DaiSy

The Library for Fast and Exact, Data Series and Vector Similarity Search

Exact similarity search over large collections of data series is a fundamental operation in modern applications, yet existing solutions are often fragmented, specialized, or tailored to specific execution environments. We present DaiSy (Data series similarity Search library), a unified library for exact data series and vector similarity search that integrates multiple state-of-the-art algorithms within a single, coherent framework. DaiSy is the first library to support exact similarity search across diverse execution environments, including implementations for disk-based, in-memory, GPU-accelerated, and distributed scalable similarity search. The library supports interfaces in both C++ and Python, enabling researchers and practitioners to easily integrate its functionality in a variety of tasks.

ALPHA VERSION: Currently, DaiSy is experimental. The library is still under active development. We welcome suggestions and bug reports.

How to Cite

DaiSy has been accepted for publication at SIGMOD Record! When using DaiSy, please cite the following artifacts:

@article{daisy2026preprint,
  title={DaiSy: A Library for Scalable Data Series Similarity Search},
  author={Del Gaudio, Francesca and Chatzakis, Manos and Ravendirane, Gayathiri and Peng, Botao and Palpanas, Themis},
  journal={arXiv preprint arXiv:2603.27719},
  year={2026}
}

@misc{daisy2026github,
  title        = {{The DaiSy Library for Fast and Exact, Data Series and Vector Similarity Search}},
  author       ={Francesca Del Gaudio, Manos Chatzakis, Gayathiri Ravendirane, Botao Peng, Panagiota Fatourou, Themis Palpanas},
  year         = {2026},
  howpublished = {\url{https://github.com/mchatzakis/daisy}}
}

Supported State-of-the-Art algorithms

We currently support several algorithms for exact similarity search, each optimized for specific use cases and environments. The following table summarizes the key features of each algorithm:

Quickstart

Dependencies

• Operating System: Linux, macOS, or Windows
• C++ Compiler: C++14 or higher (GCC 6+, Clang 3.4+, MSVC 2015+)
• CMake: Version 3.15 or higher

Optionally,

• Python: 3.10-3.12
• MPI: Required for Odyssey distributed computing algorithm
• CUDA: Required for SING GPU acceleration algorithm

Installation

To download DaiSy, use:

git clone https://github.com/MChatzakis/daisy.git

cd daisy
git submodule update --init --recursive

Based on the available hardware, you can specify the below arguments to enable/disable features.

To compile:

mkdir build && cd build

cmake ..
make

DaiSy with Python

If you intent to use only the Python interface, you can install the library directly from PyPI using pip:

pip install daisy-exact-search

If you want to use Odyssey, you will need to install mpi:

pip install daisy-exact-search[mpi]

Compatibility issues

Kindly note that we are aware for compatibility issues related to ARM processors (e.g., Apple MX processors). Due to pthread-barriers and SIMD being unavailable on ARM, we currently noticing compilations failling on ARM machines. We are currently working on possible solutions, however we recommend using DaiSy on non-ARM machines for the time being.

Others

We provide several usage examples in both C++ and Python under demos/, demonstrating how to utilize the library for various similarity search tasks. We provide several troubleshooting guides and extra resources in the docs/ directory. In this directory, we also provide useful information about how to contribute to the project, and how to implement new algorithms.

Acknowledgements

Work supported by EU Horizon projects ARMADA (101168951), TwinODIS (101160009), DataGEMS (101188416), and YΠAIΘA & NextGenerationEU project HARSH (YΠ3TA − 0560901) that is carried out within the framework of the National Recovery and Resilience Plan “Greece 2.0” with funding from the European Union – NextGenerationEU. Manos Chatzakis is supported with a PhD Scholarship from the Onassis Foundation. The logo of DaiSy was designed by Eva Chamilaki.