Microsoft SQL Server — SQLAlchemy 1.2 Documentation

Release: 1.2.12 | Release Date: September 19, 2018

# SQLAlchemy 1.2 Documentation

# Microsoft SQL Server

Support for the Microsoft SQL Server database.

## DBAPI Support

The following dialect/DBAPI options are available. Please refer to individual DBAPI sections for connect information.

- PyODBC
- mxODBC
- pymssql
- zxJDBC for Jython
- adodbapi

## Auto Increment Behavior

SQL Server provides so-called “auto incrementing” behavior using the `IDENTITY` construct, which can be placed on an integer primary key. SQLAlchemy considers `IDENTITY` within its default “autoincrement” behavior, described at `Column.autoincrement`; this means that by default, the first integer primary key column in a `Table` will be considered to be the identity column and will generate DDL as such:

    from sqlalchemy import Table, MetaData, Column, Integer

    m = MetaData()
    t = Table('t', m,
            Column('id', Integer, primary_key=True),
            Column('x', Integer))
    m.create_all(engine)

The above example will generate DDL as:

    CREATE TABLE t (
        id INTEGER NOT NULL IDENTITY(1,1),
        x INTEGER NULL,
        PRIMARY KEY (id)
    )

For the case where this default generation of `IDENTITY` is not desired, specify `autoincrement=False` on all integer primary key columns:

    m = MetaData()
    t = Table('t', m,
            Column('id', Integer, primary_key=True, autoincrement=False),
            Column('x', Integer))
    m.create_all(engine)

Note

An INSERT statement which refers to an explicit value for such a column is prohibited by SQL Server, however SQLAlchemy will detect this and modify the `IDENTITY_INSERT` flag accordingly at statement execution time. As this is not a high performing process, care should be taken to set the `autoincrement` flag appropriately for columns that will not actually require IDENTITY behavior.

### Controlling “Start” and “Increment”

Specific control over the parameters of the `IDENTITY` value is supported using the `schema.Sequence` object. While this object normally represents an explicit “sequence” for supporting backends, on SQL Server it is re-purposed to specify behavior regarding the identity column, including support of the “start” and “increment” values:

    from sqlalchemy import Table, Integer, Sequence, Column

    Table('test', metadata,
           Column('id', Integer,
                  Sequence('blah', start=100, increment=10),
                  primary_key=True),
           Column('name', String(20))
         ).create(some_engine)

would yield:

    CREATE TABLE test (
      id INTEGER NOT NULL IDENTITY(100,10) PRIMARY KEY,
      name VARCHAR(20) NULL,
      )

Note that the `start` and `increment` values for sequences are optional and will default to 1,1.

### INSERT behavior

Handling of the `IDENTITY` column at INSERT time involves two key techniques. The most common is being able to fetch the “last inserted value” for a given `IDENTITY` column, a process which SQLAlchemy performs implicitly in many cases, most importantly within the ORM.

The process for fetching this value has several variants:

- In the vast majority of cases, RETURNING is used in conjunction with INSERT statements on SQL Server in order to get newly generated primary key values:

      INSERT INTO t (x) OUTPUT inserted.id VALUES (?)

- When RETURNING is not available or has been disabled via `implicit_returning=False`, either the `scope_identity()` function or the `@@identity` variable is used; behavior varies by backend:

  - when using PyODBC, the phrase `; select scope_identity()` will be appended to the end of the INSERT statement; a second result set will be fetched in order to receive the value. Given a table as:

        t = Table('t', m, Column('id', Integer, primary_key=True),
                Column('x', Integer),
                implicit_returning=False)

    an INSERT will look like:

        INSERT INTO t (x) VALUES (?); select scope_identity()

  - Other dialects such as pymssql will call upon `SELECT scope_identity() AS lastrowid` subsequent to an INSERT statement. If the flag `use_scope_identity=False` is passed to `create_engine()`, the statement `SELECT @@identity AS lastrowid` is used instead.

A table that contains an `IDENTITY` column will prohibit an INSERT statement that refers to the identity column explicitly. The SQLAlchemy dialect will detect when an INSERT construct, created using a core `insert()` construct (not a plain string SQL), refers to the identity column, and in this case will emit `SET IDENTITY_INSERT ON` prior to the insert statement proceeding, and `SET IDENTITY_INSERT OFF` subsequent to the execution. Given this example:

    m = MetaData()
    t = Table('t', m, Column('id', Integer, primary_key=True),
                    Column('x', Integer))
    m.create_all(engine)

    engine.execute(t.insert(), {'id': 1, 'x':1}, {'id':2, 'x':2})

The above column will be created with IDENTITY, however the INSERT statement we emit is specifying explicit values. In the echo output we can see how SQLAlchemy handles this:

    CREATE TABLE t (
        id INTEGER NOT NULL IDENTITY(1,1),
        x INTEGER NULL,
        PRIMARY KEY (id)
    )

    COMMIT
    SET IDENTITY_INSERT t ON
    INSERT INTO t (id, x) VALUES (?, ?)
    ((1, 1), (2, 2))
    SET IDENTITY_INSERT t OFF
    COMMIT

This is an auxiliary use case suitable for testing and bulk insert scenarios.

