Database Performance Root Cause Analysis via KPI Correlation
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Solution Overview
Problem
IT monitoring products face challenges in efficiently performing root cause analysis of performance issues due to the elusive nature of low performance symptoms, particularly when analyzing SQL executions or functions, which makes it difficult to determine inefficient SQLs or patterns contributing to performance degradation across multiple intervals.
Innovation Solution
A method that monitors key performance indicators (KPIs) at various time intervals, calculates a KPI correlation coefficient to identify the most impactful SQLs, and generates a dynamic tabular representation to isolate bottlenecks, using weighted correlation coefficients to prioritize KPIs based on their impact on database performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual correlation of symptoms to performance periods is performed, then administrators can identify performance issues, but the analysis time becomes excessively long
Solution Approach 1:
The patent replaces manual mechanical correlation analysis with automated computational correlation analysis. The system automatically calculates correlation coefficients between SQL executions and performance metrics, substituting human analysts with algorithmic processing to identify performance issues faster and more accurately.
Solution Approach 2:
The patent creates computational models and correlations that replicate the analytical process. By establishing mathematical relationships between SQL patterns and performance outcomes, the system creates a reusable framework that automatically identifies performance issues without requiring manual analysis of each case.
2Measurement precision
If comprehensive monitoring of SQL executions is performed across multiple intervals, then the strongest correlation to database performance can be determined, but the complexity of analysis increases exponentially
Solution Approach 1:
The patent segments the complex analysis problem into distinct components: monitoring individual SQL executions, calculating correlation coefficients for each SQL, ranking SQLs by correlation strength, and identifying top contributors to performance degradation. This segmentation transforms an exponentially complex problem into a series of manageable linear steps.
Solution Approach 2:
The patent introduces correlation coefficients as a quantitative parameter to measure the relationship between SQL executions and performance metrics. By transforming qualitative performance analysis into quantitative correlation measurement, the system simplifies the complexity of analyzing comprehensive monitoring data across multiple intervals.
3Reliability
If administrators manually analyze data and metrics to identify inefficient SQLs, then root cause can be determined, but the process becomes ad-hoc and time-consuming
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors SQL executions, calculates correlation coefficients, and provides automated identification of inefficient SQLs. This closed-loop feedback system replaces ad-hoc manual analysis with a systematic, repeatable process that reliably identifies root causes while maintaining high productivity.
Solution Approach 2:
The patent enables the monitoring system to automatically identify and report inefficient SQLs without requiring administrator intervention. The system serves itself by autonomously calculating correlations, ranking SQLs, and presenting findings, thereby eliminating the time-consuming manual analysis process while maintaining reliable root cause determination.
Data Source
AI summary
A method, computer program product, and system for analyzing performance of a computer system where at a plurality of time intervals, a set of KPIs of a database and database performance outputs are monitored. A database performance value is calculated based on the monitored KPIs and one or more database performance outputs. In response to determining the database performance value is below a first threshold, a KPI correlation coefficient is calculated. The calculated KPI correlation coefficient with the greatest value is selected as a target KPI. In response to determining that a count of functions of the target KPI meets a second threshold, a dynamic tabular representation of the determined target KPI is generated. A correlation matrix is generated based on the generated tabular representation and the plurality of time intervals and a first set of target functions are identified based on the generated correlation matrix.