SQLAlchemy provides abstractions for most common database data types, and a mechanism for specifying your own custom data types.
The methods and attributes of type objects are rarely used directly.
Type objects are supplied to Table
definitions
and can be supplied as type hints to functions for occasions where
the database driver returns an incorrect type.
>>> users = Table('users', metadata,
... Column('id', Integer, primary_key=True)
... Column('login', String(32))
... )
SQLAlchemy will use the Integer
and String(32)
type
information when issuing a CREATE TABLE
statement and will use it
again when reading back rows SELECTed
from the database.
Functions that accept a type (such as Column()
) will
typically accept a type class or instance; Integer
is equivalent
to Integer()
with no construction arguments in this case.
Generic types specify a column that can read, write and store a
particular type of Python data. SQLAlchemy will choose the best
database column type available on the target database when issuing a
CREATE TABLE
statement. For complete control over which column
type is emitted in CREATE TABLE
, such as VARCHAR
see
SQL Standard and Multiple Vendor Types and the other sections of this chapter.
sqlalchemy.types.
BigInteger
¶Bases: sqlalchemy.types.Integer
A type for bigger int
integers.
Typically generates a BIGINT
in DDL, and otherwise acts like
a normal Integer
on the Python side.
sqlalchemy.types.
Boolean
(create_constraint=True, name=None, _create_events=True)¶Bases: sqlalchemy.types.TypeEngine
, sqlalchemy.types.SchemaType
A bool datatype.
Boolean typically uses BOOLEAN or SMALLINT on the DDL side, and on
the Python side deals in True
or False
.
__init__
(create_constraint=True, name=None, _create_events=True)¶Construct a Boolean.
Parameters: |
---|
sqlalchemy.types.
Date
¶Bases: sqlalchemy.types._DateAffinity
, sqlalchemy.types.TypeEngine
A type for datetime.date()
objects.
sqlalchemy.types.
DateTime
(timezone=False)¶Bases: sqlalchemy.types._DateAffinity
, sqlalchemy.types.TypeEngine
A type for datetime.datetime()
objects.
Date and time types return objects from the Python datetime
module. Most DBAPIs have built in support for the datetime
module, with the noted exception of SQLite. In the case of
SQLite, date and time types are stored as strings which are then
converted back to datetime objects when rows are returned.
For the time representation within the datetime type, some
backends include additional options, such as timezone support and
fractional seconds support. For fractional seconds, use the
dialect-specific datatype, such as mysql.TIME
. For
timezone support, use at least the TIMESTAMP
datatype,
if not the dialect-specific datatype object.
__init__
(timezone=False)¶Construct a new DateTime
.
Parameters: | timezone¶ – boolean. Indicates that the datetime type should
enable timezone support, if available on the
base date/time-holding type only. It is recommended
to make use of the TIMESTAMP datatype directly when
using this flag, as some databases include separate generic
date/time-holding types distinct from the timezone-capable
TIMESTAMP datatype, such as Oracle. |
---|
sqlalchemy.types.
Enum
(*enums, **kw)¶Bases: sqlalchemy.types.String
, sqlalchemy.types.SchemaType
Generic Enum Type.
The Enum
type provides a set of possible string values
which the column is constrained towards.
The Enum
type will make use of the backend’s native “ENUM”
type if one is available; otherwise, it uses a VARCHAR datatype and
produces a CHECK constraint. Use of the backend-native enum type
can be disabled using the Enum.native_enum
flag, and
the production of the CHECK constraint is configurable using the
Enum.create_constraint
flag.
The Enum
type also provides in-Python validation of string
values during both read and write operations. When reading a value
from the database in a result set, the string value is always checked
against the list of possible values and a LookupError
is raised
if no match is found. When passing a value to the database as a
plain string within a SQL statement, if the
Enum.validate_strings
parameter is
set to True, a LookupError
is raised for any string value that’s
not located in the given list of possible values; note that this
impacts usage of LIKE expressions with enumerated values (an unusual
use case).
