When I first created SQLAlchemy, I knew I wanted to create something significant. It was by no means the first ORM or database abstraction layer I'd written; by 2005, I'd probably written about a dozen abstraction layers in several languages, including in Java, Perl, C and C++ (really bad C and even worse C++, one that talked to ODBC and another that communicated with Microsoft's ancient DB-LIB directly). All of these abstraction layers were in the range of awful to mediocre, and certainly none were anywhere near release-quality; even by late-90's to early-2000's standards. They were all created for closed-source applications written on the job, but each one did its job very well.

It was the repetitive creation of the same patterns over and over again that made apparent the kinds of things a real toolkit should have, as well as increased the urge to actually go through with it, so that I wouldn't have to invent new database interaction layers for every new project, or worse, be compelled by management to use whatever mediocre product they had read about the week before (keeping in mind I was made to use such disasters as EJB 1.0). But at the same time it was apparent to me that I was going to need to do some research up front as well. The primary book I used for this research was Patterns of Enterprise Archictecture by Martin Fowler. When reading this book, about half the patterns were ones that I'd already used implicitly, and the other half were ones that I was previously not entirely aware of.

Sometimes I read comments from new users expressing confusion or frustration with SQLAlchemy's concepts. Maybe some of these users are not only new to SQLAlchemy but are new to database abstraction layers in general, and some maybe even to relational databases themselves. What I'd like to lay out here is just how many of POEAA's patterns SQLAlchemy is built upon. If you're new to SQLAlchemy, my hope is that this list might help to de-mystify where these patterns come from.

These links are from Catalog of Patterns of Enterprise Architecture.

• Data Mapper - The key to this pattern is that object-relational mapping is applied to a user-defined class in a transparent way, keeping the details of persistence separate from the public interface of the class. SQLAlchemy's classical mapping system, which is the usage of the mapper() function to link a class with table metadata, implemented this pattern as fully as possible. In modern SQLAlchemy, we use the Declarative pattern which combines table metadata with the class' declaration as a shortcut to using mapper(), but the persistence API remains separate.
• Unit of Work - This pattern is where the system transparently keeps track of changes to objects and periodically flushes all those pending changes out to the database. SQLAlchemy's Session implements this pattern fully in a manner similar to that of Hibernate.
• Identity Map - This is an essential pattern that establishes unique identities for each object within a particular session, based on database identity. No ORM should be without this feature, as working with object structures and applications of the most moderate complexity is vastly simplified and made more efficient with this pattern in place.
• Metadata Mapping - this chapter in the book is where the name MetaData comes from. The exact correspondence to Fowler's pattern would be the combination of mapper() and Table.
• Query Object - Both the ORM Query and the Core select() construct are built on this pattern.
• Repository - An interface that serves as the gateway to the database, in terms of object-relational mappings. This is the SQLAlchemy Session.
• Lazy Load - Load a related collection or object as you need it. SQLAlchemy, like Hibernate, has a lot of options in how attributes can load things.
• Identity Field - Represent the primary key of a table's row within the object that represents it.
• Foreign Key Mapping - Database foreign keys are represented using relationships in the object model.
• Association Table Mapping - A class can be mapped that represents information about how two objects are related to each other. Use the Association Object for this pattern.
• Embedded Value - a value inline on an object represents multiple columns. SQLAlchemy provides the Composite pattern here.
• Serialized LOB - Sometimes you just want to stuff all the objects into a BLOB. Use the PickleType or roll a JSON type.
• Inheritance Mappers - Represent class hierarchies within database tables. See Inheritance Mapping.
  • Single Table Inheritance - POEAA (1) - SQLA (1)
  • Class Table Inheritance - POEAA (2) - SQLA (2)
  • Concrete Table Inheritance - POEAA (3) - SQLA (3)
• Optimistic Offline Lock - Set up a version id on your mapping to enable this feature in SQLAlchemy.

Thanks for reading!

As luck would have it, I'm currently assigned to work with some existing Django code. It's been quite a long time I've gone without needing to do so, but now that I'm there, I can start fleshing out how some of the use cases for Django can be approximated in SQLAlchemy.

Today it's something that's really quite simple - how to mimic the Multiple Databases example in Django's docs. The Django approach is to build a "router" object into the system which can then decide on a query-by-query basis which of several databases should be used for each query. For this, they provide an interface which includes db_for_read(), db_for_write(), allow_relation() and allow_syncdb(). In particular, db_for_read() and db_for_write() are used when emitting most SQL. These receive an argument called model, which the docs state is a "type", i.e. a class. Because the database determination is based on type, we call this in SQLAlchemy vertical partitioning - databases are partitioned along types.

