voirs-sdk 0.1.0-rc.1

use super::*;
use serde_json::Value;
use std::collections::{HashMap, VecDeque};
use std::sync::atomic::{AtomicU32, AtomicU64, Ordering};
use std::time::{SystemTime, UNIX_EPOCH};
use tokio::sync::{Mutex, RwLock};
use tokio::time::{interval, Duration};
use uuid::Uuid;

/// Comprehensive telemetry provider for VoiRS analytics and monitoring
pub struct VoirsTelemetryProvider {
    config: TelemetryConfig,
    event_collector: Arc<EventCollector>,
    metrics_collector: Arc<MetricsCollector>,
    analytics_engine: Arc<AnalyticsEngine>,
    ab_testing_manager: Arc<ABTestingManager>,
    exporters: Vec<Arc<dyn TelemetryExporter>>,
}

struct EventCollector {
    event_buffer: Arc<Mutex<VecDeque<TelemetryEvent>>>,
    event_stats: Arc<EventStats>,
    sampling_controller: Arc<SamplingController>,
    batch_processor: Arc<BatchProcessor>,
}

struct EventStats {
    total_events: AtomicU64,
    events_by_type: Arc<RwLock<HashMap<String, AtomicU64>>>,
    events_per_minute: AtomicU64,
    dropped_events: AtomicU64,
    processing_errors: AtomicU64,
}

struct SamplingController {
    sampling_rules: Arc<RwLock<Vec<SamplingRule>>>,
    adaptive_sampling: bool,
    current_load: AtomicU32,
}

struct SamplingRule {
    event_type: String,
    sampling_rate: f32,
    condition: Option<SamplingCondition>,
    priority: u32,
}

#[derive(Debug, Clone)]
enum SamplingCondition {
    UserProperty(String, Value),
    EventProperty(String, Value),
    SessionProperty(String, Value),
    Custom(String),
}

struct BatchProcessor {
    batch_buffer: Arc<Mutex<Vec<TelemetryEvent>>>,
    batch_size: usize,
    flush_interval: Duration,
    compression_enabled: bool,
}

struct MetricsCollector {
    metrics_buffer: Arc<Mutex<VecDeque<Metric>>>,
    aggregators: Arc<RwLock<HashMap<String, MetricAggregator>>>,
    time_series_store: Arc<TimeSeriesStore>,
    alert_manager: Arc<AlertManager>,
}

struct MetricAggregator {
    metric_name: String,
    aggregation_type: AggregationType,
    window_size: Duration,
    values: VecDeque<TimestampedValue>,
    current_value: f64,
}

struct TimestampedValue {
    timestamp: DateTime<Utc>,
    value: f64,
    tags: HashMap<String, String>,
}

struct TimeSeriesStore {
    series: Arc<RwLock<HashMap<String, TimeSeries>>>,
    retention_policy: RetentionPolicy,
    compression_settings: CompressionSettings,
}

struct TimeSeries {
    name: String,
    data_points: VecDeque<DataPoint>,
    metadata: TimeSeriesMetadata,
}

struct TimeSeriesMetadata {
    created_at: DateTime<Utc>,
    last_updated: DateTime<Utc>,
    sample_count: u64,
    min_value: f64,
    max_value: f64,
    tags: HashMap<String, String>,
}

struct RetentionPolicy {
    max_age: Duration,
    max_points: usize,
    downsampling_rules: Vec<DownsamplingRule>,
}

struct DownsamplingRule {
    age_threshold: Duration,
    aggregation: AggregationType,
    interval: Duration,
}

struct CompressionSettings {
    enabled: bool,
    algorithm: CompressionAlgorithm,
    compression_level: u32,
}

#[derive(Debug, Clone)]
enum CompressionAlgorithm {
    Gzip,
    Zstd,
    Snappy,
}

struct AlertManager {
    alert_rules: Arc<RwLock<Vec<AlertRule>>>,
    alert_history: Arc<Mutex<VecDeque<Alert>>>,
    notification_channels: Vec<Arc<dyn NotificationChannel>>,
}

struct AlertRule {
    id: String,
    name: String,
    condition: AlertCondition,
    threshold: f64,
    duration: Duration,
    severity: AlertSeverity,
    enabled: bool,
    tags: HashMap<String, String>,
}

#[derive(Debug, Clone)]
enum AlertCondition {
    MetricAbove(String),
    MetricBelow(String),
    MetricMissing(String),
    EventRateHigh(String, f64),
    ErrorRateHigh(f64),
    Custom(String),
}

#[derive(Debug, Clone)]
enum AlertSeverity {
    Info,
    Warning,
    Error,
    Critical,
}

struct Alert {
    id: String,
    rule_id: String,
    triggered_at: DateTime<Utc>,
    resolved_at: Option<DateTime<Utc>>,
    severity: AlertSeverity,
    message: String,
    tags: HashMap<String, String>,
}

trait NotificationChannel: Send + Sync {
    fn send_alert(&self, alert: &Alert) -> Result<()>;
    fn get_channel_name(&self) -> &str;
}

struct AnalyticsEngine {
    query_processor: Arc<QueryProcessor>,
    report_generator: Arc<ReportGenerator>,
    dashboard_manager: Arc<DashboardManager>,
    real_time_processor: Arc<RealTimeProcessor>,
}

struct QueryProcessor {
    query_cache: Arc<RwLock<HashMap<String, CachedQuery>>>,
    query_optimizer: Arc<QueryOptimizer>,
    execution_engine: Arc<QueryExecutionEngine>,
}

struct CachedQuery {
    query_hash: String,
    result: AnalyticsResult,
    cached_at: DateTime<Utc>,
    ttl: Duration,
}

struct QueryOptimizer {
    optimization_rules: Vec<OptimizationRule>,
    statistics: QueryStatistics,
}

struct OptimizationRule {
    rule_type: OptimizationType,
    condition: String,
    transformation: String,
}

