oxirs-vec 0.2.4

//! OxiRS ARQ Integration for Vector-Aware Query Optimization
//!
//! This module provides comprehensive integration between oxirs-vec and oxirs-arq,
//! enabling vector-aware SPARQL query optimization and hybrid symbolic-vector queries.
//!
//! Features:
//! - Vector-aware query planning and cost modeling
//! - Hybrid SPARQL-vector execution strategies
//! - Vector service function registration
//! - Optimization hints for vector operations
//! - Performance monitoring and query analytics
//! - Neural-symbolic query processing

use crate::VectorIndex;
use anyhow::{Error as AnyhowError, Result};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant};
use tracing::{debug, span, Level};

/// Vector-aware query optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VectorQueryConfig {
    /// Enable vector-aware query planning
    pub enable_vector_planning: bool,
    /// Vector operation cost model
    pub cost_model: VectorCostModel,
    /// Optimization strategies
    pub optimization_strategies: Vec<OptimizationStrategy>,
    /// Join optimization settings
    pub join_optimization: JoinOptimizationConfig,
    /// Result streaming configuration
    pub streaming_config: StreamingConfig,
    /// Performance monitoring settings
    pub monitoring: QueryMonitoringConfig,
}

/// Vector operation cost model
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VectorCostModel {
    /// Base cost per vector operation
    pub base_cost: f64,
    /// Cost scaling factors
    pub scaling_factors: CostScalingFactors,
    /// Index-specific cost adjustments
    pub index_costs: HashMap<String, f64>,
    /// Hardware-specific adjustments
    pub hardware_adjustments: HardwareAdjustments,
}

/// Cost scaling factors for different operations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostScalingFactors {
    /// Search operation scaling
    pub search_scale: f64,
    /// Index building scaling
    pub build_scale: f64,
    /// Vector addition scaling
    pub add_scale: f64,
    /// Cross-modal operation scaling
    pub cross_modal_scale: f64,
    /// Similarity computation scaling
    pub similarity_scale: f64,
}

/// Hardware-specific cost adjustments
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareAdjustments {
    /// CPU performance factor
    pub cpu_factor: f64,
    /// Memory bandwidth factor
    pub memory_factor: f64,
    /// GPU acceleration factor
    pub gpu_factor: f64,
    /// Network latency factor
    pub network_factor: f64,
}

/// Optimization strategies for vector-aware queries
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationStrategy {
    /// Push vector filters early in execution
    VectorFilterPushdown,
    /// Reorder joins to minimize vector operations
    VectorJoinReordering,
    /// Use vector indices for filtering
    VectorIndexSelection,
    /// Batch vector operations
    VectorBatching,
    /// Cache frequently used vectors
    VectorCaching,
    /// Parallel vector execution
    VectorParallelization,
    /// Adaptive vector strategy selection
    AdaptiveVectorStrategy,
}

/// Join optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct JoinOptimizationConfig {
    /// Enable vector-aware join ordering
    pub enable_vector_join_ordering: bool,
    /// Join algorithm selection
    pub join_algorithms: Vec<VectorJoinAlgorithm>,
    /// Join cost threshold
    pub cost_threshold: f64,
    /// Enable join result caching
    pub enable_caching: bool,
}

/// Vector-aware join algorithms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum VectorJoinAlgorithm {
    /// Nested loop join with vector filtering
    VectorNestedLoop,
    /// Hash join with vector keys
    VectorHashJoin,
    /// Sort-merge join with vector ordering
    VectorSortMerge,
    /// Index-based join using vector indices
    VectorIndexJoin,
    /// Similarity-based join
    SimilarityJoin,
}

/// Streaming configuration for vector results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StreamingConfig {
    /// Enable result streaming
    pub enable_streaming: bool,
    /// Streaming buffer size
    pub buffer_size: usize,
    /// Streaming timeout
    pub timeout_ms: u64,
    /// Enable backpressure handling
    pub enable_backpressure: bool,
}

/// Query monitoring configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QueryMonitoringConfig {
    /// Enable performance monitoring
    pub enable_monitoring: bool,
    /// Monitor vector operation performance
    pub monitor_vector_ops: bool,
    /// Monitor join performance
    pub monitor_joins: bool,
    /// Monitor memory usage
    pub monitor_memory: bool,
    /// Export metrics format
    pub metrics_format: MetricsFormat,
}

