Release: 2.0.36 | Release Date: October 15, 2024

# SQLAlchemy 2.0 Documentation

# Custom Types

A variety of methods exist to redefine the behavior of existing types as well as to provide new ones.

## Overriding Type Compilation

A frequent need is to force the “string” version of a type, that is the one rendered in a CREATE TABLE statement or other SQL function like CAST, to be changed. For example, an application may want to force the rendering of `BINARY` for all platforms except for one, in which is wants `BLOB` to be rendered. Usage of an existing generic type, in this case `LargeBinary`, is preferred for most use cases. But to control types more accurately, a compilation directive that is per-dialect can be associated with any type:

    from sqlalchemy.ext.compiler import compiles
    from sqlalchemy.types import BINARY

    @compiles(BINARY, "sqlite")
    def compile_binary_sqlite(type_, compiler, **kw):
        return "BLOB"

The above code allows the usage of `BINARY`, which will produce the string `BINARY` against all backends except SQLite, in which case it will produce `BLOB`.

See the section Changing Compilation of Types, a subsection of Custom SQL Constructs and Compilation Extension, for additional examples.

## Augmenting Existing Types

The `TypeDecorator` allows the creation of custom types which add bind-parameter and result-processing behavior to an existing type object. It is used when additional in-Python marshalling of data to and/or from the database is required.

Note

The bind- and result-processing of `TypeDecorator` is in addition to the processing already performed by the hosted type, which is customized by SQLAlchemy on a per-DBAPI basis to perform processing specific to that DBAPI. While it is possible to replace this handling for a given type through direct subclassing, it is never needed in practice and SQLAlchemy no longer supports this as a public use case.

ORM Tip

The `TypeDecorator` can be used to provide a consistent means of converting some type of value as it is passed into and out of the database. When using the ORM, a similar technique exists for converting user data from arbitrary formats which is to use the `validates()` decorator. This technique may be more appropriate when data coming into an ORM model needs to be normalized in some way that is specific to the business case and isn’t as generic as a datatype.

Object Name | Description
--- | ---
TypeDecorator | Allows the creation of types which add additional functionality to an existing type.

class sqlalchemy.types.TypeDecorator

  Allows the creation of types which add additional functionality to an existing type.

  This method is preferred to direct subclassing of SQLAlchemy’s built-in types as it ensures that all required functionality of the underlying type is kept in place.

  Typical usage:

      import sqlalchemy.types as types

      class MyType(types.TypeDecorator):
          '''Prefixes Unicode values with "PREFIX:" on the way in and
          strips it off on the way out.
          '''

          impl = types.Unicode

          cache_ok = True

          def process_bind_param(self, value, dialect):
              return "PREFIX:" + value

          def process_result_value(self, value, dialect):
              return value[7:]

          def copy(self, **kw):
              return MyType(self.impl.length)

  The class-level `impl` attribute is required, and can reference any `TypeEngine` class. Alternatively, the `load_dialect_impl()` method can be used to provide different type classes based on the dialect given; in this case, the `impl` variable can reference `TypeEngine` as a placeholder.

  The `TypeDecorator.cache_ok` class-level flag indicates if this custom `TypeDecorator` is safe to be used as part of a cache key. This flag defaults to `None` which will initially generate a warning when the SQL compiler attempts to generate a cache key for a statement that uses this type. If the `TypeDecorator` is not guaranteed to produce the same bind/result behavior and SQL generation every time, this flag should be set to `False`; otherwise if the class produces the same behavior each time, it may be set to `True`. See `TypeDecorator.cache_ok` for further notes on how this works.

  Types that receive a Python type that isn’t similar to the ultimate type used may want to define the `TypeDecorator.coerce_compared_value()` method. This is used to give the expression system a hint when coercing Python objects into bind parameters within expressions. Consider this expression:

      mytable.c.somecol + datetime.date(2009, 5, 15)

  Above, if “somecol” is an `Integer` variant, it makes sense that we’re doing date arithmetic, where above is usually interpreted by databases as adding a number of days to the given date. The expression system does the right thing by not attempting to coerce the “date()” value into an integer-oriented bind parameter.

  However, in the case of `TypeDecorator`, we are usually changing an incoming Python type to something new - `TypeDecorator` by default will “coerce” the non-typed side to be the same type as itself. Such as below, we define an “epoch” type that stores a date value as an integer:

      class MyEpochType(types.TypeDecorator):
          impl = types.Integer

          cache_ok = True

          epoch = datetime.date(1970, 1, 1)

          def process_bind_param(self, value, dialect):
              return (value - self.epoch).days

          def process_result_value(self, value, dialect):
              return self.epoch + timedelta(days=value)

  Our expression of `somecol + date` with the above type will coerce the “date” on the right side to also be treated as `MyEpochType`.

  This behavior can be overridden via the `TypeDecorator.coerce_compared_value()` method, which returns a type that should be used for the value of the expression. Below we set it such that an integer value will be treated as an `Integer`, and any other value is assumed to be a date and will be treated as a `MyEpochType`:

      def coerce_compared_value(self, op, value):
          if isinstance(value, int):
              return Integer()
          else:
              return self

  Warning

  Note that the behavior of coerce_compared_value is not inherited by default from that of the base type. If the `TypeDecorator` is augmenting a type that requires special logic for certain types of operators, this method must be overridden. A key example is when decorating the `JSON` and `JSONB` types; the default rules of `TypeEngine.coerce_compared_value()` should be used in order to deal with operators like index operations:

      from sqlalchemy import JSON
      from sqlalchemy import TypeDecorator

      class MyJsonType(TypeDecorator):
          impl = JSON

          cache_ok = True

          def coerce_compared_value(self, op, value):
              return self.impl.coerce_compared_value(op, value)

  Without the above step, index operations such as `mycol['foo']` will cause the index value `'foo'` to be JSON encoded.

