// Enterprise immutability framework for Go applications
package immutable

import (
    "context"
    "fmt"
    "reflect"
    "runtime"
    "sync"
    "sync/atomic"
    "time"
    "unsafe"
)

// ImmutableManager provides enterprise-grade immutability management
type ImmutableManager struct {
    // Core components
    dataRegistry     *DataRegistry
    versionManager   *VersionManager
    copyOnWritePool  *CopyOnWritePool
    
    // Performance optimization
    memoryPool       *MemoryPool
    cacheManager     *ImmutableCache
    compactor        *MemoryCompactor
    
    // Concurrency management
    accessTracker    *ConcurrentAccessTracker
    lockManager      *LockFreeManager
    
    // Monitoring and metrics
    metricsCollector *MemoryMetrics
    performanceProfiler *PerformanceProfiler
    
    // Configuration
    config          *ImmutabilityConfig
    
    // Thread safety
    mu              sync.RWMutex
}

type ImmutabilityConfig struct {
    // Memory management
    EnableMemoryPooling     bool
    PoolInitialSize        int
    PoolMaxSize           int
    EnableCompaction      bool
    CompactionThreshold   float64
    
    // Performance settings
    EnableCaching         bool
    CacheSize            int
    CacheTTL             time.Duration
    EnablePrefetching    bool
    
    // Concurrency settings
    MaxConcurrentReaders  int
    EnableLockFreeAccess bool
    ConcurrencyLevel     int
    
    // Monitoring
    EnableMetrics        bool
    EnableProfiling      bool
    MetricsInterval      time.Duration
}

// ImmutableSlice provides enterprise-grade immutable slice implementation
type ImmutableSlice[T any] struct {
    data            []T
    version         uint64
    referenceCount  int64
    
    // Memory management
    allocator       *SliceAllocator[T]
    memoryPool      *MemoryPool
    
    // Concurrency control
    rwMutex         sync.RWMutex
    atomicOps       *AtomicOperations[T]
    
    // Performance optimization
    cachedLength    int
    cachedHash      uint64
    dirty           bool
    
    // Metadata
    metadata        *SliceMetadata
    created         time.Time
    lastAccessed    int64
}

// NewImmutableSlice creates a new immutable slice with enterprise features
func NewImmutableSlice[T any](data []T, opts ...SliceOption[T]) *ImmutableSlice[T] {
    slice := &ImmutableSlice[T]{
        data:           make([]T, len(data)),
        version:        generateVersion(),
        referenceCount: 1,
        created:        time.Now(),
        lastAccessed:   time.Now().UnixNano(),
        cachedLength:   len(data),
        dirty:          false,
    }
    
    // Apply options
    for _, opt := range opts {
        opt(slice)
    }
    
    // Initialize allocator if not provided
    if slice.allocator == nil {
        slice.allocator = NewSliceAllocator[T](DefaultAllocatorConfig())
    }
    
    // Copy data using optimized memory copy
    slice.allocator.CopySlice(slice.data, data)
    
    // Initialize atomic operations
    slice.atomicOps = NewAtomicOperations[T]()
    
    // Calculate initial hash
    slice.cachedHash = slice.calculateHash()
    
    // Initialize metadata
    slice.metadata = &SliceMetadata{
        ElementType:    reflect.TypeOf((*T)(nil)).Elem(),
        InitialSize:    len(data),
        CreatedAt:     slice.created,
        AccessPattern: NewAccessPattern(),
    }
    
    return slice
}

// Get returns an element at the specified index with bounds checking
func (s *ImmutableSlice[T]) Get(index int) (T, error) {
    var zero T
    
    // Update access tracking
    atomic.StoreInt64(&s.lastAccessed, time.Now().UnixNano())
    s.metadata.AccessPattern.RecordAccess(index)
    
    // Bounds checking
    if index < 0 || index >= s.cachedLength {
        return zero, fmt.Errorf("index %d out of bounds [0:%d]", index, s.cachedLength)
    }
    
    // Use atomic operations for thread-safe access
    return s.atomicOps.Load(s.data, index), nil
}

// Len returns the length of the slice
func (s *ImmutableSlice[T]) Len() int {
    return s.cachedLength
}

// Append creates a new immutable slice with additional elements
func (s *ImmutableSlice[T]) Append(elements ...T) *ImmutableSlice[T] {
    newLength := s.cachedLength + len(elements)
    
    // Allocate new slice with optimized capacity
    newCapacity := s.allocator.CalculateOptimalCapacity(newLength)
    newData := s.allocator.AllocateSlice(newCapacity)[:newLength]
    
    // Copy existing data
    s.allocator.CopySlice(newData[:s.cachedLength], s.data)
    
    // Copy new elements
    copy(newData[s.cachedLength:], elements)
    
    // Create new immutable slice
    return &ImmutableSlice[T]{
        data:          newData,
        version:       generateVersion(),
        referenceCount: 1,
        allocator:     s.allocator,
        memoryPool:    s.memoryPool,
        atomicOps:     s.atomicOps,
        cachedLength:  newLength,
        cachedHash:    calculateHashForSlice(newData),
        dirty:         false,
        metadata:      s.metadata.Clone(),
        created:       time.Now(),
        lastAccessed:  time.Now().UnixNano(),
    }
}

// Slice creates a new immutable slice from a subset of elements
func (s *ImmutableSlice[T]) Slice(start, end int) (*ImmutableSlice[T], error) {
    // Bounds checking
    if start < 0 || end > s.cachedLength || start > end {
        return nil, fmt.Errorf("invalid slice bounds [%d:%d] with length %d", start, end, s.cachedLength)
    }
    
    newLength := end - start
    if newLength == 0 {
        return NewImmutableSlice[T](nil), nil
    }
    
    // Allocate new slice
    newData := s.allocator.AllocateSlice(newLength)
    s.allocator.CopySlice(newData, s.data[start:end])
    
    return &ImmutableSlice[T]{
        data:          newData,
        version:       generateVersion(),
        referenceCount: 1,
        allocator:     s.allocator,
        memoryPool:    s.memoryPool,
        atomicOps:     s.atomicOps,
        cachedLength:  newLength,
        cachedHash:    calculateHashForSlice(newData),
        dirty:         false,
        metadata:      s.metadata.Clone(),
        created:       time.Now(),
        lastAccessed:  time.Now().UnixNano(),
    }, nil
}

