torsh-backend 0.1.2

//! Pool management and analytics for SciRS2 integration
//!
//! This module handles memory pool information, advanced analytics,
//! health assessment, and optimization recommendations.

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use super::config::{HealthIndicator, HealthTrend, PoolOptimizationRecommendation, RiskFactor};
use std::collections::HashMap;
use std::time::{Duration, Instant};

/// SciRS2 memory pool information
///
/// Detailed information about SciRS2 memory pools with performance
/// analytics and optimization recommendations.
#[derive(Debug, Clone)]
pub struct ScirS2PoolInfo {
    /// Pool identifier
    pub pool_id: String,

    /// Pool type (e.g., "tensor", "buffer", "scratch")
    pub pool_type: String,

    /// Pool capacity in bytes
    pub capacity: usize,

    /// Pool utilization ratio (0.0 to 1.0)
    pub utilization: f64,

    /// Pool performance metrics
    pub performance_metrics: HashMap<String, f64>,

    /// Advanced pool analytics
    pub advanced_analytics: PoolAdvancedAnalytics,

    /// Pool health assessment
    pub health_assessment: PoolHealthAssessment,

    /// Optimization recommendations
    pub optimization_recommendations: Vec<PoolOptimizationRecommendation>,
}

/// Advanced pool analytics
#[derive(Debug, Clone)]
pub struct PoolAdvancedAnalytics {
    /// Allocation patterns
    pub allocation_patterns: PoolAllocationPatterns,

    /// Usage efficiency over time
    pub efficiency_timeline: Vec<(Instant, f64)>,

    /// Peak usage times
    pub peak_usage_times: Vec<Instant>,

    /// Idle periods
    pub idle_periods: Vec<(Instant, Duration)>,

    /// Memory turnover rate
    pub turnover_rate: f64,
}

/// Pool allocation patterns
#[derive(Debug, Clone)]
pub struct PoolAllocationPatterns {
    /// Allocation frequency distribution
    pub frequency_distribution: HashMap<usize, u64>, // size -> count

    /// Temporal allocation patterns
    pub temporal_patterns: Vec<TemporalPattern>,

    /// Spatial allocation clustering
    pub spatial_clustering: SpatialClustering,

    /// Predictive allocation model
    pub predictive_model: Option<PoolPredictiveModel>,
}

/// Temporal allocation pattern
#[derive(Debug, Clone)]
pub struct TemporalPattern {
    /// Pattern name
    pub name: String,

    /// Pattern period
    pub period: Duration,

    /// Pattern strength (0.0 to 1.0)
    pub strength: f64,

    /// Pattern phase offset
    pub phase_offset: Duration,
}

/// Spatial allocation clustering
#[derive(Debug, Clone)]
pub struct SpatialClustering {
    /// Number of clusters detected
    pub cluster_count: usize,

    /// Cluster centers
    pub cluster_centers: Vec<usize>,

    /// Cluster densities
    pub cluster_densities: Vec<f64>,

    /// Inter-cluster distances
    pub inter_cluster_distances: Vec<f64>,
}

/// Pool predictive model
#[derive(Debug, Clone)]
pub struct PoolPredictiveModel {
    /// Model type
    pub model_type: String,

    /// Model accuracy
    pub accuracy: f64,

    /// Prediction horizon
    pub prediction_horizon: Duration,

    /// Next predicted allocations
    pub next_predicted_allocations: Vec<PredictedAllocation>,
}

/// Predicted allocation
#[derive(Debug, Clone)]
pub struct PredictedAllocation {
    /// Expected size
    pub size: usize,

    /// Expected time
    pub time: Instant,

    /// Confidence level
    pub confidence: f64,

    /// Allocation type
    pub allocation_type: String,
}

/// Pool health assessment
#[derive(Debug, Clone)]
pub struct PoolHealthAssessment {
    /// Overall health score (0.0 to 1.0)
    pub health_score: f64,

    /// Health indicators
    pub health_indicators: Vec<HealthIndicator>,

    /// Risk factors
    pub risk_factors: Vec<RiskFactor>,

    /// Health trend
    pub health_trend: HealthTrend,
}

impl ScirS2PoolInfo {
    /// Create new pool information
    pub fn new(pool_id: String, pool_type: String, capacity: usize) -> Self {
        Self {
            pool_id,
            pool_type,
            capacity,
            utilization: 0.0,
            performance_metrics: HashMap::new(),
            advanced_analytics: PoolAdvancedAnalytics::default(),
            health_assessment: PoolHealthAssessment::default(),
            optimization_recommendations: Vec::new(),
        }
    }

