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Adaptive Factorization in DuckDB with Paul Groß

In this episode of the DuckDB in Research series, host Jack Waudby talks with Paul Groß, PhD student at CWI Amsterdam, about his groundbreaking work on adaptive factorization, linear chained hash tables, and worst-case optimal joins in DuckDB. We explore how decades of database theory are being integrated into modern analytical systems, making DuckDB a perfect example of research meeting real-world engineering. Paul explains how his CIDR 2025 paper “Adaptive Factorization Using Linear Chained Hash Tables” helps make analytical queries faster and more efficient — and how these ideas are reshaping data systems research. Topics covered: What is adaptive factorization in DuckDB? How worst-case optimal joins work (and why they matter) Bridging research and production in database systems The challenges of query planning and optimization Insights into DuckDB’s architecture and developer experience Subscribe for more conversations at the intersection of research and industry — from query optimisation and system design to open-source innovation. #DuckDB #DatabaseResearch #QueryOptimization #DataEngineering #AnalyticalDatabases #DisseminatePodcast #DuckDBinResearch #DatabaseSystems #OpenSourceDatabase #AdaptiveFactorization #HashTables #WorstCaseOptimalJoins #CWIAmsterdam #DataScience #SQL #TechPodcast #AcademicResearch #SystemsEngineering #DuckDBTutorial #ResearchToProduction