A solution to the problem of delay in searching large-scale data
Vespa.ai provides a multi-faceted optimization solution to deal with latency issues in large-scale data scenarios:
- Optimization with HNSW Indexing: Vespa integrates the highly efficient HNSW (Hierarchical Navigable Small World) indexing algorithm, which is one of the best-performing nearest neighbor search algorithms available today, and is especially suited for handling high-dimensional vector data.
- Hybrid Query Architecture Design: Supports parallel processing of vector search, text search and structured data, reducing query latency through intelligent routing and distributed processing
- Distributed Node Scaling: nodes can be added to increase throughput based on data size, and a single cluster can handle thousands of queries per second
Specific optimization steps:
- Configuring HNSW indexing parameters (e.g., neighbors-to-explore=200, ef-construction=400) balances recall and performance
- Adopt hybrid query YQL syntax to express multiple search intents at the same time
- Plan the number of nodes according to the amount of data, it is recommended to have at least 3 worker nodes for every 1 billion data.
Expected results: according to the official test data, the query delay can be stably controlled within 100 milliseconds under 1 billion data, which is significantly better than the traditional search engine solutions.
This answer comes from the articleVespa.ai: an open source platform for building efficient AI search and recommendation systemsThe
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