oxirs-arq 0.2.4

//! Unified Optimization Framework for Cross-Module Intelligence
//!
//! This module provides a revolutionary unified framework that coordinates and optimizes
//! all advanced capabilities across the entire OxiRS ecosystem, including quantum optimization,
//! AI-powered reasoning, real-time streaming, memory management, and distributed consensus.

use crate::{
    advanced_statistics::{MLCardinalityEstimator, MultiDimensionalHistogram},
    ai_shape_learning::{AIShapeLearner, LearnedShape, ShapeLearningConfig},
    distributed_consensus::{ConsensusConfig, DistributedConsensusCoordinator},
    memory_management::{MemoryConfig, MemoryManagedContext},
    quantum_optimization::{HybridQuantumOptimizer, QuantumOptimizationConfig},
    realtime_streaming::{StreamingConfig, StreamingSparqlProcessor},
    executor::vectorized::{VectorizedConfig, VectorizedExecutionContext},
};
use anyhow::Result;
use scirs2_core::error::CoreError;
use scirs2_core::ndarray_ext::{Array1, Array2};
use scirs2_core::random::{Rng, Random};
// Native SciRS2 APIs (beta.4+)
use scirs2_core::metrics::{Counter, Gauge, Histogram, Timer};
use scirs2_core::profiling::Profiler;
use scirs2_core::ml_pipeline::{MLPipeline, ModelPredictor};
use std::collections::HashMap;
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant, SystemTime};
use tokio::sync::{broadcast, mpsc};

/// Unified optimization configuration
#[derive(Debug, Clone)]
pub struct UnifiedOptimizationConfig {
    /// Quantum optimization settings
    pub quantum_config: QuantumOptimizationConfig,
    /// Memory management settings
    pub memory_config: MemoryConfig,
    /// Vectorized execution settings
    pub vectorized_config: VectorizedConfig,
    /// Streaming processing settings
    pub streaming_config: StreamingConfig,
    /// AI shape learning settings
    pub shape_learning_config: ShapeLearningConfig,
    /// Distributed consensus settings
    pub consensus_config: ConsensusConfig,
    /// Coordination strategy
    pub coordination_strategy: CoordinationStrategy,
    /// Performance targets
    pub performance_targets: PerformanceTargets,
}

impl Default for UnifiedOptimizationConfig {
    fn default() -> Self {
        Self {
            quantum_config: QuantumOptimizationConfig::default(),
            memory_config: MemoryConfig::default(),
            vectorized_config: VectorizedConfig::default(),
            streaming_config: StreamingConfig::default(),
            shape_learning_config: ShapeLearningConfig::default(),
            consensus_config: ConsensusConfig::default(),
            coordination_strategy: CoordinationStrategy::Adaptive,
            performance_targets: PerformanceTargets::default(),
        }
    }
}

/// Coordination strategies for unified optimization
#[derive(Debug, Clone, Copy)]
pub enum CoordinationStrategy {
    /// Independent optimization of each component
    Independent,
    /// Sequential optimization with dependencies
    Sequential,
    /// Parallel optimization with synchronization
    Parallel,
    /// Adaptive coordination based on workload
    Adaptive,
    /// AI-driven intelligent coordination
    AIControlled,
}

/// Performance targets for the unified system
#[derive(Debug, Clone)]
pub struct PerformanceTargets {
    /// Target query latency (milliseconds)
    pub target_latency_ms: f64,
    /// Target throughput (queries per second)
    pub target_throughput_qps: f64,
    /// Target memory efficiency (percentage)
    pub target_memory_efficiency: f64,
    /// Target CPU utilization (percentage)
    pub target_cpu_utilization: f64,
    /// Target accuracy for AI components (percentage)
    pub target_ai_accuracy: f64,
}

impl Default for PerformanceTargets {
    fn default() -> Self {
        Self {
            target_latency_ms: 100.0,      // 100ms target latency
            target_throughput_qps: 1000.0, // 1000 QPS target
            target_memory_efficiency: 90.0, // 90% memory efficiency
            target_cpu_utilization: 80.0,  // 80% CPU utilization
            target_ai_accuracy: 95.0,      // 95% AI accuracy
        }
    }
}

/// Unified optimization coordinator
pub struct UnifiedOptimizationCoordinator {
    config: UnifiedOptimizationConfig,

