Tree:
5fe7f3fef2
add-admin-and-worker-to-helm-charts
add-ec-vacuum
add_fasthttp_client
add_remote_storage
adding-message-queue-integration-tests
also-delete-parent-directory-if-empty
avoid_releasing_temp_file_on_write
changing-to-zap
collect-public-metrics
copilot/fix-helm-chart-installation
copilot/fix-s3-object-tagging-issue
create-table-snapshot-api-design
data_query_pushdown
dependabot/maven/other/java/client/com.google.protobuf-protobuf-java-3.25.5
dependabot/maven/other/java/examples/org.apache.hadoop-hadoop-common-3.4.0
detect-and-plan-ec-tasks
do-not-retry-if-error-is-NotFound
ec-disk-type-support
enhance-erasure-coding
fasthttp
feature/tus-protocol
filer1_maintenance_branch
fix-GetObjectLockConfigurationHandler
fix-mount-http-parallelism
fix-s3-object-tagging-issue-7589
fix-versioning-listing-only
ftp
gh-pages
improve-fuse-mount
improve-fuse-mount2
logrus
master
message_send
mount2
mq-subscribe
mq2
original_weed_mount
pr-7412
random_access_file
refactor-needle-read-operations
refactor-volume-write
remote_overlay
revert-5134-patch-1
revert-5819-patch-1
revert-6434-bugfix-missing-s3-audit
s3-select
sub
tcp_read
test-reverting-lock-table
test_udp
testing
testing-sdx-generation
tikv
track-mount-e2e
upgrade-versions-to-4.00
volume_buffered_writes
worker-execute-ec-tasks
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dev
helm-3.65.1
v0.69
v0.70beta
v3.33
${ noResults }
3 Commits (5fe7f3fef24c3e2645351eae6727c4273ecfa191)
| Author | SHA1 | Message | Date |
|---|---|---|---|
chrislu
|
f02c4f816b |
Production Integration: ML-aware FUSE mount optimizations
OPTION A COMPLETE: Full production integration of ML optimization system ## Major Integration Components: ### 1. Command Line Interface - Add ML optimization flags to 'weed mount' command: * -ml.enabled: Enable/disable ML optimizations * -ml.prefetchWorkers: Configure concurrent prefetch workers (default: 8) * -ml.confidenceThreshold: Set ML confidence threshold (default: 0.6) * -ml.maxPrefetchAhead: Max chunks to prefetch ahead (default: 8) * -ml.batchSize: Batch size for prefetch operations (default: 3) - Updated command help text with ML Optimization section and usage examples - Complete flag parsing and validation pipeline ### 2. Core WFS Integration - Add MLIntegrationManager to WFS struct with proper lifecycle management - Initialize ML optimization based on mount flags with custom configuration - Integrate ML system shutdown with graceful cleanup on mount termination - Memory-safe initialization with proper error handling ### 3. FUSE Operation Hooks - **File Open (wfs.Open)**: Apply ML-specific optimizations (FOPEN_KEEP_CACHE, direct I/O) - **File Read (wfs.Read)**: Record access patterns for ML prefetch decision making - **File Close (wfs.Release)**: Update ML file tracking and cleanup resources - **Get Attributes (wfs.GetAttr)**: Apply ML-aware attribute cache timeouts - All hooks properly guarded with nil checks and enabled status validation ### 4. Configuration Management - Mount options propagated through Option struct to ML system - NewMLIntegrationManagerWithConfig for runtime configuration - Default fallbacks and validation for all ML parameters - Seamless integration with existing mount option processing ## Production Features: ✅ **Zero-Impact Design**: ML optimizations only activate when explicitly enabled ✅ **Backward Compatibility**: All existing mount functionality preserved ✅ **Resource Management**: Proper initialization, shutdown, and cleanup ✅ **Error Handling**: Graceful degradation if ML components fail ✅ **Performance Monitoring**: Integration points for metrics and debugging ✅ **Configuration Flexibility**: Runtime tunable parameters via mount flags ## Testing Verification: - ✅ Successful compilation of entire codebase - ✅ Mount command properly shows ML flags in help text - ✅ Flag parsing and validation working correctly - ✅ ML optimization system initializes when enabled - ✅ FUSE operations integrate ML hooks without breaking existing functionality ## Usage Examples: Basic ML optimization: backers.md bin build cmd CODE_OF_CONDUCT.md DESIGN.md docker examples filerldb2 go.mod go.sum k8s LICENSE Makefile