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|
# sql/sqltypes.py
# Copyright (C) 2005-2013 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
"""SQL specific types.
"""
import datetime as dt
import codecs
from .type_api import TypeEngine, TypeDecorator, to_instance
from .elements import quoted_name, type_coerce
from .default_comparator import _DefaultColumnComparator
from .. import exc, util, processors
from .base import _bind_or_error, SchemaEventTarget
from . import operators
from .. import event
from ..util import pickle
import decimal
if util.jython:
import array
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()
class Comparator(TypeEngine.Comparator):
_blank_dict = util.immutabledict()
def _adapt_expression(self, op, other_comparator):
othertype = other_comparator.type._type_affinity
return op, \
to_instance(self.type._expression_adaptations.get(op, self._blank_dict).\
get(othertype, NULLTYPE))
comparator_factory = Comparator
class Concatenable(object):
"""A mixin that marks a type as supporting 'concatenation',
typically strings."""
class Comparator(TypeEngine.Comparator):
def _adapt_expression(self, op, other_comparator):
if op is operators.add and isinstance(other_comparator,
(Concatenable.Comparator, NullType.Comparator)):
return operators.concat_op, self.expr.type
else:
return op, self.expr.type
comparator_factory = Comparator
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, collation=None,
convert_unicode=False,
unicode_error=None,
_warn_on_bytestring=False
):
"""
Create a string-holding type.
:param length: optional, a length for the column for use in
DDL and CAST expressions. May be safely omitted if no ``CREATE
TABLE`` will be issued. Certain databases may require 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 collation: Optional, a column-level collation for
use in DDL and CAST expressions. Renders using the
COLLATE keyword supported by SQLite, MySQL, and Postgresql.
E.g.::
>>> from sqlalchemy import cast, select, String
>>> print select([cast('some string', String(collation='utf8'))])
SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1
.. versionadded:: 0.8 Added support for COLLATE to all
string types.
: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 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.")
self.length = length
self.collation = collation
self.convert_unicode = convert_unicode
self.unicode_error = unicode_error
self._warn_on_bytestring = _warn_on_bytestring
def literal_processor(self, dialect):
def process(value):
value = value.replace("'", "''")
return "'%s'" % value
return process
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):
if isinstance(value, util.binary_type):
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, util.text_type):
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, util.text_type):
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 util.text_type
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.) In general, TEXT objects
do not have a length; while some databases will accept a length
argument here, it will be rejected by others.
"""
__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
def literal_processor(self, dialect):
def process(value):
return str(value)
return process
@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: self.__class__,
Numeric: Numeric,
},
operators.mul: {
Interval: Interval,
Integer: self.__class__,
Numeric: Numeric,
},
operators.div: {
Integer: self.__class__,
Numeric: Numeric,
},
operators.truediv: {
Integer: self.__class__,
Numeric: Numeric,
},
operators.sub: {
Integer: self.__class__,
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. 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
def literal_processor(self, dialect):
def process(value):
return str(value)
return process
@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: self.__class__,
Integer: self.__class__,
},
operators.div: {
Numeric: self.__class__,
Integer: self.__class__,
},
operators.truediv: {
Numeric: self.__class__,
Integer: self.__class__,
},
operators.add: {
Numeric: self.__class__,
Integer: self.__class__,
},
operators.sub: {
Numeric: self.__class__,
Integer: self.__class__,
}
}
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: self.__class__,
},
operators.div: {
Numeric: self.__class__,
},
operators.truediv: {
Numeric: self.__class__,
},
operators.add: {
Numeric: self.__class__,
},
operators.sub: {
Numeric: self.__class__,
}
}
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: self.__class__,
},
operators.sub: {
Interval: self.__class__,
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: self.__class__,
Interval: DateTime,
Time: DateTime,
},
operators.sub: {
# date - integer = date
Integer: self.__class__,
# 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: self.__class__
},
operators.sub: {
Time: Interval,
Interval: self.__class__,
},
}
class _Binary(TypeEngine):
"""Define base behavior for binary types."""
def __init__(self, length=None):
self.length = length
def literal_processor(self, dialect):
def process(value):
value = value.decode(self.dialect.encoding).replace("'", "''")
return "'%s'" % value
return process
@property
def python_type(self):
return util.binary_type
# 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):
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,
# psycopg2 as of 2.5 returns 'memoryview'
if util.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
else:
def result_processor(self, dialect, coltype):
def process(value):
if value is not None:
value = bytes(value)
return value
return process
def coerce_compared_value(self, op, value):
"""See :meth:`.TypeEngine.coerce_compared_value` for a description."""
if isinstance(value, util.string_types):
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(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`.
.. seealso::
:class:`.Enum`
:class:`.Boolean`
"""
def __init__(self, **kw):
name = kw.pop('name', None)
if name is not None:
self.name = quoted_name(name, kw.pop('quote', None))
else:
self.name = None
self.schema = kw.pop('schema', None)
self.metadata = kw.pop('metadata', None)
self.inherit_schema = kw.pop('inherit_schema', False)
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):
if self.inherit_schema:
self.schema = table.schema
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)
)
def copy(self, **kw):
return self.adapt(self.__class__)
def adapt(self, impltype, **kw):
schema = kw.pop('schema', self.schema)
metadata = kw.pop('metadata', self.metadata)
return impltype(name=self.name,
schema=schema,
metadata=metadata,
inherit_schema=self.inherit_schema,
**kw
)
@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 = _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 = _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.
.. seealso::
: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: Schema name of this type. For types that exist on the
target database as an independent schema construct (Postgresql),
this parameter specifies the named schema in which the type is
present.
