A variety of methods exist to redefine the behavior of existing types as well as to provide new ones.
A frequent need is to force the “string” version of a type, that is
the one rendered in a CREATE TABLE statement or other SQL function
like CAST, to be changed. For example, an application may want
to force the rendering of BINARY
for all platforms
except for one, in which is wants BLOB
to be rendered. Usage
of an existing generic type, in this case LargeBinary
, is
preferred for most use cases. But to control
types more accurately, a compilation directive that is per-dialect
can be associated with any type:
from sqlalchemy.ext.compiler import compiles
from sqlalchemy.types import BINARY
@compiles(BINARY, "sqlite")
def compile_binary_sqlite(type_, compiler, **kw):
return "BLOB"
The above code allows the usage of types.BINARY
, which
will produce the string BINARY
against all backends except SQLite,
in which case it will produce BLOB
.
See the section Changing Compilation of Types, a subsection of Custom SQL Constructs and Compilation Extension, for additional examples.
The TypeDecorator
allows the creation of custom types which
add bind-parameter and result-processing behavior to an existing
type object. It is used when additional in-Python marshaling of data
to and from the database is required.
Note
The bind- and result-processing of TypeDecorator
is in addition to the processing already performed by the hosted
type, which is customized by SQLAlchemy on a per-DBAPI basis to perform
processing specific to that DBAPI. To change the DBAPI-level processing
for an existing type, see the section Replacing the Bind/Result Processing of Existing Types.
sqlalchemy.types.
TypeDecorator
(*args, **kwargs)¶Bases: sqlalchemy.types.TypeEngine
Allows the creation of types which add additional functionality to an existing type.
This method is preferred to direct subclassing of SQLAlchemy’s built-in types as it ensures that all required functionality of the underlying type is kept in place.
Typical usage:
import sqlalchemy.types as types
class MyType(types.TypeDecorator):
'''Prefixes Unicode values with "PREFIX:" on the way in and
strips it off on the way out.
'''
impl = types.Unicode
def process_bind_param(self, value, dialect):
return "PREFIX:" + value
def process_result_value(self, value, dialect):
return value[7:]
def copy(self):
return MyType(self.impl.length)
The class-level “impl” attribute is required, and can reference any
TypeEngine class. Alternatively, the load_dialect_impl() method
can be used to provide different type classes based on the dialect
given; in this case, the “impl” variable can reference
TypeEngine
as a placeholder.
Types that receive a Python type that isn’t similar to the ultimate type
used may want to define the TypeDecorator.coerce_compared_value()
method. This is used to give the expression system a hint when coercing
Python objects into bind parameters within expressions. Consider this
expression:
mytable.c.somecol + datetime.date(2009, 5, 15)
Above, if “somecol” is an Integer
variant, it makes sense that
we’re doing date arithmetic, where above is usually interpreted
by databases as adding a number of days to the given date.
The expression system does the right thing by not attempting to
coerce the “date()” value into an integer-oriented bind parameter.
However, in the case of TypeDecorator
, we are usually changing an
incoming Python type to something new - TypeDecorator
by default will
“coerce” the non-typed side to be the same type as itself. Such as below,
we define an “epoch” type that stores a date value as an integer:
class MyEpochType(types.TypeDecorator):
impl = types.Integer
epoch = datetime.date(1970, 1, 1)
def process_bind_param(self, value, dialect):
return (value - self.epoch).days
def process_result_value(self, value, dialect):
return self.epoch + timedelta(days=value)
Our expression of somecol + date
with the above type will coerce the
“date” on the right side to also be treated as MyEpochType
.
This behavior can be overridden via the
coerce_compared_value()
method, which returns a type
that should be used for the value of the expression. Below we set it such
that an integer value will be treated as an Integer
, and any other
value is assumed to be a date and will be treated as a MyEpochType
:
def coerce_compared_value(self, op, value):
if isinstance(value, int):
return Integer()
else:
return self
__init__
(*args, **kwargs)¶Construct a TypeDecorator
.
