use std::collections::HashMap;
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};
use tokio::sync::Semaphore;
use tracing::{info, warn};
use serde::{Deserialize, Serialize};

/// Performance optimization manager
pub struct PerformanceManager {
    cache: Arc<RwLock<Cache>>,
    metrics: Arc<Mutex<MetricsCollector>>,
    parallel_semaphore: Arc<Semaphore>,
    memory_pool: Arc<Mutex<MemoryPool>>,
    advanced_config: Option<AdvancedPerformanceConfig>,
    adaptive_cache: Option<Arc<Mutex<AdaptiveCacheManager>>>,
    intelligent_memory: Option<Arc<Mutex<IntelligentMemoryManager>>>,
    predictor: Option<Arc<Mutex<PerformancePredictor>>>,
}

/// Cache for frequently accessed data
#[derive(Debug)]
struct Cache {
    package_cache: HashMap<String, CachedPackage>,
    deployment_cache: HashMap<String, CachedDeployment>,
    filesystem_cache: HashMap<String, CachedFilesystem>,
    last_cleanup: Instant,
}

/// Cached package information
#[derive(Debug, Clone)]
struct CachedPackage {
    data: PackageData,
    created_at: Instant,
    access_count: u64,
    last_accessed: Instant,
}

/// Cached deployment information
#[derive(Debug, Clone)]
struct CachedDeployment {
    data: DeploymentData,
    created_at: Instant,
    access_count: u64,
    last_accessed: Instant,
}

/// Cached filesystem information
#[derive(Debug, Clone)]
struct CachedFilesystem {
    data: FilesystemData,
    created_at: Instant,
    access_count: u64,
    last_accessed: Instant,
}

/// Package data structure
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PackageData {
    pub name: String,
    pub version: String,
    pub dependencies: Vec<String>,
    pub conflicts: Vec<String>,
    pub provides: Vec<String>,
    pub description: Option<String>,
    pub size: u64,
}

/// Deployment data structure
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DeploymentData {
    pub commit_checksum: String,
    pub packages: Vec<PackageData>,
    pub filesystem_info: FilesystemData,
    pub metadata: String,
    pub created_at: u64,
}

/// Filesystem data structure
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FilesystemData {
    pub total_files: u64,
    pub total_directories: u64,
    pub total_size: u64,
    pub file_types: HashMap<String, u64>,
}

/// Metrics collector for performance monitoring
#[derive(Debug)]
struct MetricsCollector {
    operation_times: HashMap<String, Vec<Duration>>,
    cache_hits: u64,
    cache_misses: u64,
    memory_usage: u64,
    parallel_operations: u64,
    errors: Vec<ErrorMetric>,
}

/// Error metric for tracking performance issues
#[derive(Debug, Clone)]
struct ErrorMetric {
    operation: String,
    error: String,
    timestamp: Instant,
    duration: Duration,
}

/// Memory pool for efficient memory management
#[derive(Debug)]
struct MemoryPool {
    buffers: Vec<Vec<u8>>,
    max_buffer_size: usize,
    total_allocated: usize,
    peak_usage: usize,
}

/// Advanced performance configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdvancedPerformanceConfig {
    pub adaptive_caching: bool,
    pub intelligent_memory_management: bool,
    pub performance_prediction: bool,
    pub auto_optimization: bool,
    pub cache_eviction_strategy: CacheEvictionStrategy,
    pub memory_pressure_threshold: f64,
    pub performance_monitoring_interval: u64,
    pub optimization_trigger_threshold: f64,
    pub max_parallel_ops: Option<usize>,
    pub max_memory_mb: Option<usize>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CacheEvictionStrategy {
    LRU,
    LFU,
    Adaptive,
    TimeBased,
}

impl Default for AdvancedPerformanceConfig {
    fn default() -> Self {
        Self {
            adaptive_caching: true,
            intelligent_memory_management: true,
            performance_prediction: true,
            auto_optimization: true,
            cache_eviction_strategy: CacheEvictionStrategy::Adaptive,
            memory_pressure_threshold: 0.8,
            performance_monitoring_interval: 60,
            optimization_trigger_threshold: 0.7,
            max_parallel_ops: None,
            max_memory_mb: None,
        }
    }
}

/// Performance prediction model
#[derive(Debug)]
struct PerformancePredictor {
    historical_data: Vec<PerformanceDataPoint>,
    prediction_model: Option<PredictionModel>,
    last_prediction: Option<PerformancePrediction>,
}

#[derive(Debug, Clone)]
struct PerformanceDataPoint {
    timestamp: chrono::DateTime<chrono::Utc>,
    operation_type: String,
    duration: Duration,
    memory_usage: u64,
    cache_hit_rate: f64,
    parallel_operations: u64,
}

#[derive(Debug, Clone)]
struct PredictionModel {
    model_type: String,
    accuracy: f64,
    last_updated: chrono::DateTime<chrono::Utc>,
}

#[derive(Debug, Clone)]
struct PerformancePrediction {
    operation_type: String,
    predicted_duration: Duration,
    confidence: f64,
    recommended_optimizations: Vec<String>,
}

/// Adaptive cache manager
#[derive(Debug)]
struct AdaptiveCacheManager {
    cache_stats: HashMap<String, CacheStats>,
    eviction_policy: CacheEvictionStrategy,
    adaptive_thresholds: HashMap<String, f64>,
    performance_history: Vec<CachePerformancePoint>,
}

#[derive(Debug, Clone)]
struct CacheStats {
    hits: u64,
    misses: u64,
    evictions: u64,
    size: usize,
    last_access: Instant,
    access_frequency: f64,
}

#[derive(Debug, Clone)]
struct CachePerformancePoint {
    timestamp: chrono::DateTime<chrono::Utc>,
    hit_rate: f64,
    memory_usage: u64,
    eviction_rate: f64,
}

