oxirs-gql 0.2.4

//! Hybrid Quantum-ML Query Optimizer
//!
//! This module integrates quantum-inspired optimization with machine learning
//! to provide the most advanced query optimization capabilities available.

use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use std::time::{Duration, Instant};
use tokio::sync::RwLock;
use tracing::info;

use crate::ast::Document;
use crate::ml_optimizer::{MLOptimizerConfig, MLQueryOptimizer, PerformancePrediction};
use crate::performance::PerformanceTracker;
use crate::quantum_optimizer::{
    OptimizationResult, QuantumOptimizerConfig, QuantumQueryOptimizer, QueryOptimizationProblem,
};

/// Hybrid optimization strategy configuration
#[derive(Debug, Clone)]
pub struct HybridOptimizerConfig {
    pub quantum_config: QuantumOptimizerConfig,
    pub ml_config: MLOptimizerConfig,
    pub optimization_strategy: OptimizationStrategy,
    pub decision_threshold: f64,
    pub adaptive_strategy_selection: bool,
    pub parallel_optimization: bool,
    pub confidence_weighting: bool,
    pub ensemble_voting: bool,
    pub performance_learning: bool,
}

impl Default for HybridOptimizerConfig {
    fn default() -> Self {
        Self {
            quantum_config: QuantumOptimizerConfig::default(),
            ml_config: MLOptimizerConfig::default(),
            optimization_strategy: OptimizationStrategy::Adaptive,
            decision_threshold: 0.8,
            adaptive_strategy_selection: true,
            parallel_optimization: true,
            confidence_weighting: true,
            ensemble_voting: true,
            performance_learning: true,
        }
    }
}

/// Optimization strategy selection
#[derive(Debug, Clone)]
pub enum OptimizationStrategy {
    QuantumOnly,
    MLOnly,
    Sequential { quantum_first: bool },
    Parallel,
    Adaptive,
    EnsembleVoting,
}

/// Hybrid optimization result
#[derive(Debug, Clone)]
pub struct HybridOptimizationResult {
    pub quantum_result: Option<OptimizationResult>,
    pub ml_prediction: Option<PerformancePrediction>,
    pub final_strategy: OptimizationStrategy,
    pub confidence_score: f64,
    pub optimization_time: Duration,
    pub selected_approach: String,
    pub ensemble_weights: Option<Vec<f64>>,
}

/// Strategy performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StrategyPerformance {
    pub strategy_name: String,
    pub success_rate: f64,
    pub average_improvement: f64,
    pub average_execution_time: Duration,
    pub confidence_accuracy: f64,
    pub usage_count: usize,
}

/// Hybrid quantum-ML query optimizer
pub struct HybridQueryOptimizer {
    config: HybridOptimizerConfig,
    quantum_optimizer: QuantumQueryOptimizer,
    ml_optimizer: MLQueryOptimizer,
    #[allow(dead_code)]
    performance_tracker: Arc<PerformanceTracker>,
    strategy_performance: Arc<RwLock<HashMap<String, StrategyPerformance>>>,
    optimization_history: Arc<RwLock<Vec<HybridOptimizationResult>>>,
}

impl HybridQueryOptimizer {
    /// Create a new hybrid optimizer
    pub fn new(
        config: HybridOptimizerConfig,
        performance_tracker: Arc<PerformanceTracker>,
    ) -> Self {
        let quantum_optimizer = QuantumQueryOptimizer::new(config.quantum_config.clone());
        let ml_optimizer =
            MLQueryOptimizer::new(config.ml_config.clone(), performance_tracker.clone());

        Self {
            config,
            quantum_optimizer,
            ml_optimizer,
            performance_tracker,
            strategy_performance: Arc::new(RwLock::new(HashMap::new())),
            optimization_history: Arc::new(RwLock::new(Vec::new())),
        }
    }

    /// Optimize query using hybrid approach
    pub async fn optimize_query(&self, document: &Document) -> Result<HybridOptimizationResult> {
        let start_time = Instant::now();

        info!("Starting hybrid quantum-ML optimization");

        // Determine optimal strategy
        let strategy = if self.config.adaptive_strategy_selection {
            self.select_adaptive_strategy(document).await?
        } else {
            self.config.optimization_strategy.clone()
        };