The specific example here has the following behavior:

1. Write operations occur on a database referred to as master.
2. Read operations are split among two different databases referred to as slave1 and slave2.
3. Operations for a specific subset of classes denoted by myapp occur on a database referred to as other.

When using SQLAlchemy, we of course don't have the concept of "apps" as a delineating factor between different types. So we'll use the old fashioned approach and just use the type itself, that is, what hierarchy it inherits from, to determine which subsystem of the application it heralds from.

The SQLAlchemy Session makes all decisions about what Engine to use within the get_bind() method. This method is given pretty much the same information that db_for_read() and db_for_write() receive, where a Mapper is passed in, if available, and we can also check if the operation is a "write" just by checking if the Session is flushing.

from sqlalchemy import Column, Integer, String, MetaData, create_engine
from sqlalchemy.orm import scoped_session, sessionmaker, Session
from sqlalchemy.ext.declarative import declarative_base
import random
import shutil

engines = {
    'master': create_engine('sqlite:///master.db',
                            logging_name='master'),
    'other': create_engine('sqlite:///other.db',
                            logging_name='other'),
    'slave1': create_engine('sqlite:///slave1.db',
                            logging_name='slave1'),
    'slave2': create_engine('sqlite:///slave2.db',
                            logging_name='slave2'),
    }

class RoutingSession(Session):

    def get_bind(self, mapper=None, clause=None):
        if mapper and issubclass(mapper.class_, OtherBase):
            return engines['other']
        elif self._flushing:
            return engines['master']
        else:
            return engines[
                    random.choice(['slave1','slave2'])
                ]

Session = scoped_session(sessionmaker(class_=RoutingSession))

class Base(object):
    id = Column(Integer, primary_key=True)
    data = Column(String(50))
    def __repr__(self):
        return "%s(id=%r, data=%r)" % (
            self.__class__.__name__,
            self.id, self.data
        )

Base = declarative_base(cls=Base)

class DefaultBase(Base):
    __abstract__ = True
    metadata = MetaData()

class OtherBase(Base):
    __abstract__ = True
    metadata = MetaData()

class Model1(DefaultBase):
    __tablename__ = 'model1'

class Model2(DefaultBase):
    __tablename__ = 'model2'

class Model3(OtherBase):
    __tablename__ = 'model3'

for eng in 'master', 'slave1', 'slave2':
    DefaultBase.metadata.create_all(engines[eng])

OtherBase.metadata.create_all(engines['other'])

s = Session()

s.add_all([
    Model1(data='m1_a'),
    Model2(data='m2_a'),
    Model1(data='m1_b'),
    Model2(data='m2_b'),
    Model3(data='m3_a'),
    Model3(data='m3_b'),
])
s.commit()

INFO sqlalchemy.engine.base.Engine.master BEGIN (implicit)
INFO sqlalchemy.engine.base.Engine.master INSERT INTO model1 (data) VALUES (?)
INFO sqlalchemy.engine.base.Engine.master ('m1_a',)
INFO sqlalchemy.engine.base.Engine.master INSERT INTO model1 (data) VALUES (?)
INFO sqlalchemy.engine.base.Engine.master ('m1_b',)
INFO sqlalchemy.engine.base.Engine.other BEGIN (implicit)
INFO sqlalchemy.engine.base.Engine.other INSERT INTO model3 (data) VALUES (?)
INFO sqlalchemy.engine.base.Engine.other ('m3_a',)
INFO sqlalchemy.engine.base.Engine.other INSERT INTO model3 (data) VALUES (?)
INFO sqlalchemy.engine.base.Engine.other ('m3_b',)
INFO sqlalchemy.engine.base.Engine.master INSERT INTO model2 (data) VALUES (?)
INFO sqlalchemy.engine.base.Engine.master ('m2_a',)
INFO sqlalchemy.engine.base.Engine.master INSERT INTO model2 (data) VALUES (?)
INFO sqlalchemy.engine.base.Engine.master ('m2_b',)
INFO sqlalchemy.engine.base.Engine.other COMMIT
INFO sqlalchemy.engine.base.Engine.master COMMIT

##### PRETEND PRETEND PRETEND ######
# now let's pretend "master' is replicating to "slave1", "slave2"
shutil.copy("master.db", "slave1.db")
shutil.copy("master.db", "slave2.db")
##### END PRETEND END PRETEND END PRETEND ######

