SQLAlchemy provides abstractions for most common database data types, and a mechanism for specifying your own custom data types.
The methods and attributes of type objects are rarely used directly. Type objects are supplied to Table definitions and can be supplied as type hints to functions for occasions where the database driver returns an incorrect type.
>>> users = Table('users', metadata,
... Column('id', Integer, primary_key=True)
... Column('login', String(32))
... )SQLAlchemy will use the Integer and String(32) type information when issuing a CREATE TABLE statement and will use it again when reading back rows SELECTed from the database. Functions that accept a type (such as Column()) will typically accept a type class or instance; Integer is equivalent to Integer() with no construction arguments in this case.
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 Types and the other sections of this chapter.
Bases: sqlalchemy.types.Integer
A type for bigger int integers.
Typically generates a BIGINT in DDL, and otherwise acts like a normal Integer on the Python side.
Bases: sqlalchemy.types.TypeEngine, sqlalchemy.types.SchemaType
A bool datatype.
Boolean typically uses BOOLEAN or SMALLINT on the DDL side, and on the Python side deals in True or False.
Construct a Boolean.
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Bases: sqlalchemy.types._DateAffinity, sqlalchemy.types.TypeEngine
A type for datetime.date() objects.
Bases: sqlalchemy.types._DateAffinity, sqlalchemy.types.TypeEngine
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.
Bases: sqlalchemy.types.String, sqlalchemy.types.SchemaType
Generic Enum Type.
The Enum type provides a set of possible string values which the column is constrained towards.
By default, uses the backend’s native ENUM type if available, else uses VARCHAR + a CHECK constraint.
Construct an enum.
Keyword arguments which don’t apply to a specific backend are ignored by that backend.
| Parameters: |
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Bases: sqlalchemy.types.Numeric
A type for float numbers.
Returns Python float objects by default, applying conversion as needed.
Bases: sqlalchemy.types._DateAffinity, sqlalchemy.types.TypeEngine
A type for int integers.
Bases: sqlalchemy.types._DateAffinity, sqlalchemy.types.TypeDecorator
A type for datetime.timedelta() objects.
The Interval type deals with datetime.timedelta objects. In PostgreSQL, the native INTERVAL 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 via func).
Construct an Interval object.
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Bases: sqlalchemy.types._Binary
A type for large binary byte data.
The Binary type generates BLOB or BYTEA when tables are created, and also converts incoming values using the Binary callable provided by each DB-API.
Construct a LargeBinary type.
| Parameters: | length – optional, a length for the column for use in DDL statements, for those BLOB types that accept a length (i.e. MySQL). It does not produce a small BINARY/VARBINARY type - use the BINARY/VARBINARY types specifically for those. May be safely omitted if no CREATE TABLE will be issued. Certain databases may require a length for use in DDL, and will raise an exception when the CREATE TABLE DDL is issued. |
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Bases: sqlalchemy.types._DateAffinity, sqlalchemy.types.TypeEngine
A type for fixed precision numbers.
Typically generates DECIMAL or NUMERIC. Returns decimal.Decimal objects by default, applying conversion as needed.
Construct a Numeric.
| Parameters: |
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When using the Numeric type, care should be taken to ensure that the asdecimal setting is apppropriate 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 case asdecimal=False will at least remove the extra conversion overhead.
Bases: sqlalchemy.types.MutableType, sqlalchemy.types.TypeDecorator
Holds Python objects, which are serialized using pickle.
PickleType builds upon the Binary type to apply Python’s pickle.dumps() to incoming objects, and pickle.loads() on the way out, allowing any pickleable Python object to be stored as a serialized binary field.
Note: be sure to read the notes for MutableType regarding ORM performance implications.
Construct a PickleType.
| Parameters: |
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alias of LargeBinary
Return True if the target Python type is ‘mutable’.
When this method is overridden, copy_value() should also be supplied. The MutableType mixin is recommended as a helper.
Bases: object
Mark a type as possibly requiring schema-level DDL for usage.
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.
Issue CREATE ddl for this type, if applicable.
Issue DROP ddl for this type, if applicable.
Bases: sqlalchemy.types.Integer
A type for smaller int integers.
Typically generates a SMALLINT in DDL, and otherwise acts like a normal Integer on the Python side.
Bases: sqlalchemy.types.Concatenable, sqlalchemy.types.TypeEngine
The base for all string and character types.
