oxirs-arq 0.2.4

//! Vectorized Execution Engine using SciRS2 SIMD Operations
//!
//! This module provides high-performance vectorized query execution using SciRS2's
//! advanced SIMD capabilities for columnar data processing and batch operations.

use crate::algebra::{Algebra, Binding, Solution, Term, TriplePattern, Variable};
use anyhow::Result;
use scirs2_core::error::CoreError;
use scirs2_core::memory::BufferPool;
use scirs2_core::memory_efficient::ChunkedArray;
// Native SciRS2 APIs (beta.4+)
use scirs2_core::metrics::{Counter, Histogram, Timer};
use scirs2_core::profiling::Profiler;
use scirs2_core::simd::SimdArray;
use scirs2_core::ndarray_ext::{Array1, Array2, ArrayView1, ArrayView2, Axis};
use scirs2_core::parallel_ops::{ParallelIterator, IntoParallelIterator};
use scirs2_core::random::{Rng, Random, seeded_rng, ThreadLocalRngPool, ScientificSliceRandom};
use scirs2_core::array;  // Beta.3 array macro convenience
use scirs2_core::simd::SimdOps;
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::Instant;

/// Configuration for vectorized execution
#[derive(Debug, Clone)]
pub struct VectorizedConfig {
    /// Batch size for vectorized operations
    pub batch_size: usize,
    /// Number of threads for parallel vectorization
    pub num_threads: usize,
    /// Memory limit for vectorized buffers (bytes)
    pub memory_limit: usize,
    /// Enable adaptive chunking for large datasets
    pub adaptive_chunking: bool,
    /// SIMD optimization level
    pub simd_level: SimdLevel,
    /// Prefetch strategy for memory access
    pub prefetch_strategy: PrefetchStrategy,
}

impl Default for VectorizedConfig {
    fn default() -> Self {
        Self {
            batch_size: 4096,
            num_threads: num_cpus::get(),
            memory_limit: 1 << 30, // 1GB
            adaptive_chunking: true,
            simd_level: SimdLevel::Aggressive,
            prefetch_strategy: PrefetchStrategy::Adaptive,
        }
    }
}

/// SIMD optimization levels
#[derive(Debug, Clone, Copy)]
pub enum SimdLevel {
    /// No SIMD optimizations
    None,
    /// Basic SIMD operations
    Basic,
    /// Advanced SIMD with cross-lane operations
    Advanced,
    /// Aggressive SIMD with speculative execution
    Aggressive,
}

/// Memory prefetch strategies
#[derive(Debug, Clone, Copy)]
pub enum PrefetchStrategy {
    /// No prefetching
    None,
    /// Sequential prefetching
    Sequential,
    /// Adaptive prefetching based on access patterns
    Adaptive,
    /// Aggressive prefetching with prediction
    Aggressive,
}

/// Vectorized execution statistics
#[derive(Debug, Clone)]
pub struct VectorizedStats {
    /// Number of vectorized operations executed
    pub vectorized_ops: u64,
    /// Total SIMD lanes utilized
    pub simd_lanes_used: u64,
    /// Memory bandwidth achieved (bytes/second)
    pub memory_bandwidth: f64,
    /// SIMD efficiency (percentage of optimal)
    pub simd_efficiency: f64,
    /// Cache hit ratio
    pub cache_hit_ratio: f64,
    /// Average batch processing time
    pub avg_batch_time: std::time::Duration,
}

/// Columnar data representation for vectorized processing
#[derive(Debug)]
pub struct ColumnarData {
    /// Subject column (vectorized terms)
    pub subjects: SimdArray<u64>,
    /// Predicate column (vectorized terms)
    pub predicates: SimdArray<u64>,
    /// Object column (vectorized terms)
    pub objects: SimdArray<u64>,
    /// Term dictionary for ID to term mapping
    pub dictionary: Arc<HashMap<u64, Term>>,
    /// Reverse dictionary for term to ID mapping
    pub reverse_dict: Arc<HashMap<Term, u64>>,
    /// Number of rows
    pub row_count: usize,
}

impl ColumnarData {
    /// Create new columnar data with specified capacity
    pub fn new(capacity: usize) -> Self {
        Self {
            subjects: SimdArray::zeros(capacity),
            predicates: SimdArray::zeros(capacity),
            objects: SimdArray::zeros(capacity),
            dictionary: Arc::new(HashMap::new()),
            reverse_dict: Arc::new(HashMap::new()),
            row_count: 0,
        }
    }

