DuckLake Performance Optimization: Faster Queries From Your Lakehouse

​DuckLake works well out of the box. Getting faster queries as your lakehouse grows mostly comes down to reading less data: skipping files you don't need, skipping row groups within the files you do read, and writing Parquet in a way that suits your queries.

​Alex Monahan and Matt Martin, co-authors of O'Reilly's DuckLake: The Definitive Guide, walk through the book's new Performance Optimization chapter and take your questions live. Matt has been partitioning and clustering large fact tables for over a decade. Alex's first major DuckLake contribution was clustering, and he calls it his favorite optimization.

​You'll learn how to:

• ​Find the bottleneck first with metadata logging, EXPLAIN ANALYZE, and JSON query profiles (and why handing those profiles to an LLM works well)

• ​Pick partition keys and sort orders so queries skip whole files and row groups, and spot when bucket partitioning works against you

• ​Sort on two columns at once with space-filling curves, something the Iceberg spec doesn't allow

• ​Tune compression, file size, and row group size together instead of one at a time

• ​Keep the catalog and storage lean with inlining, compaction, snapshot expiration, and cleanup, and know which of these actually make queries faster