Hierarchical Database Metric Visualization for Query Performance Diagnosis
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Solution Overview
Problem
Database query monitoring tools provide low-level metrics that are incomprehensible to users, and existing systems fail to collect all metrics impacting query performance, such as network activity, making it difficult for users to understand and diagnose query performance issues effectively.
Innovation Solution
A visualization system that aggregates multiple metrics at various levels of execution (query, phase, node, path, and operator levels) and allows users to select metrics and levels for intuitive representation, enabling users to drill down for detailed performance analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If low-level metrics are collected at detailed execution levels, then measurement precision is improved, but ease of operation deteriorates because users cannot comprehend the metrics
Solution Approach 1:
The patent segments the complex query execution process into multiple hierarchical levels (query level, phase level, node level, path level, operator level). Each level aggregates metrics differently, allowing users to view metrics at an appropriate level of abstraction. This segmentation resolves the contradiction by providing precise low-level measurements while enabling easy user comprehension through hierarchical aggregation.
Solution Approach 2:
The patent adds a hierarchical dimension to metric visualization by organizing metrics across five levels of execution. This dimensional transformation allows users to navigate from high-level summaries to detailed measurements and back, resolving the contradiction between measurement precision and ease of operation by providing multiple viewing dimensions.
2Measurement precision
If comprehensive metrics including network activity are collected, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal monitoring framework that can collect and visualize multiple types of metrics (CPU usage, memory usage, network activity, I/O operations) through a single system. This multi-functional approach improves measurement precision by comprehensively tracking all performance-affecting metrics while managing system complexity through unified architecture.
Solution Approach 2:
The patent introduces an intermediary visualization layer that aggregates and presents metrics from multiple sources including network activity. This intermediary layer manages the complexity of collecting comprehensive metrics by providing a unified interface that handles data aggregation, filtering, and presentation across all metric types.
3Ease of operation
If metrics are aggregated at multiple levels, then ease of operation is improved, but loss of information occurs in high-level abstractions
Solution Approach 1:
The patent segments metric aggregation into five distinct hierarchical levels, each preserving different levels of detail. This segmentation allows users to select the appropriate level of aggregation for their needs, resolving the contradiction by preventing information loss at higher levels while maintaining ease of operation through available lower-level details.
Solution Approach 2:
The patent implements dynamic navigation between hierarchical levels, allowing users to drill down from high-level summaries to detailed measurements and drill up to aggregate views. This dynamic approach resolves the contradiction by enabling users to access detailed information when needed while maintaining easy comprehension through hierarchical aggregation.
Data Source
AI summary
Described herein are techniques for generating a visualization relating to execution of a workload. Multiple measurements of a plurality of metrics relating to execution of the workload may be aggregated at multiple levels of execution. A visualization may be generated that comprises a representation of the measurements of a metric at one of the levels of execution.