{"ID":5551699,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-04T12:32:05.014436432Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00868","arxiv_id":"2607.00868","title":"From Single to Multiple Attributes: Experimental Insights on Sampling-Based Distinct Combination Estimation in GROUP-BY Queries","abstract":"Estimating the number of distinct combinations in multi-attribute GROUP-BY queries remains a significant yet underexplored challenge. Current cardinality estimation techniques primarily focus on SPJ queries (i.e., selections, projections, and joins) and neglect GROUP-BY operations; meanwhile, distinct value estimation research has mainly targeted the single-attribute setting. Although sampling-based methods, including recent approaches with learned models, can theoretically support multi-attribute estimation, their practical effectiveness remains unclear. A comprehensive empirical evaluation is thus lacking to address whether joint distribution information from samples alone is sufficient for accurate multi-attribute estimation, whether existing methods fully exploit single-attribute information and can be further optimized, and whether filtered GROUP-BY queries can be accurately estimated. To this end, we propose a specialized workload generator for multi-attribute GROUP-BY queries and generate both filtered and non-filtered queries over four real-world datasets. By evaluating existing methods across synthetic workloads and the multi-table TPC-H benchmark, we analyze the sources of GROUP-BY cardinality estimation errors and their impact on PostgreSQL's plan selection, offering key recommendations for future estimator design.","short_abstract":"Estimating the number of distinct combinations in multi-attribute GROUP-BY queries remains a significant yet underexplored challenge. Current cardinality estimation techniques primarily focus on SPJ queries (i.e., selections, projections, and joins) and neglect GROUP-BY operations; meanwhile, distinct value estimation...","url_abs":"https://arxiv.org/abs/2607.00868","url_pdf":"https://arxiv.org/pdf/2607.00868v1","authors":"[\"Yujie Zhang\",\"Xiaochun Yang\",\"Bin Wang\",\"Yuan Sui\"]","published":"2026-07-01T12:33:00Z","proceeding":"cs.DB","tasks":"[\"cs.DB\"]","methods":"[]","has_code":false}