The Type Hierarchy¶
SQLAlchemy provides abstractions for most common database data types, as well as several techniques for customization of datatypes.
Database types are represented using Python classes, all of which ultimately
extend from the base type class known as TypeEngine
. There are
two general categories of datatypes, each of which express themselves within
the typing hierarchy in different ways. The category used by an individual
datatype class can be identified based on the use of two different naming
conventions, which are “CamelCase” and “UPPERCASE”.
See also
Setting up MetaData with Table objects - in the SQLAlchemy Unified Tutorial. Illustrates
the most rudimental use of TypeEngine
type objects to
define Table
metadata and introduces the concept
of type objects in tutorial form.
The “CamelCase” datatypes¶
The rudimental types have “CamelCase” names such as String
,
Numeric
, Integer
, and DateTime
.
All of the immediate subclasses of TypeEngine
are
“CamelCase” types. The “CamelCase” types are to the greatest degree possible
database agnostic, meaning they can all be used on any database backend
where they will behave in such a way as appropriate to that backend in order to
produce the desired behavior.
An example of a straightforward “CamelCase” datatype is String
.
On most backends, using this datatype in a
table specification will correspond to the
VARCHAR
database type being used on the target backend, delivering string
values to and from the database, as in the example below:
from sqlalchemy import MetaData
from sqlalchemy import Table, Column, Integer, String
metadata_obj = MetaData()
user = Table(
"user",
metadata_obj,
Column("user_name", String, primary_key=True),
Column("email_address", String(60)),
)
When using a particular TypeEngine
class in a
Table
definition or in any SQL expression overall, if no
arguments are required it may be passed as the class itself, that is, without
instantiating it with ()
. If arguments are needed, such as the length
argument of 60 in the "email_address"
column above, the type may be
instantiated.
Another “CamelCase” datatype that expresses more backend-specific behavior
is the Boolean
datatype. Unlike String
,
which represents a string datatype that all databases have,
not every backend has a real “boolean” datatype; some make use of integers
or BIT values 0 and 1, some have boolean literal constants true
and
false
while others dont. For this datatype, Boolean
may render BOOLEAN
on a backend such as PostgreSQL, BIT
on the
MySQL backend and SMALLINT
on Oracle. As data is sent and received
from the database using this type, based on the dialect in use it may be
interpreting Python numeric or boolean values.
The typical SQLAlchemy application will likely wish to use primarily “CamelCase” types in the general case, as they will generally provide the best basic behavior and be automatically portable to all backends.
Reference for the general set of “CamelCase” datatypes is below at Generic “CamelCase” Types.
The “UPPERCASE” datatypes¶
In contrast to the “CamelCase” types are the “UPPERCASE” datatypes. These
datatypes are always inherited from a particular “CamelCase” datatype, and
always represent an exact datatype. When using an “UPPERCASE” datatype,
the name of the type is always rendered exactly as given, without regard for
whether or not the current backend supports it. Therefore the use
of “UPPERCASE” types in a SQLAlchemy application indicates that specific
datatypes are required, which then implies that the application would normally,
without additional steps taken,
be limited to those backends which use the type exactly as given. Examples
of UPPERCASE types include VARCHAR
, NUMERIC
,
INTEGER
, and TIMESTAMP
, which inherit directly
from the previously mentioned “CamelCase” types
String
,
Numeric
, Integer
, and DateTime
,
respectively.
The “UPPERCASE” datatypes that are part of sqlalchemy.types
are common
SQL types that typically expect to be available on at least two backends
if not more.
Reference for the general set of “UPPERCASE” datatypes is below at SQL Standard and Multiple Vendor “UPPERCASE” Types.
Backend-specific “UPPERCASE” datatypes¶
Most databases also have their own datatypes that
are either fully specific to those databases, or add additional arguments
that are specific to those databases. For these datatypes, specific
SQLAlchemy dialects provide backend-specific “UPPERCASE” datatypes, for a
SQL type that has no analogue on other backends. Examples of backend-specific
uppercase datatypes include PostgreSQL’s JSONB
, SQL Server’s
IMAGE
and MySQL’s TINYTEXT
.
Specific backends may also include “UPPERCASE” datatypes that extend the
arguments available from that same “UPPERCASE” datatype as found in the
sqlalchemy.types
module. An example is when creating a MySQL string
datatype, one might want to specify MySQL-specific arguments such as charset
or national
, which are available from the MySQL version
of VARCHAR
as the MySQL-only parameters
VARCHAR.charset
and VARCHAR.national
.
API documentation for backend-specific types are in the dialect-specific documentation, listed at Dialects.
Using “UPPERCASE” and Backend-specific types for multiple backends¶
Reviewing the presence of “UPPERCASE” and “CamelCase” types leads to the natural
use case of how to make use of “UPPERCASE” datatypes for backend-specific
options, but only when that backend is in use. To tie together the
database-agnostic “CamelCase” and backend-specific “UPPERCASE” systems, one
makes use of the TypeEngine.with_variant()
method in order to
compose types together to work with specific behaviors on specific backends.
Such as, to use the String
datatype, but when running on MySQL
to make use of the VARCHAR.charset
parameter of
VARCHAR
when the table is created on MySQL or MariaDB,
TypeEngine.with_variant()
may be used as below:
from sqlalchemy import MetaData
from sqlalchemy import Table, Column, Integer, String
from sqlalchemy.dialects.mysql import VARCHAR
metadata_obj = MetaData()
user = Table(
"user",
metadata_obj,
Column("user_name", String(100), primary_key=True),
Column(
"bio",
String(255).with_variant(VARCHAR(255, charset="utf8"), "mysql", "mariadb"),
),
)
In the above table definition, the "bio"
column will have string-behaviors
on all backends. On most backends it will render in DDL as VARCHAR
. However
on MySQL and MariaDB (indicated by database URLs that start with mysql
or
mariadb
), it will render as VARCHAR(255) CHARACTER SET utf8
.
See also
TypeEngine.with_variant()
- additional usage examples and notes
Generic “CamelCase” Types¶
Generic types specify a column that can read, write and store a
particular type of Python data. SQLAlchemy will choose the best
database column type available on the target database when issuing a
CREATE TABLE
statement. For complete control over which column
type is emitted in CREATE TABLE
, such as VARCHAR
see
SQL Standard and Multiple Vendor “UPPERCASE” Types and the other sections of this chapter.
Object Name | Description |
---|---|
A type for bigger |
|
A bool datatype. |
|
A type for |
|
A type for |
|
A type for double |
|
Generic Enum Type. |
|
Type representing floating point types, such as |
|
A type for |
|
A type for |
|
A type for large binary byte data. |
|
Refers to the return type of the MATCH operator. |
|
Base for non-integer numeric types, such as
|
|
Holds Python objects, which are serialized using pickle. |
|
Add capabilities to a type which allow for schema-level DDL to be associated with a type. |
|
A type for smaller |
|
The base for all string and character types. |
|
A variably sized string type. |
|
A type for |
|
A variable length Unicode string type. |
|
An unbounded-length Unicode string type. |
|
Represent a database agnostic UUID datatype. |
- class sqlalchemy.types.BigInteger¶
A type for bigger
int
integers.Typically generates a
BIGINT
in DDL, and otherwise acts like a normalInteger
on the Python side.Class signature
class
sqlalchemy.types.BigInteger
(sqlalchemy.types.Integer
)
- class sqlalchemy.types.Boolean¶
A bool datatype.
Boolean
typically uses BOOLEAN or SMALLINT on the DDL side, and on the Python side deals inTrue
orFalse
.The
Boolean
datatype currently has two levels of assertion that the values persisted are simple true/false values. For all backends, only the Python valuesNone
,True
,False
,1
or0
are accepted as parameter values. For those backends that don’t support a “native boolean” datatype, an option exists to also create a CHECK constraint on the target columnChanged in version 1.2: the
Boolean
datatype now asserts that incoming Python values are already in pure boolean form.Class signature
class
sqlalchemy.types.Boolean
(sqlalchemy.types.SchemaType
,sqlalchemy.types.Emulated
,sqlalchemy.types.TypeEngine
)-
method
sqlalchemy.types.Boolean.
__init__(create_constraint: bool = False, name: Optional[str] = None, _create_events: bool = True, _adapted_from: Optional[SchemaType] = None)¶ 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
sqlalchemy.types.Boolean.
bind_processor(dialect)¶ 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.Boolean.
literal_processor(dialect)¶ 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.Boolean.
python_type¶
-
method
sqlalchemy.types.Boolean.
result_processor(dialect, coltype)¶ 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.
-
method
- class sqlalchemy.types.Date¶
A type for
datetime.date()
objects.Members
Class signature
class
sqlalchemy.types.Date
(sqlalchemy.types._RenderISO8601NoT
,sqlalchemy.types.HasExpressionLookup
,sqlalchemy.types.TypeEngine
)-
method
sqlalchemy.types.Date.
get_dbapi_type(dbapi)¶ Return the corresponding type object from the underlying DB-API, if any.
This can be useful for calling
setinputsizes()
, for example.
