# sql/sqltypes.py
# Copyright (C) 2005-2024 the SQLAlchemy authors and contributors
# <see AUTHORS file>
#
# This module is part of SQLAlchemy and is released under
# the MIT License: https://www.opensource.org/licenses/mit-license.php
# mypy: allow-untyped-defs, allow-untyped-calls
"""SQL specific types.
"""
from __future__ import annotations
import collections.abc as collections_abc
import datetime as dt
import decimal
import enum
import json
import pickle
from typing import Any
from typing import Callable
from typing import cast
from typing import Dict
from typing import List
from typing import Optional
from typing import overload
from typing import Sequence
from typing import Tuple
from typing import Type
from typing import TYPE_CHECKING
from typing import TypeVar
from typing import Union
from uuid import UUID as _python_UUID
from . import coercions
from . import elements
from . import operators
from . import roles
from . import type_api
from .base import _NONE_NAME
from .base import NO_ARG
from .base import SchemaEventTarget
from .cache_key import HasCacheKey
from .elements import quoted_name
from .elements import Slice
from .elements import TypeCoerce as type_coerce # noqa
from .type_api import Emulated
from .type_api import NativeForEmulated # noqa
from .type_api import to_instance as to_instance
from .type_api import TypeDecorator as TypeDecorator
from .type_api import TypeEngine as TypeEngine
from .type_api import TypeEngineMixin
from .type_api import Variant # noqa
from .visitors import InternalTraversal
from .. import event
from .. import exc
from .. import inspection
from .. import util
from ..engine import processors
from ..util import langhelpers
from ..util import OrderedDict
from ..util.typing import is_literal
from ..util.typing import Literal
from ..util.typing import typing_get_args
if TYPE_CHECKING:
from ._typing import _ColumnExpressionArgument
from ._typing import _TypeEngineArgument
from .operators import OperatorType
from .schema import MetaData
from .type_api import _BindProcessorType
from .type_api import _ComparatorFactory
from .type_api import _MatchedOnType
from .type_api import _ResultProcessorType
from ..engine.interfaces import Dialect
_T = TypeVar("_T", bound="Any")
_CT = TypeVar("_CT", bound=Any)
_TE = TypeVar("_TE", bound="TypeEngine[Any]")
class HasExpressionLookup(TypeEngineMixin):
"""Mixin expression adaptations based on lookup tables.
These rules are currently used by the numeric, integer and date types
which have detailed cross-expression coercion rules.
"""
@property
def _expression_adaptations(self):
raise NotImplementedError()
class Comparator(TypeEngine.Comparator[_CT]):
__slots__ = ()
_blank_dict = util.EMPTY_DICT
def _adapt_expression(
self,
op: OperatorType,
other_comparator: TypeEngine.Comparator[Any],
) -> Tuple[OperatorType, TypeEngine[Any]]:
othertype = other_comparator.type._type_affinity
if TYPE_CHECKING:
assert isinstance(self.type, HasExpressionLookup)
lookup = self.type._expression_adaptations.get(
op, self._blank_dict
).get(othertype, self.type)
if lookup is othertype:
return (op, other_comparator.type)
elif lookup is self.type._type_affinity:
return (op, self.type)
else:
return (op, to_instance(lookup))
comparator_factory: _ComparatorFactory[Any] = Comparator
class Concatenable(TypeEngineMixin):
"""A mixin that marks a type as supporting 'concatenation',
typically strings."""
class Comparator(TypeEngine.Comparator[_T]):
__slots__ = ()
def _adapt_expression(
self,
op: OperatorType,
other_comparator: TypeEngine.Comparator[Any],
) -> Tuple[OperatorType, TypeEngine[Any]]:
if op is operators.add and isinstance(
other_comparator,
(Concatenable.Comparator, NullType.Comparator),
):
return operators.concat_op, self.expr.type
else:
return super()._adapt_expression(op, other_comparator)
comparator_factory: _ComparatorFactory[Any] = Comparator
class Indexable(TypeEngineMixin):
"""A mixin that marks a type as supporting indexing operations,
such as array or JSON structures.
"""
class Comparator(TypeEngine.Comparator[_T]):
__slots__ = ()
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: _ComparatorFactory[Any] = Comparator
class String(Concatenable, TypeEngine[str]):
"""The base for all string and character types.
In SQL, corresponds to VARCHAR.
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: Optional[int] = None,
collation: Optional[str] = None,
):
"""
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.:
.. sourcecode:: pycon+sql
>>> from sqlalchemy import cast, select, String
>>> print(select(cast('some string', String(collation='utf8'))))
{printsql}SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1
.. note::
In most cases, the :class:`.Unicode` or :class:`.UnicodeText`
datatypes should be used for a :class:`_schema.Column` that expects
to store non-ascii data. These datatypes will ensure that the
correct types are used on the database.
"""
self.length = length
self.collation = collation
def _with_collation(self, collation):
new_type = self.copy()
new_type.collation = collation
return new_type
def _resolve_for_literal(self, value):
# I was SO PROUD of my regex trick, but we dont need it.
# re.search(r"[^\u0000-\u007F]", value)
if value.isascii():
return _STRING
else:
return _UNICODE
def literal_processor(self, dialect):
def process(value):
value = value.replace("'", "''")
if dialect.identifier_preparer._double_percents:
value = value.replace("%", "%%")
return "'%s'" % value
return process
def bind_processor(self, dialect):
return None
def result_processor(self, dialect, coltype):
return None
@property
def python_type(self):
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. 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 strings that may contain non-ASCII characters, and for
some backends implies an underlying column type that is explicitly
supporting of non-ASCII data, such as ``NVARCHAR`` on Oracle and SQL
Server. This will impact the output of ``CREATE TABLE`` statements and
``CAST`` functions at the dialect level.
The character encoding used by the :class:`.Unicode` type that is used to
transmit and receive data to the database is usually determined by the
DBAPI itself. All modern DBAPIs accommodate non-ASCII strings but may have
different methods of managing database encodings; if necessary, this
encoding should be configured as detailed in the notes for the target DBAPI
in the :ref:`dialect_toplevel` section.
In modern SQLAlchemy, use of the :class:`.Unicode` datatype does not
imply any encoding/decoding behavior within SQLAlchemy itself. In Python
3, all string objects are inherently Unicode capable, and SQLAlchemy
does not produce bytestring objects nor does it accommodate a DBAPI that
does not return Python Unicode objects in result sets for string values.
.. warning:: Some database backends, particularly SQL Server with pyodbc,
are known to have undesirable behaviors regarding data that is noted
as being of ``NVARCHAR`` type as opposed to ``VARCHAR``, including
datatype mismatch errors and non-use of indexes. See the section
on :meth:`.DialectEvents.do_setinputsizes` for background on working
around unicode character issues for backends like SQL Server with
pyodbc as well as cx_Oracle.
.. seealso::
:class:`.UnicodeText` - unlengthed textual counterpart
to :class:`.Unicode`.
:meth:`.DialectEvents.do_setinputsizes`
"""
__visit_name__ = "unicode"
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"
class Integer(HasExpressionLookup, TypeEngine[int]):
"""A type for ``int`` integers."""
__visit_name__ = "integer"
if TYPE_CHECKING:
@util.ro_memoized_property
def _type_affinity(self) -> Type[Integer]: ...
def get_dbapi_type(self, dbapi):
return dbapi.NUMBER
@property
def python_type(self):
return int
def _resolve_for_literal(self, value):
if value.bit_length() >= 32:
return _BIGINTEGER
else:
return self
def literal_processor(self, dialect):
def process(value):
return str(int(value))
return process
@util.memoized_property
def _expression_adaptations(self):
return {
operators.add: {
Date: Date,
Integer: self.__class__,
Numeric: Numeric,
},
operators.mul: {
Interval: Interval,
Integer: self.__class__,
Numeric: Numeric,
},
operators.truediv: {Integer: Numeric, Numeric: Numeric},
operators.floordiv: {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"
_N = TypeVar("_N", bound=Union[decimal.Decimal, float])
class Numeric(HasExpressionLookup, TypeEngine[_N]):
"""Base for non-integer numeric types, such as
``NUMERIC``, ``FLOAT``, ``DECIMAL``, and other variants.
The :class:`.Numeric` datatype when used directly will render DDL
corresponding to precision numerics if available, such as
``NUMERIC(precision, scale)``. The :class:`.Float` subclass will
attempt to render a floating-point datatype such as ``FLOAT(precision)``.
:class:`.Numeric` returns Python ``decimal.Decimal`` objects by default,
based on the default value of ``True`` for the
:paramref:`.Numeric.asdecimal` parameter. If this parameter is set to
False, returned values are coerced to Python ``float`` objects.
The :class:`.Float` subtype, being more specific to floating point,
defaults the :paramref:`.Float.asdecimal` flag to False so that the
default Python datatype is ``float``.
