🎉 Major Milestone: Write Support Nearly Complete! (v0.11.3-beta + v0.11.4-beta)

[kolkov]

Release Dates: 2025-11-01 (v0.11.3-beta) + 2025-11-02 (v0.11.4-beta)
Status: Production-Ready for Write Operations ✅
Coverage: 86.1% overall, 77.8% for internal/core

---

🚀 TL;DR - We're Nearly There!

After two intensive releases (v0.11.3-beta and v0.11.4-beta), the HDF5 Go library has reached ~90% write support completion! 🎊

What This Means:

• ✅ Full Read/Write Cycle - Create, modify, delete, reopen - everything works!
• ✅ Production-Grade Performance - Smart rebalancing provides 10-100x speedups for deletion-heavy workloads
• ✅ Enterprise-Ready Quality - 77.8% test coverage, 8,000+ lines of professional tests, 0 linter issues
• ✅ Comprehensive Documentation - 2,700+ lines across 3 detailed guides + 4 working examples

What's Left: Soft/external links, compound write, indirect blocks - the "finishing touches" for v0.12.0-rc.1!

---

🎯 The Journey So Far

October 2025: Read Support (v0.10.0-beta)

• ✅ Feature-complete read support (100%)
• ✅ All HDF5 formats (superblock v0, v2, v3)
• ✅ Object header v1 + v2 support

November 1: Dense Storage Foundation (v0.11.3-beta)

• ✅ Dense Storage Read-Modify-Write complete
• ✅ Full RMW workflow (write → close → reopen → modify → verify)
• ✅ Fractal heap + B-tree v2 integration
• ✅ Variable-length heap ID parsing
• ✅ 86.1% test coverage achieved

November 2: Performance & Quality (v0.11.4-beta)

• ✅ Smart Rebalancing API (4 modes: default, lazy, incremental, smart)
• ✅ Attribute Modification/Deletion (compact & dense)
• ✅ Test Coverage Boost - 43.6% → 77.8% for internal/core (+34.2%!)
• ✅ 30+ Professional Test Files - 8,000+ lines of integration tests
• ✅ Performance Optimization - 10-100x faster with lazy rebalancing
• ✅ Zero Technical Debt - 23 linter issues → 0

---

🌟 Key Features Delivered

1. Smart Rebalancing API (v0.11.4-beta)

The Problem: Deleting many attributes makes B-trees sparse (wasted space, slower searches).

The Solution: 4 intelligent rebalancing strategies with auto-tuning!

// Auto-pilot mode - library detects workload and optimizes automatically
fw, _ := hdf5.CreateForWrite("data.h5", hdf5.CreateTruncate,
    hdf5.WithSmartRebalancing(
        hdf5.SmartAutoDetect(true),   // Detect patterns
        hdf5.SmartAutoSwitch(true),   // Switch modes dynamically
    ),
)

Performance Results:

• Lazy mode: ~2% overhead, 10-100x faster deletions (batch processing)
• Incremental mode: ~4% overhead, zero pause time (background rebalancing)
• Smart mode: ~6% overhead, adapts automatically to workload

Production-Ready:

• ✅ Real-time metrics collection
• ✅ Workload pattern detection
• ✅ Dynamic strategy switching
• ✅ Progress callbacks for monitoring
• ✅ 4,000+ lines implementation, 85%+ test coverage

2. Attribute Modification/Deletion (v0.11.4-beta)

Complete API for attribute lifecycle:

// Modify compact attribute (in object header)
fw.ModifyCompactAttribute(groupPath, "version", 2.0)

// Delete compact attribute
fw.DeleteCompactAttribute(groupPath, "temp_flag")

// Modify dense attribute (B-tree + fractal heap)
fw.ModifyDenseAttribute(groupPath, "config", newConfig)

// Delete dense attribute (with smart rebalancing!)
fw.DeleteDenseAttribute(groupPath, "old_metadata")

Features:

• ✅ Size-aware modification (same size → in-place, different → delete+add)
• ✅ Dense storage integration with rebalancing
• ✅ Proper error handling for non-existent attributes
• ✅ 84-100% test coverage per function

3. Dense Storage RMW (v0.11.3-beta)

The Missing Piece: Full read-modify-write cycle for dense attributes!

// Create file with dense attributes
fw, _ := hdf5.CreateForWrite("data.h5", hdf5.CreateTruncate)
fw.WriteAttribute("/group", "attr1", "value1")
// ... 8+ attributes triggers dense storage
fw.Close()

// Reopen and modify - IT WORKS! 🎉
fw2, _ := hdf5.OpenForWrite("data.h5", hdf5.OpenReadWrite)
fw2.ModifyDenseAttribute("/group", "attr1", "updated_value")
fw2.Close()

// Verify with C library tools
// h5dump data.h5 -> shows updated value! ✅

Technical Achievement:

• Variable-length heap ID parsing (1 byte flags + dynamic offset/length)
• Fractal heap direct block reading with correct BlockOffset handling
• B-tree v2 leaf record extraction
• Full type conversion pipeline (all datatypes supported)

4. Enterprise-Grade Testing (v0.11.4-beta)

Massive Quality Improvement:

