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편집 파일: sqltypes.py

# sql/sqltypes.py # Copyright (C) 2005-2021 the SQLAlchemy authors and contributors # <see AUTHORS file> # # This module is part of SQLAlchemy and is released under # the MIT License: http://www.opensource.org/licenses/mit-license.php """SQL specific types. """ import codecs import datetime as dt import decimal import json from . import elements from . import operators from . import type_api from .base import _bind_or_error from .base import NO_ARG from .base import SchemaEventTarget from .elements import _literal_as_binds from .elements import _NONE_NAME 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 from .type_api import TypeDecorator from .type_api import TypeEngine from .type_api import Variant from .. import event from .. import exc from .. import inspection from .. import processors from .. import util from ..util import compat from ..util import langhelpers from ..util import pickle if util.jython: import array class _LookupExpressionAdapter(object): """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): _blank_dict = util.immutabledict() def _adapt_expression(self, op, other_comparator): othertype = other_comparator.type._type_affinity 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 = Comparator class Concatenable(object): """A mixin that marks a type as supporting 'concatenation', typically strings.""" class Comparator(TypeEngine.Comparator): def _adapt_expression(self, op, other_comparator): if op is operators.add and isinstance( other_comparator, (Concatenable.Comparator, NullType.Comparator), ): return operators.concat_op, self.expr.type else: return super(Concatenable.Comparator, self)._adapt_expression( op, other_comparator ) comparator_factory = Comparator class Indexable(object): """A mixin that marks a type as supporting indexing operations, such as array or JSON structures. .. versionadded:: 1.1.0 """ class Comparator(TypeEngine.Comparator): def _setup_getitem(self, index): raise NotImplementedError() def __getitem__(self, index): ( adjusted_op, adjusted_right_expr, result_type, ) = self._setup_getitem(index) return self.operate( adjusted_op, adjusted_right_expr, result_type=result_type ) comparator_factory = Comparator class String(Concatenable, TypeEngine): """The base for all string and character types. In SQL, corresponds to VARCHAR. Can also take Python unicode objects and encode to the database's encoding in bind params (and the reverse for result sets.) The `length` field is usually required when the `String` type is used within a CREATE TABLE statement, as VARCHAR requires a length on most databases. """ __visit_name__ = "string" @util.deprecated_params( convert_unicode=( "1.3", "The :paramref:`.String.convert_unicode` parameter is deprecated " "and will be removed in a future release. All modern DBAPIs " "now support Python Unicode directly and this parameter is " "unnecessary.", ), unicode_error=( "1.3", "The :paramref:`.String.unicode_errors` parameter is deprecated " "and will be removed in a future release. This parameter is " "unnecessary for modern Python DBAPIs and degrades performance " "significantly.", ), ) def __init__( self, length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False, _expect_unicode=False, ): """ Create a string-holding type. :param length: optional, a length for the column for use in DDL and CAST expressions. May be safely omitted if no ``CREATE TABLE`` will be issued. Certain databases may require a ``length`` for use in DDL, and will raise an exception when the ``CREATE TABLE`` DDL is issued if a ``VARCHAR`` with no length is included. Whether the value is interpreted as bytes or characters is database specific. :param collation: Optional, a column-level collation for use in DDL and CAST expressions. Renders using the COLLATE keyword supported by SQLite, MySQL, and PostgreSQL. E.g.:: >>> from sqlalchemy import cast, select, String >>> print(select([cast('some string', String(collation='utf8'))])) SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1 :param convert_unicode: When set to ``True``, the :class:`.String` type will assume that input is to be passed as Python Unicode objects under Python 2, and results returned as Python Unicode objects. In the rare circumstance that the DBAPI does not support Python unicode under Python 2, SQLAlchemy will use its own encoder/decoder functionality on strings, referring to the value of the :paramref:`_sa.create_engine.encoding` parameter parameter passed to :func:`_sa.create_engine` as the encoding. For the extremely rare case that Python Unicode is to be encoded/decoded by SQLAlchemy on a backend that *does* natively support Python Unicode, the string value ``"force"`` can be passed here which will cause SQLAlchemy's encode/decode services to be used unconditionally. .. note:: SQLAlchemy's unicode-conversion flags and features only apply to Python 2; in Python 3, all string objects are Unicode objects. For this reason, as well as the fact that virtually all modern DBAPIs now support Unicode natively even under Python 2, the :paramref:`.String.convert_unicode` flag is inherently a legacy feature. .. note:: In the vast majority of 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 side as well as set up the correct Unicode behaviors under Python 2. .. seealso:: :paramref:`_sa.create_engine.convert_unicode` - :class:`_engine.Engine`-wide parameter :param unicode_error: Optional, a method to use to handle Unicode conversion errors. Behaves like the ``errors`` keyword argument to the standard library's ``string.decode()`` functions, requires that :paramref:`.String.convert_unicode` is set to ``"force"`` """ if unicode_error is not None and convert_unicode != "force": raise exc.ArgumentError( "convert_unicode must be 'force' " "when unicode_error is set." ) self.length = length self.collation = collation self._expect_unicode = convert_unicode or _expect_unicode self._expect_unicode_error = unicode_error self._warn_on_bytestring = _warn_on_bytestring 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): if self._expect_unicode or dialect.convert_unicode: if ( dialect.supports_unicode_binds and self._expect_unicode != "force" ): if self._warn_on_bytestring: def process(value): if isinstance(value, util.binary_type): util.warn_limited( "Unicode type received non-unicode " "bind param value %r.", (util.ellipses_string(value),), ) return value return process else: return None else: encoder = codecs.getencoder(dialect.encoding) warn_on_bytestring = self._warn_on_bytestring def process(value): if isinstance(value, util.text_type): return encoder(value, self._expect_unicode_error)[0] elif warn_on_bytestring and value is not None: util.warn_limited( "Unicode type received non-unicode bind " "param value %r.", (util.ellipses_string(value),), ) return value return process else: return None def result_processor(self, dialect, coltype): wants_unicode = self._expect_unicode or dialect.convert_unicode needs_convert = wants_unicode and ( dialect.returns_unicode_strings is not True or self._expect_unicode in ("force", "force_nocheck") ) needs_isinstance = ( needs_convert and dialect.returns_unicode_strings and self._expect_unicode != "force_nocheck" ) if needs_convert: if needs_isinstance: return processors.to_conditional_unicode_processor_factory( dialect.encoding, self._expect_unicode_error ) else: return