SQLAlchemy 2.0 Documentation
Dialects
- PostgreSQL
- MySQL and MariaDB
- SQLite
- Oracle
- Microsoft SQL Server¶
- Support for the Microsoft SQL Server database.
- External Dialects
- Auto Increment Behavior / IDENTITY Columns
- SEQUENCE support
- MAX on VARCHAR / NVARCHAR
- Collation Support
- LIMIT/OFFSET Support
- DDL Comment Support
- Transaction Isolation Level
- Temporary Table / Resource Reset for Connection Pooling
- Nullability
- Date / Time Handling
- Large Text/Binary Type Deprecation
- Multipart Schema Names
- Legacy Schema Mode
- Clustered Index Support
- Columnstore Index Support
- MSSQL-Specific Index Options
- Compatibility Levels
- Triggers
- Rowcount Support / ORM Versioning
- Enabling Snapshot Isolation
- SQL Server SQL Constructs
- SQL Server Data Types
- PyODBC
- pymssql
- aioodbc
Project Versions
- Previous: Oracle
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- On this page:
- Microsoft SQL Server
- Support for the Microsoft SQL Server database.
- External Dialects
- Auto Increment Behavior / IDENTITY Columns
- SEQUENCE support
- MAX on VARCHAR / NVARCHAR
- Collation Support
- LIMIT/OFFSET Support
- DDL Comment Support
- Transaction Isolation Level
- Temporary Table / Resource Reset for Connection Pooling
- Nullability
- Date / Time Handling
- Large Text/Binary Type Deprecation
- Multipart Schema Names
- Legacy Schema Mode
- Clustered Index Support
- Columnstore Index Support
- MSSQL-Specific Index Options
- Compatibility Levels
- Triggers
- Rowcount Support / ORM Versioning
- Enabling Snapshot Isolation
- SQL Server SQL Constructs
- SQL Server Data Types
- PyODBC
- pymssql
- aioodbc
Microsoft SQL Server¶
Support for the Microsoft SQL Server database.
The following table summarizes current support levels for database release versions.
Support type |
Versions |
---|---|
2012+ |
|
2005+ |
DBAPI Support¶
The following dialect/DBAPI options are available. Please refer to individual DBAPI sections for connect information.
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.
Using IDENTITY with Non-Integer numeric types¶
SQL Server also allows IDENTITY
to be used with NUMERIC
columns. To
implement this pattern smoothly in SQLAlchemy, the primary datatype of the
column should remain as Integer
, however the underlying implementation
type deployed to the SQL Server database can be specified as Numeric
using
TypeEngine.with_variant()
:
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)
In the above example, Integer().with_variant()
provides clear usage
information that accurately describes the intent of the code. The general
restriction that autoincrement
only applies to Integer
is established
at the metadata level and not at the per-dialect level.
When using the above pattern, the primary key identifier that comes back from
the insertion of a row, which is also the value that would be assigned to an
ORM object such as TestTable
above, will be an instance of Decimal()
and not int
when using SQL Server. The numeric return type of the
Numeric
type can be changed to return floats by passing False
to Numeric.asdecimal
. To normalize the return type of the
above Numeric(10, 0)
to return Python ints (which also support “long”
integer values in Python 3), use TypeDecorator
as follows:
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)
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 (?)
As of SQLAlchemy 2.0, the “Insert Many Values” Behavior for INSERT statements feature is also used by default to optimize many-row INSERT statements; for SQL Server the feature takes place for both RETURNING and-non RETURNING INSERT statements.
Changed in version 2.0.10: The “Insert Many Values” Behavior for INSERT statements feature for SQL Server was temporarily disabled for SQLAlchemy version 2.0.9 due to issues with row ordering. As of 2.0.10 the feature is re-enabled, with special case handling for the unit of work’s requirement for RETURNING to be ordered.
When RETURNING is not available or has been disabled via
implicit_returning=False
, either thescope_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', metadata, 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 flaguse_scope_identity=False
is passed tocreate_engine()
, the statementSELECT @@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 creates “real” sequences, i.e.,
CREATE SEQUENCE
:
>>> from sqlalchemy import Sequence
>>> from sqlalchemy.schema import CreateSequence
>>> from sqlalchemy.dialects import mssql
>>> print(CreateSequence(Sequence("my_seq", start=1)).compile(dialect=mssql.dialect()))
CREATE SEQUENCE my_seq START WITH 1
For integer primary key generation, SQL Server’s IDENTITY
construct should
generally be preferred vs. sequence.