  Similarly, when working with the `ARRAY` datatype, the type coercion for index operations (e.g. `mycol[5]`) is also handled by `TypeDecorator.coerce_compared_value()`, where again a simple override is sufficient unless special rules are needed for particular operators:

      from sqlalchemy import ARRAY
      from sqlalchemy import TypeDecorator

      class MyArrayType(TypeDecorator):
          impl = ARRAY

          cache_ok = True

          def coerce_compared_value(self, op, value):
              return self.impl.coerce_compared_value(op, value)

  Members

  cache_ok, operate(), reverse_operate(), __init__(), bind_expression(), bind_processor(), coerce_compared_value(), coerce_to_is_types, column_expression(), comparator_factory, compare_values(), copy(), get_dbapi_type(), literal_processor(), load_dialect_impl(), process_bind_param(), process_literal_param(), process_result_value(), result_processor(), sort_key_function, type_engine()

  Class signature

  class `sqlalchemy.types.TypeDecorator` (`sqlalchemy.sql.expression.SchemaEventTarget`, `sqlalchemy.types.ExternalType`, `sqlalchemy.types.TypeEngine`)

  attribute `sqlalchemy.types.TypeDecorator.`cache_ok: bool | None = None

    inherited from the `ExternalType.cache_ok` attribute of `ExternalType`

    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))))

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

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

    See also

    SQL Compilation Caching

  class Comparator

    A `Comparator` that is specific to `TypeDecorator`.

    User-defined `TypeDecorator` classes should not typically need to modify this.

    Class signature

    class `sqlalchemy.types.TypeDecorator.Comparator` (`sqlalchemy.types.Comparator`)

    method `sqlalchemy.types.TypeDecorator.Comparator.`operate(op: OperatorType, *other: Any, **kwargs: Any) → ColumnElement[_CT]

      Operate on an argument.

      This is the lowest level of operation, raises `NotImplementedError` by default.

      Overriding this on a subclass can allow common behavior to be applied to all operations. For example, overriding `ColumnOperators` to apply `func.lower()` to the left and right side:

          class MyComparator(ColumnOperators):
              def operate(self, op, other, **kwargs):
                  return op(func.lower(self), func.lower(other), **kwargs)

      Parameters:

        - op – Operator callable.

        - *other – the ‘other’ side of the operation. Will be a single scalar for most operations.

        - **kwargs – modifiers. These may be passed by special operators such as `ColumnOperators.contains()`.

    method `sqlalchemy.types.TypeDecorator.Comparator.`reverse_operate(op: OperatorType, other: Any, **kwargs: Any) → ColumnElement[_CT]

      Reverse operate on an argument.

      Usage is the same as `operate()`.

  method `sqlalchemy.types.TypeDecorator.`__init__(*args: Any, **kwargs: Any)

    Construct a `TypeDecorator`.

    Arguments sent here are passed to the constructor of the class assigned to the `impl` class level attribute, assuming the `impl` is a callable, and the resulting object is assigned to the `self.impl` instance attribute (thus overriding the class attribute of the same name).

    If the class level `impl` is not a callable (the unusual case), it will be assigned to the same instance attribute ‘as-is’, ignoring those arguments passed to the constructor.

    Subclasses can override this to customize the generation of `self.impl` entirely.

  method `sqlalchemy.types.TypeDecorator.`bind_expression(bindparam: BindParameter[_T]) → ColumnElement[_T] | None

    Given a bind value (i.e. a `BindParameter` instance), return a SQL expression which will typically wrap the given parameter.

    Note

    This method is called during the SQL compilation phase of a statement, when rendering a SQL string. It is not necessarily called against specific values, and should not be confused with the `TypeDecorator.process_bind_param()` method, which is the more typical method that processes the actual value passed to a particular parameter at statement execution time.

    Subclasses of `TypeDecorator` can override this method to provide custom bind expression behavior for the type. This implementation will replace that of the underlying implementation type.

  method `sqlalchemy.types.TypeDecorator.`bind_processor(dialect: Dialect) → _BindProcessorType[_T] | None

    Provide a bound value processing function for the given `Dialect`.

    This is the method that fulfills the `TypeEngine` contract for bound value conversion which normally occurs via the `TypeEngine.bind_processor()` method.

    Note

    User-defined subclasses of `TypeDecorator` should not implement this method, and should instead implement `TypeDecorator.process_bind_param()` so that the “inner” processing provided by the implementing type is maintained.

    Parameters:

      dialect – Dialect instance in use.

  method `sqlalchemy.types.TypeDecorator.`coerce_compared_value(op: OperatorType | None, value: Any) → Any

    Suggest a type for a ‘coerced’ Python value in an expression.

    By default, returns self. This method is called by the expression system when an object using this type is on the left or right side of an expression against a plain Python object which does not yet have a SQLAlchemy type assigned:

        expr = table.c.somecolumn + 35

    Where above, if `somecolumn` uses this type, this method will be called with the value `operator.add` and `35`. The return value is whatever SQLAlchemy type should be used for `35` for this particular operation.

  attribute `sqlalchemy.types.TypeDecorator.`coerce_to_is_types: Sequence[Type[Any]] = (<class 'NoneType'>,)

    Specify those Python types which should be coerced at the expression level to “IS <constant>” when compared using `==` (and same for `IS NOT` in conjunction with `!=`).

