Monitoring Spark on Kubernetes: Key Performance Metrics

Presented by

Alex Pierce, Pepperdata Field Engineer

About this talk

Spark on Kubernetes is growing in popularity due to improved isolation, better resource sharing, and the ability to leverage homogeneous and cloud-native infrastructure for the entire stack. However, running Spark on Kubernetes in a stable, performant, cost-efficient, and secure manner still presents complex challenges. In this webinar, Alex Pierce discusses the key performance metrics to focus on when monitoring and optimizing Spark performance on Kubernetes. Topics include: - Automation and observability for lowering costs and improving performance - Deploying, managing, monitoring, and simplifying Spark on Kubernetes: big data application monitoring, platform monitoring, and dynamic optimization - Configuring for performance and efficiency - Spark app-level dynamic allocation and cluster level autoscaling - The fastest way to improve Spark on Kubernetes performance
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Pepperdata is the Big Data performance company. Fortune 1000 enterprises depend on Pepperdata to manage and optimize the performance of Hadoop and Spark applications and infrastructure. Developers and IT Operations use Pepperdata solutions to diagnose and solve performance problems in production, increase infrastructure efficiencies, and maintain critical SLAs. Pepperdata automatically correlates performance issues between applications and operations, accelerates time to production, and increases infrastructure ROI. Pepperdata works with customer Big Data systems on-premises and in the cloud.