// ImmutableMap provides enterprise-grade immutable map implementation
type ImmutableMap[K comparable, V any] struct {
    data            map[K]V
    version         uint64
    referenceCount  int64
    
    // Memory management
    allocator       *MapAllocator[K, V]
    memoryPool      *MemoryPool
    
    // Concurrency control
    rwMutex         sync.RWMutex
    shardedLocks    []*sync.RWMutex
    shardCount      int
    
    // Performance optimization
    cachedSize      int
    cachedHash      uint64
    dirty           bool
    bloomFilter     *BloomFilter
    
    // Metadata
    metadata        *MapMetadata
    created         time.Time
    lastAccessed    int64
}

// NewImmutableMap creates a new immutable map with enterprise features
func NewImmutableMap[K comparable, V any](data map[K]V, opts ...MapOption[K, V]) *ImmutableMap[K, V] {
    m := &ImmutableMap[K, V]{
        data:           make(map[K]V, len(data)),
        version:        generateVersion(),
        referenceCount: 1,
        created:        time.Now(),
        lastAccessed:   time.Now().UnixNano(),
        cachedSize:     len(data),
        dirty:          false,
        shardCount:     calculateOptimalShardCount(len(data)),
    }
    
    // Apply options
    for _, opt := range opts {
        opt(m)
    }
    
    // Initialize allocator if not provided
    if m.allocator == nil {
        m.allocator = NewMapAllocator[K, V](DefaultMapAllocatorConfig())
    }
    
    // Initialize sharded locks for better concurrency
    m.shardedLocks = make([]*sync.RWMutex, m.shardCount)
    for i := range m.shardedLocks {
        m.shardedLocks[i] = &sync.RWMutex{}
    }
    
    // Copy data using optimized map copy
    m.allocator.CopyMap(m.data, data)
    
    // Initialize bloom filter for fast negative lookups
    m.bloomFilter = NewBloomFilter(len(data), 0.01) // 1% false positive rate
    for k := range data {
        m.bloomFilter.Add(k)
    }
    
    // Calculate initial hash
    m.cachedHash = m.calculateHash()
    
    // Initialize metadata
    m.metadata = &MapMetadata{
        KeyType:       reflect.TypeOf((*K)(nil)).Elem(),
        ValueType:     reflect.TypeOf((*V)(nil)).Elem(),
        InitialSize:   len(data),
        CreatedAt:    m.created,
        AccessPattern: NewMapAccessPattern[K](),
    }
    
    return m
}

// Get returns a value for the specified key
func (m *ImmutableMap[K, V]) Get(key K) (V, bool) {
    var zero V
    
    // Update access tracking
    atomic.StoreInt64(&m.lastAccessed, time.Now().UnixNano())
    m.metadata.AccessPattern.RecordAccess(key)
    
    // Fast negative lookup using bloom filter
    if !m.bloomFilter.Contains(key) {
        return zero, false
    }
    
    // Use sharded locking for better concurrency
    shard := m.getShardForKey(key)
    m.shardedLocks[shard].RLock()
    defer m.shardedLocks[shard].RUnlock()
    
    value, exists := m.data[key]
    return value, exists
}

// Set creates a new immutable map with the key-value pair added/updated
func (m *ImmutableMap[K, V]) Set(key K, value V) *ImmutableMap[K, V] {
    newSize := m.cachedSize
    if _, exists := m.data[key]; !exists {
        newSize++
    }
    
    // Allocate new map
    newData := m.allocator.AllocateMap(newSize)
    
    // Copy existing data
    m.allocator.CopyMap(newData, m.data)
    
    // Set new value
    newData[key] = value
    
    // Create new bloom filter
    newBloomFilter := NewBloomFilter(newSize, 0.01)
    for k := range newData {
        newBloomFilter.Add(k)
    }
    
    return &ImmutableMap[K, V]{
        data:           newData,
        version:        generateVersion(),
        referenceCount: 1,
        allocator:      m.allocator,
        memoryPool:     m.memoryPool,
        shardedLocks:   m.shardedLocks, // Reuse sharded locks
        shardCount:     m.shardCount,
        cachedSize:     newSize,
        cachedHash:     calculateHashForMap(newData),
        dirty:          false,
        bloomFilter:    newBloomFilter,
        metadata:       m.metadata.Clone(),
        created:        time.Now(),
        lastAccessed:   time.Now().UnixNano(),
    }
}

// SliceAllocator provides optimized memory allocation for slices
type SliceAllocator[T any] struct {
    pool           *sync.Pool
    sizeClasses    []int
    allocStats     *AllocationStats
    config         *AllocatorConfig
    
    // Memory tracking
    totalAllocated int64
    totalReleased  int64
    peakUsage     int64
}

func NewSliceAllocator[T any](config *AllocatorConfig) *SliceAllocator[T] {
    allocator := &SliceAllocator[T]{
        config:      config,
        sizeClasses: generateSizeClasses(config),
        allocStats:  NewAllocationStats(),
    }
    
    // Initialize memory pool
    allocator.pool = &sync.Pool{
        New: func() interface{} {
            return make([]T, 0, config.InitialCapacity)
        },
    }
    
    return allocator
}

// AllocateSlice allocates a slice with optimized capacity
func (sa *SliceAllocator[T]) AllocateSlice(capacity int) []T {
    // Find optimal size class
    sizeClass := sa.findSizeClass(capacity)
    
    // Try to get from pool first
    if pooled := sa.pool.Get(); pooled != nil {
        slice := pooled.([]T)
        if cap(slice) >= capacity {
            // Update allocation stats
            atomic.AddInt64(&sa.totalAllocated, int64(capacity*int(unsafe.Sizeof((*T)(nil)))))
            sa.allocStats.RecordAllocation(capacity)
            
            return slice[:0] // Reset length but keep capacity
        }
        // Return to pool if not suitable
        sa.pool.Put(pooled)
    }
    