    /// Update pool utilization
    pub fn update_utilization(&mut self, new_utilization: f64) {
        let _old_utilization = self.utilization;
        self.utilization = new_utilization.clamp(0.0, 1.0);

        // Record utilization change in efficiency timeline
        self.advanced_analytics
            .efficiency_timeline
            .push((Instant::now(), self.utilization));

        // Trim efficiency timeline to keep last 1000 entries
        if self.advanced_analytics.efficiency_timeline.len() > 1000 {
            self.advanced_analytics.efficiency_timeline.remove(0);
        }

        // Check for peak usage
        if self.utilization > 0.9 {
            self.advanced_analytics
                .peak_usage_times
                .push(Instant::now());
            // Keep only recent peak usage times (last 100)
            if self.advanced_analytics.peak_usage_times.len() > 100 {
                self.advanced_analytics.peak_usage_times.remove(0);
            }
        }

        // Update health assessment
        self.update_health_assessment();
    }

    /// Record allocation in the pool
    pub fn record_allocation(&mut self, size: usize) {
        // Update frequency distribution
        *self
            .advanced_analytics
            .allocation_patterns
            .frequency_distribution
            .entry(size)
            .or_insert(0) += 1;

        // Update performance metrics
        self.performance_metrics
            .insert("last_allocation_size".to_string(), size as f64);
        self.performance_metrics.insert(
            "last_allocation_time".to_string(),
            Instant::now()
                .duration_since(Instant::now() - Duration::from_secs(1))
                .as_secs_f64(),
        );
    }

    /// Analyze allocation patterns and detect temporal patterns
    pub fn analyze_patterns(&mut self) {
        // Simplified pattern detection
        let mut patterns = Vec::new();

        // Detect periodic patterns in allocation frequency
        if let Some(pattern) = self.detect_periodic_pattern() {
            patterns.push(pattern);
        }

        // Detect burst patterns
        if let Some(pattern) = self.detect_burst_pattern() {
            patterns.push(pattern);
        }

        self.advanced_analytics
            .allocation_patterns
            .temporal_patterns = patterns;

        // Update spatial clustering
        self.update_spatial_clustering();
    }

    /// Generate optimization recommendations based on current state
    pub fn generate_recommendations(&mut self) {
        self.optimization_recommendations.clear();

        // High utilization recommendation
        if self.utilization > 0.9 {
            self.optimization_recommendations.push(
                PoolOptimizationRecommendation::high_utilization(&self.pool_id, self.utilization),
            );
        }

        // Low utilization recommendation
        if self.utilization < 0.3 {
            self.optimization_recommendations.push(
                PoolOptimizationRecommendation::low_utilization(&self.pool_id, self.utilization),
            );
        }

        // Fragmentation recommendation
        if self.get_fragmentation_level() > 0.3 {
            self.optimization_recommendations.push(
                PoolOptimizationRecommendation::high_fragmentation(&self.pool_id),
            );
        }

        // Performance recommendation
        if let Some(throughput) = self.performance_metrics.get("throughput") {
            if *throughput < 100.0 {
                self.optimization_recommendations.push(
                    PoolOptimizationRecommendation::low_performance(&self.pool_id, *throughput),
                );
            }
        }
    }

    /// Get current memory usage in bytes
    pub fn used_memory(&self) -> usize {
        (self.capacity as f64 * self.utilization) as usize
    }

    /// Get available memory in bytes
    pub fn available_memory(&self) -> usize {
        self.capacity - self.used_memory()
    }

    /// Get fragmentation level (0.0 to 1.0)
    pub fn get_fragmentation_level(&self) -> f64 {
        // Simplified fragmentation calculation based on cluster analysis
        if self
            .advanced_analytics
            .allocation_patterns
            .spatial_clustering
            .cluster_count
            > 1
        {
            1.0 - (1.0
                / self
                    .advanced_analytics
                    .allocation_patterns
                    .spatial_clustering
                    .cluster_count as f64)
        } else {
            0.0
        }
    }

    /// Check if pool is healthy
    pub fn is_healthy(&self) -> bool {
        self.health_assessment.health_score > 0.7
    }

    /// Get pool efficiency over last N minutes
    pub fn efficiency_over_period(&self, minutes: u64) -> f64 {
        let cutoff = Instant::now() - Duration::from_secs(minutes * 60);
        let recent_entries: Vec<_> = self
            .advanced_analytics
            .efficiency_timeline
            .iter()
            .filter(|(timestamp, _)| *timestamp > cutoff)
            .map(|(_, efficiency)| *efficiency)
            .collect();

        if recent_entries.is_empty() {
            self.utilization
        } else {
            recent_entries.iter().sum::<f64>() / recent_entries.len() as f64
        }
    }