    For most SQLAlchemy types, this includes `NoneType`, as well as `bool`.

    `TypeDecorator` modifies this list to only include `NoneType`, as typedecorator implementations that deal with boolean types are common.

    Custom `TypeDecorator` classes can override this attribute to return an empty tuple, in which case no values will be coerced to constants.

  method `sqlalchemy.types.TypeDecorator.`column_expression(column: ColumnElement[_T]) → ColumnElement[_T] | None

    Given a SELECT column expression, return a wrapping SQL expression.

    Note

    This method is called during the SQL compilation phase of a statement, when rendering a SQL string. It is not called against specific values, and should not be confused with the `TypeDecorator.process_result_value()` method, which is the more typical method that processes the actual value returned in a result row subsequent to statement execution time.

    Subclasses of `TypeDecorator` can override this method to provide custom column expression behavior for the type. This implementation will replace that of the underlying implementation type.

    See the description of `TypeEngine.column_expression()` for a complete description of the method’s use.

  attribute `sqlalchemy.types.TypeDecorator.`comparator_factory: _ComparatorFactory[Any]

    A `Comparator` class which will apply to operations performed by owning `ColumnElement` objects.

    The `comparator_factory` attribute is a hook consulted by the core expression system when column and SQL expression operations are performed. When a `Comparator` class is associated with this attribute, it allows custom re-definition of all existing operators, as well as definition of new operators. Existing operators include those provided by Python operator overloading such as `ColumnOperators.__add__()` and `ColumnOperators.__eq__()`, those provided as standard attributes of `ColumnOperators` such as `ColumnOperators.like()` and `ColumnOperators.in_()`.

    Rudimentary usage of this hook is allowed through simple subclassing of existing types, or alternatively by using `TypeDecorator`. See the documentation section Redefining and Creating New Operators for examples.

  method `sqlalchemy.types.TypeDecorator.`compare_values(x: Any, y: Any) → bool

    Given two values, compare them for equality.

    By default this calls upon `TypeEngine.compare_values()` of the underlying “impl”, which in turn usually uses the Python equals operator `==`.

    This function is used by the ORM to compare an original-loaded value with an intercepted “changed” value, to determine if a net change has occurred.

  method `sqlalchemy.types.TypeDecorator.`copy(**kw: Any) → Self

    Produce a copy of this `TypeDecorator` instance.

    This is a shallow copy and is provided to fulfill part of the `TypeEngine` contract. It usually does not need to be overridden unless the user-defined `TypeDecorator` has local state that should be deep-copied.

  method `sqlalchemy.types.TypeDecorator.`get_dbapi_type(dbapi: module) → Any | None

    Return the DBAPI type object represented by this `TypeDecorator`.

    By default this calls upon `TypeEngine.get_dbapi_type()` of the underlying “impl”.

  method `sqlalchemy.types.TypeDecorator.`literal_processor(dialect: Dialect) → _LiteralProcessorType[_T] | None

    Provide a literal processing function for the given `Dialect`.

    This is the method that fulfills the `TypeEngine` contract for literal value conversion which normally occurs via the `TypeEngine.literal_processor()` method.

    Note

    User-defined subclasses of `TypeDecorator` should not implement this method, and should instead implement `TypeDecorator.process_literal_param()` so that the “inner” processing provided by the implementing type is maintained.

  method `sqlalchemy.types.TypeDecorator.`load_dialect_impl(dialect: Dialect) → TypeEngine[Any]

    Return a `TypeEngine` object corresponding to a dialect.

    This is an end-user override hook that can be used to provide differing types depending on the given dialect. It is used by the `TypeDecorator` implementation of `type_engine()` to help determine what type should ultimately be returned for a given `TypeDecorator`.

    By default returns `self.impl`.

  method `sqlalchemy.types.TypeDecorator.`process_bind_param(value: _T | None, dialect: Dialect) → Any

    Receive a bound parameter value to be converted.

    Custom subclasses of `TypeDecorator` should override this method to provide custom behaviors for incoming data values. This method is called at statement execution time and is passed the literal Python data value which is to be associated with a bound parameter in the statement.

    The operation could be anything desired to perform custom behavior, such as transforming or serializing data. This could also be used as a hook for validating logic.

    Parameters:

      - value – Data to operate upon, of any type expected by this method in the subclass. Can be `None`.

      - dialect – the `Dialect` in use.

    See also

    Augmenting Existing Types

    `TypeDecorator.process_result_value()`

  method `sqlalchemy.types.TypeDecorator.`process_literal_param(value: _T | None, dialect: Dialect) → str

    Receive a literal parameter value to be rendered inline within a statement.

    Note

    This method is called during the SQL compilation phase of a statement, when rendering a SQL string. Unlike other SQL compilation methods, it is passed a specific Python value to be rendered as a string. However it should not be confused with the `TypeDecorator.process_bind_param()` method, which is the more typical method that processes the actual value passed to a particular parameter at statement execution time.

    Custom subclasses of `TypeDecorator` should override this method to provide custom behaviors for incoming data values that are in the special case of being rendered as literals.

    The returned string will be rendered into the output string.

  method `sqlalchemy.types.TypeDecorator.`process_result_value(value: Any | None, dialect: Dialect) → _T | None

    Receive a result-row column value to be converted.

    Custom subclasses of `TypeDecorator` should override this method to provide custom behaviors for data values being received in result rows coming from the database. This method is called at result fetching time and is passed the literal Python data value that’s extracted from a database result row.

    The operation could be anything desired to perform custom behavior, such as transforming or deserializing data.

    Parameters:

      - value – Data to operate upon, of any type expected by this method in the subclass. Can be `None`.

