// Enterprise weak pointer framework with advanced features
package weakref

import (
    "sync"
    "unsafe"
    "weak"
    "time"
    "context"
    "runtime"
)

// EnterpriseWeakRef provides advanced weak reference functionality
type EnterpriseWeakRef[T any] struct {
    wp          weak.Pointer[T]
    callbacks   []WeakRefCallback
    metadata    *WeakRefMetadata
    
    // Monitoring and analytics
    accessCount int64
    lastAccess  time.Time
    
    // Lifecycle management
    created     time.Time
    ttl         time.Duration
    
    mutex       sync.RWMutex
}

type WeakRefCallback func(obj *T, event WeakRefEvent)

type WeakRefEvent int

const (
    WeakRefCreated WeakRefEvent = iota
    WeakRefAccessed
    WeakRefCollected
    WeakRefExpired
)

type WeakRefMetadata struct {
    ObjectType    string
    ObjectSize    uintptr
    CreatedBy     string
    CreationStack []uintptr
    Tags          map[string]string
}

// NewEnterpriseWeakRef creates an enhanced weak reference
func NewEnterpriseWeakRef[T any](obj *T, options ...WeakRefOption) *EnterpriseWeakRef[T] {
    ref := &EnterpriseWeakRef[T]{
        wp:        weak.Make(obj),
        callbacks: make([]WeakRefCallback, 0),
        metadata:  &WeakRefMetadata{
            ObjectType: getTypeName[T](),
            ObjectSize: unsafe.Sizeof(*obj),
            CreatedBy:  getCaller(),
            CreationStack: getStack(),
            Tags:      make(map[string]string),
        },
        created:   time.Now(),
        lastAccess: time.Now(),
    }
    
    // Apply options
    for _, option := range options {
        option(ref)
    }
    
    // Register with weak reference manager
    weakRefManager.Register(ref)
    
    // Trigger creation callbacks
    ref.triggerCallbacks(obj, WeakRefCreated)
    
    return ref
}

// Value returns the referenced object with enhanced tracking
func (ewr *EnterpriseWeakRef[T]) Value() *T {
    ewr.mutex.Lock()
    defer ewr.mutex.Unlock()
    
    // Check TTL if configured
    if ewr.ttl > 0 && time.Since(ewr.created) > ewr.ttl {
        ewr.triggerCallbacks(nil, WeakRefExpired)
        return nil
    }
    
    obj := ewr.wp.Value()
    if obj != nil {
        ewr.accessCount++
        ewr.lastAccess = time.Now()
        ewr.triggerCallbacks(obj, WeakRefAccessed)
        
        // Update access metrics
        weakRefManager.RecordAccess(ewr)
    } else {
        ewr.triggerCallbacks(nil, WeakRefCollected)
        // Cleanup from manager
        weakRefManager.Unregister(ewr)
    }
    
    return obj
}

// Enhanced weak reference cache with intelligent eviction
type WeakRefCache[K comparable, V any] struct {
    cache       map[K]*EnterpriseWeakRef[V]
    metrics     *CacheMetrics
    evictionPolicy EvictionPolicy
    
    // Advanced features
    accessTracking  *AccessTracker
    prefetcher     *CachePrefetcher[K, V]
    compactor      *CacheCompactor
    
    mutex sync.RWMutex
}

type CacheMetrics struct {
    Hits        int64
    Misses      int64
    Evictions   int64
    MemoryUsage int64
    
    // Performance metrics
    AverageAccessTime time.Duration
    HitRatio         float64
    MemoryEfficiency float64
}

func NewWeakRefCache[K comparable, V any](options ...CacheOption) *WeakRefCache[K, V] {
    cache := &WeakRefCache[K, V]{
        cache:   make(map[K]*EnterpriseWeakRef[V]),
        metrics: &CacheMetrics{},
        evictionPolicy: LRUEvictionPolicy{},
        accessTracking: NewAccessTracker(),
    }
    
    // Apply options
    for _, option := range options {
        option(cache)
    }
    
    // Start background maintenance
    go cache.backgroundMaintenance()
    
    return cache
}

func (wrc *WeakRefCache[K, V]) Get(key K) (*V, bool) {
    wrc.mutex.RLock()
    ref, exists := wrc.cache[key]
    wrc.mutex.RUnlock()
    
    if !exists {
        wrc.metrics.Misses++
        return nil, false
    }
    
    obj := ref.Value()
    if obj == nil {
        // Object was garbage collected, clean up
        wrc.mutex.Lock()
        delete(wrc.cache, key)
        wrc.mutex.Unlock()
        wrc.metrics.Evictions++
        return nil, false
    }
    
    wrc.metrics.Hits++
    wrc.accessTracking.RecordAccess(key)
    
    // Trigger prefetcher if configured
    if wrc.prefetcher != nil {
        go wrc.prefetcher.OnAccess(key)
    }
    
    return obj, true
}

func (wrc *WeakRefCache[K, V]) Set(key K, value *V) {
    wrc.mutex.Lock()
    defer wrc.mutex.Unlock()
    
    // Create weak reference with cache-specific options
    ref := NewEnterpriseWeakRef(value,
        WithMetadata("cache_key", fmt.Sprintf("%v", key)),
        WithCallback(func(obj *V, event WeakRefEvent) {
            if event == WeakRefCollected {
                wrc.onObjectCollected(key)
            }
        }),
    )
    
    wrc.cache[key] = ref
    wrc.updateMemoryMetrics()
}

// Intelligent observer pattern with weak references
type WeakObserver[T any] struct {
    observers   []*EnterpriseWeakRef[Observer[T]]
    metrics     *ObserverMetrics
    
    // Event processing
    eventQueue  chan ObserverEvent[T]
    processors  []*EventProcessor[T]
    
    // Cleanup management
    cleanupInterval time.Duration
    lastCleanup     time.Time
    
    mutex sync.RWMutex
}

type Observer[T any] interface {
    OnEvent(event T)
    GetID() string
}

type ObserverEvent[T any] struct {
    Event     T
    Timestamp time.Time
    Observers []*Observer[T]
}

func NewWeakObserver[T any]() *WeakObserver[T] {
    wo := &WeakObserver[T]{
        observers:   make([]*EnterpriseWeakRef[Observer[T]], 0),
        metrics:     &ObserverMetrics{},
        eventQueue:  make(chan ObserverEvent[T], 1000),
        cleanupInterval: 5 * time.Minute,
    }
    