#[derive(Debug, Clone)]
enum OptimizationType {
    IndexUsage,
    Aggregation,
    Filtering,
    Projection,
}

struct QueryStatistics {
    query_count: AtomicU64,
    average_execution_time: f64,
    cache_hit_rate: f64,
    most_expensive_queries: Vec<String>,
}

struct QueryExecutionEngine {
    executors: Vec<Arc<dyn QueryExecutor>>,
    execution_stats: ExecutionStats,
}

trait QueryExecutor: Send + Sync {
    fn can_execute(&self, query: &AnalyticsQuery) -> bool;
    fn execute(&self, query: &AnalyticsQuery) -> Result<AnalyticsResult>;
    fn get_executor_name(&self) -> &str;
}

struct ExecutionStats {
    total_queries: AtomicU64,
    successful_queries: AtomicU64,
    failed_queries: AtomicU64,
    average_execution_time: f64,
}

struct ReportGenerator {
    report_templates: Arc<RwLock<HashMap<String, ReportTemplate>>>,
    scheduled_reports: Arc<RwLock<Vec<ScheduledReport>>>,
    report_storage: Arc<dyn ReportStorage>,
}

struct ReportTemplate {
    id: String,
    name: String,
    description: String,
    queries: Vec<AnalyticsQuery>,
    format: ReportFormat,
    parameters: HashMap<String, Value>,
}

#[derive(Debug, Clone)]
enum ReportFormat {
    Json,
    Csv,
    Html,
    Pdf,
}

struct ScheduledReport {
    id: String,
    template_id: String,
    schedule: ReportSchedule,
    recipients: Vec<String>,
    enabled: bool,
    last_run: Option<DateTime<Utc>>,
    next_run: DateTime<Utc>,
}

#[derive(Debug, Clone)]
enum ReportSchedule {
    Hourly,
    Daily,
    Weekly,
    Monthly,
    Custom(String), // Cron expression
}

trait ReportStorage: Send + Sync {
    fn store_report(&self, report: &GeneratedReport) -> Result<String>;
    fn get_report(&self, report_id: &str) -> Result<GeneratedReport>;
    fn list_reports(&self, filters: &ReportFilters) -> Result<Vec<ReportMetadata>>;
    fn delete_report(&self, report_id: &str) -> Result<()>;
}

struct GeneratedReport {
    id: String,
    template_id: String,
    generated_at: DateTime<Utc>,
    format: ReportFormat,
    data: Vec<u8>,
    metadata: ReportMetadata,
}

struct ReportMetadata {
    id: String,
    name: String,
    generated_at: DateTime<Utc>,
    size_bytes: u64,
    tags: HashMap<String, String>,
}

struct ReportFilters {
    start_date: Option<DateTime<Utc>>,
    end_date: Option<DateTime<Utc>>,
    tags: HashMap<String, String>,
}

struct DashboardManager {
    dashboards: Arc<RwLock<HashMap<String, Dashboard>>>,
    dashboard_storage: Arc<dyn DashboardStorage>,
    real_time_updates: Arc<RealTimeUpdater>,
}

/// Telemetry dashboard configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Dashboard {
    /// Unique dashboard identifier
    pub id: String,
    /// Dashboard name
    pub name: String,
    /// Dashboard description
    pub description: String,
    /// List of widgets in the dashboard
    pub widgets: Vec<Widget>,
    /// Dashboard layout configuration
    pub layout: DashboardLayout,
    /// Dashboard access permissions
    pub permissions: DashboardPermissions,
    /// Dashboard creation timestamp
    pub created_at: DateTime<Utc>,
    /// Dashboard last update timestamp
    pub updated_at: DateTime<Utc>,
}

/// Dashboard widget configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Widget {
    /// Widget identifier
    pub id: String,
    /// Type of widget
    pub widget_type: WidgetType,
    /// Widget title
    pub title: String,
    /// Data query for the widget
    pub query: AnalyticsQuery,
    /// Visualization settings
    pub visualization: VisualizationSettings,
    /// Widget position in the dashboard
    pub position: WidgetPosition,
    /// Auto-refresh interval
    pub refresh_interval: Option<Duration>,
}

/// Type of dashboard widget
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum WidgetType {
    /// Line chart visualization
    LineChart,
    /// Bar chart visualization
    BarChart,
    /// Pie chart visualization
    PieChart,
    /// Counter/metric display
    Counter,
    /// Table view
    Table,
    /// Heatmap visualization
    Heatmap,
    /// Gauge/dial display
    Gauge,
}

/// Visualization settings for widgets
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VisualizationSettings {
    /// Color scheme for the visualization
    pub color_scheme: String,
    /// Whether to show legend
    pub show_legend: bool,
    /// Whether to show grid
    pub show_grid: bool,
    /// Whether animation is enabled
    pub animation_enabled: bool,
    /// Custom visualization settings
    pub custom_settings: HashMap<String, Value>,
}

/// Position and size of a widget in the dashboard
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WidgetPosition {
    /// X coordinate
    pub x: u32,
    /// Y coordinate
    pub y: u32,
    /// Widget width
    pub width: u32,
    /// Widget height
    pub height: u32,
}

/// Dashboard layout configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DashboardLayout {
    /// Grid size (columns, rows)
    pub grid_size: (u32, u32),
    /// Whether layout is responsive
    pub responsive: bool,
    /// Theme name
    pub theme: String,
}

