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dibbler/sqlalchemy/sql/sqltypes.py

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Updated SqlAlchemy + the new files
2017-04-15 18:33:29 +02:00
# sql/sqltypes.py
# Copyright (C) 2005-2017 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
import collections
import json
from . import elements
from .type_api import TypeEngine, TypeDecorator, to_instance, Variant
from .elements import quoted_name, TypeCoerce as type_coerce, _defer_name, \
Slice, _literal_as_binds
from .. import exc, util, processors
from .base import _bind_or_error, SchemaEventTarget
from . import operators
from .. import inspection
from .. import event
from ..util import pickle
from ..util import compat
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 super(Concatenable.Comparator, self)._adapt_expression(
op, other_comparator)
comparator_factory = Comparator
class Indexable(object):
"""A mixin that marks a type as supporting indexing operations,
such as array or JSON structures.
.. versionadded:: 1.1.0
"""
class Comparator(TypeEngine.Comparator):
def _setup_getitem(self, index):
raise NotImplementedError()
def __getitem__(self, index):
adjusted_op, adjusted_right_expr, result_type = \
self._setup_getitem(index)
return self.operate(
adjusted_op,
adjusted_right_expr,
result_type=result_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_limited(
"Unicode type received non-unicode "
"bind param value %r.",
(util.ellipses_string(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_limited(
"Unicode type received non-unicode bind "
"param value %r.",
(util.ellipses_string(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 in ('force', 'force_nocheck'))
needs_isinstance = (
needs_convert and
dialect.returns_unicode_strings and
self.convert_unicode != 'force_nocheck'
)
if needs_convert:
if needs_isinstance:
return processors.to_conditional_unicode_processor_factory(
dialect.encoding, self.unicode_error)
else:
return processors.to_unicode_processor_factory(
dialect.encoding, self.unicode_error)
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, such as ``NUMERIC`` or ``DECIMAL``.
This type returns Python ``decimal.Decimal`` objects by default, unless
the :paramref:`.Numeric.asdecimal` flag is set to False, in which case
they are coerced to Python ``float`` objects.
.. note::
The :class:`.Numeric` type is designed to receive data from a database
type that is explicitly known to be a decimal type
(e.g. ``DECIMAL``, ``NUMERIC``, others) and not a floating point
type (e.g. ``FLOAT``, ``REAL``, others).
If the database column on the server is in fact a floating-point type
type, such as ``FLOAT`` or ``REAL``, use the :class:`.Float`
type or a subclass, otherwise numeric coercion between
``float``/``Decimal`` may or may not function as expected.
.. note::
The Python ``decimal.Decimal`` class is generally slow
performing; cPython 3.3 has now switched to use the `cdecimal
<http://pypi.python.org/pypi/cdecimal/>`_ library natively. For
older Python versions, the ``cdecimal`` library can be patched
into any application where it will replace the ``decimal``
library fully, however this needs to be applied globally and
before any other modules have been imported, as follows::
import sys
import cdecimal
sys.modules["decimal"] = cdecimal
Note that the ``cdecimal`` and ``decimal`` libraries are **not
compatible with each other**, so patching ``cdecimal`` at the
global level is the only way it can be used effectively with
various DBAPIs that hardcode to import the ``decimal`` library.
"""
__visit_name__ = 'numeric'
_default_decimal_return_scale = 10
def __init__(self, precision=None, scale=None,
decimal_return_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.
:param decimal_return_scale: Default scale to use when converting
from floats to Python decimals. Floating point values will typically
be much longer due to decimal inaccuracy, and most floating point
database types don't have a notion of "scale", so by default the
float type looks for the first ten decimal places when converting.
Specfiying this value will override that length. Types which
do include an explicit ".scale" value, such as the base
:class:`.Numeric` as well as the MySQL float types, will use the
value of ".scale" as the default for decimal_return_scale, if not
otherwise specified.
.. versionadded:: 0.9.0
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.decimal_return_scale = decimal_return_scale
self.asdecimal = asdecimal
@property
def _effective_decimal_return_scale(self):
if self.decimal_return_scale is not None:
return self.decimal_return_scale
elif getattr(self, "scale", None) is not None:
return self.scale
else:
return self._default_decimal_return_scale
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.
return processors.to_decimal_processor_factory(
decimal.Decimal,
self.scale if self.scale is not None
else self._default_decimal_return_scale)
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):
"""Type representing floating point types, such as ``FLOAT`` or ``REAL``.
This type returns Python ``float`` objects by default, unless the
:paramref:`.Float.asdecimal` flag is set to True, in which case they
are coerced to ``decimal.Decimal`` objects.
