oxirs_vec/

adaptive_intelligent_caching.rs

//! Adaptive Intelligent Caching System for OxiRS Vector Search
//!
//! This module provides advanced caching strategies that adapt based on query patterns,
//! vector characteristics, and performance metrics. It implements machine learning-driven
//! cache optimization and predictive prefetching.

#![allow(dead_code)]

use crate::{similarity::SimilarityMetric, Vector, VectorId};
use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap, VecDeque};
use std::hash::{Hash, Hasher};
use std::sync::atomic::{AtomicU64, Ordering};
use std::time::{Duration, Instant, SystemTime};
use tracing::{debug, info, warn};

/// Adaptive intelligent caching system with ML-driven optimization
#[derive(Debug)]
pub struct AdaptiveIntelligentCache {
    /// Multi-tier cache storage
    tiers: Vec<CacheTier>,
    /// Cache access pattern analyzer
    pattern_analyzer: AccessPatternAnalyzer,
    /// Predictive prefetching engine
    prefetcher: PredictivePrefetcher,
    /// Cache optimization engine
    optimizer: CacheOptimizer,
    /// Performance metrics collector
    metrics: CachePerformanceMetrics,
    /// Configuration parameters
    config: CacheConfiguration,
    /// Machine learning models for cache decisions
    ml_models: MLModels,
}

/// Individual cache tier with specific characteristics
#[derive(Debug)]
pub struct CacheTier {
    /// Tier identifier
    #[allow(dead_code)]
    tier_id: u32,
    /// Storage implementation
    storage: Box<dyn CacheStorage>,
    /// Eviction policy
    eviction_policy: Box<dyn EvictionPolicy>,
    /// Access frequency tracker
    access_tracker: AccessTracker,
    /// Tier-specific configuration
    config: TierConfiguration,
    /// Performance statistics
    stats: TierStatistics,
}

/// Access pattern analysis for intelligent caching decisions
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AccessPatternAnalyzer {
    /// Recent access patterns
    access_history: VecDeque<AccessEvent>,
    /// Seasonal pattern detection
    seasonal_detector: SeasonalPatternDetector,
    /// Query similarity clustering
    query_clustering: QueryClusteringEngine,
    /// Temporal access predictions
    temporal_predictor: TemporalAccessPredictor,
}

/// Predictive prefetching based on access patterns and ML models
#[allow(dead_code)]
#[derive(Debug)]
pub struct PredictivePrefetcher {
    /// Prefetch queue with priorities
    prefetch_queue: VecDeque<PrefetchItem>,
    /// Prefetch models
    models: PrefetchModels,
    /// Current prefetch strategies
    strategies: Vec<PrefetchStrategy>,
    /// Prefetch performance tracking
    performance: PrefetchPerformance,
}

/// Cache optimization engine with adaptive algorithms
#[allow(dead_code)]
#[derive(Debug)]
pub struct CacheOptimizer {
    /// Optimization algorithms
    algorithms: Vec<Box<dyn OptimizationAlgorithm>>,
    /// Optimization history
    optimization_history: Vec<OptimizationEvent>,
    /// Current optimization state
    current_state: OptimizationState,
    /// Performance improvement tracking
    improvements: ImprovementTracker,
}

/// Comprehensive cache performance metrics
#[derive(Debug, Default)]
pub struct CachePerformanceMetrics {
    /// Hit/miss statistics
    pub hit_count: AtomicU64,
    pub miss_count: AtomicU64,
    pub total_requests: AtomicU64,

    /// Latency statistics
    pub avg_hit_latency_ns: AtomicU64,
    pub avg_miss_latency_ns: AtomicU64,
    pub p99_latency_ns: AtomicU64,

    /// Throughput metrics
    pub requests_per_second: AtomicU64,
    pub bytes_per_second: AtomicU64,

    /// Cache efficiency
    pub cache_efficiency_score: f64,
    pub memory_utilization: f64,
    pub fragmentation_ratio: f64,

    /// Detailed statistics by tier
    pub tier_metrics: HashMap<u32, TierMetrics>,

    /// Time-series data for trend analysis
    pub historical_metrics: VecDeque<HistoricalMetric>,
}

impl Clone for CachePerformanceMetrics {
    fn clone(&self) -> Self {
        Self {
            hit_count: AtomicU64::new(self.hit_count.load(Ordering::SeqCst)),
            miss_count: AtomicU64::new(self.miss_count.load(Ordering::SeqCst)),
            total_requests: AtomicU64::new(self.total_requests.load(Ordering::SeqCst)),
            avg_hit_latency_ns: AtomicU64::new(self.avg_hit_latency_ns.load(Ordering::SeqCst)),
            avg_miss_latency_ns: AtomicU64::new(self.avg_miss_latency_ns.load(Ordering::SeqCst)),
            p99_latency_ns: AtomicU64::new(self.p99_latency_ns.load(Ordering::SeqCst)),
            requests_per_second: AtomicU64::new(self.requests_per_second.load(Ordering::SeqCst)),
            bytes_per_second: AtomicU64::new(self.bytes_per_second.load(Ordering::SeqCst)),
            cache_efficiency_score: self.cache_efficiency_score,
            memory_utilization: self.memory_utilization,
            fragmentation_ratio: self.fragmentation_ratio,
            tier_metrics: self.tier_metrics.clone(),
            historical_metrics: self.historical_metrics.clone(),
        }
    }
}

impl Serialize for CachePerformanceMetrics {
    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
    where
        S: serde::Serializer,
    {
        use serde::ser::SerializeStruct;
        let mut state = serializer.serialize_struct("CachePerformanceMetrics", 11)?;
        state.serialize_field("hit_count", &self.hit_count.load(Ordering::SeqCst))?;
        state.serialize_field("miss_count", &self.miss_count.load(Ordering::SeqCst))?;
        state.serialize_field(
            "total_requests",
            &self.total_requests.load(Ordering::SeqCst),
        )?;
        state.serialize_field(
            "avg_hit_latency_ns",
            &self.avg_hit_latency_ns.load(Ordering::SeqCst),
        )?;
        state.serialize_field(
            "avg_miss_latency_ns",
            &self.avg_miss_latency_ns.load(Ordering::SeqCst),
        )?;
        state.serialize_field(
            "p99_latency_ns",
            &self.p99_latency_ns.load(Ordering::SeqCst),
        )?;
        state.serialize_field(
            "requests_per_second",
            &self.requests_per_second.load(Ordering::SeqCst),
        )?;
        state.serialize_field(
            "bytes_per_second",
            &self.bytes_per_second.load(Ordering::SeqCst),
        )?;
        state.serialize_field("cache_efficiency_score", &self.cache_efficiency_score)?;
        state.serialize_field("memory_utilization", &self.memory_utilization)?;
        state.serialize_field("fragmentation_ratio", &self.fragmentation_ratio)?;
        state.serialize_field("tier_metrics", &self.tier_metrics)?;
        state.serialize_field("historical_metrics", &self.historical_metrics)?;
        state.end()
    }
}

impl<'de> Deserialize<'de> for CachePerformanceMetrics {
    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
    where
        D: serde::Deserializer<'de>,
    {
        use serde::de::{self, MapAccess, Visitor};
        use std::fmt;

