//! Model Performance Tracking Repository
//!
//! Tracks model performance metrics, A/B testing results, and performance
//! degradation detection for HFT ML models with PostgreSQL integration.

use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use uuid::Uuid;

use crate::{MlDataError, PerformanceConfig, Result};
use database::{Database, DatabaseTransaction};
use sqlx::Row;

/// Performance tracking repository for ML models
#[derive(Clone)]
pub struct PerformanceRepository {
    db: Database,
    config: PerformanceConfig,
}

impl PerformanceRepository {
    pub async fn new(db: Database, config: PerformanceConfig) -> Result<Self> {
        let repo = Self { db, config };
        repo.initialize_schema().await?;
        Ok(repo)
    }

    /// Initialize database schema for performance tracking
    pub async fn initialize_schema(&self) -> Result<()> {
        // Initialize schema

        // Model performance metrics table
        self.db
            .execute(
                r#"
            CREATE TABLE IF NOT EXISTS ml_model_performance (
                id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
                model_id UUID NOT NULL,
                model_name VARCHAR NOT NULL,
                model_version VARCHAR NOT NULL,
                environment VARCHAR NOT NULL,
                timestamp TIMESTAMPTZ NOT NULL,
                metric_name VARCHAR NOT NULL,
                metric_value DOUBLE PRECISION NOT NULL,
                metric_metadata JSONB DEFAULT '{}',
                created_at TIMESTAMPTZ DEFAULT NOW()
            )
        "#,
            )
            .await?;

        // Performance benchmarks table
        self.db
            .execute(
                r#"
            CREATE TABLE IF NOT EXISTS ml_performance_benchmarks (
                id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
                benchmark_name VARCHAR NOT NULL,
                model_id UUID NOT NULL,
                model_name VARCHAR NOT NULL,
                model_version VARCHAR NOT NULL,
                environment VARCHAR NOT NULL,
                started_at TIMESTAMPTZ NOT NULL,
                completed_at TIMESTAMPTZ,
                duration_ms BIGINT,
                status benchmark_status DEFAULT 'running',
                results JSONB DEFAULT '{}',
                error_message TEXT,
                created_by VARCHAR NOT NULL,
                metadata JSONB DEFAULT '{}'
            )
        "#,
            )
            .await?;

        // A/B testing experiments table
        self.db
            .execute(
                r#"
            CREATE TABLE IF NOT EXISTS ml_ab_experiments (
                id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
                experiment_name VARCHAR NOT NULL,
                description TEXT,
                control_model_id UUID NOT NULL,
                treatment_model_id UUID NOT NULL,
                started_at TIMESTAMPTZ NOT NULL,
                ended_at TIMESTAMPTZ,
                status experiment_status DEFAULT 'running',
                traffic_split DOUBLE PRECISION NOT NULL,
                confidence_level DOUBLE PRECISION NOT NULL,
                results JSONB DEFAULT '{}',
                created_by VARCHAR NOT NULL,
                metadata JSONB DEFAULT '{}'
            )
        "#,
            )
            .await?;

        // A/B experiment metrics table
        self.db
            .execute(
                r#"
            CREATE TABLE IF NOT EXISTS ml_ab_experiment_metrics (
                id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
                experiment_id UUID NOT NULL REFERENCES ml_ab_experiments(id) ON DELETE CASCADE,
                model_variant ab_variant NOT NULL,
                timestamp TIMESTAMPTZ NOT NULL,
                metric_name VARCHAR NOT NULL,
                metric_value DOUBLE PRECISION NOT NULL,
                sample_size INTEGER NOT NULL,
                metadata JSONB DEFAULT '{}'
            )
        "#,
            )
            .await?;

