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CouchPotatoServer/libs/sqlalchemy/types.py

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# sqlalchemy/types.py
# Copyright (C) 2005-2012 the SQLAlchemy authors and contributors <see AUTHORS file>
#
# This module is part of SQLAlchemy and is released under
# the MIT License: http://www.opensource.org/licenses/mit-license.php
"""defines genericized SQL types, each represented by a subclass of
:class:`~sqlalchemy.types.AbstractType`. Dialects define further subclasses of these
types.
For more information see the SQLAlchemy documentation on types.
"""
__all__ = [ 'TypeEngine', 'TypeDecorator', 'AbstractType', 'UserDefinedType',
'INT', 'CHAR', 'VARCHAR', 'NCHAR', 'NVARCHAR','TEXT', 'Text',
'FLOAT', 'NUMERIC', 'REAL', 'DECIMAL', 'TIMESTAMP', 'DATETIME',
'CLOB', 'BLOB', 'BOOLEAN', 'SMALLINT', 'INTEGER', 'DATE', 'TIME',
'String', 'Integer', 'SmallInteger', 'BigInteger', 'Numeric',
'Float', 'DateTime', 'Date', 'Time', 'LargeBinary', 'Binary',
'Boolean', 'Unicode', 'MutableType', 'Concatenable',
'UnicodeText','PickleType', 'Interval', 'Enum' ]
import inspect
import datetime as dt
import codecs
from sqlalchemy import exc, schema
from sqlalchemy.sql import expression, operators
from sqlalchemy.util import pickle
from sqlalchemy.util.compat import decimal
from sqlalchemy.sql.visitors import Visitable
from sqlalchemy import util
from sqlalchemy import processors, events, event
import collections
default = util.importlater("sqlalchemy.engine", "default")
NoneType = type(None)
if util.jython:
import array
class AbstractType(Visitable):
"""Base for all types - not needed except for backwards
compatibility."""
class TypeEngine(AbstractType):
"""Base for built-in types."""
def copy_value(self, value):
return value
def bind_processor(self, 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``.
:param dialect: Dialect instance in use.
"""
return None
def result_processor(self, 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``.
:param dialect: Dialect instance in use.
:param coltype: DBAPI coltype argument received in cursor.description.
"""
return None
def compare_values(self, x, y):
"""Compare two values for equality."""
return x == y
def is_mutable(self):
"""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.
.. note::
This functionality is now superseded by the
``sqlalchemy.ext.mutable`` extension described in
:ref:`mutable_toplevel`.
When this method is overridden, :meth:`copy_value` should
also be supplied. The :class:`.MutableType` mixin
is recommended as a helper.
"""
return False
def get_dbapi_type(self, dbapi):
"""Return the corresponding type object from the underlying DB-API, if
any.
This can be useful for calling ``setinputsizes()``, for example.
"""
return None
@property
def python_type(self):
"""Return the Python type object expected to be returned
by instances of this type, if known.
Basically, for those types which enforce a return type,
or are known across the board to do such for all common
DBAPIs (like ``int`` for example), will return that type.
If a return type is not defined, raises
``NotImplementedError``.
Note that any type also accommodates NULL in SQL which
means you can also get back ``None`` from any type
in practice.
"""
raise NotImplementedError()
def with_variant(self, type_, dialect_name):
"""Produce a new type object that will utilize the given
type when applied to the dialect of the given name.
e.g.::
from sqlalchemy.types import String
from sqlalchemy.dialects import mysql
s = String()
s = s.with_variant(mysql.VARCHAR(collation='foo'), 'mysql')
The construction of :meth:`.TypeEngine.with_variant` is always
from the "fallback" type to that which is dialect specific.
The returned type is an instance of :class:`.Variant`, which
itself provides a :meth:`~sqlalchemy.types.Variant.with_variant` that can
be called repeatedly.
:param type_: a :class:`.TypeEngine` that will be selected
as a variant from the originating type, when a dialect
of the given name is in use.
:param dialect_name: base name of the dialect which uses
this type. (i.e. ``'postgresql'``, ``'mysql'``, etc.)
New in 0.7.2.
"""
return Variant(self, {dialect_name:type_})
def _adapt_expression(self, op, othertype):
"""evaluate the return type of <self> <op> <othertype>,
and apply any adaptations to the given operator.
"""
return op, self
@util.memoized_property
def _type_affinity(self):
"""Return a rudimental 'affinity' value expressing the general class
of type."""
typ = None
for t in self.__class__.__mro__:
if t is TypeEngine or t is UserDefinedType:
return typ
elif issubclass(t, TypeEngine):
typ = t
else:
return self.__class__
def dialect_impl(self, dialect):
"""Return a dialect-specific implementation for this :class:`.TypeEngine`."""
try:
return dialect._type_memos[self]['impl']
except KeyError:
return self._dialect_info(dialect)['impl']
def _cached_bind_processor(self, dialect):
"""Return a dialect-specific bind processor for this type."""
try:
return dialect._type_memos[self]['bind']
except KeyError:
d = self._dialect_info(dialect)
d['bind'] = bp = d['impl'].bind_processor(dialect)
return bp
def _cached_result_processor(self, dialect, coltype):
"""Return a dialect-specific result processor for this type."""
try:
return dialect._type_memos[self][coltype]
except KeyError:
d = self._dialect_info(dialect)
# key assumption: DBAPI type codes are
# constants. Else this dictionary would
# grow unbounded.
d[coltype] = rp = d['impl'].result_processor(dialect, coltype)
return rp
def _dialect_info(self, dialect):
"""Return a dialect-specific registry which
caches a dialect-specific implementation, bind processing
function, and one or more result processing functions."""
if self in dialect._type_memos:
return dialect._type_memos[self]
else:
impl = self._gen_dialect_impl(dialect)
if impl is self:
impl = self.adapt(type(self))
# this can't be self, else we create a cycle
assert impl is not self
dialect._type_memos[self] = d = {'impl':impl}
return d
def _gen_dialect_impl(self, dialect):
return dialect.type_descriptor(self)
def adapt(self, cls, **kw):
"""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.
"""
return util.constructor_copy(self, cls, **kw)
def _coerce_compared_value(self, 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
:class:`.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 method
:meth:`.TypeDecorator.coerce_compared_value` which is intended for
end-user customization of this behavior.
"""
_coerced_type = _type_map.get(type(value), NULLTYPE)
if _coerced_type is NULLTYPE or _coerced_type._type_affinity \
is self._type_affinity:
return self
else:
return _coerced_type
def _compare_type_affinity(self, other):
return self._type_affinity is other._type_affinity
def compile(self, dialect=None):
"""Produce a string-compiled form of this :class:`.TypeEngine`.
When called with no arguments, uses a "default" dialect
to produce a string result.
:param dialect: a :class:`.Dialect` instance.