>>> print s.query(Model1).all()
INFO sqlalchemy.engine.base.Engine.slave2 BEGIN (implicit)
INFO sqlalchemy.engine.base.Engine.slave2 SELECT model1.id AS model1_id, model1.data AS model1_data
FROM model1
[Model1(id=1, data='m1_a'), Model1(id=2, data='m1_b')]

>>> print s.query(Model2).all()
INFO sqlalchemy.engine.base.Engine.slave1 BEGIN (implicit)
INFO sqlalchemy.engine.base.Engine.slave1 SELECT model2.id AS model2_id, model2.data AS model2_data
FROM model2
[Model2(id=1, data='m2_a'), Model2(id=2, data='m2_b')]

>>> print s.query(Model3).all()
INFO sqlalchemy.engine.base.Engine.other BEGIN (implicit)
INFO sqlalchemy.engine.base.Engine.other SELECT model3.id AS model3_id, model3.data AS model3_data
FROM model3
[Model3(id=1, data='m3_a'), Model3(id=2, data='m3_b')]

class RoutingSession(Session):

    def get_bind(self, mapper=None, clause=None ):
        if self._name:
            return engines[self._name]
        elif mapper and issubclass(mapper.class_, OtherBase):
            return engines['other']
        elif self._flushing:
            return engines['master']
        else:
            return engines[
                    random.choice(['slave1','slave2'])
                ]

    _name = None
    def using_bind(self, name):
        s = RoutingSession()
        vars(s).update(vars(self))
        s._name = name
        return s

m1 = Session().using_bind("master").query(Model1).first()

Many of you are aware that for quite some time I've had available a new migrations tool called Alembic. I wrote the majority of this code last year and basically haven't had much time to work on it, save for a little bit of integration at my day job.

Alembic has several areas that improve upon the current crop of migration tools, including:

• Full control over how migrations run, including multiple database support, transactional DDL, etc.
• A super-minimal style of writing migrations, not requiring full table definitions for simple operations.
• No monkeypatching or repurposing of core SQLAlchemy objects.
• Ability to generate migrations as SQL scripts, critical for working in large organizations on restricted-access production systems.
• A non-linear versioning model that allows, somewhat rudimentally, for branching and merging of multiple migration file streams.
• By popular request, designs for some degree of automation will be added, where "obivous" migrations of tables or columns being added or dropped as well as simple column attribute changes can be detected and rendered into migration scripts automatically.

I get asked about migration tools quite often, and I've always mentioned that I have such a tool available in an early form, it just lacks documentation, hoping that one out of so many eager users would be able to chip in a couple of days to help get it going. Turns out it's simply not possible to get someone to document your project for you; so here at the PloneConf sprints I've taken the time at the sprints to create initial documentation. Originally I was going to do some work on Dogpile but I've literally been asked about schema migrations, either in person or via reddit or twitter, about nine times in the past three days.

The documentation for Alembic will provide an overview of the tool, including theory of operation as well as philosophy, and some indicators of features not yet implemented. I would like feedback on the current approach. Alembic is not yet released though can be checked out from Bitbucket for testing. It's a pretty simple tool, 1163 lines of code at the moment, with plenty of room to support a lot more database features in a straightforward way.

I also have an open question about the notion of "automatic" migrations - how does one discern detecting whether or not a table or column has had a name change, or if a new table/column was added and an old one dropped? Just a curiosity as I attempt to avoid reading South's source code.

In last week's post I illustrated how to use a "value agnostic type" in conjunction with an ORM-mapped class, using the @hybrid_proprerty decorator. Pressed for details about the hypothetically better TypeDecorator approach, we'll illustrate that here. The TypeDecorator approach as I've mentioned has a few non-intuitive edges, though after working out the example they're not so bad. I do in fact use the TypeDecorator approach in my day job as often as I use the pure hybrid approach.

someint = Column('int_col', Integer)
somestring = Column('str_col', String)

someint = Integer("int_col")
somestring = String("str_col")

label_of_someint = someint.label("some label")
subquery = select([somestring]).as_scalar()

from sqlalchemy.ext.hybrid import hybrid_method
from sqlalchemy import type_coerce, Numeric

class Amount(object):
    def __init__(self, amount, currency):
        self.currency = currency
        self.amount = amount

    def __add__(self, other):
        return Amount(
                self.amount +
                other.as_currency(self.currency).amount,
                self.currency
            )

    def __sub__(self, other):
        return Amount(
                self.amount -
                other.as_currency(self.currency).amount,
                self.currency
            )

    def __lt__(self, other):
        return self.amount < other.as_currency(self.currency).amount

    def __gt__(self, other):
        return self.amount > other.as_currency(self.currency).amount

    def __eq__(self, other):
        return self.amount == other.as_currency(self.currency).amount

