Social Graph Hub Caching for Data Store Load Reduction
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Solution Overview
Problem
Social networking applications face bottlenecks in data store access due to the resource-intensive nature of maintaining user views, particularly in handling queries and updates across large social networks, leading to performance issues and increased load on data stores.
Innovation Solution
The system employs a social graph to identify content sharing hubs, which act as intermediaries for data store requests, optimizing data store access by reducing the need for direct queries and updates between users, thereby minimizing data store requests and improving throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If direct data store access is used for each user view, then real-time personalized feeds can be generated, but data store access becomes a bottleneck with increased load
Solution Approach 1:
The patent introduces a data store access layer with a request schedule as an intermediary between the social networking application and the data store. This layer caches data and intelligently schedules access patterns, reducing the frequency and volume of direct data store queries while maintaining real-time feed generation capability.
Solution Approach 2:
The system performs preliminary actions by pre-fetching and caching data in the data store access layer before it is actually needed. The request schedule proactively manages data retrieval and caching, so that when feeds need to be generated, the data is already available or can be quickly retrieved, avoiding bottlenecking the data store during peak feed generation times.
2Adaptability or versatility
If user views are maintained in data stores, then personalized content delivery is enabled, but resource consumption increases significantly
Solution Approach 1:
The patent merges multiple data access operations into a unified data store access layer that serves all user views. Instead of each user view independently accessing the data store, the consolidated access layer combines requests, caches results, and serves multiple users from the same cached data, significantly reducing overall resource consumption while maintaining personalized content delivery.
Solution Approach 2:
The data store access layer implements self-service mechanisms through intelligent caching and request scheduling. It automatically determines what data to cache, when to refresh cached data, and how to serve requests from cache versus the data store, reducing resource consumption without requiring external optimization interventions.
3Measurement precision
If social graph queries are executed over the entire network, then accurate event streams are provided, but query performance degrades with network size
Solution Approach 1:
The patent segments the social graph into multiple partitions that can be independently queried. The data store access layer routes queries to relevant partitions rather than scanning the entire social graph, maintaining event stream accuracy by ensuring all relevant partitions are queried while dramatically reducing query execution time through the segmentation approach.
4Ease of operation
If data stores service all social network workloads, then complete data access is provided, but data stores become a performance bottleneck
Solution Approach 1:
The data store access layer acts as an intermediary that provides complete data access functionality while protecting the data store from being overwhelmed. It implements caching, request scheduling, and intelligent data retrieval strategies that maintain comprehensive data access capability while significantly improving system throughput by reducing the burden on the data store.
Data Source
AI summary
Disclosed herein are a system, method and architecture for optimizing a manner in which queries and updates are issued to data stores servicing an application such as a social networking application. A social graph modeling a social network may be used to generate a data store request schedule that optimizes, e.g., minimizes, data store accesses, such as without limitation queries and updates to the data store.