z3c.dobbin 0.3.0

Relational object persistance framework

Overview

Dobbin is a relational database abstraction layer supporting a semi-transparent object persistance model.

It relies on descriptive attribute and field declarations based on zope.interface and zope.schema.

Tables are created automatically with a 1:1 correspondence to an interface with no inheritance (minimal interface). As such, objects are modelled as a join between the interfaces it implements.

Authors

This package was designed and implemented by Malthe Borch, Stefan Eletzhofer. It’s licensed as ZPL.

Developer documentation

This section details the object persistence model and the relations machinery.

Introduction

Objects that need persisting are required to declare their attributes in an interface. Attributes that are not declared are considered volatile.

Concrete attributes may be strongly typed using the schema fields that correspond to their type; polymorphic attributes are available using relational properties.

Relations

There are two kinds of objects that can be related: instances and rocks. Relations are polymorphic such that they support both kinds.

An attribute can hold a single relation or many, using one of the built-in sequence types: list, tuple, set, dict.

The following fields allow polymorphic relations of any kind:

• zope.schema.Object

• zope.interface.Attribute

Additional structure can be declared using the sequence fields:

• zope.schema.List

• zope.schema.Dict

• zope.schema.Set

When translated to column in a table, all relations are soup object references; the soup specification will reflect the type.

Essentially, all polymorphic relations are many-to-many from a database perspective.

Collections

Dictionaries are keyed by (unicode) string. Soup objects may be used as keys in which case a string representation of the UUID is used. Dictionaries are polymorphic such that any kind of value may be assigned for an entry.

Developer walk-through

This section demonstrates the main functionality of the package using the doctest format.

Mapping

Dobbin creates SQLAlchemy ORM mappers from Python classes based on class specification (class or interface):

• Columns are infered from interface schema fields and attributes

• Specification is kept as dotted name in a special column

Interface specification

An mapper adapter is provided.

>>> from z3c.dobbin.mapper import getMapper
>>> component.provideAdapter(getMapper)

We begin with a database session.

>>> import ore.alchemist
>>> session = ore.alchemist.Session()

Define a schema interface:

>>> class IAlbum(interface.Interface):
...     artist = schema.TextLine(
...         title=u"Artist",
...         default=u"")
...
...     title = schema.TextLine(
...         title=u"Title",
...         default=u"")

We can then fabricate an instance that implements this interface by using the create method.

>>> from z3c.dobbin.factory import create
>>> album = create(IAlbum)

Set attributes.

>>> album.artist = "The Beach Boys"
>>> album.title = u"Pet Sounds"

Interface inheritance is supported. For instance, a vinyl record is a particular type of album.

>>> class IVinyl(IAlbum):
...     rpm = schema.Int(
...         title=u"RPM",
...         default=33)

>>> vinyl = create(IVinyl)

What actually happens on the database side is that columns are mapped to the interface that they provide.

Let’s demonstrate that the mapper instance actually implements the defined fields.

>>> vinyl.artist = "Diana Ross and The Supremes"
>>> vinyl.title = "Taking Care of Business"
>>> vinyl.rpm = 45

Or a compact disc.

>>> class ICompactDisc(IAlbum):
...     year = schema.Int(title=u"Year")

>>> cd = create(ICompactDisc)

Let’s pick a more recent Diana Ross, to fit the format.

>>> cd.artist = "Diana Ross"
>>> cd.title = "The Great American Songbook"
>>> cd.year = 2005

To verify that we’ve actually inserted objects to the database, we commit the transacation, thus flushing the current session.

>>> session.save(album)
>>> session.save(vinyl)
>>> session.save(cd)

>>> import transaction
>>> transaction.commit()

We get a reference to the database metadata object, to locate each underlying table.

>>> from ore.alchemist.interfaces import IDatabaseEngine
>>> engine = component.getUtility(IDatabaseEngine)
>>> metadata = engine.metadata

Tables are given a name based on the dotted path of the interface they describe. A utility method is provided to create a proper table name for an interface.

>>> from z3c.dobbin.mapper import encode

Verify tables for IVinyl, IAlbum and ICompactDisc.