Arguments sent here are passed to the constructor
of the class assigned to the impl
class level attribute,
assuming the impl
is a callable, and the resulting
object is assigned to the self.impl
instance attribute
(thus overriding the class attribute of the same name).
If the class level impl
is not a callable (the unusual case),
it will be assigned to the same instance attribute ‘as-is’,
ignoring those arguments passed to the constructor.
Subclasses can override this to customize the generation
of self.impl
entirely.
adapt
(cls, **kw)¶adapt()
method of TypeEngine
Produce an “adapted” form of this type, given an “impl” class to work with.
This method is used internally to associate generic types with “implementation” types that are specific to a particular dialect.
bind_expression
(bindvalue)¶bind_expression()
method of TypeEngine
“Given a bind value (i.e. a BindParameter
instance),
return a SQL expression in its place.
This is typically a SQL function that wraps the existing bound
parameter within the statement. It is used for special data types
that require literals being wrapped in some special database function
in order to coerce an application-level value into a database-specific
format. It is the SQL analogue of the
TypeEngine.bind_processor()
method.
The method is evaluated at statement compile time, as opposed to statement construction time.
Note that this method, when implemented, should always return the exact same structure, without any conditional logic, as it may be used in an executemany() call against an arbitrary number of bound parameter sets.
See also:
bind_processor
(dialect)¶Provide a bound value processing function for the
given Dialect
.
This is the method that fulfills the TypeEngine
contract for bound value conversion. TypeDecorator
will wrap a user-defined implementation of
process_bind_param()
here.
User-defined code can override this method directly,
though its likely best to use process_bind_param()
so that
the processing provided by self.impl
is maintained.
Parameters: | dialect¶ – Dialect instance in use. |
---|
This method is the reverse counterpart to the
result_processor()
method of this class.
coerce_compared_value
(op, value)¶Suggest a type for a ‘coerced’ Python value in an expression.
By default, returns self. This method is called by the expression system when an object using this type is on the left or right side of an expression against a plain Python object which does not yet have a SQLAlchemy type assigned:
expr = table.c.somecolumn + 35
Where above, if somecolumn
uses this type, this method will
be called with the value operator.add
and 35
. The return value is whatever SQLAlchemy type should
be used for 35
for this particular operation.
coerce_to_is_types
= (<type 'NoneType'>,)¶Specify those Python types which should be coerced at the expression
level to “IS <constant>” when compared using ==
(and same for
IS NOT
in conjunction with !=
.
For most SQLAlchemy types, this includes NoneType
, as well as
bool
.
TypeDecorator
modifies this list to only include NoneType
,
as typedecorator implementations that deal with boolean types are common.
Custom TypeDecorator
classes can override this attribute to
return an empty tuple, in which case no values will be coerced to
constants.
TypeDecorator.coerce_to_is_types
to allow for easier
control of __eq__()
__ne__()
operations.column_expression
(colexpr)¶column_expression()
method of TypeEngine
Given a SELECT column expression, return a wrapping SQL expression.
This is typically a SQL function that wraps a column expression
as rendered in the columns clause of a SELECT statement.
It is used for special data types that require
columns to be wrapped in some special database function in order
to coerce the value before being sent back to the application.
It is the SQL analogue of the TypeEngine.result_processor()
method.
The method is evaluated at statement compile time, as opposed to statement construction time.
See also:
compare_against_backend
(dialect, conn_type)¶compare_against_backend()
method of TypeEngine
Compare this type against the given backend type.
This function is currently not implemented for SQLAlchemy
types, and for all built in types will return None
. However,
it can be implemented by a user-defined type
where it can be consumed by schema comparison tools such as
Alembic autogenerate.
A future release of SQLAlchemy will potentially impement this method for builtin types as well.
The function should return True if this type is equivalent to the given type; the type is typically reflected from the database so should be database specific. The dialect in use is also passed. It can also return False to assert that the type is not equivalent.