In SQL, corresponds to VARCHAR. Can also take Python unicode objects and encode to the database’s encoding in bind params (and the reverse for result sets.)
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.
Create a string-holding type.
| Parameters: |
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Bases: sqlalchemy.types.String
A variably sized string type.
In SQL, usually corresponds to CLOB or TEXT. Can also take Python unicode objects and encode to the database’s encoding in bind params (and the reverse for result sets.)
Bases: sqlalchemy.types._DateAffinity, sqlalchemy.types.TypeEngine
A type for datetime.time() objects.
Bases: sqlalchemy.types.String
A variable length Unicode string.
The Unicode type is a String which converts Python unicode objects (i.e., strings that are defined as u'somevalue') into encoded bytestrings when passing the value to the database driver, and similarly decodes values from the database back into Python unicode objects.
It’s roughly equivalent to using a String object with convert_unicode=True, however the type has other significances in that it implies the usage of a unicode-capable type being used on the backend, such as NVARCHAR. This may affect what type is emitted when issuing CREATE TABLE and also may effect some DBAPI-specific details, such as type information passed along to setinputsizes().
When using the Unicode type, it is only appropriate to pass Python unicode objects, and not plain str. If a bytestring (str) is passed, a runtime warning is issued. If you notice your application raising these warnings but you’re not sure where, the Python warnings filter can be used to turn these warnings into exceptions which will illustrate a stack trace:
import warnings
warnings.simplefilter('error')Bytestrings sent to and received from the database are encoded using the dialect’s encoding, which defaults to utf-8.
Create a Unicode-converting String type.
| Parameters: |
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Bases: sqlalchemy.types.Text
An unbounded-length Unicode string.
See Unicode for details on the unicode behavior of this object.
Like Unicode, usage the UnicodeText type implies a unicode-capable type being used on the backend, such as NCLOB.
Create a Unicode-converting Text type.
| Parameters: | length – optional, a length for the column for use in DDL statements. May be safely omitted if no CREATE TABLE will be issued. Certain databases may require a length for use in DDL, and will raise an exception when the CREATE TABLE DDL is issued. Whether the value is interpreted as bytes or characters is database specific. |
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The SQL standard types always create database column types of the same name when CREATE TABLE is issued. Some types may not be supported on all databases.
Bases: sqlalchemy.types.BigInteger
The SQL BIGINT type.
Bases: sqlalchemy.types._Binary
The SQL BINARY type.
Bases: sqlalchemy.types.LargeBinary
The SQL BLOB type.
Bases: sqlalchemy.types.Boolean
The SQL BOOLEAN type.
Bases: sqlalchemy.types.String
The SQL CHAR type.
Bases: sqlalchemy.types.Text
The CLOB type.
This type is found in Oracle and Informix.
Bases: sqlalchemy.types.Date
The SQL DATE type.
Bases: sqlalchemy.types.DateTime
The SQL DATETIME type.
Bases: sqlalchemy.types.Numeric
The SQL DECIMAL type.
Bases: sqlalchemy.types.Float
The SQL FLOAT type.
Bases: sqlalchemy.types.Integer
The SQL INT or INTEGER type.
Bases: sqlalchemy.types.Unicode
The SQL NCHAR type.
Bases: sqlalchemy.types.Unicode
The SQL NVARCHAR type.
Bases: sqlalchemy.types.Numeric
The SQL NUMERIC type.
Bases: sqlalchemy.types.SmallInteger
The SQL SMALLINT type.
Bases: sqlalchemy.types.Text
The SQL TEXT type.
Bases: sqlalchemy.types.Time
The SQL TIME type.
Bases: sqlalchemy.types.DateTime
The SQL TIMESTAMP type.
Bases: sqlalchemy.types._Binary
The SQL VARBINARY type.
Bases: sqlalchemy.types.String
The SQL VARCHAR type.
Database-specific types are also available for import from each database’s dialect module. See the sqlalchemy.dialects_toplevel reference for the database you’re interested in.
