sqlalchemy.databases.mssql

External Dialects

In addition to the above DBAPI layers with native SQLAlchemy support, there are third-party dialects for other DBAPI layers that are compatible with SQL Server. See the "External Dialects" list on the :ref:`dialect_toplevel` page.

Auto Increment Behavior / IDENTITY Columns

SQL Server provides so-called "auto incrementing" behavior using the IDENTITY construct, which can be placed on any single integer column in a table. SQLAlchemy considers IDENTITY within its default "autoincrement" behavior for an integer primary key column, described at :paramref:`_schema.Column.autoincrement`. This means that by default, the first integer primary key column in a _schema.Table will be considered to be the identity column - unless it is associated with a .Sequence - 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,
    x INTEGER NULL,
    PRIMARY KEY (id)
)

For the case where this default generation of IDENTITY is not desired, specify False for the :paramref:`_schema.Column.autoincrement` flag, on the first integer primary key column:

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

To add the IDENTITY keyword to a non-primary key column, specify True for the :paramref:`_schema.Column.autoincrement` flag on the desired _schema.Column object, and ensure that :paramref:`_schema.Column.autoincrement` is set to False on any integer primary key column:

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

from sqlalchemy import Table, Integer, Column, Identity

test = Table(
    'test', metadata,
    Column(
        'id',
        Integer,
        primary_key=True,
        Identity(start=100, increment=10)
    ),
    Column('name', String(20))
)

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

from sqlalchemy import Column
from sqlalchemy import Integer
from sqlalchemy import Numeric
from sqlalchemy import String
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

class TestTable(Base):
    __tablename__ = "test"
    id = Column(
        Integer().with_variant(Numeric(10, 0), "mssql"),
        primary_key=True,
        autoincrement=True,
    )
    name = Column(String)

from sqlalchemy import TypeDecorator

class NumericAsInteger(TypeDecorator):
    '''normalize floating point return values into ints'''

    impl = Numeric(10, 0, asdecimal=False)
    cache_ok = True

    def process_result_value(self, value, dialect):
        if value is not None:
            value = int(value)
        return value

class TestTable(Base):
    __tablename__ = "test"
    id = Column(
        Integer().with_variant(NumericAsInteger, "mssql"),
        primary_key=True,
        autoincrement=True,
    )
    name = Column(String)

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

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

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

SEQUENCE support

The .Sequence object now creates "real" sequences, i.e., CREATE SEQUENCE. To provide compatibility with other dialects, .Sequence defaults to a start value of 1, even though the T-SQL defaults is -9223372036854775808.

MAX on VARCHAR / NVARCHAR

SQL Server supports the special string "MAX" within the _types.VARCHAR and _types.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 _schema.Table, the CREATE TABLE statement for this column will yield:

login VARCHAR(32) COLLATE Latin1_General_CI_AS NULL

LIMIT/OFFSET Support

MSSQL has added support for LIMIT / OFFSET as of SQL Server 2012, via the "OFFSET n ROWS" and "FETCH NEXT n ROWS" clauses. SQLAlchemy supports these syntaxes automatically if SQL Server 2012 or greater is detected.

For statements that specify only LIMIT and no OFFSET, all versions of SQL Server support the TOP keyword. This syntax is used for all SQL Server versions when no OFFSET clause is present. A statement such as:

select(some_table).limit(5)

will render similarly to:

SELECT TOP 5 col1, col2.. FROM table

For versions of SQL Server prior to SQL Server 2012, a statement that uses LIMIT and OFFSET, or just OFFSET alone, will be rendered using the ROW_NUMBER() window function. A statement such as:

select(some_table).order_by(some_table.c.col3).limit(5).offset(10)

will render similarly to:

SELECT anon_1.col1, anon_1.col2 FROM (SELECT col1, col2,
ROW_NUMBER() OVER (ORDER BY col3) AS
mssql_rn FROM table WHERE t.x = :x_1) AS
anon_1 WHERE mssql_rn > :param_1 AND mssql_rn <= :param_2 + :param_1

Note that when using LIMIT and/or OFFSET, whether using the older or newer SQL Server syntaxes, the statement must have an ORDER BY as well, else a .CompileError is raised.

Transaction Isolation Level

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

To set isolation level using _sa.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

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, _expression.TextClause 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, _expression.TextClause 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, _expression.TextClause 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 _sa.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: _types.NVARCHAR, _types.VARCHAR, _types.VARBINARY, _types.TEXT, _mssql.NTEXT, _mssql.IMAGE will always remain fixed and always output exactly that type.

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 :paramref:`_schema.Table.schema` argument of _schema.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]"
)

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 _sa.create_engine as illustrated above.

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))

MSSQL-Specific Index Options

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

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

Index("my_index", table.c.x, mssql_where=table.c.x > 10)

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

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 SQLAlchemy 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 _schema.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 _sa.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 :paramref:`_schema.Table.implicit_returning` flag to False on a particular _schema.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 https://msdn.microsoft.com/en-us/library/ms175095.aspx.