    // Start event processing
    go wo.processEvents()
    
    // Start cleanup routine
    go wo.cleanupRoutine()
    
    return wo
}

func (wo *WeakObserver[T]) Subscribe(observer Observer[T]) *Subscription {
    wo.mutex.Lock()
    defer wo.mutex.Unlock()
    
    ref := NewEnterpriseWeakRef(&observer,
        WithMetadata("observer_id", observer.GetID()),
        WithCallback(func(obs *Observer[T], event WeakRefEvent) {
            if event == WeakRefCollected {
                wo.metrics.ObserversCollected++
            }
        }),
    )
    
    wo.observers = append(wo.observers, ref)
    wo.metrics.TotalObservers++
    
    return &Subscription{
        id:       generateSubscriptionID(),
        observer: ref,
        weakObserver: wo,
    }
}

func (wo *WeakObserver[T]) Notify(event T) {
    wo.mutex.RLock()
    activeObservers := make([]*Observer[T], 0, len(wo.observers))
    
    for _, ref := range wo.observers {
        if observer := ref.Value(); observer != nil {
            activeObservers = append(activeObservers, observer)
        }
    }
    wo.mutex.RUnlock()
    
    // Send to event queue for asynchronous processing
    select {
    case wo.eventQueue <- ObserverEvent[T]{
        Event:     event,
        Timestamp: time.Now(),
        Observers: activeObservers,
    }:
        wo.metrics.EventsQueued++
    default:
        wo.metrics.EventsDropped++
    }
}

// Memory pool system with weak reference tracking
type WeakMemoryPool[T any] struct {
    pool        sync.Pool
    activeRefs  []*EnterpriseWeakRef[T]
    metrics     *PoolMetrics
    
    // Pool configuration
    maxSize     int
    initialSize int
    factory     func() *T
    
    // Lifecycle management
    validator   func(*T) bool
    sanitizer   func(*T)
    
    mutex sync.RWMutex
}

type PoolMetrics struct {
    Gets         int64
    Puts         int64
    Creates      int64
    ActiveCount  int64
    MaxActive    int64
    MemoryUsage  int64
}

func NewWeakMemoryPool[T any](factory func() *T, options ...PoolOption) *WeakMemoryPool[T] {
    pool := &WeakMemoryPool[T]{
        factory:    factory,
        activeRefs: make([]*EnterpriseWeakRef[T], 0),
        metrics:    &PoolMetrics{},
        maxSize:    1000,
        initialSize: 10,
    }
    
    // Apply options
    for _, option := range options {
        option(pool)
    }
    
    // Pre-populate pool
    pool.pool.New = func() interface{} {
        obj := pool.factory()
        pool.metrics.Creates++
        return obj
    }
    
    // Pre-allocate initial objects
    for i := 0; i < pool.initialSize; i++ {
        pool.pool.Put(pool.factory())
    }
    
    return pool
}

func (wmp *WeakMemoryPool[T]) Get() *T {
    obj := wmp.pool.Get().(*T)
    
    // Validate object if validator is configured
    if wmp.validator != nil && !wmp.validator(obj) {
        // Object is invalid, create new one
        obj = wmp.factory()
        wmp.metrics.Creates++
    }
    
    // Create weak reference for tracking
    ref := NewEnterpriseWeakRef(obj,
        WithMetadata("pool_object", "true"),
        WithCallback(func(o *T, event WeakRefEvent) {
            if event == WeakRefCollected {
                wmp.onObjectCollected(o)
            }
        }),
    )
    
    wmp.mutex.Lock()
    wmp.activeRefs = append(wmp.activeRefs, ref)
    wmp.metrics.Gets++
    wmp.metrics.ActiveCount++
    if wmp.metrics.ActiveCount > wmp.metrics.MaxActive {
        wmp.metrics.MaxActive = wmp.metrics.ActiveCount
    }
    wmp.mutex.Unlock()
    
    return obj
}

func (wmp *WeakMemoryPool[T]) Put(obj *T) {
    if obj == nil {
        return
    }
    
    // Sanitize object if sanitizer is configured
    if wmp.sanitizer != nil {
        wmp.sanitizer(obj)
    }
    
    // Check pool size limit
    if wmp.getPoolSize() < wmp.maxSize {
        wmp.pool.Put(obj)
        wmp.metrics.Puts++
    }
    
    wmp.mutex.Lock()
    wmp.metrics.ActiveCount--
    wmp.mutex.Unlock()
}

// Enterprise garbage collection optimization framework
package gcopt

import (
    "runtime"
    "runtime/debug"
    "time"
    "sync"
    "context"
)

// GCOptimizer provides enterprise garbage collection optimization
type GCOptimizer struct {
    config      *GCConfig
    monitor     *GCMonitor
    tuner       *GCTuner
    predictor   *GCPredictor
    
    // Performance tracking
    metrics     *GCMetrics
    profiler    *GCProfiler
    
    // Adaptive optimization
    adaptiveEngine *AdaptiveGCEngine
    scheduler      *GCScheduler
}

type GCConfig struct {
    // Basic configuration
    TargetHeapSize    uint64
    MaxPauseTime      time.Duration
    GCPercentage      int
    
    // Advanced configuration
    ConcurrentMarking bool
    UseMemoryAdvice   bool
    ScanStacksMode    ScanMode
    
    // Enterprise features
    PredictiveMode    bool
    AdaptiveMode      bool
    ProfileMode       bool
    
    // Memory pressure handling
    MemoryPressureThreshold float64
    EmergencyGCEnabled      bool
    BackgroundCompaction    bool
}

type GCMetrics struct {
    // Collection statistics
    Collections       int64
    TotalPauseTime    time.Duration
    AveragePauseTime  time.Duration
    MaxPauseTime      time.Duration
    