/// Intelligent memory manager
#[derive(Debug)]
struct IntelligentMemoryManager {
    memory_pressure_history: Vec<MemoryPressurePoint>,
    allocation_patterns: HashMap<String, AllocationPattern>,
    optimization_suggestions: Vec<MemoryOptimization>,
    auto_cleanup_enabled: bool,
}

#[derive(Debug, Clone)]
struct MemoryPressurePoint {
    timestamp: chrono::DateTime<chrono::Utc>,
    pressure_level: f64,
    available_memory: u64,
    total_memory: u64,
}

#[derive(Debug, Clone)]
struct AllocationPattern {
    pattern_type: String,
    frequency: u64,
    average_size: u64,
    lifetime: Duration,
}

#[derive(Debug, Clone)]
struct MemoryOptimization {
    optimization_type: String,
    description: String,
    expected_improvement: f64,
    implementation_cost: OptimizationCost,
}

#[derive(Debug, Clone)]
enum OptimizationCost {
    Low,
    Medium,
    High,
}

impl PerformanceManager {
    /// Create a new performance manager
    pub fn new(max_parallel_ops: usize, max_memory_mb: usize) -> Self {
        let cache = Arc::new(RwLock::new(Cache {
            package_cache: HashMap::new(),
            deployment_cache: HashMap::new(),
            filesystem_cache: HashMap::new(),
            last_cleanup: Instant::now(),
        }));

        let metrics = Arc::new(Mutex::new(MetricsCollector {
            operation_times: HashMap::new(),
            cache_hits: 0,
            cache_misses: 0,
            memory_usage: 0,
            parallel_operations: 0,
            errors: Vec::new(),
        }));

        let parallel_semaphore = Arc::new(Semaphore::new(max_parallel_ops));
        let memory_pool = Arc::new(Mutex::new(MemoryPool {
            buffers: Vec::new(),
            max_buffer_size: max_memory_mb * 1024 * 1024,
            total_allocated: 0,
            peak_usage: 0,
        }));

        PerformanceManager {
            cache,
            metrics,
            parallel_semaphore,
            memory_pool,
            advanced_config: None,
            adaptive_cache: None,
            intelligent_memory: None,
            predictor: None,
        }
    }

    /// Create a new performance manager with advanced features
    pub fn new_advanced(config: AdvancedPerformanceConfig) -> Self {
        let basic_config = PerformanceConfig {
            max_cache_size: 1000,
            max_memory_mb: 512,
            max_parallel_ops: 10,
            cache_ttl_seconds: 3600,
            enable_metrics: true,
            enable_memory_pool: true,
        };

        let cache = Arc::new(RwLock::new(Cache {
            package_cache: HashMap::new(),
            deployment_cache: HashMap::new(),
            filesystem_cache: HashMap::new(),
            last_cleanup: Instant::now(),
        }));

        let metrics = Arc::new(Mutex::new(MetricsCollector {
            operation_times: HashMap::new(),
            cache_hits: 0,
            cache_misses: 0,
            memory_usage: 0,
            parallel_operations: 0,
            errors: Vec::new(),
        }));

        let parallel_semaphore = Arc::new(Semaphore::new(config.max_parallel_ops.unwrap_or(10)));
        let memory_pool = Arc::new(Mutex::new(MemoryPool {
            buffers: Vec::new(),
            max_buffer_size: config.max_memory_mb.unwrap_or(512) * 1024 * 1024,
            total_allocated: 0,
            peak_usage: 0,
        }));

        // Initialize advanced components
        let adaptive_cache = if config.adaptive_caching {
            Some(Arc::new(Mutex::new(AdaptiveCacheManager {
                cache_stats: HashMap::new(),
                eviction_policy: config.cache_eviction_strategy.clone(),
                adaptive_thresholds: HashMap::new(),
                performance_history: Vec::new(),
            })))
        } else {
            None
        };

        let intelligent_memory = if config.intelligent_memory_management {
            Some(Arc::new(Mutex::new(IntelligentMemoryManager {
                memory_pressure_history: Vec::new(),
                allocation_patterns: HashMap::new(),
                optimization_suggestions: Vec::new(),
                auto_cleanup_enabled: config.auto_optimization,
            })))
        } else {
            None
        };

        let predictor = if config.performance_prediction {
            Some(Arc::new(Mutex::new(PerformancePredictor {
                historical_data: Vec::new(),
                prediction_model: None,
                last_prediction: None,
            })))
        } else {
            None
        };

        PerformanceManager {
            cache,
            metrics,
            parallel_semaphore,
            memory_pool,
            advanced_config: Some(config),
            adaptive_cache,
            intelligent_memory,
            predictor,
        }
    }

    /// Get package data with caching
    pub async fn get_package_data(&self, package_name: &str) -> Result<Option<PackageData>, Box<dyn std::error::Error + Send + Sync>> {
        let start_time = Instant::now();
        
        // Check cache first - release lock before await
        let cached_data = {
            let cache = self.cache.read().unwrap();
            cache.package_cache.get(package_name).cloned()
        };
        
        if let Some(cached_package) = cached_data {
            // Update access count and last accessed time
            {
                let mut cache = self.cache.write().unwrap();
                if let Some(cached_package) = cache.package_cache.get_mut(package_name) {
                    cached_package.access_count += 1;
                    cached_package.last_accessed = Instant::now();
                }
            }
            
            // Update metrics separately
            {
                let mut metrics = self.metrics.lock().unwrap();
                metrics.cache_hits += 1;
                metrics.operation_times.entry("cache_hit".to_string()).or_insert_with(Vec::new).push(start_time.elapsed());
            }
            
            return Ok(Some(cached_package.data.clone()));
        }

        // Cache miss - fetch from source
        {
            let mut metrics = self.metrics.lock().unwrap();
            metrics.cache_misses += 1;
        }

        let package_data = self.fetch_package_data_internal(package_name).await?;
        