    // Private helper methods

    fn detect_periodic_pattern(&self) -> Option<TemporalPattern> {
        // Simplified periodic pattern detection
        if self.advanced_analytics.peak_usage_times.len() >= 3 {
            let intervals: Vec<Duration> = self
                .advanced_analytics
                .peak_usage_times
                .windows(2)
                .map(|w| w[1].duration_since(w[0]))
                .collect();

            // Check if intervals are roughly similar (indicating periodicity)
            if intervals.len() >= 2 {
                let avg_interval: Duration = Duration::from_secs_f64(
                    intervals.iter().map(|d| d.as_secs_f64()).sum::<f64>() / intervals.len() as f64,
                );

                Some(TemporalPattern {
                    name: "Periodic Peak Usage".to_string(),
                    period: avg_interval,
                    strength: 0.7, // Simplified strength calculation
                    phase_offset: Duration::from_secs(0),
                })
            } else {
                None
            }
        } else {
            None
        }
    }

    fn detect_burst_pattern(&self) -> Option<TemporalPattern> {
        // Detect burst patterns based on utilization spikes
        let recent_efficiency: Vec<f64> = self
            .advanced_analytics
            .efficiency_timeline
            .iter()
            .take(10)
            .map(|(_, eff)| *eff)
            .collect();

        if recent_efficiency.len() >= 5 {
            let variance = self.calculate_variance(&recent_efficiency);
            if variance > 0.1 {
                Some(TemporalPattern {
                    name: "Burst Usage".to_string(),
                    period: Duration::from_secs(60), // Assume 1-minute burst cycles
                    strength: variance.min(1.0),
                    phase_offset: Duration::from_secs(0),
                })
            } else {
                None
            }
        } else {
            None
        }
    }

    fn update_spatial_clustering(&mut self) {
        // Simplified spatial clustering based on allocation size distribution
        let mut clusters = Vec::new();
        let mut densities = Vec::new();

        for (size, count) in &self
            .advanced_analytics
            .allocation_patterns
            .frequency_distribution
        {
            if *count > 10 {
                clusters.push(*size);
                densities.push(*count as f64);
            }
        }

        // Calculate inter-cluster distances
        let mut distances = Vec::new();
        for i in 0..clusters.len() {
            for j in (i + 1)..clusters.len() {
                distances.push((clusters[j] - clusters[i]) as f64);
            }
        }

        self.advanced_analytics
            .allocation_patterns
            .spatial_clustering = SpatialClustering {
            cluster_count: clusters.len(),
            cluster_centers: clusters,
            cluster_densities: densities,
            inter_cluster_distances: distances,
        };
    }

    fn update_health_assessment(&mut self) {
        let mut health_score: f64 = 1.0;
        let mut indicators = Vec::new();
        let mut risk_factors = Vec::new();

        // Utilization health indicator
        let utilization_health = if self.utilization > 0.95 {
            health_score -= 0.3;
            0.0 // Critical utilization
        } else if self.utilization > 0.85 {
            health_score -= 0.1;
            0.5 // Warning utilization
        } else {
            1.0 // Healthy utilization
        };

        indicators.push(HealthIndicator {
            name: "Pool Utilization".to_string(),
            value: self.utilization,
            healthy_range: (0.2, 0.8),
            severity: if utilization_health < 0.5 {
                super::config::HealthSeverity::Critical
            } else if utilization_health < 1.0 {
                super::config::HealthSeverity::Warning
            } else {
                super::config::HealthSeverity::Info
            },
        });

        // Fragmentation health indicator
        let fragmentation = self.get_fragmentation_level();
        if fragmentation > 0.5 {
            health_score -= 0.2;
            risk_factors.push(RiskFactor {
                risk_type: super::config::RiskType::FragmentationIncrease,
                probability: fragmentation,
                impact: 0.7,
                mitigation_strategies: vec![
                    "Consider pool defragmentation".to_string(),
                    "Adjust allocation strategy".to_string(),
                ],
            });
        }

        // Performance health indicator
        if let Some(throughput) = self.performance_metrics.get("throughput") {
            if *throughput < 100.0 {
                health_score -= 0.15;
                indicators.push(HealthIndicator {
                    name: "Pool Throughput".to_string(),
                    value: *throughput,
                    healthy_range: (100.0, 1000.0),
                    severity: super::config::HealthSeverity::Warning,
                });
            }
        }