    // Allocate new slice
    slice := make([]T, 0, sizeClass)
    
    // Update allocation stats
    atomic.AddInt64(&sa.totalAllocated, int64(sizeClass*int(unsafe.Sizeof((*T)(nil)))))
    sa.allocStats.RecordAllocation(sizeClass)
    
    // Update peak usage
    currentUsage := atomic.LoadInt64(&sa.totalAllocated) - atomic.LoadInt64(&sa.totalReleased)
    for {
        peak := atomic.LoadInt64(&sa.peakUsage)
        if currentUsage <= peak || atomic.CompareAndSwapInt64(&sa.peakUsage, peak, currentUsage) {
            break
        }
    }
    
    return slice
}

// CopySlice performs optimized slice copying
func (sa *SliceAllocator[T]) CopySlice(dst, src []T) {
    // Use optimized copy based on element type
    if sa.isSimpleType() {
        // Use memmove for simple types
        sa.fastCopy(dst, src)
    } else {
        // Use standard copy for complex types
        copy(dst, src)
    }
}

// ReleaseSlice returns a slice to the pool
func (sa *SliceAllocator[T]) ReleaseSlice(slice []T) {
    if cap(slice) <= sa.config.MaxPooledCapacity {
        // Clear the slice before returning to pool
        for i := range slice {
            var zero T
            slice[i] = zero
        }
        
        slice = slice[:0] // Reset length
        sa.pool.Put(slice)
        
        // Update release stats
        atomic.AddInt64(&sa.totalReleased, int64(cap(slice)*int(unsafe.Sizeof((*T)(nil)))))
    }
}

// MemoryPool provides enterprise-grade memory pooling
type MemoryPool struct {
    pools          map[int]*sync.Pool
    sizeClasses    []int
    allocStats     *PoolStats
    config         *PoolConfig
    
    // Monitoring
    metricsCollector *PoolMetrics
    
    // Thread safety
    mu             sync.RWMutex
}

type PoolConfig struct {
    MinSize        int
    MaxSize        int
    GrowthFactor   float64
    MaxPools       int
    CleanupInterval time.Duration
    EnableMetrics  bool
}

// AtomicOperations provides lock-free operations for immutable data structures
type AtomicOperations[T any] struct {
    compareFunc    func(a, b T) bool
    hashFunc      func(T) uint64
    elementSize   uintptr
    
    // Performance optimization
    cacheLine     int
    alignment     uintptr
}

func NewAtomicOperations[T any]() *AtomicOperations[T] {
    return &AtomicOperations[T]{
        elementSize: unsafe.Sizeof((*T)(nil)),
        cacheLine:   getCacheLineSize(),
        alignment:   getAlignmentRequirement[T](),
    }
}

// Load performs atomic load operation
func (ao *AtomicOperations[T]) Load(slice []T, index int) T {
    // For simple types, use atomic operations
    if ao.isAtomicType() {
        return ao.atomicLoad(slice, index)
    }
    
    // For complex types, use memory barriers
    runtime.Gosched() // Memory barrier
    return slice[index]
}

// Store performs atomic store operation
func (ao *AtomicOperations[T]) Store(slice []T, index int, value T) {
    // For simple types, use atomic operations
    if ao.isAtomicType() {
        ao.atomicStore(slice, index, value)
        return
    }
    
    // For complex types, use memory barriers
    slice[index] = value
    runtime.Gosched() // Memory barrier
}

// CompareAndSwap performs atomic compare-and-swap operation
func (ao *AtomicOperations[T]) CompareAndSwap(slice []T, index int, old, new T) bool {
    if ao.isAtomicType() {
        return ao.atomicCompareAndSwap(slice, index, old, new)
    }
    
    // For complex types, use comparison function
    if ao.compareFunc != nil && ao.compareFunc(slice[index], old) {
        slice[index] = new
        return true
    }
    
    return false
}

// ConcurrentAccessTracker tracks concurrent access patterns
type ConcurrentAccessTracker struct {
    accessCounts   map[string]*int64
    hotspots      *HotspotDetector
    patterns      *AccessPatternAnalyzer
    
    // Configuration
    trackingWindow time.Duration
    maxTrackedKeys int
    
    // Thread safety
    mu            sync.RWMutex
}

// PerformanceProfiler provides comprehensive performance profiling
type PerformanceProfiler struct {
    cpuProfiler    *CPUProfiler
    memoryProfiler *MemoryProfiler
    gcProfiler     *GCProfiler
    
    // Metrics collection
    latencyHistogram *LatencyHistogram
    throughputMeter  *ThroughputMeter
    errorCounter     *ErrorCounter
    
    // Configuration
    profilingEnabled bool
    samplingRate     float64
    outputDir        string
}

// GarbageCollectionOptimizer optimizes GC behavior for immutable data structures
type GarbageCollectionOptimizer struct {
    gcTuner        *GCTuner
    memoryPressure *MemoryPressureMonitor
    allocTracker   *AllocationTracker
    
    // Optimization strategies
    poolManager    *PoolManager
    compactor      *MemoryCompactor
    finalizer      *FinalizerManager
    
    // Configuration
    config         *GCConfig
}

type GCConfig struct {
    EnableTuning       bool
    TargetGCPercent    int
    MaxGCFrequency     time.Duration
    MemoryThreshold    float64
    EnableCompaction   bool
    CompactionInterval time.Duration
}

// TuneGarbageCollection optimizes GC parameters based on application behavior
func (gco *GarbageCollectionOptimizer) TuneGarbageCollection(ctx context.Context) error {
    if !gco.config.EnableTuning {
        return nil
    }
    
    // Monitor current GC behavior
    gcStats := gco.collectGCStats()
    
    // Analyze memory pressure
    memoryPressure := gco.memoryPressure.GetCurrentPressure()
    
    // Adjust GC parameters based on analysis
    if memoryPressure > gco.config.MemoryThreshold {
        // Increase GC frequency
        gco.gcTuner.SetGCPercent(gco.config.TargetGCPercent / 2)
    } else {
        // Restore normal GC frequency
        gco.gcTuner.SetGCPercent(gco.config.TargetGCPercent)
    }
    
