Accelerating Similarity Search on Really Big Data with Vector Indexing

This article discusses the role of vector indexing in accelerating similarity search and machine learning applications, particularly those that involve large datasets. It covers different types of vector inverted file (IVF) indexes and their suitability for various scenarios. The IVF_FLAT index is best suited for searching relatively small (million-scale) datasets when 100% recall is required. For scenarios where disk, CPU, or GPU memory resources are limited, the IVF_SQ8 index type is a better option as it can convert each FLOAT to UINT8 by performing scalar quantization, reducing memory consumption by 70-75%. The new hybrid GPU/CPU approach, IVF_SQ8H, offers even faster query performance compared to IVF_SQ8 with no loss in search accuracy. Finally, the article introduces Milvus, an open-source vector data management platform that can power similarity search applications across various fields.

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