/// Dashboard access permissions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DashboardPermissions {
    /// List of users with view permission
    pub viewers: Vec<String>,
    /// List of users with edit permission
    pub editors: Vec<String>,
    /// Whether dashboard is publicly accessible
    pub public: bool,
}

trait DashboardStorage: Send + Sync {
    fn save_dashboard(&self, dashboard: &Dashboard) -> Result<()>;
    fn load_dashboard(&self, dashboard_id: &str) -> Result<Dashboard>;
    fn list_dashboards(&self, user_id: &str) -> Result<Vec<DashboardMetadata>>;
    fn delete_dashboard(&self, dashboard_id: &str) -> Result<()>;
}

struct DashboardMetadata {
    id: String,
    name: String,
    description: String,
    created_at: DateTime<Utc>,
    updated_at: DateTime<Utc>,
    owner: String,
}

struct RealTimeUpdater {
    active_subscriptions: Arc<RwLock<HashMap<String, Subscription>>>,
    update_dispatcher: Arc<UpdateDispatcher>,
}

struct Subscription {
    id: String,
    dashboard_id: String,
    widget_id: String,
    user_id: String,
    last_update: DateTime<Utc>,
}

struct UpdateDispatcher {
    // WebSocket or Server-Sent Events implementation
    // For now, we'll keep it simple
}

struct RealTimeProcessor {
    stream_processors: Vec<Arc<dyn StreamProcessor>>,
    anomaly_detector: Arc<AnomalyDetector>,
    trend_analyzer: Arc<TrendAnalyzer>,
}

trait StreamProcessor: Send + Sync {
    fn process_event(&self, event: &TelemetryEvent) -> Result<Vec<ProcessedEvent>>;
    fn process_metric(&self, metric: &Metric) -> Result<Vec<ProcessedMetric>>;
    fn get_processor_name(&self) -> &str;
}

struct ProcessedEvent {
    original_event: TelemetryEvent,
    derived_metrics: Vec<Metric>,
    anomaly_score: Option<f64>,
    tags: HashMap<String, String>,
}

struct ProcessedMetric {
    original_metric: Metric,
    trend_direction: TrendDirection,
    velocity: f64,
    acceleration: f64,
    anomaly_score: Option<f64>,
}

#[derive(Debug, Clone)]
enum TrendDirection {
    Increasing,
    Decreasing,
    Stable,
    Volatile,
}

struct AnomalyDetector {
    algorithms: Vec<Arc<dyn AnomalyAlgorithm>>,
    detection_config: AnomalyDetectionConfig,
    anomaly_history: Arc<Mutex<VecDeque<Anomaly>>>,
}

trait AnomalyAlgorithm: Send + Sync {
    fn detect(&self, data: &[DataPoint]) -> Result<Vec<Anomaly>>;
    fn get_algorithm_name(&self) -> &str;
}

struct AnomalyDetectionConfig {
    sensitivity: f64,
    min_data_points: usize,
    window_size: Duration,
    enabled_algorithms: Vec<String>,
}

struct Anomaly {
    id: String,
    detected_at: DateTime<Utc>,
    metric_name: String,
    value: f64,
    expected_value: f64,
    confidence: f64,
    severity: AnomalySeverity,
    algorithm: String,
}

#[derive(Debug, Clone)]
enum AnomalySeverity {
    Low,
    Medium,
    High,
    Critical,
}

struct TrendAnalyzer {
    trend_models: HashMap<String, TrendModel>,
    forecasting_enabled: bool,
    forecast_horizon: Duration,
}

struct TrendModel {
    metric_name: String,
    model_type: TrendModelType,
    parameters: HashMap<String, f64>,
    accuracy: f64,
    last_trained: DateTime<Utc>,
}

#[derive(Debug, Clone)]
enum TrendModelType {
    Linear,
    Exponential,
    Seasonal,
    Arima,
}

struct ABTestingManager {
    experiments: Arc<RwLock<HashMap<String, Experiment>>>,
    participant_tracker: Arc<ParticipantTracker>,
    statistical_engine: Arc<StatisticalEngine>,
}

/// A/B testing experiment configuration
pub struct Experiment {
    /// Unique experiment identifier
    pub id: String,
    /// Human-readable experiment name
    pub name: String,
    /// Experiment description
    pub description: String,
    /// Current status of the experiment
    pub status: ExperimentStatus,
    /// List of experiment variants
    pub variants: Vec<Variant>,
    /// Strategy for allocating users to variants
    pub allocation: AllocationStrategy,
    /// Experiment start date
    pub start_date: DateTime<Utc>,
    /// Optional experiment end date
    pub end_date: Option<DateTime<Utc>>,
    /// Metrics used to measure success
    pub success_metrics: Vec<String>,
    /// Required sample size
    pub sample_size: u32,
    /// Statistical confidence level
    pub confidence_level: f64,
}

/// Status of an A/B testing experiment
#[derive(Debug, Clone)]
pub enum ExperimentStatus {
    /// Experiment is being prepared
    Draft,
    /// Experiment is currently running
    Running,
    /// Experiment has been temporarily paused
    Paused,
    /// Experiment has finished
    Completed,
    /// Experiment was cancelled
    Cancelled,
}

/// A variant in an A/B test
#[derive(Debug, Clone)]
pub struct Variant {
    /// Variant identifier
    pub id: String,
    /// Variant name
    pub name: String,
    /// Variant description
    pub description: String,
    /// Percentage of users allocated to this variant
    pub allocation_percentage: f32,
    /// Variant-specific configuration
    pub configuration: HashMap<String, Value>,
}