"""
# arg, return value is inconsistent with
# ClauseElement.compile()....this is a mistake.
if not dialect:
dialect = self._default_dialect
return dialect.type_compiler.process(self)
@property
def _default_dialect(self):
if self.__class__.__module__.startswith("sqlalchemy.dialects"):
tokens = self.__class__.__module__.split(".")[0:3]
mod = ".".join(tokens)
return getattr(__import__(mod).dialects, tokens[-1]).dialect()
else:
return default.DefaultDialect()
def __str__(self):
# Py3K
#return unicode(self.compile())
# Py2K
return unicode(self.compile()).\
encode('ascii', 'backslashreplace')
# end Py2K
def __init__(self, *args, **kwargs):
"""Support implementations that were passing arguments"""
if args or kwargs:
util.warn_deprecated("Passing arguments to type object "
"constructor %s is deprecated" % self.__class__)
def __repr__(self):
return util.generic_repr(self)
class UserDefinedType(TypeEngine):
"""Base for user defined types.
This should be the base of new types. Note that
for most cases, :class:`.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 process
Once the type is made, it's immediately usable::
table = Table('foo', meta,
Column('id', Integer, primary_key=True),
Column('data', MyType(16))
)
"""
__visit_name__ = "user_defined"
def _adapt_expression(self, op, othertype):
"""evaluate the return type of <self> <op> <othertype>,
and apply any adaptations to the given operator.
"""
return self.adapt_operator(op), self
def adapt_operator(self, op):
"""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 op
class TypeDecorator(TypeEngine):
"""Allows the creation of types which add additional functionality
to an existing type.
This method is preferred to direct subclassing of SQLAlchemy's
built-in types as it ensures that all required functionality of
the underlying type is kept in place.
Typical usage::
import sqlalchemy.types as types
class MyType(types.TypeDecorator):
'''Prefixes Unicode values with "PREFIX:" on the way in and
strips it off on the way out.
'''
impl = types.Unicode
def process_bind_param(self, value, dialect):
return "PREFIX:" + value
def process_result_value(self, value, dialect):
return value[7:]
def copy(self):
return MyType(self.impl.length)
The class-level "impl" attribute is required, and can reference any
TypeEngine class. Alternatively, the load_dialect_impl() method
can be used to provide different type classes based on the dialect
given; in this case, the "impl" variable can reference
``TypeEngine`` as a placeholder.
Types that receive a Python type that isn't similar to the ultimate type
used may want to define the :meth:`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
:meth:`~TypeDecorator.coerce_compared_value` method, which returns a type
that should be used for the value of the expression. Below we set it such
that an integer value will be treated as an ``Integer``, and any other
value is assumed to be a date and will be treated as a ``MyEpochType``::
def coerce_compared_value(self, op, value):
if isinstance(value, int):
return Integer()
else:
return self
"""
__visit_name__ = "type_decorator"
def __init__(self, *args, **kwargs):
"""Construct a :class:`.TypeDecorator`.
Arguments sent here are passed to the constructor
of the class assigned to the ``impl`` class level attribute,
assuming the ``impl`` is a callable, and the resulting
object is assigned to the ``self.impl`` instance attribute
(thus overriding the class attribute of the same name).
If the class level ``impl`` is not a callable (the unusual case),
it will be assigned to the same instance attribute 'as-is',
ignoring those arguments passed to the constructor.
Subclasses can override this to customize the generation
of ``self.impl`` entirely.
"""
if not hasattr(self.__class__, 'impl'):
raise AssertionError("TypeDecorator implementations "
"require a class-level variable "
"'impl' which refers to the class of "
"type being decorated")
self.impl = to_instance(self.__class__.impl, *args, **kwargs)
def _gen_dialect_impl(self, dialect):
"""
#todo
"""
adapted = dialect.type_descriptor(self)
if adapted is not self:
return adapted
# otherwise adapt the impl type, link
# to a copy of this TypeDecorator and return
# that.
typedesc = self.load_dialect_impl(dialect).dialect_impl(dialect)
tt = self.copy()
if not isinstance(tt, self.__class__):
raise AssertionError('Type object %s does not properly '
'implement the copy() method, it must '
'return an object of type %s' % (self,
self.__class__))
tt.impl = typedesc
return tt
@property
def _type_affinity(self):
"""
#todo
"""
return self.impl._type_affinity
def type_engine(self, dialect):
"""Return a dialect-specific :class:`.TypeEngine` instance for this :class:`.TypeDecorator`.
In most cases this returns a dialect-adapted form of
the :class:`.TypeEngine` type represented by ``self.impl``.
Makes usage of :meth:`dialect_impl` but also traverses
into wrapped :class:`.TypeDecorator` instances.
Behavior can be customized here by overriding :meth:`load_dialect_impl`.
"""
adapted = dialect.type_descriptor(self)
if type(adapted) is not type(self):
return adapted
elif isinstance(self.impl, TypeDecorator):
return self.impl.type_engine(dialect)
else:
return self.load_dialect_impl(dialect)
def load_dialect_impl(self, dialect):
"""Return a :class:`.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 :class:`.TypeDecorator` implementation of :meth:`type_engine`
to help determine what type should ultimately be returned
for a given :class:`.TypeDecorator`.
By default returns ``self.impl``.
"""
return self.impl
def __getattr__(self, key):
"""Proxy all other undefined accessors to the underlying
implementation."""
return getattr(self.impl, key)
def process_bind_param(self, value, dialect):
"""Receive a bound parameter value to be converted.
Subclasses override this method to return the
value that should be passed along to the underlying
:class:`.TypeEngine` object, and from there to the
DBAPI ``execute()`` method.
The operation could be anything desired to perform custom
behavior, such as transforming or serializing data.
This could also be used as a hook for validating logic.
This operation should be designed with the reverse operation
in mind, which would be the process_result_value method of
this class.
:param value: Data to operate upon, of any type expected by
this method in the subclass. Can be ``None``.
:param dialect: the :class:`.Dialect` in use.
"""
raise NotImplementedError()
def process_result_value(self, value, dialect):
"""Receive a result-row column value to be converted.
Subclasses should implement this method to operate on data
fetched from the database.
Subclasses override this method to return the
value that should be passed back to the application,
given a value that is already processed by
the underlying :class:`.TypeEngine` object, originally
from the DBAPI cursor method ``fetchone()`` or similar.
The operation could be anything desired to perform custom
behavior, such as transforming or serializing data.
This could also be used as a hook for validating logic.
:param value: Data to operate upon, of any type expected by
this method in the subclass. Can be ``None``.
:param dialect: the :class:`.Dialect` in use.
This operation should be designed to be reversible by
the "process_bind_param" method of this class.
"""
raise NotImplementedError()
def bind_processor(self, dialect):
"""Provide a bound value processing function for the
given :class:`.Dialect`.
