oxirs-arq 0.2.4

//! Advanced statistics collector

use crate::executor::QueryExecutionPlan;
use std::collections::HashMap;
use std::time::{Duration, Instant};

pub struct AdvancedStatisticsCollector {
    /// Base execution statistics
    execution_stats: Arc<RwLock<ExecutionStats>>,
    /// Query pattern analyzer
    pattern_analyzer: PatternAnalyzer,
    /// Performance predictor
    performance_predictor: PerformancePredictor,
    /// Workload classifier
    workload_classifier: WorkloadClassifier,
    /// Real-time monitoring
    real_time_monitor: RealTimeMonitor,
    /// Anomaly detector
    anomaly_detector: AnomalyDetector,
    /// Resource usage tracker
    resource_tracker: ResourceUsageTracker,
    /// Adaptive optimizer feedback
    optimizer_feedback: OptimizerFeedback,
}

/// Pattern analysis for query optimization
#[derive(Debug, Clone)]

impl AdvancedStatisticsCollector {
    /// Create a new advanced statistics collector
    pub fn new() -> Self {
        Self {
            execution_stats: Arc::new(RwLock::new(ExecutionStats::new())),
            pattern_analyzer: PatternAnalyzer::new(),
            performance_predictor: PerformancePredictor::new(),
            workload_classifier: WorkloadClassifier::new(),
            real_time_monitor: RealTimeMonitor::new(),
            anomaly_detector: AnomalyDetector::new(),
            resource_tracker: ResourceUsageTracker::new(),
            optimizer_feedback: OptimizerFeedback::new(),
        }
    }

    /// Collect statistics from query execution
    pub fn collect_execution_stats(
        &mut self,
        algebra: &Algebra,
        execution_time: Duration,
        memory_usage: usize,
        success: bool,
    ) -> Result<()> {
        // Extract query features
        let features = self.extract_query_features(algebra);

        // Create performance data point
        let data_point = PerformanceDataPoint {
            timestamp: SystemTime::now(),
            query_features: features,
            execution_time,
            memory_usage,
            success,
            error_category: if success { None } else { Some("execution_error".to_string()) },
        };

        // Update pattern analyzer
        self.pattern_analyzer.analyze_pattern(algebra, &data_point)?;

        // Update performance predictor
        self.performance_predictor.add_data_point(data_point)?;

        // Update workload classifier
        self.workload_classifier.classify_workload(algebra, execution_time)?;

        // Update real-time monitor
        self.real_time_monitor.update_metrics(&algebra, execution_time, memory_usage)?;

        // Check for anomalies
        self.anomaly_detector.detect_anomalies(&algebra, execution_time, memory_usage)?;

        // Update resource tracker
        self.resource_tracker.track_resource_usage(memory_usage, execution_time)?;

        // Provide feedback to optimizer
        self.optimizer_feedback.record_execution(&algebra, execution_time, memory_usage, success)?;

        Ok(())
    }

    /// Predict query performance
    pub fn predict_performance(&self, algebra: &Algebra) -> Result<PerformancePrediction> {
        let features = self.extract_query_features(algebra);
        self.performance_predictor.predict(&features)
    }

    /// Get current workload classification
    pub fn get_workload_classification(&self) -> WorkloadCategory {
        self.workload_classifier.get_current_classification()
    }

    /// Get real-time metrics
    pub fn get_real_time_metrics(&self) -> LiveMetrics {
        self.real_time_monitor.get_current_metrics()
    }

    /// Detect anomalies
    pub fn detect_anomalies(&self, algebra: &Algebra) -> Result<Vec<Anomaly>> {
        self.anomaly_detector.detect(&algebra)
    }

    /// Get optimization recommendations
    pub fn get_optimization_recommendations(&self, algebra: &Algebra) -> Vec<OptimizationRecommendation> {
        self.optimizer_feedback.get_recommendations(&algebra)
    }

    /// Extract query features for machine learning
    fn extract_query_features(&self, algebra: &Algebra) -> QueryFeatures {
        QueryFeatures {
            pattern_count: self.count_patterns(algebra),
            join_count: self.count_joins(algebra),
            filter_count: self.count_filters(algebra),
            union_count: self.count_unions(algebra),
            optional_count: self.count_optionals(algebra),
            graph_patterns: self.count_graph_patterns(algebra),
            path_expressions: self.count_path_expressions(algebra),
            aggregations: self.count_aggregations(algebra),
            subqueries: self.count_subqueries(algebra),
            services: self.count_services(algebra),
            estimated_cardinality: self.estimate_cardinality(algebra),
            complexity_score: self.calculate_complexity_score(algebra),
            index_coverage: self.calculate_index_coverage(algebra),
        }
    }

