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.

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Introducing Argos Dashboard

If you've seen our previous posts or tech talks, you know that we are huge fans of Hystrix, the resiliency library by Netflix. The Hystrix dashboard has been great for monitoring a single application or service, but as the number of applications and microservices increased, we found ourselves needing a more holistic view of our infrastructure. We currently trend all our Hystrix metrics into Graphite for correlation and forecasting, but we really needed something concise and real-time. Inspired by the usefulness of the Hystrix dashboard, we have created Argos Dashboard.

Argos Dashboard provides a real-time view of multiple Hystrix clusters by aggregating data for each cluster onto a single dashboard. The new dashboard has been extremely useful for monitoring how requests flow through the dependencies of each application. It also helps easily monitor what happens when we do performance and resiliency testing. Today, we are releasing Argos Dashboard on GitHub at http://www.github.com/bbcom/argos-dashboard. We hope you find Argos Dashboard valuable and look forward to feedback and pull requests.

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