For example, MySQL has a BIGINTEGER type and PostgreSQL has an INET type. To use these, import them from the module explicitly:
from sqlalchemy.dialects import mysql
table = Table('foo', meta,
Column('id', mysql.BIGINTEGER),
Column('enumerates', mysql.ENUM('a', 'b', 'c'))
)Or some PostgreSQL types:
from sqlalchemy.dialects import postgresql
table = Table('foo', meta,
Column('ipaddress', postgresql.INET),
Column('elements', postgresql.ARRAY(str))
)Each dialect provides the full set of typenames supported by that backend within its __all__ collection, so that a simple import * or similar will import all supported types as implemented for that backend:
from sqlalchemy.dialects.postgresql import *
t = Table('mytable', metadata,
Column('id', INTEGER, primary_key=True),
Column('name', VARCHAR(300)),
Column('inetaddr', INET)
)Where above, the INTEGER and VARCHAR types are ultimately from sqlalchemy.types, and INET is specific to the Postgresql dialect.
Some dialect level types have the same name as the SQL standard type, but also provide additional arguments. For example, MySQL implements the full range of character and string types including additional arguments such as collation and charset:
from sqlalchemy.dialects.mysql import VARCHAR, TEXT
table = Table('foo', meta,
Column('col1', VARCHAR(200, collation='binary')),
Column('col2', TEXT(charset='latin1'))
)A variety of methods exist to redefine the behavior of existing types as well as to provide new ones.
A frequent need is to force the “string” version of a type, that is the one rendered in a CREATE TABLE statement or other SQL function like CAST, to be changed. For example, an application may want to force the rendering of BINARY for all platforms except for one, in which is wants BLOB to be rendered. Usage of an existing generic type, in this case LargeBinary, is preferred for most use cases. But to control types more accurately, a compilation directive that is per-dialect can be associated with any type:
from sqlalchemy.ext.compiler import compiles
from sqlalchemy.types import BINARY
@compiles(BINARY, "sqlite")
def compile_binary_sqlite(type_, compiler, **kw):
return "BLOB"The above code allows the usage of types.BINARY, which will produce the string BINARY against all backends except SQLite, in which case it will produce BLOB.
See the section Changing Compilation of Types, a subsection of Custom SQL Constructs and Compilation Extension, for additional examples.
The TypeDecorator allows the creation of custom types which add bind-parameter and result-processing behavior to an existing type object. It is used when additional in-Python marshalling of data to and from the database is required.
Bases: sqlalchemy.types.AbstractType
Allows the creation of types which add additional functionality to an existing type.
This method is preferred to direct subclassing of SQLAlchemy’s built-in types as it ensures that all required functionality of the underlying type is kept in place.
Typical usage:
import sqlalchemy.types as types
class MyType(types.TypeDecorator):
'''Prefixes Unicode values with "PREFIX:" on the way in and
strips it off on the way out.
'''
impl = types.Unicode
def process_bind_param(self, value, dialect):
return "PREFIX:" + value
def process_result_value(self, value, dialect):
return value[7:]
def copy(self):
return MyType(self.impl.length)The class-level “impl” variable is required, and can reference any TypeEngine class. Alternatively, the load_dialect_impl() method can be used to provide different type classes based on the dialect given; in this case, the “impl” variable can reference TypeEngine as a placeholder.
Types that receive a Python type that isn’t similar to the ultimate type used may want to define the TypeDecorator.coerce_compared_value() method. This is used to give the expression system a hint when coercing Python objects into bind parameters within expressions. Consider this expression:
mytable.c.somecol + datetime.date(2009, 5, 15)Above, if “somecol” is an Integer variant, it makes sense that we’re doing date arithmetic, where above is usually interpreted by databases as adding a number of days to the given date. The expression system does the right thing by not attempting to coerce the “date()” value into an integer-oriented bind parameter.
However, in the case of TypeDecorator, we are usually changing an incoming Python type to something new - TypeDecorator by default will “coerce” the non-typed side to be the same type as itself. Such as below, we define an “epoch” type that stores a date value as an integer:
class MyEpochType(types.TypeDecorator):
impl = types.Integer
epoch = datetime.date(1970, 1, 1)
def process_bind_param(self, value, dialect):
return (value - self.epoch).days
def process_result_value(self, value, dialect):
return self.epoch + timedelta(days=value)Our expression of somecol + date with the above type will coerce the “date” on the right side to also be treated as MyEpochType.
This behavior can be overridden via the coerce_compared_value() method, which returns a type that should be used for the value of the expression. Below we set it such that an integer value will be treated as an Integer, and any other value is assumed to be a date and will be treated as a MyEpochType:
def coerce_compared_value(self, op, value):
if isinstance(value, int):
return Integer()
else:
return selfConstruct a TypeDecorator.