.. note::
The :class:`.Float` type is designed to receive data from a database
type that is explicitly known to be a floating point type
(e.g. ``FLOAT``, ``REAL``, others)
and not a decimal type (e.g. ``DECIMAL``, ``NUMERIC``, others).
If the database column on the server is in fact a Numeric
type, such as ``DECIMAL`` or ``NUMERIC``, use the :class:`.Numeric`
type or a subclass, otherwise numeric coercion between
``float``/``Decimal`` may or may not function as expected.
"""
__visit_name__ = 'float'
scale = None
def __init__(self, precision=None, asdecimal=False,
decimal_return_scale=None, **kwargs):
r"""
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 decimal_return_scale: Default scale to use when converting
from floats to Python decimals. Floating point values will typically
be much longer due to decimal inaccuracy, and most floating point
database types don't have a notion of "scale", so by default the
float type looks for the first ten decimal places when converting.
Specfiying this value will override that length. Note that the
MySQL float types, which do include "scale", will use "scale"
as the default for decimal_return_scale, if not otherwise specified.
.. versionadded:: 0.9.0
: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
self.decimal_return_scale = decimal_return_scale
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,
self._effective_decimal_return_scale)
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.
For the time representation within the datetime type, some
backends include additional options, such as timezone support and
fractional seconds support. For fractional seconds, use the
dialect-specific datatype, such as :class:`.mysql.TIME`. For
timezone support, use at least the :class:`~.types.TIMESTAMP` datatype,
if not the dialect-specific datatype object.
"""
__visit_name__ = 'datetime'
def __init__(self, timezone=False):
"""Construct a new :class:`.DateTime`.
:param timezone: boolean. Indicates that the datetime type should
enable timezone support, if available on the
**base date/time-holding type only**. It is recommended
to make use of the :class:`~.types.TIMESTAMP` datatype directly when
using this flag, as some databases include separate generic
date/time-holding types distinct from the timezone-capable
TIMESTAMP datatype, such as Oracle.
"""
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(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):
if dialect.dbapi is None:
return None
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 :class:`.LargeBinary` type corresponds to a large and/or unlengthed
binary type for the target platform, such as BLOB on MySQL and BYTEA for
PostgreSQL. It also handles the necessary conversions for the DBAPI.
"""
__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 binary types that accept a length,
such as the MySQL BLOB type.
"""
_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, name=None, schema=None, metadata=None,
inherit_schema=False, quote=None, _create_events=True):
if name is not None:
self.name = quoted_name(name, quote)
else:
self.name = None
self.schema = schema
self.metadata = metadata
self.inherit_schema = inherit_schema
self._create_events = _create_events
if _create_events and 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 _translate_schema(self, effective_schema, map_):
return map_.get(effective_schema, effective_schema)
def _set_parent(self, column):
column._on_table_attach(util.portable_instancemethod(self._set_table))
def _variant_mapping_for_set_table(self, column):
if isinstance(column.type, Variant):
variant_mapping = column.type.mapping.copy()
variant_mapping['_default'] = column.type.impl
else:
variant_mapping = None
return variant_mapping
def _set_table(self, column, table):
if self.inherit_schema:
self.schema = table.schema
if not self._create_events:
return
variant_mapping = self._variant_mapping_for_set_table(column)
event.listen(
table,
"before_create",
util.portable_instancemethod(
self._on_table_create,
{"variant_mapping": variant_mapping})
)
event.listen(
table,
"after_drop",
util.portable_instancemethod(
self._on_table_drop,
{"variant_mapping": variant_mapping})
)
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,
{"variant_mapping": variant_mapping})
)
event.listen(
table.metadata,
"after_drop",
util.portable_instancemethod(
self._on_metadata_drop,
{"variant_mapping": variant_mapping})
)
def copy(self, **kw):
return self.adapt(self.__class__, _create_events=True)
def adapt(self, impltype, **kw):
schema = kw.pop('schema', self.schema)
metadata = kw.pop('metadata', self.metadata)
_create_events = kw.pop('_create_events', False)
return impltype(name=self.name,
schema=schema,
inherit_schema=self.inherit_schema,
metadata=metadata,
_create_events=_create_events,
**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):
if not self._is_impl_for_variant(bind.dialect, kw):
return
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):
if not self._is_impl_for_variant(bind.dialect, kw):
return
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):
if not self._is_impl_for_variant(bind.dialect, kw):
return
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):
if not self._is_impl_for_variant(bind.dialect, kw):
return
t = self.dialect_impl(bind.dialect)
if t.__class__ is not self.__class__ and isinstance(t, SchemaType):
t._on_metadata_drop(target, bind, **kw)
def _is_impl_for_variant(self, dialect, kw):
variant_mapping = kw.pop('variant_mapping', None)
if variant_mapping is None:
return True
if dialect.name in variant_mapping and \
variant_mapping[dialect.name] is self:
return True
elif dialect.name not in variant_mapping:
return variant_mapping['_default'] is self
class Enum(String, SchemaType):
"""Generic Enum Type.
