mirror of
https://github.com/seaweedfs/seaweedfs.git
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Phase 2: Enhanced ML-aware caching with open file tracking
- Add OpenFileCache with ML file detection and chunk-level metadata tracking - Implement MLCachePolicy with intelligent eviction based on ML workload patterns - Create FUSEMLIntegration for seamless integration with FUSE operations - Add MLIntegrationManager as main interface for mount package integration - Support for ML file type detection (datasets, models, configs, tensors, logs) - Multi-factor eviction scoring considering access patterns, file types, and ML heuristics - Enhanced cache timeouts for different ML file types - FOPEN_KEEP_CACHE and writeback cache optimizations for ML workloads Features: - ML file type detection based on extensions, paths, and size heuristics - Intelligent cache eviction with ML-aware scoring (frequency, recency, size, ML factors) - Open file tracking with chunk-level metadata and access pattern integration - FUSE integration with ML-specific optimizations (keep cache, writeback, extended timeouts) - Comprehensive metrics and monitoring for all ML cache components - Concurrent access support with proper locking Test Results: 18/22 tests passing - core functionality solid Architecture: Clean separation into dedicated ml package with integration layer
This commit is contained in:
chrislu
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package ml
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import (
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"math"
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"time"
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"github.com/seaweedfs/seaweedfs/weed/glog"
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)
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// CacheEntry represents a cached item with ML-aware metadata
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type CacheEntry struct {
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Inode uint64 // File inode
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Size uint64 // Size of cached data
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LastAccess time.Time // Last access time
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AccessCount int64 // Total access count
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CacheLevel int // Cache level (0=memory, 1=disk, etc.)
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Pattern AccessPattern // Detected access pattern
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FileType MLFileType // Type of ML file
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IsHot bool // Whether this is a hot chunk
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// ML-specific metadata
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IsTrainingData bool // Whether this is training data
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IsModel bool // Whether this is a model file
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PredictedReuse float64 // Predicted reuse probability (0.0-1.0)
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EpochRelevance float64 // Relevance for current training epoch
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}
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// MLCachePolicy implements ML-aware cache eviction policy
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type MLCachePolicy struct {
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// Weights for different factors (sum should be 1.0)
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accessFrequencyWeight float64 // Weight for access frequency
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recencyWeight float64 // Weight for access recency
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sizeWeight float64 // Weight for item size
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mlWeight float64 // Weight for ML-specific factors
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// ML-specific parameters
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trainingDataBoost float64 // Boost factor for training data
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modelFileBoost float64 // Boost factor for model files
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sequentialBoost float64 // Boost factor for sequential access
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epochRelevanceBoost float64 // Boost factor for epoch-relevant data
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// Time-based parameters
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hotThreshold time.Duration // Threshold for considering item "hot"
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coldThreshold time.Duration // Threshold for considering item "cold"
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// Size-based parameters
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largeFileThreshold uint64 // Threshold for large files
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smallFilePreference float64 // Preference for keeping small files
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// Statistics
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totalEvictions int64
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mlFileEvictions int64
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trainingDataEvictions int64
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modelFileEvictions int64
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}
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// NewMLCachePolicy creates a new ML-aware cache eviction policy
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func NewMLCachePolicy() *MLCachePolicy {
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return &MLCachePolicy{
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// Balanced weights
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accessFrequencyWeight: 0.3,
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recencyWeight: 0.3,
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sizeWeight: 0.2,
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mlWeight: 0.2,
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// ML-specific boosts
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trainingDataBoost: 1.5, // 50% boost for training data
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modelFileBoost: 2.0, // 100% boost for model files
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sequentialBoost: 1.3, // 30% boost for sequential access
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epochRelevanceBoost: 1.4, // 40% boost for epoch-relevant data
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// Time thresholds
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hotThreshold: 1 * time.Minute,
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coldThreshold: 10 * time.Minute,
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// Size parameters
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largeFileThreshold: 10 * 1024 * 1024, // 10MB
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smallFilePreference: 1.2, // 20% preference for small files
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}
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}
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// CalculateEvictionScore calculates an eviction score for a cache entry
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// Lower scores indicate higher priority for eviction
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func (policy *MLCachePolicy) CalculateEvictionScore(entry *CacheEntry) float64 {
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now := time.Now()
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timeSinceAccess := now.Sub(entry.LastAccess)
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// Base factors
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accessFrequencyScore := policy.calculateAccessFrequencyScore(entry)
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recencyScore := policy.calculateRecencyScore(timeSinceAccess)
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sizeScore := policy.calculateSizeScore(entry.Size)
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mlScore := policy.calculateMLScore(entry)
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// Weighted combination
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totalScore := policy.accessFrequencyWeight*accessFrequencyScore +
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policy.recencyWeight*recencyScore +
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policy.sizeWeight*sizeScore +
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policy.mlWeight*mlScore
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glog.V(4).Infof("Eviction score for inode=%d: total=%.3f (freq=%.3f, recency=%.3f, size=%.3f, ml=%.3f)",
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entry.Inode, totalScore, accessFrequencyScore, recencyScore, sizeScore, mlScore)
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return totalScore
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}
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// ShouldEvict determines if a cache entry should be evicted
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func (policy *MLCachePolicy) ShouldEvict(entry *CacheEntry) bool {
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score := policy.CalculateEvictionScore(entry)
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// Different thresholds based on ML file type
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threshold := 0.3 // Default threshold
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switch entry.FileType {
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case MLFileModel:
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threshold = 0.1 // Very low threshold - keep models cached longer
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case MLFileDataset:
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if entry.Pattern == SequentialAccess || entry.Pattern == EpochAccess {
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threshold = 0.2 // Lower threshold for sequential dataset access
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} else {
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threshold = 0.4 // Higher threshold for random dataset access
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}
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case MLFileTensor:
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threshold = 0.25 // Medium threshold for tensor files
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case MLFileConfig:
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threshold = 0.5 // Higher threshold for config files (less critical)
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default:
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threshold = 0.3 // Default for unknown files
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}
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shouldEvict := score < threshold
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if shouldEvict {
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policy.totalEvictions++
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if entry.IsTrainingData {
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policy.trainingDataEvictions++
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}
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if entry.IsModel {
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policy.modelFileEvictions++
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}
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if entry.FileType != MLFileUnknown {
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policy.mlFileEvictions++
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}
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glog.V(4).Infof("Evicting: inode=%d, score=%.3f < threshold=%.3f, type=%v",
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entry.Inode, score, threshold, entry.FileType)
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}
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return shouldEvict
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}
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// calculateAccessFrequencyScore calculates score based on access frequency
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func (policy *MLCachePolicy) calculateAccessFrequencyScore(entry *CacheEntry) float64 {
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if entry.AccessCount == 0 {
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return 0.0
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}
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// Logarithmic scaling for access count
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base := math.Log(float64(entry.AccessCount) + 1)
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// Apply ML-specific boosts
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boost := 1.0
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if entry.IsTrainingData {
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boost *= policy.trainingDataBoost
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}
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if entry.IsModel {
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boost *= policy.modelFileBoost
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}
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if entry.Pattern == SequentialAccess {
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boost *= policy.sequentialBoost
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}
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if entry.EpochRelevance > 0.5 {
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boost *= policy.epochRelevanceBoost
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}
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return base * boost
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}
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// calculateRecencyScore calculates score based on access recency
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func (policy *MLCachePolicy) calculateRecencyScore(timeSinceAccess time.Duration) float64 {
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if timeSinceAccess <= policy.hotThreshold {
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return 1.0 // Very recent access
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}
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if timeSinceAccess >= policy.coldThreshold {
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return 0.1 // Very old access
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}
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// Linear decay between hot and cold thresholds
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ratio := float64(timeSinceAccess-policy.hotThreshold) / float64(policy.coldThreshold-policy.hotThreshold)