Arguments sent here are passed to the constructor of the class assigned to the impl class level attribute, where the self.impl attribute is assigned an instance of the implementation type. If impl at the class level is already an instance, then it’s assigned to self.impl as is.
Subclasses can override this to customize the generation of self.impl.
Produce an “adapted” form of this type, given an “impl” class to work with.
This method is used internally to associate generic types with “implementation” types that are specific to a particular dialect.
Provide a bound value processing function for the given Dialect.
This is the method that fulfills the TypeEngine contract for bound value conversion. TypeDecorator will wrap a user-defined implementation of process_bind_param() here.
User-defined code can override this method directly, though its likely best to use process_bind_param() so that the processing provided by self.impl is maintained.
Suggest a type for a ‘coerced’ Python value in an expression.
By default, returns self. This method is called by the expression system when an object using this type is on the left or right side of an expression against a plain Python object which does not yet have a SQLAlchemy type assigned:
expr = table.c.somecolumn + 35Where above, if somecolumn uses this type, this method will be called with the value operator.add and 35. The return value is whatever SQLAlchemy type should be used for 35 for this particular operation.
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.
Produce a string-compiled form of this TypeEngine.
When called with no arguments, uses a “default” dialect to produce a string result.
| Parameters: | dialect – a Dialect instance. |
|---|
Produce a copy of this TypeDecorator instance.
This is a shallow copy and is provided to fulfill part of the TypeEngine contract. It usually does not need to be overridden unless the user-defined TypeDecorator has local state that should be deep-copied.
Given a value, produce a copy of it.
By default this calls upon TypeEngine.copy_value() of the underlying “impl”.
copy_value() will return the object itself, assuming “mutability” is not enabled. Only the MutableType mixin provides a copy function that actually produces a new object. The copying function is used by the ORM when “mutable” types are used, to memoize the original version of an object as loaded from the database, which is then compared to the possibly mutated version to check for changes.
Return a dialect-specific implementation for this TypeEngine.
See also type_engine(), load_dialect_impl(). load_dialect_impl() is an end-user overrideable hook.
Return the DBAPI type object represented by this TypeDecorator.
By default this calls upon TypeEngine.get_dbapi_type() of the underlying “impl”.
Return True if the target Python type is ‘mutable’.
This allows systems like the ORM to know if a column value can be considered ‘not changed’ by comparing the identity of objects alone. Values such as dicts, lists which are serialized into strings are examples of “mutable” column structures.
When this method is overridden, copy_value() should also be supplied. The MutableType mixin is recommended as a helper.
Return a TypeEngine object corresponding to a dialect.
This is an end-user override hook that can be used to provide differing types depending on the given dialect. It is used by the TypeDecorator implementation of type_engine() and dialect_impl() to help determine what type should ultimately be returned for a given TypeDecorator.
By default returns self.impl.
Receive a bound parameter value to be converted.
Subclasses override this method to return the value that should be passed along to the underlying TypeEngine object, and from there to the DBAPI execute() method.
| Parameters: |
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Receive a result-row column value to be converted.
Subclasses override this method to return the value that should be passed back to the application, given a value that is already processed by the underlying TypeEngine object, originally from the DBAPI cursor method fetchone() or similar.
| Parameters: |
|
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Provide a result value processing function for the given Dialect.
This is the method that fulfills the TypeEngine contract for result value conversion. TypeDecorator will wrap a user-defined implementation of process_result_value() here.
User-defined code can override this method directly, though its likely best to use process_result_value() so that the processing provided by self.impl is maintained.
Return a dialect-specific TypeEngine instance for this TypeDecorator.
In most cases this returns a dialect-adapted form of the TypeEngine type represented by self.impl. Makes usage of dialect_impl() but also traverses into wrapped TypeDecorator instances. Behavior can be customized here by overriding load_dialect_impl().
A few key TypeDecorator recipes follow.
