Efficient Property Price Range Search with Binary Search Tree

Zillow aims to enhance its platform by allowing users to efficiently search for properties within specific price ranges. To achieve this, implementing a Binary Search Tree (BST) can optimize the search process, ensuring users receive quick and accurate results. A BST is an ideal data structure for this purpose as it allows for efficient searching, insertion, and deletion operations, maintaining order within the tree and providing a foundation for quick range queries.

Implementation

• Create a BST where each node represents a property, with the property price as the key. Each node will store property details such as location, size, and other relevant attributes.

• Implement a function to insert property data into the BST based on the price. This function will ensure that the tree remains balanced to maintain efficient search operations.

• Develop a function that searches the BST for properties within a specified price range. The function will traverse the tree, efficiently identifying and collecting properties that fall within the desired range.

• Implement balancing techniques (such as AVL rotations) to ensure the BST remains balanced, optimizing search performance and preventing skewed trees that could degrade efficiency.

• Create functions to update property prices and other details. Ensure that any changes to the property data trigger rebalancing operations if necessary to maintain the tree’s efficiency.

Time Complexity

• Average Case: O(log N) (Insert,search,delete)
• Worst Case: O(N)

Space Complexity : O(N)

Here is the full code implementation Code