Changed in version 1.1: the Enum
type now provides in-Python
validation of input values as well as on data being returned by
the database.
The source of enumerated values may be a list of string values, or
alternatively a PEP-435-compliant enumerated class. For the purposes
of the Enum
datatype, this class need only provide a
__members__
method.
When using an enumerated class, the enumerated objects are used both for input and output, rather than strings as is the case with a plain-string enumerated type:
import enum
class MyEnum(enum.Enum):
one = 1
two = 2
three = 3
t = Table(
'data', MetaData(),
Column('value', Enum(MyEnum))
)
connection.execute(t.insert(), {"value": MyEnum.two})
assert connection.scalar(t.select()) is MyEnum.two
Above, the string names of each element, e.g. “one”, “two”, “three”, are persisted to the database; the values of the Python Enum, here indicated as integers, are not used; the value of each enum can therefore be any kind of Python object whether or not it is persistable.
New in version 1.1: - support for PEP-435-style enumerated classes.
See also
ENUM
- PostgreSQL-specific type,
which has additional functionality.
__init__
(*enums, **kw)¶Construct an enum.
Keyword arguments which don’t apply to a specific backend are ignored by that backend.
Parameters: |
|
---|
create
(bind=None, checkfirst=False)¶create()
method of SchemaType
Issue CREATE ddl for this type, if applicable.
drop
(bind=None, checkfirst=False)¶drop()
method of SchemaType
Issue DROP ddl for this type, if applicable.
sqlalchemy.types.
Float
(precision=None, asdecimal=False, decimal_return_scale=None, **kwargs)¶Bases: sqlalchemy.types.Numeric
Type representing floating point types, such as FLOAT
or REAL
.
This type returns Python float
objects by default, unless the
Float.asdecimal
flag is set to True, in which case they
are coerced to decimal.Decimal
objects.
Note
The Float
type is designed to receive data from a database
type that is explicitly known to be a floating point type
(e.g. FLOAT
, REAL
, others)
and not a decimal type (e.g. DECIMAL
, NUMERIC
, others).
If the database column on the server is in fact a Numeric
type, such as DECIMAL
or NUMERIC
, use the Numeric
type or a subclass, otherwise numeric coercion between
float
/Decimal
may or may not function as expected.
__init__
(precision=None, asdecimal=False, decimal_return_scale=None, **kwargs)¶Construct a Float.
Parameters: |
|
---|
sqlalchemy.types.
Integer
¶Bases: sqlalchemy.types._DateAffinity
, sqlalchemy.types.TypeEngine
A type for int
integers.
sqlalchemy.types.
Interval
(native=True, second_precision=None, day_precision=None)¶Bases: sqlalchemy.types._DateAffinity
, sqlalchemy.types.TypeDecorator
A type for datetime.timedelta()
objects.
The Interval type deals with datetime.timedelta
objects. In
PostgreSQL, the native INTERVAL
type is used; for others, the
value is stored as a date which is relative to the “epoch”
(Jan. 1, 1970).
Note that the Interval
type does not currently provide date arithmetic
operations on platforms which do not support interval types natively. Such
operations usually require transformation of both sides of the expression
(such as, conversion of both sides into integer epoch values first) which
currently is a manual procedure (such as via
func
).
__init__
(native=True, second_precision=None, day_precision=None)¶Construct an Interval object.
Parameters: |
|
---|
coerce_compared_value
(op, value)¶See TypeEngine.coerce_compared_value()
for a description.
sqlalchemy.types.
LargeBinary
(length=None)¶Bases: sqlalchemy.types._Binary
A type for large binary byte data.
The LargeBinary
type corresponds to a large and/or unlengthed
binary type for the target platform, such as BLOB on MySQL and BYTEA for
PostgreSQL. It also handles the necessary conversions for the DBAPI.
sqlalchemy.types.
MatchType
(create_constraint=True, name=None, _create_events=True)¶Bases: sqlalchemy.types.Boolean
Refers to the return type of the MATCH operator.
