oxirs-chat 0.2.4

//! Advanced SPARQL Query Optimization for OxiRS Chat
//!
//! Provides sophisticated query optimization, performance analysis, and intelligent
//! query rewriting for improved SPARQL execution performance and accuracy.

use anyhow::Result;
use regex::Regex;
use serde::{Deserialize, Serialize};
use std::{
    collections::HashMap,
    time::{Duration, SystemTime},
};
use tracing::{debug, info};

use crate::{
    nl2sparql::{OptimizationHint, OptimizationHintType, SPARQLGenerationResult},
    rag::QueryIntent,
};

/// Advanced optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizerConfig {
    pub enable_query_rewriting: bool,
    pub enable_performance_analysis: bool,
    pub enable_cost_estimation: bool,
    pub enable_semantic_optimization: bool,
    pub enable_index_suggestions: bool,
    pub optimization_level: OptimizationLevel,
    pub max_optimization_time: Duration,
    pub performance_threshold: Duration, // Queries slower than this get optimized
    pub cache_optimized_queries: bool,
}

impl Default for OptimizerConfig {
    fn default() -> Self {
        Self {
            enable_query_rewriting: true,
            enable_performance_analysis: true,
            enable_cost_estimation: true,
            enable_semantic_optimization: true,
            enable_index_suggestions: true,
            optimization_level: OptimizationLevel::Balanced,
            max_optimization_time: Duration::from_millis(500),
            performance_threshold: Duration::from_millis(1000),
            cache_optimized_queries: true,
        }
    }
}

/// Optimization levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationLevel {
    Conservative,                      // Safe optimizations only
    Balanced,                          // Mix of safety and performance
    Aggressive,                        // Maximum performance optimizations
    Custom(Vec<OptimizationStrategy>), // User-defined strategies
}

/// Available optimization strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationStrategy {
    TripleReordering,
    FilterPushdown,
    JoinOptimization,
    SubqueryElimination,
    UnionSimplification,
    RedundancyRemoval,
    IndexHints,
    PropertyPathOptimization,
    AggregationOptimization,
    LimitPushdown,
}

/// Query performance analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QueryAnalysis {
    pub original_query: String,
    pub estimated_cost: QueryCost,
    pub complexity_score: f32,
    pub potential_issues: Vec<QueryIssue>,
    pub optimization_opportunities: Vec<OptimizationOpportunity>,
    pub performance_prediction: PerformancePrediction,
}

/// Query cost estimation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QueryCost {
    pub estimated_execution_time: Duration,
    pub estimated_memory_usage: usize, // in bytes
    pub estimated_network_io: usize,   // number of triples accessed
    pub complexity_factor: f32,        // 1.0 = simple, 10.0+ = very complex
}

/// Query issues that affect performance
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QueryIssue {
    pub issue_type: QueryIssueType,
    pub description: String,
    pub severity: IssueSeverity,
    pub location: Option<String>, // Part of query where issue occurs
    pub suggested_fix: Option<String>,
}

/// Types of query issues
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum QueryIssueType {
    CartesianProduct,
    UnboundVariable,
    MissingIndex,
    IneffientJoin,
    RedundantPattern,
    ComplexRegex,
    UnoptimizedUnion,
    LargeResultSet,
    DeepNesting,
    FunctionInFilter,
}

/// Issue severity levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum IssueSeverity {
    Info,
    Warning,
    Error,
    Critical,
}

/// Optimization opportunities
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationOpportunity {
    pub strategy: OptimizationStrategy,
    pub description: String,
    pub estimated_improvement: f32, // Percentage improvement expected
    pub confidence: f32,            // Confidence in the improvement estimate
    pub applicable: bool,           // Whether this optimization can be applied
}

/// Performance prediction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformancePrediction {
    pub estimated_execution_time: Duration,
    pub confidence_interval: (Duration, Duration), // Min, Max estimates
    pub bottlenecks: Vec<String>,
    pub scalability_concerns: Vec<String>,
}

/// Optimization result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationResult {
    pub original_query: String,
    pub optimized_query: String,
    pub applied_optimizations: Vec<AppliedOptimization>,
    pub performance_improvement: f32, // Expected percentage improvement
    pub optimization_time: Duration,
    pub warnings: Vec<String>,
    pub analysis: QueryAnalysis,
}

/// Details of an applied optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AppliedOptimization {
    pub strategy: OptimizationStrategy,
    pub description: String,
    pub query_before: String,
    pub query_after: String,
    pub expected_improvement: f32,
}