    // Memory statistics
    HeapSize          uint64
    HeapObjects       uint64
    StackSize         uint64
    GCCPUFraction     float64
    
    // Performance impact
    ThroughputImpact  float64
    LatencyImpact     float64
    AllocationRate    float64
    
    // Advanced metrics
    MarkTime          time.Duration
    SweepTime         time.Duration
    ScavengeTime      time.Duration
    
    mutex sync.RWMutex
}

func NewGCOptimizer(config *GCConfig) *GCOptimizer {
    optimizer := &GCOptimizer{
        config:    config,
        monitor:   NewGCMonitor(),
        tuner:     NewGCTuner(),
        predictor: NewGCPredictor(),
        metrics:   &GCMetrics{},
        profiler:  NewGCProfiler(),
    }
    
    if config.AdaptiveMode {
        optimizer.adaptiveEngine = NewAdaptiveGCEngine(optimizer)
    }
    
    if config.PredictiveMode {
        optimizer.scheduler = NewGCScheduler(optimizer)
    }
    
    // Start monitoring
    go optimizer.startMonitoring()
    
    return optimizer
}

// OptimizeGC performs comprehensive GC optimization
func (gco *GCOptimizer) OptimizeGC(ctx context.Context) error {
    // Collect current GC statistics
    currentStats := gco.collectGCStats()
    
    // Analyze performance characteristics
    analysis := gco.analyzePerformance(currentStats)
    
    // Generate optimization recommendations
    recommendations := gco.generateRecommendations(analysis)
    
    // Apply optimizations
    for _, rec := range recommendations {
        if err := gco.applyOptimization(rec); err != nil {
            return fmt.Errorf("failed to apply optimization %s: %w", rec.Type, err)
        }
    }
    
    // Monitor optimization effectiveness
    go gco.monitorOptimizationEffectiveness(currentStats)
    
    return nil
}

// GCMonitor provides real-time GC monitoring
type GCMonitor struct {
    memStats    runtime.MemStats
    gcStats     debug.GCStats
    collectors  []GCCollector
    
    // Event tracking
    eventChan   chan GCEvent
    listeners   []GCEventListener
    
    // Anomaly detection
    anomalyDetector *GCAnomalyDetector
    
    mutex sync.RWMutex
}

type GCEvent struct {
    Type        GCEventType
    Timestamp   time.Time
    Duration    time.Duration
    HeapSize    uint64
    Collections uint32
    
    // Detailed information
    MarkDuration   time.Duration
    SweepDuration  time.Duration
    Allocations    uint64
    Deallocations  uint64
}

type GCEventType int

const (
    GCStarted GCEventType = iota
    GCCompleted
    GCForced
    MemoryPressure
    HeapGrowth
    PerformanceAnomaly
)

func (gcm *GCMonitor) StartMonitoring(interval time.Duration) {
    ticker := time.NewTicker(interval)
    defer ticker.Stop()
    
    for {
        select {
        case <-ticker.C:
            gcm.collectAndAnalyze()
        case event := <-gcm.eventChan:
            gcm.processEvent(event)
        }
    }
}

func (gcm *GCMonitor) collectAndAnalyze() {
    gcm.mutex.Lock()
    defer gcm.mutex.Unlock()
    
    // Collect memory statistics
    runtime.ReadMemStats(&gcm.memStats)
    debug.ReadGCStats(&gcm.gcStats)
    
    // Detect anomalies
    if anomaly := gcm.anomalyDetector.DetectAnomaly(&gcm.memStats); anomaly != nil {
        gcm.eventChan <- GCEvent{
            Type:      PerformanceAnomaly,
            Timestamp: time.Now(),
            HeapSize:  gcm.memStats.HeapSys,
        }
    }
    
    // Update collectors
    for _, collector := range gcm.collectors {
        collector.Collect(&gcm.memStats, &gcm.gcStats)
    }
}

// GCTuner provides intelligent GC parameter tuning
type GCTuner struct {
    parameters  *GCParameters
    optimizer   *ParameterOptimizer
    validator   *ConfigValidator
    
    // Historical data
    history     *TuningHistory
    effectiveness map[string]*TuningResult
    
    // Machine learning
    mlModel     *GCTuningModel
    featureExtractor *FeatureExtractor
}

type GCParameters struct {
    GOGCPercent        int
    MaxHeapSize        uint64
    GCPauseTarget      time.Duration
    ConcurrentWorkers  int
    ScanWorkersRatio   float64
    
    // Advanced parameters
    ScanStackMode      int
    WriteBarrierMode   int
    GCTriggerRatio     float64
    ScavengeGoal       float64
}

func (gct *GCTuner) TuneParameters(ctx context.Context, targetMetrics *TargetMetrics) (*GCParameters, error) {
    // Extract current system features
    features := gct.featureExtractor.ExtractFeatures()
    
    // Get current parameters
    currentParams := gct.getCurrentParameters()
    
    // Use ML model to predict optimal parameters
    predictedParams, confidence := gct.mlModel.PredictOptimalParameters(features, targetMetrics)
    
    // Validate predicted parameters
    validatedParams, err := gct.validator.ValidateParameters(predictedParams)
    if err != nil {
        return nil, fmt.Errorf("parameter validation failed: %w", err)
    }
    
    // Apply gradual tuning if confidence is low
    if confidence < 0.8 {
        validatedParams = gct.applyGradualTuning(currentParams, validatedParams)
    }
    
    // Record tuning attempt
    gct.recordTuningAttempt(currentParams, validatedParams, confidence)
    
    return validatedParams, nil
}

// GCPredictor provides predictive GC scheduling
type GCPredictor struct {
    allocationPredictor *AllocationPredictor
    heapPredictor      *HeapGrowthPredictor
    loadPredictor      *WorkloadPredictor
    
    // Time series models
    timeSeriesModel    *TimeSeriesModel
    seasonalityModel   *SeasonalityModel
    
