Integrate sparkMeasure for Enhanced Performance Monitoring

[fjammes]

Description:

I propose integrating sparkMeasure into the Fink Broker to enhance performance monitoring and troubleshooting of Apache Spark jobs. sparkMeasure is a tool and library designed for efficient analysis of Spark workloads, simplifying the collection and examination of Spark metrics.

Benefits:

Detailed Metrics Collection: sparkMeasure provides comprehensive metrics such as execution time, CPU usage, and I/O statistics, offering deeper insights into job performance.

Performance Optimization: By analyzing these metrics, we can identify bottlenecks and optimize the broker's performance, leading to more efficient alert processing.

Resource Utilization Analysis: Understanding resource usage patterns can help in better resource allocation and scaling decisions.

Implementation Steps:

Dependency Addition: Include sparkMeasure as a dependency in the Fink Broker project.

Instrumentation: Integrate sparkMeasure's instrumentation into the broker's Spark jobs to collect relevant metrics.

Data Collection: Configure sparkMeasure to collect and store metrics data during job execution.

Analysis and Visualization: Develop tools or dashboards to analyze and visualize the collected metrics for ongoing monitoring and performance assessment.

References:

sparkMeasure GitHub Repository: https://github.com/LucaCanali/sparkMeasure
Integrating sparkMeasure will provide valuable insights into the Fink Broker's performance, facilitating proactive monitoring and optimization of our Spark-based workflows.