Approximate K Nearest Neighbor (AKNN) algorithms play a pivotal role in
various AI applications, including information retrieval, computer vision, and
natural language processing. Although numerous AKNN algorithms and benchmarks
have been developed recently to evaluate their effectiveness, the dynamic
nature of real-world data presents significant challenges that existing
benchmarks fail to address. Traditional benchmarks primarily assess retrieval
effectiveness in static contexts and often overlook update efficiency, which is
crucial for handling continuous data ingestion. This limitation results in an
incomplete assessment of an AKNN algorithms ability to adapt to changing data
patterns, thereby restricting insights into their performance in dynamic
environments. To address these gaps, we introduce CANDY, a benchmark tailored
for Continuous Approximate Nearest Neighbor Search with Dynamic Data Ingestion.
CANDY comprehensively assesses a wide range of AKNN algorithms, integrating
advanced optimizations such as machine learning-driven inference to supplant
traditional heuristic scans, and improved distance computation methods to
reduce computational overhead. Our extensive evaluations across diverse
datasets demonstrate that simpler AKNN baselines often surpass more complex
alternatives in terms of recall and latency. These findings challenge
established beliefs about the necessity of algorithmic complexity for high
performance. Furthermore, our results underscore existing challenges and
illuminate future research opportunities. We have made the datasets and
implementation methods available at: this https URL

CANDY 是一个定制的连续近似最近邻搜索基准，能够全面评估广泛的 AKNN 算法，并通过集成机器学习驱动的推断和改进的距离计算方法来提高检索效果和减少计算开销。实验证明，相对简单的 AKNN 基线通常在召回率和延迟方面超越更复杂的替代方案，这一发现对高性能所需的算法复杂性的现有信念提出了质疑，并强调了现有挑战和未来研究机会。