.. note::
When using a :class:`.Numeric` datatype against a database type that
returns Python floating point values to the driver, the accuracy of the
decimal conversion indicated by :paramref:`.Numeric.asdecimal` may be
limited. The behavior of specific numeric/floating point datatypes
is a product of the SQL datatype in use, the Python :term:`DBAPI`
in use, as well as strategies that may be present within
the SQLAlchemy dialect in use. Users requiring specific precision/
scale are encouraged to experiment with the available datatypes
in order to determine the best results.
"""
__visit_name__ = "numeric"
if TYPE_CHECKING:
@util.ro_memoized_property
def _type_affinity(self) -> Type[Numeric[_N]]: ...
_default_decimal_return_scale = 10
@overload
def __init__(
self: Numeric[decimal.Decimal],
precision: Optional[int] = ...,
scale: Optional[int] = ...,
decimal_return_scale: Optional[int] = ...,
asdecimal: Literal[True] = ...,
): ...
@overload
def __init__(
self: Numeric[float],
precision: Optional[int] = ...,
scale: Optional[int] = ...,
decimal_return_scale: Optional[int] = ...,
asdecimal: Literal[False] = ...,
): ...
def __init__(
self,
precision: Optional[int] = None,
scale: Optional[int] = None,
decimal_return_scale: Optional[int] = None,
asdecimal: bool = 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.
Specifying 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.
When using the ``Numeric`` type, care should be taken to ensure
that the asdecimal setting is appropriate 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:
# 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.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[_N]):
"""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.
When a :paramref:`.Float.precision` is not provided in a
:class:`_types.Float` type some backend may compile this type as
an 8 bytes / 64 bit float datatype. To use a 4 bytes / 32 bit float
datatype a precision <= 24 can usually be provided or the
:class:`_types.REAL` type can be used.
This is known to be the case in the PostgreSQL and MSSQL dialects
that render the type as ``FLOAT`` that's in both an alias of
``DOUBLE PRECISION``. Other third party dialects may have similar
behavior.
"""
__visit_name__ = "float"
scale = None
@overload
def __init__(
self: Float[float],
precision: Optional[int] = ...,
asdecimal: Literal[False] = ...,
decimal_return_scale: Optional[int] = ...,
): ...
@overload
def __init__(
self: Float[decimal.Decimal],
precision: Optional[int] = ...,
asdecimal: Literal[True] = ...,
decimal_return_scale: Optional[int] = ...,
): ...
def __init__(
self: Float[_N],
precision: Optional[int] = None,
asdecimal: bool = False,
decimal_return_scale: Optional[int] = None,
):
r"""
Construct a Float.
:param precision: the numeric precision for use in DDL ``CREATE
TABLE``. Backends **should** attempt to ensure this precision
indicates a number of digits for the generic
:class:`_sqltypes.Float` datatype.
.. note:: For the Oracle backend, the
:paramref:`_sqltypes.Float.precision` parameter is not accepted
when rendering DDL, as Oracle does not support float precision
specified as a number of decimal places. Instead, use the
Oracle-specific :class:`_oracle.FLOAT` datatype and specify the
:paramref:`_oracle.FLOAT.binary_precision` parameter. This is new
in version 2.0 of SQLAlchemy.
To create a database agnostic :class:`_types.Float` that
separately specifies binary precision for Oracle, use
:meth:`_types.TypeEngine.with_variant` as follows::
from sqlalchemy import Column
from sqlalchemy import Float
from sqlalchemy.dialects import oracle
Column(
"float_data",
Float(5).with_variant(oracle.FLOAT(binary_precision=16), "oracle")
)
: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.
Specifying 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.
""" # noqa: E501
self.precision = precision
self.asdecimal = asdecimal
self.decimal_return_scale = decimal_return_scale
def result_processor(self, dialect, coltype):
if self.asdecimal:
return processors.to_decimal_processor_factory(
decimal.Decimal, self._effective_decimal_return_scale
)
elif dialect.supports_native_decimal:
return processors.to_float
else:
return None
class Double(Float[_N]):
"""A type for double ``FLOAT`` floating point types.
Typically generates a ``DOUBLE`` or ``DOUBLE_PRECISION`` in DDL,
and otherwise acts like a normal :class:`.Float` on the Python
side.
.. versionadded:: 2.0
"""
__visit_name__ = "double"
class _RenderISO8601NoT:
def _literal_processor_datetime(self, dialect):
return self._literal_processor_portion(dialect, None)
def _literal_processor_date(self, dialect):
return self._literal_processor_portion(dialect, 0)
def _literal_processor_time(self, dialect):
return self._literal_processor_portion(dialect, -1)
def _literal_processor_portion(self, dialect, _portion=None):
assert _portion in (None, 0, -1)
if _portion is not None:
def process(value):
return f"""'{value.isoformat().split("T")[_portion]}'"""
else:
def process(value):
return f"""'{value.isoformat().replace("T", " ")}'"""
return process
class DateTime(
_RenderISO8601NoT, HasExpressionLookup, TypeEngine[dt.datetime]
):
"""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: bool = 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
def _resolve_for_literal(self, value):
with_timezone = value.tzinfo is not None
if with_timezone and not self.timezone:
return DATETIME_TIMEZONE
else:
return self
def literal_processor(self, dialect):
return self._literal_processor_datetime(dialect)
@property
def python_type(self):
return dt.datetime
@util.memoized_property
def _expression_adaptations(self):
# Based on
# https://www.postgresql.org/docs/current/static/functions-datetime.html.
return {
operators.add: {Interval: self.__class__},
operators.sub: {Interval: self.__class__, DateTime: Interval},
}
class Date(_RenderISO8601NoT, HasExpressionLookup, TypeEngine[dt.date]):
"""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
def literal_processor(self, dialect):
return self._literal_processor_date(dialect)
@util.memoized_property
def _expression_adaptations(self):
# Based on
# https://www.postgresql.org/docs/current/static/functions-datetime.html.
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(_RenderISO8601NoT, HasExpressionLookup, TypeEngine[dt.time]):
"""A type for ``datetime.time()`` objects."""
__visit_name__ = "time"
def __init__(self, timezone: bool = False):
self.timezone = timezone
def get_dbapi_type(self, dbapi):
return dbapi.DATETIME
@property
def python_type(self):
return dt.time
def _resolve_for_literal(self, value):
with_timezone = value.tzinfo is not None
if with_timezone and not self.timezone:
return TIME_TIMEZONE
else:
return self
@util.memoized_property
def _expression_adaptations(self):
# Based on
# https://www.postgresql.org/docs/current/static/functions-datetime.html.
return {
operators.add: {Date: DateTime, Interval: self.__class__},
operators.sub: {Time: Interval, Interval: self.__class__},
}
def literal_processor(self, dialect):
return self._literal_processor_time(dialect)
class _Binary(TypeEngine[bytes]):
"""Define base behavior for binary types."""
def __init__(self, length: Optional[int] = None):
self.length = length
@util.ro_memoized_property
def _generic_type_affinity(
self,
) -> Type[TypeEngine[bytes]]:
return LargeBinary
def literal_processor(self, dialect):
def process(value):
# TODO: this is useless for real world scenarios; implement
# real binary literals
value = value.decode(
dialect._legacy_binary_type_literal_encoding
).replace("'", "''")
return "'%s'" % value
return process
@property
def python_type(self):
return bytes
# 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'
def result_processor(self, dialect, coltype):
if dialect.returns_native_bytes:
return None
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, str):
return self
else:
return super().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: Optional[int] = 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 SchemaType(SchemaEventTarget, TypeEngineMixin):
"""Add capabilities to a type which allow for schema-level DDL to be
associated with a type.
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:`_schema.Column`.
.. seealso::
:class:`.Enum`
:class:`.Boolean`
"""
_use_schema_map = True
name: Optional[str]
def __init__(
self,
name: Optional[str] = None,
schema: Optional[str] = None,
metadata: Optional[MetaData] = None,
inherit_schema: bool = False,
quote: Optional[bool] = None,
_create_events: bool = True,
_adapted_from: Optional[SchemaType] = None,
):
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),
)
if _adapted_from:
self.dispatch = self.dispatch._join(_adapted_from.dispatch)
def _set_parent(self, parent, **kw):
# set parent hook is when this type is associated with a column.
# Column calls it for all SchemaEventTarget instances, either the
# base type and/or variants in _variant_mapping.
# we want to register a second hook to trigger when that column is
# associated with a table. in that event, we and all of our variants
# may want to set up some state on the table such as a CheckConstraint
# that will conditionally render at DDL render time.
# the base SchemaType also sets up events for
# on_table/metadata_create/drop in this method, which is used by
# "native" types with a separate CREATE/DROP e.g. Postgresql.ENUM
parent._on_table_attach(util.portable_instancemethod(self._set_table))
def _variant_mapping_for_set_table(self, column):
if column.type._variant_mapping:
variant_mapping = dict(column.type._variant_mapping)
variant_mapping["_default"] = column.type
else:
variant_mapping = None
return variant_mapping
def _set_table(self, column, table):
if self.inherit_schema:
self.schema = table.schema
elif self.metadata and self.schema is None and self.metadata.schema:
self.schema = self.metadata.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:
# if SchemaType were created w/ a metadata argument, these
# events would already have been associated with that metadata
# and would preclude an association with table.metadata
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(
cast("Type[TypeEngine[Any]]", self.__class__),
_create_events=True,
metadata=(
kw.get("_to_metadata", self.metadata)
if self.metadata is not None
else None
),
)
@overload
def adapt(self, cls: Type[_TE], **kw: Any) -> _TE: ...