        // Cache the result
        if let Some(data) = &package_data {
            // Apply adaptive eviction if cache is full
            if self.cache.read().unwrap().package_cache.len() >= 1000 {
                drop(self.cache.read().unwrap()); // Release lock before await
                self.apply_adaptive_eviction_packages(&mut self.cache.write().unwrap().package_cache).await?;
                let mut cache = self.cache.write().unwrap();
                cache.package_cache.insert(package_name.to_string(), CachedPackage {
                    data: data.clone(),
                    created_at: Instant::now(),
                    access_count: 1,
                    last_accessed: Instant::now(),
                });
            } else {
                let mut cache = self.cache.write().unwrap();
                cache.package_cache.insert(package_name.to_string(), CachedPackage {
                    data: data.clone(),
                    created_at: Instant::now(),
                    access_count: 1,
                    last_accessed: Instant::now(),
                });
            }
        }
        
        Ok(package_data)
    }

    /// Get package data with adaptive caching
    pub async fn get_package_data_adaptive(&self, package_name: &str) -> Result<Option<PackageData>, Box<dyn std::error::Error + Send + Sync>> {
        let start_time = Instant::now();
        
        // Check if adaptive caching is enabled
        if let Some(adaptive_cache) = &self.adaptive_cache {
            // Update adaptive cache statistics
            {
                let mut cache_manager = adaptive_cache.lock().unwrap();
                
                // Collect data before multiple borrows
                let threshold = cache_manager.adaptive_thresholds.get(package_name).unwrap_or(&0.5).clone();
                
                let stats = cache_manager.cache_stats.entry(package_name.to_string()).or_insert(CacheStats {
                    hits: 0,
                    misses: 0,
                    evictions: 0,
                    size: 0,
                    access_frequency: 0.0,
                    last_access: Instant::now(),
                });
                
                // Collect data before multiple borrows
                let access_frequency = stats.access_frequency;
                let evictions = stats.evictions;
                let hits = stats.hits;
                let misses = stats.misses;
                
                // Update stats
                stats.hits += 1;
                stats.access_frequency = access_frequency * 0.9 + 0.1; // Exponential moving average
                stats.last_access = Instant::now();
                
                // Calculate eviction rate
                let eviction_rate = if hits + misses > 0 {
                    evictions as f64 / (hits + misses) as f64
                } else {
                    0.0
                };
                
                // Add performance history point
                cache_manager.performance_history.push(CachePerformancePoint {
                    timestamp: chrono::Utc::now(),
                    hit_rate: access_frequency,
                    memory_usage: self.memory_pool.lock().unwrap().total_allocated as u64,
                    eviction_rate,
                });
            }
            
            // Check cache with adaptive threshold
            if let Some(cached) = self.cache.read().unwrap().package_cache.get(package_name) {
                return Ok(Some(cached.data.clone()));
            } else {
                // Cache miss - fetch from source
                let package_data = self.get_package_data(package_name).await?;
                
                // Cache the result with adaptive strategy
                if let Some(data) = &package_data {
                    let mut cache = self.cache.write().unwrap();
                    
                    // Apply adaptive eviction if needed
                    if cache.package_cache.len() >= 1000 {
                        self.apply_adaptive_eviction(&mut cache.package_cache).await?;
                    }
                    
                    cache.package_cache.insert(package_name.to_string(), CachedPackage {
                        data: data.clone(),
                        created_at: Instant::now(),
                        access_count: 1,
                        last_accessed: Instant::now(),
                    });
                }
                
                return Ok(package_data);
            }
        }
        
        // Cache miss - fetch from source
        let package_data = self.get_package_data(package_name).await?;
        
        // Cache the result with adaptive strategy
        if let Some(data) = &package_data {
            let mut cache = self.cache.write().unwrap();
            
            // Apply adaptive eviction if needed
            if cache.package_cache.len() >= 1000 {
                self.apply_adaptive_eviction(&mut cache.package_cache).await?;
            }
            
            cache.package_cache.insert(package_name.to_string(), CachedPackage {
                data: data.clone(),
                created_at: Instant::now(),
                access_count: 1,
                last_accessed: Instant::now(),
            });
        }
        
        Ok(package_data)
    }

    /// Get deployment data with caching
    pub async fn get_deployment_data(&self, commit_checksum: &str) -> Result<Option<DeploymentData>, Box<dyn std::error::Error + Send + Sync>> {
        let start_time = Instant::now();
        
        // Check cache first
        {
            let cache = self.cache.read().unwrap();
            if let Some(cached) = cache.deployment_cache.get(commit_checksum) {
                let mut metrics = self.metrics.lock().unwrap();
                metrics.cache_hits += 1;
                metrics.operation_times.entry("cache_hit".to_string()).or_insert_with(Vec::new).push(start_time.elapsed());
                
                return Ok(Some(cached.data.clone()));
            }
        }

        // Cache miss - fetch from source
        let mut metrics = self.metrics.lock().unwrap();
        metrics.cache_misses += 1;

        let deployment_data = self.fetch_deployment_data_internal(commit_checksum).await?;
        
        // Cache the result
        if let Some(data) = &deployment_data {
            let mut cache = self.cache.write().unwrap();
            cache.deployment_cache.insert(commit_checksum.to_string(), CachedDeployment {
                data: data.clone(),
                created_at: Instant::now(),
                access_count: 1,
                last_accessed: Instant::now(),
            });
        }

        metrics.operation_times.entry("deployment_fetch".to_string()).or_insert_with(Vec::new).push(start_time.elapsed());
        
        Ok(deployment_data)
    }

    /// Parallel package processing
    pub async fn process_packages_parallel(&self, packages: &[String]) -> Result<Vec<PackageData>, Box<dyn std::error::Error + Send + Sync>> {
        let start_time = Instant::now();
        let mut results = Vec::new();
        
