How to Run Apache Spark on Kubernetes: A Step-by-Step Guide

Apache Spark on Kubernetes runs Spark drivers and executors as Kubernetes pods, combining Spark’s distributed processing and task scheduling with Kubernetes resource placement, autoscaling, access controls, observability, and deployment tooling. It explains cluster and client deployment modes, contrasts Kubernetes with YARN, and presents native spark-submit for ad hoc workloads alongside Spark Operators for declarative, versioned, and scheduled application management. A nine-step walkthrough covers building a Spark image, configuring namespaces and RBAC, submitting SparkPi, deploying it through the Kubeflow Spark Operator, sizing pods, provisioning shuffle storage, monitoring applications, and cleaning up local resources. The production guidance, based on operational incidents reported by Onehouse, emphasizes sizing complete executor containers rather than JVM heaps alone, coordinating Spark dynamic allocation with node autoscalers, protecting drivers from disruption, isolating capacity, budgeting local disk for shuffle data, handling driver failures, planning network and IP capacity, securing cloud identities and images, and packaging dependencies reproducibly. It also recommends persistent event logs, workload-specific testing of scaling and recovery behavior, and careful validation of storage commit protocols, while describing Onehouse’s Quanton products as optional tools for managing Spark workloads on self-managed Kubernetes infrastructure.

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