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 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.
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:
| Algorithm | Description |
|---|---|
| Bruteforce | Naive parallel similarity search implementation |
| Lower Bound Bruteforce | Optimized bruteforce with lower bounding for the distance calculations |
| MESSI | In-memory parallel similarity search |
| PARIS | Disk-based parallel similarity search |
| SING | GPU-accelerated in-memory parallel similarity search |
| Odyssey | Distributed and parallel in-memory similarity search |
- 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
To download DaiSy, use:
git clone https://github.com/MChatzakis/daisy.git
cd daisy
git submodule update --init --recursiveBased on the available hardware, you can specify the below arguments to enable/disable features.
| Flag | Description | Default | Dependencies |
|---|---|---|---|
BUILD_PYTHON |
Enable Python bindings | OFF |
Python 3.10+ |
BUILD_BENCHMARK |
Build benchmarking tools | OFF |
GoogleBenchmark |
BUILD_TESTS |
Build test suite | OFF |
GoogleTest |
BUILD_DEMO |
Build demonstration applications | ON |
Core library |
BUILD_ODYSSEY |
Enable MPI for distributed computing | OFF |
OpenMPI/MPICH |
BUILD_SING |
Enable CUDA for GPU acceleration | OFF |
CUDA Toolkit |
DEBUG_MSG |
Enable debug output | OFF |
None |
To compile:
mkdir build && cd build
cmake ..
makeIf you intent to use only the Python interface, you can install the library directly from PyPI using pip:
pip install daisy-exact-searchIf you want to use Odyssey, you will need to install mpi:
pip install daisy-exact-search[mpi]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.
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.
DaiSy is developed by researchers at the diNo research group, LIPADE, Université Paris Cité.
It is provided with no warranty, and we encourage contributions from the community to enhance its capabilities and performance. For questions, issues, or contributions, please open an issue or submit a pull request on GitHub. DaiSy licensed under the MIT License.
For questions and suggestions through mail, you can contact us at manos.chatzaki@gmail.com.
The logo of DaiSy was designed by Eva Chamilaki.