The :class:`.Enum` type provides a set of possible string values
which the column is constrained towards.
The :class:`.Enum` type will make use of the backend's native "ENUM"
type if one is available; otherwise, it uses a VARCHAR datatype and
produces a CHECK constraint. Use of the backend-native enum type
can be disabled using the :paramref:`.Enum.native_enum` flag, and
the production of the CHECK constraint is configurable using the
:paramref:`.Enum.create_constraint` flag.
The :class:`.Enum` type also provides in-Python validation of string
values during both read and write operations. When reading a value
from the database in a result set, the string value is always checked
against the list of possible values and a ``LookupError`` is raised
if no match is found. When passing a value to the database as a
plain string within a SQL statement, if the
:paramref:`.Enum.validate_strings` parameter is
set to True, a ``LookupError`` is raised for any string value that's
not located in the given list of possible values; note that this
impacts usage of LIKE expressions with enumerated values (an unusual
use case).
.. versionchanged:: 1.1 the :class:`.Enum` type now provides in-Python
validation of input values as well as on data being returned by
the database.
The source of enumerated values may be a list of string values, or
alternatively a PEP-435-compliant enumerated class. For the purposes
of the :class:`.Enum` datatype, this class need only provide a
``__members__`` method.
When using an enumerated class, the enumerated objects are used
both for input and output, rather than strings as is the case with
a plain-string enumerated type::
import enum
class MyEnum(enum.Enum):
one = 1
two = 2
three = 3
t = Table(
'data', MetaData(),
Column('value', Enum(MyEnum))
)
connection.execute(t.insert(), {"value": MyEnum.two})
assert connection.scalar(t.select()) is MyEnum.two
Above, the string names of each element, e.g. "one", "two", "three",
are persisted to the database; the values of the Python Enum, here
indicated as integers, are **not** used; the value of each enum can
therefore be any kind of Python object whether or not it is persistable.
.. versionadded:: 1.1 - support for PEP-435-style enumerated
classes.
.. seealso::
:class:`~.postgresql.ENUM` - PostgreSQL-specific type,
which has additional functionality.
"""
__visit_name__ = 'enum'
def __init__(self, *enums, **kw):
r"""Construct an enum.
Keyword arguments which don't apply to a specific backend are ignored
by that backend.
:param \*enums: either exactly one PEP-435 compliant enumerated type
or one or more string or unicode enumeration labels. If unicode
labels are present, the `convert_unicode` flag is auto-enabled.
.. versionadded:: 1.1 a PEP-435 style enumerated class may be
passed.
: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 create_constraint: defaults to True. When creating a non-native
enumerated type, also build a CHECK constraint on the database
against the valid values.
.. versionadded:: 1.1 - added :paramref:`.Enum.create_constraint`
which provides the option to disable the production of the
CHECK constraint for a non-native enumerated type.
: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 its 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. If a PEP-435 enumerated
class was used, its name (converted to lower case) is used by
default.
: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.
:param validate_strings: when True, string values that are being
passed to the database in a SQL statement will be checked
for validity against the list of enumerated values. Unrecognized
values will result in a ``LookupError`` being raised.
