seaweedfs/ML_OPTIMIZATION_PLAN.md

SeaweedFS FUSE ML Optimization Plan

Analysis Summary

Based on examination of JuiceFS's recent 600 commits and current SeaweedFS FUSE implementation, this plan identifies key ML-focused optimizations that can be ported to SeaweedFS.

Key JuiceFS Optimizations for ML Workloads:

1. Smart Prefetching System (pkg/chunk/prefetch.go)

   • Concurrent prefetch workers (configurable parallelism)
   • Duplicate request deduplication
   • Background chunk fetching

2. Advanced Caching Architecture

   • Multi-tiered caching (memory + disk with size-based tiers)
   • Open file cache with chunk-level caching (pkg/meta/openfile.go)
   • Intelligent cache eviction based on access patterns

3. Performance Optimizations

   • Support for writeback cache mode
   • Memory cache optimization with separate allocation
   • Better cache hit detection and metrics

Current SeaweedFS Limitations:

1. Basic Caching: Simple tiered cache without smart prefetching
2. No Sequential Access Detection: Missing readahead optimizations
3. Limited Concurrency Control: Basic reader cache without pattern detection
4. No ML-Specific Optimizations: Missing batch processing awareness

Implementation Plan

Phase 1: Smart Prefetching System (Priority: High)

1.1 Create Prefetch Worker Pool

// Location: weed/mount/prefetch.go (new file)
type PrefetchManager struct {
    workers     chan *PrefetchRequest
    activeJobs  map[string]*PrefetchJob
    maxWorkers  int
    jobTimeout  time.Duration
}

type PrefetchRequest struct {
    FileId      string
    ChunkIndex  uint32
    Priority    int
    Callback    func([]byte, error)
}

1.2 Sequential Access Detection

// Location: weed/mount/access_pattern.go (new file)
type AccessPatternDetector struct {
    recentAccesses []AccessInfo
    sequentialThreshold int
    readaheadSize       int64
}

// Integration in weedfs_file_read.go
func (fh *FileHandle) detectSequentialAccess(offset int64, size int) bool {
    // Detect if current read follows sequential pattern
    // Trigger prefetch for next chunks if sequential
}

1.3 Enhanced Reader Cache with Prefetching

// Location: weed/filer/reader_cache.go (enhancement)
func (rc *ReaderCache) MaybePrefetch(chunkViews *Interval[*ChunkView]) {
    // Enhanced version with sequential detection
    // Prefetch multiple chunks ahead for sequential reads
    // Use ML-aware heuristics for prefetch distance
}

Phase 2: Enhanced Caching (Priority: High)

2.1 Open File Cache with Chunk Metadata

// Location: weed/mount/open_file_cache.go (new file)
type OpenFileCache struct {
    files    map[uint64]*OpenFile // inode -> OpenFile
    mutex    sync.RWMutex
    maxFiles int
    ttl      time.Duration
}

type OpenFile struct {
    Inode       uint64
    ChunkCache  map[uint32]*ChunkMetadata
    AccessTime  time.Time
    ReadPattern AccessPattern
}

type ChunkMetadata struct {
    Offset     uint64
    Size       uint64
    CacheLevel int // 0=memory, 1=disk, 2=not cached
    LastAccess time.Time
}

2.2 ML-Aware Cache Eviction Policy

// Location: weed/util/chunk_cache/ml_cache_policy.go (new file)
type MLCachePolicy struct {
    // Factors in:
    // - File access recency
    // - Sequential vs random access patterns
    // - File size (prefer caching smaller frequently accessed files)
    // - Training vs inference workload detection
}

func (policy *MLCachePolicy) ShouldEvict(chunk *CacheEntry) bool {
    // ML-specific eviction logic
    // Keep chunks that are part of training datasets longer
    // Prioritize model checkpoints during inference
}

2.3 Writeback Cache Support

// Location: weed/mount/weedfs.go (enhancement)
func (wfs *WFS) configureFuseOptions() {
    // Add support for FOPEN_KEEP_CACHE
    // Implement writeback cache similar to JuiceFS
    // Enable kernel caching for read-heavy ML workloads
}

Phase 3: ML Pattern Detection (Priority: Medium)

3.1 Training Data Access Pattern Detection

// Location: weed/mount/ml_patterns.go (new file)
type MLWorkloadDetector struct {
    accessHistory []AccessEvent
    patterns      []AccessPattern
}

type AccessPattern int
const (
    RandomAccess AccessPattern = iota
    SequentialAccess
    StridedAccess    // Common in image datasets
    BatchAccess      // Multiple files accessed together
    EpochAccess      // Dataset restart patterns
)

func (detector *MLWorkloadDetector) DetectPattern(accesses []AccessEvent) AccessPattern {
    // Analyze access patterns to detect:
    // - Image dataset traversal (often sequential with restarts)
    // - Model checkpoint loading (large sequential reads)
    // - Tensor file access patterns
}