## MAX on VARCHAR / NVARCHAR

SQL Server supports the special string “MAX” within the `sqltypes.VARCHAR` and `sqltypes.NVARCHAR` datatypes, to indicate “maximum length possible”. The dialect currently handles this as a length of “None” in the base type, rather than supplying a dialect-specific version of these types, so that a base type specified such as `VARCHAR(None)` can assume “unlengthed” behavior on more than one backend without using dialect-specific types.

To build a SQL Server VARCHAR or NVARCHAR with MAX length, use None:

    my_table = Table(
        'my_table', metadata,
        Column('my_data', VARCHAR(None)),
        Column('my_n_data', NVARCHAR(None))
    )

## Collation Support

Character collations are supported by the base string types, specified by the string argument “collation”:

    from sqlalchemy import VARCHAR
    Column('login', VARCHAR(32, collation='Latin1_General_CI_AS'))

When such a column is associated with a `Table`, the CREATE TABLE statement for this column will yield:

    login VARCHAR(32) COLLATE Latin1_General_CI_AS NULL

New in version 0.8: Character collations are now part of the base string types.

## LIMIT/OFFSET Support

MSSQL has no support for the LIMIT or OFFSET keywords. LIMIT is supported directly through the `TOP` Transact SQL keyword:

    select.limit

will yield:

    SELECT TOP n

If using SQL Server 2005 or above, LIMIT with OFFSET support is available through the `ROW_NUMBER OVER` construct. For versions below 2005, LIMIT with OFFSET usage will fail.

## Transaction Isolation Level

All SQL Server dialects support setting of transaction isolation level both via a dialect-specific parameter `create_engine.isolation_level` accepted by `create_engine()`, as well as the `Connection.execution_options.isolation_level` argument as passed to `Connection.execution_options()`. This feature works by issuing the command `SET TRANSACTION ISOLATION LEVEL <level>` for each new connection.

To set isolation level using `create_engine()`:

    engine = create_engine(
        "mssql+pyodbc://scott:tiger@ms_2008",
        isolation_level="REPEATABLE READ"
    )

To set using per-connection execution options:

    connection = engine.connect()
    connection = connection.execution_options(
        isolation_level="READ COMMITTED"
    )

Valid values for `isolation_level` include:

- `AUTOCOMMIT` - pyodbc / pymssql-specific
- `READ COMMITTED`
- `READ UNCOMMITTED`
- `REPEATABLE READ`
- `SERIALIZABLE`
- `SNAPSHOT` - specific to SQL Server

New in version 1.1: support for isolation level setting on Microsoft SQL Server.

New in version 1.2: added AUTOCOMMIT isolation level setting

## Nullability

MSSQL has support for three levels of column nullability. The default nullability allows nulls and is explicit in the CREATE TABLE construct:

    name VARCHAR(20) NULL

If `nullable=None` is specified then no specification is made. In other words the database’s configured default is used. This will render:

    name VARCHAR(20)

If `nullable` is `True` or `False` then the column will be `NULL` or `NOT NULL` respectively.

## Date / Time Handling

DATE and TIME are supported. Bind parameters are converted to datetime.datetime() objects as required by most MSSQL drivers, and results are processed from strings if needed. The DATE and TIME types are not available for MSSQL 2005 and previous - if a server version below 2008 is detected, DDL for these types will be issued as DATETIME.

## Large Text/Binary Type Deprecation

Per SQL Server 2012/2014 Documentation, the `NTEXT`, `TEXT` and `IMAGE` datatypes are to be removed from SQL Server in a future release. SQLAlchemy normally relates these types to the `UnicodeText`, `Text` and `LargeBinary` datatypes.

In order to accommodate this change, a new flag `deprecate_large_types` is added to the dialect, which will be automatically set based on detection of the server version in use, if not otherwise set by the user. The behavior of this flag is as follows:

- When this flag is `True`, the `UnicodeText`, `Text` and `LargeBinary` datatypes, when used to render DDL, will render the types `NVARCHAR(max)`, `VARCHAR(max)`, and `VARBINARY(max)`, respectively. This is a new behavior as of the addition of this flag.

- When this flag is `False`, the `UnicodeText`, `Text` and `LargeBinary` datatypes, when used to render DDL, will render the types `NTEXT`, `TEXT`, and `IMAGE`, respectively. This is the long-standing behavior of these types.

- The flag begins with the value `None`, before a database connection is established. If the dialect is used to render DDL without the flag being set, it is interpreted the same as `False`.

- On first connection, the dialect detects if SQL Server version 2012 or greater is in use; if the flag is still at `None`, it sets it to `True` or `False` based on whether 2012 or greater is detected.

- The flag can be set to either `True` or `False` when the dialect is created, typically via `create_engine()`:

      eng = create_engine("mssql+pymssql://user:pass@host/db",
                      deprecate_large_types=True)

- Complete control over whether the “old” or “new” types are rendered is available in all SQLAlchemy versions by using the UPPERCASE type objects instead: `NVARCHAR`, `VARCHAR`, `types.VARBINARY`, `TEXT`, `mssql.NTEXT`, `mssql.IMAGE` will always remain fixed and always output exactly that type.