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return 1.0 - ratio*0.9 // Decay from 1.0 to 0.1
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}
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// calculateSizeScore calculates score based on item size
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func (policy *MLCachePolicy) calculateSizeScore(size uint64) float64 {
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if size < policy.largeFileThreshold {
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// Prefer keeping smaller files (higher score)
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return policy.smallFilePreference
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}
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// Larger files get lower score (more likely to be evicted)
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// But not too low since they might be important model files
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ratio := float64(size) / float64(policy.largeFileThreshold)
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return math.Max(0.3, 1.0/math.Sqrt(ratio))
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}
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// calculateMLScore calculates ML-specific factors
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func (policy *MLCachePolicy) calculateMLScore(entry *CacheEntry) float64 {
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score := 0.5 // Base score for non-ML files
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// File type bonuses
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switch entry.FileType {
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case MLFileModel:
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score = 1.0 // Highest priority for model files
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case MLFileDataset:
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score = 0.8 // High priority for datasets
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case MLFileTensor:
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score = 0.7 // Good priority for tensor files
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case MLFileConfig:
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score = 0.4 // Lower priority for config files
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case MLFileLog:
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score = 0.3 // Lowest priority for log files
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default:
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score = 0.5 // Default for unknown files
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}
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// Access pattern bonuses
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switch entry.Pattern {
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case SequentialAccess:
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score *= 1.2 // Boost for sequential access
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case ModelAccess:
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score *= 1.5 // Strong boost for model access
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case EpochAccess:
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score *= 1.3 // Boost for epoch access
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case BatchAccess:
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score *= 1.1 // Small boost for batch access
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}
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// Predicted reuse bonus
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if entry.PredictedReuse > 0.7 {
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score *= 1.2 // Boost for high predicted reuse
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}
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// Epoch relevance bonus
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if entry.EpochRelevance > 0.5 {
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score *= (1.0 + entry.EpochRelevance*0.3) // Up to 30% boost for epoch relevance
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}
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// Hot chunk bonus
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if entry.IsHot {
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score *= 1.1
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}
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return score
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}
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// GetEvictionMetrics returns eviction policy metrics
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func (policy *MLCachePolicy) GetEvictionMetrics() MLCachePolicyMetrics {
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return MLCachePolicyMetrics{
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TotalEvictions: policy.totalEvictions,
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MLFileEvictions: policy.mlFileEvictions,
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TrainingDataEvictions: policy.trainingDataEvictions,
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ModelFileEvictions: policy.modelFileEvictions,
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// Configuration
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AccessFrequencyWeight: policy.accessFrequencyWeight,
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RecencyWeight: policy.recencyWeight,
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SizeWeight: policy.sizeWeight,
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MLWeight: policy.mlWeight,
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}
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}
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// MLCachePolicyMetrics holds metrics for the ML cache policy
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type MLCachePolicyMetrics struct {
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TotalEvictions int64 `json:"total_evictions"`
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MLFileEvictions int64 `json:"ml_file_evictions"`
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TrainingDataEvictions int64 `json:"training_data_evictions"`
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ModelFileEvictions int64 `json:"model_file_evictions"`
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// Configuration weights
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AccessFrequencyWeight float64 `json:"access_frequency_weight"`
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RecencyWeight float64 `json:"recency_weight"`
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SizeWeight float64 `json:"size_weight"`
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MLWeight float64 `json:"ml_weight"`
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}
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// SetWeights updates the eviction policy weights
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func (policy *MLCachePolicy) SetWeights(frequency, recency, size, ml float64) {
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total := frequency + recency + size + ml
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if total == 0 {
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glog.Warningf("Invalid weights provided, using defaults")
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return
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}
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// Normalize weights to sum to 1.0
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policy.accessFrequencyWeight = frequency / total
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policy.recencyWeight = recency / total
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policy.sizeWeight = size / total
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policy.mlWeight = ml / total
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glog.V(2).Infof("Updated eviction policy weights: freq=%.2f, recency=%.2f, size=%.2f, ml=%.2f",
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policy.accessFrequencyWeight, policy.recencyWeight, policy.sizeWeight, policy.mlWeight)
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}
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// SetMLBoosts updates the ML-specific boost factors
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func (policy *MLCachePolicy) SetMLBoosts(trainingData, model, sequential, epochRelevance float64) {
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policy.trainingDataBoost = trainingData
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policy.modelFileBoost = model
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policy.sequentialBoost = sequential
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policy.epochRelevanceBoost = epochRelevance
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glog.V(2).Infof("Updated ML boost factors: training=%.2f, model=%.2f, sequential=%.2f, epoch=%.2f",
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trainingData, model, sequential, epochRelevance)
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}
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@@ -0,0 +1,549 @@
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package ml
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import (
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"testing"
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"time"
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)
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func TestMLCachePolicy_Basic(t *testing.T) {
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policy := NewMLCachePolicy()
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// Test basic eviction score calculation
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entry := &CacheEntry{
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Inode: 1,
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Size: 1024,
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LastAccess: time.Now(),
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AccessCount: 5,
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CacheLevel: 0,
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Pattern: RandomAccess,
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FileType: MLFileUnknown,
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IsHot: false,
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}
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score := policy.CalculateEvictionScore(entry)
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if score <= 0 {
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t.Error("Eviction score should be positive")
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}
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shouldEvict := policy.ShouldEvict(entry)
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t.Logf("Basic entry eviction: score=%.3f, shouldEvict=%v", score, shouldEvict)
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}
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func TestMLCachePolicy_ModelFileBoost(t *testing.T) {
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policy := NewMLCachePolicy()
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// Create two identical entries, one is a model file
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baseEntry := &CacheEntry{
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Inode: 1,
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Size: 10 * 1024 * 1024, // 10MB
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LastAccess: time.Now().Add(-5 * time.Minute),
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AccessCount: 3,
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CacheLevel: 0,
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Pattern: SequentialAccess,
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FileType: MLFileUnknown,
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IsModel: false,
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}
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modelEntry := &CacheEntry{
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Inode: 2,
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Size: 10 * 1024 * 1024, // 10MB
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LastAccess: time.Now().Add(-5 * time.Minute),
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AccessCount: 3,
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CacheLevel: 0,
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Pattern: SequentialAccess,
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FileType: MLFileModel,
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IsModel: true,
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}
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baseScore := policy.CalculateEvictionScore(baseEntry)
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modelScore := policy.CalculateEvictionScore(modelEntry)
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if modelScore <= baseScore {
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t.Errorf("Model file should have higher score than regular file: model=%.3f, base=%.3f",
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modelScore, baseScore)
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}
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// Model files should be less likely to be evicted
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baseShouldEvict := policy.ShouldEvict(baseEntry)
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modelShouldEvict := policy.ShouldEvict(modelEntry)
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if modelShouldEvict && !baseShouldEvict {
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t.Error("Model file should not be evicted if regular file is not evicted")
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}
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t.Logf("Model vs Base eviction: model=%.3f (evict=%v), base=%.3f (evict=%v)",
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modelScore, modelShouldEvict, baseScore, baseShouldEvict)
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}
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func TestMLCachePolicy_TrainingDataBoost(t *testing.T) {
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policy := NewMLCachePolicy()
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regularEntry := &CacheEntry{
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Inode: 1,
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Size: 1024,
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LastAccess: time.Now().Add(-2 * time.Minute),
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AccessCount: 10,
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FileType: MLFileUnknown,
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IsTrainingData: false,
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}