/// Strategy for allocating users to experiment variants
#[derive(Debug, Clone)]
pub enum AllocationStrategy {
    /// Random allocation
    Random,
    /// Allocate based on user property
    UserProperty(String),
    /// Deterministic allocation based on hash
    Deterministic(String),
}

struct ParticipantTracker {
    participants: Arc<RwLock<HashMap<String, ParticipantInfo>>>,
    assignment_cache: Arc<RwLock<HashMap<String, VariantAssignment>>>,
}

struct ParticipantInfo {
    user_id: String,
    joined_at: DateTime<Utc>,
    experiments: Vec<String>,
    properties: HashMap<String, Value>,
}

struct VariantAssignment {
    experiment_id: String,
    variant_id: String,
    assigned_at: DateTime<Utc>,
    sticky: bool,
}

struct StatisticalEngine {
    test_types: Vec<StatisticalTest>,
    significance_calculator: Arc<SignificanceCalculator>,
}

#[derive(Debug, Clone)]
enum StatisticalTest {
    TTest,
    ChiSquare,
    MannWhitney,
    Bayesian,
}

struct SignificanceCalculator {
    // Statistical calculation implementations
}

trait TelemetryExporter: Send + Sync {
    fn export_events(&self, events: &[TelemetryEvent]) -> Result<()>;
    fn export_metrics(&self, metrics: &[Metric]) -> Result<()>;
    fn get_exporter_name(&self) -> &str;
}

impl VoirsTelemetryProvider {
    pub async fn new(config: TelemetryConfig) -> Result<Self> {
        let event_collector = Arc::new(EventCollector::new(&config).await?);
        let metrics_collector = Arc::new(MetricsCollector::new(&config).await?);
        let analytics_engine = Arc::new(AnalyticsEngine::new().await?);
        let ab_testing_manager = Arc::new(ABTestingManager::new().await?);

        let provider = Self {
            config: config.clone(),
            event_collector,
            metrics_collector,
            analytics_engine,
            ab_testing_manager,
            exporters: Vec::new(),
        };

        // Start background tasks
        provider.start_batch_processing().await?;
        provider.start_metrics_aggregation().await?;
        provider.start_analytics_processing().await?;

        Ok(provider)
    }

    async fn start_batch_processing(&self) -> Result<()> {
        let event_collector = self.event_collector.clone();
        let exporters = self.exporters.clone();
        let flush_interval = Duration::from_secs(self.config.flush_interval_seconds as u64);

        tokio::spawn(async move {
            let mut interval = interval(flush_interval);

            loop {
                interval.tick().await;
                let _ = Self::process_event_batch(event_collector.clone(), exporters.clone()).await;
            }
        });

        Ok(())
    }

    async fn process_event_batch(
        event_collector: Arc<EventCollector>,
        exporters: Vec<Arc<dyn TelemetryExporter>>,
    ) -> Result<()> {
        let events = event_collector.get_batch().await;

        for exporter in &exporters {
            if let Err(e) = exporter.export_events(&events) {
                tracing::error!(
                    "Failed to export events to {}: {}",
                    exporter.get_exporter_name(),
                    e
                );
            }
        }

        Ok(())
    }

    async fn start_metrics_aggregation(&self) -> Result<()> {
        let metrics_collector = self.metrics_collector.clone();

        tokio::spawn(async move {
            let mut interval = interval(Duration::from_secs(60)); // Aggregate every minute

            loop {
                interval.tick().await;
                let _ = metrics_collector.aggregate_metrics().await;
            }
        });

        Ok(())
    }

    async fn start_analytics_processing(&self) -> Result<()> {
        let analytics_engine = self.analytics_engine.clone();

        tokio::spawn(async move {
            let mut interval = interval(Duration::from_secs(300)); // Process every 5 minutes

            loop {
                interval.tick().await;
                let _ = analytics_engine.process_real_time_analytics().await;
            }
        });

        Ok(())
    }

    pub async fn create_experiment(&self, experiment: Experiment) -> Result<String> {
        let mut experiments = self.ab_testing_manager.experiments.write().await;
        let experiment_id = experiment.id.clone();
        experiments.insert(experiment_id.clone(), experiment);
        Ok(experiment_id)
    }

    pub async fn get_variant_for_user(
        &self,
        experiment_id: &str,
        user_id: &str,
    ) -> Result<Option<String>> {
        self.ab_testing_manager
            .get_variant_assignment(experiment_id, user_id)
            .await
    }

    pub async fn record_conversion(
        &self,
        experiment_id: &str,
        user_id: &str,
        metric_name: &str,
        value: f64,
    ) -> Result<()> {
        let event = TelemetryEvent {
            id: Uuid::new_v4().to_string(),
            event_type: "conversion".to_string(),
            timestamp: Utc::now(),
            user_id: Some(user_id.to_string()),
            session_id: None,
            properties: [
                (
                    "experiment_id".to_string(),
                    serde_json::Value::String(experiment_id.to_string()),
                ),
                (
                    "metric_name".to_string(),
                    serde_json::Value::String(metric_name.to_string()),
                ),
                (
                    "value".to_string(),
                    serde_json::Value::Number(
                        serde_json::Number::from_f64(value).expect("value should be present"),
                    ),
                ),
            ]
            .iter()
            .cloned()
            .collect(),
        };

        self.record_event(event).await
    }

    pub async fn create_dashboard(&self, dashboard: Dashboard) -> Result<String> {
        self.analytics_engine
            .dashboard_manager
            .create_dashboard(dashboard)
            .await
    }

    pub async fn get_dashboard_data(&self, dashboard_id: &str) -> Result<DashboardData> {
        self.analytics_engine
            .dashboard_manager
            .get_dashboard_data(dashboard_id)
            .await
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DashboardData {
    pub dashboard: Dashboard,
    pub widget_data: HashMap<String, WidgetData>,
    pub last_updated: DateTime<Utc>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WidgetData {
    pub data: AnalyticsResult,
    pub cached: bool,
    pub cache_age: Duration,
}