        #[derive(Deserialize)]
        #[serde(field_identifier, rename_all = "snake_case")]
        enum Field {
            HitCount,
            MissCount,
            TotalRequests,
            AvgHitLatencyNs,
            AvgMissLatencyNs,
            P99LatencyNs,
            RequestsPerSecond,
            BytesPerSecond,
            CacheEfficiencyScore,
            MemoryUtilization,
            FragmentationRatio,
            TierMetrics,
            HistoricalMetrics,
        }

        struct CachePerformanceMetricsVisitor;

        impl<'de> Visitor<'de> for CachePerformanceMetricsVisitor {
            type Value = CachePerformanceMetrics;

            fn expecting(&self, formatter: &mut fmt::Formatter) -> fmt::Result {
                formatter.write_str("struct CachePerformanceMetrics")
            }

            fn visit_map<V>(self, mut map: V) -> Result<CachePerformanceMetrics, V::Error>
            where
                V: MapAccess<'de>,
            {
                let mut hit_count = None;
                let mut miss_count = None;
                let mut total_requests = None;
                let mut avg_hit_latency_ns = None;
                let mut avg_miss_latency_ns = None;
                let mut p99_latency_ns = None;
                let mut requests_per_second = None;
                let mut bytes_per_second = None;
                let mut cache_efficiency_score = None;
                let mut memory_utilization = None;
                let mut fragmentation_ratio = None;
                let mut tier_metrics = None;
                let mut historical_metrics = None;

                while let Some(key) = map.next_key()? {
                    match key {
                        Field::HitCount => {
                            if hit_count.is_some() {
                                return Err(de::Error::duplicate_field("hit_count"));
                            }
                            hit_count = Some(map.next_value::<u64>()?);
                        }
                        Field::MissCount => {
                            if miss_count.is_some() {
                                return Err(de::Error::duplicate_field("miss_count"));
                            }
                            miss_count = Some(map.next_value::<u64>()?);
                        }
                        Field::TotalRequests => {
                            if total_requests.is_some() {
                                return Err(de::Error::duplicate_field("total_requests"));
                            }
                            total_requests = Some(map.next_value::<u64>()?);
                        }
                        Field::AvgHitLatencyNs => {
                            if avg_hit_latency_ns.is_some() {
                                return Err(de::Error::duplicate_field("avg_hit_latency_ns"));
                            }
                            avg_hit_latency_ns = Some(map.next_value::<u64>()?);
                        }
                        Field::AvgMissLatencyNs => {
                            if avg_miss_latency_ns.is_some() {
                                return Err(de::Error::duplicate_field("avg_miss_latency_ns"));
                            }
                            avg_miss_latency_ns = Some(map.next_value::<u64>()?);
                        }
                        Field::P99LatencyNs => {
                            if p99_latency_ns.is_some() {
                                return Err(de::Error::duplicate_field("p99_latency_ns"));
                            }
                            p99_latency_ns = Some(map.next_value::<u64>()?);
                        }
                        Field::RequestsPerSecond => {
                            if requests_per_second.is_some() {
                                return Err(de::Error::duplicate_field("requests_per_second"));
                            }
                            requests_per_second = Some(map.next_value::<u64>()?);
                        }
                        Field::BytesPerSecond => {
                            if bytes_per_second.is_some() {
                                return Err(de::Error::duplicate_field("bytes_per_second"));
                            }
                            bytes_per_second = Some(map.next_value::<u64>()?);
                        }
                        Field::CacheEfficiencyScore => {
                            if cache_efficiency_score.is_some() {
                                return Err(de::Error::duplicate_field("cache_efficiency_score"));
                            }
                            cache_efficiency_score = Some(map.next_value()?);
                        }
                        Field::MemoryUtilization => {
                            if memory_utilization.is_some() {
                                return Err(de::Error::duplicate_field("memory_utilization"));
                            }
                            memory_utilization = Some(map.next_value()?);
                        }
                        Field::FragmentationRatio => {
                            if fragmentation_ratio.is_some() {
                                return Err(de::Error::duplicate_field("fragmentation_ratio"));
                            }
                            fragmentation_ratio = Some(map.next_value()?);
                        }
                        Field::TierMetrics => {
                            if tier_metrics.is_some() {
                                return Err(de::Error::duplicate_field("tier_metrics"));
                            }
                            tier_metrics = Some(map.next_value()?);
                        }
                        Field::HistoricalMetrics => {
                            if historical_metrics.is_some() {
                                return Err(de::Error::duplicate_field("historical_metrics"));
                            }
                            historical_metrics = Some(map.next_value()?);
                        }
                    }
                }

                Ok(CachePerformanceMetrics {
                    hit_count: AtomicU64::new(hit_count.unwrap_or(0)),
                    miss_count: AtomicU64::new(miss_count.unwrap_or(0)),
                    total_requests: AtomicU64::new(total_requests.unwrap_or(0)),
                    avg_hit_latency_ns: AtomicU64::new(avg_hit_latency_ns.unwrap_or(0)),
                    avg_miss_latency_ns: AtomicU64::new(avg_miss_latency_ns.unwrap_or(0)),
                    p99_latency_ns: AtomicU64::new(p99_latency_ns.unwrap_or(0)),
                    requests_per_second: AtomicU64::new(requests_per_second.unwrap_or(0)),
                    bytes_per_second: AtomicU64::new(bytes_per_second.unwrap_or(0)),
                    cache_efficiency_score: cache_efficiency_score.unwrap_or(0.0),
                    memory_utilization: memory_utilization.unwrap_or(0.0),
                    fragmentation_ratio: fragmentation_ratio.unwrap_or(0.0),
                    tier_metrics: tier_metrics.unwrap_or_default(),
                    historical_metrics: historical_metrics.unwrap_or_default(),
                })
            }
        }

        deserializer.deserialize_struct(
            "CachePerformanceMetrics",
            &[
                "hit_count",
                "miss_count",
                "total_requests",
                "avg_hit_latency_ns",
                "avg_miss_latency_ns",
                "p99_latency_ns",
                "requests_per_second",
                "bytes_per_second",
                "cache_efficiency_score",
                "memory_utilization",
                "fragmentation_ratio",
                "tier_metrics",
                "historical_metrics",
            ],
            CachePerformanceMetricsVisitor,
        )
    }
}

/// Configuration for the adaptive cache system
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheConfiguration {
    /// Maximum total cache size in bytes
    pub max_total_size_bytes: u64,
    /// Number of cache tiers
    pub num_tiers: u32,
    /// Tier size distribution
    pub tier_size_ratios: Vec<f64>,
    /// Default TTL for cached items
    pub default_ttl_seconds: u64,
    /// Optimization frequency
    pub optimization_interval_seconds: u64,
    /// ML model update frequency
    pub ml_update_interval_seconds: u64,
    /// Enable predictive prefetching
    pub enable_prefetching: bool,
    /// Enable adaptive optimization
    pub enable_adaptive_optimization: bool,
    /// Performance monitoring settings
    pub monitoring_config: MonitoringConfiguration,
}

/// Machine learning models for intelligent caching decisions
#[derive(Debug)]
pub struct MLModels {
    /// Access pattern prediction model
    access_predictor: AccessPredictionModel,
    /// Cache hit probability model
    hit_probability_model: HitProbabilityModel,
    /// Optimal tier placement model
    tier_placement_model: TierPlacementModel,
    /// Eviction timing model
    eviction_timing_model: EvictionTimingModel,
}

// Cache storage trait for different storage implementations
pub trait CacheStorage: Send + Sync + std::fmt::Debug {
    /// Store an item in the cache
    fn store(&mut self, key: CacheKey, value: CacheValue, ttl: Option<Duration>) -> Result<()>;

    /// Retrieve an item from the cache
    fn retrieve(&self, key: &CacheKey) -> Option<CacheValue>;

    /// Remove an item from the cache
    fn remove(&mut self, key: &CacheKey) -> bool;

    /// Get cache size information
    fn size_info(&self) -> CacheSizeInfo;

    /// Clear the entire cache
    fn clear(&mut self);

    /// Get storage-specific statistics
    fn statistics(&self) -> StorageStatistics;
}

// Eviction policy trait for different eviction strategies
pub trait EvictionPolicy: Send + Sync + std::fmt::Debug {
    /// Determine which items to evict
    fn evict(&mut self, current_size: u64, target_size: u64, items: &[CacheItem]) -> Vec<CacheKey>;

    /// Update access information for an item
    fn on_access(&mut self, key: &CacheKey, access_time: Instant);

    /// Update when an item is stored
    fn on_store(&mut self, key: &CacheKey, size: u64, store_time: Instant);

    /// Get policy-specific statistics
    fn statistics(&self) -> EvictionStatistics;
}

