Newly Picked Mangoes


    0.2.1 • Public • Published

    Balanced binary search trees in ES6 Javascript for Node.js and browsers

    The underlying implementation is a Treap, which provides balance with high probability. This implementation focuses on fast iteration, and provides generators for doing so.

    Treaps are simple and fast. In most cases, it provides up to 4x faster inserts, and 2x faster erases than a similar RB tree implementation.

    Deterministic behaviour can be achieved by seeding the RNG.


    Package name is bbst

    npm install bbst --save


    npm install mocha
    // perform random test
    npm test
    // test with specific seed "seed"
    npm test seed


    The API provides insert, changeKey, find, erase, iteration, reverseIterator, size, height, print, and variants of the above (to be covered in examples below).

    First you need to include the module, which is easy in both node and a browser (look at index.html).


    const BST = require("bbst");


        <meta charset="UTF-8">
        <script src="src/treap.js"></script> 
    const BST = Treap;

    From this point on, usage is the same on both platforms; let's assume we name the class BST.

    deterministic seed

    BST.seedrandom("some value");


    const bst = new BST();


    Insert takes 1 object that is required to have a key property that can be compared. An Error will be thrown if something is inserted without a key. The inserted object can have any other property.

    bst.insert({key:1, name:"Buzz Lightyear"});

    Duplicate keys are not guarenteed to remain in insertion order.

    bst.insert({key:1, name:"An imposter"});
    // bst.find(1) will find them in a random order


    Iterating forward will do so in increasing key order

    for (let node of bst) {
      console.log(node.key);  // print keys smallest to largest
    for (let node of bst.reverseIterator()) {
      console.log(node.key);  // print keys largest to smallest

    This is very useful for game tree searches as you iterate through possible moves in place rather than creating and sorting them at each step. This is especially powerful combined with changeKey. For each step, instead of creating a new state, simply change the current one to the next one while tracking the changes. If you have n possible moves, at each step you use O(1) memory instead of O(n).

    I use this in a minimax algorithm with alpha-beta pruning so that the best moves are explored first, which often causes most of the other moves to be pruned and thus not generated.


    A small change to a key's value can be dealt with faster than deleting and reinserting the node with changeKey.

    const bst = new BST();
    for (let key = 0; key < 1000000; ++key) {
    const node = bst.find(4000);
    // takes many fewer operations since it searches from node up
    bst.changeKey(node, 4001);


    The tree comes with pretty printing that works in consoles and browsers, as well as returning the string.

    const bst = new BST();
    for (let key = 0; key < 10; ++key) {

    Results in something similar to

    ├ 1
    │ ├ 0
    │ └ 2
    │   ├ 
    │   └ 3
    └ 9
      └ 7
        ├ 5
        │ ├ 
        │ └ 6
        └ 8

    print accepts a custom printer function for printing more than just the node key.

    bst.print((node)=> {
      // return something printable, such as a string
      return node.key + ":" +;


    All failed finds will return BST.NIL. The basic find retrieves the top-most node with matching key.

    const node = bst.find(5);
    const nonNode = bst.find(11);
    assert(nonNode === BST.NIL);  // true

    If there is temporal locality (you need it soon after you found it, but not immediately after), then findAndElevate will make subsequent finds faster.

    const hotNode = bst.find(5);

    If your data has duplicate keys and you care about finding all of them or a specific one, then use findFirst and findNext. findFirst takes the key to find, findNext takes the previous node (and is O(1) time).

    bst.insert({key:5, fruit:"Pomelo"});
    bst.insert({key:5, fruit:"Tomato"});
    let found = bst.findFirst(5);
    while (found !== BST.NIL) {
      found = bst.findNext(found);


    Erase the top-most node with a matching key, or do nothing if it doesn't exist


    Erase a specific node, which is useful for duplicates and faster if you already found the node



    Get number of nodes

    let bst = new BST();
    bst.size(); // 0
    for (let key = 0; key < 10; ++key) {
    bst.size(); // 10


    Maximum pointer distance to root node. It is expected to be O(lg(bst.size())), and is more likely with larger size.

    let bst = new BST();
    bst.height(); // 0
    for (let key = 0; key < 1000; ++key) {
    bst.height(); // about lg(bst.size())


    npm i bbst

    DownloadsWeekly Downloads






    Last publish


    • johnsonzhong