lqft-python-engine 0.9.4

LQFT Engine: Billion-Scale Persistence & Vectorized 13M-Search (v0.9.4 Stable)

Log-Quantum Fractal Tree (LQFT) 🚀

📌 Project Overview

The Log-Quantum Fractal Tree (LQFT) is a high-performance, scale-invariant data structure engine designed for massive data deduplication and persistent state management. It synthesizes Hash Array Mapped Trie (HAMT) routing with Merkle-DAG structural folding to provide deterministic $O(1)$ search latency and sub-linear $O(\Sigma)$ space complexity.

By offloading core associative logic to a native C-Extension, the LQFT completely bypasses the Python Global Interpreter Lock (GIL), providing true hardware concurrency and significant memory reduction for versioned, redundant, or patterned datasets.

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🧠 Core Architecture (v0.9.4 Multi-Language Core Prep)

1. Custom Memory Arena (Slab Allocator)

Standard C malloc() introduces significant OS metadata overhead (up to 16 bytes per allocation). In v0.9.0, the LQFT utilizes a Custom Slab Allocator. The engine requests memory in 16KB "Arena Chunks" and performs O(1) bump-allocation internally, crushing the memory footprint down to ~104 bytes per node.

2. Intrinsic Free-List & ARC

When a branch is overwritten, the engine natively reclaims memory using an intrinsic linked-list. "Dead" nodes repurpose their own internal pointers to form an infinite recycle bin, avoiding expensive calls to the Operating System's free() method.

3. O(1) Cryptographic Fast-Path

Branch hash recalculation during Deletion and Updates utilizes mathematical XOR inverses (Parent ^ (Old_Child * Prime) ^ (New_Child * Prime)). This eliminates the standard 32-way branch loop, providing sub-microsecond structural folding.

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🚀 Silicon Performance Report (v0.9.2)

Environment: Python 3.12 | GCC -O3 | MinGW64 | 16-Core Parallel Architecture

Metric	Result
Raw Memory Density	~104.6 bytes per node (Eliminated 288-byte bloat)
Search Latency (p50)	~500 ns (Deterministic 13-hop limit)
Raw Write Speed	313,433 ops/sec (Zero-Copy Buffer Protocol)
Multi-Thread Scaling	Optimistic Concurrency (Read-Copy-Update)

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🛠️ Getting Started

Installation

# Clone the repository
git clone [https://github.com/ParjadM/Log-Quantum-Fractal-Tree-LQFT-.git](https://github.com/ParjadM/Log-Quantum-Fractal-Tree-LQFT-.git)
cd Log-Quantum-Fractal-Tree-LQFT-

# Build the native C-extension
python setup.py build_ext --inplace

High-Speed Usage (Zero-Copy)

import lqft_c_engine
import array

# Prepare raw 64-bit buffer
raw_buffer = array.array('Q', [0x1, 0x2, 0x3])
lqft_c_engine.insert_batch_raw(bytes(raw_buffer), "enterprise_payload")

# Sub-microsecond search
result = lqft_c_engine.search(0x1)
print(result) # "enterprise_payload"

# Safe memory arena reclamation
lqft_c_engine.free_all()

⚖️ License

MIT License - Parjad Minooei (2026).