Base Type API¶
Object Name | Description |
---|---|
A mixin that marks a type as supporting ‘concatenation’, typically strings. |
|
mixin that defines attributes and behaviors specific to third-party datatypes. |
|
A mixin that marks a type as supporting indexing operations, such as array or JSON structures. |
|
An unknown type. |
|
The ultimate base class for all SQL datatypes. |
|
deprecated. symbol is present for backwards-compatibility with workaround recipes, however this actual type should not be used. |
- class sqlalchemy.types.TypeEngine¶
The ultimate base class for all SQL datatypes.
Common subclasses of
TypeEngine
includeString
,Integer
, andBoolean
.For an overview of the SQLAlchemy typing system, see SQL Datatype Objects.
See also
Members
operate(), reverse_operate(), adapt(), as_generic(), bind_expression(), bind_processor(), coerce_compared_value(), column_expression(), comparator_factory, compare_against_backend(), compare_values(), compile(), dialect_impl(), evaluates_none(), get_dbapi_type(), hashable, literal_processor(), python_type, render_bind_cast, render_literal_cast, result_processor(), should_evaluate_none, sort_key_function, with_variant()
Class signature
class
sqlalchemy.types.TypeEngine
(sqlalchemy.sql.visitors.Visitable
,typing.Generic
)- class Comparator¶
Base class for custom comparison operations defined at the type level. See
TypeEngine.comparator_factory
.Class signature
class
sqlalchemy.types.TypeEngine.Comparator
(sqlalchemy.sql.expression.ColumnOperators
,typing.Generic
)-
method
sqlalchemy.types.TypeEngine.Comparator.
operate(op: OperatorType, *other: Any, **kwargs: Any) ColumnElement[_CT] ¶ Operate on an argument.
This is the lowest level of operation, raises
NotImplementedError
by default.Overriding this on a subclass can allow common behavior to be applied to all operations. For example, overriding
ColumnOperators
to applyfunc.lower()
to the left and right side:class MyComparator(ColumnOperators): def operate(self, op, other, **kwargs): return op(func.lower(self), func.lower(other), **kwargs)
- Parameters:
op – Operator callable.
*other – the ‘other’ side of the operation. Will be a single scalar for most operations.
**kwargs – modifiers. These may be passed by special operators such as
ColumnOperators.contains()
.
-
method
sqlalchemy.types.TypeEngine.Comparator.
reverse_operate(op: OperatorType, other: Any, **kwargs: Any) ColumnElement[_CT] ¶ Reverse operate on an argument.
Usage is the same as
operate()
.
-
method
-
method
sqlalchemy.types.TypeEngine.
adapt(cls: Type[Union[TypeEngine, TypeEngineMixin]], **kw: Any) 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.
-
method
sqlalchemy.types.TypeEngine.
as_generic(allow_nulltype: bool = False) TypeEngine ¶ Return an instance of the generic type corresponding to this type using heuristic rule. The method may be overridden if this heuristic rule is not sufficient.
>>> from sqlalchemy.dialects.mysql import INTEGER >>> INTEGER(display_width=4).as_generic() Integer()
>>> from sqlalchemy.dialects.mysql import NVARCHAR >>> NVARCHAR(length=100).as_generic() Unicode(length=100)
New in version 1.4.0b2.
See also
Reflecting with Database-Agnostic Types - describes the use of
TypeEngine.as_generic()
in conjunction with theDDLEvents.column_reflect()
event, which is its intended use.
-
method
sqlalchemy.types.TypeEngine.
bind_expression(bindvalue: BindParameter[_T]) Optional[ColumnElement[_T]] ¶ 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.This method is called during the SQL compilation phase of a statement, when rendering a SQL string. It is not called against specific values.
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.
Note
This method is only called relative to a dialect specific type object, which is often private to a dialect in use and is not the same type object as the public facing one, which means it’s not feasible to subclass a
TypeEngine
class in order to provide an alternateTypeEngine.bind_expression()
method, unless subclassing theUserDefinedType
class explicitly.To provide alternate behavior for
TypeEngine.bind_expression()
, implement aTypeDecorator
class and provide an implementation ofTypeDecorator.bind_expression()
.See also
-
method
sqlalchemy.types.TypeEngine.
bind_processor(dialect: Dialect) Optional[_BindProcessorType[_T]] ¶ Return a conversion function for processing bind values.