.. versionadded:: 1.1.0b2
"""
values, objects = self._parse_into_values(enums, kw)
self._setup_for_values(values, objects, kw)
self.native_enum = kw.pop('native_enum', True)
convert_unicode = kw.pop('convert_unicode', None)
self.create_constraint = kw.pop('create_constraint', True)
self.validate_strings = kw.pop('validate_strings', False)
if convert_unicode is None:
for e in self.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
self._valid_lookup[None] = self._object_lookup[None] = None
String.__init__(self,
length=length,
convert_unicode=convert_unicode,
)
SchemaType.__init__(self, **kw)
def _parse_into_values(self, enums, kw):
if len(enums) == 1 and hasattr(enums[0], '__members__'):
self.enum_class = enums[0]
values = list(self.enum_class.__members__)
objects = [self.enum_class.__members__[k] for k in values]
kw.setdefault('name', self.enum_class.__name__.lower())
return values, objects
else:
self.enum_class = None
return enums, enums
def _setup_for_values(self, values, objects, kw):
self.enums = list(values)
self._valid_lookup = dict(
zip(objects, values)
)
self._object_lookup = dict(
(value, key) for key, value in self._valid_lookup.items()
)
self._valid_lookup.update(
[(value, value) for value in self._valid_lookup.values()]
)
def _db_value_for_elem(self, elem):
try:
return self._valid_lookup[elem]
except KeyError:
# for unknown string values, we return as is. While we can
# validate these if we wanted, that does not allow for lesser-used
# end-user use cases, such as using a LIKE comparison with an enum,
# or for an application that wishes to apply string tests to an
# ENUM (see [ticket:3725]). While we can decide to differentiate
# here between an INSERT statement and a criteria used in a SELECT,
# for now we're staying conservative w/ behavioral changes (perhaps
# someone has a trigger that handles strings on INSERT)
if not self.validate_strings and \
isinstance(elem, compat.string_types):
return elem
else:
raise LookupError(
'"%s" is not among the defined enum values' % elem)
class Comparator(String.Comparator):
def _adapt_expression(self, op, other_comparator):
op, typ = super(Enum.Comparator, self)._adapt_expression(
op, other_comparator)
if op is operators.concat_op:
typ = String(
self.type.length,
convert_unicode=self.type.convert_unicode)
return op, typ
comparator_factory = Comparator
def _object_value_for_elem(self, elem):
try:
return self._object_lookup[elem]
except KeyError:
raise LookupError(
'"%s" is not among the defined enum values' % elem)
def __repr__(self):
return util.generic_repr(self,
additional_kw=[('native_enum', True)],
to_inspect=[Enum, SchemaType],
)
def _should_create_constraint(self, compiler, **kw):
if not self._is_impl_for_variant(compiler.dialect, kw):
return False
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)
if not self.create_constraint:
return
variant_mapping = self._variant_mapping_for_set_table(column)
e = schema.CheckConstraint(
type_coerce(column, self).in_(self.enums),
name=_defer_name(self.name),
_create_rule=util.portable_instancemethod(
self._should_create_constraint,
{"variant_mapping": variant_mapping}),
_type_bound=True
)
assert e.table is table
def copy(self, **kw):
return SchemaType.copy(self, **kw)
def adapt(self, impltype, **kw):
schema = kw.pop('schema', self.schema)
metadata = kw.pop('metadata', self.metadata)
_create_events = kw.pop('_create_events', False)
if issubclass(impltype, Enum):
if self.enum_class is not None:
args = [self.enum_class]
else:
args = self.enums
return impltype(name=self.name,
schema=schema,
metadata=metadata,
convert_unicode=self.convert_unicode,
native_enum=self.native_enum,
inherit_schema=self.inherit_schema,
validate_strings=self.validate_strings,
_create_events=_create_events,
*args,
**kw)
else:
# TODO: why would we be here?
return super(Enum, self).adapt(impltype, **kw)
def literal_processor(self, dialect):
parent_processor = super(Enum, self).literal_processor(dialect)
def process(value):
value = self._db_value_for_elem(value)
if parent_processor:
value = parent_processor(value)
return value
return process
def bind_processor(self, dialect):
def process(value):
value = self._db_value_for_elem(value)
if parent_processor:
value = parent_processor(value)
return value
parent_processor = super(Enum, self).bind_processor(dialect)
return process
def result_processor(self, dialect, coltype):
parent_processor = super(Enum, self).result_processor(
dialect, coltype)
def process(value):
if parent_processor:
value = parent_processor(value)
value = self._object_value_for_elem(value)
return value
return process
@property
def python_type(self):
if self.enum_class:
return self.enum_class
else:
return super(Enum, self).python_type
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, _create_events=True):
"""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
self._create_events = _create_events
def _should_create_constraint(self, compiler, **kw):
if not self._is_impl_for_variant(compiler.dialect, kw):
return False
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
variant_mapping = self._variant_mapping_for_set_table(column)
e = schema.CheckConstraint(
type_coerce(column, self).in_([0, 1]),
name=_defer_name(self.name),
_create_rule=util.portable_instancemethod(
self._should_create_constraint,
{"variant_mapping": variant_mapping}),
_type_bound=True
)
assert e.table is table
@property
def python_type(self):
return bool
def literal_processor(self, dialect):
if dialect.supports_native_boolean:
def process(value):
return "true" if value else "false"
else:
def process(value):
return str(1 if value else 0)
return process
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 JSON(Indexable, TypeEngine):
"""Represent a SQL JSON type.
.. note:: :class:`.types.JSON` is provided as a facade for vendor-specific
JSON types. Since it supports JSON SQL operations, it only
works on backends that have an actual JSON type, currently
PostgreSQL as well as certain versions of MySQL.