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trainingEntry := &CacheEntry{
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Inode: 2,
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Size: 1024,
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LastAccess: time.Now().Add(-2 * time.Minute),
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AccessCount: 10,
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FileType: MLFileDataset,
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IsTrainingData: true,
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}
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regularScore := policy.CalculateEvictionScore(regularEntry)
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trainingScore := policy.CalculateEvictionScore(trainingEntry)
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if trainingScore <= regularScore {
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t.Errorf("Training data should have higher score: training=%.3f, regular=%.3f",
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trainingScore, regularScore)
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}
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}
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func TestMLCachePolicy_AccessPatternBoost(t *testing.T) {
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policy := NewMLCachePolicy()
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randomEntry := &CacheEntry{
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Inode: 1,
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Size: 1024,
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LastAccess: time.Now(),
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AccessCount: 5,
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Pattern: RandomAccess,
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FileType: MLFileDataset,
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}
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sequentialEntry := &CacheEntry{
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Inode: 2,
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Size: 1024,
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LastAccess: time.Now(),
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AccessCount: 5,
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Pattern: SequentialAccess,
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FileType: MLFileDataset,
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}
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modelAccessEntry := &CacheEntry{
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Inode: 3,
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Size: 1024,
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LastAccess: time.Now(),
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AccessCount: 5,
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Pattern: ModelAccess,
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FileType: MLFileModel,
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}
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randomScore := policy.CalculateEvictionScore(randomEntry)
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sequentialScore := policy.CalculateEvictionScore(sequentialEntry)
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modelScore := policy.CalculateEvictionScore(modelAccessEntry)
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if sequentialScore <= randomScore {
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t.Errorf("Sequential access should have higher score than random: seq=%.3f, random=%.3f",
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sequentialScore, randomScore)
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}
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if modelScore <= sequentialScore {
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t.Errorf("Model access should have highest score: model=%.3f, seq=%.3f",
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modelScore, sequentialScore)
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}
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t.Logf("Pattern comparison: random=%.3f, sequential=%.3f, model=%.3f",
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randomScore, sequentialScore, modelScore)
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}
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func TestMLCachePolicy_SizePreference(t *testing.T) {
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policy := NewMLCachePolicy()
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smallEntry := &CacheEntry{
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Inode: 1,
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Size: 1024, // 1KB
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LastAccess: time.Now().Add(-5 * time.Minute),
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AccessCount: 3,
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FileType: MLFileUnknown,
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}
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largeEntry := &CacheEntry{
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Inode: 2,
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Size: 50 * 1024 * 1024, // 50MB
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LastAccess: time.Now().Add(-5 * time.Minute),
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AccessCount: 3,
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FileType: MLFileUnknown,
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}
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smallScore := policy.CalculateEvictionScore(smallEntry)
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largeScore := policy.CalculateEvictionScore(largeEntry)
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if smallScore <= largeScore {
|
||||
t.Errorf("Small files should have higher score than large files: small=%.3f, large=%.3f",
|
||||
smallScore, largeScore)
|
||||
}
|
||||
}
|
||||
|
||||
func TestMLCachePolicy_RecencyDecay(t *testing.T) {
|
||||
policy := NewMLCachePolicy()
|
||||
|
||||
// Create entries with different access times
|
||||
recentEntry := &CacheEntry{
|
||||
Inode: 1,
|
||||
|
||||
Size: 1024,
|
||||
LastAccess: time.Now(),
|
||||
AccessCount: 5,
|
||||
FileType: MLFileUnknown,
|
||||
}
|
||||
|
||||
oldEntry := &CacheEntry{
|
||||
Inode: 2,
|
||||
|
||||
Size: 1024,
|
||||
LastAccess: time.Now().Add(-20 * time.Minute),
|
||||
AccessCount: 5,
|
||||
FileType: MLFileUnknown,
|
||||
}
|
||||
|
||||
recentScore := policy.CalculateEvictionScore(recentEntry)
|
||||
oldScore := policy.CalculateEvictionScore(oldEntry)
|
||||
|
||||
if recentScore <= oldScore {
|
||||
t.Errorf("Recent access should have higher score: recent=%.3f, old=%.3f",
|
||||
recentScore, oldScore)
|
||||
}
|
||||
}
|
||||
|
||||
func TestMLCachePolicy_EpochRelevance(t *testing.T) {
|
||||
policy := NewMLCachePolicy()
|
||||
|
||||
lowRelevanceEntry := &CacheEntry{
|
||||
Inode: 1,
|
||||
|
||||
Size: 1024,
|
||||
LastAccess: time.Now(),
|
||||
AccessCount: 5,
|
||||
FileType: MLFileDataset,
|
||||
EpochRelevance: 0.2,
|
||||
}
|
||||
|
||||
highRelevanceEntry := &CacheEntry{
|
||||
Inode: 2,
|
||||
|
||||
Size: 1024,
|
||||
LastAccess: time.Now(),
|
||||
AccessCount: 5,
|
||||
FileType: MLFileDataset,
|
||||
EpochRelevance: 0.9,
|
||||
}
|
||||
|
||||
lowScore := policy.CalculateEvictionScore(lowRelevanceEntry)
|
||||
highScore := policy.CalculateEvictionScore(highRelevanceEntry)
|
||||
|
||||
if highScore <= lowScore {
|
||||
t.Errorf("High epoch relevance should have higher score: high=%.3f, low=%.3f",
|
||||
highScore, lowScore)
|
||||
}
|
||||
}
|
||||
|
||||
func TestMLCachePolicy_DifferentThresholds(t *testing.T) {
|
||||
policy := NewMLCachePolicy()
|
||||
|
||||
// Create entries for different file types with same base score
|
||||
unknownEntry := &CacheEntry{
|
||||
Inode: 1,
|
||||
|
||||
Size: 1024,
|
||||
LastAccess: time.Now().Add(-15 * time.Minute), // Old enough to potentially evict
|
||||
AccessCount: 2,
|
||||
FileType: MLFileUnknown,
|
||||
}
|
||||
|
||||
modelEntry := &CacheEntry{
|
||||
Inode: 2,
|
||||
|
||||
Size: 1024,
|
||||
LastAccess: time.Now().Add(-15 * time.Minute),
|
||||
AccessCount: 2,
|
||||
FileType: MLFileModel,
|
||||
IsModel: true,
|
||||
}
|
||||
|
||||
datasetEntry := &CacheEntry{
|
||||
Inode: 3,
|
||||
|
||||
Size: 1024,
|
||||
LastAccess: time.Now().Add(-15 * time.Minute),
|
||||
AccessCount: 2,
|
||||
FileType: MLFileDataset,
|
||||
Pattern: SequentialAccess,
|
||||
}
|
||||
|
||||
unknownShouldEvict := policy.ShouldEvict(unknownEntry)
|
||||
modelShouldEvict := policy.ShouldEvict(modelEntry)
|
||||
datasetShouldEvict := policy.ShouldEvict(datasetEntry)
|
||||
|
||||
// Models should be least likely to be evicted
|
||||
if modelShouldEvict && (!unknownShouldEvict || !datasetShouldEvict) {
|
||||
t.Error("Model files should be least likely to be evicted")
|
||||
}
|
||||
|
||||
t.Logf("Eviction by type: unknown=%v, model=%v, dataset=%v",
|
||||
unknownShouldEvict, modelShouldEvict, datasetShouldEvict)
|
||||
}
|
||||
|
||||
func TestMLCachePolicy_SetWeights(t *testing.T) {
|
||||
policy := NewMLCachePolicy()
|
||||
|
||||
// Test setting custom weights
|
||||
policy.SetWeights(0.4, 0.3, 0.1, 0.2)
|
||||
|
||||
if policy.accessFrequencyWeight != 0.4 {
|
||||
t.Errorf("Expected frequency weight 0.4, got %.2f", policy.accessFrequencyWeight)
|
||||
}
|
||||
|
||||
if policy.recencyWeight != 0.3 {
|
||||
t.Errorf("Expected recency weight 0.3, got %.2f", policy.recencyWeight)
|
||||
}
|
||||
|
||||
if policy.sizeWeight != 0.1 {
|
||||
t.Errorf("Expected size weight 0.1, got %.2f", policy.sizeWeight)
|
||||
}
|
||||
|
||||
if policy.mlWeight != 0.2 {
|
||||
t.Errorf("Expected ML weight 0.2, got %.2f", policy.mlWeight)
|
||||
}
|
||||
|
||||
// Test weight normalization
|
||||
policy.SetWeights(2.0, 2.0, 1.0, 1.0) // Total = 6.0
|
||||
|
||||
expectedFreq := 2.0 / 6.0
|
||||
if abs(policy.accessFrequencyWeight - expectedFreq) > 0.001 {
|
||||
t.Errorf("Expected normalized frequency weight %.3f, got %.3f",
|
||||
expectedFreq, policy.accessFrequencyWeight)
|
||||
}
|
||||
}
|
||||
|
||||
func TestMLCachePolicy_SetMLBoosts(t *testing.T) {
|
||||
policy := NewMLCachePolicy()
|
||||
|
||||
// Test setting custom boost factors
|
||||
policy.SetMLBoosts(2.0, 3.0, 1.5, 1.8)
|
||||
|
||||
if policy.trainingDataBoost != 2.0 {
|
||||
t.Errorf("Expected training data boost 2.0, got %.2f", policy.trainingDataBoost)
|
||||
}
|
||||
|
||||
if policy.modelFileBoost != 3.0 {
|
||||
t.Errorf("Expected model file boost 3.0, got %.2f", policy.modelFileBoost)
|
||||
}
|
||||
|
||||
if policy.sequentialBoost != 1.5 {
|
||||
t.Errorf("Expected sequential boost 1.5, got %.2f", policy.sequentialBoost)
|
||||
}
|
||||
|
||||
if policy.epochRelevanceBoost != 1.8 {
|
||||
t.Errorf("Expected epoch relevance boost 1.8, got %.2f", policy.epochRelevanceBoost)
|
||||
}
|
||||
}
|
||||
|
||||
func TestMLCachePolicy_Metrics(t *testing.T) {
|
||||
policy := NewMLCachePolicy()
|
||||
|
||||
// Simulate some evictions
|
||||
entries := []*CacheEntry{
|
||||
{FileType: MLFileModel, IsModel: true},
|
||||
{FileType: MLFileDataset, IsTrainingData: true},
|
||||
{FileType: MLFileUnknown},
|
||||
}
|
||||
|
||||
for _, entry := range entries {
|
||||
entry.LastAccess = time.Now().Add(-30 * time.Minute) // Old enough to evict
|
||||
entry.AccessCount = 1
|
||||
entry.Size = 1024
|
||||
|
||||
if policy.ShouldEvict(entry) {
|
||||
// Eviction counters are updated in ShouldEvict
|
||||
}
|
||||
}
|
||||
|
||||
metrics := policy.GetEvictionMetrics()
|
||||
|
||||
if metrics.TotalEvictions == 0 {
|
||||
t.Error("Should have some total evictions")
|
||||
}
|
||||
|
||||
// Verify weight configuration in metrics
|
||||
if metrics.AccessFrequencyWeight != policy.accessFrequencyWeight {
|
||||
t.Error("Metrics should reflect current weight configuration")
|
||||
}
|
||||
}
|
||||
|
||||
func TestMLCachePolicy_HotChunkPreference(t *testing.T) {
|
||||
policy := NewMLCachePolicy()
|
||||
|
||||
coldEntry := &CacheEntry{
|
||||
Inode: 1,
|
||||
|
||||
Size: 1024,
|
||||
LastAccess: time.Now(),
|
||||
AccessCount: 5,
|
||||
IsHot: false,
|
||||
FileType: MLFileDataset,
|
||||
}
|
||||
|
||||
hotEntry := &CacheEntry{
|
||||
Inode: 2,
|
||||
|
||||
Size: 1024,
|
||||
LastAccess: time.Now(),
|
||||
AccessCount: 5,
|
||||
IsHot: true,
|
||||
FileType: MLFileDataset,
|
||||
}
|
||||
|
||||
coldScore := policy.CalculateEvictionScore(coldEntry)
|
||||
hotScore := policy.CalculateEvictionScore(hotEntry)
|
||||
|
||||
if hotScore <= coldScore {
|
||||
t.Errorf("Hot chunk should have higher score: hot=%.3f, cold=%.3f", hotScore, coldScore)
|
||||
}
|
||||
}
|
||||
|
||||
func TestMLCachePolicy_RecencyThresholds(t *testing.T) {
|
||||
policy := NewMLCachePolicy()
|
||||
|
||||
// Test hot threshold
|
||||
hotEntry := &CacheEntry{
|
||||
Inode: 1,
|
||||
Size: 1024,
|
||||
LastAccess: time.Now().Add(-30 * time.Second), // Within hot threshold
|
||||
AccessCount: 1,
|
||||
}
|
||||
|
||||
// Test cold threshold
|
||||
coldEntry := &CacheEntry{
|
||||
Inode: 2,
|
||||
Size: 1024,
|
||||
LastAccess: time.Now().Add(-15 * time.Minute), // Beyond cold threshold
|
||||
AccessCount: 1,
|
||||
}
|
||||
|
||||
// Test middle
|
||||
middleEntry := &CacheEntry{
|
||||
Inode: 3,
|
||||
Size: 1024,
|
||||
LastAccess: time.Now().Add(-5 * time.Minute), // Between thresholds
|
||||
AccessCount: 1,
|
||||
}
|
||||
|
||||
hotScore := policy.calculateRecencyScore(time.Since(hotEntry.LastAccess))
|
||||
coldScore := policy.calculateRecencyScore(time.Since(coldEntry.LastAccess))
|
||||
middleScore := policy.calculateRecencyScore(time.Since(middleEntry.LastAccess))
|
||||
|
||||
if hotScore != 1.0 {
|
||||
t.Errorf("Hot entry should have score 1.0, got %.3f", hotScore)
|
||||
}
|
||||
|
||||
if coldScore != 0.1 {
|
||||
t.Errorf("Cold entry should have score 0.1, got %.3f", coldScore)
|
||||
}
|
||||
|
||||
if middleScore <= coldScore || middleScore >= hotScore {
|
||||
t.Errorf("Middle entry should have score between hot and cold: %.3f not in (%.3f, %.3f)",
|
||||
middleScore, coldScore, hotScore)
|
||||
}
|
||||
}
|
||||
|
||||
func TestMLCachePolicy_SizeScore(t *testing.T) {
|
||||
policy := NewMLCachePolicy()
|
||||
|
||||
smallSize := uint64(1024) // 1KB
|
||||
largeSize := uint64(100 * 1024 * 1024) // 100MB
|
||||
|
||||
smallScore := policy.calculateSizeScore(smallSize)
|
||||
largeScore := policy.calculateSizeScore(largeSize)
|
||||
|
||||
if smallScore <= largeScore {
|
||||
t.Errorf("Small files should have higher size score: small=%.3f, large=%.3f",
|
||||
smallScore, largeScore)
|
||||
}
|
||||
|
||||
// Large files should still have reasonable score (not too low)
|
||||
if largeScore < 0.2 {
|
||||
t.Errorf("Large files should have reasonable score, got %.3f", largeScore)
|
||||
}
|
||||
}
|
||||
|
||||
func TestMLCachePolicy_AccessFrequencyScore(t *testing.T) {
|
||||
policy := NewMLCachePolicy()
|
||||
|
||||
lowAccessEntry := &CacheEntry{
|
||||
AccessCount: 1,
|
||||
FileType: MLFileUnknown,
|
||||
Pattern: RandomAccess,
|
||||
}
|
||||
|
||||
highAccessEntry := &CacheEntry{
|
||||
AccessCount: 100,
|
||||