processors.to_unicode_processor_factory( dialect.encoding, self._expect_unicode_error ) else: return None @property def python_type(self): if self._expect_unicode: return util.text_type else: return str def get_dbapi_type(self, dbapi): return dbapi.STRING @classmethod def _warn_deprecated_unicode(cls): util.warn_deprecated( "The convert_unicode on Engine and String as well as the " "unicode_error flag on String are deprecated. All modern " "DBAPIs now support Python Unicode natively under Python 2, and " "under Python 3 all strings are inherently Unicode. These flags " "will be removed in a future release." ) class Text(String): """A variably sized string type. In SQL, usually corresponds to CLOB or TEXT. Can also take Python unicode objects and encode to the database's encoding in bind params (and the reverse for result sets.) In general, TEXT objects do not have a length; while some databases will accept a length argument here, it will be rejected by others. """ __visit_name__ = "text" class Unicode(String): """A variable length Unicode string type. The :class:`.Unicode` type is a :class:`.String` subclass that assumes input and output as Python ``unicode`` data, and in that regard is equivalent to the usage of the ``convert_unicode`` flag with the :class:`.String` type. However, unlike plain :class:`.String`, it also implies an underlying column type that is explicitly supporting of non-ASCII data, such as ``NVARCHAR`` on Oracle and SQL Server. This can impact the output of ``CREATE TABLE`` statements and ``CAST`` functions at the dialect level, and can also affect the handling of bound parameters in some specific DBAPI scenarios. The encoding used by the :class:`.Unicode` type is usually determined by the DBAPI itself; most modern DBAPIs feature support for Python ``unicode`` objects as bound values and result set values, and the encoding should be configured as detailed in the notes for the target DBAPI in the :ref:`dialect_toplevel` section. For those DBAPIs which do not support, or are not configured to accommodate Python ``unicode`` objects directly, SQLAlchemy does the encoding and decoding outside of the DBAPI. The encoding in this scenario is determined by the ``encoding`` flag passed to :func:`_sa.create_engine`. When using the :class:`.Unicode` type, it is only appropriate to pass Python ``unicode`` objects, and not plain ``str``. If a plain ``str`` is passed under Python 2, a warning is emitted. If you notice your application emitting these warnings but you're not sure of the source of them, the Python ``warnings`` filter, documented at http://docs.python.org/library/warnings.html, can be used to turn these warnings into exceptions which will illustrate a stack trace:: import warnings warnings.simplefilter('error') For an application that wishes to pass plain bytestrings and Python ``unicode`` objects to the ``Unicode`` type equally, the bytestrings must first be decoded into unicode. The recipe at :ref:`coerce_to_unicode` illustrates how this is done. .. seealso:: :class:`.UnicodeText` - unlengthed textual counterpart to :class:`.Unicode`. """ __visit_name__ = "unicode" def __init__(self, length=None, **kwargs): """ Create a :class:`.Unicode` object. Parameters are the same as that of :class:`.String`, with the exception that ``convert_unicode`` defaults to ``True``. """ kwargs.setdefault("_expect_unicode", True) kwargs.setdefault("_warn_on_bytestring", True) super(Unicode, self).__init__(length=length, **kwargs) class UnicodeText(Text): """An unbounded-length Unicode string type. See :class:`.Unicode` for details on the unicode behavior of this object. Like :class:`.Unicode`, usage the :class:`.UnicodeText` type implies a unicode-capable type being used on the backend, such as ``NCLOB``, ``NTEXT``. """ __visit_name__ = "unicode_text" def __init__(self, length=None, **kwargs): """ Create a Unicode-converting Text type. Parameters are the same as that of :class:`_expression.TextClause`, with the exception that ``convert_unicode`` defaults to ``True``. """ kwargs.setdefault("_expect_unicode", True) kwargs.setdefault("_warn_on_bytestring", True) super(UnicodeText, self).__init__(length=length, **kwargs) def _warn_deprecated_unicode(self): pass class Integer(_LookupExpressionAdapter, TypeEngine): """A type for ``int`` integers.""" __visit_name__ = "integer" def get_dbapi_type(self, dbapi): return dbapi.NUMBER @property def python_type(self): return int def literal_processor(self, dialect): def process(value): return str(value) return process @util.memoized_property def _expression_adaptations(self): # TODO: need a dictionary object that will # handle operators generically here, this is incomplete return { operators.add: { Date: Date, Integer: self.__class__, Numeric: Numeric, }, operators.mul: { Interval: Interval, Integer: self.__class__, Numeric: Numeric, }, operators.div: {Integer: self.__class__, Numeric: Numeric}, operators.truediv: {Integer: self.__class__, Numeric: Numeric}, operators.sub: {Integer: self.__class__, Numeric: Numeric}, } class SmallInteger(Integer): """A type for smaller ``int`` integers. Typically generates a ``SMALLINT`` in DDL, and otherwise acts like a normal :class:`.Integer` on the Python side. """ __visit_name__ = "small_integer" class BigInteger(Integer): """A type for bigger ``int`` integers. Typically generates a ``BIGINT`` in DDL, and otherwise acts like a normal :class:`.Integer` on the Python side. """ __visit_name__ = "big_integer" class Numeric(_LookupExpressionAdapter, TypeEngine): """A type for fixed precision numbers, such as ``NUMERIC`` or ``DECIMAL``. This type returns Python ``decimal.Decimal`` objects by default, unless the :paramref:`.Numeric.asdecimal` flag is set to False, in which case they are coerced to Python ``float`` objects. .. note:: The :class:`.Numeric` type is designed to receive data from a database type that is explicitly known to be a decimal type (e.g. ``DECIMAL``, ``NUMERIC``, others) and not a floating point type (e.g. ``FLOAT``, ``REAL``, others). If the database column on the server is in fact a floating-point type, such as ``FLOAT`` or ``REAL``, use the :class:`.Float` type or a subclass, otherwise numeric coercion between ``float``/``Decimal`` may or may not function as expected. .. note:: The Python ``decimal.Decimal`` class is generally slow performing; cPython 3.3 has now switched to use the `cdecimal <http://pypi.python.org/pypi/cdecimal/>`_ library natively. For older Python versions, the ``cdecimal`` library can be patched into any application where it will replace the ``decimal`` library fully, however this needs to be applied globally and before any other modules have been imported, as follows:: import sys import cdecimal sys.modules["decimal"] = cdecimal Note that the ``cdecimal`` and ``decimal`` libraries are **not compatible with each other**, so patching ``cdecimal`` at the global level is the only way it can be used effectively with various DBAPIs that hardcode to import the ``decimal`` library. """ __visit_name__ = "numeric" _default_decimal_return_scale = 10 def __init__( self, precision=None, scale=None, decimal_return_scale=None, asdecimal=True, ): """ Construct a Numeric. :param precision: the numeric precision for use in DDL ``CREATE TABLE``. :param scale: the numeric scale for use in DDL ``CREATE TABLE``. :param asdecimal: default True. Return whether or not values should be sent as Python Decimal objects, or as floats. Different DBAPIs send one or the other based on datatypes - the Numeric type will ensure that return values are one or the other across DBAPIs consistently. :param decimal_return_scale: Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don't have a notion of "scale", so by default the float type looks for the first ten decimal places when converting. 