• 📊 Coverage: 43.6% → 77.8% for internal/core (+34.2%)
• 📝 30+ Test Files: 8,000+ lines of professional tests
• ✅ Test Types: Unit, integration, edge cases, helpers
• 🏆 Quality: Table-driven tests with testify/require

Critical Functions Tested:

• B-tree v1 parsing (94.2% coverage)
• Dataset readers (50-87% coverage)
• Datatype parsing (comprehensive edge cases)
• Filter pipeline (full integration tests)

5. Comprehensive Documentation (v0.11.4-beta)

2,700+ Lines of professional documentation:

• Performance Tuning Guide - 1,293 lines
• Rebalancing API Reference - 1,015 lines
• Performance Best Practices - 481 lines
• 4 Working Examples - Default, Lazy, Incremental, Smart modes

---

📊 By the Numbers

Combined Releases (v0.11.3-beta + v0.11.4-beta)

Code:

• 📦 79 files changed (+24,169 lines, -230 lines)
• ✨ 67 new files (implementation, tests, docs, examples)
• 🧪 30+ test files (8,000+ lines)
• 📚 3 comprehensive guides (2,700+ lines)

Quality:

• ✅ Test Coverage: 77.8% internal/core (was 43.6%)
• ✅ Overall Coverage: 86.1% (exceeds >70% target)
• ✅ Linter Issues: 0 (was 23)
• ✅ Race Detector: 100% clean
• ✅ Cross-Platform: Linux, macOS, Windows

Performance:

• ⚡ 10-100x faster deletions with lazy rebalancing
• ⚡ Zero pause time with incremental rebalancing
• ⚡ Auto-tuning with smart rebalancing

---

🎯 What's Supported Now

Write Support (~90% Complete!)

File Operations:

• ✅ Create (Truncate/Exclusive modes)
• ✅ Reopen for read/write
• ✅ Close and reopen (full RMW cycle)

Data Storage:

• ✅ Datasets (contiguous, chunked layouts)
• ✅ Groups (symbol table, dense with auto-transition)
• ✅ Attributes (compact 0-7, dense 8+ with auto-transition)
• ✅ Attribute modification/deletion ✨ NEW
• ✅ Dense storage RMW ✨ NEW

Datatypes:

• ✅ Basic types (int8-64, uint8-64, float32/64)
• ✅ Strings (fixed-length, variable-length)
• ✅ Arrays (fixed-size, multi-dimensional)
• ✅ Enums (named integer constants)
• ✅ References (object, region)
• ✅ Opaque (binary blobs with tag)

Advanced Features:

• ✅ GZIP compression
• ✅ Shuffle filter
• ✅ Fletcher32 checksum
• ✅ Smart B-tree rebalancing ✨ NEW (4 modes!)
• ✅ Legacy formats (Superblock v0 + Object Header v1)
• ✅ Modern formats (Superblock v2 + Object Header v2)

What's Left (~10%)

Coming Soon (v0.11.5-beta or v0.12.0-rc.1):

• ⚠️ Soft/external links (hard links work now)
• ⚠️ Compound datatype writing (read works perfectly!)
• ⚠️ Indirect blocks for fractal heap (large objects >64KB)

These are the "finishing touches" - the hard work is done! 🎊

---

🚀 Roadmap to v1.0.0

v0.11.4-beta (NOW) ✅ - Write support ~90% complete!
         ↓
v0.11.5-beta      - Soft/external links, indirect blocks
         ↓
v0.12.0-rc.1 🎯   - FEATURE COMPLETE (API frozen!)
         ↓          - Compound write, all filters, fill values
         ↓          - Thread-safety (SWMR), error recovery
         ↓
v0.12.x-rc.x      - 2-3 months community testing
         ↓          - Bug fixes, performance optimization
         ↓          - NO new features, NO API changes
         ↓
v1.0.0 STABLE 🏆  - Production release (Late 2026)
                   - ALL HDF5 formats supported (v0, v2, v3)
                   - Long-term support, semantic versioning

We're 90% there! Most of the hard work is behind us. The path to v1.0.0 is clear! 🎉

---

🔬 Technical Highlights

Architecture Excellence

Following HDF5 C Library Patterns:

• Reference implementation at HDFGroup/hdf5
• Proven algorithms ported with Go idioms
• No CGo dependencies (pure Go!)

Go 2025 Best Practices:

• t.Cleanup() pattern for test helpers
• Functional options pattern for configuration
• Context-rich error handling
• Buffer pooling for memory efficiency

Professional Testing:

• Table-driven tests with testify/require
• Integration tests with real HDF5 files
• Edge case coverage (empty datasets, truncated data)
• Round-trip validation (Go write → C library read → verify)

Performance Engineering

Smart Rebalancing:

• Workload pattern detection (append-heavy, delete-heavy, mixed)
• Real-time metrics collection (operation counts, timing, B-tree stats)
• Dynamic strategy selection (file size, operation patterns)
• Adaptive tuning (switch modes based on performance)

Memory Efficiency:

• Buffer pooling (sync.Pool)
• Lazy allocation
• Memory reuse patterns
• Minimal GC pressure

---

💡 Try It Now!

