feat: New blog announcement for Kubeflow Spark Operator Benchmarks #164
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Signed-off-by: Vara Bonthu <[email protected]>
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Looks great @vara-bonthu, thank you for working on this!
@varodrig @franciscojavierarceo @hbelmiro @akgraner @kubeflow/kubeflow-steering-committee @kubeflow/wg-data-leads @jbottum Appreciate your review on this!
Signed-off-by: Vara Bonthu <[email protected]>
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I think, we should be good to move this forward.
Let's address changes in the followup PRs if that is needed.
Thanks again for this great work @vara-bonthu and team!
/lgtm
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This PR adds a new blog post titled "🚀 Announcing the Kubeflow Spark Operator Benchmarking Results and Toolkit", focusing on performance benchmarking for the Spark Operator on Kubernetes. The blog highlights key scaling challenges, such as CPU saturation, API server slowdowns, and job scheduling inefficiencies, and provides best practices to optimize large-scale Spark workloads. It also introduces a Benchmarking Toolkit and a Grafana Dashboard to help users monitor and improve performance.
The motivation behind this blog is to share benchmarking insights and practical tuning strategies to help users efficiently run thousands of Spark jobs on Kubernetes. By implementing these optimizations, users can improve job throughput, resource utilization, and system stability. This post serves as a valuable resource for the community to enhance Spark Operator deployments.