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. .. versionadded:: 0.9.0 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: util.warn( "Dialect %s+%s does *not* support Decimal " "objects natively, and SQLAlchemy must " "convert from floating point - rounding " "errors and other issues may occur. Please " "consider storing Decimal numbers as strings " "or integers on this platform for lossless " "storage." % (dialect.name, dialect.driver) ) # we're a "numeric", DBAPI returns floats, convert. return processors.to_decimal_processor_factory( decimal.Decimal, self.scale if self.scale is not None else self._default_decimal_return_scale, ) else: if dialect.supports_native_decimal: return processors.to_float else: return None @util.memoized_property def _expression_adaptations(self): return { operators.mul: { Interval: Interval, Numeric: self.__class__, Integer: self.__class__, }, operators.div: {Numeric: self.__class__, Integer: self.__class__}, operators.truediv: { Numeric: self.__class__, Integer: self.__class__, }, operators.add: {Numeric: self.__class__, Integer: self.__class__}, operators.sub: {Numeric: self.__class__, Integer: self.__class__}, } class Float(Numeric): """Type representing floating point types, such as ``FLOAT`` or ``REAL``. This type returns Python ``float`` objects by default, unless the :paramref:`.Float.asdecimal` flag is set to True, in which case they are coerced to ``decimal.Decimal`` objects. .. note:: The :class:`.Float` type is designed to receive data from a database type that is explicitly known to be a floating point type (e.g. ``FLOAT``, ``REAL``, others) and not a decimal type (e.g. ``DECIMAL``, ``NUMERIC``, others). If the database column on the server is in fact a Numeric type, such as ``DECIMAL`` or ``NUMERIC``, use the :class:`.Numeric` type or a subclass, otherwise numeric coercion between ``float``/``Decimal`` may or may not function as expected. """ __visit_name__ = "float" scale = None def __init__( self, precision=None, asdecimal=False, decimal_return_scale=None ): r""" Construct a Float. :param precision: the numeric precision for use in DDL ``CREATE TABLE``. :param asdecimal: the same flag as that of :class:`.Numeric`, but defaults to ``False``. Note that setting this flag to ``True`` results in floating point conversion. :param decimal_return_scale: Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don't have a notion of "scale", so by default the float type looks for the first ten decimal places when converting. 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. .. versionadded:: 0.9.0 """ 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 DateTime(_LookupExpressionAdapter, TypeEngine): """A type for ``datetime.datetime()`` objects. Date and time types return objects from the Python ``datetime`` module. Most DBAPIs have built in support for the datetime module, with the noted exception of SQLite. In the case of SQLite, date and time types are stored as strings which are then converted back to datetime objects when rows are returned. For the time representation within the datetime type, some backends include additional options, such as timezone support and fractional seconds support. For fractional seconds, use the dialect-specific datatype, such as :class:`.mysql.TIME`. For timezone support, use at least the :class:`_types.TIMESTAMP` datatype, if not the dialect-specific datatype object. """ __visit_name__ = "datetime" def __init__(self, timezone=False): """Construct a new :class:`.DateTime`. :param timezone: boolean. Indicates that the datetime type should enable timezone support, if available on the **base date/time-holding type only**. It is recommended to make use of the :class:`_types.TIMESTAMP` datatype directly when using this flag, as some databases include separate generic date/time-holding types distinct from the timezone-capable TIMESTAMP datatype, such as Oracle. """ self.timezone = timezone def get_dbapi_type(self, dbapi): return dbapi.DATETIME @property def python_type(self): return dt.datetime @util.memoized_property def _expression_adaptations(self): # Based on http://www.postgresql.org/docs/current/\ # static/functions-datetime.html. return { operators.add: {Interval: self.__class__}, operators.sub: {Interval: self.__class__, DateTime: Interval}, } class Date(_LookupExpressionAdapter, TypeEngine): """A type for ``datetime.date()`` objects.""" __visit_name__ = "date" def get_dbapi_type(self, dbapi): return dbapi.DATETIME @property def python_type(self): return dt.date @util.memoized_property def _expression_adaptations(self): # Based on http://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(_LookupExpressionAdapter, TypeEngine): """A type for ``datetime.time()`` objects.""" __visit_name__ = "time" def __init__(self, timezone=False): self.timezone = timezone def get_dbapi_type(self, dbapi): return dbapi.DATETIME @property def python_type(self): return dt.time @util.memoized_property def _expression_adaptations(self): # Based on http://www.postgresql.org/docs/current/\ # static/functions-datetime.html. return { operators.add: {Date: DateTime, Interval: self.__class__}, operators.sub: {Time: Interval, Interval: self.__class__}, } class _Binary(TypeEngine): """Define base behavior for binary types.""" def __init__(self, length=None): self.length = length def literal_processor(self, dialect): def process(value): value = value.decode(dialect.encoding).replace("'", "''") return "'%s'" % value return process @property def python_type(self): return util.binary_type # Python 3 - sqlite3 doesn't need the `Binary` conversion # here, though pg8000 does to indicate "bytea" def bind_processor(self, dialect): if dialect.dbapi is None: return None DBAPIBinary = dialect.dbapi.Binary def process(value): if value is not None: return DBAPIBinary(value) else: return None return process # Python 3 has native bytes() type # both sqlite3 and pg8000 seem to return it, # psycopg2 as of 2.5 returns 'memoryview' if util.py2k: def result_processor(self, dialect, coltype): if util.jython: def process(value): if value is not None: if isinstance(value, array.array): return value.tostring() return str(value) else: return None else: process = processors.to_str return process else: def result_processor(self, dialect, coltype): def process(value): if value is not None: value = bytes(value) return value return process def coerce_compared_value(self, op, value): """See :meth:`.TypeEngine.coerce_compared_value` for a description.""" if isinstance(value, util.string_types): return self else: return super(_Binary, self).coerce_compared_value(op, value) def get_dbapi_type(self, dbapi): return dbapi.BINARY class LargeBinary(_Binary): """A type for large binary byte data. The :class:`.LargeBinary` type corresponds to a large and/or unlengthed binary type for the target platform, such as BLOB on MySQL and BYTEA for PostgreSQL. It also handles the necessary conversions for the DBAPI. """ __visit_name__ = "large_binary" def __init__(self, length=None): """ Construct a LargeBinary type. :param length: optional, a length for the column for use in DDL statements, for those binary types that accept a length, such as the MySQL BLOB type. """ _Binary.