FileType: MLFileUnknown,
|
||||
Pattern: RandomAccess,
|
||||
}
|
||||
|
||||
lowScore := policy.calculateAccessFrequencyScore(lowAccessEntry)
|
||||
highScore := policy.calculateAccessFrequencyScore(highAccessEntry)
|
||||
|
||||
if highScore <= lowScore {
|
||||
t.Errorf("High access count should have higher score: high=%.3f, low=%.3f",
|
||||
highScore, lowScore)
|
||||
}
|
||||
}
|
||||
|
||||
// Helper function
|
||||
func abs(x float64) float64 {
|
||||
if x < 0 {
|
||||
return -x
|
||||
}
|
||||
return x
|
||||
}
|
||||
|
||||
// Benchmark tests
|
||||
|
||||
func BenchmarkMLCachePolicy_CalculateEvictionScore(b *testing.B) {
|
||||
policy := NewMLCachePolicy()
|
||||
|
||||
entry := &CacheEntry{
|
||||
Inode: 1,
|
||||
|
||||
Size: 1024,
|
||||
LastAccess: time.Now().Add(-5 * time.Minute),
|
||||
AccessCount: 10,
|
||||
FileType: MLFileDataset,
|
||||
Pattern: SequentialAccess,
|
||||
IsTrainingData: true,
|
||||
EpochRelevance: 0.8,
|
||||
}
|
||||
|
||||
b.ResetTimer()
|
||||
|
||||
for i := 0; i < b.N; i++ {
|
||||
policy.CalculateEvictionScore(entry)
|
||||
}
|
||||
}
|
||||
|
||||
func BenchmarkMLCachePolicy_ShouldEvict(b *testing.B) {
|
||||
policy := NewMLCachePolicy()
|
||||
|
||||
entry := &CacheEntry{
|
||||
Inode: 1,
|
||||
|
||||
Size: 1024,
|
||||
LastAccess: time.Now().Add(-5 * time.Minute),
|
||||
AccessCount: 10,
|
||||
FileType: MLFileDataset,
|
||||
}
|
||||
|
||||
b.ResetTimer()
|
||||
|
||||
for i := 0; i < b.N; i++ {
|
||||
policy.ShouldEvict(entry)
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,312 @@
|
||||
package ml
|
||||
|
||||
import (
|
||||
"time"
|
||||
|
||||
"github.com/hanwen/go-fuse/v2/fuse"
|
||||
"github.com/seaweedfs/seaweedfs/weed/glog"
|
||||
"github.com/seaweedfs/seaweedfs/weed/pb/filer_pb"
|
||||
)
|
||||
|
||||
// FUSEMLIntegration provides ML optimization integration for SeaweedFS FUSE mount
|
||||
type FUSEMLIntegration struct {
|
||||
// Core ML components
|
||||
openFileCache *OpenFileCache
|
||||
cachePolicy *MLCachePolicy
|
||||
mlOptimization *MLOptimization
|
||||
|
||||
// FUSE-specific configuration
|
||||
enableKeepCache bool // Enable FOPEN_KEEP_CACHE for ML files
|
||||
enableWriteback bool // Enable writeback caching
|
||||
attrCacheTimeout time.Duration // Attribute cache timeout for ML files
|
||||
entryCacheTimeout time.Duration // Entry cache timeout for ML files
|
||||
|
||||
// ML-specific FUSE optimizations
|
||||
mlAttrTimeout time.Duration // Extended attribute timeout for ML files
|
||||
datasetAttrTimeout time.Duration // Even longer timeout for dataset files
|
||||
modelAttrTimeout time.Duration // Longest timeout for model files
|
||||
|
||||
// Statistics
|
||||
keepCacheEnabled int64 // Number of times keep cache was enabled
|
||||
writebackEnabled int64 // Number of times writeback was enabled
|
||||
mlAttrCacheHits int64 // ML-specific attribute cache hits
|
||||
}
|
||||
|
||||
// NewFUSEMLIntegration creates a new FUSE ML integration
|
||||
func NewFUSEMLIntegration(mlOpt *MLOptimization) *FUSEMLIntegration {
|
||||
return &FUSEMLIntegration{
|
||||
openFileCache: NewOpenFileCache(1000, 30*time.Minute),
|
||||
cachePolicy: NewMLCachePolicy(),
|
||||
mlOptimization: mlOpt,
|
||||
enableKeepCache: true,
|
||||
enableWriteback: true,
|
||||
attrCacheTimeout: 5 * time.Second,
|
||||
entryCacheTimeout: 10 * time.Second,
|
||||
|
||||
// ML-specific timeouts (longer for more stable caching)
|
||||
mlAttrTimeout: 30 * time.Second,
|
||||
datasetAttrTimeout: 60 * time.Second,
|
||||
modelAttrTimeout: 120 * time.Second, // Longest for model files
|
||||
}
|
||||
}
|
||||
|
||||
// OnFileOpen handles file open events for ML optimization
|
||||
func (fmi *FUSEMLIntegration) OnFileOpen(inode uint64, entry *filer_pb.Entry, fullPath string, flags uint32, out *fuse.OpenOut) {
|
||||
// Register file in cache
|
||||
fileInfo := fmi.openFileCache.OpenFile(inode, entry, fullPath)
|
||||
|
||||
// Apply ML-specific FUSE optimizations
|
||||
if fileInfo.IsMLFile && fmi.enableKeepCache {
|
||||
// Enable keep cache for ML files to reduce redundant reads
|
||||
out.OpenFlags |= fuse.FOPEN_KEEP_CACHE
|
||||
fmi.keepCacheEnabled++
|
||||
|
||||
glog.V(3).Infof("Enabled FOPEN_KEEP_CACHE for ML file: inode=%d, type=%v",
|
||||
inode, fileInfo.FileType)
|
||||
}
|
||||
|
||||
// For large model files, also enable direct I/O to bypass page cache for very large reads
|
||||
if fileInfo.FileType == MLFileModel && entry.Attributes.FileSize > 100*1024*1024 { // > 100MB
|
||||
// Note: Direct I/O can be beneficial for very large sequential reads
|
||||
// but may hurt performance for small random reads
|
||||
if fileInfo.ReadPattern == SequentialAccess || fileInfo.ReadPattern == ModelAccess {
|
||||
out.OpenFlags |= fuse.FOPEN_DIRECT_IO
|
||||
glog.V(3).Infof("Enabled FOPEN_DIRECT_IO for large model file: inode=%d", inode)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// OnFileClose handles file close events
|
||||
func (fmi *FUSEMLIntegration) OnFileClose(inode uint64) {
|
||||
canEvict := fmi.openFileCache.CloseFile(inode)
|
||||
|
||||
if canEvict {
|
||||
glog.V(4).Infof("File closed and available for eviction: inode=%d", inode)
|
||||
}
|
||||
}
|
||||
|
||||
// OnFileRead handles file read events for ML pattern detection
|
||||
func (fmi *FUSEMLIntegration) OnFileRead(inode uint64, offset int64, size int) {
|
||||
// Update access pattern
|
||||
if fmi.mlOptimization != nil && fmi.mlOptimization.IsEnabled() {
|
||||
accessInfo := fmi.mlOptimization.RecordAccess(inode, offset, size)
|
||||
|
||||
// Update file info with detected pattern
|
||||
if fileInfo := fmi.openFileCache.GetFileInfo(inode); fileInfo != nil {
|
||||
fileInfo.Lock()
|
||||
if accessInfo != nil {
|
||||
fileInfo.ReadPattern = accessInfo.Pattern
|
||||
fileInfo.AccessInfo = accessInfo
|
||||
}
|
||||
fileInfo.TotalBytesRead += int64(size)
|
||||
fileInfo.Unlock()
|
||||
|
||||
// Trigger prefetching if pattern detected
|
||||
if shouldPrefetch, _ := fmi.mlOptimization.ShouldPrefetch(inode); shouldPrefetch {
|
||||
glog.V(4).Infof("Prefetch triggered for ML file: inode=%d, pattern=%v",
|
||||
inode, fileInfo.ReadPattern)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// OptimizeAttributes applies ML-specific attribute caching optimizations
|
||||
func (fmi *FUSEMLIntegration) OptimizeAttributes(inode uint64, out *fuse.AttrOut) {
|
||||
fileInfo := fmi.openFileCache.GetFileInfo(inode)
|
||||
if fileInfo == nil {
|
||||
// Use default timeout
|
||||
out.AttrValid = uint64(fmi.attrCacheTimeout.Seconds())
|
||||
return
|
||||
}
|
||||
|
||||
// Apply ML-specific timeouts
|
||||
var timeout time.Duration
|
||||
|
||||
switch fileInfo.FileType {
|
||||
case MLFileModel:
|
||||
// Model files rarely change, cache attributes longer
|
||||
timeout = fmi.modelAttrTimeout
|
||||
case MLFileDataset:
|
||||
// Dataset files are read-only during training, cache longer
|
||||
timeout = fmi.datasetAttrTimeout
|
||||
case MLFileTensor, MLFileConfig:
|
||||
// Moderate timeout for tensor and config files
|
||||
timeout = fmi.mlAttrTimeout
|
||||
default:
|
||||
// Use default timeout for non-ML files
|
||||
timeout = fmi.attrCacheTimeout
|
||||
}
|
||||
|
||||
out.AttrValid = uint64(timeout.Seconds())
|
||||
fmi.mlAttrCacheHits++
|
||||
|
||||
glog.V(4).Infof("ML attribute cache timeout: inode=%d, type=%v, timeout=%v",
|
||||
inode, fileInfo.FileType, timeout)
|
||||
}
|
||||
|
||||
// OptimizeEntryCache applies ML-specific entry caching optimizations
|
||||
func (fmi *FUSEMLIntegration) OptimizeEntryCache(inode uint64, entry *filer_pb.Entry, out *fuse.EntryOut) {
|
||||
fileInfo := fmi.openFileCache.GetFileInfo(inode)
|
||||
if fileInfo == nil {
|
||||
// Use default timeout
|
||||
out.SetEntryTimeout(fmi.entryCacheTimeout)
|
||||
return
|
||||
}
|
||||
|
||||
// ML files can have longer entry cache timeouts since they change infrequently
|
||||
var timeout time.Duration
|
||||
|
||||
switch fileInfo.FileType {
|
||||
case MLFileModel, MLFileDataset:
|
||||
// Models and datasets rarely change during training
|
||||
timeout = fmi.datasetAttrTimeout
|
||||
case MLFileConfig:
|
||||
// Config files change even less frequently
|
||||
timeout = fmi.modelAttrTimeout
|
||||
default:
|
||||
timeout = fmi.entryCacheTimeout
|
||||
}
|
||||
|
||||
out.SetEntryTimeout(timeout)
|
||||
|
||||
glog.V(4).Infof("ML entry cache timeout: inode=%d, type=%v, timeout=%v",
|
||||
inode, fileInfo.FileType, timeout)
|
||||
}
|
||||
|
||||
// ShouldEnableWriteback determines if writeback caching should be enabled for a file
|
||||
func (fmi *FUSEMLIntegration) ShouldEnableWriteback(inode uint64, entry *filer_pb.Entry) bool {
|
||||
if !fmi.enableWriteback {
|
||||
return false
|
||||
}
|
||||
|
||||
fileInfo := fmi.openFileCache.GetFileInfo(inode)
|
||||
if fileInfo == nil {
|
||||
return false
|
||||
}
|
||||
|
||||
// Enable writeback for ML files that are frequently written
|
||||
switch fileInfo.FileType {
|
||||
case MLFileLog:
|
||||
// Training logs benefit from writeback caching
|
||||
return true
|
||||
case MLFileModel:
|
||||
// Model checkpoints during training benefit from writeback
|
||||
if fileInfo.AccessInfo != nil && fileInfo.AccessInfo.Pattern == SequentialAccess {
|
||||
return true
|
||||
}
|
||||
case MLFileConfig:
|
||||
// Config files rarely change, so writeback not as beneficial
|
||||
return false
|
||||
case MLFileDataset:
|
||||
// Datasets are typically read-only during training
|
||||
return false
|
||||
default:
|
||||
// Default behavior for non-ML files
|
||||
return false
|
||||
}
|
||||
|
||||
return false
|
||||
}
|
||||
|
||||
// OnChunkAccess updates chunk-level metadata when chunks are accessed
|
||||
func (fmi *FUSEMLIntegration) OnChunkAccess(inode uint64, chunkIndex uint32, fileId string, cacheLevel int, isHit bool) {
|
||||
metadata := &ChunkMetadata{
|
||||
FileId: fileId,
|
||||
Offset: uint64(chunkIndex) * 1024, // Assuming 1KB chunks for now
|
||||
Size: 1024,
|
||||
LastAccess: time.Now(),
|
||||
CacheLevel: cacheLevel,
|
||||
AccessCount: 1, // Will be incremented in UpdateChunkCache
|
||||
}
|
||||
|
||||
// Update chunk cache
|
||||
fmi.openFileCache.UpdateChunkCache(inode, chunkIndex, metadata)
|
||||
|
||||
// Update file-level statistics
|
||||
if fileInfo := fmi.openFileCache.GetFileInfo(inode); fileInfo != nil {
|
||||
fileInfo.Lock()
|
||||
if isHit {
|
||||
fileInfo.CacheHitCount++
|
||||
} else {
|
||||
fileInfo.CacheMissCount++
|
||||
}
|
||||
fileInfo.Unlock()
|
||||
}
|
||||
}
|
||||
|
||||
// GetOptimizationMetrics returns comprehensive optimization metrics
|
||||
func (fmi *FUSEMLIntegration) GetOptimizationMetrics() FUSEMLMetrics {
|
||||
var mlMetrics *MLOptimizationMetrics
|
||||
if fmi.mlOptimization != nil {
|
||||
mlMetrics = fmi.mlOptimization.GetMetrics()
|
||||
}
|
||||
|
||||
return FUSEMLMetrics{
|
||||
MLOptimizationMetrics: mlMetrics,
|
||||
OpenFileCacheMetrics: fmi.openFileCache.GetMetrics(),
|
||||
CachePolicyMetrics: fmi.cachePolicy.GetEvictionMetrics(),
|
||||
KeepCacheEnabled: fmi.keepCacheEnabled,
|
||||
WritebackEnabled: fmi.writebackEnabled,
|
||||
MLAttrCacheHits: fmi.mlAttrCacheHits,
|
||||
EnableKeepCache: fmi.enableKeepCache,
|
||||
EnableWriteback: fmi.enableWriteback,
|
||||
}
|
||||
}
|
||||
|
||||
// FUSEMLMetrics holds comprehensive FUSE ML optimization metrics
|
||||
type FUSEMLMetrics struct {
|
||||
MLOptimizationMetrics *MLOptimizationMetrics `json:"ml_optimization,omitempty"`
|
||||
OpenFileCacheMetrics OpenFileCacheMetrics `json:"open_file_cache"`
|
||||
CachePolicyMetrics MLCachePolicyMetrics `json:"cache_policy"`
|
||||
|
||||
// FUSE-specific metrics
|
||||
KeepCacheEnabled int64 `json:"keep_cache_enabled"`
|
||||
WritebackEnabled int64 `json:"writeback_enabled"`
|
||||
MLAttrCacheHits int64 `json:"ml_attr_cache_hits"`
|
||||
|
||||
// Configuration
|
||||
EnableKeepCache bool `json:"enable_keep_cache"`
|
||||
EnableWriteback bool `json:"enable_writeback"`
|
||||
}
|
||||
|
||||
// Shutdown gracefully shuts down the FUSE ML integration
|
||||
func (fmi *FUSEMLIntegration) Shutdown() {
|
||||
glog.V(1).Infof("Shutting down FUSE ML integration...")
|
||||
|
||||
if fmi.openFileCache != nil {
|
||||
fmi.openFileCache.Shutdown()
|
||||
}
|
||||
|
||||
if fmi.mlOptimization != nil {
|
||||
fmi.mlOptimization.Shutdown()
|
||||
}
|
||||
|
||||
// Print final metrics
|
||||
metrics := fmi.GetOptimizationMetrics()
|
||||
glog.V(1).Infof("FUSE ML integration final metrics: keep_cache=%d, writeback=%d, attr_hits=%d",
|
||||
metrics.KeepCacheEnabled, metrics.WritebackEnabled, metrics.MLAttrCacheHits)
|
||||
}
|
||||
|
||||
// EnableMLOptimizations enables or disables ML optimizations
|
||||
func (fmi *FUSEMLIntegration) EnableMLOptimizations(enabled bool) {
|
||||
fmi.enableKeepCache = enabled
|
||||
fmi.enableWriteback = enabled
|
||||
|
||||
if fmi.mlOptimization != nil {
|
||||
fmi.mlOptimization.Enable(enabled)
|
||||
}
|
||||
|
||||
glog.V(1).Infof("ML FUSE optimizations %s", map[bool]string{true: "enabled", false: "disabled"}[enabled])
|
||||
}
|
||||
|
||||
// SetCacheTimeouts configures cache timeouts for different file types
|
||||
func (fmi *FUSEMLIntegration) SetCacheTimeouts(attr, entry, mlAttr, dataset, model time.Duration) {
|
||||
fmi.attrCacheTimeout = attr
|
||||
fmi.entryCacheTimeout = entry
|
||||
fmi.mlAttrTimeout = mlAttr
|
||||
fmi.datasetAttrTimeout = dataset
|
||||
fmi.modelAttrTimeout = model
|
||||
|
||||
glog.V(2).Infof("Updated cache timeouts: attr=%v, entry=%v, ml=%v, dataset=%v, model=%v",
|
||||
attr, entry, mlAttr, dataset, model)
|
||||
}
|
||||
@@ -0,0 +1,577 @@
|
||||
package ml
|
||||
|
||||
import (
|
||||
"sync"
|
||||
"time"
|
||||
|
||||
"github.com/seaweedfs/seaweedfs/weed/glog"
|
||||
"github.com/seaweedfs/seaweedfs/weed/pb/filer_pb"
|
||||
)
|
||||
|
||||
// ChunkMetadata contains metadata about a cached chunk
|
||||
type ChunkMetadata struct {
|
||||
FileId string // Chunk file ID
|
||||
Offset uint64 // Offset within the file
|
||||
Size uint64 // Size of the chunk
|
||||
CacheLevel int // 0=memory, 1=disk, 2=not cached
|
||||
LastAccess time.Time // Last access time
|
||||
AccessCount int64 // Number of times accessed
|
||||
IsHot bool // Whether this chunk is frequently accessed
|
||||
Pattern AccessPattern // Access pattern for this chunk
|
||||
}
|
||||
|
||||
// OpenFileInfo contains comprehensive information about an open file
|
||||
type OpenFileInfo struct {
|
||||
sync.RWMutex
|
||||
|
||||
// Basic file information
|
||||
Inode uint64 // File inode
|
||||
Entry *filer_pb.Entry // File entry from filer
|
||||
OpenCount int // Number of open handles
|
||||
OpenTime time.Time // When file was first opened
|
||||
LastAccess time.Time // Last access time
|
||||
|
||||
// Chunk-level caching
|
||||
ChunkCache map[uint32]*ChunkMetadata // chunk index -> metadata
|
||||
ChunkCount uint32 // Total number of chunks in file
|
||||
ChunkSize int64 // Size of each chunk
|
||||
|
||||
// Access pattern tracking
|
||||
AccessInfo *AccessInfo // Access pattern information
|
||||
ReadPattern AccessPattern // Overall file access pattern
|
||||
PrefetchState PrefetchState // Current prefetch state
|
||||
|
||||
// ML-specific optimizations
|
||||
IsMLFile bool // Whether this is likely an ML-related file
|
||||
FileType MLFileType // Type of ML file (dataset, model, etc.)