Tip
The default start value for T-SQL is -2**63
instead of 1 as
in most other SQL databases. Users should explicitly set the
Sequence.start
to 1 if that’s the expected default:
seq = Sequence("my_sequence", start=1)
New in version 1.4: added SQL Server support for Sequence
Changed in version 2.0: The SQL Server dialect will no longer implicitly
render “START WITH 1” for CREATE SEQUENCE
, which was the behavior
first implemented in version 1.4.
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.
DDL Comment Support¶
Comment support, which includes DDL rendering for attributes such as
Table.comment
and Column.comment
, as
well as the ability to reflect these comments, is supported assuming a
supported version of SQL Server is in use. If a non-supported version such as
Azure Synapse is detected at first-connect time (based on the presence
of the fn_listextendedproperty
SQL function), comment support including
rendering and table-comment reflection is disabled, as both features rely upon
SQL Server stored procedures and functions that are not available on all
backend types.
To force comment support to be on or off, bypassing autodetection, set the
parameter supports_comments
within create_engine()
:
e = create_engine("mssql+pyodbc://u:p@dsn", supports_comments=False)
New in version 2.0: Added support for table and column comments for the SQL Server dialect, including DDL generation and reflection.
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-specificREAD COMMITTED
READ UNCOMMITTED
REPEATABLE READ
SERIALIZABLE
SNAPSHOT
- specific to SQL Server
There are also more options for isolation level configurations, such as
“sub-engine” objects linked to a main Engine
which each apply
different isolation level settings. See the discussion at
Setting Transaction Isolation Levels including DBAPI Autocommit for background.
Temporary Table / Resource Reset for Connection Pooling¶
The QueuePool
connection pool implementation used
by the SQLAlchemy Engine
object includes
reset on return behavior that will invoke
the DBAPI .rollback()
method when connections are returned to the pool.
While this rollback will clear out the immediate state used by the previous
transaction, it does not cover a wider range of session-level state, including
temporary tables as well as other server state such as prepared statement
handles and statement caches. An undocumented SQL Server procedure known
as sp_reset_connection
is known to be a workaround for this issue which
will reset most of the session state that builds up on a connection, including
temporary tables.
To install sp_reset_connection
as the means of performing reset-on-return,
the PoolEvents.reset()
event hook may be used, as demonstrated in the
example below. The create_engine.pool_reset_on_return
parameter
is set to None
so that the custom scheme can replace the default behavior
completely. The custom hook implementation calls .rollback()
in any case,
as it’s usually important that the DBAPI’s own tracking of commit/rollback
will remain consistent with the state of the transaction:
from sqlalchemy import create_engine
from sqlalchemy import event
mssql_engine = create_engine(
"mssql+pyodbc://scott:tiger^5HHH@mssql2017:1433/test?driver=ODBC+Driver+17+for+SQL+Server",
# disable default reset-on-return scheme
pool_reset_on_return=None,
)
@event.listens_for(mssql_engine, "reset")
def _reset_mssql(dbapi_connection, connection_record, reset_state):
if not reset_state.terminate_only:
dbapi_connection.execute("{call sys.sp_reset_connection}")
# so that the DBAPI itself knows that the connection has been
# reset
dbapi_connection.rollback()
Changed in version 2.0.0b3: Added additional state arguments to
the PoolEvents.reset()
event and additionally ensured the event
is invoked for all “reset” occurrences, so that it’s appropriate
as a place for custom “reset” handlers. Previous schemes which
use the PoolEvents.checkin()
handler remain usable as well.
See also
Reset On Return - in the Connection Pooling documentation
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
, theUnicodeText
,TextClause
andLargeBinary
datatypes, when used to render DDL, will render the typesNVARCHAR(max)
,VARCHAR(max)
, andVARBINARY(max)
, respectively. This is a new behavior as of the addition of this flag.When this flag is
False
, theUnicodeText
,TextClause
andLargeBinary
datatypes, when used to render DDL, will render the typesNTEXT
,TEXT
, andIMAGE
, 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 asFalse
.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 toTrue
orFalse
based on whether 2012 or greater is detected.The flag can be set to either
True
orFalse
when the dialect is created, typically viacreate_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.