__init__(self, length=length) @util.deprecated_cls( "0.6", "The :class:`.Binary` class is deprecated and will be removed " "in a future relase. Please use :class:`.LargeBinary`.", ) class Binary(LargeBinary): def __init__(self, *arg, **kw): LargeBinary.__init__(self, *arg, **kw) class SchemaType(SchemaEventTarget): """Mark a type as possibly requiring schema-level DDL for usage. Supports types that must be explicitly created/dropped (i.e. PG ENUM type) as well as types that are complimented by table or schema level constraints, triggers, and other rules. :class:`.SchemaType` classes can also be targets for the :meth:`.DDLEvents.before_parent_attach` and :meth:`.DDLEvents.after_parent_attach` events, where the events fire off surrounding the association of the type object with a parent :class:`_schema.Column`. .. seealso:: :class:`.Enum` :class:`.Boolean` """ def __init__( self, name=None, schema=None, metadata=None, inherit_schema=False, quote=None, _create_events=True, ): if name is not None: self.name = quoted_name(name, quote) else: self.name = None self.schema = schema self.metadata = metadata self.inherit_schema = inherit_schema self._create_events = _create_events if _create_events and self.metadata: event.listen( self.metadata, "before_create", util.portable_instancemethod(self._on_metadata_create), ) event.listen( self.metadata, "after_drop", util.portable_instancemethod(self._on_metadata_drop), ) def _translate_schema(self, effective_schema, map_): return map_.get(effective_schema, effective_schema) def _set_parent(self, column, **kw): column._on_table_attach(util.portable_instancemethod(self._set_table)) def _variant_mapping_for_set_table(self, column): if isinstance(column.type, Variant): variant_mapping = column.type.mapping.copy() variant_mapping["_default"] = column.type.impl else: variant_mapping = None return variant_mapping def _set_table(self, column, table): if self.inherit_schema: self.schema = table.schema if not self._create_events: return variant_mapping = self._variant_mapping_for_set_table(column) event.listen( table, "before_create", util.portable_instancemethod( self._on_table_create, {"variant_mapping": variant_mapping} ), ) event.listen( table, "after_drop", util.portable_instancemethod( self._on_table_drop, {"variant_mapping": variant_mapping} ), ) if self.metadata is None: # TODO: what's the difference between self.metadata # and table.metadata here ? event.listen( table.metadata, "before_create", util.portable_instancemethod( self._on_metadata_create, {"variant_mapping": variant_mapping}, ), ) event.listen( table.metadata, "after_drop", util.portable_instancemethod( self._on_metadata_drop, {"variant_mapping": variant_mapping}, ), ) def copy(self, **kw): return self.adapt(self.__class__, _create_events=True) def adapt(self, impltype, **kw): schema = kw.pop("schema", self.schema) metadata = kw.pop("metadata", self.metadata) _create_events = kw.pop("_create_events", False) return impltype( name=self.name, schema=schema, inherit_schema=self.inherit_schema, metadata=metadata, _create_events=_create_events, **kw ) @property def bind(self): return self.metadata and self.metadata.bind or None def create(self, bind=None, checkfirst=False): """Issue CREATE DDL for this type, if applicable.""" if bind is None: bind = _bind_or_error(self) t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t.create(bind=bind, checkfirst=checkfirst) def drop(self, bind=None, checkfirst=False): """Issue DROP DDL for this type, if applicable.""" if bind is None: bind = _bind_or_error(self) t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t.drop(bind=bind, checkfirst=checkfirst) def _on_table_create(self, target, bind, **kw): if not self._is_impl_for_variant(bind.dialect, kw): return t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t._on_table_create(target, bind, **kw) def _on_table_drop(self, target, bind, **kw): if not self._is_impl_for_variant(bind.dialect, kw): return t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t._on_table_drop(target, bind, **kw) def _on_metadata_create(self, target, bind, **kw): if not self._is_impl_for_variant(bind.dialect, kw): return t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t._on_metadata_create(target, bind, **kw) def _on_metadata_drop(self, target, bind, **kw): if not self._is_impl_for_variant(bind.dialect, kw): return t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t._on_metadata_drop(target, bind, **kw) def _is_impl_for_variant(self, dialect, kw): variant_mapping = kw.pop("variant_mapping", None) if variant_mapping is None: return True if ( dialect.name in variant_mapping and variant_mapping[dialect.name] is self ): return True elif dialect.name not in variant_mapping: return variant_mapping["_default"] is self class Enum(Emulated, String, SchemaType): """Generic Enum Type. The :class:`.Enum` type provides a set of possible string values which the column is constrained towards. The :class:`.Enum` type will make use of the backend's native "ENUM" type if one is available; otherwise, it uses a VARCHAR datatype and produces a CHECK constraint. Use of the backend-native enum type can be disabled using the :paramref:`.Enum.native_enum` flag, and the production of the CHECK constraint is configurable using the :paramref:`.Enum.create_constraint` flag. The :class:`.Enum` type also provides in-Python validation of string values during both read and write operations. When reading a value from the database in a result set, the string value is always checked against the list of possible values and a ``LookupError`` is raised if no match is found. When passing a value to the database as a plain string within a SQL statement, if the :paramref:`.Enum.validate_strings` parameter is set to True, a ``LookupError`` is raised for any string value that's not located in the given list of possible values; note that this impacts usage of LIKE expressions with enumerated values (an unusual use case). .. versionchanged:: 1.1 the :class:`.Enum` type now provides in-Python validation of input values as well as on data being returned by the database. The source of enumerated values may be a list of string values, or alternatively a PEP-435-compliant enumerated class. For the purposes of the :class:`.Enum` datatype, this class need only provide a ``__members__`` method. When using an enumerated class, the enumerated objects are used both for input and output, rather than strings as is the case with a plain-string enumerated type:: import enum class MyEnum(enum.Enum): one = 1 two = 2 three = 3 t = Table( 'data', MetaData(), Column('value', Enum(MyEnum)) ) connection.execute(t.insert(), {"value": MyEnum.two}) assert connection.scalar(t.select()) is MyEnum.two Above, the string names of each element, e.g. "one", "two", "three", are persisted to the database; the values of the Python Enum, here indicated as integers, are **not** used; the value of each enum can therefore be any kind of Python object whether or not it is persistable. 