SQLAlchemy 1.1 Documentation
Column and Data Types¶
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¶
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 Types`_ and the other sections of this chapter.
-
class
sqlalchemy.types.
BigInteger
¶ Bases:
sqlalchemy.types.Integer
A type for bigger
int
integers.Typically generates a
BIGINT
in DDL, and otherwise acts like a normalInteger
on the Python side.
-
class
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
orFalse
.-
__init__
(create_constraint=True, name=None, _create_events=True)¶ Construct a Boolean.
매개 변수:
-
-
class
sqlalchemy.types.
Date
¶ Bases:
sqlalchemy.types._DateAffinity
,sqlalchemy.types.TypeEngine
A type for
datetime.date()
objects.
-
class
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.
-
class
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 theEnum.native_enum
flag, and the production of the CHECK constraint is configurable using theEnum.create_constraint
flag.The
Enum
type also provides in-Python validation of both input values and database-returned values. ALookupError
is raised for any Python value that’s not located in the given list of possible values.버전 1.1으로 변경: 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 = "one" two = "two" three = "three" t = Table( 'data', MetaData(), Column('value', Enum(MyEnum)) ) connection.execute(t.insert(), {"value": MyEnum.two}) assert connection.scalar(t.select()) is MyEnum.two
버전 1.1에 추가: - support for PEP-435-style enumerated classes.
더 보기
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.
매개 변수: - *enums¶ –
either exactly one PEP-435 compliant enumerated type or one or more string or unicode enumeration labels. If unicode labels are present, the convert_unicode flag is auto-enabled.
버전 1.1에 추가: a PEP-435 style enumerated class may be passed.
- convert_unicode¶ – Enable unicode-aware bind parameter and result-set processing for this Enum’s data. This is set automatically based on the presence of unicode label strings.
- create_constraint¶ –
defaults to True. When creating a non-native enumerated type, also build a CHECK constraint on the database against the valid values.
버전 1.1에 추가: - added
Enum.create_constraint
which provides the option to disable the production of the CHECK constraint for a non-native enumerated type. - metadata¶ – Associate this type directly with a
MetaData
object. For types that exist on the target database as an independent schema construct (Postgresql), this type will be created and dropped withincreate_all()
anddrop_all()
operations. If the type is not associated with anyMetaData
object, it will associate itself with eachTable
in which it is used, and will be created when any of those individual tables are created, after a check is performed for its existence. The type is only dropped whendrop_all()
is called for thatTable
object’s metadata, however. - name¶ – The name of this type. This is required for Postgresql and any future supported database which requires an explicitly named type, or an explicitly named constraint in order to generate the type and/or a table that uses it. If a PEP-435 enumerated class was used, its name (converted to lower case) is used by default.
- native_enum¶ – Use the database’s native ENUM type when available. Defaults to True. When False, uses VARCHAR + check constraint for all backends.
- schema¶ –
Schema name of this type. For types that exist on the target database as an independent schema construct (Postgresql), this parameter specifies the named schema in which the type is present.
- quote¶ – Set explicit quoting preferences for the type’s name.
- inherit_schema¶ – When
True
, the “schema” from the owningTable
will be copied to the “schema” attribute of thisEnum
, replacing whatever value was passed for theschema
attribute. This also takes effect when using theTable.tometadata()
operation.
- *enums¶ –
-
create
(bind=None, checkfirst=False)¶ - inherited from the
create()
method ofSchemaType
Issue CREATE ddl for this type, if applicable.
-
drop
(bind=None, checkfirst=False)¶ - inherited from the
drop()
method ofSchemaType
Issue DROP ddl for this type, if applicable.
-
-
class
sqlalchemy.types.
Float
(precision=None, asdecimal=False, decimal_return_scale=None, **kwargs)¶ Bases:
sqlalchemy.types.Numeric
Type representing floating point types, such as
FLOAT
orREAL
.This type returns Python
float
objects by default, unless theFloat.asdecimal
flag is set to True, in which case they are coerced todecimal.Decimal
objects.주석
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 asDECIMAL
orNUMERIC
, use theNumeric
type or a subclass, otherwise numeric coercion betweenfloat
/Decimal
may or may not function as expected.-
__init__
(precision=None, asdecimal=False, decimal_return_scale=None, **kwargs)¶ Construct a Float.
매개 변수: - precision¶ – the numeric precision for use in DDL
CREATE TABLE
. - asdecimal¶ – the same flag as that of
Numeric
, but defaults toFalse
. Note that setting this flag toTrue
results in floating point conversion. - decimal_return_scale¶ –
Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don’t have a notion of “scale”, so by default the float type looks for the first ten decimal places when converting. Specfiying this value will override that length. Note that the MySQL float types, which do include “scale”, will use “scale” as the default for decimal_return_scale, if not otherwise specified.
버전 0.9.0에 추가.
