sqlatypemodel.model_type package¶

Submodules¶

sqlatypemodel.model_type.model_type module¶

SQLAlchemy TypeDecorator for storing Pydantic models as JSON.

class sqlatypemodel.model_type.model_type.ModelType(model: type[T], dumper: Callable[[T], dict[str, Any]] | None = None, loader: Callable[[dict[str, Any]], T] | None = None, *args: Any, **kwargs: Any)[source]¶

Bases: TypeDecorator, Generic[T]

SQLAlchemy TypeDecorator for storing Pydantic models as JSON.

This type handles the automatic serialization and deserialization of Pydantic models (and compatible classes) to and from JSON. It also ensures that mutation tracking is restored when objects are loaded from the database.

impl¶

The underlying SQLAlchemy type (JSON).

Type:: Union[TypeEngine[Any], Type[TypeEngine[Any]]]

cache_ok¶

Whether the type is safe to cache (True).

Type:: Optional[bool]

__init__(model: type[T], dumper: Callable[[T], dict[str, Any]] | None = None, loader: Callable[[dict[str, Any]], T] | None = None, *args: Any, **kwargs: Any) → None[source]¶

Initialize the ModelType.

Parameters:

model – A Pydantic model class (or compatible) to be stored.
dumper – Custom serialization function (Model -> Dict).
loader – Custom deserialization function (Dict -> Model).
*args – Additional arguments for TypeDecorator.
**kwargs – Additional keyword arguments for TypeDecorator.

Raises:

ValueError – If serialization or deserialization functions cannot be resolved.

cache_ok: bool | None = True¶

Indicate if statements using this ExternalType are “safe to cache”.

The default value None will emit a warning and then not allow caching of a statement which includes this type. Set to False to disable statements using this type from being cached at all without a warning. When set to True, the object’s class and selected elements from its state will be used as part of the cache key. For example, using a TypeDecorator:

class MyType(TypeDecorator):
    impl = String

    cache_ok = True

    def __init__(self, choices):
        self.choices = tuple(choices)
        self.internal_only = True

The cache key for the above type would be equivalent to:

>>> MyType(["a", "b", "c"])._static_cache_key
(<class '__main__.MyType'>, ('choices', ('a', 'b', 'c')))

The caching scheme will extract attributes from the type that correspond to the names of parameters in the __init__() method. Above, the “choices” attribute becomes part of the cache key but “internal_only” does not, because there is no parameter named “internal_only”.

The requirements for cacheable elements is that they are hashable and also that they indicate the same SQL rendered for expressions using this type every time for a given cache value.

To accommodate for datatypes that refer to unhashable structures such as dictionaries, sets and lists, these objects can be made “cacheable” by assigning hashable structures to the attributes whose names correspond with the names of the arguments. For example, a datatype which accepts a dictionary of lookup values may publish this as a sorted series of tuples. Given a previously un-cacheable type as:

class LookupType(UserDefinedType):
    """a custom type that accepts a dictionary as a parameter.

    this is the non-cacheable version, as "self.lookup" is not
    hashable.

    """

    def __init__(self, lookup):
        self.lookup = lookup

    def get_col_spec(self, **kw):
        return "VARCHAR(255)"

    def bind_processor(self, dialect): ...  # works with "self.lookup" ...

Where “lookup” is a dictionary. The type will not be able to generate a cache key:

>>> type_ = LookupType({"a": 10, "b": 20})
>>> type_._static_cache_key
<stdin>:1: SAWarning: UserDefinedType LookupType({'a': 10, 'b': 20}) will not
produce a cache key because the ``cache_ok`` flag is not set to True.
Set this flag to True if this type object's state is safe to use
in a cache key, or False to disable this warning.
symbol('no_cache')

If we did set up such a cache key, it wouldn’t be usable. We would get a tuple structure that contains a dictionary inside of it, which cannot itself be used as a key in a “cache dictionary” such as SQLAlchemy’s statement cache, since Python dictionaries aren’t hashable:

>>> # set cache_ok = True
>>> type_.cache_ok = True

>>> # this is the cache key it would generate
>>> key = type_._static_cache_key
>>> key
(<class '__main__.LookupType'>, ('lookup', {'a': 10, 'b': 20}))

>>> # however this key is not hashable, will fail when used with
>>> # SQLAlchemy statement cache
>>> some_cache = {key: "some sql value"}
Traceback (most recent call last): File "<stdin>", line 1,
in <module> TypeError: unhashable type: 'dict'

The type may be made cacheable by assigning a sorted tuple of tuples to the “.lookup” attribute:

class LookupType(UserDefinedType):
    """a custom type that accepts a dictionary as a parameter.