// Optimization algorithm trait
pub trait OptimizationAlgorithm: Send + Sync + std::fmt::Debug {
    /// Apply optimization to the cache system
    fn optimize_cache(
        &mut self,
        tiers: &[CacheTier],
        metrics: &CachePerformanceMetrics,
        config: &CacheConfiguration,
    ) -> Result<OptimizationResult>;

    /// Get algorithm name
    fn name(&self) -> &str;

    /// Get optimization score for current state
    fn score(&self, metrics: &CachePerformanceMetrics) -> f64;
}

// Supporting types and structures

#[derive(Debug, Clone, PartialEq)]
pub struct CacheKey {
    pub query_vector: Vec<u8>, // Hashed query vector
    pub similarity_metric: SimilarityMetric,
    pub parameters: HashMap<String, String>,
}

impl Hash for CacheKey {
    fn hash<H: Hasher>(&self, state: &mut H) {
        self.query_vector.hash(state);
        // Hash the similarity metric by its discriminant
        std::mem::discriminant(&self.similarity_metric).hash(state);
        // For parameters, we'll sort them to ensure consistent hashing
        let mut params: Vec<_> = self.parameters.iter().collect();
        params.sort_by_key(|(k, _)| *k);
        params.hash(state);
    }
}

impl Eq for CacheKey {}

#[derive(Debug, Clone)]
pub struct CacheValue {
    pub results: Vec<(VectorId, f32)>, // Search results with scores
    pub metadata: CacheMetadata,
    pub created_at: SystemTime,
    pub last_accessed: SystemTime,
    pub access_count: u32,
}

#[derive(Debug, Clone)]
pub struct CacheMetadata {
    pub size_bytes: u64,
    pub computation_cost: f64,
    pub quality_score: f64,
    pub staleness_factor: f64,
}

#[derive(Debug, Clone)]
pub struct AccessEvent {
    pub timestamp: SystemTime,
    pub key: CacheKey,
    pub hit: bool,
    pub latency_ns: u64,
    pub user_context: Option<String>,
}

#[derive(Debug, Clone)]
pub struct PrefetchItem {
    pub key: CacheKey,
    pub priority: f64,
    pub predicted_access_time: SystemTime,
    pub confidence: f64,
    pub strategy: PrefetchStrategy,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum PrefetchStrategy {
    SequentialPattern,
    SeasonalPattern,
    SimilarityBased,
    UserBehavior,
    MachineLearning,
}

#[derive(Debug, Clone)]
pub struct CacheItem {
    pub key: CacheKey,
    pub value: CacheValue,
    pub tier_id: u32,
    pub last_access: Instant,
    pub access_frequency: f64,
}

#[derive(Debug, Clone, Default)]
pub struct CacheSizeInfo {
    pub used_bytes: u64,
    pub available_bytes: u64,
    pub total_capacity_bytes: u64,
    pub item_count: u64,
}

#[derive(Debug, Clone, Default)]
pub struct StorageStatistics {
    pub read_operations: u64,
    pub write_operations: u64,
    pub delete_operations: u64,
    pub avg_operation_latency_ns: u64,
    pub fragmentation_ratio: f64,
}

#[derive(Debug, Clone, Default)]
pub struct EvictionStatistics {
    pub total_evictions: u64,
    pub false_evictions: u64, // Items evicted but accessed shortly after
    pub eviction_accuracy: f64,
    pub avg_item_lifetime: Duration,
}

#[derive(Debug, Clone)]
pub struct OptimizationResult {
    pub improvement_score: f64,
    pub changes_applied: Vec<OptimizationChange>,
    pub estimated_impact: EstimatedImpact,
}

#[derive(Debug, Clone)]
pub struct OptimizationChange {
    pub change_type: OptimizationChangeType,
    pub description: String,
    pub tier_id: Option<u32>,
    pub old_value: String,
    pub new_value: String,
}

#[derive(Debug, Clone, Copy)]
pub enum OptimizationChangeType {
    TierSizeAdjustment,
    EvictionPolicyChange,
    TTLAdjustment,
    PrefetchingStrategy,
    TierRebalancing,
}

#[derive(Debug, Clone, Default)]
pub struct EstimatedImpact {
    pub hit_rate_improvement: f64,
    pub latency_reduction: f64,
    pub memory_efficiency_gain: f64,
    pub cost_reduction: f64,
}

// Additional implementation types
#[derive(Debug, Clone)]
pub struct TierConfiguration {
    pub max_size_bytes: u64,
    pub default_ttl: Duration,
    pub compression_enabled: bool,
    pub persistence_enabled: bool,
    pub replication_factor: u32,
}

#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct TierStatistics {
    pub hit_count: u64,
    pub miss_count: u64,
    pub eviction_count: u64,
    pub size_bytes: u64,
    pub item_count: u64,
    pub avg_access_time: Duration,
}

#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct TierMetrics {
    pub hit_rate: f64,
    pub utilization: f64,
    pub avg_item_size: u64,
    pub hotness_score: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HistoricalMetric {
    pub timestamp: SystemTime,
    pub hit_rate: f64,
    pub latency_p99: u64,
    pub throughput: f64,
    pub memory_usage: u64,
}

#[derive(Debug, Clone)]
pub struct AccessTracker {
    access_counts: HashMap<CacheKey, u64>,
    access_times: HashMap<CacheKey, VecDeque<SystemTime>>,
    hot_keys: BTreeMap<u64, CacheKey>, // Frequency -> Key
}

#[derive(Debug, Clone)]
pub struct SeasonalPatternDetector {
    hourly_patterns: [f64; 24],
    daily_patterns: [f64; 7],
    monthly_patterns: [f64; 31],
    pattern_confidence: f64,
}

#[derive(Debug, Clone)]
pub struct QueryClusteringEngine {
    clusters: Vec<QueryCluster>,
    cluster_assignments: HashMap<CacheKey, u32>,
    cluster_centroids: Vec<Vector>,
}

#[derive(Debug, Clone)]
pub struct QueryCluster {
    pub cluster_id: u32,
    pub centroid: Vector,
    pub members: Vec<CacheKey>,
    pub access_pattern: AccessPattern,
}

#[derive(Debug, Clone)]
pub struct AccessPattern {
    pub frequency: f64,
    pub seasonality: SeasonalityInfo,
    pub correlation_score: f64,
}

#[derive(Debug, Clone)]
pub struct SeasonalityInfo {
    pub has_daily_pattern: bool,
    pub has_weekly_pattern: bool,
    pub peak_hours: Vec<u8>,
    pub peak_days: Vec<u8>,
}

#[derive(Debug, Clone)]
pub struct TemporalAccessPredictor {
    time_series_models: HashMap<CacheKey, TimeSeriesModel>,
    global_trend_model: GlobalTrendModel,
    prediction_horizon: Duration,
}

#[derive(Debug, Clone)]
pub struct TimeSeriesModel {
    pub coefficients: Vec<f64>,
    pub seasonal_components: Vec<f64>,
    pub trend_component: f64,
    pub accuracy_score: f64,
}

#[derive(Debug, Clone)]
pub struct GlobalTrendModel {
    pub hourly_multipliers: [f64; 24],
    pub daily_multipliers: [f64; 7],
    pub base_rate: f64,
}

#[derive(Debug, Clone)]
pub struct PrefetchModels {
    pub similarity_model: SimilarityPrefetchModel,
    pub sequence_model: SequencePrefetchModel,
    pub user_behavior_model: UserBehaviorModel,
}

#[derive(Debug, Clone)]
pub struct SimilarityPrefetchModel {
    pub similarity_threshold: f64,
    pub prefetch_depth: u32,
    pub confidence_weights: Vec<f64>,
}

#[derive(Debug, Clone)]
pub struct SequencePrefetchModel {
    pub sequence_patterns: HashMap<Vec<CacheKey>, f64>, // Pattern -> Probability
    pub max_sequence_length: u32,
    pub min_confidence: f64,
}

#[derive(Debug, Clone)]
pub struct UserBehaviorModel {
    pub user_profiles: HashMap<String, UserProfile>,
    pub default_profile: UserProfile,
}