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 delimiters 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.
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
.
For indexes this option can be combined with the mssql_columnstore
one
to create a clustered columnstore index.
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))
Columnstore Index Support¶
The MSSQL dialect supports columnstore indexes via the mssql_columnstore
option. This option is available to Index
. It be combined with
the mssql_clustered
option to create a clustered columnstore index.
To generate a columnstore index:
Index("my_index", table.c.x, mssql_columnstore=True)
which renders the index as CREATE COLUMNSTORE INDEX my_index ON table (x)
.
To generate a clustered columnstore index provide no columns:
idx = Index("my_index", mssql_clustered=True, mssql_columnstore=True)
# required to associate the index with the table
table.append_constraint(idx)
the above renders the index as
CREATE CLUSTERED COLUMNSTORE INDEX my_index ON table
.
New in version 2.0.18.
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
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}
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. Previous versions of SQLAlchemy therefore had limitations for features such as the “ORM Versioning” feature that relies upon accurate rowcounts in order to match version numbers with matched rows.
SQLAlchemy 2.0 now retrieves the “rowcount” manually for these particular use cases based on counting the rows that arrived back within RETURNING; so while the driver still has this limitation, the ORM Versioning feature is no longer impacted by it. As of SQLAlchemy 2.0.5, ORM versioning has been fully re-enabled for the pyodbc driver.
Changed in version 2.0.5: ORM versioning support is restored for the pyodbc driver. Previously, a warning would be emitted during ORM flush that versioning was not supported.
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.
SQL Server SQL Constructs¶
Object Name | Description |
---|---|
try_cast(expression, type_) |
Produce a |
- function sqlalchemy.dialects.mssql.try_cast(expression: _ColumnExpressionOrLiteralArgument[Any], type_: _TypeEngineArgument[_T]) → TryCast[_T]¶
Produce a
TRY_CAST
expression for backends which support it; this is aCAST
which returns NULL for un-castable conversions.In SQLAlchemy, this construct is supported only by the SQL Server dialect, and will raise a
CompileError
if used on other included backends. However, third party backends may also support this construct.Tip
As
try_cast()
originates from the SQL Server dialect, it’s importable both fromsqlalchemy.
as well as fromsqlalchemy.dialects.mssql
.try_cast()
returns an instance ofTryCast
and generally behaves similarly to theCast
construct; at the SQL level, the difference betweenCAST
andTRY_CAST
is thatTRY_CAST
returns NULL for an un-castable expression, such as attempting to cast a string"hi"
to an integer value.E.g.:
from sqlalchemy import select, try_cast, Numeric stmt = select( try_cast(product_table.c.unit_price, Numeric(10, 4)) )
The above would render on Microsoft SQL Server as:
SELECT TRY_CAST (product_table.unit_price AS NUMERIC(10, 4)) FROM product_table
New in version 2.0.14:
try_cast()
has been generalized from the SQL Server dialect into a general use construct that may be supported by additional dialects.
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,
DOUBLE_PRECISION,
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 |
---|---|
MSSQL BIT type. |
|
the SQL Server DOUBLE PRECISION datatype. |
|
MSSQL JSON type. |
|
MSSQL NTEXT type, for variable-length unicode text up to 2^30 characters. |
|
the SQL Server REAL datatype. |
|
Implement the SQL Server ROWVERSION type. |
|
Implement the SQL Server TIMESTAMP type. |
|
MSSQL XML type. |
- class sqlalchemy.dialects.mssql.BIT¶
MSSQL BIT type.
Both pyodbc and pymssql return values from BIT columns as Python <class ‘bool’> so just subclass Boolean.
Members
Class signature
class
sqlalchemy.dialects.mssql.BIT
(sqlalchemy.types.Boolean
)-
method
sqlalchemy.dialects.mssql.BIT.
__init__(create_constraint: bool = False, name: str | None = None, _create_events: bool = True, _adapted_from: SchemaType | None = None)¶ inherited from the
sqlalchemy.types.Boolean.__init__
method ofBoolean
Construct a Boolean.
- Parameters:
create_constraint¶ –
defaults to False. If the boolean is generated as an int/smallint, also create a CHECK constraint on the table that ensures 1 or 0 as a value.