@overload
def adapt(
self, cls: Type[TypeEngineMixin], **kw: Any
) -> TypeEngine[Any]: ...
def adapt(
self, cls: Type[Union[TypeEngine[Any], TypeEngineMixin]], **kw: Any
) -> TypeEngine[Any]:
kw.setdefault("_create_events", False)
kw.setdefault("_adapted_from", self)
return super().adapt(cls, **kw)
def create(self, bind, checkfirst=False):
"""Issue CREATE DDL for this type, if applicable."""
t = self.dialect_impl(bind.dialect)
if isinstance(t, SchemaType) and t.__class__ is not self.__class__:
t.create(bind, checkfirst=checkfirst)
def drop(self, bind, checkfirst=False):
"""Issue DROP DDL for this type, if applicable."""
t = self.dialect_impl(bind.dialect)
if isinstance(t, SchemaType) and t.__class__ is not self.__class__:
t.drop(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 isinstance(t, SchemaType) and t.__class__ is not self.__class__:
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 isinstance(t, SchemaType) and t.__class__ is not self.__class__:
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 isinstance(t, SchemaType) and t.__class__ is not self.__class__:
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 isinstance(t, SchemaType) and t.__class__ is not self.__class__:
t._on_metadata_drop(target, bind, **kw)
def _is_impl_for_variant(self, dialect, kw):
variant_mapping = kw.pop("variant_mapping", None)
if not variant_mapping:
return True
# for types that have _variant_mapping, all the impls in the map
# that are SchemaEventTarget subclasses get set up as event holders.
# this is so that constructs that need
# to be associated with the Table at dialect-agnostic time etc. like
# CheckConstraints can be set up with that table. they then add
# to these constraints a DDL check_rule that among other things
# will check this _is_impl_for_variant() method to determine when
# the dialect is known that we are part of the table's DDL sequence.
# since PostgreSQL is the only DB that has ARRAY this can only
# be integration tested by PG-specific tests
def _we_are_the_impl(typ):
return (
typ is self
or isinstance(typ, ARRAY)
and typ.item_type is self # type: ignore[comparison-overlap]
)
if dialect.name in variant_mapping and _we_are_the_impl(
variant_mapping[dialect.name]
):
return True
elif dialect.name not in variant_mapping:
return _we_are_the_impl(variant_mapping["_default"])
class Enum(String, SchemaType, Emulated, TypeEngine[Union[str, enum.Enum]]):
"""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.
An option also exists to automatically produce a CHECK constraint
when the VARCHAR (so called "non-native") variant is produced;
see 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).
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
from sqlalchemy 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.
In order to persist the values and not the names, the
:paramref:`.Enum.values_callable` parameter may be used. The value of
this parameter is a user-supplied callable, which is intended to be used
with a PEP-435-compliant enumerated class and returns a list of string
values to be persisted. For a simple enumeration that uses string values,
a callable such as ``lambda x: [e.value for e in x]`` is sufficient.
.. seealso::
:ref:`orm_declarative_mapped_column_enums` - background on using
the :class:`_sqltypes.Enum` datatype with the ORM's
:ref:`ORM Annotated Declarative <orm_declarative_mapped_column>`
feature.
:class:`_postgresql.ENUM` - PostgreSQL-specific type,
which has additional functionality.
:class:`.mysql.ENUM` - MySQL-specific type
"""
__visit_name__ = "enum"
def __init__(self, *enums: object, **kw: Any):
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 labels.
:param create_constraint: defaults to False. When creating a
non-native enumerated type, also build a CHECK constraint on the
database against the valid values.
.. note:: it is strongly recommended that the CHECK constraint
have an explicit name in order to support schema-management
concerns. This can be established either by setting the
:paramref:`.Enum.name` parameter or by setting up an
appropriate naming convention; see
:ref:`constraint_naming_conventions` for background.
.. versionchanged:: 1.4 - this flag now defaults to False, meaning
no CHECK constraint is generated 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.
The value of the :paramref:`_schema.MetaData.schema` parameter of
the :class:`_schema.MetaData` object, if set, will be used as the
default value of the :paramref:`_types.Enum.schema` on this object
if an explicit value is not otherwise supplied.
.. versionchanged:: 1.4.12 :class:`_types.Enum` inherits the
:paramref:`_schema.MetaData.schema` parameter of the
:class:`_schema.MetaData` object if present, when passed using
the :paramref:`_types.Enum.metadata` parameter.
: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. When False, the VARCHAR length can be
controlled with :paramref:`.Enum.length`; currently "length" is
ignored if native_enum=True.
:param length: Allows specifying a custom length for the VARCHAR
when a non-native enumeration datatype is used. By default it uses
the length of the longest value.
.. versionchanged:: 2.0.0 The :paramref:`.Enum.length` parameter
is used unconditionally for ``VARCHAR`` rendering regardless of
the :paramref:`.Enum.native_enum` parameter, for those backends
where ``VARCHAR`` is used for enumerated datatypes.
: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.
If not present, the schema name will be taken from the
:class:`_schema.MetaData` collection if passed as
:paramref:`_types.Enum.metadata`, for a :class:`_schema.MetaData`
that includes the :paramref:`_schema.MetaData.schema` parameter.
.. versionchanged:: 1.4.12 :class:`_types.Enum` inherits the
:paramref:`_schema.MetaData.schema` parameter of the
:class:`_schema.MetaData` object if present, when passed using
the :paramref:`_types.Enum.metadata` parameter.
Otherwise, if the :paramref:`_types.Enum.inherit_schema` flag is set
to ``True``, the schema will be inherited from the associated
:class:`_schema.Table` object if any; when
:paramref:`_types.Enum.inherit_schema` is at its default of
``False``, the owning table's schema is **not** used.
:param quote: Set explicit quoting preferences for the type's name.
:param inherit_schema: When ``True``, the "schema" from the owning
:class:`_schema.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:`_schema.Table.to_metadata` 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.
:param values_callable: A callable which will be passed the PEP-435
compliant enumerated type, which should then return a list of string
values to be persisted. This allows for alternate usages such as
using the string value of an enum to be persisted to the database
instead of its name. The callable must return the values to be
persisted in the same order as iterating through the Enum's
``__member__`` attribute. For example
``lambda x: [i.value for i in x]``.
.. versionadded:: 1.2.3
:param sort_key_function: a Python callable which may be used as the
"key" argument in the Python ``sorted()`` built-in. The SQLAlchemy
ORM requires that primary key columns which are mapped must
be sortable in some way. When using an unsortable enumeration
object such as a Python 3 ``Enum`` object, this parameter may be
used to set a default sort key function for the objects. By
default, the database value of the enumeration is used as the
sorting function.
.. versionadded:: 1.3.8
:param omit_aliases: A boolean that when true will remove aliases from
pep 435 enums. defaults to ``True``.
.. versionchanged:: 2.0 This parameter now defaults to True.
"""
self._enum_init(enums, kw)
@property
def _enums_argument(self):
if self.enum_class is not None:
return [self.enum_class]
else:
return self.enums
def _enum_init(self, enums, kw):
"""internal init for :class:`.Enum` and subclasses.
friendly init helper used by subclasses to remove
all the Enum-specific keyword arguments from kw. Allows all
other arguments in kw to pass through.
