Parallel HNSW Vector Index Construction Inside RDBMS
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Solution Overview
Problem
Existing methods for similarity search in relational databases incur high data management complexity and performance degradation due to data manipulation, and dedicated vector engines are not practical for enterprise environments.
Innovation Solution
Construct a main-memory Hierarchical Navigable Small World (HNSW) vector index within the relational database management system (RDBMS) using parallelism mechanisms to minimize synchronization and enhance performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a main-memory HNSW vector index is constructed within RDBMS, then search performance (recall and QPS) is improved, but device complexity increases
Solution Approach 1:
The patent implements multi-functionality by integrating HNSW index construction into the existing RDBMS framework, allowing the database to simultaneously serve traditional relational querying and vector similarity search functions. The system leverages existing RDBMS components (memory management, concurrency control, query processing) to support the new vector index capabilities, thereby improving search performance without requiring a completely separate vector database system.
Solution Approach 2:
The patent applies segmentation by dividing the HNSW index construction process into manageable phases: data loading, graph construction, and index validation. Each phase is handled by dedicated modules that can operate independently, reducing the overall complexity of implementing a sophisticated vector index within the RDBMS architecture.
2Productivity
If parallel construction algorithms are used, then construction speed is improved, but synchronization complexity increases
Solution Approach 1:
The patent segments the parallel construction process into independent worker threads, each responsible for processing specific portions of the data. This segmentation allows concurrent HNSW graph construction without requiring complex synchronization mechanisms, as each worker operates on disjoint data subsets.
Solution Approach 2:
The parallel construction algorithm implements self-service by allowing each worker thread to independently manage its own execution and data processing. Workers automatically handle their assigned data portions without requiring centralized coordination or complex synchronization protocols, thereby achieving high construction speed with minimal synchronization overhead.
3Productivity
If vector data is migrated to external vector databases, then similarity search performance is improved, but data management complexity and loading costs increase
Solution Approach 1:
The patent merges the vector index construction functionality with the existing RDBMS system, eliminating the need for separate vector database infrastructure. By combining relational data storage and vector similarity search capabilities within a single database system, the solution improves search performance while reducing data management complexity and eliminating costly data migration operations.
Solution Approach 2:
The RDBMS is enhanced with multi-functionality to simultaneously handle traditional relational queries and vector similarity searches. The system integrates vector data types, HNSW index structures, and similarity search algorithms directly into the database engine, allowing customers to maintain all data in a single location without migrating to external vector databases.
Data Source
AI summary
The present disclosure relates to efficiently constructing in-memory HNSW vector indexes in a database management system (DBMS). A DBMS may store in memory a segmented array, wherein the segmented array may store representations of a plurality of vectors from a database. A hierarchical navigable small world (HNSW) vector index may be constructed in the memory, wherein the HNSW vector index may index the plurality of vectors. A similarity search may be performed using the HNSW vector index.