>>> session.bind = metadata.bind
>>> session.execute(metadata.tables[encode(IVinyl)].select()).fetchall()
[(2, 45)]

>>> session.execute(metadata.tables[encode(IAlbum)].select()).fetchall()
[(1, u'Pet Sounds', u'The Beach Boys'),
 (2, u'Taking Care of Business', u'Diana Ross and The Supremes'),
 (3, u'The Great American Songbook', u'Diana Ross')]

>>> session.execute(metadata.tables[encode(ICompactDisc)].select()).fetchall()
[(3, 2005)]

Concrete class specification

Now we’ll create a mapper based on a concrete class. We’ll let the class implement the interface that describes the attributes we want to store, but also provides a custom method.

>>> class Vinyl(object):
...     interface.implements(IVinyl)
...
...     artist = title = u""
...     rpm = 33
...
...     def __repr__(self):
...         return "<Vinyl %s: %s (@ %d RPM)>" % \
...                (self.artist, self.title, self.rpm)

Although the symbols we define in this test report that they’re available from the __builtin__ module, they really aren’t.

We’ll manually add these symbols.

>>> import __builtin__
>>> __builtin__.IVinyl = IVinyl
>>> __builtin__.IAlbum = IAlbum
>>> __builtin__.Vinyl = Vinyl

Create an instance using the create factory.

>>> vinyl = create(Vinyl)

Verify that we’ve instantiated and instance of our class.

>>> isinstance(vinyl, Vinyl)
True

Copy the attributes from the Diana Ross vinyl record.

>>> diana = session.query(IVinyl.__mapper__).filter_by(
...     artist=u"Diana Ross and The Supremes")[0]
>>> vinyl.artist = diana.artist
>>> vinyl.title = diana.title
>>> vinyl.rpm = diana.rpm

Verify that the methods on our Vinyl-class are available on the mapper.

>>> repr(vinyl)
'<Vinyl Diana Ross and The Supremes: Taking Care of Business (@ 45 RPM)>'

If we’re mapping a concrete class, and run into class properties, we won’t instrument them even if they’re declared by the schema.

>>> class Experimental(Vinyl):
...     @property
...     def rpm(self):
...         return len(self.title+self.artist)

>>> experimental = create(Experimental)
>>> experimental.artist = vinyl.artist
>>> experimental.title = vinyl.title

Let’s see how fast this record should be played back.

>>> experimental.rpm
50

Instances of mappers automatically join the object soup.

>>> mapper = getMapper(Vinyl)
>>> instance = mapper()
>>> instance.uuid is not None
True

Relations

Relations are columns that act as references to other objects.

As an example, let’s create an object holds a reference to some favorite item. We use zope.schema.Object to declare this reference; relations are polymorphic and we needn’t declare the schema of the referenced object in advance.

>>> class IFavorite(interface.Interface):
...     item = schema.Object(title=u"Item", schema=interface.Interface)

>>> __builtin__.IFavorite = IFavorite

Let’s make our Diana Ross record a favorite.

>>> favorite = create(IFavorite)
>>> favorite.item = vinyl
>>> favorite.item
<Vinyl Diana Ross and The Supremes: Taking Care of Business (@ 45 RPM)>

>>> session.save(favorite)

We’ll commit the transaction and lookup the object by its UUID.

>>> transaction.commit()

>>> from z3c.dobbin.soup import lookup
>>> favorite = lookup(favorite.uuid)

When we retrieve the related items, it’s automatically reconstructed to match the specification to which it was associated.

>>> favorite.item
<Vinyl Diana Ross and The Supremes: Taking Care of Business (@ 45 RPM)>

We can create relations to objects that are not mapped. Let’s model an accessory item.

>>> class IAccessory(interface.Interface):
...     name = schema.TextLine(title=u"Name of accessory")

>>> class Accessory(object):
...     interface.implements(IAccessory)
...
...     def __repr__(self):
...          return "<Accessory '%s'>" % self.name

If we now instantiate an accessory and assign it as a favorite item, we’ll implicitly create a mapper from the class specification and insert it into the database.

>>> cleaner = Accessory()
>>> cleaner.name = u"Record cleaner"

Set up relation.

>>> favorite.item = cleaner

Let’s try and get back our record cleaner item.

>>> __builtin__.Accessory = Accessory
>>> favorite.item
<Accessory 'Record cleaner'>

Within the same transaction, the relation will return the original object, maintaining integrity.

>>> favorite.item is cleaner
True

The session keeps a copy of the pending object until the transaction is ended.

>>> cleaner in session._d_pending.values()
True

However, once we commit the transaction, the relation is no longer attached to the relation source, and the correct data will be persisted in the database.