/// Main SPARQL optimizer
pub struct AdvancedSPARQLOptimizer {
    config: OptimizerConfig,
    query_analyzer: QueryAnalyzer,
    rewriter: QueryRewriter,
    #[allow(dead_code)]
    cost_estimator: CostEstimator,
    optimization_cache: HashMap<String, OptimizationResult>,
}

impl AdvancedSPARQLOptimizer {
    pub fn new(config: OptimizerConfig) -> Self {
        Self {
            query_analyzer: QueryAnalyzer::new(&config),
            rewriter: QueryRewriter::new(&config),
            cost_estimator: CostEstimator::new(&config),
            optimization_cache: HashMap::new(),
            config,
        }
    }

    /// Optimize a SPARQL query with comprehensive analysis
    pub async fn optimize_query(
        &mut self,
        query: &str,
        intent: &QueryIntent,
        _generation_result: Option<&SPARQLGenerationResult>,
    ) -> Result<OptimizationResult> {
        let start_time = SystemTime::now();

        // Check cache first
        let cache_key = self.generate_cache_key(query, intent);
        if self.config.cache_optimized_queries {
            if let Some(cached_result) = self.optimization_cache.get(&cache_key) {
                debug!("Using cached optimization for query");
                return Ok(cached_result.clone());
            }
        }

        info!(
            "Starting advanced optimization for query: {}",
            query.chars().take(100).collect::<String>()
        );

        // Step 1: Analyze the query
        let analysis = self.query_analyzer.analyze(query, intent).await?;
        debug!(
            "Query analysis complete: complexity={:.2}, issues={}",
            analysis.complexity_score,
            analysis.potential_issues.len()
        );

        // Step 2: Apply optimizations based on analysis
        let optimized_query = self
            .rewriter
            .rewrite_query(query, &analysis, intent)
            .await?;

        // Step 3: Calculate performance improvement
        let original_cost = &analysis.estimated_cost;
        let optimized_analysis = self
            .query_analyzer
            .analyze(&optimized_query, intent)
            .await?;
        let optimized_cost = &optimized_analysis.estimated_cost;

        let performance_improvement = self.calculate_improvement(original_cost, optimized_cost);

        // Step 4: Collect applied optimizations
        let applied_optimizations = self.rewriter.get_applied_optimizations();

        let optimization_time = start_time.elapsed().unwrap_or(Duration::ZERO);

        let result = OptimizationResult {
            original_query: query.to_string(),
            optimized_query: optimized_query.clone(),
            applied_optimizations,
            performance_improvement,
            optimization_time,
            warnings: self.collect_warnings(&analysis, &optimized_analysis),
            analysis,
        };

        // Cache the result
        if self.config.cache_optimized_queries {
            self.optimization_cache.insert(cache_key, result.clone());
        }

        info!(
            "Optimization complete: {:.1}% improvement expected in {:.1}ms",
            performance_improvement,
            optimization_time.as_millis()
        );

        Ok(result)
    }

    /// Generate optimization hints for existing generation results
    pub async fn generate_optimization_hints(
        &self,
        generation_result: &SPARQLGenerationResult,
    ) -> Result<Vec<OptimizationHint>> {
        let analysis = self
            .query_analyzer
            .analyze(&generation_result.query, &QueryIntent::Explanation)
            .await?;
        let mut hints = Vec::new();

        // Generate hints based on analysis
        for issue in &analysis.potential_issues {
            let hint_type = match issue.issue_type {
                QueryIssueType::CartesianProduct => OptimizationHintType::UseFilter,
                QueryIssueType::MissingIndex => OptimizationHintType::AddIndex,
                QueryIssueType::IneffientJoin => OptimizationHintType::ReorderTriples,
                QueryIssueType::RedundantPattern => OptimizationHintType::SimplifyExpression,
                _ => OptimizationHintType::SimplifyExpression,
            };

            hints.push(OptimizationHint {
                hint_type,
                description: issue.description.clone(),
                estimated_improvement: Some(20.0), // Conservative estimate
            });
        }

        for opportunity in &analysis.optimization_opportunities {
            if opportunity.applicable && opportunity.estimated_improvement > 10.0 {
                hints.push(OptimizationHint {
                    hint_type: OptimizationHintType::SimplifyExpression,
                    description: opportunity.description.clone(),
                    estimated_improvement: Some(opportunity.estimated_improvement),
                });
            }
        }