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. .. versionadded:: 1.1 - support for PEP-435-style enumerated classes. .. seealso:: :class:`_postgresql.ENUM` - PostgreSQL-specific type, which has additional functionality. :class:`.mysql.ENUM` - MySQL-specific type """ __visit_name__ = "enum" @util.deprecated_params( convert_unicode=( "1.3", "The :paramref:`.Enum.convert_unicode` parameter is deprecated " "and will be removed in a future release. All modern DBAPIs " "now support Python Unicode directly and this parameter is " "unnecessary.", ) ) def __init__(self, *enums, **kw): r"""Construct an enum. Keyword arguments which don't apply to a specific backend are ignored by that backend. :param \*enums: either exactly one PEP-435 compliant enumerated type or one or more string or unicode enumeration labels. If unicode labels are present, the `convert_unicode` flag is auto-enabled. .. versionadded:: 1.1 a PEP-435 style enumerated class may be passed. :param convert_unicode: Enable unicode-aware bind parameter and result-set processing for this Enum's data. This is set automatically based on the presence of unicode label strings. :param create_constraint: defaults to True. When creating a non-native enumerated type, also build a CHECK constraint on the database against the valid values. .. versionadded:: 1.1 - added :paramref:`.Enum.create_constraint` which provides the option to disable the production of the CHECK constraint for a non-native enumerated type. :param metadata: Associate this type directly with a ``MetaData`` object. For types that exist on the target database as an independent schema construct (PostgreSQL), this type will be created and dropped within ``create_all()`` and ``drop_all()`` operations. If the type is not associated with any ``MetaData`` object, it will associate itself with each ``Table`` in which it is used, and will be created when any of those individual tables are created, after a check is performed for its existence. The type is only dropped when ``drop_all()`` is called for that ``Table`` object's metadata, however. :param name: The name of this type. This is required for PostgreSQL and any future supported database which requires an explicitly named type, or an explicitly named constraint in order to generate the type and/or a table that uses it. If a PEP-435 enumerated class was used, its name (converted to lower case) is used by default. :param native_enum: Use the database's native ENUM type when available. Defaults to True. When False, uses VARCHAR + check constraint for all backends. The VARCHAR length can be controlled with :paramref:`.Enum.length` :param length: Allows specifying a custom length for the VARCHAR when :paramref:`.Enum.native_enum` is False. By default it uses the length of the longest value. .. versionadded:: 1.3.16 :param schema: Schema name of this type. For types that exist on the target database as an independent schema construct (PostgreSQL), this parameter specifies the named schema in which the type is present. .. note:: The ``schema`` of the :class:`.Enum` type does not by default make use of the ``schema`` established on the owning :class:`_schema.Table`. If this behavior is desired, set the ``inherit_schema`` flag to ``True``. :param quote: Set explicit quoting preferences for the type's name. :param inherit_schema: When ``True``, the "schema" from the owning :class:`_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.tometadata` operation. :param validate_strings: when True, string values that are being passed to the database in a SQL statement will be checked for validity against the list of enumerated values. Unrecognized values will result in a ``LookupError`` being raised. .. versionadded:: 1.1.0b2 :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. .. 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 """ 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", True) 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) values, objects = self._parse_into_values(enums, kw) self._setup_for_values(values, objects, kw) convert_unicode = kw.pop("convert_unicode", None) self.validate_strings = kw.pop("validate_strings", False) if convert_unicode is None: for e in self.enums: # this is all py2k logic that can go away for py3k only, # "expect unicode" will always be implicitly true if isinstance(e, util.text_type): _expect_unicode = True break else: _expect_unicode = False else: _expect_unicode = convert_unicode if self.enums: length = max(len(x) for x in self.enums) else: length = 0 if not self.native_enum and length_arg is not NO_ARG: if 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(Enum, self).__init__( length=length, _expect_unicode=_expect_unicode ) if self.enum_class: 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), ) 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.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 _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, compat.string_types ): return elem else: util.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), ) ), replace_context=err, ) class Comparator(String.Comparator): def _adapt_expression(self, op, other_comparator): op, typ = super(Enum.Comparator, self)._adapt_expression( op, other_comparator ) if op is operators.concat_op: typ = String( self.type.length, _expect_unicode=self.type._expect_unicode ) return op, typ comparator_factory = Comparator def _object_value_for_elem(self, elem): try: return self._object_lookup[elem] except KeyError as err: util.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), ) ), replace_context=err, ) def __repr__(self): return util.generic_repr( self, additional_kw=[("native_enum", True)], to_inspect=[Enum, SchemaType], ) def adapt_to_emulated(self, impltype, **kw): kw.setdefault("_expect_unicode", self._expect_unicode) kw.setdefault("validate_strings", self.validate_strings) kw.setdefault("name", self.name) kw.setdefault("schema", self.schema) kw.setdefault("inherit_schema", self.inherit_schema) kw.setdefault("metadata", self.metadata) kw.setdefault("_create_events", False) 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) assert "_enums" in kw return impltype(**kw) def adapt(self, impltype, **kw): kw["_enums"] = self._enums_argument return super(Enum, self).adapt(impltype, **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.dependencies("sqlalchemy.sql.schema") def _set_table(self, schema, column, table): SchemaType._set_table(self, column, table) if not self.create_constraint: return variant_mapping = self._variant_mapping_for_set_table(column) e = schema.CheckConstraint( type_coerce(column, self).in_(self.enums), name=_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(Enum, self).literal_processor(dialect) def process(value): value = self._db_value_for_elem(value) if parent_processor: value = parent_processor(value) return value return process def bind_processor(self, dialect): def process(value): value = self._db_value_for_elem(value) if parent_processor: value = parent_processor(value) return value parent_processor = super(Enum, self).bind_processor(dialect) return process def result_processor(self, dialect, coltype): parent_processor = super(Enum, self).result_processor(dialect, coltype) def process(value): if parent_processor: value = parent_processor(value) value = self._object_value_for_elem(value) return value return process 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(Enum, self).python_type class PickleType(TypeDecorator): """Holds Python objects, which are serialized using pickle. PickleType builds upon the Binary type to apply Python's ``pickle.dumps()`` to incoming objects, and ``pickle.loads()`` on the way out, allowing any pickleable Python object to be stored as a serialized binary field. To allow ORM change events to propagate for elements associated with :class:`.PickleType`, see :ref:`mutable_toplevel`. """ impl = LargeBinary def __init__( self, protocol=pickle.HIGHEST_PROTOCOL, pickler=None, comparator=None ): """ Construct a PickleType. :param protocol: defaults to ``pickle.HIGHEST_PROTOCOL``. :param pickler: defaults to cPickle.pickle or pickle.pickle if cPickle is not available. May be any object with pickle-compatible ``dumps`` and ``loads`` methods. :param comparator: a 2-arg callable predicate used to compare values of this type. If left as ``None``, the Python "equals" operator is used to compare values. """ self.protocol = protocol self.pickler = pickler or pickle self.comparator = comparator super(PickleType, self).