-
method
sqlalchemy.types.Date.
literal_processor(dialect)¶ 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.Date.
python_type¶
-
method
- class sqlalchemy.types.DateTime¶
A type for
datetime.datetime()
objects.Date and time types return objects from the Python
datetime
module. Most DBAPIs have built in support for the datetime module, with the noted exception of SQLite. In the case of SQLite, date and time types are stored as strings which are then converted back to datetime objects when rows are returned.For the time representation within the datetime type, some backends include additional options, such as timezone support and fractional seconds support. For fractional seconds, use the dialect-specific datatype, such as
TIME
. For timezone support, use at least theTIMESTAMP
datatype, if not the dialect-specific datatype object.Members
__init__(), get_dbapi_type(), literal_processor(), python_type
Class signature
class
sqlalchemy.types.DateTime
(sqlalchemy.types._RenderISO8601NoT
,sqlalchemy.types.HasExpressionLookup
,sqlalchemy.types.TypeEngine
)-
method
sqlalchemy.types.DateTime.
__init__(timezone: bool = False)¶ 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
sqlalchemy.types.DateTime.
get_dbapi_type(dbapi)¶ Return the corresponding type object from the underlying DB-API, if any.
This can be useful for calling
setinputsizes()
, for example.
-
method
sqlalchemy.types.DateTime.
literal_processor(dialect)¶ 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.DateTime.
python_type¶
-
method
- class sqlalchemy.types.Enum¶
Generic Enum Type.
The
Enum
type provides a set of possible string values which the column is constrained towards.The
Enum
type will make use of the backend’s native “ENUM” type if one is available; otherwise, it uses a VARCHAR datatype. An option also exists to automatically produce a CHECK constraint when the VARCHAR (so called “non-native”) variant is produced; see theEnum.create_constraint
flag.The
Enum
type also provides in-Python validation of string values during both read and write operations. When reading a value from the database in a result set, the string value is always checked against the list of possible values and aLookupError
is raised if no match is found. When passing a value to the database as a plain string within a SQL statement, if theEnum.validate_strings
parameter is set to True, aLookupError
is raised for any string value that’s not located in the given list of possible values; note that this impacts usage of LIKE expressions with enumerated values (an unusual use case).The source of enumerated values may be a list of string values, or alternatively a PEP-435-compliant enumerated class. For the purposes of the
Enum
datatype, this class need only provide a__members__
method.When using an enumerated class, the enumerated objects are used both for input and output, rather than strings as is the case with a plain-string enumerated type:
import enum from sqlalchemy import Enum class MyEnum(enum.Enum): one = 1 two = 2 three = 3 t = Table( 'data', MetaData(), Column('value', Enum(MyEnum)) ) connection.execute(t.insert(), {"value": MyEnum.two}) assert connection.scalar(t.select()) is MyEnum.two
Above, the string names of each element, e.g. “one”, “two”, “three”, are persisted to the database; the values of the Python Enum, here indicated as integers, are not used; the value of each enum can therefore be any kind of Python object whether or not it is persistable.
In order to persist the values and not the names, the
Enum.values_callable
parameter may be used. The value of this parameter is a user-supplied callable, which is intended to be used with a PEP-435-compliant enumerated class and returns a list of string values to be persisted. For a simple enumeration that uses string values, a callable such aslambda x: [e.value for e in x]
is sufficient.See also
Using Python Enum or pep-586 Literal types in the type map - background on using the
Enum
datatype with the ORM’s ORM Annotated Declarative feature.ENUM
- PostgreSQL-specific type, which has additional functionality.ENUM
- MySQL-specific typeMembers
Class signature
class
sqlalchemy.types.Enum
(sqlalchemy.types.String
,sqlalchemy.types.SchemaType
,sqlalchemy.types.Emulated
,sqlalchemy.types.TypeEngine
)-
method
sqlalchemy.types.Enum.
__init__(*enums: object, **kw: Any)¶ Construct an enum.
Keyword arguments which don’t apply to a specific backend are ignored by that backend.
- Parameters:
*enums – either exactly one PEP-435 compliant enumerated type or one or more string labels.
create_constraint –
defaults to False. When creating a non-native enumerated type, also build a CHECK constraint on the database against the valid values.
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
Enum.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.
metadata –
Associate this type directly with a
MetaData
object. For types that exist on the target database as an independent schema construct (PostgreSQL), this type will be created and dropped withincreate_all()
anddrop_all()
operations. If the type is not associated with anyMetaData
object, it will associate itself with eachTable
in which it is used, and will be created when any of those individual tables are created, after a check is performed for its existence. The type is only dropped whendrop_all()
is called for thatTable
object’s metadata, however.The value of the
MetaData.schema
parameter of theMetaData
object, if set, will be used as the default value of theEnum.schema
on this object if an explicit value is not otherwise supplied.Changed in version 1.4.12:
Enum
inherits theMetaData.schema
parameter of theMetaData
object if present, when passed using theEnum.metadata
parameter.name – The name of this type. This is required for PostgreSQL and any future supported database which requires an explicitly named type, or an explicitly named constraint in order to generate the type and/or a table that uses it. If a PEP-435 enumerated class was used, its name (converted to lower case) is used by default.
native_enum – Use the database’s native ENUM type when available. Defaults to True. When False, uses VARCHAR + check constraint for all backends. When False, the VARCHAR length can be controlled with
Enum.length
; currently “length” is ignored if native_enum=True.length –
Allows specifying a custom length for the VARCHAR when a non-native enumeration datatype is used. By default it uses the length of the longest value.
Changed in version 2.0.0: The
Enum.length
parameter is used unconditionally forVARCHAR
rendering regardless of theEnum.native_enum
parameter, for those backends whereVARCHAR
is used for enumerated datatypes.schema –
Schema name of this type. For types that exist on the target database as an independent schema construct (PostgreSQL), this parameter specifies the named schema in which the type is present.
If not present, the schema name will be taken from the
MetaData
collection if passed asEnum.metadata
, for aMetaData
that includes theMetaData.schema
parameter.Changed in version 1.4.12:
Enum
inherits theMetaData.schema
parameter of theMetaData
object if present, when passed using theEnum.metadata
parameter.Otherwise, if the
Enum.inherit_schema
flag is set toTrue
, the schema will be inherited from the associatedTable
object if any; whenEnum.inherit_schema
is at its default ofFalse
, the owning table’s schema is not used.quote – Set explicit quoting preferences for the type’s name.
inherit_schema – When
True
, the “schema” from the owningTable
will be copied to the “schema” attribute of thisEnum
, replacing whatever value was passed for theschema
attribute. This also takes effect when using theTable.to_metadata()
operation.validate_strings – when True, string values that are being passed to the database in a SQL statement will be checked for validity against the list of enumerated values. Unrecognized values will result in a
LookupError
being raised.values_callable –
A callable which will be passed the PEP-435 compliant enumerated type, which should then return a list of string values to be persisted. This allows for alternate usages such as using the string value of an enum to be persisted to the database instead of its name.
New in version 1.2.3.
sort_key_function –
a Python callable which may be used as the “key” argument in the Python
sorted()
built-in. The SQLAlchemy ORM requires that primary key columns which are mapped must be sortable in some way. When using an unsortable enumeration object such as a Python 3Enum
object, this parameter may be used to set a default sort key function for the objects. By default, the database value of the enumeration is used as the sorting function.New in version 1.3.8.
omit_aliases –
A boolean that when true will remove aliases from pep 435 enums. defaults to
True
.Changed in version 2.0: This parameter now defaults to True.
-
method
sqlalchemy.types.Enum.
create(bind, checkfirst=False)¶ inherited from the
SchemaType.create()
method ofSchemaType
Issue CREATE DDL for this type, if applicable.
-
method
sqlalchemy.types.Enum.
drop(bind, checkfirst=False)¶ inherited from the
SchemaType.drop()
method ofSchemaType
Issue DROP DDL for this type, if applicable.
-
method
- class sqlalchemy.types.Double¶
A type for double
FLOAT
floating point types.Typically generates a
DOUBLE
orDOUBLE_PRECISION
in DDL, and otherwise acts like a normalFloat
on the Python side.New in version 2.0.
Class signature
- class sqlalchemy.types.Float¶
Type representing floating point types, such as
FLOAT
orREAL
.This type returns Python
float
objects by default, unless theFloat.asdecimal
flag is set to True, in which case they are coerced todecimal.Decimal
objects.Members
Class signature
-
method
sqlalchemy.types.Float.
__init__(precision: Optional[int] = None, asdecimal: bool = False, decimal_return_scale: Optional[int] = None)¶ Construct a Float.
- Parameters:
precision –
the numeric precision for use in DDL
CREATE TABLE
. Backends should attempt to ensure this precision indicates a number of digits for the genericFloat
datatype.Note
For the Oracle backend, the
Float.precision
parameter is not accepted when rendering DDL, as Oracle does not support float precision specified as a number of decimal places. Instead, use the Oracle-specificFLOAT
datatype and specify theFLOAT.binary_precision
parameter. This is new in version 2.0 of SQLAlchemy.To create a database agnostic
Float
that separately specifies binary precision for Oracle, useTypeEngine.with_variant()
as follows:from sqlalchemy import Column from sqlalchemy import Float from sqlalchemy.dialects import oracle Column( "float_data", Float(5).with_variant(oracle.FLOAT(binary_precision=16), "oracle") )
asdecimal – the same flag as that of
Numeric
, but defaults toFalse
. Note that setting this flag toTrue
results in floating point conversion.decimal_return_scale – Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don’t have a notion of “scale”, so by default the float type looks for the first ten decimal places when converting. Specifying this value will override that length. Note that the MySQL float types, which do include “scale”, will use “scale” as the default for decimal_return_scale, if not otherwise specified.
