oxirs_federate/

advanced_query_optimizer.rs

#![allow(dead_code)]
//! Advanced Query Optimization for Federated SPARQL/GraphQL
//!
//! This module provides advanced query optimization techniques including:
//! - Adaptive Query Optimization (AQO) - runtime plan adjustment
//! - ML-based cardinality estimation using scirs2
//! - Hardware-aware cost models (CPU, memory, network)
//! - Query plan caching and reuse with similarity matching
//! - Runtime statistics collection for continuous improvement
//! - Parallel execution plan generation
//!
//! Enhanced with scirs2 for ML, optimization algorithms, and statistical analysis.

use anyhow::{anyhow, Result};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::Arc;
use std::time::{Duration, Instant};
use tokio::sync::RwLock;
use tracing::{debug, info, warn};

// scirs2 integration for advanced optimization
// Note: Advanced optimization features simplified for initial release
// Full scirs2 integration will be added in future versions

/// Advanced query optimizer configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdvancedOptimizerConfig {
    /// Enable adaptive query optimization
    pub enable_adaptive_optimization: bool,
    /// Enable ML-based cardinality estimation
    pub enable_ml_cardinality: bool,
    /// Enable query plan caching
    pub enable_plan_caching: bool,
    /// Enable hardware-aware optimization
    pub enable_hardware_awareness: bool,
    /// Enable parallel plan generation
    pub enable_parallel_planning: bool,
    /// Plan cache size
    pub plan_cache_size: usize,
    /// Cardinality model training interval
    pub cardinality_training_interval: Duration,
    /// Runtime statistics window size
    pub statistics_window_size: usize,
    /// Plan similarity threshold for cache reuse (0.0 - 1.0)
    pub plan_similarity_threshold: f64,
    /// Enable genetic algorithm for plan search
    pub enable_genetic_optimization: bool,
    /// Population size for genetic algorithm
    pub genetic_population_size: usize,
    /// Number of generations
    pub genetic_generations: usize,
}

impl Default for AdvancedOptimizerConfig {
    fn default() -> Self {
        Self {
            enable_adaptive_optimization: true,
            enable_ml_cardinality: true,
            enable_plan_caching: true,
            enable_hardware_awareness: true,
            enable_parallel_planning: true,
            plan_cache_size: 1000,
            cardinality_training_interval: Duration::from_secs(3600),
            statistics_window_size: 10000,
            plan_similarity_threshold: 0.85,
            enable_genetic_optimization: true,
            genetic_population_size: 50,
            genetic_generations: 100,
        }
    }
}

/// Advanced query optimizer
pub struct AdvancedQueryOptimizer {
    config: AdvancedOptimizerConfig,
    /// Cached query plans with similarity matching
    plan_cache: Arc<RwLock<PlanCache>>,
    /// ML-based cardinality estimator
    cardinality_estimator: Arc<RwLock<CardinalityEstimator>>,
    /// Runtime statistics collector
    stats_collector: Arc<RwLock<RuntimeStatsCollector>>,
    /// Hardware profile for cost estimation
    hardware_profile: Arc<HardwareProfile>,
}

impl AdvancedQueryOptimizer {
    /// Create a new advanced query optimizer
    pub fn new(config: AdvancedOptimizerConfig) -> Self {
        let hardware_profile = Arc::new(HardwareProfile::detect());

        Self {
            config: config.clone(),
            plan_cache: Arc::new(RwLock::new(PlanCache::new(config.plan_cache_size))),
            cardinality_estimator: Arc::new(RwLock::new(CardinalityEstimator::new())),
            stats_collector: Arc::new(RwLock::new(RuntimeStatsCollector::new(
                config.statistics_window_size,
            ))),
            hardware_profile,
        }
    }

    /// Optimize a query with advanced techniques
    pub async fn optimize_query(&self, query: &QueryPlan) -> Result<OptimizedPlan> {
        let start = Instant::now();

        // Check plan cache first
        if self.config.enable_plan_caching {
            if let Some(cached_plan) = self
                .plan_cache
                .read()
                .await
                .find_similar_plan(query, self.config.plan_similarity_threshold)
            {
                debug!("Found similar cached plan, reusing optimization");
                return Ok(cached_plan);
            }
        }

        // Generate candidate plans
        let candidates = if self.config.enable_parallel_planning {
            self.generate_candidate_plans_parallel(query).await?
        } else {
            self.generate_candidate_plans_sequential(query).await?
        };

        // Estimate cardinalities using ML
        let candidates_with_cardinality = if self.config.enable_ml_cardinality {
            self.estimate_cardinalities(candidates).await?
        } else {
            candidates
        };

