Micro-partitioned Search System Using Similarity Graphs
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Solution Overview
Problem
Managing large online catalogs efficiently to provide timely and relevant search results is challenging due to high computational resource consumption and time requirements, especially when dealing with billions of items.
Innovation Solution
A micro-partitioned search system that preprocesses the catalog into similarity graphs, identifies representative items, and uses machine-learned models to compare queries to subsets of items, reducing the number of comparisons needed to generate relevant search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system compares queries to all items in the catalog, then search result relevance is improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent divides the large catalog into multiple micro-partitions based on item features and relationships. Each micro-partition contains a subset of items, allowing the system to search only relevant partitions for a given query instead of scanning all items. This segmentation maintains search relevance by preserving item relationships while dramatically reducing computational resources needed for each search operation.
Solution Approach 2:
The system performs preliminary processing to build the micro-partition structure and item relationships before search operations. By pre-organizing items into meaningful partitions and establishing relationships between items in advance, the system avoids performing exhaustive comparisons during actual search operations, thus reducing real-time computational resource consumption while maintaining relevance.
2Reliability
If the system compares queries to all items in the catalog, then search result completeness is improved, but time required to provide results increases
Solution Approach 1:
The catalog is segmented into micro-partitions that group items by their features and relationships. During search, the system identifies which micro-partitions are relevant to the query and searches only those partitions, rather than scanning all items in the catalog. This maintains result completeness for relevant items while significantly reducing the time required to provide results.
Solution Approach 2:
The system performs partial action by searching only the necessary micro-partitions that are relevant to the query rather than performing excessive action of scanning all items in the catalog. This approach achieves sufficient completeness for user needs while avoiding the time cost of exhaustive searching.
3Productivity
If the catalog is organized into micro-partitions, then search efficiency is improved, but system complexity increases
Solution Approach 1:
The system implements segmentation by dividing the catalog into micro-partitions based on item features and relationships. While this creates a more complex data structure, it dramatically improves search efficiency by enabling targeted searches of only relevant partitions. The complexity is managed through automated partitioning algorithms that organize items based on their characteristics.
Solution Approach 2:
The micro-partition structure acts as an intermediary layer between the raw catalog data and the search query processing. This intermediary organization enables efficient routing of queries to relevant item subsets, improving search efficiency while the system manages the complexity of maintaining and querying this intermediate structure.
Data Source
AI summary
Devices and techniques are generally described for search using a micro-partitioned catalog. In some examples, a search system can determine a plurality of items in an online catalog. The search system can generate a similarity graph based on vector representations of the plurality of items and determine an item as a representative item for a cluster of items. The search system can receive a query and determine the cluster of items as a candidate cluster based on a comparison between the query and the representative item. The search system can determine a candidate item of the cluster of items based on a comparison between the query and the cluster of items.