-
method
sqlalchemy.types.Float.
result_processor(dialect, coltype)¶ 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.
-
method
- class sqlalchemy.types.Integer¶
A type for
int
integers.Members
Class signature
class
sqlalchemy.types.Integer
(sqlalchemy.types.HasExpressionLookup
,sqlalchemy.types.TypeEngine
)-
method
sqlalchemy.types.Integer.
get_dbapi_type(dbapi)¶ Return the corresponding type object from the underlying DB-API, if any.
This can be useful for calling
setinputsizes()
, for example.
-
method
sqlalchemy.types.Integer.
literal_processor(dialect)¶ 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.Integer.
python_type¶
-
method
- class sqlalchemy.types.Interval¶
A type for
datetime.timedelta()
objects.The Interval type deals with
datetime.timedelta
objects. In PostgreSQL, the nativeINTERVAL
type is used; for others, the value is stored as a date which is relative to the “epoch” (Jan. 1, 1970).Note that the
Interval
type does not currently provide date arithmetic operations on platforms which do not support interval types natively. Such operations usually require transformation of both sides of the expression (such as, conversion of both sides into integer epoch values first) which currently is a manual procedure (such as viaexpression.func
).Members
__init__(), adapt_to_emulated(), bind_processor(), cache_ok, coerce_compared_value(), comparator_factory, impl, python_type, result_processor()
Class signature
class
sqlalchemy.types.Interval
(sqlalchemy.types.Emulated
,sqlalchemy.types._AbstractInterval
,sqlalchemy.types.TypeDecorator
)- class Comparator¶
Class signature
class
sqlalchemy.types.Interval.Comparator
(sqlalchemy.types.Comparator
,sqlalchemy.types.Comparator
)
-
method
sqlalchemy.types.Interval.
__init__(native: bool = True, second_precision: Optional[int] = None, day_precision: Optional[int] = None)¶ Construct an Interval object.
- Parameters:
native – when True, use the actual INTERVAL type provided by the database, if supported (currently PostgreSQL, Oracle). Otherwise, represent the interval data as an epoch value regardless.
second_precision – For native interval types which support a “fractional seconds precision” parameter, i.e. Oracle and PostgreSQL
day_precision – for native interval types which support a “day precision” parameter, i.e. Oracle.
-
method
sqlalchemy.types.Interval.
adapt_to_emulated(impltype, **kw)¶ Given an impl class, adapt this type to the impl assuming “emulated”.
The impl should also be an “emulated” version of this type, most likely the same class as this type itself.
e.g.: sqltypes.Enum adapts to the Enum class.
-
method
sqlalchemy.types.Interval.
bind_processor(dialect: Dialect) _BindProcessorType[dt.timedelta] ¶ 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.
-
attribute
sqlalchemy.types.Interval.
cache_ok: Optional[bool] = True¶ 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
-
method
sqlalchemy.types.Interval.
coerce_compared_value(op, value)¶ 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.
-
attribute
sqlalchemy.types.Interval.
comparator_factory¶ alias of
Comparator
-
attribute
sqlalchemy.types.Interval.
impl¶ alias of
DateTime
-
attribute
sqlalchemy.types.Interval.
python_type¶
-
method
sqlalchemy.types.Interval.
result_processor(dialect: Dialect, coltype: Any) _ResultProcessorType[dt.timedelta] ¶ 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.
- class sqlalchemy.types.LargeBinary¶
A type for large binary byte data.
The
LargeBinary
type corresponds to a large and/or unlengthed binary type for the target platform, such as BLOB on MySQL and BYTEA for PostgreSQL. It also handles the necessary conversions for the DBAPI.Members
Class signature
class
sqlalchemy.types.LargeBinary
(sqlalchemy.types._Binary
)-
method
sqlalchemy.types.LargeBinary.
__init__(length: Optional[int] = None)¶ 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.types.MatchType¶
Refers to the return type of the MATCH operator.
As the
ColumnOperators.match()
is probably the most open-ended operator in generic SQLAlchemy Core, we can’t assume the return type at SQL evaluation time, as MySQL returns a floating point, not a boolean, and other backends might do something different. So this type acts as a placeholder, currently subclassingBoolean
. The type allows dialects to inject result-processing functionality if needed, and on MySQL will return floating-point values.Class signature
- class sqlalchemy.types.Numeric¶
Base for non-integer numeric types, such as
NUMERIC
,FLOAT
,DECIMAL
, and other variants.The
Numeric
datatype when used directly will render DDL corresponding to precision numerics if available, such asNUMERIC(precision, scale)
. TheFloat
subclass will attempt to render a floating-point datatype such asFLOAT(precision)
.Numeric
returns Pythondecimal.Decimal
objects by default, based on the default value ofTrue
for theNumeric.asdecimal
parameter. If this parameter is set to False, returned values are coerced to Pythonfloat
objects.The
Float
subtype, being more specific to floating point, defaults theFloat.asdecimal
flag to False so that the default Python datatype isfloat
.Note
When using a
Numeric
datatype against a database type that returns Python floating point values to the driver, the accuracy of the decimal conversion indicated byNumeric.asdecimal
may be limited. The behavior of specific numeric/floating point datatypes is a product of the SQL datatype in use, the Python DBAPI in use, as well as strategies that may be present within the SQLAlchemy dialect in use. Users requiring specific precision/ scale are encouraged to experiment with the available datatypes in order to determine the best results.Members
__init__(), bind_processor(), get_dbapi_type(), literal_processor(), python_type, result_processor()
Class signature
class
sqlalchemy.types.Numeric
(sqlalchemy.types.HasExpressionLookup
,sqlalchemy.types.TypeEngine
)-
method
sqlalchemy.types.Numeric.
__init__(precision: Optional[int] = None, scale: Optional[int] = None, decimal_return_scale: Optional[int] = None, asdecimal: bool = True)¶ Construct a Numeric.
- Parameters:
precision – the numeric precision for use in DDL
CREATE TABLE
.scale – the numeric scale for use in DDL
CREATE TABLE
.asdecimal – default True. Return whether or not values should be sent as Python Decimal objects, or as floats. Different DBAPIs send one or the other based on datatypes - the Numeric type will ensure that return values are one or the other across DBAPIs consistently.
decimal_return_scale – Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don’t have a notion of “scale”, so by default the float type looks for the first ten decimal places when converting. Specifying this value will override that length. Types which do include an explicit “.scale” value, such as the base
Numeric
as well as the MySQL float types, will use the value of “.scale” as the default for decimal_return_scale, if not otherwise specified.
When using the
Numeric
type, care should be taken to ensure that the asdecimal setting is appropriate for the DBAPI in use - when Numeric applies a conversion from Decimal->float or float-> Decimal, this conversion incurs an additional performance overhead for all result columns received.DBAPIs that return Decimal natively (e.g. psycopg2) will have better accuracy and higher performance with a setting of
True
, as the native translation to Decimal reduces the amount of floating- point issues at play, and the Numeric type itself doesn’t need to apply any further conversions. However, another DBAPI which returns floats natively will incur an additional conversion overhead, and is still subject to floating point data loss - in which caseasdecimal=False
will at least remove the extra conversion overhead.
-
method
sqlalchemy.types.Numeric.
bind_processor(dialect)¶ 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.Numeric.
get_dbapi_type(dbapi)¶ Return the corresponding type object from the underlying DB-API, if any.
This can be useful for calling
setinputsizes()
, for example.
-
method
sqlalchemy.types.Numeric.
literal_processor(dialect)¶ 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.Numeric.
python_type¶
-
method
sqlalchemy.types.Numeric.
result_processor(dialect, coltype)¶ 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.
-
method
- class sqlalchemy.types.PickleType¶
Holds Python objects, which are serialized using pickle.
PickleType builds upon the Binary type to apply Python’s
pickle.dumps()
to incoming objects, andpickle.loads()
on the way out, allowing any pickleable Python object to be stored as a serialized binary field.To allow ORM change events to propagate for elements associated with
PickleType
, see Mutation Tracking.Members
__init__(), bind_processor(), cache_ok, compare_values(), impl, result_processor()
Class signature
class
sqlalchemy.types.PickleType
(sqlalchemy.types.TypeDecorator
)-
method
sqlalchemy.types.PickleType.
__init__(protocol: int = 5, pickler: Any = None, comparator: Optional[Callable[[Any, Any], bool]] = None, impl: Optional[_TypeEngineArgument[Any]] = None)¶ Construct a PickleType.
- Parameters:
protocol – defaults to
pickle.HIGHEST_PROTOCOL
.pickler – defaults to pickle. May be any object with pickle-compatible
dumps
andloads
methods.comparator – a 2-arg callable predicate used to compare values of this type. If left as
None
, the Python “equals” operator is used to compare values.impl –
A binary-storing
TypeEngine
class or instance to use in place of the defaultLargeBinary
. For example the :class: _mysql.LONGBLOB class may be more effective when using MySQL.New in version 1.4.20.
