avl-tree-typed
Advanced tools
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
npm i avl-tree-typed --save
yarn add avl-tree-typed
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 // 12
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 // 12
type Datum = { timestamp: Date; temperature: number };
// Fixed dataset of CPU temperature readings
const cpuData: Datum[] = [
{ timestamp: new Date('2024-12-02T00:00:00'), temperature: 55.1 },
{ timestamp: new Date('2024-12-02T00:01:00'), temperature: 56.3 },
{ timestamp: new Date('2024-12-02T00:02:00'), temperature: 54.8 },
{ timestamp: new Date('2024-12-02T00:03:00'), temperature: 57.2 },
{ timestamp: new Date('2024-12-02T00:04:00'), temperature: 58.0 },
{ timestamp: new Date('2024-12-02T00:05:00'), temperature: 59.4 },
{ timestamp: new Date('2024-12-02T00:06:00'), temperature: 60.1 },
{ timestamp: new Date('2024-12-02T00:07:00'), temperature: 61.3 },
{ timestamp: new Date('2024-12-02T00:08:00'), temperature: 62.0 },
{ timestamp: new Date('2024-12-02T00:09:00'), temperature: 63.5 },
{ timestamp: new Date('2024-12-02T00:10:00'), temperature: 64.0 },
{ timestamp: new Date('2024-12-02T00:11:00'), temperature: 62.8 },
{ timestamp: new Date('2024-12-02T00:12:00'), temperature: 61.5 },
{ timestamp: new Date('2024-12-02T00:13:00'), temperature: 60.2 },
{ timestamp: new Date('2024-12-02T00:14:00'), temperature: 59.8 },
{ timestamp: new Date('2024-12-02T00:15:00'), temperature: 58.6 },
{ timestamp: new Date('2024-12-02T00:16:00'), temperature: 57.4 },
{ timestamp: new Date('2024-12-02T00:17:00'), temperature: 56.2 },
{ timestamp: new Date('2024-12-02T00:18:00'), temperature: 55.7 },
{ timestamp: new Date('2024-12-02T00:19:00'), temperature: 54.5 },
{ timestamp: new Date('2024-12-02T00:20:00'), temperature: 53.2 },
{ timestamp: new Date('2024-12-02T00:21:00'), temperature: 52.8 },
{ timestamp: new Date('2024-12-02T00:22:00'), temperature: 51.9 },
{ timestamp: new Date('2024-12-02T00:23:00'), temperature: 50.5 },
{ timestamp: new Date('2024-12-02T00:24:00'), temperature: 49.8 },
{ timestamp: new Date('2024-12-02T00:25:00'), temperature: 48.7 },
{ timestamp: new Date('2024-12-02T00:26:00'), temperature: 47.5 },
{ timestamp: new Date('2024-12-02T00:27:00'), temperature: 46.3 },
{ timestamp: new Date('2024-12-02T00:28:00'), temperature: 45.9 },
{ timestamp: new Date('2024-12-02T00:29:00'), temperature: 45.0 }
];
// Create an AVL tree to store CPU temperature data
const cpuTemperatureTree = new AVLTree<Date, number, Datum>(cpuData, {
toEntryFn: ({ timestamp, temperature }) => [timestamp, temperature]
});
// Query a specific time range (e.g., from 00:05 to 00:15)
const rangeStart = new Date('2024-12-02T00:05:00');
const rangeEnd = new Date('2024-12-02T00:15:00');
const rangeResults = cpuTemperatureTree.rangeSearch([rangeStart, rangeEnd], node => ({
minute: node ? node.key.getMinutes() : 0,
temperature: cpuTemperatureTree.get(node ? node.key : undefined)
}));
console.log(rangeResults); // [
// { minute: 5, temperature: 59.4 },
// { minute: 6, temperature: 60.1 },
// { minute: 7, temperature: 61.3 },
// { minute: 8, temperature: 62 },
// { minute: 9, temperature: 63.5 },
// { minute: 10, temperature: 64 },
// { minute: 11, temperature: 62.8 },
// { minute: 12, temperature: 61.5 },
// { minute: 13, temperature: 60.2 },
// { minute: 14, temperature: 59.8 },
// { minute: 15, temperature: 58.6 }
// ]
| Data Structure | Unit Test | Performance Test | API Docs |
|---|---|---|---|
| AVL Tree |  | | AVLTree |
| Data Structure Typed | C++ STL | java.util | Python collections |
|---|---|---|---|
| AVLTree<K, V> | - | - | - |
| 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 |
| 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 |
| 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. |
FAQs
Standard AVL tree
The npm package avl-tree-typed receives a total of 401 weekly downloads. As such, avl-tree-typed popularity was classified as not popular.
We found that avl-tree-typed demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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