"""
self.native_enum = kw.pop("native_enum", True)
self.create_constraint = kw.pop("create_constraint", False)
self.values_callable = kw.pop("values_callable", None)
self._sort_key_function = kw.pop("sort_key_function", NO_ARG)
length_arg = kw.pop("length", NO_ARG)
self._omit_aliases = kw.pop("omit_aliases", True)
_disable_warnings = kw.pop("_disable_warnings", False)
values, objects = self._parse_into_values(enums, kw)
self._setup_for_values(values, objects, kw)
self.validate_strings = kw.pop("validate_strings", False)
if self.enums:
self._default_length = length = max(len(x) for x in self.enums)
else:
self._default_length = length = 0
if length_arg is not NO_ARG:
if (
not _disable_warnings
and length_arg is not None
and length_arg < length
):
raise ValueError(
"When provided, length must be larger or equal"
" than the length of the longest enum value. %s < %s"
% (length_arg, length)
)
length = length_arg
self._valid_lookup[None] = self._object_lookup[None] = None
super().__init__(length=length)
# assign name to the given enum class if no other name, and this
# enum is not an "empty" enum. if the enum is "empty" we assume
# this is a template enum that will be used to generate
# new Enum classes.
if self.enum_class and values:
kw.setdefault("name", self.enum_class.__name__.lower())
SchemaType.__init__(
self,
name=kw.pop("name", None),
schema=kw.pop("schema", None),
metadata=kw.pop("metadata", None),
inherit_schema=kw.pop("inherit_schema", False),
quote=kw.pop("quote", None),
_create_events=kw.pop("_create_events", True),
_adapted_from=kw.pop("_adapted_from", None),
)
def _parse_into_values(self, enums, kw):
if not enums and "_enums" in kw:
enums = kw.pop("_enums")
if len(enums) == 1 and hasattr(enums[0], "__members__"):
self.enum_class = enums[0]
_members = self.enum_class.__members__
if self._omit_aliases is True:
# remove aliases
members = OrderedDict(
(n, v) for n, v in _members.items() if v.name == n
)
else:
members = _members
if self.values_callable:
values = self.values_callable(self.enum_class)
else:
values = list(members)
objects = [members[k] for k in members]
return values, objects
else:
self.enum_class = None
return enums, enums
def _resolve_for_literal(self, value: Any) -> Enum:
tv = type(value)
typ = self._resolve_for_python_type(tv, tv, tv)
assert typ is not None
return typ
def _resolve_for_python_type(
self,
python_type: Type[Any],
matched_on: _MatchedOnType,
matched_on_flattened: Type[Any],
) -> Optional[Enum]:
# "generic form" indicates we were placed in a type map
# as ``sqlalchemy.Enum(enum.Enum)`` which indicates we need to
# get enumerated values from the datatype
we_are_generic_form = self._enums_argument == [enum.Enum]
native_enum = None
if not we_are_generic_form and python_type is matched_on:
# if we have enumerated values, and the incoming python
# type is exactly the one that matched in the type map,
# then we use these enumerated values and dont try to parse
# what's incoming
enum_args = self._enums_argument
elif is_literal(python_type):
# for a literal, where we need to get its contents, parse it out.
enum_args = typing_get_args(python_type)
bad_args = [arg for arg in enum_args if not isinstance(arg, str)]
if bad_args:
raise exc.ArgumentError(
f"Can't create string-based Enum datatype from non-string "
f"values: {', '.join(repr(x) for x in bad_args)}. Please "
f"provide an explicit Enum datatype for this Python type"
)
native_enum = False
elif isinstance(python_type, type) and issubclass(
python_type, enum.Enum
):
# same for an enum.Enum
enum_args = [python_type]
else:
enum_args = self._enums_argument
# make a new Enum that looks like this one.
# arguments or other rules
kw = self._make_enum_kw({})
if native_enum is False:
kw["native_enum"] = False
kw["length"] = NO_ARG if self.length == 0 else self.length
return cast(
Enum,
self._generic_type_affinity(_enums=enum_args, **kw), # type: ignore # noqa: E501
)
def _setup_for_values(self, values, objects, kw):
self.enums = list(values)
self._valid_lookup = dict(zip(reversed(objects), reversed(values)))
self._object_lookup = dict(zip(values, objects))
self._valid_lookup.update(
[
(value, self._valid_lookup[self._object_lookup[value]])
for value in values
]
)
@property
def sort_key_function(self):
if self._sort_key_function is NO_ARG:
return self._db_value_for_elem
else:
return self._sort_key_function
@property
def native(self):
return self.native_enum
def _db_value_for_elem(self, elem):
try:
return self._valid_lookup[elem]
except KeyError as err:
# 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, str):
return elem
else:
raise LookupError(
"'%s' is not among the defined enum values. "
"Enum name: %s. Possible values: %s"
% (
elem,
self.name,
langhelpers.repr_tuple_names(self.enums),
)
) from err
class Comparator(String.Comparator[str]):
__slots__ = ()
type: String
def _adapt_expression(
self,
op: OperatorType,
other_comparator: TypeEngine.Comparator[Any],
) -> Tuple[OperatorType, TypeEngine[Any]]:
op, typ = super()._adapt_expression(op, other_comparator)
if op is operators.concat_op:
typ = String(self.type.length)
return op, typ
comparator_factory = Comparator
def _object_value_for_elem(self, elem):
try:
return self._object_lookup[elem]
except KeyError as err:
raise LookupError(
"'%s' is not among the defined enum values. "
"Enum name: %s. Possible values: %s"
% (
elem,
self.name,
langhelpers.repr_tuple_names(self.enums),
)
) from err
def __repr__(self):
return util.generic_repr(
self,
additional_kw=[
("native_enum", True),
("create_constraint", False),
("length", self._default_length),
],
to_inspect=[Enum, SchemaType],
)
def as_generic(self, allow_nulltype=False):
try:
args = self.enums
except AttributeError:
raise NotImplementedError(
"TypeEngine.as_generic() heuristic "
"is undefined for types that inherit Enum but do not have "
"an `enums` attribute."
) from None
return util.constructor_copy(
self, self._generic_type_affinity, *args, _disable_warnings=True
)
def _make_enum_kw(self, kw):
kw.setdefault("validate_strings", self.validate_strings)
if self.name:
kw.setdefault("name", self.name)
kw.setdefault("schema", self.schema)
kw.setdefault("inherit_schema", self.inherit_schema)
kw.setdefault("metadata", self.metadata)
kw.setdefault("native_enum", self.native_enum)
kw.setdefault("values_callable", self.values_callable)
kw.setdefault("create_constraint", self.create_constraint)
kw.setdefault("length", self.length)
kw.setdefault("omit_aliases", self._omit_aliases)
return kw
def adapt_to_emulated(self, impltype, **kw):
self._make_enum_kw(kw)
kw["_disable_warnings"] = True
kw.setdefault("_create_events", False)
assert "_enums" in kw
return impltype(**kw)
def adapt(self, cls, **kw):
kw["_enums"] = self._enums_argument
kw["_disable_warnings"] = True
return super().adapt(cls, **kw)
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.preload_module("sqlalchemy.sql.schema")
def _set_table(self, column, table):
schema = util.preloaded.sql_schema
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, String()).in_(self.enums),
name=_NONE_NAME if self.name is None else self.name,
_create_rule=util.portable_instancemethod(
self._should_create_constraint,
{"variant_mapping": variant_mapping},
),
_type_bound=True,
)
assert e.table is table
def literal_processor(self, dialect):
parent_processor = super().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):
parent_processor = super().bind_processor(dialect)
def process(value):
value = self._db_value_for_elem(value)
if parent_processor:
value = parent_processor(value)
return value
return process
def result_processor(self, dialect, coltype):
parent_processor = super().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
def copy(self, **kw):
return SchemaType.copy(self, **kw)
@property
def python_type(self):
if self.enum_class:
return self.enum_class
else:
return super().python_type
class PickleType(TypeDecorator[object]):
"""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
cache_ok = True
def __init__(
self,
protocol: int = pickle.HIGHEST_PROTOCOL,
pickler: Any = None,
comparator: Optional[Callable[[Any, Any], bool]] = None,
impl: Optional[_TypeEngineArgument[Any]] = None,
):
"""
Construct a PickleType.
:param protocol: defaults to ``pickle.HIGHEST_PROTOCOL``.
:param pickler: defaults to pickle. 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.
:param impl: A binary-storing :class:`_types.TypeEngine` class or
instance to use in place of the default :class:`_types.LargeBinary`.
For example the :class: `_mysql.LONGBLOB` class may be more effective
when using MySQL.
.. versionadded:: 1.4.20
"""
self.protocol = protocol
self.pickler = pickler or pickle
self.comparator = comparator
super().__init__()
if impl:
# custom impl is not necessarily a LargeBinary subclass.
# make an exception to typing for this
self.impl = to_instance(impl) # type: ignore
def __reduce__(self):
return PickleType, (self.protocol, None, self.comparator)
def bind_processor(self, dialect):
impl_processor = self.impl_instance.bind_processor(dialect)
dumps = self.pickler.dumps
protocol = self.protocol
if impl_processor:
fixed_impl_processor = impl_processor
def process(value):
if value is not None:
value = dumps(value, protocol)
return fixed_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_instance.result_processor(dialect, coltype)
loads = self.pickler.loads
if impl_processor:
fixed_impl_processor = impl_processor
def process(value):
value = fixed_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(SchemaType, Emulated, TypeEngine[bool]):
"""A bool datatype.
:class:`.Boolean` typically uses BOOLEAN or SMALLINT on the DDL side,
and on the Python side deals in ``True`` or ``False``.
