CREATE STATISTICS (Transact-SQL)

Raksts
05/22/2024

Applies to: SQL Server Azure SQL Database Azure SQL Managed Instance Azure Synapse Analytics SQL analytics endpoint in Microsoft Fabric Warehouse in Microsoft Fabric

Creates query optimization statistics on one or more columns of a table, an indexed view, or an external table. For most queries, the query optimizer already generates the necessary statistics for a high-quality query plan; in a few cases, you need to create extra statistics with CREATE STATISTICS or modify the query design to improve query performance.

To learn more, see Statistics.

Note

For more information on statistics in Microsoft Fabric, see Statistics in Fabric data warehousing.

Transact-SQL syntax conventions

Syntax

Syntax for SQL Server, Azure SQL Database, and Azure SQL Managed Instance.

-- Create statistics on an external table
CREATE STATISTICS statistics_name
ON { table_or_indexed_view_name } ( column [ , ...n ] )
    [ WITH FULLSCAN ] ;

-- Create statistics on a regular table or indexed view
CREATE STATISTICS statistics_name
ON { table_or_indexed_view_name } ( column [ , ...n ] )
    [ WHERE <filter_predicate> ]
    [ WITH
        [ FULLSCAN
            [ [ , ] PERSIST_SAMPLE_PERCENT = { ON | OFF } ]
          | SAMPLE number { PERCENT | ROWS }
            [ [ , ] PERSIST_SAMPLE_PERCENT = { ON | OFF } ]
          | <update_stats_stream_option> [ , ...n ]
        [ [ , ] NORECOMPUTE ]
        [ [ , ] INCREMENTAL = { ON | OFF } ]
        [ [ , ] MAXDOP = max_degree_of_parallelism ]
        [ [ , ] AUTO_DROP = { ON | OFF } ]
        ]
    ];

<filter_predicate> ::=
    <conjunct> [ AND <conjunct> ]

<conjunct> ::=
    <disjunct> | <comparison>

<disjunct> ::=
        column_name IN (constant , ...)

<comparison> ::=
        column_name <comparison_op> constant

<comparison_op> ::=
    IS | IS NOT | = | <> | != | > | >= | !> | < | <= | !<

<update_stats_stream_option> ::=
    [ STATS_STREAM = stats_stream ]
    [ ROWCOUNT = numeric_constant ]
    [ PAGECOUNT = numeric_contant ]

Syntax for Azure Synapse Analytics and Analytics Platform System (PDW).

CREATE STATISTICS statistics_name
    ON { database_name.schema_name.table_name | schema_name.table_name | table_name }
    ( column_name  [ , ...n ] )
    [ WHERE <filter_predicate> ]
    [ WITH {
           FULLSCAN
           | SAMPLE number PERCENT
      }
    ]
[ ; ]

<filter_predicate> ::=
    <conjunct> [ AND <conjunct> ]

<conjunct> ::=
    <disjunct> | <comparison>

<disjunct> ::=
        column_name IN (constant , ...)

<comparison> ::=
        column_name <comparison_op> constant

<comparison_op> ::=
    IS | IS NOT | = | <> | != | > | >= | !> | < | <= | !<

Syntax for Microsoft Fabric.

CREATE STATISTICS statistics_name
    ON { database_name.schema_name.table_name | schema_name.table_name | table_name }
    ( column_name )
    [ WITH {
           FULLSCAN
           | SAMPLE number PERCENT
      }
    ]
[ ; ]

Arguments

statistics_name

The name of the statistics to create.

table_or_indexed_view_name

The name of the table, indexed view, or external table on which to create the statistics. To create statistics on another database, specify a qualified table name.

column [ ,...n ]

One or more columns to be included in the statistics. The columns should be in priority order from left to right. Only the first column is used for creating the histogram. All columns are used for cross-column correlation statistics called densities.

You can specify any column that can be specified as an index key column with the following exceptions:

xml, full-text, and FILESTREAM columns can't be specified.
Computed columns can be specified only if the ARITHABORT and QUOTED_IDENTIFIER database settings are ON.
CLR user-defined type columns can be specified if the type supports binary ordering. Computed columns defined as method invocations of a user-defined type column can be specified if the methods are marked deterministic.

WHERE <filter_predicate>

Specifies an expression for selecting a subset of rows to include when creating the statistics object. Statistics that are created with a filter predicate are called filtered statistics. The filter predicate uses simple comparison logic and can't reference a computed column, a UDT column, a spatial data type column, or a hierarchyID data type column. Comparisons using NULL literals aren't allowed with the comparison operators. Use the IS NULL and IS NOT NULL operators instead.