        // Performance alerts table
        self.db
            .execute(
                r#"
            CREATE TABLE IF NOT EXISTS ml_performance_alerts (
                id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
                model_id UUID NOT NULL,
                model_name VARCHAR NOT NULL,
                alert_type alert_type NOT NULL,
                severity alert_severity NOT NULL,
                metric_name VARCHAR NOT NULL,
                threshold_value DOUBLE PRECISION NOT NULL,
                actual_value DOUBLE PRECISION NOT NULL,
                triggered_at TIMESTAMPTZ NOT NULL,
                resolved_at TIMESTAMPTZ,
                status alert_status DEFAULT 'active',
                message TEXT NOT NULL,
                metadata JSONB DEFAULT '{}'
            )
        "#,
            )
            .await?;

        // Create enums
        self.db.execute(r#"
            DO $$ BEGIN
                CREATE TYPE benchmark_status AS ENUM ('running', 'completed', 'failed', 'cancelled');
            EXCEPTION
                WHEN duplicate_object THEN null;
            END $$;
        "#).await?;

        self.db.execute(r#"
            DO $$ BEGIN
                CREATE TYPE experiment_status AS ENUM ('draft', 'running', 'paused', 'completed', 'failed');
            EXCEPTION
                WHEN duplicate_object THEN null;
            END $$;
        "#).await?;

        self.db
            .execute(
                r#"
            DO $$ BEGIN
                CREATE TYPE ab_variant AS ENUM ('control', 'treatment');
            EXCEPTION
                WHEN duplicate_object THEN null;
            END $$;
        "#,
            )
            .await?;

        self.db
            .execute(
                r#"
            DO $$ BEGIN
                CREATE TYPE alert_type AS ENUM ('degradation', 'anomaly', 'threshold', 'drift');
            EXCEPTION
                WHEN duplicate_object THEN null;
            END $$;
        "#,
            )
            .await?;

        self.db
            .execute(
                r#"
            DO $$ BEGIN
                CREATE TYPE alert_severity AS ENUM ('low', 'medium', 'high', 'critical');
            EXCEPTION
                WHEN duplicate_object THEN null;
            END $$;
        "#,
            )
            .await?;

        self.db
            .execute(
                r#"
            DO $$ BEGIN
                CREATE TYPE alert_status AS ENUM ('active', 'acknowledged', 'resolved');
            EXCEPTION
                WHEN duplicate_object THEN null;
            END $$;
        "#,
            )
            .await?;

        // Indexes for performance
        self.db.execute("CREATE INDEX IF NOT EXISTS idx_performance_model_timestamp ON ml_model_performance(model_id, timestamp DESC)").await?;
        self.db.execute("CREATE INDEX IF NOT EXISTS idx_performance_metric_name ON ml_model_performance(metric_name, timestamp DESC)").await?;
        self.db.execute("CREATE INDEX IF NOT EXISTS idx_benchmarks_model ON ml_performance_benchmarks(model_id, started_at DESC)").await?;
        self.db.execute("CREATE INDEX IF NOT EXISTS idx_experiments_status ON ml_ab_experiments(status, started_at DESC)").await?;
        self.db.execute("CREATE INDEX IF NOT EXISTS idx_alerts_model ON ml_performance_alerts(model_id, triggered_at DESC)").await?;

        Ok(())
    }

    /// Record model performance metrics
    pub async fn record_metrics(&self, request: RecordMetricsRequest) -> Result<()> {
        let mut tx = self.db.begin_transaction().await?;

        let metric_count = request.metrics.len();
        for metric in &request.metrics {
            let query = format!(
                r#"INSERT INTO ml_model_performance
                   (model_id, model_name, model_version, environment, timestamp,
                    metric_name, metric_value, metric_metadata)
                   VALUES ('{}', '{}', '{}', '{}', '{}', '{}', {}, '{}')"#,
                request.model_id,
                request.model_name,
                request.model_version,
                request.environment,
                metric.timestamp.to_rfc3339(),
                metric.name,
                metric.value,
                metric.metadata.to_string().replace("'", "''")
            );
            tx.execute(&query).await?;
            // Check for performance alerts
            self.check_performance_threshold(&mut tx, &request, metric)
                .await?;
        }

        tx.commit().await?;

        tracing::info!(
            "Recorded {} metrics for model {} in {}",
            metric_count,
            request.model_name,
            request.environment
        );
        Ok(())
    }

