What

Brief

This is a standalone Doubly Linked List data structure from the data-structure-typed collection. If you wish to access more data structures or advanced features, you can transition to directly installing the complete data-structure-typed package

How

install

npm

npm i doubly-linked-list-typed --save

yarn

yarn add doubly-linked-list-typed

snippet

basic DoublyLinkedList creation and push operation

 // Create a simple DoublyLinkedList with initial values
    const list = new DoublyLinkedList([1, 2, 3, 4, 5]);

    // Verify the list maintains insertion order
    console.log([...list]); // [1, 2, 3, 4, 5];

    // Check length
    console.log(list.length); // 5;

    // Push a new element to the end
    list.push(6);
    console.log(list.length); // 6;
    console.log([...list]); // [1, 2, 3, 4, 5, 6];

DoublyLinkedList pop and shift operations

 const list = new DoublyLinkedList<number>([10, 20, 30, 40, 50]);

    // Pop removes from the end
    const last = list.pop();
    console.log(last); // 50;

    // Shift removes from the beginning
    const first = list.shift();
    console.log(first); // 10;

    // Verify remaining elements
    console.log([...list]); // [20, 30, 40];
    console.log(list.length); // 3;

DoublyLinkedList for...of iteration and map operation

 const list = new DoublyLinkedList<number>([1, 2, 3, 4, 5]);

    // Iterate through list
    const doubled = list.map(value => value * 2);
    console.log(doubled.length); // 5;

    // Use for...of loop
    const result: number[] = [];
    for (const item of list) {
      result.push(item);
    }
    console.log(result); // [1, 2, 3, 4, 5];

Browser history

 const browserHistory = new DoublyLinkedList<string>();

    browserHistory.push('home page');
    browserHistory.push('search page');
    browserHistory.push('details page');

    console.log(browserHistory.last); // 'details page';
    console.log(browserHistory.pop()); // 'details page';
    console.log(browserHistory.last); // 'search page';

DoublyLinkedList for LRU cache implementation

 interface CacheEntry {
      key: string;
      value: string;
    }

    // Simulate LRU cache using DoublyLinkedList
    // DoublyLinkedList is perfect because:
    // - O(1) delete from any position
    // - O(1) push to end
    // - Bidirectional traversal for LRU policy

    const cacheList = new DoublyLinkedList<CacheEntry>();
    const maxSize = 3;

    // Add cache entries
    cacheList.push({ key: 'user:1', value: 'Alice' });
    cacheList.push({ key: 'user:2', value: 'Bob' });
    cacheList.push({ key: 'user:3', value: 'Charlie' });

    // Try to add a new entry when cache is full
    if (cacheList.length >= maxSize) {
      // Remove the oldest (first) entry
      const evicted = cacheList.shift();
      console.log(evicted?.key); // 'user:1';
    }

    // Add new entry
    cacheList.push({ key: 'user:4', value: 'Diana' });

    // Verify current cache state
    console.log(cacheList.length); // 3;
    const cachedKeys = [...cacheList].map(entry => entry.key);
    console.log(cachedKeys); // ['user:2', 'user:3', 'user:4'];

    // Access entry (in real LRU, this would move it to end)
    const foundEntry = [...cacheList].find(entry => entry.key === 'user:2');
    console.log(foundEntry?.value); // 'Bob';

API docs & Examples

API Docs

Live Examples

Examples Repository

Data Structures

Data Structure	Unit Test	Performance Test	API Docs
Doubly Linked List			DoublyLinkedList

Standard library data structure comparison

Data Structure Typed	C++ STL	java.util	Python collections
DoublyLinkedList<E>	list<T>	LinkedList<E>	-

Benchmark

doubly-linked-list

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	221.57	4.51	0.03
1,000,000 unshift	229.02	4.37	0.07
1,000,000 unshift & shift	169.21	5.91	0.02
1,000,000 insertBefore	314.48	3.18	0.07

Built-in classic algorithms

Algorithm	Function Description	Iteration Type

Software Engineering Design Standards

Principle	Description
Practicality	Follows ES6 and ESNext standards, offering unified and considerate optional parameters, and simplifies method names.
Extensibility	Adheres to OOP (Object-Oriented Programming) principles, allowing inheritance for all data structures.
Modularization	Includes data structure modularization and independent NPM packages.
Efficiency	All methods provide time and space complexity, comparable to native JS performance.
Maintainability	Follows open-source community development standards, complete documentation, continuous integration, and adheres to TDD (Test-Driven Development) patterns.
Testability	Automated and customized unit testing, performance testing, and integration testing.
Portability	Plans for porting to Java, Python, and C++, currently achieved to 80%.
Reusability	Fully decoupled, minimized side effects, and adheres to OOP.
Security	Carefully designed security for member variables and methods. Read-write separation. Data structure software does not need to consider other security aspects.
Scalability	Data structure software does not involve load issues.