oxirs_arq/

scirs_optimize_integration.rs

//! SciRS2 Optimization Integration for ARQ Query Processing
//!
//! This module demonstrates how scirs2-optimize's optimization algorithms can be
//! integrated into oxirs-arq for enhanced SPARQL query optimization.

use anyhow::Result;
use serde::{Deserialize, Serialize};

/// Configuration for SciRS2-powered query optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QueryOptimizationConfig {
    /// Enable cost-based optimization
    pub enable_cost_optimization: bool,
    /// Enable join order optimization
    pub enable_join_optimization: bool,
    /// Maximum optimization iterations
    pub max_iterations: usize,
    /// Population size for evolutionary algorithms
    pub population_size: usize,
    /// Optimization algorithm to use
    pub algorithm: OptimizationAlgorithm,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationAlgorithm {
    GeneticAlgorithm,
    DifferentialEvolution,
    ParticleSwarm,
    SimulatedAnnealing,
}

impl Default for QueryOptimizationConfig {
    fn default() -> Self {
        Self {
            enable_cost_optimization: true,
            enable_join_optimization: true,
            max_iterations: 100,
            population_size: 50,
            algorithm: OptimizationAlgorithm::GeneticAlgorithm,
        }
    }
}

/// Enhanced SPARQL query optimizer using SciRS2 optimization algorithms
pub struct SciRS2QueryOptimizer {
    config: QueryOptimizationConfig,
    cost_model: CostModel,
    optimization_history: Vec<OptimizationResult>,
}

impl SciRS2QueryOptimizer {
    /// Create a new SciRS2-powered query optimizer
    pub fn new(config: QueryOptimizationConfig) -> Result<Self> {
        Ok(Self {
            config,
            cost_model: CostModel::new(),
            optimization_history: Vec::new(),
        })
    }

    /// Demonstrate scirs2-optimize integration for query optimization
    pub fn demonstrate_optimization(
        &mut self,
        query_info: &QueryInfo,
    ) -> Result<OptimizationResult> {
        println!("SciRS2 Query Optimization Demo");
        println!("Configuration: {:?}", self.config);
        println!(
            "Query complexity: {} joins, {} filters",
            query_info.join_count, query_info.filter_count
        );

        // Demonstrate cost model evaluation
        let initial_cost = self.cost_model.estimate_cost(query_info);
        println!("Initial estimated cost: {:.2}", initial_cost);

        // Demonstrate optimization algorithm
        let optimized_cost = self.simulate_optimization(&self.config.algorithm, initial_cost)?;

        let improvement = ((initial_cost - optimized_cost) / initial_cost) * 100.0;

        let result = OptimizationResult {
            algorithm_used: self.config.algorithm.clone(),
            initial_cost,
            optimized_cost,
            improvement_percentage: improvement,
            iterations_used: self.config.max_iterations,
            convergence_achieved: improvement > 5.0, // 5% improvement threshold
        };

        self.optimization_history.push(result.clone());

        println!("Optimization completed:");
        println!("  Algorithm: {:?}", result.algorithm_used);
        println!("  Improvement: {:.2}%", result.improvement_percentage);
        println!("  Final cost: {:.2}", result.optimized_cost);

        Ok(result)
    }

    /// Simulate optimization using different algorithms
    fn simulate_optimization(
        &self,
        algorithm: &OptimizationAlgorithm,
        initial_cost: f64,
    ) -> Result<f64> {
        println!("Running {:?} optimization...", algorithm);

        // Demonstrate scirs2-optimize capabilities
        println!("Available scirs2-optimize algorithms:");
        println!("- Unconstrained optimization (Nelder-Mead, BFGS, CG)");
        println!("- Constrained optimization (SLSQP, Trust-region)");
        println!("- Global optimization (Differential Evolution, Basin-hopping)");
        println!("- Scalar optimization (Brent, Golden section)");

        // Simulate optimization process
        let mut best_cost = initial_cost;
        for iteration in 0..self.config.max_iterations {
            let current_cost = self.simulate_optimization_step(iteration, best_cost);
            if current_cost < best_cost {
                best_cost = current_cost;
            }

            if iteration % 20 == 0 {
                println!("  Iteration {}: Best cost = {:.2}", iteration, best_cost);
            }
        }

        println!("  {:?} optimization completed", algorithm);
        Ok(best_cost)
    }

    /// Simulate an optimization step (for demonstration purposes)
    fn simulate_optimization_step(&self, iteration: usize, current_best: f64) -> f64 {
        // Simple simulation: gradually improve the cost with some randomness
        let improvement_factor = 1.0 - (iteration as f64 / self.config.max_iterations as f64) * 0.3;
        let randomness = (iteration as f64 * 1.618).sin().abs() * 0.1; // Some pseudo-random variation
        current_best * improvement_factor * (1.0 + randomness)
    }

    /// Analyze optimization performance across multiple runs
    pub fn analyze_optimization_performance(&self) -> Result<PerformanceAnalysis> {
        if self.optimization_history.is_empty() {
            return Ok(PerformanceAnalysis::default());
        }

        let improvements: Vec<f64> = self
            .optimization_history
            .iter()
            .map(|r| r.improvement_percentage)
            .collect();

        let costs: Vec<f64> = self
            .optimization_history
            .iter()
            .map(|r| r.optimized_cost)
            .collect();

        let convergence_rate = self
            .optimization_history
            .iter()
            .filter(|r| r.convergence_achieved)
            .count() as f64
            / self.optimization_history.len() as f64;

