| test name | time taken (ms) | executions per sec | sample deviation |
|---|---|---|---|
| 10,000 add randomly | 31.32 | 31.93 | 3.67e-4 |
| 10,000 add & delete randomly | 70.90 | 14.10 | 0.00 |
| 10,000 addMany | 40.58 | 24.64 | 4.87e-4 |
| 10,000 get | 27.31 | 36.62 | 2.00e-4 |
Package Exports
- avl-tree-typed
- avl-tree-typed/dist/index.js
This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (avl-tree-typed) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.
Readme
What
Brief
This is a standalone AVL Tree 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 avl-tree-typed --saveyarn
yarn add avl-tree-typedmethods

snippet
TS
import {AVLTree, AVLTreeNode} from 'data-structure-typed';
// /* or if you prefer */ import {AVLTree} from 'avl-tree-typed';
const avlTree = new AVLTree<AVLTreeNode<number>>();
const idsOrVals = [11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5];
avlTree.addMany(idsOrVals, idsOrVals);
const node6 = avlTree.get(6);
node6 && avlTree.getHeight(node6) // 3
node6 && avlTree.getDepth(node6) // 1
const getNodeById = avlTree.get(10, 'id');
getNodeById?.id // 10
const getMinNodeByRoot = avlTree.getLeftMost();
getMinNodeByRoot?.id // 1
const node15 = avlTree.get(15);
const getMinNodeBySpecificNode = node15 && avlTree.getLeftMost(node15);
getMinNodeBySpecificNode?.id // 12
const subTreeSum = node15 && avlTree.subTreeSum(node15);
subTreeSum // 70
const lesserSum = avlTree.lesserSum(10);
lesserSum // 45
const node11 = avlTree.get(11);
node11?.id // 11
const dfs = avlTree.DFS('in', 'node');
dfs[0].id // 1
avlTree.perfectlyBalance();
const bfs = avlTree.BFS('node');
avlTree.isPerfectlyBalanced() && bfs[0].id // 8
avlTree.remove(11, true)[0].deleted?.id // 11
avlTree.isAVLBalanced(); // true
node15 && avlTree.getHeight(node15) // 2
avlTree.remove(1, true)[0].deleted?.id // 1
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 4
avlTree.remove(4, true)[0].deleted?.id // 4
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 4
avlTree.remove(10, true)[0].deleted?.id // 10
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 3
avlTree.remove(15, true)[0].deleted?.id // 15
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 3
avlTree.remove(5, true)[0].deleted?.id // 5
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 3
avlTree.remove(13, true)[0].deleted?.id // 13
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 3
avlTree.remove(3, true)[0].deleted?.id // 3
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 3
avlTree.remove(8, true)[0].deleted?.id // 8
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 3
avlTree.remove(6, true)[0].deleted?.id // 6
avlTree.remove(6, true).length // 0
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 2
avlTree.remove(7, true)[0].deleted?.id // 7
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 2
avlTree.remove(9, true)[0].deleted?.id // 9
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 2
avlTree.remove(14, true)[0].deleted?.id // 14
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 1
avlTree.isAVLBalanced(); // true
const lastBFSIds = avlTree.BFS();
lastBFSIds[0] // 12
const lastBFSNodes = avlTree.BFS('node');
lastBFSNodes[0].id // 12JS
const {AVLTree} = require('data-structure-typed');
// /* or if you prefer */ const {AVLTree} = require('avl-tree-typed');
const avlTree = new AVLTree();
const idsOrVals = [11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5];
avlTree.addMany(idsOrVals, idsOrVals);
const node6 = avlTree.get(6);
node6 && avlTree.getHeight(node6) // 3
node6 && avlTree.getDepth(node6) // 1
const getNodeById = avlTree.get(10, 'id');
getNodeById?.id // 10
const getMinNodeByRoot = avlTree.getLeftMost();
getMinNodeByRoot?.id // 1
const node15 = avlTree.get(15);
const getMinNodeBySpecificNode = node15 && avlTree.getLeftMost(node15);
getMinNodeBySpecificNode?.id // 12
const subTreeSum = node15 && avlTree.subTreeSum(node15);
subTreeSum // 70
const lesserSum = avlTree.lesserSum(10);
lesserSum // 45
const node11 = avlTree.get(11);
node11?.id // 11
const dfs = avlTree.DFS('in', 'node');
dfs[0].id // 1
avlTree.perfectlyBalance();
const bfs = avlTree.BFS('node');
avlTree.isPerfectlyBalanced() && bfs[0].id // 8
avlTree.remove(11, true)[0].deleted?.id // 11
avlTree.isAVLBalanced(); // true
node15 && avlTree.getHeight(node15) // 2
avlTree.remove(1, true)[0].deleted?.id // 1
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 4
avlTree.remove(4, true)[0].deleted?.id // 4
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 4
avlTree.remove(10, true)[0].deleted?.id // 10
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 3
avlTree.remove(15, true)[0].deleted?.id // 15
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 3
avlTree.remove(5, true)[0].deleted?.id // 5
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 3
avlTree.remove(13, true)[0].deleted?.id // 13
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 3
avlTree.remove(3, true)[0].deleted?.id // 3
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 3
avlTree.remove(8, true)[0].deleted?.id // 8
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 3
avlTree.remove(6, true)[0].deleted?.id // 6
avlTree.remove(6, true).length // 0
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 2
avlTree.remove(7, true)[0].deleted?.id // 7
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 2
avlTree.remove(9, true)[0].deleted?.id // 9
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 2
avlTree.remove(14, true)[0].deleted?.id // 14
avlTree.isAVLBalanced(); // true
avlTree.getHeight() // 1
avlTree.isAVLBalanced(); // true
const lastBFSIds = avlTree.BFS();
lastBFSIds[0] // 12
const lastBFSNodes = avlTree.BFS('node');
lastBFSNodes[0].id // 12API docs & Examples
Data Structures
| Data Structure | Unit Test | Performance Test | API Docs |
|---|---|---|---|
| AVL Tree | AVLTree |
Standard library data structure comparison
| Data Structure Typed | C++ STL | java.util | Python collections |
|---|---|---|---|
| AVLTree<K, V> | - | - | - |
Benchmark
avl-tree
Built-in classic algorithms
| Algorithm | Function Description | Iteration Type |
|---|---|---|
| Binary Tree DFS | Traverse a binary tree in a depth-first manner, starting from the root node, first visiting the left subtree, and then the right subtree, using recursion. | Recursion + Iteration |
| Binary Tree BFS | Traverse a binary tree in a breadth-first manner, starting from the root node, visiting nodes level by level from left to right. | Iteration |
| Binary Tree Morris | Morris traversal is an in-order traversal algorithm for binary trees with O(1) space complexity. It allows tree traversal without additional stack or recursion. | Iteration |
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. |