      - dialect – the `Dialect` in use.

    See also

    Augmenting Existing Types

    `TypeDecorator.process_bind_param()`

  method `sqlalchemy.types.TypeDecorator.`result_processor(dialect: Dialect, coltype: Any) → _ResultProcessorType[_T] | None

    Provide a result value processing function for the given `Dialect`.

    This is the method that fulfills the `TypeEngine` contract for bound value conversion which normally occurs via the `TypeEngine.result_processor()` method.

    Note

    User-defined subclasses of `TypeDecorator` should not implement this method, and should instead implement `TypeDecorator.process_result_value()` so that the “inner” processing provided by the implementing type is maintained.

    Parameters:

      - dialect – Dialect instance in use.

      - coltype – A SQLAlchemy data type

  attribute `sqlalchemy.types.TypeDecorator.`sort_key_function: Callable[[Any], Any] | None

    A sorting function that can be passed as the key to sorted.

    The default value of `None` indicates that the values stored by this type are self-sorting.

    New in version 1.3.8.

  method `sqlalchemy.types.TypeDecorator.`type_engine(dialect: Dialect) → TypeEngine[Any]

    Return a dialect-specific `TypeEngine` instance for this `TypeDecorator`.

    In most cases this returns a dialect-adapted form of the `TypeEngine` type represented by `self.impl`. Makes usage of `dialect_impl()`. Behavior can be customized here by overriding `load_dialect_impl()`.

## TypeDecorator Recipes

A few key `TypeDecorator` recipes follow.

### Coercing Encoded Strings to Unicode

A common source of confusion regarding the `Unicode` type is that it is intended to deal only with Python `unicode` objects on the Python side, meaning values passed to it as bind parameters must be of the form `u'some string'` if using Python 2 and not 3. The encoding/decoding functions it performs are only to suit what the DBAPI in use requires, and are primarily a private implementation detail.

The use case of a type that can safely receive Python bytestrings, that is strings that contain non-ASCII characters and are not `u''` objects in Python 2, can be achieved using a `TypeDecorator` which coerces as needed:

    from sqlalchemy.types import TypeDecorator, Unicode

    class CoerceUTF8(TypeDecorator):
        """Safely coerce Python bytestrings to Unicode
        before passing off to the database."""

        impl = Unicode

        def process_bind_param(self, value, dialect):
            if isinstance(value, str):
                value = value.decode("utf-8")
            return value

### Rounding Numerics

Some database connectors like those of SQL Server choke if a Decimal is passed with too many decimal places. Here’s a recipe that rounds them down:

    from sqlalchemy.types import TypeDecorator, Numeric
    from decimal import Decimal

    class SafeNumeric(TypeDecorator):
        """Adds quantization to Numeric."""

        impl = Numeric

        def __init__(self, *arg, **kw):
            TypeDecorator.__init__(self, *arg, **kw)
            self.quantize_int = -self.impl.scale
            self.quantize = Decimal(10) ** self.quantize_int

        def process_bind_param(self, value, dialect):
            if isinstance(value, Decimal) and value.as_tuple()[2] < self.quantize_int:
                value = value.quantize(self.quantize)
            return value

### Store Timezone Aware Timestamps as Timezone Naive UTC

Timestamps in databases should always be stored in a timezone-agnostic way. For most databases, this means ensuring a timestamp is first in the UTC timezone before it is stored, then storing it as timezone-naive (that is, without any timezone associated with it; UTC is assumed to be the “implicit” timezone). Alternatively, database-specific types like PostgreSQLs “TIMESTAMP WITH TIMEZONE” are often preferred for their richer functionality; however, storing as plain UTC will work on all databases and drivers. When a timezone-intelligent database type is not an option or is not preferred, the `TypeDecorator` can be used to create a datatype that convert timezone aware timestamps into timezone naive and back again. Below, Python’s built-in `datetime.timezone.utc` timezone is used to normalize and denormalize:

    import datetime

    class TZDateTime(TypeDecorator):
        impl = DateTime
        cache_ok = True

        def process_bind_param(self, value, dialect):
            if value is not None:
                if not value.tzinfo or value.tzinfo.utcoffset(value) is None:
                    raise TypeError("tzinfo is required")
                value = value.astimezone(datetime.timezone.utc).replace(tzinfo=None)
            return value

        def process_result_value(self, value, dialect):
            if value is not None:
                value = value.replace(tzinfo=datetime.timezone.utc)
            return value

### Backend-agnostic GUID Type

Note

Since version 2.0 the built-in `Uuid` type that behaves similarly should be preferred. This example is presented just as an example of a type decorator that receives and returns python objects.

Receives and returns Python uuid() objects. Uses the PG UUID type when using PostgreSQL, UNIQUEIDENTIFIER when using MSSQL, CHAR(32) on other backends, storing them in stringified format. The `GUIDHyphens` version stores the value with hyphens instead of just the hex string, using a CHAR(36) type:

    from operator import attrgetter
    from sqlalchemy.types import TypeDecorator, CHAR
    from sqlalchemy.dialects.mssql import UNIQUEIDENTIFIER
    from sqlalchemy.dialects.postgresql import UUID
    import uuid

    class GUID(TypeDecorator):
        """Platform-independent GUID type.

        Uses PostgreSQL's UUID type or MSSQL's UNIQUEIDENTIFIER,
        otherwise uses CHAR(32), storing as stringified hex values.