    /// Get performance metrics for a model
    pub async fn get_performance(
        &self,
        model_id: Uuid,
        metric_names: Option<Vec<String>>,
        time_range: Option<(DateTime<Utc>, DateTime<Utc>)>,
        limit: Option<usize>,
    ) -> Result<ModelPerformance> {
        let mut conn = self.db.acquire().await?;

        let _query = r#"
            SELECT timestamp, metric_name, metric_value, metric_metadata
            FROM ml_model_performance
            WHERE model_id = $1
        "#
        .to_string();

        // Using sqlx query builder pattern
        let mut query_builder = sqlx::QueryBuilder::new(
            "SELECT timestamp, metric_name, metric_value, metric_metadata FROM ml_model_performance WHERE model_id = "
        );
        query_builder.push_bind(model_id);

        // Add metric name filter
        if let Some(ref names) = metric_names {
            query_builder.push(" AND metric_name = ANY(");
            query_builder.push_bind(names);
            query_builder.push(")");
        }

        // Add time range filter
        if let Some((start, end)) = time_range {
            query_builder.push(" AND timestamp >= ");
            query_builder.push_bind(start);
            query_builder.push(" AND timestamp <= ");
            query_builder.push_bind(end);
        }

        query_builder.push(" ORDER BY timestamp DESC");

        // Add limit
        if let Some(limit_val) = limit {
            query_builder.push(" LIMIT ");
            query_builder.push_bind(limit_val as i64);
        }

        let query = query_builder.build();
        let rows = query.fetch_all(&mut *conn).await?;

        let mut metrics = Vec::new();
        for row in rows {
            metrics.push(PerformanceMetric {
                timestamp: row.get("timestamp"),
                name: row.get("metric_name"),
                value: row.get("metric_value"),
                metadata: row.get("metric_metadata"),
            });
        }

        // Calculate summary statistics
        let summary = self.calculate_performance_summary(&metrics);
        let last_updated = metrics.first().map(|m| m.timestamp);

        Ok(ModelPerformance {
            model_id,
            metrics,
            summary,
            last_updated,
        })
    }

    /// Start a performance benchmark
    pub async fn start_benchmark(&self, request: StartBenchmarkRequest) -> Result<BenchmarkResult> {
        let benchmark_id = Uuid::new_v4();

        let mut conn = self.db.acquire().await?;
        sqlx::query(
            r#"INSERT INTO ml_performance_benchmarks 
               (id, benchmark_name, model_id, model_name, model_version, environment,
                started_at, created_by, metadata)
               VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9)"#,
        )
        .bind(benchmark_id)
        .bind(&request.benchmark_name)
        .bind(request.model_id)
        .bind(&request.model_name)
        .bind(&request.model_version)
        .bind(&request.environment)
        .bind(request.started_at)
        .bind(&request.created_by)
        .bind(&request.metadata)
        .execute(conn.as_mut())
        .await?;

        let benchmark = BenchmarkResult {
            id: benchmark_id,
            benchmark_name: request.benchmark_name,
            model_id: request.model_id,
            model_name: request.model_name,
            model_version: request.model_version,
            environment: request.environment,
            started_at: request.started_at,
            completed_at: None,
            duration_ms: None,
            status: BenchmarkStatus::Running,
            results: serde_json::Value::Object(serde_json::Map::new()),
            error_message: None,
            created_by: request.created_by,
            metadata: request.metadata,
        };

        tracing::info!(
            "Started benchmark {} for model {}",
            benchmark.benchmark_name,
            benchmark.model_name
        );

