Index Structure

Index structures, crucial for efficient data retrieval, are undergoing significant advancements driven by the need for faster and more accurate searches in massive datasets. Current research focuses on integrating machine learning models (learned indexes) with traditional structures like LSM trees to optimize performance for various data types, including high-dimensional embeddings and string keys, addressing challenges like index collapse and content drift. These improvements are impacting diverse applications, from recommendation systems and key-value stores to speech processing and similarity search, enabling faster query response times and improved accuracy in large-scale data analysis.