bigdata-lib 0.2

A Python library for various big data structures including graphs, trees, and more.

BigDataLib

BigDataLib is a Python library that provides direct implementations of various big data structures, allowing you to work with these structures efficiently using simple functions. This library includes support for graphs, trees, and more.

Features

• Adjacency Matrix: Efficient representation and manipulation of graph edges.
• Adjacency List: Lightweight representation of graph edges with fast access.
• Trie (Prefix Tree): Fast prefix-based search and insertion for strings.
• Segment Tree: Efficient range queries and updates.
• Fenwick Tree (Binary Indexed Tree): Fast updates and prefix sum queries.
• Red-Black Tree: Self-balancing binary search tree with efficient insertions and deletions.
• AVL Tree: Self-balancing binary search tree maintaining height balance.
• Skip List: Probabilistic data structure for fast search, insertion, and deletion.
• B-Tree: Balanced tree used for efficient data retrieval in databases.
• B+ Tree: Similar to B-Tree but with all values stored in leaf nodes.

Installation

You can install the library using pip:

pip install bigdata_lib

Usage

1.Adjacency Matrix

The adjacency matrix is a 2D array used to represent graph edges. It is memory-intensive but straightforward.

from bigdata_lib.adjacency_matrix import create_adjacency_matrix, add_edge_matrix, display_matrix

matrix = create_adjacency_matrix(5)
add_edge_matrix(matrix, 1, 2)
display_matrix(matrix)

2.Adjacency List

The adjacency list is a dictionary where keys are nodes and values are lists of adjacent nodes.

from bigdata_lib.adjacency_list import add_edge_list, display_list

graph = {}
add_edge_list(graph, 1, 2)
display_list(graph)

3.Trie (Prefix Tree)

A trie is a tree used for storing strings in a way that allows for fast retrieval of prefixes.

from bigdata_lib.trie_structure import Trie, TrieNode

trie = Trie()
trie.insert("example")
print(trie.search("example"))  # Output: True
print(trie.search("test"))     # Output: False

4.Segment Tree

A segment tree is used for storing intervals or segments, allowing efficient querying and updating.

from bigdata_lib.segment_tree import SegmentTree

arr = [1, 3, 5, 7, 9, 11]
segment_tree = SegmentTree(arr)
print(segment_tree.query(1, 3))  # Output: 15
segment_tree.update(1, 10)
print(segment_tree.query(1, 3))  # Output: 22

5.Fenwick Tree (Binary Indexed Tree)

A Fenwick Tree is used for efficient range queries and updates.

from bigdata_lib.fenwick_tree import FenwickTree

arr = [1, 3, 5, 7, 9, 11]
fenwick_tree = FenwickTree(arr)
print(fenwick_tree.query(3))  # Output: 16
fenwick_tree.update(1, 10)
print(fenwick_tree.query(3))  # Output: 26

6.Red-Black Tree

A Red-Black Tree is a self-balancing binary search tree with specific rules to ensure balanced height.

from bigdata_lib.red_black_tree import RedBlackTree

rb_tree = RedBlackTree()
rb_tree.insert(10)
rb_tree.insert(20)
rb_tree.insert(15)
# Implement traversal or other operations as needed

7.AVL Tree

An AVL Tree is a self-balancing binary search tree where the height difference between left and right subtrees is maintained.

from bigdata_lib.avl_tree import AVLTree

avl_tree = AVLTree()
root = None
root = avl_tree.insert(root, 10)
root = avl_tree.insert(root, 20)
root = avl_tree.insert(root, 15)
# Implement traversal or other operations as needed

8.Skip List

A Skip List is a probabilistic data structure that allows fast search, insertion, and deletion operations.

from bigdata_lib.skip_list import SkipList

skip_list = SkipList(max_level=3, p=0.5)
skip_list.insert(3)
skip_list.insert(6)
skip_list.insert(7)
print(skip_list.search(6))  # Output: True
print(skip_list.search(4))  # Output: False

9.B-Tree

A B-Tree is a balanced tree used for efficient data retrieval and insertion, often used in database systems.

from bigdata_lib.b_tree import BTree

b_tree = BTree(t=2)
b_tree.insert(10)
b_tree.insert(20)
b_tree.insert(5)
# Implement traversal or other operations as needed

10.B+ Tree

A B+ Tree is similar to a B-Tree but with all values stored in leaf nodes, allowing efficient range queries.

from bigdata_lib.bplus_tree import BPlusTree

bplus_tree = BPlusTree(t=2)
bplus_tree.insert(10)
bplus_tree.insert(20)
bplus_tree.insert(5)
# Implement traversal or other operations as needed

Contributing

Contributions are welcome! Please submit a pull request or open an issue to suggest improvements.

License

This project is licensed under the MIT License - see the LICENSE file for details.