Microsoft SQL Server

Microsoft SQL Server

Support for the Microsoft SQL Server database.

The following table summarizes current support levels for database release versions.

**Supported Microsoft SQL Server versions**
Support type	Versions
Fully tested in CI	2017
Normal support	2012+
Best effort	2005+

DBAPI Support

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

PyODBC
mxODBC
pymssql

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 Dialects 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 Column.autoincrement. This means that by default, the first integer primary key column in a 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 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 Column.autoincrement flag on the desired Column object, and ensure that 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)

Changed in version 1.4: Added Identity construct in a Column to specify the start and increment parameters of an IDENTITY. These replace the use of the Sequence object in order to specify these values.

Deprecated since version 1.4: The mssql_identity_start and mssql_identity_increment parameters to Column are deprecated and should we replaced by an Identity object. Specifying both ways of configuring an IDENTITY will result in a compile error. These options are also no longer returned as part of the dialect_options key in Inspector.get_columns(). Use the information in the identity key instead.

Deprecated since version 1.3: The use of Sequence to specify IDENTITY characteristics is deprecated and will be removed in a future release. Please use the Identity object parameters Identity.start and Identity.increment.

Changed in version 1.4: Removed the ability to use a Sequence object to modify IDENTITY characteristics. Sequence objects now only manipulate true T-SQL SEQUENCE types.

Note

There can only be one IDENTITY column on the table. When using autoincrement=True to enable the IDENTITY keyword, SQLAlchemy does not guard against multiple columns specifying the option simultaneously. The SQL Server database will instead reject the CREATE TABLE statement.

Note

An INSERT statement which attempts to provide a value for a column that is marked with IDENTITY will be rejected by SQL Server. In order for the value to be accepted, a session-level option “SET IDENTITY_INSERT” must be enabled. The SQLAlchemy SQL Server dialect will perform this operation automatically when using a core Insert construct; if the execution specifies a value for the IDENTITY column, the “IDENTITY_INSERT” option will be enabled for the span of that statement’s invocation.However, this scenario is not high performing and should not be relied upon for normal use. If a table doesn’t actually require IDENTITY behavior in its integer primary key column, the keyword should be disabled when creating the table by ensuring that autoincrement=False is set.

Controlling “Start” and “Increment”

Specific control over the “start” and “increment” values for the IDENTITY generator are provided using the Identity.start and Identity.increment parameters passed to the Identity object:

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))
)

The CREATE TABLE for the above Table object would be:

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

Note

The Identity object supports many other parameter in addition to start and increment. These are not supported by SQL Server and will be ignored when generating the CREATE TABLE ddl.

Changed in version 1.3.19: The Identity object is now used to affect the IDENTITY generator for a Column under SQL Server. Previously, the Sequence object was used. As SQL Server now supports real sequences as a separate construct, Sequence will be functional in the normal way starting from SQLAlchemy version 1.4.

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)
with engine.begin() as conn:
    conn.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.

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.

New in version 1.4.0.

MAX on VARCHAR / NVARCHAR

SQL Server supports the special string “MAX” within the VARCHAR and 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

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.

Changed in version 1.4: support added for SQL Server “OFFSET n ROWS” and “FETCH NEXT n ROWS” syntax.

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 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 1.2: 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, 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, 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, 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 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, VARBINARY, TEXT, NTEXT, 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.

Deprecated since version 1.4: The legacy_schema_aliasing flag is now deprecated and will be removed in a future release.

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)

Filtered Indexes

The mssql_where option renders WHERE(condition) for the given string names:

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

would render the index as CREATE INDEX my_index ON table (x) WHERE x > 10.

New in version 1.3.4.

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)

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 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 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 SQL Constructs

Object Name	Description
`try_cast`(arg, *kw)	Create a TRY_CAST expression.

function sqlalchemy.dialects.mssql.``try_cast(\arg, **kw*)

Create a TRY_CAST expression.