        Ok(hints)
    }

    /// Analyze query performance without optimization
    pub async fn analyze_performance(
        &self,
        query: &str,
        intent: &QueryIntent,
    ) -> Result<QueryAnalysis> {
        self.query_analyzer.analyze(query, intent).await
    }

    /// Get optimization statistics
    pub fn get_optimization_stats(&self) -> OptimizationStats {
        let total_optimizations = self.optimization_cache.len();
        let avg_improvement = if total_optimizations > 0 {
            self.optimization_cache
                .values()
                .map(|r| r.performance_improvement)
                .sum::<f32>()
                / total_optimizations as f32
        } else {
            0.0
        };

        let cache_hit_rate = 0.0; // Would need to track cache hits/misses

        OptimizationStats {
            total_optimizations,
            average_improvement: avg_improvement,
            cache_hit_rate,
            total_optimization_time: self
                .optimization_cache
                .values()
                .map(|r| r.optimization_time)
                .sum(),
        }
    }

    fn generate_cache_key(&self, query: &str, intent: &QueryIntent) -> String {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        query.hash(&mut hasher);
        intent.hash(&mut hasher);
        format!("opt_{:x}", hasher.finish())
    }

    fn calculate_improvement(&self, original: &QueryCost, optimized: &QueryCost) -> f32 {
        let time_improvement =
            if original.estimated_execution_time > optimized.estimated_execution_time {
                let saved = original.estimated_execution_time - optimized.estimated_execution_time;
                (saved.as_millis() as f32 / original.estimated_execution_time.as_millis() as f32)
                    * 100.0
            } else {
                0.0
            };

        let memory_improvement =
            if original.estimated_memory_usage > optimized.estimated_memory_usage {
                let saved = original.estimated_memory_usage - optimized.estimated_memory_usage;
                (saved as f32 / original.estimated_memory_usage as f32) * 100.0
            } else {
                0.0
            };

        let complexity_improvement = if original.complexity_factor > optimized.complexity_factor {
            let saved = original.complexity_factor - optimized.complexity_factor;
            (saved / original.complexity_factor) * 100.0
        } else {
            0.0
        };

        // Weighted average: time is most important
        (time_improvement * 0.5 + memory_improvement * 0.3 + complexity_improvement * 0.2).max(0.0)
    }

    fn collect_warnings(&self, original: &QueryAnalysis, optimized: &QueryAnalysis) -> Vec<String> {
        let mut warnings = Vec::new();

        // Check if optimization introduced new issues
        for issue in &optimized.potential_issues {
            if !original
                .potential_issues
                .iter()
                .any(|orig| orig.issue_type == issue.issue_type)
            {
                warnings.push(format!(
                    "Optimization introduced new issue: {}",
                    issue.description
                ));
            }
        }

        // Check if complexity increased unexpectedly
        if optimized.complexity_score > original.complexity_score * 1.1 {
            warnings.push("Optimization may have increased query complexity".to_string());
        }

        warnings
    }
}

/// Query analysis component
struct QueryAnalyzer {
    _config: OptimizerConfig,
    complexity_patterns: Vec<ComplexityPattern>,
}

impl QueryAnalyzer {
    fn new(config: &OptimizerConfig) -> Self {
        let complexity_patterns = vec![
            ComplexityPattern {
                pattern: Regex::new(r"(?i)\bUNION\b").expect("regex pattern should be valid"),
                complexity_weight: 2.0,
                _description: "UNION operations increase complexity".to_string(),
            },
            ComplexityPattern {
                pattern: Regex::new(r"(?i)\bOPTIONAL\b").expect("regex pattern should be valid"),
                complexity_weight: 1.5,
                _description: "OPTIONAL patterns add complexity".to_string(),
            },
            ComplexityPattern {
                pattern: Regex::new(r"(?i)\bFILTER\s+regex\(")
                    .expect("regex pattern should be valid"),
                complexity_weight: 3.0,
                _description: "REGEX filters are expensive".to_string(),
            },
            ComplexityPattern {
                pattern: Regex::new(r"\{\s*SELECT").expect("regex pattern should be valid"),
                complexity_weight: 2.5,
                _description: "Subqueries increase complexity".to_string(),
            },
        ];

        Self {
            _config: config.clone(),
            complexity_patterns,
        }
    }

    async fn analyze(&self, query: &str, intent: &QueryIntent) -> Result<QueryAnalysis> {
        // Calculate complexity score
        let complexity_score = self.calculate_complexity(query);

        // Estimate cost
        let estimated_cost = self.estimate_cost(query, complexity_score);

        // Identify issues
        let potential_issues = self.identify_issues(query);

