DuckDB Internals: Speed Through In-Process Design
Original: DuckDB Internals: Why Is DuckDB Fast? (Part 1)
Why This Matters
DuckDB's in-process architecture and columnar design enable single-node performance competitive with million-dollar clusters, affecting analytical database market dynamics.
DuckDB, evolved from a 2019 CWI Amsterdam research project, has become widely adopted as an in-process analytical SQL database. Its speed derives from columnar storage, vectorized execution, and elimination of network serialization overhead compared to traditional server-based databases.
DuckDB is an in-process analytical SQL database optimized for queries scanning millions of rows rather than single-record lookups. Unlike server-based databases such as Snowflake, Postgres, BigQuery, and Redshift, DuckDB loads as a library within programs without requiring a separate server or network connections. The database ships as a single binary under 20 MB with no external dependencies, installable via pip, brew, or C++ linking. It can directly query Parquet, CSV, and JSON files without CREATE TABLE statements. DuckDB's adoption spans notebooks, ETL pipelines, dashboards, CI test runners, and embedded analytics in SaaS products. Companies including MotherDuck (cloud data warehouse), Hex, Omni, Evidence, Fivetran, and Rill have built products around it. The first part of a three-part technical series examines DuckDB's speed advantages. Key design choices include in-process execution eliminating TCP serialization, columnar compressed storage with zonemaps, vectorized execution, morsel-driven parallelism, and snapshot isolation with optimistic MVCC. In-process execution avoids the overhead of encoding query results into wire protocols and decoding them on client machines—a process that often exceeds query execution time on large result sets.