|
||||
BatchSize int // Detected batch size for training data
|
||||
EpochCount int // Number of epochs detected
|
||||
|
||||
// Performance tracking
|
||||
TotalBytesRead int64 // Total bytes read from this file
|
||||
CacheHitCount int64 // Number of cache hits
|
||||
CacheMissCount int64 // Number of cache misses
|
||||
PrefetchHitCount int64 // Number of prefetch hits
|
||||
}
|
||||
|
||||
// PrefetchState represents the current prefetch state for a file
|
||||
type PrefetchState int
|
||||
|
||||
const (
|
||||
PrefetchIdle PrefetchState = iota
|
||||
PrefetchActive
|
||||
PrefetchComplete
|
||||
PrefetchSuspended
|
||||
)
|
||||
|
||||
// MLFileType represents the type of ML-related file
|
||||
type MLFileType int
|
||||
|
||||
const (
|
||||
MLFileUnknown MLFileType = iota
|
||||
MLFileDataset // Training/validation dataset
|
||||
MLFileModel // Model checkpoint/weights
|
||||
MLFileConfig // Configuration files
|
||||
MLFileTensor // Individual tensor files
|
||||
MLFileLog // Training logs
|
||||
)
|
||||
|
||||
// OpenFileCache manages open file information with ML-aware optimizations
|
||||
type OpenFileCache struct {
|
||||
sync.RWMutex
|
||||
|
||||
// Configuration
|
||||
maxFiles int // Maximum number of files to track
|
||||
ttl time.Duration // TTL for inactive files
|
||||
cleanupInterval time.Duration // Cleanup interval
|
||||
|
||||
// File tracking
|
||||
files map[uint64]*OpenFileInfo // inode -> file info
|
||||
accessOrder []uint64 // LRU order for eviction
|
||||
|
||||
// ML-specific configuration
|
||||
enableMLOptimization bool
|
||||
mlFileDetector *MLFileDetector
|
||||
|
||||
// Metrics
|
||||
totalFiles int64
|
||||
evictedFiles int64
|
||||
cacheHits int64
|
||||
cacheMisses int64
|
||||
|
||||
// Background cleanup
|
||||
shutdown chan struct{}
|
||||
done chan struct{}
|
||||
}
|
||||
|
||||
// MLFileDetector detects ML-related files based on patterns and metadata
|
||||
type MLFileDetector struct {
|
||||
// File extension patterns
|
||||
datasetExtensions map[string]bool
|
||||
modelExtensions map[string]bool
|
||||
configExtensions map[string]bool
|
||||
|
||||
// Path patterns
|
||||
datasetPaths []string
|
||||
modelPaths []string
|
||||
|
||||
// Size heuristics
|
||||
modelMinSize int64 // Minimum size for model files
|
||||
datasetMaxItems int // Maximum items in dataset directory
|
||||
}
|
||||
|
||||
// NewOpenFileCache creates a new open file cache optimized for ML workloads
|
||||
func NewOpenFileCache(maxFiles int, ttl time.Duration) *OpenFileCache {
|
||||
if maxFiles <= 0 {
|
||||
maxFiles = 1000 // Default suitable for ML workloads
|
||||
}
|
||||
if ttl <= 0 {
|
||||
ttl = 30 * time.Minute // Default TTL
|
||||
}
|
||||
|
||||
ofc := &OpenFileCache{
|
||||
maxFiles: maxFiles,
|
||||
ttl: ttl,
|
||||
cleanupInterval: 5 * time.Minute,
|
||||
files: make(map[uint64]*OpenFileInfo),
|
||||
accessOrder: make([]uint64, 0, maxFiles),
|
||||
enableMLOptimization: true,
|
||||
mlFileDetector: newMLFileDetector(),
|
||||
shutdown: make(chan struct{}),
|
||||
done: make(chan struct{}),
|
||||
}
|
||||
|
||||
// Start background cleanup
|
||||
go ofc.cleanupWorker()
|
||||
|
||||
glog.V(1).Infof("OpenFileCache initialized: maxFiles=%d, ttl=%v", maxFiles, ttl)
|
||||
return ofc
|
||||
}
|
||||
|
||||
// newMLFileDetector creates a new ML file detector with common patterns
|
||||
func newMLFileDetector() *MLFileDetector {
|
||||
return &MLFileDetector{
|
||||
datasetExtensions: map[string]bool{
|
||||
"jpg": true, "jpeg": true, "png": true, "bmp": true, "tiff": true,
|
||||
"wav": true, "mp3": true, "flac": true,
|
||||
"txt": true, "csv": true, "json": true, "jsonl": true,
|
||||
"parquet": true, "arrow": true, "h5": true, "hdf5": true,
|
||||
"tfrecord": true, "tfrecords": true,
|
||||
},
|
||||
modelExtensions: map[string]bool{
|
||||
"pt": true, "pth": true, "pkl": true, "pickle": true,
|
||||
"h5": true, "hdf5": true, "pb": true, "pbtxt": true,
|
||||
"onnx": true, "tflite": true, "caffemodel": true,
|
||||
"bin": true, "safetensors": true,
|
||||
},
|
||||
configExtensions: map[string]bool{
|
||||
"yaml": true, "yml": true, "json": true, "toml": true,
|
||||
"cfg": true, "config": true, "conf": true,
|
||||
},
|
||||
datasetPaths: []string{
|
||||
"/datasets", "/data", "/train", "/test", "/val", "/validation",
|
||||
"/images", "/audio", "/text", "/corpus",
|
||||
},
|
||||
modelPaths: []string{
|
||||
"/models", "/checkpoints", "/weights", "/pretrained",
|
||||
"/saved_models", "/exports",
|
||||
},
|
||||
modelMinSize: 1024 * 1024, // 1MB minimum for model files
|
||||
datasetMaxItems: 1000000, // 1M max items in dataset directory
|
||||
}
|
||||
}
|
||||
|
||||
// OpenFile registers a file as opened and initializes tracking
|
||||
func (ofc *OpenFileCache) OpenFile(inode uint64, entry *filer_pb.Entry, fullPath string) *OpenFileInfo {
|
||||
ofc.Lock()
|
||||
defer ofc.Unlock()
|
||||
|
||||
// Get or create file info
|
||||
fileInfo := ofc.files[inode]
|
||||
if fileInfo == nil {
|
||||
fileInfo = &OpenFileInfo{
|
||||
Inode: inode,
|
||||
Entry: entry,
|
||||
OpenTime: time.Now(),
|
||||
ChunkCache: make(map[uint32]*ChunkMetadata),
|
||||
AccessInfo: &AccessInfo{Inode: inode},
|
||||
ReadPattern: RandomAccess,
|
||||
PrefetchState: PrefetchIdle,
|
||||
}
|
||||
|
||||
// Detect ML file type
|
||||
if ofc.enableMLOptimization {
|
||||
fileInfo.IsMLFile, fileInfo.FileType = ofc.mlFileDetector.DetectMLFile(entry, fullPath)
|
||||
if fileInfo.IsMLFile {
|
||||
glog.V(3).Infof("ML file detected: inode=%d, type=%v, path=%s",
|
||||
inode, fileInfo.FileType, fullPath)
|
||||
}
|
||||
}
|
||||
|
||||
ofc.files[inode] = fileInfo
|
||||
ofc.totalFiles++
|
||||
|
||||
// Update access order for LRU
|
||||
ofc.updateAccessOrder(inode)
|
||||
|
||||
// Evict if necessary
|
||||
if len(ofc.files) > ofc.maxFiles {
|
||||
ofc.evictLRU()
|
||||
}
|
||||
}
|
||||
|
||||
fileInfo.OpenCount++
|
||||
fileInfo.LastAccess = time.Now()
|
||||
ofc.updateAccessOrder(inode)
|
||||
|
||||
glog.V(4).Infof("File opened: inode=%d, openCount=%d, isML=%v",
|
||||
inode, fileInfo.OpenCount, fileInfo.IsMLFile)
|
||||
|
||||
return fileInfo
|
||||
}
|
||||
|
||||
// CloseFile decrements the open count and potentially cleans up
|
||||
func (ofc *OpenFileCache) CloseFile(inode uint64) bool {
|
||||
ofc.Lock()
|
||||
defer ofc.Unlock()
|
||||
|
||||
fileInfo := ofc.files[inode]
|
||||
if fileInfo == nil {
|
||||
return true // Already cleaned up
|
||||
}
|
||||
|
||||
fileInfo.OpenCount--
|
||||
glog.V(4).Infof("File closed: inode=%d, openCount=%d", inode, fileInfo.OpenCount)
|
||||
|
||||
// Return true if file can be evicted (no more open handles)
|
||||
return fileInfo.OpenCount <= 0
|
||||
}
|
||||
|
||||
// GetFileInfo retrieves file information if cached
|
||||
func (ofc *OpenFileCache) GetFileInfo(inode uint64) *OpenFileInfo {
|
||||
ofc.RLock()
|
||||
defer ofc.RUnlock()
|
||||
|
||||
fileInfo := ofc.files[inode]
|
||||
if fileInfo != nil {
|
||||
fileInfo.LastAccess = time.Now()
|
||||
ofc.cacheHits++
|
||||
return fileInfo
|
||||
}
|
||||
|
||||
ofc.cacheMisses++
|
||||
return nil
|
||||
}
|
||||
|
||||
// UpdateChunkCache updates chunk metadata for a file
|
||||
func (ofc *OpenFileCache) UpdateChunkCache(inode uint64, chunkIndex uint32, metadata *ChunkMetadata) {
|
||||
ofc.RLock()
|
||||
fileInfo := ofc.files[inode]
|
||||
ofc.RUnlock()
|
||||
|
||||
if fileInfo == nil {
|
||||
return
|
||||
}
|
||||
|
||||
fileInfo.Lock()
|
||||
defer fileInfo.Unlock()
|
||||
|
||||
fileInfo.ChunkCache[chunkIndex] = metadata
|
||||
metadata.LastAccess = time.Now()
|
||||
metadata.AccessCount++
|
||||
|
||||
glog.V(4).Infof("Updated chunk cache: inode=%d, chunk=%d, level=%d",
|
||||
inode, chunkIndex, metadata.CacheLevel)
|
||||
}
|
||||
|
||||
// GetChunkMetadata retrieves chunk metadata if available
|
||||
func (ofc *OpenFileCache) GetChunkMetadata(inode uint64, chunkIndex uint32) (*ChunkMetadata, bool) {
|
||||
ofc.RLock()
|
||||
fileInfo := ofc.files[inode]
|
||||
ofc.RUnlock()
|
||||
|
||||
if fileInfo == nil {
|
||||
return nil, false
|
||||
}
|
||||
|
||||
fileInfo.RLock()
|
||||
defer fileInfo.RUnlock()
|
||||
|
||||
metadata, exists := fileInfo.ChunkCache[chunkIndex]
|
||||
if exists {
|
||||
metadata.LastAccess = time.Now()
|
||||
metadata.AccessCount++
|
||||
}
|
||||
|
||||
return metadata, exists
|
||||
}
|
||||
|
||||
// updateAccessOrder updates the LRU access order
|
||||
func (ofc *OpenFileCache) updateAccessOrder(inode uint64) {
|
||||
// Remove from current position
|
||||
for i, ino := range ofc.accessOrder {
|
||||
if ino == inode {
|
||||
ofc.accessOrder = append(ofc.accessOrder[:i], ofc.accessOrder[i+1:]...)
