DuckDB's storage engine is a key reason for its high performance in analytical workloads. This article breaks down the entire data flow: starting from a logical table, data is organized into Row Groups, each containing Column Data structures. These are further divided into Column Segments, which are the units of compression and storage. The segments are written to disk blocks, managed by the Buffer Manager, which handles caching and I/O. Understanding this hierarchy helps developers optimize queries, choose appropriate compression, and debug performance issues. The article also touches on how vectors and DataChunks are used in query execution, linking storage to processing. For anyone building data-intensive applications with DuckDB, this architectural knowledge is essential for tuning and scaling.
A deep dive into DuckDB's storage architecture, from logical tables to physical disk blocks, explaining Row Groups, Column Segments, and the Buffer Manager.