The :class:`.Boolean` datatype currently has two levels of assertion
that the values persisted are simple true/false values. For all
backends, only the Python values ``None``, ``True``, ``False``, ``1``
or ``0`` are accepted as parameter values. For those backends that
don't support a "native boolean" datatype, an option exists to
also create a CHECK constraint on the target column
.. versionchanged:: 1.2 the :class:`.Boolean` datatype now asserts that
incoming Python values are already in pure boolean form.
"""
__visit_name__ = "boolean"
native = True
def __init__(
self,
create_constraint: bool = False,
name: Optional[str] = None,
_create_events: bool = True,
_adapted_from: Optional[SchemaType] = None,
):
"""Construct a Boolean.
:param create_constraint: defaults to False. 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.
.. note:: it is strongly recommended that the CHECK constraint
have an explicit name in order to support schema-management
concerns. This can be established either by setting the
:paramref:`.Boolean.name` parameter or by setting up an
appropriate naming convention; see
:ref:`constraint_naming_conventions` for background.
.. versionchanged:: 1.4 - this flag now defaults to False, meaning
no CHECK constraint is generated for a non-native enumerated
type.
: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
if _adapted_from:
self.dispatch = self.dispatch._join(_adapted_from.dispatch)
def copy(self, **kw):
# override SchemaType.copy() to not include to_metadata logic
return self.adapt(
cast("Type[TypeEngine[Any]]", self.__class__),
_create_events=True,
)
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
and compiler.dialect.non_native_boolean_check_constraint
)
@util.preload_module("sqlalchemy.sql.schema")
def _set_table(self, column, table):
schema = util.preloaded.sql_schema
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=_NONE_NAME if self.name is None else 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
_strict_bools = frozenset([None, True, False])
def _strict_as_bool(self, value):
if value not in self._strict_bools:
if not isinstance(value, int):
raise TypeError("Not a boolean value: %r" % (value,))
else:
raise ValueError(
"Value %r is not None, True, or False" % (value,)
)
return value
def literal_processor(self, dialect):
compiler = dialect.statement_compiler(dialect, None)
true = compiler.visit_true(None)
false = compiler.visit_false(None)
def process(value):
return true if self._strict_as_bool(value) else false
return process
def bind_processor(self, dialect):
_strict_as_bool = self._strict_as_bool
_coerce: Union[Type[bool], Type[int]]
if dialect.supports_native_boolean:
_coerce = bool
else:
_coerce = int
def process(value):
value = _strict_as_bool(value)
if value is not None:
value = _coerce(value)
return value
return process
def result_processor(self, dialect, coltype):
if dialect.supports_native_boolean:
return None
else:
return processors.int_to_boolean
class _AbstractInterval(HasExpressionLookup, TypeEngine[dt.timedelta]):
@util.memoized_property
def _expression_adaptations(self):
# Based on
# https://www.postgresql.org/docs/current/static/functions-datetime.html.
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__},
}
@util.ro_non_memoized_property
def _type_affinity(self) -> Type[Interval]:
return Interval
class Interval(Emulated, _AbstractInterval, TypeDecorator[dt.timedelta]):
"""A type for ``datetime.timedelta()`` objects.
The Interval type deals with ``datetime.timedelta`` objects. In
PostgreSQL and Oracle, 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.fromtimestamp(0, dt.timezone.utc).replace(tzinfo=None)
cache_ok = True
def __init__(
self,
native: bool = True,
second_precision: Optional[int] = None,
day_precision: Optional[int] = 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().__init__()
self.native = native
self.second_precision = second_precision
self.day_precision = day_precision
class Comparator(
TypeDecorator.Comparator[_CT],
_AbstractInterval.Comparator[_CT],
):
__slots__ = ()
comparator_factory = Comparator
@property
def python_type(self):
return dt.timedelta
def adapt_to_emulated(self, impltype, **kw):
return _AbstractInterval.adapt(self, impltype, **kw)
def coerce_compared_value(self, op, value):
return self.impl_instance.coerce_compared_value(op, value)
def bind_processor(
self, dialect: Dialect
) -> _BindProcessorType[dt.timedelta]:
if TYPE_CHECKING:
assert isinstance(self.impl_instance, DateTime)
impl_processor = self.impl_instance.bind_processor(dialect)
epoch = self.epoch
if impl_processor:
fixed_impl_processor = impl_processor
def process(
value: Optional[dt.timedelta],
) -> Any:
if value is not None:
dt_value = epoch + value
else:
dt_value = None
return fixed_impl_processor(dt_value)
else:
def process(
value: Optional[dt.timedelta],
) -> Any:
if value is not None:
dt_value = epoch + value
else:
dt_value = None
return dt_value
return process
def result_processor(
self, dialect: Dialect, coltype: Any
) -> _ResultProcessorType[dt.timedelta]:
if TYPE_CHECKING:
assert isinstance(self.impl_instance, DateTime)
impl_processor = self.impl_instance.result_processor(dialect, coltype)
epoch = self.epoch
if impl_processor:
fixed_impl_processor = impl_processor
def process(value: Any) -> Optional[dt.timedelta]:
dt_value = fixed_impl_processor(value)
if dt_value is None:
return None
return dt_value - epoch
else:
def process(value: Any) -> Optional[dt.timedelta]:
if value is None:
return None
return value - epoch # type: ignore
return process
class JSON(Indexable, TypeEngine[Any]):
"""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 - see :class:`sqlalchemy.dialects.postgresql.JSON` and
:class:`sqlalchemy.dialects.postgresql.JSONB` for backend-specific
notes
* MySQL - see
:class:`sqlalchemy.dialects.mysql.JSON` for backend-specific notes
* SQLite as of version 3.9 - see
:class:`sqlalchemy.dialects.sqlite.JSON` for backend-specific notes
* Microsoft SQL Server 2016 and later - see
:class:`sqlalchemy.dialects.mssql.JSON` for backend-specific notes
: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"}}
)
**JSON-Specific Expression Operators**
The :class:`_types.JSON`
datatype provides these additional SQL 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')]
* Data casters for specific JSON element types, subsequent to an index
or path operation being invoked::
data_table.c.data["some key"].as_integer()
.. versionadded:: 1.3.11
Additional operations may be available from the dialect-specific versions
of :class:`_types.JSON`, such as
:class:`sqlalchemy.dialects.postgresql.JSON` and
:class:`sqlalchemy.dialects.postgresql.JSONB` which both offer additional
PostgreSQL-specific operations.
**Casting JSON Elements to Other Types**
Index operations, i.e. those invoked by calling upon the expression using
the Python bracket operator as in ``some_column['some key']``, return an
expression object whose type defaults to :class:`_types.JSON` by default,
so that
further JSON-oriented instructions may be called upon the result type.
However, it is likely more common that an index operation is expected
to return a specific scalar element, such as a string or integer. In
order to provide access to these elements in a backend-agnostic way,
a series of data casters are provided:
* :meth:`.JSON.Comparator.as_string` - return the element as a string
* :meth:`.JSON.Comparator.as_boolean` - return the element as a boolean
* :meth:`.JSON.Comparator.as_float` - return the element as a float
* :meth:`.JSON.Comparator.as_integer` - return the element as an integer
These data casters are implemented by supporting dialects in order to
assure that comparisons to the above types will work as expected, such as::
# integer comparison
data_table.c.data["some_integer_key"].as_integer() == 5
# boolean comparison
data_table.c.data["some_boolean"].as_boolean() == True
.. versionadded:: 1.3.11 Added type-specific casters for the basic JSON
data element types.
.. note::
The data caster functions are new in version 1.3.11, and supersede
the previous documented approaches of using CAST; for reference,
this looked like::
from sqlalchemy import cast, type_coerce
from sqlalchemy import String, JSON
cast(
data_table.c.data['some_key'], String
) == type_coerce(55, JSON)
The above case now works directly as::
data_table.c.data['some_key'].as_integer() == 5
For details on the previous comparison approach within the 1.3.x
series, see the documentation for SQLAlchemy 1.2 or the included HTML
files in the doc/ directory of the version's distribution.
**Detecting Changes in JSON columns when using the ORM**
The :class:`_types.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, most typically
using the :class:`.MutableDict` class. 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.
Alternatively, assigning a JSON structure to an ORM element that
replaces the old one will always trigger a change event.
**Support for JSON null vs. SQL NULL**
When working with NULL values, the :class:`_types.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`. This symbol may be passed as a parameter value
specifically when using the :class:`_types.JSON` datatype, which contains
special logic that interprets this symbol to mean that the column value
should be SQL NULL as opposed to JSON ``"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:`_types.JSON.NULL`::
conn.execute(table.insert(), {"json_value": JSON.NULL})
The :class:`_types.JSON` type supports a flag
:paramref:`_types.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:`_types.JSON.NULL` and :func:`.null` in order to indicate NULL
values, but care must be taken as to the value of the
:paramref:`_types.JSON.none_as_null` in these cases.