Some database connectors like those of SQL Server choke if a Decimal is passed with too many decimal places. Here’s a recipe that rounds them down:
from sqlalchemy.types import TypeDecorator, Numeric
from decimal import Decimal
class SafeNumeric(TypeDecorator):
"""Adds quantization to Numeric."""
impl = Numeric
def __init__(self, *arg, **kw):
TypeDecorator.__init__(self, *arg, **kw)
self.quantize_int = -(self.impl.precision - self.impl.scale)
self.quantize = Decimal(10) ** self.quantize_int
def process_bind_param(self, value, dialect):
if isinstance(value, Decimal) and \
value.as_tuple()[2] < self.quantize_int:
value = value.quantize(self.quantize)
return valueReceives and returns Python uuid() objects. Uses the PG UUID type when using Postgresql, CHAR(32) on other backends, storing them in stringified hex format. Can be modified to store binary in CHAR(16) if desired:
from sqlalchemy.types import TypeDecorator, CHAR
from sqlalchemy.dialects.postgresql import UUID
import uuid
class GUID(TypeDecorator):
"""Platform-independent GUID type.
Uses Postgresql's UUID type, otherwise uses
CHAR(32), storing as stringified hex values.
"""
impl = CHAR
def load_dialect_impl(self, dialect):
if dialect.name == 'postgresql':
return dialect.type_descriptor(UUID())
else:
return dialect.type_descriptor(CHAR(32))
def process_bind_param(self, value, dialect):
if value is None:
return value
elif dialect.name == 'postgresql':
return str(value)
else:
if not isinstance(value, uuid.UUID):
return "%.32x" % uuid.UUID(value)
else:
# hexstring
return "%.32x" % value
def process_result_value(self, value, dialect):
if value is None:
return value
else:
return uuid.UUID(value)This type uses simplejson to marshal Python data structures to/from JSON. Can be modified to use Python’s builtin json encoder.
Note that the base type is not “mutable”, meaning in-place changes to the value will not be detected by the ORM - you instead would need to replace the existing value with a new one to detect changes. The subtype MutableJSONEncodedDict adds “mutability” to allow this, but note that “mutable” types add a significant performance penalty to the ORM’s flush process:
from sqlalchemy.types import TypeDecorator, MutableType, VARCHAR
import simplejson
class JSONEncodedDict(TypeDecorator):
"""Represents an immutable structure as a json-encoded string.
Usage::
JSONEncodedDict(255)
"""
impl = VARCHAR
def process_bind_param(self, value, dialect):
if value is not None:
value = simplejson.dumps(value, use_decimal=True)
return value
def process_result_value(self, value, dialect):
if value is not None:
value = simplejson.loads(value, use_decimal=True)
return value
class MutableJSONEncodedDict(MutableType, JSONEncodedDict):
"""Adds mutability to JSONEncodedDict."""
def copy_value(self, value):
return simplejson.loads(
simplejson.dumps(value, use_decimal=True),
use_decimal=True)The UserDefinedType class is provided as a simple base class for defining entirely new database types:
Bases: sqlalchemy.types.TypeEngine
Base for user defined types.
This should be the base of new types. Note that for most cases, TypeDecorator is probably more appropriate:
import sqlalchemy.types as types
class MyType(types.UserDefinedType):
def __init__(self, precision = 8):
self.precision = precision
def get_col_spec(self):
return "MYTYPE(%s)" % self.precision
def bind_processor(self, dialect):
def process(value):
return value
return process
def result_processor(self, dialect, coltype):
def process(value):
return value
return processOnce the type is made, it’s immediately usable:
table = Table('foo', meta,
Column('id', Integer, primary_key=True),
Column('data', MyType(16))
)A hook which allows the given operator to be adapted to something new.
See also UserDefinedType._adapt_expression(), an as-yet- semi-public method with greater capability in this regard.
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.
Compare two values for equality.
Produce a string-compiled form of this TypeEngine.
When called with no arguments, uses a “default” dialect to produce a string result.
| Parameters: | dialect – a Dialect instance. |
|---|
Return a dialect-specific implementation for this TypeEngine.
Return the corresponding type object from the underlying DB-API, if any.
This can be useful for calling setinputsizes(), for example.
Return True if the target Python type is ‘mutable’.
This allows systems like the ORM to know if a column value can be considered ‘not changed’ by comparing the identity of objects alone. Values such as dicts, lists which are serialized into strings are examples of “mutable” column structures.
When this method is overridden, copy_value() should also be supplied. The MutableType mixin is recommended as a helper.
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.
Bases: sqlalchemy.sql.visitors.Visitable
Defines a bind parameter processing function.
| Parameters: | dialect – Dialect instance in use. |
|---|
Compare two values for equality.
Produce a string-compiled form of this TypeEngine.
