kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search (2501.06121v1)

Abstract: Approximate Nearest Neighbors (ANN) search is a crucial task in several applications like recommender systems and information retrieval. Current state-of-the-art ANN libraries, although being performance-oriented, often lack modularity and ease of use. This translates into them not being fully suitable for easy prototyping and testing of research ideas, an important feature to enable. We address these limitations by introducing kANNolo, a novel research-oriented ANN library written in Rust and explicitly designed to combine usability with performance effectively. kANNolo is the first ANN library that supports dense and sparse vector representations made available on top of different similarity measures, e.g., euclidean distance and inner product. Moreover, it also supports vector quantization techniques, e.g., Product Quantization, on top of the indexing strategies implemented. These functionalities are managed through Rust traits, allowing shared behaviors to be handled abstractly. This abstraction ensures flexibility and facilitates an easy integration of new components. In this work, we detail the architecture of kANNolo and demonstrate that its flexibility does not compromise performance. The experimental analysis shows that kANNolo achieves state-of-the-art performance in terms of speed-accuracy trade-off while allowing fast and easy prototyping, thus making kANNolo a valuable tool for advancing ANN research. Source code available on GitHub: https://github.com/TusKANNy/kannolo.

• The paper introduces kANNolo, a modular, research-oriented library for ANN search that simplifies prototyping without compromising performance.
• Its design uses Rust traits for flexible indexing and quantization, ensuring consistent handling of both dense and sparse data.
• Experimental results demonstrate up to an 11.1× speedup on dense datasets and a 2.1× improvement on sparse datasets.

An Overview of kANN: A Novel Approach to Approximate k-Nearest Neighbors Search

The paper "kANN: Sweet and Smooth Approximate $k$-Nearest Neighbors Search" introduces kANN, a new research-oriented library for Approximate Nearest Neighbors (ANN) search implemented in Rust. ANN search is crucial in numerous computer science domains, including image processing, information retrieval, and recommendation systems. Traditional libraries, despite their performance-oriented nature, often lack modularity and ease of use, which are essential for rapid prototyping and experimentation. The authors aim to bridge this gap with kANN, which emphasizes ease of modification and integration without sacrificing performance.

Architecture and Design

kANN is designed with a modular architecture comprising several key components:

• One-Dimensional Arrays: Managed by the DArray1 trait, this component handles both dense and sparse vectors, providing a unified interface for indexing and search operations.
• Quantizers: These transform high-dimensional data into compact representations. The library employs a Quantizer trait that supports various quantization methods, including the standard Product Quantization (PQ).
• Query Evaluator: The QueryEvaluator trait calculates distances or similarities between dataset items and query points, ensuring a consistent interface across different quantization methods and data representations.
• Dataset Trait: Acts as a collection of one-dimensional arrays equipped with a quantizer, facilitating seamless integration with the query evaluator during search operations.

The core strength of kANN lies in its abstraction via Rust traits, which not only provide flexibility in managing these components but also facilitate easy integration of new elements. This modularity allows researchers to focus on developing and integrating novel indexing and quantization techniques with minimal effort.

Performance and Experimental Results

The experimental analysis highlights the effectiveness of kANN in achieving a superior speed-accuracy trade-off. The library was benchmarked against existing state-of-the-art ANN libraries on several datasets, including Sift1M and MsMarco. Key findings include:

• Dense Data Performance: kANN exhibits competitive results with leading ANN libraries on the Sift1M dataset and surpasses competitors on the MsMarco dataset. Notably, kANN achieves up to an 11.1× speedup due to its efficient graph-based indexing.
• Sparse Data Performance: On sparse datasets, kANN demonstrates an up to 2.1× speedup over its closest competitors, underscoring its utility in both dense and sparse retrieval tasks.

This performance is achieved while maintaining the library's modularity and ease of use, setting it apart as a versatile tool for ANN research.

Implications and Future Work

The introduction of kANN has several implications for the ANN research community:

• Flexibility in Research: By prioritizing modularity, kANN empowers researchers to experiment with different indexing and quantization techniques without the constraints posed by more rigid libraries.
• Potential for Expansion: The library’s design anticipates future extensions, such as additional indexing and quantization methods, which could broaden its applicability and support more complex research inquiries.
• Performance-Driven Development: The combination of integral performance and modularity ensures that kANN serves as both a testbed for innovative research ideas and a tool for practical ANN applications.

The authors propose extending kANN's capabilities by integrating additional indexing methods, a move that will enhance its utility across a wider range of use cases. This envisaged development trajectory aligns with the needs of an evolving landscape of machine learning research, where adaptability and performance are paramount.

In summary, kANN offers a compelling solution to existing limitations in ANN search libraries by delivering high performance without compromising on flexibility, thus serving as a valuable asset for researchers and practitioners alike.