-
method
sqlalchemy.types.PickleType.
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 which normally occurs via theTypeEngine.bind_processor()
method.Note
User-defined subclasses of
TypeDecorator
should not implement this method, and should instead implementTypeDecorator.process_bind_param()
so that the “inner” processing provided by the implementing type is maintained.- Parameters:
dialect – Dialect instance in use.
-
attribute
sqlalchemy.types.PickleType.
cache_ok: Optional[bool] = True¶ 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
-
method
sqlalchemy.types.PickleType.
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.
-
attribute
sqlalchemy.types.PickleType.
impl¶ alias of
LargeBinary
-
method
sqlalchemy.types.PickleType.
result_processor(dialect, coltype)¶ Provide a result value processing function for the given
Dialect
.This is the method that fulfills the
TypeEngine
contract for bound value conversion which normally occurs via theTypeEngine.result_processor()
method.Note
User-defined subclasses of
TypeDecorator
should not implement this method, and should instead implementTypeDecorator.process_result_value()
so that the “inner” processing provided by the implementing type is maintained.- Parameters:
dialect – Dialect instance in use.
coltype – A SQLAlchemy data type
-
method
- class sqlalchemy.types.SchemaType¶
Add capabilities to a type which allow for schema-level DDL to be associated with a type.
Supports types that must be explicitly created/dropped (i.e. PG ENUM type) as well as types that are complimented by table or schema level constraints, triggers, and other rules.
SchemaType
classes can also be targets for theDDLEvents.before_parent_attach()
andDDLEvents.after_parent_attach()
events, where the events fire off surrounding the association of the type object with a parentColumn
.Class signature
class
sqlalchemy.types.SchemaType
(sqlalchemy.sql.expression.SchemaEventTarget
,sqlalchemy.types.TypeEngineMixin
)-
method
sqlalchemy.types.SchemaType.
adapt(cls: Type[Union[TypeEngine, TypeEngineMixin]], **kw: Any) TypeEngine ¶
-
method
sqlalchemy.types.SchemaType.
copy(**kw)¶
-
method
sqlalchemy.types.SchemaType.
create(bind, checkfirst=False)¶ Issue CREATE DDL for this type, if applicable.
-
method
sqlalchemy.types.SchemaType.
drop(bind, checkfirst=False)¶ Issue DROP DDL for this type, if applicable.
-
attribute
sqlalchemy.types.SchemaType.
name: Optional[str]¶
-
method
- class sqlalchemy.types.SmallInteger¶
A type for smaller
int
integers.Typically generates a
SMALLINT
in DDL, and otherwise acts like a normalInteger
on the Python side.Class signature
class
sqlalchemy.types.SmallInteger
(sqlalchemy.types.Integer
)
- class sqlalchemy.types.String¶
The base for all string and character types.
In SQL, corresponds to VARCHAR.
The length field is usually required when the String type is used within a CREATE TABLE statement, as VARCHAR requires a length on most databases.
Members
__init__(), bind_processor(), get_dbapi_type(), literal_processor(), python_type, result_processor()
Class signature
class
sqlalchemy.types.String
(sqlalchemy.types.Concatenable
,sqlalchemy.types.TypeEngine
)-
method
sqlalchemy.types.String.
__init__(length: Optional[int] = None, collation: Optional[str] = None)¶ 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')))) {printsql}SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1
Note
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
sqlalchemy.types.String.
bind_processor(dialect)¶ 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.String.
get_dbapi_type(dbapi)¶ Return the corresponding type object from the underlying DB-API, if any.
This can be useful for calling
setinputsizes()
, for example.
-
method
sqlalchemy.types.String.
literal_processor(dialect)¶ 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.String.
python_type¶
-
method
sqlalchemy.types.String.
result_processor(dialect, coltype)¶ 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.
-
method
- class sqlalchemy.types.Text¶
A variably sized string type.
In SQL, usually corresponds to CLOB or TEXT. In general, TEXT objects do not have a length; while some databases will accept a length argument here, it will be rejected by others.
Class signature
- class sqlalchemy.types.Time¶
A type for
datetime.time()
objects.Members
Class signature
class
sqlalchemy.types.Time
(sqlalchemy.types._RenderISO8601NoT
,sqlalchemy.types.HasExpressionLookup
,sqlalchemy.types.TypeEngine
)-
method
sqlalchemy.types.Time.
get_dbapi_type(dbapi)¶ Return the corresponding type object from the underlying DB-API, if any.
This can be useful for calling
setinputsizes()
, for example.
-
method
sqlalchemy.types.Time.
literal_processor(dialect)¶ 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.Time.
python_type¶
-
method
- class sqlalchemy.types.Unicode¶
A variable length Unicode string type.
The
Unicode
type is aString
subclass that assumes input and output strings that may contain non-ASCII characters, and for some backends implies an underlying column type that is explicitly supporting of non-ASCII data, such asNVARCHAR
on Oracle and SQL Server. This will impact the output ofCREATE TABLE
statements andCAST
functions at the dialect level.The character encoding used by the
Unicode
type that is used to transmit and receive data to the database is usually determined by the DBAPI itself. All modern DBAPIs accommodate non-ASCII strings but may have different methods of managing database encodings; if necessary, this encoding should be configured as detailed in the notes for the target DBAPI in the Dialects section.In modern SQLAlchemy, use of the
Unicode
datatype does not imply any encoding/decoding behavior within SQLAlchemy itself. In Python 3, all string objects are inherently Unicode capable, and SQLAlchemy does not produce bytestring objects nor does it accommodate a DBAPI that does not return Python Unicode objects in result sets for string values.Warning
Some database backends, particularly SQL Server with pyodbc, are known to have undesirable behaviors regarding data that is noted as being of
NVARCHAR
type as opposed toVARCHAR
, including datatype mismatch errors and non-use of indexes. See the section onDialectEvents.do_setinputsizes()
for background on working around unicode character issues for backends like SQL Server with pyodbc as well as cx_Oracle.Members
Class signature
-
method
sqlalchemy.types.Unicode.
__init__(length=None, **kwargs)¶ Create a
Unicode
object.Parameters are the same as that of
String
.
-
method
- class sqlalchemy.types.UnicodeText¶
An unbounded-length Unicode string type.
See
Unicode
for details on the unicode behavior of this object.Like
Unicode
, usage theUnicodeText
type implies a unicode-capable type being used on the backend, such asNCLOB
,NTEXT
.Members
Class signature
-
method
sqlalchemy.types.UnicodeText.
__init__(length=None, **kwargs)¶ Create a Unicode-converting Text type.
Parameters are the same as that of
TextClause
.
-
method
- class sqlalchemy.types.Uuid¶
Represent a database agnostic UUID datatype.
For backends that have no “native” UUID datatype, the value will make use of
CHAR(32)
and store the UUID as a 32-character alphanumeric hex string.For backends which are known to support
UUID
directly or a similar uuid-storing datatype such as SQL Server’sUNIQUEIDENTIFIER
, a “native” mode enabled by default allows these types will be used on those backends.In its default mode of use, the
Uuid
datatype expects Python uuid objects, from the Python uuid module:import uuid from sqlalchemy import Uuid from sqlalchemy import Table, Column, MetaData, String metadata_obj = MetaData() t = Table( "t", metadata_obj, Column('uuid_data', Uuid, primary_key=True), Column("other_data", String) ) with engine.begin() as conn: conn.execute( t.insert(), {"uuid_data": uuid.uuid4(), "other_data", "some data"} )
To have the
Uuid
datatype work with string-based Uuids (e.g. 32 character hexadecimal strings), pass theUuid.as_uuid
parameter with the valueFalse
.New in version 2.0.
See also
UUID
- represents exactly theUUID
datatype without any backend-agnostic behaviors.Members
__init__(), bind_processor(), coerce_compared_value(), literal_processor(), python_type, result_processor()
Class signature
class
sqlalchemy.types.Uuid
(sqlalchemy.types.Emulated
,sqlalchemy.types.TypeEngine
)-
method
sqlalchemy.types.Uuid.
__init__(as_uuid: bool = True, native_uuid: bool = True)¶ Construct a
Uuid
type.- Parameters:
as_uuid=True –
if True, values will be interpreted as Python uuid objects, converting to/from string via the DBAPI.
native_uuid=True – if True, backends that support either the
UUID
datatype directly, or a UUID-storing value (such as SQL Server’sUNIQUEIDENTIFIER
will be used by those backends. If False, aCHAR(32)
datatype will be used for all backends regardless of native support.
-
method
sqlalchemy.types.Uuid.
bind_processor(dialect)¶ 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.Uuid.
coerce_compared_value(op, value)¶ See
TypeEngine.coerce_compared_value()
for a description.
-
method
sqlalchemy.types.Uuid.
literal_processor(dialect)¶ 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.Uuid.
python_type¶
-
method
sqlalchemy.types.Uuid.
result_processor(dialect, coltype)¶ 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.
-
method
SQL Standard and Multiple Vendor “UPPERCASE” Types¶
This category of types refers to types that are either part of the
SQL standard, or are potentially found within a subset of database backends.