impl EventCollector {
    async fn new(config: &TelemetryConfig) -> Result<Self> {
        Ok(Self {
            event_buffer: Arc::new(Mutex::new(VecDeque::new())),
            event_stats: Arc::new(EventStats::new()),
            sampling_controller: Arc::new(SamplingController::new(config.sampling_rate)),
            batch_processor: Arc::new(BatchProcessor::new(
                config.batch_size,
                Duration::from_secs(config.flush_interval_seconds as u64),
            )),
        })
    }

    async fn get_batch(&self) -> Vec<TelemetryEvent> {
        let mut buffer = self.event_buffer.lock().await;
        let batch_size = self.batch_processor.batch_size;
        let mut batch = Vec::with_capacity(batch_size);

        for _ in 0..batch_size {
            if let Some(event) = buffer.pop_front() {
                batch.push(event);
            } else {
                break;
            }
        }

        batch
    }
}

impl EventStats {
    fn new() -> Self {
        Self {
            total_events: AtomicU64::new(0),
            events_by_type: Arc::new(RwLock::new(HashMap::new())),
            events_per_minute: AtomicU64::new(0),
            dropped_events: AtomicU64::new(0),
            processing_errors: AtomicU64::new(0),
        }
    }
}

impl SamplingController {
    fn new(default_rate: f32) -> Self {
        Self {
            sampling_rules: Arc::new(RwLock::new(vec![SamplingRule {
                event_type: "*".to_string(),
                sampling_rate: default_rate,
                condition: None,
                priority: 0,
            }])),
            adaptive_sampling: true,
            current_load: AtomicU32::new(0),
        }
    }
}

impl BatchProcessor {
    fn new(batch_size: u32, flush_interval: Duration) -> Self {
        Self {
            batch_buffer: Arc::new(Mutex::new(Vec::new())),
            batch_size: batch_size as usize,
            flush_interval,
            compression_enabled: true,
        }
    }
}

impl MetricsCollector {
    async fn new(_config: &TelemetryConfig) -> Result<Self> {
        Ok(Self {
            metrics_buffer: Arc::new(Mutex::new(VecDeque::new())),
            aggregators: Arc::new(RwLock::new(HashMap::new())),
            time_series_store: Arc::new(TimeSeriesStore::new()),
            alert_manager: Arc::new(AlertManager::new()),
        })
    }

    async fn aggregate_metrics(&self) -> Result<()> {
        let mut aggregators = self.aggregators.write().await;
        let now = Utc::now();

        for (_, aggregator) in aggregators.iter_mut() {
            aggregator.aggregate(now);
        }

        Ok(())
    }
}

impl MetricAggregator {
    fn aggregate(&mut self, now: DateTime<Utc>) {
        // Remove old values outside the window
        let cutoff = now - self.window_size;
        self.values.retain(|v| v.timestamp > cutoff);

        // Calculate aggregated value
        if !self.values.is_empty() {
            self.current_value = match self.aggregation_type {
                AggregationType::Sum => self.values.iter().map(|v| v.value).sum(),
                AggregationType::Average => {
                    self.values.iter().map(|v| v.value).sum::<f64>() / self.values.len() as f64
                }
                AggregationType::Count => self.values.len() as f64,
                AggregationType::Min => self
                    .values
                    .iter()
                    .map(|v| v.value)
                    .fold(f64::INFINITY, f64::min),
                AggregationType::Max => self
                    .values
                    .iter()
                    .map(|v| v.value)
                    .fold(f64::NEG_INFINITY, f64::max),
                AggregationType::Percentile(p) => {
                    let mut sorted_values: Vec<f64> = self.values.iter().map(|v| v.value).collect();
                    sorted_values
                        .sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
                    let index = ((p / 100.0) * (sorted_values.len() - 1) as f32) as usize;
                    sorted_values.get(index).copied().unwrap_or(0.0)
                }
            };
        }
    }
}

impl TimeSeriesStore {
    fn new() -> Self {
        Self {
            series: Arc::new(RwLock::new(HashMap::new())),
            retention_policy: RetentionPolicy {
                max_age: Duration::from_secs(30 * 24 * 3600), // 30 days
                max_points: 100_000,
                downsampling_rules: Vec::new(),
            },
            compression_settings: CompressionSettings {
                enabled: true,
                algorithm: CompressionAlgorithm::Gzip,
                compression_level: 6,
            },
        }
    }
}

impl AlertManager {
    fn new() -> Self {
        Self {
            alert_rules: Arc::new(RwLock::new(Vec::new())),
            alert_history: Arc::new(Mutex::new(VecDeque::new())),
            notification_channels: Vec::new(),
        }
    }
}

impl AnalyticsEngine {
    async fn new() -> Result<Self> {
        Ok(Self {
            query_processor: Arc::new(QueryProcessor::new()),
            report_generator: Arc::new(ReportGenerator::new()),
            dashboard_manager: Arc::new(DashboardManager::new()),
            real_time_processor: Arc::new(RealTimeProcessor::new()),
        })
    }

    async fn process_real_time_analytics(&self) -> Result<()> {
        // Process real-time analytics
        tracing::debug!("Processing real-time analytics");
        Ok(())
    }
}

impl QueryProcessor {
    fn new() -> Self {
        Self {
            query_cache: Arc::new(RwLock::new(HashMap::new())),
            query_optimizer: Arc::new(QueryOptimizer::new()),
            execution_engine: Arc::new(QueryExecutionEngine::new()),
        }
    }
}

impl QueryOptimizer {
    fn new() -> Self {
        Self {
            optimization_rules: Vec::new(),
            statistics: QueryStatistics {
                query_count: AtomicU64::new(0),
                average_execution_time: 0.0,
                cache_hit_rate: 0.0,
                most_expensive_queries: Vec::new(),
            },
        }
    }
}