    @hybrid_method
    def as_currency(self, other_currency):
        return Amount(
                    currency_lookup[(self.currency, other_currency)] * self.amount,
                    other_currency)

    @as_currency.expression
    def as_currency(self, target, other_currency):
        if hasattr(target, '__clause_element__'):
            target = target.__clause_element__()
        currency = target.type.currency
        return Amount(
                    currency_lookup[(currency, other_currency)]
                     * type_coerce(target, Numeric),
                    other_currency)

    def __str__(self):
        return "%s %s" % (self.amount, self.currency)

    def __repr__(self):
        return "Amount(%s)" % self

Amount.as_currency(mytable.c.balance, "usd")

from sqlalchemy import TypeDecorator, Numeric

class AmountType(TypeDecorator):
    impl = Numeric

    def __init__(self, currency):
        self.currency = currency
        super(AmountType, self).__init__()

    def process_bind_param(self, value, dialect):
        if value is not None:
            value = value.as_currency(self.currency).amount
        return value

    def process_result_value(self, value, dialect):
        if value is not None:
            value = Amount(value, self.currency)
        return value

class BankAccount(Base):
    __tablename__ = 'bank_account'
    id = Column(Integer, primary_key=True)

    balance = Column('balance', AmountType("usd"))

    def __repr__(self):
        return "BankAccount(%s)" % self.balance

session.add_all([
    account,
    BankAccount(balance=Amount(10000, "cad")),
    BankAccount(balance=Amount(5700, "usd")),
    BankAccount(balance=Amount(89682, "aud")),
])

>>> print session.query(BankAccount).\
...    filter(BankAccount.balance == Amount(10000, "cad")).one()
SELECT bank_account.id AS bank_account_id,
        bank_account.balance AS bank_account_balance
FROM bank_account
WHERE bank_account.balance = ?
(9886.11,)
BankAccount(9886.1100 usd)

>>> print Amount.as_currency(BankAccount.balance, "cad")
:param_1 * bank_account.balance cad

>>> print session.query(BankAccount).\
...    filter(Amount.as_currency(BankAccount.balance, "cad") > Amount(9999, "cad")).\
...    filter(Amount.as_currency(BankAccount.balance, "cad") < Amount(10001, "cad")).\
...    one()
SELECT bank_account.id AS bank_account_id, bank_account.balance AS bank_account_balance
FROM bank_account
WHERE ? * bank_account.balance > ? AND ? * bank_account.balance < ?
(1.01152, 9999.0, 1.01152, 10001.0)
BankAccount(9886.1100 usd)

>>> print session.query(BankAccount.balance).all()
SELECT bank_account.balance AS bank_account_balance FROM bank_account
[(Amount(4000.0000 usd),), (Amount(9886.1100 usd),), (Amount(5700.0000 usd),), (Amount(92338.3808 usd),)]

>>> from sqlalchemy import select
>>> bank_account = BankAccount.__table__
>>> for amount, in session.execute(
...            select([bank_account.c.balance]).\
...            where(Amount.as_currency(bank_account.c.balance, "cad") > Amount(9999, "cad"))
...            ).fetchall():
...     print "Amount:", amount, \
...                 "As USD:", amount.as_currency("usd"), \
...                 "As CAD:", amount.as_currency("cad")
SELECT bank_account.balance FROM bank_account
WHERE ? * bank_account.balance > ?
(1.01152, 9999.0)
Amount: 9886.1100 usd As USD: 9886.1100 usd As CAD: 9999.9980 cad
Amount: 92338.3808 usd As USD: 92338.3808 usd As CAD: 93402.1190 cad

Update: This is now part one of a two part series. Be sure to read Value Agnostic Types, Part II for an updated approach using TypeDecorator.

A "hybrid" in SQLAlchemy is this awesome idea thought up by Ants Aasma. The hybrid takes advantage of the ability of Python classes to have an attribute which has customizable behavior both at the class level as well as at the instance level. What's that mean? Just this:

class my_descriptor(object):
    def __get__(self, instance, owner):
        if instance is None:
            return "I'm at the class level!"
        else:
            return "I'm at the instance level!"

class MyClass(object):
    some_attribute = my_descriptor()

Above, my_descriptor is what's known as a Python Descriptor. It's a way to have an attribute on an object where the value produced comes from a function. Above, we've made one that will tell you one thing when invoked on the class, i.e. class level behavior:

>>> MyClass.some_attribute
I'm at the class level!