Parameters: |
---|
New in version 1.0.3.
compare_values
(x, y)¶Given two values, compare them for equality.
By default this calls upon TypeEngine.compare_values()
of the underlying “impl”, which in turn usually
uses the Python equals operator ==
.
This function is used by the ORM to compare an original-loaded value with an intercepted “changed” value, to determine if a net change has occurred.
compile
(dialect=None)¶compile()
method of TypeEngine
Produce a string-compiled form of this TypeEngine
.
When called with no arguments, uses a “default” dialect to produce a string result.
Parameters: | dialect¶ – a Dialect instance. |
---|
copy
()¶Produce a copy of this TypeDecorator
instance.
This is a shallow copy and is provided to fulfill part of
the TypeEngine
contract. It usually does not
need to be overridden unless the user-defined TypeDecorator
has local state that should be deep-copied.
dialect_impl
(dialect)¶dialect_impl()
method of TypeEngine
Return a dialect-specific implementation for this
TypeEngine
.
get_dbapi_type
(dbapi)¶Return the DBAPI type object represented by this
TypeDecorator
.
By default this calls upon TypeEngine.get_dbapi_type()
of the
underlying “impl”.
literal_processor
(dialect)¶Provide a literal processing function for the given
Dialect
.
Subclasses here will typically override
TypeDecorator.process_literal_param()
instead of this method
directly.
By default, this method makes use of
TypeDecorator.process_bind_param()
if that method is
implemented, where TypeDecorator.process_literal_param()
is
not. The rationale here is that TypeDecorator
typically
deals with Python conversions of data that are above the layer of
database presentation. With the value converted by
TypeDecorator.process_bind_param()
, the underlying type will
then handle whether it needs to be presented to the DBAPI as a bound
parameter or to the database as an inline SQL value.
New in version 0.9.0.
load_dialect_impl
(dialect)¶Return a TypeEngine
object corresponding to a dialect.
This is an end-user override hook that can be used to provide
differing types depending on the given dialect. It is used
by the TypeDecorator
implementation of type_engine()
to help determine what type should ultimately be returned
for a given TypeDecorator
.
By default returns self.impl
.
process_bind_param
(value, dialect)¶Receive a bound parameter value to be converted.
Subclasses override this method to return the
value that should be passed along to the underlying
TypeEngine
object, and from there to the
DBAPI execute()
method.
The operation could be anything desired to perform custom behavior, such as transforming or serializing data. This could also be used as a hook for validating logic.
This operation should be designed with the reverse operation in mind, which would be the process_result_value method of this class.
Parameters: |
---|
process_literal_param
(value, dialect)¶Receive a literal parameter value to be rendered inline within a statement.
This method is used when the compiler renders a literal value without using binds, typically within DDL such as in the “server default” of a column or an expression within a CHECK constraint.
The returned string will be rendered into the output string.
New in version 0.9.0.
process_result_value
(value, dialect)¶Receive a result-row column value to be converted.
Subclasses should implement this method to operate on data fetched from the database.
Subclasses override this method to return the
value that should be passed back to the application,
given a value that is already processed by
the underlying TypeEngine
object, originally
from the DBAPI cursor method fetchone()
or similar.
The operation could be anything desired to perform custom behavior, such as transforming or serializing data. This could also be used as a hook for validating logic.
Parameters: |
---|
This operation should be designed to be reversible by the “process_bind_param” method of this class.
python_type
¶python_type
attribute of TypeEngine
Return the Python type object expected to be returned by instances of this type, if known.
Basically, for those types which enforce a return type,
or are known across the board to do such for all common
DBAPIs (like int
for example), will return that type.
If a return type is not defined, raises
NotImplementedError
.
Note that any type also accommodates NULL in SQL which
means you can also get back None
from any type
in practice.
result_processor
(dialect, coltype)¶Provide a result value processing function for the given
Dialect
.
This is the method that fulfills the TypeEngine
contract for result value conversion. TypeDecorator
will wrap a user-defined implementation of
process_result_value()
here.