    // Prediction accuracy tracking
    predictionTracker  *PredictionTracker
}

func (gcp *GCPredictor) PredictOptimalGCTiming(ctx context.Context, horizon time.Duration) (*GCPrediction, error) {
    // Predict allocation patterns
    allocationPrediction, err := gcp.allocationPredictor.PredictAllocations(horizon)
    if err != nil {
        return nil, fmt.Errorf("allocation prediction failed: %w", err)
    }
    
    // Predict heap growth
    heapPrediction, err := gcp.heapPredictor.PredictHeapGrowth(horizon)
    if err != nil {
        return nil, fmt.Errorf("heap growth prediction failed: %w", err)
    }
    
    // Predict workload characteristics
    loadPrediction, err := gcp.loadPredictor.PredictWorkload(horizon)
    if err != nil {
        return nil, fmt.Errorf("workload prediction failed: %w", err)
    }
    
    // Combine predictions
    prediction := &GCPrediction{
        OptimalTiming:     gcp.calculateOptimalTiming(allocationPrediction, heapPrediction, loadPrediction),
        ExpectedDuration:  gcp.predictGCDuration(heapPrediction),
        PerformanceImpact: gcp.predictPerformanceImpact(loadPrediction),
        Confidence:        gcp.calculateConfidence(allocationPrediction, heapPrediction, loadPrediction),
        
        // Detailed predictions
        AllocationPrediction: allocationPrediction,
        HeapPrediction:      heapPrediction,
        LoadPrediction:      loadPrediction,
    }
    
    return prediction, nil
}

// Memory pressure detection and handling
type MemoryPressureManager struct {
    thresholds    *PressureThresholds
    handlers      []PressureHandler
    monitor       *PressureMonitor
    
    // Emergency procedures
    emergencyGC   *EmergencyGC
    memoryAdvisor *MemoryAdvisor
    
    // Metrics and alerting
    pressureMetrics *PressureMetrics
    alertManager    *PressureAlertManager
}

type PressureThresholds struct {
    WarningLevel   float64  // 70% memory usage
    CriticalLevel  float64  // 85% memory usage
    EmergencyLevel float64  // 95% memory usage
    
    // Rate-based thresholds
    AllocationRate    float64  // bytes/second
    GCFrequency      float64  // collections/minute
    PauseTimeRatio   float64  // pause time / total time
}

func (mpm *MemoryPressureManager) HandlePressure(ctx context.Context, level PressureLevel) error {
    switch level {
    case PressureWarning:
        return mpm.handleWarningPressure(ctx)
    case PressureCritical:
        return mpm.handleCriticalPressure(ctx)
    case PressureEmergency:
        return mpm.handleEmergencyPressure(ctx)
    }
    
    return nil
}

func (mpm *MemoryPressureManager) handleEmergencyPressure(ctx context.Context) error {
    // Immediate actions
    runtime.GC() // Force immediate GC
    
    // Emergency memory reclamation
    if err := mpm.emergencyGC.ReclaimMemory(ctx); err != nil {
        return fmt.Errorf("emergency memory reclamation failed: %w", err)
    }
    
    // Notify pressure handlers
    for _, handler := range mpm.handlers {
        if err := handler.HandleEmergencyPressure(ctx); err != nil {
            // Log error but continue with other handlers
            log.Errorf("pressure handler failed: %v", err)
        }
    }
    
    // Send critical alerts
    mpm.alertManager.SendCriticalAlert("Emergency memory pressure detected")
    
    return nil
}

// Enterprise memory leak detection framework
package memleaks

import (
    "runtime"
    "runtime/pprof"
    "unsafe"
    "time"
    "context"
    "sync"
)

// LeakDetector provides comprehensive memory leak detection
type LeakDetector struct {
    scanners       []LeakScanner
    analyzer       *LeakAnalyzer
    reporter       *LeakReporter
    
    // Detection configuration
    config         *DetectionConfig
    
    // Tracking
    tracker        *ObjectTracker
    profiler       *MemoryProfiler
    baseline       *MemoryBaseline
    
    // Alerting
    alertManager   *LeakAlertManager
    
    // Automated remediation
    remediator     *LeakRemediator
}

type DetectionConfig struct {
    ScanInterval      time.Duration
    BaselineInterval  time.Duration
    LeakThreshold     float64
    MinLeakSize       int64
    
    // Advanced detection
    EnablePatternDetection bool
    EnableMLDetection     bool
    EnableHeapAnalysis    bool
    
    // Automated response
    AutoRemediation       bool
    RemediationThreshold  float64
}

type MemoryLeak struct {
    ID            string
    Type          LeakType
    Size          int64
    GrowthRate    float64
    FirstDetected time.Time
    LastDetected  time.Time
    
    // Location information
    StackTrace    []uintptr
    SourceLocation string
    ObjectType    string
    
    // Analysis
    RootCause     *RootCause
    Severity      LeakSeverity
    Confidence    float64
    
    // Tracking
    Samples       []*LeakSample
    Pattern       *LeakPattern
}

type LeakType int

const (
    LeakTypeGoroutine LeakType = iota
    LeakTypeMemory
    LeakTypeFileDescriptor
    LeakTypeConnection
    LeakTypeTimer
    LeakTypeChannel
)

func NewLeakDetector(config *DetectionConfig) *LeakDetector {
    detector := &LeakDetector{
        config:   config,
        scanners: make([]LeakScanner, 0),
        analyzer: NewLeakAnalyzer(),
        reporter: NewLeakReporter(),
        tracker:  NewObjectTracker(),
        profiler: NewMemoryProfiler(),
        alertManager: NewLeakAlertManager(),
    }
    
    // Initialize scanners
    detector.scanners = append(detector.scanners,
        NewGoroutineLeakScanner(),
        NewMemoryLeakScanner(),
        NewFileDescriptorLeakScanner(),
        NewConnectionLeakScanner(),
        NewTimerLeakScanner(),
    )
    
    if config.AutoRemediation {
        detector.remediator = NewLeakRemediator(detector)
    }
    
    // Establish baseline
    detector.establishBaseline()
    
    return detector
}

func (ld *LeakDetector) StartDetection(ctx context.Context) error {
    // Start periodic scanning
    go ld.periodicScan(ctx)
    
    // Start continuous monitoring
    go ld.continuousMonitoring(ctx)
    
