How do column statistics improve query performance?

Column statistics significantly enhance query performance by providing essential information about the distribution of data within specific columns. By maintaining an overview of how much data exists within ranges of column values, the query optimizer can make more informed decisions when formulating execution plans.

When the optimizer understands the data distribution, it can accurately estimate the number of rows that will be returned for a query. This capability allows it to choose the most efficient execution plan, like selecting appropriate indexes or deciding on join strategies. For instance, if a particular value in a column is highly skewed (where a small number of distinct values occur very frequently), the optimizer can avoid scanning large swaths of data unnecessarily, thus speeding up query performance.

The other options do not capture the core function of column statistics effectively. Keeping track of which columns are being queried offers some insight but does not specifically relate to how data distribution impacts performance. Caching column values could theoretically enhance performance but is more about memory management than statistics. Optimizing the execution plan is the end result of insights gained from statistics rather than a function of statistics themselves. Thus, the focus on data ranges and their cardinality is what makes column statistics a vital tool for improving the processing speed of queries.