        // Execute optimization based on selected strategy
        let result = match strategy {
            OptimizationStrategy::QuantumOnly => self.execute_quantum_only(document).await?,
            OptimizationStrategy::MLOnly => self.execute_ml_only(document).await?,
            OptimizationStrategy::Sequential { quantum_first } => {
                self.execute_sequential(document, quantum_first).await?
            }
            OptimizationStrategy::Parallel => self.execute_parallel(document).await?,
            OptimizationStrategy::Adaptive => self.execute_adaptive(document).await?,
            OptimizationStrategy::EnsembleVoting => self.execute_ensemble_voting(document).await?,
        };

        let optimization_time = start_time.elapsed();

        let hybrid_result = HybridOptimizationResult {
            quantum_result: result.0,
            ml_prediction: result.1,
            final_strategy: strategy.clone(),
            confidence_score: result.2,
            optimization_time,
            selected_approach: self.strategy_name(&strategy),
            ensemble_weights: result.3,
        };

        // Update performance tracking
        if self.config.performance_learning {
            self.update_strategy_performance(&hybrid_result).await?;
        }

        // Store in history
        self.optimization_history
            .write()
            .await
            .push(hybrid_result.clone());

        info!("Hybrid optimization completed in {:?}", optimization_time);
        Ok(hybrid_result)
    }

    /// Select the best strategy adaptively based on query characteristics
    async fn select_adaptive_strategy(&self, document: &Document) -> Result<OptimizationStrategy> {
        // Extract query features for strategy selection
        let features = self.ml_optimizer.extract_features(document)?;

        // Get historical performance data
        let performance_data = self.strategy_performance.read().await;

        // Simple adaptive strategy selection based on query complexity
        if features.complexity_score > 500.0 && features.max_depth > 8.0 {
            // High complexity queries may benefit from quantum optimization
            if let Some(quantum_perf) = performance_data.get("quantum") {
                if quantum_perf.success_rate > 0.7 {
                    return Ok(OptimizationStrategy::QuantumOnly);
                }
            }
            Ok(OptimizationStrategy::EnsembleVoting)
        } else if features.field_count > 20.0 {
            // Many fields may benefit from ML pattern recognition
            Ok(OptimizationStrategy::MLOnly)
        } else if self.config.parallel_optimization {
            // Medium complexity - try parallel approach
            Ok(OptimizationStrategy::Parallel)
        } else {
            // Default to sequential quantum-first
            Ok(OptimizationStrategy::Sequential {
                quantum_first: true,
            })
        }
    }

    /// Execute quantum-only optimization
    async fn execute_quantum_only(
        &self,
        document: &Document,
    ) -> Result<(
        Option<OptimizationResult>,
        Option<PerformancePrediction>,
        f64,
        Option<Vec<f64>>,
    )> {
        let problem = self.create_optimization_problem(document).await?;

        // Try different quantum approaches and select the best
        let mut best_result = None;
        let mut best_energy = f64::INFINITY;

        // Quantum annealing
        if self.config.quantum_config.enable_quantum_annealing {
            if let Ok(result) = self
                .quantum_optimizer
                .quantum_anneal_optimization(&problem)
                .await
            {
                if result.energy < best_energy {
                    best_energy = result.energy;
                    best_result = Some(result);
                }
            }
        }

        // Variational optimization
        if self.config.quantum_config.enable_variational_optimization {
            if let Ok(result) = self
                .quantum_optimizer
                .variational_optimization(&problem)
                .await
            {
                if result.energy < best_energy {
                    best_energy = result.energy;
                    best_result = Some(result);
                }
            }
        }

        // Quantum search
        if self.config.quantum_config.enable_quantum_search {
            if let Ok(result) = self
                .quantum_optimizer
                .quantum_search_optimization(&problem)
                .await
            {
                if result.energy < best_energy {
                    best_result = Some(result);
                }
            }
        }

        let confidence = if best_result.is_some() { 0.8 } else { 0.1 };
        Ok((best_result, None, confidence, None))
    }

    /// Execute ML-only optimization
    async fn execute_ml_only(
        &self,
        document: &Document,
    ) -> Result<(
        Option<OptimizationResult>,
        Option<PerformancePrediction>,
        f64,
        Option<Vec<f64>>,
    )> {
        let prediction = self.ml_optimizer.predict_performance(document).await?;
        let confidence = prediction.confidence_score;

