Vector search scales with data size by employing a combination of efficient indexing, distributed storage, and parallel processing. As datasets grow, vector databases must be able to handle increasingly complex queries without sacrificing performance. One key factor in scaling is the use of indexing structures such as HNSW, which organize vectors in a way that optimizes search time as the database grows. These structures reduce the need to compare each query vector to every data point, allowing the system to focus on the most relevant results. Additionally, vector databases like Milvus and Zilliz Cloud are designed for horizontal scaling, meaning they can distribute data across multiple servers, allowing for better load balancing and faster searches. As more data is added, these systems can automatically scale their infrastructure, ensuring consistent performance. Parallel processing capabilities further enhance scaling by allowing searches to be performed across multiple processors or even GPUs, significantly increasing query throughput. To maintain low-latency searches as data grows, some systems also use hardware acceleration, such as using GPUs for vector computation. This ensures that the vector search process remains efficient even as the dataset increases in size, enabling real-time performance for applications such as recommendation engines or large-scale semantic search. Thus, by combining optimized indexing, distributed storage, parallel processing, and hardware acceleration, vector search can scale effectively as data size increases.
How does vector search scale with data size?
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