DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916938871996416 |
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| author | Adams, Philip Li, Menghao Zhang, Shi Tan, Li Chen, Qi Li, Mingqin Li, Zengzhong Risvik, Knut Simhadri, Harsha Vardhan |
| author_facet | Adams, Philip Li, Menghao Zhang, Shi Tan, Li Chen, Qi Li, Mingqin Li, Zengzhong Risvik, Knut Simhadri, Harsha Vardhan |
| contents | We present DISTRIBUTEDANN, a distributed vector search service that makes it possible to search over a single 50 billion vector graph index spread across over a thousand machines that offers 26ms median query latency and processes over 100,000 queries per second. This is 6x more efficient than existing partitioning and routing strategies that route the vector query to a subset of partitions in a scale out vector search system. DISTRIBUTEDANN is built using two well-understood components: a distributed key-value store and an in-memory ANN index. DISTRIBUTEDANN has replaced conventional scale-out architectures for serving the Bing search engine, and we share our experience from making this transition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06046 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers Adams, Philip Li, Menghao Zhang, Shi Tan, Li Chen, Qi Li, Mingqin Li, Zengzhong Risvik, Knut Simhadri, Harsha Vardhan Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Information Retrieval E.1; H.3.3 We present DISTRIBUTEDANN, a distributed vector search service that makes it possible to search over a single 50 billion vector graph index spread across over a thousand machines that offers 26ms median query latency and processes over 100,000 queries per second. This is 6x more efficient than existing partitioning and routing strategies that route the vector query to a subset of partitions in a scale out vector search system. DISTRIBUTEDANN is built using two well-understood components: a distributed key-value store and an in-memory ANN index. DISTRIBUTEDANN has replaced conventional scale-out architectures for serving the Bing search engine, and we share our experience from making this transition. |
| title | DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers |
| topic | Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Information Retrieval E.1; H.3.3 |
| url | https://arxiv.org/abs/2509.06046 |