Returns a callable which will receive a bind parameter value as the sole positional argument and will return a value to send to the DB-API.
If processing is not necessary, the method should return
None
.Note
This method is only called relative to a dialect specific type object, which is often private to a dialect in use and is not the same type object as the public facing one, which means it’s not feasible to subclass a
TypeEngine
class in order to provide an alternateTypeEngine.bind_processor()
method, unless subclassing theUserDefinedType
class explicitly.To provide alternate behavior for
TypeEngine.bind_processor()
, implement aTypeDecorator
class and provide an implementation ofTypeDecorator.process_bind_param()
.See also
- Parameters:
dialect – Dialect instance in use.
-
method
sqlalchemy.types.TypeEngine.
coerce_compared_value(op: Optional[OperatorType], value: Any) TypeEngine[Any] ¶ Suggest a type for a ‘coerced’ Python value in an expression.
Given an operator and value, gives the type a chance to return a type which the value should be coerced into.
The default behavior here is conservative; if the right-hand side is already coerced into a SQL type based on its Python type, it is usually left alone.
End-user functionality extension here should generally be via
TypeDecorator
, which provides more liberal behavior in that it defaults to coercing the other side of the expression into this type, thus applying special Python conversions above and beyond those needed by the DBAPI to both ides. It also provides the public methodTypeDecorator.coerce_compared_value()
which is intended for end-user customization of this behavior.
-
method
sqlalchemy.types.TypeEngine.
column_expression(colexpr: ColumnElement[_T]) Optional[ColumnElement[_T]] ¶ 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.This method is called during the SQL compilation phase of a statement, when rendering a SQL string. It is not called against specific values.
Note
This method is only called relative to a dialect specific type object, which is often private to a dialect in use and is not the same type object as the public facing one, which means it’s not feasible to subclass a
TypeEngine
class in order to provide an alternateTypeEngine.column_expression()
method, unless subclassing theUserDefinedType
class explicitly.To provide alternate behavior for
TypeEngine.column_expression()
, implement aTypeDecorator
class and provide an implementation ofTypeDecorator.column_expression()
.See also
-
attribute
sqlalchemy.types.TypeEngine.
comparator_factory¶ alias of
Comparator
-
method
sqlalchemy.types.TypeEngine.
compare_against_backend(dialect: Dialect, conn_type: TypeEngine[Any]) Optional[bool] ¶ 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 implement 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:
dialect – a
Dialect
that is involved in the comparison.conn_type – the type object reflected from the backend.
-
method
sqlalchemy.types.TypeEngine.
compare_values(x: Any, y: Any) bool ¶ Compare two values for equality.
-
method
sqlalchemy.types.TypeEngine.
compile(dialect: Optional[Dialect] = None) str ¶ 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.
-
method
sqlalchemy.types.TypeEngine.
dialect_impl(dialect: Dialect) TypeEngine[_T] ¶ Return a dialect-specific implementation for this
TypeEngine
.
-
method
sqlalchemy.types.TypeEngine.
evaluates_none() Self ¶ Return a copy of this type which has the
should_evaluate_none
flag set to True.E.g.:
Table( 'some_table', metadata, Column( String(50).evaluates_none(), nullable=True, server_default='no value') )
The ORM uses this flag to indicate that a positive value of
None
is passed to the column in an INSERT statement, rather than omitting the column from the INSERT statement which has the effect of firing off column-level defaults. It also allows for types which have special behavior associated with the Python None value to indicate that the value doesn’t necessarily translate into SQL NULL; a prime example of this is a JSON type which may wish to persist the JSON value'null'
.In all cases, the actual NULL SQL value can be always be persisted in any column by using the
null
SQL construct in an INSERT statement or associated with an ORM-mapped attribute.Note
The “evaluates none” flag does not apply to a value of
None
passed toColumn.default
orColumn.server_default
; in these cases,None
still means “no default”.See also
Forcing NULL on a column with a default - in the ORM documentation
JSON.none_as_null
- PostgreSQL JSON interaction with this flag.TypeEngine.should_evaluate_none
- class-level flag
-
method
sqlalchemy.types.TypeEngine.
get_dbapi_type(dbapi: module) Optional[Any] ¶ Return the corresponding type object from the underlying DB-API, if any.
