https://arxiv.org/abs/2111.08566

Skip to main content
Cornell University
We gratefully acknowledge support from the Simons Foundation, member
institutions, and all contributors. Donate
 
arxiv logo > cs > arXiv:2111.08566
[                    ]

Help | Advanced Search

[All fields        ]
Search
arXiv logo
Cornell University Logo
[                    ] GO
quick links

  * Login
  * Help Pages
  * About

Computer Science > Databases

arXiv:2111.08566 (cs)
[Submitted on 5 Nov 2021]

Title:SPANN: Highly-efficient Billion-scale Approximate Nearest
Neighbor Search

Authors:Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu,
Zengzhong Li, Mao Yang, Jingdong Wang
View a PDF of the paper titled SPANN: Highly-efficient Billion-scale
Approximate Nearest Neighbor Search, by Qi Chen and 7 other authors
View PDF

    Abstract:The in-memory algorithms for approximate nearest
    neighbor search (ANNS) have achieved great success for fast
    high-recall search, but are extremely expensive when handling
    very large scale database. Thus, there is an increasing request
    for the hybrid ANNS solutions with small memory and inexpensive
    solid-state drive (SSD). In this paper, we present a simple but
    efficient memory-disk hybrid indexing and search system, named
    SPANN, that follows the inverted index methodology. It stores the
    centroid points of the posting lists in the memory and the large
    posting lists in the disk. We guarantee both disk-access
    efficiency (low latency) and high recall by effectively reducing
    the disk-access number and retrieving high-quality posting lists.
    In the index-building stage, we adopt a hierarchical balanced
    clustering algorithm to balance the length of posting lists and
    augment the posting list by adding the points in the closure of
    the corresponding clusters. In the search stage, we use a
    query-aware scheme to dynamically prune the access of unnecessary
    posting lists. Experiment results demonstrate that SPANN is 2$\
    times$ faster than the state-of-the-art ANNS solution DiskANN to
    reach the same recall quality $90\%$ with same memory cost in
    three billion-scale datasets. It can reach $90\%$ recall@1 and
    recall@10 in just around one millisecond with only 32GB memory
    cost. Code is available at: {\footnotesize\color{blue}{\url{this
    https URL}}}.

Comments: Accepted to NeurIPS 2021
          Databases (cs.DB); Artificial Intelligence (cs.AI);
Subjects: Computer Vision and Pattern Recognition (cs.CV);
          Information Retrieval (cs.IR); Machine Learning (cs.LG)
Cite as:  arXiv:2111.08566 [cs.DB]
          (or arXiv:2111.08566v1 [cs.DB] for this version)
          https://doi.org/10.48550/arXiv.2111.08566
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Jingdong Wang [view email]
[v1] Fri, 5 Nov 2021 06:28:15 UTC (127 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled SPANN: Highly-efficient
    Billion-scale Approximate Nearest Neighbor Search, by Qi Chen and
    7 other authors
  * View PDF
  * TeX Source
  * Other Formats

license icon view license
Current browse context:
cs.DB
< prev   |   next >
new | recent | 2021-11
Change to browse by:
cs
cs.AI
cs.CV
cs.IR
cs.LG

References & Citations

  * NASA ADS
  * Google Scholar
  * Semantic Scholar

DBLP - CS Bibliography

listing | bibtex
Qi Chen
Bing Zhao
Mao Yang
Jingdong Wang
a export BibTeX citation Loading...

BibTeX formatted citation

x
[loading...          ]
Data provided by:

Bookmark

BibSonomy logo Reddit logo
(*) Bibliographic Tools

Bibliographic and Citation Tools

[ ] Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
[ ] Connected Papers Toggle
Connected Papers (What is Connected Papers?)
[ ] Litmaps Toggle
Litmaps (What is Litmaps?)
[ ] scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
( ) Code, Data, Media

Code, Data and Media Associated with this Article

[ ] alphaXiv Toggle
alphaXiv (What is alphaXiv?)
[ ] Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
[ ] DagsHub Toggle
DagsHub (What is DagsHub?)
[ ] GotitPub Toggle
Gotit.pub (What is GotitPub?)
[ ] Huggingface Toggle
Hugging Face (What is Huggingface?)
[ ] Links to Code Toggle
Papers with Code (What is Papers with Code?)
[ ] ScienceCast Toggle
ScienceCast (What is ScienceCast?)
( ) Demos

Demos

[ ] Replicate Toggle
Replicate (What is Replicate?)
[ ] Spaces Toggle
Hugging Face Spaces (What is Spaces?)
[ ] Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
( ) Related Papers

Recommenders and Search Tools

[ ] Link to Influence Flower
Influence Flower (What are Influence Flowers?)
[ ] Core recommender toggle
CORE Recommender (What is CORE?)

  * Author
  * Venue
  * Institution
  * Topic

( ) About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and
share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have
embraced and accepted our values of openness, community, excellence,
and user data privacy. arXiv is committed to these values and only
works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?
Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is
MathJax?)

  * About
  * Help

  * Click here to contact arXiv Contact
  * Click here to subscribe Subscribe

  * Copyright
  * Privacy Policy

  * Web Accessibility Assistance
  * arXiv Operational Status
    Get status notifications via email or slack