Web Content Feature Variant Performance Isolation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting performance degradation of new web application features are inadequate, as they fail to isolate the impact of feature variants on client devices and are not suited for sophisticated deployment techniques, leading to unnoticed performance issues in smaller user groups.
Innovation Solution
A system that sends different versions of web content to client devices, with one version including a particular feature and the other not, to collect performance data and compare processing metrics, enabling the detection of performance degradation and sending alerts when thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If overall performance data is compared before and after general release of a new feature, then performance monitoring is simplified, but the ability to isolate variant performance is lost
Solution Approach 1:
The patent segments performance data by creating separate performance streams for different feature variants. Each variant's performance is tracked independently through distinct data collection and processing pipelines, enabling precise isolation and comparison of variant-specific performance metrics without mixing data from different variants
Solution Approach 2:
The patent introduces intermediary components including performance tracking code embedded in variant-specific web content, data reception modules that separate incoming performance data by variant, and processing systems that maintain distinct performance profiles for each variant. These intermediaries enable precise variant isolation while maintaining manageable system architecture
2Reliability
If performance data is collected from all users simultaneously, then statistical measurements are robust, but performance degradation in smaller user groups is lost
Solution Approach 1:
The patent divides the user base into separate segments based on which feature variant they receive. Each segment's performance data is collected and analyzed independently, allowing robust statistical measurements within each segment while maintaining the ability to detect performance degradation in smaller groups through dedicated analysis pipelines
Solution Approach 2:
The patent collects performance data from all users but applies partial analysis focused on specific variant segments. By maintaining separate performance streams and applying targeted analysis to each variant's data subset, the system achieves both comprehensive data collection and focused detection capability for minority groups
3Adaptability or versatility
If separate variants are created for multiple new features, then feature testing flexibility is improved, but performance isolation becomes more difficult
Solution Approach 1:
The patent implements a universal performance tracking framework that handles multiple feature variants through a common architecture. The system uses standardized performance streams, data collection mechanisms, and processing pipelines that can accommodate any number of variants, maintaining flexibility while reducing isolation complexity through consistent handling methods
Solution Approach 2:
The patent creates separate copies of performance tracking code embedded in each variant's web content. Each copy is tailored to its specific variant but follows a uniform structure, enabling independent performance isolation for each feature while maintaining overall system consistency and managing complexity through pattern repetition
Data Source
AI summary
Computerized techniques are described for detecting performance degradation during features ramp up. A first version of web content is sent to first client devices. The first version of the web content lacks a particular feature. First performance data that indicates performance of each first client device with respect to processing the web content is received. A second version of the web content is sent to second client devices. The second version of the web content has the particular feature. Second performance data that indicates performance of each second client device with respect to processing the web content is received. The first performance data is compared with the second performance data to determine a difference between a processing metric associated with the first version of the web content and a processing metric associated with the second version of the web content.