- **kwargs¶ – deprecated. Additional arguments here are ignored
by the default
Float
type. For database specific floats that support additional arguments, see that dialect’s documentation for details, such assqlalchemy.dialects.mysql.FLOAT
.
- precision¶ – the numeric precision for use in DDL
-
-
class
sqlalchemy.types.
Integer
¶ Bases:
sqlalchemy.types._DateAffinity
,sqlalchemy.types.TypeEngine
A type for
int
integers.
-
class
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 nativeINTERVAL
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 viafunc
).-
__init__
(native=True, second_precision=None, day_precision=None)¶ Construct an Interval object.
매개 변수: - native¶ – when True, use the actual INTERVAL type provided by the database, if supported (currently Postgresql, Oracle). Otherwise, represent the interval data as an epoch value regardless.
- second_precision¶ – For native interval types which support a “fractional seconds precision” parameter, i.e. Oracle and Postgresql
- day_precision¶ – for native interval types which support a “day precision” parameter, i.e. Oracle.
-
coerce_compared_value
(op, value)¶ See
TypeEngine.coerce_compared_value()
for a description.
-
-
class
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.
-
class
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 subclassingBoolean
. The type allows dialects to inject result-processing functionality if needed, and on MySQL will return floating-point values.버전 1.0.0에 추가.
-
class
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
orDECIMAL
.This type returns Python
decimal.Decimal
objects by default, unless theNumeric.asdecimal
flag is set to False, in which case they are coerced to Pythonfloat
objects.주석
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 asFLOAT
orREAL
, use theFloat
type or a subclass, otherwise numeric coercion betweenfloat
/Decimal
may or may not function as expected.주석
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, thecdecimal
library can be patched into any application where it will replace thedecimal
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
anddecimal
libraries are not compatible with each other, so patchingcdecimal
at the global level is the only way it can be used effectively with various DBAPIs that hardcode to import thedecimal
library.-
__init__
(precision=None, scale=None, decimal_return_scale=None, asdecimal=True)¶ Construct a Numeric.
매개 변수: - precision¶ – the numeric precision for use in DDL
CREATE TABLE
. - scale¶ – the numeric scale for use in DDL
CREATE TABLE
. - asdecimal¶ – default True. Return whether or not values should be sent as Python Decimal objects, or as floats. Different DBAPIs send one or the other based on datatypes - the Numeric type will ensure that return values are one or the other across DBAPIs consistently.
- decimal_return_scale¶ –
Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don’t have a notion of “scale”, so by default the float type looks for the first ten decimal places when converting. Specfiying this value will override that length. Types which do include an explicit ”.scale” value, such as the base
Numeric
as well as the MySQL float types, will use the value of ”.scale” as the default for decimal_return_scale, if not otherwise specified.버전 0.9.0에 추가.
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 caseasdecimal=False
will at least remove the extra conversion overhead.- precision¶ – the numeric precision for use in DDL
-
-
class
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, andpickle.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.
매개 변수: - protocol¶ – defaults to
pickle.HIGHEST_PROTOCOL
. - pickler¶ – defaults to cPickle.pickle or pickle.pickle if
cPickle is not available. May be any object with
pickle-compatible
dumps` and ``loads
methods. - comparator¶ – a 2-arg callable predicate used
to compare values of this type. If left as
None
, the Python “equals” operator is used to compare values.
- protocol¶ – defaults to
-
impl
¶ alias of
LargeBinary
-
-
class
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 theDDLEvents.before_parent_attach()
andDDLEvents.after_parent_attach()
events, where the events fire off surrounding the association of the type object with a parentColumn
.-
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.
-
-
class
sqlalchemy.types.
SmallInteger
¶ Bases:
sqlalchemy.types.Integer
A type for smaller
int
integers.Typically generates a
SMALLINT
in DDL, and otherwise acts like a normalInteger
on the Python side.
-
class
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.
매개 변수: - length¶ – optional, a length for the column for use in
DDL and CAST expressions. May be safely omitted if no
CREATE TABLE
will be issued. Certain databases may require alength
for use in DDL, and will raise an exception when theCREATE TABLE
DDL is issued if aVARCHAR
with no length is included. Whether the value is interpreted as bytes or characters is database specific. - collation¶ –
Optional, a column-level collation for use in DDL and CAST expressions. Renders using the COLLATE keyword supported by SQLite, MySQL, and Postgresql. E.g.:
>>> from sqlalchemy import cast, select, String >>> print select([cast('some string', String(collation='utf8'))]) SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1
버전 0.8에 추가: Added support for COLLATE to all string types.