/// Metrics export formats
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MetricsFormat {
    Prometheus,
    JSON,
    CSV,
    Custom(String),
}

impl Default for VectorQueryConfig {
    fn default() -> Self {
        Self {
            enable_vector_planning: true,
            cost_model: VectorCostModel {
                base_cost: 1.0,
                scaling_factors: CostScalingFactors {
                    search_scale: 1.5,
                    build_scale: 10.0,
                    add_scale: 0.5,
                    cross_modal_scale: 2.0,
                    similarity_scale: 1.0,
                },
                index_costs: {
                    let mut costs = HashMap::new();
                    costs.insert("hnsw".to_string(), 1.2);
                    costs.insert("ivf".to_string(), 1.0);
                    costs.insert("flat".to_string(), 2.0);
                    costs
                },
                hardware_adjustments: HardwareAdjustments {
                    cpu_factor: 1.0,
                    memory_factor: 1.0,
                    gpu_factor: 0.3, // GPU is 3x faster
                    network_factor: 1.5,
                },
            },
            optimization_strategies: vec![
                OptimizationStrategy::VectorFilterPushdown,
                OptimizationStrategy::VectorJoinReordering,
                OptimizationStrategy::VectorIndexSelection,
                OptimizationStrategy::VectorBatching,
            ],
            join_optimization: JoinOptimizationConfig {
                enable_vector_join_ordering: true,
                join_algorithms: vec![
                    VectorJoinAlgorithm::VectorIndexJoin,
                    VectorJoinAlgorithm::SimilarityJoin,
                    VectorJoinAlgorithm::VectorHashJoin,
                ],
                cost_threshold: 1000.0,
                enable_caching: true,
            },
            streaming_config: StreamingConfig {
                enable_streaming: true,
                buffer_size: 1000,
                timeout_ms: 30000,
                enable_backpressure: true,
            },
            monitoring: QueryMonitoringConfig {
                enable_monitoring: true,
                monitor_vector_ops: true,
                monitor_joins: true,
                monitor_memory: true,
                metrics_format: MetricsFormat::JSON,
            },
        }
    }
}

/// Vector-aware query planner
pub struct VectorQueryPlanner {
    /// Configuration
    config: VectorQueryConfig,
    /// Available vector indices
    vector_indices: Arc<RwLock<HashMap<String, Arc<dyn VectorIndex>>>>,
    /// Query statistics for cost modeling
    query_stats: Arc<RwLock<QueryStatistics>>,
    /// Optimization cache
    optimization_cache: Arc<RwLock<HashMap<String, OptimizationPlan>>>,
    /// Performance monitor
    performance_monitor: Arc<RwLock<VectorQueryPerformance>>,
}

/// Query statistics for optimization
#[derive(Debug, Clone, Default)]
pub struct QueryStatistics {
    /// Total queries processed
    pub total_queries: usize,
    /// Vector operation counts
    pub vector_op_counts: HashMap<String, usize>,
    /// Average execution times
    pub avg_execution_times: HashMap<String, Duration>,
    /// Join statistics
    pub join_stats: JoinStatistics,
    /// Index usage statistics
    pub index_usage: HashMap<String, IndexUsageStats>,
}

/// Join operation statistics
#[derive(Debug, Clone, Default)]
pub struct JoinStatistics {
    /// Total joins performed
    pub total_joins: usize,
    /// Join algorithm usage
    pub algorithm_usage: HashMap<String, usize>,
    /// Average join cardinality
    pub avg_cardinality: f64,
    /// Join selectivity estimates
    pub selectivity_estimates: HashMap<String, f64>,
}

/// Index usage statistics
#[derive(Debug, Clone, Default)]
pub struct IndexUsageStats {
    /// Times index was used
    pub usage_count: usize,
    /// Average search time
    pub avg_search_time: Duration,
    /// Average result count
    pub avg_result_count: f64,
    /// Cache hit rate
    pub cache_hit_rate: f32,
}

/// Vector query performance metrics
#[derive(Debug, Clone, Default)]
pub struct VectorQueryPerformance {
    /// Query execution metrics
    pub execution_metrics: ExecutionMetrics,
    /// Resource utilization metrics
    pub resource_metrics: ResourceMetrics,
    /// Quality metrics
    pub quality_metrics: QualityMetrics,
    /// Trend analysis
    pub trend_analysis: TrendAnalysis,
}

/// Query execution performance metrics
#[derive(Debug, Clone, Default)]
pub struct ExecutionMetrics {
    /// Total execution time
    pub total_time: Duration,
    /// Vector operation time
    pub vector_op_time: Duration,
    /// Join operation time
    pub join_time: Duration,
    /// Planning time
    pub planning_time: Duration,
    /// Result materialization time
    pub materialization_time: Duration,
}

/// Resource utilization metrics
#[derive(Debug, Clone, Default)]
pub struct ResourceMetrics {
    /// CPU utilization percentage
    pub cpu_utilization: f32,
    /// Memory usage in bytes
    pub memory_usage: usize,
    /// GPU utilization percentage
    pub gpu_utilization: f32,
    /// Network I/O bytes
    pub network_io: usize,
    /// Disk I/O bytes
    pub disk_io: usize,
}