TryCast is a subclass of SQLAlchemy’s Cast construct, and works in the same way, except that the SQL expression rendered is “TRY_CAST” rather than “CAST”:

from sqlalchemy import select
from sqlalchemy import Numeric
from sqlalchemy.dialects.mssql import try_cast
stmt = select(
    try_cast(product_table.c.unit_price, Numeric(10, 4))
)

The above would render:

SELECT TRY_CAST (product_table.unit_price AS NUMERIC(10, 4))
FROM product_table

New in version 1.3.7.

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, JSON, 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:

Object Name	Description
`BIT`
`CHAR`	The SQL CHAR type.
`DATETIME2`
`DATETIMEOFFSET`
`IMAGE`
`JSON`	MSSQL JSON type.
`MONEY`
`NCHAR`	The SQL NCHAR type.
`NTEXT`	MSSQL NTEXT type, for variable-length unicode text up to 2^30 characters.
`NVARCHAR`	The SQL NVARCHAR type.
`REAL`
`ROWVERSION`	Implement the SQL Server ROWVERSION type.
`SMALLDATETIME`
`SMALLMONEY`
`SQL_VARIANT`
`TEXT`	The SQL TEXT type.
`TIME`
`TIMESTAMP`	Implement the SQL Server TIMESTAMP type.
`TINYINT`
`UNIQUEIDENTIFIER`
`VARCHAR`	The SQL VARCHAR type.
`XML`	MSSQL XML type.

class sqlalchemy.dialects.mssql.``BIT

Class signature

class sqlalchemy.dialects.mssql.BIT (sqlalchemy.types.TypeEngine)

method sqlalchemy.dialects.mssql.BIT.__init__()

inherited from the builtins.object.__init__ method of builtins.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, _expect_unicode=False)

The SQL CHAR type.

Class signature

class sqlalchemy.dialects.mssql.CHAR (sqlalchemy.types.String)

method sqlalchemy.dialects.mssql.CHAR.__init__(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False, _expect_unicode=False)

