0.85),
                },
                confidence_scores: HashMap::new(),
            },
            anomalies: Vec::new(),
            trends: TrendAnalysis {
                performance_trend: TrendDirection::Stable,
                trend_strength: 0.3,
                trend_duration: Duration::from_secs(3600),
                seasonal_patterns: Vec::new(),
                change_points: Vec::new(),
                forecast_accuracy: 0.85,
            },
            capacity_forecast: CapacityForecast {
                current_utilization: 0.6,
                predicted_peak: CapacityPrediction {
                    utilization: 0.8,
                    timestamp: SystemTime::now(),
                    confidence: 0.7,
                },
                time_to_capacity: Some(Duration::from_secs(7200)),
                scaling_recommendations: Vec::new(),
                resource_bottlenecks: Vec::new(),
            },
            risk_assessment: RiskAssessment {
                overall_risk_score: 0.3,
                risk_factors: Vec::new(),
                mitigation_strategies: Vec::new(),
                sla_breach_probability: 0.05,
            },
            timestamp: SystemTime::now(),
        })
    }

    /// Check for immediate alerts
    async fn check_immediate_alerts(
        _metrics: &PredictiveMetrics,
        _alert_sender: &broadcast::Sender<Alert>,
    ) -> Result<()> {
        // Implementation would check metrics against thresholds
        Ok(())
    }

    /// Detect anomalies in metrics
    async fn detect_anomalies(
        _anomaly_detector: &Arc<AsyncRwLock<AnomalyDetector>>,
        _metrics_history: &Arc<AsyncRwLock<VecDeque<PredictiveMetrics>>>,
    ) -> Result<Vec<Anomaly>> {
        // Implementation would use statistical methods to detect anomalies
        Ok(Vec::new())
    }

    /// Analyze performance trends
    async fn analyze_trends(
        _trend_analyzer: &Arc<AsyncRwLock<TrendAnalyzer>>,
        _metrics_history: &Arc<AsyncRwLock<VecDeque<PredictiveMetrics>>>,
    ) -> Result<TrendAnalysis> {
        // Implementation would analyze historical trends
        Ok(TrendAnalysis {
            performance_trend: TrendDirection::Stable,
            trend_strength: 0.3,
            trend_duration: Duration::from_secs(3600),
            seasonal_patterns: Vec::new(),
            change_points: Vec::new(),
            forecast_accuracy: 0.85,
        })
    }

    /// Plan capacity requirements
    async fn plan_capacity(
        _capacity_planner: &Arc<AsyncRwLock<CapacityPlanner>>,
        _metrics_history: &Arc<AsyncRwLock<VecDeque<PredictiveMetrics>>>,
        _config: &PredictiveAnalyticsConfig,
    ) -> Result<CapacityForecast> {
        // Implementation would forecast capacity needs
        Ok(CapacityForecast {
            current_utilization: 0.6,
            predicted_peak: CapacityPrediction {
                utilization: 0.8,
                timestamp: SystemTime::now(),
                confidence: 0.7,
            },
            time_to_capacity: Some(Duration::from_secs(7200)),
            scaling_recommendations: Vec::new(),
            resource_bottlenecks: Vec::new(),
        })
    }

    /// Get current metrics
    pub async fn get_current_metrics(&self) -> Result<Option<PredictiveMetrics>> {
        let history = self.metrics_history.read().await;
        Ok(history.back().cloned())
    }

    /// Get metrics history
    pub async fn get_metrics_history(&self, duration: Duration) -> Result<Vec<PredictiveMetrics>> {
        let history = self.metrics_history.read().await;
        let cutoff = SystemTime::now() - duration;

        Ok(history
            .iter()
            .filter(|m| m.timestamp >= cutoff)
            .cloned()
            .collect())
    }
}

/// Alert structure
#[derive(Debug, Clone)]
pub struct Alert {
    pub alert_type: AlertType,
    pub severity: AlertSeverity,
    pub message: String,
    pub timestamp: SystemTime,
    pub metadata: HashMap<String, String>,
}

/// Anomaly detection algorithms
#[derive(Debug)]
pub struct AnomalyDetector {
    #[allow(dead_code)]
    threshold: f64,
    #[allow(dead_code)]
    window_size: usize,
    #[allow(dead_code)]
    statistical_models: HashMap<String, StatisticalModel>,
}

impl AnomalyDetector {
    pub fn new(config: &PredictiveAnalyticsConfig) -> Self {
        Self {
            threshold: config.anomaly_threshold,
            window_size: 100,
            statistical_models: HashMap::new(),
        }
    }
}

/// Trend analysis algorithms
#[derive(Debug)]
pub struct TrendAnalyzer {
    #[allow(dead_code)]
    window_size: Duration,
    #[allow(dead_code)]
    seasonal_detection: bool,
    #[allow(dead_code)]
    change_point_detection: bool,
}

impl TrendAnalyzer {
    pub fn new(config: &PredictiveAnalyticsConfig) -> Self {
        Self {
            window_size: config.trend_window,
            seasonal_detection: true,
            change_point_detection: true,
        }
    }
}

/// Capacity planning algorithms
#[derive(Debug)]
pub struct CapacityPlanner {
    #[allow(dead_code)]
    prediction_horizon: Duration,
    #[allow(dead_code)]
    scaling_policies: Vec<ScalingPolicy>,
}

impl CapacityPlanner {
    pub fn new(config: &PredictiveAnalyticsConfig) -> Self {
        Self {
            prediction_horizon: config.prediction_window,
            scaling_policies: Vec::new(),
        }
    }
}

/// Alert management
#[derive(Debug)]
pub struct AlertManager {
    #[allow(dead_code)]
    config: AlertConfig,
    #[allow(dead_code)]
    active_alerts: HashMap<String, Alert>,
    #[allow(dead_code)]
    cooldown_timers: HashMap<String, SystemTime>,
}

impl AlertManager {
    pub fn new(config: AlertConfig) -> Self {
        Self {
            config,
            active_alerts: HashMap::new(),
            cooldown_timers: HashMap::new(),
        }
    }
}

/// Statistical model for anomaly detection
#[derive(Debug)]
pub struct StatisticalModel {
    #[allow(dead_code)]
    mean: f64,
    #[allow(dead_code)]
    std_dev: f64,
    #[allow(dead_code)]
    observations: VecDeque<f64>,
    #[allow(dead_code)]
    model_type: ModelType,
}

#[derive(Debug)]
pub enum ModelType {
    ZScore,
    IsolationForest,
    LSTM,
    MovingAverage,
}

/// Scaling policy
#[derive(Debug)]
pub struct ScalingPolicy {
    pub metric: String,
    pub threshold: f64,
    pub action: ScalingAction,
    pub cooldown: Duration,
}