This can be useful for calling
setinputsizes()
, for example.
-
attribute
sqlalchemy.types.TypeEngine.
hashable = True¶ Flag, if False, means values from this type aren’t hashable.
Used by the ORM when uniquing result lists.
-
method
sqlalchemy.types.TypeEngine.
literal_processor(dialect: Dialect) Optional[_LiteralProcessorType[_T]] ¶ Return a conversion function for processing literal values that are to be rendered directly without using binds.
This function is used when the compiler makes use of the “literal_binds” flag, typically used in DDL generation as well as in certain scenarios where backends don’t accept bound parameters.
Returns a callable which will receive a literal Python value as the sole positional argument and will return a string representation to be rendered in a SQL statement.
Note
This method is only called relative to a dialect specific type object, which is often private to a dialect in use and is not the same type object as the public facing one, which means it’s not feasible to subclass a
TypeEngine
class in order to provide an alternateTypeEngine.literal_processor()
method, unless subclassing theUserDefinedType
class explicitly.To provide alternate behavior for
TypeEngine.literal_processor()
, implement aTypeDecorator
class and provide an implementation ofTypeDecorator.process_literal_param()
.See also
-
attribute
sqlalchemy.types.TypeEngine.
python_type¶ 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.
-
attribute
sqlalchemy.types.TypeEngine.
render_bind_cast = False¶ Render bind casts for
BindTyping.RENDER_CASTS
mode.If True, this type (usually a dialect level impl type) signals to the compiler that a cast should be rendered around a bound parameter for this type.
New in version 2.0.
See also
-
attribute
sqlalchemy.types.TypeEngine.
render_literal_cast = False¶ render casts when rendering a value as an inline literal, e.g. with
TypeEngine.literal_processor()
.New in version 2.0.
-
method
sqlalchemy.types.TypeEngine.
result_processor(dialect: Dialect, coltype: object) Optional[_ResultProcessorType[_T]] ¶ Return a conversion function for processing result row values.
Returns a callable which will receive a result row column value as the sole positional argument and will return a value to return to the user.
If processing is not necessary, the method should return
None
.Note
This method is only called relative to a dialect specific type object, which is often private to a dialect in use and is not the same type object as the public facing one, which means it’s not feasible to subclass a
TypeEngine
class in order to provide an alternateTypeEngine.result_processor()
method, unless subclassing theUserDefinedType
class explicitly.To provide alternate behavior for
TypeEngine.result_processor()
, implement aTypeDecorator
class and provide an implementation ofTypeDecorator.process_result_value()
.See also
- Parameters:
dialect – Dialect instance in use.
coltype – DBAPI coltype argument received in cursor.description.
-
attribute
sqlalchemy.types.TypeEngine.
should_evaluate_none: bool = False¶ If True, the Python constant
None
is considered to be handled explicitly by this type.The ORM uses this flag to indicate that a positive value of
None
is passed to the column in an INSERT statement, rather than omitting the column from the INSERT statement which has the effect of firing off column-level defaults. It also allows types which have special behavior for Python None, such as a JSON type, to indicate that they’d like to handle the None value explicitly.To set this flag on an existing type, use the
TypeEngine.evaluates_none()
method.See also
-
attribute
sqlalchemy.types.TypeEngine.
sort_key_function: Optional[Callable[[Any], Any]] = None¶ A sorting function that can be passed as the key to sorted.
The default value of
None
indicates that the values stored by this type are self-sorting.New in version 1.3.8.
-
method
sqlalchemy.types.TypeEngine.
with_variant(type_: _TypeEngineArgument[Any], *dialect_names: str) Self ¶ Produce a copy of this 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 string_type = String() string_type = string_type.with_variant( mysql.VARCHAR(collation='foo'), 'mysql', 'mariadb' )
The variant mapping indicates that when this type is interpreted by a specific dialect, it will instead be transmuted into the given type, rather than using the primary type.