        // Process packages sequentially for now to avoid Send issues
        for package in packages {
            match self.get_package_data(package).await {
                Ok(Some(package_data)) => {
                    results.push(package_data);
                }
                Ok(None) => {
                    warn!("Package not found: {}", package);
                }
                Err(e) => {
                    // Update metrics
                    {
                        let mut metrics = self.metrics.lock().unwrap();
                        metrics.errors.push(ErrorMetric {
                            operation: "parallel_package_processing".to_string(),
                            error: e.to_string(),
                            timestamp: Instant::now(),
                            duration: start_time.elapsed(),
                        });
                    }
                }
            }
        }

        // Update metrics
        {
            let mut metrics = self.metrics.lock().unwrap();
            metrics.parallel_operations += 1;
            metrics.operation_times.entry("parallel_processing".to_string()).or_insert_with(Vec::new).push(start_time.elapsed());
        }
        
        Ok(results)
    }

    /// Memory-optimized file processing
    pub async fn process_files_memory_optimized(&self, file_paths: &[String]) -> Result<Vec<FileData>, Box<dyn std::error::Error>> {
        let start_time = Instant::now();
        let mut results = Vec::new();
        
        // Get memory buffer from pool
        let buffer = self.get_memory_buffer().await?;
        
        for file_path in file_paths {
            let file_data = self.process_file_with_buffer(file_path, &buffer).await?;
            results.push(file_data);
        }
        
        // Return buffer to pool
        self.return_memory_buffer(buffer).await?;
        
        let mut metrics = self.metrics.lock().unwrap();
        metrics.operation_times.entry("memory_optimized_processing".to_string()).or_insert_with(Vec::new).push(start_time.elapsed());
        
        Ok(results)
    }

    /// Cache cleanup to prevent memory leaks
    pub async fn cleanup_cache(&self) -> Result<(), Box<dyn std::error::Error>> {
        let start_time = Instant::now();
        
        let mut cache = self.cache.write().unwrap();
        let now = Instant::now();
        
        // Remove expired entries (older than 1 hour)
        let max_age = Duration::from_secs(3600);
        
        cache.package_cache.retain(|_, cached| {
            now.duration_since(cached.created_at) < max_age
        });
        
        cache.deployment_cache.retain(|_, cached| {
            now.duration_since(cached.created_at) < max_age
        });
        
        cache.filesystem_cache.retain(|_, cached| {
            now.duration_since(cached.created_at) < max_age
        });
        
        cache.last_cleanup = now;
        
        let mut metrics = self.metrics.lock().unwrap();
        metrics.operation_times.entry("cache_cleanup".to_string()).or_insert_with(Vec::new).push(start_time.elapsed());
        
        info!("Cache cleanup completed");
        Ok(())
    }

    /// Get performance metrics
    pub fn get_metrics(&self) -> PerformanceMetrics {
        let cache = self.cache.read().unwrap();
        let metrics = self.metrics.lock().unwrap();
        let memory_pool = self.memory_pool.lock().unwrap();
        
        let avg_operation_times: HashMap<String, Duration> = metrics.operation_times
            .iter()
            .map(|(operation, times)| {
                let avg = times.iter().sum::<Duration>() / times.len() as u32;
                (operation.clone(), avg)
            })
            .collect();
        
        PerformanceMetrics {
            cache_hits: metrics.cache_hits,
            cache_misses: metrics.cache_misses,
            cache_hit_rate: if metrics.cache_hits + metrics.cache_misses > 0 {
                metrics.cache_hits as f64 / (metrics.cache_hits + metrics.cache_misses) as f64
            } else {
                0.0
            },
            memory_usage_mb: memory_pool.total_allocated as f64 / 1024.0 / 1024.0,
            peak_memory_usage_mb: memory_pool.peak_usage as f64 / 1024.0 / 1024.0,
            parallel_operations: metrics.parallel_operations,
            error_count: metrics.errors.len(),
            avg_operation_times,
            cache_size: cache.package_cache.len() + cache.deployment_cache.len() + cache.filesystem_cache.len(),
        }
    }

    /// Optimize memory usage
    pub async fn optimize_memory(&self) -> Result<(), Box<dyn std::error::Error>> {
        let start_time = Instant::now();
        
        // Clean up cache
        self.cleanup_cache().await?;
        
        // Compact memory pool
        let mut memory_pool = self.memory_pool.lock().unwrap();
        memory_pool.buffers.retain(|buffer| buffer.len() > 0);
        memory_pool.buffers.shrink_to_fit();
        
        let mut metrics = self.metrics.lock().unwrap();
        metrics.operation_times.entry("memory_optimization".to_string()).or_insert_with(Vec::new).push(start_time.elapsed());
        
        info!("Memory optimization completed");
        Ok(())
    }

    /// Intelligent memory optimization
    pub async fn optimize_memory_intelligent(&self) -> Result<Vec<MemoryOptimization>, Box<dyn std::error::Error + Send + Sync>> {
        if let Some(intelligent_memory) = &self.intelligent_memory {
            let mut memory_manager = intelligent_memory.lock().unwrap();
            
            // Analyze memory pressure
            let current_pressure = self.calculate_memory_pressure().await.map_err(|e| format!("Memory pressure calculation failed: {}", e))?;
            memory_manager.memory_pressure_history.push(MemoryPressurePoint {
                timestamp: chrono::Utc::now(),
                pressure_level: current_pressure,
                available_memory: 0, // Would get from system
                total_memory: 0,     // Would get from system
            });
            
            // Generate optimization suggestions
            let mut optimizations = Vec::new();
            
            if current_pressure > 0.8 {
                optimizations.push(MemoryOptimization {
                    optimization_type: "Aggressive cleanup".to_string(),
                    description: "High memory pressure detected, performing aggressive cleanup".to_string(),
                    expected_improvement: 0.3,
                    implementation_cost: OptimizationCost::Low,
                });
            }
            
            if current_pressure > 0.6 {
                optimizations.push(MemoryOptimization {
                    optimization_type: "Cache optimization".to_string(),
                    description: "Optimizing cache usage to reduce memory footprint".to_string(),
                    expected_improvement: 0.2,
                    implementation_cost: OptimizationCost::Medium,
                });
            }
            
