Pinterest [Kubernetes User Case Study]

Pinterest was born on the cloud — running on AWS since day one in 2010 — but even cloud native companies can experience some growing pains. Since its launch, Pinterest has become a household name, with more than 200 million active monthly users and 100 billion objects saved. Underneath the hood, there are 1,000 microservices running and hundreds of thousands of data jobs.

Challenge

After eight years in existence, Pinterest had grown into 1,000 microservices and multiple layers of infrastructure and diverse set-up tools and platforms. In 2016 the company launched a roadmap towards a new compute platform, led by the vision of creating the fastest path from an idea to production, without making engineers worry about the underlying infrastructure.

Solution

The first phase involved moving services to Docker containers. Once these services went into production in early 2017, the team began looking at orchestration to help create efficiencies and manage them in a decentralized way. In July 2017, after an eight-week evaluation period, the team chose Kubernetes over other orchestration platforms. Kubernetes lacked certain things at the time — for example, they wanted Spark on Kubernetes but later they realized that the dev cycles they would put in to even try building that is well worth the outcome, both for Pinterest as well as the community.

Impact

By moving to Kubernetes their team was able to build on-demand scaling and new failover policies, in addition to simplifying the overall deployment and management of a complicated piece of infrastructure such as Jenkins. They not only saw reduced build times but also huge efficiency wins. For instance, their team reclaimed over 80 percent of capacity during non-peak hours. As a result, the Jenkins Kubernetes cluster now uses 30 percent less instance-hours per-day when compared to the previous static cluster.

After years of being a cloud native pioneer, Pinterest is eager to share its ongoing journey.

“We are in the position to run things at scale, in a public cloud environment, and test things out in a way that a lot of people might not be able to do and to contribute back some of those learnings.” — Micheal Benedict, Product Manager for the Cloud and the Data Infrastructure Group at Pinterest.