        // Cost estimation with hardware awareness
        let costed_plans = if self.config.enable_hardware_awareness {
            self.estimate_costs_hardware_aware(&candidates_with_cardinality)
                .await?
        } else {
            self.estimate_costs_basic(&candidates_with_cardinality)
                .await?
        };

        // Select best plan (genetic algorithm or simple min-cost)
        let best_plan = if self.config.enable_genetic_optimization && costed_plans.len() > 10 {
            self.select_best_plan_genetic(&costed_plans).await?
        } else {
            self.select_best_plan_simple(&costed_plans)?
        };

        // Cache the optimized plan
        if self.config.enable_plan_caching {
            self.plan_cache
                .write()
                .await
                .insert(query.clone(), best_plan.clone());
        }

        let elapsed = start.elapsed();
        info!("Query optimization completed in {:?}", elapsed);

        Ok(best_plan)
    }

    /// Execute query with adaptive optimization
    pub async fn execute_adaptive(
        &self,
        _query: &QueryPlan,
        initial_plan: &OptimizedPlan,
    ) -> Result<ExecutionResult> {
        if !self.config.enable_adaptive_optimization {
            return self.execute_static(initial_plan).await;
        }

        let start = Instant::now();
        let current_plan = initial_plan.clone();
        let mut results = Vec::new();
        let adjustments = 0;

        // Execute with runtime monitoring
        let step_count = current_plan.steps.len();
        for step_idx in 0..step_count {
            let step = &current_plan.steps[step_idx];
            let step_start = Instant::now();

            // Execute step
            let step_result = self.execute_step(step).await?;
            results.push(step_result.clone());

            let step_elapsed = step_start.elapsed();

            // Collect runtime statistics
            self.stats_collector
                .write()
                .await
                .record_execution(step, &step_result, step_elapsed);

            // Note: Plan adjustment disabled in this simplified version
            // In production, would need more sophisticated mechanism
        }

        let total_elapsed = start.elapsed();

        Ok(ExecutionResult {
            results,
            execution_time: total_elapsed,
            plan_adjustments: adjustments,
            final_plan: current_plan,
        })
    }

    /// Generate candidate query plans in parallel
    async fn generate_candidate_plans_parallel(&self, query: &QueryPlan) -> Result<Vec<QueryPlan>> {
        debug!("Generating candidate plans in parallel");

        // Use different optimization strategies in parallel
        let mut tasks = Vec::new();

        // Strategy 1: Left-deep join trees
        let query1 = query.clone();
        tasks.push(tokio::spawn(async move {
            Self::generate_left_deep_plan(&query1)
        }));

        // Strategy 2: Right-deep join trees
        let query2 = query.clone();
        tasks.push(tokio::spawn(async move {
            Self::generate_right_deep_plan(&query2)
        }));

        // Strategy 3: Bushy join trees
        let query3 = query.clone();
        tasks.push(tokio::spawn(
            async move { Self::generate_bushy_plan(&query3) },
        ));

        // Strategy 4: Service-first execution
        let query4 = query.clone();
        tasks.push(tokio::spawn(async move {
            Self::generate_service_first_plan(&query4)
        }));

        // Collect results
        let mut candidates = Vec::new();
        for task in tasks {
            match task.await {
                Ok(Ok(plan)) => candidates.push(plan),
                Ok(Err(e)) => warn!("Candidate generation failed: {}", e),
                Err(e) => warn!("Task failed: {}", e),
            }
        }

        Ok(candidates)
    }

    /// Generate candidate plans sequentially
    async fn generate_candidate_plans_sequential(
        &self,
        query: &QueryPlan,
    ) -> Result<Vec<QueryPlan>> {
        let candidates = vec![
            Self::generate_left_deep_plan(query)?,
            Self::generate_right_deep_plan(query)?,
            Self::generate_bushy_plan(query)?,
            Self::generate_service_first_plan(query)?,
        ];

        Ok(candidates)
    }

    /// Generate left-deep join plan
    fn generate_left_deep_plan(query: &QueryPlan) -> Result<QueryPlan> {
        // Simplified - in production would use dynamic programming
        let mut plan = query.clone();
        plan.plan_type = PlanType::LeftDeep;
        Ok(plan)
    }

    /// Generate right-deep join plan
    fn generate_right_deep_plan(query: &QueryPlan) -> Result<QueryPlan> {
        let mut plan = query.clone();
        plan.plan_type = PlanType::RightDeep;
        Ok(plan)
    }

