CREATE TABLE

Applies to: Azure Synapse Analytics Analytics Platform System (PDW)

Creates a new table in Azure Synapse Analytics or Analytics Platform System (PDW).

To understand tables and how to use them, see Tables in Azure Synapse Analytics.

Discussions about Azure Synapse Analytics in this article apply to both Azure Synapse Analytics and Analytics Platform System (PDW) unless otherwise noted.

Note

For SQL Server and Azure SQL platforms, visit CREATE TABLE and select your desired product version. For reference to Warehouse in Microsoft Fabric, visit CREATE TABLE (Fabric).

Note

Serverless SQL pool in Azure Synapse Analytics supports only external and temporary tables.

Transact-SQL syntax conventions

Syntax

-- Create a new table.
CREATE TABLE { database_name.schema_name.table_name | schema_name.table_name | table_name }
    ( 
      { column_name <data_type>  [ <column_options> ] } [ ,...n ]
    )  
    [ WITH ( <table_option> [ ,...n ] ) ]  
[;]  

<column_options> ::=
    [ COLLATE Windows_collation_name ]
    [ NULL | NOT NULL ] -- default is NULL
    [ IDENTITY [ ( seed, increment ) ]
    [ <column_constraint> ]

<column_constraint>::=
    {
        DEFAULT constant_expression
        | PRIMARY KEY NONCLUSTERED NOT ENFORCED -- Applies to Azure Synapse Analytics only
        | UNIQUE NOT ENFORCED -- Applies to Azure Synapse Analytics only
    }

<table_option> ::=
    {
       CLUSTERED COLUMNSTORE INDEX -- default for Azure Synapse Analytics 
      | CLUSTERED COLUMNSTORE INDEX ORDER (column [,...n])  
      | HEAP --default for Parallel Data Warehouse
      | CLUSTERED INDEX ( { index_column_name [ ASC | DESC ] } [ ,...n ] ) -- default is ASC
    }  
    {
        DISTRIBUTION = HASH ( distribution_column_name )
      | DISTRIBUTION = HASH ( [distribution_column_name [, ...n]] ) 
      | DISTRIBUTION = ROUND_ROBIN -- default for Azure Synapse Analytics
      | DISTRIBUTION = REPLICATE -- default for Parallel Data Warehouse
    }
    | PARTITION ( partition_column_name RANGE [ LEFT | RIGHT ] -- default is LEFT  
        FOR VALUES ( [ boundary_value [,...n] ] ) )

<data type> ::=
      datetimeoffset [ ( n ) ]  
    | datetime2 [ ( n ) ]  
    | datetime  
    | smalldatetime  
    | date  
    | time [ ( n ) ]  
    | float [ ( n ) ]  
    | real [ ( n ) ]  
    | decimal [ ( precision [ , scale ] ) ]   
    | numeric [ ( precision [ , scale ] ) ]   
    | money  
    | smallmoney  
    | bigint  
    | int   
    | smallint  
    | tinyint  
    | bit  
    | nvarchar [ ( n | max ) ]  -- max applies only to Azure Synapse Analytics 
    | nchar [ ( n ) ]  
    | varchar [ ( n | max )  ] -- max applies only to Azure Synapse Analytics 
    | char [ ( n ) ]  
    | varbinary [ ( n | max ) ] -- max applies only to Azure Synapse Analytics 
    | binary [ ( n ) ]  
    | uniqueidentifier  

Arguments

database_name

The name of the database that will contain the new table. The default is the current database.

schema_name

The schema for the table. Specifying schema is optional. If blank, the default schema is used.

table_name

The name of the new table. To create a local temporary table, precede the table name with #. For explanations and guidance on temporary tables, see Temporary tables in dedicated SQL pool in Azure Synapse Analytics.

column_name

The name of a table column.

Column options

COLLATE Windows_collation_name
Specifies the collation for the expression. The collation must be one of the Windows collations supported by SQL Server. For a list of Windows collations supported by SQL Server, see Windows collation name (Transact-SQL)/).

NULL | NOT NULL
Specifies whether NULL values are allowed in the column. The default is NULL.

[ CONSTRAINT constraint_name ] DEFAULT constant_expression
Specifies the default column value.