**Customizing the JSON Serializer**
The JSON serializer and deserializer used by :class:`_types.JSON`
defaults to
Python's ``json.dumps`` and ``json.loads`` functions; in the case of the
psycopg2 dialect, psycopg2 may be using its own custom loader function.
In order to affect the serializer / deserializer, they are currently
configurable at the :func:`_sa.create_engine` level via the
:paramref:`_sa.create_engine.json_serializer` and
:paramref:`_sa.create_engine.json_deserializer` parameters. For example,
to turn off ``ensure_ascii``::
engine = create_engine(
"sqlite://",
json_serializer=lambda obj: json.dumps(obj, ensure_ascii=False))
.. versionchanged:: 1.3.7
SQLite dialect's ``json_serializer`` and ``json_deserializer``
parameters renamed from ``_json_serializer`` and
``_json_deserializer``.
.. seealso::
:class:`sqlalchemy.dialects.postgresql.JSON`
:class:`sqlalchemy.dialects.postgresql.JSONB`
:class:`sqlalchemy.dialects.mysql.JSON`
:class:`sqlalchemy.dialects.sqlite.JSON`
"""
__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:`_types.JSON.none_as_null` flag; the
:attr:`_types.JSON.NULL`
constant can be used to always resolve to JSON ``"null"`` regardless
of this setting. This is in contrast to the :func:`_expression.null`
construct,
which always resolves to SQL NULL. E.g.::
from sqlalchemy import null
from sqlalchemy.dialects.postgresql import JSON
# will *always* insert SQL NULL
obj1 = MyObject(json_value=null())
# will *always* insert JSON string "null"
obj2 = MyObject(json_value=JSON.NULL)
session.add_all([obj1, obj2])
session.commit()
In order to set JSON NULL as a default value for a column, the most
transparent method is to use :func:`_expression.text`::
Table(
'my_table', metadata,
Column('json_data', JSON, default=text("'null'"))
)
While it is possible to use :attr:`_types.JSON.NULL` in this context, the
:attr:`_types.JSON.NULL` value will be returned as the value of the
column,
which in the context of the ORM or other repurposing of the default
value, may not be desirable. Using a SQL expression means the value
will be re-fetched from the database within the context of retrieving
generated defaults.
"""
def __init__(self, none_as_null: bool = 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, which may be passed directly as a parameter
value that is specially interpreted by the :class:`_types.JSON` type
as SQL NULL::
from sqlalchemy import null
conn.execute(table.insert(), {"data": null()})
.. note::
:paramref:`_types.JSON.none_as_null` does **not** apply to the
values passed to :paramref:`_schema.Column.default` and
:paramref:`_schema.Column.server_default`; a value of ``None``
passed for these parameters means "no default present".
Additionally, when used in SQL comparison expressions, the
Python value ``None`` continues to refer to SQL null, and not
JSON NULL. The :paramref:`_types.JSON.none_as_null` flag refers
explicitly to the **persistence** of the value within an
INSERT or UPDATE statement. The :attr:`_types.JSON.NULL`
value should be used for SQL expressions that wish to compare to
JSON null.
.. seealso::
:attr:`.types.JSON.NULL`
"""
self.none_as_null = none_as_null
class JSONElementType(TypeEngine[Any]):
"""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, str):
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, str):
value = string_processor(value)
else:
raise NotImplementedError()
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 JSONIntIndexType(JSONIndexType):
"""Placeholder for the datatype of a JSON index value.
This allows execution-time processing of JSON index values
for special syntaxes.
"""
class JSONStrIndexType(JSONIndexType):
"""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.
"""
__visit_name__ = "json_path"
class Comparator(Indexable.Comparator[_T], Concatenable.Comparator[_T]):
"""Define comparison operations for :class:`_types.JSON`."""
__slots__ = ()
def _setup_getitem(self, index):
if not isinstance(index, str) and isinstance(
index, collections_abc.Sequence
):
index = coercions.expect(
roles.BinaryElementRole,
index,
expr=self.expr,
operator=operators.json_path_getitem_op,
bindparam_type=JSON.JSONPathType,
)
operator = operators.json_path_getitem_op
else:
index = coercions.expect(
roles.BinaryElementRole,
index,
expr=self.expr,
operator=operators.json_getitem_op,
bindparam_type=(
JSON.JSONIntIndexType
if isinstance(index, int)
else JSON.JSONStrIndexType
),
)
operator = operators.json_getitem_op
return operator, index, self.type
def as_boolean(self):
"""Consider an indexed value as boolean.
This is similar to using :class:`_sql.type_coerce`, and will
usually not apply a ``CAST()``.
e.g.::
stmt = select(
mytable.c.json_column['some_data'].as_boolean()
).where(
mytable.c.json_column['some_data'].as_boolean() == True
)
.. versionadded:: 1.3.11
"""
return self._binary_w_type(Boolean(), "as_boolean")
def as_string(self):
"""Consider an indexed value as string.
This is similar to using :class:`_sql.type_coerce`, and will
usually not apply a ``CAST()``.
e.g.::
stmt = select(
mytable.c.json_column['some_data'].as_string()
).where(
mytable.c.json_column['some_data'].as_string() ==
'some string'
)
.. versionadded:: 1.3.11
"""
return self._binary_w_type(Unicode(), "as_string")
def as_integer(self):
"""Consider an indexed value as integer.
This is similar to using :class:`_sql.type_coerce`, and will
usually not apply a ``CAST()``.
e.g.::
stmt = select(
mytable.c.json_column['some_data'].as_integer()
).where(
mytable.c.json_column['some_data'].as_integer() == 5
)
.. versionadded:: 1.3.11
"""
return self._binary_w_type(Integer(), "as_integer")
def as_float(self):
"""Consider an indexed value as float.
This is similar to using :class:`_sql.type_coerce`, and will
usually not apply a ``CAST()``.
e.g.::
stmt = select(
mytable.c.json_column['some_data'].as_float()
).where(
mytable.c.json_column['some_data'].as_float() == 29.75
)
.. versionadded:: 1.3.11
"""
return self._binary_w_type(Float(), "as_float")
def as_numeric(self, precision, scale, asdecimal=True):
"""Consider an indexed value as numeric/decimal.
This is similar to using :class:`_sql.type_coerce`, and will
usually not apply a ``CAST()``.
e.g.::
stmt = select(
mytable.c.json_column['some_data'].as_numeric(10, 6)
).where(
mytable.c.
json_column['some_data'].as_numeric(10, 6) == 29.75
)
.. versionadded:: 1.4.0b2
"""
return self._binary_w_type(
Numeric(precision, scale, asdecimal=asdecimal), "as_numeric"
)
def as_json(self):
"""Consider an indexed value as JSON.
This is similar to using :class:`_sql.type_coerce`, and will
usually not apply a ``CAST()``.
e.g.::
stmt = select(mytable.c.json_column['some_data'].as_json())
This is typically the default behavior of indexed elements in any
case.
Note that comparison of full JSON structures may not be
supported by all backends.
.. versionadded:: 1.3.11
"""
return self.expr
def _binary_w_type(self, typ, method_name):
if not isinstance(
self.expr, elements.BinaryExpression
) or self.expr.operator not in (
operators.json_getitem_op,
operators.json_path_getitem_op,
):
raise exc.InvalidRequestError(
"The JSON cast operator JSON.%s() only works with a JSON "
"index expression e.g. col['q'].%s()"
% (method_name, method_name)
)
expr = self.expr._clone()
expr.type = typ
return expr
comparator_factory = Comparator
@property
def python_type(self):
return dict
@property # type: ignore # mypy property bug
def should_evaluate_none(self):
"""Alias of :attr:`_types.JSON.none_as_null`"""
return not self.none_as_null
@should_evaluate_none.setter
def should_evaluate_none(self, value):
self.none_as_null = not value
@util.memoized_property
def _str_impl(self):
return String()
def _make_bind_processor(self, string_process, json_serializer):
if string_process:
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)
return string_process(serialized)
else:
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
return json_serializer(value)
return process
def bind_processor(self, dialect):
string_process = self._str_impl.bind_processor(dialect)
json_serializer = dialect._json_serializer or json.dumps
return self._make_bind_processor(string_process, json_serializer)
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(
SchemaEventTarget, Indexable, Concatenable, TypeEngine[Sequence[Any]]
):
"""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 PostgreSQL-specific
:class:`sqlalchemy.dialects.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:`_functions.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 ``[]`` operator produces 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:`_expression.Update.values`
method is used::
mytable.update().values({
mytable.c.data[5]: 7,
mytable.c.data[2:7]: [1, 2, 3]
})
Indexed access is one-based by default;
for zero-based index conversion, set :paramref:`_types.ARRAY.zero_indexes`.
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.