        // Determine health trend
        let trend = if self.advanced_analytics.efficiency_timeline.len() >= 2 {
            let recent = self
                .advanced_analytics
                .efficiency_timeline
                .last()
                .expect("efficiency_timeline should have at least 2 elements")
                .1;
            let previous = self.advanced_analytics.efficiency_timeline
                [self.advanced_analytics.efficiency_timeline.len() - 2]
                .1;

            if recent > previous + 0.05 {
                HealthTrend::Improving
            } else if recent < previous - 0.05 {
                HealthTrend::Declining
            } else {
                HealthTrend::Stable
            }
        } else {
            HealthTrend::Stable
        };

        self.health_assessment = PoolHealthAssessment {
            health_score: health_score.max(0.0),
            health_indicators: indicators,
            risk_factors,
            health_trend: trend,
        };
    }

    fn calculate_variance(&self, values: &[f64]) -> f64 {
        if values.is_empty() {
            return 0.0;
        }

        let mean = values.iter().sum::<f64>() / values.len() as f64;
        let variance = values.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / values.len() as f64;

        variance
    }
}

impl Default for PoolAdvancedAnalytics {
    fn default() -> Self {
        Self {
            allocation_patterns: PoolAllocationPatterns::default(),
            efficiency_timeline: Vec::new(),
            peak_usage_times: Vec::new(),
            idle_periods: Vec::new(),
            turnover_rate: 0.0,
        }
    }
}

impl Default for PoolAllocationPatterns {
    fn default() -> Self {
        Self {
            frequency_distribution: HashMap::new(),
            temporal_patterns: Vec::new(),
            spatial_clustering: SpatialClustering::default(),
            predictive_model: None,
        }
    }
}

impl Default for SpatialClustering {
    fn default() -> Self {
        Self {
            cluster_count: 0,
            cluster_centers: Vec::new(),
            cluster_densities: Vec::new(),
            inter_cluster_distances: Vec::new(),
        }
    }
}

impl Default for PoolHealthAssessment {
    fn default() -> Self {
        Self {
            health_score: 1.0,
            health_indicators: Vec::new(),
            risk_factors: Vec::new(),
            health_trend: HealthTrend::Stable,
        }
    }
}

impl PoolOptimizationRecommendation {
    /// Create a high utilization recommendation
    pub fn high_utilization(pool_id: &str, utilization: f64) -> Self {
        use super::config::{PoolOptimizationType, RecommendationPriority, ResourceRequirements};

        Self {
            recommendation_type: PoolOptimizationType::CapacityAdjustment,
            priority: RecommendationPriority::High,
            description: format!(
                "Pool '{}' has high utilization: {:.1}%",
                pool_id,
                utilization * 100.0
            ),
            implementation_steps: vec![
                "Analyze current allocation patterns".to_string(),
                "Increase pool capacity by 50%".to_string(),
                "Monitor utilization for 24 hours".to_string(),
                "Adjust capacity as needed".to_string(),
            ],
            expected_benefits: vec![
                "Reduced allocation latency".to_string(),
                "Lower risk of allocation failures".to_string(),
                "Improved overall performance".to_string(),
            ],
            resource_requirements: ResourceRequirements {
                additional_memory: (utilization * 1024.0 * 1024.0 * 100.0) as usize, // 100MB estimate
                cpu_overhead: 0.05,
                implementation_time: Duration::from_secs(3600), // 1 hour
                maintenance_overhead: 0.02,
            },
        }
    }

    /// Create a low utilization recommendation
    pub fn low_utilization(pool_id: &str, utilization: f64) -> Self {
        use super::config::{PoolOptimizationType, RecommendationPriority, ResourceRequirements};

        Self {
            recommendation_type: PoolOptimizationType::CapacityAdjustment,
            priority: RecommendationPriority::Medium,
            description: format!(
                "Pool '{}' has low utilization: {:.1}%",
                pool_id,
                utilization * 100.0
            ),
            implementation_steps: vec![
                "Analyze allocation patterns over 7 days".to_string(),
                "Consider reducing pool capacity by 30%".to_string(),
                "Monitor for performance degradation".to_string(),
            ],
            expected_benefits: vec![
                "Reduced memory overhead".to_string(),
                "Lower maintenance costs".to_string(),
                "Better resource utilization".to_string(),
            ],
            resource_requirements: ResourceRequirements {
                additional_memory: 0, // Reducing memory
                cpu_overhead: 0.01,
                implementation_time: Duration::from_secs(1800), // 30 minutes
                maintenance_overhead: -0.01,                    // Reduced maintenance
            },
        }
    }

