Apache Spark on Kubernetes - Lessons Learned from Launching Millions of Spark Executors
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Overview
Syllabus
Intro
Data Platform
Elastic Self Service Spark
Code to Deployment
Security
Monitoring
Orchestration Architecture
Varying Workload Pattern
One Interface over Multi-Cloud
Optimize Kubernetes for Spark Workload
Granular Concurrency Check at Orchestration
Avoid Partially Running Applications
Timeout Partially Running Applications
Mitigate Cluster Storage Stress
Utilization-based Allocation Recommendation
Dynamic Allocation
Push-button Cloud Management
Scale up Spark on Kubernetes
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