Apache Spark History Server
Enables AI agents to analyze job performance, identify bottlenecks, and provide intelligent insights from Apache Spark History Server data.
About
This software transforms Apache Spark infrastructure monitoring by enabling AI agents to interact directly with your Spark History Server. It acts as a Model Context Protocol (MCP) server, bridging AI agents with your existing Spark data to facilitate intelligent job analysis, performance monitoring, and detailed insights. Users can leverage natural language queries to analyze performance metrics, compare jobs, investigate failures, and generate comprehensive reports from historical execution data, thereby enhancing the efficiency and understanding of their big data operations.
Key Features
- 6 GitHub stars
- Investigate job failures with detailed error analysis
- Query Spark job details via natural language
- Analyze and compare Spark application performance metrics
- Identify performance bottlenecks and provide recommendations
- Track resource utilization and executor performance
Use Cases
- Investigate and optimize the performance of Spark ETL jobs
- Compare Spark job performance and environments for regression detection
- Analyze the root cause of Spark job failures
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