And another at the instance level, i.e. instance level behavior:

>>> my_object = MyClass()
>>> my_object.some_attribute
I'm at the instance level!

That's all we're dealing with here. As it turns out, descriptors are very commonly used in object relational mappers and other "declarative" kinds of systems, where attributes are defined at the class level and maintain behavior on the class, as well as on instances. Suppose we have a SQLAlchemy class like this:

from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, Numeric
Base = declarative_base()

class BankAccount(Base):
    __tablename__ = 'bank_account'
    id = Column(Integer, primary_key=True)

    balance = Column(Numeric)
    pending = Column(Numeric)

We've made a new class BankAccount, which represents something like a bank account. It contains fields representing the current account balance, as well as a "pending" balance - say it's checks that haven't cleared yet.

Let's set up a sqlite database as usual and save some accounts:

from sqlalchemy import create_engine
engine = create_engine('sqlite://', echo=True)
Base.metadata.create_all(engine)

from sqlalchemy.orm import Session
session = Session(engine)
session.add_all([
    BankAccount(balance=4000, pending=500),
    BankAccount(balance=10000, pending=0),
])
session.commit()

Descriptors are used here as follows: first, the BankAccount class, as a result of being mapped by SQLAlchemy, has three descriptors on it, named id, balance, and pending. These descriptors have class- and instance-level behavior. When invoked at the class level, they produce SQL expressions:

>>> print BankAccount.balance + BankAccount.pending
balance + pending
>>> print select([BankAccount.id, BankAccount.balance])
SELECT id, balance FROM bank_account

Invoked at the instance level, they provide access to values that are present in __dict__, the same way any other Python object works:

>>> account = BankAccount()
>>> account.balance = 1000
>>> account.pending = 500
>>> account.__dict__
{'balance': 1000, 'pending': 500, '_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x1014dde50>}

(_sa_instance_state is SQLAlchemy's tracking object for this particular BankAccount). SQLAlchemy's descriptors, used at the instance level, also keep track of changes to data, and can also invoke queries against the database to load more data if needed.

class BankAccount(Base):
    __tablename__ = 'bank_account'
    id = Column(Integer, primary_key=True)

    balance = Column(Numeric)
    pending = Column(Numeric)

    @property
    def total(self):
        return self.balance + self.pending

>>> BankAccount(balance=1000, pending=500).total
1500

self.balance + self.pending

>>> print session.query(BankAccount.balance + BankAccount.pending)
SELECT bank_account.balance + bank_account.pending AS anon_1
FROM bank_account

from sqlalchemy.ext.hybrid import hybrid_property

class BankAccount(Base):
    __tablename__ = 'bank_account'
    id = Column(Integer, primary_key=True)

    balance = Column(Numeric)
    pending = Column(Numeric)

    @hybrid_property
    def total(self):
        return self.balance + self.pending

>>> print session.query(BankAccount.total).filter(BankAccount.total.between(3000, 5000)).all()
SELECT bank_account.balance + bank_account.pending AS anon_1
FROM bank_account
WHERE bank_account.balance + bank_account.pending BETWEEN ? AND ?
(3000, 5000)
[(Decimal('4500.0000000000'),)]

>>> myaccount = session.query(BankAccount).first()
>>> print myaccount.total
4500.0000000000

from decimal import Decimal

# US dollars, British pounds, Canadian dollar, Euro, Australian dollar
symbols = ('usd', 'gbp', 'cad', 'eur', 'aud')
currency_lookup = dict(
            ((currency_from, currency_to), Decimal(str(rate)))
            for currency_to, values in zip(
                symbols,
                [
                    (1, 1.59009, 0.988611, 1.37979, 1.02962),
                    (0.628895, 1, 0.621732, 0.867748, 0.647525),
                    (1.01152, 1.6084, 1, 1.39569, 1.04148),
                    (0.724743, 1.1524, 0.716489, 1, 0.746213),
                    (0.971228, 1.54434, 0.960166, 1.34009, 1),
                ])
            for currency_from, rate in zip(symbols, values)
    )