:class:`.types.JSON` is part of the Core in support of the growing
popularity of native JSON datatypes.
The :class:`.types.JSON` type stores arbitrary JSON format data, e.g.::
data_table = Table('data_table', metadata,
Column('id', Integer, primary_key=True),
Column('data', JSON)
)
with engine.connect() as conn:
conn.execute(
data_table.insert(),
data = {"key1": "value1", "key2": "value2"}
)
The base :class:`.types.JSON` provides these two operations:
* Keyed index operations::
data_table.c.data['some key']
* Integer index operations::
data_table.c.data[3]
* Path index operations::
data_table.c.data[('key_1', 'key_2', 5, ..., 'key_n')]
Additional operations are available from the dialect-specific versions
of :class:`.types.JSON`, such as :class:`.postgresql.JSON` and
:class:`.postgresql.JSONB`, each of which offer more operators than
just the basic type.
Index operations return an expression object whose type defaults to
:class:`.JSON` by default, so that further JSON-oriented instructions
may be called upon the result type. Note that there are backend-specific
idiosyncracies here, including that the Postgresql database does not generally
compare a "json" to a "json" structure without type casts. These idiosyncracies
can be accommodated in a backend-neutral way by by making explicit use
of the :func:`.cast` and :func:`.type_coerce` constructs.
Comparison of specific index elements of a :class:`.JSON` object
to other objects work best if the **left hand side is CAST to a string**
and the **right hand side is rendered as a json string**; a future SQLAlchemy
feature such as a generic "astext" modifier may simplify this at some point:
* **Compare an element of a JSON structure to a string**::
from sqlalchemy import cast, type_coerce
from sqlalchemy import String, JSON
cast(
data_table.c.data['some_key'], String
) == '"some_value"'
cast(
data_table.c.data['some_key'], String
) == type_coerce("some_value", JSON)
* **Compare an element of a JSON structure to an integer**::
from sqlalchemy import cast, type_coerce
from sqlalchemy import String, JSON
cast(data_table.c.data['some_key'], String) == '55'
cast(
data_table.c.data['some_key'], String
) == type_coerce(55, JSON)
* **Compare an element of a JSON structure to some other JSON structure** - note
that Python dictionaries are typically not ordered so care should be taken
here to assert that the JSON structures are identical::
from sqlalchemy import cast, type_coerce
from sqlalchemy import String, JSON
import json
cast(
data_table.c.data['some_key'], String
) == json.dumps({"foo": "bar"})
cast(
data_table.c.data['some_key'], String
) == type_coerce({"foo": "bar"}, JSON)
The :class:`.JSON` type, when used with the SQLAlchemy ORM, does not
detect in-place mutations to the structure. In order to detect these, the
:mod:`sqlalchemy.ext.mutable` extension must be used. This extension will
allow "in-place" changes to the datastructure to produce events which
will be detected by the unit of work. See the example at :class:`.HSTORE`
for a simple example involving a dictionary.
When working with NULL values, the :class:`.JSON` type recommends the
use of two specific constants in order to differentiate between a column
that evaluates to SQL NULL, e.g. no value, vs. the JSON-encoded string
of ``"null"``. To insert or select against a value that is SQL NULL,
use the constant :func:`.null`::
from sqlalchemy import null
conn.execute(table.insert(), json_value=null())
To insert or select against a value that is JSON ``"null"``, use the
constant :attr:`.JSON.NULL`::
conn.execute(table.insert(), json_value=JSON.NULL)
The :class:`.JSON` type supports a flag
:paramref:`.JSON.none_as_null` which when set to True will result
in the Python constant ``None`` evaluating to the value of SQL
NULL, and when set to False results in the Python constant
``None`` evaluating to the value of JSON ``"null"``. The Python
value ``None`` may be used in conjunction with either
:attr:`.JSON.NULL` and :func:`.null` in order to indicate NULL
values, but care must be taken as to the value of the
:paramref:`.JSON.none_as_null` in these cases.
.. seealso::
:class:`.postgresql.JSON`
:class:`.postgresql.JSONB`
:class:`.mysql.JSON`
.. versionadded:: 1.1
"""
__visit_name__ = 'JSON'
hashable = False
NULL = util.symbol('JSON_NULL')
"""Describe the json value of NULL.