inherited from the sqlalchemy.types.String.__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
```
  - convert_unicode –
    
    When set to True, the String type will assume that input is to be passed as Python Unicode objects under Python 2, and results returned as Python Unicode objects. In the rare circumstance that the DBAPI does not support Python unicode under Python 2, SQLAlchemy will use its own encoder/decoder functionality on strings, referring to the value of the create_engine.encoding parameter parameter passed to create_engine() as the encoding.
    
    Deprecated since version 1.3: The String.convert_unicode parameter is deprecated and will be removed in a future release. All modern DBAPIs now support Python Unicode directly and this parameter is unnecessary.
    
    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 string value "force" can be passed here which will cause SQLAlchemy’s encode/decode services to be used unconditionally.
    
    Note
    
    SQLAlchemy’s unicode-conversion flags and features only apply to Python 2; in Python 3, all string objects are Unicode objects. For this reason, as well as the fact that virtually all modern DBAPIs now support Unicode natively even under Python 2, the String.convert_unicode flag is inherently a legacy feature.
    
    Note
    
    In the vast majority of cases, the Unicode or UnicodeText datatypes should be used for a Column that expects to store non-ascii data. These datatypes will ensure that the correct types are used on the database side as well as set up the correct Unicode behaviors under Python 2.
    
    See also
    
    create_engine.convert_unicode - Engine-wide parameter
  - 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, requires that String.convert_unicode is set to "force"
    
    Deprecated since version 1.3: The String.unicode_errors parameter is deprecated and will be removed in a future release. This parameter is unnecessary for modern Python DBAPIs and degrades performance significantly.

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

Class signature

class sqlalchemy.dialects.mssql.DATETIME2 (sqlalchemy.dialects.mssql.base._DateTimeBase, sqlalchemy.types.DateTime)

method sqlalchemy.dialects.mssql.DATETIME2.__init__(precision=None, \*kw*)

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

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

Class signature

class sqlalchemy.dialects.mssql.DATETIMEOFFSET (sqlalchemy.dialects.mssql.base._DateTimeBase, sqlalchemy.types.DateTime)

method sqlalchemy.dialects.mssql.DATETIMEOFFSET.__init__(precision=None, \*kw*)

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

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

Class signature

class sqlalchemy.dialects.mssql.IMAGE (sqlalchemy.types.LargeBinary)

method sqlalchemy.dialects.mssql.IMAGE.__init__(length=None)

inherited from the sqlalchemy.types.LargeBinary.__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.``JSON(none_as_null=False)

MSSQL JSON type.

MSSQL supports JSON-formatted data as of SQL Server 2016.

The JSON datatype at the DDL level will represent the datatype as NVARCHAR(max), but provides for JSON-level comparison functions as well as Python coercion behavior.

JSON is used automatically whenever the base JSON datatype is used against a SQL Server backend.

See also

JSON - main documentation for the generic cross-platform JSON datatype.

The JSON type supports persistence of JSON values as well as the core index operations provided by JSON datatype, by adapting the operations to render the JSON_VALUE or JSON_QUERY functions at the database level.

The SQL Server JSON type necessarily makes use of the JSON_QUERY and JSON_VALUE functions when querying for elements of a JSON object. These two functions have a major restriction in that they are mutually exclusive based on the type of object to be returned. The JSON_QUERY function only returns a JSON dictionary or list, but not an individual string, numeric, or boolean element; the JSON_VALUE function only returns an individual string, numeric, or boolean element. both functions either return NULL or raise an error if they are not used against the correct expected value.

To handle this awkward requirement, indexed access rules are as follows:

When extracting a sub element from a JSON that is itself a JSON dictionary or list, the Comparator.as_json() accessor should be used:

stmt = select(
    data_table.c.data["some key"].as_json()
).where(
    data_table.c.data["some key"].as_json() == {"sub": "structure"}
)

When extracting a sub element from a JSON that is a plain boolean, string, integer, or float, use the appropriate method among Comparator.as_boolean(), Comparator.as_string(), Comparator.as_integer(), Comparator.as_float():
```
stmt = select(
    data_table.c.data["some key"].as_string()
).where(
    data_table.c.data["some key"].as_string() == "some string"
)
```

New in version 1.4.

Class signature

class sqlalchemy.dialects.mssql.JSON (sqlalchemy.types.JSON)

method sqlalchemy.dialects.mssql.JSON.__init__(none_as_null=False)

inherited from the sqlalchemy.types.JSON.__init__ method of JSON

Construct a JSON type.
- Parameters
  
  none_as_null=False –
  
  if True, persist the value None as a SQL NULL value, not the JSON encoding of null. Note that when this flag is False, the null() construct can still be used to persist a NULL value:
```
from sqlalchemy import null
conn.execute(table.insert(), data=null())
```
  Note
  
  JSON.none_as_null does not apply to the values passed to Column.default and Column.server_default; a value of None passed for these parameters means “no default present”.
  
  See also
  
  JSON.NULL

class sqlalchemy.dialects.mssql.``MONEY

Class signature

class sqlalchemy.dialects.mssql.MONEY (sqlalchemy.types.TypeEngine)

method sqlalchemy.dialects.mssql.MONEY.__init__()

inherited from the builtins.object.__init__ method of builtins.object

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

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

The SQL NCHAR type.

Class signature

class sqlalchemy.dialects.mssql.NCHAR (sqlalchemy.types.Unicode)

method sqlalchemy.dialects.mssql.NCHAR.__init__(length=None, \*kwargs*)

inherited from the sqlalchemy.types.Unicode.__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*)

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

Class signature

class sqlalchemy.dialects.mssql.NTEXT (sqlalchemy.types.UnicodeText)

method sqlalchemy.dialects.mssql.NTEXT.__init__(length=None, \*kwargs*)

inherited from the sqlalchemy.types.UnicodeText.__init__ method of UnicodeText

Create a Unicode-converting Text type.

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

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

The SQL NVARCHAR type.

Class signature

class sqlalchemy.dialects.mssql.NVARCHAR (sqlalchemy.types.Unicode)

method sqlalchemy.dialects.mssql.NVARCHAR.__init__(length=None, \*kwargs*)

inherited from the sqlalchemy.types.Unicode.__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*)

Class signature

class sqlalchemy.dialects.mssql.REAL (sqlalchemy.types.REAL)

method sqlalchemy.dialects.mssql.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. Specifying 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.

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

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 TIMESTAMP.

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

New in version 1.2.

See also

TIMESTAMP

Class signature

class sqlalchemy.dialects.mssql.ROWVERSION (sqlalchemy.dialects.mssql.base.TIMESTAMP)

method sqlalchemy.dialects.mssql.ROWVERSION.__init__(convert_int=False)

inherited from the sqlalchemy.dialects.mssql.base.TIMESTAMP.__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)

Class signature

class sqlalchemy.dialects.mssql.SMALLDATETIME (sqlalchemy.dialects.mssql.base._DateTimeBase, sqlalchemy.types.DateTime)

method sqlalchemy.dialects.mssql.SMALLDATETIME.__init__(timezone=False)

inherited from the sqlalchemy.types.DateTime.__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

Class signature

class sqlalchemy.dialects.mssql.SMALLMONEY (sqlalchemy.types.TypeEngine)

method sqlalchemy.dialects.mssql.SMALLMONEY.__init__()

inherited from the builtins.object.__init__ method of builtins.object

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

class sqlalchemy.dialects.mssql.``SQL_VARIANT

Class signature

class sqlalchemy.dialects.mssql.SQL_VARIANT (sqlalchemy.types.TypeEngine)

method sqlalchemy.dialects.mssql.SQL_VARIANT.__init__()

inherited from the builtins.object.__init__ method of builtins.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, _expect_unicode=False)

The SQL TEXT type.

Class signature

class sqlalchemy.dialects.mssql.TEXT (sqlalchemy.types.Text)

method sqlalchemy.dialects.mssql.TEXT.__init__(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False, _expect_unicode=False)

inherited from the sqlalchemy.types.String.__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
```
  - convert_unicode –
    