Note
it is strongly recommended that the CHECK constraint have an explicit name in order to support schema-management concerns. This can be established either by setting the
Boolean.name
parameter or by setting up an appropriate naming convention; see Configuring Constraint Naming Conventions for background.Changed in version 1.4: - this flag now defaults to False, meaning no CHECK constraint is generated for a non-native enumerated type.
name¶ – if a CHECK constraint is generated, specify the name of the constraint.
-
method
- class sqlalchemy.dialects.mssql.CHAR
The SQL CHAR type.
Class signature
class
sqlalchemy.dialects.mssql.CHAR
(sqlalchemy.types.String
)-
method
sqlalchemy.dialects.mssql.CHAR.
__init__(length: int | None = None, collation: str | None = None) inherited from the
sqlalchemy.types.String.__init__
method ofString
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 alength
for use in DDL, and will raise an exception when theCREATE TABLE
DDL is issued if aVARCHAR
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_1Note
In most cases, the
Unicode
orUnicodeText
datatypes should be used for aColumn
that expects to store non-ascii data. These datatypes will ensure that the correct types are used on the database.
-
method
- class sqlalchemy.dialects.mssql.DATETIME2¶
Class signature
class
sqlalchemy.dialects.mssql.DATETIME2
(sqlalchemy.dialects.mssql.base._DateTimeBase
,sqlalchemy.types.DateTime
)
- class sqlalchemy.dialects.mssql.DATETIMEOFFSET¶
Class signature
class
sqlalchemy.dialects.mssql.DATETIMEOFFSET
(sqlalchemy.dialects.mssql.base._DateTimeBase
,sqlalchemy.types.DateTime
)
- class sqlalchemy.dialects.mssql.DOUBLE_PRECISION¶
the SQL Server DOUBLE PRECISION datatype.
New in version 2.0.11.
Class signature
class
sqlalchemy.dialects.mssql.DOUBLE_PRECISION
(sqlalchemy.types.DOUBLE_PRECISION
)
- class sqlalchemy.dialects.mssql.IMAGE¶
Members
Class signature
class
sqlalchemy.dialects.mssql.IMAGE
(sqlalchemy.types.LargeBinary
)-
method
sqlalchemy.dialects.mssql.IMAGE.
__init__(length: int | None = None)¶ inherited from the
sqlalchemy.types.LargeBinary.__init__
method ofLargeBinary
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.
-
method
- class sqlalchemy.dialects.mssql.JSON¶
MSSQL JSON type.
MSSQL supports JSON-formatted data as of SQL Server 2016.
The
JSON
datatype at the DDL level will represent the datatype asNVARCHAR(max)
, but provides for JSON-level comparison functions as well as Python coercion behavior.JSON
is used automatically whenever the baseJSON
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 byJSON
datatype, by adapting the operations to render theJSON_VALUE
orJSON_QUERY
functions at the database level.The SQL Server
JSON
type necessarily makes use of theJSON_QUERY
andJSON_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. TheJSON_QUERY
function only returns a JSON dictionary or list, but not an individual string, numeric, or boolean element; theJSON_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.
Members
Class signature
class
sqlalchemy.dialects.mssql.JSON
(sqlalchemy.types.JSON
)-
method
sqlalchemy.dialects.mssql.JSON.
__init__(none_as_null: bool = False)¶ inherited from the
sqlalchemy.types.JSON.__init__
method ofJSON
Construct a
JSON
type.- Parameters:
none_as_null=False¶ –
if True, persist the value
None
as a SQL NULL value, not the JSON encoding ofnull
. Note that when this flag is False, thenull()
construct can still be used to persist a NULL value, which may be passed directly as a parameter value that is specially interpreted by theJSON
type as SQL NULL:from sqlalchemy import null conn.execute(table.insert(), {"data": null()})
Note
JSON.none_as_null
does not apply to the values passed toColumn.default
andColumn.server_default
; a value ofNone
passed for these parameters means “no default present”.Additionally, when used in SQL comparison expressions, the Python value
None
continues to refer to SQL null, and not JSON NULL. TheJSON.none_as_null
flag refers explicitly to the persistence of the value within an INSERT or UPDATE statement. TheJSON.NULL
value should be used for SQL expressions that wish to compare to JSON null.See also
- class sqlalchemy.dialects.mssql.MONEY¶
Class signature
class
sqlalchemy.dialects.mssql.MONEY
(sqlalchemy.types.TypeEngine
)
- class sqlalchemy.dialects.mssql.NCHAR
The SQL NCHAR type.
