red-black-tree-typed
Version:
464 lines (364 loc) • 14.8 kB
Markdown







This is a standalone Red Black 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](https://www.npmjs.com/package/data-structure-typed) package
```bash
npm i red-black-tree-typed --save
```
```bash
yarn add red-black-tree-typed
```
```typescript
import {RedBlackTree} from 'data-structure-typed';
// /* or if you prefer */ import {RedBlackTree} from 'red-black-tree-typed';
const rbTree = new RedBlackTree<number>();
const idsOrVals = [11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5];
rbTree.addMany(idsOrVals);
const node6 = rbTree.getNode(6);
node6 && rbTree.getHeight(node6) // 3
node6 && rbTree.getDepth(node6) // 1
const getNodeById = rbTree.getNodeByKey(10);
getNodeById?.id // 10
const getMinNodeByRoot = rbTree.getLeftMost();
getMinNodeByRoot?.id // 1
const node15 = rbTree.getNodeByKey(15);
const getMinNodeBySpecificNode = node15 && rbTree.getLeftMost(node15);
getMinNodeBySpecificNode?.id // 12
const lesserSum = rbTree.lesserSum(10);
lesserSum // 45
const node11 = rbTree.getNodeByKey(11);
node11?.id // 11
const dfs = rbTree.dfs('in');
dfs[0].id // 1
rbTree.perfectlyBalance();
const bfs = rbTree.bfs('node');
rbTree.isPerfectlyBalanced() && bfs[0].id // 8
rbTree.delete(11, true)[0].deleted?.id // 11
rbTree.isAVLBalanced(); // true
node15 && rbTree.getHeight(node15) // 2
rbTree.delete(1, true)[0].deleted?.id // 1
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 4
rbTree.delete(4, true)[0].deleted?.id // 4
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 4
rbTree.delete(10, true)[0].deleted?.id // 10
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 3
rbTree.delete(15, true)[0].deleted?.id // 15
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 3
rbTree.delete(5, true)[0].deleted?.id // 5
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 3
rbTree.delete(13, true)[0].deleted?.id // 13
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 3
rbTree.delete(3, true)[0].deleted?.id // 3
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 3
rbTree.delete(8, true)[0].deleted?.id // 8
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 3
rbTree.delete(6, true)[0].deleted?.id // 6
rbTree.delete(6, true).length // 0
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 2
rbTree.delete(7, true)[0].deleted?.id // 7
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 2
rbTree.delete(9, true)[0].deleted?.id // 9
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 2
rbTree.delete(14, true)[0].deleted?.id // 14
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 1
rbTree.isAVLBalanced(); // true
const lastBFSIds = rbTree.BFS();
lastBFSIds[0] // 12
const lastBFSNodes = rbTree.BFS('node');
lastBFSNodes[0].id // 12
```
```javascript
const {RedBlackTree} = require('data-structure-typed');
// /* or if you prefer */ const {RedBlackTree} = require('red-black-tree-typed');
const rbTree = new RedBlackTree();
const idsOrVals = [11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5];
rbTree.addMany(idsOrVals, idsOrVals);
const node6 = rbTree.getNodeByKey(6);
node6 && rbTree.getHeight(node6) // 3
node6 && rbTree.getDepth(node6) // 1
const getNodeById = rbTree.get(10, 'id');
getNodeById?.id // 10
const getMinNodeByRoot = rbTree.getLeftMost();
getMinNodeByRoot?.id // 1
const node15 = rbTree.getNodeByKey(15);
const getMinNodeBySpecificNode = node15 && rbTree.getLeftMost(node15);
getMinNodeBySpecificNode?.id // 12
const node11 = rbTree.getNodeByKey(11);
node11?.id // 11
const dfs = rbTree.dfs('in');
dfs[0].id // 1
rbTree.perfectlyBalance();
const bfs = rbTree.bfs('node');
rbTree.isPerfectlyBalanced() && bfs[0].id // 8
rbTree.delete(11, true)[0].deleted?.id // 11
rbTree.isAVLBalanced(); // true
node15 && rbTree.getHeight(node15) // 2
rbTree.delete(1, true)[0].deleted?.id // 1
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 4
rbTree.delete(4, true)[0].deleted?.id // 4
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 4
rbTree.delete(10, true)[0].deleted?.id // 10
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 3
rbTree.delete(15, true)[0].deleted?.id // 15
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 3
rbTree.delete(5, true)[0].deleted?.id // 5
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 3
rbTree.delete(13, true)[0].deleted?.id // 13
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 3
rbTree.delete(3, true)[0].deleted?.id // 3
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 3
rbTree.delete(8, true)[0].deleted?.id // 8
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 3
rbTree.delete(6, true)[0].deleted?.id // 6
rbTree.delete(6, true).length // 0
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 2
rbTree.delete(7, true)[0].deleted?.id // 7
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 2
rbTree.delete(9, true)[0].deleted?.id // 9
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 2
rbTree.delete(14, true)[0].deleted?.id // 14
rbTree.isAVLBalanced(); // true
rbTree.getHeight() // 1
rbTree.isAVLBalanced(); // true
const lastBFSIds = rbTree.bfs();
lastBFSIds[0] // 12
const lastBFSNodes = rbTree.bfs('node');
lastBFSNodes[0].id // 12
```
[//]: # (No deletion!!! Start of Example Replace Section)
```typescript
interface Employee {