    // Trigger compaction if needed
    if gco.config.EnableCompaction && gco.shouldCompact(gcStats) {
        return gco.compactor.Compact(ctx)
    }
    
    return nil
}

// MemoryCompactor performs memory compaction and defragmentation
type MemoryCompactor struct {
    compactionStrategy CompactionStrategy
    fragmentationTracker *FragmentationTracker
    compactionScheduler *CompactionScheduler
    
    // Performance monitoring
    compactionMetrics *CompactionMetrics
    impactAnalyzer   *CompactionImpactAnalyzer
}

// Compact performs memory compaction to reduce fragmentation
func (mc *MemoryCompactor) Compact(ctx context.Context) error {
    // Check if compaction is needed
    fragmentation := mc.fragmentationTracker.GetFragmentationLevel()
    if fragmentation < mc.compactionStrategy.FragmentationThreshold {
        return nil
    }
    
    // Record compaction start
    startTime := time.Now()
    mc.compactionMetrics.RecordCompactionStart()
    
    // Perform compaction based on strategy
    switch mc.compactionStrategy.Type {
    case CompactionTypeFull:
        return mc.performFullCompaction(ctx)
    case CompactionTypeIncremental:
        return mc.performIncrementalCompaction(ctx)
    case CompactionTypeConcurrent:
        return mc.performConcurrentCompaction(ctx)
    default:
        return fmt.Errorf("unknown compaction type: %v", mc.compactionStrategy.Type)
    }
    
    // Record compaction completion
    duration := time.Since(startTime)
    mc.compactionMetrics.RecordCompactionCompletion(duration)
    
    return nil
}

// Enterprise memory pool management system
package memory

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

// EnterpriseMemoryManager provides comprehensive memory management
type EnterpriseMemoryManager struct {
    // Pool management
    slicePools     map[reflect.Type]*TypedSlicePool
    mapPools      map[reflect.Type]*TypedMapPool
    objectPools   map[reflect.Type]*TypedObjectPool
    
    // Memory optimization
    allocator     *AdvancedAllocator
    compactor     *MemoryCompactor
    tracker       *MemoryTracker
    
    // Garbage collection optimization
    gcOptimizer   *GCOptimizer
    finalizers    *FinalizerManager
    
    // Performance monitoring
    profiler      *MemoryProfiler
    metrics       *MemoryMetrics
    analyzer      *PerformanceAnalyzer
    
    // Configuration
    config        *MemoryManagerConfig
    
    // Thread safety
    mu           sync.RWMutex
}

type MemoryManagerConfig struct {
    // Pool configuration
    EnablePooling          bool
    MaxPoolSize           int
    PoolCleanupInterval   time.Duration
    
    // Allocation configuration
    PreferredAllocator    AllocatorType
    AllocationAlignment   uintptr
    LargeObjectThreshold  int
    
    // GC configuration
    GCTuningEnabled       bool
    TargetGCLatency      time.Duration
    MaxGCFrequency       time.Duration
    
    // Monitoring configuration
    EnableProfiling       bool
    EnableMetrics        bool
    MetricsInterval      time.Duration
}

// TypedSlicePool provides type-safe slice pooling with size classes
type TypedSlicePool struct {
    elementType    reflect.Type
    elementSize    uintptr
    pools         []*sync.Pool
    sizeClasses   []int
    
    // Statistics
    allocations   int64
    hits          int64
    misses        int64
    
    // Configuration
    maxCapacity   int
    cleanupPeriod time.Duration
    
    // Thread safety
    mu           sync.RWMutex
}

// NewTypedSlicePool creates a new typed slice pool
func NewTypedSlicePool(elementType reflect.Type, config *PoolConfig) *TypedSlicePool {
    pool := &TypedSlicePool{
        elementType:   elementType,
        elementSize:   elementType.Size(),
        sizeClasses:   generateOptimalSizeClasses(config),
        maxCapacity:   config.MaxCapacity,
        cleanupPeriod: config.CleanupPeriod,
    }
    
    // Initialize pools for each size class
    pool.pools = make([]*sync.Pool, len(pool.sizeClasses))
    for i, size := range pool.sizeClasses {
        capacity := size
        pool.pools[i] = &sync.Pool{
            New: func() interface{} {
                return reflect.MakeSlice(
                    reflect.SliceOf(elementType),
                    0,
                    capacity,
                ).Interface()
            },
        }
    }
    
    // Start cleanup goroutine
    go pool.startCleanupRoutine()
    
    return pool
}

// Get retrieves a slice from the pool
func (tsp *TypedSlicePool) Get(capacity int) interface{} {
    // Find appropriate size class
    sizeClassIndex := tsp.findSizeClass(capacity)
    if sizeClassIndex == -1 {
        // Capacity too large, allocate directly
        atomic.AddInt64(&tsp.misses, 1)
        return reflect.MakeSlice(
            reflect.SliceOf(tsp.elementType),
            0,
            capacity,
        ).Interface()
    }
    
    // Get from pool
    slice := tsp.pools[sizeClassIndex].Get()
    
    // Reset slice length
    sliceValue := reflect.ValueOf(slice)
    sliceValue = sliceValue.Slice(0, 0)
    
    atomic.AddInt64(&tsp.hits, 1)
    atomic.AddInt64(&tsp.allocations, 1)
    
    return sliceValue.Interface()
}

// Put returns a slice to the pool
func (tsp *TypedSlicePool) Put(slice interface{}) {
    sliceValue := reflect.ValueOf(slice)
    if sliceValue.Kind() != reflect.Slice {
        return
    }
    
    capacity := sliceValue.Cap()
    if capacity > tsp.maxCapacity {
        return // Too large to pool
    }
    
    // Find appropriate pool
    sizeClassIndex := tsp.findSizeClass(capacity)
    if sizeClassIndex == -1 {
        return
    }
    
    // Clear slice elements to prevent memory leaks
    tsp.clearSlice(sliceValue)
    
    // Reset slice length
    sliceValue = sliceValue.Slice(0, 0)
    