>>> print currency_lookup[("usd", "aud")] * 5000
4856.140000

class Amount(object):
    def __init__(self, amount, currency):
        self.currency = currency
        self.amount = amount

    def __add__(self, other):
        return Amount(
                self.amount +
                other.as_currency(self.currency).amount,
                self.currency
            )

    def __sub__(self, other):
        return Amount(
                self.amount -
                other.as_currency(self.currency).amount,
                self.currency
            )

    def __lt__(self, other):
        return self.amount < other.as_currency(self.currency).amount

    def __gt__(self, other):
        return self.amount > other.as_currency(self.currency).amount

    def __eq__(self, other):
        return self.amount == other.as_currency(self.currency).amount

    def as_currency(self, other_currency):
        return Amount(
                    currency_lookup[(self.currency, other_currency)] * self.amount,
                    other_currency)

    def __str__(self):
        return "%2.4f %s" % (self.amount, self.currency)

>>> # Convert 5000 US dollars into Canadian dollars
>>> print Amount(5000, "usd").as_currency("cad")
5057.6000 cad

>>> # Calculate 5000 USD + 1000 USD:
>>> print Amount(5000, "usd") + Amount(1000, "usd")
6000.000 usd

>>> # Calculate 5000 USD + 500 GBP:
>>> print Amount(5000, "usd") + Amount(500, "gbp")
5795.0450 usd

>>> # Take the above and convert to CAD:
>>> print (Amount(5000, "usd") + Amount(500, "gbp")).as_currency("cad")
5861.8039 cad

>>> from sqlalchemy.sql import column
>>> balance_col = column('balance')
>>> print Amount(balance_col, "aud").as_currency("eur").amount
:balance_1 * balance

from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, Numeric
from sqlalchemy.ext.hybrid import hybrid_property
Base = declarative_base()

class BankAccount(Base):
    __tablename__ = 'bank_account'
    id = Column(Integer, primary_key=True)

    _balance = Column('balance', Numeric)

    @hybrid_property
    def balance(self):
        return Amount(self._balance, "usd")

    @balance.setter
    def balance(self, value):
        self._balance = value.as_currency("usd").amount

    def __repr__(self):
        return "BankAccount(%s)" % self.balance

>>> account = BankAccount(balance=Amount(4000, "usd"))
>>> print account.balance
4000.0000 usd

>>> print account.balance.as_currency("gbp")
2515.5800 gbp

>>> print account.balance > Amount(500, "cad")
True

>>> print account.balance + Amount(500, "cad") - Amount(50, "eur")
4425.3160 usd

from sqlalchemy import create_engine
from sqlalchemy.orm import Session
from sqlalchemy import func

engine = create_engine('sqlite://', echo=True)
Base.metadata.create_all(engine)

session = Session(engine)

session.add_all([
    account,
    BankAccount(balance=Amount(10000, "cad")),
    BankAccount(balance=Amount(5700, "usd")),
    BankAccount(balance=Amount(89682, "aud")),
])
session.commit()

>>> print session.query(BankAccount).\
...    filter(BankAccount.balance == Amount(10000, "cad")).one()
SELECT bank_account.balance AS bank_account_balance, bank_account.id AS bank_account_id
FROM bank_account
WHERE bank_account.balance = ?
(9886.11,)
BankAccount(9886.1100 usd)

>>> print session.query(BankAccount).\
...    filter(BankAccount.balance.as_currency("cad") > Amount(9999, "cad")).\
...    filter(BankAccount.balance.as_currency("cad") < Amount(10001, "cad")).\
...    one()
SELECT bank_account.balance AS bank_account_balance, bank_account.id AS bank_account_id
FROM bank_account
WHERE ? * bank_account.balance > ? AND ? * bank_account.balance < ?
(1.01152, 9999.0, 1.01152, 10001.0)
BankAccount(9886.1100 usd)

>>> # print all amounts as CAD
>>> print session.query(BankAccount.balance.as_currency("cad").amount).all()
SELECT ? * bank_account.balance AS anon_1
FROM bank_account
(1.01152,)
[(Decimal('4046.0800000000'),), (Decimal('9999.9979872000'),), (Decimal('5765.6640000000'),), (Decimal('93402.1189872768'),)]

>>> # return the average balance in EUR
>>> print session.query(func.avg(BankAccount.balance.as_currency("eur").amount)).scalar()
SELECT avg(? * bank_account.balance) AS avg_1
FROM bank_account
(0.724743,)
20279.1228162

Beaker is a widely used caching and HTTP session library published by Ben Bangert. It dates back to the early days of Pylons, when Pylons moved off of the Myghty base to the Python Paste infrastructure. Beaker's guts in fact were originally the internals to the caching and session system of Myghty itself, which in turn was loosely based on the caching code from HTML::Mason (which if those two colons don't look familiar means we've made the jump across the ages to Perl).