This value is used to force the JSON value of ``"null"`` to be
used as the value. A value of Python ``None`` will be recognized
either as SQL NULL or JSON ``"null"``, based on the setting
of the :paramref:`.JSON.none_as_null` flag; the :attr:`.JSON.NULL`
constant can be used to always resolve to JSON ``"null"`` regardless
of this setting. This is in contrast to the :func:`.sql.null` construct,
which always resolves to SQL NULL. E.g.::
from sqlalchemy import null
from sqlalchemy.dialects.postgresql import JSON
obj1 = MyObject(json_value=null()) # will *always* insert SQL NULL
obj2 = MyObject(json_value=JSON.NULL) # will *always* insert JSON string "null"
session.add_all([obj1, obj2])
session.commit()
"""
def __init__(self, none_as_null=False):
"""Construct a :class:`.types.JSON` type.
:param none_as_null=False: if True, persist the value ``None`` as a
SQL NULL value, not the JSON encoding of ``null``. Note that
when this flag is False, the :func:`.null` construct can still
be used to persist a NULL value::
from sqlalchemy import null
conn.execute(table.insert(), data=null())
.. note::
:paramref:`.JSON.none_as_null` does **not** apply to the
values passed to :paramref:`.Column.default` and
:paramref:`.Column.server_default`; a value of ``None`` passed for
these parameters means "no default present".
.. seealso::
:attr:`.types.JSON.NULL`
"""
self.none_as_null = none_as_null
class JSONElementType(TypeEngine):
"""common function for index / path elements in a JSON expression."""
_integer = Integer()
_string = String()
def string_bind_processor(self, dialect):
return self._string._cached_bind_processor(dialect)
def string_literal_processor(self, dialect):
return self._string._cached_literal_processor(dialect)
def bind_processor(self, dialect):
int_processor = self._integer._cached_bind_processor(dialect)
string_processor = self.string_bind_processor(dialect)
def process(value):
if int_processor and isinstance(value, int):
value = int_processor(value)
elif string_processor and isinstance(value, util.string_types):
value = string_processor(value)
return value
return process
def literal_processor(self, dialect):
int_processor = self._integer._cached_literal_processor(dialect)
string_processor = self.string_literal_processor(dialect)
def process(value):
if int_processor and isinstance(value, int):
value = int_processor(value)
elif string_processor and isinstance(value, util.string_types):
value = string_processor(value)
return value
return process
class JSONIndexType(JSONElementType):
"""Placeholder for the datatype of a JSON index value.
This allows execution-time processing of JSON index values
for special syntaxes.
"""
class JSONPathType(JSONElementType):
"""Placeholder type for JSON path operations.
This allows execution-time processing of a path-based
index value into a specific SQL syntax.
"""
class Comparator(Indexable.Comparator, Concatenable.Comparator):
"""Define comparison operations for :class:`.types.JSON`."""
@util.dependencies('sqlalchemy.sql.default_comparator')
def _setup_getitem(self, default_comparator, index):
if not isinstance(index, util.string_types) and \
isinstance(index, collections.Sequence):
index = default_comparator._check_literal(
self.expr, operators.json_path_getitem_op,
index, bindparam_type=JSON.JSONPathType
)
operator = operators.json_path_getitem_op
else:
index = default_comparator._check_literal(
self.expr, operators.json_getitem_op,
index, bindparam_type=JSON.JSONIndexType
)
operator = operators.json_getitem_op
return operator, index, self.type
comparator_factory = Comparator
@property
def python_type(self):
return dict
@property
def should_evaluate_none(self):
return not self.none_as_null
@util.memoized_property
def _str_impl(self):
return String(convert_unicode=True)
def bind_processor(self, dialect):
string_process = self._str_impl.bind_processor(dialect)
json_serializer = dialect._json_serializer or json.dumps
def process(value):
if value is self.NULL:
value = None
elif isinstance(value, elements.Null) or (
value is None and self.none_as_null
):
return None
serialized = json_serializer(value)
if string_process:
serialized = string_process(serialized)
return serialized
return process
def result_processor(self, dialect, coltype):
string_process = self._str_impl.result_processor(dialect, coltype)
json_deserializer = dialect._json_deserializer or json.loads
def process(value):
if value is None:
return None
if string_process:
value = string_process(value)
return json_deserializer(value)
return process
class ARRAY(Indexable, Concatenable, TypeEngine):
"""Represent a SQL Array type.
.. note:: This type serves as the basis for all ARRAY operations.
However, currently **only the PostgreSQL backend has support
for SQL arrays in SQLAlchemy**. It is recommended to use the
:class:`.postgresql.ARRAY` type directly when using ARRAY types
with PostgreSQL, as it provides additional operators specific
to that backend.
:class:`.types.ARRAY` is part of the Core in support of various SQL standard
functions such as :class:`.array_agg` which explicitly involve arrays;
however, with the exception of the PostgreSQL backend and possibly
some third-party dialects, no other SQLAlchemy built-in dialect has
support for this type.
