Feature #1: Select Closest Drivers
Understand how to solve the problem of selecting the k closest Uber drivers from a user’s location using Elixir. Explore distance calculations on a Cartesian plane, implement a max-Heap data structure to store and update driver distances, and analyze the time and space complexity of the approach for efficient ride allocation.
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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