>>> cleaner.name = u"CD cleaner"

>>> session.flush()
>>> session.update(favorite)

>>> favorite.item.name
u'CD cleaner'

This behavior should work well in a request-response type environment, where the request will typically end with a commit.

Collections

We can instrument properties that behave like collections by using the sequence and mapping schema fields.

Let’s set up a record collection as an ordered list.

>>> class ICollection(interface.Interface):
...     records = schema.List(
...         title=u"Records",
...         value_type=schema.Object(schema=IAlbum)
...         )

>>> __builtin__.ICollection = ICollection

>>> collection = create(ICollection)
>>> collection.records
[]

Add the Diana Ross record, and save the collection to the session.

>>> collection.records.append(diana)
>>> session.save(collection)

We can get our collection back.

>>> collection = lookup(collection.uuid)

Let’s verify that we’ve stored the Diana Ross record.

>>> record = collection.records[0]

>>> record.artist, record.title
(u'Diana Ross and The Supremes', u'Taking Care of Business')

>>> session.flush()

When we create a new, transient object and append it to a list, it’s automatically saved on the session.

>>> collection = lookup(collection.uuid)

>>> kool = create(IVinyl)
>>> kool.artist = u"Kool & the Gang"
>>> kool.title = u"Music Is the Message"
>>> kool.rpm = 33

>>> collection.records.append(kool)
>>> [record.artist for record in collection.records]
[u'Diana Ross and The Supremes', u'Kool & the Gang']

>>> session.flush()
>>> session.update(collection)

We can remove items.

>>> collection.records.remove(kool)
>>> len(collection.records) == 1
True

And extend.

>>> collection.records.extend((kool,))
>>> len(collection.records) == 2
True

Items can appear twice in the list.

>>> collection.records.append(kool)
>>> len(collection.records) == 3
True

We can add concrete instances to collections.

>>> marvin = Vinyl()
>>> marvin.artist = u"Marvin Gaye"
>>> marvin.title = u"Let's get it on"
>>> marvin.rpm = 33

>>> collection.records.append(marvin)
>>> len(collection.records) == 4
True

And remove them, too.

>>> collection.records.remove(marvin)
>>> len(collection.records) == 3
True

The standard list methods are available.

>>> collection.records = [marvin, vinyl]
>>> collection.records.sort(key=lambda record: record.artist)
>>> collection.records
[<Vinyl Diana Ross and The Supremes: Taking Care of Business (@ 45 RPM)>,
 <Vinyl Marvin Gaye: Let's get it on (@ 33 RPM)>]

>>> collection.records.reverse()
>>> collection.records
[<Vinyl Marvin Gaye: Let's get it on (@ 33 RPM)>,
 <Vinyl Diana Ross and The Supremes: Taking Care of Business (@ 45 RPM)>]

>>> collection.records.index(vinyl)
1

>>> collection.records.pop()
<Vinyl Diana Ross and The Supremes: Taking Care of Business (@ 45 RPM)>

>>> collection.records.insert(0, vinyl)
>>> collection.records
[<Vinyl Diana Ross and The Supremes: Taking Care of Business (@ 45 RPM)>,
 <Vinyl Marvin Gaye: Let's get it on (@ 33 RPM)>]

>>> collection.records.count(vinyl)
1

>>> collection.records[1] = vinyl
>>> collection.records.count(vinyl)
2

For good measure, let’s create a new instance without adding any elements to its list.

>>> empty_collection = create(ICollection)
>>> session.save(empty_collection)

To demonstrate the mapping implementation, let’s set up a catalog for our record collection. We’ll index the records by their ASIN string.

>>> class ICatalog(interface.Interface):
...     index = schema.Dict(
...         title=u"Record index")

>>> catalog = create(ICatalog)
>>> session.save(catalog)

Add a record to the index.

>>> catalog.index[u"B00004WZ5Z"] = diana
>>> catalog.index[u"B00004WZ5Z"]
<Mapper (__builtin__.IVinyl) at ...>

Verify state after commit.

>>> transaction.commit()
>>> catalog.index[u"B00004WZ5Z"]
<Mapper (__builtin__.IVinyl) at ...>

Let’s check that the standard dict methods are supported.

>>> catalog.index.values()
[<Mapper (__builtin__.IVinyl) at ...>]

>>> tuple(catalog.index.itervalues())
(<Mapper (__builtin__.IVinyl) at ...>,)

>>> catalog.index.setdefault(u"B00004WZ5Z", None)
<Mapper (__builtin__.IVinyl) at ...>

>>> catalog.index.pop(u"B00004WZ5Z")
<Mapper (__builtin__.IVinyl) at ...>

>>> len(catalog.index)
0

Concrete instances are supported.