As the ColumnOperators.match()
is probably the most open-ended
operator in generic SQLAlchemy Core, we can’t assume the return type
at SQL evaluation time, as MySQL returns a floating point, not a boolean,
and other backends might do something different. So this type
acts as a placeholder, currently subclassing Boolean
.
The type allows dialects to inject result-processing functionality
if needed, and on MySQL will return floating-point values.
New in version 1.0.0.
sqlalchemy.types.
Numeric
(precision=None, scale=None, decimal_return_scale=None, asdecimal=True)¶Bases: sqlalchemy.types._DateAffinity
, sqlalchemy.types.TypeEngine
A type for fixed precision numbers, such as NUMERIC
or DECIMAL
.
This type returns Python decimal.Decimal
objects by default, unless
the Numeric.asdecimal
flag is set to False, in which case
they are coerced to Python float
objects.
Note
The Numeric
type is designed to receive data from a database
type that is explicitly known to be a decimal type
(e.g. DECIMAL
, NUMERIC
, others) and not a floating point
type (e.g. FLOAT
, REAL
, others).
If the database column on the server is in fact a floating-point type
type, such as FLOAT
or REAL
, use the Float
type or a subclass, otherwise numeric coercion between
float
/Decimal
may or may not function as expected.
Note
The Python decimal.Decimal
class is generally slow
performing; cPython 3.3 has now switched to use the cdecimal library natively. For
older Python versions, the cdecimal
library can be patched
into any application where it will replace the decimal
library fully, however this needs to be applied globally and
before any other modules have been imported, as follows:
import sys
import cdecimal
sys.modules["decimal"] = cdecimal
Note that the cdecimal
and decimal
libraries are not
compatible with each other, so patching cdecimal
at the
global level is the only way it can be used effectively with
various DBAPIs that hardcode to import the decimal
library.
__init__
(precision=None, scale=None, decimal_return_scale=None, asdecimal=True)¶Construct a Numeric.
Parameters: |
|
---|
When using the Numeric
type, care should be taken to ensure
that the asdecimal setting is apppropriate for the DBAPI in use -
when Numeric applies a conversion from Decimal->float or float->
Decimal, this conversion incurs an additional performance overhead
for all result columns received.
DBAPIs that return Decimal natively (e.g. psycopg2) will have
better accuracy and higher performance with a setting of True
,
as the native translation to Decimal reduces the amount of floating-
point issues at play, and the Numeric type itself doesn’t need
to apply any further conversions. However, another DBAPI which
returns floats natively will incur an additional conversion
overhead, and is still subject to floating point data loss - in
which case asdecimal=False
will at least remove the extra
conversion overhead.
sqlalchemy.types.
PickleType
(protocol=2, pickler=None, comparator=None)¶Bases: sqlalchemy.types.TypeDecorator
Holds Python objects, which are serialized using pickle.
PickleType builds upon the Binary type to apply Python’s
pickle.dumps()
to incoming objects, and pickle.loads()
on
the way out, allowing any pickleable Python object to be stored as
a serialized binary field.
To allow ORM change events to propagate for elements associated
with PickleType
, see Mutation Tracking.
__init__
(protocol=2, pickler=None, comparator=None)¶Construct a PickleType.
Parameters: |
|
---|
impl
¶alias of LargeBinary
sqlalchemy.types.
SchemaType
(name=None, schema=None, metadata=None, inherit_schema=False, quote=None, _create_events=True)¶Bases: sqlalchemy.sql.expression.SchemaEventTarget
Mark a type as possibly requiring schema-level DDL for usage.
Supports types that must be explicitly created/dropped (i.e. PG ENUM type) as well as types that are complimented by table or schema level constraints, triggers, and other rules.