    The dictionary is stored both as itself in a private variable,
    and published in a public variable as a sorted tuple of tuples,
    which is hashable and will also return the same value for any
    two equivalent dictionaries.  Note it assumes the keys and
    values of the dictionary are themselves hashable.

    """

    cache_ok = True

    def __init__(self, lookup):
        self._lookup = lookup

        # assume keys/values of "lookup" are hashable; otherwise
        # they would also need to be converted in some way here
        self.lookup = tuple((key, lookup[key]) for key in sorted(lookup))

    def get_col_spec(self, **kw):
        return "VARCHAR(255)"

    def bind_processor(self, dialect): ...  # works with "self._lookup" ...

Where above, the cache key for LookupType({"a": 10, "b": 20}) will be:

>>> LookupType({"a": 10, "b": 20})._static_cache_key
(<class '__main__.LookupType'>, ('lookup', (('a', 10), ('b', 20))))

Added in version 1.4.14: - added the cache_ok flag to allow some configurability of caching for TypeDecorator classes.

Added in version 1.4.28: - added the ExternalType mixin which generalizes the cache_ok flag to both the TypeDecorator and UserDefinedType classes.

sqlatypemodel.model_type.protocols module¶

Protocol definitions for Pydantic-compatible models.

This module defines the structural typing protocols that allow sqlatypemodel to work with any class that implements the Pydantic model interface, not just actual Pydantic BaseModel subclasses.

Example

>>> from sqlatypemodel.protocols import PydanticModelProtocol
>>>
>>> class CustomModel:
...     def model_dump(self, mode: str = "python") -> dict:
...         return {"field": self.field}
...
...     @classmethod
...     def model_validate(cls, obj):
...         instance = cls()
...         instance.field = obj["field"]
...         return instance
>>>
>>> # CustomModel now conforms to PydanticModelProtocol
>>> isinstance(CustomModel(), PydanticModelProtocol)
True

class sqlatypemodel.model_type.protocols.PydanticModelProtocol(*args, **kwargs)[source]¶

Bases: Protocol

Protocol defining the minimal interface for Pydantic-compatible models.

This protocol enables type-safe serialization and deserialization in SQLAlchemy for any class that implements: - model_dump() for serialization to dictionary - model_validate() for deserialization from dictionary

The protocol is runtime-checkable, allowing isinstance() checks.

This protocol does not define any attributes, only methods.

Example

>>> from pydantic import BaseModel
>>>
>>> class Config(BaseModel):
...     theme: str
...     debug: bool = False
>>>
>>> # Pydantic models automatically conform
>>> isinstance(Config(theme="dark"), PydanticModelProtocol)
True

model_dump(*, mode: str = 'python') → dict[str, Any][source]¶

Serialize the model into a dictionary.

Parameters:: mode – Serialization mode. Common values are “python” (default) and “json” for JSON-compatible output.
Returns:: A dictionary representation of the model data.

Example

>>> config = Config(theme="dark", debug=True)
>>> config.model_dump()
{'theme': 'dark', 'debug': True}
>>> config.model_dump(mode="json")
{'theme': 'dark', 'debug': True}

classmethod model_validate(obj: Any) → PydanticModelProtocol[source]¶

Create a model instance from input data.

This class method validates and converts input data (typically a dictionary) into an instance of the model.

Parameters:: obj – The input data to validate. Usually a dictionary, but implementations may accept other types.
Returns:: A validated instance of the model.
Raises:: ValidationError – If the input data is invalid.

Example

>>> data = {"theme": "light", "debug": False}
>>> config = Config.model_validate(data)
>>> config.theme
'light'

Module contents¶

SQLAlchemy TypeDecorator for Pydantic model serialization.