>>> vinyl = Vinyl()
>>> vinyl.artist = diana.artist
>>> vinyl.title = diana.title
>>> vinyl.rpm = diana.rpm

>>> catalog.index[u"B00004WZ5Z"] = vinyl
>>> len(catalog.index)
1

>>> catalog.index.popitem()
(u'B00004WZ5Z',
 <Vinyl Diana Ross and The Supremes: Taking Care of Business (@ 45 RPM)>)

>>> catalog.index = {u"B00004WZ5Z": vinyl}
>>> len(catalog.index)
1

>>> catalog.index.clear()
>>> len(catalog.index)
0

We may use a mapped object as index.

>>> catalog.index[diana] = diana
>>> catalog.index.keys()[0] == diana.uuid
True

>>> transaction.commit()

>>> catalog.index[diana]
<Mapper (__builtin__.IVinyl) at ...>

>>> class IDiscography(ICatalog):
...     records = schema.Dict(
...         title=u"Discographies by artist",
...         value_type=schema.List())

Polymorphic relations

We can create relations to instances as well as immutable objects (rocks).

Integers, floats and unicode strings are straight-forward.

>>> favorite.item = 42; transaction.commit()
>>> favorite.item
42

>>> favorite.item = 42.01; transaction.commit()
>>> 42 < favorite.item <= 42.01
True

>>> favorite.item = u"My favorite number is 42."; transaction.commit()
>>> favorite.item
u'My favorite number is 42.'

Normal strings need explicit coercing to str.

>>> favorite.item = "My favorite number is 42."; transaction.commit()
>>> str(favorite.item)
'My favorite number is 42.'

Or sequences of relations.

>>> favorite.item = (u"green", u"blue", u"red"); transaction.commit()
>>> favorite.item
(u'green', u'blue', u'red')

Dictionaries.

>>> favorite.item = {u"green": 0x00FF00, u"blue": 0x0000FF, u"red": 0xFF0000}
>>> transaction.commit()
>>> favorite.item
{u'blue': 255, u'green': 65280, u'red': 16711680}

>>> favorite.item[u"black"] = 0x000000
>>> favorite.item
{u'blue': 255, u'green': 65280, u'black': 0, u'red': 16711680}

When we create relations to mutable objects, a hook is made into the transaction machinery to keep track of the pending state.

>>> some_list = [u"green", u"blue", u"red"]; transaction.commit()
>>> favorite.item = some_list
>>> favorite.item
[u'green', u'blue', u'red']

Amorphic relations.

>>> favorite.item = ((1, u"green"), (2, u"blue"), (3, u"red")); transaction.commit()
>>> favorite.item
((1, u'green'), (2, u'blue'), (3, u'red'))

Security

The security model from Zope is applied to mappers.

>>> from zope.security.checker import getCheckerForInstancesOf

Our Vinyl class does not have a security checker defined.

>>> mapper = getMapper(Vinyl)
>>> getCheckerForInstancesOf(mapper) is None
True

Let’s set a checker and regenerate the mapper.

>>> from zope.security.checker import defineChecker, CheckerPublic
>>> defineChecker(Vinyl, CheckerPublic)

>>> from z3c.dobbin.mapper import createMapper
>>> mapper = createMapper(Vinyl)
>>> getCheckerForInstancesOf(mapper) is CheckerPublic
True

Known limitations

Certain names are disallowed, and will be ignored when constructing the mapper.

>>> class IKnownLimitations(interface.Interface):
...     __name__ = schema.TextLine()

>>> from z3c.dobbin.interfaces import IMapper
>>> mapper = IMapper(IKnownLimitations)
>>> mapper.__name__
'Mapper'

Cleanup

Commit session.

>>> transaction.commit()

Change log

0.3 dev

• Implemented rest of list methods. [malthe]

• Refactoring of table bootstrapping; internal tables now using a naming convention less likely to clash with existing tables. [malthe]

• Added support for schema.Dict (including polymorphic dictionary relation). [malthe]

• Implemented polymorphic relations for a subset of the basic types (int, str, unicode, tuple and list). [malthe]

0.2.9

• Tables are now only created once per minimal interface; this fixes issue on both SQLite and Postgres when we create mappers with an explicit polymorphic class. [malthe]

• First entry in change-log.