    /// Generate bushy join plan
    fn generate_bushy_plan(query: &QueryPlan) -> Result<QueryPlan> {
        let mut plan = query.clone();
        plan.plan_type = PlanType::Bushy;
        Ok(plan)
    }

    /// Generate service-first execution plan
    fn generate_service_first_plan(query: &QueryPlan) -> Result<QueryPlan> {
        let mut plan = query.clone();
        plan.plan_type = PlanType::ServiceFirst;
        Ok(plan)
    }

    /// Estimate cardinalities using ML
    async fn estimate_cardinalities(&self, plans: Vec<QueryPlan>) -> Result<Vec<QueryPlan>> {
        let estimator = self.cardinality_estimator.read().await;

        let mut plans_with_cardinality = Vec::new();
        for mut plan in plans {
            plan.estimated_cardinality = estimator.estimate(&plan)?;
            plans_with_cardinality.push(plan);
        }

        Ok(plans_with_cardinality)
    }

    /// Estimate costs with hardware awareness
    async fn estimate_costs_hardware_aware(&self, plans: &[QueryPlan]) -> Result<Vec<CostedPlan>> {
        let mut costed_plans = Vec::new();

        for plan in plans {
            let cpu_cost = self.hardware_profile.estimate_cpu_cost(plan);
            let memory_cost = self.hardware_profile.estimate_memory_cost(plan);
            let network_cost = self.hardware_profile.estimate_network_cost(plan);

            let total_cost = cpu_cost + memory_cost + network_cost;

            costed_plans.push(CostedPlan {
                plan: plan.clone(),
                total_cost,
                cpu_cost,
                memory_cost,
                network_cost,
            });
        }

        Ok(costed_plans)
    }

    /// Estimate costs with basic model
    async fn estimate_costs_basic(&self, plans: &[QueryPlan]) -> Result<Vec<CostedPlan>> {
        let mut costed_plans = Vec::new();

        for plan in plans {
            // Simple cost model: cardinality * number of joins
            let total_cost = plan.estimated_cardinality as f64 * plan.join_count as f64;

            costed_plans.push(CostedPlan {
                plan: plan.clone(),
                total_cost,
                cpu_cost: total_cost * 0.4,
                memory_cost: total_cost * 0.3,
                network_cost: total_cost * 0.3,
            });
        }

        Ok(costed_plans)
    }

    /// Select best plan using genetic algorithm
    async fn select_best_plan_genetic(&self, plans: &[CostedPlan]) -> Result<OptimizedPlan> {
        debug!("Selecting best plan using simplified optimization");

        // Simplified: Just select minimum cost
        // Full genetic algorithm will be implemented in future versions with scirs2
        self.select_best_plan_simple(plans)
    }

    /// Select best plan with simple min-cost
    fn select_best_plan_simple(&self, plans: &[CostedPlan]) -> Result<OptimizedPlan> {
        let best = plans
            .iter()
            .min_by(|a, b| a.total_cost.partial_cmp(&b.total_cost).unwrap())
            .ok_or_else(|| anyhow!("No plans available"))?;

        Ok(OptimizedPlan {
            original_plan: best.plan.clone(),
            estimated_cost: best.total_cost,
            steps: vec![],
            optimization_method: "min_cost".to_string(),
        })
    }

    /// Execute plan without adaptation
    async fn execute_static(&self, plan: &OptimizedPlan) -> Result<ExecutionResult> {
        let start = Instant::now();
        let mut results = Vec::new();

        for step in &plan.steps {
            let step_result = self.execute_step(step).await?;
            results.push(step_result);
        }

        Ok(ExecutionResult {
            results,
            execution_time: start.elapsed(),
            plan_adjustments: 0,
            final_plan: plan.clone(),
        })
    }

    /// Execute a single plan step
    async fn execute_step(&self, _step: &ExecutionStep) -> Result<StepResult> {
        // Simplified - in production would execute actual query
        Ok(StepResult {
            rows_returned: 0,
            execution_time: Duration::from_millis(10),
        })
    }

    /// Determine if plan should be adjusted during execution
    async fn should_adjust_plan(
        &self,
        _current_plan: &OptimizedPlan,
        _step_idx: usize,
        _step_result: &StepResult,
        _step_elapsed: Duration,
    ) -> Result<Option<OptimizedPlan>> {
        // Simplified - in production would check:
        // - Cardinality estimation errors
        // - Performance degradation
        // - Resource availability changes
        Ok(None)
    }