/// Query result quality metrics
#[derive(Debug, Clone, Default)]
pub struct QualityMetrics {
    /// Result accuracy score
    pub accuracy_score: f32,
    /// Result completeness score
    pub completeness_score: f32,
    /// Result relevance score
    pub relevance_score: f32,
    /// Confidence score
    pub confidence_score: f32,
}

/// Performance trend analysis
#[derive(Debug, Clone, Default)]
pub struct TrendAnalysis {
    /// Performance trend over time
    pub performance_trend: Vec<(Instant, f64)>,
    /// Resource usage trend
    pub resource_trend: Vec<(Instant, f64)>,
    /// Quality trend
    pub quality_trend: Vec<(Instant, f64)>,
    /// Optimization effectiveness
    pub optimization_effectiveness: f64,
}

/// Optimization plan for vector queries
#[derive(Debug, Clone)]
pub struct OptimizationPlan {
    /// Plan ID
    pub plan_id: String,
    /// Optimization steps
    pub steps: Vec<OptimizationStep>,
    /// Estimated cost
    pub estimated_cost: f64,
    /// Estimated execution time
    pub estimated_time: Duration,
    /// Expected quality score
    pub expected_quality: f32,
    /// Plan metadata
    pub metadata: HashMap<String, String>,
}

/// Individual optimization step
#[derive(Debug, Clone)]
pub struct OptimizationStep {
    /// Step type
    pub step_type: OptimizationStepType,
    /// Step parameters
    pub parameters: HashMap<String, serde_json::Value>,
    /// Estimated cost
    pub cost: f64,
    /// Dependencies
    pub dependencies: Vec<String>,
}

/// Types of optimization steps
#[derive(Debug, Clone)]
pub enum OptimizationStepType {
    /// Vector index selection
    IndexSelection {
        index_type: String,
        selection_criteria: SelectionCriteria,
    },
    /// Filter pushdown optimization
    FilterPushdown {
        filter_type: FilterType,
        pushdown_level: usize,
    },
    /// Join reordering
    JoinReordering {
        original_order: Vec<String>,
        optimized_order: Vec<String>,
    },
    /// Vector batching
    VectorBatching {
        batch_size: usize,
        batching_strategy: BatchingStrategy,
    },
    /// Caching setup
    CachingSetup {
        cache_type: CacheType,
        cache_size: usize,
    },
    /// Parallel execution
    ParallelExecution {
        parallelism_level: usize,
        execution_strategy: ParallelStrategy,
    },
}

/// Index selection criteria
#[derive(Debug, Clone)]
pub enum SelectionCriteria {
    Performance,
    Memory,
    Accuracy,
    Hybrid(Vec<f32>), // Weights for different criteria
}

/// Filter types for optimization
#[derive(Debug, Clone)]
pub enum FilterType {
    SimilarityFilter,
    ThresholdFilter,
    RangeFilter,
    CompositeFilter,
}

/// Batching strategies
#[derive(Debug, Clone)]
pub enum BatchingStrategy {
    SizeBased,
    TimeBased,
    Adaptive,
    ContentBased,
}

/// Cache types
#[derive(Debug, Clone)]
pub enum CacheType {
    VectorCache,
    ResultCache,
    IndexCache,
    QueryCache,
}

/// Parallel execution strategies
#[derive(Debug, Clone)]
pub enum ParallelStrategy {
    TaskParallel,
    DataParallel,
    PipelineParallel,
    Hybrid,
}

/// Vector function registry for SPARQL integration
pub struct VectorFunctionRegistry {
    /// Registered functions
    functions: Arc<RwLock<HashMap<String, Arc<dyn VectorFunction>>>>,
    /// Function metadata
    function_metadata: Arc<RwLock<HashMap<String, FunctionMetadata>>>,
    /// Type checker
    type_checker: Arc<VectorTypeChecker>,
    /// Performance monitor
    performance_monitor: Arc<RwLock<FunctionPerformanceMonitor>>,
}

impl std::fmt::Debug for VectorFunctionRegistry {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("VectorFunctionRegistry")
            .field("functions", &"<HashMap<String, Arc<dyn VectorFunction>>>")
            .field("function_metadata", &self.function_metadata)
            .field("type_checker", &"<Arc<VectorTypeChecker>>")
            .field("performance_monitor", &self.performance_monitor)
            .finish()
    }
}

/// Vector function trait for SPARQL integration
pub trait VectorFunction: Send + Sync {
    /// Function name
    fn name(&self) -> &str;

    /// Function signature
    fn signature(&self) -> FunctionSignature;

    /// Execute function
    fn execute(
        &self,
        args: &[FunctionArgument],
        context: &ExecutionContext,
    ) -> Result<FunctionResult>;