.. container:: topic
**Detecting Changes in ARRAY columns when using the ORM**
The :class:`_sqltypes.ARRAY` type, when used with the SQLAlchemy ORM,
does not detect in-place mutations to the array. In order to detect
these, the :mod:`sqlalchemy.ext.mutable` extension must be used, using
the :class:`.MutableList` class::
from sqlalchemy import ARRAY
from sqlalchemy.ext.mutable import MutableList
class SomeOrmClass(Base):
# ...
data = Column(MutableList.as_mutable(ARRAY(Integer)))
This extension will allow "in-place" changes such to the array
such as ``.append()`` to produce events which will be detected by the
unit of work. Note that changes to elements **inside** the array,
including subarrays that are mutated in place, are **not** detected.
Alternatively, assigning a new array value to an ORM element that
replaces the old one will always trigger a change event.
.. seealso::
:class:`sqlalchemy.dialects.postgresql.ARRAY`
"""
__visit_name__ = "ARRAY"
_is_array = True
zero_indexes = False
"""If True, Python zero-based indexes should be interpreted as one-based
on the SQL expression side."""
def __init__(
self,
item_type: _TypeEngineArgument[Any],
as_tuple: bool = False,
dimensions: Optional[int] = None,
zero_indexes: bool = 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
class Comparator(
Indexable.Comparator[Sequence[Any]],
Concatenable.Comparator[Sequence[Any]],
):
"""Define comparison operations for :class:`_types.ARRAY`.
More operators are available on the dialect-specific form
of this type. See :class:`.postgresql.ARRAY.Comparator`.
"""
__slots__ = ()
type: ARRAY
def _setup_getitem(self, index):
arr_type = self.type
return_type: TypeEngine[Any]
if isinstance(index, slice):
return_type = arr_type
if arr_type.zero_indexes:
index = slice(index.start + 1, index.stop + 1, index.step)
slice_ = Slice(
index.start, index.stop, index.step, _name=self.expr.key
)
return operators.getitem, slice_, return_type
else:
if arr_type.zero_indexes:
index += 1
if arr_type.dimensions is None or arr_type.dimensions == 1:
return_type = arr_type.item_type
else:
adapt_kw = {"dimensions": arr_type.dimensions - 1}
return_type = arr_type.adapt(
arr_type.__class__, **adapt_kw
)
return operators.getitem, index, return_type
def contains(self, *arg, **kw):
"""``ARRAY.contains()`` not implemented for the base ARRAY type.
Use the dialect-specific ARRAY type.
.. seealso::
:class:`_postgresql.ARRAY` - PostgreSQL specific version.
"""
raise NotImplementedError(
"ARRAY.contains() not implemented for the base "
"ARRAY type; please use the dialect-specific ARRAY type"
)
@util.preload_module("sqlalchemy.sql.elements")
def any(self, other, operator=None):
"""Return ``other operator ANY (array)`` clause.
.. legacy:: This method is an :class:`_types.ARRAY` - specific
construct that is now superseded by the :func:`_sql.any_`
function, which features a different calling style. The
:func:`_sql.any_` function is also mirrored at the method level
via the :meth:`_sql.ColumnOperators.any_` method.
Usage of array-specific :meth:`_types.ARRAY.Comparator.any`
is as follows::
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:`_expression.any_`
:meth:`.types.ARRAY.Comparator.all`
"""
elements = util.preloaded.sql_elements
operator = operator if operator else operators.eq
arr_type = self.type
return elements.CollectionAggregate._create_any(self.expr).operate(
operators.mirror(operator),
coercions.expect(
roles.BinaryElementRole,
element=other,
operator=operator,
expr=self.expr,
bindparam_type=arr_type.item_type,
),
)
@util.preload_module("sqlalchemy.sql.elements")
def all(self, other, operator=None):
"""Return ``other operator ALL (array)`` clause.
.. legacy:: This method is an :class:`_types.ARRAY` - specific
construct that is now superseded by the :func:`_sql.all_`
function, which features a different calling style. The
:func:`_sql.all_` function is also mirrored at the method level
via the :meth:`_sql.ColumnOperators.all_` method.
Usage of array-specific :meth:`_types.ARRAY.Comparator.all`
is as follows::
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:`_expression.all_`
:meth:`.types.ARRAY.Comparator.any`
"""
elements = util.preloaded.sql_elements
operator = operator if operator else operators.eq
arr_type = self.type
return elements.CollectionAggregate._create_all(self.expr).operate(
operators.mirror(operator),
coercions.expect(
roles.BinaryElementRole,
element=other,
operator=operator,
expr=self.expr,
bindparam_type=arr_type.item_type,
),
)
comparator_factory = Comparator
@property
def hashable(self):
return self.as_tuple
@property
def python_type(self):
return list
def compare_values(self, x, y):
return x == y
def _set_parent(self, parent, outer=False, **kw):
"""Support SchemaEventTarget"""
if not outer and isinstance(self.item_type, SchemaEventTarget):
self.item_type._set_parent(parent, **kw)
def _set_parent_with_dispatch(self, parent, **kw):
"""Support SchemaEventTarget"""
super()._set_parent_with_dispatch(parent, outer=True)
if isinstance(self.item_type, SchemaEventTarget):
self.item_type._set_parent_with_dispatch(parent)
def literal_processor(self, dialect):
item_proc = self.item_type.dialect_impl(dialect).literal_processor(
dialect
)
if item_proc is None:
return None
def to_str(elements):
return f"[{', '.join(elements)}]"
def process(value):
inner = self._apply_item_processor(
value, item_proc, self.dimensions, to_str
)
return inner
return process
def _apply_item_processor(self, arr, itemproc, dim, collection_callable):
"""Helper method that can be used by bind_processor(),
literal_processor(), etc. to apply an item processor to elements of
an array value, taking into account the 'dimensions' for this
array type.
See the Postgresql ARRAY datatype for usage examples.
.. versionadded:: 2.0
"""
if dim is None:
arr = list(arr)
if (
dim == 1
or dim is None
and (
# this has to be (list, tuple), or at least
# not hasattr('__iter__'), since Py3K strings
# etc. have __iter__
not arr
or not isinstance(arr[0], (list, tuple))
)
):
if itemproc:
return collection_callable(itemproc(x) for x in arr)
else:
return collection_callable(arr)
else:
return collection_callable(
(
self._apply_item_processor(
x,
itemproc,
dim - 1 if dim is not None else None,
collection_callable,
)
if x is not None
else None
)
for x in arr
)
class TupleType(TypeEngine[Tuple[Any, ...]]):
"""represent the composite type of a Tuple."""
_is_tuple_type = True
types: List[TypeEngine[Any]]
def __init__(self, *types: _TypeEngineArgument[Any]):
self._fully_typed = NULLTYPE not in types
self.types = [
item_type() if isinstance(item_type, type) else item_type
for item_type in types
]
def coerce_compared_value(
self, op: Optional[OperatorType], value: Any
) -> TypeEngine[Any]:
if value is type_api._NO_VALUE_IN_LIST:
return super().coerce_compared_value(op, value)
else:
return TupleType(
*[
typ.coerce_compared_value(op, elem)
for typ, elem in zip(self.types, value)
]
)
def _resolve_values_to_types(self, value: Any) -> TupleType:
if self._fully_typed:
return self
else:
return TupleType(
*[
_resolve_value_to_type(elem) if typ is NULLTYPE else typ
for typ, elem in zip(self.types, value)
]
)
def result_processor(self, dialect, coltype):
raise NotImplementedError(
"The tuple type does not support being fetched "
"as a column in a result row."
)
class REAL(Float[_N]):
"""The SQL REAL type.
.. seealso::
:class:`_types.Float` - documentation for the base type.
"""
__visit_name__ = "REAL"
class FLOAT(Float[_N]):
"""The SQL FLOAT type.
.. seealso::
:class:`_types.Float` - documentation for the base type.
"""
__visit_name__ = "FLOAT"
class DOUBLE(Double[_N]):
"""The SQL DOUBLE type.
.. versionadded:: 2.0
.. seealso::
:class:`_types.Double` - documentation for the base type.
"""
__visit_name__ = "DOUBLE"
class DOUBLE_PRECISION(Double[_N]):
"""The SQL DOUBLE PRECISION type.
.. versionadded:: 2.0
.. seealso::
:class:`_types.Double` - documentation for the base type.
"""
__visit_name__ = "DOUBLE_PRECISION"
class NUMERIC(Numeric[_N]):
"""The SQL NUMERIC type.
.. seealso::
:class:`_types.Numeric` - documentation for the base type.
"""
__visit_name__ = "NUMERIC"
class DECIMAL(Numeric[_N]):
"""The SQL DECIMAL type.
.. seealso::
:class:`_types.Numeric` - documentation for the base type.
"""
__visit_name__ = "DECIMAL"
class INTEGER(Integer):
"""The SQL INT or INTEGER type.
.. seealso::
:class:`_types.Integer` - documentation for the base type.
"""
__visit_name__ = "INTEGER"
INT = INTEGER
class SMALLINT(SmallInteger):
"""The SQL SMALLINT type.