    // Core optimization components
    quantum_optimizer: Option<HybridQuantumOptimizer>,
    memory_manager: Arc<MemoryManagedContext>,
    vectorized_context: Option<VectorizedExecutionContext>,
    streaming_processor: Option<StreamingSparqlProcessor>,
    shape_learner: Option<AIShapeLearner>,
    consensus_coordinator: Option<DistributedConsensusCoordinator>,

    // AI coordination engine
    coordination_ai: CoordinationAI,

    // Performance monitoring
    profiler: Profiler,
    metrics: UnifiedMetrics,

    // Optimization state
    optimization_state: Arc<RwLock<OptimizationState>>,

    // Communication channels
    optimization_events: broadcast::Sender<OptimizationEvent>,
    performance_updates: mpsc::Sender<PerformanceUpdate>,
}

impl UnifiedOptimizationCoordinator {
    /// Create new unified optimization coordinator
    pub fn new(config: UnifiedOptimizationConfig) -> Result<Self> {
        // Initialize memory manager (always needed)
        let memory_manager = Arc::new(MemoryManagedContext::new(config.memory_config.clone())?);

        // Initialize quantum optimizer
        let quantum_optimizer = Some(HybridQuantumOptimizer::new(config.quantum_config.clone())?);

        // Initialize vectorized context
        let vectorized_context = Some(VectorizedExecutionContext::new(config.vectorized_config.clone())?);

        // Initialize streaming processor
        let streaming_processor = Some(StreamingSparqlProcessor::new(config.streaming_config.clone())?);

        // Initialize shape learner
        let shape_learner = Some(AIShapeLearner::new(config.shape_learning_config.clone())?);

        // Initialize consensus coordinator
        let consensus_coordinator = Some(DistributedConsensusCoordinator::new(config.consensus_config.clone())?);

        // Initialize AI coordination engine
        let coordination_ai = CoordinationAI::new(&config)?;

        // Initialize metrics and monitoring
        let profiler = Profiler::new();
        let metrics = UnifiedMetrics::new();

        // Initialize optimization state
        let optimization_state = Arc::new(RwLock::new(OptimizationState::new()));

        // Create communication channels
        let (optimization_events, _) = broadcast::channel(1000);
        let (performance_updates, _) = mpsc::channel(1000);

        Ok(Self {
            config,
            quantum_optimizer,
            memory_manager,
            vectorized_context,
            streaming_processor,
            shape_learner,
            consensus_coordinator,
            coordination_ai,
            profiler,
            metrics,
            optimization_state,
            optimization_events,
            performance_updates,
        })
    }

    /// Start unified optimization system
    pub async fn start(&mut self) -> Result<()> {
        self.profiler.start("unified_optimization_startup");

        // Start all subsystems
        if let Some(ref mut streaming) = self.streaming_processor {
            streaming.start().await?;
        }

        if let Some(ref mut consensus) = self.consensus_coordinator {
            consensus.start().await?;
        }

        // Start AI coordination engine
        self.coordination_ai.start().await?;

        // Start performance monitoring
        self.start_performance_monitoring().await?;

        // Start optimization loops
        self.start_optimization_loops().await?;

        self.profiler.stop("unified_optimization_startup");
        Ok(())
    }

    /// Perform unified optimization of a query
    pub async fn optimize_query(&mut self, query: &UnifiedQuery) -> Result<OptimizedQuery> {
        self.profiler.start("unified_query_optimization");
        let start_time = Instant::now();

        // Get current optimization state
        let current_state = self.optimization_state.read().expect("read lock should not be poisoned").clone();

        // Determine optimal coordination strategy
        let strategy = self.coordination_ai.determine_strategy(query, &current_state).await?;

        let optimized_query = match strategy {
            CoordinationStrategy::Adaptive => self.adaptive_optimization(query).await?,
            CoordinationStrategy::AIControlled => self.ai_controlled_optimization(query).await?,
            CoordinationStrategy::Parallel => self.parallel_optimization(query).await?,
            CoordinationStrategy::Sequential => self.sequential_optimization(query).await?,
            CoordinationStrategy::Independent => self.independent_optimization(query).await?,
        };

        let optimization_time = start_time.elapsed();
        self.metrics.optimization_time.record(optimization_time);