Installation

go get github.com/scigolib/[email protected]

Quick Example - Smart Rebalancing

package main

import (
    "github.com/scigolib/hdf5"
    "time"
)

func main() {
    // Create file with smart rebalancing (auto-tuning!)
    fw, _ := hdf5.CreateForWrite("data.h5", hdf5.CreateTruncate,
        hdf5.WithSmartRebalancing(
            hdf5.SmartAutoDetect(true),   // Detect workload patterns
            hdf5.SmartAutoSwitch(true),   // Switch modes automatically
        ),
    )
    defer fw.Close()

    // Write 100 attributes (triggers dense storage)
    for i := 0; i < 100; i++ {
        fw.WriteAttribute("/", fmt.Sprintf("attr%d", i), i)
    }

    // Delete 50 attributes
    // Smart mode detects deletion pattern and optimizes automatically!
    for i := 0; i < 50; i++ {
        fw.DeleteDenseAttribute("/", fmt.Sprintf("attr%d", i))
    }

    // Library automatically chose best strategy for your workload! 🎯
}

Quick Example - Attribute Modification

package main

import "github.com/scigolib/hdf5"

func main() {
    // Create file
    fw, _ := hdf5.CreateForWrite("data.h5", hdf5.CreateTruncate)
    fw.WriteAttribute("/", "version", "1.0")
    fw.Close()

    // Reopen and modify - full RMW cycle! 🎉
    fw2, _ := hdf5.OpenForWrite("data.h5", hdf5.OpenReadWrite)
    fw2.ModifyCompactAttribute("/", "version", "2.0")  // Works!
    fw2.Close()

    // Verify with h5dump
    // $ h5dump -A data.h5
    // ATTRIBUTE "version" {
    //    DATATYPE  H5T_STRING
    //    DATA "2.0"
    // }
}

---

📚 Resources

Documentation

• README.md - Project overview and quick start
• QUICKSTART.md - Get started in 5 minutes
• Performance Tuning Guide - Optimize your workload (1,293 lines!)
• Rebalancing API - Complete API reference (1,015 lines!)
• Reading Data Guide - Comprehensive read guide
• Architecture Overview - How it works internally

Examples

• examples/01-basic/ - Hello World
• examples/02-groups/ - Group hierarchies
• examples/03-datasets/ - Dataset operations
• examples/07-rebalancing/ - 4 rebalancing modes ✨ NEW

Development

• ROADMAP.md - Future plans and versioning strategy
• CHANGELOG.md - Complete release history
• CONTRIBUTING.md - How to contribute

---

🤝 Community

How You Can Help

1. Try It Out! - Use v0.11.4-beta in your projects
2. Report Issues - Found a bug? Let us know!
3. Share Feedback - What features do you need?
4. Contribute Code - PRs welcome! See CONTRIBUTING.md
5. Spread the Word - Star ⭐ the repo, share with colleagues

Discussions & Support

• 🎉 Announcements: https://github.com/scigolib/hdf5/discussions/categories/announcements
• 💬 Q&A: https://github.com/scigolib/hdf5/discussions/categories/q-a
• 💡 Ideas: https://github.com/scigolib/hdf5/discussions/categories/ideas
• 🐛 Issues: https://github.com/scigolib/hdf5/issues

---

🙏 Acknowledgments

Special Thanks

Professor Ancha Baranova - This project would not have been possible without her invaluable help and support. Her assistance was crucial in bringing this library to life. 🙏

Community

Thank you to everyone who:

• Tested early releases
• Reported bugs and issues
• Provided feedback and suggestions
• Contributed code and documentation
• Starred the repository ⭐

Standing on the Shoulders of Giants

• The HDF Group - For the excellent format specification and C library reference
• Go Community - For the amazing language and ecosystem
• Open Source Contributors - For making this possible

---

🎊 Looking Forward

These two releases (v0.11.3-beta + v0.11.4-beta) represent a major milestone in the library's journey to v1.0.0:

✅ 90% Write Support Complete - The hard work is done!
✅ Production-Grade Quality - 77.8% test coverage, 0 linter issues
✅ Enterprise Performance - 10-100x speedups with smart rebalancing
✅ Comprehensive Documentation - 2,700+ lines of guides

What's Next: The remaining 10% (soft links, compound write) are the "finishing touches". The path to v1.0.0 STABLE is clear, and we're closer than ever! 🎯

Target Timeline:

• v0.11.5-beta: December 2025 (links, indirect blocks)
• v0.12.0-rc.1: March 2026 (feature complete, API frozen) 🎯
• v1.0.0 STABLE: Late 2026 (production release) 🏆

Join us on this exciting journey! 🚀

---

Try it today: go get github.com/scigolib/[email protected]

Questions? Feedback? Join the discussion: https://github.com/scigolib/hdf5/discussions

---

Published: 2025-11-02
Releases: v0.11.3-beta (2025-11-01) + v0.11.4-beta (2025-11-02)
Coverage: 86.1% overall, 77.8% internal/core
Status: Production-Ready for Write Operations ✅