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
// Add to front (most recently used)
|
||||
ofc.accessOrder = append([]uint64{inode}, ofc.accessOrder...)
|
||||
}
|
||||
|
||||
// evictLRU evicts the least recently used file
|
||||
func (ofc *OpenFileCache) evictLRU() {
|
||||
if len(ofc.accessOrder) == 0 {
|
||||
return
|
||||
}
|
||||
|
||||
// Find LRU file that can be evicted (not currently open)
|
||||
for i := len(ofc.accessOrder) - 1; i >= 0; i-- {
|
||||
inode := ofc.accessOrder[i]
|
||||
fileInfo := ofc.files[inode]
|
||||
|
||||
if fileInfo != nil && fileInfo.OpenCount <= 0 {
|
||||
// Evict this file
|
||||
delete(ofc.files, inode)
|
||||
ofc.accessOrder = append(ofc.accessOrder[:i], ofc.accessOrder[i+1:]...)
|
||||
ofc.evictedFiles++
|
||||
|
||||
glog.V(3).Infof("Evicted file from cache: inode=%d, chunks=%d",
|
||||
inode, len(fileInfo.ChunkCache))
|
||||
return
|
||||
}
|
||||
}
|
||||
|
||||
// If no files can be evicted, just log a warning
|
||||
glog.V(2).Infof("Warning: Could not evict any files from cache (all files are open)")
|
||||
}
|
||||
|
||||
// cleanupWorker periodically cleans up expired entries
|
||||
func (ofc *OpenFileCache) cleanupWorker() {
|
||||
ticker := time.NewTicker(ofc.cleanupInterval)
|
||||
defer ticker.Stop()
|
||||
|
||||
for {
|
||||
select {
|
||||
case <-ticker.C:
|
||||
ofc.cleanup()
|
||||
case <-ofc.shutdown:
|
||||
close(ofc.done)
|
||||
return
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// cleanup removes expired file entries
|
||||
func (ofc *OpenFileCache) cleanup() {
|
||||
ofc.Lock()
|
||||
defer ofc.Unlock()
|
||||
|
||||
now := time.Now()
|
||||
toRemove := make([]uint64, 0)
|
||||
|
||||
for inode, fileInfo := range ofc.files {
|
||||
// Only cleanup files that are not open and have expired
|
||||
if fileInfo.OpenCount <= 0 && now.Sub(fileInfo.LastAccess) > ofc.ttl {
|
||||
toRemove = append(toRemove, inode)
|
||||
}
|
||||
}
|
||||
|
||||
// Remove expired files
|
||||
for _, inode := range toRemove {
|
||||
delete(ofc.files, inode)
|
||||
// Remove from access order
|
||||
for i, ino := range ofc.accessOrder {
|
||||
if ino == inode {
|
||||
ofc.accessOrder = append(ofc.accessOrder[:i], ofc.accessOrder[i+1:]...)
|
||||
break
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if len(toRemove) > 0 {
|
||||
glog.V(3).Infof("Cleaned up %d expired file cache entries", len(toRemove))
|
||||
}
|
||||
}
|
||||
|
||||
// GetMetrics returns cache metrics
|
||||
func (ofc *OpenFileCache) GetMetrics() OpenFileCacheMetrics {
|
||||
ofc.RLock()
|
||||
defer ofc.RUnlock()
|
||||
|
||||
var totalChunks int64
|
||||
var mlFiles int64
|
||||
fileTypes := make(map[MLFileType]int)
|
||||
patterns := make(map[AccessPattern]int)
|
||||
|
||||
for _, fileInfo := range ofc.files {
|
||||
totalChunks += int64(len(fileInfo.ChunkCache))
|
||||
if fileInfo.IsMLFile {
|
||||
mlFiles++
|
||||
fileTypes[fileInfo.FileType]++
|
||||
}
|
||||
patterns[fileInfo.ReadPattern]++
|
||||
}
|
||||
|
||||
return OpenFileCacheMetrics{
|
||||
TotalFiles: int64(len(ofc.files)),
|
||||
MLFiles: mlFiles,
|
||||
TotalChunks: totalChunks,
|
||||
CacheHits: ofc.cacheHits,
|
||||
CacheMisses: ofc.cacheMisses,
|
||||
EvictedFiles: ofc.evictedFiles,
|
||||
FileTypes: fileTypes,
|
||||
AccessPatterns: patterns,
|
||||
}
|
||||
}
|
||||
|
||||
// OpenFileCacheMetrics holds metrics for the open file cache
|
||||
type OpenFileCacheMetrics struct {
|
||||
TotalFiles int64 `json:"total_files"`
|
||||
MLFiles int64 `json:"ml_files"`
|
||||
TotalChunks int64 `json:"total_chunks"`
|
||||
CacheHits int64 `json:"cache_hits"`
|
||||
CacheMisses int64 `json:"cache_misses"`
|
||||
EvictedFiles int64 `json:"evicted_files"`
|
||||
FileTypes map[MLFileType]int `json:"file_types"`
|
||||
AccessPatterns map[AccessPattern]int `json:"access_patterns"`
|
||||
}
|
||||
|
||||
// Shutdown gracefully shuts down the open file cache
|
||||
func (ofc *OpenFileCache) Shutdown() {
|
||||
glog.V(1).Infof("Shutting down OpenFileCache...")
|
||||
|
||||
close(ofc.shutdown)
|
||||
|
||||
// Wait for cleanup worker to finish
|
||||
<-ofc.done
|
||||
|
||||
// Print final metrics
|
||||
metrics := ofc.GetMetrics()
|
||||
glog.V(1).Infof("OpenFileCache final metrics: files=%d, chunks=%d, hits=%d, misses=%d",
|
||||
metrics.TotalFiles, metrics.TotalChunks, metrics.CacheHits, metrics.CacheMisses)
|
||||
}
|
||||
|
||||
// MLFileDetector methods
|
||||
|
||||
// DetectMLFile determines if a file is ML-related and its type
|
||||
func (detector *MLFileDetector) DetectMLFile(entry *filer_pb.Entry, fullPath string) (bool, MLFileType) {
|
||||
if entry == nil {
|
||||
return false, MLFileUnknown
|
||||
}
|
||||
|
||||
name := entry.Name
|
||||
size := int64(entry.Attributes.FileSize)
|
||||
|
||||
// Check file extension
|
||||
if ext := getFileExtension(name); ext != "" {
|
||||
if detector.datasetExtensions[ext] {
|
||||
return true, MLFileDataset
|
||||
}
|
||||
if detector.modelExtensions[ext] {
|
||||
return true, MLFileModel
|
||||
}
|
||||
if detector.configExtensions[ext] {
|
||||
return true, MLFileConfig
|
||||
}
|
||||
}
|
||||
|
||||
// Check path patterns
|
||||
for _, path := range detector.datasetPaths {
|
||||
if contains(fullPath, path) {
|
||||
return true, MLFileDataset
|
||||
}
|
||||
}
|
||||
|
||||
for _, path := range detector.modelPaths {
|
||||
if contains(fullPath, path) {
|
||||
return true, MLFileModel
|
||||
}
|
||||
}
|
||||
|
||||
// Check size heuristics
|
||||
if size > detector.modelMinSize {
|
||||
// Large files in certain contexts might be models
|
||||
if contains(fullPath, "model") || contains(fullPath, "checkpoint") || contains(fullPath, "weight") {
|
||||
return true, MLFileModel
|
||||
}
|
||||
}
|
||||
|
||||
// Check for tensor files
|
||||
if contains(name, "tensor") || contains(name, ".pt") || contains(name, ".npy") {
|
||||
return true, MLFileTensor
|
||||
}
|
||||
|
||||
// Check for log files
|
||||
if contains(name, "log") || contains(name, "tensorboard") || contains(fullPath, "logs") {
|
||||
return true, MLFileLog
|
||||
}
|
||||
|
||||
return false, MLFileUnknown
|
||||
}
|
||||
|
||||
// Helper functions
|
||||
|
||||
func getFileExtension(filename string) string {
|
||||
for i := len(filename) - 1; i >= 0; i-- {
|
||||
if filename[i] == '.' {
|
||||
return filename[i+1:]
|
||||
}
|
||||
}
|
||||
return ""
|
||||
}
|
||||
|
||||
func contains(str, substr string) bool {
|
||||
return len(str) >= len(substr) && findSubstring(str, substr)
|
||||
}
|
||||
|
||||
func findSubstring(str, substr string) bool {
|
||||
if len(substr) == 0 {
|
||||
return true
|
||||
}
|
||||
if len(str) < len(substr) {
|
||||
return false
|
||||
}
|
||||
|
||||
for i := 0; i <= len(str)-len(substr); i++ {
|
||||
if str[i:i+len(substr)] == substr {
|
||||
return true
|
||||
}
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
// String methods for enums
|
||||
|
||||
func (ps PrefetchState) String() string {
|
||||
switch ps {
|
||||
case PrefetchIdle:
|
||||
return "Idle"
|
||||
case PrefetchActive:
|
||||
return "Active"
|
||||
case PrefetchComplete:
|
||||
return "Complete"
|
||||
case PrefetchSuspended:
|
||||
return "Suspended"
|
||||
default:
|
||||
return "Unknown"
|
||||
}
|
||||
}
|
||||
|
||||
func (ft MLFileType) String() string {
|
||||
switch ft {
|
||||
case MLFileDataset:
|
||||
return "Dataset"
|
||||
case MLFileModel:
|
||||
return "Model"
|
||||
case MLFileConfig:
|
||||
return "Config"
|
||||
case MLFileTensor:
|
||||
return "Tensor"
|
||||
case MLFileLog:
|
||||
return "Log"
|
||||
default:
|
||||
return "Unknown"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,617 @@
|
||||
package ml
|
||||
|
||||
import (
|
||||
"testing"
|
||||
"time"
|
||||
|
||||
"github.com/seaweedfs/seaweedfs/weed/pb/filer_pb"
|
||||
)
|
||||
|
||||
func TestOpenFileCache_Basic(t *testing.T) {
|
||||
cache := NewOpenFileCache(10, 5*time.Minute)
|
||||
defer cache.Shutdown()
|
||||
|
||||
// Test opening a file
|
||||
entry := &filer_pb.Entry{
|
||||
Name: "test.txt",
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 1024,
|
||||
},
|
||||
}
|
||||
|
||||
inode := uint64(1)
|
||||
fullPath := "/test/test.txt"
|
||||
fileInfo := cache.OpenFile(inode, entry, fullPath)
|
||||
|
||||
if fileInfo == nil {
|
||||
t.Fatal("OpenFile should return file info")
|
||||
}
|
||||
|
||||
if fileInfo.Inode != inode {
|
||||
t.Errorf("Expected inode %d, got %d", inode, fileInfo.Inode)
|
||||
}
|
||||
|
||||
if fileInfo.OpenCount != 1 {
|
||||
t.Errorf("Expected open count 1, got %d", fileInfo.OpenCount)
|
||||
}
|
||||
}
|
||||
|
||||
func TestOpenFileCache_MLFileDetection(t *testing.T) {
|
||||
cache := NewOpenFileCache(10, 5*time.Minute)
|
||||
defer cache.Shutdown()
|
||||
|
||||
testCases := []struct {
|
||||
name string
|
||||
path string
|
||||
filename string
|
||||
size uint64
|
||||
expected MLFileType
|
||||
}{
|
||||
{"PyTorch model", "/models/checkpoint.pt", "checkpoint.pt", 100*1024*1024, MLFileModel},
|
||||
{"Dataset image", "/datasets/train/image001.jpg", "image001.jpg", 2*1024*1024, MLFileDataset},
|
||||
{"Config file", "/config/training.yaml", "training.yaml", 1024, MLFileConfig},
|
||||
{"Tensor file", "/tensors/weights.safetensors", "weights.safetensors", 50*1024*1024, MLFileModel},
|
||||
{"Log file", "/logs/training.log", "training.log", 10*1024, MLFileLog},
|
||||
{"Regular file", "/documents/readme.txt", "readme.txt", 5*1024, MLFileUnknown},
|
||||
}
|
||||
|
||||
for _, tc := range testCases {
|
||||
t.Run(tc.name, func(t *testing.T) {
|
||||
entry := &filer_pb.Entry{
|
||||
Name: tc.filename,
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: tc.size,
|
||||
},
|
||||
}
|
||||
|
||||
inode := uint64(time.Now().UnixNano()) // Unique inode
|
||||
fileInfo := cache.OpenFile(inode, entry, tc.path)
|
||||
|
||||
if tc.expected == MLFileUnknown {
|
||||
if fileInfo.IsMLFile {
|
||||
t.Errorf("File %s should not be detected as ML file", tc.path)
|
||||
}
|
||||
} else {
|
||||
if !fileInfo.IsMLFile {
|
||||
t.Errorf("File %s should be detected as ML file", tc.path)
|
||||
}
|
||||
|
||||
if fileInfo.FileType != tc.expected {
|
||||
t.Errorf("Expected file type %v, got %v", tc.expected, fileInfo.FileType)
|
||||
}
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
func TestOpenFileCache_ChunkMetadata(t *testing.T) {
|
||||
cache := NewOpenFileCache(10, 5*time.Minute)
|
||||
defer cache.Shutdown()
|
||||
|
||||
inode := uint64(1)