__init__() def __reduce__(self): return PickleType, (self.protocol, None, self.comparator) def bind_processor(self, dialect): impl_processor = self.impl.bind_processor(dialect) dumps = self.pickler.dumps protocol = self.protocol if impl_processor: def process(value): if value is not None: value = dumps(value, protocol) return impl_processor(value) else: def process(value): if value is not None: value = dumps(value, protocol) return value return process def result_processor(self, dialect, coltype): impl_processor = self.impl.result_processor(dialect, coltype) loads = self.pickler.loads if impl_processor: def process(value): value = impl_processor(value) if value is None: return None return loads(value) else: def process(value): if value is None: return None return loads(value) return process def compare_values(self, x, y): if self.comparator: return self.comparator(x, y) else: return x == y class Boolean(Emulated, TypeEngine, SchemaType): """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, a CHECK constraint is also created on the target column. Production of the CHECK constraint can be disabled by passing the :paramref:`.Boolean.create_constraint` flag set to ``False``. .. 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=True, name=None, _create_events=True): """Construct a Boolean. :param create_constraint: defaults to True. If the boolean is generated as an int/smallint, also create a CHECK constraint on the table that ensures 1 or 0 as a value. :param name: if a CHECK constraint is generated, specify the name of the constraint. """ self.create_constraint = create_constraint self.name = name self._create_events = _create_events def _should_create_constraint(self, compiler, **kw): if not self._is_impl_for_variant(compiler.dialect, kw): return False return ( not compiler.dialect.supports_native_boolean and compiler.dialect.non_native_boolean_check_constraint ) @util.dependencies("sqlalchemy.sql.schema") def _set_table(self, schema, column, table): if not self.create_constraint: return variant_mapping = self._variant_mapping_for_set_table(column) e = schema.CheckConstraint( type_coerce(column, self).in_([0, 1]), name=_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 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(_LookupExpressionAdapter, TypeEngine): @util.memoized_property def _expression_adaptations(self): # Based on http://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__}, operators.div: {Numeric: self.__class__}, } @property def _type_affinity(self): return Interval def coerce_compared_value(self, op, value): """See :meth:`.TypeEngine.coerce_compared_value` for a description.""" return self.impl.coerce_compared_value(op, value) class Interval(Emulated, _AbstractInterval, TypeDecorator): """A type for ``datetime.timedelta()`` objects. The Interval type deals with ``datetime.timedelta`` objects. In PostgreSQL, the native ``INTERVAL`` type is used; for others, the value is stored as a date which is relative to the "epoch" (Jan. 1, 1970). Note that the ``Interval`` type does not currently provide date arithmetic operations on platforms which do not support interval types natively. Such operations usually require transformation of both sides of the expression (such as, conversion of both sides into integer epoch values first) which currently is a manual procedure (such as via :attr:`~sqlalchemy.sql.expression.func`). """ impl = DateTime epoch = dt.datetime.utcfromtimestamp(0) def __init__(self, native=True, second_precision=None, day_precision=None): """Construct an Interval object. :param native: when True, use the actual INTERVAL type provided by the database, if supported (currently PostgreSQL, Oracle). Otherwise, represent the interval data as an epoch value regardless. :param second_precision: For native interval types which support a "fractional seconds precision" parameter, i.e. Oracle and PostgreSQL :param day_precision: for native interval types which support a "day precision" parameter, i.e. Oracle. """ super(Interval, self).__init__() self.native = native self.second_precision = second_precision self.day_precision = day_precision @property def python_type(self): return dt.timedelta def adapt_to_emulated(self, impltype, **kw): return _AbstractInterval.adapt(self, impltype, **kw) def bind_processor(self, dialect): impl_processor = self.impl.bind_processor(dialect) epoch = self.epoch if impl_processor: def process(value): if value is not None: value = epoch + value return impl_processor(value) else: def process(value): if value is not None: value = epoch + value return value return process def result_processor(self, dialect, coltype): impl_processor = self.impl.result_processor(dialect, coltype) epoch = self.epoch if impl_processor: def process(value): value = impl_processor(value) if value is None: return None return value - epoch else: def process(value): if value is None: return None return value - epoch return process class JSON(Indexable, TypeEngine): """Represent a SQL JSON type. .. note:: :class:`_types.JSON` is provided as a facade for vendor-specific JSON types. Since it supports JSON SQL operations, it only works on backends that have an actual JSON type, currently: * PostgreSQL * MySQL as of version 5.7 (MariaDB as of the 10.2 series does not) * SQLite as of version 3.9 :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:`_postgresql.JSON` and :class:`_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. 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. **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`:: 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:`_postgresql.JSON` :class:`_postgresql.JSONB` :class:`.mysql.JSON` :class:`_sqlite.JSON` .. versionadded:: 1.1 """ __visit_name__ = "JSON" hashable = False NULL = util.symbol("JSON_NULL") """Describe the json value of NULL. This value is used to force the JSON value of ``"null"`` to be used as the value. A value of Python ``None`` will be recognized either as SQL NULL or JSON ``"null"``, based on the setting of the :paramref:`_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=False): """Construct a :class:`_types.JSON` type. :param none_as_null=False: if True, persist the value ``None`` as a SQL NULL value, not the JSON encoding of ``null``. Note that when this flag is False, the :func:`.null` construct can still be used to persist a NULL value:: from sqlalchemy import null conn.execute(table.insert(), data=null()) .. note:: :paramref:`_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". .. seealso:: :attr:`.types.JSON.NULL` """ self.none_as_null = none_as_null class JSONElementType(TypeEngine): """Common function for index / path elements in a JSON