.. note::
The ``schema`` of the :class:`.Enum` type does not
by default make use of the ``schema`` established on the
owning :class:`.Table`. If this behavior is desired,
set the ``inherit_schema`` flag to ``True``.
:param quote: Set explicit quoting preferences for the type's name.
:param inherit_schema: When ``True``, the "schema" from the owning
:class:`.Table` will be copied to the "schema" attribute of this
:class:`.Enum`, replacing whatever value was passed for the
``schema`` attribute. This also takes effect when using the
:meth:`.Table.tometadata` operation.
.. versionadded:: 0.8
"""
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, util.text_type):
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 __repr__(self):
return util.generic_repr(self, [
("native_enum", True),
("name", None)
])
def _should_create_constraint(self, compiler):
return not self.native_enum or \
not compiler.dialect.supports_native_enum
@util.dependencies("sqlalchemy.sql.schema")
def _set_table(self, schema, column, table):
if self.native_enum:
SchemaType._set_table(self, column, table)
e = schema.CheckConstraint(
type_coerce(column, self).in_(self.enums),
name=self.name,
_create_rule=util.portable_instancemethod(
self._should_create_constraint)
)
table.append_constraint(e)
def adapt(self, impltype, **kw):
schema = kw.pop('schema', self.schema)
metadata = kw.pop('metadata', self.metadata)
if issubclass(impltype, Enum):
return impltype(name=self.name,
schema=schema,
metadata=metadata,
convert_unicode=self.convert_unicode,
native_enum=self.native_enum,
inherit_schema=self.inherit_schema,
*self.enums,
**kw
)
else:
return super(Enum, self).adapt(impltype, **kw)
class PickleType(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.
To allow ORM change events to propagate for elements associated
with :class:`.PickleType`, see :ref:`mutable_toplevel`.
"""
impl = LargeBinary
def __init__(self, protocol=pickle.HIGHEST_PROTOCOL,
pickler=None, 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 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.comparator = comparator
super(PickleType, self).__init__()
def __reduce__(self):
return PickleType, (self.protocol,
None,
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 compare_values(self, x, y):
if self.comparator:
return self.comparator(x, y)
else:
return x == y
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
@util.dependencies("sqlalchemy.sql.schema")
def _set_table(self, schema, column, table):
if not self.create_constraint:
return
e = schema.CheckConstraint(
type_coerce(column, self).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: self.__class__,
DateTime: DateTime,
Time: Time,
},
operators.sub: {
Interval: self.__class__
},
operators.mul: {
Numeric: self.__class__
},
operators.truediv: {
Numeric: self.__class__
},
operators.div: {
Numeric: self.__class__
}
}
@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'
class NullType(TypeEngine):
"""An unknown type.
:class:`.NullType` is used as a default type for those cases where
a type cannot be determined, including:
* During table reflection, when the type of a column is not recognized
by the :class:`.Dialect`
* When constructing SQL expressions using plain Python objects of
unknown types (e.g. ``somecolumn == my_special_object``)
* When a new :class:`.Column` is created, and the given type is passed
as ``None`` or is not passed at all.
The :class:`.NullType` can be used within SQL expression invocation
without issue, it just has no behavior either at the expression construction
level or at the bind-parameter/result processing level. :class:`.NullType`
will result in a :class:`.CompileException` if the compiler is asked to render
the type itself, such as if it is used in a :func:`.cast` operation
or within a schema creation operation such as that invoked by
:meth:`.MetaData.create_all` or the :class:`.CreateTable` construct.
"""
__visit_name__ = 'null'
_isnull = True
def literal_processor(self, dialect):
def process(value):
return "NULL"
return process
class Comparator(TypeEngine.Comparator):
def _adapt_expression(self, op, other_comparator):
if isinstance(other_comparator, NullType.Comparator) or \
not operators.is_commutative(op):
return op, self.expr.type
else:
return other_comparator._adapt_expression(op, self)
comparator_factory = Comparator
NULLTYPE = NullType()
BOOLEANTYPE = Boolean()
STRINGTYPE = String()
INTEGERTYPE = Integer()
_type_map = {
int: Integer(),
float: Numeric(),
bool: BOOLEANTYPE,
decimal.Decimal: Numeric(),
dt.date: Date(),
dt.datetime: DateTime(),
dt.time: Time(),
dt.timedelta: Interval(),
util.NoneType: NULLTYPE
}
if util.py3k:
_type_map[bytes] = LargeBinary()
_type_map[str] = Unicode()
else:
_type_map[unicode] = Unicode()
_type_map[str] = String()
# back-assign to type_api
from . import type_api
type_api.BOOLEANTYPE = BOOLEANTYPE
type_api.STRINGTYPE = STRINGTYPE
type_api.INTEGERTYPE = INTEGERTYPE
type_api.NULLTYPE = NULLTYPE
type_api._type_map = _type_map
# this one, there's all kinds of ways to play it, but at the EOD
# there's just a giant dependency cycle between the typing system and
# the expression element system, as you might expect. We can use
# importlaters or whatnot, but the typing system just necessarily has
# to have some kind of connection like this. right now we're injecting the
# _DefaultColumnComparator implementation into the TypeEngine.Comparator interface.
# Alternatively TypeEngine.Comparator could have an "impl" injected, though
# just injecting the base is simpler, error free, and more performant.
class Comparator(_DefaultColumnComparator):
BOOLEANTYPE = BOOLEANTYPE
TypeEngine.Comparator.__bases__ = (Comparator, ) + TypeEngine.Comparator.__bases__
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