This is the method that fulfills the :class:`.TypeEngine`
contract for bound value conversion. :class:`.TypeDecorator`
will wrap a user-defined implementation of
:meth:`process_bind_param` here.
User-defined code can override this method directly,
though its likely best to use :meth:`process_bind_param` so that
the processing provided by ``self.impl`` is maintained.
:param dialect: Dialect instance in use.
This method is the reverse counterpart to the
:meth:`result_processor` method of this class.
"""
if self.__class__.process_bind_param.func_code \
is not TypeDecorator.process_bind_param.func_code:
process_param = self.process_bind_param
impl_processor = self.impl.bind_processor(dialect)
if impl_processor:
def process(value):
return impl_processor(process_param(value, dialect))
else:
def process(value):
return process_param(value, dialect)
return process
else:
return self.impl.bind_processor(dialect)
def result_processor(self, dialect, coltype):
"""Provide a result value processing function for the given :class:`.Dialect`.
This is the method that fulfills the :class:`.TypeEngine`
contract for result value conversion. :class:`.TypeDecorator`
will wrap a user-defined implementation of
:meth:`process_result_value` here.
User-defined code can override this method directly,
though its likely best to use :meth:`process_result_value` so that
the processing provided by ``self.impl`` is maintained.
:param dialect: Dialect instance in use.
:param coltype: An SQLAlchemy data type
This method is the reverse counterpart to the
:meth:`bind_processor` method of this class.
"""
if self.__class__.process_result_value.func_code \
is not TypeDecorator.process_result_value.func_code:
process_value = self.process_result_value
impl_processor = self.impl.result_processor(dialect,
coltype)
if impl_processor:
def process(value):
return process_value(impl_processor(value), dialect)
else:
def process(value):
return process_value(value, dialect)
return process
else:
return self.impl.result_processor(dialect, coltype)
def coerce_compared_value(self, op, value):
"""Suggest a type for a 'coerced' Python value in an expression.
By default, returns self. This method is called by
the expression system when an object using this type is
on the left or right side of an expression against a plain Python
object which does not yet have a SQLAlchemy type assigned::
expr = table.c.somecolumn + 35
Where above, if ``somecolumn`` uses this type, this method will
be called with the value ``operator.add``
and ``35``. The return value is whatever SQLAlchemy type should
be used for ``35`` for this particular operation.
"""
return self
def _coerce_compared_value(self, op, value):
"""See :meth:`.TypeEngine._coerce_compared_value` for a description."""
return self.coerce_compared_value(op, value)
def copy(self):
"""Produce a copy of this :class:`.TypeDecorator` instance.
This is a shallow copy and is provided to fulfill part of
the :class:`.TypeEngine` contract. It usually does not
need to be overridden unless the user-defined :class:`.TypeDecorator`
has local state that should be deep-copied.
"""
instance = self.__class__.__new__(self.__class__)
instance.__dict__.update(self.__dict__)
return instance
def get_dbapi_type(self, dbapi):
"""Return the DBAPI type object represented by this :class:`.TypeDecorator`.
By default this calls upon :meth:`.TypeEngine.get_dbapi_type` of the
underlying "impl".
"""
return self.impl.get_dbapi_type(dbapi)
def copy_value(self, value):
"""Given a value, produce a copy of it.
By default this calls upon :meth:`.TypeEngine.copy_value`
of the underlying "impl".
:meth:`.copy_value` will return the object
itself, assuming "mutability" is not enabled.
Only the :class:`.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.
Modern implementations should use the
``sqlalchemy.ext.mutable`` extension described in
:ref:`mutable_toplevel` for intercepting in-place
changes to values.
"""
return self.impl.copy_value(value)
def compare_values(self, x, y):
"""Given two values, compare them for equality.
By default this calls upon :meth:`.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.
"""
return self.impl.compare_values(x, y)
def is_mutable(self):
"""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.
.. note::
This functionality is now superseded by the
``sqlalchemy.ext.mutable`` extension described in
:ref:`mutable_toplevel`.
"""
return self.impl.is_mutable()
def _adapt_expression(self, op, othertype):
"""
#todo
"""
op, typ =self.impl._adapt_expression(op, othertype)
if typ is self.impl:
return op, self
else:
return op, typ
class Variant(TypeDecorator):
"""A wrapping type that selects among a variety of
implementations based on dialect in use.
The :class:`.Variant` type is typically constructed
using the :meth:`.TypeEngine.with_variant` method.
New in 0.7.2.
"""
def __init__(self, base, mapping):
"""Construct a new :class:`.Variant`.
:param base: the base 'fallback' type
:param mapping: dictionary of string dialect names to :class:`.TypeEngine`
instances.
"""
self.impl = base
self.mapping = mapping
def load_dialect_impl(self, dialect):
if dialect.name in self.mapping:
return self.mapping[dialect.name]
else:
return self.impl
def with_variant(self, type_, dialect_name):
"""Return a new :class:`.Variant` which adds the given
type + dialect name to the mapping, in addition to the
mapping present in this :class:`.Variant`.
:param type_: a :class:`.TypeEngine` that will be selected
as a variant from the originating type, when a dialect
of the given name is in use.
:param dialect_name: base name of the dialect which uses
this type. (i.e. ``'postgresql'``, ``'mysql'``, etc.)
New in 0.7.2.
"""
if dialect_name in self.mapping:
raise exc.ArgumentError(
"Dialect '%s' is already present in "
"the mapping for this Variant" % dialect_name)
mapping = self.mapping.copy()
mapping[dialect_name] = type_
return Variant(self.impl, mapping)
class MutableType(object):
"""A mixin that marks a :class:`.TypeEngine` as representing
a mutable Python object type. This functionality is used
only by the ORM.
.. note::
:class:`.MutableType` is superseded as of SQLAlchemy 0.7
by the ``sqlalchemy.ext.mutable`` extension described in
:ref:`mutable_toplevel`. This extension provides an event
driven approach to in-place mutation detection that does not
incur the severe performance penalty of the :class:`.MutableType`
approach.
"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 :class:`.MutableType` usually allows a flag called
``mutable=False`` to enable/disable the "mutability" flag,
represented on this class by :meth:`is_mutable`. Examples
include :class:`.PickleType` and
:class:`~sqlalchemy.dialects.postgresql.base.ARRAY`. Setting
this flag to ``True`` enables mutability-specific behavior
by the ORM.
The :meth:`copy_value` and :meth:`compare_values` functions
represent a copy and compare function for values of this
type - implementing subclasses should override these
appropriately.
.. warning::
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 :class:`.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.
As of SQLAlchemy 0.7, the ``sqlalchemy.ext.mutable`` is provided which
allows an event driven approach to in-place mutation detection. This
approach should now be favored over the usage of :class:`.MutableType`
with ``mutable=True``. ``sqlalchemy.ext.mutable`` is described in
:ref:`mutable_toplevel`.
