SPTAG

SPTAG: A library for fast approximate nearest neighbor search

SPTAG

SPTAG (Space Partition Tree And Graph) is a library for large scale vector approximate nearest neighbor search scenario released by Microsoft Research (MSR) and Microsoft Bing.

What's NEW

• Result Iterator with Relaxed Monotonicity Signal Support
• New Research Paper SPFresh: Incremental In-Place Update for Billion-Scale Vector Search - published in SOSP 2023
• New Research Paper VBASE: Unifying Online Vector Similarity Search and Relational Queries via Relaxed Monotonicity - published in OSDI 2023

Introduction

This library assumes that the samples are represented as vectors and that the vectors can be compared by L2 distances or cosine distances. Vectors returned for a query vector are the vectors that have smallest L2 distance or cosine distances with the query vector.

SPTAG provides two methods: kd-tree and relative neighborhood graph (SPTAG-KDT) and balanced k-means tree and relative neighborhood graph (SPTAG-BKT). SPTAG-KDT is advantageous in index building cost, and SPTAG-BKT is advantageous in search accuracy in very high-dimensional data.

How it works

SPTAG is inspired by the NGS approach [WangL12]. It contains two basic modules: index builder and searcher. The RNG is built on the k-nearest neighborhood graph [WangWZTG12, WangWJLZZH14] for boosting the connectivity. Balanced k-means trees are used to replace kd-trees to avoid the inaccurate distance bound estimation in kd-trees for very high-dimensional vectors. The search begins with the search in the space partition trees for finding several seeds to start the search in the RNG. The searches in the trees and the graph are iteratively conducted.

Highlights

• Fresh update: Support online vector deletion and insertion
• Distributed serving: Search over multiple machines

Build

Requirements

• swig >= 4.0.2
• cmake >= 3.12.0
• boost >= 1.67.0

Fast clone

set GIT_LFS_SKIP_SMUDGE=1
git clone --recurse-submodules https://github.com/microsoft/SPTAG

OR

git config --global filter.lfs.smudge "git-lfs smudge --skip -- %f"
git config --global filter.lfs.process "git-lfs filter-process --skip"

Install

For Linux: Compile SPDK

cd ThirdParty/spdk
./scripts/pkgdep.sh
CC=gcc-9 ./configure
CC=gcc-9 make -j

Compile isal-l_crypto

cd ThirdParty/isal-l_crypto
./autogen.sh
./configure
make -j

Build RocksDB

mkdir build && cd build
cmake -DUSE_RTTI=1 -DWITH_JEMALLOC=1 -DWITH_SNAPPY=1 -DCMAKE_C_COMPILER=gcc-7 -DCMAKE_CXX_COMPILER=g++-7 -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-fPIC" ..
make -j
sudo make install

Build SPTAG

mkdir build
cd build && cmake -DSPDK=OFF -DROCKSDB=OFF .. && make

It will generate a Release folder in the code directory which contains all the build targets.

For Windows:

mkdir build
cd build && cmake -A x64 -DSPDK=OFF -DROCKSDB=OFF ..

It will generate a SPTAGLib.sln in the build directory. Compiling the ALL_BUILD project in the Visual Studio (at least 2019) will generate a Release directory which contains all the build targets.

For detailed instructions on installing Windows binaries, please see here

Using Docker:

docker build -t sptag .

Will build a docker container with binaries in /app/Release/.

Verify

Run the SPTAGTest (or Test.exe) in the Release folder to verify all the tests have passed.

Usage

The detailed usage can be found in Get started. There is also an end-to-end tutorial for building vector search online service using Python Wrapper in Python Tutorial. The detailed parameters tunning can be found in Parameters.

References

Please cite SPTAG in your publications if it helps your research:

@inproceedings{xu2023spfresh,
  title={SPFresh: Incremental In-Place Update for Billion-Scale Vector Search},
  author={Xu, Yuming and Liang, Hengyu and Li, Jin and Xu, Shuotao and Chen, Qi and Zhang, Qianxi and Li, Cheng and Yang, Ziyue and Yang, Fan and Yang, Yuqing and others},
  booktitle={Proceedings of the 29th Symposium on Operating Systems Principles},
  pages={545--561},
  year={2023}
}

@inproceedings{zhang2023vbase,
  title={$\{$VBASE$\}$: Unifying Online Vector Similarity Search and Relational Queries via Relaxed Monotonicity},
  author={Zhang, Qianxi and Xu, Shuotao and Chen, Qi and Sui, Guoxin and Xie, Jiadong and Cai, Zhizhen and Chen, Yaoqi and He, Yinxuan and Yang, Yuqing and Yang, Fan and others},
  booktitle={17th USENIX Symposium on Operating Systems Design and Implementation (OSDI 23)},
  year={2023}
}

@inproceedings{ChenW21,
  author = {Qi Chen and 
            Bing Zhao and 
            Haidong Wang and 
            Mingqin Li and 
            Chuanjie Liu and 
            Zengzhong Li and 
            Mao Yang and 
            Jingdong Wang},
  title = {SPANN: Highly-efficient Billion-scale Approximate Nearest Neighbor Search},
  booktitle = {35th Conference on Neural Information Processing Systems (NeurIPS 2021)},
  year = {2021}
}

@manual{ChenW18,
  author    = {Qi Chen and
               Haidong Wang and
               Mingqin Li and 
               Gang Ren and
               Scarlett Li and
               Jeffery Zhu and
               Jason Li and
               Chuanjie Liu and
               Lintao Zhang and
               Jingdong Wang},
  title     = {SPTAG: A library for fast approximate nearest neighbor search},
  url       = {https://github.com/Microsoft/SPTAG},
  year      = {2018}
}

@inproceedings{WangL12,
  author    = {Jingdong Wang and
               Shipeng Li},
  title     = {Query-driven iterated neighborhood graph search for large scale indexing},
  booktitle = {ACM Multimedia 2012},
  pages     = {179--188},
  year      = {2012}
}

@inproceedings{WangWZTGL12,
  author    = {Jing Wang and
               Jingdong Wang and
               Gang Zeng and
               Zhuowen Tu and
               Rui Gan and
               Shipeng Li},
  title     = {Scalable k-NN graph construction for visual descriptors},
  booktitle = {CVPR 2012},
  pages     = {1106--1113},
  year      = {2012}
}

@article{WangWJLZZH14,
  author    = {Jingdong Wang and
               Naiyan Wang and
               You Jia and
               Jian Li and
               Gang Zeng and
               Hongbin Zha and
               Xian{-}Sheng Hua},
  title     = {Trinary-Projection Trees for Approximate Nearest Neighbor Search},
  journal   = {{IEEE} Trans. Pattern Anal. Mach. Intell.},
  volume    = {36},
  number    = {2},
  pages     = {388--403},
  year      = {2014
}

Contribute

This project welcomes contributions and suggestions from all the users.

We use GitHub issues for tracking suggestions and bugs.

License

The entire codebase is under MIT license