- convert_unicode¶ –
When set to
True
, theString
type will assume that input is to be passed as Pythonunicode
objects, and results returned as Pythonunicode
objects. If the DBAPI in use does not support Python unicode (which is fewer and fewer these days), SQLAlchemy will encode/decode the value, using the value of theencoding
parameter passed tocreate_engine()
as the encoding.When using a DBAPI that natively supports Python unicode objects, this flag generally does not need to be set. For columns that are explicitly intended to store non-ASCII data, the
Unicode
orUnicodeText
types should be used regardless, which feature the same behavior ofconvert_unicode
but also indicate an underlying column type that directly supports unicode, such asNVARCHAR
.For the extremely rare case that Python
unicode
is to be encoded/decoded by SQLAlchemy on a backend that does natively support Pythonunicode
, the valueforce
can be passed here which will cause SQLAlchemy’s encode/decode services to be used unconditionally. - unicode_error¶ – Optional, a method to use to handle Unicode
conversion errors. Behaves like the
errors
keyword argument to the standard library’sstring.decode()
functions. This flag requires thatconvert_unicode
is set toforce
- otherwise, SQLAlchemy is not guaranteed to handle the task of unicode conversion. Note that this flag adds significant performance overhead to row-fetching operations for backends that already return unicode objects natively (which most DBAPIs do). This flag should only be used as a last resort for reading strings from a column with varied or corrupted encodings.
- length¶ – optional, a length for the column for use in
DDL and CAST expressions. May be safely omitted if no
-
-
class
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.
-
class
sqlalchemy.types.
Time
(timezone=False)¶ Bases:
sqlalchemy.types._DateAffinity
,sqlalchemy.types.TypeEngine
A type for
datetime.time()
objects.
-
class
sqlalchemy.types.
Unicode
(length=None, **kwargs)¶ Bases:
sqlalchemy.types.String
A variable length Unicode string type.
The
Unicode
type is aString
subclass that assumes input and output as Pythonunicode
data, and in that regard is equivalent to the usage of theconvert_unicode
flag with theString
type. However, unlike plainString
, it also implies an underlying column type that is explicitly supporting of non-ASCII data, such asNVARCHAR
on Oracle and SQL Server. This can impact the output ofCREATE TABLE
statements andCAST
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 Pythonunicode
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 theencoding
flag passed tocreate_engine()
.When using the
Unicode
type, it is only appropriate to pass Pythonunicode
objects, and not plainstr
. If a plainstr
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 Pythonwarnings
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 theUnicode
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
.
-
class
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 theUnicodeText
type implies a unicode-capable type being used on the backend, such asNCLOB
,NTEXT
.
SQL Standard and Multiple Vendor Types¶
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.
-
class
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.
주석
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 asarray_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 inspectionWhen 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 operatorstypes.ARRAY.Comparator.any()
andtypes.ARRAY.Comparator.all()
. The PostgreSQL-specific version oftypes.ARRAY
also provides additional operators.버전 1.1.0에 추가.
더 보기
-
class
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) ) )
매개 변수:
-
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) ) )
매개 변수:
-
-
ARRAY.
__init__
(item_type, as_tuple=False, dimensions=None, zero_indexes=False)¶ Construct an
types.ARRAY
.E.g.:
Column('myarray', ARRAY(Integer))
Arguments are:
매개 변수: - item_type¶ – The data type of items of this array. Note that
dimensionality is irrelevant here, so multi-dimensional arrays like
INTEGER[][]
, are constructed asARRAY(Integer)
, not asARRAY(ARRAY(Integer))
or such. - as_tuple=False¶ – Specify whether return results should be converted to tuples from lists. This parameter is not generally needed as a Python list corresponds well to a SQL array.
- dimensions¶ – if non-None, the ARRAY will assume a fixed
number of dimensions. This impacts how the array is declared
on the database, how it goes about interpreting Python and
result values, as well as how expression behavior in conjunction
with the “getitem” operator works. See the description at
types.ARRAY
for additional detail. - zero_indexes=False¶ – when True, index values will be converted between Python zero-based and SQL one-based indexes, e.g. a value of one will be added to all index values before passing to the database.
- item_type¶ – The data type of items of this array. Note that
dimensionality is irrelevant here, so multi-dimensional arrays like
-
ARRAY.
comparator_factory
¶ alias of
Comparator
-
ARRAY.
zero_indexes
= False¶ if True, Python zero-based indexes should be interpreted as one-based on the SQL expression side.
-
class
sqlalchemy.types.
BIGINT
¶ Bases:
sqlalchemy.types.BigInteger
The SQL BIGINT type.
-
class
sqlalchemy.types.
BINARY
(length=None)¶ Bases:
sqlalchemy.types._Binary
The SQL BINARY type.
-
class
sqlalchemy.types.
BLOB
(length=None)¶ Bases:
sqlalchemy.types.LargeBinary
The SQL BLOB type.
-
class
sqlalchemy.types.
BOOLEAN
(create_constraint=True, name=None, _create_events=True)¶ Bases:
sqlalchemy.types.Boolean
The SQL BOOLEAN type.