.. seealso::
:class:`_types.SmallInteger` - documentation for the base type.
"""
__visit_name__ = "SMALLINT"
class BIGINT(BigInteger):
"""The SQL BIGINT type.
.. seealso::
:class:`_types.BigInteger` - documentation for the base 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: bool = False):
"""Construct a new :class:`_types.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().__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[None]):
"""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:`_schema.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:`_schema.MetaData.create_all` or the
:class:`.CreateTable`
construct.
"""
__visit_name__ = "null"
_isnull = True
def literal_processor(self, dialect):
return None
class Comparator(TypeEngine.Comparator[_T]):
__slots__ = ()
def _adapt_expression(
self,
op: OperatorType,
other_comparator: TypeEngine.Comparator[Any],
) -> Tuple[OperatorType, TypeEngine[Any]]:
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 TableValueType(HasCacheKey, TypeEngine[Any]):
"""Refers to a table value type."""
_is_table_value = True
_traverse_internals = [
("_elements", InternalTraversal.dp_clauseelement_list),
]
def __init__(self, *elements: Union[str, _ColumnExpressionArgument[Any]]):
self._elements = [
coercions.expect(roles.StrAsPlainColumnRole, elem)
for elem in elements
]
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.
"""
_UUID_RETURN = TypeVar("_UUID_RETURN", str, _python_UUID)
class Uuid(Emulated, TypeEngine[_UUID_RETURN]):
"""Represent a database agnostic UUID datatype.
For backends that have no "native" UUID datatype, the value will
make use of ``CHAR(32)`` and store the UUID as a 32-character alphanumeric
hex string.
For backends which are known to support ``UUID`` directly or a similar
uuid-storing datatype such as SQL Server's ``UNIQUEIDENTIFIER``, a
"native" mode enabled by default allows these types will be used on those
backends.
In its default mode of use, the :class:`_sqltypes.Uuid` datatype expects
**Python uuid objects**, from the Python
`uuid <https://docs.python.org/3/library/uuid.html>`_
module::
import uuid
from sqlalchemy import Uuid
from sqlalchemy import Table, Column, MetaData, String
metadata_obj = MetaData()
t = Table(
"t",
metadata_obj,
Column('uuid_data', Uuid, primary_key=True),
Column("other_data", String)
)
with engine.begin() as conn:
conn.execute(
t.insert(),
{"uuid_data": uuid.uuid4(), "other_data", "some data"}
)
To have the :class:`_sqltypes.Uuid` datatype work with string-based
Uuids (e.g. 32 character hexadecimal strings), pass the
:paramref:`_sqltypes.Uuid.as_uuid` parameter with the value ``False``.
.. versionadded:: 2.0
.. seealso::
:class:`_sqltypes.UUID` - represents exactly the ``UUID`` datatype
without any backend-agnostic behaviors.
"""
__visit_name__ = "uuid"
collation: Optional[str] = None
@overload
def __init__(
self: Uuid[_python_UUID],
as_uuid: Literal[True] = ...,
native_uuid: bool = ...,
): ...
@overload
def __init__(
self: Uuid[str],
as_uuid: Literal[False] = ...,
native_uuid: bool = ...,
): ...
def __init__(self, as_uuid: bool = True, native_uuid: bool = True):
"""Construct a :class:`_sqltypes.Uuid` type.
:param as_uuid=True: if True, values will be interpreted
as Python uuid objects, converting to/from string via the
DBAPI.
.. versionchanged: 2.0 ``as_uuid`` now defaults to ``True``.
:param native_uuid=True: if True, backends that support either the
``UUID`` datatype directly, or a UUID-storing value
(such as SQL Server's ``UNIQUEIDENTIFIER`` will be used by those
backends. If False, a ``CHAR(32)`` datatype will be used for
all backends regardless of native support.
"""
self.as_uuid = as_uuid
self.native_uuid = native_uuid
@property
def python_type(self):
return _python_UUID if self.as_uuid else str
@property
def native(self):
return self.native_uuid
def coerce_compared_value(self, op, value):
"""See :meth:`.TypeEngine.coerce_compared_value` for a description."""
if isinstance(value, str):
return self
else:
return super().coerce_compared_value(op, value)
def bind_processor(self, dialect):
character_based_uuid = (
not dialect.supports_native_uuid or not self.native_uuid
)
if character_based_uuid:
if self.as_uuid:
def process(value):
if value is not None:
value = value.hex
return value
return process
else:
def process(value):
if value is not None:
value = value.replace("-", "")
return value
return process
else:
return None
def result_processor(self, dialect, coltype):
character_based_uuid = (
not dialect.supports_native_uuid or not self.native_uuid
)
if character_based_uuid:
if self.as_uuid:
def process(value):
if value is not None:
value = _python_UUID(value)
return value
return process
else:
def process(value):
if value is not None:
value = str(_python_UUID(value))
return value
return process
else:
if not self.as_uuid:
def process(value):
if value is not None:
value = str(value)
return value
return process
else:
return None
def literal_processor(self, dialect):
character_based_uuid = (
not dialect.supports_native_uuid or not self.native_uuid
)
if not self.as_uuid:
def process(value):
return f"""'{value.replace("-", "").replace("'", "''")}'"""
return process
else:
if character_based_uuid:
def process(value):
return f"""'{value.hex}'"""
return process
else:
def process(value):
return f"""'{str(value).replace("'", "''")}'"""
return process
class UUID(Uuid[_UUID_RETURN], type_api.NativeForEmulated):
"""Represent the SQL UUID type.
This is the SQL-native form of the :class:`_types.Uuid` database agnostic
datatype, and is backwards compatible with the previous PostgreSQL-only
version of ``UUID``.
The :class:`_sqltypes.UUID` datatype only works on databases that have a
SQL datatype named ``UUID``. It will not function for backends which don't
have this exact-named type, including SQL Server. For backend-agnostic UUID
values with native support, including for SQL Server's ``UNIQUEIDENTIFIER``
datatype, use the :class:`_sqltypes.Uuid` datatype.
.. versionadded:: 2.0
.. seealso::
:class:`_sqltypes.Uuid`
"""
__visit_name__ = "UUID"
@overload
def __init__(self: UUID[_python_UUID], as_uuid: Literal[True] = ...): ...
@overload
def __init__(self: UUID[str], as_uuid: Literal[False] = ...): ...
def __init__(self, as_uuid: bool = True):
"""Construct a :class:`_sqltypes.UUID` type.
:param as_uuid=True: if True, values will be interpreted
as Python uuid objects, converting to/from string via the
DBAPI.
.. versionchanged: 2.0 ``as_uuid`` now defaults to ``True``.
"""
self.as_uuid = as_uuid
self.native_uuid = True
@classmethod
def adapt_emulated_to_native(cls, impl, **kw):
kw.setdefault("as_uuid", impl.as_uuid)
return cls(**kw)
NULLTYPE = NullType()
BOOLEANTYPE = Boolean()
STRINGTYPE = String()
INTEGERTYPE = Integer()
NUMERICTYPE: Numeric[decimal.Decimal] = Numeric()
MATCHTYPE = MatchType()
TABLEVALUE = TableValueType()
DATETIME_TIMEZONE = DateTime(timezone=True)
TIME_TIMEZONE = Time(timezone=True)
_BIGINTEGER = BigInteger()
_DATETIME = DateTime()
_TIME = Time()
_STRING = String()
_UNICODE = Unicode()
_type_map: Dict[Type[Any], TypeEngine[Any]] = {
int: Integer(),
float: Float(),
bool: BOOLEANTYPE,
_python_UUID: Uuid(),
decimal.Decimal: Numeric(),
dt.date: Date(),
dt.datetime: _DATETIME,
dt.time: _TIME,
dt.timedelta: Interval(),
type(None): NULLTYPE,
bytes: LargeBinary(),
str: _STRING,
enum.Enum: Enum(enum.Enum),
Literal: Enum(enum.Enum), # type: ignore[dict-item]
}
_type_map_get = _type_map.get
def _resolve_value_to_type(value: Any) -> TypeEngine[Any]:
_result_type = _type_map_get(type(value), False)
if _result_type is False:
_result_type = getattr(value, "__sa_type_engine__", 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._resolve_for_literal( # type: ignore [union-attr]
value
)
# back-assign to type_api
type_api.BOOLEANTYPE = BOOLEANTYPE
type_api.STRINGTYPE = STRINGTYPE
type_api.INTEGERTYPE = INTEGERTYPE
type_api.NULLTYPE = NULLTYPE
type_api.NUMERICTYPE = NUMERICTYPE
type_api.MATCHTYPE = MATCHTYPE
type_api.INDEXABLE = INDEXABLE = Indexable
type_api.TABLEVALUE = TABLEVALUE
type_api._resolve_value_to_type = _resolve_value_to_type
advanced_alchemy