Peter Vary

AI workloads increasingly depend on vector indexes for fast similarity search, while analytical queries often require specialized data layouts for efficient execution. Secondary indexes such as primary key index , B-Tree, and vector indexes enable engines to dramatically reduce query latency and unlock new capabilities including feature engineering, text search, and AI-driven queries.

Machine learning and feature engineering workloads increasingly rely on very wide tables with thousands of columns, exposing fundamental limits in current columnar layouts. These limits increase I/O and metadata complexity while slowing down query planning.