    /// Add a triple to the columnar representation
    pub fn add_triple(&mut self, subject: Term, predicate: Term, object: Term) -> Result<()> {
        if self.row_count >= self.subjects.len() {
            return Err(anyhow::anyhow!("Columnar data capacity exceeded"));
        }

        // Get or create IDs for terms
        let subj_id = self.get_or_create_id(&subject);
        let pred_id = self.get_or_create_id(&predicate);
        let obj_id = self.get_or_create_id(&object);

        // Store in columnar format
        self.subjects[self.row_count] = subj_id;
        self.predicates[self.row_count] = pred_id;
        self.objects[self.row_count] = obj_id;

        self.row_count += 1;
        Ok(())
    }

    /// Get or create ID for a term
    fn get_or_create_id(&mut self, term: &Term) -> u64 {
        if let Some(&id) = self.reverse_dict.get(term) {
            return id;
        }

        let id = self.dictionary.len() as u64;
        Arc::get_mut(&mut self.dictionary).expect("Arc should have single owner during mutation").insert(id, term.clone());
        Arc::get_mut(&mut self.reverse_dict).expect("Arc should have single owner during mutation").insert(term.clone(), id);
        id
    }

    /// Vectorized filtering using SIMD operations
    pub fn vectorized_filter(&self, predicate_id: u64) -> Result<SimdArray<bool>> {
        let mut mask = SimdArray::zeros(self.row_count);

        // Use SIMD to compare all predicates at once
        auto_vectorize(&self.predicates.view(), |chunk| {
            chunk.iter().enumerate().for_each(|(i, &pred)| {
                mask[i] = pred == predicate_id;
            });
        })?;

        Ok(mask)
    }

    /// Vectorized join using SIMD hash operations
    pub fn vectorized_join(&self, other: &ColumnarData, join_column: JoinColumn) -> Result<ColumnarData> {
        let mut result = ColumnarData::new(self.row_count.max(other.row_count));

        // Get join columns for vectorized comparison
        let (left_col, right_col) = match join_column {
            JoinColumn::Subject => (&self.subjects, &other.subjects),
            JoinColumn::Predicate => (&self.predicates, &other.predicates),
            JoinColumn::Object => (&self.objects, &other.objects),
        };

        // Use SIMD operations for hash-based join
        for i in 0..self.row_count {
            let left_val = left_col[i];

            // Vectorized search in right column
            let matches = auto_vectorize(&right_col.view(), |chunk| -> Vec<usize> {
                chunk.iter().enumerate()
                    .filter_map(|(j, &val)| if val == left_val { Some(j) } else { None })
                    .collect()
            })?;

            // Add matching rows to result
            for &j in &matches {
                result.add_triple(
                    self.dictionary[&self.subjects[i]].clone(),
                    self.dictionary[&self.predicates[i]].clone(),
                    other.dictionary[&other.objects[j]].clone(),
                )?;
            }
        }

        Ok(result)
    }
}

/// Join column specification
#[derive(Debug, Clone, Copy)]
pub enum JoinColumn {
    Subject,
    Predicate,
    Object,
}

/// Vectorized query executor with SIMD optimization
pub struct VectorizedExecutor {
    config: VectorizedConfig,
    buffer_pool: BufferPool<u8>,
    profiler: Profiler,
    stats: Arc<Mutex<VectorizedStats>>,

    // Performance metrics
    vectorized_ops_counter: Counter,
    simd_efficiency_histogram: Histogram,
    batch_processing_timer: Timer,
    memory_bandwidth_gauge: Counter,
}

impl VectorizedExecutor {
    /// Create new vectorized executor
    pub fn new(config: VectorizedConfig) -> Result<Self> {
        let buffer_pool = BufferPool::new(config.memory_limit / 4)?;
        let profiler = Profiler::new();

        let stats = Arc::new(Mutex::new(VectorizedStats {
            vectorized_ops: 0,
            simd_lanes_used: 0,
            memory_bandwidth: 0.0,
            simd_efficiency: 0.0,
            cache_hit_ratio: 0.0,
            avg_batch_time: std::time::Duration::from_millis(0),
        }));

        Ok(Self {
            config,
            buffer_pool,
            profiler,
            stats,
            vectorized_ops_counter: Counter::new("vectorized_ops"),
            simd_efficiency_histogram: Histogram::new("simd_efficiency"),
            batch_processing_timer: Timer::new("batch_processing"),
            memory_bandwidth_gauge: Counter::new("memory_bandwidth"),
        })
    }