SchemaType
classes can also be targets for the
DDLEvents.before_parent_attach()
and
DDLEvents.after_parent_attach()
events, where the events fire off
surrounding the association of the type object with a parent
Column
.
adapt
(impltype, **kw)¶bind
¶copy
(**kw)¶create
(bind=None, checkfirst=False)¶Issue CREATE ddl for this type, if applicable.
drop
(bind=None, checkfirst=False)¶Issue DROP ddl for this type, if applicable.
sqlalchemy.types.
SmallInteger
¶Bases: sqlalchemy.types.Integer
A type for smaller int
integers.
Typically generates a SMALLINT
in DDL, and otherwise acts like
a normal Integer
on the Python side.
sqlalchemy.types.
String
(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)¶Bases: sqlalchemy.types.Concatenable
, sqlalchemy.types.TypeEngine
The base for all string and character types.
In SQL, corresponds to VARCHAR. Can also take Python unicode objects and encode to the database’s encoding in bind params (and the reverse for result sets.)
The length field is usually required when the String type is used within a CREATE TABLE statement, as VARCHAR requires a length on most databases.
__init__
(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)¶Create a string-holding type.
Parameters: |
|
---|
sqlalchemy.types.
Text
(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)¶Bases: sqlalchemy.types.String
A variably sized string type.
In SQL, usually corresponds to CLOB or TEXT. Can also take Python unicode objects and encode to the database’s encoding in bind params (and the reverse for result sets.) In general, TEXT objects do not have a length; while some databases will accept a length argument here, it will be rejected by others.
sqlalchemy.types.
Time
(timezone=False)¶Bases: sqlalchemy.types._DateAffinity
, sqlalchemy.types.TypeEngine
A type for datetime.time()
objects.
sqlalchemy.types.
Unicode
(length=None, **kwargs)¶Bases: sqlalchemy.types.String
A variable length Unicode string type.
The Unicode
type is a String
subclass
that assumes input and output as Python unicode
data,
and in that regard is equivalent to the usage of the
convert_unicode
flag with the String
type.
However, unlike plain String
, it also implies an
underlying column type that is explicitly supporting of non-ASCII
data, such as NVARCHAR
on Oracle and SQL Server.
This can impact the output of CREATE TABLE
statements
and CAST
functions at the dialect level, and can
also affect the handling of bound parameters in some
specific DBAPI scenarios.
The encoding used by the Unicode
type is usually
determined by the DBAPI itself; most modern DBAPIs
feature support for Python unicode
objects as bound
values and result set values, and the encoding should
be configured as detailed in the notes for the target
DBAPI in the Dialects section.
For those DBAPIs which do not support, or are not configured
to accommodate Python unicode
objects
directly, SQLAlchemy does the encoding and decoding
outside of the DBAPI. The encoding in this scenario
is determined by the encoding
flag passed to
create_engine()
.
When using the Unicode
type, it is only appropriate
to pass Python unicode
objects, and not plain str
.
If a plain str
is passed under Python 2, a warning
is emitted. If you notice your application emitting these warnings but
you’re not sure of the source of them, the Python
warnings
filter, documented at
http://docs.python.org/library/warnings.html,
can be used to turn these warnings into exceptions
which will illustrate a stack trace:
import warnings
warnings.simplefilter('error')
For an application that wishes to pass plain bytestrings
and Python unicode
objects to the Unicode
type
equally, the bytestrings must first be decoded into
unicode. The recipe at Coercing Encoded Strings to Unicode illustrates
how this is done.
See also:
UnicodeText
- unlengthed textual counterpart toUnicode
.
sqlalchemy.types.
UnicodeText
(length=None, **kwargs)¶Bases: sqlalchemy.types.Text
An unbounded-length Unicode string type.
See Unicode
for details on the unicode
behavior of this object.
Like Unicode
, usage the UnicodeText
type implies a
unicode-capable type being used on the backend, such as
NCLOB
, NTEXT
.
This category of types refers to types that are either part of the
SQL standard, or are potentially found within a subset of database backends.
