oxirs-arq 0.2.4

//! Optimization feedback loop

use serde::{Deserialize, Serialize};
use std::collections::HashMap;


/// Reward function for optimization learning
#[derive(Debug, Clone)]
pub struct RewardFunction {
    pub performance_weight: f64,
    pub resource_weight: f64,
    pub stability_weight: f64,
    pub user_satisfaction_weight: f64,
}

/// Exploration strategies for learning
#[derive(Debug, Clone)]
pub enum ExplorationStrategy {
    EpsilonGreedy(f64),
    Ucb(f64),
    ThompsonSampling,
    BoltzmannExploration(f64),
}

/// Feedback loop controller
#[derive(Debug, Clone)]
pub struct FeedbackLoopController {
    pub control_algorithm: ControlAlgorithm,
    pub setpoint: f64,
    pub error_tolerance: f64,
    pub control_parameters: ControlParameters,
}

/// Control algorithms
#[derive(Debug, Clone)]
pub enum ControlAlgorithm {
    Pid,
    ModelPredictiveControl,
    FuzzyControl,
    AdaptiveControl,
}

/// Control parameters
#[derive(Debug, Clone)]
pub struct ControlParameters {
    pub proportional_gain: f64,
    pub integral_gain: f64,
    pub derivative_gain: f64,
    pub setpoint_weight: f64,
}


impl OptimizerFeedback {
    pub fn new() -> Self {
        Self {
            effectiveness_tracker: OptimizationEffectivenessTracker::new(),
            adaptive_parameters: AdaptiveOptimizationParameters::new(),
            learning_optimizer: LearningBasedOptimizer::new(),
            feedback_controller: FeedbackLoopController::new(),
        }
    }

    pub fn record_execution(&mut self, algebra: &Algebra, execution_time: Duration, memory_usage: usize, success: bool) -> Result<()> {
        // Implementation would record execution for feedback
        Ok(())
    }

    pub fn get_recommendations(&self, algebra: &Algebra) -> Vec<OptimizationRecommendation> {
        // Implementation would generate optimization recommendations
        vec![
            OptimizationRecommendation {
                optimization_type: OptimizationType::JoinReordering,
                priority: Priority::High,
                expected_improvement: 0.3,
                confidence: 0.8,
                description: "Reorder joins to reduce intermediate result sizes".to_string(),
                implementation_cost: ImplementationCost::Low,
            }
        ]
    }
}

impl OptimizationEffectivenessTracker {
    pub fn new() -> Self {
        Self {
            optimization_history: Vec::new(),
            effectiveness_metrics: EffectivenessMetrics::default(),
            regression_detector: RegressionDetector::new(),
        }
    }
}

impl Default for EffectivenessMetrics {
    fn default() -> Self {
        Self {
            success_rate: 0.85,
            average_improvement: 0.25,
            regression_rate: 0.05,
            stability_score: 0.9,
        }
    }
}

impl RegressionDetector {
    pub fn new() -> Self {
        Self {
            baseline_metrics: PerformanceMetrics::default(),
            regression_threshold: 0.1,
            detection_window: Duration::from_secs(300),
            regression_alerts: Vec::new(),
        }
    }
}

impl Default for PerformanceMetrics {
    fn default() -> Self {
        Self {
            execution_time: Duration::from_millis(100),
            memory_usage: 1024 * 1024,
            cpu_usage: 0.5,
            throughput: 10.0,
            error_rate: 0.01,
        }
    }
}

impl AdaptiveOptimizationParameters {
    pub fn new() -> Self {
        Self {
            parameter_map: HashMap::new(),
            adaptation_strategy: AdaptationStrategy::GradientDescent,
            learning_rate: 0.01,
            stability_threshold: 0.95,
        }
    }
}

impl LearningBasedOptimizer {
    pub fn new() -> Self {
        Self {
            learning_algorithm: LearningAlgorithm::QLearning,
            feature_extractor: FeatureExtractor::new(),
            reward_function: RewardFunction::default(),
            exploration_strategy: ExplorationStrategy::EpsilonGreedy(0.1),
        }
    }
}