    /// Get optimization hints
    fn optimization_hints(&self) -> Vec<OptimizationHint>;

    /// Cost estimation
    fn estimate_cost(&self, args: &[FunctionArgument]) -> f64;
}

/// Function signature definition
#[derive(Debug, Clone)]
pub struct FunctionSignature {
    /// Parameter types
    pub parameters: Vec<ParameterType>,
    /// Return type
    pub return_type: ReturnType,
    /// Variadic parameters
    pub variadic: bool,
    /// Required parameters count
    pub required_params: usize,
}

/// Parameter types for functions
#[derive(Debug, Clone)]
pub enum ParameterType {
    Vector,
    Scalar(ScalarType),
    Graph,
    URI,
    Literal(LiteralType),
    Variable,
}

/// Scalar types
#[derive(Debug, Clone)]
pub enum ScalarType {
    Integer,
    Float,
    String,
    Boolean,
}

/// Literal types
#[derive(Debug, Clone)]
pub enum LiteralType {
    String,
    Number,
    Boolean,
    DateTime,
    Custom(String),
}

/// Return types
#[derive(Debug, Clone)]
pub enum ReturnType {
    Vector,
    Scalar(ScalarType),
    ResultSet,
    Boolean,
    Void,
}

/// Function arguments
#[derive(Debug, Clone)]
pub enum FunctionArgument {
    Vector(Vec<f32>),
    Scalar(ScalarValue),
    URI(String),
    Literal(String, Option<String>), // Value, datatype
    Variable(String),
}

/// Scalar values
#[derive(Debug, Clone)]
pub enum ScalarValue {
    Integer(i64),
    Float(f64),
    String(String),
    Boolean(bool),
}

/// Function execution context
pub struct ExecutionContext {
    /// Available vector indices
    pub vector_indices: Arc<RwLock<HashMap<String, Arc<dyn VectorIndex>>>>,
    /// Query context
    pub query_context: QueryContext,
    /// Performance monitor
    pub performance_monitor: Arc<RwLock<VectorQueryPerformance>>,
    /// Configuration
    pub config: VectorQueryConfig,
}

impl std::fmt::Debug for ExecutionContext {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("ExecutionContext")
            .field("vector_indices", &"<HashMap<String, Arc<dyn VectorIndex>>>")
            .field("query_context", &self.query_context)
            .field("performance_monitor", &self.performance_monitor)
            .field("config", &self.config)
            .finish()
    }
}

/// Query execution context
#[derive(Debug, Clone)]
pub struct QueryContext {
    /// Query ID
    pub query_id: String,
    /// Execution timestamp
    pub timestamp: Instant,
    /// Variable bindings
    pub bindings: HashMap<String, String>,
    /// Active dataset
    pub dataset: Option<String>,
    /// Query metadata
    pub metadata: HashMap<String, String>,
}

/// Function execution result
#[derive(Debug, Clone)]
pub enum FunctionResult {
    Vector(Vec<f32>),
    Scalar(ScalarValue),
    ResultSet(Vec<HashMap<String, String>>),
    Boolean(bool),
    Void,
}

/// Function metadata
#[derive(Debug, Clone)]
pub struct FunctionMetadata {
    /// Function description
    pub description: String,
    /// Author
    pub author: String,
    /// Version
    pub version: String,
    /// Categories
    pub categories: Vec<String>,
    /// Performance characteristics
    pub performance_info: PerformanceInfo,
}

/// Performance information for functions
#[derive(Debug, Clone)]
pub struct PerformanceInfo {
    /// Time complexity
    pub time_complexity: String,
    /// Space complexity
    pub space_complexity: String,
    /// Typical execution time
    pub typical_time: Duration,
    /// Memory usage
    pub memory_usage: usize,
}

/// Optimization hints for functions
#[derive(Debug, Clone)]
pub enum OptimizationHint {
    /// Prefer specific index type
    PreferIndex(String),
    /// Can be batched
    Batchable,
    /// Can be cached
    Cacheable,
    /// Can be parallelized
    Parallelizable,
    /// Memory intensive
    MemoryIntensive,
    /// CPU intensive
    CpuIntensive,
    /// GPU accelerable
    GpuAccelerable,
}

/// Vector type checker
#[derive(Debug)]
pub struct VectorTypeChecker {
    /// Type rules
    type_rules: HashMap<String, TypeRule>,
    /// Conversion rules
    conversion_rules: HashMap<(String, String), ConversionRule>,
}

/// Type checking rules
#[derive(Debug, Clone)]
pub struct TypeRule {
    /// Compatible types
    pub compatible_types: Vec<String>,
    /// Conversion cost
    pub conversion_costs: HashMap<String, f64>,
    /// Validation function
    pub validator: Option<String>,
}