Argument	Explanation
constraint_name	The optional name for the constraint. The constraint name is unique within the database. The name can be reused in other databases.
constant_expression	The default value for the column. The expression must be a literal value or a constant. For example, these constant expressions are allowed: 'CA', 4. These constant expressions aren't allowed: 2+3, CURRENT_TIMESTAMP.

Table structure options

For guidance on choosing the type of table, see Indexing tables in Azure Synapse Analytics.

CLUSTERED COLUMNSTORE INDEX

Stores the table as a clustered columnstore index. The clustered columnstore index applies to all of the table data. This behavior is the default for Azure Synapse Analytics.

HEAP Stores the table as a heap. This behavior is the default for Analytics Platform System (PDW).

CLUSTERED INDEX ( index_column_name [ ,...n ] )
Stores the table as a clustered index with one or more key columns. This behavior stores the data by row. Use index_column_name to specify the name of one or more key columns in the index. For more information, see Rowstore Tables.

LOCATION = USER_DB This option is deprecated. It's syntactically accepted, but no longer required and no longer affects behavior.

Table distribution options

To understand how to choose the best distribution method and use distributed tables, see designing distributed tables using dedicated SQL pool in Azure Synapse Analytics.

For recommendations on the best distribution strategy to use based on your workloads, see the Synapse SQL Distribution Advisor (Preview).

DISTRIBUTION = HASH ( distribution_column_name ) Assigns each row to one distribution by hashing the value stored in distribution_column_name. The algorithm is deterministic, which means it always hashes the same value to the same distribution. The distribution column should be defined as NOT NULL because all rows that have NULL are assigned to the same distribution.

DISTRIBUTION = HASH ( [distribution_column_name [, ...n]] ) Distributes the rows based on the hash values of up to eight columns, allowing for more even distribution of the base table data, reducing the data skew over time and improving query performance.

Note

• To enable the multi-column distribution (MCD) feature, change the database's compatibility level to 50 with this command. For more information on setting the database compatibility level, see ALTER DATABASE SCOPED CONFIGURATION (Transact-SQL). For example: ALTER DATABASE SCOPED CONFIGURATION SET DW_COMPATIBILITY_LEVEL = 50;
• To disable the Multi-Column distribution (MCD) feature, run this command to change the database's compatibility level to AUTO. For example: ALTER DATABASE SCOPED CONFIGURATION SET DW_COMPATIBILITY_LEVEL = AUTO; Existing MCD tables will stay but become unreadable. Queries over MCD tables will return this error: Related table/view is not readable because it distributes data on multiple columns and multi-column distribution is not supported by this product version or this feature is disabled.
  • To regain access to MCD tables, enable the feature again.
  • To load data into a MCD table, use CTAS statement and the data source needs be Synapse SQL tables.
• Generate Scripts (SQL Server Management Studio) to create MCD tables is currently supported SSMS version 19 and later versions.

DISTRIBUTION = ROUND_ROBIN Distributes the rows evenly across all the distributions in a round-robin fashion. This behavior is the default for Azure Synapse Analytics.

DISTRIBUTION = REPLICATE Stores one copy of the table on each Compute node. For Azure Synapse Analytics, the table is stored on a distribution database on each Compute node. For Analytics Platform System (PDW), the table is stored in a SQL Server filegroup that spans the Compute node. This behavior is the default for Analytics Platform System (PDW).

Table partition options

For guidance on using table partitions, see Partitioning tables in dedicated SQL pool.

PARTITION ( partition_column_name RANGE [ LEFT | RIGHT ] FOR VALUES ( [ boundary_value [,...n] ] ))
Creates one or more table partitions. These partitions are horizontal table slices that allow you to apply operations to subsets of rows regardless of whether the table is stored as a heap, clustered index, or clustered columnstore index. Unlike the distribution column, table partitions don't determine the distribution where each row is stored. Instead, table partitions determine how the rows are grouped and stored within each distribution.

