DSA Toolbox 📚

Welcome to DSA Toolbox, a comprehensive library of data structures and algorithms designed to streamline your development process! This library is built for JavaScript and TypeScript, offering both fundamental and advanced data structures, search algorithms, sort algorithms, and more.

Whether you're building a lightweight application or handling large datasets, DSA Toolbox provides an optimized library to make your coding journey efficient and fun.

✨ Features

DSA Toolbox offers a variety of powerful toolbox:

🔎 Search Algorithms

• Binary Search
• Exponential Search
• Hybrid Search
• Linear Search
• Ternary Search

🧮 Sort Algorithms

• HeapSort
• MergeSort
• TimSort

🔄 Cache Algorithms

• LFU (Least Frequently Used)
• LRU (Least Recently Used)

🏗️ Data Structures

• Heaps: MaxHeap, MinHeap
• Linked Lists: Singly Linked List, Doubly Linked List
• Queues & Stacks
• Treaps (Binary Search Tree with heap properties)
• Trees: AVL Tree, Red-Black Tree, Binary Search Tree, B-Tree, Trie
• Probabilistic Structures: Bloom Filter, HyperLogLog, CountMinSketch, SkipList, MinHash, SimHash, TDigest

📊 Functional Programming (FP)

• Composition: Function composition for declarative programming
• Currying: Transforming functions into unary functions
• Functors: CanApply for safe function application
• Monads:
  • Option: Safe handling of optional values (Some, None)
  • Result<T, E>: Error handling without exceptions (Ok, Err)
  • Effect<T, E>: Deferred computations with error safety
• Pattern Matching: Expressive control flow using Match (matcher, case-of)
• Lenses & Optics: Immutable state manipulation (Lens, Prism, Traversal)
• Trampoline: Converts deep recursion into iteration to prevent stack overflows
• Transducers: Composable data transformations with high performance (map, filter, reduce fused)

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Benchmarks

Data Structures Benchmarks

(index)	dataStructure	operation	size	time (ms)
0	Native Array	Insert	1000	0.0042
1	Native Array	Search	1000	0.0015
2	Native Array	Delete	1000	0.0008
3	Queue	Insert (Enqueue)	1000	0.1306
4	Queue	Delete (Dequeue)	1000	0.1355
5	Stack	Insert (Push)	1000	0.1216
6	Stack	Delete (Pop)	1000	0.1128
7	Binary Search Tree	Insert	1000	0.3302
8	Binary Search Tree	Search	1000	0.0431
9	Binary Search Tree	Delete	1000	0.0308
10	Red-Black Tree	Insert	1000	0.8018
11	Red-Black Tree	Search	1000	0.0202
12	Red-Black Tree	Delete	1000	0.0791
13	AVL Tree	Insert	1000	0.6278
14	AVL Tree	Search	1000	0.0117
15	AVL Tree	Delete	1000	0.0189
16	Trie	Insert	1000	0.3451
17	Trie	Search	1000	0.0157
18	Trie	Delete	1000	0.0184
19	Min Heap	Insert	1000	0.1854
20	Min Heap	Extract

Suggested Applications:

• Small Data: For datasets around 1,000 elements, Native Arrays, Queues, and Stacks provide excellent performance.
• Medium Data: Up to 10,000 elements, use AVL Tree for balanced tree operations and Min/Max Heap for priority-based insert/extract operations.
• Large Data: At 100,000 elements, consider using B-Trees or Red-Black Trees for optimized insertion and search performance.
• Extra Large Data: Beyond 1,000,000 elements, B-Trees, with their balanced and efficient nature, are superior for handling large datasets, especially for search and delete operations.

Algorithms Benchmarks

(index)	algorithm	operation	size	time (ms)
0	Heap Sort	Sort	1000	1.1344
1	Merge Sort	Sort	1000	0.6770
2	Tim Sort	Sort	1000	0.5847
3	Native Sort	Sort	1000	0.1421
4	Binary Search	Search	1000	0.0278
5	Exponential Search	Search	1000	0.0265
6	Hybrid Search	Search	1000	0.0377
7	Linear Search	Search	1000	0.0218
8	Ternary Search	Search	1000	0.0259
9	Heap Sort	Sort	10000	2.6921
10	Merge Sort	Sort	10000	3.4185
11	Tim Sort	Sort	10000	2.1583
12	Native Sort	Sort	10000	1.2181
13	Binary Search	Search	10000	0.0050
14	Exponential Search	Search	10000	0.0062
15	Hybrid Search	Search	10000	3.5105
16	Linear Search	Search	10000	0.0740
17	Ternary Search	Search	10000	0.0067
18	Heap Sort	Sort	100000	18.3146
19	Merge Sort	Sort	100000	24.6636
20	Tim Sort	Sort	100000