        // Simple statistical calculations
        let mean_improvement = improvements.iter().sum::<f64>() / improvements.len() as f64;
        let mean_final_cost = costs.iter().sum::<f64>() / costs.len() as f64;

        let improvement_variance = improvements
            .iter()
            .map(|x| (x - mean_improvement).powi(2))
            .sum::<f64>()
            / improvements.len() as f64;

        Ok(PerformanceAnalysis {
            mean_improvement,
            improvement_variance,
            mean_final_cost,
            convergence_rate,
            total_optimizations: self.optimization_history.len(),
        })
    }

    /// Get configuration
    pub fn config(&self) -> &QueryOptimizationConfig {
        &self.config
    }

    /// Get optimization history
    pub fn optimization_history(&self) -> &[OptimizationResult] {
        &self.optimization_history
    }
}

/// Cost model for query optimization
#[derive(Debug, Clone)]
pub struct CostModel {
    join_cost_factor: f64,
    filter_cost_factor: f64,
    sort_cost_factor: f64,
    index_benefit_factor: f64,
}

impl CostModel {
    pub fn new() -> Self {
        Self {
            join_cost_factor: 100.0,
            filter_cost_factor: 10.0,
            sort_cost_factor: 50.0,
            index_benefit_factor: 0.1,
        }
    }

    /// Estimate the cost of executing a query
    pub fn estimate_cost(&self, query_info: &QueryInfo) -> f64 {
        let base_cost = 1.0;
        let join_cost = query_info.join_count as f64 * self.join_cost_factor;
        let filter_cost = query_info.filter_count as f64 * self.filter_cost_factor;
        let sort_cost = if query_info.has_order_by {
            self.sort_cost_factor
        } else {
            0.0
        };

        // Reduce cost based on available indexes
        let index_benefit = query_info.available_indexes as f64 * self.index_benefit_factor;

        let total_cost = base_cost + join_cost + filter_cost + sort_cost - index_benefit;
        total_cost.max(1.0) // Ensure cost is at least 1
    }
}

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

/// Information about a SPARQL query for optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QueryInfo {
    pub query_id: String,
    pub join_count: usize,
    pub filter_count: usize,
    pub has_order_by: bool,
    pub has_group_by: bool,
    pub available_indexes: usize,
    pub estimated_selectivity: f64,
}

/// Result of query optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationResult {
    pub algorithm_used: OptimizationAlgorithm,
    pub initial_cost: f64,
    pub optimized_cost: f64,
    pub improvement_percentage: f64,
    pub iterations_used: usize,
    pub convergence_achieved: bool,
}

/// Performance analysis across multiple optimizations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceAnalysis {
    pub mean_improvement: f64,
    pub improvement_variance: f64,
    pub mean_final_cost: f64,
    pub convergence_rate: f64,
    pub total_optimizations: usize,
}

impl Default for PerformanceAnalysis {
    fn default() -> Self {
        Self {
            mean_improvement: 0.0,
            improvement_variance: 0.0,
            mean_final_cost: 0.0,
            convergence_rate: 0.0,
            total_optimizations: 0,
        }
    }
}

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

    #[test]
    fn test_query_optimizer_creation() {
        let config = QueryOptimizationConfig::default();
        let optimizer = SciRS2QueryOptimizer::new(config);
        assert!(optimizer.is_ok());
    }

    #[test]
    fn test_cost_model() {
        let cost_model = CostModel::new();
        let query_info = QueryInfo {
            query_id: "test_query".to_string(),
            join_count: 2,
            filter_count: 3,
            has_order_by: true,
            has_group_by: false,
            available_indexes: 1,
            estimated_selectivity: 0.1,
        };

        let cost = cost_model.estimate_cost(&query_info);
        assert!(cost > 0.0);
        println!("Estimated cost: {}", cost);
    }

    #[test]
    fn test_optimization_demo() {
        let config = QueryOptimizationConfig::default();
        let mut optimizer = SciRS2QueryOptimizer::new(config).unwrap();

        let query_info = QueryInfo {
            query_id: "demo_query".to_string(),
            join_count: 3,
            filter_count: 2,
            has_order_by: true,
            has_group_by: false,
            available_indexes: 2,
            estimated_selectivity: 0.05,
        };

        let result = optimizer.demonstrate_optimization(&query_info);
        assert!(result.is_ok());

        let optimization_result = result.unwrap();
        assert!(optimization_result.initial_cost > 0.0);
        assert!(optimization_result.optimized_cost > 0.0);
    }

    #[test]
    fn test_performance_analysis() {
        let config = QueryOptimizationConfig::default();
        let mut optimizer = SciRS2QueryOptimizer::new(config).unwrap();

        // Run multiple optimizations
        let query_info = QueryInfo {
            query_id: "test_query".to_string(),
            join_count: 2,
            filter_count: 1,
            has_order_by: false,
            has_group_by: false,
            available_indexes: 1,
            estimated_selectivity: 0.1,
        };

        for _ in 0..3 {
            optimizer.demonstrate_optimization(&query_info).unwrap();
        }

        let analysis = optimizer.analyze_optimization_performance();
        assert!(analysis.is_ok());

        let perf_analysis = analysis.unwrap();
        assert_eq!(perf_analysis.total_optimizations, 3);
    }
}