        // Find optimization opportunities
        let optimization_opportunities = self.find_optimization_opportunities(query, intent);

        // Performance prediction
        let performance_prediction = self.predict_performance(query, &estimated_cost);

        Ok(QueryAnalysis {
            original_query: query.to_string(),
            estimated_cost,
            complexity_score,
            potential_issues,
            optimization_opportunities,
            performance_prediction,
        })
    }

    fn calculate_complexity(&self, query: &str) -> f32 {
        let mut complexity = 1.0;

        // Base complexity from query length
        complexity += (query.len() as f32 / 1000.0).min(5.0);

        // Pattern-based complexity
        for pattern in &self.complexity_patterns {
            let matches = pattern.pattern.find_iter(query).count() as f32;
            complexity += matches * pattern.complexity_weight;
        }

        // Triple pattern count
        let triple_patterns = query.matches(" . ").count() as f32;
        complexity += triple_patterns * 0.1;

        // Variable count
        let variable_count = Regex::new(r"\?[a-zA-Z][a-zA-Z0-9_]*")
            .expect("regex pattern should be valid")
            .find_iter(query)
            .count() as f32;
        complexity += variable_count * 0.05;

        complexity.max(1.0)
    }

    fn estimate_cost(&self, query: &str, complexity: f32) -> QueryCost {
        // Simplified cost estimation model
        let base_time = Duration::from_millis(100);
        let estimated_execution_time =
            Duration::from_millis((base_time.as_millis() as f32 * complexity) as u64);

        let base_memory = 1024 * 1024; // 1MB base
        let estimated_memory_usage = (base_memory as f32 * complexity.sqrt()) as usize;

        let triple_patterns = query.matches(" . ").count();
        let estimated_network_io = (triple_patterns * 100).max(100); // Minimum 100 triples

        QueryCost {
            estimated_execution_time,
            estimated_memory_usage,
            estimated_network_io,
            complexity_factor: complexity,
        }
    }

    fn identify_issues(&self, query: &str) -> Vec<QueryIssue> {
        let mut issues = Vec::new();

        // Check for Cartesian products
        if !query.to_uppercase().contains("FILTER") && query.matches(" . ").count() > 3 {
            issues.push(QueryIssue {
                issue_type: QueryIssueType::CartesianProduct,
                description: "Query may produce Cartesian product without proper filters"
                    .to_string(),
                severity: IssueSeverity::Warning,
                location: None,
                suggested_fix: Some("Add FILTER clauses to constrain results".to_string()),
            });
        }

        // Check for expensive regex
        if Regex::new(r"(?i)regex\(")
            .expect("regex pattern should be valid")
            .is_match(query)
        {
            issues.push(QueryIssue {
                issue_type: QueryIssueType::ComplexRegex,
                description: "REGEX operations can be expensive".to_string(),
                severity: IssueSeverity::Info,
                location: None,
                suggested_fix: Some(
                    "Consider using CONTAINS or STARTS_WITH if possible".to_string(),
                ),
            });
        }

        // Check for missing LIMIT
        if !query.to_uppercase().contains("LIMIT") && query.to_uppercase().contains("SELECT") {
            issues.push(QueryIssue {
                issue_type: QueryIssueType::LargeResultSet,
                description: "Query without LIMIT may return large result sets".to_string(),
                severity: IssueSeverity::Info,
                location: None,
                suggested_fix: Some("Add LIMIT clause to control result size".to_string()),
            });
        }

        // Check for unbound variables in FILTER
        let filter_regex = Regex::new(r"(?i)FILTER\s*\([^)]*\?([a-zA-Z][a-zA-Z0-9_]*)[^)]*\)")
            .expect("regex pattern should be valid");
        if let Some(captures) = filter_regex.captures(query) {
            let var_name = &captures[1];
            let var_pattern = format!(r"\?{var_name}\s+[^?]*\.");
            if !Regex::new(&var_pattern)
                .expect("regex pattern should be valid")
                .is_match(query)
            {
                issues.push(QueryIssue {
                    issue_type: QueryIssueType::UnboundVariable,
                    description: format!(
                        "Variable ?{var_name} used in FILTER may not be properly bound"
                    ),
                    severity: IssueSeverity::Warning,
                    location: Some(format!("FILTER clause with ?{var_name}")),
                    suggested_fix: Some(
                        "Ensure variable is bound in a triple pattern before use in FILTER"
                            .to_string(),
                    ),
                });
            }
        }

        issues
    }

    fn find_optimization_opportunities(
        &self,
        query: &str,
        intent: &QueryIntent,
    ) -> Vec<OptimizationOpportunity> {
        let mut opportunities = Vec::new();