An :class:`.types.ARRAY` type is constructed given the "type"
of element::
mytable = Table("mytable", metadata,
Column("data", ARRAY(Integer))
)
The above type represents an N-dimensional array,
meaning a supporting backend such as PostgreSQL will interpret values
with any number of dimensions automatically. To produce an INSERT
construct that passes in a 1-dimensional array of integers::
connection.execute(
mytable.insert(),
data=[1,2,3]
)
The :class:`.types.ARRAY` type can be constructed given a fixed number
of dimensions::
mytable = Table("mytable", metadata,
Column("data", ARRAY(Integer, dimensions=2))
)
Sending a number of dimensions is optional, but recommended if the
datatype is to represent arrays of more than one dimension. This number
is used:
* When emitting the type declaration itself to the database, e.g.
``INTEGER[][]``
* When translating Python values to database values, and vice versa, e.g.
an ARRAY of :class:`.Unicode` objects uses this number to efficiently
access the string values inside of array structures without resorting
to per-row type inspection
* When used with the Python ``getitem`` accessor, the number of dimensions
serves to define the kind of type that the ``[]`` operator should
return, e.g. for an ARRAY of INTEGER with two dimensions::
>>> expr = table.c.column[5] # returns ARRAY(Integer, dimensions=1)
>>> expr = expr[6] # returns Integer
For 1-dimensional arrays, an :class:`.types.ARRAY` instance with no
dimension parameter will generally assume single-dimensional behaviors.
SQL expressions of type :class:`.types.ARRAY` have support for "index" and
"slice" behavior. The Python ``[]`` operator works normally here, given
integer indexes or slices. Arrays default to 1-based indexing.
The operator produces binary expression
constructs which will produce the appropriate SQL, both for
SELECT statements::
select([mytable.c.data[5], mytable.c.data[2:7]])
as well as UPDATE statements when the :meth:`.Update.values` method
is used::
mytable.update().values({
mytable.c.data[5]: 7,
mytable.c.data[2:7]: [1, 2, 3]
})
The :class:`.types.ARRAY` type also provides for the operators
:meth:`.types.ARRAY.Comparator.any` and :meth:`.types.ARRAY.Comparator.all`.
The PostgreSQL-specific version of :class:`.types.ARRAY` also provides additional
operators.
.. versionadded:: 1.1.0
.. seealso::
:class:`.postgresql.ARRAY`
"""
__visit_name__ = 'ARRAY'
zero_indexes = False
"""if True, Python zero-based indexes should be interpreted as one-based
on the SQL expression side."""
class Comparator(Indexable.Comparator, Concatenable.Comparator):
"""Define comparison operations for :class:`.types.ARRAY`.
More operators are available on the dialect-specific form
of this type. See :class:`.postgresql.ARRAY.Comparator`.
"""
def _setup_getitem(self, index):
if isinstance(index, slice):
return_type = self.type
if self.type.zero_indexes:
index = slice(
index.start + 1,
index.stop + 1,
index.step
)
index = Slice(
_literal_as_binds(
index.start, name=self.expr.key,
type_=type_api.INTEGERTYPE),
_literal_as_binds(
index.stop, name=self.expr.key,
type_=type_api.INTEGERTYPE),
_literal_as_binds(
index.step, name=self.expr.key,
type_=type_api.INTEGERTYPE)
)
else:
if self.type.zero_indexes:
index += 1
if self.type.dimensions is None or self.type.dimensions == 1:
return_type = self.type.item_type
else:
adapt_kw = {'dimensions': self.type.dimensions - 1}
return_type = self.type.adapt(
self.type.__class__, **adapt_kw)
return operators.getitem, index, return_type
@util.dependencies("sqlalchemy.sql.elements")
def any(self, elements, other, operator=None):
"""Return ``other operator ANY (array)`` clause.
Argument places are switched, because ANY requires array
expression to be on the right hand-side.
E.g.::
from sqlalchemy.sql import operators
conn.execute(
select([table.c.data]).where(
table.c.data.any(7, operator=operators.lt)
)
)
:param other: expression to be compared
:param operator: an operator object from the
:mod:`sqlalchemy.sql.operators`
package, defaults to :func:`.operators.eq`.
.. seealso::
:func:`.sql.expression.any_`
:meth:`.types.ARRAY.Comparator.all`
"""
operator = operator if operator else operators.eq
return operator(
elements._literal_as_binds(other),
elements.CollectionAggregate._create_any(self.expr)
)
@util.dependencies("sqlalchemy.sql.elements")
def all(self, elements, other, operator=None):
"""Return ``other operator ALL (array)`` clause.
