Learned Index

Learned indexes represent a paradigm shift in data indexing, leveraging machine learning models to predict the location of keys within a dataset, thereby improving search efficiency and reducing storage needs. Current research focuses on enhancing the adaptability of learned indexes to updates, exploring various model architectures like deep neural networks and normalizing flows, and optimizing index selection for diverse workloads using reinforcement learning. This approach offers significant potential for improving the performance of database systems and other applications requiring efficient key-value lookups, particularly in scenarios with high update frequency or complex data distributions.