id: number;
name: string;
}
// Create tree with employee data
const employees = new RedBlackTree<number, Employee>([
[],
[],
[]
]);
// Retrieve employee by ID
const alice = employees.get(1);
console.log(alice?.name); // 'Alice';
// Verify sorted order by ID
console.log([...employees.keys()]); // [1, 2, 3];
```
```typescript
interface Product {
name: string;
price: number;
}
const products = new RedBlackTree<number, Product>([
[],
[],
[],
[]
]);
// Find products in price range [20, 45]
const pricesInRange = products.rangeSearch([20, 45], node => {
return products.get(node)?.name;
});
console.log(pricesInRange); // ['Item B', 'Item C'];
```
```typescript
interface StockPrice {
symbol: string;
volume: number;
timestamp: Date;
}
// Simulate real-time stock price index
const priceIndex = new RedBlackTree<number, StockPrice>([
[],
[],
[],
[],
[]
]);
// Find highest-priced stock
const maxPrice = priceIndex.getRightMost();
console.log(priceIndex.get(maxPrice)?.symbol); // 'AMZN';
// Find stocks in price range [200, 400] for portfolio balancing
const stocksInRange = priceIndex.rangeSearch([200, 400], node => {
const stock = priceIndex.get(node);
return {
symbol: stock?.symbol,
price: node,
volume: stock?.volume
};
});
console.log(stocksInRange.length); // 3;
console.log(stocksInRange.some((s: any) => s.symbol === 'GOOGL')); // true;
console.log(stocksInRange.some((s: any) => s.symbol === 'META')); // true;
console.log(stocksInRange.some((s: any) => s.symbol === 'MSFT')); // true;
```
[//]: # (No deletion!!! End of Example Replace Section)
[](https://data-structure-typed-docs.vercel.app)
[](https://vivid-algorithm.vercel.app)
<a href="https://github.com/zrwusa/vivid-algorithm" target="_blank">Examples Repository</a>
<table>
<thead>
<tr>
<th>Data Structure</th>
<th>Unit Test</th>
<th>Performance Test</th>
<th>API Docs</th>
</tr>
</thead>
<tbody>
<tr>
<td>Red Black Tree</td>
<td><img src="https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/assets/tick.svg" alt=""></td>
<td><img src="https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/assets/tick.svg" alt=""></td>
<td><a href="https://data-structure-typed-docs.vercel.app/classes/RedBlackTree.html"><span>RedBlackTree</span></a></td>
</tr>
</tbody>
</table>
<table>
<thead>
<tr>
<th>Data Structure Typed</th>
<th>C++ STL</th>
<th>java.util</th>
<th>Python collections</th>
</tr>
</thead>
<tbody>
<tr>
<td>RedBlackTree<K, V></td>
<td>map<K, V></td>
<td>TreeMap<K, V></td>
<td>-</td>
</tr>
</tbody>
</table>
[//]: # (No deletion!!! Start of Replace Section)
<div class="json-to-html-collapse clearfix 0">
<div class='collapsible level0' ><span class='json-to-html-label'>rb-tree</span></div>
<div class="content"><table style="display: table; width:100%; table-layout: fixed;"><tr><th>test name</th><th>time taken (ms)</th><th>executions per sec</th><th>sample deviation</th></tr><tr><td>100,000 add</td><td>85.85</td><td>11.65</td><td>0.00</td></tr><tr><td>100,000 add & delete randomly</td><td>211.54</td><td>4.73</td><td>0.00</td></tr><tr><td>100,000 getNode</td><td>37.92</td><td>26.37</td><td>1.65e-4</td></tr></table></div>
</div>
[//]: # (No deletion!!! End of Replace Section)
## Built-in classic algorithms
<table>
<thead>
<tr>
<th>Algorithm</th>
<th>Function Description</th>
<th>Iteration Type</th>
</tr>
</thead>
<tbody>
<tr>
<td>Binary Tree DFS</td>
<td>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.
</td>
<td>Recursion + Iteration</td>
</tr>
<tr>
<td>Binary Tree BFS</td>
<td>Traverse a binary tree in a breadth-first manner, starting from the root node, visiting nodes level by level
from left to right.
</td>
<td>Iteration</td>
</tr>
<tr>
<td>Binary Tree Morris</td>
<td>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.
</td>
<td>Iteration</td>
</tr>
</tbody>
</table>
## Software Engineering Design Standards
<table>
<tr>
<th>Principle</th>
<th>Description</th>
</tr>
<tr>
<td>Practicality</td>
<td>Follows ES6 and ESNext standards, offering unified and considerate optional parameters, and simplifies method names.</td>
</tr>
<tr>
<td>Extensibility</td>
<td>Adheres to OOP (Object-Oriented Programming) principles, allowing inheritance for all data structures.</td>
</tr>
<tr>
<td>Modularization</td>
<td>Includes data structure modularization and independent NPM packages.</td>
</tr>
<tr>
<td>Efficiency</td>
<td>All methods provide time and space complexity, comparable to native JS performance.</td>
</tr>
<tr>
<td>Maintainability</td>
<td>Follows open-source community development standards, complete documentation, continuous integration, and adheres to TDD (Test-Driven Development) patterns.</td>
</tr>
<tr>
<td>Testability</td>
<td>Automated and customized unit testing, performance testing, and integration testing.</td>
</tr>
<tr>
<td>Portability</td>
<td>Plans for porting to Java, Python, and C++, currently achieved to 80%.</td>
</tr>
<tr>
<td>Reusability</td>
<td>Fully decoupled, minimized side effects, and adheres to OOP.</td>
</tr>
<tr>
<td>Security</td>
<td>Carefully designed security for member variables and methods. Read-write separation. Data structure software does not need to consider other security aspects.</td>
</tr>
<tr>
<td>Scalability</td>
<td>Data structure software does not involve load issues.</td>
</tr>
</table>