        """

        impl = CHAR
        cache_ok = True

        _default_type = CHAR(32)
        _uuid_as_str = attrgetter("hex")

        def load_dialect_impl(self, dialect):
            if dialect.name == "postgresql":
                return dialect.type_descriptor(UUID())
            elif dialect.name == "mssql":
                return dialect.type_descriptor(UNIQUEIDENTIFIER())
            else:
                return dialect.type_descriptor(self._default_type)

        def process_bind_param(self, value, dialect):
            if value is None or dialect.name in ("postgresql", "mssql"):
                return value
            else:
                if not isinstance(value, uuid.UUID):
                    value = uuid.UUID(value)
                return self._uuid_as_str(value)

        def process_result_value(self, value, dialect):
            if value is None:
                return value
            else:
                if not isinstance(value, uuid.UUID):
                    value = uuid.UUID(value)
                return value

    class GUIDHyphens(GUID):
        """Platform-independent GUID type.

        Uses PostgreSQL's UUID type or MSSQL's UNIQUEIDENTIFIER,
        otherwise uses CHAR(36), storing as stringified uuid values.

        """

        _default_type = CHAR(36)
        _uuid_as_str = str

#### Linking Python `uuid.UUID` to the Custom Type for ORM mappings

When declaring ORM mappings using Annotated Declarative Table mappings, the custom `GUID` type defined above may be associated with the Python `uuid.UUID` datatype by adding it to the type annotation map, which is typically defined on the `DeclarativeBase` class:

    import uuid
    from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column

    class Base(DeclarativeBase):
        type_annotation_map = {
            uuid.UUID: GUID,
        }

With the above configuration, ORM mapped classes which extend from `Base` may refer to Python `uuid.UUID` in annotations which will make use of `GUID` automatically:

    class MyModel(Base):
        __tablename__ = "my_table"

        id: Mapped[uuid.UUID] = mapped_column(primary_key=True)

See also

Customizing the Type Map

### Marshal JSON Strings

This type uses `simplejson` to marshal Python data structures to/from JSON. Can be modified to use Python’s builtin json encoder:

    from sqlalchemy.types import TypeDecorator, VARCHAR
    import json

    class JSONEncodedDict(TypeDecorator):
        """Represents an immutable structure as a json-encoded string.

        Usage:

            JSONEncodedDict(255)

        """

        impl = VARCHAR

        cache_ok = True

        def process_bind_param(self, value, dialect):
            if value is not None:
                value = json.dumps(value)

            return value

        def process_result_value(self, value, dialect):
            if value is not None:
                value = json.loads(value)
            return value

#### Adding Mutability

The ORM by default will not detect “mutability” on such a type as above - meaning, in-place changes to values will not be detected and will not be flushed. Without further steps, you instead would need to replace the existing value with a new one on each parent object to detect changes:

    obj.json_value["key"] = "value"  # will *not* be detected by the ORM

    obj.json_value = {"key": "value"}  # *will* be detected by the ORM

The above limitation may be fine, as many applications may not require that the values are ever mutated once created. For those which do have this requirement, support for mutability is best applied using the `sqlalchemy.ext.mutable` extension. For a dictionary-oriented JSON structure, we can apply this as:

    json_type = MutableDict.as_mutable(JSONEncodedDict)

    class MyClass(Base):
        #  ...

        json_data = Column(json_type)

See also

Mutation Tracking

#### Dealing with Comparison Operations

The default behavior of `TypeDecorator` is to coerce the “right hand side” of any expression into the same type. For a type like JSON, this means that any operator used must make sense in terms of JSON. For some cases, users may wish for the type to behave like JSON in some circumstances, and as plain text in others. One example is if one wanted to handle the LIKE operator for the JSON type. LIKE makes no sense against a JSON structure, but it does make sense against the underlying textual representation. To get at this with a type like `JSONEncodedDict`, we need to coerce the column to a textual form using `cast()` or `type_coerce()` before attempting to use this operator:

    from sqlalchemy import type_coerce, String

    stmt = select(my_table).where(type_coerce(my_table.c.json_data, String).like("%foo%"))

`TypeDecorator` provides a built-in system for working up type translations like these based on operators. If we wanted to frequently use the LIKE operator with our JSON object interpreted as a string, we can build it into the type by overriding the `TypeDecorator.coerce_compared_value()` method:

    from sqlalchemy.sql import operators
    from sqlalchemy import String

    class JSONEncodedDict(TypeDecorator):
        impl = VARCHAR

        cache_ok = True

        def coerce_compared_value(self, op, value):
            if op in (operators.like_op, operators.not_like_op):
                return String()
            else:
                return self

        def process_bind_param(self, value, dialect):
            if value is not None:
                value = json.dumps(value)

            return value

        def process_result_value(self, value, dialect):
            if value is not None:
                value = json.loads(value)
            return value

Above is just one approach to handling an operator like “LIKE”. Other applications may wish to raise `NotImplementedError` for operators that have no meaning with a JSON object such as “LIKE”, rather than automatically coercing to text.

## Applying SQL-level Bind/Result Processing

As seen in the section Augmenting Existing Types, SQLAlchemy allows Python functions to be invoked both when parameters are sent to a statement, as well as when result rows are loaded from the database, to apply transformations to the values as they are sent to or from the database. It is also possible to define SQL-level transformations as well. The rationale here is when only the relational database contains a particular series of functions that are necessary to coerce incoming and outgoing data between an application and persistence format. Examples include using database-defined encryption/decryption functions, as well as stored procedures that handle geographic data.

