In SQL Server, table statistics are metadata objects that store information about the data distribution within one or more columns of a table. These statistics are crucial for the query optimizer, which uses them to estimate the number of rows that a query’s predicates will return. This estimation, known as cardinality estimation, is the foundation of a good execution plan. For example, if a query filters on a column with a skewed data distribution (i.e., some values appear much more frequently than others), the optimizer can use statistics to choose a more efficient access method, such as a clustered index scan over a non-clustered index seek, avoiding a costly lookup operation.
As a DBA, I believe keeping statistics up to date is paramount for maintaining optimal query performance. Stale statistics, which don’t accurately reflect changes in the underlying data (e.g., due to frequent INSERT, UPDATE, or DELETE operations), can lead the optimizer to make poor cardinality estimates. This results in inefficient execution plans that could use more system resources and run longer than necessary. Regularly updating statistics either manually with UPDATE STATISTICS or by allowing the database engine’s automatic update feature (AUTO_UPDATE_STATISTICS) to do so ensures the query optimizer has the most accurate information available. This proactive maintenance helps prevent performance degradation and ensures that queries continue to run efficiently as the data in the database evolves.