    When set to True, the String type will assume that input is to be passed as Python Unicode objects under Python 2, and results returned as Python Unicode objects. In the rare circumstance that the DBAPI does not support Python unicode under Python 2, SQLAlchemy will use its own encoder/decoder functionality on strings, referring to the value of the create_engine.encoding parameter parameter passed to create_engine() as the encoding.
    
    Deprecated since version 1.3: The String.convert_unicode parameter is deprecated and will be removed in a future release. All modern DBAPIs now support Python Unicode directly and this parameter is unnecessary.
    
    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 string value "force" can be passed here which will cause SQLAlchemy’s encode/decode services to be used unconditionally.
    
    Note
    
    SQLAlchemy’s unicode-conversion flags and features only apply to Python 2; in Python 3, all string objects are Unicode objects. For this reason, as well as the fact that virtually all modern DBAPIs now support Unicode natively even under Python 2, the String.convert_unicode flag is inherently a legacy feature.
    
    Note
    
    In the vast majority of cases, the Unicode or UnicodeText datatypes should be used for a Column that expects to store non-ascii data. These datatypes will ensure that the correct types are used on the database side as well as set up the correct Unicode behaviors under Python 2.
    
    See also
    
    create_engine.convert_unicode - Engine-wide parameter
  - 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, requires that String.convert_unicode is set to "force"
    
    Deprecated since version 1.3: The String.unicode_errors parameter is deprecated and will be removed in a future release. This parameter is unnecessary for modern Python DBAPIs and degrades performance significantly.

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

Class signature

class sqlalchemy.dialects.mssql.TIME (sqlalchemy.types.TIME)

method sqlalchemy.dialects.mssql.TIME.__init__(precision=None, \*kwargs*)

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

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

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

ROWVERSION

Class signature

class sqlalchemy.dialects.mssql.TIMESTAMP (sqlalchemy.types._Binary)

method sqlalchemy.dialects.mssql.TIMESTAMP.__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

Class signature

class sqlalchemy.dialects.mssql.TINYINT (sqlalchemy.types.Integer)

method sqlalchemy.dialects.mssql.TINYINT.__init__()

inherited from the builtins.object.__init__ method of builtins.object

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

class sqlalchemy.dialects.mssql.``UNIQUEIDENTIFIER

Class signature

class sqlalchemy.dialects.mssql.UNIQUEIDENTIFIER (sqlalchemy.types.TypeEngine)

method sqlalchemy.dialects.mssql.UNIQUEIDENTIFIER.__init__()

inherited from the builtins.object.__init__ method of builtins.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, _expect_unicode=False)

The SQL VARCHAR type.