User-defined code can override this method directly,
though its likely best to use process_result_value()
so that
the processing provided by self.impl
is maintained.
Parameters: |
---|
This method is the reverse counterpart to the
bind_processor()
method of this class.
type_engine
(dialect)¶Return a dialect-specific TypeEngine
instance
for this TypeDecorator
.
In most cases this returns a dialect-adapted form of
the TypeEngine
type represented by self.impl
.
Makes usage of dialect_impl()
but also traverses
into wrapped TypeDecorator
instances.
Behavior can be customized here by overriding
load_dialect_impl()
.
with_variant
(type_, dialect_name)¶with_variant()
method of TypeEngine
Produce a new type object that will utilize the given type when applied to the dialect of the given name.
e.g.:
from sqlalchemy.types import String
from sqlalchemy.dialects import mysql
s = String()
s = s.with_variant(mysql.VARCHAR(collation='foo'), 'mysql')
The construction of TypeEngine.with_variant()
is always
from the “fallback” type to that which is dialect specific.
The returned type is an instance of Variant
, which
itself provides a Variant.with_variant()
that can be called repeatedly.
Parameters: |
|
---|
New in version 0.7.2.
A few key TypeDecorator
recipes follow.
A common source of confusion regarding the Unicode
type
is that it is intended to deal only with Python unicode
objects
on the Python side, meaning values passed to it as bind parameters
must be of the form u'some string'
if using Python 2 and not 3.
The encoding/decoding functions it performs are only to suit what the
DBAPI in use requires, and are primarily a private implementation detail.
The use case of a type that can safely receive Python bytestrings,
that is strings that contain non-ASCII characters and are not u''
objects in Python 2, can be achieved using a TypeDecorator
which coerces as needed:
from sqlalchemy.types import TypeDecorator, Unicode
class CoerceUTF8(TypeDecorator):
"""Safely coerce Python bytestrings to Unicode
before passing off to the database."""
impl = Unicode
def process_bind_param(self, value, dialect):
if isinstance(value, str):
value = value.decode('utf-8')
return value
Some database connectors like those of SQL Server choke if a Decimal is passed with too many decimal places. Here’s a recipe that rounds them down:
from sqlalchemy.types import TypeDecorator, Numeric
from decimal import Decimal
class SafeNumeric(TypeDecorator):
"""Adds quantization to Numeric."""
impl = Numeric
def __init__(self, *arg, **kw):
TypeDecorator.__init__(self, *arg, **kw)
self.quantize_int = - self.impl.scale
self.quantize = Decimal(10) ** self.quantize_int
def process_bind_param(self, value, dialect):
if isinstance(value, Decimal) and \
value.as_tuple()[2] < self.quantize_int:
value = value.quantize(self.quantize)
return value
Receives and returns Python uuid() objects. Uses the PG UUID type when using Postgresql, CHAR(32) on other backends, storing them in stringified hex format. Can be modified to store binary in CHAR(16) if desired:
from sqlalchemy.types import TypeDecorator, CHAR
from sqlalchemy.dialects.postgresql import UUID
import uuid
class GUID(TypeDecorator):
"""Platform-independent GUID type.
Uses Postgresql's UUID type, otherwise uses
CHAR(32), storing as stringified hex values.
"""
impl = CHAR
def load_dialect_impl(self, dialect):
if dialect.name == 'postgresql':
return dialect.type_descriptor(UUID())
else:
return dialect.type_descriptor(CHAR(32))
def process_bind_param(self, value, dialect):
if value is None:
return value
elif dialect.name == 'postgresql':
return str(value)
else:
if not isinstance(value, uuid.UUID):
return "%.32x" % uuid.UUID(value)
else:
# hexstring
return "%.32x" % value
def process_result_value(self, value, dialect):
if value is None:
return value
else:
return uuid.UUID(value)
This type uses simplejson
to marshal Python data structures
to/from JSON. Can be modified to use Python’s builtin json encoder:
from sqlalchemy.types import TypeDecorator, VARCHAR
import json
class JSONEncodedDict(TypeDecorator):
"""Represents an immutable structure as a json-encoded string.