New in version 1.0.0.

## Multipart Schema Names

SQL Server schemas sometimes require multiple parts to their “schema” qualifier, that is, including the database name and owner name as separate tokens, such as `mydatabase.dbo.some_table`. These multipart names can be set at once using the `Table.schema` argument of `Table`:

    Table(
        "some_table", metadata,
        Column("q", String(50)),
        schema="mydatabase.dbo"
    )

When performing operations such as table or component reflection, a schema argument that contains a dot will be split into separate “database” and “owner” components in order to correctly query the SQL Server information schema tables, as these two values are stored separately. Additionally, when rendering the schema name for DDL or SQL, the two components will be quoted separately for case sensitive names and other special characters. Given an argument as below:

    Table(
        "some_table", metadata,
        Column("q", String(50)),
        schema="MyDataBase.dbo"
    )

The above schema would be rendered as `[MyDataBase].dbo`, and also in reflection, would be reflected using “dbo” as the owner and “MyDataBase” as the database name.

To control how the schema name is broken into database / owner, specify brackets (which in SQL Server are quoting characters) in the name. Below, the “owner” will be considered as `MyDataBase.dbo` and the “database” will be None:

    Table(
        "some_table", metadata,
        Column("q", String(50)),
        schema="[MyDataBase.dbo]"
    )

To individually specify both database and owner name with special characters or embedded dots, use two sets of brackets:

    Table(
        "some_table", metadata,
        Column("q", String(50)),
        schema="[MyDataBase.Period].[MyOwner.Dot]"
    )

Changed in version 1.2: the SQL Server dialect now treats brackets as identifier delimeters splitting the schema into separate database and owner tokens, to allow dots within either name itself.

## Legacy Schema Mode

Very old versions of the MSSQL dialect introduced the behavior such that a schema-qualified table would be auto-aliased when used in a SELECT statement; given a table:

    account_table = Table(
        'account', metadata,
        Column('id', Integer, primary_key=True),
        Column('info', String(100)),
        schema="customer_schema"
    )

this legacy mode of rendering would assume that “customer_schema.account” would not be accepted by all parts of the SQL statement, as illustrated below:

    >>> eng = create_engine("mssql+pymssql://mydsn", legacy_schema_aliasing=True)
    >>> print(account_table.select().compile(eng))
    SELECT account_1.id, account_1.info
    FROM customer_schema.account AS account_1

This mode of behavior is now off by default, as it appears to have served no purpose; however in the case that legacy applications rely upon it, it is available using the `legacy_schema_aliasing` argument to `create_engine()` as illustrated above.

Changed in version 1.1: the `legacy_schema_aliasing` flag introduced in version 1.0.5 to allow disabling of legacy mode for schemas now defaults to False.

## Clustered Index Support

The MSSQL dialect supports clustered indexes (and primary keys) via the `mssql_clustered` option. This option is available to `Index`, `UniqueConstraint`. and `PrimaryKeyConstraint`.

To generate a clustered index:

    Index("my_index", table.c.x, mssql_clustered=True)

which renders the index as `CREATE CLUSTERED INDEX my_index ON table (x)`.

To generate a clustered primary key use:

    Table('my_table', metadata,
          Column('x', ...),
          Column('y', ...),
          PrimaryKeyConstraint("x", "y", mssql_clustered=True))

which will render the table, for example, as:

    CREATE TABLE my_table (x INTEGER NOT NULL, y INTEGER NOT NULL,
                           PRIMARY KEY CLUSTERED (x, y))

Similarly, we can generate a clustered unique constraint using:

    Table('my_table', metadata,
          Column('x', ...),
          Column('y', ...),
          PrimaryKeyConstraint("x"),
          UniqueConstraint("y", mssql_clustered=True),
          )

To explicitly request a non-clustered primary key (for example, when a separate clustered index is desired), use:

    Table('my_table', metadata,
          Column('x', ...),
          Column('y', ...),
          PrimaryKeyConstraint("x", "y", mssql_clustered=False))

which will render the table, for example, as:

    CREATE TABLE my_table (x INTEGER NOT NULL, y INTEGER NOT NULL,
                           PRIMARY KEY NONCLUSTERED (x, y))

Changed in version 1.1: the `mssql_clustered` option now defaults to None, rather than False. `mssql_clustered=False` now explicitly renders the NONCLUSTERED clause, whereas None omits the CLUSTERED clause entirely, allowing SQL Server defaults to take effect.

## MSSQL-Specific Index Options

In addition to clustering, the MSSQL dialect supports other special options for `Index`.

### INCLUDE

The `mssql_include` option renders INCLUDE(colname) for the given string names:

    Index("my_index", table.c.x, mssql_include=['y'])

would render the index as `CREATE INDEX my_index ON table (x) INCLUDE (y)`

New in version 0.8.

### Index ordering

Index ordering is available via functional expressions, such as:

    Index("my_index", table.c.x.desc())

would render the index as `CREATE INDEX my_index ON table (x DESC)`

New in version 0.8.