Class signature
class
sqlalchemy.dialects.mssql.NCHAR
(sqlalchemy.types.Unicode
)-
method
sqlalchemy.dialects.mssql.NCHAR.
__init__(length: int | None = None, collation: str | None = None) inherited from the
sqlalchemy.types.String.__init__
method ofString
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 alength
for use in DDL, and will raise an exception when theCREATE TABLE
DDL is issued if aVARCHAR
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_1Note
In most cases, the
Unicode
orUnicodeText
datatypes should be used for aColumn
that expects to store non-ascii data. These datatypes will ensure that the correct types are used on the database.
-
method
- class sqlalchemy.dialects.mssql.NTEXT¶
MSSQL NTEXT type, for variable-length unicode text up to 2^30 characters.
Members
Class signature
class
sqlalchemy.dialects.mssql.NTEXT
(sqlalchemy.types.UnicodeText
)-
method
sqlalchemy.dialects.mssql.NTEXT.
__init__(length: int | None = None, collation: str | None = None)¶ inherited from the
sqlalchemy.types.String.__init__
method ofString
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 alength
for use in DDL, and will raise an exception when theCREATE TABLE
DDL is issued if aVARCHAR
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_1Note
In most cases, the
Unicode
orUnicodeText
datatypes should be used for aColumn
that expects to store non-ascii data. These datatypes will ensure that the correct types are used on the database.
-
method
- class sqlalchemy.dialects.mssql.NVARCHAR
The SQL NVARCHAR type.
Class signature
class
sqlalchemy.dialects.mssql.NVARCHAR
(sqlalchemy.types.Unicode
)-
method
sqlalchemy.dialects.mssql.NVARCHAR.
__init__(length: int | None = None, collation: str | None = None) inherited from the
sqlalchemy.types.String.__init__
method ofString
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 alength
for use in DDL, and will raise an exception when theCREATE TABLE
DDL is issued if aVARCHAR
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_1Note
In most cases, the
Unicode
orUnicodeText
datatypes should be used for aColumn
that expects to store non-ascii data. These datatypes will ensure that the correct types are used on the database.
-
method
- class sqlalchemy.dialects.mssql.REAL¶
the SQL Server REAL datatype.
Class signature
class
sqlalchemy.dialects.mssql.REAL
(sqlalchemy.types.REAL
)
- class sqlalchemy.dialects.mssql.ROWVERSION¶
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
Members
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 ofTIMESTAMP
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.
-
method
- class sqlalchemy.dialects.mssql.SMALLDATETIME¶
Members
Class signature
class
sqlalchemy.dialects.mssql.SMALLDATETIME
(sqlalchemy.dialects.mssql.base._DateTimeBase
,sqlalchemy.types.DateTime
)-
method
sqlalchemy.dialects.mssql.SMALLDATETIME.
__init__(timezone: bool = False)¶ inherited from the
sqlalchemy.types.DateTime.__init__
method ofDateTime
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.
-
method
- class sqlalchemy.dialects.mssql.SMALLMONEY¶
Class signature
class
sqlalchemy.dialects.mssql.SMALLMONEY
(sqlalchemy.types.TypeEngine
)
- class sqlalchemy.dialects.mssql.SQL_VARIANT¶
Class signature
class
sqlalchemy.dialects.mssql.SQL_VARIANT
(sqlalchemy.types.TypeEngine
)
- class sqlalchemy.dialects.mssql.TEXT
The SQL TEXT type.
Class signature
class
sqlalchemy.dialects.mssql.TEXT
(sqlalchemy.types.Text
)-
method
sqlalchemy.dialects.mssql.TEXT.
__init__(length: int | None = None, collation: str | None = None) inherited from the
sqlalchemy.types.String.__init__
method ofString
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 alength
for use in DDL, and will raise an exception when theCREATE TABLE
DDL is issued if aVARCHAR
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_1Note
In most cases, the
Unicode
orUnicodeText
datatypes should be used for aColumn
that expects to store non-ascii data. These datatypes will ensure that the correct types are used on the database.