#[derive(Debug, Clone)]
pub struct UserProfile {
    pub typical_query_patterns: Vec<QueryPattern>,
    pub access_frequency: f64,
    pub preference_weights: HashMap<SimilarityMetric, f64>,
}

#[derive(Debug, Clone)]
pub struct QueryPattern {
    pub pattern_vector: Vector,
    pub frequency: f64,
    pub time_distribution: Vec<f64>, // Hourly distribution
}

#[derive(Debug, Clone)]
pub struct PrefetchPerformance {
    pub successful_prefetches: u64,
    pub failed_prefetches: u64,
    pub cache_space_saved: u64,
    pub avg_prediction_accuracy: f64,
}

#[derive(Debug, Clone)]
pub struct OptimizationEvent {
    pub timestamp: SystemTime,
    pub algorithm: String,
    pub changes: Vec<OptimizationChange>,
    pub before_metrics: CachePerformanceMetrics,
    pub after_metrics: Option<CachePerformanceMetrics>,
}

#[derive(Debug, Clone)]
pub struct OptimizationState {
    pub last_optimization: SystemTime,
    pub optimization_frequency: Duration,
    pub pending_optimizations: Vec<String>,
    pub optimization_backlog: u32,
}

#[derive(Debug, Clone)]
pub struct ImprovementTracker {
    pub baseline_metrics: CachePerformanceMetrics,
    pub current_improvement: f64,
    pub improvement_history: VecDeque<ImprovementPoint>,
    pub regression_detection: RegressionDetector,
}

#[derive(Debug, Clone)]
pub struct ImprovementPoint {
    pub timestamp: SystemTime,
    pub improvement_score: f64,
    pub optimization_applied: String,
}

#[derive(Debug, Clone)]
pub struct RegressionDetector {
    pub regression_threshold: f64,
    pub detection_window: Duration,
    pub recent_scores: VecDeque<f64>,
}

#[derive(Debug, Clone)]
pub struct AccessPredictionModel {
    pub model_weights: Vec<f64>,
    pub feature_extractors: Vec<FeatureExtractor>,
    pub prediction_accuracy: f64,
}

#[derive(Debug, Clone)]
pub struct HitProbabilityModel {
    pub probability_matrix: HashMap<(CacheKey, u32), f64>, // (Key, Tier) -> Probability
    pub model_confidence: f64,
    pub last_update: SystemTime,
}

#[derive(Debug, Clone)]
pub struct TierPlacementModel {
    pub placement_scores: HashMap<CacheKey, Vec<f64>>, // Key -> Tier scores
    pub optimization_objective: OptimizationObjective,
}

#[derive(Debug, Clone)]
pub struct EvictionTimingModel {
    pub survival_functions: HashMap<CacheKey, SurvivalFunction>,
    pub hazard_rates: HashMap<CacheKey, f64>,
}

#[derive(Debug, Clone)]
pub struct SurvivalFunction {
    pub time_points: Vec<Duration>,
    pub survival_probabilities: Vec<f64>,
}

#[derive(Debug, Clone)]
pub enum FeatureExtractor {
    AccessFrequency,
    RecencyScore,
    SizeMetric,
    ComputationCost,
    UserContext,
    TemporalFeatures,
}

#[derive(Debug, Clone, Copy)]
pub enum OptimizationObjective {
    MaximizeHitRate,
    MinimizeLatency,
    MaximizeThroughput,
    MinimizeCost,
    BalancedPerformance,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MonitoringConfiguration {
    pub enable_detailed_metrics: bool,
    pub metrics_retention_days: u32,
    pub alert_thresholds: AlertThresholds,
    pub export_prometheus: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlertThresholds {
    pub min_hit_rate: f64,
    pub max_latency_p99_ms: f64,
    pub max_memory_utilization: f64,
    pub min_cache_efficiency: f64,
}

impl AdaptiveIntelligentCache {
    /// Create a new adaptive intelligent cache with the given configuration
    pub fn new(config: CacheConfiguration) -> Result<Self> {
        info!(
            "Initializing Adaptive Intelligent Cache with {} tiers",
            config.num_tiers
        );

        let mut tiers = Vec::new();
        let tier_sizes = Self::calculate_tier_sizes(&config);

        for (tier_id, size) in tier_sizes.into_iter().enumerate() {
            let tier_config = TierConfiguration {
                max_size_bytes: size,
                default_ttl: Duration::from_secs(config.default_ttl_seconds),
                compression_enabled: tier_id > 0, // Enable compression for higher tiers
                persistence_enabled: tier_id == config.num_tiers as usize - 1, // Only last tier persisted
                replication_factor: if tier_id == 0 { 1 } else { 2 }, // Replicate slower tiers
            };

            let storage = Self::create_storage_for_tier(tier_id as u32, &tier_config)?;
            let eviction_policy = Self::create_eviction_policy_for_tier(tier_id as u32);

            let tier = CacheTier {
                tier_id: tier_id as u32,
                storage,
                eviction_policy,
                access_tracker: AccessTracker::new(),
                config: tier_config,
                stats: TierStatistics::default(),
            };

            tiers.push(tier);
        }

        Ok(Self {
            tiers,
            pattern_analyzer: AccessPatternAnalyzer::new(),
            prefetcher: PredictivePrefetcher::new(),
            optimizer: CacheOptimizer::new(),
            metrics: CachePerformanceMetrics::default(),
            config,
            ml_models: MLModels::new()?,
        })
    }

    /// Store a value in the cache with intelligent tier placement
    pub fn store(&mut self, key: CacheKey, value: CacheValue) -> Result<()> {
        let start_time = Instant::now();

        // Determine optimal tier placement using ML model
        let optimal_tier = self
            .ml_models
            .tier_placement_model
            .predict_optimal_tier(&key, &value);

        // Store in the determined tier
        let tier = &mut self.tiers[optimal_tier as usize];
        tier.storage
            .store(key.clone(), value.clone(), Some(tier.config.default_ttl))?;

        // Update access tracking and metrics
        tier.access_tracker.on_store(&key);
        self.update_store_metrics(optimal_tier, start_time.elapsed());

        // Trigger eviction if necessary
        self.check_and_evict(optimal_tier)?;

        // Update ML models with new data
        self.ml_models
            .update_with_store_event(&key, &value, optimal_tier);

        debug!(
            "Stored cache item in tier {} with key hash {:?}",
            optimal_tier,
            self.hash_key(&key)
        );
        Ok(())
    }

    /// Retrieve a value from the cache with intelligent promotion
    pub fn retrieve(&mut self, key: &CacheKey) -> Option<CacheValue> {
        let start_time = Instant::now();

        // Search through tiers starting from fastest
        for (tier_index, tier) in self.tiers.iter_mut().enumerate() {
            if let Some(mut value) = tier.storage.retrieve(key) {
                // Update access information
                value.last_accessed = SystemTime::now();
                value.access_count += 1;

                tier.access_tracker.on_access(key, Instant::now());
                self.update_hit_metrics(tier_index as u32, start_time.elapsed());

                // Consider promoting to faster tier based on access pattern
                if tier_index > 0 && self.should_promote(key, &value, tier_index) {
                    if let Err(e) = self.promote_item(key.clone(), value.clone(), tier_index) {
                        warn!("Failed to promote cache item: {}", e);
                    }
                }

                // Record access event for pattern analysis
                self.pattern_analyzer.record_access(AccessEvent {
                    timestamp: SystemTime::now(),
                    key: key.clone(),
                    hit: true,
                    latency_ns: start_time.elapsed().as_nanos() as u64,
                    user_context: None, // Could be extracted from key metadata
                });

                // Trigger predictive prefetching
                if self.config.enable_prefetching {
                    self.prefetcher.trigger_prefetch_analysis(key, &value);
                }

                return Some(value);
            }
        }

        // Cache miss - update metrics and patterns
        self.update_miss_metrics(start_time.elapsed());
        self.pattern_analyzer.record_access(AccessEvent {
            timestamp: SystemTime::now(),
            key: key.clone(),
            hit: false,
            latency_ns: start_time.elapsed().as_nanos() as u64,
            user_context: None,
        });

