The threshold is different whether a table is temporary or permanent. Starting with SQL Server With this change, statistics on large tables will be updated more frequently. However, if a database has a compatibility level below , then the SQL Server This means the statistics will be updated every 44, modifications.
While recommended for all scenarios, enabling the trace flag is optional. However, you can use the following guidance for enabling the trace flag in your pre-SQL Server The Query Optimizer checks for out-of-date statistics before compiling a query and before executing a cached query plan. Before compiling a query, the Query Optimizer uses the columns, tables, and indexed views in the query predicate to determine which statistics might be out-of-date.
Before executing a cached query plan, the Database Engine verifies that the query plan references up-to-date statistics. This option also applies to filtered statistics. You can use the sys. By default, the asynchronous statistics update option is OFF, and the Query Optimizer updates statistics synchronously. With synchronous statistics updates, queries always compile and execute with up-to-date statistics.
When statistics are out-of-date, the Query Optimizer waits for updated statistics before compiling and executing the query. With asynchronous statistics updates, queries compile with existing statistics even if the existing statistics are out-of-date. The Query Optimizer could choose a suboptimal query plan if statistics are out-of-date when the query compiles.
Statistics are typically updated soon thereafter. Queries that compile after the stats updates complete will benefit from using the updated statistics. Consider using synchronous statistics when you perform operations that change the distribution of data, such as truncating a table or performing a bulk update of a large percentage of the rows. If you do not manually update the statistics after completing the operation, using synchronous statistics will ensure statistics are up-to-date before executing queries on the changed data.
Consider using asynchronous statistics to achieve more predictable query response times for the following scenarios:. Your application frequently executes the same query, similar queries, or similar cached query plans. Your query response times might be more predictable with asynchronous statistics updates than with synchronous statistics updates because the Query Optimizer can execute incoming queries without waiting for up-to-date statistics. This avoids delaying some queries and not others.
Your application has experienced client request time outs caused by one or more queries waiting for updated statistics. In some cases, waiting for synchronous statistics could cause applications with aggressive time outs to fail.
Asynchronous statistics update is performed by a background request. When the request is ready to write updated statistics to the database, it attempts to acquire a schema modification lock on the statistics metadata object. If a different session is already holding a lock on the same object, asynchronous statistics update is blocked until the schema modification lock can be acquired. Similarly, sessions that need to acquire a schema stability Sch-S lock on the statistics metadata object to compile a query may be blocked by the asynchronous statistics update background session, which is already holding or waiting to acquire the schema modification lock.
Therefore, for workloads with very frequent query compilations and frequent statistics updates, using asynchronous statistics may increase the likelihood of concurrency issues due to lock blocking.
With this configuration enabled, the background request will wait to acquire the schema modification Sch-M lock and persist the updated statistics on a separate low-priority queue, allowing other requests to continue compiling queries with existing statistics.
Once no other session is holding a lock on the statistics metadata object, the background request will acquire its schema modification lock and update statistics. In the unlikely event that the background request cannot acquire the lock within a timeout period of several minutes, the asynchronous statistics update will be aborted, and the statistics will not be updated until another automatic statistics update is triggered, or until statistics are updated manually.
The default is OFF. When new partitions are added to a large table, statistics should be updated to include the new partitions.
Also, scanning the entire table isn't necessary because only the statistics on the new partitions might be needed. The incremental option creates and stores statistics on a per partition basis, and when updated, only refreshes statistics on those partitions that need new statistics.
If per partition statistics are not supported the option is ignored and a warning is generated. Incremental stats are not supported for following statistics types:. The Query Optimizer creates statistics for indexes on tables or views when the index is created.
These statistics are created on the key columns of the index. If the index is a filtered index, the Query Optimizer creates filtered statistics on the same subset of rows specified for the filtered index.
Instead, the Query Optimizer uses the default sampling algorithm to generate statistics. After upgrading a database with partitioned indexes, you may notice a difference in the histogram data for these indexes. This change in behavior may not affect query performance. These additional statistics can capture statistical correlations that the Query Optimizer does not account for when it creates statistics for indexes or single columns.
