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SQL queries, with the AND, OR, and NOT operators, constitute a broad class ofhighly used queries. Thus, their cardinality estimation is important for queryoptimization. In addition, a query planner requires the set-theoreticcardinality (i.e., without duplicates) for queries with DISTINCT as well as inplanning; for example, when considering sorting options. Yet, despite theimportance of estimating query cardinalities in the presence of DISTINCT, AND,OR, and NOT, many cardinality estimation methods are limited to estimatingcardinalities of only conjunctive queries with duplicates counted.  The focus of this work is on two methods for handling this deficiency thatcan be applied to any limited cardinality estimation model. First, we describea specialized deep learning scheme, PUNQ, which is tailored to representingconjunctive SQL queries and predicting the percentage of unique rows in thequery's result with duplicate rows. Using the predicted percentages obtainedvia PUNQ, we are able to transform any cardinality estimation method that onlyestimates for conjunctive queries, and which estimates cardinalities withduplicates (e.g., MSCN), to a method that estimates queries cardinalitieswithout duplicates. This enables estimating cardinalities of queries with theDISTINCT keyword. In addition, we describe a recursive algorithm, GenCrd, forextending any cardinality estimation method M that only handles conjunctivequeries to one that estimates cardinalities for more general queries (thatinclude AND, OR, and NOT), without changing the method M itself.  Our evaluation is carried out on a challenging, real-world database withgeneral queries that include either the DISTINCT keyword or the AND, OR, andNOT operators. Experimentally, we show that the proposed methods obtainaccurate cardinality estimates with the same level of accuracy as that of theoriginal transformed methods.

NN-based Transformation of Any SQL Cardinality Estimator for Handling DISTINCT, AND, OR and NOT