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QEVIS: Multi-Grained Visualization of Distributed Query Execution.

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    This summary is machine-generated.

    QEVIS offers enhanced visualization for distributed query execution in systems like Apache Hive and Spark. It provides fine-grained, multi-view insights to pinpoint performance issues and execution anomalies.

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    Area of Science:

    • Computer Science
    • Data Engineering
    • Data Visualization

    Background:

    • Large-scale data analytics rely on distributed query processing systems (e.g., Apache Hive, Spark).
    • Understanding query execution is vital for performance optimization and error resolution.
    • Existing visualization tools lack fine-grained task-level details and system status linkages, hindering problem identification.

    Purpose of the Study:

    • To introduce QEVIS, a novel visualization system for distributed query execution.
    • To address limitations of existing tools by providing fine-grained, multi-view analysis.
    • To facilitate easier identification of performance bottlenecks and execution anomalies.

    Main Methods:

    • Developed a query logical plan layout algorithm for clear progress visualization.
    • Proposed novel scoring methods to quantify and visualize job and machine anomaly degrees.
    • Implemented a scatter plot-based task view for analyzing atomic task distributions and cross-view exploration features.

    Main Results:

    • QEVIS visualizes distributed query execution at atomic task and system levels.
    • Anomaly scoring and task distribution patterns effectively highlight performance issues.
    • Integrated auxiliary views and interactions enable efficient root cause analysis of execution problems.

    Conclusions:

    • QEVIS overcomes limitations of existing tools by offering comprehensive, multi-granularity visualization.
    • The system aids engineers in diagnosing and resolving performance issues in distributed query processing.
    • QEVIS has been successfully deployed in a production environment, demonstrating practical effectiveness.