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SQL Anywhere 11.0.1 » SQL Anywhere Server - SQL Usage » Query Processing » Query optimization and execution » How the optimizer works » Optimizer estimates and column statistics


How the optimizer uses column statistics

The most important component of the column statistics used by the optimizer are histograms. Histograms store information about the distribution of values in a column. In SQL Anywhere, a histogram represents the data distribution for a column by dividing the domain of the column into a set of consecutive value ranges (also called buckets) and by remembering, for each value range (or bucket), the number of rows in the table for which the column value falls in the bucket.

SQL Anywhere pays particular attention to single column values that are present in a large number of rows in the table. Significant single value selectivities are maintained in singleton histogram buckets (for example, buckets that encompass a single value in the column domain). SQL Anywhere tries to maintain a minimum number of singleton buckets in each histogram, usually between 10 and 100 depending upon the size of the table. Additionally, all single values with selectivities greater than 1% are kept as singleton buckets. As a result, a histogram for a given column remembers the top N single value selectivities for the column where the value of N is dependent upon the size of the table and the number of single value selectivities that are greater than 1%.

Once the minimum number of value ranges has been met, low-selectivity frequencies are replaced by large-selectivity frequencies as they come along. The histogram will only have more than the minimum number of singleton value ranges after it has seen enough values with a selectivity of greater than 1%.

For more information about column statistics, see SYSCOLSTAT system view.