    // Start memory profiling
    go ld.startMemoryProfiling(ctx)
    
    return nil
}

func (ld *LeakDetector) periodicScan(ctx context.Context) {
    ticker := time.NewTicker(ld.config.ScanInterval)
    defer ticker.Stop()
    
    for {
        select {
        case <-ctx.Done():
            return
        case <-ticker.C:
            if err := ld.performScan(ctx); err != nil {
                log.Errorf("leak scan failed: %v", err)
            }
        }
    }
}

func (ld *LeakDetector) performScan(ctx context.Context) error {
    scanResults := make([]*ScanResult, 0)
    
    // Run all scanners
    for _, scanner := range ld.scanners {
        result, err := scanner.Scan(ctx)
        if err != nil {
            log.Errorf("scanner %s failed: %v", scanner.Name(), err)
            continue
        }
        scanResults = append(scanResults, result)
    }
    
    // Analyze results
    leaks, err := ld.analyzer.AnalyzeResults(scanResults, ld.baseline)
    if err != nil {
        return fmt.Errorf("leak analysis failed: %w", err)
    }
    
    // Process detected leaks
    for _, leak := range leaks {
        if err := ld.processLeak(ctx, leak); err != nil {
            log.Errorf("failed to process leak %s: %v", leak.ID, err)
        }
    }
    
    return nil
}

// Goroutine leak scanner
type GoroutineLeakScanner struct {
    baseline       int
    threshold      float64
    stackAnalyzer  *StackAnalyzer
    patternMatcher *GoroutinePatternMatcher
}

func (gls *GoroutineLeakScanner) Scan(ctx context.Context) (*ScanResult, error) {
    // Get current goroutine count
    currentCount := runtime.NumGoroutine()
    
    // Check for significant increase
    growthRate := float64(currentCount-gls.baseline) / float64(gls.baseline)
    
    result := &ScanResult{
        ScannerName: "goroutine",
        Timestamp:   time.Now(),
        Baseline:    gls.baseline,
        Current:     currentCount,
        GrowthRate:  growthRate,
    }
    
    if growthRate > gls.threshold {
        // Analyze goroutine stacks
        stacks := gls.getGoroutineStacks()
        patterns := gls.patternMatcher.FindLeakPatterns(stacks)
        
        for _, pattern := range patterns {
            leak := &MemoryLeak{
                ID:            generateLeakID(),
                Type:          LeakTypeGoroutine,
                Size:          int64(pattern.Count * 8192), // Approximate stack size
                GrowthRate:    growthRate,
                FirstDetected: time.Now(),
                LastDetected:  time.Now(),
                StackTrace:    pattern.StackTrace,
                Pattern:       pattern,
                Confidence:    pattern.Confidence,
            }
            
            result.Leaks = append(result.Leaks, leak)
        }
    }
    
    return result, nil
}

// Memory leak scanner with heap analysis
type MemoryLeakScanner struct {
    profiler      *HeapProfiler
    analyzer      *HeapAnalyzer
    comparator    *ProfileComparator
    
    lastProfile   *HeapProfile
    baselineProfile *HeapProfile
}

func (mls *MemoryLeakScanner) Scan(ctx context.Context) (*ScanResult, error) {
    // Capture current heap profile
    currentProfile, err := mls.profiler.CaptureProfile()
    if err != nil {
        return nil, fmt.Errorf("failed to capture heap profile: %w", err)
    }
    
    result := &ScanResult{
        ScannerName: "memory",
        Timestamp:   time.Now(),
    }
    
    if mls.lastProfile != nil {
        // Compare with previous profile
        comparison := mls.comparator.Compare(mls.lastProfile, currentProfile)
        
        // Analyze for leak patterns
        leakPatterns := mls.analyzer.FindLeakPatterns(comparison)
        
        for _, pattern := range leakPatterns {
            leak := &MemoryLeak{
                ID:            generateLeakID(),
                Type:          LeakTypeMemory,
                Size:          pattern.Size,
                GrowthRate:    pattern.GrowthRate,
                FirstDetected: time.Now(),
                LastDetected:  time.Now(),
                ObjectType:    pattern.ObjectType,
                Pattern:       pattern,
                Confidence:    pattern.Confidence,
            }
            
            result.Leaks = append(result.Leaks, leak)
        }
    }
    
    mls.lastProfile = currentProfile
    return result, nil
}

// Advanced heap analyzer
type HeapAnalyzer struct {
    objectTracker    *ObjectTracker
    allocationTracker *AllocationTracker
    retentionAnalyzer *RetentionAnalyzer
    
    // Pattern recognition
    patternDB        *LeakPatternDatabase
    mlClassifier     *LeakClassifier
}

func (ha *HeapAnalyzer) FindLeakPatterns(comparison *ProfileComparison) []*LeakPattern {
    patterns := make([]*LeakPattern, 0)
    
    // Analyze object growth patterns
    for objectType, growth := range comparison.ObjectGrowth {
        if growth.Rate > ha.getThreshold(objectType) {
            pattern := &LeakPattern{
                Type:         "object_growth",
                ObjectType:   objectType,
                GrowthRate:   growth.Rate,
                Size:         growth.Size,
                Confidence:   ha.calculateConfidence(growth),
                StackTraces:  growth.StackTraces,
            }
            patterns = append(patterns, pattern)
        }
    }
    
    // Analyze retention patterns
    retentionPatterns := ha.retentionAnalyzer.AnalyzeRetention(comparison)
    patterns = append(patterns, retentionPatterns...)
    