Class signature

class sqlalchemy.dialects.mssql.VARCHAR (sqlalchemy.types.String)

method sqlalchemy.dialects.mssql.VARCHAR.__init__(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False, _expect_unicode=False)

inherited from the sqlalchemy.types.String.__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
```
  - convert_unicode –
    
    When set to True, the String type will assume that input is to be passed as Python Unicode objects under Python 2, and results returned as Python Unicode objects. In the rare circumstance that the DBAPI does not support Python unicode under Python 2, SQLAlchemy will use its own encoder/decoder functionality on strings, referring to the value of the create_engine.encoding parameter parameter passed to create_engine() as the encoding.
    
    Deprecated since version 1.3: The String.convert_unicode parameter is deprecated and will be removed in a future release. All modern DBAPIs now support Python Unicode directly and this parameter is unnecessary.
    
    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 string value "force" can be passed here which will cause SQLAlchemy’s encode/decode services to be used unconditionally.
    
    Note
    
    SQLAlchemy’s unicode-conversion flags and features only apply to Python 2; in Python 3, all string objects are Unicode objects. For this reason, as well as the fact that virtually all modern DBAPIs now support Unicode natively even under Python 2, the String.convert_unicode flag is inherently a legacy feature.
    
    Note
    
    In the vast majority of cases, the Unicode or UnicodeText datatypes should be used for a Column that expects to store non-ascii data. These datatypes will ensure that the correct types are used on the database side as well as set up the correct Unicode behaviors under Python 2.
    
    See also
    
    create_engine.convert_unicode - Engine-wide parameter
  - 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, requires that String.convert_unicode is set to "force"
    
    Deprecated since version 1.3: The String.unicode_errors parameter is deprecated and will be removed in a future release. This parameter is unnecessary for modern Python DBAPIs and degrades performance significantly.

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

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.

Class signature

class sqlalchemy.dialects.mssql.XML (sqlalchemy.types.Text)

method sqlalchemy.dialects.mssql.XML.__init__(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False, _expect_unicode=False)

inherited from the sqlalchemy.types.String.__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
```
  - convert_unicode –
    
    When set to True, the String type will assume that input is to be passed as Python Unicode objects under Python 2, and results returned as Python Unicode objects. In the rare circumstance that the DBAPI does not support Python unicode under Python 2, SQLAlchemy will use its own encoder/decoder functionality on strings, referring to the value of the create_engine.encoding parameter parameter passed to create_engine() as the encoding.
    
    Deprecated since version 1.3: The String.convert_unicode parameter is deprecated and will be removed in a future release. All modern DBAPIs now support Python Unicode directly and this parameter is unnecessary.
    
    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 string value "force" can be passed here which will cause SQLAlchemy’s encode/decode services to be used unconditionally.
    
    Note
    
    SQLAlchemy’s unicode-conversion flags and features only apply to Python 2; in Python 3, all string objects are Unicode objects. For this reason, as well as the fact that virtually all modern DBAPIs now support Unicode natively even under Python 2, the String.convert_unicode flag is inherently a legacy feature.
    
    Note
    
    In the vast majority of cases, the Unicode or UnicodeText datatypes should be used for a Column that expects to store non-ascii data. These datatypes will ensure that the correct types are used on the database side as well as set up the correct Unicode behaviors under Python 2.
    
    See also
    
    create_engine.convert_unicode - Engine-wide parameter
  - 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, requires that String.convert_unicode is set to "force"
    
    Deprecated since version 1.3: The String.unicode_errors parameter is deprecated and will be removed in a future release. This parameter is unnecessary for modern Python DBAPIs and degrades performance significantly.

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 connection in ODBC means that a pre-existing ODBC datasource is configured on the client machine. The application then specifies the name of this datasource, which encompasses details such as the specific ODBC driver in use as well as the network address of the database. Assuming a datasource is configured on the client, 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 also supported by pyodbc. These are often easier to use than a DSN and have the additional advantage that the specific database name to connect towards may be specified locally in the URL, rather than it being fixed as part of a datasource configuration.