See also

Functional Indexes

## Compatibility Levels

MSSQL supports the notion of setting compatibility levels at the database level. This allows, for instance, to run a database that is compatible with SQL2000 while running on a SQL2005 database server. `server_version_info` will always return the database server version information (in this case SQL2005) and not the compatibility level information. Because of this, if running under a backwards compatibility mode SQAlchemy may attempt to use T-SQL statements that are unable to be parsed by the database server.

## Triggers

SQLAlchemy by default uses OUTPUT INSERTED to get at newly generated primary key values via IDENTITY columns or other server side defaults. MS-SQL does not allow the usage of OUTPUT INSERTED on tables that have triggers. To disable the usage of OUTPUT INSERTED on a per-table basis, specify `implicit_returning=False` for each `Table` which has triggers:

    Table('mytable', metadata,
        Column('id', Integer, primary_key=True),
        # ...,
        implicit_returning=False
    )

Declarative form:

    class MyClass(Base):
        # ...
        __table_args__ = {'implicit_returning':False}

This option can also be specified engine-wide using the `implicit_returning=False` argument on `create_engine()`.

## Rowcount Support / ORM Versioning

The SQL Server drivers may have limited ability to return the number of rows updated from an UPDATE or DELETE statement.

As of this writing, the PyODBC driver is not able to return a rowcount when OUTPUT INSERTED is used. This impacts the SQLAlchemy ORM’s versioning feature in many cases where server-side value generators are in use in that while the versioning operations can succeed, the ORM cannot always check that an UPDATE or DELETE statement matched the number of rows expected, which is how it verifies that the version identifier matched. When this condition occurs, a warning will be emitted but the operation will proceed.

The use of OUTPUT INSERTED can be disabled by setting the `Table.implicit_returning` flag to `False` on a particular `Table`, which in declarative looks like:

    class MyTable(Base):
        __tablename__ = 'mytable'
        id = Column(Integer, primary_key=True)
        stuff = Column(String(10))
        timestamp = Column(TIMESTAMP(), default=text('DEFAULT'))
        __mapper_args__ = {
            'version_id_col': timestamp,
            'version_id_generator': False,
        }
        __table_args__ = {
            'implicit_returning': False
        }

## Enabling Snapshot Isolation

SQL Server has a default transaction isolation mode that locks entire tables, and causes even mildly concurrent applications to have long held locks and frequent deadlocks. Enabling snapshot isolation for the database as a whole is recommended for modern levels of concurrency support. This is accomplished via the following ALTER DATABASE commands executed at the SQL prompt:

    ALTER DATABASE MyDatabase SET ALLOW_SNAPSHOT_ISOLATION ON

    ALTER DATABASE MyDatabase SET READ_COMMITTED_SNAPSHOT ON

Background on SQL Server snapshot isolation is available at http://msdn.microsoft.com/en-us/library/ms175095.aspx.

## SQL Server Data Types

As with all SQLAlchemy dialects, all UPPERCASE types that are known to be valid with SQL server are importable from the top level dialect, whether they originate from `sqlalchemy.types` or from the local dialect:

    from sqlalchemy.dialects.mssql import \
        BIGINT, BINARY, BIT, CHAR, DATE, DATETIME, DATETIME2, \
        DATETIMEOFFSET, DECIMAL, FLOAT, IMAGE, INTEGER, MONEY, \
        NCHAR, NTEXT, NUMERIC, NVARCHAR, REAL, SMALLDATETIME, \
        SMALLINT, SMALLMONEY, SQL_VARIANT, TEXT, TIME, \
        TIMESTAMP, TINYINT, UNIQUEIDENTIFIER, VARBINARY, VARCHAR

Types which are specific to SQL Server, or have SQL Server-specific construction arguments, are as follows:

class `sqlalchemy.dialects.mssql.` `BIT`

  Bases: `sqlalchemy.types.TypeEngine`

  `__init__`
    inherited from the `__init__` attribute of `object`

    Initialize self. See help(type(self)) for accurate signature.

class `sqlalchemy.dialects.mssql.` `CHAR`(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)

  Bases: `sqlalchemy.types.String`

  The SQL CHAR type.

  `__init__`(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)
    inherited from the `__init__()` method of `String`

    Create a string-holding type.