Usage::
JSONEncodedDict(255)
"""
impl = VARCHAR
def process_bind_param(self, value, dialect):
if value is not None:
value = json.dumps(value)
return value
def process_result_value(self, value, dialect):
if value is not None:
value = json.loads(value)
return value
Note that the ORM by default will not detect “mutability” on such a type -
meaning, in-place changes to values will not be detected and will not be
flushed. Without further steps, you instead would need to replace the existing
value with a new one on each parent object to detect changes. Note that
there’s nothing wrong with this, as many applications may not require that the
values are ever mutated once created. For those which do have this requirement,
support for mutability is best applied using the sqlalchemy.ext.mutable
extension - see the example in Mutation Tracking.
Most augmentation of type behavior at the bind/result level
is achieved using TypeDecorator
. For the rare scenario
where the specific processing applied by SQLAlchemy at the DBAPI
level needs to be replaced, the SQLAlchemy type can be subclassed
directly, and the bind_processor()
or result_processor()
methods can be overridden. Doing so requires that the
adapt()
method also be overridden. This method is the mechanism
by which SQLAlchemy produces DBAPI-specific type behavior during
statement execution. Overriding it allows a copy of the custom
type to be used in lieu of a DBAPI-specific type. Below we subclass
the types.TIME
type to have custom result processing behavior.
The process()
function will receive value
from the DBAPI
cursor directly:
class MySpecialTime(TIME):
def __init__(self, special_argument):
super(MySpecialTime, self).__init__()
self.special_argument = special_argument
def result_processor(self, dialect, coltype):
import datetime
time = datetime.time
def process(value):
if value is not None:
microseconds = value.microseconds
seconds = value.seconds
minutes = seconds / 60
return time(
minutes / 60,
minutes % 60,
seconds - minutes * 60,
microseconds)
else:
return None
return process
def adapt(self, impltype):
return MySpecialTime(self.special_argument)
As seen in the sections Augmenting Existing Types and Replacing the Bind/Result Processing of Existing Types, SQLAlchemy allows Python functions to be invoked both when parameters are sent to a statement, as well as when result rows are loaded from the database, to apply transformations to the values as they are sent to or from the database. It is also possible to define SQL-level transformations as well. The rationale here is when only the relational database contains a particular series of functions that are necessary to coerce incoming and outgoing data between an application and persistence format. Examples include using database-defined encryption/decryption functions, as well as stored procedures that handle geographic data. The Postgis extension to Postgresql includes an extensive array of SQL functions that are necessary for coercing data into particular formats.
Any TypeEngine
, UserDefinedType
or TypeDecorator
subclass
can include implementations of
TypeEngine.bind_expression()
and/or TypeEngine.column_expression()
, which
when defined to return a non-None
value should return a ColumnElement
expression to be injected into the SQL statement, either surrounding
bound parameters or a column expression. For example, to build a Geometry
type which will apply the Postgis function ST_GeomFromText
to all outgoing
values and the function ST_AsText
to all incoming data, we can create
our own subclass of UserDefinedType
which provides these methods
in conjunction with func
:
from sqlalchemy import func
from sqlalchemy.types import UserDefinedType
class Geometry(UserDefinedType):
def get_col_spec(self):
return "GEOMETRY"
def bind_expression(self, bindvalue):
return func.ST_GeomFromText(bindvalue, type_=self)
def column_expression(self, col):
return func.ST_AsText(col, type_=self)
We can apply the Geometry
type into Table
metadata
and use it in a select()
construct:
geometry = Table('geometry', metadata,
Column('geom_id', Integer, primary_key=True),
Column('geom_data', Geometry)
)
print select([geometry]).where(
geometry.c.geom_data == 'LINESTRING(189412 252431,189631 259122)')
The resulting SQL embeds both functions as appropriate. ST_AsText