impl QueryExecutionEngine {
    fn new() -> Self {
        Self {
            executors: Vec::new(),
            execution_stats: ExecutionStats {
                total_queries: AtomicU64::new(0),
                successful_queries: AtomicU64::new(0),
                failed_queries: AtomicU64::new(0),
                average_execution_time: 0.0,
            },
        }
    }
}

impl ReportGenerator {
    fn new() -> Self {
        Self {
            report_templates: Arc::new(RwLock::new(HashMap::new())),
            scheduled_reports: Arc::new(RwLock::new(Vec::new())),
            report_storage: Arc::new(LocalReportStorage::new()),
        }
    }
}

impl DashboardManager {
    fn new() -> Self {
        Self {
            dashboards: Arc::new(RwLock::new(HashMap::new())),
            dashboard_storage: Arc::new(LocalDashboardStorage::new()),
            real_time_updates: Arc::new(RealTimeUpdater::new()),
        }
    }

    async fn create_dashboard(&self, dashboard: Dashboard) -> Result<String> {
        let dashboard_id = dashboard.id.clone();
        let mut dashboards = self.dashboards.write().await;
        dashboards.insert(dashboard_id.clone(), dashboard);
        Ok(dashboard_id)
    }

    async fn get_dashboard_data(&self, dashboard_id: &str) -> Result<DashboardData> {
        let dashboards = self.dashboards.read().await;
        if let Some(dashboard) = dashboards.get(dashboard_id) {
            let mut widget_data = HashMap::new();

            // Populate widget data for each widget in the dashboard
            for widget in &dashboard.widgets {
                let data = self.get_widget_data(widget).await?;
                widget_data.insert(widget.id.clone(), data);
            }

            Ok(DashboardData {
                dashboard: dashboard.clone(),
                widget_data,
                last_updated: Utc::now(),
            })
        } else {
            Err(VoirsError::config_error(format!(
                "Dashboard {} not found",
                dashboard_id
            )))
        }
    }

    async fn get_widget_data(&self, _widget: &Widget) -> Result<WidgetData> {
        // Mock implementation - return dummy widget data
        // Create a mock AnalyticsSummary
        let mock_summary = super::AnalyticsSummary {
            total_points: 0,
            min_value: 0.0,
            max_value: 0.0,
            average_value: 0.0,
            sum_value: 0.0,
        };

        let mock_result = AnalyticsResult {
            data_points: vec![],
            summary: mock_summary,
        };

        Ok(WidgetData {
            data: mock_result,
            cached: false,
            cache_age: Duration::from_secs(0),
        })
    }

    fn parse_aggregation_type(&self, value: Option<&serde_json::Value>) -> AggregationType {
        match value {
            Some(serde_json::Value::String(s)) => match s.as_str() {
                "sum" => AggregationType::Sum,
                "average" => AggregationType::Average,
                "count" => AggregationType::Count,
                "min" => AggregationType::Min,
                "max" => AggregationType::Max,
                _ => AggregationType::Count,
            },
            _ => AggregationType::Count,
        }
    }
}

impl RealTimeUpdater {
    fn new() -> Self {
        Self {
            active_subscriptions: Arc::new(RwLock::new(HashMap::new())),
            update_dispatcher: Arc::new(UpdateDispatcher {}),
        }
    }
}

impl RealTimeProcessor {
    fn new() -> Self {
        Self {
            stream_processors: Vec::new(),
            anomaly_detector: Arc::new(AnomalyDetector::new()),
            trend_analyzer: Arc::new(TrendAnalyzer::new()),
        }
    }
}

impl AnomalyDetector {
    fn new() -> Self {
        Self {
            algorithms: Vec::new(),
            detection_config: AnomalyDetectionConfig {
                sensitivity: 0.8,
                min_data_points: 10,
                window_size: Duration::from_secs(3600), // 1 hour
                enabled_algorithms: vec!["statistical".to_string()],
            },
            anomaly_history: Arc::new(Mutex::new(VecDeque::new())),
        }
    }
}

impl TrendAnalyzer {
    fn new() -> Self {
        Self {
            trend_models: HashMap::new(),
            forecasting_enabled: false,
            forecast_horizon: Duration::from_secs(24 * 3600), // 1 day
        }
    }
}

impl ABTestingManager {
    async fn new() -> Result<Self> {
        Ok(Self {
            experiments: Arc::new(RwLock::new(HashMap::new())),
            participant_tracker: Arc::new(ParticipantTracker::new()),
            statistical_engine: Arc::new(StatisticalEngine::new()),
        })
    }

    async fn get_variant_assignment(
        &self,
        experiment_id: &str,
        user_id: &str,
    ) -> Result<Option<String>> {
        // Check if user is already assigned
        let assignments = self.participant_tracker.assignment_cache.read().await;
        let assignment_key = format!("{}:{}", experiment_id, user_id);

        if let Some(assignment) = assignments.get(&assignment_key) {
            return Ok(Some(assignment.variant_id.clone()));
        }

        // Assign user to a variant
        let experiments = self.experiments.read().await;
        if let Some(experiment) = experiments.get(experiment_id) {
            if matches!(experiment.status, ExperimentStatus::Running) {
                // Simple random assignment for now
                let variant_index = user_id.len() % experiment.variants.len();
                if let Some(variant) = experiment.variants.get(variant_index) {
                    return Ok(Some(variant.id.clone()));
                }
            }
        }

        Ok(None)
    }
}

impl ParticipantTracker {
    fn new() -> Self {
        Self {
            participants: Arc::new(RwLock::new(HashMap::new())),
            assignment_cache: Arc::new(RwLock::new(HashMap::new())),
        }
    }
}

impl StatisticalEngine {
    fn new() -> Self {
        Self {
            test_types: vec![StatisticalTest::TTest, StatisticalTest::ChiSquare],
            significance_calculator: Arc::new(SignificanceCalculator {}),
        }
    }
}