-
class
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.
-
class
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.
-
class
sqlalchemy.types.
DATE
¶ Bases:
sqlalchemy.types.Date
The SQL DATE type.
-
class
sqlalchemy.types.
DATETIME
(timezone=False)¶ Bases:
sqlalchemy.types.DateTime
The SQL DATETIME type.
-
class
sqlalchemy.types.
DECIMAL
(precision=None, scale=None, decimal_return_scale=None, asdecimal=True)¶ Bases:
sqlalchemy.types.Numeric
The SQL DECIMAL type.
-
class
sqlalchemy.types.
FLOAT
(precision=None, asdecimal=False, decimal_return_scale=None, **kwargs)¶ Bases:
sqlalchemy.types.Float
The SQL FLOAT type.
-
class
sqlalchemy.types.
JSON
(none_as_null=False)¶ Bases:
sqlalchemy.types.Indexable
,sqlalchemy.types.TypeEngine
Represent a SQL JSON type.
주석
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 aspostgresql.JSON
andpostgresql.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.The
JSON
type, when used with the SQLAlchemy ORM, does not detect in-place mutations to the structure. In order to detect these, thesqlalchemy.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 atHSTORE
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 constantnull()
:from sqlalchemy import null conn.execute(table.insert(), json_value=null())
To insert or select against a value that is JSON
"null"
, use the constantJSON.NULL
:conn.execute(table.insert(), json_value=JSON.NULL)
The
JSON
type supports a flagJSON.none_as_null
which when set to True will result in the Python constantNone
evaluating to the value of SQL NULL, and when set to False results in the Python constantNone
evaluating to the value of JSON"null"
. The Python valueNone
may be used in conjunction with eitherJSON.NULL
andnull()
in order to indicate NULL values, but care must be taken as to the value of theJSON.none_as_null
in these cases.버전 1.1에 추가.
-
class
Comparator
(expr)¶ Bases:
sqlalchemy.types.Comparator
,sqlalchemy.types.Comparator
Define comparison operations for
types.JSON
.
-
class
JSON.
JSONIndexType
¶ Bases:
sqlalchemy.types.TypeEngine
Placeholder for the datatype of a JSON index value.
This allows execution-time processing of JSON index values for special syntaxes.
-
class
JSON.
JSONPathType
¶ Bases:
sqlalchemy.types.TypeEngine
Placeholder type for JSON path operations.
This allows execution-time processing of a path-based index value into a specific SQL syntax.
-
JSON.
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 PythonNone
will be recognized either as SQL NULL or JSON"null"
, based on the setting of theJSON.none_as_null
flag; theJSON.NULL
constant can be used to always resolve to JSON"null"
regardless of this setting. This is in contrast to thesql.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()
-
JSON.
__init__
(none_as_null=False)¶ Construct a
types.JSON
type.매개 변수: none_as_null=False¶ – if True, persist the value
None
as a SQL NULL value, not the JSON encoding ofnull
. Note that when this flag is False, thenull()
construct can still be used to persist a NULL value:from sqlalchemy import null conn.execute(table.insert(), data=null())
더 보기
-
JSON.
comparator_factory
¶ alias of
Comparator
-
class
sqlalchemy.types.
INTEGER
¶ Bases:
sqlalchemy.types.Integer
The SQL INT or INTEGER type.
-
class
sqlalchemy.types.
NCHAR
(length=None, **kwargs)¶ Bases:
sqlalchemy.types.Unicode
The SQL NCHAR type.
-
class
sqlalchemy.types.
NVARCHAR
(length=None, **kwargs)¶ Bases:
sqlalchemy.types.Unicode
The SQL NVARCHAR type.
-
class
sqlalchemy.types.
NUMERIC
(precision=None, scale=None, decimal_return_scale=None, asdecimal=True)¶ Bases:
sqlalchemy.types.Numeric
The SQL NUMERIC type.
-
class
sqlalchemy.types.
REAL
(precision=None, asdecimal=False, decimal_return_scale=None, **kwargs)¶ Bases:
sqlalchemy.types.Float
The SQL REAL type.
-
class
sqlalchemy.types.
SMALLINT
¶ Bases:
sqlalchemy.types.SmallInteger
The SQL SMALLINT type.
-
class
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.
-
class
sqlalchemy.types.
TIME
(timezone=False)¶ Bases:
sqlalchemy.types.Time
The SQL TIME type.
-
class
sqlalchemy.types.
TIMESTAMP
(timezone=False)¶ Bases:
sqlalchemy.types.DateTime
The SQL TIMESTAMP type.
-
class
sqlalchemy.types.
VARBINARY
(length=None)¶ Bases:
sqlalchemy.types._Binary
The SQL VARBINARY type.
-
class
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.
Vendor-Specific Types¶
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'))
)