"""
def is_mutable(self):
"""Return True if the target Python type is 'mutable'.
For :class:`.MutableType`, this method is set to
return ``True``.
"""
return True
def copy_value(self, value):
"""Unimplemented."""
raise NotImplementedError()
def compare_values(self, x, y):
"""Compare *x* == *y*."""
return x == y
def to_instance(typeobj, *arg, **kw):
if typeobj is None:
return NULLTYPE
if util.callable(typeobj):
return typeobj(*arg, **kw)
else:
return typeobj
def adapt_type(typeobj, colspecs):
if isinstance(typeobj, type):
typeobj = typeobj()
for t in typeobj.__class__.__mro__[0:-1]:
try:
impltype = colspecs[t]
break
except KeyError:
pass
else:
# couldnt adapt - so just return the type itself
# (it may be a user-defined type)
return typeobj
# if we adapted the given generic type to a database-specific type,
# but it turns out the originally given "generic" type
# is actually a subclass of our resulting type, then we were already
# given a more specific type than that required; so use that.
if (issubclass(typeobj.__class__, impltype)):
return typeobj
return typeobj.adapt(impltype)
class NullType(TypeEngine):
"""An unknown type.
NullTypes will stand in if :class:`~sqlalchemy.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 :meth:`~sqlalchemy.Table.create` operation.
"""
__visit_name__ = 'null'
def _adapt_expression(self, op, othertype):
if isinstance(othertype, NullType) or not operators.is_commutative(op):
return op, self
else:
return othertype._adapt_expression(op, self)
NullTypeEngine = NullType
class Concatenable(object):
"""A mixin that marks a type as supporting 'concatenation',
typically strings."""
def _adapt_expression(self, op, othertype):
if op is operators.add and issubclass(othertype._type_affinity,
(Concatenable, NullType)):
return operators.concat_op, self
else:
return op, self
class _DateAffinity(object):
"""Mixin date/time specific expression adaptations.
Rules are implemented within Date,Time,Interval,DateTime, Numeric,
Integer. Based on http://www.postgresql.org/docs/current/static
/functions-datetime.html.
"""
@property
def _expression_adaptations(self):
raise NotImplementedError()
_blank_dict = util.immutabledict()
def _adapt_expression(self, op, othertype):
othertype = othertype._type_affinity
return op, \
self._expression_adaptations.get(op, self._blank_dict).\
get(othertype, NULLTYPE)
class String(Concatenable, 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.
"""
__visit_name__ = 'string'
def __init__(self, length=None, convert_unicode=False,
assert_unicode=None, unicode_error=None,
_warn_on_bytestring=False
):
"""
Create a string-holding type.
:param 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 if a ``VARCHAR``
with no length is included. Whether the value is
interpreted as bytes or characters is database specific.
:param convert_unicode: When set to ``True``, the
:class:`.String` type will assume that
input is to be passed as Python ``unicode`` objects,
and results returned as Python ``unicode`` objects.
If the DBAPI in use does not support Python unicode
(which is fewer and fewer these days), SQLAlchemy
will encode/decode the value, using the
value of the ``encoding`` parameter passed to
:func:`.create_engine` as the encoding.
When using a DBAPI that natively supports Python
unicode objects, this flag generally does not
need to be set. For columns that are explicitly
intended to store non-ASCII data, the :class:`.Unicode`
or :class:`UnicodeText`
types should be used regardless, which feature
the same behavior of ``convert_unicode`` but
also indicate an underlying column type that
directly supports unicode, such as ``NVARCHAR``.
For the extremely rare case that Python ``unicode``
is to be encoded/decoded by SQLAlchemy on a backend
that does natively support Python ``unicode``,
the value ``force`` can be passed here which will
cause SQLAlchemy's encode/decode services to be
used unconditionally.
:param assert_unicode: Deprecated. A warning is emitted
when a non-``unicode`` object is passed to the
:class:`.Unicode` subtype of :class:`.String`,
or the :class:`.UnicodeText` subtype of :class:`.Text`.
See :class:`.Unicode` for information on how to
control this warning.
:param unicode_error: Optional, a method to use to handle Unicode
conversion errors. Behaves like the ``errors`` keyword argument to
the standard library's ``string.decode()`` functions. This flag
requires that ``convert_unicode`` is set to ``force`` - otherwise,
SQLAlchemy is not guaranteed to handle the task of unicode
conversion. Note that this flag adds significant performance
overhead to row-fetching operations for backends that already
return unicode objects natively (which most DBAPIs do). This
flag should only be used as a last resort for reading
strings from a column with varied or corrupted encodings.
"""
if unicode_error is not None and convert_unicode != 'force':
raise exc.ArgumentError("convert_unicode must be 'force' "
"when unicode_error is set.")
if assert_unicode:
util.warn_deprecated('assert_unicode is deprecated. '
'SQLAlchemy emits a warning in all '
'cases where it would otherwise like '
'to encode a Python unicode object '
'into a specific encoding but a plain '
'bytestring is received. This does '
'*not* apply to DBAPIs that coerce '
'Unicode natively.')
self.length = length
self.convert_unicode = convert_unicode
self.unicode_error = unicode_error
self._warn_on_bytestring = _warn_on_bytestring
def bind_processor(self, dialect):
if self.convert_unicode or dialect.convert_unicode:
if dialect.supports_unicode_binds and \
self.convert_unicode != 'force':
if self._warn_on_bytestring:
def process(value):
# Py3K
#if isinstance(value, bytes):
# Py2K
if isinstance(value, str):
# end Py2K
util.warn("Unicode type received non-unicode bind "
"param value.")
return value
return process
else:
return None
else:
encoder = codecs.getencoder(dialect.encoding)
warn_on_bytestring = self._warn_on_bytestring
def process(value):
if isinstance(value, unicode):
return encoder(value, self.unicode_error)[0]
elif warn_on_bytestring and value is not None:
util.warn("Unicode type received non-unicode bind "
"param value")
return value
return process
else:
return None
def result_processor(self, dialect, coltype):
wants_unicode = self.convert_unicode or dialect.convert_unicode
needs_convert = wants_unicode and \
(dialect.returns_unicode_strings is not True or
self.convert_unicode == 'force')
if needs_convert:
to_unicode = processors.to_unicode_processor_factory(
dialect.encoding, self.unicode_error)
if dialect.returns_unicode_strings:
# we wouldn't be here unless convert_unicode='force'
# was specified, or the driver has erratic unicode-returning
# habits. since we will be getting back unicode
# in most cases, we check for it (decode will fail).
def process(value):
if isinstance(value, unicode):
return value
else:
return to_unicode(value)
return process
else:
# here, we assume that the object is not unicode,
# avoiding expensive isinstance() check.
return to_unicode
else:
return None
@property
def python_type(self):
if self.convert_unicode:
return unicode
else:
return str
def get_dbapi_type(self, dbapi):
return dbapi.STRING
class Text(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.)
