Building Stateless Serverless Vector DBs via Block-based Data Partitioning
Summary: First experimental comparison of data partitioning for stateless FaaS vector DBs, showing clustering-based partitions fail on dynamic/bursty datasets due to complexity and load‑imbalance. Block-based partitioning yields up to 5.8× faster partitioning, ~63% lower costs and Milvus-comparable recall, enabling cost-efficient serverless vector DBs for sparse workloads. (summarized by gpt-5-mini on Feb 11 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 78 | A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces | 1998 | VLDB | 0.00056385781 |
| 494 | Milvus: A Purpose-Built Vector Data Management System | 2021 | SIGMOD | 0.00021769407 |
| 730 | AnalyticDB-V: A Hybrid Analytical Engine Towards Query Fusion for Structured and Unstructured Data | 2020 | VLDB | 0.00017443615 |
| 1,188 | word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data | 2020 | PODS | 0.00013431099 |
| 2,264 | Manu: A Cloud Native Vector Database Management System | 2022 | VLDB | 9.1587362e-05 |
| 4,831 | Vexless: A Serverless Vector Data Management System Using Cloud Functions | 2024 | SIGMOD | 5.8883791e-05 |
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