This feature allows the value of a database column to be set to a SQL expression instead of a literal value. It’s especially useful for atomic updates, calling stored procedures, etc. All you do is assign an expression to an attribute:
class SomeClass(object):
pass
mapper(SomeClass, some_table)
someobject = session.query(SomeClass).get(5)
# set 'value' attribute to a SQL expression adding one
someobject.value = some_table.c.value + 1
# issues "UPDATE some_table SET value=value+1"
session.commit()
This technique works both for INSERT and UPDATE statements. After the
flush/commit operation, the value
attribute on someobject
above is
expired, so that when next accessed the newly generated value will be loaded
from the database.
SQL expressions and strings can be executed via the
Session
within its transactional context.
This is most easily accomplished using the
execute()
method, which returns a
ResultProxy
in the same manner as an
Engine
or
Connection
:
Session = sessionmaker(bind=engine)
session = Session()
# execute a string statement
result = session.execute("select * from table where id=:id", {'id':7})
# execute a SQL expression construct
result = session.execute(select([mytable]).where(mytable.c.id==7))
The current Connection
held by the
Session
is accessible using the
connection()
method:
connection = session.connection()
The examples above deal with a Session
that’s
bound to a single Engine
or
Connection
. To execute statements using a
Session
which is bound either to multiple
engines, or none at all (i.e. relies upon bound metadata), both
execute()
and
connection()
accept a mapper
keyword
argument, which is passed a mapped class or
Mapper
instance, which is used to locate the
proper context for the desired engine:
Session = sessionmaker()
session = Session()
# need to specify mapper or class when executing
result = session.execute("select * from table where id=:id", {'id':7}, mapper=MyMappedClass)
result = session.execute(select([mytable], mytable.c.id==7), mapper=MyMappedClass)
connection = session.connection(MyMappedClass)
The ORM considers any attribute that was never set on an object as a “default” case; the attribute will be omitted from the INSERT statement:
class MyObject(Base):
__tablename__ = 'my_table'
id = Column(Integer, primary_key=True)
data = Column(String(50), nullable=True)
obj = MyObject(id=1)
session.add(obj)
session.commit() # INSERT with the 'data' column omitted; the database
# itself will persist this as the NULL value
Omitting a column from the INSERT means that the column will have the NULL value set, unless the column has a default set up, in which case the default value will be persisted. This holds true both from a pure SQL perspective with server-side defaults, as well as the behavior of SQLAlchemy’s insert behavior with both client-side and server-side defaults:
class MyObject(Base):
__tablename__ = 'my_table'
id = Column(Integer, primary_key=True)
data = Column(String(50), nullable=True, server_default="default")
obj = MyObject(id=1)
session.add(obj)
session.commit() # INSERT with the 'data' column omitted; the database
# itself will persist this as the value 'default'
However, in the ORM, even if one assigns the Python value None
explicitly
to the object, this is treated the same as though the value were never
assigned:
class MyObject(Base):
__tablename__ = 'my_table'
id = Column(Integer, primary_key=True)
data = Column(String(50), nullable=True, server_default="default")
obj = MyObject(id=1, data=None)
session.add(obj)
session.commit() # INSERT with the 'data' column explicitly set to None;
# the ORM still omits it from the statement and the
# database will still persist this as the value 'default'
The above operation will persist into the data
column the
server default value of "default"
and not SQL NULL, even though None
was passed; this is a long-standing behavior of the ORM that many applications
hold as an assumption.
So what if we want to actually put NULL into this column, even though the
column has a default value? There are two approaches. One is that
on a per-instance level, we assign the attribute using the
null
SQL construct:
from sqlalchemy import null
obj = MyObject(id=1, data=null())
session.add(obj)
session.commit() # INSERT with the 'data' column explicitly set as null();
# the ORM uses this directly, bypassing all client-
# and server-side defaults, and the database will
# persist this as the NULL value
The null
SQL construct always translates into the SQL
NULL value being directly present in the target INSERT statement.
If we’d like to be able to use the Python value None
and have this
also be persisted as NULL despite the presence of column defaults,
we can configure this for the ORM using a Core-level modifier
TypeEngine.evaluates_none()
, which indicates
a type where the ORM should treat the value None
the same as any other
value and pass it through, rather than omitting it as a “missing” value:
class MyObject(Base):
__tablename__ = 'my_table'
id = Column(Integer, primary_key=True)
data = Column(
String(50).evaluates_none(), # indicate that None should always be passed
nullable=True, server_default="default")
obj = MyObject(id=1, data=None)
session.add(obj)
session.commit() # INSERT with the 'data' column explicitly set to None;
# the ORM uses this directly, bypassing all client-
# and server-side defaults, and the database will
# persist this as the NULL value
Evaluating None
The TypeEngine.evaluates_none()
modifier is primarily intended to
signal a type where the Python value “None” is significant, the primary
example being a JSON type which may want to persist the JSON null
value
rather than SQL NULL. We are slightly repurposing it here in order to
signal to the ORM that we’d like None
to be passed into the type whenever
present, even though no special type-level behaviors are assigned to it.