"""
__visit_name__ = 'text'
class Unicode(String):
"""A variable length Unicode string type.
The :class:`.Unicode` type is a :class:`.String` subclass
that assumes input and output as Python ``unicode`` data,
and in that regard is equivalent to the usage of the
``convert_unicode`` flag with the :class:`.String` type.
However, unlike plain :class:`.String`, it also implies an
underlying column type that is explicitly supporting of non-ASCII
data, such as ``NVARCHAR`` on Oracle and SQL Server.
This can impact the output of ``CREATE TABLE`` statements
and ``CAST`` functions at the dialect level, and can
also affect the handling of bound parameters in some
specific DBAPI scenarios.
The encoding used by the :class:`.Unicode` type is usually
determined by the DBAPI itself; most modern DBAPIs
feature support for Python ``unicode`` objects as bound
values and result set values, and the encoding should
be configured as detailed in the notes for the target
DBAPI in the :ref:`dialect_toplevel` section.
For those DBAPIs which do not support, or are not configured
to accommodate Python ``unicode`` objects
directly, SQLAlchemy does the encoding and decoding
outside of the DBAPI. The encoding in this scenario
is determined by the ``encoding`` flag passed to
:func:`.create_engine`.
When using the :class:`.Unicode` type, it is only appropriate
to pass Python ``unicode`` objects, and not plain ``str``.
If a plain ``str`` is passed under Python 2, a warning
is emitted. If you notice your application emitting these warnings but
you're not sure of the source of them, the Python
``warnings`` filter, documented at
http://docs.python.org/library/warnings.html,
can be used to turn these warnings into exceptions
which will illustrate a stack trace::
import warnings
warnings.simplefilter('error')
For an application that wishes to pass plain bytestrings
and Python ``unicode`` objects to the ``Unicode`` type
equally, the bytestrings must first be decoded into
unicode. The recipe at :ref:`coerce_to_unicode` illustrates
how this is done.
See also:
:class:`.UnicodeText` - unlengthed textual counterpart
to :class:`.Unicode`.
"""
__visit_name__ = 'unicode'
def __init__(self, length=None, **kwargs):
"""
Create a :class:`.Unicode` object.
Parameters are the same as that of :class:`.String`,
with the exception that ``convert_unicode``
defaults to ``True``.
"""
kwargs.setdefault('convert_unicode', True)
kwargs.setdefault('_warn_on_bytestring', True)
super(Unicode, self).__init__(length=length, **kwargs)
class UnicodeText(Text):
"""An unbounded-length Unicode string type.
See :class:`.Unicode` for details on the unicode
behavior of this object.
Like :class:`.Unicode`, usage the :class:`.UnicodeText` type implies a
unicode-capable type being used on the backend, such as
``NCLOB``, ``NTEXT``.
"""
__visit_name__ = 'unicode_text'
def __init__(self, length=None, **kwargs):
"""
Create a Unicode-converting Text type.
Parameters are the same as that of :class:`.Text`,
with the exception that ``convert_unicode``
defaults to ``True``.
"""
kwargs.setdefault('convert_unicode', True)
kwargs.setdefault('_warn_on_bytestring', True)
super(UnicodeText, self).__init__(length=length, **kwargs)
class Integer(_DateAffinity, TypeEngine):
"""A type for ``int`` integers."""
__visit_name__ = 'integer'
def get_dbapi_type(self, dbapi):
return dbapi.NUMBER
@property
def python_type(self):
return int
@util.memoized_property
def _expression_adaptations(self):
# TODO: need a dictionary object that will
# handle operators generically here, this is incomplete
return {
operators.add:{
Date:Date,
Integer:Integer,
Numeric:Numeric,
},
operators.mul:{
Interval:Interval,
Integer:Integer,
Numeric:Numeric,
},
# Py2K
operators.div:{
Integer:Integer,
Numeric:Numeric,
},
# end Py2K
operators.truediv:{
Integer:Integer,
Numeric:Numeric,
},
operators.sub:{
Integer:Integer,
Numeric:Numeric,
},
}
class SmallInteger(Integer):
"""A type for smaller ``int`` integers.
Typically generates a ``SMALLINT`` in DDL, and otherwise acts like
a normal :class:`.Integer` on the Python side.
"""
__visit_name__ = 'small_integer'
class BigInteger(Integer):
"""A type for bigger ``int`` integers.
Typically generates a ``BIGINT`` in DDL, and otherwise acts like
a normal :class:`.Integer` on the Python side.
"""
__visit_name__ = 'big_integer'
class Numeric(_DateAffinity, TypeEngine):
"""A type for fixed precision numbers.
Typically generates DECIMAL or NUMERIC. Returns
``decimal.Decimal`` objects by default, applying
conversion as needed.
.. note::
The `cdecimal <http://pypi.python.org/pypi/cdecimal/>`_ library
is a high performing alternative to Python's built-in
``decimal.Decimal`` type, which performs very poorly in high volume
situations. SQLAlchemy 0.7 is tested against ``cdecimal`` and supports
it fully. The type is not necessarily supported by DBAPI
implementations however, most of which contain an import for plain
``decimal`` in their source code, even though some such as psycopg2
provide hooks for alternate adapters. SQLAlchemy imports ``decimal``
globally as well. While the alternate ``Decimal`` class can be patched
into SQLA's ``decimal`` module, overall the most straightforward and
foolproof way to use "cdecimal" given current DBAPI and Python support
is to patch it directly into sys.modules before anything else is
imported::
import sys
import cdecimal
sys.modules["decimal"] = cdecimal
While the global patch is a little ugly, it's particularly
important to use just one decimal library at a time since
Python Decimal and cdecimal Decimal objects
are not currently compatible *with each other*::
>>> import cdecimal
>>> import decimal
>>> decimal.Decimal("10") == cdecimal.Decimal("10")
False
SQLAlchemy will provide more natural support of
cdecimal if and when it becomes a standard part of Python
installations and is supported by all DBAPIs.
"""
__visit_name__ = 'numeric'
def __init__(self, precision=None, scale=None, asdecimal=True):
"""
Construct a Numeric.
:param precision: the numeric precision for use in DDL ``CREATE
TABLE``.
:param scale: the numeric scale for use in DDL ``CREATE TABLE``.
:param 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.
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.