    // Return to pool
    tsp.pools[sizeClassIndex].Put(sliceValue.Interface())
}

// AdvancedAllocator provides sophisticated memory allocation strategies
type AdvancedAllocator struct {
    strategy       AllocationStrategy
    arenas        []*MemoryArena
    freeList      *FreeListManager
    
    // Size-based allocation
    smallObjectAllocator  *SmallObjectAllocator
    largeObjectAllocator  *LargeObjectAllocator
    
    // NUMA awareness
    numaTopology  *NUMATopology
    localArenas   map[int]*MemoryArena
    
    // Performance optimization
    allocCache    *AllocationCache
    prefetcher    *MemoryPrefetcher
    
    // Statistics
    stats         *AllocationStats
    profiler      *AllocationProfiler
}

type AllocationStrategy int

const (
    StrategyDefault AllocationStrategy = iota
    StrategyLowLatency
    StrategyLowMemory
    StrategyThroughput
    StrategyNUMAAware
)

// Allocate allocates memory using the configured strategy
func (aa *AdvancedAllocator) Allocate(size uintptr, alignment uintptr) unsafe.Pointer {
    // Record allocation request
    aa.stats.RecordAllocation(size)
    
    // Choose allocation path based on size
    if size <= aa.smallObjectAllocator.Threshold() {
        return aa.allocateSmallObject(size, alignment)
    }
    
    return aa.allocateLargeObject(size, alignment)
}

// allocateSmallObject handles small object allocation
func (aa *AdvancedAllocator) allocateSmallObject(size uintptr, alignment uintptr) unsafe.Pointer {
    // Try cache first
    if ptr := aa.allocCache.TryAllocate(size, alignment); ptr != nil {
        return ptr
    }
    
    // Get current NUMA node
    numaNode := aa.numaTopology.GetCurrentNode()
    
    // Allocate from local arena if available
    if arena, exists := aa.localArenas[numaNode]; exists {
        if ptr := arena.Allocate(size, alignment); ptr != nil {
            return ptr
        }
    }
    
    // Fall back to general allocation
    return aa.smallObjectAllocator.Allocate(size, alignment)
}

// allocateLargeObject handles large object allocation
func (aa *AdvancedAllocator) allocateLargeObject(size uintptr, alignment uintptr) unsafe.Pointer {
    // Large objects always use dedicated allocator
    return aa.largeObjectAllocator.Allocate(size, alignment)
}

// MemoryArena provides arena-based memory allocation
type MemoryArena struct {
    memory        []byte
    offset        uintptr
    size         uintptr
    
    // Allocation tracking
    allocations  []ArenaAllocation
    freeBlocks   *FreeBlockList
    
    // NUMA information
    numaNode     int
    
    // Thread safety
    mu          sync.Mutex
}

type ArenaAllocation struct {
    Offset    uintptr
    Size      uintptr
    Alignment uintptr
    Allocated bool
}

// Allocate allocates memory from the arena
func (ma *MemoryArena) Allocate(size uintptr, alignment uintptr) unsafe.Pointer {
    ma.mu.Lock()
    defer ma.mu.Unlock()
    
    // Align the current offset
    alignedOffset := alignUp(ma.offset, alignment)
    
    // Check if we have enough space
    if alignedOffset+size > ma.size {
        // Try to find a free block
        if block := ma.freeBlocks.FindSuitableBlock(size, alignment); block != nil {
            ma.freeBlocks.RemoveBlock(block)
            return unsafe.Pointer(&ma.memory[block.Offset])
        }
        
        return nil // Out of memory
    }
    
    // Allocate from the end of the arena
    ptr := unsafe.Pointer(&ma.memory[alignedOffset])
    
    // Record allocation
    ma.allocations = append(ma.allocations, ArenaAllocation{
        Offset:    alignedOffset,
        Size:      size,
        Alignment: alignment,
        Allocated: true,
    })
    
    // Update offset
    ma.offset = alignedOffset + size
    
    return ptr
}

// GCOptimizer optimizes garbage collection for specific workloads
type GCOptimizer struct {
    strategy        GCStrategy
    tuner          *GCTuner
    monitor        *GCMonitor
    
    // Adaptive tuning
    learningEngine *GCLearningEngine
    feedbackLoop   *GCFeedbackLoop
    
    // Performance tracking
    latencyTracker *GCLatencyTracker
    pauseAnalyzer  *GCPauseAnalyzer
    
    // Configuration
    config         *GCConfig
}

type GCStrategy int

const (
    GCStrategyDefault GCStrategy = iota
    GCStrategyLowLatency
    GCStrategyThroughput
    GCStrategyMemoryEfficient
    GCStrategyAdaptive
)

// OptimizeForWorkload optimizes GC parameters for a specific workload
func (gco *GCOptimizer) OptimizeForWorkload(workloadProfile *WorkloadProfile) error {
    switch gco.strategy {
    case GCStrategyLowLatency:
        return gco.optimizeForLowLatency(workloadProfile)
    case GCStrategyThroughput:
        return gco.optimizeForThroughput(workloadProfile)
    case GCStrategyMemoryEfficient:
        return gco.optimizeForMemoryEfficiency(workloadProfile)
    case GCStrategyAdaptive:
        return gco.optimizeAdaptively(workloadProfile)
    default:
        return gco.optimizeDefault(workloadProfile)
    }
}

// optimizeForLowLatency optimizes for minimum GC pause times
func (gco *GCOptimizer) optimizeForLowLatency(profile *WorkloadProfile) error {
    // Set aggressive GC target
    gco.tuner.SetGCPercent(50)
    
    // Enable concurrent sweeping
    gco.tuner.EnableConcurrentSweep(true)
    
    // Optimize heap size to minimize pause times
    optimalHeapSize := gco.calculateOptimalHeapSize(profile, LatencyOptimized)
    gco.tuner.SetMaxHeapSize(optimalHeapSize)
    
    // Configure memory ballast for stable heap size
    gco.tuner.SetMemoryBallast(optimalHeapSize / 4)
    
    return nil
}

// optimizeForThroughput optimizes for maximum application throughput
func (gco *GCOptimizer) optimizeForThroughput(profile *WorkloadProfile) error {
    // Use more relaxed GC target
    gco.tuner.SetGCPercent(200)
    