New in version 1.1: added the TypeEngine.evaluates_none()
method
in order to indicate that a “None” value should be treated as significant.
Vertical partitioning places different kinds of objects, or different tables, across multiple databases:
engine1 = create_engine('postgresql://db1')
engine2 = create_engine('postgresql://db2')
Session = sessionmaker(twophase=True)
# bind User operations to engine 1, Account operations to engine 2
Session.configure(binds={User:engine1, Account:engine2})
session = Session()
Above, operations against either class will make usage of the Engine
linked to that class. Upon a flush operation, similar rules take place
to ensure each class is written to the right database.
The transactions among the multiple databases can optionally be coordinated via two phase commit, if the underlying backend supports it. See Enabling Two-Phase Commit for an example.
More comprehensive rule-based class-level partitioning can be built by
overriding the Session.get_bind()
method. Below we illustrate
a custom Session
which delivers the following rules:
master
.MyOtherClass
all
occur on the other
engine.slave1
or slave2
database.engines = {
'master':create_engine("sqlite:///master.db"),
'other':create_engine("sqlite:///other.db"),
'slave1':create_engine("sqlite:///slave1.db"),
'slave2':create_engine("sqlite:///slave2.db"),
}
from sqlalchemy.orm import Session, sessionmaker
import random
class RoutingSession(Session):
def get_bind(self, mapper=None, clause=None):
if mapper and issubclass(mapper.class_, MyOtherClass):
return engines['other']
elif self._flushing:
return engines['master']
else:
return engines[
random.choice(['slave1','slave2'])
]
The above Session
class is plugged in using the class_
argument to sessionmaker
:
Session = sessionmaker(class_=RoutingSession)
This approach can be combined with multiple MetaData
objects,
using an approach such as that of using the declarative __abstract__
keyword, described at __abstract__.
Horizontal partitioning partitions the rows of a single table (or a set of tables) across multiple databases.
See the “sharding” example: Horizontal Sharding.
Note
Bulk Operations mode is a new series of operations made available
on the Session
object for the purpose of invoking INSERT and
UPDATE statements with greatly reduced Python overhead, at the expense
of much less functionality, automation, and error checking.
As of SQLAlchemy 1.0, these features should be considered as “beta”, and
additionally are intended for advanced users.
New in version 1.0.0.
Bulk operations on the Session
include Session.bulk_save_objects()
,
Session.bulk_insert_mappings()
, and Session.bulk_update_mappings()
.
The purpose of these methods is to directly expose internal elements of the unit of work system,
such that facilities for emitting INSERT and UPDATE statements given dictionaries
or object states can be utilized alone, bypassing the normal unit of work
mechanics of state, relationship and attribute management. The advantages
to this approach is strictly one of reduced Python overhead:
relationship()
, and the topological sort of all operations to
be performed is completely bypassed. This reduces a great amount of
Python overhead.Session
, even when the operation is complete, meaning there’s no
overhead in attaching them or managing their state in terms of the identity
map or session.Session.bulk_insert_mappings()
and Session.bulk_update_mappings()
methods accept lists of plain Python dictionaries, not objects; this further
reduces a large amount of overhead associated with instantiating mapped
objects and assigning state to them, which normally is also subject to
expensive tracking of history on a per-attribute basis.Session.bulk_save_objects()
, when objects of different types are passed,
the INSERT and UPDATE statements are necessarily broken up into per-type
groups. In order to reduce the number of batch INSERT or UPDATE statements
passed to the DBAPI, ensure that the incoming list of objects
are grouped by type.executemany()
blocks, which
perform vastly better than individual statement invocations.executemany()
blocks can be used.The performance behavior of the bulk routines should be studied using the Performance example suite. This is a series of example scripts which illustrate Python call-counts across a variety of scenarios, including bulk insert and update scenarios.
See also
Performance - includes detailed examples of bulk operations contrasted against traditional Core and ORM methods, including performance metrics.
The methods each work in the context of the Session
object’s
transaction, like any other:
s = Session()
objects = [
User(name="u1"),
User(name="u2"),
User(name="u3")
]
s.bulk_save_objects(objects)
For Session.bulk_insert_mappings()
, and Session.bulk_update_mappings()
,
dictionaries are passed:
s.bulk_insert_mappings(User,
[dict(name="u1"), dict(name="u2"), dict(name="u3")]
)
The bulk methods offer performance that under particular circumstances
can be close to that of using the core Insert
and
Update
constructs in an “executemany” context (for a description
of “executemany”, see Executing Multiple Statements in the Core tutorial).
In order to achieve this, the
Session.bulk_insert_mappings.return_defaults
flag should be disabled so that rows can be batched together. The example
suite in Performance should be carefully studied in order
to gain familiarity with how fast bulk performance can be achieved.
The bulk insert / update methods lose a significant amount of functionality versus traditional ORM use. The following is a listing of features that are not available when using these methods:
relationship()
linkagesMapperEvents.before_insert()
, etc. The bulk
session methods have no event support.Features that are available include:
Session.bulk_save_objects.return_defaults
flag must be used,
which will greatly reduce the performance benefits