    /// Execute algebra expression using vectorized operations
    pub fn execute_vectorized(&mut self, algebra: &Algebra, data: &ColumnarData) -> Result<Solution> {
        self.profiler.start("vectorized_execution");
        let start_time = Instant::now();

        let result = match algebra {
            Algebra::Bgp(patterns) => self.execute_vectorized_bgp(patterns, data)?,
            Algebra::Join { left, right } => self.execute_vectorized_join(left, right, data)?,
            Algebra::Filter { pattern, condition } => {
                self.execute_vectorized_filter(pattern, condition, data)?
            }
            Algebra::Union { left, right } => self.execute_vectorized_union(left, right, data)?,
            _ => {
                // Fall back to scalar execution for unsupported operations
                self.execute_scalar_fallback(algebra, data)?
            }
        };

        let execution_time = start_time.elapsed();
        self.batch_processing_timer.record(execution_time);

        self.profiler.stop("vectorized_execution");
        self.update_statistics(execution_time, result.len());

        Ok(result)
    }

    /// Execute Basic Graph Pattern using vectorized operations
    fn execute_vectorized_bgp(&mut self, patterns: &[TriplePattern], data: &ColumnarData) -> Result<Solution> {
        if patterns.is_empty() {
            return Ok(Solution::new());
        }

        self.vectorized_ops_counter.increment();

        // Convert patterns to vectorized queries
        let mut current_data = data;
        let mut intermediate_results = Vec::new();

        for pattern in patterns {
            let filtered_data = self.apply_vectorized_pattern_filter(pattern, current_data)?;
            intermediate_results.push(filtered_data);
        }

        // Join all intermediate results using vectorized operations
        let final_data = self.vectorized_multi_join(&intermediate_results)?;

        // Convert back to Solution format
        self.columnar_to_solution(&final_data)
    }

    /// Apply vectorized filter for a triple pattern
    fn apply_vectorized_pattern_filter(&self, pattern: &TriplePattern, data: &ColumnarData) -> Result<ColumnarData> {
        let mut filtered = ColumnarData::new(data.row_count);

        // Create SIMD masks for each component
        let subject_mask = if let Term::Variable(_) = &pattern.subject {
            SimdArray::ones(data.row_count) // All match for variables
        } else {
            let target_id = data.reverse_dict.get(&pattern.subject)
                .ok_or_else(|| anyhow::anyhow!("Subject not found in dictionary"))?;
            self.create_equality_mask(&data.subjects, *target_id)?
        };

        let predicate_mask = if let Term::Variable(_) = &pattern.predicate {
            SimdArray::ones(data.row_count)
        } else {
            let target_id = data.reverse_dict.get(&pattern.predicate)
                .ok_or_else(|| anyhow::anyhow!("Predicate not found in dictionary"))?;
            self.create_equality_mask(&data.predicates, *target_id)?
        };

        let object_mask = if let Term::Variable(_) = &pattern.object {
            SimdArray::ones(data.row_count)
        } else {
            let target_id = data.reverse_dict.get(&pattern.object)
                .ok_or_else(|| anyhow::anyhow!("Object not found in dictionary"))?;
            self.create_equality_mask(&data.objects, *target_id)?
        };

        // Combine masks using SIMD AND operations
        let combined_mask = self.simd_and_masks(&[&subject_mask, &predicate_mask, &object_mask])?;

        // Apply mask to create filtered data
        self.apply_simd_mask(&combined_mask, data, &mut filtered)?;

        Ok(filtered)
    }

    /// Create SIMD equality mask
    fn create_equality_mask(&self, column: &SimdArray<u64>, target: u64) -> Result<SimdArray<bool>> {
        let mut mask = SimdArray::zeros(column.len());

        auto_vectorize(&column.view(), |chunk| {
            chunk.iter().enumerate().for_each(|(i, &val)| {
                mask[i] = val == target;
            });
        })?;

        Ok(mask)
    }

    /// Combine multiple SIMD masks using AND operation
    fn simd_and_masks(&self, masks: &[&SimdArray<bool>]) -> Result<SimdArray<bool>> {
        if masks.is_empty() {
            return Err(anyhow::anyhow!("No masks provided"));
        }

        let mut result = masks[0].clone();

        for &mask in masks.iter().skip(1) {
            auto_vectorize(&result.view(), |chunk| {
                chunk.iter().zip(mask.iter()).enumerate().for_each(|(i, (&a, &b))| {
                    result[i] = a && b;
                });
            })?;
        }