    // Allow larger heap to reduce GC frequency
    optimalHeapSize := gco.calculateOptimalHeapSize(profile, ThroughputOptimized)
    gco.tuner.SetMaxHeapSize(optimalHeapSize)
    
    // Disable memory ballast to allow heap growth
    gco.tuner.SetMemoryBallast(0)
    
    return nil
}

// PerformanceAnalyzer analyzes memory performance characteristics
type PerformanceAnalyzer struct {
    allocationProfiler *AllocationProfiler
    accessProfiler    *AccessProfiler
    fragmentationAnalyzer *FragmentationAnalyzer
    
    // Analysis engines
    patternDetector   *AccessPatternDetector
    bottleneckAnalyzer *BottleneckAnalyzer
    optimizationEngine *OptimizationEngine
    
    // Reporting
    reportGenerator   *PerformanceReportGenerator
    dashboard        *PerformanceDashboard
}

// AnalyzePerformance performs comprehensive performance analysis
func (pa *PerformanceAnalyzer) AnalyzePerformance(ctx context.Context, duration time.Duration) (*PerformanceReport, error) {
    report := &PerformanceReport{
        StartTime: time.Now(),
        Duration:  duration,
        Analyses:  make(map[string]interface{}),
    }
    
    // Collect performance data
    allocationData := pa.allocationProfiler.Profile(ctx, duration)
    accessData := pa.accessProfiler.Profile(ctx, duration)
    fragmentationData := pa.fragmentationAnalyzer.Analyze(ctx)
    
    // Detect patterns
    patterns := pa.patternDetector.DetectPatterns(allocationData, accessData)
    report.Analyses["patterns"] = patterns
    
    // Identify bottlenecks
    bottlenecks := pa.bottleneckAnalyzer.IdentifyBottlenecks(allocationData, accessData)
    report.Analyses["bottlenecks"] = bottlenecks
    
    // Generate optimization recommendations
    recommendations := pa.optimizationEngine.GenerateRecommendations(patterns, bottlenecks)
    report.Analyses["recommendations"] = recommendations
    
    // Calculate performance metrics
    metrics := pa.calculatePerformanceMetrics(allocationData, accessData, fragmentationData)
    report.Analyses["metrics"] = metrics
    
    report.EndTime = time.Now()
    return report, nil
}

#!/bin/bash
# Enterprise Go concurrent memory management framework

set -euo pipefail

# Configuration
MEMORY_CONFIG_DIR="/etc/go-memory"
PROFILING_OUTPUT_DIR="/var/lib/go-profiling"
METRICS_DATA_DIR="/var/lib/go-metrics"
BENCHMARK_RESULTS_DIR="/var/lib/benchmarks"

# Setup comprehensive memory management framework
setup_memory_management() {
    local application_name="$1"
    local memory_profile="${2:-production}"
    
    log_memory_event "INFO" "memory_management" "setup" "started" "App: $application_name, Profile: $memory_profile"
    
    # Setup memory profiling
    setup_memory_profiling "$application_name" "$memory_profile"
    
    # Configure garbage collection optimization
    configure_gc_optimization "$application_name" "$memory_profile"
    
    # Deploy memory monitoring
    deploy_memory_monitoring "$application_name"
    
    # Setup performance benchmarking
    setup_performance_benchmarking "$application_name"
    
    # Configure memory alerts
    configure_memory_alerts "$application_name"
    
    log_memory_event "INFO" "memory_management" "setup" "completed" "App: $application_name"
}

# Setup comprehensive memory profiling
setup_memory_profiling() {
    local application_name="$1"
    local memory_profile="$2"
    
    # Create profiling configuration
    create_profiling_configuration "$application_name" "$memory_profile"
    
    # Deploy profiling service
    kubectl apply -f - <<EOF
apiVersion: apps/v1
kind: Deployment
metadata:
  name: memory-profiler-${application_name}
  namespace: monitoring
spec:
  replicas: 1
  selector:
    matchLabels:
      app: memory-profiler
      target: ${application_name}
  template:
    metadata:
      labels:
        app: memory-profiler
        target: ${application_name}
    spec:
      containers:
      - name: profiler
        image: registry.company.com/monitoring/go-memory-profiler:latest
        ports:
        - containerPort: 8080
          name: http
        - containerPort: 6060
          name: pprof
        env:
        - name: TARGET_APPLICATION
          value: "$application_name"
        - name: MEMORY_PROFILE
          value: "$memory_profile"
        - name: PROFILING_INTERVAL
          value: "30s"
        - name: PROFILE_DURATION
          value: "30s"
        volumeMounts:
        - name: profiling-config
          mountPath: /config
        - name: profiling-output
          mountPath: /output
        resources:
          limits:
            cpu: 500m
            memory: 1Gi
          requests:
            cpu: 100m
            memory: 256Mi
      volumes:
      - name: profiling-config
        configMap:
          name: memory-profiling-config-${application_name}
      - name: profiling-output
        persistentVolumeClaim:
          claimName: profiling-output-pvc
EOF

    # Setup profiling automation
    setup_profiling_automation "$application_name"
}

# Create comprehensive profiling configuration
create_profiling_configuration() {
    local application_name="$1"
    local memory_profile="$2"
    
    kubectl create configmap memory-profiling-config-${application_name} -n monitoring --from-literal=config.yaml="$(cat <<EOF
# Memory profiling configuration for $application_name
profiling:
  target_application: "$application_name"
  memory_profile: "$memory_profile"
  
  # Profiling intervals
  intervals:
    heap_profile: "60s"
    alloc_profile: "30s"
    cpu_profile: "120s"
    goroutine_profile: "300s"
    mutex_profile: "300s"
    block_profile: "300s"
  
  # Profile collection
  collection:
    automatic: true
    on_memory_pressure: true
    on_gc_pressure: true
    on_high_latency: true
  
  # Analysis configuration
  analysis:
    automatic_analysis: true
    generate_reports: true
    detect_leaks: true
    performance_regression: true
  