The key neato thing nestled deep inside of Beaker is what I later was told is called a "dogpile lock". This is a simple, but slightly less simple than dict.get() type of system whereby the cache can put one thread to work on generating a new value for a certain cache key, while all the other threads and even other processes can all continue to return the expired value, until the new value is ready. I spent weeks getting this aspect to do what it was supposed to. It does things in a slightly more complex but comprehensive way than Mason did; it coordinates workers using a mutex and/or lockfiles, instead of a simple counter that estimates the creation time for a new value.

The storage backends are implemented in terms of a "container", which deals with an individual key, including checking if it's expired, regenerating it, and so forth, and a "namespace", which is the entryway to storing the data. It deals with keys and values like everything else does, and has backends for pickled files, DBM files, relational databases, memcached, and GAE. The general idea here came from Myghty but has been highly modified since then.

Beaker then adds something of a coarse-grained facade over this model, including something of a generic get/put API as well as a system of function decorators that cache the return value of a function. In recent releases it also supports the notion of a "region", which is just a way to package up a particular cache configuration under a single name that can later be referenced by high level caching operations.

Beaker also implements an HTTP session object on top of the same storage backends, taking this from Myghty as well. Implementing HTTP sessions on top of the cache backends was a completely off-the-cuff idea in Myghty, and over the years in Beaker it's had to be wrangled and re-wrangled over basic issues resulting from this mismatch.

As far as the backends themselves, at this point all of them have accumulated a fair degree of woe. The file- and DBM- backends basically perform pretty poorly (DBM maybe not as much). The memcached backend has always been problematic - we slowly learned that the cmemcache library is basically a non starter, and that pylibmc is dramatically faster than the usual memcache library. The memcached backend slowly sprouted an awkward way to select among these backends and more awkwardness to attempt to accommodate pylibmc's special concurrency API. Then when users started combining the memcached backend with HTTP sessions, all kinds of new issues occurred, namely that any particular key might be removed from memcached at any time, that caused us to rework the session implementation even more awkwardly to store the whole session under one "key", defeating the purpose of how the session object works with other backends. As for the SQLAlchemy backends, there are two, and I've no idea why.

The HTTP session as a "bag of random keys", to paraphrase a term I think PJE originally used, is not something I really do these days, as I'm pretty skeptical of the approach of storing a bag of key/value pairs inside of a big serialized BLOB as a solution to anything, really. A few keys in a cookie-based session of very limited size is fine. But a backend-oriented system where you're loading up a giant bag of dozens of keys referencing full object structures inside of a good sized serialized blob inside of a file, database, or memcached-type of system, is both horribly inefficient and a sign of sloppy, ad-hoc coding.

Beaker eventually provided a purely client side encrypted cookie session as an alternative to the server-based sessions. This session implementation stores all the data in an encrypted string (Ben spent a lot of time and got a lot of advice getting the encryption right here) which is stored completely on the client. All of the performance, scalability and failover issues introduced by typical memory or file based HTTP session implementations is solved immediately. So this session implementation, I use lots. I only put a few critical keys in it to track the user, but anything that is more significant is part of the object model which is persisted in the database as a first class object.

The only downside to the cookie-only session is that, unless you coordinate the state of the session with server-side state (which IMHO you should), you can't "force" it to expire sooner than it normally would, or otherwise prevent the replay of previous state, within an attack scenario. While an attacker can't craft a session, (s)he can re-use the same cookie over and over again, and that can only be guarded against by comparing its state to that of a memo on the server; a single "generation" counter can achieve this. I.e. session comes in, generation is "5", the server says it should be "7", reject it. So it may be considered that this turns the encrypted cookie session into a fancy session cookie for a server-side session. But I am actually OK with that. I put very, very little into unstructured sessions.

What I'm getting at here is that I'm not super-interested in most of what Beaker has - a lot of the APIs are awkward, all of the storage backends are either mostly useless or slow and crufty, and I don't care much for the server-side session thing especially using backends that were designed for caching. Overall Beaker creates a coarse opacity on top of a collection of things that I think good application developers should be much more aware of. I really think developers should know how their tools work.