            // Apply optimizations if auto-optimization is enabled
            if memory_manager.auto_cleanup_enabled {
                for optimization in &optimizations {
                    self.apply_memory_optimization(optimization).await.map_err(|e| format!("Memory optimization failed: {}", e))?;
                }
            }
            
            memory_manager.optimization_suggestions = optimizations.clone();
            
            Ok(optimizations)
        } else {
            Err("Intelligent memory management not enabled".into())
        }
    }

    /// Predict performance for an operation
    pub async fn predict_performance(&self, operation_type: &str, input_size: usize) -> Result<PerformancePrediction, Box<dyn std::error::Error>> {
        if let Some(predictor) = &self.predictor {
            let mut predictor = predictor.lock().unwrap();
            
            // Add current data point
            let current_metrics = self.metrics.lock().unwrap();
            let avg_duration = current_metrics.operation_times
                .get(operation_type)
                .and_then(|times| {
                    if times.is_empty() {
                        None
                    } else {
                        Some(times.iter().sum::<Duration>() / times.len() as u32)
                    }
                })
                .unwrap_or(Duration::from_millis(100));
            
            predictor.historical_data.push(PerformanceDataPoint {
                timestamp: chrono::Utc::now(),
                operation_type: operation_type.to_string(),
                duration: avg_duration,
                memory_usage: current_metrics.memory_usage,
                cache_hit_rate: if current_metrics.cache_hits + current_metrics.cache_misses > 0 {
                    current_metrics.cache_hits as f64 / (current_metrics.cache_hits + current_metrics.cache_misses) as f64
                } else {
                    0.0
                },
                parallel_operations: current_metrics.parallel_operations,
            });
            
            // Simple prediction model (linear regression)
            let prediction = self.calculate_performance_prediction(&predictor.historical_data, operation_type, input_size);
            
            predictor.last_prediction = Some(prediction.clone());
            
            Ok(prediction)
        } else {
            Err("Performance prediction not enabled".into())
        }
    }

    /// Calculate performance prediction using simple linear regression
    fn calculate_performance_prediction(&self, historical_data: &[PerformanceDataPoint], operation_type: &str, input_size: usize) -> PerformancePrediction {
        let relevant_data: Vec<_> = historical_data
            .iter()
            .filter(|d| d.operation_type == operation_type)
            .collect();
        
        if relevant_data.len() < 2 {
            return PerformancePrediction {
                operation_type: operation_type.to_string(),
                predicted_duration: Duration::from_millis(100),
                confidence: 0.1,
                recommended_optimizations: vec!["Insufficient data for accurate prediction".to_string()],
            };
        }
        
        // Simple linear regression: duration = a * input_size + b
        let n = relevant_data.len() as f64;
        let sum_x: f64 = relevant_data.iter().map(|d| d.duration.as_millis() as f64).sum();
        let sum_y: f64 = relevant_data.iter().map(|d| d.memory_usage as f64).sum();
        let sum_xy: f64 = relevant_data.iter()
            .map(|d| d.duration.as_millis() as f64 * d.memory_usage as f64)
            .sum();
        let sum_x2: f64 = relevant_data.iter()
            .map(|d| (d.duration.as_millis() as f64).powi(2))
            .sum();
        
        let slope = (n * sum_xy - sum_x * sum_y) / (n * sum_x2 - sum_x * sum_x);
        let intercept = (sum_y - slope * sum_x) / n;
        
        let predicted_duration_ms = slope * input_size as f64 + intercept;
        let predicted_duration = Duration::from_millis(predicted_duration_ms.max(1.0) as u64);
        
        // Calculate confidence based on data consistency
        let avg_duration = sum_x / n;
        let variance = relevant_data.iter()
            .map(|d| (d.duration.as_millis() as f64 - avg_duration).powi(2))
            .sum::<f64>() / n;
        let confidence = (1.0 / (1.0 + variance / 1000.0)).min(1.0);
        
        // Generate optimization recommendations
        let mut recommendations = Vec::new();
        if predicted_duration > Duration::from_secs(5) {
            recommendations.push("Consider parallel processing".to_string());
        }
        if input_size > 1000 {
            recommendations.push("Consider caching intermediate results".to_string());
        }
        if confidence < 0.5 {
            recommendations.push("Collect more performance data".to_string());
        }
        
        PerformancePrediction {
            operation_type: operation_type.to_string(),
            predicted_duration,
            confidence,
            recommended_optimizations: recommendations,
        }
    }

    /// Apply adaptive cache eviction
    async fn apply_adaptive_eviction(&self, cache: &mut HashMap<String, CachedPackage>) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        if let Some(adaptive_cache) = &self.adaptive_cache {
            let cache_manager = adaptive_cache.lock().unwrap();
            
            // Collect data before multiple borrows
            let eviction_policy = cache_manager.eviction_policy.clone();
            
            drop(cache_manager); // Release lock
            
            match eviction_policy {
                CacheEvictionStrategy::LRU => {
                    // Remove least recently used items
                    let mut items: Vec<_> = cache.iter().map(|(k, v)| (k.clone(), v.last_accessed)).collect();
                    items.sort_by(|a, b| a.1.cmp(&b.1));
                    
                    // Remove 10% of items
                    let to_remove = (items.len() / 10).max(1);
                    for (key, _) in items.iter().take(to_remove) {
                        cache.remove(key);
                    }
                }
                CacheEvictionStrategy::LFU => {
                    // Remove least frequently used items
                    let mut items: Vec<_> = cache.iter().map(|(k, v)| (k.clone(), v.access_count)).collect();
                    items.sort_by(|a, b| a.1.cmp(&b.1));
                    
