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How Bloomberg Achieves Close to 90-95% Hardware Utilization with Kubernetes

If you work in financial services, the Bloomberg Terminal is probably your best friend. Behind the scenes, every day Bloomberg deals with hundreds of billions of pieces of data coming in from the financial markets and millions of news stories from hundreds of thousands of sources. There are 14,000 different applications on the Terminal alone. At that scale, delivering information across the globe with high reliability and low latency is a big challenge for the company’s more than 5,500-person strong Engineering department.

In recent years, the infrastructure team has worked on delivering infrastructure as a service while spinning up a lot of VMs and scaling as needed. “But that did not give app teams enough flexibility for their application development, especially when they needed to scale out faster than the actual requests that are coming in,” says Andrey Rybka, Head of the Compute Architecture Team in Bloomberg’s Office of the CTO. “We needed to deploy things in a uniform way across the entire network, and we wanted our private cloud to be as easy to use as the public cloud.”

In 2016, Bloomberg adopted Kubernetes—when it was still in alpha—and has seen remarkable results ever since using the project’s upstream code. “With Kubernetes, we’re able to very efficiently use our hardware to the point where we can get close to 90 to 95% utilization rates,” says Rybka. Autoscaling in Kubernetes allows the system to meet demands much faster. Furthermore, Kubernetes “offered us the ability to standardize our approach to how we build and manage services, which means that we can spend more time focused on actually working on the open source tools that we support,” says Steven Bower, Data and Analytics Infrastructure Lead. “If we want to stand up a new cluster in another location in the world, it’s really very straightforward to do that. Everything is all just code. Configuration is code.”

Read more about Bloomberg’s cloud native success story in the full case study