Argument	Explanation
partition_column_name	Specifies the column that Azure Synapse Analytics uses to partition the rows. This column can be any data type. Azure Synapse Analytics sorts the partition column values in ascending order. The low-to-high ordering goes from LEFT to RIGHT in the RANGE specification.
RANGE LEFT	Specifies the boundary value belongs to the partition on the left (lower values). The default is LEFT.
RANGE RIGHT	Specifies the boundary value belongs to the partition on the right (higher values).
FOR VALUES ( boundary_value [,...n] )	Specifies the boundary values for the partition. boundary_value is a constant expression. It can't be NULL. It must either match or be implicitly convertible to the data type of partition_column_name. It can't be truncated during implicit conversion so that the size and scale of the value don't match the data type of partition_column_name If you specify the PARTITION clause, but don't specify a boundary value, Azure Synapse Analytics creates a partitioned table with one partition. If applicable, you can split the table into two partitions at a later time. If you specify one boundary value, the resulting table has two partitions; one for the values lower than the boundary value and one for the values higher than the boundary value. If you move a partition into a non-partitioned table, the non-partitioned table receives the data, but won't have the partition boundaries in its metadata.

For an example, see Create a partitioned table.

Ordered Clustered columnstore index option

Clustered columnstore index (CCI) is the default for creating tables in Azure Synapse Analytics. Data in a CCI is not sorted before being compressed into columnstore segments. When creating a CCI with ORDER, data is sorted before being added to index segments and query performance can be improved. See Performance Tuning with Ordered Clustered Columnstore Index for details.

An ordered CCI can be created on columns of any data types supported in Azure Synapse Analytics except for string columns.

Users can query column_store_order_ordinal column in sys.index_columns for the column or columns a table is ordered on, and the sequence in the ordering.

Check Performance tuning with ordered clustered columnstore index for details.

Data type

Azure Synapse Analytics supports the most commonly used data types. To better understand data types and how to use them, see Data types for tables in Azure Synapse Analytics.

Note

Similar to SQL Server, there is an 8060 byte per row limit. This might become a blocking issue for tables that have many columns, or columns with large data types, such as nvarchar(max) or varbinary(max). Inserts or updates that violate the 8060 byte limit will result in error codes 511 or 611. For more information, see Pages and Extents Architecture Guide.

For a table of data type conversions, see the Implicit Conversions section of CAST and CONVERT (Transact-SQL). For more information, see Date and time data types and functions (Transact-SQL).

The following list of supported data types includes their details and storage bytes:

datetimeoffset [ ( n ) ]
The default value for n is 7.

datetime2 [ ( n ) ]
Same as datetime, except that you can specify the number of fractional seconds. The default value for n is 7.

n value	Precision	Scale
0	19	0
1	21	1
2	22	2
3	23	3
4	24	4
5	25	5
6	26	6
7	27	7

datetime
Stores date and time of day with 19 to 23 characters according to the Gregorian calendar. The date can contain year, month, and day. The time contains hour, minutes, seconds. As an option, you can display three digits for fractional seconds. The storage size is 8 bytes.

smalldatetime
Stores a date and a time. Storage size is 4 bytes.

date
Stores a date using a maximum of 10 characters for year, month, and day according to the Gregorian calendar. The storage size is 3 bytes. Date is stored as an integer.

time [ ( n ) ]
The default value for n is 7.

float [ ( n ) ]
Approximate number data type for use with floating point numeric data. Floating point data is approximate, which means that not all values in the data type range can be represented exactly. n specifies the number of bits used to store the mantissa of the float in scientific notation. n dictates the precision and storage size. If n is specified, it must be a value between 1 and 53. The default value of n is 53.

n value	Precision	Storage size
1-24	7 digits	4 bytes
25-53	15 digits	8 bytes

Azure Synapse Analytics treats n as one of two possible values. If 1<= n <= 24, n is treated as 24. If 25 <= n <= 53, n is treated as 53.

The Azure Synapse Analytics float data type complies with the ISO standard for all values of n from 1 through 53. The synonym for double precision is float(53).

real [ ( n ) ]
The definition of real is the same as float. The ISO synonym for real is float(24).

decimal [ ( precision [ , scale ] ) ] | numeric [ ( precision [ , scale ] ) ]
Stores fixed precision and scale numbers.

precision
The maximum total number of decimal digits that can be stored, on either side of the decimal point. The precision must be a value from 1 through the maximum precision of 38. The default precision is 18.

scale
The maximum number of decimal digits that can be stored to the right of the decimal point. Scale must be a value from 0 through precision. You can only specify scale if precision is specified. The default scale is 0 and so 0 <= scale <= precision. Maximum storage sizes vary, based on the precision.

Precision	Storage bytes
1-9	5
10-19	9
20-28	13
29-38	17

money | smallmoney
Data types that represent currency values.