Unlike the “generic” types, the SQL standard/multi-vendor types have no
guarantee of working on all backends, and will only work on those backends
that explicitly support them by name. That is, the type will always emit
its exact name in DDL with CREATE TABLE
is issued.
Object Name | Description |
---|---|
Represent a SQL Array type. |
|
The SQL BIGINT type. |
|
The SQL BINARY type. |
|
The SQL BLOB type. |
|
The SQL BOOLEAN type. |
|
The SQL CHAR type. |
|
The CLOB type. |
|
The SQL DATE type. |
|
The SQL DATETIME type. |
|
The SQL DECIMAL type. |
|
The SQL DOUBLE type. |
|
The SQL DOUBLE PRECISION type. |
|
The SQL FLOAT type. |
|
alias of |
|
The SQL INT or INTEGER type. |
|
Represent a SQL JSON type. |
|
The SQL NCHAR type. |
|
The SQL NUMERIC type. |
|
The SQL NVARCHAR type. |
|
The SQL REAL type. |
|
The SQL SMALLINT type. |
|
The SQL TEXT type. |
|
The SQL TIME type. |
|
The SQL TIMESTAMP type. |
|
Represent the SQL UUID type. |
|
The SQL VARBINARY type. |
|
The SQL VARCHAR type. |
- class sqlalchemy.types.ARRAY¶
Represent a SQL Array type.
Note
This type serves as the basis for all ARRAY operations. However, currently only the PostgreSQL backend has support for SQL arrays in SQLAlchemy. It is recommended to use the PostgreSQL-specific
sqlalchemy.dialects.postgresql.ARRAY
type directly when using ARRAY types with PostgreSQL, as it provides additional operators specific to that backend.ARRAY
is part of the Core in support of various SQL standard functions such asarray_agg
which explicitly involve arrays; however, with the exception of the PostgreSQL backend and possibly some third-party dialects, no other SQLAlchemy built-in dialect has support for this type.An
ARRAY
type is constructed given the “type” of element:mytable = Table("mytable", metadata, Column("data", ARRAY(Integer)) )
The above type represents an N-dimensional array, meaning a supporting backend such as PostgreSQL will interpret values with any number of dimensions automatically. To produce an INSERT construct that passes in a 1-dimensional array of integers:
connection.execute( mytable.insert(), {"data": [1,2,3]} )
The
ARRAY
type can be constructed given a fixed number of dimensions:mytable = Table("mytable", metadata, Column("data", ARRAY(Integer, dimensions=2)) )
Sending a number of dimensions is optional, but recommended if the datatype is to represent arrays of more than one dimension. This number is used:
When emitting the type declaration itself to the database, e.g.
INTEGER[][]
When translating Python values to database values, and vice versa, e.g. an ARRAY of
Unicode
objects uses this number to efficiently access the string values inside of array structures without resorting to per-row type inspectionWhen used with the Python
getitem
accessor, the number of dimensions serves to define the kind of type that the[]
operator should return, e.g. for an ARRAY of INTEGER with two dimensions:>>> expr = table.c.column[5] # returns ARRAY(Integer, dimensions=1) >>> expr = expr[6] # returns Integer
For 1-dimensional arrays, an
ARRAY
instance with no dimension parameter will generally assume single-dimensional behaviors.SQL expressions of type
ARRAY
have support for “index” and “slice” behavior. The Python[]
operator works normally here, given integer indexes or slices. Arrays default to 1-based indexing. The operator produces binary expression constructs which will produce the appropriate SQL, both for SELECT statements:select(mytable.c.data[5], mytable.c.data[2:7])
as well as UPDATE statements when the
Update.values()
method is used:mytable.update().values({ mytable.c.data[5]: 7, mytable.c.data[2:7]: [1, 2, 3] })
The
ARRAY
type also provides for the operatorsComparator.any()
andComparator.all()
. The PostgreSQL-specific version ofARRAY
also provides additional operators.Detecting Changes in ARRAY columns when using the ORM
The
ARRAY
type, when used with the SQLAlchemy ORM, does not detect in-place mutations to the array. In order to detect these, thesqlalchemy.ext.mutable
extension must be used, using theMutableList
class:from sqlalchemy import ARRAY from sqlalchemy.ext.mutable import MutableList class SomeOrmClass(Base): # ... data = Column(MutableList.as_mutable(ARRAY(Integer)))
This extension will allow “in-place” changes such to the array such as
.append()
to produce events which will be detected by the unit of work. Note that changes to elements inside the array, including subarrays that are mutated in place, are not detected.Alternatively, assigning a new array value to an ORM element that replaces the old one will always trigger a change event.
See also
Members
all(), any(), contains(), __init__(), comparator_factory, compare_values(), hashable, literal_processor(), python_type, zero_indexes
Class signature
class
sqlalchemy.types.ARRAY
(sqlalchemy.sql.expression.SchemaEventTarget
,sqlalchemy.types.Indexable
,sqlalchemy.types.Concatenable
,sqlalchemy.types.TypeEngine
)- class Comparator¶
Define comparison operations for
ARRAY
.More operators are available on the dialect-specific form of this type. See
Comparator
.Class signature
class
sqlalchemy.types.ARRAY.Comparator
(sqlalchemy.types.Comparator
,sqlalchemy.types.Comparator
)-
method
sqlalchemy.types.ARRAY.Comparator.
all(other, operator=None)¶ Return
other operator ALL (array)
clause.Note
This method is an
ARRAY
- specific construct that is now superseded by theany_()
function, which features a different calling style. Theany_()
function is also mirrored at the method level via theColumnOperators.any_()
method.Usage of array-specific
Comparator.all()
is as follows:from sqlalchemy.sql import operators conn.execute( select(table.c.data).where( table.c.data.all(7, operator=operators.lt) ) )
- Parameters:
other – expression to be compared
operator – an operator object from the
sqlalchemy.sql.operators
package, defaults toeq()
.
-
method
sqlalchemy.types.ARRAY.Comparator.
any(other, operator=None)¶ Return
other operator ANY (array)
clause.Note
This method is an
ARRAY
- specific construct that is now superseded by theany_()
function, which features a different calling style. Theany_()
function is also mirrored at the method level via theColumnOperators.any_()
method.Usage of array-specific
Comparator.any()
is as follows:from sqlalchemy.sql import operators conn.execute( select(table.c.data).where( table.c.data.any(7, operator=operators.lt) ) )
- Parameters:
other – expression to be compared
operator – an operator object from the
sqlalchemy.sql.operators
package, defaults toeq()
.
-
method
sqlalchemy.types.ARRAY.Comparator.
contains(*arg, **kw)¶ Implement the ‘contains’ operator.
Produces a LIKE expression that tests against a match for the middle of a string value:
column LIKE '%' || <other> || '%'
E.g.:
stmt = select(sometable).\ where(sometable.c.column.contains("foobar"))
Since the operator uses
LIKE
, wildcard characters"%"
and"_"
that are present inside the <other> expression will behave like wildcards as well. For literal string values, theColumnOperators.contains.autoescape
flag may be set toTrue
to apply escaping to occurrences of these characters within the string value so that they match as themselves and not as wildcard characters. Alternatively, theColumnOperators.contains.escape
parameter will establish a given character as an escape character which can be of use when the target expression is not a literal string.- Parameters:
other – expression to be compared. This is usually a plain string value, but can also be an arbitrary SQL expression. LIKE wildcard characters
%
and_
are not escaped by default unless theColumnOperators.contains.autoescape
flag is set to True.autoescape –
boolean; when True, establishes an escape character within the LIKE expression, then applies it to all occurrences of
"%"
,"_"
and the escape character itself within the comparison value, which is assumed to be a literal string and not a SQL expression.An expression such as:
somecolumn.contains("foo%bar", autoescape=True)
Will render as:
somecolumn LIKE '%' || :param || '%' ESCAPE '/'
With the value of
:param
as"foo/%bar"
.escape –
a character which when given will render with the
ESCAPE
keyword to establish that character as the escape character. This character can then be placed preceding occurrences of%
and_
to allow them to act as themselves and not wildcard characters.An expression such as:
somecolumn.contains("foo/%bar", escape="^")
Will render as:
somecolumn LIKE '%' || :param || '%' ESCAPE '^'
The parameter may also be combined with
ColumnOperators.contains.autoescape
:somecolumn.contains("foo%bar^bat", escape="^", autoescape=True)
Where above, the given literal parameter will be converted to
"foo^%bar^^bat"
before being passed to the database.
-
method
-
method
sqlalchemy.types.ARRAY.
__init__(item_type: _TypeEngineArgument[Any], as_tuple: bool = False, dimensions: Optional[int] = None, zero_indexes: bool = False)¶ Construct an
ARRAY
.E.g.:
Column('myarray', ARRAY(Integer))
Arguments are:
- Parameters:
item_type – The data type of items of this array. Note that dimensionality is irrelevant here, so multi-dimensional arrays like
INTEGER[][]
, are constructed asARRAY(Integer)
, not asARRAY(ARRAY(Integer))
or such.as_tuple=False – Specify whether return results should be converted to tuples from lists. This parameter is not generally needed as a Python list corresponds well to a SQL array.
dimensions – if non-None, the ARRAY will assume a fixed number of dimensions. This impacts how the array is declared on the database, how it goes about interpreting Python and result values, as well as how expression behavior in conjunction with the “getitem” operator works. See the description at
ARRAY
for additional detail.zero_indexes=False – when True, index values will be converted between Python zero-based and SQL one-based indexes, e.g. a value of one will be added to all index values before passing to the database.