    - Parameters: | length – optional, a length for the column for use in DDL and CAST expressions. May be safely omitted if no `CREATE TABLE` will be issued. Certain databases may require a `length` for use in DDL, and will raise an exception when the `CREATE TABLE` DDL is issued if a `VARCHAR` with no length is included. Whether the value is interpreted as bytes or characters is database specific. collation – Optional, a column-level collation for use in DDL and CAST expressions. Renders using the COLLATE keyword supported by SQLite, MySQL, and PostgreSQL. E.g.:
          >>> from sqlalchemy import cast, select, String
          >>> print select([cast('some string', String(collation='utf8'))])
          SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1
    New in version 0.8: Added support for COLLATE to all string types. convert_unicode – When set to `True`, the `String` type will assume that input is to be passed as Python `unicode` objects, and results returned as Python `unicode` objects. If the DBAPI in use does not support Python unicode (which is fewer and fewer these days), SQLAlchemy will encode/decode the value, using the value of the `encoding` parameter passed to `create_engine()` as the encoding. When using a DBAPI that natively supports Python unicode objects, this flag generally does not need to be set. For columns that are explicitly intended to store non-ASCII data, the `Unicode` or `UnicodeText` types should be used regardless, which feature the same behavior of `convert_unicode` but also indicate an underlying column type that directly supports unicode, such as `NVARCHAR`. For the extremely rare case that Python `unicode` is to be encoded/decoded by SQLAlchemy on a backend that does natively support Python `unicode`, the value `force` can be passed here which will cause SQLAlchemy’s encode/decode services to be used unconditionally. unicode_error – Optional, a method to use to handle Unicode conversion errors. Behaves like the `errors` keyword argument to the standard library’s `string.decode()` functions. This flag requires that `convert_unicode` is set to `force` - otherwise, SQLAlchemy is not guaranteed to handle the task of unicode conversion. Note that this flag adds significant performance overhead to row-fetching operations for backends that already return unicode objects natively (which most DBAPIs do). This flag should only be used as a last resort for reading strings from a column with varied or corrupted encodings.

class `sqlalchemy.dialects.mssql.` `DATETIME2`(precision=None, **kw)

  Bases: `sqlalchemy.dialects.mssql.base._DateTimeBase`, `sqlalchemy.types.DateTime`

  `__init__`(precision=None, **kw)

    Initialize self. See help(type(self)) for accurate signature.

class `sqlalchemy.dialects.mssql.` `DATETIMEOFFSET`(precision=None, **kwargs)

  Bases: `sqlalchemy.types.TypeEngine`

  `__init__`(precision=None, **kwargs)

    Initialize self. See help(type(self)) for accurate signature.

class `sqlalchemy.dialects.mssql.` `IMAGE`(length=None)

  Bases: `sqlalchemy.types.LargeBinary`

  `__init__`(length=None)
    inherited from the `__init__()` method of `LargeBinary`

    Construct a LargeBinary type.

    Parameters: | length – optional, a length for the column for use in DDL statements, for those binary types that accept a length, such as the MySQL BLOB type.

class `sqlalchemy.dialects.mssql.` `MONEY`

  Bases: `sqlalchemy.types.TypeEngine`

  `__init__`
    inherited from the `__init__` attribute of `object`

    Initialize self. See help(type(self)) for accurate signature.

class `sqlalchemy.dialects.mssql.` `NCHAR`(length=None, **kwargs)

  Bases: `sqlalchemy.types.Unicode`

  The SQL NCHAR type.

  `__init__`(length=None, **kwargs)
    inherited from the `__init__()` method of `Unicode`

    Create a `Unicode` object.

    Parameters are the same as that of `String`, with the exception that `convert_unicode` defaults to `True`.

class `sqlalchemy.dialects.mssql.` `NTEXT`(length=None, **kwargs)

  Bases: `sqlalchemy.types.UnicodeText`

  MSSQL NTEXT type, for variable-length unicode text up to 2^30 characters.

  `__init__`(length=None, **kwargs)
    inherited from the `__init__()` method of `UnicodeText`

    Create a Unicode-converting Text type.

    Parameters are the same as that of `Text`, with the exception that `convert_unicode` defaults to `True`.

class `sqlalchemy.dialects.mssql.` `NVARCHAR`(length=None, **kwargs)

  Bases: `sqlalchemy.types.Unicode`

  The SQL NVARCHAR type.

  `__init__`(length=None, **kwargs)
    inherited from the `__init__()` method of `Unicode`

    Create a `Unicode` object.

    Parameters are the same as that of `String`, with the exception that `convert_unicode` defaults to `True`.

class `sqlalchemy.dialects.mssql.` `REAL`(**kw)

  Bases: `sqlalchemy.types.REAL`

  `__init__`(**kw)

    Construct a Float.

    Parameters: | precision – the numeric precision for use in DDL `CREATE TABLE`. asdecimal – the same flag as that of `Numeric`, but defaults to `False`. Note that setting this flag to `True` results in floating point conversion. decimal_return_scale – Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don’t have a notion of “scale”, so by default the float type looks for the first ten decimal places when converting. Specfiying this value will override that length. Note that the MySQL float types, which do include “scale”, will use “scale” as the default for decimal_return_scale, if not otherwise specified. New in version 0.9.0. **kwargs – deprecated. Additional arguments here are ignored by the default `Float` type. For database specific floats that support additional arguments, see that dialect’s documentation for details, such as `sqlalchemy.dialects.mysql.FLOAT`.

class `sqlalchemy.dialects.mssql.` `ROWVERSION`(convert_int=False)

  Bases: `sqlalchemy.dialects.mssql.base.TIMESTAMP`

  Implement the SQL Server ROWVERSION type.

  The ROWVERSION datatype is a SQL Server synonym for the TIMESTAMP datatype, however current SQL Server documentation suggests using ROWVERSION for new datatypes going forward.

  The ROWVERSION datatype does not reflect (e.g. introspect) from the database as itself; the returned datatype will be `mssql.TIMESTAMP`.

  This is a read-only datatype that does not support INSERT of values.

  New in version 1.2.