"""
self.precision = precision
self.scale = scale
self.asdecimal = asdecimal
def get_dbapi_type(self, dbapi):
return dbapi.NUMBER
@property
def python_type(self):
if self.asdecimal:
return decimal.Decimal
else:
return float
def bind_processor(self, dialect):
if dialect.supports_native_decimal:
return None
else:
return processors.to_float
def result_processor(self, dialect, coltype):
if self.asdecimal:
if dialect.supports_native_decimal:
# we're a "numeric", DBAPI will give us Decimal directly
return None
else:
util.warn('Dialect %s+%s does *not* support Decimal '
'objects natively, and SQLAlchemy must '
'convert from floating point - rounding '
'errors and other issues may occur. Please '
'consider storing Decimal numbers as strings '
'or integers on this platform for lossless '
'storage.' % (dialect.name, dialect.driver))
# we're a "numeric", DBAPI returns floats, convert.
if self.scale is not None:
return processors.to_decimal_processor_factory(
decimal.Decimal, self.scale)
else:
return processors.to_decimal_processor_factory(
decimal.Decimal)
else:
if dialect.supports_native_decimal:
return processors.to_float
else:
return None
@util.memoized_property
def _expression_adaptations(self):
return {
operators.mul:{
Interval:Interval,
Numeric:Numeric,
Integer:Numeric,
},
# Py2K
operators.div:{
Numeric:Numeric,
Integer:Numeric,
},
# end Py2K
operators.truediv:{
Numeric:Numeric,
Integer:Numeric,
},
operators.add:{
Numeric:Numeric,
Integer:Numeric,
},
operators.sub:{
Numeric:Numeric,
Integer:Numeric,
}
}
class Float(Numeric):
"""A type for ``float`` numbers.
Returns Python ``float`` objects by default, applying
conversion as needed.
"""
__visit_name__ = 'float'
scale = None
def __init__(self, precision=None, asdecimal=False, **kwargs):
"""
Construct a Float.
:param precision: the numeric precision for use in DDL ``CREATE
TABLE``.
:param asdecimal: the same flag as that of :class:`.Numeric`, but
defaults to ``False``. Note that setting this flag to ``True``
results in floating point conversion.
:param \**kwargs: deprecated. Additional arguments here are ignored
by the default :class:`.Float` type. For database specific
floats that support additional arguments, see that dialect's
documentation for details, such as :class:`sqlalchemy.dialects.mysql.FLOAT`.
"""
self.precision = precision
self.asdecimal = asdecimal
if kwargs:
util.warn_deprecated("Additional keyword arguments "
"passed to Float ignored.")
def result_processor(self, dialect, coltype):
if self.asdecimal:
return processors.to_decimal_processor_factory(decimal.Decimal)
else:
return None
@util.memoized_property
def _expression_adaptations(self):
return {
operators.mul:{
Interval:Interval,
Numeric:Float,
},
# Py2K
operators.div:{
Numeric:Float,
},
# end Py2K
operators.truediv:{
Numeric:Float,
},
operators.add:{
Numeric:Float,
},
operators.sub:{
Numeric:Float,
}
}
class DateTime(_DateAffinity, 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.
"""
__visit_name__ = 'datetime'
def __init__(self, timezone=False):
"""Construct a new :class:`.DateTime`.
:param timezone: boolean. If True, and supported by the
backend, will produce 'TIMESTAMP WITH TIMEZONE'. For backends
that don't support timezone aware timestamps, has no
effect.
"""
self.timezone = timezone
def get_dbapi_type(self, dbapi):
return dbapi.DATETIME
@property
def python_type(self):
return dt.datetime
@util.memoized_property
def _expression_adaptations(self):
return {
operators.add:{
Interval:DateTime,
},
operators.sub:{
Interval:DateTime,
DateTime:Interval,
},
}
class Date(_DateAffinity,TypeEngine):
"""A type for ``datetime.date()`` objects."""
__visit_name__ = 'date'
def get_dbapi_type(self, dbapi):
return dbapi.DATETIME
@property
def python_type(self):
return dt.date
@util.memoized_property
def _expression_adaptations(self):
return {
operators.add:{
Integer:Date,
Interval:DateTime,
Time:DateTime,
},
operators.sub:{
# date - integer = date
Integer:Date,
# date - date = integer.
Date:Integer,
Interval:DateTime,
# date - datetime = interval,
# this one is not in the PG docs
# but works
DateTime:Interval,
},
}
class Time(_DateAffinity,TypeEngine):
"""A type for ``datetime.time()`` objects."""
__visit_name__ = 'time'
def __init__(self, timezone=False):
self.timezone = timezone
def get_dbapi_type(self, dbapi):
return dbapi.DATETIME
@property
def python_type(self):
return dt.time
@util.memoized_property
def _expression_adaptations(self):
return {
operators.add:{
Date:DateTime,
Interval:Time
},
operators.sub:{
Time:Interval,
Interval:Time,
},
}
class _Binary(TypeEngine):
"""Define base behavior for binary types."""
def __init__(self, length=None):
self.length = length
@property
def python_type(self):
# Py3K
#return bytes
# Py2K
return str
# end Py2K
# Python 3 - sqlite3 doesn't need the `Binary` conversion
# here, though pg8000 does to indicate "bytea"
def bind_processor(self, dialect):
DBAPIBinary = dialect.dbapi.Binary
def process(value):
x = self
if value is not None:
return DBAPIBinary(value)
else:
return None
return process
# Python 3 has native bytes() type
# both sqlite3 and pg8000 seem to return it
# (i.e. and not 'memoryview')
# Py2K
def result_processor(self, dialect, coltype):
if util.jython:
def process(value):
if value is not None:
if isinstance(value, array.array):
return value.tostring()
return str(value)
else:
return None
else:
process = processors.to_str
return process
# end Py2K
def _coerce_compared_value(self, op, value):
"""See :meth:`.TypeEngine._coerce_compared_value` for a description."""
if isinstance(value, basestring):
return self
else:
return super(_Binary, self)._coerce_compared_value(op, value)
def get_dbapi_type(self, dbapi):
return dbapi.BINARY
class LargeBinary(_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.
"""
__visit_name__ = 'large_binary'
def __init__(self, length=None):
"""
Construct a LargeBinary type.
:param 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.
"""
_Binary.__init__(self, length=length)
class Binary(LargeBinary):
"""Deprecated. Renamed to LargeBinary."""
def __init__(self, *arg, **kw):
util.warn_deprecated('The Binary type has been renamed to '
'LargeBinary.')
LargeBinary.__init__(self, *arg, **kw)
class SchemaType(events.SchemaEventTarget):
"""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.
:class:`.SchemaType` classes can also be targets for the
:meth:`.DDLEvents.before_parent_attach` and :meth:`.DDLEvents.after_parent_attach`
events, where the events fire off surrounding the association of
the type object with a parent :class:`.Column`.
