Cloud-Native Vector Search: A Comprehensive Performance Analysis
Zhaoheng Li, Wei Ding, Silu Huang, Zikang Wang, Yuanjin Lin, Ke Wu, Yongjoo Park, Jianjun Chen

TL;DR
This paper systematically analyzes cloud-native vector search, comparing cluster and graph indexes, and explores how indexing, search parameters, and caching affect performance in cloud environments.
Contribution
It provides a comprehensive study of index choices, parameter tuning, and caching strategies for cloud-native vector search, highlighting when graph indexes outperform cluster indexes.
Findings
Graph indexes are preferred for high concurrency and high-dimensional data.
Cloud-native search requires different index and parameter configurations than on-disk search.
Certain index optimizations can conflict with caching, affecting performance.
Abstract
Vector search has been widely employed in recommender system and retrieval-augmented-generation pipelines, commonly performed with vector indexes to efficiently find similar items in large datasets. Recent growths in both data and task complexity have motivated placing vector indexes onto remote storage -- cloud-native vector search, which cloud providers have recently introduced services for. Yet, despite varying workload characteristics and various available vector index forms, providers default to using cluster-based indexes, which on paper do adapt well to differences between disk and cloud-based environment: their fetch granularities and lack of notable intra-query dependencies aligns with the large optimal fetch sizes and minimizes costly round-trips (i.e., as opposed to graph-based indexes) to remote storage, respectively. This paper systematically studies cloud-native vector…
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Taxonomy
TopicsData Management and Algorithms · Information Retrieval and Search Behavior · Advanced Image and Video Retrieval Techniques
