Preloaded Content Selection Graph Validation for Rapid Response
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Solution Overview
Problem
Conventional data retrieval techniques struggle to handle spikes in content selection requests efficiently, particularly for popular content, leading to overwhelming demands on the system.
Innovation Solution
Preloading and preformatting content selection graphs in an in-memory data store, such as a Redis cache, with multiple graphs for different client-specific details, allowing rapid retrieval and return of formatted responses without the need for real-time data shaping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data retrieval techniques are used, then data can be retrieved on-demand, but the system becomes overwhelmed during spikes in content selection requests
Solution Approach 1:
The patent pre-generates and pre-loads content selection graphs into an in-memory data store (Redis cache) before requests arrive. This preliminary action ensures that when spikes in requests occur, the system can serve pre-formatted data without overwhelming computational resources, thereby maintaining both high productivity and system reliability under load.
Solution Approach 2:
The patent creates multiple copies of content selection graphs, each formatted for different client-specific details (device type, software version, etc.). These copies are stored in the in-memory cache, allowing the system to rapidly serve appropriate pre-formatted data without real-time processing, thus handling request spikes efficiently while maintaining system stability.
2Adaptability or versatility
If data is formatted and shaped in real-time based on client-specific details, then responses are customized, but retrieval speed decreases during peak demand
Solution Approach 1:
The system performs data formatting and shaping in advance, creating multiple pre-formatted versions of content selection graphs for different client specifications. This eliminates real-time formatting overhead, allowing rapid retrieval of already-customized data during peak demand while maintaining full adaptability to different client types.
Solution Approach 2:
Multiple copies of the content selection graph are created, each pre-formatted for specific client characteristics (device type, software version, etc.). The system can quickly retrieve the appropriate pre-formatted copy without real-time processing, thus maintaining both client-specific adaptability and high retrieval speed during peak demand.
3Productivity
If multiple graphs are preloaded for different client-specific combinations, then retrieval efficiency improves, but memory usage increases
Solution Approach 1:
The patent segments the content selection data into multiple graphs, each tailored to specific client combinations (device type, software version, etc.). This segmentation allows the system to load only necessary portions into memory based on anticipated demand patterns, improving retrieval efficiency for specific client types while managing overall memory consumption through selective pre-loading.
Data Source
AI summary
The described technology is generally directed towards validating content selection graphs for use in an in-memory content selection graph data store. When a content selection graph data is generated, the graph nodes can correspond to prebuilt response data. The response data for any of the nodes can be verified against rules based on the type of a node, for example, as well as based on client-specific information for types of clients. The overall validation process can validate the content selection graph for subsequent use, can result in errors or warnings being logged (which may or may not render the graph unusable, e.g., depending on severity), or can fail the content selection graph. If validated, the content selection graph can be used to rapidly return response data when content from the graph is requested.