Custom Types — SQLAlchemy 2.0.0b1 documentation

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Sqlalchemy/docs/latest/core/custom types

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 _types.BINARY, which will produce the string BINARY against all backends except SQLite, in which case it will produce BLOB.

See the section type_compilation_extension, 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.


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.


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.

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:
                raise TypeError("tzinfo is required")
            value = value.astimezone(datetime.timezone.utc).replace(
        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

Receives and returns Python uuid() objects. Uses the PG UUID type when using PostgreSQL, CHAR(32) on other backends, storing them in stringified hex format. Can be modified to store binary in CHAR(16) if desired:

from sqlalchemy.types import TypeDecorator, CHAR
from sqlalchemy.dialects.postgresql import UUID
import uuid

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

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

    impl = CHAR
    cache_ok = True

    def load_dialect_impl(self, dialect):
        if == 'postgresql':
            return dialect.type_descriptor(UUID())
            return dialect.type_descriptor(CHAR(32))

    def process_bind_param(self, value, dialect):
        if value is None:
            return value
        elif == 'postgresql':
            return str(value)
            if not isinstance(value, uuid.UUID):
                return "%.32x" % uuid.UUID(value).int
                # hexstring
                return "%.32x" %

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

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.




    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)

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()
            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. The PostGIS extension to PostgreSQL includes an extensive array of SQL functions that are necessary for coercing data into particular formats.

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 _expression.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 _schema.Table metadata and use it in a construct:

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

  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 against a label() of our expression, the string label is moved to the outside of the wrapped expression:



SELECT ST_AsText(geometry.geom_data) AS my_data
FROM geometry

Another example is we decorate _postgresql.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)),
                    PGPString("this is my passphrase")),

engine = create_engine("postgresql://scott:[email protected]/test", echo=True)
with engine.begin() as conn:

    conn.execute(message.insert(), username="some user",
                                message="this is my 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 ('%ed%')).

The Core expression constructs in all cases consult the type of the expression in order to determine the behavior of existing operators, as well as to locate additional operators that aren’t part of the built-in set. The TypeEngine base class defines a root “comparison” implementation TypeEngine.Comparator, and many specific types provide their own sub-implementations of this class. User-defined TypeEngine.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:

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 TypeEngine.Comparator class associated with the Integer type.


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

The implementation for ColumnOperators.__add__() is consulted by an owning SQL expression, by instantiating the TypeEngine.Comparator with itself as the expr attribute. The mechanics of the expression system are such that operations continue recursively until an expression object produces a new SQL expression construct. Above, we could just as well have said self.expr.op("goofy")(other) instead of self.op("goofy")(other).

When using Operators.op() for comparison operations that return a boolean result, the :paramref:`.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)

New methods added to a TypeEngine.Comparator are exposed on an owning SQL expression using a __getattr__ scheme, which exposes methods added to TypeEngine.Comparator onto the owning _expression.ColumnElement. 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(
log(:log_1, :log_2)

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,

Using the above type:

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

See also



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.

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 _schema.Column object within it would have an instance of VARCHAR present inside of it.

The implication of this is that if a _schema.Table object makes use of type objects that don’t correspond directly to the database-native type name, if we create a new _schema.Table object against a new _schema.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, as this is a Python object that was created by us directly, it is PickleType:


However, if we create another instance of _schema.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)


Typically, when an application defines explicit _schema.Table metadata with custom types, there is no need to use table reflection because the necessary _schema.Table metadata is already present. However, for the case where an application, or a combination of them, need to make use of both explicit _schema.Table metadata which includes custom, Python-level datatypes, as well as _schema.Table objects which set up their _schema.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 _schema.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 _schema.Column definition that includes our desired in-Python datatype, the PickleType. The reflection process will leave this _schema.Column object intact:


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 _schema.Table that includes a column with a BLOB datatype, the resulting datatype will be stored in the _schema.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.