        // Triple reordering opportunity
        if query.matches(" . ").count() > 2 {
            opportunities.push(OptimizationOpportunity {
                strategy: OptimizationStrategy::TripleReordering,
                description: "Reorder triple patterns for better join performance".to_string(),
                estimated_improvement: 15.0,
                confidence: 0.7,
                applicable: true,
            });
        }

        // Filter pushdown opportunity
        if query.to_uppercase().contains("FILTER") && query.to_uppercase().contains("OPTIONAL") {
            let filter_pos = query.to_uppercase().find("FILTER").unwrap_or(0);
            let optional_pos = query.to_uppercase().find("OPTIONAL").unwrap_or(0);

            if filter_pos > optional_pos {
                opportunities.push(OptimizationOpportunity {
                    strategy: OptimizationStrategy::FilterPushdown,
                    description: "Move FILTER clauses before OPTIONAL for better performance"
                        .to_string(),
                    estimated_improvement: 25.0,
                    confidence: 0.8,
                    applicable: true,
                });
            }
        }

        // LIMIT pushdown for list queries
        if matches!(intent, QueryIntent::Listing) && !query.to_uppercase().contains("LIMIT") {
            opportunities.push(OptimizationOpportunity {
                strategy: OptimizationStrategy::LimitPushdown,
                description: "Add LIMIT clause for list queries to improve performance".to_string(),
                estimated_improvement: 30.0,
                confidence: 0.9,
                applicable: true,
            });
        }

        // Union simplification
        if query.matches("UNION").count() > 1 {
            opportunities.push(OptimizationOpportunity {
                strategy: OptimizationStrategy::UnionSimplification,
                description: "Simplify multiple UNION clauses if possible".to_string(),
                estimated_improvement: 20.0,
                confidence: 0.6,
                applicable: true,
            });
        }

        opportunities
    }

    fn predict_performance(&self, _query: &str, cost: &QueryCost) -> PerformancePrediction {
        let base_time = cost.estimated_execution_time;
        let confidence_interval = (
            Duration::from_millis((base_time.as_millis() as f32 * 0.5) as u64),
            Duration::from_millis((base_time.as_millis() as f32 * 2.0) as u64),
        );

        let mut bottlenecks = Vec::new();
        let mut scalability_concerns = Vec::new();

        if cost.complexity_factor > 5.0 {
            bottlenecks.push("High query complexity".to_string());
            scalability_concerns.push("Performance may degrade with larger datasets".to_string());
        }

        if cost.estimated_memory_usage > 10 * 1024 * 1024 {
            // > 10MB
            bottlenecks.push("High memory usage".to_string());
        }

        if cost.estimated_network_io > 10000 {
            bottlenecks.push("Large number of triple accesses".to_string());
            scalability_concerns.push("Network I/O may become bottleneck".to_string());
        }

        PerformancePrediction {
            estimated_execution_time: base_time,
            confidence_interval,
            bottlenecks,
            scalability_concerns,
        }
    }
}

/// Query rewriting component
struct QueryRewriter {
    config: OptimizerConfig,
    applied_optimizations: Vec<AppliedOptimization>,
}

impl QueryRewriter {
    fn new(config: &OptimizerConfig) -> Self {
        Self {
            config: config.clone(),
            applied_optimizations: Vec::new(),
        }
    }

    async fn rewrite_query(
        &mut self,
        query: &str,
        _analysis: &QueryAnalysis,
        intent: &QueryIntent,
    ) -> Result<String> {
        self.applied_optimizations.clear();
        let mut optimized_query = query.to_string();

        let strategies = match &self.config.optimization_level {
            OptimizationLevel::Conservative => vec![
                OptimizationStrategy::RedundancyRemoval,
                OptimizationStrategy::LimitPushdown,
            ],
            OptimizationLevel::Balanced => vec![
                OptimizationStrategy::RedundancyRemoval,
                OptimizationStrategy::FilterPushdown,
                OptimizationStrategy::TripleReordering,
                OptimizationStrategy::LimitPushdown,
            ],
            OptimizationLevel::Aggressive => vec![
                OptimizationStrategy::RedundancyRemoval,
                OptimizationStrategy::FilterPushdown,
                OptimizationStrategy::TripleReordering,
                OptimizationStrategy::JoinOptimization,
                OptimizationStrategy::UnionSimplification,
                OptimizationStrategy::LimitPushdown,
                OptimizationStrategy::SubqueryElimination,
            ],
            OptimizationLevel::Custom(strategies) => strategies.clone(),
        };