expression.""" _integer = Integer() _string = String() def string_bind_processor(self, dialect): return self._string._cached_bind_processor(dialect) def string_literal_processor(self, dialect): return self._string._cached_literal_processor(dialect) def bind_processor(self, dialect): int_processor = self._integer._cached_bind_processor(dialect) string_processor = self.string_bind_processor(dialect) def process(value): if int_processor and isinstance(value, int): value = int_processor(value) elif string_processor and isinstance(value, util.string_types): value = string_processor(value) return value return process def literal_processor(self, dialect): int_processor = self._integer._cached_literal_processor(dialect) string_processor = self.string_literal_processor(dialect) def process(value): if int_processor and isinstance(value, int): value = int_processor(value) elif string_processor and isinstance(value, util.string_types): value = string_processor(value) return value return process class JSONIndexType(JSONElementType): """Placeholder for the datatype of a JSON index value. This allows execution-time processing of JSON index values for special syntaxes. """ class JSONPathType(JSONElementType): """Placeholder type for JSON path operations. This allows execution-time processing of a path-based index value into a specific SQL syntax. """ class Comparator(Indexable.Comparator, Concatenable.Comparator): """Define comparison operations for :class:`_types.JSON`.""" @util.dependencies("sqlalchemy.sql.default_comparator") def _setup_getitem(self, default_comparator, index): if not isinstance(index, util.string_types) and isinstance( index, compat.collections_abc.Sequence ): index = default_comparator._check_literal( self.expr, operators.json_path_getitem_op, index, bindparam_type=JSON.JSONPathType, ) operator = operators.json_path_getitem_op else: index = default_comparator._check_literal( self.expr, operators.json_getitem_op, index, bindparam_type=JSON.JSONIndexType, ) operator = operators.json_getitem_op return operator, index, self.type def as_boolean(self): """Cast an indexed value as boolean. 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): """Cast an indexed value as string. 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(String(), "as_string") def as_integer(self): """Cast an indexed value as integer. 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): """Cast an indexed value as float. 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 """ # note there's no Numeric or Decimal support here yet return self._binary_w_type(Float(), "as_float") def as_json(self): """Cast an indexed value as JSON. This is 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 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(_expect_unicode=True) def bind_processor(self, dialect): string_process = self._str_impl.bind_processor(dialect) json_serializer = dialect._json_serializer or json.dumps def process(value): if value is self.NULL: value = None elif isinstance(value, elements.Null) or ( value is None and self.none_as_null ): return None serialized = json_serializer(value) if string_process: serialized = string_process(serialized) return serialized return process def result_processor(self, dialect, coltype): string_process = self._str_impl.result_processor(dialect, coltype) json_deserializer = dialect._json_deserializer or json.loads def process(value): if value is None: return None if string_process: value = string_process(value) return json_deserializer(value) return process class ARRAY(SchemaEventTarget, Indexable, Concatenable, TypeEngine): """Represent a SQL Array type. .. note:: This type serves as the basis for all ARRAY operations. However, currently **only the PostgreSQL backend has support for SQL arrays in SQLAlchemy**. It is recommended to use the :class:`_postgresql.ARRAY` type directly when using ARRAY types with PostgreSQL, as it provides additional operators specific to that backend. :class:`_types.ARRAY` is part of the Core in support of various SQL standard functions such as :class:`_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 Python ``[]`` operator works normally here, given integer indexes or slices. Arrays default to 1-based indexing. The operator produces binary expression constructs which will produce the appropriate SQL, both for SELECT statements:: select([mytable.c.data[5], mytable.c.data[2:7]]) as well as UPDATE statements when the :meth:`_expression.Update.values` method is used:: mytable.update().values({ mytable.c.data[5]: 7, mytable.c.data[2:7]: [1, 2, 3] }) The :class:`_types.ARRAY` type also provides for the operators :meth:`.types.ARRAY.Comparator.any` and :meth:`.types.ARRAY.Comparator.all`. The PostgreSQL-specific version of :class:`_types.ARRAY` also provides additional operators. .. versionadded:: 1.1.0 .. seealso:: :class:`_postgresql.ARRAY` """ __visit_name__ = "ARRAY" _is_array = True zero_indexes = False """If True, Python zero-based indexes should be interpreted as one-based on the SQL expression side.""" class Comparator(Indexable.Comparator, Concatenable.Comparator): """Define comparison operations for :class:`_types.ARRAY`. More operators are available on the dialect-specific form of this type. See :class:`.postgresql.ARRAY.Comparator`. """ def _setup_getitem(self, index): if isinstance(index, slice): return_type = self.type if self.type.zero_indexes: index = slice(index.start + 1, index.stop + 1, index.step) index = Slice( _literal_as_binds( index.start, name=self.expr.key, type_=type_api.INTEGERTYPE, ), _literal_as_binds( index.stop, name=self.expr.key, type_=type_api.INTEGERTYPE, ), _literal_as_binds( index.step, name=self.expr.key, type_=type_api.INTEGERTYPE, ), ) else: if self.type.zero_indexes: index += 1 if self.type.dimensions is None or self.type.dimensions == 1: return_type = self.type.item_type else: adapt_kw = {"dimensions": self.type.dimensions - 1} return_type = self.type.adapt( self.type.__class__, **adapt_kw ) return operators.getitem, index, return_type def contains(self, *arg, **kw): raise NotImplementedError( "ARRAY.contains() not implemented for the base " "ARRAY type; please use the dialect-specific ARRAY type" ) @util.dependencies("sqlalchemy.sql.elements") def any(self, elements, other, operator=None): """Return ``other operator ANY (array)`` clause. Argument places are switched, because ANY requires array expression to be on the right hand-side. E.g.:: from sqlalchemy.sql import operators conn.execute( select([table.c.data]).where( table.c.data.any(7, operator=operators.lt) ) ) :param other: expression to be compared :param operator: an operator object from the :mod:`sqlalchemy.sql.operators` package, defaults to :func:`.operators.eq`. .. seealso:: :func:`_expression.any_` :meth:`.types.ARRAY.Comparator.all` """ operator = operator if operator else operators.eq # send plain BinaryExpression so that negate remains at None, # leading to NOT expr for negation. return elements.BinaryExpression( elements._literal_as_binds(other), elements.CollectionAggregate._create_any(self.expr), operator, ) @util.dependencies("sqlalchemy.sql.elements") def all(self, elements, other, operator=None): """Return ``other operator ALL (array)`` clause. Argument places are switched, because ALL requires array expression to be on the right hand-side. E.g.:: from sqlalchemy.sql import operators conn.execute( select([table.c.data]).where( table.c.data.all(7, operator=operators.lt) ) ) :param