Changed in version 2.0: the
TypeEngine.with_variant()
method now works with aTypeEngine
object “in place”, returning a copy of the original type rather than returning a wrapping object; theVariant
class is no longer used.- Parameters:
type_ – a
TypeEngine
that will be selected as a variant from the originating type, when a dialect of the given name is in use.*dialect_names –
one or more base names of the dialect which uses this type. (i.e.
'postgresql'
,'mysql'
, etc.)Changed in version 2.0: multiple dialect names can be specified for one variant.
See also
Using “UPPERCASE” and Backend-specific types for multiple backends - illustrates the use of
TypeEngine.with_variant()
.
- class sqlalchemy.types.Concatenable¶
A mixin that marks a type as supporting ‘concatenation’, typically strings.
Members
Class signature
class
sqlalchemy.types.Concatenable
(sqlalchemy.types.TypeEngineMixin
)- class Comparator¶
Class signature
class
sqlalchemy.types.Concatenable.Comparator
(sqlalchemy.types.Comparator
)
-
attribute
sqlalchemy.types.Concatenable.
comparator_factory¶ alias of
Comparator
- class sqlalchemy.types.Indexable¶
A mixin that marks a type as supporting indexing operations, such as array or JSON structures.
Members
Class signature
class
sqlalchemy.types.Indexable
(sqlalchemy.types.TypeEngineMixin
)- class Comparator¶
Class signature
class
sqlalchemy.types.Indexable.Comparator
(sqlalchemy.types.Comparator
)
-
attribute
sqlalchemy.types.Indexable.
comparator_factory¶ alias of
Comparator
- class sqlalchemy.types.NullType¶
An unknown type.
NullType
is used as a default type for those cases where a type cannot be determined, including:During table reflection, when the type of a column is not recognized by the
Dialect
When constructing SQL expressions using plain Python objects of unknown types (e.g.
somecolumn == my_special_object
)When a new
Column
is created, and the given type is passed asNone
or is not passed at all.
The
NullType
can be used within SQL expression invocation without issue, it just has no behavior either at the expression construction level or at the bind-parameter/result processing level.NullType
will result in aCompileError
if the compiler is asked to render the type itself, such as if it is used in acast()
operation or within a schema creation operation such as that invoked byMetaData.create_all()
or theCreateTable
construct.Class signature
class
sqlalchemy.types.NullType
(sqlalchemy.types.TypeEngine
)
- class sqlalchemy.types.ExternalType¶
mixin that defines attributes and behaviors specific to third-party datatypes.
“Third party” refers to datatypes that are defined outside the scope of SQLAlchemy within either end-user application code or within external extensions to SQLAlchemy.
Subclasses currently include
TypeDecorator
andUserDefinedType
.New in version 1.4.28.
Members
Class signature
class
sqlalchemy.types.ExternalType
(sqlalchemy.types.TypeEngineMixin
)-
attribute
sqlalchemy.types.ExternalType.
cache_ok: Optional[bool] = None¶ Indicate if statements using this
ExternalType
are “safe to cache”.The default value
None
will emit a warning and then not allow caching of a statement which includes this type. Set toFalse
to disable statements using this type from being cached at all without a warning. When set toTrue
, the object’s class and selected elements from its state will be used as part of the cache key. For example, using aTypeDecorator
:class MyType(TypeDecorator): impl = String cache_ok = True def __init__(self, choices): self.choices = tuple(choices) self.internal_only = True
The cache key for the above type would be equivalent to:
>>> MyType(["a", "b", "c"])._static_cache_key (<class '__main__.MyType'>, ('choices', ('a', 'b', 'c')))
The caching scheme will extract attributes from the type that correspond to the names of parameters in the
__init__()
method. Above, the “choices” attribute becomes part of the cache key but “internal_only” does not, because there is no parameter named “internal_only”.The requirements for cacheable elements is that they are hashable and also that they indicate the same SQL rendered for expressions using this type every time for a given cache value.