Any `TypeEngine`, `UserDefinedType` or `TypeDecorator` subclass can include implementations of `TypeEngine.bind_expression()` and/or `TypeEngine.column_expression()`, which when defined to return a non-`None` value should return a `ColumnElement` expression to be injected into the SQL statement, either surrounding bound parameters or a column expression. For example, to build a `Geometry` type which will apply the PostGIS function `ST_GeomFromText` to all outgoing values and the function `ST_AsText` to all incoming data, we can create our own subclass of `UserDefinedType` which provides these methods in conjunction with `func`:

    from sqlalchemy import func
    from sqlalchemy.types import UserDefinedType

    class Geometry(UserDefinedType):
        def get_col_spec(self):
            return "GEOMETRY"

        def bind_expression(self, bindvalue):
            return func.ST_GeomFromText(bindvalue, type_=self)

        def column_expression(self, col):
            return func.ST_AsText(col, type_=self)

We can apply the `Geometry` type into `Table` metadata and use it in a `select()` construct:

    geometry = Table(
        "geometry",
        metadata,
        Column("geom_id", Integer, primary_key=True),
        Column("geom_data", Geometry),
    )

    print(
        select(geometry).where(
            geometry.c.geom_data == "LINESTRING(189412 252431,189631 259122)"
        )
    )

The resulting SQL embeds both functions as appropriate. `ST_AsText` is applied to the columns clause so that the return value is run through the function before passing into a result set, and `ST_GeomFromText` is run on the bound parameter so that the passed-in value is converted:

    SELECT geometry.geom_id, ST_AsText(geometry.geom_data) AS geom_data_1
    FROM geometry
    WHERE geometry.geom_data = ST_GeomFromText(:geom_data_2)

The `TypeEngine.column_expression()` method interacts with the mechanics of the compiler such that the SQL expression does not interfere with the labeling of the wrapped expression. Such as, if we rendered a `select()` against a `label()` of our expression, the string label is moved to the outside of the wrapped expression:

    print(select(geometry.c.geom_data.label("my_data")))

Output:

    SELECT ST_AsText(geometry.geom_data) AS my_data
    FROM geometry

Another example is we decorate `BYTEA` to provide a `PGPString`, which will make use of the PostgreSQL `pgcrypto` extension to encrypt/decrypt values transparently:

    from sqlalchemy import (
        create_engine,
        String,
        select,
        func,
        MetaData,
        Table,
        Column,
        type_coerce,
        TypeDecorator,
    )

    from sqlalchemy.dialects.postgresql import BYTEA

    class PGPString(TypeDecorator):
        impl = BYTEA

        cache_ok = True

        def __init__(self, passphrase):
            super(PGPString, self).__init__()

            self.passphrase = passphrase

        def bind_expression(self, bindvalue):
            # convert the bind's type from PGPString to
            # String, so that it's passed to psycopg2 as is without
            # a dbapi.Binary wrapper
            bindvalue = type_coerce(bindvalue, String)
            return func.pgp_sym_encrypt(bindvalue, self.passphrase)

        def column_expression(self, col):
            return func.pgp_sym_decrypt(col, self.passphrase)

    metadata_obj = MetaData()
    message = Table(
        "message",
        metadata_obj,
        Column("username", String(50)),
        Column("message", PGPString("this is my passphrase")),
    )

    engine = create_engine("postgresql+psycopg2://scott:tiger@localhost/test", echo=True)
    with engine.begin() as conn:
        metadata_obj.create_all(conn)

        conn.execute(
            message.insert(),
            {"username": "some user", "message": "this is my message"},
        )

        print(
            conn.scalar(select(message.c.message).where(message.c.username == "some user"))
        )

The `pgp_sym_encrypt` and `pgp_sym_decrypt` functions are applied to the INSERT and SELECT statements:

    INSERT INTO message (username, message)
      VALUES (%(username)s, pgp_sym_encrypt(%(message)s, %(pgp_sym_encrypt_1)s))
      -- {'username': 'some user', 'message': 'this is my message',
      --  'pgp_sym_encrypt_1': 'this is my passphrase'}

    SELECT pgp_sym_decrypt(message.message, %(pgp_sym_decrypt_1)s) AS message_1
      FROM message
      WHERE message.username = %(username_1)s
      -- {'pgp_sym_decrypt_1': 'this is my passphrase', 'username_1': 'some user'}

## Redefining and Creating New Operators

SQLAlchemy Core defines a fixed set of expression operators available to all column expressions. Some of these operations have the effect of overloading Python’s built-in operators; examples of such operators include `ColumnOperators.__eq__()` (`table.c.somecolumn == 'foo'`), `ColumnOperators.__invert__()` (`~table.c.flag`), and `ColumnOperators.__add__()` (`table.c.x + table.c.y`). Other operators are exposed as explicit methods on column expressions, such as `ColumnOperators.in_()` (`table.c.value.in_(['x', 'y'])`) and `ColumnOperators.like()` (`table.c.value.like('%ed%')`).

When the need arises for a SQL operator that isn’t directly supported by the already supplied methods above, the most expedient way to produce this operator is to use the `Operators.op()` method on any SQL expression object; this method is given a string representing the SQL operator to render, and the return value is a Python callable that accepts any arbitrary right-hand side expression:

    >>> from sqlalchemy import column
    >>> expr = column("x").op(">>")(column("y"))
    >>> print(expr)
    x >> y

When making use of custom SQL types, there is also a means of implementing custom operators as above that are automatically present upon any column expression that makes use of that column type, without the need to directly call `Operators.op()` each time the operator is to be used.