other: expression to be compared :param operator: an operator object from the :mod:`sqlalchemy.sql.operators` package, defaults to :func:`.operators.eq`. .. seealso:: :func:`_expression.all_` :meth:`.types.ARRAY.Comparator.any` """ operator = operator if operator else operators.eq # send plain BinaryExpression so that negate remains at None, # leading to NOT expr for negation. return elements.BinaryExpression( elements._literal_as_binds(other), elements.CollectionAggregate._create_all(self.expr), operator, ) comparator_factory = Comparator def __init__( self, item_type, as_tuple=False, dimensions=None, zero_indexes=False ): """Construct an :class:`_types.ARRAY`. E.g.:: Column('myarray', ARRAY(Integer)) Arguments are: :param item_type: The data type of items of this array. Note that dimensionality is irrelevant here, so multi-dimensional arrays like ``INTEGER[][]``, are constructed as ``ARRAY(Integer)``, not as ``ARRAY(ARRAY(Integer))`` or such. :param as_tuple=False: Specify whether return results should be converted to tuples from lists. This parameter is not generally needed as a Python list corresponds well to a SQL array. :param dimensions: if non-None, the ARRAY will assume a fixed number of dimensions. This impacts how the array is declared on the database, how it goes about interpreting Python and result values, as well as how expression behavior in conjunction with the "getitem" operator works. See the description at :class:`_types.ARRAY` for additional detail. :param zero_indexes=False: when True, index values will be converted between Python zero-based and SQL one-based indexes, e.g. a value of one will be added to all index values before passing to the database. """ if isinstance(item_type, ARRAY): raise ValueError( "Do not nest ARRAY types; ARRAY(basetype) " "handles multi-dimensional arrays of basetype" ) if isinstance(item_type, type): item_type = item_type() self.item_type = item_type self.as_tuple = as_tuple self.dimensions = dimensions self.zero_indexes = zero_indexes @property def hashable(self): return self.as_tuple @property def python_type(self): return list def compare_values(self, x, y): return x == y def _set_parent(self, column, **kw): """Support SchemaEventTarget""" if isinstance(self.item_type, SchemaEventTarget): self.item_type._set_parent(column, **kw) def _set_parent_with_dispatch(self, parent, **kw): """Support SchemaEventTarget""" super(ARRAY, self)._set_parent_with_dispatch(parent) if isinstance(self.item_type, SchemaEventTarget): self.item_type._set_parent_with_dispatch(parent) class REAL(Float): """The SQL REAL type.""" __visit_name__ = "REAL" class FLOAT(Float): """The SQL FLOAT type.""" __visit_name__ = "FLOAT" class NUMERIC(Numeric): """The SQL NUMERIC type.""" __visit_name__ = "NUMERIC" class DECIMAL(Numeric): """The SQL DECIMAL type.""" __visit_name__ = "DECIMAL" class INTEGER(Integer): """The SQL INT or INTEGER type.""" __visit_name__ = "INTEGER" INT = INTEGER class SMALLINT(SmallInteger): """The SQL SMALLINT type.""" __visit_name__ = "SMALLINT" class BIGINT(BigInteger): """The SQL BIGINT type.""" __visit_name__ = "BIGINT" class TIMESTAMP(DateTime): """The SQL TIMESTAMP type. :class:`_types.TIMESTAMP` datatypes have support for timezone storage on some backends, such as PostgreSQL and Oracle. Use the :paramref:`~types.TIMESTAMP.timezone` argument in order to enable "TIMESTAMP WITH TIMEZONE" for these backends. """ __visit_name__ = "TIMESTAMP" def __init__(self, timezone=False): """Construct a new :class:`_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(TIMESTAMP, self).__init__(timezone=timezone) def get_dbapi_type(self, dbapi): return dbapi.TIMESTAMP class DATETIME(DateTime): """The SQL DATETIME type.""" __visit_name__ = "DATETIME" class DATE(Date): """The SQL DATE type.""" __visit_name__ = "DATE" class TIME(Time): """The SQL TIME type.""" __visit_name__ = "TIME" class TEXT(Text): """The SQL TEXT type.""" __visit_name__ = "TEXT" class CLOB(Text): """The CLOB type. This type is found in Oracle and Informix. """ __visit_name__ = "CLOB" class VARCHAR(String): """The SQL VARCHAR type.""" __visit_name__ = "VARCHAR" class NVARCHAR(Unicode): """The SQL NVARCHAR type.""" __visit_name__ = "NVARCHAR" class CHAR(String): """The SQL CHAR type.""" __visit_name__ = "CHAR" class NCHAR(Unicode): """The SQL NCHAR type.""" __visit_name__ = "NCHAR" class BLOB(LargeBinary): """The SQL BLOB type.""" __visit_name__ = "BLOB" class BINARY(_Binary): """The SQL BINARY type.""" __visit_name__ = "BINARY" class VARBINARY(_Binary): """The SQL VARBINARY type.""" __visit_name__ = "VARBINARY" class BOOLEAN(Boolean): """The SQL BOOLEAN type.""" __visit_name__ = "BOOLEAN" class NullType(TypeEngine): """An unknown type. :class:`.NullType` is used as a default type for those cases where a type cannot be determined, including: * During table reflection, when the type of a column is not recognized by the :class:`.Dialect` * When constructing SQL expressions using plain Python objects of unknown types (e.g. ``somecolumn == my_special_object``) * When a new :class:`_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 hashable = False def literal_processor(self, dialect): def process(value): return "NULL" return process class Comparator(TypeEngine.Comparator): def _adapt_expression(self, op, other_comparator): if isinstance( other_comparator, NullType.Comparator ) or not operators.is_commutative(op): return op, self.expr.type else: return other_comparator._adapt_expression(op, self) comparator_factory = Comparator class MatchType(Boolean): """Refers to the return type of the MATCH operator. As the :meth:`.ColumnOperators.match` is probably the most open-ended operator in generic SQLAlchemy Core, we can't assume the return type at SQL evaluation time, as MySQL returns a floating point, not a boolean, and other backends might do something different. So this type acts as a placeholder, currently subclassing :class:`.Boolean`. The type allows dialects to inject result-processing functionality if needed, and on MySQL will return floating-point values. .. versionadded:: 1.0.0 """ NULLTYPE = NullType() BOOLEANTYPE = Boolean() STRINGTYPE = String() INTEGERTYPE = Integer() MATCHTYPE = MatchType() _type_map = { int: Integer(), float: Float(), bool: BOOLEANTYPE, decimal.Decimal: Numeric(), dt.date: Date(), dt.datetime: DateTime(), dt.time: Time(), dt.timedelta: Interval(), util.NoneType: NULLTYPE, } if util.py3k: _type_map[bytes] = LargeBinary() # noqa _type_map[str] = Unicode() else: _type_map[unicode] = Unicode() # noqa _type_map[str] = String() _type_map_get = _type_map.get def _resolve_value_to_type(value): _result_type = _type_map_get(type(value), False) if _result_type is False: # use inspect() to detect SQLAlchemy built-in # objects. insp = inspection.inspect(value, False) if ( insp is not None and # foil mock.Mock() and other impostors by ensuring # the inspection target itself self-inspects insp.__class__ in inspection._registrars ): raise exc.ArgumentError( "Object %r is not legal as a SQL literal value" % value ) return NULLTYPE else: return _result_type # back-assign to type_api type_api.BOOLEANTYPE = BOOLEANTYPE type_api.STRINGTYPE = STRINGTYPE type_api.INTEGERTYPE = INTEGERTYPE type_api.NULLTYPE = NULLTYPE type_api.MATCHTYPE = MATCHTYPE type_api.INDEXABLE = Indexable type_api._resolve_value_to_type = _resolve_value_to_type TypeEngine.Comparator.BOOLEANTYPE = BOOLEANTYPE