MotherDuck Research
Advancing the future of data + AI.
MotherDuck: DuckDB in the Cloud and in the Client
We describe and demo MotherDuck: a new service that connects DuckDB to the cloud. MotherDuck provides the concept of hybrid query processing: the ability to execute queries partly on the client and partly in the cloud.
Results on Results: Building New Results from Cached Partial Results
An intelligent recovery framework for real-time SQL previews. When composing partial cached results produces too few rows, a Data Lineage heuristic selects the cheapest upstream dependency to re-fetch — enabling fluid exploratory analysis in MotherDuck's hybrid architecture.
Cost-Based Hybrid Query Optimization in MotherDuck
This thesis tackles a core challenge in MotherDuck's hybrid execution model: deciding which parts of a query should run locally vs. in the cloud. The result is a cost-based optimizer that delivers up to 17x speedups on long-running analytical queries.
Query-Log-Informed Schema Descriptions and their Impact on Text-to-SQL
Automatically generating schema documentation from historical query logs to improve LLM-powered Text-to-SQL. Tested on both the BIRD benchmark and MotherDuck’s production data warehouse, query pattern descriptions boost SQL generation accuracy by up to 16% on real-world data.
Declarative Caching in MotherDuck
This thesis introduces Accelerated Approximate Views — a new SQL-level caching mechanism for MotherDuck’s hybrid execution model. By partially materializing query results on the client, AAVs reduce latency for interactive data exploration in both native and WebAssembly environments.