                    // Remove 10% of items
                    let to_remove = (items.len() / 10).max(1);
                    for (key, _) in items.iter().take(to_remove) {
                        cache.remove(key);
                    }
                }
                CacheEvictionStrategy::Adaptive => {
                    // Use adaptive strategy based on access patterns
                    let mut items: Vec<_> = cache.iter().map(|(k, v)| {
                        let score = v.access_count as f64 / v.last_accessed.elapsed().as_secs() as f64;
                        (k.clone(), score)
                    }).collect();
                    items.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));
                    
                    // Remove 10% of items
                    let to_remove = (items.len() / 10).max(1);
                    for (key, _) in items.iter().take(to_remove) {
                        cache.remove(key);
                    }
                }
                CacheEvictionStrategy::TimeBased => {
                    // Remove items older than threshold
                    let threshold = Instant::now() - Duration::from_secs(3600); // 1 hour
                    cache.retain(|_, item| item.created_at > threshold);
                }
            }
        }
        
        Ok(())
    }

    /// Apply adaptive cache eviction for packages
    async fn apply_adaptive_eviction_packages(&self, cache: &mut HashMap<String, CachedPackage>) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        if let Some(adaptive_cache) = &self.adaptive_cache {
            let cache_manager = adaptive_cache.lock().unwrap();
            
            // Collect data before multiple borrows
            let eviction_policy = cache_manager.eviction_policy.clone();
            let adaptive_thresholds = cache_manager.adaptive_thresholds.clone();
            
            drop(cache_manager); // Release lock
            
            match eviction_policy {
                CacheEvictionStrategy::LRU => {
                    // Remove least recently used items
                    let mut items: Vec<_> = cache.iter().map(|(k, v)| (k.clone(), v.last_accessed)).collect();
                    items.sort_by(|a, b| a.1.cmp(&b.1));
                    
                    // Remove 10% of items
                    let to_remove = (items.len() / 10).max(1);
                    for (key, _) in items.iter().take(to_remove) {
                        cache.remove(key);
                    }
                }
                CacheEvictionStrategy::LFU => {
                    // Remove least frequently used items
                    let mut items: Vec<_> = cache.iter().map(|(k, v)| (k.clone(), v.access_count)).collect();
                    items.sort_by(|a, b| a.1.cmp(&b.1));
                    
                    // Remove 10% of items
                    let to_remove = (items.len() / 10).max(1);
                    for (key, _) in items.iter().take(to_remove) {
                        cache.remove(key);
                    }
                }
                CacheEvictionStrategy::Adaptive => {
                    // Use adaptive strategy based on access patterns
                    let mut items: Vec<_> = cache.iter().map(|(k, v)| {
                        let score = v.access_count as f64 / v.last_accessed.elapsed().as_secs() as f64;
                        (k.clone(), score)
                    }).collect();
                    items.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));
                    
                    // Remove 10% of items
                    let to_remove = (items.len() / 10).max(1);
                    for (key, _) in items.iter().take(to_remove) {
                        cache.remove(key);
                    }
                }
                CacheEvictionStrategy::TimeBased => {
                    // Remove items older than threshold
                    let threshold = Instant::now() - Duration::from_secs(3600); // 1 hour
                    cache.retain(|_, item| item.created_at > threshold);
                }
            }
        }
        
        Ok(())
    }

    /// Apply adaptive cache eviction for deployments
    async fn apply_adaptive_eviction_deployments(&self, cache: &mut HashMap<String, CachedDeployment>) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        if let Some(adaptive_cache) = &self.adaptive_cache {
            let cache_manager = adaptive_cache.lock().unwrap();
            
            // Collect data before multiple borrows
            let eviction_policy = cache_manager.eviction_policy.clone();
            
            drop(cache_manager); // Release lock
            
            match eviction_policy {
                CacheEvictionStrategy::LRU => {
                    // Remove least recently used items
                    let mut items: Vec<_> = cache.iter().map(|(k, v)| (k.clone(), v.last_accessed)).collect();
                    items.sort_by(|a, b| a.1.cmp(&b.1));
                    
                    // Remove 10% of items
                    let to_remove = (items.len() / 10).max(1);
                    for (key, _) in items.iter().take(to_remove) {
                        cache.remove(key);
                    }
                }
                CacheEvictionStrategy::LFU => {
                    // Remove least frequently used items
                    let mut items: Vec<_> = cache.iter().map(|(k, v)| (k.clone(), v.access_count)).collect();
                    items.sort_by(|a, b| a.1.cmp(&b.1));
                    
                    // Remove 10% of items
                    let to_remove = (items.len() / 10).max(1);
                    for (key, _) in items.iter().take(to_remove) {
                        cache.remove(key);
                    }
                }
                CacheEvictionStrategy::Adaptive => {
                    // Use adaptive strategy based on access patterns
                    let mut items: Vec<_> = cache.iter().map(|(k, v)| {
                        let score = v.access_count as f64 / v.last_accessed.elapsed().as_secs() as f64;
                        (k.clone(), score)
                    }).collect();
                    items.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));
                    
                    // Remove 10% of items
                    let to_remove = (items.len() / 10).max(1);
                    for (key, _) in items.iter().take(to_remove) {
                        cache.remove(key);
                    }
                }
                CacheEvictionStrategy::TimeBased => {
                    // Remove items older than threshold
                    let threshold = Instant::now() - Duration::from_secs(3600); // 1 hour
                    cache.retain(|_, item| item.created_at > threshold);
                }
            }
        }
        
        Ok(())
    }

    /// Apply adaptive cache eviction for filesystem
    async fn apply_adaptive_eviction_filesystem(&self, cache: &mut HashMap<String, CachedFilesystem>) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        if let Some(adaptive_cache) = &self.adaptive_cache {
            let cache_manager = adaptive_cache.lock().unwrap();
            