        Ok((None, Some(prediction), confidence, None))
    }

    /// Execute sequential optimization
    async fn execute_sequential(
        &self,
        document: &Document,
        quantum_first: bool,
    ) -> Result<(
        Option<OptimizationResult>,
        Option<PerformancePrediction>,
        f64,
        Option<Vec<f64>>,
    )> {
        if quantum_first {
            let (quantum_result, _, quantum_confidence, _) =
                self.execute_quantum_only(document).await?;

            // Use quantum result to inform ML optimization
            let ml_prediction = self.ml_optimizer.predict_performance(document).await?;

            let combined_confidence = (quantum_confidence + ml_prediction.confidence_score) / 2.0;
            Ok((
                quantum_result,
                Some(ml_prediction),
                combined_confidence,
                None,
            ))
        } else {
            let (_, ml_prediction, ml_confidence, _) = self.execute_ml_only(document).await?;

            // Use ML insights to guide quantum optimization
            let problem = self.create_optimization_problem(document).await?;
            let quantum_result = self
                .quantum_optimizer
                .quantum_anneal_optimization(&problem)
                .await
                .ok();

            let quantum_confidence = if quantum_result.is_some() { 0.7 } else { 0.1 };
            let combined_confidence = (ml_confidence + quantum_confidence) / 2.0;

            Ok((quantum_result, ml_prediction, combined_confidence, None))
        }
    }

    /// Execute parallel optimization
    async fn execute_parallel(
        &self,
        document: &Document,
    ) -> Result<(
        Option<OptimizationResult>,
        Option<PerformancePrediction>,
        f64,
        Option<Vec<f64>>,
    )> {
        // Run quantum and ML optimizations in parallel
        let quantum_task = {
            let doc = document.clone();
            let optimizer = &self.quantum_optimizer;
            async move {
                let problem = self.create_optimization_problem(&doc).await?;
                optimizer.quantum_anneal_optimization(&problem).await
            }
        };

        let ml_task = {
            let doc = document.clone();
            let optimizer = &self.ml_optimizer;
            async move { optimizer.predict_performance(&doc).await }
        };

        // Wait for both to complete
        let (quantum_result, ml_result) = tokio::join!(quantum_task, ml_task);

        let quantum_opt = quantum_result.ok();
        let ml_pred = ml_result.ok();

        // Combine confidence scores
        let quantum_conf = if quantum_opt.is_some() { 0.8 } else { 0.1 };
        let ml_conf = ml_pred.as_ref().map(|p| p.confidence_score).unwrap_or(0.1);
        let combined_confidence = (quantum_conf + ml_conf) / 2.0;

        Ok((quantum_opt, ml_pred, combined_confidence, None))
    }

    /// Execute adaptive optimization
    async fn execute_adaptive(
        &self,
        document: &Document,
    ) -> Result<(
        Option<OptimizationResult>,
        Option<PerformancePrediction>,
        f64,
        Option<Vec<f64>>,
    )> {
        // Start with ML prediction to guide strategy
        let ml_prediction = self.ml_optimizer.predict_performance(document).await?;

        // Use ML insights to determine quantum strategy
        let strategy = if ml_prediction.predicted_execution_time > Duration::from_millis(1000) {
            // High execution time predicted - use quantum optimization
            OptimizationStrategy::QuantumOnly
        } else if ml_prediction.confidence_score < 0.5 {
            // Low confidence - use ensemble approach
            OptimizationStrategy::EnsembleVoting
        } else {
            // Good ML prediction - use sequential ML-first
            OptimizationStrategy::Sequential {
                quantum_first: false,
            }
        };

        // Execute the determined strategy
        match strategy {
            OptimizationStrategy::QuantumOnly => self.execute_quantum_only(document).await,
            OptimizationStrategy::Sequential { quantum_first } => {
                self.execute_sequential(document, quantum_first).await
            }
            OptimizationStrategy::EnsembleVoting => self.execute_ensemble_voting(document).await,
            _ => self.execute_parallel(document).await,
        }
    }

    /// Execute ensemble voting optimization
    async fn execute_ensemble_voting(
        &self,
        document: &Document,
    ) -> Result<(
        Option<OptimizationResult>,
        Option<PerformancePrediction>,
        f64,
        Option<Vec<f64>>,
    )> {
        // Get results from multiple approaches
        let (quantum_result, _, quantum_conf, _) = self.execute_quantum_only(document).await?;
        let (_, ml_prediction, ml_conf, _) = self.execute_ml_only(document).await?;