is applied to the columns clause so that the return value is run through
the function before passing into a result set, and ST_GeomFromText
is run on the bound parameter so that the passed-in value is converted:
SELECT geometry.geom_id, ST_AsText(geometry.geom_data) AS geom_data_1
FROM geometry
WHERE geometry.geom_data = ST_GeomFromText(:geom_data_2)
The TypeEngine.column_expression()
method interacts with the
mechanics of the compiler such that the SQL expression does not interfere
with the labeling of the wrapped expression. Such as, if we rendered
a select()
against a label()
of our expression, the string
label is moved to the outside of the wrapped expression:
print select([geometry.c.geom_data.label('my_data')])
Output:
SELECT ST_AsText(geometry.geom_data) AS my_data
FROM geometry
For an example of subclassing a built in type directly, we subclass
postgresql.BYTEA
to provide a PGPString
, which will make use of the
Postgresql pgcrypto
extension to encrpyt/decrypt values
transparently:
from sqlalchemy import create_engine, String, select, func, \
MetaData, Table, Column, type_coerce
from sqlalchemy.dialects.postgresql import BYTEA
class PGPString(BYTEA):
def __init__(self, passphrase, length=None):
super(PGPString, self).__init__(length)
self.passphrase = passphrase
def bind_expression(self, bindvalue):
# convert the bind's type from PGPString to
# String, so that it's passed to psycopg2 as is without
# a dbapi.Binary wrapper
bindvalue = type_coerce(bindvalue, String)
return func.pgp_sym_encrypt(bindvalue, self.passphrase)
def column_expression(self, col):
return func.pgp_sym_decrypt(col, self.passphrase)
metadata = MetaData()
message = Table('message', metadata,
Column('username', String(50)),
Column('message',
PGPString("this is my passphrase", length=1000)),
)
engine = create_engine("postgresql://scott:tiger@localhost/test", echo=True)
with engine.begin() as conn:
metadata.create_all(conn)
conn.execute(message.insert(), username="some user",
message="this is my message")
print conn.scalar(
select([message.c.message]).\
where(message.c.username == "some user")
)
The pgp_sym_encrypt
and pgp_sym_decrypt
functions are applied
to the INSERT and SELECT statements:
INSERT INTO message (username, message)
VALUES (%(username)s, pgp_sym_encrypt(%(message)s, %(pgp_sym_encrypt_1)s))
{'username': 'some user', 'message': 'this is my message',
'pgp_sym_encrypt_1': 'this is my passphrase'}
SELECT pgp_sym_decrypt(message.message, %(pgp_sym_decrypt_1)s) AS message_1
FROM message
WHERE message.username = %(username_1)s
{'pgp_sym_decrypt_1': 'this is my passphrase', 'username_1': 'some user'}
New in version 0.8: Added the TypeEngine.bind_expression()
and
TypeEngine.column_expression()
methods.
See also:
SQLAlchemy Core defines a fixed set of expression operators available to all column expressions.
Some of these operations have the effect of overloading Python’s built in operators;
examples of such operators include
ColumnOperators.__eq__()
(table.c.somecolumn == 'foo'
),
ColumnOperators.__invert__()
(~table.c.flag
),
and ColumnOperators.__add__()
(table.c.x + table.c.y
). Other operators are exposed as
explicit methods on column expressions, such as
ColumnOperators.in_()
(table.c.value.in_(['x', 'y'])
) and ColumnOperators.like()
(table.c.value.like('%ed%')
).
The Core expression constructs in all cases consult the type of the expression in order to determine
the behavior of existing operators, as well as to locate additional operators that aren’t part of
the built in set. The TypeEngine
base class defines a root “comparison” implementation
TypeEngine.Comparator
, and many specific types provide their own sub-implementations of this
class. User-defined TypeEngine.Comparator
implementations can be built directly into a
simple subclass of a particular type in order to override or define new operations. Below,
we create a Integer
subclass which overrides the ColumnOperators.__add__()
operator:
from sqlalchemy import Integer
class MyInt(Integer):
class comparator_factory(Integer.Comparator):
def __add__(self, other):
return self.op("goofy")(other)
The above configuration creates a new class MyInt
, which
establishes the TypeEngine.comparator_factory
attribute as
referring to a new class, subclassing the TypeEngine.Comparator
class
associated with the Integer
type.
