Spark Job Observability and Analysis

Overview

The Spark Job Observability and Analysis feature provides deep, intelligent insight into every Spark job executed within the IOMETE Lakehouse Platform.
After each job run, IOMETE automatically analyzes execution metrics, resource utilization, and job configurations to deliver actionable recommendations for improving performance and optimizing cost.
This capability helps engineers understand how their Spark jobs behave in production — identifying under- or over-provisioned resources, inefficient configurations, and tuning opportunities — all directly integrated into the job history and monitoring UI.

Planned Features

• Post-Execution Insights: Automatically analyze completed Spark jobs and summarize performance, stage timings, and resource utilization.
• Optimization Recommendations: Suggest Spark configuration changes such as executorMemory, executorCores, and shuffle parameters based on observed behavior.
• Resource Usage Evaluation: Detect CPU underutilization or memory pressure and recommend right-sizing cluster configurations.
• Disk Spill Detection: Identify shuffle or join spills to disk and suggest memory or partitioning optimizations.
• Bottleneck Analysis: Highlight slow stages, skewed partitions, or long-running tasks with actionable next steps.
• Query Plan Insights: Break down query execution plans and pinpoint inefficient operations or excessive shuffles.
• Cost Awareness: Estimate compute cost for each job and recommend configurations for improved cost-performance balance.
• Performance Trend Tracking: Compare runs of the same job to highlight regressions or improvements over time.
• Integration with Lakehouse Monitoring: Surface job-level insights in the global observability and alerting layer.
• Contextual Guidance: Provide plain-language explanations and tuning suggestions directly in the job details view (e.g., “Your job spilled 4.5GB to disk. Increasing executor memory to 8GB may prevent this.”).

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