        // Calculate ensemble weights based on historical performance
        let performance_data = self.strategy_performance.read().await;
        let quantum_weight = performance_data
            .get("quantum")
            .map(|p| p.success_rate)
            .unwrap_or(0.5);
        let ml_weight = performance_data
            .get("ml")
            .map(|p| p.success_rate)
            .unwrap_or(0.5);

        let total_weight = quantum_weight + ml_weight;
        let normalized_quantum_weight = quantum_weight / total_weight;
        let normalized_ml_weight = ml_weight / total_weight;

        // Weighted confidence score
        let ensemble_confidence =
            (quantum_conf * normalized_quantum_weight) + (ml_conf * normalized_ml_weight);

        let weights = vec![normalized_quantum_weight, normalized_ml_weight];

        Ok((
            quantum_result,
            ml_prediction,
            ensemble_confidence,
            Some(weights),
        ))
    }

    /// Create optimization problem from GraphQL document
    async fn create_optimization_problem(
        &self,
        document: &Document,
    ) -> Result<QueryOptimizationProblem> {
        use crate::quantum_optimizer::{
            ConstraintType, ObjectiveFunction, OptimizationConstraint, VariableDomain,
        };

        // Extract query characteristics
        let features = self.ml_optimizer.extract_features(document)?;

        // Create constraints based on query complexity
        let mut constraints = Vec::new();

        if features.max_depth > 5.0 {
            constraints.push(OptimizationConstraint {
                constraint_type: ConstraintType::ExecutionTimeLimit,
                variables: vec!["execution_time".to_string()],
                parameters: {
                    let mut params = HashMap::new();
                    params.insert("max_time_ms".to_string(), 5000.0);
                    params
                },
            });
        }

        if features.field_count > 20.0 {
            constraints.push(OptimizationConstraint {
                constraint_type: ConstraintType::MemoryLimit,
                variables: vec!["memory_usage".to_string()],
                parameters: {
                    let mut params = HashMap::new();
                    params.insert("max_memory_mb".to_string(), 512.0);
                    params
                },
            });
        }

        // Create variable domains
        let mut variable_domains = HashMap::new();
        variable_domains.insert(
            "execution_time".to_string(),
            VariableDomain {
                min_value: 0.0,
                max_value: 10000.0,
                discrete_values: None,
            },
        );
        variable_domains.insert(
            "memory_usage".to_string(),
            VariableDomain {
                min_value: 0.0,
                max_value: 1024.0,
                discrete_values: None,
            },
        );

        Ok(QueryOptimizationProblem {
            constraints,
            objective_function: ObjectiveFunction::MinimizeExecutionTime,
            variable_domains,
        })
    }

    /// Update strategy performance metrics
    async fn update_strategy_performance(&self, result: &HybridOptimizationResult) -> Result<()> {
        let mut performance_data = self.strategy_performance.write().await;
        let strategy_name = result.selected_approach.clone();

        let current_perf =
            performance_data
                .entry(strategy_name.clone())
                .or_insert(StrategyPerformance {
                    strategy_name: strategy_name.clone(),
                    success_rate: 0.0,
                    average_improvement: 0.0,
                    average_execution_time: Duration::from_millis(0),
                    confidence_accuracy: 0.0,
                    usage_count: 0,
                });

        // Update metrics (simplified)
        current_perf.usage_count += 1;
        let success = result.confidence_score > self.config.decision_threshold;
        current_perf.success_rate = (current_perf.success_rate
            * (current_perf.usage_count - 1) as f64
            + if success { 1.0 } else { 0.0 })
            / current_perf.usage_count as f64;

        // Update average execution time
        let total_time = current_perf.average_execution_time.as_millis() as f64
            * (current_perf.usage_count - 1) as f64
            + result.optimization_time.as_millis() as f64;
        current_perf.average_execution_time =
            Duration::from_millis((total_time / current_perf.usage_count as f64) as u64);

