Optimizer estimates and statistics

The optimizer chooses a strategy for processing a statement based on column statistics stored in the database and on heuristics (educated guesses). For each access plan considered by the optimizer, an estimated result size (number of rows) must be computed. For example, for each join method or index access based on the selectivity estimations of the predicates used in the query, an estimated result size is calculated. The estimated result sizes are used to compute the estimated disk access and CPU cost for each operator such as a join method, a group by method, or a sequential scan, used in the plan. Column statistics are the primary data used by the optimizer to compute selectivity estimation of predicates. Therefore, they are vital to estimating correctly the cost of an access plan.

If column statistics become stale, or are missing, performance can degrade since inaccurate statistics may result in an inefficient execution plan. If you suspect that poor performance is due to inaccurate column statistics, you should recreate them.

How the optimizer uses 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%.

Unlike base tables, procedure calls executed in the FROM clause do not have column statistics. Therefore, the optimizer uses defaults or guesses for all selectivity estimates on data coming from a procedure call. The execution time of a procedure call, and the total number of rows in its result set, are estimated using statistics collected from previous calls. These statistics are maintained in the stats column of the ISYSPROCEDURE system table.

How the optimizer uses heuristics

For each table in a potential execution plan, the optimizer estimates the number of rows that will form part of the results. The number of rows depends on the size of the table and the restrictions in the WHERE clause or the ON clause of the query.

Given the histogram on a column, SQL Anywhere estimates the number of rows satisfying a given query predicate on the column by adding up the number of rows in all value ranges that overlap the values satisfying the specified predicate. For value ranges in the histograms that are partially contained in the query result set, SQL Anywhere uses interpolation within the value range.

Often, the optimizer uses more sophisticated heuristics. For example, the optimizer only uses default estimates when better statistics are unavailable. As well, the optimizer makes use of indexes and keys to improve its guess of the number of rows. The following are a few single-column examples:

Equating a column to a value: estimate one row when the column has a unique index or is the primary key.
A comparison of an indexed column to a constant: probe the index to estimate the percentage of rows that satisfy the comparison.
Equating a foreign key to a primary key (key join): use relative table sizes in determining an estimate. For example, if a 5000 row table has a foreign key to a 1000 row table, the optimizer guesses that there are five foreign key rows for each primary key row.

Optimizer estimates and statistics

How the optimizer uses statistics

How the optimizer uses heuristics

See also