-
attribute
sqlalchemy.types.ARRAY.
comparator_factory¶ alias of
Comparator
-
method
sqlalchemy.types.ARRAY.
compare_values(x, y)¶ Compare two values for equality.
-
attribute
sqlalchemy.types.ARRAY.
hashable¶ Flag, if False, means values from this type aren’t hashable.
Used by the ORM when uniquing result lists.
-
method
sqlalchemy.types.ARRAY.
literal_processor(dialect)¶ 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.ARRAY.
python_type¶
-
attribute
sqlalchemy.types.ARRAY.
zero_indexes = False¶ If True, Python zero-based indexes should be interpreted as one-based on the SQL expression side.
- class sqlalchemy.types.BIGINT¶
The SQL BIGINT type.
See also
BigInteger
- documentation for the base type.Class signature
- class sqlalchemy.types.BINARY¶
The SQL BINARY type.
Class signature
class
sqlalchemy.types.BINARY
(sqlalchemy.types._Binary
)
- class sqlalchemy.types.BLOB¶
The SQL BLOB type.
Members
Class signature
-
method
sqlalchemy.types.BLOB.
__init__(length: Optional[int] = 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.types.BOOLEAN¶
The SQL BOOLEAN type.
Members
Class signature
-
method
sqlalchemy.types.BOOLEAN.
__init__(create_constraint: bool = False, name: Optional[str] = None, _create_events: bool = True, _adapted_from: Optional[SchemaType] = 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.types.CHAR¶
The SQL CHAR type.
Members
Class signature
-
method
sqlalchemy.types.CHAR.
__init__(length: Optional[int] = None, collation: Optional[str] = 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')))) {printsql}SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1
Note
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.types.CLOB¶
The CLOB type.
This type is found in Oracle and Informix.
Members
Class signature
-
method
sqlalchemy.types.CLOB.
__init__(length: Optional[int] = None, collation: Optional[str] = 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')))) {printsql}SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1
Note
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.types.DATE¶
The SQL DATE type.
Class signature
- class sqlalchemy.types.DATETIME¶
The SQL DATETIME type.
Members
Class signature
-
method
sqlalchemy.types.DATETIME.
__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.types.DECIMAL¶
The SQL DECIMAL type.
See also
Numeric
- documentation for the base type.Members
Class signature
-
method
sqlalchemy.types.DECIMAL.
__init__(precision: Optional[int] = None, scale: Optional[int] = None, decimal_return_scale: Optional[int] = None, asdecimal: bool = True)¶ inherited from the
sqlalchemy.types.Numeric.__init__
method ofNumeric
Construct a Numeric.
- Parameters:
precision – the numeric precision for use in DDL
CREATE TABLE
.scale – the numeric scale for use in DDL
CREATE TABLE
.asdecimal – default True. Return whether or not values should be sent as Python Decimal objects, or as floats. Different DBAPIs send one or the other based on datatypes - the Numeric type will ensure that return values are one or the other across DBAPIs consistently.
decimal_return_scale – Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don’t have a notion of “scale”, so by default the float type looks for the first ten decimal places when converting. Specifying this value will override that length. Types which do include an explicit “.scale” value, such as the base
Numeric
as well as the MySQL float types, will use the value of “.scale” as the default for decimal_return_scale, if not otherwise specified.
When using the
Numeric
type, care should be taken to ensure that the asdecimal setting is appropriate for the DBAPI in use - when Numeric applies a conversion from Decimal->float or float-> Decimal, this conversion incurs an additional performance overhead for all result columns received.DBAPIs that return Decimal natively (e.g. psycopg2) will have better accuracy and higher performance with a setting of
True
, as the native translation to Decimal reduces the amount of floating- point issues at play, and the Numeric type itself doesn’t need to apply any further conversions. However, another DBAPI which returns floats natively will incur an additional conversion overhead, and is still subject to floating point data loss - in which caseasdecimal=False
will at least remove the extra conversion overhead.
-
method
- class sqlalchemy.types.DOUBLE¶
The SQL DOUBLE type.
New in version 2.0.
See also
Double
- documentation for the base type.Members
Class signature
-
method
sqlalchemy.types.DOUBLE.
__init__(precision: Optional[int] = None, asdecimal: bool = False, decimal_return_scale: Optional[int] = None)¶ inherited from the
sqlalchemy.types.Float.__init__
method ofFloat
Construct a Float.
- Parameters:
precision –
the numeric precision for use in DDL
CREATE TABLE
. Backends should attempt to ensure this precision indicates a number of digits for the genericFloat
datatype.Note
For the Oracle backend, the
Float.precision
parameter is not accepted when rendering DDL, as Oracle does not support float precision specified as a number of decimal places. Instead, use the Oracle-specificFLOAT
datatype and specify theFLOAT.binary_precision
parameter. This is new in version 2.0 of SQLAlchemy.To create a database agnostic
Float
that separately specifies binary precision for Oracle, useTypeEngine.with_variant()
as follows:from sqlalchemy import Column from sqlalchemy import Float from sqlalchemy.dialects import oracle Column( "float_data", Float(5).with_variant(oracle.FLOAT(binary_precision=16), "oracle") )
asdecimal – the same flag as that of
Numeric
, but defaults toFalse
. Note that setting this flag toTrue
results in floating point conversion.decimal_return_scale – Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don’t have a notion of “scale”, so by default the float type looks for the first ten decimal places when converting. Specifying this value will override that length. Note that the MySQL float types, which do include “scale”, will use “scale” as the default for decimal_return_scale, if not otherwise specified.
-
method
- class sqlalchemy.types.DOUBLE_PRECISION¶
The SQL DOUBLE PRECISION type.
New in version 2.0.
See also
Double
- documentation for the base type.Members
Class signature
class
sqlalchemy.types.DOUBLE_PRECISION
(sqlalchemy.types.Double
)-
method
sqlalchemy.types.DOUBLE_PRECISION.
__init__(precision: Optional[int] = None, asdecimal: bool = False, decimal_return_scale: Optional[int] = None)¶ inherited from the
sqlalchemy.types.Float.__init__
method ofFloat
Construct a Float.
- Parameters:
precision –
the numeric precision for use in DDL
CREATE TABLE
. Backends should attempt to ensure this precision indicates a number of digits for the genericFloat
datatype.Note
For the Oracle backend, the
Float.precision
parameter is not accepted when rendering DDL, as Oracle does not support float precision specified as a number of decimal places. Instead, use the Oracle-specificFLOAT
datatype and specify theFLOAT.binary_precision
parameter. This is new in version 2.0 of SQLAlchemy.To create a database agnostic
Float
that separately specifies binary precision for Oracle, useTypeEngine.with_variant()
as follows:from sqlalchemy import Column from sqlalchemy import Float from sqlalchemy.dialects import oracle Column( "float_data", Float(5).with_variant(oracle.FLOAT(binary_precision=16), "oracle") )
asdecimal – the same flag as that of
Numeric
, but defaults toFalse
. Note that setting this flag toTrue
results in floating point conversion.decimal_return_scale – Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don’t have a notion of “scale”, so by default the float type looks for the first ten decimal places when converting. Specifying this value will override that length. Note that the MySQL float types, which do include “scale”, will use “scale” as the default for decimal_return_scale, if not otherwise specified.
-
method
- class sqlalchemy.types.FLOAT¶
The SQL FLOAT type.
See also
Float
- documentation for the base type.Members
Class signature
-
method
sqlalchemy.types.FLOAT.
__init__(precision: Optional[int] = None, asdecimal: bool = False, decimal_return_scale: Optional[int] = None)¶ inherited from the
sqlalchemy.types.Float.__init__
method ofFloat
Construct a Float.
- Parameters:
precision –
the numeric precision for use in DDL
CREATE TABLE
. Backends should attempt to ensure this precision indicates a number of digits for the genericFloat
datatype.Note
For the Oracle backend, the
Float.precision
parameter is not accepted when rendering DDL, as Oracle does not support float precision specified as a number of decimal places. Instead, use the Oracle-specificFLOAT
datatype and specify theFLOAT.binary_precision
parameter. This is new in version 2.0 of SQLAlchemy.To create a database agnostic
Float
that separately specifies binary precision for Oracle, useTypeEngine.with_variant()
as follows:from sqlalchemy import Column from sqlalchemy import Float from sqlalchemy.dialects import oracle Column( "float_data", Float(5).with_variant(oracle.FLOAT(binary_precision=16), "oracle") )
asdecimal – the same flag as that of
Numeric
, but defaults toFalse
. Note that setting this flag toTrue
results in floating point conversion.decimal_return_scale – Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don’t have a notion of “scale”, so by default the float type looks for the first ten decimal places when converting. Specifying this value will override that length. Note that the MySQL float types, which do include “scale”, will use “scale” as the default for decimal_return_scale, if not otherwise specified.
