Granular Database Query Performance Analysis
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Solution Overview
Problem
Current database query performance analysis methods lack granularity, often leading to false positives and inefficient troubleshooting, as they do not accurately identify the root causes of performance issues, which can result in exacerbating existing problems or creating new ones during remediation efforts.
Innovation Solution
Implementing granular performance analysis for database queries, which involves collecting and evaluating performance metrics across the entire system stack, using machine learning techniques to build models that classify query performance attributes, and applying performance goals to specific query portions to identify and address sub-optimal performance, thereby providing accurate root cause analysis and preventing false diagnoses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If granular performance analysis is implemented for database queries, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the database system into multiple hierarchical levels (query level, operator level, and physical operation level) to enable granular performance analysis. Each level captures specific performance metrics, allowing precise measurement without overwhelming system complexity. The segmentation enables targeted collection of performance data at appropriate granularities for different aspects of query execution.
Solution Approach 2:
The patent introduces performance analysis entities that act as intermediaries between the database query execution and the performance monitoring system. These entities collect, aggregate, and analyze performance metrics at appropriate levels of granularity, mediating between the complex database operations and the performance analysis requirements, thus improving measurement precision while managing system complexity.
2Reliability
If performance metrics are collected across the entire system stack, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent segments performance metric collection into hierarchical levels (query-level, operator-level, and physical operation-level metrics). This segmentation allows the system to collect comprehensive performance data across the entire stack while organizing it in a structured manner that enables efficient analysis, improving root cause analysis reliability without proportionally increasing data collection time.
Solution Approach 2:
The patent implements preliminary instrumentation of the database system to automatically capture performance metrics at defined points during query execution. By pre-positioning measurement points and defining what metrics to collect at each level, the system prepares the data collection framework in advance, reducing the time required during actual performance analysis while ensuring comprehensive data capture for reliable root cause identification.
3Productivity
If machine learning techniques are used to build performance models, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the machine learning modeling process into multiple hierarchical levels corresponding to different aspects of query performance (query-level models, operator-level models, and physical operation-level models). Each model focuses on specific performance attributes at its level, improving analysis productivity by providing targeted insights while managing complexity through modular model construction and deployment.
Data Source
AI summary
Granular performance analysis may be performed for database queries. Database query performance may be monitored. For some database queries, performance of portions of the database query may be measure. The measure performance of the portions may be compared with performance goals that correspond to the portions of the database query. Portions that do not meet or exceed the corresponding performance goals may be identified so that an indication of the identified portions may be provided.