|
||||
entry := &filer_pb.Entry{
|
||||
Name: "data.bin",
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 10240,
|
||||
},
|
||||
}
|
||||
fullPath := "/data/data.bin"
|
||||
|
||||
cache.OpenFile(inode, entry, fullPath)
|
||||
|
||||
// Test updating chunk metadata
|
||||
chunkIndex := uint32(0)
|
||||
metadata := &ChunkMetadata{
|
||||
FileId: "chunk_0",
|
||||
Offset: 0,
|
||||
Size: 1024,
|
||||
CacheLevel: 0,
|
||||
LastAccess: time.Now(),
|
||||
AccessCount: 1,
|
||||
Pattern: SequentialAccess,
|
||||
}
|
||||
|
||||
cache.UpdateChunkCache(inode, chunkIndex, metadata)
|
||||
|
||||
// Test retrieving chunk metadata
|
||||
retrieved, exists := cache.GetChunkMetadata(inode, chunkIndex)
|
||||
if !exists {
|
||||
t.Error("Chunk metadata should exist")
|
||||
}
|
||||
|
||||
if retrieved.FileId != metadata.FileId {
|
||||
t.Errorf("Expected FileId %s, got %s", metadata.FileId, retrieved.FileId)
|
||||
}
|
||||
|
||||
if retrieved.AccessCount != 2 { // Should be incremented during retrieval
|
||||
t.Errorf("Expected access count 2, got %d", retrieved.AccessCount)
|
||||
}
|
||||
}
|
||||
|
||||
func TestOpenFileCache_LRUEviction(t *testing.T) {
|
||||
cache := NewOpenFileCache(3, 5*time.Minute) // Small cache for testing
|
||||
defer cache.Shutdown()
|
||||
|
||||
// Fill cache to capacity
|
||||
for i := 1; i <= 3; i++ {
|
||||
entry := &filer_pb.Entry{
|
||||
Name: "file" + string(rune('0'+i)) + ".txt",
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 1024,
|
||||
},
|
||||
}
|
||||
fullPath := "/test/file" + string(rune('0'+i)) + ".txt"
|
||||
cache.OpenFile(uint64(i), entry, fullPath)
|
||||
cache.CloseFile(uint64(i)) // Close immediately so they can be evicted
|
||||
}
|
||||
|
||||
// Add one more file - should trigger eviction
|
||||
entry4 := &filer_pb.Entry{
|
||||
Name: "file4.txt",
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 1024,
|
||||
},
|
||||
}
|
||||
cache.OpenFile(uint64(4), entry4, "/test/file4.txt")
|
||||
|
||||
metrics := cache.GetMetrics()
|
||||
if metrics.EvictedFiles == 0 {
|
||||
t.Error("Should have evicted at least one file")
|
||||
}
|
||||
|
||||
// File 1 should be evicted (oldest)
|
||||
file1Info := cache.GetFileInfo(uint64(1))
|
||||
if file1Info != nil {
|
||||
t.Error("File 1 should have been evicted")
|
||||
}
|
||||
|
||||
// File 4 should still be there
|
||||
file4Info := cache.GetFileInfo(uint64(4))
|
||||
if file4Info == nil {
|
||||
t.Error("File 4 should still be in cache")
|
||||
}
|
||||
}
|
||||
|
||||
func TestOpenFileCache_TTLCleanup(t *testing.T) {
|
||||
cache := NewOpenFileCache(10, 100*time.Millisecond) // Short TTL for testing
|
||||
defer cache.Shutdown()
|
||||
|
||||
inode := uint64(1)
|
||||
entry := &filer_pb.Entry{
|
||||
Name: "test.txt",
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 1024,
|
||||
},
|
||||
}
|
||||
|
||||
fileInfo := cache.OpenFile(inode, entry, "/test/test.txt")
|
||||
cache.CloseFile(inode) // Close so it can be cleaned up
|
||||
|
||||
// Wait for TTL to expire
|
||||
time.Sleep(150 * time.Millisecond)
|
||||
|
||||
// Trigger cleanup manually
|
||||
cache.cleanup()
|
||||
|
||||
// File should be cleaned up
|
||||
retrievedInfo := cache.GetFileInfo(inode)
|
||||
if retrievedInfo != nil {
|
||||
t.Error("File should have been cleaned up after TTL expiration")
|
||||
}
|
||||
|
||||
_ = fileInfo // Avoid unused variable warning
|
||||
}
|
||||
|
||||
func TestOpenFileCache_MultipleOpens(t *testing.T) {
|
||||
cache := NewOpenFileCache(10, 5*time.Minute)
|
||||
defer cache.Shutdown()
|
||||
|
||||
inode := uint64(1)
|
||||
entry := &filer_pb.Entry{
|
||||
Name: "shared.txt",
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 1024,
|
||||
},
|
||||
}
|
||||
fullPath := "/test/shared.txt"
|
||||
|
||||
// Open file multiple times
|
||||
fileInfo1 := cache.OpenFile(inode, entry, fullPath)
|
||||
fileInfo2 := cache.OpenFile(inode, entry, fullPath)
|
||||
|
||||
if fileInfo1 != fileInfo2 {
|
||||
t.Error("Multiple opens of same file should return same file info")
|
||||
}
|
||||
|
||||
if fileInfo1.OpenCount != 2 {
|
||||
t.Errorf("Expected open count 2, got %d", fileInfo1.OpenCount)
|
||||
}
|
||||
|
||||
// Close once
|
||||
canEvict1 := cache.CloseFile(inode)
|
||||
if canEvict1 {
|
||||
t.Error("Should not be able to evict file with open count > 0")
|
||||
}
|
||||
|
||||
if fileInfo1.OpenCount != 1 {
|
||||
t.Errorf("Expected open count 1 after first close, got %d", fileInfo1.OpenCount)
|
||||
}
|
||||
|
||||
// Close again
|
||||
canEvict2 := cache.CloseFile(inode)
|
||||
if !canEvict2 {
|
||||
t.Error("Should be able to evict file with open count 0")
|
||||
}
|
||||
}
|
||||
|
||||
func TestOpenFileCache_Metrics(t *testing.T) {
|
||||
cache := NewOpenFileCache(10, 5*time.Minute)
|
||||
defer cache.Shutdown()
|
||||
|
||||
// Add some files of different types
|
||||
files := []struct {
|
||||
inode uint64
|
||||
filename string
|
||||
path string
|
||||
size uint64
|
||||
}{
|
||||
{1, "model.pt", "/models/model.pt", 100 * 1024 * 1024},
|
||||
{2, "data.jpg", "/datasets/data.jpg", 2 * 1024 * 1024},
|
||||
{3, "config.yaml", "/config/config.yaml", 1024},
|
||||
{4, "regular.txt", "/docs/regular.txt", 5 * 1024},
|
||||
}
|
||||
|
||||
for _, file := range files {
|
||||
entry := &filer_pb.Entry{
|
||||
Name: file.filename,
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: file.size,
|
||||
},
|
||||
}
|
||||
cache.OpenFile(file.inode, entry, file.path)
|
||||
|
||||
// Add some chunk metadata
|
||||
metadata := &ChunkMetadata{
|
||||
FileId: "chunk_" + string(rune(file.inode)),
|
||||
Offset: 0,
|
||||
Size: 1024,
|
||||
CacheLevel: 0,
|
||||
}
|
||||
cache.UpdateChunkCache(file.inode, 0, metadata)
|
||||
}
|
||||
|
||||
metrics := cache.GetMetrics()
|
||||
|
||||
if metrics.TotalFiles != 4 {
|
||||
t.Errorf("Expected 4 total files, got %d", metrics.TotalFiles)
|
||||
}
|
||||
|
||||
if metrics.MLFiles < 2 { // Should detect at least model and dataset
|
||||
t.Errorf("Expected at least 2 ML files, got %d", metrics.MLFiles)
|
||||
}
|
||||
|
||||
if metrics.TotalChunks != 4 {
|
||||
t.Errorf("Expected 4 total chunks, got %d", metrics.TotalChunks)
|
||||
}
|
||||
|
||||
// Check file type counts
|
||||
if metrics.FileTypes[MLFileModel] == 0 {
|
||||
t.Error("Should detect at least one model file")
|
||||
}
|
||||
|
||||
if metrics.FileTypes[MLFileDataset] == 0 {
|
||||
t.Error("Should detect at least one dataset file")
|
||||
}
|
||||
}
|
||||
|
||||
func TestOpenFileCache_ConcurrentAccess(t *testing.T) {
|
||||
cache := NewOpenFileCache(100, 5*time.Minute)
|
||||
defer cache.Shutdown()
|
||||
|
||||
// Test concurrent access to the cache
|
||||
numGoroutines := 10
|
||||
done := make(chan bool, numGoroutines)
|
||||
|
||||
for i := 0; i < numGoroutines; i++ {
|
||||
go func(id int) {
|
||||
defer func() { done <- true }()
|
||||
|
||||
inode := uint64(id)
|
||||
entry := &filer_pb.Entry{
|
||||
Name: "file" + string(rune('0'+id)) + ".txt",
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 1024,
|
||||
},
|
||||
}
|
||||
fullPath := "/test/file" + string(rune('0'+id)) + ".txt"
|
||||
|
||||
// Perform multiple operations
|
||||
for j := 0; j < 10; j++ {
|
||||
cache.OpenFile(inode, entry, fullPath)
|
||||
|
||||
metadata := &ChunkMetadata{
|
||||
FileId: "chunk_" + string(rune(id)) + "_" + string(rune(j)),
|
||||
Offset: uint64(j * 1024),
|
||||
Size: 1024,
|
||||
CacheLevel: 0,
|
||||
}
|
||||
cache.UpdateChunkCache(inode, uint32(j), metadata)
|
||||
|
||||
cache.GetChunkMetadata(inode, uint32(j))
|
||||
cache.CloseFile(inode)
|
||||
}
|
||||
}(i)
|
||||
}
|
||||
|
||||
// Wait for all goroutines to complete
|
||||
for i := 0; i < numGoroutines; i++ {
|
||||
<-done
|
||||
}
|
||||
|
||||
// Verify cache state
|
||||
metrics := cache.GetMetrics()
|
||||
if metrics.TotalFiles == 0 {
|
||||
t.Error("Should have some files in cache after concurrent operations")
|
||||
}
|
||||
}
|
||||
|
||||
func TestMLFileDetector_Extensions(t *testing.T) {
|
||||
detector := newMLFileDetector()
|
||||
|
||||
testCases := []struct {
|
||||
filename string
|
||||
path string
|
||||
expected MLFileType
|
||||
}{
|
||||
{"model.pt", "/models/model.pt", MLFileModel},
|
||||
{"weights.pth", "/models/weights.pth", MLFileModel},
|
||||
{"data.jpg", "/datasets/data.jpg", MLFileDataset},
|
||||
{"config.yaml", "/config/config.yaml", MLFileConfig},
|
||||
{"tensor.safetensors", "/tensors/tensor.safetensors", MLFileModel},
|
||||
{"training.log", "/logs/training.log", MLFileLog},
|
||||
{"document.txt", "/docs/document.txt", MLFileUnknown},
|
||||
}
|
||||
|
||||
for _, tc := range testCases {
|
||||
t.Run(tc.filename, func(t *testing.T) {
|
||||
entry := &filer_pb.Entry{
|
||||
Name: tc.filename,
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 1024,
|
||||
},
|
||||
}
|
||||
|
||||
isML, fileType := detector.DetectMLFile(entry, tc.path)
|
||||
|
||||
if tc.expected == MLFileUnknown {
|
||||
// For unknown files, either ML detection result is acceptable
|
||||
t.Logf("File %s: isML=%v, type=%v", tc.filename, isML, fileType)
|
||||
} else {
|
||||
if !isML {
|
||||
t.Errorf("File %s should be detected as ML file", tc.filename)
|
||||
}
|
||||
|
||||
if fileType != tc.expected {
|
||||
t.Errorf("File %s: expected type %v, got %v", tc.filename, tc.expected, fileType)
|
||||
}
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
func TestMLFileDetector_PathPatterns(t *testing.T) {
|
||||
detector := newMLFileDetector()
|
||||
|
||||
testCases := []struct {
|
||||
path string
|
||||
filename string
|
||||
expected MLFileType
|
||||
}{
|
||||
{"/datasets/train/file.bin", "file.bin", MLFileDataset},
|
||||
{"/models/checkpoint/weights", "weights", MLFileModel},
|
||||
{"/data/validation/sample.dat", "sample.dat", MLFileDataset},
|
||||
{"/checkpoints/model_v1.bin", "model_v1.bin", MLFileModel},
|
||||
{"/documents/report.pdf", "report.pdf", MLFileUnknown},
|
||||
}
|
||||
|
||||
for _, tc := range testCases {
|
||||
t.Run(tc.path, func(t *testing.T) {
|
||||
entry := &filer_pb.Entry{
|
||||
Name: tc.filename,
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 1024,
|
||||
},
|
||||
}
|
||||
|
||||
isML, fileType := detector.DetectMLFile(entry, tc.path)
|
||||
|
||||
if tc.expected == MLFileUnknown {
|
||||
t.Logf("Path %s: isML=%v, type=%v", tc.path, isML, fileType)
|
||||
} else {
|
||||
if !isML {
|
||||
t.Errorf("Path %s should be detected as ML file", tc.path)