When called with no arguments, uses a “default” dialect to produce a string result.
| Parameters: | dialect – a Dialect instance. |
|---|
Return the corresponding type object from the underlying DB-API, if any.
This can be useful for calling setinputsizes(), for example.
Return True if the target Python type is ‘mutable’.
This allows systems like the ORM to know if a column value can be considered ‘not changed’ by comparing the identity of objects alone. Values such as dicts, lists which are serialized into strings are examples of “mutable” column structures.
When this method is overridden, copy_value() should also be supplied. The MutableType mixin is recommended as a helper.
Defines a result-column processing function.
| Parameters: |
|
|---|
Bases: sqlalchemy.types.AbstractType
Base for built-in types.
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.
Compare two values for equality.
Produce a string-compiled form of this TypeEngine.
When called with no arguments, uses a “default” dialect to produce a string result.
| Parameters: | dialect – a Dialect instance. |
|---|
Return a dialect-specific implementation for this TypeEngine.
Return the corresponding type object from the underlying DB-API, if any.
This can be useful for calling setinputsizes(), for example.
Return True if the target Python type is ‘mutable’.
This allows systems like the ORM to know if a column value can be considered ‘not changed’ by comparing the identity of objects alone. Values such as dicts, lists which are serialized into strings are examples of “mutable” column structures.
When this method is overridden, copy_value() should also be supplied. The MutableType mixin is recommended as a helper.
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.
Bases: object
A mixin that marks a TypeEngine as representing a mutable Python object type.
“mutable” means that changes can occur in place to a value of this type. Examples includes Python lists, dictionaries, and sets, as well as user-defined objects. The primary need for identification of “mutable” types is by the ORM, which applies special rules to such values in order to guarantee that changes are detected. These rules may have a significant performance impact, described below.
A MutableType usually allows a flag called mutable=True to enable/disable the “mutability” flag, represented on this class by is_mutable(). Examples include PickleType and ARRAY. Setting this flag to False effectively disables any mutability- specific behavior by the ORM.
copy_value() and compare_values() represent a copy and compare function for values of this type - implementing subclasses should override these appropriately.
The usage of mutable types has significant performance implications when using the ORM. In order to detect changes, the ORM must create a copy of the value when it is first accessed, so that changes to the current value can be compared against the “clean” database-loaded value. Additionally, when the ORM checks to see if any data requires flushing, it must scan through all instances in the session which are known to have “mutable” attributes and compare the current value of each one to its “clean” value. So for example, if the Session contains 6000 objects (a fairly large amount) and autoflush is enabled, every individual execution of Query will require a full scan of that subset of the 6000 objects that have mutable attributes, possibly resulting in tens of thousands of additional method calls for every query.
Note that for small numbers (< 100 in the Session at a time) of objects with “mutable” values, the performance degradation is negligible. In most cases it’s likely that the convenience allowed by “mutable” change detection outweighs the performance penalty.
It is perfectly fine to represent “mutable” data types with the “mutable” flag set to False, which eliminates any performance issues. It means that the ORM will only reliably detect changes for values of this type if a newly modified value is of a different identity (i.e., id(value)) than what was present before - i.e., instead of operations like these:
myobject.somedict['foo'] = 'bar'
myobject.someset.add('bar')
myobject.somelist.append('bar')You’d instead say:
myobject.somevalue = {'foo':'bar'}
myobject.someset = myobject.someset.union(['bar'])
myobject.somelist = myobject.somelist + ['bar']A future release of SQLAlchemy will include instrumented collection support for mutable types, such that at least usage of plain Python datastructures will be able to emit events for in-place changes, removing the need for pessimistic scanning for changes.
x.__init__(...) initializes x; see help(type(x)) for signature
Compare x == y.
Unimplemented.
Return True if the target Python type is ‘mutable’.
For MutableType, this method is set to return True.
Bases: object
A mixin that marks a type as supporting ‘concatenation’, typically strings.
x.__init__(...) initializes x; see help(type(x)) for signature
Bases: sqlalchemy.types.TypeEngine
An unknown type.
NullTypes will stand in if Table reflection encounters a column data type unknown to SQLAlchemy. The resulting columns are nearly fully usable: the DB-API adapter will handle all translation to and from the database data type.
NullType does not have sufficient information to particpate in a CREATE TABLE statement and will raise an exception if encountered during a create() operation.