Dynamic Bucketization for Real-Time Access Pattern Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for managing large electronic catalogs face inefficiencies in resource utilization and user experience due to extreme fragmentation in data storage and retrieval, particularly when handling large shopping lists or carts, leading to high storage and network resource usage and slow hydration processes.
Innovation Solution
Implementing a dynamic hydration system that assigns items to buckets based on real-time access patterns, allowing items frequently accessed together to be stored and hydrated together, while minimizing storage and network resource usage by dividing items into access windows and dynamically adjusting their distribution based on client requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If items are stored and retrieved individually in a highly fragmented manner, then data retrieval flexibility is improved, but storage and network resource usage increases significantly
Solution Approach 1:
The patent divides the catalog items into multiple buckets based on access patterns, and further segments each bucket into access windows. This segmentation allows the system to retrieve only the necessary portions of data (specific buckets and windows) rather than loading entire catalogs, thus reducing storage and network resource usage while maintaining retrieval flexibility.
Solution Approach 2:
The system dynamically adjusts the distribution of items across buckets and access windows based on real-time access patterns. This dynamic reorganization optimizes resource utilization by grouping frequently accessed items together, reducing the amount of data that needs to be retrieved and transmitted over the network.
2Speed
If all items are loaded into memory for fast access, then hydration speed is improved, but memory resource consumption increases
Solution Approach 1:
The patent segments items into buckets and access windows, allowing the system to load only the necessary segments into memory based on current access patterns. This selective loading maintains fast hydration speed for frequently accessed items while significantly reducing overall memory resource consumption compared to loading all items.
Solution Approach 2:
The system performs preliminary organization of items into buckets and access windows based on historical access patterns, so that when hydration is needed, the required data is already positioned for efficient retrieval and loading, optimizing both speed and memory usage.
3Device complexity
If data is organized statically in fixed structures, then system complexity is reduced, but adaptability to changing access patterns deteriorates
Solution Approach 1:
The patent implements a dynamic system that automatically reorganizes items into buckets and access windows based on observed access patterns. This dynamic adaptation allows the system to optimize for changing user behavior without requiring manual reconfiguration, achieving high adaptability while maintaining manageable complexity through automated processes.
Solution Approach 2:
The system performs self-organization by automatically analyzing access patterns and redistributing items across buckets and windows without external intervention. This self-service capability enables the system to adapt to changing access patterns autonomously, balancing adaptability with operational simplicity.
Data Source
AI summary
Methods, systems, and computer-readable media for dynamic bucketization based on real-time access patterns are disclosed. Item identifiers corresponding to items in a data store are distributed among of buckets based at least in part on one or more bucket parameters. The item identifiers are divided into access windows within the buckets. Items corresponding to item identifiers in one or more access windows are loaded from the data store. Based at least in part on one or more access requests from a client with respect to the loaded items, one or more item identifiers are moved between access windows. The updated access windows with different sets of items are stored back to the data store.