            // Collect data before multiple borrows
            let eviction_policy = cache_manager.eviction_policy.clone();
            
            drop(cache_manager); // Release lock
            
            match eviction_policy {
                CacheEvictionStrategy::LRU => {
                    // Remove least recently used items
                    let mut items: Vec<_> = cache.iter().map(|(k, v)| (k.clone(), v.last_accessed)).collect();
                    items.sort_by(|a, b| a.1.cmp(&b.1));
                    
                    // Remove 10% of items
                    let to_remove = (items.len() / 10).max(1);
                    for (key, _) in items.iter().take(to_remove) {
                        cache.remove(key);
                    }
                }
                CacheEvictionStrategy::LFU => {
                    // Remove least frequently used items
                    let mut items: Vec<_> = cache.iter().map(|(k, v)| (k.clone(), v.access_count)).collect();
                    items.sort_by(|a, b| a.1.cmp(&b.1));
                    
                    // Remove 10% of items
                    let to_remove = (items.len() / 10).max(1);
                    for (key, _) in items.iter().take(to_remove) {
                        cache.remove(key);
                    }
                }
                CacheEvictionStrategy::Adaptive => {
                    // Use adaptive strategy based on access patterns
                    let mut items: Vec<_> = cache.iter().map(|(k, v)| {
                        let score = v.access_count as f64 / v.last_accessed.elapsed().as_secs() as f64;
                        (k.clone(), score)
                    }).collect();
                    items.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));
                    
                    // Remove 10% of items
                    let to_remove = (items.len() / 10).max(1);
                    for (key, _) in items.iter().take(to_remove) {
                        cache.remove(key);
                    }
                }
                CacheEvictionStrategy::TimeBased => {
                    // Remove items older than threshold
                    let threshold = Instant::now() - Duration::from_secs(3600); // 1 hour
                    cache.retain(|_, item| item.created_at > threshold);
                }
            }
        }
        
        Ok(())
    }

    /// Calculate current memory pressure
    async fn calculate_memory_pressure(&self) -> Result<f64, Box<dyn std::error::Error + Send + Sync>> {
        let memory_pool = self.memory_pool.lock().unwrap();
        let current_usage = memory_pool.total_allocated as f64;
        let max_usage = memory_pool.max_buffer_size as f64;
        
        Ok(current_usage / max_usage)
    }

    /// Apply memory optimization
    async fn apply_memory_optimization(&self, optimization: &MemoryOptimization) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        match optimization.optimization_type.as_str() {
            "Aggressive cleanup" => {
                // Perform aggressive cache cleanup
                let mut cache = self.cache.write().unwrap();
                cache.package_cache.clear();
                cache.deployment_cache.clear();
                cache.filesystem_cache.clear();
                
                // Clear memory pool
                let mut memory_pool = self.memory_pool.lock().unwrap();
                memory_pool.buffers.clear();
                memory_pool.total_allocated = 0;
            }
            "Cache optimization" => {
                // Optimize cache by removing least useful items
                // Apply eviction to each cache type separately
                {
                    let mut cache = self.cache.write().unwrap();
                    self.apply_adaptive_eviction_packages(&mut cache.package_cache).await?;
                    self.apply_adaptive_eviction_deployments(&mut cache.deployment_cache).await?;
                    self.apply_adaptive_eviction_filesystem(&mut cache.filesystem_cache).await?;
                }
            }
            _ => {
                warn!("Unknown optimization type: {}", optimization.optimization_type);
            }
        }
        
        Ok(())
    }

    /// Get advanced performance metrics
    pub fn get_advanced_metrics(&self) -> AdvancedPerformanceMetrics {
        let basic_metrics = self.get_metrics();
        
        AdvancedPerformanceMetrics {
            basic_metrics,
            cache_efficiency: self.calculate_cache_efficiency(),
            memory_efficiency: self.calculate_memory_efficiency(),
            parallel_efficiency: self.calculate_parallel_efficiency(),
            prediction_accuracy: self.calculate_prediction_accuracy(),
            optimization_effectiveness: self.calculate_optimization_effectiveness(),
        }
    }

    /// Calculate cache efficiency
    fn calculate_cache_efficiency(&self) -> f64 {
        let metrics = self.metrics.lock().unwrap();
        if metrics.cache_hits + metrics.cache_misses > 0 {
            metrics.cache_hits as f64 / (metrics.cache_hits + metrics.cache_misses) as f64
        } else {
            0.0
        }
    }

    /// Calculate memory efficiency
    fn calculate_memory_efficiency(&self) -> f64 {
        let memory_pool = self.memory_pool.lock().unwrap();
        if memory_pool.max_buffer_size > 0 {
            1.0 - (memory_pool.total_allocated as f64 / memory_pool.max_buffer_size as f64)
        } else {
            0.0
        }
    }

    /// Calculate parallel efficiency
    fn calculate_parallel_efficiency(&self) -> f64 {
        let metrics = self.metrics.lock().unwrap();
        if metrics.parallel_operations > 0 {
            // Simple efficiency calculation based on operation times
            let avg_time = metrics.operation_times.values()
                .flat_map(|times| times.iter())
                .sum::<Duration>();
            let total_operations = metrics.operation_times.values()
                .map(|times| times.len())
                .sum::<usize>();
            
            if total_operations > 0 {
                let avg_operation_time = avg_time / total_operations as u32;
                // Efficiency decreases with longer operations
                1.0 / (1.0 + avg_operation_time.as_millis() as f64 / 1000.0)
            } else {
                0.0
            }
        } else {
            0.0
        }
    }

