🔢 Snaplit - Rust-powered Data Structures in Python.

❓ What is Snaplit?

Snaplit is a Python library providing mid- to high-level data structures that leverage an extensive, high-performance Rust backend.

The library is designed to maximize type safety, runtime speed & memory efficiency, offering Python developers the ability to work with performant data structures without leaving the Python ecosystem. Snaplit seamlessly exposes Rust-native structures through Python bindings, allowing users to achieve near-native performance for memory-intensive and computationally demanding operations.

📚 Core Philosophy

Snaplit is built upon 3 primary guiding principles:

1. Rust efficiency in Python - All core algorithms are implemented in Rust, ensuring predictable memory layout, zero-cost abstractions and low-level safety guarantees.
2. Type safety & Reliability - Rust's strict type system reduces runtime errors, while Python bindings provide a user-friendly interface.
3. Performance without compromise - Snaplit targets both high-throughput perations and memory-constrained environments, making it suitable for large-scale or high-frequency workloads.

💭 Why use Snaplit Data Structure?

• Predictable performance: Algorithms like 'Robin Hood Hashing' or custom tree structures provide near-constant-time lookups and insertions.
• Memory efficiency: Rust's ownership model and precise memory layout minimize overhead compared to native Python implementations.
• Pythonic API: Fully idiomatic Python interface without compromising speed.
• Extensibility: New Rust-based data structures can be exposed to Python without major refactoring.

</> Installation

Utilise any Python-related package manager to add Snaplit to virtual environment:

# Pip 
pip install snaplit
# Conda
conda install snaplit
# Poetry
poetry add snaplit

📈 Linear Data Structures

# Import statement
from snaplit.linear import (
    LinkedList, Stack, ArrayStack, Queue,
    PriorityQueue, CircularBuffer, RingBuffer
)

🌳 Tree Data Structures

# Import statement
from snaplit.trees import AVLTree, BinarySearchTree, Trie

📶 Graph Data Structures

# Import statement
from snaplit.graph import (
    BaseGraph, Digraph, WeightedGraph, WeightedDigraph, Hypergraph
)

％ Probability Data Structures

# Import statement
from snaplit.probability import BloomFilter, CuckooFilter, Flatlist

#️⃣ Hashing Data Structures

# Import statement
from snaplit.hashing import SnapMap, RhoodMap

🔮 Future Roadmap

A short roadmap of additional advanced data structures to be added in the future.

• Perfect LinkedList - A Doubly-Linked Circular List structure.
• XOR Filter - Probabilistic data structure for membership checks.
• HyperLogLog (HLL) - Probabilistic data structure for estimating unique values.
• Red-Black Tree - Self-balanncing Binary Search Tree structure.
• B+ Tree - Self-balancing Binary Search Tree structure.
• DAG - Doubly-linked Acyclical Graph structure (similar to Digraph).
• Suffix Tree - Character-based search tree structure.
• Adjacency Array - Linked List strucutre utilising array-based backend.
• Quadratic HashMap - Ordinary HashMap implementation with Quadratic Probing.
• Quadratic HashSet - Ordinary Set structure with Quadratic Probing.

🤝 Contribution

Snaplit is open to contributions from both Python and Rust communities!

If you'd like to report a bug, suggest an additional feature or contribute code:

• Open an issue or pull request on Github
• Contact developers directly via the listed e-mail

📝 Licensing

Snaplit project is distributed under the MIT License - free for both commercial & personal use.