Amazon EMR Serverless observability, Part 1: Monitor Amazon EMR Serverless workers in near real time using Amazon CloudWatch

This article discusses how to monitor and optimize Amazon EMR Serverless jobs using new CloudWatch metrics for worker-level resource utilization. The new metrics provide insights into vCPU, memory, storage, and I/O allocation and usage for driver and executor workers.

Specifically, the article covers:

• Benefits of monitoring EMR Serverless jobs with CloudWatch metrics like optimizing resource utilization, diagnosing common errors, getting near real-time insights, configuring alerts, and historical analysis
• How to use a CloudWatch dashboard solution to view aggregated worker metrics for an EMR Serverless application
• Optimizing resource utilization by adjusting Spark configurations like executor memory, cores, and dynamic allocation based on actual usage
• Diagnosing and resolving driver errors like out-of-memory by monitoring driver metrics
• Diagnosing and resolving executor errors like disk space issues by monitoring aggregated executor metrics
• Conclusion highlighting the value of these metrics for managing EMR Serverless applications