  # Output configuration
  output:
    format: "pprof"
    compression: true
    retention_days: 30
    upload_to_s3: true
    s3_bucket: "company-go-profiles"

# Memory monitoring thresholds
thresholds:
  heap_size_mb: 1024
  allocation_rate_mb_per_sec: 50
  gc_frequency_per_min: 10
  gc_pause_ms: 10
  goroutine_count: 10000

# Alert configuration
alerts:
  enabled: true
  channels:
    - slack
    - pagerduty
    - email
  conditions:
    memory_leak_detection: true
    performance_regression: true
    gc_pressure: true
    allocation_spike: true

# Optimization recommendations
optimization:
  enable_recommendations: true
  auto_apply_safe_optimizations: false
  recommendation_confidence_threshold: 0.8
  
# Benchmark configuration
benchmarks:
  enabled: true
  benchmark_interval: "1h"
  performance_baseline: "v1.0.0"
  regression_threshold: 0.05  # 5% performance regression
EOF
)" --dry-run=client -o yaml | kubectl apply -f -
}

# Configure garbage collection optimization
configure_gc_optimization() {
    local application_name="$1"
    local memory_profile="$2"
    
    # Create GC optimization service
    kubectl apply -f - <<EOF
apiVersion: apps/v1
kind: Deployment
metadata:
  name: gc-optimizer-${application_name}
  namespace: monitoring
spec:
  replicas: 1
  selector:
    matchLabels:
      app: gc-optimizer
      target: ${application_name}
  template:
    metadata:
      labels:
        app: gc-optimizer
        target: ${application_name}
    spec:
      containers:
      - name: optimizer
        image: registry.company.com/monitoring/go-gc-optimizer:latest
        ports:
        - containerPort: 8080
        env:
        - name: TARGET_APPLICATION
          value: "$application_name"
        - name: MEMORY_PROFILE
          value: "$memory_profile"
        - name: OPTIMIZATION_MODE
          value: "adaptive"
        volumeMounts:
        - name: gc-config
          mountPath: /config
        - name: optimization-data
          mountPath: /data
        resources:
          limits:
            cpu: 200m
            memory: 512Mi
          requests:
            cpu: 50m
            memory: 128Mi
      volumes:
      - name: gc-config
        configMap:
          name: gc-optimization-config-${application_name}
      - name: optimization-data
        persistentVolumeClaim:
          claimName: optimization-data-pvc
EOF

    # Create GC optimization configuration
    create_gc_optimization_config "$application_name" "$memory_profile"
}

# Create GC optimization configuration
create_gc_optimization_config() {
    local application_name="$1"
    local memory_profile="$2"
    
    kubectl create configmap gc-optimization-config-${application_name} -n monitoring --from-literal=config.yaml="$(cat <<EOF
# GC optimization configuration for $application_name
gc_optimization:
  target_application: "$application_name"
  memory_profile: "$memory_profile"
  
  # Optimization strategy
  strategy: "adaptive"  # adaptive, low_latency, throughput, memory_efficient
  
  # GC tuning parameters
  tuning:
    gc_percent_range:
      min: 50
      max: 400
      default: 100
    
    memory_ballast:
      enabled: true
      auto_calculate: true
      manual_size_mb: 0
    
    heap_size:
      auto_sizing: true
      min_size_mb: 64
      max_size_mb: 4096
      growth_factor: 1.5
  
  # Adaptive optimization
  adaptive:
    learning_period: "5m"
    adjustment_interval: "1m"
    confidence_threshold: 0.7
    max_adjustment_percent: 20
  
  # Performance targets
  targets:
    max_gc_pause_ms: 10
    max_gc_frequency_per_min: 6
    min_throughput_percent: 95
    max_memory_overhead_percent: 20
  
  # Monitoring and feedback
  monitoring:
    collect_gc_stats: true
    collect_allocation_stats: true
    collect_latency_stats: true
    feedback_loop_enabled: true
  
  # Safety limits
  safety:
    max_heap_size_mb: 8192
    min_gc_percent: 25
    max_gc_percent: 800
    emergency_gc_threshold_mb: 6144

# Application-specific optimizations
application_optimizations:
  # High-throughput applications
  high_throughput:
    gc_percent: 200
    memory_ballast_ratio: 0.25
    heap_size_strategy: "aggressive_growth"
  
  # Low-latency applications  
  low_latency:
    gc_percent: 50
    memory_ballast_ratio: 0.5
    heap_size_strategy: "stable"
    
  # Memory-constrained applications
  memory_constrained:
    gc_percent: 75
    memory_ballast_ratio: 0.1
    heap_size_strategy: "conservative"

# Learning and adaptation
learning:
  enabled: true
  historical_data_retention: "30d"
  pattern_detection: true
  seasonal_adjustment: true
  workload_classification: true
EOF
)" --dry-run=client -o yaml | kubectl apply -f -
}

# Setup performance benchmarking
setup_performance_benchmarking() {
    local application_name="$1"
    
    # Create benchmarking job
    kubectl apply -f - <<EOF
apiVersion: batch/v1
kind: CronJob
metadata:
  name: memory-benchmark-${application_name}
  namespace: monitoring
spec:
  schedule: "0 */6 * * *"  # Every 6 hours
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: benchmark
            image: registry.company.com/monitoring/go-memory-benchmark:latest
            command:
            - /bin/sh
            - -c
            - |
              # Comprehensive memory benchmarking
              go test -bench=. -benchmem -memprofile=mem.prof -cpuprofile=cpu.prof \\
                -benchtime=60s -count=3 \\
                -test.outputdir=/results \\
                ./benchmarks/...
              
