Memory Management in DuckDB

DuckDB employs effective memory management strategies to optimize query processing while handling large datasets. The system utilizes a streaming execution engine that processes data in small chunks, allowing for larger-than-memory operations without fully materializing data in memory. This method is particularly efficient for simple queries, such as aggregations with a limited number of unique groups. However, for more complex queries that generate larger intermediate results, DuckDB implements a disk spilling mechanism, temporarily writing excess data to disk to prevent out-of-memory errors. The memory limit is adjustable, defaulting to 80% of the system's physical RAM, and can be configured alongside the temporary directory settings.

Additionally, DuckDB's buffer manager caches pages from persistent storage, optimizing memory usage by retaining frequently accessed data while evicting less critical information as needed. This dual approach of streaming execution and intermediate spilling, combined with a robust buffer management system, ensures efficient memory utilization. Users can monitor memory usage through built-in profiling tools, which provide insights into memory allocation across various components. Overall, DuckDB's memory management is designed to enhance performance while accommodating the challenges posed by larger-than-memory datasets, with ongoing improvements being made to support increasingly complex queries.