Month: May 2018

Importance of Statistics in SQL Server

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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… Continue Reading

Query Predicates in SQL Server

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Sometimes people speak “SQL” and expect you know all the terminology. In recent a conversation about query plans I was ranting on and on about how the predicates were written when the person stopped me and asked a very simple question. Like a light bulb going off, it reminded me that not everyone knows what the other person is talking about when terminology is mentioned without clarification. So, this post will answer that persons very valid simple question. What is a SQL Predicate? Predicates are expressions that evaluate to TRUE, FALSE, UNKNOWN. There are two types of predicates Filtered Predicates… Continue Reading

5 Ways to Examine Cardinality Metrics

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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… Continue Reading