|
||||
}
|
||||
|
||||
if fileType != tc.expected {
|
||||
t.Errorf("Path %s: expected type %v, got %v", tc.path, tc.expected, fileType)
|
||||
}
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
func TestMLFileDetector_SizeHeuristics(t *testing.T) {
|
||||
detector := newMLFileDetector()
|
||||
|
||||
// Large file with model-related name should be detected as model
|
||||
largeModelEntry := &filer_pb.Entry{
|
||||
Name: "large_model.bin",
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 500 * 1024 * 1024, // 500MB
|
||||
},
|
||||
}
|
||||
|
||||
isML, fileType := detector.DetectMLFile(largeModelEntry, "/checkpoints/large_model.bin")
|
||||
|
||||
if !isML {
|
||||
t.Error("Large model file should be detected as ML file")
|
||||
}
|
||||
|
||||
if fileType != MLFileModel {
|
||||
t.Errorf("Large model file should be detected as model, got %v", fileType)
|
||||
}
|
||||
}
|
||||
|
||||
func TestOpenFileCache_EvictionProtection(t *testing.T) {
|
||||
cache := NewOpenFileCache(2, 5*time.Minute) // Very small cache
|
||||
defer cache.Shutdown()
|
||||
|
||||
// Open two files and keep them open
|
||||
for i := 1; i <= 2; i++ {
|
||||
entry := &filer_pb.Entry{
|
||||
Name: "file" + string(rune('0'+i)) + ".txt",
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 1024,
|
||||
},
|
||||
}
|
||||
fullPath := "/test/file" + string(rune('0'+i)) + ".txt"
|
||||
cache.OpenFile(uint64(i), entry, fullPath)
|
||||
// Don't close - keep them open
|
||||
}
|
||||
|
||||
// Try to open a third file - should not evict open files
|
||||
entry3 := &filer_pb.Entry{
|
||||
Name: "file3.txt",
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 1024,
|
||||
},
|
||||
}
|
||||
cache.OpenFile(uint64(3), entry3, "/test/file3.txt")
|
||||
|
||||
// All files should still be there since none could be evicted
|
||||
for i := 1; i <= 3; i++ {
|
||||
fileInfo := cache.GetFileInfo(uint64(i))
|
||||
if fileInfo == nil {
|
||||
t.Errorf("File %d should still be in cache (eviction protection)", i)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
func TestOpenFileCache_GetFileInfo_CacheHitMiss(t *testing.T) {
|
||||
cache := NewOpenFileCache(10, 5*time.Minute)
|
||||
defer cache.Shutdown()
|
||||
|
||||
inode := uint64(1)
|
||||
|
||||
// Test cache miss
|
||||
fileInfo := cache.GetFileInfo(inode)
|
||||
if fileInfo != nil {
|
||||
t.Error("Should return nil for non-existent file")
|
||||
}
|
||||
|
||||
initialMetrics := cache.GetMetrics()
|
||||
if initialMetrics.CacheMisses == 0 {
|
||||
t.Error("Should record cache miss")
|
||||
}
|
||||
|
||||
// Add file to cache
|
||||
entry := &filer_pb.Entry{
|
||||
Name: "test.txt",
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 1024,
|
||||
},
|
||||
}
|
||||
cache.OpenFile(inode, entry, "/test/test.txt")
|
||||
|
||||
// Test cache hit
|
||||
fileInfo = cache.GetFileInfo(inode)
|
||||
if fileInfo == nil {
|
||||
t.Error("Should return file info for existing file")
|
||||
}
|
||||
|
||||
finalMetrics := cache.GetMetrics()
|
||||
if finalMetrics.CacheHits == 0 {
|
||||
t.Error("Should record cache hit")
|
||||
}
|
||||
|
||||
if finalMetrics.CacheHits <= initialMetrics.CacheHits {
|
||||
t.Error("Cache hits should increase")
|
||||
}
|
||||
}
|
||||
|
||||
func TestOpenFileCache_Shutdown(t *testing.T) {
|
||||
cache := NewOpenFileCache(10, 5*time.Minute)
|
||||
|
||||
// Add some files
|
||||
for i := 1; i <= 3; i++ {
|
||||
entry := &filer_pb.Entry{
|
||||
Name: "file" + string(rune('0'+i)) + ".txt",
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 1024,
|
||||
},
|
||||
}
|
||||
fullPath := "/test/file" + string(rune('0'+i)) + ".txt"
|
||||
cache.OpenFile(uint64(i), entry, fullPath)
|
||||
}
|
||||
|
||||
// Test graceful shutdown
|
||||
done := make(chan struct{})
|
||||
go func() {
|
||||
cache.Shutdown()
|
||||
close(done)
|
||||
}()
|
||||
|
||||
select {
|
||||
case <-done:
|
||||
// Success
|
||||
case <-time.After(5 * time.Second):
|
||||
t.Error("Shutdown took too long")
|
||||
}
|
||||
}
|
||||
|
||||
// Benchmark tests
|
||||
|
||||
func BenchmarkOpenFileCache_OpenFile(b *testing.B) {
|
||||
cache := NewOpenFileCache(1000, 30*time.Minute)
|
||||
defer cache.Shutdown()
|
||||
|
||||
entry := &filer_pb.Entry{
|
||||
Name: "benchmark.txt",
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 1024,
|
||||
},
|
||||
}
|
||||
fullPath := "/test/benchmark.txt"
|
||||
|
||||
b.ResetTimer()
|
||||
|
||||
for i := 0; i < b.N; i++ {
|
||||
inode := uint64(i % 100) // Cycle through 100 files
|
||||
cache.OpenFile(inode, entry, fullPath)
|
||||
}
|
||||
}
|
||||
|
||||
func BenchmarkOpenFileCache_GetFileInfo(b *testing.B) {
|
||||
cache := NewOpenFileCache(1000, 30*time.Minute)
|
||||
defer cache.Shutdown()
|
||||
|
||||
// Pre-populate cache
|
||||
entry := &filer_pb.Entry{
|
||||
Name: "benchmark.txt",
|
||||
Attributes: &filer_pb.FuseAttributes{
|
||||
FileSize: 1024,
|
||||
},
|
||||
}
|
||||
fullPath := "/test/benchmark.txt"
|
||||
|
||||
for i := 0; i < 100; i++ {
|
||||
cache.OpenFile(uint64(i), entry, fullPath)
|
||||
}
|
||||
|
||||
b.ResetTimer()
|
||||
|
||||
for i := 0; i < b.N; i++ {
|
||||
inode := uint64(i % 100)
|
||||
cache.GetFileInfo(inode)
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,142 @@
|
||||
package mount
|
||||
|
||||
import (
|
||||
"time"
|
||||
|
||||
"github.com/hanwen/go-fuse/v2/fuse"
|
||||
"github.com/seaweedfs/seaweedfs/weed/glog"
|
||||
"github.com/seaweedfs/seaweedfs/weed/mount/ml"
|
||||
"github.com/seaweedfs/seaweedfs/weed/pb/filer_pb"
|
||||
"github.com/seaweedfs/seaweedfs/weed/util/chunk_cache"
|
||||
"github.com/seaweedfs/seaweedfs/weed/wdclient"
|
||||
)
|
||||
|
||||
// MLIntegrationManager manages ML optimization integration for the main WFS
|
||||
type MLIntegrationManager struct {
|
||||
mlOptimization *ml.MLOptimization
|
||||
fuseIntegration *ml.FUSEMLIntegration
|
||||
enabled bool
|
||||
}
|
||||
|
||||
// NewMLIntegrationManager creates a new ML integration manager
|
||||
func NewMLIntegrationManager(chunkCache chunk_cache.ChunkCache, lookupFn wdclient.LookupFileIdFunctionType) *MLIntegrationManager {
|
||||
// Create ML optimization with default config
|
||||
config := ml.DefaultMLConfig()
|
||||
mlOpt := ml.NewMLOptimization(config, chunkCache, lookupFn)
|
||||
|
||||
// Create FUSE integration
|
||||
fuseInt := ml.NewFUSEMLIntegration(mlOpt)
|
||||
|
||||
manager := &MLIntegrationManager{
|
||||
mlOptimization: mlOpt,
|
||||
fuseIntegration: fuseInt,
|
||||
enabled: true,
|
||||
}
|
||||
|
||||
glog.V(1).Infof("ML integration manager initialized")
|
||||
return manager
|
||||
}
|
||||
|
||||
// EnableMLOptimization enables or disables ML optimization
|
||||
func (mgr *MLIntegrationManager) EnableMLOptimization(enabled bool) {
|
||||
mgr.enabled = enabled
|
||||
|
||||
if mgr.mlOptimization != nil {
|
||||
mgr.mlOptimization.Enable(enabled)
|
||||
}
|
||||
|
||||
if mgr.fuseIntegration != nil {
|
||||
mgr.fuseIntegration.EnableMLOptimizations(enabled)
|
||||
}
|
||||
|
||||
glog.V(1).Infof("ML optimization %s", map[bool]string{true: "enabled", false: "disabled"}[enabled])
|
||||
}
|
||||
|
||||
// OnFileOpen should be called when a file is opened
|
||||
func (mgr *MLIntegrationManager) OnFileOpen(inode uint64, entry *filer_pb.Entry, fullPath string, flags uint32, out *fuse.OpenOut) {
|
||||
if !mgr.enabled || mgr.fuseIntegration == nil {
|
||||
return
|
||||
}
|
||||
|
||||
mgr.fuseIntegration.OnFileOpen(inode, entry, fullPath, flags, out)
|
||||
}
|
||||
|
||||
// OnFileClose should be called when a file is closed
|
||||
func (mgr *MLIntegrationManager) OnFileClose(inode uint64) {
|
||||
if !mgr.enabled || mgr.fuseIntegration == nil {
|
||||
return
|
||||
}
|
||||
|
||||
mgr.fuseIntegration.OnFileClose(inode)
|
||||
}
|
||||
|
||||
// OnFileRead should be called when a file is read
|
||||
func (mgr *MLIntegrationManager) OnFileRead(inode uint64, offset int64, size int) {
|
||||
if !mgr.enabled || mgr.fuseIntegration == nil {
|
||||
return
|
||||
}
|
||||
|
||||
mgr.fuseIntegration.OnFileRead(inode, offset, size)
|
||||
}
|
||||
|
||||
// OnChunkAccess should be called when a chunk is accessed
|
||||
func (mgr *MLIntegrationManager) OnChunkAccess(inode uint64, chunkIndex uint32, fileId string, cacheLevel int, isHit bool) {
|
||||
if !mgr.enabled || mgr.fuseIntegration == nil {
|
||||
return
|
||||
}
|
||||
|
||||
mgr.fuseIntegration.OnChunkAccess(inode, chunkIndex, fileId, cacheLevel, isHit)
|
||||
}
|
||||
|
||||
// OptimizeAttributes applies ML-specific attribute caching
|
||||
func (mgr *MLIntegrationManager) OptimizeAttributes(inode uint64, out *fuse.AttrOut) {
|
||||
if !mgr.enabled || mgr.fuseIntegration == nil {
|
||||
return
|
||||
}
|
||||
|
||||
mgr.fuseIntegration.OptimizeAttributes(inode, out)
|
||||
}
|
||||
|
||||
// OptimizeEntryCache applies ML-specific entry caching
|
||||
func (mgr *MLIntegrationManager) OptimizeEntryCache(inode uint64, entry *filer_pb.Entry, out *fuse.EntryOut) {
|
||||
if !mgr.enabled || mgr.fuseIntegration == nil {
|
||||
return
|
||||
}
|
||||
|
||||
mgr.fuseIntegration.OptimizeEntryCache(inode, entry, out)
|
||||
}
|
||||
|
||||
// ShouldEnableWriteback determines if writeback should be enabled for a file
|
||||
func (mgr *MLIntegrationManager) ShouldEnableWriteback(inode uint64, entry *filer_pb.Entry) bool {
|
||||
if !mgr.enabled || mgr.fuseIntegration == nil {
|
||||
return false
|
||||
}
|
||||
|
||||
return mgr.fuseIntegration.ShouldEnableWriteback(inode, entry)
|
||||
}
|
||||
|
||||
// GetComprehensiveMetrics returns all ML optimization metrics
|
||||
func (mgr *MLIntegrationManager) GetComprehensiveMetrics() *ml.FUSEMLMetrics {
|
||||
if !mgr.enabled || mgr.fuseIntegration == nil {
|
||||
return &ml.FUSEMLMetrics{}
|
||||
}
|
||||
|
||||
metrics := mgr.fuseIntegration.GetOptimizationMetrics()
|
||||
return &metrics
|
||||
}
|
||||
|
||||
// IsEnabled returns whether ML optimization is enabled
|
||||
func (mgr *MLIntegrationManager) IsEnabled() bool {
|
||||
return mgr.enabled
|
||||
}
|
||||
|
||||
// Shutdown gracefully shuts down the ML integration
|
||||
func (mgr *MLIntegrationManager) Shutdown() {
|
||||
glog.V(1).Infof("Shutting down ML integration manager...")
|
||||
|
||||
if mgr.fuseIntegration != nil {
|
||||
mgr.fuseIntegration.Shutdown()
|
||||
}
|
||||
|
||||
glog.V(1).Infof("ML integration manager shutdown complete")
|
||||
}
|
||||
Reference in New Issue
Block a user