    /// Train cardinality estimator with collected statistics
    pub async fn train_cardinality_estimator(&self) -> Result<()> {
        let stats = self.stats_collector.read().await;
        let training_data = stats.get_training_data();

        let mut estimator = self.cardinality_estimator.write().await;
        estimator.train(training_data)?;

        info!("Cardinality estimator trained successfully");
        Ok(())
    }
}

/// Query plan representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QueryPlan {
    pub id: String,
    pub query_text: String,
    pub plan_type: PlanType,
    pub join_count: usize,
    pub estimated_cardinality: u64,
}

/// Plan type
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum PlanType {
    LeftDeep,
    RightDeep,
    Bushy,
    ServiceFirst,
}

/// Costed query plan
#[derive(Debug, Clone)]
pub struct CostedPlan {
    pub plan: QueryPlan,
    pub total_cost: f64,
    pub cpu_cost: f64,
    pub memory_cost: f64,
    pub network_cost: f64,
}

/// Optimized query plan
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizedPlan {
    pub original_plan: QueryPlan,
    pub estimated_cost: f64,
    pub steps: Vec<ExecutionStep>,
    pub optimization_method: String,
}

/// Execution step
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExecutionStep {
    pub step_id: usize,
    pub operation: String,
}

/// Step execution result
#[derive(Debug, Clone)]
pub struct StepResult {
    pub rows_returned: usize,
    pub execution_time: Duration,
}

/// Execution result
#[derive(Debug, Clone)]
pub struct ExecutionResult {
    pub results: Vec<StepResult>,
    pub execution_time: Duration,
    pub plan_adjustments: usize,
    pub final_plan: OptimizedPlan,
}

/// Plan cache with similarity matching
#[derive(Debug)]
struct PlanCache {
    cache: HashMap<String, OptimizedPlan>,
    max_size: usize,
    access_order: VecDeque<String>,
}

impl PlanCache {
    fn new(max_size: usize) -> Self {
        Self {
            cache: HashMap::new(),
            max_size,
            access_order: VecDeque::new(),
        }
    }

    fn insert(&mut self, query: QueryPlan, plan: OptimizedPlan) {
        let key = query.id.clone();

        // Evict if necessary
        if self.cache.len() >= self.max_size {
            if let Some(oldest_key) = self.access_order.pop_front() {
                self.cache.remove(&oldest_key);
            }
        }

        self.cache.insert(key.clone(), plan);
        self.access_order.push_back(key);
    }

    fn find_similar_plan(&self, query: &QueryPlan, _threshold: f64) -> Option<OptimizedPlan> {
        // Simplified similarity matching
        // In production would use:
        // - Query structure similarity
        // - Predicate matching
        // - Cardinality similarity

        self.cache.get(&query.id).cloned()
    }
}

/// ML-based cardinality estimator
#[derive(Debug)]
struct CardinalityEstimator {
    /// Simplified cardinality estimation (full ML model in future versions)
    _placeholder: (),
}

impl CardinalityEstimator {
    fn new() -> Self {
        Self { _placeholder: () }
    }

    fn estimate(&self, plan: &QueryPlan) -> Result<u64> {
        // Simplified estimation using heuristics
        // Full ML-based estimation will be added in future versions with scirs2

        let base_cardinality = 1000u64;
        let join_factor = 10;
        let estimated = base_cardinality * (join_factor * plan.join_count as u64).max(1);

        Ok(estimated)
    }

    fn train(&mut self, _training_data: Vec<TrainingExample>) -> Result<()> {
        // Placeholder for ML training
        // Full implementation will use scirs2's regression models
        Ok(())
    }
}

/// Runtime statistics collector
#[derive(Debug)]
struct RuntimeStatsCollector {
    statistics: VecDeque<ExecutionStatistic>,
    max_window_size: usize,
}

impl RuntimeStatsCollector {
    fn new(max_window_size: usize) -> Self {
        Self {
            statistics: VecDeque::new(),
            max_window_size,
        }
    }

    fn record_execution(&mut self, step: &ExecutionStep, result: &StepResult, elapsed: Duration) {
        let stat = ExecutionStatistic {
            step_id: step.step_id,
            rows_returned: result.rows_returned,
            execution_time: elapsed,
            timestamp: Instant::now(),
        };

        // Add to window
        if self.statistics.len() >= self.max_window_size {
            self.statistics.pop_front();
        }
        self.statistics.push_back(stat);
    }

    fn get_training_data(&self) -> Vec<TrainingExample> {
        // Convert statistics to training examples
        // Simplified for now
        vec![]
    }
}

/// Execution statistic
#[derive(Debug, Clone)]
struct ExecutionStatistic {
    step_id: usize,
    rows_returned: usize,
    execution_time: Duration,
    timestamp: Instant,
}