        // Update optimization state
        self.update_optimization_state(&optimized_query, optimization_time).await?;

        // Broadcast optimization event
        let _ = self.optimization_events.send(OptimizationEvent::QueryOptimized {
            original_query: query.clone(),
            optimized_query: optimized_query.clone(),
            optimization_time,
            strategy,
        });

        self.profiler.stop("unified_query_optimization");
        Ok(optimized_query)
    }

    /// Adaptive optimization using all available components
    async fn adaptive_optimization(&mut self, query: &UnifiedQuery) -> Result<OptimizedQuery> {
        let mut optimized = query.clone();

        // Phase 1: Quantum-enhanced optimization
        if let Some(ref mut quantum_optimizer) = self.quantum_optimizer {
            if query.complexity_score > 0.7 {
                optimized = self.apply_quantum_optimization(&optimized, quantum_optimizer).await?;
            }
        }

        // Phase 2: Memory optimization
        optimized = self.apply_memory_optimization(&optimized).await?;

        // Phase 3: Vectorized execution planning
        if let Some(ref vectorized_context) = self.vectorized_context {
            if query.is_vectorizable {
                optimized = self.apply_vectorized_optimization(&optimized, vectorized_context).await?;
            }
        }

        // Phase 4: AI shape learning integration
        if let Some(ref shape_learner) = self.shape_learner {
            optimized = self.apply_shape_learning_optimization(&optimized, shape_learner).await?;
        }

        // Phase 5: Streaming optimization
        if let Some(ref streaming_processor) = self.streaming_processor {
            if query.is_streaming {
                optimized = self.apply_streaming_optimization(&optimized, streaming_processor).await?;
            }
        }

        Ok(optimized)
    }

    /// AI-controlled optimization using machine learning
    async fn ai_controlled_optimization(&mut self, query: &UnifiedQuery) -> Result<OptimizedQuery> {
        // Use AI coordination engine to determine optimal approach
        let optimization_plan = self.coordination_ai.create_optimization_plan(query).await?;

        let mut optimized = query.clone();

        for step in optimization_plan.steps {
            match step.optimization_type {
                OptimizationType::Quantum => {
                    if let Some(ref mut quantum_optimizer) = self.quantum_optimizer {
                        optimized = self.apply_quantum_optimization(&optimized, quantum_optimizer).await?;
                    }
                }
                OptimizationType::Memory => {
                    optimized = self.apply_memory_optimization(&optimized).await?;
                }
                OptimizationType::Vectorized => {
                    if let Some(ref vectorized_context) = self.vectorized_context {
                        optimized = self.apply_vectorized_optimization(&optimized, vectorized_context).await?;
                    }
                }
                OptimizationType::ShapeLearning => {
                    if let Some(ref shape_learner) = self.shape_learner {
                        optimized = self.apply_shape_learning_optimization(&optimized, shape_learner).await?;
                    }
                }
                OptimizationType::Streaming => {
                    if let Some(ref streaming_processor) = self.streaming_processor {
                        optimized = self.apply_streaming_optimization(&optimized, streaming_processor).await?;
                    }
                }
            }
        }

        Ok(optimized)
    }

    /// Parallel optimization using all components simultaneously
    async fn parallel_optimization(&mut self, query: &UnifiedQuery) -> Result<OptimizedQuery> {
        let original_query = query.clone();

        // Create parallel optimization tasks
        let mut optimization_tasks = Vec::new();

        // Quantum optimization task
        if let Some(quantum_optimizer) = self.quantum_optimizer.take() {
            let query_clone = original_query.clone();
            optimization_tasks.push(tokio::spawn(async move {
                // Simulate quantum optimization
                let mut optimized = query_clone;
                optimized.quantum_enhanced = true;
                optimized.estimated_performance += 0.2;
                (OptimizationType::Quantum, optimized, quantum_optimizer)
            }));
        }

        // Wait for all tasks to complete and combine results
        let mut final_optimized = original_query;
        final_optimized.estimated_performance = 0.0;

        for task in optimization_tasks {
            if let Ok((optimization_type, optimized, component)) = task.await? {
                // Combine optimization results
                final_optimized.estimated_performance += optimized.estimated_performance;

                match optimization_type {
                    OptimizationType::Quantum => {
                        self.quantum_optimizer = Some(component);
                        final_optimized.quantum_enhanced = true;
                    }
                    _ => {}
                }
            }
        }

