Ashwin Ramesh

Ashwin did his undergrad in computer science at IIT Madras and likes working on distributed systems. He likes working out, hiking, swimming and everything adventurous. Loves travelling to new places and trying out new food.

Build a Realtime Recommendation Engine: Part 2

This is part 2 of a two-part series on recommendations using Dgraph. Check our part 1 here. In the last post, we looked at how many applications and web apps no longer present static data, but rather generate interesting recommendations to users.

Build a Realtime Recommendation Engine: Part 1

Preface In today’s world, user experience is paramount. It’s no longer about basic CRUD, just serving user data; it’s about mining the data to generate interesting predictions and suggesting actions to the user.

Can it really scale?

In this post, we’ll look at how Dgraph performs on varying the number of nodes in the cluster, specs of the machine and load on the server to answer the ultimate question: Can it really scale?