Usage:
>>> sometable = Table("sometable", metadata, Column("data", MyInt))
>>> print sometable.c.data + 5
sometable.data goofy :data_1
The implementation for ColumnOperators.__add__()
is consulted
by an owning SQL expression, by instantiating the TypeEngine.Comparator
with
itself as the expr
attribute. The mechanics of the expression
system are such that operations continue recursively until an
expression object produces a new SQL expression construct. Above, we
could just as well have said self.expr.op("goofy")(other)
instead
of self.op("goofy")(other)
.
New methods added to a TypeEngine.Comparator
are exposed on an
owning SQL expression
using a __getattr__
scheme, which exposes methods added to
TypeEngine.Comparator
onto the owning ColumnElement
.
For example, to add a log()
function
to integers:
from sqlalchemy import Integer, func
class MyInt(Integer):
class comparator_factory(Integer.Comparator):
def log(self, other):
return func.log(self.expr, other)
Using the above type:
>>> print sometable.c.data.log(5)
log(:log_1, :log_2)
Unary operations
are also possible. For example, to add an implementation of the
Postgresql factorial operator, we combine the UnaryExpression
construct
along with a custom_op
to produce the factorial expression:
from sqlalchemy import Integer
from sqlalchemy.sql.expression import UnaryExpression
from sqlalchemy.sql import operators
class MyInteger(Integer):
class comparator_factory(Integer.Comparator):
def factorial(self):
return UnaryExpression(self.expr,
modifier=operators.custom_op("!"),
type_=MyInteger)
Using the above type:
>>> from sqlalchemy.sql import column
>>> print column('x', MyInteger).factorial()
x !
See also:
New in version 0.8: The expression system was enhanced to support customization of operators on a per-type level.
The UserDefinedType
class is provided as a simple base class
for defining entirely new database types. Use this to represent native
database types not known by SQLAlchemy. If only Python translation behavior
is needed, use TypeDecorator
instead.
sqlalchemy.types.
UserDefinedType
¶Bases: sqlalchemy.types.TypeEngine
Base for user defined types.
This should be the base of new types. Note that
for most cases, TypeDecorator
is probably
more appropriate:
import sqlalchemy.types as types
class MyType(types.UserDefinedType):
def __init__(self, precision = 8):
self.precision = precision
def get_col_spec(self, **kw):
return "MYTYPE(%s)" % self.precision
def bind_processor(self, dialect):
def process(value):
return value
return process
def result_processor(self, dialect, coltype):
def process(value):
return value
return process
Once the type is made, it’s immediately usable:
table = Table('foo', meta,
Column('id', Integer, primary_key=True),
Column('data', MyType(16))
)
The get_col_spec()
method will in most cases receive a keyword
argument type_expression
which refers to the owning expression
of the type as being compiled, such as a Column
or
cast()
construct. This keyword is only sent if the method
accepts keyword arguments (e.g. **kw
) in its argument signature;
introspection is used to check for this in order to support legacy
forms of this function.
New in version 1.0.0: the owning expression is passed to
the get_col_spec()
method via the keyword argument
type_expression
, if it receives **kw
in its signature.
coerce_compared_value
(op, value)¶Suggest a type for a ‘coerced’ Python value in an expression.
Default behavior for UserDefinedType
is the
same as that of TypeDecorator
; by default it returns
self
, assuming the compared value should be coerced into
the same type as this one. See
TypeDecorator.coerce_compared_value()
for more detail.
Changed in version 0.8: UserDefinedType.coerce_compared_value()
now returns self
by default, rather than falling onto the
more fundamental behavior of
TypeEngine.coerce_compared_value()
.