Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment
CoRR(2024)
摘要
High-dimensional vector similarity search (HVSS) is receiving a spotlight as
a powerful tool for various data science and AI applications. As vector data
grows larger, in-memory indexes become extremely expensive because they
necessitate substantial expansion of main memory resources. One possible
solution is to use disk-based implementation, which stores and searches vector
data in high-performance devices like NVMe SSDs. However, HVSS for data
segments is still challenging in vector databases, where one machine has
multiple segments for system features (like scaling) purposes. In this setting,
each segment has limited memory and disk space, so HVSS on the data segment
needs to balance accuracy, efficiency, and space cost. Existing disk-based
methods are sub-optimal because they do not consider all these requirements
together. In this paper, we present Starling, an I/O-efficient disk-resident
graph index framework that optimizes data layout and search strategy in the
segment. It has two main components: (1) a data layout that includes an
in-memory navigation graph and a reordered disk-based graph with locality
enhancement, which reduces the search path length and disk bandwidth wastage;
and (2) a block search strategy that minimizes expensive disk I/Os when
executing a vector query. We conduct extensive experiments to verify Starling's
effectiveness, efficiency, and scalability. On a data segment with 2GB memory
and 10GB disk capacity, Starling can maintain up to 33 million vectors in 128
dimensions, and serve HVSS with more than 0.9 average precision and top-10
recall rate, and latency of under 1 millisecond. The results show that Starling
exhibits 43.9× higher throughput with 98
state-of-the-art methods under the same accuracy.
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