-
method
-
attribute
sqlalchemy.types..
sqlalchemy.types.INT¶ alias of
INTEGER
- class sqlalchemy.types.JSON¶
Represent a SQL JSON type.
Note
JSON
is provided as a facade for vendor-specific JSON types. Since it supports JSON SQL operations, it only works on backends that have an actual JSON type, currently:PostgreSQL - see
sqlalchemy.dialects.postgresql.JSON
andsqlalchemy.dialects.postgresql.JSONB
for backend-specific notesMySQL - see
sqlalchemy.dialects.mysql.JSON
for backend-specific notesSQLite as of version 3.9 - see
sqlalchemy.dialects.sqlite.JSON
for backend-specific notesMicrosoft SQL Server 2016 and later - see
sqlalchemy.dialects.mssql.JSON
for backend-specific notes
JSON
is part of the Core in support of the growing popularity of native JSON datatypes.The
JSON
type stores arbitrary JSON format data, e.g.:data_table = Table('data_table', metadata, Column('id', Integer, primary_key=True), Column('data', JSON) ) with engine.connect() as conn: conn.execute( data_table.insert(), {"data": {"key1": "value1", "key2": "value2"}} )
JSON-Specific Expression Operators
The
JSON
datatype provides these additional SQL operations:Keyed index operations:
data_table.c.data['some key']
Integer index operations:
data_table.c.data[3]
Path index operations:
data_table.c.data[('key_1', 'key_2', 5, ..., 'key_n')]
Data casters for specific JSON element types, subsequent to an index or path operation being invoked:
data_table.c.data["some key"].as_integer()
New in version 1.3.11.
Additional operations may be available from the dialect-specific versions of
JSON
, such assqlalchemy.dialects.postgresql.JSON
andsqlalchemy.dialects.postgresql.JSONB
which both offer additional PostgreSQL-specific operations.Casting JSON Elements to Other Types
Index operations, i.e. those invoked by calling upon the expression using the Python bracket operator as in
some_column['some key']
, return an expression object whose type defaults toJSON
by default, so that further JSON-oriented instructions may be called upon the result type. However, it is likely more common that an index operation is expected to return a specific scalar element, such as a string or integer. In order to provide access to these elements in a backend-agnostic way, a series of data casters are provided:Comparator.as_string()
- return the element as a stringComparator.as_boolean()
- return the element as a booleanComparator.as_float()
- return the element as a floatComparator.as_integer()
- return the element as an integer
These data casters are implemented by supporting dialects in order to assure that comparisons to the above types will work as expected, such as:
# integer comparison data_table.c.data["some_integer_key"].as_integer() == 5 # boolean comparison data_table.c.data["some_boolean"].as_boolean() == True
New in version 1.3.11: Added type-specific casters for the basic JSON data element types.
Note
The data caster functions are new in version 1.3.11, and supersede the previous documented approaches of using CAST; for reference, this looked like:
from sqlalchemy import cast, type_coerce from sqlalchemy import String, JSON cast( data_table.c.data['some_key'], String ) == type_coerce(55, JSON)
The above case now works directly as:
data_table.c.data['some_key'].as_integer() == 5
For details on the previous comparison approach within the 1.3.x series, see the documentation for SQLAlchemy 1.2 or the included HTML files in the doc/ directory of the version’s distribution.
Detecting Changes in JSON columns when using the ORM
The
JSON
type, when used with the SQLAlchemy ORM, does not detect in-place mutations to the structure. In order to detect these, thesqlalchemy.ext.mutable
extension must be used, most typically using theMutableDict
class. This extension will allow “in-place” changes to the datastructure to produce events which will be detected by the unit of work. See the example atHSTORE
for a simple example involving a dictionary.Alternatively, assigning a JSON structure to an ORM element that replaces the old one will always trigger a change event.
Support for JSON null vs. SQL NULL
When working with NULL values, the
JSON
type recommends the use of two specific constants in order to differentiate between a column that evaluates to SQL NULL, e.g. no value, vs. the JSON-encoded string of"null"
. To insert or select against a value that is SQL NULL, use the constantnull()
. This symbol may be passed as a parameter value specifically when using theJSON
datatype, which contains special logic that interprets this symbol to mean that the column value should be SQL NULL as opposed to JSON"null"
:from sqlalchemy import null conn.execute(table.insert(), {"json_value": null()})
To insert or select against a value that is JSON
"null"
, use the constantJSON.NULL
:conn.execute(table.insert(), {"json_value": JSON.NULL})
The
JSON
type supports a flagJSON.none_as_null
which when set to True will result in the Python constantNone
evaluating to the value of SQL NULL, and when set to False results in the Python constantNone
evaluating to the value of JSON"null"
. The Python valueNone
may be used in conjunction with eitherJSON.NULL
andnull()
in order to indicate NULL values, but care must be taken as to the value of theJSON.none_as_null
in these cases.Customizing the JSON Serializer
The JSON serializer and deserializer used by
JSON
defaults to Python’sjson.dumps
andjson.loads
functions; in the case of the psycopg2 dialect, psycopg2 may be using its own custom loader function.In order to affect the serializer / deserializer, they are currently configurable at the
create_engine()
level via thecreate_engine.json_serializer
andcreate_engine.json_deserializer
parameters. For example, to turn offensure_ascii
:engine = create_engine( "sqlite://", json_serializer=lambda obj: json.dumps(obj, ensure_ascii=False))
Changed in version 1.3.7: SQLite dialect’s
json_serializer
andjson_deserializer
parameters renamed from_json_serializer
and_json_deserializer
.See also
sqlalchemy.dialects.postgresql.JSON
sqlalchemy.dialects.postgresql.JSONB
Members
as_boolean(), as_float(), as_integer(), as_json(), as_numeric(), as_string(), bind_processor(), literal_processor(), NULL, __init__(), bind_processor(), comparator_factory, hashable, python_type, result_processor(), should_evaluate_none
Class signature
class
sqlalchemy.types.JSON
(sqlalchemy.types.Indexable
,sqlalchemy.types.TypeEngine
)- class Comparator¶
Define comparison operations for
JSON
.Class signature
class
sqlalchemy.types.JSON.Comparator
(sqlalchemy.types.Comparator
,sqlalchemy.types.Comparator
)-
method
sqlalchemy.types.JSON.Comparator.
as_boolean()¶ Cast an indexed value as boolean.
e.g.:
stmt = select( mytable.c.json_column['some_data'].as_boolean() ).where( mytable.c.json_column['some_data'].as_boolean() == True )
New in version 1.3.11.
-
method
sqlalchemy.types.JSON.Comparator.
as_float()¶ Cast an indexed value as float.
e.g.:
stmt = select( mytable.c.json_column['some_data'].as_float() ).where( mytable.c.json_column['some_data'].as_float() == 29.75 )
New in version 1.3.11.
-
method
sqlalchemy.types.JSON.Comparator.
as_integer()¶ Cast an indexed value as integer.
e.g.:
stmt = select( mytable.c.json_column['some_data'].as_integer() ).where( mytable.c.json_column['some_data'].as_integer() == 5 )
New in version 1.3.11.
-
method
sqlalchemy.types.JSON.Comparator.
as_json()¶ Cast an indexed value as JSON.
e.g.:
stmt = select(mytable.c.json_column['some_data'].as_json())
This is typically the default behavior of indexed elements in any case.
Note that comparison of full JSON structures may not be supported by all backends.
New in version 1.3.11.
-
method
sqlalchemy.types.JSON.Comparator.
as_numeric(precision, scale, asdecimal=True)¶ Cast an indexed value as numeric/decimal.
e.g.:
stmt = select( mytable.c.json_column['some_data'].as_numeric(10, 6) ).where( mytable.c. json_column['some_data'].as_numeric(10, 6) == 29.75 )
New in version 1.4.0b2.
-
method
sqlalchemy.types.JSON.Comparator.
as_string()¶ Cast an indexed value as string.
e.g.:
stmt = select( mytable.c.json_column['some_data'].as_string() ).where( mytable.c.json_column['some_data'].as_string() == 'some string' )
New in version 1.3.11.
-
method
- class JSONElementType¶
Common function for index / path elements in a JSON expression.
Class signature
class
sqlalchemy.types.JSON.JSONElementType
(sqlalchemy.types.TypeEngine
)-
method
sqlalchemy.types.JSON.JSONElementType.
bind_processor(dialect)¶ 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.JSON.JSONElementType.
literal_processor(dialect)¶ 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
-
method
- class JSONIndexType¶
Placeholder for the datatype of a JSON index value.
This allows execution-time processing of JSON index values for special syntaxes.
Class signature
class
sqlalchemy.types.JSON.JSONIndexType
(sqlalchemy.types.JSONElementType
)
- class JSONIntIndexType¶
Placeholder for the datatype of a JSON index value.
This allows execution-time processing of JSON index values for special syntaxes.
Class signature
class
sqlalchemy.types.JSON.JSONIntIndexType
(sqlalchemy.types.JSONIndexType
)
- class JSONPathType¶
Placeholder type for JSON path operations.
This allows execution-time processing of a path-based index value into a specific SQL syntax.
