Database Anomaly Detection Using Pending Query Time Series
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Solution Overview
Problem
Hosted database management systems require users to have significant experience in database design and optimization, and there is a need for improved assistance in detecting and mitigating anomalous database load conditions.
Innovation Solution
A system that uses machine learning models to analyze time series data of pending queries and wait states to detect anomalies, identify root causes, and generate performance tuning recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If hosted database management systems provide automated anomaly detection and mitigation, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The database management system automatically detects anomalies, identifies root causes, and generates mitigation recommendations without requiring user intervention. The system monitors its own performance metrics, applies machine learning models to detect issues, and provides self-service anomaly mitigation capabilities.
Solution Approach 2:
The patent introduces an intermediary anomaly mitigation system that acts as a bridge between the database operations and users. This intermediary automatically processes anomaly detection and generates recommendations, shielding users from the complexity of database optimization while providing actionable insights.
2Measurement precision
If the system collects and analyzes extensive time series data and wait state information, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system continuously collects and pre-processes time series data and wait state information in the background before anomalies occur. This preliminary data collection and preparation enables rapid anomaly detection and analysis when issues arise, without adding time delays to the anomaly response process.
Solution Approach 2:
The patent replaces manual anomaly analysis with automated machine learning models that process time series data and wait state information. This substitution of automated computational mechanisms for manual analysis improves measurement precision while reducing the time loss associated with human investigation and diagnosis.
Data Source
AI summary
An anomalous period of operation of a database management system is detected by analyzing a time series of data points indicating the number of database queries pending processing by the system. Conditions associated with execution of the pending database queries are recorded and analyzed to identify conditions correlated with the anomalous period of operation. A recommendation for tuning the database is generated based on analysis of the conditions.