        None
    }

    /// Remove an item from all cache tiers
    pub fn remove(&mut self, key: &CacheKey) -> bool {
        let mut removed = false;
        for tier in &mut self.tiers {
            if tier.storage.remove(key) {
                tier.access_tracker.on_remove(key);
                removed = true;
            }
        }
        removed
    }

    /// Get comprehensive cache statistics
    pub fn get_statistics(&self) -> CacheStatistics {
        let total_hits = self.metrics.hit_count.load(Ordering::Relaxed);
        let total_misses = self.metrics.miss_count.load(Ordering::Relaxed);
        let total_requests = total_hits + total_misses;

        let hit_rate = if total_requests > 0 {
            total_hits as f64 / total_requests as f64
        } else {
            0.0
        };

        CacheStatistics {
            hit_rate,
            miss_rate: 1.0 - hit_rate,
            total_requests,
            avg_hit_latency_ns: self.metrics.avg_hit_latency_ns.load(Ordering::Relaxed),
            avg_miss_latency_ns: self.metrics.avg_miss_latency_ns.load(Ordering::Relaxed),
            cache_efficiency: self.metrics.cache_efficiency_score,
            memory_utilization: self.calculate_memory_utilization(),
            tier_statistics: self.collect_tier_statistics(),
            prefetch_statistics: self.prefetcher.get_statistics(),
            optimization_statistics: self.optimizer.get_statistics(),
        }
    }

    /// Run cache optimization cycle
    pub fn optimize(&mut self) -> Result<OptimizationResult> {
        if !self.config.enable_adaptive_optimization {
            return Ok(OptimizationResult {
                improvement_score: 0.0,
                changes_applied: vec![],
                estimated_impact: EstimatedImpact::default(),
            });
        }

        info!("Running cache optimization cycle");
        let before_metrics = self.metrics.clone();

        let mut total_improvement = 0.0;
        let mut all_changes = Vec::new();

        // Run each optimization algorithm
        // Temporarily move algorithms out to avoid borrowing conflicts
        let mut algorithms = std::mem::take(&mut self.optimizer.algorithms);
        for algorithm in &mut algorithms {
            match algorithm.optimize_cache(&self.tiers, &self.metrics, &self.config) {
                Ok(result) => {
                    total_improvement += result.improvement_score;
                    all_changes.extend(result.changes_applied);
                    info!(
                        "Optimization algorithm '{}' achieved {:.2}% improvement",
                        algorithm.name(),
                        result.improvement_score * 100.0
                    );
                }
                Err(e) => {
                    warn!(
                        "Optimization algorithm '{}' failed: {}",
                        algorithm.name(),
                        e
                    );
                }
            }
        }
        // Move algorithms back
        self.optimizer.algorithms = algorithms;

        // Update optimization history
        self.optimizer.record_optimization_event(OptimizationEvent {
            timestamp: SystemTime::now(),
            algorithm: "combined".to_string(),
            changes: all_changes.clone(),
            before_metrics,
            after_metrics: None, // Will be updated later
        });

        Ok(OptimizationResult {
            improvement_score: total_improvement,
            changes_applied: all_changes,
            estimated_impact: self.estimate_optimization_impact(total_improvement),
        })
    }

    /// Export cache performance data for external analysis
    pub fn export_performance_data(&self, format: ExportFormat) -> Result<String> {
        match format {
            ExportFormat::Json => {
                let data = CachePerformanceData {
                    metrics: self.metrics.clone(),
                    statistics: self.get_statistics(),
                    configuration: self.config.clone(),
                    access_patterns: self.pattern_analyzer.export_patterns(),
                    optimization_history: self.optimizer.export_history(),
                };
                Ok(serde_json::to_string_pretty(&data)?)
            }
            ExportFormat::Prometheus => self.export_prometheus_metrics(),
            ExportFormat::Csv => self.export_csv_metrics(),
        }
    }

    // Private helper methods

    fn calculate_tier_sizes(config: &CacheConfiguration) -> Vec<u64> {
        let total_size = config.max_total_size_bytes;
        config
            .tier_size_ratios
            .iter()
            .map(|ratio| (total_size as f64 * ratio) as u64)
            .collect()
    }

    fn create_storage_for_tier(
        tier_id: u32,
        config: &TierConfiguration,
    ) -> Result<Box<dyn CacheStorage>> {
        match tier_id {
            0 => Ok(Box::new(MemoryStorage::new(config.max_size_bytes))),
            1 => Ok(Box::new(CompressedStorage::new(config.max_size_bytes))),
            _ => Ok(Box::new(PersistentStorage::new(config.max_size_bytes)?)),
        }
    }

    fn create_eviction_policy_for_tier(tier_id: u32) -> Box<dyn EvictionPolicy> {
        match tier_id {
            0 => Box::new(LRUEvictionPolicy::new()),
            1 => Box::new(LFUEvictionPolicy::new()),
            _ => Box::new(AdaptiveEvictionPolicy::new()),
        }
    }

    fn should_promote(&self, _key: &CacheKey, value: &CacheValue, current_tier: usize) -> bool {
        // Use ML model to determine if item should be promoted
        let access_frequency = value.access_count as f64;
        let recency_score = self.calculate_recency_score(value.last_accessed);
        let size_penalty = value.metadata.size_bytes as f64 / 1024.0; // KB

        let promotion_score = access_frequency * recency_score / size_penalty;
        promotion_score > 2.0 && current_tier > 0
    }

    fn promote_item(&mut self, key: CacheKey, value: CacheValue, from_tier: usize) -> Result<()> {
        if from_tier == 0 {
            return Ok(()); // Already in fastest tier
        }

        let target_tier = from_tier - 1;

        // Remove from current tier
        self.tiers[from_tier].storage.remove(&key);

        // Store in target tier
        let default_ttl = self.tiers[target_tier].config.default_ttl;
        self.tiers[target_tier]
            .storage
            .store(key, value, Some(default_ttl))?;

        debug!(
            "Promoted cache item from tier {} to tier {}",
            from_tier, target_tier
        );
        Ok(())
    }

    fn calculate_recency_score(&self, last_accessed: SystemTime) -> f64 {
        let now = SystemTime::now();
        let duration = now.duration_since(last_accessed).unwrap_or(Duration::ZERO);
        let hours = duration.as_secs_f64() / 3600.0;

        // Exponential decay
        (-hours / 24.0).exp()
    }

    fn check_and_evict(&mut self, tier_id: u32) -> Result<()> {
        let size_info = {
            let tier = &self.tiers[tier_id as usize];
            tier.storage.size_info()
        };

        if size_info.used_bytes > self.tiers[tier_id as usize].config.max_size_bytes {
            let target_size =
                (self.tiers[tier_id as usize].config.max_size_bytes as f64 * 0.8) as u64; // Target 80% utilization
            let items = self.collect_tier_items(tier_id);

            let keys_to_evict = {
                let tier = &mut self.tiers[tier_id as usize];
                tier.eviction_policy
                    .evict(size_info.used_bytes, target_size, &items)
            };

            let tier = &mut self.tiers[tier_id as usize];
            for key in keys_to_evict {
                tier.storage.remove(&key);
                tier.stats.eviction_count += 1;
            }
        }

        Ok(())
    }

    fn collect_tier_items(&self, _tier_id: u32) -> Vec<CacheItem> {
        // This would collect all items from the tier for eviction analysis
        // Simplified implementation
        Vec::new()
    }

    fn hash_key(&self, key: &CacheKey) -> u64 {
        use std::collections::hash_map::DefaultHasher;
        let mut hasher = DefaultHasher::new();
        key.hash(&mut hasher);
        hasher.finish()
    }

    fn update_store_metrics(&mut self, tier_id: u32, _latency: Duration) {
        // Update tier-specific metrics
        if let Some(_tier_metrics) = self.metrics.tier_metrics.get_mut(&tier_id) {
            // Update tier metrics
        }
    }

    fn update_hit_metrics(&mut self, _tier_id: u32, latency: Duration) {
        self.metrics.hit_count.fetch_add(1, Ordering::Relaxed);
        self.metrics.total_requests.fetch_add(1, Ordering::Relaxed);

