Component-Level Search Runtime Monitoring for Bottleneck Isolation
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Solution Overview
Problem
Conventional query systems struggle to isolate and characterize search performance degradation due to various influencing factors, leading to unreliable search runtime metrics, which hinders effective data management and analysis in IT environments.
Innovation Solution
A data intake and query system that tracks search performance at a component level, utilizing a search head and monitoring application to analyze search variances and visualize search runtimes, enabling detailed insights into search performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional query systems measure search performance using overall search runtime, then the measurement is simple to implement, but the metric is unreliable and cannot isolate performance degradation to specific factors
Solution Approach 1:
The patent segments the overall search runtime into multiple component-level metrics including query parsing time, index scanning time, filter evaluation time, and result aggregation time. By dividing the monolithic search process into discrete measurable components, the system achieves precise identification of performance bottlenecks while maintaining manageable monitoring complexity through structured data collection.
Solution Approach 2:
The patent introduces a monitoring application as an intermediary layer between the search head and the performance metrics. This intermediary collects, aggregates, and analyzes component-level metrics from multiple searches, transforming raw data into actionable performance insights without requiring direct modification of the search execution engine, thus balancing measurement precision with system complexity.
2Adaptability or versatility
If the system stores and analyzes all raw search data without pre-processing, then data analysis flexibility is maximized, but storage requirements and processing overhead increase significantly
Solution Approach 1:
The patent extracts only the essential component-level metrics (parsing time, scanning time, filter time, aggregation time) from the raw search data while discarding redundant information. This selective extraction maintains data analysis flexibility for performance optimization while significantly reducing storage requirements compared to retaining all raw search data.
Solution Approach 2:
The monitoring application performs preliminary aggregation and filtering of search metrics before storage, pre-processing the data to retain only performance-relevant components. This preliminary action enables flexible analysis of performance trends while minimizing storage volume by eliminating redundant raw data early in the data pipeline.
Data Source
AI summary
A computer-implemented method for tracking search performance includes executing a search query at a first computing device and computing a runtime for each of one or more components of the search query. The method also includes comparing a respective runtime for each of the one or more components with a respective threshold value, wherein the respective threshold value for each of the one or more components is based on a respective statistic determined using runtimes from prior executions of the search query. Further, responsive to determining that the respective runtime is higher than the threshold value, the method includes transmitting an alert to a second computing device in a cloud computing environment.