Building an Upload Microservice using AWS Lambda

One of Bodybuilding.com’s core missions is to help people transform their lives through the adoption of a healthy lifestyle and exercise. One way we motivate people to begin such a journey is through our yearly “transformation challenge.” Over the course of 12 weeks, participants in the challenge are encouraged to adopt a training plan and set new habits that will lead to a fitter, healthier body. Prizes are awarded to the people who make the most profound transformation over the course of the contest. 

In order to participate, contestants are required to upload photos of themselves before and after the challenge. As one might imagine, the time period shortly before and shortly after each challenge tends to produce a spike in photo-upload traffic to our servers. Historically, these periods have been problematic for the scalability of our backend systems. In order to improve our end-user experience (not to mention our own), we decided to re-architect our photo-upload system in late 2015.

This tech talk describes how we created a new microservice-based architecture using Amazon Web Services technologies as well as a Java/Spring stack. Our goal was to greatly improve our ability to handle spikes in image-upload traffic without manual intervention. As a result, we created autoscaling RESTful services using Spring Boot and AWS Elastic Beanstalk. Image-processing logic has been encapsulated into highly scalable Amazon Lambda functions.

We recently launched the 2016 transformation challenge. With our new image-upload infrastructure in place, we have been able to handle peak loads without a hitch.