When using a hostname connection, the driver name must also be specified in the query parameters of the URL. As these names usually have spaces in them, the name must be URL encoded which means using plus signs for spaces:

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

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, authentication. Note that in order for the dialect to recognize these keywords (including the driver keyword above) they must be all lowercase. Multiple additional keyword arguments must be separated by an ampersand (&), not a semicolon:

engine = create_engine(
    "mssql+pyodbc://scott:tiger@myhost:port/databasename"
    "?driver=ODBC+Driver+17+for+SQL+Server"
    "&authentication=ActiveDirectoryIntegrated"
)

Pass through exact Pyodbc string

A PyODBC connection string can also be sent in pyodbc’s format directly, as specified in ConnectionStrings into the driver using the parameter odbc_connect. The delimeters must be URL encoded, as illustrated below using urllib.parse.quote_plus:

import urllib
params = urllib.parse.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)

Pyodbc Pooling / connection close behavior

PyODBC uses internal pooling by default, which means connections will be longer lived than they are within SQLAlchemy itself. As SQLAlchemy has its own pooling behavior, it is often preferable to disable this behavior. This behavior can only be disabled globally at the PyODBC module level, before any connections are made:

import pyodbc
pyodbc.pooling = False
# don't use the engine before pooling is set to False
engine = create_engine("mssql+pyodbc://user:pass@dsn")

If this variable is left at its default value of True, the application will continue to maintain active database connections, even when the SQLAlchemy engine itself fully discards a connection or if the engine is disposed.

See also

pooling - in the PyODBC documentation.

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.

Fast Executemany Mode

The Pyodbc driver has added support for a “fast executemany” mode of execution which greatly reduces round trips for a DBAPI executemany() call when using Microsoft ODBC drivers, for limited size batches that fit in memory. The feature is enabled by setting the flag .fast_executemany on the DBAPI cursor when an executemany call is to be used. The SQLAlchemy pyodbc SQL Server dialect supports setting this flag automatically when the .fast_executemany flag is passed to create_engine() ; note that the ODBC driver must be the Microsoft driver in order to use this flag:

engine = create_engine(
    "mssql+pyodbc://scott:tiger@mssql2017:1433/test?driver=ODBC+Driver+13+for+SQL+Server",
    fast_executemany=True)

Warning

The pyodbc fast_executemany mode buffers all rows in memory and is not compatible with very large batches of data. A future version of SQLAlchemy may support this flag as a per-execution option instead.

New in version 1.3.

See also

fast executemany - on github

Setinputsizes Support

The pyodbc cursor.setinputsizes() method can be used if necessary. To enable this hook, pass use_setinputsizes=True to create_engine():

engine = create_engine("mssql+pyodbc://...", use_setinputsizes=True)

The behavior of the hook can then be customized, as may be necessary particularly if fast_executemany is in use, via the DialectEvents.do_setinputsizes() hook. See that method for usage examples.

Changed in version 1.4.1: The pyodbc dialects will not use setinputsizes unless use_setinputsizes=True is passed.

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>

Deprecated since version 1.4: The mxODBC DBAPI is deprecated and will be removed in a future version. Please use one of the supported DBAPIs to connect to mssql.

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 Executable.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.

Connecting

Connect String:

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

pymssql is a Python module that provides a Python DBAPI interface around FreeTDS.

Note

pymssql is currently not included in SQLAlchemy’s continuous integration (CI) testing.

Modern versions of this driver worked very well with SQL Server and FreeTDS from Linux and were highly recommended. However, pymssql is currently unmaintained and has fallen behind the progress of the Microsoft ODBC driver in its support for newer features of SQL Server. The latest official release of pymssql at the time of this document is version 2.1.4 (August, 2018) and it lacks support for:

table-valued parameters (TVPs),
datetimeoffset columns using timezone-aware datetime objects (values are sent and retrieved as strings), and
encrypted connections (e.g., to Azure SQL), when pymssql is installed from the pre-built wheels. Support for encrypted connections requires building pymssql from source, which can be a nuisance, especially under Windows.

The above features are all supported by mssql+pyodbc when using Microsoft’s ODBC Driver for SQL Server (msodbcsql), which is now available for Windows, (several flavors of) Linux, and macOS.