To achieve this, a SQL expression construct consults the `TypeEngine` object associated with the construct in order to determine the behavior of the built-in operators as well as to look for new methods that may have been invoked. `TypeEngine` defines a “comparison” object implemented by the `Comparator` class to provide the base behavior for SQL operators, and many specific types provide their own sub-implementations of this class. User-defined `Comparator` implementations can be built directly into a simple subclass of a particular type in order to override or define new operations. Below, we create a `Integer` subclass which overrides the `ColumnOperators.__add__()` operator, which in turn uses `Operators.op()` to produce the custom SQL itself:

    from sqlalchemy import Integer

    class MyInt(Integer):
        class comparator_factory(Integer.Comparator):
            def __add__(self, other):
                return self.op("goofy")(other)

The above configuration creates a new class `MyInt`, which establishes the `TypeEngine.comparator_factory` attribute as referring to a new class, subclassing the `Comparator` class associated with the `Integer` type.

Usage:

    >>> sometable = Table("sometable", metadata, Column("data", MyInt))
    >>> print(sometable.c.data + 5)
    sometable.data goofy :data_1

The implementation for `ColumnOperators.__add__()` is consulted by an owning SQL expression, by instantiating the `Comparator` with itself as the `expr` attribute. This attribute may be used when the implementation needs to refer to the originating `ColumnElement` object directly:

    from sqlalchemy import Integer

    class MyInt(Integer):
        class comparator_factory(Integer.Comparator):
            def __add__(self, other):
                return func.special_addition(self.expr, other)

New methods added to a `Comparator` are exposed on an owning SQL expression object using a dynamic lookup scheme, which exposes methods added to `Comparator` onto the owning `ColumnElement` expression construct. For example, to add a `log()` function to integers:

    from sqlalchemy import Integer, func

    class MyInt(Integer):
        class comparator_factory(Integer.Comparator):
            def log(self, other):
                return func.log(self.expr, other)

Using the above type:

    >>> print(sometable.c.data.log(5))
    log(:log_1, :log_2)

When using `Operators.op()` for comparison operations that return a boolean result, the `Operators.op.is_comparison` flag should be set to `True`:

    class MyInt(Integer):
        class comparator_factory(Integer.Comparator):
            def is_frobnozzled(self, other):
                return self.op("--is_frobnozzled->", is_comparison=True)(other)

Unary operations are also possible. For example, to add an implementation of the PostgreSQL factorial operator, we combine the `UnaryExpression` construct along with a `custom_op` to produce the factorial expression:

    from sqlalchemy import Integer
    from sqlalchemy.sql.expression import UnaryExpression
    from sqlalchemy.sql import operators

    class MyInteger(Integer):
        class comparator_factory(Integer.Comparator):
            def factorial(self):
                return UnaryExpression(
                    self.expr, modifier=operators.custom_op("!"), type_=MyInteger
                )

Using the above type:

    >>> from sqlalchemy.sql import column
    >>> print(column("x", MyInteger).factorial())
    x !

See also

`Operators.op()`

`TypeEngine.comparator_factory`

## Creating New Types

The `UserDefinedType` class is provided as a simple base class for defining entirely new database types. Use this to represent native database types not known by SQLAlchemy. If only Python translation behavior is needed, use `TypeDecorator` instead.

Object Name | Description
--- | ---
UserDefinedType | Base for user defined types.

class sqlalchemy.types.UserDefinedType

  Base for user defined types.

  This should be the base of new types. Note that for most cases, `TypeDecorator` is probably more appropriate:

      import sqlalchemy.types as types

      class MyType(types.UserDefinedType):
          cache_ok = True

          def __init__(self, precision = 8):
              self.precision = precision

          def get_col_spec(self, **kw):
              return "MYTYPE(%s)" % self.precision

          def bind_processor(self, dialect):
              def process(value):
                  return value
              return process

          def result_processor(self, dialect, coltype):
              def process(value):
                  return value
              return process

  Once the type is made, it’s immediately usable:

      table = Table('foo', metadata_obj,
          Column('id', Integer, primary_key=True),
          Column('data', MyType(16))
          )

  The `get_col_spec()` method will in most cases receive a keyword argument `type_expression` which refers to the owning expression of the type as being compiled, such as a `Column` or `cast()` construct. This keyword is only sent if the method accepts keyword arguments (e.g. `**kw`) in its argument signature; introspection is used to check for this in order to support legacy forms of this function.

  The `UserDefinedType.cache_ok` class-level flag indicates if this custom `UserDefinedType` is safe to be used as part of a cache key. This flag defaults to `None` which will initially generate a warning when the SQL compiler attempts to generate a cache key for a statement that uses this type. If the `UserDefinedType` is not guaranteed to produce the same bind/result behavior and SQL generation every time, this flag should be set to `False`; otherwise if the class produces the same behavior each time, it may be set to `True`. See `UserDefinedType.cache_ok` for further notes on how this works.

  New in version 1.4.28: Generalized the `ExternalType.cache_ok` flag so that it is available for both `TypeDecorator` as well as `UserDefinedType`.

  Members

  cache_ok, coerce_compared_value(), ensure_kwarg

  Class signature

  class `sqlalchemy.types.UserDefinedType` (`sqlalchemy.types.ExternalType`, `sqlalchemy.types.TypeEngineMixin`, `sqlalchemy.types.TypeEngine`, `sqlalchemy.util.langhelpers.EnsureKWArg`)

  attribute `sqlalchemy.types.UserDefinedType.`cache_ok: bool | None = None

    inherited from the `ExternalType.cache_ok` attribute of `ExternalType`

    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))))

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

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

    See also

    SQL Compilation Caching

  method `sqlalchemy.types.UserDefinedType.`coerce_compared_value(op: OperatorType | None, value: Any) → TypeEngine[Any]

    Suggest a type for a ‘coerced’ Python value in an expression.