Unlike the “generic” types, the SQL standard/multi-vendor types have no
guarantee of working on all backends, and will only work on those backends
that explicitly support them by name. That is, the type will always emit
its exact name in DDL with CREATE TABLE
is issued.
sqlalchemy.types.
ARRAY
(item_type, as_tuple=False, dimensions=None, zero_indexes=False)¶Bases: sqlalchemy.types.Indexable
, sqlalchemy.types.Concatenable
, sqlalchemy.types.TypeEngine
Represent a SQL Array type.
Note
This type serves as the basis for all ARRAY operations.
However, currently only the PostgreSQL backend has support
for SQL arrays in SQLAlchemy. It is recommended to use the
postgresql.ARRAY
type directly when using ARRAY types
with PostgreSQL, as it provides additional operators specific
to that backend.
types.ARRAY
is part of the Core in support of various SQL standard
functions such as array_agg
which explicitly involve arrays;
however, with the exception of the PostgreSQL backend and possibly
some third-party dialects, no other SQLAlchemy built-in dialect has
support for this type.
An types.ARRAY
type is constructed given the “type”
of element:
mytable = Table("mytable", metadata,
Column("data", ARRAY(Integer))
)
The above type represents an N-dimensional array, meaning a supporting backend such as PostgreSQL will interpret values with any number of dimensions automatically. To produce an INSERT construct that passes in a 1-dimensional array of integers:
connection.execute(
mytable.insert(),
data=[1,2,3]
)
The types.ARRAY
type can be constructed given a fixed number
of dimensions:
mytable = Table("mytable", metadata,
Column("data", ARRAY(Integer, dimensions=2))
)
Sending a number of dimensions is optional, but recommended if the datatype is to represent arrays of more than one dimension. This number is used:
When emitting the type declaration itself to the database, e.g.
INTEGER[][]
When translating Python values to database values, and vice versa, e.g.
an ARRAY of Unicode
objects uses this number to efficiently
access the string values inside of array structures without resorting
to per-row type inspection
When used with the Python getitem
accessor, the number of dimensions
serves to define the kind of type that the []
operator should
return, e.g. for an ARRAY of INTEGER with two dimensions:
>>> expr = table.c.column[5] # returns ARRAY(Integer, dimensions=1)
>>> expr = expr[6] # returns Integer
For 1-dimensional arrays, an types.ARRAY
instance with no
dimension parameter will generally assume single-dimensional behaviors.
SQL expressions of type types.ARRAY
have support for “index” and
“slice” behavior. The Python []
operator works normally here, given
integer indexes or slices. Arrays default to 1-based indexing.
The operator produces binary expression
constructs which will produce the appropriate SQL, both for
SELECT statements:
select([mytable.c.data[5], mytable.c.data[2:7]])
as well as UPDATE statements when the Update.values()
method
is used:
mytable.update().values({
mytable.c.data[5]: 7,
mytable.c.data[2:7]: [1, 2, 3]
})
The types.ARRAY
type also provides for the operators
types.ARRAY.Comparator.any()
and types.ARRAY.Comparator.all()
.
The PostgreSQL-specific version of types.ARRAY
also provides additional
operators.
New in version 1.1.0.
See also
Comparator
(expr)¶Bases: sqlalchemy.types.Comparator
, sqlalchemy.types.Comparator
Define comparison operations for types.ARRAY
.
More operators are available on the dialect-specific form
of this type. See postgresql.ARRAY.Comparator
.
all
(other, operator=None)¶Return other operator ALL (array)
clause.
Argument places are switched, because ALL requires array expression to be on the right hand-side.
E.g.:
from sqlalchemy.sql import operators
conn.execute(
select([table.c.data]).where(
table.c.data.all(7, operator=operators.lt)
)
)
Parameters: |
---|
any
(other, operator=None)¶Return other operator ANY (array)
clause.
Argument places are switched, because ANY requires array expression to be on the right hand-side.
