# mypy: ignore-errors from ... import types as sqltypes # technically, all the dialect-specific datatypes that don't have any special # behaviors would be private with names like _MSJson. However, we haven't been # doing this for mysql.JSON or sqlite.JSON which both have JSON / JSONIndexType # / JSONPathType in their json.py files, so keep consistent with that # sub-convention for now. A future change can update them all to be # package-private at once. class JSON(sqltypes.JSON): """MSSQL JSON type. MSSQL supports JSON-formatted data as of SQL Server 2016. The :class:`_mssql.JSON` datatype at the DDL level will represent the datatype as ``NVARCHAR(max)``, but provides for JSON-level comparison functions as well as Python coercion behavior. :class:`_mssql.JSON` is used automatically whenever the base :class:`_types.JSON` datatype is used against a SQL Server backend. .. seealso:: :class:`_types.JSON` - main documentation for the generic cross-platform JSON datatype. The :class:`_mssql.JSON` type supports persistence of JSON values as well as the core index operations provided by :class:`_types.JSON` datatype, by adapting the operations to render the ``JSON_VALUE`` or ``JSON_QUERY`` functions at the database level. The SQL Server :class:`_mssql.JSON` type necessarily makes use of the ``JSON_QUERY`` and ``JSON_VALUE`` functions when querying for elements of a JSON object. These two functions have a major restriction in that they are **mutually exclusive** based on the type of object to be returned. The ``JSON_QUERY`` function **only** returns a JSON dictionary or list, but not an individual string, numeric, or boolean element; the ``JSON_VALUE`` function **only** returns an individual string, numeric, or boolean element. **both functions either return NULL or raise an error if they are not used against the correct expected value**. To handle this awkward requirement, indexed access rules are as follows: 1. When extracting a sub element from a JSON that is itself a JSON dictionary or list, the :meth:`_types.JSON.Comparator.as_json` accessor should be used:: stmt = select( data_table.c.data["some key"].as_json() ).where( data_table.c.data["some key"].as_json() == {"sub": "structure"} ) 2. When extracting a sub element from a JSON that is a plain boolean, string, integer, or float, use the appropriate method among :meth:`_types.JSON.Comparator.as_boolean`, :meth:`_types.JSON.Comparator.as_string`, :meth:`_types.JSON.Comparator.as_integer`, :meth:`_types.JSON.Comparator.as_float`:: stmt = select( data_table.c.data["some key"].as_string() ).where( data_table.c.data["some key"].as_string() == "some string" ) .. versionadded:: 1.4 """ # note there was a result processor here that was looking for "number", # but none of the tests seem to exercise it. # Note: these objects currently match exactly those of MySQL, however since # these are not generalizable to all JSON implementations, remain separately # implemented for each dialect. class _FormatTypeMixin: def _format_value(self, value): raise NotImplementedError() def bind_processor(self, dialect): super_proc = self.string_bind_processor(dialect) def process(value): value = self._format_value(value) if super_proc: value = super_proc(value) return value return process def literal_processor(self, dialect): super_proc = self.string_literal_processor(dialect) def process(value): value = self._format_value(value) if super_proc: value = super_proc(value) return value return process class JSONIndexType(_FormatTypeMixin, sqltypes.JSON.JSONIndexType): def _format_value(self, value): if isinstance(value, int): value = "$[%s]" % value else: value = '$."%s"' % value return value class JSONPathType(_FormatTypeMixin, sqltypes.JSON.JSONPathType): def _format_value(self, value): return "$%s" % ( "".join( [ "[%s]" % elem if isinstance(elem, int) else '."%s"' % elem for elem in value ] ) )