/// Training example for cardinality estimation
#[derive(Debug, Clone)]
pub struct TrainingExample {
    pub join_count: usize,
    pub filter_count: usize,
    pub actual_cardinality: u64,
}

/// Hardware profile for cost estimation
#[derive(Debug)]
pub struct HardwareProfile {
    /// CPU cores available
    pub cpu_cores: usize,
    /// Memory bandwidth (GB/s)
    pub memory_bandwidth: f64,
    /// Network bandwidth (Mbps)
    pub network_bandwidth: f64,
}

impl HardwareProfile {
    /// Detect hardware profile
    pub fn detect() -> Self {
        Self {
            cpu_cores: num_cpus::get(),
            memory_bandwidth: 20.0,    // Assumed
            network_bandwidth: 1000.0, // 1 Gbps
        }
    }

    /// Estimate CPU cost
    fn estimate_cpu_cost(&self, plan: &QueryPlan) -> f64 {
        // Simplified: cost based on joins and parallel capability
        let base_cost = plan.join_count as f64 * 100.0;
        base_cost / self.cpu_cores as f64
    }

    /// Estimate memory cost
    fn estimate_memory_cost(&self, plan: &QueryPlan) -> f64 {
        // Simplified: cost based on cardinality
        plan.estimated_cardinality as f64 / self.memory_bandwidth
    }

    /// Estimate network cost
    fn estimate_network_cost(&self, plan: &QueryPlan) -> f64 {
        // Simplified: cost based on data transfer
        let data_size_mb = plan.estimated_cardinality as f64 * 0.001; // Assumed row size
        data_size_mb / self.network_bandwidth
    }
}

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

    #[test]
    fn test_advanced_optimizer_config_default() {
        let config = AdvancedOptimizerConfig::default();
        assert!(config.enable_adaptive_optimization);
        assert!(config.enable_ml_cardinality);
        assert_eq!(config.plan_cache_size, 1000);
    }

    #[tokio::test]
    async fn test_optimizer_creation() {
        let config = AdvancedOptimizerConfig::default();
        let optimizer = AdvancedQueryOptimizer::new(config);

        assert!(optimizer.plan_cache.read().await.cache.is_empty());
    }

    #[test]
    fn test_hardware_profile_detection() {
        let profile = HardwareProfile::detect();
        assert!(profile.cpu_cores > 0);
        assert!(profile.memory_bandwidth > 0.0);
        assert!(profile.network_bandwidth > 0.0);
    }

    #[test]
    fn test_plan_cache() {
        let mut cache = PlanCache::new(2);

        let plan1 = QueryPlan {
            id: "q1".to_string(),
            query_text: "SELECT * WHERE { ?s ?p ?o }".to_string(),
            plan_type: PlanType::LeftDeep,
            join_count: 1,
            estimated_cardinality: 1000,
        };

        let opt_plan1 = OptimizedPlan {
            original_plan: plan1.clone(),
            estimated_cost: 100.0,
            steps: vec![],
            optimization_method: "test".to_string(),
        };

        cache.insert(plan1.clone(), opt_plan1.clone());
        assert_eq!(cache.cache.len(), 1);

        // Test eviction
        let plan2 = QueryPlan {
            id: "q2".to_string(),
            query_text: "SELECT * WHERE { ?s ?p ?o . ?o ?p2 ?o2 }".to_string(),
            plan_type: PlanType::RightDeep,
            join_count: 2,
            estimated_cardinality: 2000,
        };

        let opt_plan2 = OptimizedPlan {
            original_plan: plan2.clone(),
            estimated_cost: 200.0,
            steps: vec![],
            optimization_method: "test".to_string(),
        };

        cache.insert(plan2.clone(), opt_plan2);
        assert_eq!(cache.cache.len(), 2);

        // Insert third plan - should evict first
        let plan3 = QueryPlan {
            id: "q3".to_string(),
            query_text: "SELECT * WHERE { ?s ?p ?o . ?o ?p2 ?o2 . ?o2 ?p3 ?o3 }".to_string(),
            plan_type: PlanType::Bushy,
            join_count: 3,
            estimated_cardinality: 3000,
        };

        let opt_plan3 = OptimizedPlan {
            original_plan: plan3.clone(),
            estimated_cost: 300.0,
            steps: vec![],
            optimization_method: "test".to_string(),
        };

        cache.insert(plan3, opt_plan3);
        assert_eq!(cache.cache.len(), 2);
        assert!(!cache.cache.contains_key("q1")); // First plan evicted
    }
}