This module provides ModelType for storing and tracking Pydantic, Dataclass, and Attrs models in database JSON columns.

class sqlatypemodel.model_type.ModelType(model: type[T], dumper: Callable[[T], dict[str, Any]] | None = None, loader: Callable[[dict[str, Any]], T] | None = None, *args: Any, **kwargs: Any)[source]

Bases: TypeDecorator, Generic[T]

SQLAlchemy TypeDecorator for storing Pydantic models as JSON.

impl

The underlying SQLAlchemy type (JSON).

Type:: Union[TypeEngine[Any], Type[TypeEngine[Any]]]

cache_ok

Whether the type is safe to cache (True).

Type:: Optional[bool]

__init__(model: type[T], dumper: Callable[[T], dict[str, Any]] | None = None, loader: Callable[[dict[str, Any]], T] | None = None, *args: Any, **kwargs: Any) → None[source]

Initialize the ModelType.

Parameters:

model – A Pydantic model class (or compatible) to be stored.
dumper – Custom serialization function (Model -> Dict).
loader – Custom deserialization function (Dict -> Model).
*args – Additional arguments for TypeDecorator.
**kwargs – Additional keyword arguments for TypeDecorator.

Raises:

ValueError – If serialization or deserialization functions cannot be resolved.

cache_ok: bool | None = True

Indicate if statements using this ExternalType are “safe to cache”.

class MyType(TypeDecorator):
    impl = String

    cache_ok = True

    def __init__(self, choices):
        self.choices = tuple(choices)
        self.internal_only = True

The cache key for the above type would be equivalent to:

>>> MyType(["a", "b", "c"])._static_cache_key
(<class '__main__.MyType'>, ('choices', ('a', 'b', 'c')))

The requirements for cacheable elements is that they are hashable and also that they indicate the same SQL rendered for expressions using this type every time for a given cache value.

class LookupType(UserDefinedType):
    """a custom type that accepts a dictionary as a parameter.

    this is the non-cacheable version, as "self.lookup" is not
    hashable.

    """

    def __init__(self, lookup):
        self.lookup = lookup

    def get_col_spec(self, **kw):
        return "VARCHAR(255)"

    def bind_processor(self, dialect): ...  # works with "self.lookup" ...

Where “lookup” is a dictionary. The type will not be able to generate a cache key:

>>> type_ = LookupType({"a": 10, "b": 20})
>>> type_._static_cache_key
<stdin>:1: SAWarning: UserDefinedType LookupType({'a': 10, 'b': 20}) will not
produce a cache key because the ``cache_ok`` flag is not set to True.
Set this flag to True if this type object's state is safe to use
in a cache key, or False to disable this warning.
symbol('no_cache')

>>> # set cache_ok = True
>>> type_.cache_ok = True

>>> # this is the cache key it would generate
>>> key = type_._static_cache_key
>>> key
(<class '__main__.LookupType'>, ('lookup', {'a': 10, 'b': 20}))

>>> # however this key is not hashable, will fail when used with
>>> # SQLAlchemy statement cache
>>> some_cache = {key: "some sql value"}
Traceback (most recent call last): File "<stdin>", line 1,
in <module> TypeError: unhashable type: 'dict'

The type may be made cacheable by assigning a sorted tuple of tuples to the “.lookup” attribute:

class LookupType(UserDefinedType):
    """a custom type that accepts a dictionary as a parameter.

    The dictionary is stored both as itself in a private variable,
    and published in a public variable as a sorted tuple of tuples,
    which is hashable and will also return the same value for any
    two equivalent dictionaries.  Note it assumes the keys and
    values of the dictionary are themselves hashable.

    """

    cache_ok = True

    def __init__(self, lookup):
        self._lookup = lookup

        # assume keys/values of "lookup" are hashable; otherwise
        # they would also need to be converted in some way here
        self.lookup = tuple((key, lookup[key]) for key in sorted(lookup))

    def get_col_spec(self, **kw):
        return "VARCHAR(255)"

    def bind_processor(self, dialect): ...  # works with "self._lookup" ...

Where above, the cache key for LookupType({"a": 10, "b": 20}) will be:

>>> LookupType({"a": 10, "b": 20})._static_cache_key
(<class '__main__.LookupType'>, ('lookup', (('a', 10), ('b', 20))))

Added in version 1.4.14: - added the cache_ok flag to allow some configurability of caching for TypeDecorator classes.

Added in version 1.4.28: - added the ExternalType mixin which generalizes the cache_ok flag to both the TypeDecorator and UserDefinedType classes.