Configuration Graph Data Traversal for Offline Client Caching
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Solution Overview
Problem
Existing systems struggle to efficiently manage data retrieval and updates in client devices operating in both online and offline modes, particularly in scenarios where client applications require varying functionalities without necessitating updates to their executable code.
Innovation Solution
A recursive data traversal model that utilizes a configuration graph to efficiently construct and fetch data from a datastore, allowing for the generation of additional queries based on retrieved objects, and a data control model that includes object type definitions, metadata modules, and polymorphic entities to manage data interactions and provide customizable functionalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a client device operates in offline mode, then data access continues without network connectivity, but the client cannot receive or submit updates to the backend server
Solution Approach 1:
The system segments data access into online and offline modes, with the offline mode containing a local copy of data that can be accessed without network connectivity. This segmentation allows the client to continue operating with cached data while preventing update synchronization when disconnected.
Solution Approach 2:
The system performs preliminary actions by caching data locally before offline operation occurs. The offline mode is prepared in advance with necessary data copies, allowing seamless transition to offline operation without interrupting data access continuity.
2Adaptability or versatility
If the client application requires varying functionalities, then the application can adapt to different scenarios, but updating executable code is required which complicates the system
Solution Approach 1:
The system implements dynamic configuration through configuration graphs that can be modified at runtime without requiring executable code updates. The configuration graph structure allows the client application to adapt its behavior and functionality by loading different configuration data, making the system dynamic and flexible while avoiding the complexity of code updates.
Solution Approach 2:
Instead of updating executable code to change functionality, the system copies and loads different configuration graphs that define application behavior. This approach allows varying functionalities to be achieved through data copying rather than code modification, reducing the complexity associated with code updates.
3Productivity
If data is retrieved using traditional methods, then data can be fetched from the backend server, but communication bandwidth and memory usage increase
Solution Approach 1:
The system applies partial action by retrieving only the specific data instances needed based on the configuration graph traversal, rather than fetching entire datasets. This selective data retrieval reduces communication bandwidth usage and memory consumption while maintaining data retrieval efficiency for the required information.
Solution Approach 2:
The system extracts and retrieves only the necessary data instances from the backend server based on configuration graph traversal results, rather than retrieving complete datasets. This extraction approach minimizes bandwidth consumption and memory usage by obtaining only the specific data needed for the current operation.
Data Source
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AI summary
One or more embodiments interpret a configuration graph to efficiently and optimally construct requests and fetch data from a datastore. The values of objects of a requested data type are used to generate additional queries for pre-fetching data from the datastore. Specifically, the values are used to query for and retrieve a corresponding subset of objects of another, related data type. Recursively querying for and retrieving objects of related data types based on already retrieved objects builds a data cache of relevant objects. The cached, relevant objects may be useful in subsequent queries that are likely to follow the initial query.