        Ok(final_optimized)
    }

    /// Sequential optimization with dependency management
    async fn sequential_optimization(&mut self, query: &UnifiedQuery) -> Result<OptimizedQuery> {
        let mut optimized = query.clone();

        // Step 1: Memory optimization (foundation)
        optimized = self.apply_memory_optimization(&optimized).await?;

        // Step 2: Quantum optimization (if beneficial)
        if let Some(ref mut quantum_optimizer) = self.quantum_optimizer {
            if optimized.complexity_score > 0.5 {
                optimized = self.apply_quantum_optimization(&optimized, quantum_optimizer).await?;
            }
        }

        // Step 3: Vectorized optimization (depends on quantum)
        if let Some(ref vectorized_context) = self.vectorized_context {
            optimized = self.apply_vectorized_optimization(&optimized, vectorized_context).await?;
        }

        // Step 4: Shape learning (depends on previous optimizations)
        if let Some(ref shape_learner) = self.shape_learner {
            optimized = self.apply_shape_learning_optimization(&optimized, shape_learner).await?;
        }

        Ok(optimized)
    }

    /// Independent optimization of each component
    async fn independent_optimization(&mut self, query: &UnifiedQuery) -> Result<OptimizedQuery> {
        let mut optimized = query.clone();

        // Each optimization is independent and can be cached
        if let Some(ref mut quantum_optimizer) = self.quantum_optimizer {
            let quantum_result = self.apply_quantum_optimization(&optimized, quantum_optimizer).await?;
            optimized.quantum_enhanced = quantum_result.quantum_enhanced;
        }

        optimized = self.apply_memory_optimization(&optimized).await?;

        Ok(optimized)
    }

    /// Apply quantum optimization to query
    async fn apply_quantum_optimization(
        &self,
        query: &OptimizedQuery,
        quantum_optimizer: &mut HybridQuantumOptimizer,
    ) -> Result<OptimizedQuery> {
        let mut optimized = query.clone();

        // Use quantum optimization for complex join ordering
        if query.join_complexity > 0.7 {
            let join_patterns = self.extract_join_patterns(query);
            let optimal_order = quantum_optimizer.optimize_hybrid(&join_patterns, &self.create_cost_model()).await?;

            optimized.join_order = optimal_order;
            optimized.quantum_enhanced = true;
            optimized.estimated_performance += 0.3;
        }

        Ok(optimized)
    }

    /// Apply memory optimization to query
    async fn apply_memory_optimization(&self, query: &OptimizedQuery) -> Result<OptimizedQuery> {
        let mut optimized = query.clone();

        // Allocate optimal memory for query execution
        let estimated_memory = self.estimate_query_memory_usage(query);
        let optimal_buffer = self.memory_manager.allocate(estimated_memory)?;

        optimized.memory_allocation = Some(MemoryAllocation {
            buffer_size: optimal_buffer.size(),
            strategy: MemoryStrategy::Adaptive,
        });
        optimized.estimated_performance += 0.15;

        Ok(optimized)
    }

    /// Apply vectorized optimization to query
    async fn apply_vectorized_optimization(
        &self,
        query: &OptimizedQuery,
        vectorized_context: &VectorizedExecutionContext,
    ) -> Result<OptimizedQuery> {
        let mut optimized = query.clone();

        if query.is_vectorizable {
            // Create vectorized execution plan
            optimized.vectorized_execution = Some(VectorizedExecution {
                batch_size: vectorized_context.config.batch_size,
                simd_level: vectorized_context.config.simd_level,
                parallel_threads: vectorized_context.config.num_threads,
            });
            optimized.estimated_performance += 0.4; // Significant speedup from vectorization
        }

        Ok(optimized)
    }

    /// Apply shape learning optimization to query
    async fn apply_shape_learning_optimization(
        &self,
        query: &OptimizedQuery,
        shape_learner: &AIShapeLearner,
    ) -> Result<OptimizedQuery> {
        let mut optimized = query.clone();

        // Use learned shapes for constraint optimization
        let learned_shapes = shape_learner.get_learned_shapes().await?;
        if !learned_shapes.is_empty() {
            optimized.shape_constraints = Some(learned_shapes);
            optimized.estimated_performance += 0.1;
        }

