Zeng, Xianzhi, et al. "CANDY: A Benchmark for Continuous Approximate Nearest Neighbor Search with Dynamic Data Ingestion."

Online Product Quantization (O-PQ) is a variant of product quantization designed to support dynamic or streaming data. It enables adaptive updating of quantization codebooks and codes in real-time, making it suitable for vector databases that handle evolving datasets.

ANN-Benchmarks is a benchmarking platform specifically for evaluating the performance of approximate nearest neighbor (ANN) search algorithms, which are foundational to vector database evaluation and comparison.

BEIR (Benchmarking IR) is a benchmark suite for evaluating information retrieval and vector search systems across multiple tasks and datasets. Useful for comparing vector database performance.

Amazon OpenSearch's k-NN plugin enables scalable, efficient vector search using ANN algorithms (IVF, HNSW) directly within a managed OpenSearch cluster. It is directly relevant for building, querying, and scaling vector databases on AWS.

An open-source library for approximate nearest neighbor search in high-dimensional spaces, often used as a backend for vector databases and search engines.

DiskANN is a graph-based approximate nearest neighbor search (ANNS) system optimized for fast and accurate billion-point nearest neighbor search on a single node, leveraging SSD storage. It is highly relevant for large-scale vector database applications requiring efficient vector search at scale.