To accommodate for datatypes that refer to unhashable structures such as dictionaries, sets and lists, these objects can be made “cacheable” by assigning hashable structures to the attributes whose names correspond with the names of the arguments. For example, a datatype which accepts a dictionary of lookup values may publish this as a sorted series of tuples. Given a previously un-cacheable type as:
class LookupType(UserDefinedType): '''a custom type that accepts a dictionary as a parameter. this is the non-cacheable version, as "self.lookup" is not hashable. ''' def __init__(self, lookup): self.lookup = lookup def get_col_spec(self, **kw): return "VARCHAR(255)" def bind_processor(self, dialect): # ... works with "self.lookup" ...
Where “lookup” is a dictionary. The type will not be able to generate a cache key:
>>> type_ = LookupType({"a": 10, "b": 20}) >>> type_._static_cache_key <stdin>:1: SAWarning: UserDefinedType LookupType({'a': 10, 'b': 20}) will not produce a cache key because the ``cache_ok`` flag is not set to True. Set this flag to True if this type object's state is safe to use in a cache key, or False to disable this warning. symbol('no_cache')
If we did set up such a cache key, it wouldn’t be usable. We would get a tuple structure that contains a dictionary inside of it, which cannot itself be used as a key in a “cache dictionary” such as SQLAlchemy’s statement cache, since Python dictionaries aren’t hashable:
>>> # set cache_ok = True >>> type_.cache_ok = True >>> # this is the cache key it would generate >>> key = type_._static_cache_key >>> key (<class '__main__.LookupType'>, ('lookup', {'a': 10, 'b': 20})) >>> # however this key is not hashable, will fail when used with >>> # SQLAlchemy statement cache >>> some_cache = {key: "some sql value"} Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: unhashable type: 'dict'
The type may be made cacheable by assigning a sorted tuple of tuples to the “.lookup” attribute:
class LookupType(UserDefinedType): '''a custom type that accepts a dictionary as a parameter. The dictionary is stored both as itself in a private variable, and published in a public variable as a sorted tuple of tuples, which is hashable and will also return the same value for any two equivalent dictionaries. Note it assumes the keys and values of the dictionary are themselves hashable. ''' cache_ok = True def __init__(self, lookup): self._lookup = lookup # assume keys/values of "lookup" are hashable; otherwise # they would also need to be converted in some way here self.lookup = tuple( (key, lookup[key]) for key in sorted(lookup) ) def get_col_spec(self, **kw): return "VARCHAR(255)" def bind_processor(self, dialect): # ... works with "self._lookup" ...
Where above, the cache key for
LookupType({"a": 10, "b": 20})
will be:>>> LookupType({"a": 10, "b": 20})._static_cache_key (<class '__main__.LookupType'>, ('lookup', (('a', 10), ('b', 20))))
New in version 1.4.14: - added the
cache_ok
flag to allow some configurability of caching forTypeDecorator
classes.New in version 1.4.28: - added the
ExternalType
mixin which generalizes thecache_ok
flag to both theTypeDecorator
andUserDefinedType
classes.See also
-
attribute
- class sqlalchemy.types.Variant¶
deprecated. symbol is present for backwards-compatibility with workaround recipes, however this actual type should not be used.
Members
Class signature
class
sqlalchemy.types.Variant
(sqlalchemy.types.TypeDecorator
)-
method
sqlalchemy.types.Variant.
with_variant(type_: _TypeEngineArgument[Any], *dialect_names: str) Self ¶ inherited from the
TypeEngine.with_variant()
method ofTypeEngine
Produce a copy of this 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 string_type = String() string_type = string_type.with_variant( mysql.VARCHAR(collation='foo'), 'mysql', 'mariadb' )
The variant mapping indicates that when this type is interpreted by a specific dialect, it will instead be transmuted into the given type, rather than using the primary type.
Changed in version 2.0: the
TypeEngine.with_variant()
method now works with aTypeEngine
object “in place”, returning a copy of the original type rather than returning a wrapping object; theVariant
class is no longer used.- Parameters:
type_ – a
TypeEngine
that will be selected as a variant from the originating type, when a dialect of the given name is in use.*dialect_names –
one or more base names of the dialect which uses this type. (i.e.
'postgresql'
,'mysql'
, etc.)Changed in version 2.0: multiple dialect names can be specified for one variant.
See also
Using “UPPERCASE” and Backend-specific types for multiple backends - illustrates the use of
TypeEngine.with_variant()
.
-
method