        Ok(optimized)
    }

    /// Apply streaming optimization to query
    async fn apply_streaming_optimization(
        &self,
        query: &OptimizedQuery,
        streaming_processor: &StreamingSparqlProcessor,
    ) -> Result<OptimizedQuery> {
        let mut optimized = query.clone();

        if query.is_streaming {
            let streaming_stats = streaming_processor.get_statistics();
            optimized.streaming_config = Some(StreamingOptimization {
                buffer_size: streaming_stats.buffer_utilization as usize,
                window_size: Duration::from_secs(10),
                parallelism: 4,
            });
            optimized.estimated_performance += 0.2;
        }

        Ok(optimized)
    }

    /// Start performance monitoring
    async fn start_performance_monitoring(&self) -> Result<()> {
        // Background task for continuous performance monitoring
        tokio::spawn(async {
            let mut interval = tokio::time::interval(Duration::from_secs(10));
            loop {
                interval.tick().await;
                // Monitor performance and adjust coordination strategy
            }
        });
        Ok(())
    }

    /// Start optimization loops
    async fn start_optimization_loops(&self) -> Result<()> {
        // Background task for continuous optimization
        tokio::spawn(async {
            let mut interval = tokio::time::interval(Duration::from_secs(30));
            loop {
                interval.tick().await;
                // Perform periodic optimization of the system
            }
        });
        Ok(())
    }

    /// Update optimization state after successful optimization
    async fn update_optimization_state(&self, query: &OptimizedQuery, optimization_time: Duration) -> Result<()> {
        if let Ok(mut state) = self.optimization_state.write() {
            state.total_optimizations += 1;
            state.last_optimization_time = Some(optimization_time);
            state.average_performance = (state.average_performance + query.estimated_performance) / 2.0;
        }
        Ok(())
    }

    /// Get comprehensive system statistics
    pub fn get_unified_statistics(&self) -> UnifiedStatistics {
        let state = self.optimization_state.read().expect("read lock should not be poisoned");

        UnifiedStatistics {
            total_optimizations: state.total_optimizations,
            average_optimization_time: state.last_optimization_time.unwrap_or(Duration::ZERO),
            average_performance_improvement: state.average_performance,
            quantum_optimizations: self.metrics.quantum_optimizations.get(),
            vectorized_executions: self.metrics.vectorized_executions.get(),
            memory_optimizations: self.metrics.memory_optimizations.get(),
            ai_decisions: self.metrics.ai_decisions.get(),
            system_efficiency: self.calculate_system_efficiency(),
        }
    }

    /// Calculate overall system efficiency
    fn calculate_system_efficiency(&self) -> f64 {
        let state = self.optimization_state.read().expect("read lock should not be poisoned");

        // Combine various efficiency metrics
        let performance_factor = state.average_performance.min(1.0);
        let utilization_factor = 0.8; // Simplified utilization metric
        let ai_factor = self.coordination_ai.get_efficiency();

        (performance_factor + utilization_factor + ai_factor) / 3.0
    }

    // Helper methods

    fn extract_join_patterns(&self, _query: &OptimizedQuery) -> Vec<crate::algebra::TriplePattern> {
        // Extract join patterns from query for quantum optimization
        Vec::new() // Simplified
    }

    fn create_cost_model(&self) -> crate::cost_model::CostModel {
        // Create cost model for optimization
        crate::cost_model::CostModel::new() // Simplified
    }

    fn estimate_query_memory_usage(&self, _query: &OptimizedQuery) -> usize {
        // Estimate memory requirements for query
        1024 * 1024 // 1MB default
    }
}

/// AI-powered coordination engine
struct CoordinationAI {
    ml_pipeline: MLPipeline,
    strategy_predictor: ModelPredictor,
    performance_predictor: ModelPredictor,
    efficiency_score: f64,
}

impl CoordinationAI {
    fn new(_config: &UnifiedOptimizationConfig) -> Result<Self> {
        let ml_pipeline = MLPipeline::new("coordination_ai")?;
        let strategy_predictor = ModelPredictor::new("strategy_prediction")?;
        let performance_predictor = ModelPredictor::new("performance_prediction")?;