impl FeatureExtractor {
    pub fn new() -> Self {
        Self {
            query_features: vec!["pattern_count".to_string(), "join_count".to_string()],
            system_features: vec!["cpu_usage".to_string(), "memory_usage".to_string()],
            contextual_features: vec!["time_of_day".to_string(), "workload_type".to_string()],
            feature_normalization: FeatureNormalization::ZScore,
        }
    }
}

impl Default for RewardFunction {
    fn default() -> Self {
        Self {
            performance_weight: 0.4,
            resource_weight: 0.3,
            stability_weight: 0.2,
            user_satisfaction_weight: 0.1,
        }
    }
}

impl FeedbackLoopController {
    pub fn new() -> Self {
        Self {
            control_algorithm: ControlAlgorithm::Pid,
            setpoint: 100.0, // Target execution time in ms
            error_tolerance: 5.0,
            control_parameters: ControlParameters::default(),
        }
    }
}

impl Default for ControlParameters {
    fn default() -> Self {
        Self {
            proportional_gain: 1.0,
            integral_gain: 0.1,
            derivative_gain: 0.01,
            setpoint_weight: 1.0,
        }
    }
}

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

    #[test]
    fn test_advanced_statistics_collector_creation() {
        let collector = AdvancedStatisticsCollector::new();
        assert_eq!(collector.real_time_monitor.live_metrics.current_qps, 10.0);
    }

    #[test]
    fn test_pattern_analyzer() {
        let mut analyzer = PatternAnalyzer::new();
        let algebra = Algebra::Bgp(vec![]);
        let data_point = PerformanceDataPoint {
            timestamp: SystemTime::now(),
            query_features: QueryFeatures {
                pattern_count: 1,
                join_count: 0,
                filter_count: 0,
                union_count: 0,
                optional_count: 0,
                graph_patterns: 0,
                path_expressions: 0,
                aggregations: 0,
                subqueries: 0,
                services: 0,
                estimated_cardinality: 100,
                complexity_score: 1.0,
                index_coverage: 0.8,
            },
            execution_time: Duration::from_millis(50),
            memory_usage: 1024,
            success: true,
            error_category: None,
        };

        assert!(analyzer.analyze_pattern(&algebra, &data_point).is_ok());
    }

    #[test]
    fn test_performance_predictor() {
        let mut predictor = PerformancePredictor::new();
        let data_point = PerformanceDataPoint {
            timestamp: SystemTime::now(),
            query_features: QueryFeatures {
                pattern_count: 2,
                join_count: 1,
                filter_count: 1,
                union_count: 0,
                optional_count: 0,
                graph_patterns: 0,
                path_expressions: 0,
                aggregations: 0,
                subqueries: 0,
                services: 0,
                estimated_cardinality: 500,
                complexity_score: 3.5,
                index_coverage: 0.6,
            },
            execution_time: Duration::from_millis(150),
            memory_usage: 2048,
            success: true,
            error_category: None,
        };

        assert!(predictor.add_data_point(data_point).is_ok());
    }

    #[test]
    fn test_workload_classifier() {
        let classifier = WorkloadClassifier::new();
        let classification = classifier.get_current_classification();
        assert_eq!(classification.name, "balanced");
    }

    #[test]
    fn test_anomaly_detector() {
        let detector = AnomalyDetector::new();
        let algebra = Algebra::Bgp(vec![]);
        let anomalies = detector.detect(&algebra).unwrap();
        assert!(anomalies.is_empty());
    }

    #[test]
    fn test_resource_tracker() {
        let tracker = ResourceUsageTracker::new();
        assert_eq!(tracker.memory_tracker.current_usage, 0);
        assert_eq!(tracker.cpu_tracker.current_usage, 0.0);
    }

    #[test]
    fn test_optimizer_feedback() {
        let feedback = OptimizerFeedback::new();
        let algebra = Algebra::Bgp(vec![]);
        let recommendations = feedback.get_recommendations(&algebra);
        assert!(!recommendations.is_empty());
        assert_eq!(recommendations[0].priority, Priority::High);
    }
}