Here are some examples of filter predicates for the Production.BillOfMaterials table:

WHERE StartDate > '20000101' AND EndDate <= '20000630'
WHERE ComponentID IN (533, 324, 753)
WHERE StartDate IN ('20000404', '20000905') AND EndDate IS NOT NULL

For more information about filter predicates, see Create filtered indexes.

FULLSCAN

Applies to: SQL Server 2016 (13.x) SP 1 CU 4, SQL Server 2017 (14.x) CU 1, and later versions

Compute statistics by scanning all rows. FULLSCAN and SAMPLE 100 PERCENT have the same results. FULLSCAN can't be used with the SAMPLE option.

When omitted, SQL Server uses sampling to create the statistics, and determines the sample size that is required to create a high quality query plan.

In Warehouse in Microsoft Fabric, only single-column FULLSCAN and single-column SAMPLE-based statistics are supported. When no option is included, SAMPLE statistics are created.

SAMPLE number { PERCENT | ROWS }

Specifies the approximate percentage, or number of rows, in the table or indexed view for the query optimizer to use when it creates statistics. For PERCENT, number can be from 0 through 100 and for ROWS, number can be from 0 to the total number of rows. The actual percentage or number of rows the query optimizer samples might not match the percentage or number specified. For example, the query optimizer scans all rows on a data page.

SAMPLE is useful for special cases in which the query plan, based on default sampling, isn't optimal. In most situations, it's not necessary to specify SAMPLE because the query optimizer already uses sampling and determines the statistically significant sample size by default, as required to create high-quality query plans.

SAMPLE can't be used with the FULLSCAN option. When SAMPLE or FULLSCAN aren't specified, the query optimizer uses sampled data and computes the sample size by default.

We recommend against specifying 0 PERCENT or 0 ROWS. When 0 PERCENT or 0 ROWS is specified, the statistics object is created, but doesn't contain statistics data.

In Warehouse in Microsoft Fabric, only single-column FULLSCAN and single-column SAMPLE-based statistics are supported. When no option is included, FULLSCAN statistics are created.

PERSIST_SAMPLE_PERCENT = { ON | OFF }

When ON, the statistics retain the creation sampling percentage for subsequent updates that don't explicitly specify a sampling percentage. When OFF, statistics sampling percentage gets reset to default sampling in subsequent updates that don't explicitly specify a sampling percentage. The default is OFF.

Note

If the table is truncated, all statistics built on the truncated HoBT will revert to using the default sampling percentage.

STATS_STREAM = stats_stream

Identified for informational purposes only. Not supported. Future compatibility is not guaranteed.

NORECOMPUTE

Disable the automatic statistics update option, AUTO_STATISTICS_UPDATE, for statistics_name. If this option is specified, the query optimizer will complete any in-progress statistics updates for statistics_name and disable future updates.

To re-enable statistics updates, remove the statistics with DROP STATISTICS and then run CREATE STATISTICS without the NORECOMPUTE option.

Warning

If you disable automatic updating of statistics, it might prevent the Query Optimizer from picking optimal execution plans for queries that involve the table. You should use this option sparingly, and only by a qualified database administrator.

For more information about the AUTO_STATISTICS_UPDATE option, see ALTER DATABASE SET options. For more information about disabling and re-enabling statistics updates, see Statistics.

INCREMENTAL = { ON | OFF }

Applies to: SQL Server 2014 (12.x) and later versions

When ON, the statistics created are per partition statistics. When OFF, stats are combined for all partitions. The default is OFF.

If per partition statistics aren't supported, an error is generated. Incremental stats aren't supported for following statistics types:

Statistics created with indexes that aren't partition-aligned with the base table.
Statistics created on Always On readable secondary databases.
Statistics created on read-only databases.
Statistics created on filtered indexes.
Statistics created on views.
Statistics created on internal tables.
Statistics created with spatial indexes or XML indexes.

MAXDOP = max_degree_of_parallelism

Applies to: SQL Server 2016 (13.x) SP 2, SQL Server 2017 (14.x) CU 3, and later versions

Overrides the max degree of parallelism configuration option during the statistic operation. For more information, see Configure the max degree of parallelism (server configuration option). Use MAXDOP to limit the number of processors used in a parallel plan execution. The maximum is 64 processors.

max_degree_of_parallelism can be:

1: Suppresses parallel plan generation.
>1: Restricts the maximum number of processors used in a parallel index operation to the specified number.
0 (default): Uses the actual number of processors or fewer based on the current system workload.

update_stats_stream_option

Identified for informational purposes only. Not supported. Future compatibility is not guaranteed.

AUTO_DROP = { ON | OFF }

Applies to: SQL Server 2022 (16.x) and later versions, and Azure SQL Database, Azure SQL Managed Instance

Before SQL Server 2022 (16.x), if statistics are manually created by a user or third party tool on a user database, those statistics objects can block or interfere with schema changes the customer might desire.

