# sqlalchemy/types.py # Copyright (C) 2005-2012 the SQLAlchemy authors and contributors # # 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 , 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 , 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 `_ 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 }