Argument places are switched, because ALL requires array
expression to be on the right hand-side.
E.g.::
from sqlalchemy.sql import operators
conn.execute(
select([table.c.data]).where(
table.c.data.all(7, operator=operators.lt)
)
)
:param other: expression to be compared
:param operator: an operator object from the
:mod:`sqlalchemy.sql.operators`
package, defaults to :func:`.operators.eq`.
.. seealso::
:func:`.sql.expression.all_`
:meth:`.types.ARRAY.Comparator.any`
"""
operator = operator if operator else operators.eq
return operator(
elements._literal_as_binds(other),
elements.CollectionAggregate._create_all(self.expr)
)
comparator_factory = Comparator
def __init__(self, item_type, as_tuple=False, dimensions=None,
zero_indexes=False):
"""Construct an :class:`.types.ARRAY`.
E.g.::
Column('myarray', ARRAY(Integer))
Arguments are:
:param item_type: The data type of items of this array. Note that
dimensionality is irrelevant here, so multi-dimensional arrays like
``INTEGER[][]``, are constructed as ``ARRAY(Integer)``, not as
``ARRAY(ARRAY(Integer))`` or such.
:param as_tuple=False: Specify whether return results
should be converted to tuples from lists. This parameter is
not generally needed as a Python list corresponds well
to a SQL array.
:param dimensions: if non-None, the ARRAY will assume a fixed
number of dimensions. This impacts how the array is declared
on the database, how it goes about interpreting Python and
result values, as well as how expression behavior in conjunction
with the "getitem" operator works. See the description at
:class:`.types.ARRAY` for additional detail.
:param zero_indexes=False: when True, index values will be converted
between Python zero-based and SQL one-based indexes, e.g.
a value of one will be added to all index values before passing
to the database.
"""
if isinstance(item_type, ARRAY):
raise ValueError("Do not nest ARRAY types; ARRAY(basetype) "
"handles multi-dimensional arrays of basetype")
if isinstance(item_type, type):
item_type = item_type()
self.item_type = item_type
self.as_tuple = as_tuple
self.dimensions = dimensions
self.zero_indexes = zero_indexes
@property
def hashable(self):
return self.as_tuple
@property
def python_type(self):
return list
def compare_values(self, x, y):
return x == y
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.
:class:`~.types.TIMESTAMP` datatypes have support for timezone
storage on some backends, such as PostgreSQL and Oracle. Use the
:paramref:`~types.TIMESTAMP.timezone` argument in order to enable
"TIMESTAMP WITH TIMEZONE" for these backends.
"""
__visit_name__ = 'TIMESTAMP'
def __init__(self, timezone=False):
"""Construct a new :class:`.TIMESTAMP`.
:param timezone: boolean. Indicates that the TIMESTAMP type should
enable timezone support, if available on the target database.
On a per-dialect basis is similar to "TIMESTAMP WITH TIMEZONE".
If the target database does not support timezones, this flag is
ignored.
"""
super(TIMESTAMP, self).__init__(timezone=timezone)
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 :exc:`.CompileError` 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
hashable = False
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
class MatchType(Boolean):
"""Refers to the return type of the MATCH operator.
As the :meth:`.ColumnOperators.match` is probably the most open-ended
operator in generic SQLAlchemy Core, we can't assume the return type
at SQL evaluation time, as MySQL returns a floating point, not a boolean,
and other backends might do something different. So this type
acts as a placeholder, currently subclassing :class:`.Boolean`.
The type allows dialects to inject result-processing functionality
if needed, and on MySQL will return floating-point values.
.. versionadded:: 1.0.0
"""
NULLTYPE = NullType()
BOOLEANTYPE = Boolean()
STRINGTYPE = String()
INTEGERTYPE = Integer()
MATCHTYPE = MatchType()
_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()
_type_map_get = _type_map.get
def _resolve_value_to_type(value):
_result_type = _type_map_get(type(value), False)
if _result_type is False:
# use inspect() to detect SQLAlchemy built-in
# objects.
insp = inspection.inspect(value, False)
if (
insp is not None and
# foil mock.Mock() and other impostors by ensuring
# the inspection target itself self-inspects
insp.__class__ in inspection._registrars
):
raise exc.ArgumentError(
"Object %r is not legal as a SQL literal value" % value)
return NULLTYPE
else:
return _result_type
# 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.MATCHTYPE = MATCHTYPE
type_api.INDEXABLE = Indexable
type_api._resolve_value_to_type = _resolve_value_to_type
TypeEngine.Comparator.BOOLEANTYPE = BOOLEANTYPE