DuckDB's vectorized execution model is a key factor in its analytical performance. This article offers a comprehensive walkthrough of the pipeline, starting from how ColumnSegments in storage are scanned into DataChunks for processing. It then explains the critical optimization of using selection vectors during filtering, which avoids expensive data copying and maintains cache efficiency. Finally, it details the Morsel-Driven Hash Join algorithm, which partitions work into small 'morsels' for parallel execution across threads. Understanding these internals is essential for engineers building or optimizing data-intensive applications, as DuckDB's design choices—such as columnar storage, vectorized processing, and morsel-driven parallelism—are becoming industry standards for in-process analytical databases.
A deep dive into DuckDB's execution pipeline covering storage layer scanning, DataChunk filtering with selection vectors, and Morsel-Driven Hash Join.