With this option set your query will run with the currently cached query plan and the statistics will be updated in the background. Once the statistics update is complete the next query executed will generate and use a new query plan using the updated statistics. Determining whether statistics are out of date is a really difficult question to answer.
There are many different factors that can affect table, index or column statistics and cause them to generate poor performing query plans. You could have a table with many inserts, updates or deletes but depending on which values were affected it might not have much if any impact on the statistics.
The opposite could also be true. If queries are only looking for new values e. I guess to answer the question, how to tell if statistics are out of date, the best answer one can give is you need to understand how DML queries access your tables and base your decision on that information. With that said we can use the sys. Below is the TSQL for this query as well as sample output. Employee JobTitle ;. Related Articles. How to get index usage information in SQL Server.
Heap Tables. Popular Articles. The predicate cannot use the Bikes category name because it is stored in the Production. ProductCategory table, and all columns in the filter expression must be in the same table. The Query Optimizer can use the BikeWeights filtered statistics to improve the query plan for the following query that selects all of the bikes that weigh more than If an error or other event prevents the Query Optimizer from creating statistics, the Query Optimizer creates the query plan without using statistics.
The Query Optimizer marks the statistics as missing and attempts to regenerate the statistics the next time the query is executed. Missing statistics are indicated as warnings table name in red text when the execution plan of a query is graphically displayed using SQL Server Management Studio.
When statistics on a read-only database or read-only snapshot are missing or stale, the Database Engine creates and maintains temporary statistics in tempdb. Scripts for the temporary statistics can be created and reproduced on a read-write database. Only SQL Server can create and update temporary statistics. However, you can delete temporary statistics and monitor statistics properties using the same tools that you use for permanent statistics:.
Because temporary statistics are stored in tempdb , a restart of the SQL Server service causes all temporary statistics to disappear. The Query Optimizer determines when statistics might be out-of-date and then updates them when they are needed for a query plan. Updating statistics ensures that queries compile with up-to-date statistics. However, updating statistics causes queries to recompile. We recommend not updating statistics too frequently because there is a performance tradeoff between improving query plans and the time it takes to recompile queries.
The specific tradeoffs depend on your application. To determine when statistics were last updated, use the sys. If query response times are slow or unpredictable, ensure that queries have up-to-date statistics before performing additional troubleshooting steps. Insert operations append new values to ascending or descending columns.
The number of rows added might be too small to trigger a statistics update. If statistics are not up-to-date and queries select from the most recently added rows, the current statistics will not have cardinality estimates for these new values.
This can result in inaccurate cardinality estimates and slow query performance. For example, a query that selects from the most recent sales order dates will have inaccurate cardinality estimates if the statistics are not updated to include cardinality estimates for the most recent sales order dates.
Consider updating statistics after performing maintenance procedures that change the distribution of data, such as truncating a table or performing a bulk insert of a large percentage of the rows.
This can avoid future delays in query processing while queries wait for automatic statistics updates. Operations such as rebuilding, defragmenting, or reorganizing an index do not change the distribution of data.
Leverage solutions such as Adaptive Index Defrag to automatically manage index defragmentation and statistics updates for one or more databases. This procedure automatically chooses whether to rebuild or reorganize an index according to its fragmentation level, amongst other parameters, and update statistics with a linear threshold.
Certain query implementations, such as local variables and complex expressions in the query predicate, can lead to suboptimal query plans. Following query design guidelines for using statistics effectively can help to avoid this. You can improve query plans by applying query design guidelines that use statistics effectively to improve cardinality estimates for expressions, variables, and functions used in query predicates.
When the Query Optimizer does not know the value of an expression, variable, or function, it does not know which value to lookup in the histogram and therefore cannot retrieve the best cardinality estimate from the histogram.
Instead, the Query Optimizer bases the cardinality estimate on the average number of rows per distinct value for all of the sampled rows in the histogram. This leads to suboptimal cardinality estimates and can hurt query performance. For more information about histograms, see histogram section in this page or sys. The following guidelines describe how to write queries to improve query plans by improving cardinality estimates.
Whenever possible, simplify expressions with constants in them. The Query Optimizer does not evaluate all functions and expressions containing constants prior to determining cardinality estimates. For example, simplify the expression ABS to If the expression uses multiple variables, consider creating a computed column for the expression and then create statistics or an index on the computed column.
If the query predicate uses a local variable, consider rewriting the query to use a parameter instead of a local variable. The value of a local variable is not known when the Query Optimizer creates the query execution plan.
When a query uses a parameter, the Query Optimizer uses the cardinality estimate for the first actual parameter value that is passed to the stored procedure.
Consider using a standard table or temporary table to hold the results of multi-statement table-valued functions mstvf.
The Query Optimizer does not create statistics for multi-statement table-valued functions. With this approach the Query Optimizer can create statistics on the table columns and use them to create a better query plan.
Consider using a standard table or temporary table as a replacement for table variables. The Query Optimizer does not create statistics for table variables. In most cases this is very minimal and you will not notice this, that is why this is the default behavior. You will receive results in a format like the screenshot below. A value of 1 is enabled and 0 is disabled. It depends on the specifics of your installation, on the table sizes and number of indexes, the frequency of data change, the queries run, etc You should weigh the pros and cons of this option before changing your current settings.
The benefits and disadvantages can be unpredictable if your server mixes OLTP and data warehousing functions. Also, I recommend always running tests before you change something on your production environment.
Related Articles. How to get index usage information in SQL Server. For more information, see Configure the max degree of parallelism Server Configuration Option. The maximum is 64 processors. Identified for informational purposes only. Not supported. Future compatibility is not guaranteed. Leverage solutions such as Adaptive Index Defrag to automatically manage index defragmentation and statistics updates for one or more databases.
This procedure automatically chooses whether to rebuild or reorganize an index according to its fragmentation level, amongst other parameters, and update statistics with a linear threshold. The following example updates the statistics for all indexes on the SalesOrderDetail table. The following example creates and then updates the statistics for the Name and ProductNumber columns in the Product table.
The following example updates the Products statistics in the Product table, forces a full scan of all rows in the Product table, and turns off automatic statistics for the Products statistics. The following example updates the CustomerStats1 statistics on the Customer table.
The following example updates the CustomerStats1 statistics, based on scanning all of the rows in the Customer table.
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