    // Feature extraction helper methods
    fn count_patterns(&self, algebra: &Algebra) -> usize {
        match algebra {
            Algebra::Bgp(patterns) => patterns.len(),
            Algebra::Join { left, right } => {
                self.count_patterns(left) + self.count_patterns(right)
            }
            Algebra::LeftJoin { left, right, .. } => {
                self.count_patterns(left) + self.count_patterns(right)
            }
            Algebra::Union { left, right } => {
                self.count_patterns(left) + self.count_patterns(right)
            }
            Algebra::Filter { pattern, .. } => self.count_patterns(pattern),
            _ => 0,
        }
    }

    fn count_joins(&self, algebra: &Algebra) -> usize {
        match algebra {
            Algebra::Join { left, right } => {
                1 + self.count_joins(left) + self.count_joins(right)
            }
            Algebra::LeftJoin { left, right, .. } => {
                1 + self.count_joins(left) + self.count_joins(right)
            }
            _ => 0,
        }
    }

    fn count_filters(&self, algebra: &Algebra) -> usize {
        match algebra {
            Algebra::Filter { pattern, .. } => 1 + self.count_filters(pattern),
            Algebra::Join { left, right } => {
                self.count_filters(left) + self.count_filters(right)
            }
            _ => 0,
        }
    }

    fn count_unions(&self, algebra: &Algebra) -> usize {
        match algebra {
            Algebra::Union { left, right } => {
                1 + self.count_unions(left) + self.count_unions(right)
            }
            _ => 0,
        }
    }

    fn count_optionals(&self, algebra: &Algebra) -> usize {
        match algebra {
            Algebra::LeftJoin { left, right, .. } => {
                1 + self.count_optionals(left) + self.count_optionals(right)
            }
            _ => 0,
        }
    }

    fn count_graph_patterns(&self, _algebra: &Algebra) -> usize {
        // Implementation would count GRAPH patterns
        0
    }

    fn count_path_expressions(&self, _algebra: &Algebra) -> usize {
        // Implementation would count property path expressions
        0
    }

    fn count_aggregations(&self, _algebra: &Algebra) -> usize {
        // Implementation would count GROUP BY and aggregation functions
        0
    }

    fn count_subqueries(&self, _algebra: &Algebra) -> usize {
        // Implementation would count nested SELECT expressions
        0
    }

    fn count_services(&self, algebra: &Algebra) -> usize {
        match algebra {
            Algebra::Service { .. } => 1,
            _ => 0,
        }
    }

    fn estimate_cardinality(&self, _algebra: &Algebra) -> usize {
        // Implementation would estimate result set size
        1000
    }

    fn calculate_complexity_score(&self, algebra: &Algebra) -> f64 {
        // Weighted complexity calculation
        let patterns = self.count_patterns(algebra) as f64 * 1.0;
        let joins = self.count_joins(algebra) as f64 * 2.0;
        let filters = self.count_filters(algebra) as f64 * 0.5;
        let unions = self.count_unions(algebra) as f64 * 1.5;

        patterns + joins + filters + unions
    }

    fn calculate_index_coverage(&self, _algebra: &Algebra) -> f64 {
        // Implementation would calculate percentage of patterns covered by indexes
        0.8
    }
}

/// Performance prediction result
#[derive(Debug, Clone)]
pub struct PerformancePrediction {
    pub predicted_execution_time: Duration,
    pub predicted_memory_usage: usize,
    pub confidence_interval: (Duration, Duration),
    pub risk_assessment: RiskLevel,
    pub optimization_suggestions: Vec<String>,
}

/// Anomaly detection result
#[derive(Debug, Clone)]
pub struct Anomaly {
    pub anomaly_type: AnomalyType,
    pub severity: AnomalySeverity,
    pub description: String,
    pub detected_at: SystemTime,
    pub confidence: f64,
    pub affected_components: Vec<String>,
}

/// Types of anomalies
#[derive(Debug, Clone)]
pub enum AnomalyType {
    PerformanceRegression,
    ResourceExhaustion,
    UnusualPattern,
    ErrorSpike,
    SystemOverload,
}

/// Optimization recommendation
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
    pub optimization_type: OptimizationType,
    pub priority: Priority,
    pub expected_improvement: f64,
    pub confidence: f64,
    pub description: String,
    pub implementation_cost: ImplementationCost,
}

/// Priority levels
#[derive(Debug, Clone)]
pub enum Priority {
    Low,
    Medium,
    High,
    Critical,
}

/// Implementation cost assessment
#[derive(Debug, Clone)]
pub enum ImplementationCost {
    Low,
    Medium,
    High,
    VeryHigh,
}

// Implementation stubs for the complex types