/// Type conversion rules
#[derive(Debug, Clone)]
pub struct ConversionRule {
    /// Source type
    pub source_type: String,
    /// Target type
    pub target_type: String,
    /// Conversion cost
    pub cost: f64,
    /// Lossy conversion
    pub lossy: bool,
    /// Converter function
    pub converter: String,
}

/// Function performance monitor
#[derive(Debug, Default)]
pub struct FunctionPerformanceMonitor {
    /// Function call counts
    pub call_counts: HashMap<String, usize>,
    /// Execution times
    pub execution_times: HashMap<String, Vec<Duration>>,
    /// Memory usage
    pub memory_usage: HashMap<String, Vec<usize>>,
    /// Error rates
    pub error_rates: HashMap<String, f32>,
    /// Performance trends
    pub trends: HashMap<String, Vec<(Instant, f64)>>,
}

impl VectorQueryPlanner {
    /// Create a new vector-aware query planner
    pub fn new(config: VectorQueryConfig) -> Self {
        Self {
            config,
            vector_indices: Arc::new(RwLock::new(HashMap::new())),
            query_stats: Arc::new(RwLock::new(QueryStatistics::default())),
            optimization_cache: Arc::new(RwLock::new(HashMap::new())),
            performance_monitor: Arc::new(RwLock::new(VectorQueryPerformance::default())),
        }
    }

    /// Register a vector index for query optimization
    pub fn register_vector_index(&self, name: String, index: Arc<dyn VectorIndex>) -> Result<()> {
        let mut indices = self
            .vector_indices
            .write()
            .expect("vector_indices write lock should not be poisoned");
        indices.insert(name, index);
        Ok(())
    }

    /// Create an optimization plan for a query
    pub fn create_optimization_plan(&self, query: &VectorQuery) -> Result<OptimizationPlan> {
        let span = span!(Level::DEBUG, "create_optimization_plan");
        let _enter = span.enter();

        // Generate plan ID
        let plan_id = format!("plan_{}", uuid::Uuid::new_v4());

        // Analyze query for optimization opportunities
        let optimization_opportunities = self.analyze_query(query)?;

        // Generate optimization steps
        let mut steps = Vec::new();
        for opportunity in optimization_opportunities {
            let step = self.generate_optimization_step(opportunity, query)?;
            steps.push(step);
        }

        // Estimate total cost and time
        let estimated_cost = steps.iter().map(|s| s.cost).sum();
        let estimated_time = self.estimate_execution_time(&steps, query)?;

        // Calculate expected quality
        let expected_quality = self.estimate_quality_score(&steps, query)?;

        let plan = OptimizationPlan {
            plan_id: plan_id.clone(),
            steps,
            estimated_cost,
            estimated_time,
            expected_quality,
            metadata: {
                let mut metadata = HashMap::new();
                metadata.insert("created_at".to_string(), chrono::Utc::now().to_rfc3339());
                metadata.insert("query_type".to_string(), query.query_type.clone());
                metadata
            },
        };

        // Cache the plan
        {
            let mut cache = self
                .optimization_cache
                .write()
                .expect("optimization_cache write lock should not be poisoned");
            cache.insert(plan_id, plan.clone());
        }

        debug!("Created optimization plan with {} steps", plan.steps.len());
        Ok(plan)
    }

    /// Analyze query for optimization opportunities
    fn analyze_query(&self, query: &VectorQuery) -> Result<Vec<OptimizationOpportunity>> {
        let mut opportunities = Vec::new();

        // Check for vector filter pushdown opportunities
        if query.has_vector_filters() {
            opportunities.push(OptimizationOpportunity::FilterPushdown);
        }

        // Check for join reordering opportunities
        if query.has_joins() && query.join_count() > 1 {
            opportunities.push(OptimizationOpportunity::JoinReordering);
        }

        // Check for index selection opportunities
        if query.has_vector_operations() {
            opportunities.push(OptimizationOpportunity::IndexSelection);
        }

        // Check for batching opportunities
        if query.has_multiple_similar_operations() {
            opportunities.push(OptimizationOpportunity::Batching);
        }

        // Check for caching opportunities
        if query.has_repeated_subqueries() {
            opportunities.push(OptimizationOpportunity::Caching);
        }

        Ok(opportunities)
    }

    /// Generate optimization step for opportunity
    fn generate_optimization_step(
        &self,
        opportunity: OptimizationOpportunity,
        query: &VectorQuery,
    ) -> Result<OptimizationStep> {
        match opportunity {
            OptimizationOpportunity::FilterPushdown => Ok(OptimizationStep {
                step_type: OptimizationStepType::FilterPushdown {
                    filter_type: FilterType::SimilarityFilter,
                    pushdown_level: 2,
                },
                parameters: HashMap::new(),
                cost: self.estimate_filter_pushdown_cost(query),
                dependencies: Vec::new(),
            }),
            OptimizationOpportunity::JoinReordering => {
                let original_order = query.get_join_order();
                let optimized_order = self.optimize_join_order(&original_order, query)?;

