Sports activities and serverless – the architecture for data-driven systems. From the article, you'll learn how to build the architecture for data-driven systems. We decided to go with a Serverless approach and since we focus mostly on AWS, we went with AWS Lambda.
A few years ago at The Software House a new initiative was launched – TSH Challenge. The idea was simple, let’s count the kilometers we run/walk/ride and convert those into points, so we can motivate each other to a healthier lifestyle. The idea was a great success and soon it evolved into a lottery where each point gave you a lottery ticket, allowing you to win something. We could end it here, but this was just a beginning. Let’s talk about sports activities and serverless and how we build an architecture for data-driven systems.
The first version of the app didn’t come with any special requirements or features. We only wanted to calculate the points and track user activities. In fact, it required little coding from us, because most of the functionalities were handled by Endomondo.
It had a feature called challenges that we could use to gather necessary information from all the users from our group.
We had to build a parser, which was done with a simple PHP script, executed every few days on a DigitalOcean droplet. Yeah, it doesn’t sound exciting at all. It was really simple! Of course, the initial design had some flaws, we soon discovered…
At the end of 2020, Endomondo announced they were shutting down the platform. Every user had time until December 31 to transfer activities into a different system. It was fine for a standard user, yet much more complex for systems that depended on Endomondo special features, like those challenges we were using. 😅
So since we didn’t have a choice, but to change a provider, we decided that maybe it is also a good moment to rethink the whole application and finally remove all the flaws we found.
We started making our decisions regarding the architecture and technology based on a few drivers:
Support any kind of activity.
Flexibility of points recalculation at any time.
Node.js- and AWS-based.
The second point was quite a crucial requirement because we wanted to rate each type of activity based on variable conditions. Let’s take walking, cycling and workout as an example. We based points for cycling and walking on distance, however, workouts are based on duration. Now, let’s add some more complexity to that equation.
One kilometre of cycling shouldn’t be worth the same as 1 kilometre of walking. It is far easier to cover one kilometre on a bike than on your feet.
So, we needed to move most of the calculation system from the provider to our system. Making it also less dependent on a specific integration however, it is not like we could use any provider. We still wanted to achieve real-time updates, so we had to make sure to have access to up-to-date rankings constantly.
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