        Ok(())
    }

    /// Get strategy name for tracking
    fn strategy_name(&self, strategy: &OptimizationStrategy) -> String {
        match strategy {
            OptimizationStrategy::QuantumOnly => "quantum".to_string(),
            OptimizationStrategy::MLOnly => "ml".to_string(),
            OptimizationStrategy::Sequential {
                quantum_first: true,
            } => "sequential_quantum_first".to_string(),
            OptimizationStrategy::Sequential {
                quantum_first: false,
            } => "sequential_ml_first".to_string(),
            OptimizationStrategy::Parallel => "parallel".to_string(),
            OptimizationStrategy::Adaptive => "adaptive".to_string(),
            OptimizationStrategy::EnsembleVoting => "ensemble".to_string(),
        }
    }

    /// Get optimization history
    pub async fn get_optimization_history(&self) -> Vec<HybridOptimizationResult> {
        self.optimization_history.read().await.clone()
    }

    /// Get strategy performance statistics
    pub async fn get_strategy_performance(&self) -> HashMap<String, StrategyPerformance> {
        self.strategy_performance.read().await.clone()
    }

    /// Record query execution for learning
    pub async fn record_execution(
        &self,
        document: &Document,
        execution_time: Duration,
        success: bool,
    ) -> Result<()> {
        // Create training sample for ML optimizer
        let metrics = crate::performance::OperationMetrics {
            operation_name: Some("hybrid_optimization".to_string()),
            operation_type: crate::ast::OperationType::Query,
            query_hash: 0, // Would be computed from the document
            execution_time,
            parsing_time: Duration::from_millis(0),
            validation_time: Duration::from_millis(0),
            planning_time: Duration::from_millis(0),
            field_count: 0,      // Would be computed from the document
            depth: 0,            // Would be computed from the document
            complexity_score: 0, // Would be computed from the document
            cache_hit: false,
            error_count: if success { 0 } else { 1 },
            timestamp: std::time::SystemTime::now(),
            client_info: crate::performance::ClientInfo::default(),
        };

        self.ml_optimizer
            .record_execution(document, &metrics)
            .await?;

        Ok(())
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::ast::*;
    use crate::performance::PerformanceTracker;

    #[tokio::test]
    async fn test_hybrid_optimizer_creation() {
        let config = HybridOptimizerConfig::default();
        let performance_tracker = Arc::new(PerformanceTracker::new());

        let optimizer = HybridQueryOptimizer::new(config, performance_tracker);

        // Test strategy selection
        let simple_document = create_test_document();
        let strategy = optimizer
            .select_adaptive_strategy(&simple_document)
            .await
            .expect("should succeed");

        // Should select a reasonable strategy
        matches!(
            strategy,
            OptimizationStrategy::Sequential { .. }
                | OptimizationStrategy::MLOnly
                | OptimizationStrategy::Parallel
        );
    }

    #[tokio::test]
    async fn test_strategy_performance_tracking() {
        let config = HybridOptimizerConfig::default();
        let performance_tracker = Arc::new(PerformanceTracker::new());
        let optimizer = HybridQueryOptimizer::new(config, performance_tracker);

        let result = HybridOptimizationResult {
            quantum_result: None,
            ml_prediction: None,
            final_strategy: OptimizationStrategy::MLOnly,
            confidence_score: 0.9,
            optimization_time: Duration::from_millis(100),
            selected_approach: "ml".to_string(),
            ensemble_weights: None,
        };

        optimizer
            .update_strategy_performance(&result)
            .await
            .expect("should succeed");

        let performance_data = optimizer.get_strategy_performance().await;
        assert!(performance_data.contains_key("ml"));
        assert_eq!(performance_data["ml"].usage_count, 1);
    }

    fn create_test_document() -> Document {
        Document {
            definitions: vec![Definition::Operation(OperationDefinition {
                operation_type: OperationType::Query,
                name: Some("TestQuery".to_string()),
                variable_definitions: vec![],
                directives: vec![],
                selection_set: SelectionSet {
                    selections: vec![Selection::Field(Field {
                        alias: None,
                        name: "user".to_string(),
                        arguments: vec![],
                        directives: vec![],
                        selection_set: Some(SelectionSet {
                            selections: vec![Selection::Field(Field {
                                alias: None,
                                name: "id".to_string(),
                                arguments: vec![],
                                directives: vec![],
                                selection_set: None,
                            })],
                        }),
                    })],
                },
            })],
        }
    }
}