data-structure-typed/SPECIFICATION.md

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Pain Points Addressed

Enhancing the Performance of Simulated Data Structures in JS/TS

• Queue: While many resort to using Arrays to simulate Queues and Deques, the time complexity of Array.shift is O(n). We have tackled this challenge by implementing a Queue and Deque with O(1) time complexity for enqueue and dequeue operations.

• HashMap: Opting for a pure HashMap over the built-in Map (technically a LinkedHashMap) can boost algorithmic speed. However, the performance is compromised due to the necessity of considering insertion order. We have independently implemented an optimized HashMap.

• Stack: In JS, simulating a Stack with an Array is acceptable, and its performance is on par with a genuine Stack.

Introducing Missing Native Data Structures in JS/TS

• Heap / Priority Queue: Algorithms with O(log n) time complexity have been pivotal in improving efficiency since the dawn of computers. A Heap supports insertion, deletion, and search with O(log n) time complexity, coupled with the ability to obtain the minimum / maximum value in O(1) time.

• Red Black Tree: Developers well-versed in databases, file systems, Linux virtual memory management, and network routing tables often have a nuanced understanding of Red-Black Trees. It stands out as the least operation-intensive among all balanced binary search trees, offering optimal performance balance in CRUD operations.

• Linked List: In scenarios where insertion or deletion of elements with O(1) time complexity is required at a specific index in an ordered collection, JS lacks a provided data structure. Hence, the need for a LinkedList to implement this functionality.

• Trie: Efficient for fast string queries and space-efficient string storage, yet not commonly found in the standard libraries of most programming languages.

• Graph: This data structure is not commonly found in the standard libraries of most languages, making it a non-issue in JS.

Advantages

Performance:

• The performance of some ours data structures has surpassed JS's built-in data structures (Queue, Deque, HashMap), while most are comparable to or even surpass those in other languages. Some are still undergoing refinement (Graph, AVL Tree).

Uniformity

• 1. Implementation or constraint of forEach, filter, map, every, some, reduce, find, has, hasValue, get, print, isEmpty, clear, clone methods in the base class.
• 2. Use of generators to uniformly implement [Symbol.iterator], entries, keys, values. Delaying iterator execution prevents performance loss and provides control during traversal.
• 3. All deletion methods uniformly use the widely adopted delete in ES6, while addition methods uniformly use add. Compatibility with some specifications in Java.
• 4. The first parameter for all constructors is data, and the second parameter is configuration, maintaining uniformity. The first parameter accepts any iterable type for seamless conversion between data structures.
• 5. Uniform return types, for example, the add method consistently returns a boolean.

Convenience and Simplicity of APIs

• Inspired by ES6, Java, ESNext, TypeScript, Python, featuring methods like forEach, filter, map, every, some, reduce, find, has, hasValue and get.

Use of Commonly Understood Industry Standard Naming

• enqueue, dequeue, push, pop, poll, push, unshift, shift, pop, isEmpty, clear, print, clone.

Implementation of Customizable Features Whenever Possible

• Such as providing callback functions (lambda expressions) for all traversal methods.

Comprehensive Documentation

• The documentation not only explains the purpose of methods but also annotates time and space complexity across the entire series.