        for strategy in strategies {
            let before_optimization = optimized_query.clone();

            optimized_query = match strategy {
                OptimizationStrategy::RedundancyRemoval => {
                    self.remove_redundancy(&optimized_query)?
                }
                OptimizationStrategy::FilterPushdown => self.pushdown_filters(&optimized_query)?,
                OptimizationStrategy::TripleReordering => self.reorder_triples(&optimized_query)?,
                OptimizationStrategy::LimitPushdown => {
                    self.pushdown_limit(&optimized_query, intent)?
                }
                OptimizationStrategy::UnionSimplification => {
                    self.simplify_unions(&optimized_query)?
                }
                OptimizationStrategy::JoinOptimization => self.optimize_joins(&optimized_query)?,
                OptimizationStrategy::SubqueryElimination => {
                    self.eliminate_subqueries(&optimized_query)?
                }
                _ => optimized_query, // Other strategies not implemented yet
            };

            if optimized_query != before_optimization {
                self.applied_optimizations.push(AppliedOptimization {
                    strategy: strategy.clone(),
                    description: format!("Applied {strategy:?} optimization"),
                    query_before: before_optimization,
                    query_after: optimized_query.clone(),
                    expected_improvement: 10.0, // Simplified estimate
                });
            }
        }

        Ok(optimized_query)
    }

    fn get_applied_optimizations(&self) -> Vec<AppliedOptimization> {
        self.applied_optimizations.clone()
    }

    fn remove_redundancy(&self, query: &str) -> Result<String> {
        // Remove duplicate SELECT DISTINCT
        let redundant_distinct = Regex::new(r"(?i)SELECT\s+DISTINCT\s+DISTINCT")?;
        let optimized = redundant_distinct.replace_all(query, "SELECT DISTINCT");

        // Remove redundant WHERE clauses
        let redundant_where = Regex::new(r"(?i)WHERE\s+WHERE")?;
        let optimized = redundant_where.replace_all(&optimized, "WHERE");

        Ok(optimized.to_string())
    }

    fn pushdown_filters(&self, query: &str) -> Result<String> {
        // Move FILTER clauses before OPTIONAL when possible
        let filter_after_optional =
            Regex::new(r"(?i)(OPTIONAL\s*\{[^}]*\})\s*(FILTER\s*\([^)]*\))")?;
        let optimized = filter_after_optional.replace_all(query, "$2 $1");
        Ok(optimized.to_string())
    }

    fn reorder_triples(&self, query: &str) -> Result<String> {
        // Simple heuristic: move more selective patterns first
        // This is a simplified implementation
        Ok(query.to_string())
    }

    fn pushdown_limit(&self, query: &str, intent: &QueryIntent) -> Result<String> {
        if matches!(intent, QueryIntent::Listing) && !query.to_uppercase().contains("LIMIT") {
            if query.to_uppercase().contains("ORDER BY") {
                // Add LIMIT after ORDER BY
                let with_limit =
                    Regex::new(r"(?i)(ORDER\s+BY\s+[^}]+)")?.replace(query, "$1 LIMIT 100");
                Ok(with_limit.to_string())
            } else {
                // Add LIMIT at the end
                Ok(format!("{} LIMIT 100", query.trim_end()))
            }
        } else {
            Ok(query.to_string())
        }
    }

    fn simplify_unions(&self, query: &str) -> Result<String> {
        // Simplify consecutive UNION patterns (simplified implementation)
        Ok(query.to_string())
    }

    fn optimize_joins(&self, query: &str) -> Result<String> {
        // Join optimization (simplified implementation)
        Ok(query.to_string())
    }

    fn eliminate_subqueries(&self, query: &str) -> Result<String> {
        // Subquery elimination (simplified implementation)
        Ok(query.to_string())
    }
}

/// Cost estimation component
struct CostEstimator {
    _config: OptimizerConfig,
}

impl CostEstimator {
    fn new(config: &OptimizerConfig) -> Self {
        Self {
            _config: config.clone(),
        }
    }
}

/// Complexity pattern for analysis
struct ComplexityPattern {
    pattern: Regex,
    complexity_weight: f32,
    _description: String,
}

/// Optimization statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationStats {
    pub total_optimizations: usize,
    pub average_improvement: f32,
    pub cache_hit_rate: f32,
    pub total_optimization_time: Duration,
}