Feature #1: Select Closest Drivers
Explore techniques to find the k nearest drivers to a user's location on a Cartesian plane by calculating Euclidean distances. Understand how to use a max-heap data structure to efficiently keep track of the closest drivers. Gain insights into the time and space complexities involved in optimizing driver selection.
We'll cover the following...
Description
The first functionality we’ll be building is to locate the nearest available drivers within the vicinity of a user. There are numerous Uber drivers roaming around in a city. For simplicity, we’ll consider the city as a Cartesian plane and assign the coordinates (0, 0) to the user’s location. When a user wants to book a ride, we’ll simply find k drivers with the shortest distance on the Cartesian plane. Here, k is the minimum threshold for choosing the closest drivers.
We’ll be provided a list containing a set of points on the Cartesian plane and a number k. Each set of points will represent the location of a driver. We need to find the k closest drivers from the user’s location.
Solution
The Euclidean distance between a point P(x,y) and the origin can be calculated using the following formula:
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