        Ok(benchmark)
    }

    /// Complete a performance benchmark
    pub async fn complete_benchmark(
        &self,
        benchmark_id: Uuid,
        results: serde_json::Value,
        error_message: Option<String>,
    ) -> Result<()> {
        let mut conn = self.db.acquire().await?;
        let completed_at = Utc::now();

        // Get start time to calculate duration
        let start_time: DateTime<Utc> =
            sqlx::query_scalar("SELECT started_at FROM ml_performance_benchmarks WHERE id = $1")
                .bind(benchmark_id)
                .fetch_one(conn.as_mut())
                .await?;

        let duration_ms = (completed_at - start_time).num_milliseconds();
        let status = if error_message.is_some() {
            BenchmarkStatus::Failed
        } else {
            BenchmarkStatus::Completed
        };

        sqlx::query(
            r#"UPDATE ml_performance_benchmarks 
               SET completed_at = $1, duration_ms = $2, status = $3, 
                   results = $4, error_message = $5
               WHERE id = $6"#,
        )
        .bind(completed_at)
        .bind(duration_ms)
        .bind(status.to_string())
        .bind(&results)
        .bind(&error_message)
        .bind(benchmark_id)
        .execute(conn.as_mut())
        .await?;

        tracing::info!("Completed benchmark {} in {}ms", benchmark_id, duration_ms);
        Ok(())
    }

    /// Create A/B testing experiment
    pub async fn create_experiment(
        &self,
        request: CreateExperimentRequest,
    ) -> Result<AbTestExperiment> {
        let mut conn = self.db.acquire().await?;
        let experiment_id = Uuid::new_v4();

        sqlx::query(
            r#"INSERT INTO ml_ab_experiments 
               (id, experiment_name, description, control_model_id, treatment_model_id,
                started_at, traffic_split, confidence_level, created_by, metadata)
               VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10)"#,
        )
        .bind(experiment_id)
        .bind(&request.experiment_name)
        .bind(&request.description)
        .bind(request.control_model_id)
        .bind(request.treatment_model_id)
        .bind(request.started_at)
        .bind(request.traffic_split)
        .bind(request.confidence_level)
        .bind(&request.created_by)
        .bind(&request.metadata)
        .execute(conn.as_mut())
        .await?;

        let experiment = AbTestExperiment {
            id: experiment_id,
            experiment_name: request.experiment_name,
            description: request.description,
            control_model_id: request.control_model_id,
            treatment_model_id: request.treatment_model_id,
            started_at: request.started_at,
            ended_at: None,
            status: ExperimentStatus::Running,
            traffic_split: request.traffic_split,
            confidence_level: request.confidence_level,
            results: serde_json::Value::Object(serde_json::Map::new()),
            created_by: request.created_by,
            metadata: request.metadata,
            metrics: Vec::new(),
        };

        tracing::info!("Created A/B experiment: {}", experiment.experiment_name);
        Ok(experiment)
    }

    /// Record A/B experiment metrics
    pub async fn record_experiment_metrics(
        &self,
        experiment_id: Uuid,
        metrics: Vec<ExperimentMetric>,
    ) -> Result<()> {
        let mut tx = self.db.begin_transaction().await?;

        let metric_count = metrics.len();
        for metric in metrics {
            let query = format!(
                r#"INSERT INTO ml_ab_experiment_metrics
                   (experiment_id, model_variant, timestamp, metric_name,
                    metric_value, sample_size, metadata)
                   VALUES ('{}', '{}', '{}', '{}', {}, {}, '{}')"#,
                experiment_id,
                metric.variant.to_string(),
                metric.timestamp.to_rfc3339(),
                metric.metric_name,
                metric.metric_value,
                metric.sample_size,
                metric.metadata.to_string().replace("'", "''")
            );
            tx.execute(&query).await?;
        }

        tx.commit().await?;
        tracing::info!("Recorded {} experiment metrics", metric_count);
        Ok(())
    }

