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Project: Binary Search Trees

JavaScript Course


You have learned about binary search trees, where you take a group of data items and turn them into a tree full of nodes, with each left node being “lower” than each right node. The tree starts with the “root node” and any node with no children is called a “leaf node”. You have also learned about tree traversal algorithms like breadth-first and depth-first.

Now, let’s take a look at balanced binary search trees (BST). A BST allows fast operations for lookup, insertion, and deletion of data items. Read this article on building balanced BSTs. Here is a video on building balanced BSTs as well. Although the last resource does not use JavaScript, you should understand it well enough to develop your own pseudocode.


You’ll build a balanced BST in this assignment. Do not use duplicate values because they make it more complicated and result in trees that are much harder to balance. Therefore, be sure to always remove duplicate values or check for an existing value before inserting.

  1. Build a Node class/factory. It should have an attribute for the data it stores as well as its left and right children.

  2. Build a Tree class/factory which accepts an array when initialized. The Tree class should have a root attribute, which uses the return value of buildTree which you’ll write next.

  3. Write a buildTree(array) function that takes an array of data (e.g., [1, 7, 4, 23, 8, 9, 4, 3, 5, 7, 9, 67, 6345, 324]) and turns it into a balanced binary tree full of Node objects appropriately placed (don’t forget to sort and remove duplicates!). The buildTree function should return the level-0 root node.

    Tip: If you would like to visualize your binary search tree, here is a prettyPrint() function that will console.log your tree in a structured format. This function will expect to receive the root of your tree as the value for the node parameter.

     const prettyPrint = (node, prefix = "", isLeft = true) => {
       if (node === null) {
       if (node.right !== null) {
         prettyPrint(node.right, `${prefix}${isLeft ? "│   " : "    "}`, false);
       console.log(`${prefix}${isLeft ? "└── " : "┌── "}${node.data}`);
       if (node.left !== null) {
         prettyPrint(node.left, `${prefix}${isLeft ? "    " : "│   "}`, true);
  4. Write insert(value) and deleteItem(value) functions that insert/delete the given value. You’ll have to deal with several cases for delete, such as when a node has children or not. If you need additional resources, check out these two articles on inserting and deleting, or this video on BST inserting/removing with several visual examples.

    You may be tempted to implement these methods using the original input array used to build the tree, but it’s important for the efficiency of these operations that you don’t do this. If we refer back to the Big O Cheatsheet, we’ll see that binary search trees can insert/delete in O(log n) time, which is a significant performance boost over arrays for the same operations. To get this added efficiency, your implementation of these methods should traverse the tree and manipulate the nodes and their connections.

  5. Write a find(value) function that returns the node with the given value.

  6. Write a levelOrder(callback) function that accepts a callback function as its parameter. levelOrder should traverse the tree in breadth-first level order and call the callback on each node as it traverses, passing the whole node as an argument, similarly to how Array.prototype.forEach might work for arrays. levelOrder may be implemented using either iteration or recursion (try implementing both!). If no callback function is provided, throw an Error reporting that a callback is required. Tip: You will want to use an array acting as a queue to keep track of all the child nodes that you have yet to traverse and to add new ones to the list (video on level order traversal).

  7. Write inOrder(callback), preOrder(callback), and postOrder(callback) functions that also accept a callback as a parameter. Each of these functions should traverse the tree in their respective depth-first order and pass each node to the provided callback. The functions should throw an Error if no callback is given as an argument, like with levelOrder.

  8. Write a height(node) function that returns the given node’s height. Height is defined as the number of edges in the longest path from a given node to a leaf node.

  9. Write a depth(node) function that returns the given node’s depth. Depth is defined as the number of edges in the path from a given node to the tree’s root node.

  10. Write an isBalanced function that checks if the tree is balanced. A balanced tree is one where the difference between heights of the left subtree and the right subtree of every node is not more than 1.

  11. Write a rebalance function that rebalances an unbalanced tree. Tip: You’ll want to use a traversal method to provide a new array to the buildTree function.

Tie it all together

Write a driver script that does the following:

  1. Create a binary search tree from an array of random numbers < 100. You can create a function that returns an array of random numbers every time you call it if you wish.
  2. Confirm that the tree is balanced by calling isBalanced.
  3. Print out all elements in level, pre, post, and in order.
  4. Unbalance the tree by adding several numbers > 100.
  5. Confirm that the tree is unbalanced by calling isBalanced.
  6. Balance the tree by calling rebalance.
  7. Confirm that the tree is balanced by calling isBalanced.
  8. Print out all elements in level, pre, post, and in order.

Additional resources

This section contains helpful links to related content. It isn’t required, so consider it supplemental.

  • Yicheng Gong has some excellent videos that help visualize the call stack when traversing binary search trees: In-order, Post-order, and Pre-order Traversal Algorithms.

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