"""
def __init__(self, **kw):
self.name = kw.pop('name', None)
self.quote = kw.pop('quote', None)
self.schema = kw.pop('schema', None)
self.metadata = kw.pop('metadata', None)
if self.metadata:
event.listen(
self.metadata,
"before_create",
util.portable_instancemethod(self._on_metadata_create)
)
event.listen(
self.metadata,
"after_drop",
util.portable_instancemethod(self._on_metadata_drop)
)
def _set_parent(self, column):
column._on_table_attach(util.portable_instancemethod(self._set_table))
def _set_table(self, column, table):
event.listen(
table,
"before_create",
util.portable_instancemethod(
self._on_table_create)
)
event.listen(
table,
"after_drop",
util.portable_instancemethod(self._on_table_drop)
)
if self.metadata is None:
# TODO: what's the difference between self.metadata
# and table.metadata here ?
event.listen(
table.metadata,
"before_create",
util.portable_instancemethod(self._on_metadata_create)
)
event.listen(
table.metadata,
"after_drop",
util.portable_instancemethod(self._on_metadata_drop)
)
@property
def bind(self):
return self.metadata and self.metadata.bind or None
def create(self, bind=None, checkfirst=False):
"""Issue CREATE ddl for this type, if applicable."""
if bind is None:
bind = schema._bind_or_error(self)
t = self.dialect_impl(bind.dialect)
if t.__class__ is not self.__class__ and isinstance(t, SchemaType):
t.create(bind=bind, checkfirst=checkfirst)
def drop(self, bind=None, checkfirst=False):
"""Issue DROP ddl for this type, if applicable."""
if bind is None:
bind = schema._bind_or_error(self)
t = self.dialect_impl(bind.dialect)
if t.__class__ is not self.__class__ and isinstance(t, SchemaType):
t.drop(bind=bind, checkfirst=checkfirst)
def _on_table_create(self, target, bind, **kw):
t = self.dialect_impl(bind.dialect)
if t.__class__ is not self.__class__ and isinstance(t, SchemaType):
t._on_table_create(target, bind, **kw)
def _on_table_drop(self, target, bind, **kw):
t = self.dialect_impl(bind.dialect)
if t.__class__ is not self.__class__ and isinstance(t, SchemaType):
t._on_table_drop(target, bind, **kw)
def _on_metadata_create(self, target, bind, **kw):
t = self.dialect_impl(bind.dialect)
if t.__class__ is not self.__class__ and isinstance(t, SchemaType):
t._on_metadata_create(target, bind, **kw)
def _on_metadata_drop(self, target, bind, **kw):
t = self.dialect_impl(bind.dialect)
if t.__class__ is not self.__class__ and isinstance(t, SchemaType):
t._on_metadata_drop(target, bind, **kw)
class Enum(String, 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.
See also:
:class:`~.postgresql.ENUM` - PostgreSQL-specific type,
which has additional functionality.
"""
__visit_name__ = 'enum'
def __init__(self, *enums, **kw):
"""Construct an enum.
Keyword arguments which don't apply to a specific backend are ignored
by that backend.
:param \*enums: string or unicode enumeration labels. If unicode
labels are present, the `convert_unicode` flag is auto-enabled.
:param convert_unicode: Enable unicode-aware bind parameter and
result-set processing for this Enum's data. This is set
automatically based on the presence of unicode label strings.
:param 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 within ``create_all()`` and ``drop_all()``
operations. If the type is not associated with any ``MetaData``
object, it will associate itself with each ``Table`` in which it is
used, and will be created when any of those individual tables are
created, after a check is performed for it's existence. The type is
only dropped when ``drop_all()`` is called for that ``Table``
object's metadata, however.
:param 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.
:param native_enum: Use the database's native ENUM type when
available. Defaults to True. When False, uses VARCHAR + check
constraint for all backends.
:param schema: Schemaname 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.
:param quote: Force quoting to be on or off on the type's name. If
left as the default of `None`, the usual schema-level "case
sensitive"/"reserved name" rules are used to determine if this
type's name should be quoted.
"""
self.enums = enums
self.native_enum = kw.pop('native_enum', True)
convert_unicode= kw.pop('convert_unicode', None)
if convert_unicode is None:
for e in enums:
if isinstance(e, unicode):
convert_unicode = True
break
else:
convert_unicode = False
if self.enums:
length =max(len(x) for x in self.enums)
else:
length = 0
String.__init__(self,
length =length,
convert_unicode=convert_unicode,
)
SchemaType.__init__(self, **kw)
def _should_create_constraint(self, compiler):
return not self.native_enum or \
not compiler.dialect.supports_native_enum
def _set_table(self, column, table):
if self.native_enum:
SchemaType._set_table(self, column, table)
e = schema.CheckConstraint(
column.in_(self.enums),
name=self.name,
_create_rule=util.portable_instancemethod(
self._should_create_constraint)
)
table.append_constraint(e)
def adapt(self, impltype, **kw):
if issubclass(impltype, Enum):
return impltype(name=self.name,
quote=self.quote,
schema=self.schema,
metadata=self.metadata,
convert_unicode=self.convert_unicode,
native_enum=self.native_enum,
*self.enums,
**kw
)
else:
return super(Enum, self).adapt(impltype, **kw)
class PickleType(MutableType, 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.
"""
impl = LargeBinary
def __init__(self, protocol=pickle.HIGHEST_PROTOCOL,
pickler=None, mutable=False, comparator=None):
"""
Construct a PickleType.
:param protocol: defaults to ``pickle.HIGHEST_PROTOCOL``.
:param pickler: defaults to cPickle.pickle or pickle.pickle if
cPickle is not available. May be any object with
pickle-compatible ``dumps` and ``loads`` methods.
:param mutable: defaults to False; implements
:meth:`AbstractType.is_mutable`. When ``True``, incoming
objects will be compared against copies of themselves
using the Python "equals" operator, unless the
``comparator`` argument is present. See
:class:`.MutableType` for details on "mutable" type
behavior. (default changed from ``True`` in
0.7.0).
.. note::
This functionality is now superseded by the
``sqlalchemy.ext.mutable`` extension described in
:ref:`mutable_toplevel`.
:param 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.
"""
self.protocol = protocol
self.pickler = pickler or pickle
self.mutable = mutable
self.comparator = comparator
super(PickleType, self).__init__()
def __reduce__(self):
return PickleType, (self.protocol,
None,
self.mutable,
self.comparator)
def bind_processor(self, dialect):
impl_processor = self.impl.bind_processor(dialect)
dumps = self.pickler.dumps
protocol = self.protocol
if impl_processor:
def process(value):
if value is not None:
value = dumps(value, protocol)
return impl_processor(value)
else:
def process(value):
if value is not None:
value = dumps(value, protocol)
return value
return process
def result_processor(self, dialect, coltype):
impl_processor = self.impl.result_processor(dialect, coltype)
loads = self.pickler.loads
if impl_processor:
def process(value):
value = impl_processor(value)
if value is None:
return None
return loads(value)
else:
def process(value):
if value is None:
return None
return loads(value)
return process
def copy_value(self, value):
if self.mutable:
return self.pickler.loads(
self.pickler.dumps(value, self.protocol))
else:
return value
def compare_values(self, x, y):
if self.comparator:
return self.comparator(x, y)
else:
return x == y
def is_mutable(self):
"""Return True if the target Python type is 'mutable'.
