🚀 Announcing the Kubeflow Spark Operator Benchmarking Results

🔍 What’s Included? ❌ The Challenges: Why Benchmarking Matters 🛠 Tuning Best Practices for Spark Operator Deploy Multiple Spark Operator Instances Disable Webhooks for Faster Job Starts Increase Controller Workers Enable a Batch Scheduler (Volcano / YuniKorn) Optimize API Server Scaling Distribute Spark Jobs Across Multiple Namespaces Monitor & Tune Using the Open-Source Grafana Dashboard 📖 Learn More & Get Started Kubernetes has become the go-to platform for running large-scale Apache Spark workloads. But as workloads scale, how do you ensure your Spark jobs run efficiently without hitting bottlenecks? Managing thousands of concurrent Spark jobs can introduce severe performance challenges —from CPU saturation in the Spark Operator to Kubernetes API slowdowns and job scheduling inefficiencies. To address these challenges, we are excited to introduce the Kubeflow Spark Operator Benchmarking Results and Toolkit —a comprehensive framework to analyze performance, pinpoint bottlenecks, and optimize your Spark on Kubernetes deployments. This benchmarking effort provides three key outcomes to help you take full control of your Spark on Kubernetes deployment: ✅ Benchmarking Results – A detailed evaluation of performance insights and tuning recommendations for large-scale Spark workloads. 🛠 Benchmarking Test Toolkit – A fully reproducible test suite to help users evaluate their own Spark Operator performance and validate improvements. 📊 Open-Sourced Grafana Dashboard – A battle-tested visualization tool designed specifically to track large-scale Spark Operator deployments, providing real-time monitoring of job processing efficiency, API latencies, and system health. Running thousands of Spark jobs on Kubernetes at scale uncovers several performance roadblocks that can cripple efficiency if left unresolved: 🚦 Spark Operator Becomes CPU-Bound : When handling thousands of Spark jobs, the controller pod maxes out CPU resources, limiting job submission rates. 🐢 High API Server Latency : As workloads scale, Kubernetes API responsiveness degrades—job status updates slow down, affecting observability and scheduling efficiency. 🕒 Webhook Overhead Slows Job Starts : Using webhooks adds ~60 seconds of extra latency per job, reducing throughput in high-concurrency environments. 💥 Namespace Overload Causes Failures : Running 6,000+ SparkApplications in a single namespace resulted in pod failures due to excessive environment variables and service object overload. 💡 So, how do you fix these issues and optimize your Spark Operator deployment? That’s where our benchmarking results and toolkit come in. Based on our benchmarking findings, we provide clear, actionable recommendations for improving Spark Operator performance at scale.