E.g.:
from sqlalchemy.sql import operators
conn.execute(
select([table.c.data]).where(
table.c.data.any(7, operator=operators.lt)
)
)
Parameters: |
---|
__init__
(item_type, as_tuple=False, dimensions=None, zero_indexes=False)¶Construct an types.ARRAY
.
E.g.:
Column('myarray', ARRAY(Integer))
Arguments are:
Parameters: |
|
---|
comparator_factory
¶alias of Comparator
zero_indexes
= False¶if True, Python zero-based indexes should be interpreted as one-based on the SQL expression side.
sqlalchemy.types.
BIGINT
¶Bases: sqlalchemy.types.BigInteger
The SQL BIGINT type.
sqlalchemy.types.
BINARY
(length=None)¶Bases: sqlalchemy.types._Binary
The SQL BINARY type.
sqlalchemy.types.
BLOB
(length=None)¶Bases: sqlalchemy.types.LargeBinary
The SQL BLOB type.
sqlalchemy.types.
BOOLEAN
(create_constraint=True, name=None, _create_events=True)¶Bases: sqlalchemy.types.Boolean
The SQL BOOLEAN type.
sqlalchemy.types.
CHAR
(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)¶Bases: sqlalchemy.types.String
The SQL CHAR type.
sqlalchemy.types.
CLOB
(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)¶Bases: sqlalchemy.types.Text
The CLOB type.
This type is found in Oracle and Informix.
sqlalchemy.types.
DATE
¶Bases: sqlalchemy.types.Date
The SQL DATE type.
sqlalchemy.types.
DATETIME
(timezone=False)¶Bases: sqlalchemy.types.DateTime
The SQL DATETIME type.
sqlalchemy.types.
DECIMAL
(precision=None, scale=None, decimal_return_scale=None, asdecimal=True)¶Bases: sqlalchemy.types.Numeric
The SQL DECIMAL type.
sqlalchemy.types.
FLOAT
(precision=None, asdecimal=False, decimal_return_scale=None, **kwargs)¶Bases: sqlalchemy.types.Float
The SQL FLOAT type.
sqlalchemy.types.
JSON
(none_as_null=False)¶Bases: sqlalchemy.types.Indexable
, sqlalchemy.types.TypeEngine
Represent a SQL JSON type.
Note
types.JSON
is provided as a facade for vendor-specific
JSON types. Since it supports JSON SQL operations, it only
works on backends that have an actual JSON type, currently
PostgreSQL as well as certain versions of MySQL.
types.JSON
is part of the Core in support of the growing
popularity of native JSON datatypes.
The types.JSON
type stores arbitrary JSON format data, e.g.:
data_table = Table('data_table', metadata,
Column('id', Integer, primary_key=True),
Column('data', JSON)
)
with engine.connect() as conn:
conn.execute(
data_table.insert(),
data = {"key1": "value1", "key2": "value2"}
)
The base types.JSON
provides these two operations:
Keyed index operations:
data_table.c.data['some key']
Integer index operations:
data_table.c.data[3]
Path index operations:
data_table.c.data[('key_1', 'key_2', 5, ..., 'key_n')]
Additional operations are available from the dialect-specific versions
of types.JSON
, such as postgresql.JSON
and
postgresql.JSONB
, each of which offer more operators than
just the basic type.
Index operations return an expression object whose type defaults to
JSON
by default, so that further JSON-oriented instructions
may be called upon the result type. Note that there are backend-specific
idiosyncracies here, including that the Postgresql database does not generally
compare a “json” to a “json” structure without type casts. These idiosyncracies
can be accommodated in a backend-neutral way by by making explicit use
of the cast()
and type_coerce()
constructs.
