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.

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About William Benton

William Benton is passionate about making it easier for machine learning practitioners to benefit from advanced infrastructure and making it possible for organizations to manage machine learning systems. His recent roles have included defining product strategy and professional services offerings related to data science and machine learning, leading teams of data scientists and engineers, and contributing to many open source communities related to data, ML, and distributed systems. Will was an early advocate of building machine learning systems on Kubernetes and developed and popularized the “intelligent applications” idiom for machine learning systems in the cloud. He has also conducted research and development related to static program analysis, language runtimes, cluster configuration management, and music technology.