Class signature
class
sqlalchemy.types.JSON.JSONPathType
(sqlalchemy.types.JSONElementType
)
- class JSONStrIndexType¶
Placeholder for the datatype of a JSON index value.
This allows execution-time processing of JSON index values for special syntaxes.
Class signature
class
sqlalchemy.types.JSON.JSONStrIndexType
(sqlalchemy.types.JSONIndexType
)
-
attribute
sqlalchemy.types.JSON.
NULL = symbol('JSON_NULL')¶ Describe the json value of NULL.
This value is used to force the JSON value of
"null"
to be used as the value. A value of PythonNone
will be recognized either as SQL NULL or JSON"null"
, based on the setting of theJSON.none_as_null
flag; theJSON.NULL
constant can be used to always resolve to JSON"null"
regardless of this setting. This is in contrast to thenull()
construct, which always resolves to SQL NULL. E.g.:from sqlalchemy import null from sqlalchemy.dialects.postgresql import JSON # will *always* insert SQL NULL obj1 = MyObject(json_value=null()) # will *always* insert JSON string "null" obj2 = MyObject(json_value=JSON.NULL) session.add_all([obj1, obj2]) session.commit()
In order to set JSON NULL as a default value for a column, the most transparent method is to use
text()
:Table( 'my_table', metadata, Column('json_data', JSON, default=text("'null'")) )
While it is possible to use
JSON.NULL
in this context, theJSON.NULL
value will be returned as the value of the column, which in the context of the ORM or other repurposing of the default value, may not be desirable. Using a SQL expression means the value will be re-fetched from the database within the context of retrieving generated defaults.
-
method
sqlalchemy.types.JSON.
__init__(none_as_null: bool = False)¶ 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
-
method
sqlalchemy.types.JSON.
bind_processor(dialect)¶ 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.
-
attribute
sqlalchemy.types.JSON.
comparator_factory¶ alias of
Comparator
-
attribute
sqlalchemy.types.JSON.
hashable = False¶ Flag, if False, means values from this type aren’t hashable.
Used by the ORM when uniquing result lists.
-
attribute
sqlalchemy.types.JSON.
python_type¶
-
method
sqlalchemy.types.JSON.
result_processor(dialect, coltype)¶ 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.JSON.
should_evaluate_none: bool¶ 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
- class sqlalchemy.types.INTEGER¶
The SQL INT or INTEGER type.
See also
Integer
- documentation for the base type.Class signature
- class sqlalchemy.types.NCHAR¶
The SQL NCHAR type.
Members
Class signature
-
method
sqlalchemy.types.NCHAR.
__init__(length=None, **kwargs)¶ inherited from the
sqlalchemy.types.Unicode.__init__
method ofUnicode
Create a
Unicode
object.Parameters are the same as that of
String
.
-
method
- class sqlalchemy.types.NVARCHAR¶
The SQL NVARCHAR type.
Members
Class signature
-
method
sqlalchemy.types.NVARCHAR.
__init__(length=None, **kwargs)¶ inherited from the
sqlalchemy.types.Unicode.__init__
method ofUnicode
Create a
Unicode
object.Parameters are the same as that of
String
.
-
method
- class sqlalchemy.types.NUMERIC¶
The SQL NUMERIC type.
See also
Numeric
- documentation for the base type.Members
Class signature
-
method
sqlalchemy.types.NUMERIC.
__init__(precision: Optional[int] = None, scale: Optional[int] = None, decimal_return_scale: Optional[int] = None, asdecimal: bool = True)¶ inherited from the
sqlalchemy.types.Numeric.__init__
method ofNumeric
Construct a Numeric.
- Parameters:
precision – the numeric precision for use in DDL
CREATE TABLE
.scale – the numeric scale for use in DDL
CREATE TABLE
.asdecimal – default True. Return whether or not values should be sent as Python Decimal objects, or as floats. Different DBAPIs send one or the other based on datatypes - the Numeric type will ensure that return values are one or the other across DBAPIs consistently.
decimal_return_scale – Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don’t have a notion of “scale”, so by default the float type looks for the first ten decimal places when converting. Specifying this value will override that length. Types which do include an explicit “.scale” value, such as the base
Numeric
as well as the MySQL float types, will use the value of “.scale” as the default for decimal_return_scale, if not otherwise specified.
When using the
Numeric
type, care should be taken to ensure that the asdecimal setting is appropriate for the DBAPI in use - when Numeric applies a conversion from Decimal->float or float-> Decimal, this conversion incurs an additional performance overhead for all result columns received.DBAPIs that return Decimal natively (e.g. psycopg2) will have better accuracy and higher performance with a setting of
True
, as the native translation to Decimal reduces the amount of floating- point issues at play, and the Numeric type itself doesn’t need to apply any further conversions. However, another DBAPI which returns floats natively will incur an additional conversion overhead, and is still subject to floating point data loss - in which caseasdecimal=False
will at least remove the extra conversion overhead.
-
method
- class sqlalchemy.types.REAL¶
The SQL REAL type.
See also
Float
- documentation for the base type.Members
Class signature
-
method
sqlalchemy.types.REAL.
__init__(precision: Optional[int] = None, asdecimal: bool = False, decimal_return_scale: Optional[int] = None)¶ inherited from the
sqlalchemy.types.Float.__init__
method ofFloat
Construct a Float.
- Parameters:
precision –
the numeric precision for use in DDL
CREATE TABLE
. Backends should attempt to ensure this precision indicates a number of digits for the genericFloat
datatype.Note
For the Oracle backend, the
Float.precision
parameter is not accepted when rendering DDL, as Oracle does not support float precision specified as a number of decimal places. Instead, use the Oracle-specificFLOAT
datatype and specify theFLOAT.binary_precision
parameter. This is new in version 2.0 of SQLAlchemy.To create a database agnostic
Float
that separately specifies binary precision for Oracle, useTypeEngine.with_variant()
as follows:from sqlalchemy import Column from sqlalchemy import Float from sqlalchemy.dialects import oracle Column( "float_data", Float(5).with_variant(oracle.FLOAT(binary_precision=16), "oracle") )
asdecimal – the same flag as that of
Numeric
, but defaults toFalse
. Note that setting this flag toTrue
results in floating point conversion.decimal_return_scale – Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don’t have a notion of “scale”, so by default the float type looks for the first ten decimal places when converting. Specifying this value will override that length. Note that the MySQL float types, which do include “scale”, will use “scale” as the default for decimal_return_scale, if not otherwise specified.
-
method
- class sqlalchemy.types.SMALLINT¶
The SQL SMALLINT type.
See also
SmallInteger
- documentation for the base type.Class signature
class
sqlalchemy.types.SMALLINT
(sqlalchemy.types.SmallInteger
)
- class sqlalchemy.types.TEXT¶
The SQL TEXT type.
Members
Class signature
-
method
sqlalchemy.types.TEXT.
__init__(length: Optional[int] = None, collation: Optional[str] = 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')))) {printsql}SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1
Note
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.types.TIME¶
The SQL TIME type.
Class signature
- class sqlalchemy.types.TIMESTAMP¶
The SQL TIMESTAMP type.
TIMESTAMP
datatypes have support for timezone storage on some backends, such as PostgreSQL and Oracle. Use theTIMESTAMP.timezone
argument in order to enable “TIMESTAMP WITH TIMEZONE” for these backends.Members
Class signature
class
sqlalchemy.types.TIMESTAMP
(sqlalchemy.types.DateTime
)-
method
sqlalchemy.types.TIMESTAMP.
__init__(timezone: bool = False)¶ Construct a new
TIMESTAMP
.- Parameters:
timezone – boolean. Indicates that the TIMESTAMP type should enable timezone support, if available on the target database. On a per-dialect basis is similar to “TIMESTAMP WITH TIMEZONE”. If the target database does not support timezones, this flag is ignored.
-
method
sqlalchemy.types.TIMESTAMP.
get_dbapi_type(dbapi)¶ Return the corresponding type object from the underlying DB-API, if any.
This can be useful for calling
setinputsizes()
, for example.
-
method
- class sqlalchemy.types.UUID¶
Represent the SQL UUID type.
This is the SQL-native form of the
Uuid
database agnostic datatype, and is backwards compatible with the previous PostgreSQL-only version ofUUID
.The
UUID
datatype only works on databases that have a SQL datatype namedUUID
. It will not function for backends which don’t have this exact-named type, including SQL Server. For backend-agnostic UUID values with native support, including for SQL Server’sUNIQUEIDENTIFIER
datatype, use theUuid
datatype.New in version 2.0.
See also
Members
Class signature
class
sqlalchemy.types.UUID
(sqlalchemy.types.Uuid
,sqlalchemy.types.NativeForEmulated
)-
method
sqlalchemy.types.UUID.
__init__(as_uuid: bool = True)¶ Construct a
UUID
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.types.VARBINARY¶
The SQL VARBINARY type.
Class signature
class
sqlalchemy.types.VARBINARY
(sqlalchemy.types._Binary
)
- class sqlalchemy.types.VARCHAR¶
The SQL VARCHAR type.
Members
Class signature
-
method
sqlalchemy.types.VARCHAR.
__init__(length: Optional[int] = None, collation: Optional[str] = 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')))) {printsql}SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1
Note
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