3.2 Dataset Traversal Optimization

// Location: weed/mount/dataset_optimizer.go (new file)
func (opt *DatasetOptimizer) OptimizeForTraining() {
    // Pre-load dataset metadata
    // Prefetch next batch of files during current batch processing
    // Implement epoch boundary detection and cache warming
}

Phase 4: Batch Optimization (Priority: Medium)

4.1 Batch Read Aggregation

// Location: weed/mount/batch_reader.go (new file)
type BatchReader struct {
    pendingReads []ReadRequest
    batchSize    int
    timeout      time.Duration
}

func (br *BatchReader) AggregateReads() {
    // Combine multiple small reads into larger requests
    // Optimize for common ML access patterns
    // Reduce network overhead for distributed training
}

4.2 Tensor File Optimization

// Location: weed/mount/tensor_optimizer.go (new file)
func (to *TensorOptimizer) OptimizeForTensorFlow() {
    // Detect TFRecord, PyTorch .pt files
    // Optimize chunk sizes for tensor data
    // Implement tensor-aware prefetching
}

Phase 5: Configuration and Monitoring (Priority: Low)

5.1 ML-Specific Mount Options

// Location: weed/command/mount.go (enhancement)
var mlOptions = struct {
    enableMLOptimization *bool
    prefetchWorkers      *int
    mlCacheSize         *int64
    trainingMode        *bool
    datasetPath         *string
}

// New mount flags:
// -ml.optimization=true
// -ml.prefetchWorkers=8  
// -ml.cacheSize=1GB
// -ml.trainingMode=true
// -ml.datasetPath=/datasets

5.2 Performance Metrics

// Location: weed/mount/ml_metrics.go (new file)
type MLMetrics struct {
    PrefetchHitRate     float64
    SequentialDetected  int64
    CacheHitsByPattern  map[AccessPattern]int64
    BatchEfficiency     float64
}

func (metrics *MLMetrics) Export() {
    // Export to Prometheus/Grafana for monitoring
    // Track ML-specific performance indicators
}

Testing Plan

Unit Testing Strategy

Phase 1 Tests

1. Prefetch Manager Tests

   // Location: weed/mount/prefetch_test.go
   func TestPrefetchManager_WorkerPool(t *testing.T)
   func TestPrefetchManager_DuplicateRequests(t *testing.T)
   func TestPrefetchManager_PriorityQueue(t *testing.T)
   func TestPrefetchManager_Timeout(t *testing.T)
   

2. Access Pattern Detection Tests

   // Location: weed/mount/access_pattern_test.go
   func TestSequentialDetection(t *testing.T)
   func TestRandomAccessDetection(t *testing.T)
   func TestStridedAccessDetection(t *testing.T)
   func TestPatternTransition(t *testing.T)
   

Phase 2 Tests

3. Open File Cache Tests

   // Location: weed/mount/open_file_cache_test.go
   func TestOpenFileCache_Basic(t *testing.T)
   func TestOpenFileCache_Eviction(t *testing.T)
   func TestOpenFileCache_ChunkMetadata(t *testing.T)
   func TestOpenFileCache_Concurrent(t *testing.T)
   

4. ML Cache Policy Tests

   // Location: weed/util/chunk_cache/ml_cache_policy_test.go
   func TestMLCachePolicy_TrainingWorkload(t *testing.T)
   func TestMLCachePolicy_InferenceWorkload(t *testing.T)
   func TestMLCachePolicy_EvictionHeuristics(t *testing.T)
   

Phase 3 Tests

5. ML Pattern Detection Tests

   // Location: weed/mount/ml_patterns_test.go
   func TestMLWorkloadDetector_ImageDataset(t *testing.T)
   func TestMLWorkloadDetector_TextDataset(t *testing.T)
   func TestMLWorkloadDetector_ModelCheckpoints(t *testing.T)
   func TestMLWorkloadDetector_EpochBoundary(t *testing.T)
   

Phase 4 Tests

6. Batch Optimization Tests

   // Location: weed/mount/batch_reader_test.go
   func TestBatchReader_Aggregation(t *testing.T)
   func TestBatchReader_Timeout(t *testing.T)
   func TestBatchReader_TensorFiles(t *testing.T)
   

Integration Testing

Test Environment Setup

#!/bin/bash
# test/ml_integration/setup.sh

# Setup SeaweedFS cluster for ML testing
make clean
make

# Start master server
./weed master &
sleep 2

# Start volume servers
./weed volume -dir=./vol1 -mserver=localhost:9333 -port=8080 &
./weed volume -dir=./vol2 -mserver=localhost:9333 -port=8081 &
sleep 2