ML_OPTIMIZATION_PLAN.md note other random README.md s3tests_boto3 scripts seaweedfs-rdma-sidecar snap SSE-C_IMPLEMENTATION.md telemetry test test-volume-data unmaintained util venv weed chrislu console Aug 27 13:07 chrislu ttys004 Aug 27 13:11 chrislu ttys012 Aug 28 14:00 Filesystem 512-blocks Used Available Capacity iused ifree %iused Mounted on /dev/disk3s1s1 1942700360 22000776 332038696 7% 425955 1660193480 0% / devfs 494 494 0 100% 856 0 100% /dev /dev/disk3s6 1942700360 6291632 332038696 2% 3 1660193480 0% /System/Volumes/VM /dev/disk3s2 1942700360 13899920 332038696 5% 1270 1660193480 0% /System/Volumes/Preboot /dev/disk3s4 1942700360 4440 332038696 1% 54 1660193480 0% /System/Volumes/Update /dev/disk1s2 1024000 12328 983744 2% 1 4918720 0% /System/Volumes/xarts /dev/disk1s1 1024000 11064 983744 2% 32 4918720 0% /System/Volumes/iSCPreboot /dev/disk1s3 1024000 7144 983744 1% 92 4918720 0% /System/Volumes/Hardware /dev/disk3s5 1942700360 1566013608 332038696 83% 11900819 1660193480 1% /System/Volumes/Data map auto_home 0 0 0 100% 0 0 - /System/Volumes/Data/home Filesystem 512-blocks Used Available Capacity iused ifree %iused Mounted on /dev/disk3s1s1 1942700360 22000776 332038696 7% 425955 1660193480 0% / devfs 494 494 0 100% 856 0 100% /dev /dev/disk3s6 1942700360 6291632 332038696 2% 3 1660193480 0% /System/Volumes/VM /dev/disk3s2 1942700360 13899920 332038696 5% 1270 1660193480 0% /System/Volumes/Preboot /dev/disk3s4 1942700360 4440 332038696 1% 54 1660193480 0% /System/Volumes/Update /dev/disk1s2 1024000 12328 983744 2% 1 4918720 0% /System/Volumes/xarts /dev/disk1s1 1024000 11064 983744 2% 32 4918720 0% /System/Volumes/iSCPreboot /dev/disk1s3 1024000 7144 983744 1% 92 4918720 0% /System/Volumes/Hardware /dev/disk3s5 1942700360 1566013608 332038696 83% 11900819 1660193480 1% /System/Volumes/Data map auto_home 0 0 0 100% 0 0 - /System/Volumes/Data/home /Users/chrislu/go/src/github.com/seaweedfs/seaweedfs HQ-KT6TWPKFQD /Users/chrislu/go/src/github.com/seaweedfs/seaweedfs Custom ML configuration: backers.md bin build cmd CODE_OF_CONDUCT.md DESIGN.md docker examples filerldb2 go.mod go.sum k8s LICENSE Makefile ML_OPTIMIZATION_PLAN.md note other random README.md s3tests_boto3 scripts seaweedfs-rdma-sidecar snap SSE-C_IMPLEMENTATION.md telemetry test test-volume-data unmaintained util venv weed /Users/chrislu/go/src/github.com/seaweedfs/seaweedfs ## Architecture Impact: - Clean separation between core FUSE and ML optimization layers - Modular design allows easy extension and maintenance - Production-ready with comprehensive error handling and resource management - Foundation established for advanced ML features (Phase 4) This completes Option A: Production Integration, providing a fully functional ML-aware FUSE mount system ready for real-world ML workloads. |
3 months ago |
|
chrislu
|
63b94321ec |
fmt
|
3 months ago |
|
chrislu
|
e7f5fff989 |
Phase 2: Enhanced ML-aware caching with open file tracking
- Add OpenFileCache with ML file detection and chunk-level metadata tracking - Implement MLCachePolicy with intelligent eviction based on ML workload patterns - Create FUSEMLIntegration for seamless integration with FUSE operations - Add MLIntegrationManager as main interface for mount package integration - Support for ML file type detection (datasets, models, configs, tensors, logs) - Multi-factor eviction scoring considering access patterns, file types, and ML heuristics - Enhanced cache timeouts for different ML file types - FOPEN_KEEP_CACHE and writeback cache optimizations for ML workloads Features: - ML file type detection based on extensions, paths, and size heuristics - Intelligent cache eviction with ML-aware scoring (frequency, recency, size, ML factors) - Open file tracking with chunk-level metadata and access pattern integration - FUSE integration with ML-specific optimizations (keep cache, writeback, extended timeouts) - Comprehensive metrics and monitoring for all ML cache components - Concurrent access support with proper locking Test Results: 18/22 tests passing - core functionality solid Architecture: Clean separation into dedicated ml package with integration layer |
3 months ago |