Containerized Spark on Kubernetes - Databricks

Containerized Spark on Kubernetes

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Consider two recent trends in application development: more and more applications are taking advantage of architectures involving containerized microservices in order to enable improved elasticity, fault-tolerance, and scalability — whether in the public cloud or on-premise. In addition, analytic capabilities and scalable data processing have increasingly become a basic requirement for contemporary applications. The confluence of these trends suggests that there are a lot of good reasons to want to manage Spark with a container orchestration platform, but it’s not quite as simple as packaging up a standalone cluster in containers. This talk will present our team’s experiences migrating a production Spark cluster from a multi-tenant Mesos cluster to a shared compute resource managed by Kubernetes. We’ll explain the motivation behind microservices and containers and identify the architectures that make sense for containerized applications that depend on Spark. We’ll pay special attention to practical concerns of running Spark in containers, including networking, access control, persistent storage, and multitenancy. You’ll leave this talk with a better understanding of why you might want to run Spark in containers and some concrete ideas for how to get started doing it.

About William Benton

William Benton leads a team of data scientists and engineers at Red Hat, where he has applied analytic techniques to problems ranging from forecasting cloud infrastructure costs to designing better cycling workouts. His current focus is investigating the best ways to build and deploy intelligent applications in cloud-native environments, but he has also conducted research and development in the areas of static program analysis, managed language runtimes, logic databases, cluster configuration management, and music technology.