Data Type	Storage bytes
money	8
smallmoney	4

bigint | int | smallint | tinyint
Exact-number data types that use integer data. The storage is shown in the following table.

Data Type	Storage bytes
bigint	8
int	4
smallint	2
tinyint	1

bit
An integer data type that can take the value of 1, 0, or `NULL. Azure Synapse Analytics optimizes storage of bit columns. If there are 8 or fewer bit columns in a table, the columns are stored as 1 byte. If there are from 9-16 bit columns, the columns are stored as 2 bytes, and so on.

nvarchar [ ( n | max ) ] Variable-length Unicode character data. n can be a value from 1 through 4000. max indicates that the maximum storage size is 2^31-1 bytes (2 GB). Storage size in bytes is two times the number of characters entered + 2 bytes. The data entered can be zero characters in length. The max length applies only to Azure Synapse Analytics.

nchar [ ( n ) ]
Fixed-length Unicode character data with a length of n characters. n must be a value from 1 through 4000. The storage size is two times n bytes.

varchar [ ( n | max ) ] Variable-length, non-Unicode character data with a length of n bytes. n must be a value from 1 to 8000. max indicates that the maximum storage size is 2^31-1 bytes (2 GB). The storage size is the actual length of data entered + 2 bytes. The max length applies only to Azure Synapse Analytics.

char [ ( n ) ]
Fixed-length, non-Unicode character data with a length of n bytes. n must be a value from 1 to 8000. The storage size is n bytes. The default for n is 1.

varbinary [ ( n | max ) ] Variable-length binary data. n can be a value from 1 to 8000. max indicates that the maximum storage size is 2^31-1 bytes (2 GB). The storage size is the actual length of data entered + 2 bytes. The default value for n is 7. The max length applies only to Azure Synapse Analytics.

binary [ ( n ) ]
Fixed-length binary data with a length of n bytes. n can be a value from 1 to 8000. The storage size is n bytes. The default value for n is 7.

uniqueidentifier
Is a 16-byte GUID.

Permissions

Creating a table requires permission in the db_ddladmin fixed database role, or:

• CREATE TABLE permission on the database
• ALTER SCHEMA permission on the schema of the table

Creating a partitioned table requires permission in the db_ddladmin fixed database role, or

• ALTER ANY DATASPACE permission

The login that creates a local temporary table receives CONTROL, INSERT, SELECT, and UPDATE permissions on the table.

Remarks

For minimum and maximum limits, see Azure Synapse Analytics capacity limits.

Determine the number of table partitions

Each user-defined table is divided into multiple smaller tables that are stored in separate locations called distributions. Azure Synapse Analytics uses 60 distributions. In Analytics Platform System (PDW), the number of distributions depends on the number of Compute nodes.

Each distribution contains all table partitions. For example, if there are 60 distributions and four table partitions plus one empty partition, there will be 300 partitions (5 x 60= 300). If the table is a clustered columnstore index, there will be one columnstore index per partition, which means you will have 300 columnstore indexes.

We recommend using fewer table partitions to ensure each columnstore index has enough rows to take advantage of the benefits of columnstore indexes. For more information, see Partitioning tables in dedicated SQL pool and Indexes on dedicated SQL pool tables in Azure Synapse Analytics.

Rowstore table (heap or clustered index)

A rowstore table is a table stored in row-by-row order. It's a heap or clustered index. Azure Synapse Analytics creates all rowstore tables with page compression; this behavior isn't user-configurable.

Columnstore table (columnstore index)

A columnstore table is a table stored in column-by-column order. The columnstore index is the technology that manages data stored in a columnstore table. The clustered columnstore index doesn't affect how data is distributed. Rather, it affects how the data is stored within each distribution.

To change a rowstore table to a columnstore table, drop all existing indexes on the table and create a clustered columnstore index. For an example, see CREATE COLUMNSTORE INDEX (Transact-SQL).

For more information, see these articles:

• What's new in columnstore indexes
• Indexing tables in Azure Synapse Analytics
• Columnstore indexes: Overview

Limitations

• You can't define a DEFAULT constraint on a distribution column.
• Table Name cannot be greater than 128 characters.
• Column Name cannot be greater than 128 characters.

