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Phase 2: Enhanced ML-aware caching with open file tracking
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 layerimprove-fuse-mount
chrislu
3 months ago
6 changed files with 2510 additions and 0 deletions
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313weed/mount/ml/cache_policy.go
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549weed/mount/ml/cache_policy_test.go
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312weed/mount/ml/fuse_integration.go
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577weed/mount/ml/open_file_cache.go
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617weed/mount/ml/open_file_cache_test.go
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142weed/mount/ml_integration.go
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package ml |
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|
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import ( |
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"math" |
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"time" |
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|
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"github.com/seaweedfs/seaweedfs/weed/glog" |
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) |
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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func TestMLCachePolicy_SizePreference(t *testing.T) { |
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policy := NewMLCachePolicy() |
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|
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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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|
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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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|
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smallScore := policy.CalculateEvictionScore(smallEntry) |
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largeScore := policy.CalculateEvictionScore(largeEntry) |
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|
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if smallScore <= largeScore { |
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t.Errorf("Small files should have higher score than large files: small=%.3f, large=%.3f", |
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smallScore, largeScore) |
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} |
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} |
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|
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func TestMLCachePolicy_RecencyDecay(t *testing.T) { |
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policy := NewMLCachePolicy() |
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|
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// Create entries with different access times
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recentEntry := &CacheEntry{ |
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Inode: 1, |
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|
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Size: 1024, |
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LastAccess: time.Now(), |
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AccessCount: 5, |
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FileType: MLFileUnknown, |
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} |
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|
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oldEntry := &CacheEntry{ |
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Inode: 2, |
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|
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Size: 1024, |
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LastAccess: time.Now().Add(-20 * time.Minute), |
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AccessCount: 5, |
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FileType: MLFileUnknown, |
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} |
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|
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recentScore := policy.CalculateEvictionScore(recentEntry) |
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oldScore := policy.CalculateEvictionScore(oldEntry) |
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|
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if recentScore <= oldScore { |
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t.Errorf("Recent access should have higher score: recent=%.3f, old=%.3f", |
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recentScore, oldScore) |
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} |
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} |
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|
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func TestMLCachePolicy_EpochRelevance(t *testing.T) { |
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policy := NewMLCachePolicy() |
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|
|||
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") |
|||
} |
|||
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