  See also

  `mssql.TIMESTAMP`

  `__init__`(convert_int=False)
    inherited from the `__init__()` method of `TIMESTAMP`

    Construct a TIMESTAMP or ROWVERSION type.

    Parameters: | convert_int – if True, binary integer values will be converted to integers on read.

    New in version 1.2.

class `sqlalchemy.dialects.mssql.` `SMALLDATETIME`(timezone=False)

  Bases: `sqlalchemy.dialects.mssql.base._DateTimeBase`, `sqlalchemy.types.DateTime`

  `__init__`(timezone=False)
    inherited from the `__init__()` method of `DateTime`

    Construct a new `DateTime`.

    Parameters: | timezone – boolean. Indicates that the datetime type should enable timezone support, if available on the base date/time-holding type only. It is recommended to make use of the `TIMESTAMP` datatype directly when using this flag, as some databases include separate generic date/time-holding types distinct from the timezone-capable TIMESTAMP datatype, such as Oracle.

class `sqlalchemy.dialects.mssql.` `SMALLMONEY`

  Bases: `sqlalchemy.types.TypeEngine`

  `__init__`
    inherited from the `__init__` attribute of `object`

    Initialize self. See help(type(self)) for accurate signature.

class `sqlalchemy.dialects.mssql.` `SQL_VARIANT`

  Bases: `sqlalchemy.types.TypeEngine`

  `__init__`
    inherited from the `__init__` attribute of `object`

    Initialize self. See help(type(self)) for accurate signature.

class `sqlalchemy.dialects.mssql.` `TEXT`(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)

  Bases: `sqlalchemy.types.Text`

  The SQL TEXT type.

  `__init__`(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)
    inherited from the `__init__()` method of `String`

    Create a string-holding type.

    - Parameters: | length – optional, a length for the column for use in DDL and CAST expressions. May be safely omitted if no `CREATE TABLE` will be issued. Certain databases may require a `length` for use in DDL, and will raise an exception when the `CREATE TABLE` DDL is issued if a `VARCHAR` with no length is included. Whether the value is interpreted as bytes or characters is database specific. collation – Optional, a column-level collation for use in DDL and CAST expressions. Renders using the COLLATE keyword supported by SQLite, MySQL, and PostgreSQL. E.g.:
          >>> from sqlalchemy import cast, select, String
          >>> print select([cast('some string', String(collation='utf8'))])
          SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1
    New in version 0.8: Added support for COLLATE to all string types. convert_unicode – When set to `True`, the `String` type will assume that input is to be passed as Python `unicode` objects, and results returned as Python `unicode` objects. If the DBAPI in use does not support Python unicode (which is fewer and fewer these days), SQLAlchemy will encode/decode the value, using the value of the `encoding` parameter passed to `create_engine()` as the encoding. When using a DBAPI that natively supports Python unicode objects, this flag generally does not need to be set. For columns that are explicitly intended to store non-ASCII data, the `Unicode` or `UnicodeText` types should be used regardless, which feature the same behavior of `convert_unicode` but also indicate an underlying column type that directly supports unicode, such as `NVARCHAR`. For the extremely rare case that Python `unicode` is to be encoded/decoded by SQLAlchemy on a backend that does natively support Python `unicode`, the value `force` can be passed here which will cause SQLAlchemy’s encode/decode services to be used unconditionally. unicode_error – Optional, a method to use to handle Unicode conversion errors. Behaves like the `errors` keyword argument to the standard library’s `string.decode()` functions. This flag requires that `convert_unicode` is set to `force` - otherwise, SQLAlchemy is not guaranteed to handle the task of unicode conversion. Note that this flag adds significant performance overhead to row-fetching operations for backends that already return unicode objects natively (which most DBAPIs do). This flag should only be used as a last resort for reading strings from a column with varied or corrupted encodings.

class `sqlalchemy.dialects.mssql.` `TIME`(precision=None, **kwargs)

  Bases: `sqlalchemy.types.TIME`

  `__init__`(precision=None, **kwargs)

    Initialize self. See help(type(self)) for accurate signature.

class `sqlalchemy.dialects.mssql.` `TIMESTAMP`(convert_int=False)

  Bases: `sqlalchemy.types._Binary`

  Implement the SQL Server TIMESTAMP type.

  Note this is completely different than the SQL Standard TIMESTAMP type, which is not supported by SQL Server. It is a read-only datatype that does not support INSERT of values.

  New in version 1.2.

  See also

  `mssql.ROWVERSION`

  `__init__`(convert_int=False)

    Construct a TIMESTAMP or ROWVERSION type.

    Parameters: | convert_int – if True, binary integer values will be converted to integers on read.

    New in version 1.2.

class `sqlalchemy.dialects.mssql.` `TINYINT`

  Bases: `sqlalchemy.types.Integer`

  `__init__`
    inherited from the `__init__` attribute of `object`

    Initialize self. See help(type(self)) for accurate signature.

class `sqlalchemy.dialects.mssql.` `UNIQUEIDENTIFIER`

  Bases: `sqlalchemy.types.TypeEngine`

  `__init__`
    inherited from the `__init__` attribute of `object`

    Initialize self. See help(type(self)) for accurate signature.

class `sqlalchemy.dialects.mssql.` `VARCHAR`(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)

  Bases: `sqlalchemy.types.String`

  The SQL VARCHAR type.