Your application might have additional statistical correlations in the table data that, if calculated into a statistics object, could enable the Query Optimizer to improve query plans. For example, filtered statistics on a subset of data rows or multicolumn statistics on query predicate columns might improve the query plan. When a query predicate contains multiple columns that have cross-column relationships and dependencies, statistics on the multiple columns might improve the query plan.
Statistics on multiple columns contain cross-column correlation statistics, called densities , that are not available in single-column statistics. Densities can improve cardinality estimates when query results depend on data relationships among multiple columns. If the columns are already in the same index, the multicolumn statistics object already exists and it is not necessary to create it manually. It requires more system resources to maintain an index than a statistics object.
If the application does not require the multicolumn index, you can economize on system resources by creating the statistics object without creating the index. When creating multicolumn statistics, the order of the columns in the statistics object definition affects the effectiveness of densities for making cardinality estimates.
The statistics object stores densities for each prefix of key columns in the statistics object definition. For more information about densities, see Density section in this page. To create densities that are useful for cardinality estimates, the columns in the query predicate must match one of the prefixes of columns in the statistics object definition. For example, the following example creates a multicolumn statistics object on the columns LastName , MiddleName , and FirstName.
The density is not available for LastName, FirstName. If the query uses LastName and FirstName without using MiddleName , the density is not available for cardinality estimates. When the Query Optimizer creates statistics for single columns and indexes, it creates the statistics for the values in all rows.
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Could there be a situation where a duplicate single-column stat is picked over the corresponding index in query plan generation? Also, how can one calculate the size footprint of these duplicate stats in the database? Being able to get this value would strengthen the argument for dumping those.
Is it a correct value? Thanks for reading! For other statistics e. And it would also depend on the number of rows in the table. I would update statistics after restoring from a backup of a lower version. This is a particularly important when going from SQL to a higher version, as there were changes in the stats blog, but even for later versions I tend to recommend it.
Hey Erin, I was just trying to figure out how two indexes on a table had a percent change over percent yet the statistics did not recompute via autostats. If you step back from the forest and give it some thought the concept makes total sense. To avoid the time to recompute the stats and risk a bad plan turn this switch ON. Reason : The statistics were not uptodate. So I tried to make a script of my own to update statistics if necessary.
Logic : First thing I do is retrieve all views and tables in the database and the amount of records. You should write a post about it! If those stats are auto-created and are not tied to indexes, then I would hope they would be used at some point, and I would probably still update them. However, I would also consider not updating them and then monitoring them over time to see if the auto-update occurs. Tracking stats usage is a lot harder than tracking index usage.
Great Post Erin. DB has auto update and auto create stats. I thought, in step 4, it should update the statistics. So, I think the stats were not used?? I could not comprehend completely here. Did you have an index on this table?
I could try and repro, but would need the entire code. Hey Erin, This is my personal observation, obviously your vast experience may be different. Coming to point Carles Nualart raised, how will optimizer find time to update stats will full resample for very large table at query run time? Just yesterday I was trying to fix slow query problem in a large partitioned table, and looked at estimated plan where optimizer though this particular partition in this table has only I will try to replicate some of this scenario and send you my POC for your valued feedback.
You are correct that on larger tables, this sample rate gets even smaller. There is no way to change that sample size unless you explicitly specify it which you cannot do with auto-update. This lowers the threshold at which stats are updated for very large tables. It samples a certain number of pages and however long it takes to read those pages, it takes… Once the stat has been updated, then the query will be compiled and run.
When statistics are captured, they are a representation of data in the column. I always recommend proactively updating statistics with a job. Whether you look for a percentage of rows modified or something else e. Now, the majority of the time, the default sample works well.
All the rest use the default sample. When the transaction that has the statistics update in it meaning, the statistics blob in the system table was updated is shipped over to the secondary and applied, then the statistics will be updated on the secondary.
You might see that both queries will load the index statistics when they will be executed first. A second execution will not look for the stats as expected. Keep on writing — I love your blog posts about stats! We encountered a strange problem running update statistics with full scan on our SQL Server After update statistics completed , the server restarted by itself with logging anything in the event log.
Is this behavior expected after the statistics are done? That is not expected behavior. Surprisingly after the update statement the stats did not auto update. Hope that helps. I think I should had framed my question better. The thing that is confusing me is that why does sys. Also in your comments you mentioned about TF I ran this update with TF
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