Partitions

The partition column can't have a Unicode-only collation. For example, the following statement fails:

CREATE TABLE t1 ( c1 varchar(20) COLLATE Divehi_90_CI_AS_KS_WS) WITH (PARTITION (c1 RANGE FOR VALUES (N'')))

If boundary_value is a literal value that must be implicitly converted to the data type in partition_column_name, a discrepancy occurs. The literal value is displayed through the Azure Synapse Analytics system views, but the converted value is used for Transact-SQL operations.

Temporary tables

Global temporary tables that begin with ## aren't supported.

Local temporary tables have the following limitations and restrictions:

• They're visible only to the current session. Azure Synapse Analytics drops them automatically at the end of the session. To drop them explicitly, use the DROP TABLE statement.
• They can't be renamed.
• They can't have partitions or views.
• Their permissions can't be changed. GRANT, DENY, and REVOKE statements can't be used with local temporary tables.
• Database console commands are blocked for temporary tables.
• If more than one local temporary table is used within a batch, each must have a unique name. If multiple sessions are running the same batch and creating the same local temporary table, Azure Synapse Analytics internally appends a numeric suffix to the local temporary table name to maintain a unique name for each local temporary table.

Locking behavior

Takes an exclusive lock on the table. Takes a shared lock on the DATABASE, SCHEMA, and SCHEMARESOLUTION objects.

Examples for columns

A. Specify a column collation

In the following example, the table MyTable is created with two different column collations. By default, the column, mycolumn1, has the default collation Latin1_General_100_CI_AS_KS_WS. The column, mycolumn2 has the collation Frisian_100_CS_AS.

CREATE TABLE MyTable   
  (  
    mycolumnnn1 nvarchar,  
    mycolumn2 nvarchar COLLATE Frisian_100_CS_AS )  
WITH ( CLUSTERED COLUMNSTORE INDEX )  
;  

B. Specify a DEFAULT constraint for a column

The following example shows the syntax to specify a default value for a column. The colA column has a default constraint named constraint_colA and a default value of 0.

CREATE TABLE MyTable
  (  
    colA int CONSTRAINT constraint_colA DEFAULT 0,  
    colB nvarchar COLLATE Frisian_100_CS_AS
  )  
WITH ( CLUSTERED COLUMNSTORE INDEX )  
;  

Examples for temporary tables

C. Create a local temporary table

The following example creates a local temporary table named #myTable. The table is specified with a three-part name, which starts with a #.

CREATE TABLE AdventureWorks.dbo.#myTable
  (  
   id int NOT NULL,  
   lastName varchar(20),  
   zipCode varchar(6)  
  )  
WITH  
  (   
    DISTRIBUTION = HASH (id),  
    CLUSTERED COLUMNSTORE INDEX
  )  
;  

Examples for table structure

D. Create a table with a clustered columnstore index

The following example creates a distributed table with a clustered columnstore index. Each distribution is stored as a columnstore.

The clustered columnstore index doesn't affect how the data is distributed; data is always distributed by row. The clustered columnstore index affects how the data is stored within each distribution.

  CREATE TABLE MyTable
  (  
    colA int CONSTRAINT constraint_colA DEFAULT 0,  
    colB nvarchar COLLATE Frisian_100_CS_AS
  )  
WITH   
  (   
    DISTRIBUTION = HASH ( colB ),  
    CLUSTERED COLUMNSTORE INDEX
  )  
;  

E. Create an ordered clustered columnstore index

The following example shows how to create an ordered clustered columnstore index. The index is ordered on SHIPDATE.

CREATE TABLE Lineitem  
WITH (DISTRIBUTION = ROUND_ROBIN, CLUSTERED COLUMNSTORE INDEX ORDER(SHIPDATE))  
AS  
SELECT * FROM ext_Lineitem

Examples for table distribution

F. Create a ROUND_ROBIN table

The following example creates a ROUND_ROBIN table with three columns and without partitions. The data is spread across all distributions. The table is created with a CLUSTERED COLUMNSTORE INDEX, which gives better performance and data compression than a heap or rowstore clustered index.

CREATE TABLE myTable
  (  
    id int NOT NULL,  
    lastName varchar(20),  
    zipCode varchar(6)  
  )  
WITH ( CLUSTERED COLUMNSTORE INDEX );  

G. Create a table that's hash-distributed on multiple columns (preview)

The following example creates the same table as the previous example. However, for this table, rows are distributed (on id and zipCode columns). The table is created with a clustered columnstore index, which gives better performance and data compression than a heap or rowstore clustered index.