Beaker has some isolated features which I think are great. These are the dogpile lock, the encrypted client-side cookie session, the concept of "cache regions" whereby a set of cache configuration is referencable by a single name, and some nice function decorators - these allow you to apply caching to a function, similarly to:

@cached(region="file_based", namespace="somenamespace")
def return_some_data(x, y, z):
    # ...

I'm not sure what's in store for Beaker. But what I'm doing is:

• Created Dogpile, which is just the "dogpile lock" portion of Beaker, cleaned up and turned into a very clear, succinct system which you can use to build your own caching implementation. The README includes a full example using Pylibmc.
• In Mako, for either 0.5.1 or 0.6.0 (not sure yet, though I just put out 0.5.0 the other day for unrelated reasons) the built-in Beaker support is genericized into a plugin system. You can write your own plugins very easily for Mako using Dogpile or similar, if you want caching inside your templates that isn't based off of Beaker. The system supports entrypoints so if a Beaker2 comes out, it would publish a cache backend that Mako could then use.

So we have that. Then, for the community I would like to see:

• The solution for the key/value backend (or not). Today, high-performance, sophisticated key/value stores are ubiquitous. We of course have memcached. We also have Riak and Redis which appear very feasible for temporary storage, I'm not sure if Tokyo Tyrant is still popular. And of course there's the more permanent-oriented systems like Mongo and Cassandra. I am sure that someone has built some kind of generic facade over these, and there are probably many. These are the key/value systems you should be using, either via a modern facade or one of the libraries directly; Beaker's system, which was originally built to store keys inside of dictionaries or pickled files, is absolutely nothing more than a quaint, historical novelty in comparison.
• A new library that is just the encrpyted cookie session. I'd use this. It would be very nice for this portion of Beaker to be its own thing. Until then I may just rip out that part of Beaker and stick it in a lib/ folder somewhere in my work projects for internal use.
• For server-side HTTP sessions, I really think people should be rolling solutions for this as needed, probably using the encrypted cookie session for client side state linked to first class model objects in the datastore (relational or non-relational, doesn't matter). If accessing the datastore is a performance issue, you'd be using caching for those data structures, the same way you'd cache data structures that are not specific to a user (probably based on primary key or on their originating query). In the end this is similar to HTTP sessions stored directly in the cache backend, except there's a well defined model layer in between.
• People that really insist on the pattern of server-side HTTP sessions as bags of keys inside of serialized blobs on a server should be served by some new library that someone else can maintain. I think I am done with supporting this pattern and I think Ben may be as well.

The theme I've been pushing in my recent talk on SQLAlchemy and I think I'm pushing here, is that if you're writing a big application, one which you'll be spending months or years with, it is very much worth it to spend some time spending a day or so building up foundational patterns. I really have never understood the point of, "I just got the whole app up and running from start to finish on the plane!" Well great, you got the app up in three hours, now you can spend the next six months reworking the whole thing to actually scale (or if it was only a proof-of-concept, then why does it need a cache or even a database?). Did three hours for a quick and dirty implementation versus three days to do it right really improve your life within the scope of the total time spent? Breaking up Beaker into components and encouraging people to use patterns instead of relying upon opaque, pushbutton libraries is part of that idea.

The video of my PyGotham talk, SQLAlchemy, an Architectural Retrospective is now available at blip.tv. This talk details my current thinking on SQLAlchemy philosophy and then proceeds through architectural overviews of several key areas, including the core operation of the Unit of Work. The accompanying slides are available in PDF form as well. Enjoy !

Just finished my SQLAlchemy talk at PyGotham. In this talk, I get into my latest thinking on SQLAlchemy's usage philosophy and cover a few architectural features that I discuss in my upcoming chapter for Architecture of Open Source Applications. Here's the slides in PDF Form: SQLAlchemy - an Architectural Retrospective. There should eventually be a video at some point I'll keep you posted !

Update - The video is now available.

When I wrote SQLAlchemy, a key intent was as a working rebuttal to the "ORM is vietnam" argument. (edit: for those who don't know where that phrase originates, it starts with this very famous post.)

This was achieved by: a. tweaking the key assumption that relational concepts must be hidden. They don't. Practicality beats purity.

And b. not underestimating the task, i.e. unit of work, cached collections and associations backed by eager loading (solving N+1 problem).

Hibernate does both, but suffers from the limitations of Java - complex, heavyhanded. Python is what makes SQLAlchemy possible.

Our users can now write super-SQL-fluent apps succinctly and quickly; that's all the proof one should need that ORM is definitely worth it.