Fetch Strategy Optimizer for Relational Data Loading
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing lazy loading strategy for relational data in Java-based enterprise systems is inefficient, particularly for large-size applications with complex business logic, as it requires additional time-consuming and resource-intensive database communications to load data only when needed, leading to delays in data processing.
Innovation Solution
A fetch strategy optimizer dynamically switches from lazy loading to eager loading based on the application context, explicitly fetching and loading related entities when required, thereby reducing unnecessary database communications and optimizing data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lazy loading strategy is used to defer data loading until access, then memory resource usage is reduced, but data processing time increases due to additional database communications
Solution Approach 1:
The system dynamically adjusts the fetch strategy from lazy loading to eager loading based on runtime analysis of application context. The fetch strategy optimizer monitors access patterns and business logic requirements, switching to eager loading when related entities are frequently accessed together, thereby reducing database communication overhead and improving data processing time while maintaining efficient memory usage.
Solution Approach 2:
The invention changes the loading parameter from lazy (deferred) to eager (immediate) based on analyzed access patterns. By modifying the fetch strategy parameter dynamically, the system optimizes the balance between memory resource usage and data processing time, eliminating unnecessary database communications when related entities are needed together.
2Loss of time
If lazy loading is used for all related data, then initial loading time is reduced, but additional database communications occur when data is accessed
Solution Approach 1:
The system performs preliminary analysis of application context and access patterns to determine which related entities will be accessed together. Based on this preliminary action, the fetch strategy optimizer proactively switches to eager loading for identified entity sets, preventing additional database communications during data access and improving overall data processing efficiency.
Solution Approach 2:
The fetch strategy optimizer implements feedback mechanisms by monitoring actual access patterns and business logic requirements. This feedback loop allows the system to adjust fetch strategies dynamically, switching from lazy to eager loading when patterns indicate that related entities are accessed together, thereby eliminating redundant database communications and improving productivity.
3Productivity
If eager loading is used to fetch all related entities explicitly, then data processing speed improves, but memory resource consumption increases
Solution Approach 1:
The system applies local quality by implementing context-specific fetch strategies rather than a uniform approach. The fetch strategy optimizer analyzes application context to identify specific scenarios where eager loading benefits data processing speed, applying eager loading only to those localized cases while maintaining lazy loading elsewhere, thereby optimizing data processing speed without excessive memory resource consumption.
Data Source
AI summary
A method for optimizing loading of relational data for applications is disclosed. The method includes monitoring a plurality of requests selecting entities and processes to be executed for the selected entities. The plurality of requests is generated in an application server. The method also includes associating an application context with each of the processes to be executed for the selected entities. The application context includes the process to be executed using an entity stored in a database external to the application server. The method further includes generating a fetch plan for the entity stored in the database and modifying, the generated fetch plan in view of the selected entities and the associated application context. The fetch plan may include lazy loading of the entity stored in the database (e.g., by loading the entity from the database and deferring loading of one or more attributes of the entity form the database).