How Stale Are Sql Server Table Statistics

SQL Programming

I have always been fascinated by the inner workings of databases, and one aspect that has particularly caught my attention is the staleness of SQL Server table statistics. As a database enthusiast, I find it crucial to understand the impact of outdated statistics on query performance and optimization.

So, how stale can SQL Server table statistics really be? Let’s dive into the details to uncover the answer.

Understanding Table Statistics

Before we discuss the staleness of table statistics, let’s first grasp what statistics are and how they contribute to query optimization. In SQL Server, statistics are objects that contain information about the distribution and density of column values in a table or indexed view.

When the SQL Server query optimizer generates an execution plan, it relies on table statistics to estimate the number of rows that will be accessed or returned. These estimations play a crucial role in determining the most efficient way to execute a query.

The Impact of Stale Statistics

Stale statistics occur when the data distribution in a table changes significantly, but the statistics haven’t been updated to reflect this change. As a result, the optimizer may make inaccurate estimations, leading to suboptimal query plans and degraded performance.

Imagine a scenario where you have a table containing customer information. Initially, the table statistics accurately reflect the distribution of ages among your customers. However, as time passes, new customers join, and the age distribution changes. If the table statistics are not updated, the query optimizer may still rely on old information and make incorrect assumptions about the data distribution.

The consequence of relying on stale statistics can range from slightly slower query performance to severe performance degradation. In some cases, it can even lead to the execution of inefficient query plans, causing disk IO and resource consumption to skyrocket.

Determining Staleness

Now that we understand the impact of stale statistics, how can we determine their level of staleness? SQL Server provides a useful DMV (Dynamic Management View) called sys.dm_db_stats_properties that allows us to check the modification counter and the last update time for each statistic.

By querying this DMV, we can compare the modification counter against the number of rows in the table to get an idea of how many modifications have occurred since the last statistic update. This information helps us assess the level of staleness and decide whether it’s necessary to refresh the statistics.

Refreshing Table Statistics

Fortunately, SQL Server provides multiple ways to refresh table statistics. The most common methods include using the UPDATE STATISTICS statement or enabling the auto-update statistics database option.

The UPDATE STATISTICS statement allows you to update statistics on a specific table or a specific index within a table. You can choose to update all statistics or selectively update only those that are most crucial to query optimization.

On the other hand, enabling the auto-update statistics option ensures that SQL Server automatically updates statistics for a table when a threshold of modifications or data changes is reached. This option can be set at the database level or the table level, providing flexibility in managing statistics updates.


Table statistics play a vital role in optimizing query performance in SQL Server. Understanding the staleness of these statistics is crucial for maintaining efficient execution plans and avoiding performance degradation.

By regularly monitoring and refreshing table statistics, you can ensure that the optimizer has accurate information about the data distribution in your tables. This, in turn, leads to better query plans and improved overall performance.

So, next time you encounter performance issues with your SQL Server database, don’t forget to consider the staleness of table statistics as a possible culprit. Keeping them up-to-date might just be the key to unlocking optimal query execution.