-
method
- class sqlalchemy.dialects.mssql.TIME¶
Class signature
class
sqlalchemy.dialects.mssql.TIME
(sqlalchemy.types.TIME
)
- class sqlalchemy.dialects.mssql.TIMESTAMP¶
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
Members
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.
-
method
- class sqlalchemy.dialects.mssql.TINYINT¶
Class signature
class
sqlalchemy.dialects.mssql.TINYINT
(sqlalchemy.types.Integer
)
- class sqlalchemy.dialects.mssql.UNIQUEIDENTIFIER¶
Members
Class signature
class
sqlalchemy.dialects.mssql.UNIQUEIDENTIFIER
(sqlalchemy.types.Uuid
)-
method
sqlalchemy.dialects.mssql.UNIQUEIDENTIFIER.
__init__(as_uuid: bool = True)¶ Construct a
UNIQUEIDENTIFIER
type.- Parameters:
as_uuid=True¶ –
if True, values will be interpreted as Python uuid objects, converting to/from string via the DBAPI.
-
method
- class sqlalchemy.dialects.mssql.VARBINARY
The MSSQL VARBINARY type.
This type adds additional features to the core
VARBINARY
type, including “deprecate_large_types” mode where eitherVARBINARY(max)
or IMAGE is rendered, as well as the SQL ServerFILESTREAM
option.See also
Class signature
class
sqlalchemy.dialects.mssql.VARBINARY
(sqlalchemy.types.VARBINARY
,sqlalchemy.types.LargeBinary
)-
method
sqlalchemy.dialects.mssql.VARBINARY.
__init__(length=None, filestream=False) Construct a VARBINARY type.
- Parameters:
-
method
- class sqlalchemy.dialects.mssql.VARCHAR
The SQL VARCHAR type.
Class signature
class
sqlalchemy.dialects.mssql.VARCHAR
(sqlalchemy.types.String
)-
method
sqlalchemy.dialects.mssql.VARCHAR.
__init__(length: int | None = None, collation: str | None = None) inherited from the
sqlalchemy.types.String.__init__
method ofString
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 alength
for use in DDL, and will raise an exception when theCREATE TABLE
DDL is issued if aVARCHAR
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_1Note
In most cases, the
Unicode
orUnicodeText
datatypes should be used for aColumn
that expects to store non-ascii data. These datatypes will ensure that the correct types are used on the database.
-
method
- class sqlalchemy.dialects.mssql.XML¶
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”.
Members
Class signature
-
method
sqlalchemy.dialects.mssql.XML.
__init__(length: int | None = None, collation: str | None = None)¶ inherited from the
sqlalchemy.types.String.__init__
method ofString
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 alength
for use in DDL, and will raise an exception when theCREATE TABLE
DDL is issued if aVARCHAR
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_1Note
In most cases, the
Unicode
orUnicodeText
datatypes should be used for aColumn
that expects to store non-ascii data. These datatypes will ensure that the correct types are used on the database.
-
method
PyODBC¶
Support for the Microsoft SQL Server database via the PyODBC driver.
DBAPI¶
Documentation and download information (if applicable) for PyODBC is available at: https://pypi.org/project/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=some_dsn;UID=scott;PWD=tiger
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=ODBC+Driver+17+for+SQL+Server")
The driver
keyword is significant to the pyodbc dialect and must be
specified in lowercase.
Any other names passed in the query string are passed through in the pyodbc
connect string, such as authentication
, TrustServerCertificate
, etc.
Multiple keyword arguments must be separated by an ampersand (&
); these
will be translated to semicolons when the pyodbc connect string is generated
internally:
e = create_engine(
"mssql+pyodbc://scott:tiger@mssql2017:1433/test?"