When this method is overridden, :meth:`copy_value` should
also be supplied. The :class:`.MutableType` mixin
is recommended as a helper.
"""
return self.mutable
class Boolean(TypeEngine, SchemaType):
"""A bool datatype.
Boolean typically uses BOOLEAN or SMALLINT on the DDL side, and on
the Python side deals in ``True`` or ``False``.
"""
__visit_name__ = 'boolean'
def __init__(self, create_constraint=True, name=None):
"""Construct a Boolean.
:param create_constraint: defaults to True. 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.
:param name: if a CHECK constraint is generated, specify
the name of the constraint.
"""
self.create_constraint = create_constraint
self.name = name
def _should_create_constraint(self, compiler):
return not compiler.dialect.supports_native_boolean
def _set_table(self, column, table):
if not self.create_constraint:
return
e = schema.CheckConstraint(
column.in_([0, 1]),
name=self.name,
_create_rule=util.portable_instancemethod(
self._should_create_constraint)
)
table.append_constraint(e)
@property
def python_type(self):
return bool
def bind_processor(self, dialect):
if dialect.supports_native_boolean:
return None
else:
return processors.boolean_to_int
def result_processor(self, dialect, coltype):
if dialect.supports_native_boolean:
return None
else:
return processors.int_to_boolean
class Interval(_DateAffinity, 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
:attr:`~sqlalchemy.sql.expression.func`).
"""
impl = DateTime
epoch = dt.datetime.utcfromtimestamp(0)
def __init__(self, native=True,
second_precision=None,
day_precision=None):
"""Construct an Interval object.
:param 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.
:param second_precision: For native interval types
which support a "fractional seconds precision" parameter,
i.e. Oracle and Postgresql
:param day_precision: for native interval types which
support a "day precision" parameter, i.e. Oracle.
"""
super(Interval, self).__init__()
self.native = native
self.second_precision = second_precision
self.day_precision = day_precision
def adapt(self, cls, **kw):
if self.native and hasattr(cls, '_adapt_from_generic_interval'):
return cls._adapt_from_generic_interval(self, **kw)
else:
return self.__class__(
native=self.native,
second_precision=self.second_precision,
day_precision=self.day_precision,
**kw)
@property
def python_type(self):
return dt.timedelta
def bind_processor(self, dialect):
impl_processor = self.impl.bind_processor(dialect)
epoch = self.epoch
if impl_processor:
def process(value):
if value is not None:
value = epoch + value
return impl_processor(value)
else:
def process(value):
if value is not None:
value = epoch + value
return value
return process
def result_processor(self, dialect, coltype):
impl_processor = self.impl.result_processor(dialect, coltype)
epoch = self.epoch
if impl_processor:
def process(value):
value = impl_processor(value)
if value is None:
return None
return value - epoch
else:
def process(value):
if value is None:
return None
return value - epoch
return process
@util.memoized_property
def _expression_adaptations(self):
return {
operators.add:{
Date:DateTime,
Interval:Interval,
DateTime:DateTime,
Time:Time,
},
operators.sub:{
Interval:Interval
},
operators.mul:{
Numeric:Interval
},
operators.truediv: {
Numeric:Interval
},
# Py2K
operators.div: {
Numeric:Interval
}
# end Py2K
}
@property
def _type_affinity(self):
return Interval
def _coerce_compared_value(self, op, value):
"""See :meth:`.TypeEngine._coerce_compared_value` for a description."""
return self.impl._coerce_compared_value(op, value)
class REAL(Float):
"""The SQL REAL type."""
__visit_name__ = 'REAL'
class FLOAT(Float):
"""The SQL FLOAT type."""
__visit_name__ = 'FLOAT'
class NUMERIC(Numeric):
"""The SQL NUMERIC type."""
__visit_name__ = 'NUMERIC'
class DECIMAL(Numeric):
"""The SQL DECIMAL type."""
__visit_name__ = 'DECIMAL'
class INTEGER(Integer):
"""The SQL INT or INTEGER type."""
__visit_name__ = 'INTEGER'
INT = INTEGER
class SMALLINT(SmallInteger):
"""The SQL SMALLINT type."""
__visit_name__ = 'SMALLINT'
class BIGINT(BigInteger):
"""The SQL BIGINT type."""
__visit_name__ = 'BIGINT'
class TIMESTAMP(DateTime):
"""The SQL TIMESTAMP type."""
__visit_name__ = 'TIMESTAMP'
def get_dbapi_type(self, dbapi):
return dbapi.TIMESTAMP
class DATETIME(DateTime):
"""The SQL DATETIME type."""
__visit_name__ = 'DATETIME'
class DATE(Date):
"""The SQL DATE type."""
__visit_name__ = 'DATE'
class TIME(Time):
"""The SQL TIME type."""
__visit_name__ = 'TIME'
class TEXT(Text):
"""The SQL TEXT type."""
__visit_name__ = 'TEXT'
class CLOB(Text):
"""The CLOB type.
This type is found in Oracle and Informix.
"""
__visit_name__ = 'CLOB'
class VARCHAR(String):
"""The SQL VARCHAR type."""
__visit_name__ = 'VARCHAR'
class NVARCHAR(Unicode):
"""The SQL NVARCHAR type."""
__visit_name__ = 'NVARCHAR'
class CHAR(String):
"""The SQL CHAR type."""
__visit_name__ = 'CHAR'
class NCHAR(Unicode):
"""The SQL NCHAR type."""
__visit_name__ = 'NCHAR'
class BLOB(LargeBinary):
"""The SQL BLOB type."""
__visit_name__ = 'BLOB'
class BINARY(_Binary):
"""The SQL BINARY type."""
__visit_name__ = 'BINARY'
class VARBINARY(_Binary):
"""The SQL VARBINARY type."""
__visit_name__ = 'VARBINARY'
class BOOLEAN(Boolean):
"""The SQL BOOLEAN type."""
__visit_name__ = 'BOOLEAN'
NULLTYPE = NullType()
BOOLEANTYPE = Boolean()
STRINGTYPE = String()
_type_map = {
str: String(),
# Py3K
#bytes : LargeBinary(),
# Py2K
unicode : Unicode(),
# end Py2K
int : Integer(),
float : Numeric(),
bool: BOOLEANTYPE,
decimal.Decimal : Numeric(),
dt.date : Date(),
dt.datetime : DateTime(),
dt.time : Time(),
dt.timedelta : Interval(),
NoneType: NULLTYPE
}