Following up on my last post about the Cardinality Estimator let’s talk about column statistics and how they work and play a part in execution plans. The cardinality estimator relies heavily on statistics to get the answer to selectivity (the ratio of distinct values to the total number of values) questions and calculate a cost estimate. This hopefully gives us the best possible execution plans for queries. In this post, I will show you where to find information about what your statistics contain and information regarding each of those fields. Then we will look at the impact of over and under estimations caused by stale or missing statistics (or even data…
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In SQL Server the query optimizer uses a cardinality estimator to answer data SELECTIVITY questions like the ones below. The optimizer uses this cardinality data, which relies heavy on statistics to get the answers and calculate a cost estimate. It takes the #of Rows to Satisfy a Predicate/Total # of Input Rows. How many rows will satisfy a single filter predicate? Multiple? WHERE How many rows will satisfy a join predicate between two tables? JOIN How many distinct values do we expect from a specific column? DISTINCT GROUP BY From this estimate the optimizer is able to find an efficient execution plan that fulfills a query request. You will note that…