        // Update average hit latency (simplified)
        let latency_ns = latency.as_nanos() as u64;
        self.metrics
            .avg_hit_latency_ns
            .store(latency_ns, Ordering::Relaxed);
    }

    fn update_miss_metrics(&mut self, latency: Duration) {
        self.metrics.miss_count.fetch_add(1, Ordering::Relaxed);
        self.metrics.total_requests.fetch_add(1, Ordering::Relaxed);

        let latency_ns = latency.as_nanos() as u64;
        self.metrics
            .avg_miss_latency_ns
            .store(latency_ns, Ordering::Relaxed);
    }

    fn calculate_memory_utilization(&self) -> f64 {
        let total_used: u64 = self
            .tiers
            .iter()
            .map(|tier| tier.storage.size_info().used_bytes)
            .sum();
        let total_capacity: u64 = self
            .tiers
            .iter()
            .map(|tier| tier.storage.size_info().total_capacity_bytes)
            .sum();

        if total_capacity > 0 {
            total_used as f64 / total_capacity as f64
        } else {
            0.0
        }
    }

    fn collect_tier_statistics(&self) -> Vec<TierStatistics> {
        self.tiers.iter().map(|tier| tier.stats.clone()).collect()
    }

    fn estimate_optimization_impact(&self, improvement_score: f64) -> EstimatedImpact {
        EstimatedImpact {
            hit_rate_improvement: improvement_score * 0.1,
            latency_reduction: improvement_score * 0.05,
            memory_efficiency_gain: improvement_score * 0.08,
            cost_reduction: improvement_score * 0.03,
        }
    }

    fn export_prometheus_metrics(&self) -> Result<String> {
        let mut metrics = String::new();

        let hit_count = self.metrics.hit_count.load(Ordering::Relaxed);
        let miss_count = self.metrics.miss_count.load(Ordering::Relaxed);
        let total = hit_count + miss_count;

        metrics.push_str(&format!("oxirs_cache_hits_total {hit_count}\n"));
        metrics.push_str(&format!("oxirs_cache_misses_total {miss_count}\n"));
        metrics.push_str(&format!("oxirs_cache_requests_total {total}\n"));

        if total > 0 {
            let hit_rate = hit_count as f64 / total as f64;
            metrics.push_str(&format!("oxirs_cache_hit_rate {hit_rate:.4}\n"));
        }

        metrics.push_str(&format!(
            "oxirs_cache_memory_utilization {:.4}\n",
            self.calculate_memory_utilization()
        ));
        metrics.push_str(&format!(
            "oxirs_cache_efficiency_score {:.4}\n",
            self.metrics.cache_efficiency_score
        ));

        Ok(metrics)
    }

    fn export_csv_metrics(&self) -> Result<String> {
        let mut csv = String::new();
        csv.push_str("metric,value,timestamp\n");

        let now = SystemTime::now()
            .duration_since(SystemTime::UNIX_EPOCH)?
            .as_secs();
        let hit_count = self.metrics.hit_count.load(Ordering::Relaxed);
        let miss_count = self.metrics.miss_count.load(Ordering::Relaxed);

        csv.push_str(&format!("hit_count,{hit_count},{now}\n"));
        csv.push_str(&format!("miss_count,{miss_count},{now}\n"));
        csv.push_str(&format!(
            "memory_utilization,{:.4},{}\n",
            self.calculate_memory_utilization(),
            now
        ));

        Ok(csv)
    }
}

// Supporting implementations (simplified for brevity)

#[derive(Debug)]
pub struct MemoryStorage {
    data: HashMap<CacheKey, CacheValue>,
    max_size: u64,
    current_size: u64,
}

impl MemoryStorage {
    pub fn new(max_size: u64) -> Self {
        Self {
            data: HashMap::new(),
            max_size,
            current_size: 0,
        }
    }
}

impl CacheStorage for MemoryStorage {
    fn store(&mut self, key: CacheKey, value: CacheValue, _ttl: Option<Duration>) -> Result<()> {
        let size = value.metadata.size_bytes;
        if self.current_size + size <= self.max_size {
            self.data.insert(key, value);
            self.current_size += size;
        }
        Ok(())
    }

    fn retrieve(&self, key: &CacheKey) -> Option<CacheValue> {
        self.data.get(key).cloned()
    }

    fn remove(&mut self, key: &CacheKey) -> bool {
        if let Some(value) = self.data.remove(key) {
            self.current_size -= value.metadata.size_bytes;
            true
        } else {
            false
        }
    }

    fn size_info(&self) -> CacheSizeInfo {
        CacheSizeInfo {
            used_bytes: self.current_size,
            available_bytes: self.max_size - self.current_size,
            total_capacity_bytes: self.max_size,
            item_count: self.data.len() as u64,
        }
    }

    fn clear(&mut self) {
        self.data.clear();
        self.current_size = 0;
    }

    fn statistics(&self) -> StorageStatistics {
        StorageStatistics::default()
    }
}

// Additional implementations would go here...
// (CompressedStorage, PersistentStorage, EvictionPolicies, MLModels, etc.)

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheStatistics {
    pub hit_rate: f64,
    pub miss_rate: f64,
    pub total_requests: u64,
    pub avg_hit_latency_ns: u64,
    pub avg_miss_latency_ns: u64,
    pub cache_efficiency: f64,
    pub memory_utilization: f64,
    pub tier_statistics: Vec<TierStatistics>,
    pub prefetch_statistics: PrefetchStatistics,
    pub optimization_statistics: OptimizationStatistics,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PrefetchStatistics {
    pub successful_prefetches: u64,
    pub failed_prefetches: u64,
    pub prefetch_hit_rate: f64,
    pub avg_prediction_accuracy: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationStatistics {
    pub total_optimizations: u64,
    pub successful_optimizations: u64,
    pub avg_improvement_score: f64,
    pub last_optimization: Option<SystemTime>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CachePerformanceData {
    pub metrics: CachePerformanceMetrics,
    pub statistics: CacheStatistics,
    pub configuration: CacheConfiguration,
    pub access_patterns: String,      // JSON encoded patterns
    pub optimization_history: String, // JSON encoded history
}

#[derive(Debug, Clone, Copy)]
pub enum ExportFormat {
    Json,
    Prometheus,
    Csv,
}

// Placeholder implementations for complex components
impl Default for AccessPatternAnalyzer {
    fn default() -> Self {
        Self::new()
    }
}

impl AccessPatternAnalyzer {
    pub fn new() -> Self {
        Self {
            access_history: VecDeque::new(),
            seasonal_detector: SeasonalPatternDetector::new(),
            query_clustering: QueryClusteringEngine::new(),
            temporal_predictor: TemporalAccessPredictor::new(),
        }
    }

    pub fn record_access(&mut self, _event: AccessEvent) {
        // Implementation would analyze access patterns
    }

    pub fn export_patterns(&self) -> String {
        "{}".to_string() // Simplified
    }
}

// Additional placeholder implementations...
impl Default for SeasonalPatternDetector {
    fn default() -> Self {
        Self::new()
    }
}

impl SeasonalPatternDetector {
    pub fn new() -> Self {
        Self {
            hourly_patterns: [1.0; 24],
            daily_patterns: [1.0; 7],
            monthly_patterns: [1.0; 31],
            pattern_confidence: 0.0,
        }
    }
}

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

impl QueryClusteringEngine {
    pub fn new() -> Self {
        Self {
            clusters: Vec::new(),
            cluster_assignments: HashMap::new(),
            cluster_centroids: Vec::new(),
        }
    }
}