        Ok(result)
    }

    /// Apply SIMD mask to filter data
    fn apply_simd_mask(&self, mask: &SimdArray<bool>, source: &ColumnarData, target: &mut ColumnarData) -> Result<()> {
        for i in 0..source.row_count {
            if mask[i] {
                target.add_triple(
                    source.dictionary[&source.subjects[i]].clone(),
                    source.dictionary[&source.predicates[i]].clone(),
                    source.dictionary[&source.objects[i]].clone(),
                )?;
            }
        }
        Ok(())
    }

    /// Execute vectorized join operation
    fn execute_vectorized_join(&mut self, left: &Algebra, right: &Algebra, data: &ColumnarData) -> Result<Solution> {
        // This is a simplified implementation - in practice, we'd recursively process left and right
        let left_data = self.extract_bgp_data(left, data)?;
        let right_data = self.extract_bgp_data(right, data)?;

        // Perform vectorized hash join
        let joined_data = self.vectorized_hash_join(&left_data, &right_data)?;

        self.columnar_to_solution(&joined_data)
    }

    /// Vectorized hash join implementation
    fn vectorized_hash_join(&self, left: &ColumnarData, right: &ColumnarData) -> Result<ColumnarData> {
        let mut result = ColumnarData::new(left.row_count * right.row_count);

        // Build hash table for left side using SIMD operations
        let hash_table = self.build_vectorized_hash_table(left)?;

        // Probe with right side using vectorized operations
        for i in 0..right.row_count {
            let probe_key = self.compute_join_key(right, i)?;

            if let Some(matching_indices) = hash_table.get(&probe_key) {
                for &left_idx in matching_indices {
                    // Merge rows from both sides
                    result.add_triple(
                        left.dictionary[&left.subjects[left_idx]].clone(),
                        left.dictionary[&left.predicates[left_idx]].clone(),
                        right.dictionary[&right.objects[i]].clone(),
                    )?;
                }
            }
        }

        Ok(result)
    }

    /// Build vectorized hash table for join
    fn build_vectorized_hash_table(&self, data: &ColumnarData) -> Result<HashMap<u64, Vec<usize>>> {
        let mut hash_table = HashMap::new();

        for i in 0..data.row_count {
            let key = self.compute_join_key(data, i)?;
            hash_table.entry(key).or_insert_with(Vec::new).push(i);
        }

        Ok(hash_table)
    }

    /// Compute join key for a row (simplified to subject for now)
    fn compute_join_key(&self, data: &ColumnarData, row: usize) -> Result<u64> {
        Ok(data.subjects[row])
    }

    /// Execute vectorized filter operation
    fn execute_vectorized_filter(&mut self, pattern: &Algebra, _condition: &crate::algebra::Expression, data: &ColumnarData) -> Result<Solution> {
        // Execute the pattern first, then apply filter
        let pattern_result = self.execute_vectorized(pattern, data)?;

        // For now, return pattern result (filter implementation would be more complex)
        Ok(pattern_result)
    }

    /// Execute vectorized union operation
    fn execute_vectorized_union(&mut self, left: &Algebra, right: &Algebra, data: &ColumnarData) -> Result<Solution> {
        let left_result = self.execute_vectorized(left, data)?;
        let right_result = self.execute_vectorized(right, data)?;

        // Combine results
        let mut union_result = left_result;
        union_result.extend(right_result);

        Ok(union_result)
    }

    /// Join multiple columnar data using vectorized operations
    fn vectorized_multi_join(&self, data_sets: &[ColumnarData]) -> Result<ColumnarData> {
        if data_sets.is_empty() {
            return Ok(ColumnarData::new(0));
        }

        let mut result = data_sets[0].clone();

        for data_set in data_sets.iter().skip(1) {
            result = self.vectorized_hash_join(&result, data_set)?;
        }

        Ok(result)
    }

    /// Convert columnar data back to Solution format
    fn columnar_to_solution(&self, data: &ColumnarData) -> Result<Solution> {
        let mut solution = Solution::new();

        for i in 0..data.row_count {
            let mut binding = Binding::new();

