TreeCat: Standalone Catalog Engine for Large Data Systems

Abstract

With ever-increasing volume and heterogeneity of data, advent of new specialized compute engines, and demand for complex use cases, large-scale data systems require a performant catalog system that can satisfy diverse needs. We argue that existing solutions, including recent lakehouse storage formats, have fundamental limitations and that there is a strong motivation for a specialized database engine, dedicated to serve as the catalog. We present the design and implementation of TreeCat, a database engine that features a hierarchical data model with a path-based query language, a storage format optimized for efficient range queries and versioning, and a correlated scan operation that enables fast query execution. A key performance challenge is supporting concurrent read and write operations from many different clients while providing strict consistency guarantees. To this end, we present a novel MVOCC (multi-versioned optimistic concurrency control) protocol that guarantees serializable isolation. We conduct a comprehensive experimental evaluation comparing our concurrency control scheme with prior techniques, and evaluating our overall system against Hive Metastore, Delta Lake, and Iceberg.