Workload Metric Aggregation for Deviation Path Identification
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Solution Overview
Problem
Database query performance monitoring tools provide low-level metrics that are difficult for users to comprehend, often resulting in information overload, and may not collect all relevant metrics, such as network activity, making it challenging to diagnose performance issues effectively.
Innovation Solution
A method to identify a path in a workload responsible for deviant behavior by aggregating and comparing multiple metrics at various levels of execution, such as query, phase, node, and operator levels, and presenting the findings through visualizations to aid users in troubleshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple low-level metrics are collected at detailed execution levels, then measurement precision is improved, but information overload occurs and ease of operation deteriorates
Solution Approach 1:
The patent segments metrics by multiple dimensions including execution level (query, phase, node, operator), metric type (CPU, memory, network, I/O), and temporal characteristics. This segmentation allows users to focus on specific segments relevant to their diagnostic needs rather than being overwhelmed by all metrics simultaneously, resolving the contradiction between comprehensive measurement and ease of comprehension.
Solution Approach 2:
The patent introduces intermediate aggregation layers that transform low-level raw metrics into higher-level meaningful insights. Aggregation functions summarize metrics across different granularities, and correlation analysis acts as an intermediary to identify relationships between metrics. These intermediaries bridge the gap between detailed measurements and user comprehension, allowing precise measurements to be presented in digestible formats.
2Reliability
If comprehensive metrics are collected across all execution levels, then reliability of diagnosis is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic metric collection and aggregation that adapts to diagnostic needs. The system dynamically determines which metrics to collect and aggregate based on query characteristics, execution patterns, and diagnostic requirements. This dynamic approach maintains comprehensive monitoring capability while reducing the fixed complexity of the system by only processing relevant metrics at appropriate levels.
Solution Approach 2:
The patent adds multiple dimensional layers to metric organization including execution level hierarchy (query→phase→node→operator), temporal dimensions (real-time, historical, aggregated), and semantic dimensions (resource usage, performance, efficiency). This multi-dimensional organization transforms a complex flat metric space into a structured hierarchical space, making comprehensive monitoring manageable through dimensional decomposition.
3Measurement precision
If detailed low-level metrics are provided to users, then measurement precision is improved, but loss of time for analysis increases
Solution Approach 1:
The patent performs preliminary aggregation and correlation analysis of metrics before they reach the user. Aggregation functions pre-compute summaries at various execution levels, and correlation analysis pre-identifies relationships between metrics that may indicate performance issues. This preliminary processing reduces the time users need to spend analyzing raw data while preserving measurement precision through maintained access to detailed metrics when needed.
Solution Approach 2:
The patent implements feedback mechanisms where the system monitors metric patterns and automatically provides diagnostic insights when anomalies are detected. Correlation analysis provides feedback about relationships between metrics, and the system can alert users to potential issues before they become critical. This feedback loop reduces troubleshooting time by guiding users to relevant findings rather than requiring manual analysis of all metrics.
Data Source
AI summary
Described herein are techniques for identifying a path in a workload that may be associated with a deviation. A workload may be associated with multiple measurements of a plurality of metrics generated during execution of the workload. The multiple measurements may be aggregated at multiple levels of execution. One or more measurements may be compared to one or more other measurements or estimates to determine whether there is a deviation from an expected correlation. If determined that there is a deviation, a path can be identified in the workload that may be associated with the deviation.