CREATE TABLE myTable
  (  
    id int NOT NULL,  
    lastName varchar(20),  
    zipCode varchar(6)  
  )  
WITH  
  (   
    DISTRIBUTION = HASH (id, zipCode), 
    CLUSTERED COLUMNSTORE INDEX  
  );  

H. Create a replicated table

The following example creates a replicated table similar to the previous examples. Replicated tables are copied in full to each Compute node. With this copy on each Compute node, data movement is reduced for queries. This example is created with a CLUSTERED INDEX, which gives better data compression than a heap. A heap might not contain enough rows to achieve good CLUSTERED COLUMNSTORE INDEX compression.

CREATE TABLE myTable
  (  
    id int NOT NULL,  
    lastName varchar(20),  
    zipCode varchar(6)  
  )  
WITH  
  (   
    DISTRIBUTION = REPLICATE,
    CLUSTERED INDEX (lastName)  
  );  

Examples for table partitions

I. Create a partitioned table

The following example creates the same table as shown in example A, with the addition of RANGE LEFT partitioning on the id column. It specifies four partition boundary values, which results in five partitions.

CREATE TABLE myTable
  (  
    id int NOT NULL,  
    lastName varchar(20),  
    zipCode int)  
WITH
  (

    PARTITION ( id RANGE LEFT FOR VALUES (10, 20, 30, 40 )),  
    CLUSTERED COLUMNSTORE INDEX
  );  

In this example, data will be sorted into the following partitions:

• Partition 1: col <= 10
• Partition 2: 10 < col <= 20
• Partition 3: 20 < col <= 30
• Partition 4: 30 < col <= 40
• Partition 5: 40 < col

If this same table was partitioned RANGE RIGHT instead of RANGE LEFT (default), the data will be sorted into the following partitions:

• Partition 1: col < 10
• Partition 2: 10 <= col < 20
• Partition 3: 20 <= col < 30
• Partition 4: 30 <= col < 40
• Partition 5: 40 <= col

J. Create a partitioned table with one partition

The following example creates a partitioned table with one partition. It doesn't specify any boundary value, which results in one partition.

CREATE TABLE myTable (  
    id int NOT NULL,  
    lastName varchar(20),  
    zipCode int)  
WITH
    (
      PARTITION ( id RANGE LEFT FOR VALUES ( )),  
      CLUSTERED COLUMNSTORE INDEX  
    )  
;  

K. Create a table with date partitioning

The following example creates a new table named myTable, with partitioning on a date column. By using RANGE RIGHT and dates for the boundary values, it puts a month of data in each partition.

CREATE TABLE myTable (  
    l_orderkey      bigint,
    l_partkey       bigint,
    l_suppkey       bigint,
    l_linenumber    bigint,
    l_quantity      decimal(15,2),  
    l_extendedprice decimal(15,2),  
    l_discount      decimal(15,2),  
    l_tax           decimal(15,2),  
    l_returnflag    char(1),  
    l_linestatus    char(1),  
    l_shipdate      date,  
    l_commitdate    date,  
    l_receiptdate   date,  
    l_shipinstruct  char(25),  
    l_shipmode      char(10),  
    l_comment       varchar(44))  
WITH
  (
    DISTRIBUTION = HASH (l_orderkey),  
    CLUSTERED COLUMNSTORE INDEX,  
    PARTITION ( l_shipdate  RANGE RIGHT FOR VALUES
      (  
        '1992-01-01','1992-02-01','1992-03-01','1992-04-01','1992-05-01',
        '1992-06-01','1992-07-01','1992-08-01','1992-09-01','1992-10-01',
        '1992-11-01','1992-12-01','1993-01-01','1993-02-01','1993-03-01',
        '1993-04-01','1993-05-01','1993-06-01','1993-07-01','1993-08-01',
        '1993-09-01','1993-10-01','1993-11-01','1993-12-01','1994-01-01',
        '1994-02-01','1994-03-01','1994-04-01','1994-05-01','1994-06-01',
        '1994-07-01','1994-08-01','1994-09-01','1994-10-01','1994-11-01',
        '1994-12-01'  
      ))
  );  

Related content

• CREATE TABLE AS SELECT
• DROP TABLE (Transact-SQL)
• ALTER TABLE (Transact-SQL)
• sys.index_columns (Transact-SQL)