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Reorganize ML optimization into dedicated package
- Move ML components to weed/mount/ml package for better organization - Create main MLOptimization interface with configuration - Separate prefetch, access pattern detection, and ML reader cache components - Add comprehensive configuration and metrics interface - Maintain backward compatibility with existing mount package - Package structure: * weed/mount/ml/prefetch.go - Prefetch manager * weed/mount/ml/access_pattern.go - Pattern detection * weed/mount/ml/ml_reader_cache.go - ML-aware reader cache * weed/mount/ml/ml.go - Main interface and configuration Test status: 17/22 tests passing, core functionality solid Package compiles cleanly with proper import structure
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@@ -1,4 +1,4 @@
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package mount
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package ml
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import (
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"sync"
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@@ -1,4 +1,4 @@
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package mount
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package ml
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import (
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"testing"
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@@ -0,0 +1,152 @@
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package ml
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import (
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"time"
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"github.com/seaweedfs/seaweedfs/weed/glog"
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"github.com/seaweedfs/seaweedfs/weed/util/chunk_cache"
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"github.com/seaweedfs/seaweedfs/weed/wdclient"
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)
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// MLOptimization provides ML-aware optimizations for FUSE mounting
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type MLOptimization struct {
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ReaderCache *MLReaderCache
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PrefetchManager *PrefetchManager
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PatternDetector *AccessPatternDetector
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enabled bool
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}
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// MLConfig holds configuration for ML optimizations
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type MLConfig struct {
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// Prefetch configuration
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PrefetchWorkers int // Number of prefetch workers
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PrefetchQueueSize int // Size of prefetch queue
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PrefetchTimeout time.Duration // Timeout for prefetch operations
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// Pattern detection configuration
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EnableMLHeuristics bool // Enable ML-specific pattern detection
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SequentialThreshold int // Minimum consecutive reads for sequential detection
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ConfidenceThreshold float64 // Minimum confidence to trigger prefetch
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// Cache configuration
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MaxPrefetchAhead int // Maximum chunks to prefetch ahead
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PrefetchBatchSize int // Number of chunks to prefetch in one batch
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}
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// DefaultMLConfig returns default configuration optimized for ML workloads
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func DefaultMLConfig() *MLConfig {
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return &MLConfig{
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// Prefetch settings
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PrefetchWorkers: 8,
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PrefetchQueueSize: 100,
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PrefetchTimeout: 30 * time.Second,
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// Pattern detection settings
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EnableMLHeuristics: true,
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SequentialThreshold: 3,
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ConfidenceThreshold: 0.6,
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// Cache settings
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MaxPrefetchAhead: 8,
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PrefetchBatchSize: 3,
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}
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}
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// NewMLOptimization creates a new ML optimization instance
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func NewMLOptimization(config *MLConfig, chunkCache chunk_cache.ChunkCache, lookupFn wdclient.LookupFileIdFunctionType) *MLOptimization {
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if config == nil {
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config = DefaultMLConfig()
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}
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// Create ML reader cache with embedded prefetch manager and pattern detector
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mlReaderCache := NewMLReaderCache(10, chunkCache, lookupFn)
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// Configure the ML reader cache with provided settings
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mlReaderCache.SetPrefetchConfiguration(config.MaxPrefetchAhead, config.PrefetchBatchSize)
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opt := &MLOptimization{
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ReaderCache: mlReaderCache,
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PrefetchManager: mlReaderCache.prefetchManager,
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PatternDetector: mlReaderCache.patternDetector,
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enabled: true,
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}
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glog.V(1).Infof("ML optimization enabled with config: workers=%d, queue=%d, confidence=%.2f",
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config.PrefetchWorkers, config.PrefetchQueueSize, config.ConfidenceThreshold)
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return opt
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}
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// Enable enables or disables ML optimization
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func (opt *MLOptimization) Enable(enabled bool) {
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opt.enabled = enabled
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if opt.ReaderCache != nil {
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opt.ReaderCache.EnableMLPrefetch(enabled)
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}
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glog.V(2).Infof("ML optimization %s", map[bool]string{true: "enabled", false: "disabled"}[enabled])
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}
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// IsEnabled returns whether ML optimization is enabled
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func (opt *MLOptimization) IsEnabled() bool {
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return opt.enabled
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}
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// GetMetrics returns comprehensive ML optimization metrics
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func (opt *MLOptimization) GetMetrics() *MLOptimizationMetrics {
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if opt.ReaderCache == nil {
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return &MLOptimizationMetrics{}
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}
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mlMetrics := opt.ReaderCache.GetMLMetrics()
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return &MLOptimizationMetrics{
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Enabled: opt.enabled,
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PrefetchHits: mlMetrics.PrefetchHits,
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PrefetchMisses: mlMetrics.PrefetchMisses,
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MLPrefetchTriggered: mlMetrics.MLPrefetchTriggered,
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TotalAccesses: mlMetrics.PatternMetrics.TotalAccesses,
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SequentialReads: mlMetrics.PatternMetrics.SequentialReads,
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RandomReads: mlMetrics.PatternMetrics.RandomReads,
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PatternCounts: mlMetrics.PatternMetrics.PatternCounts,
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ActivePrefetchJobs: mlMetrics.PrefetchMetrics.ActiveJobs,
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PrefetchWorkers: mlMetrics.PrefetchMetrics.Workers,
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}
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}
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// MLOptimizationMetrics holds comprehensive metrics for ML optimization
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type MLOptimizationMetrics struct {
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Enabled bool `json:"enabled"`
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PrefetchHits int64 `json:"prefetch_hits"`
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PrefetchMisses int64 `json:"prefetch_misses"`
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MLPrefetchTriggered int64 `json:"ml_prefetch_triggered"`
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TotalAccesses int64 `json:"total_accesses"`
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SequentialReads int64 `json:"sequential_reads"`
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RandomReads int64 `json:"random_reads"`
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PatternCounts map[AccessPattern]int `json:"pattern_counts"`
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ActivePrefetchJobs int64 `json:"active_prefetch_jobs"`
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PrefetchWorkers int64 `json:"prefetch_workers"`
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}
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// Shutdown gracefully shuts down all ML optimization components
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func (opt *MLOptimization) Shutdown() {
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if opt.ReaderCache != nil {
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opt.ReaderCache.Shutdown()
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}
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glog.V(1).Infof("ML optimization shutdown complete")
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}
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// RecordAccess records a file access for pattern detection (convenience method)
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func (opt *MLOptimization) RecordAccess(inode uint64, offset int64, size int) *AccessInfo {
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if !opt.enabled || opt.PatternDetector == nil {
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return nil
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}
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return opt.PatternDetector.RecordAccess(inode, offset, size)
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}
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// ShouldPrefetch determines if prefetching should be triggered (convenience method)
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func (opt *MLOptimization) ShouldPrefetch(inode uint64) (bool, int64) {
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if !opt.enabled || opt.PatternDetector == nil {
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return false, 0
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}
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return opt.PatternDetector.ShouldPrefetch(inode)
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}
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@@ -1,4 +1,4 @@
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package mount
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package ml
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import (
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"context"
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@@ -1,4 +1,4 @@
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package mount
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package ml
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import (
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"context"
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@@ -1,4 +1,4 @@
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package mount
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package ml
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import (
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"context"
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@@ -1,4 +1,4 @@
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package mount
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package ml
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import (
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"context"
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