  `__init__`(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)
    inherited from the `__init__()` method of `String`

    Create a string-holding type.

    - Parameters: | length – optional, a length for the column for use in DDL and CAST expressions. May be safely omitted if no `CREATE TABLE` will be issued. Certain databases may require a `length` for use in DDL, and will raise an exception when the `CREATE TABLE` DDL is issued if a `VARCHAR` with no length is included. Whether the value is interpreted as bytes or characters is database specific. collation – Optional, a column-level collation for use in DDL and CAST expressions. Renders using the COLLATE keyword supported by SQLite, MySQL, and PostgreSQL. E.g.:
          >>> from sqlalchemy import cast, select, String
          >>> print select([cast('some string', String(collation='utf8'))])
          SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1
    New in version 0.8: Added support for COLLATE to all string types. convert_unicode – When set to `True`, the `String` type will assume that input is to be passed as Python `unicode` objects, and results returned as Python `unicode` objects. If the DBAPI in use does not support Python unicode (which is fewer and fewer these days), SQLAlchemy will encode/decode the value, using the value of the `encoding` parameter passed to `create_engine()` as the encoding. When using a DBAPI that natively supports Python unicode objects, this flag generally does not need to be set. For columns that are explicitly intended to store non-ASCII data, the `Unicode` or `UnicodeText` types should be used regardless, which feature the same behavior of `convert_unicode` but also indicate an underlying column type that directly supports unicode, such as `NVARCHAR`. For the extremely rare case that Python `unicode` is to be encoded/decoded by SQLAlchemy on a backend that does natively support Python `unicode`, the value `force` can be passed here which will cause SQLAlchemy’s encode/decode services to be used unconditionally. unicode_error – Optional, a method to use to handle Unicode conversion errors. Behaves like the `errors` keyword argument to the standard library’s `string.decode()` functions. This flag requires that `convert_unicode` is set to `force` - otherwise, SQLAlchemy is not guaranteed to handle the task of unicode conversion. Note that this flag adds significant performance overhead to row-fetching operations for backends that already return unicode objects natively (which most DBAPIs do). This flag should only be used as a last resort for reading strings from a column with varied or corrupted encodings.

class `sqlalchemy.dialects.mssql.` `XML`(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)

  Bases: `sqlalchemy.types.Text`

  MSSQL XML type.

  This is a placeholder type for reflection purposes that does not include any Python-side datatype support. It also does not currently support additional arguments, such as “CONTENT”, “DOCUMENT”, “xml_schema_collection”.

  New in version 1.1.11.

  `__init__`(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)
    inherited from the `__init__()` method of `String`

    Create a string-holding type.

    - Parameters: | length – optional, a length for the column for use in DDL and CAST expressions. May be safely omitted if no `CREATE TABLE` will be issued. Certain databases may require a `length` for use in DDL, and will raise an exception when the `CREATE TABLE` DDL is issued if a `VARCHAR` with no length is included. Whether the value is interpreted as bytes or characters is database specific. collation – Optional, a column-level collation for use in DDL and CAST expressions. Renders using the COLLATE keyword supported by SQLite, MySQL, and PostgreSQL. E.g.:
          >>> from sqlalchemy import cast, select, String
          >>> print select([cast('some string', String(collation='utf8'))])
          SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1
    New in version 0.8: Added support for COLLATE to all string types. convert_unicode – When set to `True`, the `String` type will assume that input is to be passed as Python `unicode` objects, and results returned as Python `unicode` objects. If the DBAPI in use does not support Python unicode (which is fewer and fewer these days), SQLAlchemy will encode/decode the value, using the value of the `encoding` parameter passed to `create_engine()` as the encoding. When using a DBAPI that natively supports Python unicode objects, this flag generally does not need to be set. For columns that are explicitly intended to store non-ASCII data, the `Unicode` or `UnicodeText` types should be used regardless, which feature the same behavior of `convert_unicode` but also indicate an underlying column type that directly supports unicode, such as `NVARCHAR`. For the extremely rare case that Python `unicode` is to be encoded/decoded by SQLAlchemy on a backend that does natively support Python `unicode`, the value `force` can be passed here which will cause SQLAlchemy’s encode/decode services to be used unconditionally. unicode_error – Optional, a method to use to handle Unicode conversion errors. Behaves like the `errors` keyword argument to the standard library’s `string.decode()` functions. This flag requires that `convert_unicode` is set to `force` - otherwise, SQLAlchemy is not guaranteed to handle the task of unicode conversion. Note that this flag adds significant performance overhead to row-fetching operations for backends that already return unicode objects natively (which most DBAPIs do). This flag should only be used as a last resort for reading strings from a column with varied or corrupted encodings.

## PyODBC

Support for the Microsoft SQL Server database via the PyODBC driver.

