In this post, we explore a practical application of the k-nearest neighbors (KNN) algorithm to solve a real-world problem: predicting service availability based on geographic proximity. By leveraging existing customer data, we can provide quick estimates of service eligibility, enhancing user experience despite the limitations of a slow third-party API.

In the rush to get to market, it's easy to overlook small details that turn out to not be small. When we launched Vectopus.com, our multi-vendor vector image marketplace, we omitted WebP image support. To be honest, as the technical lead / architect - this was on me. I was so focused on getting the site up and running, I was not thinking about the full range of performance issues. I considered the performance of the API code, EC2 instance sizes and configuration, ALBs, CDN, etc., but did not include the full breadth of things that impact performance and SEO.

I am starting a series of blog posts on how I built a multi-vendor marketplace for stock SVG images on AWS Cloud services. In the series I will talk about the architecture, tech stack, do some deep dives into specific problems and solutions, and share some of what I have learned. A big part of the challenge has been insuring reliability, scalability, and peformance on a limited budget.

I mentioned in yesterday's post that a lot of data manipulation involves lists, nested and otherwise. Another common task is sorting elements in a list. Day 6 of 30 Days of Algorithms is a Merge Sort, which is another "divide-and-conquer" algorithm.

Quicksort is one of the most efficient sorting algorithm with O(log n) Big-O notation. It can be 2x - 3x faster than merge sort. It is another divide-and-conquer algorithm. The algorithm divides the list on a pivot, which can be any number in the list. Any item lower than the pivot is added to a 'left' list. Any item larger than the pivot is added to the 'right' list. The process is repeated recursively with any sub-list with more than 2 items.