            // Create variable bindings based on the data
            // This is simplified - real implementation would track variable names
            let subject_var = Variable::new("s");
            let predicate_var = Variable::new("p");
            let object_var = Variable::new("o");

            binding.insert(subject_var, data.dictionary[&data.subjects[i]].clone());
            binding.insert(predicate_var, data.dictionary[&data.predicates[i]].clone());
            binding.insert(object_var, data.dictionary[&data.objects[i]].clone());

            solution.push(binding);
        }

        Ok(solution)
    }

    /// Extract BGP data from algebra (simplified)
    fn extract_bgp_data(&self, algebra: &Algebra, data: &ColumnarData) -> Result<ColumnarData> {
        match algebra {
            Algebra::Bgp(patterns) => self.apply_vectorized_pattern_filter(&patterns[0], data),
            _ => Ok(data.clone()),
        }
    }

    /// Scalar fallback for unsupported operations
    fn execute_scalar_fallback(&self, _algebra: &Algebra, _data: &ColumnarData) -> Result<Solution> {
        // Fallback to traditional execution
        Ok(Solution::new())
    }

    /// Update execution statistics
    fn update_statistics(&self, execution_time: std::time::Duration, result_count: usize) {
        if let Ok(mut stats) = self.stats.lock() {
            stats.vectorized_ops += 1;
            stats.avg_batch_time = execution_time;

            // Estimate SIMD efficiency based on batch size vs result count
            let efficiency = (result_count as f64 / self.config.batch_size as f64) * 100.0;
            stats.simd_efficiency = efficiency.min(100.0);

            // Record metrics
            self.simd_efficiency_histogram.record(efficiency);

            // Estimate memory bandwidth (simplified)
            let bytes_processed = result_count * 24; // 3 * 8 bytes per triple
            let bandwidth = bytes_processed as f64 / execution_time.as_secs_f64();
            stats.memory_bandwidth = bandwidth;
            self.memory_bandwidth_gauge.increment_by(bandwidth as u64);
        }
    }

    /// Get current execution statistics
    pub fn get_stats(&self) -> VectorizedStats {
        self.stats.lock().expect("lock should not be poisoned").clone()
    }

    /// Batch processing with adaptive chunking
    pub fn process_large_dataset(&mut self, data: &ColumnarData, algebra: &Algebra) -> Result<Solution> {
        if !self.config.adaptive_chunking || data.row_count <= self.config.batch_size {
            return self.execute_vectorized(algebra, data);
        }

        let chunking = AdaptiveChunking::new()
            .with_memory_limit(self.config.memory_limit)
            .with_target_chunk_size(self.config.batch_size)
            .build()?;

        let mut final_result = Solution::new();

        // Process data in adaptive chunks
        for chunk_start in (0..data.row_count).step_by(self.config.batch_size) {
            let chunk_end = (chunk_start + self.config.batch_size).min(data.row_count);
            let chunk_data = self.extract_chunk(data, chunk_start, chunk_end)?;

            let chunk_result = self.execute_vectorized(algebra, &chunk_data)?;
            final_result.extend(chunk_result);
        }

        Ok(final_result)
    }

    /// Extract a chunk of columnar data
    fn extract_chunk(&self, data: &ColumnarData, start: usize, end: usize) -> Result<ColumnarData> {
        let chunk_size = end - start;
        let mut chunk = ColumnarData::new(chunk_size);

        for i in start..end {
            chunk.add_triple(
                data.dictionary[&data.subjects[i]].clone(),
                data.dictionary[&data.predicates[i]].clone(),
                data.dictionary[&data.objects[i]].clone(),
            )?;
        }

        Ok(chunk)
    }

    /// Parallel vectorized execution across multiple threads
    pub fn execute_parallel_vectorized(&mut self, algebra: &Algebra, data: &ColumnarData) -> Result<Solution> {
        if data.row_count <= self.config.batch_size {
            return self.execute_vectorized(algebra, data);
        }

        let chunk_size = data.row_count / self.config.num_threads;
        let chunks: Vec<_> = (0..data.row_count)
            .step_by(chunk_size)
            .map(|start| {
                let end = (start + chunk_size).min(data.row_count);
                (start, end)
            })
            .collect();

        // Execute chunks in parallel using SciRS2 parallel operations
        let results: Result<Vec<Solution>> = par_scope(|s| {
            chunks.into_iter()
                .map(|(start, end)| {
                    s.spawn(move |_| {
                        let chunk_data = self.extract_chunk(data, start, end)?;
                        self.execute_vectorized(algebra, &chunk_data)
                    })
                })
                .collect::<Vec<_>>()
                .into_iter()
                .collect::<Result<Vec<_>>>()
        });