"driver=ODBC+Driver+18+for+SQL+Server&TrustServerCertificate=yes"
"&authentication=ActiveDirectoryIntegrated"
)
The equivalent URL can be constructed using URL
:
from sqlalchemy.engine import URL
connection_url = URL.create(
"mssql+pyodbc",
username="scott",
password="tiger",
host="mssql2017",
port=1433,
database="test",
query={
"driver": "ODBC Driver 18 for SQL Server",
"TrustServerCertificate": "yes",
"authentication": "ActiveDirectoryIntegrated",
},
)
Pass through exact Pyodbc string¶
A PyODBC connection string can also be sent in pyodbc’s format directly, as
specified in the PyODBC documentation,
using the parameter odbc_connect
. A URL
object
can help make this easier:
from sqlalchemy.engine import URL
connection_string = "DRIVER={SQL Server Native Client 10.0};SERVER=dagger;DATABASE=test;UID=user;PWD=password"
connection_url = URL.create("mssql+pyodbc", query={"odbc_connect": connection_string})
engine = create_engine(connection_url)
Connecting to databases with access tokens¶
Some database servers are set up to only accept access tokens for login. For
example, SQL Server allows the use of Azure Active Directory tokens to connect
to databases. This requires creating a credential object using the
azure-identity
library. More information about the authentication step can be
found in Microsoft’s documentation.
After getting an engine, the credentials need to be sent to pyodbc.connect
each time a connection is requested. One way to do this is to set up an event
listener on the engine that adds the credential token to the dialect’s connect
call. This is discussed more generally in Generating dynamic authentication tokens. For
SQL Server in particular, this is passed as an ODBC connection attribute with
a data structure described by Microsoft.
The following code snippet will create an engine that connects to an Azure SQL database using Azure credentials:
import struct
from sqlalchemy import create_engine, event
from sqlalchemy.engine.url import URL
from azure import identity
SQL_COPT_SS_ACCESS_TOKEN = 1256 # Connection option for access tokens, as defined in msodbcsql.h
TOKEN_URL = "https://database.windows.net/" # The token URL for any Azure SQL database
connection_string = "mssql+pyodbc://@my-server.database.windows.net/myDb?driver=ODBC+Driver+17+for+SQL+Server"
engine = create_engine(connection_string)
azure_credentials = identity.DefaultAzureCredential()
@event.listens_for(engine, "do_connect")
def provide_token(dialect, conn_rec, cargs, cparams):
# remove the "Trusted_Connection" parameter that SQLAlchemy adds
cargs[0] = cargs[0].replace(";Trusted_Connection=Yes", "")
# create token credential
raw_token = azure_credentials.get_token(TOKEN_URL).token.encode("utf-16-le")
token_struct = struct.pack(f"<I{len(raw_token)}s", len(raw_token), raw_token)
# apply it to keyword arguments
cparams["attrs_before"] = {SQL_COPT_SS_ACCESS_TOKEN: token_struct}
Tip
The Trusted_Connection
token is currently added by the SQLAlchemy
pyodbc dialect when no username or password is present. This needs
to be removed per Microsoft’s
documentation for Azure access tokens,
stating that a connection string when using an access token must not contain
UID
, PWD
, Authentication
or Trusted_Connection
parameters.
Avoiding transaction-related exceptions on Azure Synapse Analytics¶
Azure Synapse Analytics has a significant difference in its transaction
handling compared to plain SQL Server; in some cases an error within a Synapse
transaction can cause it to be arbitrarily terminated on the server side, which
then causes the DBAPI .rollback()
method (as well as .commit()
) to
fail. The issue prevents the usual DBAPI contract of allowing .rollback()
to pass silently if no transaction is present as the driver does not expect
this condition. The symptom of this failure is an exception with a message
resembling ‘No corresponding transaction found. (111214)’ when attempting to
emit a .rollback()
after an operation had a failure of some kind.
This specific case can be handled by passing ignore_no_transaction_on_rollback=True
to
the SQL Server dialect via the create_engine()
function as follows:
engine = create_engine(connection_url, ignore_no_transaction_on_rollback=True)
Using the above parameter, the dialect will catch ProgrammingError
exceptions raised during connection.rollback()
and emit a warning
if the error message contains code 111214
, however will not raise
an exception.
New in version 1.4.40: Added the
ignore_no_transaction_on_rollback=True
parameter.
Enable autocommit for Azure SQL Data Warehouse (DW) connections¶
Azure SQL Data Warehouse does not support transactions, and that can cause problems with SQLAlchemy’s “autobegin” (and implicit commit/rollback) behavior. We can avoid these problems by enabling autocommit at both the pyodbc and engine levels:
connection_url = sa.engine.URL.create(
"mssql+pyodbc",
username="scott",
password="tiger",
host="dw.azure.example.com",
database="mydb",
query={
"driver": "ODBC Driver 17 for SQL Server",
"autocommit": "True",
},
)
engine = create_engine(connection_url).execution_options(
isolation_level="AUTOCOMMIT"
)
Avoiding sending large string parameters as TEXT/NTEXT¶
By default, for historical reasons, Microsoft’s ODBC drivers for SQL Server send long string parameters (greater than 4000 SBCS characters or 2000 Unicode characters) as TEXT/NTEXT values. TEXT and NTEXT have been deprecated for many years and are starting to cause compatibility issues with newer versions of SQL_Server/Azure. For example, see this issue.