# Start filer
./weed filer -master=localhost:9333 &
sleep 2

ML Workload Simulation

// Location: test/ml_integration/ml_workload_test.go
func TestMLWorkloadSimulation(t *testing.T) {
    // Simulate PyTorch DataLoader access patterns
    // Test with ImageNet-style dataset structure
    // Measure cache hit rates and throughput
}

func TestSequentialDatasetTraversal(t *testing.T) {
    // Test epoch-based dataset iteration
    // Verify prefetch effectiveness
    // Check memory usage patterns
}

func TestConcurrentTrainingWorkers(t *testing.T) {
    // Simulate multiple training processes
    // Test batch read aggregation
    // Verify no cache conflicts
}

Performance Benchmarks

// Location: test/ml_integration/benchmark_test.go
func BenchmarkSequentialRead(b *testing.B) {
    // Compare before/after optimization
    // Measure throughput improvements
}

func BenchmarkRandomRead(b *testing.B) {
    // Test cache effectiveness for random access
}

func BenchmarkConcurrentReads(b *testing.B) {
    // Test scalability with multiple readers
}

Load Testing

Test Datasets

1. Image Dataset: 100K images, 224x224 RGB (common CNN input)
2. Text Dataset: 10M text samples (NLP training data)
3. Model Checkpoints: Large PyTorch/TensorFlow model files
4. Mixed Workload: Combination of training and inference access patterns

Load Test Scenarios

// Location: test/ml_load/scenarios.go

type LoadTestScenario struct {
    Name            string
    Workers         int
    Duration        time.Duration
    AccessPattern   AccessPattern
    DatasetType     string
    ExpectedMetrics PerformanceMetrics
}

var scenarios = []LoadTestScenario{
    {
        Name:          "CNN Training",
        Workers:       4,
        Duration:      5 * time.Minute,
        AccessPattern: SequentialAccess,
        DatasetType:   "ImageDataset",
    },
    {
        Name:          "NLP Training", 
        Workers:       8,
        Duration:      10 * time.Minute,
        AccessPattern: BatchAccess,
        DatasetType:   "TextDataset",
    },
    // More scenarios...
}

Continuous Integration Tests

GitHub Actions Workflow

# Location: .github/workflows/ml-optimization-test.yml
name: ML Optimization Tests

on: [push, pull_request]

jobs:
  ml-unit-tests:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - uses: actions/setup-go@v2
        with:
          go-version: 1.21
      - run: go test ./weed/mount/... -tags=ml_optimization
      
  ml-integration-tests:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - run: make
      - run: ./test/ml_integration/run_tests.sh
      
  ml-performance-tests:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - run: go test -bench=. ./test/ml_integration/

Implementation Timeline

Week 1-2: Foundation + Testing Setup

• Implement basic prefetch worker pool
• Add sequential access detection
• Create access pattern detector
• Testing: Unit tests for prefetch manager and access pattern detection
• Commit: "Phase 1: Add smart prefetching foundation with tests"

Week 3-4: Enhanced Caching + Integration Tests

• Implement open file cache with chunk metadata
• Add ML-aware cache eviction policies
• Enable writeback cache support
• Testing: Integration tests for caching system
• Commit: "Phase 2: Enhanced ML-aware caching with comprehensive tests"

Week 5-6: ML Patterns + Load Testing

• Create ML workload detector
• Implement dataset traversal optimization
• Add training-specific optimizations
• Testing: ML pattern detection tests and load testing setup
• Commit: "Phase 3: ML pattern detection with load testing framework"

Week 7-8: Batch Optimization + Performance Testing

• Implement batch read aggregation
• Add tensor file optimizations
• Integration testing and performance tuning
• Testing: Performance benchmarks and optimization verification
• Commit: "Phase 4: Batch optimization with performance benchmarks"

Week 9-10: Configuration, Monitoring & CI

• Add ML-specific mount options
• Implement performance metrics
• Documentation and final testing
• Testing: End-to-end testing and CI pipeline setup
• Commit: "Phase 5: ML monitoring and configuration with full test suite"

Expected Performance Improvements

1. Sequential Read Throughput: 3-5x improvement for large file streaming
2. Training Data Loading: 2-3x faster dataset iteration
3. Cache Hit Rate: 40-60% improvement with ML-aware caching
4. Memory Efficiency: 20-30% reduction in memory usage through better eviction
5. Network Overhead: 50% reduction through batch aggregation

Testing Success Criteria

Performance Benchmarks

• Sequential read throughput >= 3x baseline
• Cache hit rate >= 60% for training workloads
• Memory usage increase <= 20% despite additional caching
• Prefetch accuracy >= 80% for sequential access

Functional Tests

• All unit tests pass with >= 90% code coverage
• Integration tests pass for common ML frameworks
• Load tests complete without memory leaks
• Concurrent access tests show no data corruption

Compatibility Tests

• Existing FUSE functionality unaffected
• No performance regression for non-ML workloads
• Works with PyTorch, TensorFlow, and generic file access
• Cross-platform compatibility (Linux, macOS)