    /// Get active performance alerts
    pub async fn get_active_alerts(&self, model_id: Option<Uuid>) -> Result<Vec<PerformanceAlert>> {
        let mut conn = self.db.acquire().await?;

        // Using sqlx query builder pattern
        let rows = if let Some(model_id) = model_id {
            sqlx::query_as::<
                _,
                (
                    Uuid,
                    Uuid,
                    String,
                    String,
                    String,
                    String,
                    f64,
                    f64,
                    DateTime<Utc>,
                    String,
                    serde_json::Value,
                ),
            >(
                r#"SELECT id, model_id, model_name, alert_type, severity, metric_name,
                          threshold_value, actual_value, triggered_at, message, metadata
                   FROM ml_performance_alerts 
                   WHERE model_id = $1 AND status = 'active'
                   ORDER BY triggered_at DESC"#,
            )
            .bind(model_id)
            .fetch_all(conn.as_mut())
            .await?
        } else {
            sqlx::query_as::<
                _,
                (
                    Uuid,
                    Uuid,
                    String,
                    String,
                    String,
                    String,
                    f64,
                    f64,
                    DateTime<Utc>,
                    String,
                    serde_json::Value,
                ),
            >(
                r#"SELECT id, model_id, model_name, alert_type, severity, metric_name,
                          threshold_value, actual_value, triggered_at, message, metadata
                   FROM ml_performance_alerts 
                   WHERE status = 'active'
                   ORDER BY triggered_at DESC"#,
            )
            .fetch_all(conn.as_mut())
            .await?
        };

        let mut alerts = Vec::new();
        for (
            id,
            model_id,
            model_name,
            alert_type,
            severity,
            metric_name,
            threshold_value,
            actual_value,
            triggered_at,
            message,
            metadata,
        ) in rows
        {
            alerts.push(PerformanceAlert {
                id,
                model_id,
                model_name,
                alert_type: self.parse_alert_type(&alert_type)?,
                severity: self.parse_alert_severity(&severity)?,
                metric_name,
                threshold_value,
                actual_value,
                triggered_at,
                resolved_at: None,
                status: AlertStatus::Active,
                message,
                metadata,
            });
        }

        Ok(alerts)
    }

    /// Check performance thresholds and create alerts
    async fn check_performance_threshold(
        &self,
        tx: &mut DatabaseTransaction,
        request: &RecordMetricsRequest,
        metric: &PerformanceMetric,
    ) -> Result<()> {
        // Check for degradation based on configuration threshold
        if let Some(threshold) = self.get_metric_threshold(&metric.name) {
            let degradation_detected = match metric.name.as_str() {
                "accuracy" | "precision" | "recall" | "f1_score" => {
                    metric.value < threshold - self.config.degradation_threshold
                },
                "latency_ms" | "error_rate" => {
                    metric.value > threshold + self.config.degradation_threshold
                },
                _ => false,
            };

            if degradation_detected {
                let alert_id = Uuid::new_v4();
                let message = format!(
                    "Performance degradation detected for {}: {} (threshold: {})",
                    metric.name, metric.value, threshold
                );
                let query = format!(
                    r#"INSERT INTO ml_performance_alerts 
                       (id, model_id, model_name, alert_type, severity, metric_name,
                        threshold_value, actual_value, triggered_at, message)
                       VALUES ('{}', '{}', '{}', 'degradation', 'medium', '{}', {}, {}, '{}', '{}')"#,
                    alert_id,
                    request.model_id,
                    request.model_name,
                    metric.name,
                    threshold,
                    metric.value,
                    metric.timestamp.to_rfc3339(),
                    message.replace("'", "''")
                );
                tx.execute(&query).await?;
                tracing::warn!(
                    "Performance alert triggered for model {} metric {}",
                    request.model_name,
                    metric.name
                );
            }
        }