### DBAPI

Documentation and download information (if applicable) for PyODBC is available at: http://pypi.python.org/pypi/pyodbc/

### Connecting

Connect String:

    mssql+pyodbc://<username>:<password>@<dsnname>

### Connecting to PyODBC

The URL here is to be translated to PyODBC connection strings, as detailed in ConnectionStrings.

#### DSN Connections

A DSN-based connection is preferred overall when using ODBC. A basic DSN-based connection looks like:

    engine = create_engine("mssql+pyodbc://scott:tiger@some_dsn")

Which above, will pass the following connection string to PyODBC:

    dsn=mydsn;UID=user;PWD=pass

If the username and password are omitted, the DSN form will also add the `Trusted_Connection=yes` directive to the ODBC string.

#### Hostname Connections

Hostname-based connections are not preferred, however are supported. The ODBC driver name must be explicitly specified:

    engine = create_engine("mssql+pyodbc://scott:tiger@myhost:port/databasename?driver=SQL+Server+Native+Client+10.0")

Changed in version 1.0.0: Hostname-based PyODBC connections now require the SQL Server driver name specified explicitly. SQLAlchemy cannot choose an optimal default here as it varies based on platform and installed drivers.

Other keywords interpreted by the Pyodbc dialect to be passed to `pyodbc.connect()` in both the DSN and hostname cases include: `odbc_autotranslate`, `ansi`, `unicode_results`, `autocommit`.

#### Pass through exact Pyodbc string

A PyODBC connection string can also be sent exactly as specified in ConnectionStrings into the driver using the parameter `odbc_connect`. The delimeters must be URL escaped, however, as illustrated below using `urllib.quote_plus`:

    import urllib
    params = urllib.quote_plus("DRIVER={SQL Server Native Client 10.0};SERVER=dagger;DATABASE=test;UID=user;PWD=password")

    engine = create_engine("mssql+pyodbc:///?odbc_connect=%s" % params)

### Driver / Unicode Support

PyODBC works best with Microsoft ODBC drivers, particularly in the area of Unicode support on both Python 2 and Python 3.

Using the FreeTDS ODBC drivers on Linux or OSX with PyODBC is not recommended; there have been historically many Unicode-related issues in this area, including before Microsoft offered ODBC drivers for Linux and OSX. Now that Microsoft offers drivers for all platforms, for PyODBC support these are recommended. FreeTDS remains relevant for non-ODBC drivers such as pymssql where it works very well.

### Rowcount Support

Pyodbc only has partial support for rowcount. See the notes at Rowcount Support / ORM Versioning for important notes when using ORM versioning.

## mxODBC

Support for the Microsoft SQL Server database via the mxODBC driver.

### DBAPI

Documentation and download information (if applicable) for mxODBC is available at: http://www.egenix.com/

### Connecting

Connect String:

    mssql+mxodbc://<username>:<password>@<dsnname>

### Execution Modes

mxODBC features two styles of statement execution, using the `cursor.execute()` and `cursor.executedirect()` methods (the second being an extension to the DBAPI specification). The former makes use of a particular API call specific to the SQL Server Native Client ODBC driver known SQLDescribeParam, while the latter does not.

mxODBC apparently only makes repeated use of a single prepared statement when SQLDescribeParam is used. The advantage to prepared statement reuse is one of performance. The disadvantage is that SQLDescribeParam has a limited set of scenarios in which bind parameters are understood, including that they cannot be placed within the argument lists of function calls, anywhere outside the FROM, or even within subqueries within the FROM clause - making the usage of bind parameters within SELECT statements impossible for all but the most simplistic statements.

For this reason, the mxODBC dialect uses the “native” mode by default only for INSERT, UPDATE, and DELETE statements, and uses the escaped string mode for all other statements.

This behavior can be controlled via `execution_options()` using the `native_odbc_execute` flag with a value of `True` or `False`, where a value of `True` will unconditionally use native bind parameters and a value of `False` will unconditionally use string-escaped parameters.

## pymssql

Support for the Microsoft SQL Server database via the pymssql driver.

### DBAPI

Documentation and download information (if applicable) for pymssql is available at: http://pymssql.org/

### Connecting

Connect String:

    mssql+pymssql://<username>:<password>@<freetds_name>/?charset=utf8

pymssql is a Python module that provides a Python DBAPI interface around FreeTDS. Compatible builds are available for Linux, MacOSX and Windows platforms.

Modern versions of this driver work very well with SQL Server and FreeTDS from Linux and is highly recommended.

## zxjdbc

Support for the Microsoft SQL Server database via the zxJDBC for Jython driver.

Note

Jython is not supported by current versions of SQLAlchemy. The zxjdbc dialect should be considered as experimental.

### DBAPI

Drivers for this database are available at: http://jtds.sourceforge.net/

### Connecting

Connect String:

    mssql+zxjdbc://user:pass@host:port/dbname[?key=value&key=value...]

## AdoDBAPI

Support for the Microsoft SQL Server database via the adodbapi driver.

### DBAPI

Documentation and download information (if applicable) for adodbapi is available at: http://adodbapi.sourceforge.net/

### Connecting

Connect String:

    mssql+adodbapi://<username>:<password>@<dsnname>

Note

The adodbapi dialect is not implemented SQLAlchemy versions 0.6 and above at this time.