Optimizing query performance involving correlated columns in SingleStore` is a nuanced topic that highlights the importance of understanding the size and distribution of table data, as well as the impact of correlated columns on selectivity estimates. The use of correlation statistics, such as Cramer's V statistic, can significantly improve the efficiency of query plans by fine-tuning how the optimizer combines the selectivity of single-column filters when using histogram estimation. By recognizing the strength of association between two categorical variables and setting an appropriate correlation coefficient, users can tailor the optimizer's behavior to suit the specific relationships between columns, ultimately leading to better overall database performance. The optimization techniques discussed in this article demonstrate SingleStore's robust query optimizer and its ability to seamlessly navigate the complexities of correlated columns and data distribution.