a tumblelog
05 Feb 2020

Scaling to 100k Users

Many startups have been there - what feels like legions of new users are signing up for accounts every day and the engineering team is scrambling to keep things running.

It’s a good a problem to have, but information on how to take a web app from 0 to hundreds of thousands of users can be scarce. Usually solutions come from either massive fires popping up or by identifying bottlenecks (and often times both).

With that said, I’ve noticed that many of the main patterns for taking a side project to something highly scalable are relatively formulaic.

This is an attempt to distill the basics around that formula into writing. We’re going to take our new photo sharing website, Graminsta, from 1 to 100k users.

Source: Scaling to 100k Users, an article by Alex Pareto.

An Introduction to Big Data: Clustering

Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have highly dissimilar properties and/or features. Clustering is a method of unsupervised learning and is a common technique for statistical data analysis used in many fields.

Source: An Introduction to Big Data: Clustering, an article by James Le.