Starting with SQL Server 2022 (16.x), the AUTO_DROP option is enabled by default on all new and migrated databases. The AUTO_DROP property allows the creation of statistics objects in a mode such that a subsequent schema change is not blocked by the statistic object, but instead the statistics are dropped as necessary. In this way, manually created statistics with AUTO_DROP enabled behave like autocreated statistics.

Note

Trying to set or unset the Auto_Drop property on auto-created statistics might raise errors. Auto-created statistics always uses auto drop. Some backups, when restored, might have this property set incorrectly until the next time the statistics object is updated (manually or automatically). However, auto-created statistics always behave like auto drop statistics. When restoring a database to SQL Server 2022 (16.x) from a previous version, it's recommended to execute sp_updatestats on the database, setting the proper metadata for the statistics AUTO_DROP feature.

For more information, see AUTO_DROP option.

Permissions

Requires one of these permissions:

ALTER TABLE
User is the table owner
Membership in the db_ddladmin fixed database role

Remarks

SQL Server can use tempdb to sort the sampled rows before building statistics.

Statistics for external tables

When creating external table statistics, SQL Server imports the external table into a temporary SQL Server table, and then creates the statistics. For samples statistics, only the sampled rows are imported. If you have a large external table, it's faster to use the default sampling instead of the full scan option.

When the external table is using DELIMITEDTEXT, CSV, PARQUET, or DELTA as data types, external tables only support statistics for one column per CREATE STATISTICS command.

Statistics with a filtered condition

Filtered statistics can improve query performance for queries that select from well-defined subsets of data. Filtered statistics use a filter predicate in the WHERE clause to select the subset of data that is included in the statistics.

When to use CREATE STATISTICS

For more information about when to use CREATE STATISTICS, see Statistics.

Reference dependencies for filtered statistics

The sys.sql_expression_dependencies catalog view tracks each column in the filtered statistics predicate as a referencing dependency. Consider the operations that you perform on table columns, before creating filtered statistics. You can't drop, rename, or alter the definition of a table column that is defined in a filtered statistics predicate.

Limitations

Updating statistics isn't supported on external tables. To update statistics on an external table, drop and re-create the statistics.
You can list up to 64 columns per statistics object.
The MAXDOP option isn't compatible with STATS_STREAM, ROWCOUNT, and PAGECOUNT options.
The MAXDOP option is limited by the Resource Governor workload group MAX_DOP setting, if used.
CREATE and DROP STATISTICS on external tables aren't supported in Azure SQL Database.

Examples

The Transact-SQL code samples in this article use the AdventureWorks2022 or AdventureWorksDW2022 sample database, which you can download from the Microsoft SQL Server Samples and Community Projects home page.

A. Use CREATE STATISTICS with SAMPLE number PERCENT

The following example creates the ContactMail1 statistics, using a random sample of 5 percent of the BusinessEntityID and EmailPromotion columns of the Person table of the AdventureWorks2022 database.

CREATE STATISTICS ContactMail1
    ON Person.Person (BusinessEntityID, EmailPromotion)
    WITH SAMPLE 5 PERCENT;

B. Use CREATE STATISTICS with FULLSCAN and NORECOMPUTE

The following example creates the NamePurchase statistics for all rows in the BusinessEntityID and EmailPromotion columns of the Person table and disables automatic recomputing of statistics.

CREATE STATISTICS NamePurchase
    ON AdventureWorks2022.Person.Person (BusinessEntityID, EmailPromotion)
    WITH FULLSCAN, NORECOMPUTE;

C. Use CREATE STATISTICS to create filtered statistics

The following example creates the filtered statistics ContactPromotion1. The Database Engine samples 50 percent of the data and then selects the rows with EmailPromotion equal to 2.

CREATE STATISTICS ContactPromotion1
    ON Person.Person (BusinessEntityID, LastName, EmailPromotion)
WHERE EmailPromotion = 2
WITH SAMPLE 50 PERCENT;
GO

D. Create statistics on an external table

The only decision you need to make when you create statistics on an external table, besides providing the list of columns, is whether to create the statistics by sampling the rows or by scanning all of the rows. CREATE and DROP STATISTICS on external tables aren't supported in Azure SQL Database.

Since SQL Server imports data from the external table into a temporary table to create statistics, the full scan option takes much longer. For a large table, the default sampling method is usually sufficient.