Comparison of specific index elements of a JSON
object
to other objects work best if the left hand side is CAST to a string
and the right hand side is rendered as a json string; a future SQLAlchemy
feature such as a generic “astext” modifier may simplify this at some point:
Compare an element of a JSON structure to a string:
from sqlalchemy import cast, type_coerce
from sqlalchemy import String, JSON
cast(
data_table.c.data['some_key'], String
) == '"some_value"'
cast(
data_table.c.data['some_key'], String
) == type_coerce("some_value", JSON)
Compare an element of a JSON structure to an integer:
from sqlalchemy import cast, type_coerce
from sqlalchemy import String, JSON
cast(data_table.c.data['some_key'], String) == '55'
cast(
data_table.c.data['some_key'], String
) == type_coerce(55, JSON)
Compare an element of a JSON structure to some other JSON structure - note that Python dictionaries are typically not ordered so care should be taken here to assert that the JSON structures are identical:
from sqlalchemy import cast, type_coerce
from sqlalchemy import String, JSON
import json
cast(
data_table.c.data['some_key'], String
) == json.dumps({"foo": "bar"})
cast(
data_table.c.data['some_key'], String
) == type_coerce({"foo": "bar"}, JSON)
The JSON
type, when used with the SQLAlchemy ORM, does not
detect in-place mutations to the structure. In order to detect these, the
sqlalchemy.ext.mutable
extension must be used. This extension will
allow “in-place” changes to the datastructure to produce events which
will be detected by the unit of work. See the example at HSTORE
for a simple example involving a dictionary.
When working with NULL values, the JSON
type recommends the
use of two specific constants in order to differentiate between a column
that evaluates to SQL NULL, e.g. no value, vs. the JSON-encoded string
of "null"
. To insert or select against a value that is SQL NULL,
use the constant null()
:
from sqlalchemy import null
conn.execute(table.insert(), json_value=null())
To insert or select against a value that is JSON "null"
, use the
constant JSON.NULL
:
conn.execute(table.insert(), json_value=JSON.NULL)
The JSON
type supports a flag
JSON.none_as_null
which when set to True will result
in the Python constant None
evaluating to the value of SQL
NULL, and when set to False results in the Python constant
None
evaluating to the value of JSON "null"
. The Python
value None
may be used in conjunction with either
JSON.NULL
and null()
in order to indicate NULL
values, but care must be taken as to the value of the
JSON.none_as_null
in these cases.
New in version 1.1.
Comparator
(expr)¶Bases: sqlalchemy.types.Comparator
, sqlalchemy.types.Comparator
Define comparison operations for types.JSON
.
JSONElementType
¶Bases: sqlalchemy.types.TypeEngine
common function for index / path elements in a JSON expression.
JSONIndexType
¶Bases: sqlalchemy.types.JSONElementType
Placeholder for the datatype of a JSON index value.
This allows execution-time processing of JSON index values for special syntaxes.
JSONPathType
¶Bases: sqlalchemy.types.JSONElementType
Placeholder type for JSON path operations.
This allows execution-time processing of a path-based index value into a specific SQL syntax.
NULL
= symbol('JSON_NULL')¶Describe the json value of NULL.
This value is used to force the JSON value of "null"
to be
used as the value. A value of Python None
will be recognized
either as SQL NULL or JSON "null"
, based on the setting
of the JSON.none_as_null
flag; the JSON.NULL
constant can be used to always resolve to JSON "null"
regardless
of this setting. This is in contrast to the sql.null()
construct,
which always resolves to SQL NULL. E.g.:
from sqlalchemy import null
from sqlalchemy.dialects.postgresql import JSON
obj1 = MyObject(json_value=null()) # will *always* insert SQL NULL
obj2 = MyObject(json_value=JSON.NULL) # will *always* insert JSON string "null"
session.add_all([obj1, obj2])
session.commit()
In order to set JSON NULL as a default value for a column, the most
transparent method is to use text()
:
Table(
'my_table', metadata,
Column('json_data', JSON, default=text("'null'"))
)
While it is possible to use JSON.NULL
in this context, the
JSON.NULL
value will be returned as the value of the column,
which in the context of the ORM or other repurposing of the default
value, may not be desirable. Using a SQL expression means the value
will be re-fetched from the database within the context of retrieving
generated defaults.