Starting with ODBC Driver 18 for SQL Server we can override the legacy
behavior and pass long strings as varchar(max)/nvarchar(max) using the
LongAsMax=Yes
connection string parameter:
connection_url = sa.engine.URL.create(
"mssql+pyodbc",
username="scott",
password="tiger",
host="mssqlserver.example.com",
database="mydb",
query={
"driver": "ODBC Driver 18 for SQL Server",
"LongAsMax": "Yes",
},
)
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¶
Previous limitations with the SQLAlchemy ORM’s “versioned rows” feature with Pyodbc have been resolved as of SQLAlchemy 2.0.5. See the notes at Rowcount Support / ORM Versioning.
Fast Executemany Mode¶
The PyODBC driver includes 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 attribute .fast_executemany
on the DBAPI
cursor when an executemany call is to be used. The SQLAlchemy PyODBC SQL
Server dialect supports this parameter by passing the
fast_executemany
parameter to
create_engine()
, when using the Microsoft ODBC driver only:
engine = create_engine(
"mssql+pyodbc://scott:tiger@mssql2017:1433/test?driver=ODBC+Driver+17+for+SQL+Server",
fast_executemany=True)
Changed in version 2.0.9: - the fast_executemany
parameter now has its
intended effect of this PyODBC feature taking effect for all INSERT
statements that are executed with multiple parameter sets, which don’t
include RETURNING. Previously, SQLAlchemy 2.0’s insertmanyvalues
feature would cause fast_executemany
to not be used in most cases
even if specified.
New in version 1.3.
See also
fast executemany - on github
Setinputsizes Support¶
As of version 2.0, the pyodbc cursor.setinputsizes()
method is used for
all statement executions, except for cursor.executemany()
calls when
fast_executemany=True where it is not supported (assuming
insertmanyvalues is kept enabled,
“fastexecutemany” will not take place for INSERT statements in any case).
The use of cursor.setinputsizes()
can be disabled by passing
use_setinputsizes=False
to create_engine()
.
When use_setinputsizes
is left at its default of True
, the
specific per-type symbols passed to cursor.setinputsizes()
can be
programmatically customized using the DialectEvents.do_setinputsizes()
hook. See that method for usage examples.
Changed in version 2.0: The mssql+pyodbc dialect now defaults to using
use_setinputsizes=True
for all statement executions with the exception of
cursor.executemany() calls when fast_executemany=True. The behavior can
be turned off by passing use_setinputsizes=False
to
create_engine()
.
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.
Changed in version 2.0.5: pymssql was restored to SQLAlchemy’s continuous integration testing
aioodbc¶
Support for the Microsoft SQL Server database via the aioodbc driver.
DBAPI¶
Documentation and download information (if applicable) for aioodbc is available at: https://pypi.org/project/aioodbc/
Connecting¶
Connect String:
mssql+aioodbc://<username>:<password>@<dsnname>
Support for the SQL Server database in asyncio style, using the aioodbc driver which itself is a thread-wrapper around pyodbc.
New in version 2.0.23: Added the mssql+aioodbc dialect which builds on top of the pyodbc and general aio* dialect architecture.
Using a special asyncio mediation layer, the aioodbc dialect is usable as the backend for the SQLAlchemy asyncio extension package.
Most behaviors and caveats for this driver are the same as that of the pyodbc dialect used on SQL Server; see PyODBC for general background.
This dialect should normally be used only with the
create_async_engine()
engine creation function; connection
styles are otherwise equivalent to those documented in the pyodbc section:
from sqlalchemy.ext.asyncio import create_async_engine
engine = create_async_engine(
"mssql+aioodbc://scott:tiger@mssql2017:1433/test?"
"driver=ODBC+Driver+18+for+SQL+Server&TrustServerCertificate=yes"
)
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