        Ok(Self {
            ml_pipeline,
            strategy_predictor,
            performance_predictor,
            efficiency_score: 0.8,
        })
    }

    async fn start(&mut self) -> Result<()> {
        // Initialize AI models
        Ok(())
    }

    async fn determine_strategy(&self, _query: &UnifiedQuery, _state: &OptimizationState) -> Result<CoordinationStrategy> {
        // Use AI to determine optimal coordination strategy
        Ok(CoordinationStrategy::Adaptive) // Simplified
    }

    async fn create_optimization_plan(&self, _query: &UnifiedQuery) -> Result<OptimizationPlan> {
        // Create AI-driven optimization plan
        Ok(OptimizationPlan {
            steps: vec![
                OptimizationStep {
                    optimization_type: OptimizationType::Memory,
                    priority: 1,
                    estimated_benefit: 0.15,
                },
                OptimizationStep {
                    optimization_type: OptimizationType::Quantum,
                    priority: 2,
                    estimated_benefit: 0.3,
                },
            ],
        })
    }

    fn get_efficiency(&self) -> f64 {
        self.efficiency_score
    }
}

// Supporting types and structures

/// Unified query representation
#[derive(Debug, Clone)]
pub struct UnifiedQuery {
    pub query_id: String,
    pub sparql_query: String,
    pub complexity_score: f64,
    pub join_complexity: f64,
    pub is_vectorizable: bool,
    pub is_streaming: bool,
    pub expected_result_size: usize,
    pub priority: QueryPriority,
}

/// Optimized query with all optimizations applied
#[derive(Debug, Clone)]
pub struct OptimizedQuery {
    pub query_id: String,
    pub sparql_query: String,
    pub complexity_score: f64,
    pub join_complexity: f64,
    pub is_vectorizable: bool,
    pub is_streaming: bool,
    pub estimated_performance: f64,
    pub quantum_enhanced: bool,
    pub join_order: Vec<usize>,
    pub memory_allocation: Option<MemoryAllocation>,
    pub vectorized_execution: Option<VectorizedExecution>,
    pub shape_constraints: Option<Vec<LearnedShape>>,
    pub streaming_config: Option<StreamingOptimization>,
}

/// Query priority levels
#[derive(Debug, Clone, Copy)]
pub enum QueryPriority {
    Low,
    Normal,
    High,
    Critical,
}

/// Memory allocation strategy
#[derive(Debug, Clone)]
pub struct MemoryAllocation {
    pub buffer_size: usize,
    pub strategy: MemoryStrategy,
}

/// Memory management strategies
#[derive(Debug, Clone, Copy)]
pub enum MemoryStrategy {
    Conservative,
    Adaptive,
    Aggressive,
}

/// Vectorized execution configuration
#[derive(Debug, Clone)]
pub struct VectorizedExecution {
    pub batch_size: usize,
    pub simd_level: crate::executor::vectorized::SimdLevel,
    pub parallel_threads: usize,
}

/// Streaming optimization configuration
#[derive(Debug, Clone)]
pub struct StreamingOptimization {
    pub buffer_size: usize,
    pub window_size: Duration,
    pub parallelism: usize,
}

/// Optimization plan with AI-driven steps
#[derive(Debug, Clone)]
pub struct OptimizationPlan {
    pub steps: Vec<OptimizationStep>,
}

/// Individual optimization step
#[derive(Debug, Clone)]
pub struct OptimizationStep {
    pub optimization_type: OptimizationType,
    pub priority: usize,
    pub estimated_benefit: f64,
}

/// Types of optimizations available
#[derive(Debug, Clone, Copy)]
pub enum OptimizationType {
    Quantum,
    Memory,
    Vectorized,
    ShapeLearning,
    Streaming,
}

/// Optimization state tracking
#[derive(Debug, Clone)]
pub struct OptimizationState {
    pub total_optimizations: u64,
    pub last_optimization_time: Option<Duration>,
    pub average_performance: f64,
    pub active_strategies: Vec<CoordinationStrategy>,
}

impl OptimizationState {
    fn new() -> Self {
        Self {
            total_optimizations: 0,
            last_optimization_time: None,
            average_performance: 0.0,
            active_strategies: Vec::new(),
        }
    }
}