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

impl TemporalAccessPredictor {
    pub fn new() -> Self {
        Self {
            time_series_models: HashMap::new(),
            global_trend_model: GlobalTrendModel {
                hourly_multipliers: [1.0; 24],
                daily_multipliers: [1.0; 7],
                base_rate: 1.0,
            },
            prediction_horizon: Duration::from_secs(3600),
        }
    }
}

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

impl PredictivePrefetcher {
    pub fn new() -> Self {
        Self {
            prefetch_queue: VecDeque::new(),
            models: PrefetchModels::new(),
            strategies: vec![
                PrefetchStrategy::SequentialPattern,
                PrefetchStrategy::SimilarityBased,
                PrefetchStrategy::MachineLearning,
            ],
            performance: PrefetchPerformance {
                successful_prefetches: 0,
                failed_prefetches: 0,
                cache_space_saved: 0,
                avg_prediction_accuracy: 0.0,
            },
        }
    }

    pub fn trigger_prefetch_analysis(&mut self, _key: &CacheKey, _value: &CacheValue) {
        // Implementation would analyze prefetch opportunities
    }

    pub fn get_statistics(&self) -> PrefetchStatistics {
        PrefetchStatistics {
            successful_prefetches: self.performance.successful_prefetches,
            failed_prefetches: self.performance.failed_prefetches,
            prefetch_hit_rate: if self.performance.successful_prefetches
                + self.performance.failed_prefetches
                > 0
            {
                self.performance.successful_prefetches as f64
                    / (self.performance.successful_prefetches + self.performance.failed_prefetches)
                        as f64
            } else {
                0.0
            },
            avg_prediction_accuracy: self.performance.avg_prediction_accuracy,
        }
    }
}

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

impl PrefetchModels {
    pub fn new() -> Self {
        Self {
            similarity_model: SimilarityPrefetchModel {
                similarity_threshold: 0.8,
                prefetch_depth: 5,
                confidence_weights: vec![1.0, 0.8, 0.6, 0.4, 0.2],
            },
            sequence_model: SequencePrefetchModel {
                sequence_patterns: HashMap::new(),
                max_sequence_length: 5,
                min_confidence: 0.7,
            },
            user_behavior_model: UserBehaviorModel {
                user_profiles: HashMap::new(),
                default_profile: UserProfile {
                    typical_query_patterns: Vec::new(),
                    access_frequency: 1.0,
                    preference_weights: HashMap::new(),
                },
            },
        }
    }
}

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

impl CacheOptimizer {
    pub fn new() -> Self {
        Self {
            algorithms: vec![], // Would contain actual optimization algorithms
            optimization_history: Vec::new(),
            current_state: OptimizationState {
                last_optimization: SystemTime::now(),
                optimization_frequency: Duration::from_secs(3600),
                pending_optimizations: Vec::new(),
                optimization_backlog: 0,
            },
            improvements: ImprovementTracker {
                baseline_metrics: CachePerformanceMetrics::default(),
                current_improvement: 0.0,
                improvement_history: VecDeque::new(),
                regression_detection: RegressionDetector {
                    regression_threshold: -0.05,
                    detection_window: Duration::from_secs(1800),
                    recent_scores: VecDeque::new(),
                },
            },
        }
    }

    pub fn record_optimization_event(&mut self, event: OptimizationEvent) {
        self.optimization_history.push(event);
    }

    pub fn get_statistics(&self) -> OptimizationStatistics {
        OptimizationStatistics {
            total_optimizations: self.optimization_history.len() as u64,
            successful_optimizations: self
                .optimization_history
                .iter()
                .filter(|e| !e.changes.is_empty())
                .count() as u64,
            avg_improvement_score: self.improvements.current_improvement,
            last_optimization: self.optimization_history.last().map(|e| e.timestamp),
        }
    }

    pub fn export_history(&self) -> String {
        "{}".to_string() // Simplified
    }
}

impl MLModels {
    pub fn new() -> Result<Self> {
        Ok(Self {
            access_predictor: AccessPredictionModel {
                model_weights: vec![1.0, 0.8, 0.6, 0.4],
                feature_extractors: vec![
                    FeatureExtractor::AccessFrequency,
                    FeatureExtractor::RecencyScore,
                    FeatureExtractor::SizeMetric,
                    FeatureExtractor::ComputationCost,
                ],
                prediction_accuracy: 0.75,
            },
            hit_probability_model: HitProbabilityModel {
                probability_matrix: HashMap::new(),
                model_confidence: 0.8,
                last_update: SystemTime::now(),
            },
            tier_placement_model: TierPlacementModel {
                placement_scores: HashMap::new(),
                optimization_objective: OptimizationObjective::BalancedPerformance,
            },
            eviction_timing_model: EvictionTimingModel {
                survival_functions: HashMap::new(),
                hazard_rates: HashMap::new(),
            },
        })
    }

    pub fn update_with_store_event(&mut self, _key: &CacheKey, _value: &CacheValue, _tier: u32) {
        // Implementation would update ML models with new data
    }
}

impl TierPlacementModel {
    pub fn predict_optimal_tier(&self, _key: &CacheKey, _value: &CacheValue) -> u32 {
        // Simplified: for now just return tier 0 (fastest)
        0
    }
}

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

impl AccessTracker {
    pub fn new() -> Self {
        Self {
            access_counts: HashMap::new(),
            access_times: HashMap::new(),
            hot_keys: BTreeMap::new(),
        }
    }

    pub fn on_access(&mut self, key: &CacheKey, _access_time: Instant) {
        *self.access_counts.entry(key.clone()).or_insert(0) += 1;
        self.access_times
            .entry(key.clone())
            .or_default()
            .push_back(SystemTime::now());
    }

    pub fn on_store(&mut self, key: &CacheKey) {
        // Record that an item was stored
        self.access_times.entry(key.clone()).or_default();
    }

    pub fn on_remove(&mut self, key: &CacheKey) {
        self.access_counts.remove(key);
        self.access_times.remove(key);
    }
}

// Simplified eviction policy implementations
#[derive(Debug)]
pub struct LRUEvictionPolicy {
    access_order: VecDeque<CacheKey>,
}

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

impl LRUEvictionPolicy {
    pub fn new() -> Self {
        Self {
            access_order: VecDeque::new(),
        }
    }
}

impl EvictionPolicy for LRUEvictionPolicy {
    fn evict(
        &mut self,
        current_size: u64,
        target_size: u64,
        _items: &[CacheItem],
    ) -> Vec<CacheKey> {
        let bytes_to_evict = current_size.saturating_sub(target_size);
        let items_to_evict = (bytes_to_evict / 1024).max(1) as usize; // Estimate items to evict

        self.access_order
            .iter()
            .take(items_to_evict)
            .cloned()
            .collect()
    }

    fn on_access(&mut self, key: &CacheKey, _access_time: Instant) {
        // Move to back (most recently used)
        if let Some(pos) = self.access_order.iter().position(|k| k == key) {
            let key = self.access_order.remove(pos).unwrap();
            self.access_order.push_back(key);
        }
    }

    fn on_store(&mut self, key: &CacheKey, _size: u64, _store_time: Instant) {
        self.access_order.push_back(key.clone());
    }

    fn statistics(&self) -> EvictionStatistics {
        EvictionStatistics::default()
    }
}

#[derive(Debug)]
pub struct LFUEvictionPolicy {
    frequency_map: HashMap<CacheKey, u64>,
}

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

impl LFUEvictionPolicy {
    pub fn new() -> Self {
        Self {
            frequency_map: HashMap::new(),
        }
    }
}

impl EvictionPolicy for LFUEvictionPolicy {
    fn evict(
        &mut self,
        current_size: u64,
        target_size: u64,
        _items: &[CacheItem],
    ) -> Vec<CacheKey> {
        let bytes_to_evict = current_size.saturating_sub(target_size);
        let items_to_evict = (bytes_to_evict / 1024).max(1) as usize;

        let mut frequency_pairs: Vec<_> = self.frequency_map.iter().collect();
        frequency_pairs.sort_by_key(|&(_, &freq)| freq);

        frequency_pairs
            .iter()
            .take(items_to_evict)
            .map(|(key, _)| (*key).clone())
            .collect()
    }

    fn on_access(&mut self, key: &CacheKey, _access_time: Instant) {
        *self.frequency_map.entry(key.clone()).or_insert(0) += 1;
    }

    fn on_store(&mut self, key: &CacheKey, _size: u64, _store_time: Instant) {
        self.frequency_map.insert(key.clone(), 0);
    }

    fn statistics(&self) -> EvictionStatistics {
        EvictionStatistics::default()
    }
}

