Case Studies

Sportslobster Social Network

Learn how the world’s largest sports social network for fans is using Stream to power their feeds and user experience.

The Challenge

Sportlobster initially invested in an in-house solution; however, as the company grew, they ran into some limitations at scale. According to Andy Raines (Engineer at Sportslobster):

“Whilst our in-house solution was working fairly well for the majority of our users, there were some outliers which would cause us to have unpredictable query times which were proving challenging to tune at a SQL level. Due to this we would occasionally see long queries running on our database, as well as providing a poor user experience for the users in question. Beyond that we knew our existing solution would not work for some of the more advanced features we wanted to offer within our product (popularity ranking for example) without some major rework.”

The solution was for Sportlobster to quickly move their platform to Stream, where they safely scaled to billions of feed updates. Sportlobster uses a combination of flat, aggregated and ranked feeds.

The Results

Feeds are extremely fast and on average load in 20ms. Integrating Stream was significantly more affordable than building, maintaining and hosting an in-house solution. In addition it brings the added benefits of:

“Stream was the perfect fit…This means we can spend our precious engineering time on other parts of our product…”

Advanced features such as ranked feeds are in use and help keep user retention and engagement high. Stream’s extensive feature set allows Sportlobster to easily adapt to changing product requirements with simple code modifications. The team at Sportlobster is looking to add several new features to improve the feed experience.

You can learn more about Stream’s feed personalization here. The ranking methods can easily be configured via Stream’s dashboard.

As a closing question we asked if they have any tips for new users of Stream.

Andy: “Read the online docs, use an SDK and don't try to do anything too smart - Stream can take care of almost everything for you. If you have any questions then reach out - the engineers are great. If you have a large existing dataset like we did, make sure to start planning the migration nice and early as it can take some time get get right, especially when you have lots of moving parts and dependencies like we did.”

You can learn more about Stream’s support for importing large sets of historical data in the docs.

Andy Raines

Andy Raines is a Backend Engineer at Sportlobster. He has a MEng in Electronic Engineering from the University of York. You can connect with him on Linkedin or Github.