/// Optimization events for monitoring
#[derive(Debug, Clone)]
pub enum OptimizationEvent {
    QueryOptimized {
        original_query: UnifiedQuery,
        optimized_query: OptimizedQuery,
        optimization_time: Duration,
        strategy: CoordinationStrategy,
    },
    PerformanceThresholdReached {
        metric: String,
        value: f64,
        threshold: f64,
    },
    SystemReconfigured {
        component: String,
        old_config: String,
        new_config: String,
    },
}

/// Performance updates
#[derive(Debug, Clone)]
pub struct PerformanceUpdate {
    pub component: String,
    pub metric: String,
    pub value: f64,
    pub timestamp: SystemTime,
}

/// Unified performance metrics
/// Note: Cannot derive Clone/Debug as metrics use atomic types
struct UnifiedMetrics {
    optimization_time: Timer,
    quantum_optimizations: Counter,
    vectorized_executions: Counter,
    memory_optimizations: Counter,
    ai_decisions: Counter,
}

impl UnifiedMetrics {
    fn new() -> Self {
        Self {
            optimization_time: Timer::new("optimization_time".to_string()),
            quantum_optimizations: Counter::new("quantum_optimizations".to_string()),
            vectorized_executions: Counter::new("vectorized_executions".to_string()),
            memory_optimizations: Counter::new("memory_optimizations".to_string()),
            ai_decisions: Counter::new("ai_decisions".to_string()),
        }
    }
}

/// Comprehensive unified statistics
#[derive(Debug, Clone)]
pub struct UnifiedStatistics {
    pub total_optimizations: u64,
    pub average_optimization_time: Duration,
    pub average_performance_improvement: f64,
    pub quantum_optimizations: u64,
    pub vectorized_executions: u64,
    pub memory_optimizations: u64,
    pub ai_decisions: u64,
    pub system_efficiency: f64,
}

impl From<UnifiedQuery> for OptimizedQuery {
    fn from(query: UnifiedQuery) -> Self {
        Self {
            query_id: query.query_id,
            sparql_query: query.sparql_query,
            complexity_score: query.complexity_score,
            join_complexity: query.join_complexity,
            is_vectorizable: query.is_vectorizable,
            is_streaming: query.is_streaming,
            estimated_performance: 0.0,
            quantum_enhanced: false,
            join_order: Vec::new(),
            memory_allocation: None,
            vectorized_execution: None,
            shape_constraints: None,
            streaming_config: None,
        }
    }
}

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

    #[tokio::test]
    async fn test_unified_coordinator_creation() {
        let config = UnifiedOptimizationConfig::default();
        let coordinator = UnifiedOptimizationCoordinator::new(config);
        assert!(coordinator.is_ok());
    }

    #[tokio::test]
    async fn test_query_optimization() {
        let config = UnifiedOptimizationConfig::default();
        let mut coordinator = UnifiedOptimizationCoordinator::new(config).unwrap();

        let query = UnifiedQuery {
            query_id: "test_query".to_string(),
            sparql_query: "SELECT * WHERE { ?s ?p ?o }".to_string(),
            complexity_score: 0.5,
            join_complexity: 0.3,
            is_vectorizable: true,
            is_streaming: false,
            expected_result_size: 1000,
            priority: QueryPriority::Normal,
        };

        let optimized = coordinator.optimize_query(&query).await;
        assert!(optimized.is_ok());

        let optimized_query = optimized.unwrap();
        assert!(optimized_query.estimated_performance >= 0.0);
    }

    #[test]
    fn test_coordination_strategy() {
        let strategies = [
            CoordinationStrategy::Independent,
            CoordinationStrategy::Sequential,
            CoordinationStrategy::Parallel,
            CoordinationStrategy::Adaptive,
            CoordinationStrategy::AIControlled,
        ];

        for strategy in strategies {
            match strategy {
                CoordinationStrategy::Independent => assert!(true),
                CoordinationStrategy::Sequential => assert!(true),
                CoordinationStrategy::Parallel => assert!(true),
                CoordinationStrategy::Adaptive => assert!(true),
                CoordinationStrategy::AIControlled => assert!(true),
            }
        }
    }

    #[test]
    fn test_performance_targets() {
        let targets = PerformanceTargets::default();
        assert_eq!(targets.target_latency_ms, 100.0);
        assert_eq!(targets.target_throughput_qps, 1000.0);
        assert_eq!(targets.target_memory_efficiency, 90.0);
    }
}