This article explains how to monitor Apache Spark applications on Amazon EMR by publishing detailed metrics to Amazon CloudWatch for performance optimization and bottleneck identification.

• Custom Spark metrics sink collects and publishes metrics to CloudWatch every 30 seconds
• CloudWatch agent aggregates metric data from each EMR cluster node
• Metricfilter.json defines which metrics to capture, avoiding unnecessary data
• Solution includes bootstrap script, metrics library, and CloudFormation template
• Supports EMR versions 5.x.x, 6.x.x, and 7.x.x with version-specific JAR files
• CloudWatch dashboard provides real-time visibility into Spark job, stage, and task performance
• Metrics include I/O, garbage collection, memory, CPU, and executor-level data
• Custom metrics incur CloudWatch charges; EMR metrics do not
• Metrics can trigger CloudWatch alarms and SNS notifications for automation

This solution enables effective real-time monitoring of Spark applications on EMR through CloudWatch integration, helping identify performance issues and optimize resource utilization.