Liquid clustering is a technique for Delta Lake tables that serves as an alternative to partitioning and Z-ordering. Historically, optimizing Delta tables for read performance required defining partition and Z-order keys at table creation based on anticipated query patterns. Modifying these keys later necessitates a full data rewrite. In contrast, clustering employs a tree-based algorithm to cluster data based on designated keys, which can be incrementally changed without rewriting all data. This provides greater flexibility to support diverse query patterns, thereby reducing compute costs and enhancing read performance. Furthermore, the Databricks Runtime for Delta Lake supports automatic liquid clustering by analyzing historical query workloads, identifying optimal columns, and clustering data accordingly. Both standalone Delta Lake and Databricks Runtime users can leverage liquid clustering to optimize read performance.