--Create statistics on an external table and use default sampling.
CREATE STATISTICS CustomerStats1 ON DimCustomer (CustomerKey, EmailAddress);

--Create statistics on an external table and scan all the rows
CREATE STATISTICS CustomerStats1 ON DimCustomer (CustomerKey, EmailAddress) WITH FULLSCAN;

E. Use CREATE STATISTICS with FULLSCAN and PERSIST_SAMPLE_PERCENT

The following example creates the NamePurchase statistics for all rows in the BusinessEntityID and EmailPromotion columns of the Person table and sets a 100 percent sampling percentage for all subsequent updates that don't explicitly specify a sampling percentage.

CREATE STATISTICS NamePurchase
    ON AdventureWorks2022.Person.Person (BusinessEntityID, EmailPromotion)
    WITH FULLSCAN, PERSIST_SAMPLE_PERCENT = ON;

Examples using AdventureWorksDW database

F. Create statistics on two columns

The following example creates the CustomerStats1 statistics, based on the CustomerKey and EmailAddress columns of the DimCustomer table. The statistics are created based on a statistically significant sampling of the rows in the Customer table.

CREATE STATISTICS CustomerStats1 ON DimCustomer (CustomerKey, EmailAddress);

G. Create statistics by using a full scan

The following example creates the CustomerStatsFullScan statistics, based on scanning all of the rows in the DimCustomer table.

CREATE STATISTICS CustomerStatsFullScan
ON DimCustomer (CustomerKey, EmailAddress) WITH FULLSCAN;

H. Create statistics by specifying the sample percentage

The following example creates the CustomerStatsSampleScan statistics, based on scanning 50 percent of the rows in the DimCustomer table.

CREATE STATISTICS CustomerStatsSampleScan
ON DimCustomer (CustomerKey, EmailAddress) WITH SAMPLE 50 PERCENT;

I. Use CREATE STATISTICS with AUTO_DROP

To use auto drop statistics, just add the following to the "WITH" clause of statistics create or update.

CREATE STATISTICS CustomerStats1 ON DimCustomer (CustomerKey, EmailAddress) WITH AUTO_DROP = ON

To evaluate the auto drop setting on existing statistics, use the auto_drop column in sys.stats:

SELECT object_id, [name], auto_drop
FROM sys.stats;

Kopīgot, izmantojot

CREATE STATISTICS (Transact-SQL)

Syntax

Arguments

statistics_name

table_or_indexed_view_name

column [ ,...n ]

WHERE <filter_predicate>

FULLSCAN

SAMPLE number { PERCENT | ROWS }

PERSIST_SAMPLE_PERCENT = { ON | OFF }

STATS_STREAM = stats_stream

NORECOMPUTE

INCREMENTAL = { ON | OFF }

MAXDOP = max_degree_of_parallelism

update_stats_stream_option

AUTO_DROP = { ON | OFF }

Permissions

Remarks

Statistics for external tables

Statistics with a filtered condition

When to use CREATE STATISTICS

Reference dependencies for filtered statistics

Limitations

Examples

A. Use CREATE STATISTICS with SAMPLE number PERCENT

B. Use CREATE STATISTICS with FULLSCAN and NORECOMPUTE

C. Use CREATE STATISTICS to create filtered statistics

D. Create statistics on an external table

E. Use CREATE STATISTICS with FULLSCAN and PERSIST_SAMPLE_PERCENT

Examples using AdventureWorksDW database

F. Create statistics on two columns

G. Create statistics by using a full scan

H. Create statistics by specifying the sample percentage

I. Use CREATE STATISTICS with AUTO_DROP

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Kopīgot, izmantojot

CREATE STATISTICS (Transact-SQL)

Syntax

Arguments

statistics_name

table_or_indexed_view_name

column [ ,...n ]

WHERE <filter_predicate>

FULLSCAN

SAMPLE number { PERCENT | ROWS }

PERSIST_SAMPLE_PERCENT = { ON | OFF }

STATS_STREAM = stats_stream

NORECOMPUTE

INCREMENTAL = { ON | OFF }

MAXDOP = max_degree_of_parallelism

update_stats_stream_option

AUTO_DROP = { ON | OFF }

Permissions

Remarks

Statistics for external tables

Statistics with a filtered condition

When to use CREATE STATISTICS

Reference dependencies for filtered statistics

Limitations

Examples

A. Use CREATE STATISTICS with SAMPLE number PERCENT

B. Use CREATE STATISTICS with FULLSCAN and NORECOMPUTE

C. Use CREATE STATISTICS to create filtered statistics

D. Create statistics on an external table

E. Use CREATE STATISTICS with FULLSCAN and PERSIST_SAMPLE_PERCENT

Examples using AdventureWorksDW database

F. Create statistics on two columns

G. Create statistics by using a full scan

H. Create statistics by specifying the sample percentage

I. Use CREATE STATISTICS with AUTO_DROP

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