                Ok(OptimizationStep {
                    step_type: OptimizationStepType::JoinReordering {
                        original_order,
                        optimized_order,
                    },
                    parameters: HashMap::new(),
                    cost: self.estimate_join_reorder_cost(query),
                    dependencies: Vec::new(),
                })
            }
            OptimizationOpportunity::IndexSelection => {
                let best_index = self.select_optimal_index(query)?;

                Ok(OptimizationStep {
                    step_type: OptimizationStepType::IndexSelection {
                        index_type: best_index,
                        selection_criteria: SelectionCriteria::Hybrid(vec![0.4, 0.3, 0.3]), // Performance, Memory, Accuracy
                    },
                    parameters: HashMap::new(),
                    cost: self.estimate_index_selection_cost(query),
                    dependencies: Vec::new(),
                })
            }
            OptimizationOpportunity::Batching => Ok(OptimizationStep {
                step_type: OptimizationStepType::VectorBatching {
                    batch_size: self.calculate_optimal_batch_size(query),
                    batching_strategy: BatchingStrategy::Adaptive,
                },
                parameters: HashMap::new(),
                cost: self.estimate_batching_cost(query),
                dependencies: Vec::new(),
            }),
            OptimizationOpportunity::Caching => Ok(OptimizationStep {
                step_type: OptimizationStepType::CachingSetup {
                    cache_type: CacheType::ResultCache,
                    cache_size: 1000,
                },
                parameters: HashMap::new(),
                cost: self.estimate_caching_cost(query),
                dependencies: Vec::new(),
            }),
        }
    }

    /// Estimate execution time for optimization steps
    fn estimate_execution_time(
        &self,
        steps: &[OptimizationStep],
        query: &VectorQuery,
    ) -> Result<Duration> {
        let base_time = self.estimate_base_execution_time(query);
        let optimization_factor = self.calculate_optimization_factor(steps);

        Ok(Duration::from_secs_f64(
            base_time.as_secs_f64() * optimization_factor,
        ))
    }

    /// Estimate quality score for optimization plan
    fn estimate_quality_score(
        &self,
        steps: &[OptimizationStep],
        _query: &VectorQuery,
    ) -> Result<f32> {
        let base_quality = 0.8; // Base quality score
        let quality_improvement = steps
            .iter()
            .map(|step| self.estimate_step_quality_impact(step))
            .sum::<f32>();

        Ok((base_quality + quality_improvement).min(1.0))
    }

    /// Helper methods for cost estimation
    fn estimate_filter_pushdown_cost(&self, _query: &VectorQuery) -> f64 {
        10.0 // Simplified cost
    }

    fn estimate_join_reorder_cost(&self, _query: &VectorQuery) -> f64 {
        20.0
    }

    fn estimate_index_selection_cost(&self, _query: &VectorQuery) -> f64 {
        5.0
    }

    fn estimate_batching_cost(&self, _query: &VectorQuery) -> f64 {
        15.0
    }

    fn estimate_caching_cost(&self, _query: &VectorQuery) -> f64 {
        8.0
    }

    fn estimate_base_execution_time(&self, _query: &VectorQuery) -> Duration {
        Duration::from_millis(100) // Simplified estimation
    }

    fn calculate_optimization_factor(&self, _steps: &[OptimizationStep]) -> f64 {
        0.7 // 30% improvement
    }

    fn estimate_step_quality_impact(&self, _step: &OptimizationStep) -> f32 {
        0.05 // 5% quality improvement per step
    }

    fn optimize_join_order(
        &self,
        original_order: &[String],
        _query: &VectorQuery,
    ) -> Result<Vec<String>> {
        // Simplified join reordering
        let mut optimized = original_order.to_vec();
        optimized.reverse(); // Simple reordering strategy
        Ok(optimized)
    }

    fn select_optimal_index(&self, _query: &VectorQuery) -> Result<String> {
        // Select best index based on query characteristics
        Ok("hnsw".to_string()) // Default to HNSW
    }

    fn calculate_optimal_batch_size(&self, _query: &VectorQuery) -> usize {
        1000 // Default batch size
    }

    /// Update query statistics after execution
    pub fn update_statistics(
        &self,
        query: &VectorQuery,
        execution_time: Duration,
        _result_count: usize,
    ) -> Result<()> {
        let mut stats = self
            .query_stats
            .write()
            .expect("query_stats write lock should not be poisoned");

        stats.total_queries += 1;

