Dynamic Data Logging Framework for Application Lifecycle Management
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Solution Overview
Problem
Current data-logging techniques in application programming are inefficient, particularly in mobile applications, as they require recompilation and redeployment for changes, consume excessive resources, and continue logging data regardless of the application's lifecycle stage, leading to resource wastage and potential errors.
Innovation Solution
A log framework that dynamically controls data sampling based on the application's lifecycle stages, allowing for adjustable sampling rates and parameters such as the number of users, events, and data volume, eliminating the need for code modifications and optimizing resource usage by adapting to changing user interactions and product stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data logging is configured to log all data items, then data collection completeness is improved, but computing resource consumption increases
Solution Approach 1:
The patent implements dynamic data logging where the sampling rate and logging parameters are adjusted based on the application's lifecycle stage. During development and testing phases, the system logs all or most data items with high sampling rates. When the application reaches production phase, the system automatically reduces the sampling rate and selects only critical data items for logging, thereby maintaining data completeness where needed while reducing overall resource consumption.
2Measurement precision
If data logging parameters are changed frequently, then monitoring accuracy is improved, but deployment time increases
Solution Approach 1:
The patent incorporates data logging parameter configurations directly into the application binary during the build process. Different logging profiles (development, testing, production) are pre-configured and embedded within the application package. When deploying, the appropriate profile is selected based on the target environment, eliminating the need for separate compilation and deployment for each logging configuration. This allows frequent monitoring accuracy adjustments without incurring repeated compilation and deployment time costs.
3Duration of action of stationary object
If data logging continues throughout product lifecycle, then long-term data availability is improved, but resource wastage increases
Solution Approach 1:
The patent implements lifecycle-aware data logging where logging parameters such as sampling rate, data items selected, and log file retention are dynamically changed based on the application's lifecycle stage. In early stages (development, testing), the system maintains high logging intensity with comprehensive data capture. As the application transitions to production and stabilizes, the system automatically adjusts parameters to reduce logging intensity, capturing only essential operational data. This ensures long-term data availability for critical phases while preventing resource wastage during stable production operation.
Data Source
AI summary
The embodiments are related to a log framework for controlling data sampling at client devices based on a lifecycle of a product. A product can be an application executing on a client device and/or a feature of the application. The sampling of data depends on a lifecycle of the product. For example, data may be sampled at a higher rate during a launch phase of the product, e.g., as more data may be required to analyze the behavior of the product, and then may be decreased to a lower rate when the product matures. Similarly, for a product that is in an experiment phase for a specified duration, data may be logged during the experiment phase, e.g., at a constant sampling rate, and then the logging may be terminated at the expiry of the experiment phase, thereby saving resources, e.g., processing capacity, storage capacity of the client device.