        Ok(())
    }

    /// Get metric threshold (placeholder - would be configurable)
    fn get_metric_threshold(&self, metric_name: &str) -> Option<f64> {
        match metric_name {
            "accuracy" => Some(0.95),
            "precision" => Some(0.90),
            "recall" => Some(0.90),
            "f1_score" => Some(0.90),
            "latency_ms" => Some(100.0),
            "error_rate" => Some(0.01),
            _ => None,
        }
    }

    /// Calculate performance summary statistics
    fn calculate_performance_summary(&self, metrics: &[PerformanceMetric]) -> PerformanceSummary {
        let mut metric_groups: HashMap<String, Vec<f64>> = HashMap::new();

        for metric in metrics {
            metric_groups
                .entry(metric.name.clone())
                .or_default()
                .push(metric.value);
        }

        let mut summaries = HashMap::new();
        for (name, values) in metric_groups {
            if !values.is_empty() {
                let sum: f64 = values.iter().sum();
                let mean = sum / values.len() as f64;
                let min = values.iter().fold(f64::INFINITY, |a, &b| a.min(b));
                let max = values.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));

                summaries.insert(
                    name,
                    MetricSummary {
                        count: values.len(),
                        mean,
                        min,
                        max,
                        latest: values[0], // First value is latest due to DESC order
                    },
                );
            }
        }

        PerformanceSummary {
            total_metrics: metrics.len(),
            metric_summaries: summaries,
            time_range: if let (Some(first), Some(last)) = (metrics.first(), metrics.last()) {
                Some((last.timestamp, first.timestamp))
            } else {
                None
            },
        }
    }

    /// Parse alert type from database
    fn parse_alert_type(&self, type_str: &str) -> Result<AlertType> {
        match type_str {
            "degradation" => Ok(AlertType::Degradation),
            "anomaly" => Ok(AlertType::Anomaly),
            "threshold" => Ok(AlertType::Threshold),
            "drift" => Ok(AlertType::Drift),
            _ => Err(MlDataError::Validation {
                message: format!("Invalid alert type: {}", type_str),
            }),
        }
    }

    /// Parse alert severity from database
    fn parse_alert_severity(&self, severity_str: &str) -> Result<AlertSeverity> {
        match severity_str {
            "low" => Ok(AlertSeverity::Low),
            "medium" => Ok(AlertSeverity::Medium),
            "high" => Ok(AlertSeverity::High),
            "critical" => Ok(AlertSeverity::Critical),
            _ => Err(MlDataError::Validation {
                message: format!("Invalid alert severity: {}", severity_str),
            }),
        }
    }

    /// Health check for performance repository
    pub async fn health_check(&self) -> Result<bool> {
        let mut conn = self.db.acquire().await?;
        let _: i64 = sqlx::query_scalar("SELECT COUNT(*) FROM ml_model_performance")
            .fetch_one(conn.as_mut())
            .await?;
        Ok(true)
    }
}

/// Request to record performance metrics
#[derive(Debug)]
pub struct RecordMetricsRequest {
    pub model_id: Uuid,
    pub model_name: String,
    pub model_version: String,
    pub environment: String,
    pub metrics: Vec<PerformanceMetric>,
}

/// Individual performance metric
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetric {
    pub timestamp: DateTime<Utc>,
    pub name: String,
    pub value: f64,
    pub metadata: serde_json::Value,
}

/// Model performance data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelPerformance {
    pub model_id: Uuid,
    pub metrics: Vec<PerformanceMetric>,
    pub summary: PerformanceSummary,
    pub last_updated: Option<DateTime<Utc>>,
}

/// Performance summary statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceSummary {
    pub total_metrics: usize,
    pub metric_summaries: HashMap<String, MetricSummary>,
    pub time_range: Option<(DateTime<Utc>, DateTime<Utc>)>,
}

/// Summary statistics for a specific metric
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MetricSummary {
    pub count: usize,
    pub mean: f64,
    pub min: f64,
    pub max: f64,
    pub latest: f64,
}

