Memlyze

Visual memory profiler with <5% overhead
Find leaks, optimize allocations, understand your memory

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Why Memlyze?

Visual Timeline Scrub through time, see when leaks started

Fast <5% overhead, handles millions of allocations

Interactive Click any spike → see exact code location

Smart Finds reference chains, detects leaks automatically

Compared to Alternatives

Tool	Overhead	Visualization	Languages	Production Ready
Valgrind	❌ 20-50x slower	📝 Text only	C/C++	❌ Too slow
Chrome DevTools	⚠️ 10-20x slower	📸 Snapshots	JavaScript	⚠️ Dev only
Python tracemalloc	⚠️ ~10% overhead	📝 Text only	Python	⚠️ Limited
Memlyze	✅ <5% overhead	📊 Interactive timeline	Python, Node, Rust	✅ Yes!

Quick Start

1. Install

pip install -e tracer/

2. Record a trace

memlyze record python your_app.py

3. Analyze

memlyze analyze trace.mlyze

Example Usage

Python API import Memlyze # Start tracing tracer = Memlyze.start( output_file="trace\.mlyze", sample_rate=1.0, max_stack_depth=10, track_gc=True ) # Run your application run_my_app() # Stop and save tracer.stop()

CLI Usage # Basic tracing Memlyze record python app.py # Low overhead (10% sampling) Memlyze record \ --sample-rate 0.1 \ python app.py # Production monitoring (1%) Memlyze record \ --sample-rate 0.01 \ --max-stack-depth 5 \ python app.py

Project Status

Phase	Status	Features
Phase 1 Python Tracer MVP	✅ COMPLETE	✅ Binary format specification ✅ Memory tracer with stack traces ✅ Efficient file writer ✅ CLI tool ✅ Example programs
Phase 2 Analysis Engine	🚧 NEXT	🚧 Rust-based trace parser 🚧 Timeline builder 🚧 Leak detection 🚧 JSON export
Phase 3 Web Visualization	📅 Planned	📅 React app 📅 Canvas timeline 📅 Heatmap/flamegraph 📅 WASM integration

Architecture

Memlyze/
├── tracer/              # Python memory tracer
│   ├── Memlyze/        # Core library
│   │   ├── tracer.py    # Allocation tracking
│   │   ├── format.py    # Binary format
│   │   └── writer.py    # File I/O
│   └── setup.py
├── analyzer/            # Rust analysis engine (Phase 2)
├── web-ui/              # React visualization (Phase 3)
├── cli/                 # Rust CLI tool (Phase 4)
├── examples/            # Sample programs
└── benchmarks/          # Performance tests

Documentation

• Binary Format Specification
• Architecture Guide
• Performance Tuning
• Contributing

Performance Targets

CPU Overhead	<5%	Fast enough for production
Memory Overhead	<100MB	For 1M allocations
Trace File Size	<1MB	Per 100k allocations (compressed)
Analysis Speed	<1 second	Process 1M allocations

License

MIT License - See LICENSE file for details

Contributing

Contributions welcome! See CONTRIBUTING.md for details.

Credits

Built with ❤️ by RAR