Zeng, Xianzhi, et al. "CANDY: A Benchmark for Continuous Approximate Nearest Neighbor Search with Dynamic Data Ingestion."
A 2024 paper introducing CANDY, a benchmark for continuous ANN search with a focus on dynamic data ingestion, crucial for next-generation vector databases.
About this tool
Zeng, Xianzhi, et al. "CANDY: A Benchmark for Continuous Approximate Nearest Neighbor Search with Dynamic Data Ingestion."
- Category: Benchmarks & Evaluation
- Tags: benchmark, ann, dynamic-data, vector-search
- Source: arXiv:2406.19651
Description
CANDY is a benchmark introduced in 2024 for evaluating continuous approximate nearest neighbor (ANN) search systems, with a special focus on dynamic data ingestion. This is particularly relevant for assessing next-generation vector databases that must support both efficient similarity search and frequent data updates.
Features
- Provides a standardized benchmark for continuous ANN search.
- Focuses on scenarios with dynamic (frequently updated) data.
- Useful for evaluating vector database systems' performance under realistic, evolving workloads.
- Supports research and development of efficient ANN algorithms adaptable to dynamic environments.
Pricing
Not applicable; this is an academic benchmark paper.