__init__
(none_as_null=False)¶Construct a types.JSON
type.
Parameters: | none_as_null=False¶ – if True, persist the value from sqlalchemy import null
conn.execute(table.insert(), data=null()) Note
See also |
---|
comparator_factory
¶alias of Comparator
sqlalchemy.types.
INTEGER
¶Bases: sqlalchemy.types.Integer
The SQL INT or INTEGER type.
sqlalchemy.types.
NCHAR
(length=None, **kwargs)¶Bases: sqlalchemy.types.Unicode
The SQL NCHAR type.
sqlalchemy.types.
NVARCHAR
(length=None, **kwargs)¶Bases: sqlalchemy.types.Unicode
The SQL NVARCHAR type.
sqlalchemy.types.
NUMERIC
(precision=None, scale=None, decimal_return_scale=None, asdecimal=True)¶Bases: sqlalchemy.types.Numeric
The SQL NUMERIC type.
sqlalchemy.types.
REAL
(precision=None, asdecimal=False, decimal_return_scale=None, **kwargs)¶Bases: sqlalchemy.types.Float
The SQL REAL type.
sqlalchemy.types.
SMALLINT
¶Bases: sqlalchemy.types.SmallInteger
The SQL SMALLINT type.
sqlalchemy.types.
TEXT
(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)¶Bases: sqlalchemy.types.Text
The SQL TEXT type.
sqlalchemy.types.
TIME
(timezone=False)¶Bases: sqlalchemy.types.Time
The SQL TIME type.
sqlalchemy.types.
TIMESTAMP
(timezone=False)¶Bases: sqlalchemy.types.DateTime
The SQL TIMESTAMP type.
TIMESTAMP
datatypes have support for timezone
storage on some backends, such as PostgreSQL and Oracle. Use the
timezone
argument in order to enable
“TIMESTAMP WITH TIMEZONE” for these backends.
__init__
(timezone=False)¶Construct a new TIMESTAMP
.
Parameters: | timezone¶ – boolean. Indicates that the TIMESTAMP type should enable timezone support, if available on the target database. On a per-dialect basis is similar to “TIMESTAMP WITH TIMEZONE”. If the target database does not support timezones, this flag is ignored. |
---|
sqlalchemy.types.
VARBINARY
(length=None)¶Bases: sqlalchemy.types._Binary
The SQL VARBINARY type.
sqlalchemy.types.
VARCHAR
(length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False)¶Bases: sqlalchemy.types.String
The SQL VARCHAR type.
Database-specific types are also available for import from each database’s dialect module. See the Dialects reference for the database you’re interested in.
For example, MySQL has a BIGINT
type and PostgreSQL has an
INET
type. To use these, import them from the module explicitly:
from sqlalchemy.dialects import mysql
table = Table('foo', metadata,
Column('id', mysql.BIGINT),
Column('enumerates', mysql.ENUM('a', 'b', 'c'))
)
Or some PostgreSQL types:
from sqlalchemy.dialects import postgresql
table = Table('foo', metadata,
Column('ipaddress', postgresql.INET),
Column('elements', postgresql.ARRAY(String))
)
Each dialect provides the full set of typenames supported by that backend within its __all__ collection, so that a simple import * or similar will import all supported types as implemented for that backend:
from sqlalchemy.dialects.postgresql import *
t = Table('mytable', metadata,
Column('id', INTEGER, primary_key=True),
Column('name', VARCHAR(300)),
Column('inetaddr', INET)
)
Where above, the INTEGER and VARCHAR types are ultimately from sqlalchemy.types, and INET is specific to the PostgreSQL dialect.
Some dialect level types have the same name as the SQL standard type, but also provide additional arguments. For example, MySQL implements the full range of character and string types including additional arguments such as collation and charset:
from sqlalchemy.dialects.mysql import VARCHAR, TEXT
table = Table('foo', meta,
Column('col1', VARCHAR(200, collation='binary')),
Column('col2', TEXT(charset='latin1'))
)