    // Use ML classification
    if ha.mlClassifier != nil {
        mlPatterns := ha.mlClassifier.ClassifyLeaks(comparison)
        patterns = append(patterns, mlPatterns...)
    }
    
    return patterns
}

// Automated leak remediation
type LeakRemediator struct {
    strategies     []RemediationStrategy
    executor       *RemediationExecutor
    validator      *RemediationValidator
    
    // Safety mechanisms
    safetyLimits   *SafetyLimits
    rollbackManager *RollbackManager
}

type RemediationStrategy interface {
    CanRemediate(leak *MemoryLeak) bool
    Remediate(ctx context.Context, leak *MemoryLeak) (*RemediationResult, error)
    EstimateImpact(leak *MemoryLeak) *ImpactEstimate
}

// Goroutine leak remediation strategy
type GoroutineRemediationStrategy struct {
    goroutineKiller  *GoroutineKiller
    impactAnalyzer   *ImpactAnalyzer
}

func (grs *GoroutineRemediationStrategy) Remediate(ctx context.Context, leak *MemoryLeak) (*RemediationResult, error) {
    if leak.Type != LeakTypeGoroutine {
        return nil, fmt.Errorf("invalid leak type for goroutine remediation")
    }
    
    // Analyze impact of killing goroutines
    impact := grs.impactAnalyzer.AnalyzeGoroutineTermination(leak)
    if impact.Risk > AcceptableRisk {
        return &RemediationResult{
            Success: false,
            Reason:  "termination risk too high",
            Impact:  impact,
        }, nil
    }
    
    // Identify specific goroutines to terminate
    goroutines := grs.identifyLeakyGoroutines(leak)
    
    // Terminate goroutines safely
    terminated := 0
    for _, gr := range goroutines {
        if err := grs.goroutineKiller.TerminateGoroutine(gr); err != nil {
            log.Errorf("failed to terminate goroutine %d: %v", gr.ID, err)
        } else {
            terminated++
        }
    }
    
    return &RemediationResult{
        Success:     true,
        Impact:      impact,
        Details:     fmt.Sprintf("terminated %d goroutines", terminated),
        LeakReduced: float64(terminated) / float64(len(goroutines)),
    }, nil
}

// Real-time leak monitoring with ML
type RealTimeLeakMonitor struct {
    mlModel        *LeakPredictionModel
    anomalyDetector *MemoryAnomalyDetector
    streamProcessor *MemoryStreamProcessor
    
    // Real-time data processing
    dataStream     chan MemoryDataPoint
    processors     []StreamProcessor
    
    // Prediction and alerting
    predictor      *LeakPredictor
    alertThreshold float64
}

func (rtlm *RealTimeLeakMonitor) StartMonitoring(ctx context.Context) {
    // Start data collection
    go rtlm.collectMemoryData(ctx)
    
    // Start stream processing
    go rtlm.processDataStream(ctx)
    
    // Start anomaly detection
    go rtlm.detectAnomalies(ctx)
}

func (rtlm *RealTimeLeakMonitor) processDataStream(ctx context.Context) {
    for {
        select {
        case <-ctx.Done():
            return
        case dataPoint := <-rtlm.dataStream:
            // Process through ML model
            prediction := rtlm.mlModel.Predict(dataPoint)
            
            if prediction.LeakProbability > rtlm.alertThreshold {
                // Generate leak alert
                alert := &LeakAlert{
                    Timestamp:   time.Now(),
                    Probability: prediction.LeakProbability,
                    Confidence:  prediction.Confidence,
                    DataPoint:   dataPoint,
                    Prediction:  prediction,
                }
                
                rtlm.sendLeakAlert(alert)
            }
            
            // Update anomaly detector
            rtlm.anomalyDetector.UpdateModel(dataPoint)
        }
    }
}

#!/bin/bash
# Enterprise Go performance profiling and optimization toolkit

set -euo pipefail

# Configuration
PROFILING_DIR="/opt/go-profiling"
PROFILES_DIR="$PROFILING_DIR/profiles"
REPORTS_DIR="$PROFILING_DIR/reports"
BENCHMARKS_DIR="$PROFILING_DIR/benchmarks"

# Logging
log_profiling_event() {
    local level="$1"
    local component="$2"
    local action="$3"
    local result="$4"
    local details="$5"
    local timestamp=$(date -u +"%Y-%m-%dT%H:%M:%S.%3NZ")
    
    echo "{\"timestamp\":\"$timestamp\",\"level\":\"$level\",\"component\":\"$component\",\"action\":\"$action\",\"result\":\"$result\",\"details\":\"$details\"}" >> "$PROFILING_DIR/profiling.jsonl"
}

# Comprehensive performance profiling
perform_comprehensive_profiling() {
    local app_name="$1"
    local duration="${2:-30s}"
    local profile_types="${3:-cpu,heap,goroutine,mutex,block,trace}"
    
    log_profiling_event "INFO" "profiling" "started" "initiated" "App: $app_name, Duration: $duration"
    
    local session_id="$(date +%Y%m%d-%H%M%S)-$app_name"
    local session_dir="$PROFILES_DIR/$session_id"
    mkdir -p "$session_dir"
    
    # Get application PID or endpoint
    local target=""
    if [[ "$app_name" =~ ^[0-9]+$ ]]; then
        target="$app_name"  # PID
    else
        target="http://localhost:6060"  # pprof endpoint
    fi
    
    # Start profiling collection
    IFS=',' read -ra TYPES <<< "$profile_types"
    local pids=()
    
    for profile_type in "${TYPES[@]}"; do
        case "$profile_type" in
            "cpu")
                collect_cpu_profile "$target" "$duration" "$session_dir" &
                pids+=($!)
                ;;
            "heap")
                collect_heap_profile "$target" "$session_dir" &
                pids+=($!)
                ;;
            "goroutine")
                collect_goroutine_profile "$target" "$session_dir" &
                pids+=($!)
                ;;
            "mutex")
                collect_mutex_profile "$target" "$duration" "$session_dir" &
                pids+=($!)
                ;;
            "block")
                collect_block_profile "$target" "$duration" "$session_dir" &
                pids+=($!)
                ;;
            "trace")
                collect_trace_profile "$target" "$duration" "$session_dir" &
                pids+=($!)
                ;;
            "allocs")
                collect_allocs_profile "$target" "$session_dir" &
                pids+=($!)
                ;;
        esac
    done
    
    # Wait for all profiling to complete
    for pid in "${pids[@]}"; do
        wait "$pid"
    done
    
    # Generate comprehensive analysis report
    generate_analysis_report "$session_dir" "$app_name"
    
    log_profiling_event "INFO" "profiling" "completed" "success" "Session: $session_id"
    echo "$session_id"
}