/// Performance benchmark request
#[derive(Debug)]
pub struct StartBenchmarkRequest {
    pub benchmark_name: String,
    pub model_id: Uuid,
    pub model_name: String,
    pub model_version: String,
    pub environment: String,
    pub started_at: DateTime<Utc>,
    pub created_by: String,
    pub metadata: serde_json::Value,
}

/// Benchmark result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkResult {
    pub id: Uuid,
    pub benchmark_name: String,
    pub model_id: Uuid,
    pub model_name: String,
    pub model_version: String,
    pub environment: String,
    pub started_at: DateTime<Utc>,
    pub completed_at: Option<DateTime<Utc>>,
    pub duration_ms: Option<i64>,
    pub status: BenchmarkStatus,
    pub results: serde_json::Value,
    pub error_message: Option<String>,
    pub created_by: String,
    pub metadata: serde_json::Value,
}

/// Benchmark status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum BenchmarkStatus {
    Running,
    Completed,
    Failed,
    Cancelled,
}

impl ToString for BenchmarkStatus {
    fn to_string(&self) -> String {
        match self {
            BenchmarkStatus::Running => "running".to_string(),
            BenchmarkStatus::Completed => "completed".to_string(),
            BenchmarkStatus::Failed => "failed".to_string(),
            BenchmarkStatus::Cancelled => "cancelled".to_string(),
        }
    }
}

/// Create A/B experiment request
#[derive(Debug)]
pub struct CreateExperimentRequest {
    pub experiment_name: String,
    pub description: Option<String>,
    pub control_model_id: Uuid,
    pub treatment_model_id: Uuid,
    pub started_at: DateTime<Utc>,
    pub traffic_split: f64,
    pub confidence_level: f64,
    pub created_by: String,
    pub metadata: serde_json::Value,
}

/// A/B testing experiment
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AbTestExperiment {
    pub id: Uuid,
    pub experiment_name: String,
    pub description: Option<String>,
    pub control_model_id: Uuid,
    pub treatment_model_id: Uuid,
    pub started_at: DateTime<Utc>,
    pub ended_at: Option<DateTime<Utc>>,
    pub status: ExperimentStatus,
    pub traffic_split: f64,
    pub confidence_level: f64,
    pub results: serde_json::Value,
    pub created_by: String,
    pub metadata: serde_json::Value,
    pub metrics: Vec<ExperimentMetric>,
}

/// Experiment status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ExperimentStatus {
    Draft,
    Running,
    Paused,
    Completed,
    Failed,
}

/// Experiment metric data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExperimentMetric {
    pub variant: AbVariant,
    pub timestamp: DateTime<Utc>,
    pub metric_name: String,
    pub metric_value: f64,
    pub sample_size: i32,
    pub metadata: serde_json::Value,
}

/// A/B test variant
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AbVariant {
    Control,
    Treatment,
}

impl ToString for AbVariant {
    fn to_string(&self) -> String {
        match self {
            AbVariant::Control => "control".to_string(),
            AbVariant::Treatment => "treatment".to_string(),
        }
    }
}

/// Performance alert
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceAlert {
    pub id: Uuid,
    pub model_id: Uuid,
    pub model_name: String,
    pub alert_type: AlertType,
    pub severity: AlertSeverity,
    pub metric_name: String,
    pub threshold_value: f64,
    pub actual_value: f64,
    pub triggered_at: DateTime<Utc>,
    pub resolved_at: Option<DateTime<Utc>>,
    pub status: AlertStatus,
    pub message: String,
    pub metadata: serde_json::Value,
}

/// Alert type
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AlertType {
    Degradation,
    Anomaly,
    Threshold,
    Drift,
}

/// Alert severity
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AlertSeverity {
    Low,
    Medium,
    High,
    Critical,
}

/// Alert status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AlertStatus {
    Active,
    Acknowledged,
    Resolved,
}

// TECHNICAL DEBT ELIMINATED - Use HashMap<String, Vec<PerformanceMetric>> directly