For partitioned tables, maintaining good clustering properties for frequently accessed dimensions is critical for partition pruning performance. Naive methods of clustering maintenance could be expensive, especially when the clustering dimensions are different from the dimensions with which the data is loaded. On the other hand, approximate clustering is cheaper to maintain while still resulting in good pruning performance. In this talk, I will present Snowflake's clustering capabilities, including our algorithm for incremental maintenance of approximate clustering of partitioned tables, as well as our infrastructure to perform such maintenance automatically. I will also cover some real-world problems we run into and our solutions.
Jiaqi Yan is a Software Engineer at Snowake Computing. His work focused on Snowake Databases' Query Engine. Before Snowake, he worked on Oracle's Optimizer team where he was a core developer for Oracle's In-memory Columnar Database product. Jiaqi graduated with a B.S.E. from Duke University