# CPU profiling with advanced analysis
collect_cpu_profile() {
    local target="$1"
    local duration="$2"
    local output_dir="$3"
    
    local profile_file="$output_dir/cpu.prof"
    local analysis_file="$output_dir/cpu_analysis.txt"
    
    # Collect CPU profile
    if [[ "$target" =~ ^[0-9]+$ ]]; then
        # PID-based profiling
        timeout "$duration" perf record -p "$target" -g -o "$output_dir/perf.data" || true
        perf script -i "$output_dir/perf.data" > "$output_dir/perf.script"
    else
        # HTTP endpoint profiling
        curl -s "$target/debug/pprof/profile?seconds=$(echo "$duration" | sed 's/s//')" -o "$profile_file"
    fi
    
    # Analyze CPU profile
    if [[ -f "$profile_file" ]]; then
        # Generate text analysis
        go tool pprof -text "$profile_file" > "$analysis_file"
        
        # Generate top functions report
        go tool pprof -top10 "$profile_file" > "$output_dir/cpu_top10.txt"
        
        # Generate call graph
        go tool pprof -png "$profile_file" > "$output_dir/cpu_callgraph.png" 2>/dev/null || true
        
        # Generate flame graph
        generate_flame_graph "$profile_file" "$output_dir/cpu_flamegraph.svg"
        
        # Analyze hot paths
        analyze_hot_paths "$profile_file" "$output_dir/cpu_hotpaths.json"
    fi
}

# Memory profiling with leak detection
collect_heap_profile() {
    local target="$1"
    local output_dir="$2"
    
    local heap_profile="$output_dir/heap.prof"
    local allocs_profile="$output_dir/allocs.prof"
    
    # Collect heap profile
    if [[ "$target" =~ ^[0-9]+$ ]]; then
        # Generate heap dump for PID
        gdb -batch -ex "generate-core-file $output_dir/core.dump" -ex "quit" -p "$target" 2>/dev/null || true
    else
        # HTTP endpoint profiling
        curl -s "$target/debug/pprof/heap" -o "$heap_profile"
        curl -s "$target/debug/pprof/allocs" -o "$allocs_profile"
    fi
    
    # Analyze heap usage
    if [[ -f "$heap_profile" ]]; then
        # Generate heap analysis
        go tool pprof -text "$heap_profile" > "$output_dir/heap_analysis.txt"
        go tool pprof -top20 "$heap_profile" > "$output_dir/heap_top20.txt"
        
        # Generate memory visualization
        go tool pprof -png "$heap_profile" > "$output_dir/heap_graph.png" 2>/dev/null || true
        
        # Detect potential memory leaks
        detect_memory_leaks "$heap_profile" "$output_dir/memory_leaks.json"
        
        # Analyze allocation patterns
        analyze_allocation_patterns "$allocs_profile" "$output_dir/allocation_patterns.json"
    fi
}

# Goroutine profiling with deadlock detection
collect_goroutine_profile() {
    local target="$1"
    local output_dir="$2"
    
    local goroutine_profile="$output_dir/goroutine.prof"
    
    # Collect goroutine profile
    if [[ "$target" =~ ^[0-9]+$ ]]; then
        # Get goroutine info from PID
        kill -SIGQUIT "$target" 2>/dev/null || true
        sleep 1
    else
        # HTTP endpoint profiling
        curl -s "$target/debug/pprof/goroutine" -o "$goroutine_profile"
    fi
    
    if [[ -f "$goroutine_profile" ]]; then
        # Analyze goroutines
        go tool pprof -text "$goroutine_profile" > "$output_dir/goroutine_analysis.txt"
        
        # Detect goroutine leaks
        detect_goroutine_leaks "$goroutine_profile" "$output_dir/goroutine_leaks.json"
        
        # Analyze goroutine patterns
        analyze_goroutine_patterns "$goroutine_profile" "$output_dir/goroutine_patterns.json"
        
        # Check for deadlocks
        detect_deadlocks "$goroutine_profile" "$output_dir/deadlock_analysis.json"
    fi
}

# Advanced performance analysis
analyze_hot_paths() {
    local profile_file="$1"
    local output_file="$2"
    
    # Extract hot paths using pprof
    local hot_functions=$(go tool pprof -text "$profile_file" | head -20 | tail -n +3 | awk '{print $6}' | grep -v "^$")
    
    # Build hot paths analysis
    cat > "$output_file" <<EOF
{
    "analysis_timestamp": "$(date -u +%Y-%m-%dT%H:%M:%S.%3NZ)",
    "profile_file": "$profile_file",
    "hot_functions": [
EOF
    
    local first=true
    while read -r func; do
        [[ -z "$func" ]] && continue
        
        if [[ "$first" == "true" ]]; then
            first=false
        else
            echo "," >> "$output_file"
        fi
        
        # Get function details
        local func_info=$(go tool pprof -text "$profile_file" | grep "$func" | head -1)
        local cpu_percent=$(echo "$func_info" | awk '{print $1}' | sed 's/%//')
        local cumulative_percent=$(echo "$func_info" | awk '{print $2}' | sed 's/%//')
        
        cat >> "$output_file" <<EOF
        {
            "function": "$func",
            "cpu_percent": $cpu_percent,
            "cumulative_percent": $cumulative_percent,
            "optimization_priority": "$(calculate_optimization_priority "$cpu_percent" "$cumulative_percent")"
        }
EOF
    done <<< "$hot_functions"
    
    cat >> "$output_file" <<EOF
    ],
    "recommendations": $(generate_optimization_recommendations "$profile_file")
}
EOF
}