// Storage implementations
struct LocalReportStorage;
impl LocalReportStorage {
    fn new() -> Self {
        Self
    }
}

impl ReportStorage for LocalReportStorage {
    fn store_report(&self, _report: &GeneratedReport) -> Result<String> {
        Ok(Uuid::new_v4().to_string())
    }

    fn get_report(&self, _report_id: &str) -> Result<GeneratedReport> {
        Err(VoirsError::config_error("Report not found".to_string()))
    }

    fn list_reports(&self, _filters: &ReportFilters) -> Result<Vec<ReportMetadata>> {
        Ok(Vec::new())
    }

    fn delete_report(&self, _report_id: &str) -> Result<()> {
        Ok(())
    }
}

struct LocalDashboardStorage;
impl LocalDashboardStorage {
    fn new() -> Self {
        Self
    }
}

impl DashboardStorage for LocalDashboardStorage {
    fn save_dashboard(&self, _dashboard: &Dashboard) -> Result<()> {
        Ok(())
    }

    fn load_dashboard(&self, _dashboard_id: &str) -> Result<Dashboard> {
        Err(VoirsError::config_error("Dashboard not found".to_string()))
    }

    fn list_dashboards(&self, _user_id: &str) -> Result<Vec<DashboardMetadata>> {
        Ok(Vec::new())
    }

    fn delete_dashboard(&self, _dashboard_id: &str) -> Result<()> {
        Ok(())
    }
}

#[async_trait::async_trait]
impl TelemetryProvider for VoirsTelemetryProvider {
    async fn record_event(&self, event: TelemetryEvent) -> Result<()> {
        // Apply sampling
        if self
            .event_collector
            .sampling_controller
            .should_sample(&event)
            .await
        {
            let mut buffer = self.event_collector.event_buffer.lock().await;
            buffer.push_back(event);

            // Update stats
            self.event_collector
                .event_stats
                .total_events
                .fetch_add(1, Ordering::Relaxed);
        } else {
            self.event_collector
                .event_stats
                .dropped_events
                .fetch_add(1, Ordering::Relaxed);
        }

        Ok(())
    }

    async fn record_metric(&self, metric: Metric) -> Result<()> {
        let mut buffer = self.metrics_collector.metrics_buffer.lock().await;
        buffer.push_back(metric);
        Ok(())
    }

    async fn flush(&self) -> Result<()> {
        // Flush event buffer
        let events = self.event_collector.get_batch().await;
        for exporter in &self.exporters {
            exporter.export_events(&events)?;
        }

        // Flush metrics buffer
        let mut buffer = self.metrics_collector.metrics_buffer.lock().await;
        let metrics: Vec<Metric> = buffer.drain(..).collect();
        for exporter in &self.exporters {
            exporter.export_metrics(&metrics)?;
        }

        Ok(())
    }

    async fn get_analytics(&self, query: AnalyticsQuery) -> Result<AnalyticsResult> {
        self.analytics_engine
            .query_processor
            .execute_query(query)
            .await
    }
}

impl QueryProcessor {
    async fn execute_query(&self, query: AnalyticsQuery) -> Result<AnalyticsResult> {
        // Implement comprehensive query execution based on metric name and aggregation
        let data_points = self.generate_mock_data_points(&query).await;
        let summary = self.calculate_summary(&data_points, &query.aggregation);

        Ok(AnalyticsResult {
            data_points,
            summary,
        })
    }

    async fn generate_mock_data_points(&self, query: &AnalyticsQuery) -> Vec<DataPoint> {
        let mut data_points = Vec::new();
        let duration = query.end_time - query.start_time;
        let interval_hours = duration.num_hours().max(1);

        // Generate realistic mock data based on metric type
        for i in 0..interval_hours {
            let timestamp = query.start_time + chrono::Duration::hours(i);
            let value = match query.metric_name.as_str() {
                "events" => self.generate_event_count(i),
                "cpu_usage" => self.generate_cpu_usage(i),
                "memory_usage" => self.generate_memory_usage(i),
                "response_time" => self.generate_response_time(i),
                "error_rate" => self.generate_error_rate(i),
                "throughput" => self.generate_throughput(i),
                _ => (i as f64 * 10.0) + (i as f64 % 7.0) * 5.0, // Default pattern
            };

            data_points.push(DataPoint {
                timestamp,
                value,
                dimensions: self.generate_tags(&query.group_by, i),
            });
        }

        data_points
    }

    fn generate_event_count(&self, hour: i64) -> f64 {
        // Simulate daily traffic pattern with peak during business hours
        let base_count = 100.0;
        let peak_multiplier = if hour % 24 >= 9 && hour % 24 <= 17 {
            3.0
        } else {
            1.0
        };
        let random_factor = 0.8 + (hour % 5) as f64 * 0.1; // Add some variance
        base_count * peak_multiplier * random_factor
    }

    fn generate_cpu_usage(&self, hour: i64) -> f64 {
        // Simulate CPU usage with some fluctuation
        let base_usage = 45.0;
        let variation = ((hour as f64 * 0.3).sin() * 15.0) + ((hour % 3) as f64 * 5.0);
        (base_usage + variation).clamp(10.0, 95.0)
    }

    fn generate_memory_usage(&self, hour: i64) -> f64 {
        // Simulate memory usage with gradual increase and occasional drops
        let base_usage = 60.0;
        let trend = (hour as f64 * 0.5) % 20.0; // Gradual increase with resets
        let variation = (hour as f64 * 0.2).cos() * 8.0;
        (base_usage + trend + variation).clamp(30.0, 90.0)
    }