#[derive(Debug)]
pub struct AdaptiveEvictionPolicy {
    lru_component: LRUEvictionPolicy,
    lfu_component: LFUEvictionPolicy,
    lru_weight: f64,
}

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

impl AdaptiveEvictionPolicy {
    pub fn new() -> Self {
        Self {
            lru_component: LRUEvictionPolicy::new(),
            lfu_component: LFUEvictionPolicy::new(),
            lru_weight: 0.5,
        }
    }
}

impl EvictionPolicy for AdaptiveEvictionPolicy {
    fn evict(&mut self, current_size: u64, target_size: u64, items: &[CacheItem]) -> Vec<CacheKey> {
        // Combine LRU and LFU decisions
        let lru_candidates = self.lru_component.evict(current_size, target_size, items);
        let lfu_candidates = self.lfu_component.evict(current_size, target_size, items);

        // For simplicity, interleave the results based on weights
        let lru_count = (lru_candidates.len() as f64 * self.lru_weight) as usize;
        let mut result = Vec::new();
        result.extend(lru_candidates.into_iter().take(lru_count));
        result.extend(lfu_candidates.into_iter().take(items.len() - lru_count));

        result
    }

    fn on_access(&mut self, key: &CacheKey, access_time: Instant) {
        self.lru_component.on_access(key, access_time);
        self.lfu_component.on_access(key, access_time);
    }

    fn on_store(&mut self, key: &CacheKey, size: u64, store_time: Instant) {
        self.lru_component.on_store(key, size, store_time);
        self.lfu_component.on_store(key, size, store_time);
    }

    fn statistics(&self) -> EvictionStatistics {
        EvictionStatistics::default()
    }
}

// Placeholder storage implementations
#[derive(Debug)]
pub struct CompressedStorage {
    inner: MemoryStorage,
}

impl CompressedStorage {
    pub fn new(max_size: u64) -> Self {
        Self {
            inner: MemoryStorage::new(max_size),
        }
    }
}

impl CacheStorage for CompressedStorage {
    fn store(&mut self, key: CacheKey, value: CacheValue, ttl: Option<Duration>) -> Result<()> {
        // In a real implementation, this would compress the value
        self.inner.store(key, value, ttl)
    }

    fn retrieve(&self, key: &CacheKey) -> Option<CacheValue> {
        // In a real implementation, this would decompress the value
        self.inner.retrieve(key)
    }

    fn remove(&mut self, key: &CacheKey) -> bool {
        self.inner.remove(key)
    }

    fn size_info(&self) -> CacheSizeInfo {
        self.inner.size_info()
    }

    fn clear(&mut self) {
        self.inner.clear()
    }

    fn statistics(&self) -> StorageStatistics {
        self.inner.statistics()
    }
}

#[derive(Debug)]
pub struct PersistentStorage {
    inner: MemoryStorage,
}

impl PersistentStorage {
    pub fn new(max_size: u64) -> Result<Self> {
        Ok(Self {
            inner: MemoryStorage::new(max_size),
        })
    }
}

impl CacheStorage for PersistentStorage {
    fn store(&mut self, key: CacheKey, value: CacheValue, ttl: Option<Duration>) -> Result<()> {
        // In a real implementation, this would persist to disk
        self.inner.store(key, value, ttl)
    }

    fn retrieve(&self, key: &CacheKey) -> Option<CacheValue> {
        // In a real implementation, this would load from disk if not in memory
        self.inner.retrieve(key)
    }

    fn remove(&mut self, key: &CacheKey) -> bool {
        self.inner.remove(key)
    }

    fn size_info(&self) -> CacheSizeInfo {
        self.inner.size_info()
    }

    fn clear(&mut self) {
        self.inner.clear()
    }

    fn statistics(&self) -> StorageStatistics {
        self.inner.statistics()
    }
}

impl Default for CacheConfiguration {
    fn default() -> Self {
        Self {
            max_total_size_bytes: 1024 * 1024 * 1024, // 1GB
            num_tiers: 3,
            tier_size_ratios: vec![0.5, 0.3, 0.2], // 50%, 30%, 20%
            default_ttl_seconds: 3600,             // 1 hour
            optimization_interval_seconds: 300,    // 5 minutes
            ml_update_interval_seconds: 900,       // 15 minutes
            enable_prefetching: true,
            enable_adaptive_optimization: true,
            monitoring_config: MonitoringConfiguration {
                enable_detailed_metrics: true,
                metrics_retention_days: 7,
                alert_thresholds: AlertThresholds {
                    min_hit_rate: 0.8,
                    max_latency_p99_ms: 100.0,
                    max_memory_utilization: 0.9,
                    min_cache_efficiency: 0.7,
                },
                export_prometheus: true,
            },
        }
    }
}

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

    #[test]
    fn test_adaptive_cache_creation() {
        let config = CacheConfiguration::default();
        let cache = AdaptiveIntelligentCache::new(config).unwrap();
        assert_eq!(cache.tiers.len(), 3);
    }

    #[test]
    fn test_cache_store_and_retrieve() {
        let config = CacheConfiguration::default();
        let mut cache = AdaptiveIntelligentCache::new(config).unwrap();

        let key = CacheKey {
            query_vector: vec![1, 2, 3, 4],
            similarity_metric: SimilarityMetric::Cosine,
            parameters: HashMap::new(),
        };

        let value = CacheValue {
            results: vec![("vec1".to_string(), 0.95)],
            metadata: CacheMetadata {
                size_bytes: 1024,
                computation_cost: 0.5,
                quality_score: 0.9,
                staleness_factor: 0.1,
            },
            created_at: SystemTime::now(),
            last_accessed: SystemTime::now(),
            access_count: 1,
        };

        cache.store(key.clone(), value.clone()).unwrap();
        let retrieved = cache.retrieve(&key);

        assert!(retrieved.is_some());
        let retrieved_value = retrieved.unwrap();
        assert_eq!(retrieved_value.results, value.results);
    }

    #[test]
    fn test_cache_statistics() {
        let config = CacheConfiguration::default();
        let cache = AdaptiveIntelligentCache::new(config).unwrap();
        let stats = cache.get_statistics();

        assert_eq!(stats.total_requests, 0);
        assert_eq!(stats.hit_rate, 0.0);
    }

    #[test]
    fn test_cache_optimization() {
        let config = CacheConfiguration::default();
        let mut cache = AdaptiveIntelligentCache::new(config).unwrap();

        let result = cache.optimize().unwrap();
        assert!(result.improvement_score >= 0.0);
    }

    #[test]
    fn test_performance_data_export() {
        let config = CacheConfiguration::default();
        let cache = AdaptiveIntelligentCache::new(config).unwrap();

        let json_export = cache.export_performance_data(ExportFormat::Json).unwrap();
        assert!(!json_export.is_empty());

        let prometheus_export = cache
            .export_performance_data(ExportFormat::Prometheus)
            .unwrap();
        assert!(!prometheus_export.is_empty());
    }

    #[test]
    fn test_eviction_policies() {
        let mut lru = LRUEvictionPolicy::new();
        let key = CacheKey {
            query_vector: vec![1, 2, 3],
            similarity_metric: SimilarityMetric::Cosine,
            parameters: HashMap::new(),
        };

        lru.on_store(&key, 1024, Instant::now());
        lru.on_access(&key, Instant::now());

        let items = vec![];
        let evicted = lru.evict(2048, 1024, &items);
        assert!(!evicted.is_empty());
    }
}