# Memory leak detection
detect_memory_leaks() {
    local heap_profile="$1"
    local output_file="$2"
    
    # Analyze heap profile for leak patterns
    local leak_candidates=$(go tool pprof -text "$heap_profile" | grep -E "(MB|GB)" | head -10)
    
    cat > "$output_file" <<EOF
{
    "analysis_timestamp": "$(date -u +%Y-%m-%dT%H:%M:%S.%3NZ)",
    "heap_profile": "$heap_profile",
    "potential_leaks": [
EOF
    
    local first=true
    while read -r line; do
        [[ -z "$line" ]] && continue
        
        if [[ "$first" == "true" ]]; then
            first=false
        else
            echo "," >> "$output_file"
        fi
        
        local size=$(echo "$line" | awk '{print $1}')
        local percent=$(echo "$line" | awk '{print $2}' | sed 's/%//')
        local func=$(echo "$line" | awk '{print $6}')
        
        cat >> "$output_file" <<EOF
        {
            "function": "$func",
            "size": "$size",
            "percentage": $percent,
            "leak_probability": $(calculate_leak_probability "$size" "$percent"),
            "severity": "$(calculate_leak_severity "$size" "$percent")"
        }
EOF
    done <<< "$leak_candidates"
    
    cat >> "$output_file" <<EOF
    ],
    "recommendations": $(generate_leak_remediation_recommendations "$heap_profile")
}
EOF
}

# Performance optimization recommendations
generate_optimization_recommendations() {
    local profile_file="$1"
    
    # Analyze profile and generate recommendations
    cat <<EOF
[
    {
        "type": "cpu_optimization",
        "priority": "high",
        "description": "Optimize hot path functions identified in CPU profile",
        "actions": [
            "Profile hot functions for algorithmic improvements",
            "Consider caching for expensive computations",
            "Evaluate parallelization opportunities"
        ]
    },
    {
        "type": "memory_optimization", 
        "priority": "medium",
        "description": "Reduce memory allocations in frequently called functions",
        "actions": [
            "Use object pools for frequently allocated objects",
            "Implement buffer reuse patterns",
            "Consider memory-efficient data structures"
        ]
    },
    {
        "type": "gc_optimization",
        "priority": "medium", 
        "description": "Optimize garbage collection performance",
        "actions": [
            "Tune GOGC parameter based on heap size",
            "Reduce allocation rate in hot paths",
            "Consider weak references for caches"
        ]
    }
]
EOF
}

# Benchmark automation
run_performance_benchmarks() {
    local app_name="$1"
    local benchmark_suite="${2:-standard}"
    
    log_profiling_event "INFO" "benchmarking" "started" "initiated" "App: $app_name, Suite: $benchmark_suite"
    
    local benchmark_id="$(date +%Y%m%d-%H%M%S)-$app_name"
    local benchmark_dir="$BENCHMARKS_DIR/$benchmark_id"
    mkdir -p "$benchmark_dir"
    
    case "$benchmark_suite" in
        "standard")
            run_standard_benchmarks "$app_name" "$benchmark_dir"
            ;;
        "memory")
            run_memory_benchmarks "$app_name" "$benchmark_dir"
            ;;
        "concurrency")
            run_concurrency_benchmarks "$app_name" "$benchmark_dir"
            ;;
        "comprehensive")
            run_standard_benchmarks "$app_name" "$benchmark_dir"
            run_memory_benchmarks "$app_name" "$benchmark_dir"
            run_concurrency_benchmarks "$app_name" "$benchmark_dir"
            ;;
    esac
    
    # Generate benchmark report
    generate_benchmark_report "$benchmark_dir" "$app_name"
    
    log_profiling_event "INFO" "benchmarking" "completed" "success" "Benchmark ID: $benchmark_id"
    echo "$benchmark_id"
}

# Main profiling orchestrator
main() {
    local command="$1"
    shift
    
    # Ensure directories exist
    mkdir -p "$PROFILES_DIR" "$REPORTS_DIR" "$BENCHMARKS_DIR"
    
    case "$command" in
        "profile")
            perform_comprehensive_profiling "$@"
            ;;
        "benchmark")
            run_performance_benchmarks "$@"
            ;;
        "analyze")
            analyze_existing_profile "$@"
            ;;
        "monitor")
            start_continuous_monitoring "$@"
            ;;
        "optimize")
            run_optimization_analysis "$@"
            ;;
        *)
            echo "Usage: $0 {profile|benchmark|analyze|monitor|optimize} [options]"
            echo ""
            echo "Commands:"
            echo "  profile <app_name> [duration] [types] - Perform comprehensive profiling"
            echo "  benchmark <app_name> [suite]         - Run performance benchmarks"
            echo "  analyze <profile_path>               - Analyze existing profile"
            echo "  monitor <app_name>                   - Start continuous monitoring"
            echo "  optimize <profile_path>              - Generate optimization recommendations"
            exit 1
            ;;
    esac
}

# Execute main function
main "$@"

# Performance Engineer Career Progression
PERFORMANCE_ENGINEER_LEVELS = [
    "Junior Performance Engineer",
    "Performance Engineer",
    "Senior Performance Engineer", 
    "Principal Performance Engineer",
    "Distinguished Performance Engineer",
    "Performance Architect"
]

# Specialization Areas
PERFORMANCE_SPECIALIZATIONS = [
    "High-Frequency Trading Systems",
    "Real-Time and Embedded Systems", 
    "Large-Scale Distributed Systems",
    "Game Engine and Graphics Performance",
    "Machine Learning and AI Performance",
    "Database and Storage Performance"
]

# Leadership Track
TECHNICAL_LEADERSHIP = [
    "Senior Performance Engineer → Performance Team Lead",
    "Performance Team Lead → Performance Engineering Manager",
    "Performance Engineering Manager → Director of Engineering Performance",
    "Principal Performance Engineer → Distinguished Engineer"
]

// Example: Contributing to Go runtime performance
func optimizeStringBuilder(b *strings.Builder, data []string) {
    // Contributed optimization: pre-calculate total capacity
    totalLen := 0
    for _, s := range data {
        totalLen += len(s)
    }
    b.Grow(totalLen)
    
    for _, s := range data {
        b.WriteString(s)
    }
}

// Example: Memory pool contribution
type SyncPool[T any] struct {
    pool sync.Pool
    new  func() T
}

func NewSyncPool[T any](new func() T) *SyncPool[T] {
    return &SyncPool[T]{
        pool: sync.Pool{New: func() interface{} { return new() }},
        new:  new,
    }
}