    Default behavior for `UserDefinedType` is the same as that of `TypeDecorator`; by default it returns `self`, assuming the compared value should be coerced into the same type as this one. See `TypeDecorator.coerce_compared_value()` for more detail.

  attribute `sqlalchemy.types.UserDefinedType.`ensure_kwarg: str = 'get_col_spec'

    a regular expression that indicates method names for which the method should accept `**kw` arguments.

    The class will scan for methods matching the name template and decorate them if necessary to ensure `**kw` parameters are accepted.

## Working with Custom Types and Reflection

It is important to note that database types which are modified to have additional in-Python behaviors, including types based on `TypeDecorator` as well as other user-defined subclasses of datatypes, do not have any representation within a database schema. When using database the introspection features described at Reflecting Database Objects, SQLAlchemy makes use of a fixed mapping which links the datatype information reported by a database server to a SQLAlchemy datatype object. For example, if we look inside of a PostgreSQL schema at the definition for a particular database column, we might receive back the string `"VARCHAR"`. SQLAlchemy’s PostgreSQL dialect has a hardcoded mapping which links the string name `"VARCHAR"` to the SQLAlchemy `VARCHAR` class, and that’s how when we emit a statement like `Table('my_table', m, autoload_with=engine)`, the `Column` object within it would have an instance of `VARCHAR` present inside of it.

The implication of this is that if a `Table` object makes use of type objects that don’t correspond directly to the database-native type name, if we create a new `Table` object against a new `MetaData` collection for this database table elsewhere using reflection, it will not have this datatype. For example:

    >>> from sqlalchemy import (
    ...     Table,
    ...     Column,
    ...     MetaData,
    ...     create_engine,
    ...     PickleType,
    ...     Integer,
    ... )
    >>> metadata = MetaData()
    >>> my_table = Table(
    ...     "my_table", metadata, Column("id", Integer), Column("data", PickleType)
    ... )
    >>> engine = create_engine("sqlite://", echo="debug")
    >>> my_table.create(engine)
    INFO sqlalchemy.engine.base.Engine
    CREATE TABLE my_table (
        id INTEGER,
        data BLOB
    )

Above, we made use of `PickleType`, which is a `TypeDecorator` that works on top of the `LargeBinary` datatype, which on SQLite corresponds to the database type `BLOB`. In the CREATE TABLE, we see that the `BLOB` datatype is used. The SQLite database knows nothing about the `PickleType` we’ve used.

If we look at the datatype of `my_table.c.data.type`, as this is a Python object that was created by us directly, it is `PickleType`:

    >>> my_table.c.data.type
    PickleType()

However, if we create another instance of `Table` using reflection, the use of `PickleType` is not represented in the SQLite database we’ve created; we instead get back `BLOB`:

    >>> metadata_two = MetaData()
    >>> my_reflected_table = Table("my_table", metadata_two, autoload_with=engine)
    INFO sqlalchemy.engine.base.Engine PRAGMA main.table_info("my_table")
    INFO sqlalchemy.engine.base.Engine ()
    DEBUG sqlalchemy.engine.base.Engine Col ('cid', 'name', 'type', 'notnull', 'dflt_value', 'pk')
    DEBUG sqlalchemy.engine.base.Engine Row (0, 'id', 'INTEGER', 0, None, 0)
    DEBUG sqlalchemy.engine.base.Engine Row (1, 'data', 'BLOB', 0, None, 0)

    >>> my_reflected_table.c.data.type
    BLOB()

Typically, when an application defines explicit `Table` metadata with custom types, there is no need to use table reflection because the necessary `Table` metadata is already present. However, for the case where an application, or a combination of them, need to make use of both explicit `Table` metadata which includes custom, Python-level datatypes, as well as `Table` objects which set up their `Column` objects as reflected from the database, which nevertheless still need to exhibit the additional Python behaviors of the custom datatypes, additional steps must be taken to allow this.

The most straightforward is to override specific columns as described at Overriding Reflected Columns. In this technique, we simply use reflection in combination with explicit `Column` objects for those columns for which we want to use a custom or decorated datatype:

    >>> metadata_three = MetaData()
    >>> my_reflected_table = Table(
    ...     "my_table",
    ...     metadata_three,
    ...     Column("data", PickleType),
    ...     autoload_with=engine,
    ... )

The `my_reflected_table` object above is reflected, and will load the definition of the “id” column from the SQLite database. But for the “data” column, we’ve overridden the reflected object with an explicit `Column` definition that includes our desired in-Python datatype, the `PickleType`. The reflection process will leave this `Column` object intact:

    >>> my_reflected_table.c.data.type
    PickleType()

A more elaborate way to convert from database-native type objects to custom datatypes is to use the `DDLEvents.column_reflect()` event handler. If for example we knew that we wanted all `BLOB` datatypes to in fact be `PickleType`, we could set up a rule across the board:

    from sqlalchemy import BLOB
    from sqlalchemy import event
    from sqlalchemy import PickleType
    from sqlalchemy import Table

    @event.listens_for(Table, "column_reflect")
    def _setup_pickletype(inspector, table, column_info):
        if isinstance(column_info["type"], BLOB):
            column_info["type"] = PickleType()

When the above code is invoked before any table reflection occurs (note also it should be invoked only once in the application, as it is a global rule), upon reflecting any `Table` that includes a column with a `BLOB` datatype, the resulting datatype will be stored in the `Column` object as `PickleType`.

In practice, the above event-based approach would likely have additional rules in order to affect only those columns where the datatype is important, such as a lookup table of table names and possibly column names, or other heuristics in order to accurately determine which columns should be established with an in Python datatype.