    /// Calculate prediction accuracy
    fn calculate_prediction_accuracy(&self) -> f64 {
        if let Some(predictor) = &self.predictor {
            let predictor = predictor.lock().unwrap();
            if let Some(last_prediction) = &predictor.last_prediction {
                // Simple accuracy calculation based on confidence
                last_prediction.confidence
            } else {
                0.0
            }
        } else {
            0.0
        }
    }

    /// Calculate optimization effectiveness
    fn calculate_optimization_effectiveness(&self) -> f64 {
        if let Some(intelligent_memory) = &self.intelligent_memory {
            let memory_manager = intelligent_memory.lock().unwrap();
            let recent_optimizations = memory_manager.optimization_suggestions.len();
            
            // Effectiveness based on number of optimizations applied
            if recent_optimizations > 0 {
                (recent_optimizations as f64).min(1.0)
            } else {
                0.0
            }
        } else {
            0.0
        }
    }

    // Helper methods
    pub async fn get_memory_buffer(&self) -> Result<Vec<u8>, Box<dyn std::error::Error>> {
        let mut memory_pool = self.memory_pool.lock().unwrap();
        
        if let Some(buffer) = memory_pool.buffers.pop() {
            memory_pool.total_allocated -= buffer.len();
            Ok(buffer)
        } else {
            // Create new buffer if pool is empty
            let buffer = vec![0u8; 1024 * 1024]; // 1MB buffer
            memory_pool.total_allocated += buffer.len();
            memory_pool.peak_usage = memory_pool.peak_usage.max(memory_pool.total_allocated);
            Ok(buffer)
        }
    }

    /// Return memory buffer to pool
    async fn return_memory_buffer(&self, buffer: Vec<u8>) -> Result<(), Box<dyn std::error::Error>> {
        let buffer_len = buffer.len();
        let mut memory_pool = self.memory_pool.lock().unwrap();
        
        if memory_pool.total_allocated + buffer_len <= memory_pool.max_buffer_size {
            memory_pool.buffers.push(buffer);
            memory_pool.total_allocated += buffer_len;
        }
        
        Ok(())
    }

    async fn process_file_with_buffer(&self, file_path: &str, buffer: &[u8]) -> Result<FileData, Box<dyn std::error::Error>> {
        // Simulate file processing with buffer
        tokio::time::sleep(Duration::from_millis(5)).await;
        
        Ok(FileData {
            path: file_path.to_string(),
            size: buffer.len() as u64,
            processed: true,
        })
    }

    async fn fetch_package_data_internal(&self, package_name: &str) -> Result<Option<PackageData>, Box<dyn std::error::Error + Send + Sync>> {
        // Simulate fetching from APT database
        tokio::time::sleep(Duration::from_millis(10)).await;
        
        Ok(Some(PackageData {
            name: package_name.to_string(),
            version: "1.0.0".to_string(),
            dependencies: vec!["libc6".to_string()],
            conflicts: Vec::new(),
            provides: Vec::new(),
            description: Some("Sample package".to_string()),
            size: 1024,
        }))
    }

    async fn fetch_deployment_data_internal(&self, commit_checksum: &str) -> Result<Option<DeploymentData>, Box<dyn std::error::Error + Send + Sync>> {
        // Simulate fetching from OSTree repository
        tokio::time::sleep(Duration::from_millis(20)).await;
        
        Ok(Some(DeploymentData {
            commit_checksum: commit_checksum.to_string(),
            packages: vec![PackageData {
                name: "sample-package".to_string(),
                version: "1.0.0".to_string(),
                dependencies: vec!["libc6".to_string()],
                conflicts: Vec::new(),
                provides: Vec::new(),
                description: Some("Sample package".to_string()),
                size: 2048,
            }],
            filesystem_info: FilesystemData {
                total_files: 1000,
                total_directories: 100,
                total_size: 1024 * 1024,
                file_types: HashMap::new(),
            },
            metadata: "Sample deployment metadata".to_string(),
            created_at: chrono::Utc::now().timestamp() as u64,
        }))
    }
}

impl Clone for PerformanceManager {
    fn clone(&self) -> Self {
        PerformanceManager {
            cache: self.cache.clone(),
            metrics: self.metrics.clone(),
            parallel_semaphore: self.parallel_semaphore.clone(),
            memory_pool: self.memory_pool.clone(),
            advanced_config: self.advanced_config.clone(),
            adaptive_cache: self.adaptive_cache.clone(),
            intelligent_memory: self.intelligent_memory.clone(),
            predictor: self.predictor.clone(),
        }
    }
}

/// Performance metrics structure
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    pub cache_hits: u64,
    pub cache_misses: u64,
    pub cache_hit_rate: f64,
    pub memory_usage_mb: f64,
    pub peak_memory_usage_mb: f64,
    pub parallel_operations: u64,
    pub error_count: usize,
    pub avg_operation_times: HashMap<String, Duration>,
    pub cache_size: usize,
}

/// Advanced performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdvancedPerformanceMetrics {
    pub basic_metrics: PerformanceMetrics,
    pub cache_efficiency: f64,
    pub memory_efficiency: f64,
    pub parallel_efficiency: f64,
    pub prediction_accuracy: f64,
    pub optimization_effectiveness: f64,
}

/// File data structure
#[derive(Debug, Clone)]
pub struct FileData {
    pub path: String,
    pub size: u64,
    pub processed: bool,
}

/// Performance optimization configuration
#[derive(Debug, Clone)]
pub struct PerformanceConfig {
    pub max_cache_size: usize,
    pub max_memory_mb: usize,
    pub max_parallel_ops: usize,
    pub cache_ttl_seconds: u64,
    pub enable_metrics: bool,
    pub enable_memory_pool: bool,
}

impl Default for PerformanceConfig {
    fn default() -> Self {
        PerformanceConfig {
            max_cache_size: 1000,
            max_memory_mb: 512,
            max_parallel_ops: 10,
            cache_ttl_seconds: 3600,
            enable_metrics: true,
            enable_memory_pool: true,
        }
    }
}