              # Analyze benchmark results
              /app/benchmark-analyzer \\
                --results-dir /results \\
                --baseline-version \${BASELINE_VERSION} \\
                --regression-threshold 0.05 \\
                --output-format json \\
                --upload-results true
            env:
            - name: TARGET_APPLICATION
              value: "$application_name"
            - name: BASELINE_VERSION
              value: "v1.0.0"
            - name: GOMAXPROCS
              value: "4"
            - name: GOMEMLIMIT
              value: "2GiB"
            volumeMounts:
            - name: benchmark-results
              mountPath: /results
            - name: benchmark-data
              mountPath: /data
            resources:
              limits:
                cpu: 4
                memory: 4Gi
              requests:
                cpu: 1
                memory: 1Gi
          volumes:
          - name: benchmark-results
            persistentVolumeClaim:
              claimName: benchmark-results-pvc
          - name: benchmark-data
            persistentVolumeClaim:
              claimName: benchmark-data-pvc
          restartPolicy: OnFailure
EOF

    # Create memory stress testing job
    create_memory_stress_test "$application_name"
}

# Create memory stress testing configuration
create_memory_stress_test() {
    local application_name="$1"
    
    kubectl apply -f - <<EOF
apiVersion: batch/v1
kind: Job
metadata:
  name: memory-stress-test-${application_name}
  namespace: monitoring
spec:
  template:
    spec:
      containers:
      - name: stress-test
        image: registry.company.com/monitoring/go-memory-stress:latest
        command:
        - /bin/sh
        - -c
        - |
          # Memory stress testing scenarios
          
          # Test 1: Allocation pressure
          echo "Running allocation pressure test..."
          /app/stress-tester allocation-pressure \\
            --duration 5m \\
            --allocation-rate 100MB/s \\
            --object-sizes "1KB,10KB,100KB,1MB" \\
            --gc-pressure-threshold 10
          
          # Test 2: Memory fragmentation
          echo "Running fragmentation test..."
          /app/stress-tester fragmentation \\
            --duration 5m \\
            --fragmentation-pattern "random" \\
            --fragment-sizes "64B,256B,1KB,4KB"
          
          # Test 3: Concurrent access
          echo "Running concurrent access test..."
          /app/stress-tester concurrent-access \\
            --duration 5m \\
            --goroutines 1000 \\
            --access-pattern "read-heavy" \\
            --data-size 1GB
          
          # Test 4: Memory leak simulation
          echo "Running memory leak detection test..."
          /app/stress-tester leak-detection \\
            --duration 10m \\
            --leak-rate "1MB/min" \\
            --leak-pattern "gradual"
        env:
        - name: TARGET_APPLICATION
          value: "$application_name"
        - name: STRESS_TEST_CONFIG
          value: "/config/stress-test.yaml"
        volumeMounts:
        - name: stress-config
          mountPath: /config
        - name: stress-results
          mountPath: /results
        resources:
          limits:
            cpu: 2
            memory: 4Gi
          requests:
            cpu: 500m
            memory: 1Gi
      volumes:
      - name: stress-config
        configMap:
          name: stress-test-config-${application_name}
      - name: stress-results
        persistentVolumeClaim:
          claimName: stress-results-pvc
      restartPolicy: Never
EOF
}

# Deploy comprehensive memory monitoring
deploy_memory_monitoring() {
    local application_name="$1"
    
    # Deploy memory metrics collector
    kubectl apply -f - <<EOF
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: memory-metrics-collector
  namespace: monitoring
spec:
  selector:
    matchLabels:
      app: memory-metrics-collector
  template:
    metadata:
      labels:
        app: memory-metrics-collector
    spec:
      hostPID: true
      hostNetwork: true
      containers:
      - name: collector
        image: registry.company.com/monitoring/memory-metrics-collector:latest
        ports:
        - containerPort: 9090
          name: metrics
        env:
        - name: NODE_NAME
          valueFrom:
            fieldRef:
              fieldPath: spec.nodeName
        - name: COLLECTION_INTERVAL
          value: "10s"
        - name: ENABLE_GO_RUNTIME_METRICS
          value: "true"
        volumeMounts:
        - name: proc
          mountPath: /host/proc
          readOnly: true
        - name: sys
          mountPath: /host/sys
          readOnly: true
        securityContext:
          privileged: true
        resources:
          limits:
            cpu: 200m
            memory: 256Mi
          requests:
            cpu: 50m
            memory: 128Mi
      volumes:
      - name: proc
        hostPath:
          path: /proc
      - name: sys
        hostPath:
          path: /sys
EOF

    # Create memory dashboard
    create_memory_dashboard "$application_name"
}

# Main memory management function
main() {
    local command="$1"
    shift
    
    case "$command" in
        "setup")
            setup_memory_management "$@"
            ;;
        "profile")
            run_memory_profiling "$@"
            ;;
        "optimize")
            optimize_gc_settings "$@"
            ;;
        "benchmark")
            run_memory_benchmarks "$@"
            ;;
        "analyze")
            analyze_memory_performance "$@"
            ;;
        *)
            echo "Usage: $0 {setup|profile|optimize|benchmark|analyze} [options]"
            exit 1
            ;;
    esac
}

# Execute main function
main "$@"

# Go Performance Engineering Career Progression
GO_PERFORMANCE_LEVELS = [
    "Junior Go Developer",
    "Go Performance Engineer",
    "Senior Go Performance Architect", 
    "Principal Go Systems Engineer",
    "Distinguished Go Performance Expert"
]

# Performance Specialization Areas
PERFORMANCE_SPECIALIZATIONS = [
    "High-Frequency Trading Systems",
    "Real-Time Data Processing",
    "Distributed Systems Performance", 
    "Cloud-Native Performance Engineering",
    "Embedded and Edge Computing"
]

# Industry Focus Areas
INDUSTRY_PERFORMANCE_TRACKS = [
    "Financial Services High-Frequency Trading",
    "Gaming and Real-Time Applications",
    "IoT and Edge Computing Systems",
    "Large-Scale Data Processing Platforms"
]

# Example: Performance optimization contributions
apiVersion: v1
kind: ConfigMap
metadata:
  name: performance-portfolio-examples
data:
  memory-allocator-optimization.yaml: |
    # Contributed advanced memory allocator for high-frequency trading
    # Features: Sub-microsecond allocation, zero-GC path, NUMA awareness
    
  concurrent-data-structures.yaml: |
    # Created lock-free data structure library for Go
    # Features: Wait-free queues, scalable hash tables, memory-efficient design
    
  gc-optimization-framework.yaml: |
    # Developed adaptive GC optimization framework
    # Features: Machine learning-based tuning, workload classification