        // Update operation counts
        for op in &query.vector_operations {
            *stats.vector_op_counts.entry(op.clone()).or_insert(0) += 1;
        }

        // Update execution times
        stats
            .avg_execution_times
            .insert(query.query_type.clone(), execution_time);

        Ok(())
    }

    /// Get performance metrics
    pub fn get_performance_metrics(&self) -> Result<VectorQueryPerformance> {
        let performance = self
            .performance_monitor
            .read()
            .expect("performance_monitor read lock should not be poisoned");
        Ok(performance.clone())
    }
}

impl Default for VectorFunctionRegistry {
    fn default() -> Self {
        Self::new()
    }
}

impl VectorFunctionRegistry {
    /// Create a new vector function registry
    pub fn new() -> Self {
        Self {
            functions: Arc::new(RwLock::new(HashMap::new())),
            function_metadata: Arc::new(RwLock::new(HashMap::new())),
            type_checker: Arc::new(VectorTypeChecker::new()),
            performance_monitor: Arc::new(RwLock::new(FunctionPerformanceMonitor::default())),
        }
    }

    /// Register a vector function
    pub fn register_function(
        &self,
        function: Arc<dyn VectorFunction>,
        metadata: FunctionMetadata,
    ) -> Result<()> {
        let name = function.name().to_string();

        // Validate function signature
        self.type_checker
            .validate_signature(&function.signature())?;

        // Register function
        {
            let mut functions = self
                .functions
                .write()
                .expect("functions write lock should not be poisoned");
            functions.insert(name.clone(), function);
        }

        // Register metadata
        {
            let mut meta = self
                .function_metadata
                .write()
                .expect("function_metadata write lock should not be poisoned");
            meta.insert(name, metadata);
        }

        Ok(())
    }

    /// Execute a registered function
    pub fn execute_function(
        &self,
        name: &str,
        args: &[FunctionArgument],
        context: &ExecutionContext,
    ) -> Result<FunctionResult> {
        let function = {
            let functions = self
                .functions
                .read()
                .expect("functions read lock should not be poisoned");
            functions
                .get(name)
                .ok_or_else(|| AnyhowError::msg(format!("Function not found: {name}")))?
                .clone()
        };

        // Type check arguments
        self.type_checker
            .check_arguments(&function.signature(), args)?;

        // Execute function
        let start_time = Instant::now();
        let result = function.execute(args, context)?;
        let execution_time = start_time.elapsed();

        // Update performance metrics
        self.update_function_performance(name, execution_time)?;

        Ok(result)
    }

    /// Update function performance metrics
    fn update_function_performance(&self, name: &str, execution_time: Duration) -> Result<()> {
        let mut monitor = self
            .performance_monitor
            .write()
            .expect("performance_monitor write lock should not be poisoned");

        // Update call count
        *monitor.call_counts.entry(name.to_string()).or_insert(0) += 1;

        // Update execution times
        monitor
            .execution_times
            .entry(name.to_string())
            .or_default()
            .push(execution_time);

        // Add performance trend point
        monitor
            .trends
            .entry(name.to_string())
            .or_default()
            .push((Instant::now(), execution_time.as_secs_f64()));

        Ok(())
    }

    /// Get function performance statistics
    pub fn get_function_stats(&self, name: &str) -> Result<FunctionStats> {
        let monitor = self
            .performance_monitor
            .read()
            .expect("performance_monitor read lock should not be poisoned");

        let call_count = monitor.call_counts.get(name).copied().unwrap_or(0);
        let execution_times = monitor
            .execution_times
            .get(name)
            .cloned()
            .unwrap_or_default();

        let avg_time = if !execution_times.is_empty() {
            execution_times.iter().sum::<Duration>() / execution_times.len() as u32
        } else {
            Duration::ZERO
        };

        Ok(FunctionStats {
            name: name.to_string(),
            call_count,
            avg_execution_time: avg_time,
            total_execution_time: execution_times.iter().sum(),
            error_rate: monitor.error_rates.get(name).copied().unwrap_or(0.0),
        })
    }
}

impl Default for VectorTypeChecker {
    fn default() -> Self {
        Self::new()
    }
}

impl VectorTypeChecker {
    /// Create a new type checker
    pub fn new() -> Self {
        Self {
            type_rules: HashMap::new(),
            conversion_rules: HashMap::new(),
        }
    }

    /// Validate function signature
    pub fn validate_signature(&self, _signature: &FunctionSignature) -> Result<()> {
        // Simplified validation
        Ok(())
    }

