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 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.
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.
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(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¶
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 dialect.name == "postgresql":
return dialect.type_descriptor(UUID())
else:
return dialect.type_descriptor(CHAR(32))
def process_bind_param(self, value, dialect):
if value is None:
return value
elif dialect.name == "postgresql":
return str(value)
else:
if not isinstance(value, uuid.UUID):
return "%.32x" % uuid.UUID(value).int
else:
# hexstring
return "%.32x" % value.int
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
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
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. 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 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://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'}
See also
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.
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.