Search Query Generation Using Data Partitioning and Pre-computed Indices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Social networking systems face challenges in efficiently managing and retrieving large volumes of user and relationship data, particularly in providing diverse and relevant search results that account for user location and social graph relationships, while maintaining performance and reliability.
Innovation Solution
The system employs partitioning of data storage by object types, with each partition hosting specific object types and using search indices to rank and aggregate results, and incorporates operators like WAND and SOR to translate search requests into queries that consider user location and social graph information, ensuring diverse and relevant search results are presented.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system stores and processes large volumes of user and relationship data to provide comprehensive search results, then the diversity and relevance of search results improve, but the system performance and retrieval efficiency deteriorate
Solution Approach 1:
The patent divides the large-scale social graph data into multiple partitions or clusters based on user relationships and attributes. Each partition contains a subset of users and their relationships, allowing the search system to process queries across distributed partitions rather than scanning the entire dataset. This segmentation enables parallel processing and reduces the computational burden on individual nodes, maintaining retrieval efficiency while preserving comprehensive search capabilities.
2Measurement precision
If the system incorporates user location and social graph relationships to rank search results, then the relevance and personalization of results improve, but the computational complexity and processing time increase
Solution Approach 1:
The system pre-computes and stores social graph metrics (such as friendship strength, interaction frequency, and relationship depth) and user location data in advance within the distributed database. When a search query arrives, the system retrieves these pre-computed values rather than calculating them in real-time. This preliminary action significantly reduces the computational complexity during query processing while maintaining high relevance and personalization in search results.
3Quantity of substance
If the system uses distributed data storage and multiple search indices to manage large datasets, then the system scalability and data capacity improve, but the query coordination and result aggregation overhead increase
Solution Approach 1:
The patent creates different types of search indices optimized for specific query patterns and data access patterns. Each index is tailored to handle particular kinds of searches (e.g., one index for location-based queries, another for relationship-based queries). This local optimization allows the system to efficiently handle diverse query types across distributed partitions without requiring uniform complex aggregation logic, reducing the time overhead for coordinating and aggregating results from multiple sources.
Data Source
AI summary
In one embodiment, a method comprises, by a computer system, receiving a search term, determining a search constraint for a search query, and generating a search query. The search query includes the search term and the search constraint. The search query requires that at least a first number of search results that are returned in response to the search query match both the search term and the search constraint. The search query permits at least a second number of the search results to match only the search term. The computer system provides the search query for a search against a store of data.