    /// Check function arguments against signature
    pub fn check_arguments(
        &self,
        signature: &FunctionSignature,
        args: &[FunctionArgument],
    ) -> Result<()> {
        if args.len() < signature.required_params {
            return Err(AnyhowError::msg("Insufficient arguments"));
        }

        if !signature.variadic && args.len() > signature.parameters.len() {
            return Err(AnyhowError::msg("Too many arguments"));
        }

        // Type check each argument
        for (i, arg) in args.iter().enumerate() {
            if i < signature.parameters.len() {
                self.check_argument_type(arg, &signature.parameters[i])?;
            }
        }

        Ok(())
    }

    /// Check individual argument type
    fn check_argument_type(
        &self,
        arg: &FunctionArgument,
        expected_type: &ParameterType,
    ) -> Result<()> {
        match (arg, expected_type) {
            (FunctionArgument::Vector(_), ParameterType::Vector) => Ok(()),
            (FunctionArgument::Scalar(_), ParameterType::Scalar(_)) => Ok(()),
            (FunctionArgument::URI(_), ParameterType::URI) => Ok(()),
            (FunctionArgument::Literal(_, _), ParameterType::Literal(_)) => Ok(()),
            (FunctionArgument::Variable(_), ParameterType::Variable) => Ok(()),
            _ => Err(AnyhowError::msg("Type mismatch")),
        }
    }
}

/// Vector query representation
#[derive(Debug, Clone)]
pub struct VectorQuery {
    /// Query type
    pub query_type: String,
    /// Vector operations in query
    pub vector_operations: Vec<String>,
    /// Join operations
    pub joins: Vec<String>,
    /// Filter conditions
    pub filters: Vec<String>,
    /// Query metadata
    pub metadata: HashMap<String, String>,
}

impl VectorQuery {
    /// Check if query has vector filters
    pub fn has_vector_filters(&self) -> bool {
        self.filters
            .iter()
            .any(|f| f.contains("vector") || f.contains("similarity"))
    }

    /// Check if query has joins
    pub fn has_joins(&self) -> bool {
        !self.joins.is_empty()
    }

    /// Get join count
    pub fn join_count(&self) -> usize {
        self.joins.len()
    }

    /// Check if query has vector operations
    pub fn has_vector_operations(&self) -> bool {
        !self.vector_operations.is_empty()
    }

    /// Check if query has multiple similar operations
    pub fn has_multiple_similar_operations(&self) -> bool {
        self.vector_operations.len() > 1
    }

    /// Check if query has repeated subqueries
    pub fn has_repeated_subqueries(&self) -> bool {
        // Simplified check
        self.metadata.contains_key("repeated_patterns")
    }

    /// Get join order
    pub fn get_join_order(&self) -> Vec<String> {
        self.joins.clone()
    }
}

/// Optimization opportunities
#[derive(Debug, Clone)]
pub enum OptimizationOpportunity {
    FilterPushdown,
    JoinReordering,
    IndexSelection,
    Batching,
    Caching,
}

/// Function performance statistics
#[derive(Debug, Clone)]
pub struct FunctionStats {
    /// Function name
    pub name: String,
    /// Total call count
    pub call_count: usize,
    /// Average execution time
    pub avg_execution_time: Duration,
    /// Total execution time
    pub total_execution_time: Duration,
    /// Error rate
    pub error_rate: f32,
}

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

    #[test]
    fn test_vector_query_planner_creation() -> Result<()> {
        let config = VectorQueryConfig::default();
        let planner = VectorQueryPlanner::new(config);

        assert_eq!(planner.vector_indices.read().expect("test value").len(), 0);
        Ok(())
    }

    #[test]
    fn test_vector_function_registry() -> Result<()> {
        let registry = VectorFunctionRegistry::new();

        assert_eq!(registry.functions.read().expect("test value").len(), 0);
        Ok(())
    }

    #[test]
    fn test_optimization_plan_creation() -> Result<()> {
        let config = VectorQueryConfig::default();
        let planner = VectorQueryPlanner::new(config);

        let query = VectorQuery {
            query_type: "test".to_string(),
            vector_operations: vec!["similarity".to_string()],
            joins: vec!["inner_join".to_string()],
            filters: vec!["vector_filter".to_string()],
            metadata: HashMap::new(),
        };

        let plan = planner.create_optimization_plan(&query)?;
        assert!(!plan.steps.is_empty());
        Ok(())
    }

    #[test]
    fn test_vector_query_analysis() {
        let query = VectorQuery {
            query_type: "test".to_string(),
            vector_operations: vec!["similarity".to_string()],
            joins: vec!["join1".to_string(), "join2".to_string()],
            filters: vec!["similarity_filter".to_string()],
            metadata: HashMap::new(),
        };

        assert!(query.has_vector_filters());
        assert!(query.has_joins());
        assert!(query.has_vector_operations());
        assert_eq!(query.join_count(), 2);
    }
}