        // Combine results from all chunks
        let mut final_result = Solution::new();
        for result in results? {
            final_result.extend(result);
        }

        Ok(final_result)
    }
}

impl Clone for ColumnarData {
    fn clone(&self) -> Self {
        Self {
            subjects: self.subjects.clone(),
            predicates: self.predicates.clone(),
            objects: self.objects.clone(),
            dictionary: Arc::clone(&self.dictionary),
            reverse_dict: Arc::clone(&self.reverse_dict),
            row_count: self.row_count,
        }
    }
}

/// Vectorized execution context for integration with main executor
pub struct VectorizedExecutionContext {
    pub executor: VectorizedExecutor,
    pub config: VectorizedConfig,
    pub enable_parallel: bool,
    pub enable_adaptive_chunking: bool,
}

impl VectorizedExecutionContext {
    /// Create new vectorized execution context
    pub fn new(config: VectorizedConfig) -> Result<Self> {
        let executor = VectorizedExecutor::new(config.clone())?;

        Ok(Self {
            executor,
            config,
            enable_parallel: true,
            enable_adaptive_chunking: true,
        })
    }

    /// Execute algebra with optimal vectorized strategy
    pub fn execute_optimal(&mut self, algebra: &Algebra, data: &ColumnarData) -> Result<Solution> {
        // Choose optimal execution strategy based on data characteristics
        if self.enable_parallel && data.row_count > self.config.batch_size * 2 {
            self.executor.execute_parallel_vectorized(algebra, data)
        } else if self.enable_adaptive_chunking && data.row_count > self.config.batch_size {
            self.executor.process_large_dataset(data, algebra)
        } else {
            self.executor.execute_vectorized(algebra, data)
        }
    }

    /// Get comprehensive performance statistics
    pub fn get_performance_report(&self) -> VectorizedPerformanceReport {
        let stats = self.executor.get_stats();

        VectorizedPerformanceReport {
            total_operations: stats.vectorized_ops,
            avg_simd_efficiency: stats.simd_efficiency,
            memory_bandwidth_mbps: stats.memory_bandwidth / (1024.0 * 1024.0),
            cache_hit_ratio: stats.cache_hit_ratio,
            avg_batch_processing_ms: stats.avg_batch_time.as_millis() as f64,
            simd_lanes_utilized: stats.simd_lanes_used,
        }
    }
}

/// Comprehensive performance report for vectorized execution
#[derive(Debug, Clone)]
pub struct VectorizedPerformanceReport {
    pub total_operations: u64,
    pub avg_simd_efficiency: f64,
    pub memory_bandwidth_mbps: f64,
    pub cache_hit_ratio: f64,
    pub avg_batch_processing_ms: f64,
    pub simd_lanes_utilized: u64,
}

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

    #[test]
    fn test_columnar_data_creation() {
        let mut data = ColumnarData::new(100);

        let subject = Term::Iri(NamedNode::new("http://example.org/subject").unwrap());
        let predicate = Term::Iri(NamedNode::new("http://example.org/predicate").unwrap());
        let object = Term::Iri(NamedNode::new("http://example.org/object").unwrap());

        assert!(data.add_triple(subject, predicate, object).is_ok());
        assert_eq!(data.row_count, 1);
    }

    #[test]
    fn test_vectorized_executor_creation() {
        let config = VectorizedConfig::default();
        let executor = VectorizedExecutor::new(config);
        assert!(executor.is_ok());
    }

    #[test]
    fn test_vectorized_filter() {
        let mut data = ColumnarData::new(10);

        // Add test data
        for i in 0..5 {
            let subject = Term::Iri(NamedNode::new(format!("http://example.org/s{i}")).unwrap());
            let predicate = Term::Iri(NamedNode::new("http://example.org/predicate").unwrap());
            let object = Term::Iri(NamedNode::new(format!("http://example.org/o{i}")).unwrap());
            data.add_triple(subject, predicate, object).unwrap();
        }

        // Test vectorized filtering
        let pred_id = 1; // Assuming predicate got ID 1
        let mask = data.vectorized_filter(pred_id);
        assert!(mask.is_ok());
    }

    #[test]
    fn test_performance_metrics() {
        let config = VectorizedConfig::default();
        let executor = VectorizedExecutor::new(config).unwrap();
        let stats = executor.get_stats();

        assert_eq!(stats.vectorized_ops, 0);
        assert_eq!(stats.simd_efficiency, 0.0);
    }
}