    /// Create a high fragmentation recommendation
    pub fn high_fragmentation(pool_id: &str) -> Self {
        use super::config::{PoolOptimizationType, RecommendationPriority, ResourceRequirements};

        Self {
            recommendation_type: PoolOptimizationType::GarbageCollectionTuning,
            priority: RecommendationPriority::High,
            description: format!("Pool '{}' has high fragmentation", pool_id),
            implementation_steps: vec![
                "Enable pool defragmentation".to_string(),
                "Adjust allocation strategy to reduce fragmentation".to_string(),
                "Consider memory compaction".to_string(),
                "Monitor fragmentation metrics".to_string(),
            ],
            expected_benefits: vec![
                "Improved memory efficiency".to_string(),
                "Reduced allocation failures".to_string(),
                "Better cache locality".to_string(),
            ],
            resource_requirements: ResourceRequirements {
                additional_memory: 1024 * 1024 * 50, // 50MB for defragmentation
                cpu_overhead: 0.15,
                implementation_time: Duration::from_secs(7200), // 2 hours
                maintenance_overhead: 0.05,
            },
        }
    }

    /// Create a low performance recommendation
    pub fn low_performance(pool_id: &str, throughput: f64) -> Self {
        use super::config::{PoolOptimizationType, RecommendationPriority, ResourceRequirements};

        Self {
            recommendation_type: PoolOptimizationType::AccessPatternOptimization,
            priority: RecommendationPriority::Medium,
            description: format!(
                "Pool '{}' has low throughput: {:.1} ops/sec",
                pool_id, throughput
            ),
            implementation_steps: vec![
                "Analyze access patterns".to_string(),
                "Optimize allocation strategy".to_string(),
                "Consider cache warming".to_string(),
                "Monitor performance improvements".to_string(),
            ],
            expected_benefits: vec![
                "Increased throughput".to_string(),
                "Reduced allocation latency".to_string(),
                "Better resource utilization".to_string(),
            ],
            resource_requirements: ResourceRequirements {
                additional_memory: 1024 * 1024 * 20, // 20MB for caching
                cpu_overhead: 0.08,
                implementation_time: Duration::from_secs(5400), // 90 minutes
                maintenance_overhead: 0.03,
            },
        }
    }
}

/// Pool statistics aggregator
pub struct PoolStatsAggregator {
    pools: HashMap<String, ScirS2PoolInfo>,
}

impl PoolStatsAggregator {
    /// Create new pool aggregator
    pub fn new() -> Self {
        Self {
            pools: HashMap::new(),
        }
    }

    /// Add or update pool information
    pub fn update_pool(&mut self, pool: ScirS2PoolInfo) {
        self.pools.insert(pool.pool_id.clone(), pool);
    }

    /// Get pool information
    pub fn get_pool(&self, pool_id: &str) -> Option<&ScirS2PoolInfo> {
        self.pools.get(pool_id)
    }

    /// Get all pools
    pub fn get_all_pools(&self) -> &HashMap<String, ScirS2PoolInfo> {
        &self.pools
    }

    /// Calculate system-wide pool metrics
    pub fn calculate_system_metrics(&self) -> SystemPoolMetrics {
        let mut total_capacity = 0;
        let mut total_used = 0;
        let mut health_scores = Vec::new();
        let mut high_utilization_count = 0;
        let mut recommendation_count = 0;

        for pool in self.pools.values() {
            total_capacity += pool.capacity;
            total_used += pool.used_memory();
            health_scores.push(pool.health_assessment.health_score);
            recommendation_count += pool.optimization_recommendations.len();

            if pool.utilization > 0.8 {
                high_utilization_count += 1;
            }
        }

        let system_utilization = if total_capacity > 0 {
            total_used as f64 / total_capacity as f64
        } else {
            0.0
        };

        let average_health = if !health_scores.is_empty() {
            health_scores.iter().sum::<f64>() / health_scores.len() as f64
        } else {
            0.0
        };

        SystemPoolMetrics {
            total_pools: self.pools.len(),
            total_capacity,
            total_used,
            system_utilization,
            average_health_score: average_health,
            high_utilization_pools: high_utilization_count,
            total_recommendations: recommendation_count,
        }
    }
}

/// System-wide pool metrics
#[derive(Debug, Clone)]
pub struct SystemPoolMetrics {
    pub total_pools: usize,
    pub total_capacity: usize,
    pub total_used: usize,
    pub system_utilization: f64,
    pub average_health_score: f64,
    pub high_utilization_pools: usize,
    pub total_recommendations: usize,
}