    fn generate_response_time(&self, hour: i64) -> f64 {
        // Simulate response time in milliseconds
        let base_time = 150.0;
        let peak_delay = if hour % 24 >= 9 && hour % 24 <= 17 {
            50.0
        } else {
            0.0
        };
        let variation = (hour as f64 * 0.4).sin().abs() * 30.0;
        base_time + peak_delay + variation
    }

    fn generate_error_rate(&self, hour: i64) -> f64 {
        // Simulate error rate as percentage
        let base_rate = 0.5;
        let spike = if hour % 13 == 0 { 2.0 } else { 0.0 }; // Occasional spikes
        let variation = (hour as f64 * 0.1).sin().abs() * 0.3;
        (base_rate + spike + variation).clamp(0.0, 5.0)
    }

    fn generate_throughput(&self, hour: i64) -> f64 {
        // Simulate requests per second
        let base_throughput = 50.0;
        let business_hours_boost = if hour % 24 >= 9 && hour % 24 <= 17 {
            30.0
        } else {
            0.0
        };
        let variation = (hour as f64 * 0.2).cos() * 10.0;
        (base_throughput + business_hours_boost + variation).max(5.0)
    }

    fn generate_tags(&self, group_by: &[String], hour: i64) -> HashMap<String, String> {
        let mut tags = HashMap::new();

        for group in group_by {
            match group.as_str() {
                "hour" => {
                    tags.insert("hour".to_string(), (hour % 24).to_string());
                }
                "region" => {
                    let regions = ["us-west-1", "us-east-1", "eu-west-1"];
                    tags.insert(
                        "region".to_string(),
                        regions[hour as usize % regions.len()].to_string(),
                    );
                }
                "service" => {
                    let services = ["synthesis", "recognition", "evaluation"];
                    tags.insert(
                        "service".to_string(),
                        services[hour as usize % services.len()].to_string(),
                    );
                }
                _ => {
                    tags.insert(group.clone(), format!("value_{}", hour % 10));
                }
            }
        }

        tags
    }

    fn calculate_summary(
        &self,
        data_points: &[DataPoint],
        aggregation: &AggregationType,
    ) -> AnalyticsSummary {
        if data_points.is_empty() {
            return AnalyticsSummary {
                total_points: 0,
                min_value: 0.0,
                max_value: 0.0,
                average_value: 0.0,
                sum_value: 0.0,
            };
        }

        let values: Vec<f64> = data_points.iter().map(|dp| dp.value).collect();
        let sum_value: f64 = values.iter().sum();
        let min_value = values.iter().fold(f64::INFINITY, |a, &b| a.min(b));
        let max_value = values.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
        let average_value = sum_value / values.len() as f64;

        AnalyticsSummary {
            total_points: data_points.len() as u32,
            min_value,
            max_value,
            average_value,
            sum_value,
        }
    }
}

impl SamplingController {
    async fn should_sample(&self, _event: &TelemetryEvent) -> bool {
        // Simple sampling logic - in practice this would be more sophisticated
        true
    }
}

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

    #[tokio::test]
    async fn test_telemetry_provider_creation() {
        let config = TelemetryConfig::default();
        let provider = VoirsTelemetryProvider::new(config).await;
        assert!(provider.is_ok());
    }

    #[tokio::test]
    async fn test_event_recording() {
        let config = TelemetryConfig::default();
        let provider = VoirsTelemetryProvider::new(config).await.unwrap();

        let event = TelemetryEvent {
            id: Uuid::new_v4().to_string(),
            event_type: "test".to_string(),
            timestamp: Utc::now(),
            user_id: Some("user123".to_string()),
            session_id: Some("session456".to_string()),
            properties: HashMap::new(),
        };

        let result = provider.record_event(event).await;
        assert!(result.is_ok());
    }

    #[tokio::test]
    async fn test_metric_recording() {
        let config = TelemetryConfig::default();
        let provider = VoirsTelemetryProvider::new(config).await.unwrap();

        let metric = Metric {
            name: "test_metric".to_string(),
            value: 42.0,
            unit: "count".to_string(),
            timestamp: Utc::now(),
            tags: HashMap::new(),
        };

        let result = provider.record_metric(metric).await;
        assert!(result.is_ok());
    }

    #[tokio::test]
    async fn test_experiment_creation() {
        let config = TelemetryConfig::default();
        let provider = VoirsTelemetryProvider::new(config).await.unwrap();

        let experiment = Experiment {
            id: "test_experiment".to_string(),
            name: "Test Experiment".to_string(),
            description: "A test experiment".to_string(),
            status: ExperimentStatus::Draft,
            variants: vec![
                Variant {
                    id: "variant_a".to_string(),
                    name: "Variant A".to_string(),
                    description: "Control variant".to_string(),
                    allocation_percentage: 50.0,
                    configuration: HashMap::new(),
                },
                Variant {
                    id: "variant_b".to_string(),
                    name: "Variant B".to_string(),
                    description: "Treatment variant".to_string(),
                    allocation_percentage: 50.0,
                    configuration: HashMap::new(),
                },
            ],
            allocation: AllocationStrategy::Random,
            start_date: Utc::now(),
            end_date: None,
            success_metrics: vec!["conversion_rate".to_string()],
            sample_size: 1000,
            confidence_level: 0.95,
        };

        let result = provider.create_experiment(experiment).await;
        assert!(result.is_ok());
    }

    #[test]
    fn test_trend_direction() {
        let directions = vec![
            TrendDirection::Increasing,
            TrendDirection::Decreasing,
            TrendDirection::Stable,
            TrendDirection::Volatile,
        ];

        assert_eq!(directions.len(), 4);
    }

    #[test]
    fn test_statistical_tests() {
        let tests = vec![
            StatisticalTest::TTest,
            StatisticalTest::ChiSquare,
            StatisticalTest::MannWhitney,
            StatisticalTest::Bayesian,
        ];

        assert_eq!(tests.len(), 4);
    }
}