Using persistent in your application

Inheriting from persistent.Persistent

The basic mechanism for making your application’s objects persistent is mix-in interitance. Instances whose classes derive from persistent.Persistent are automatically capable of being created as ghost instances, being associated with a database connection (called the jar), and notifying the connection when they have been changed.

Relationship to a Data Manager and its Cache

Except immediately after their creation, persistent objects are normally associated with a data manager (also referred to as a jar). An object’s data manager is stored in its _p_jar attribute. The data manager is responsible for loading and saving the state of the persistent object to some sort of backing store, including managing any interactions with transaction machinery.

Each data manager maintains an object cache, which keeps track of the currently loaded objects, as well as any objects they reference which have not yet been loaded: such an object is called a ghost. The cache is stored on the data manager in its _cache attribute.

A persistent object remains in the ghost state until the application attempts to access or mutate one of its attributes: at that point, the object requests that its data manager load its state. The persistent object also notifies the cache that it has been loaded, as well as on each subsequent attribute access. The cache keeps a “most-recently-used” list of its objects, and removes objects in least-recently-used order when it is asked to reduce its working set.

The examples below use a stub data manager class, and its stub cache class:

>>> class Cache(object):
...     def __init__(self):
...         self._mru = []
...     def mru(self, oid):
...         self._mru.append(oid)
>>> from zope.interface import implements
>>> from persistent.interfaces import IPersistentDataManager
>>> class DM(object):
...     implements(IPersistentDataManager)
...     def __init__(self):
...         self._cache = Cache()
...         self.registered = 0
...     def register(self, ob):
...         self.registered += 1
...     def setstate(self, ob):
...         ob.__setstate__({'x': 42})

Note

Notic that the DM class always sets the x attribute to the value 42 when activating an object.

Persistent objects without a Data Manager

Before aersistent instance has been associtated with a a data manager ( i.e., its _p_jar is still None).

The examples below use a class, P, defined as:

>>> from persistent import Persistent
>>> from persistent.interfaces import GHOST, UPTODATE, CHANGED
>>> class P(Persistent):
...    def __init__(self):
...        self.x = 0
...    def inc(self):
...        self.x += 1

Instances of the derived P class which are not (yet) assigned to a data manager behave as other Python instances, except that they have some extra attributes:

>>> p = P()
>>> p.x
0

The _p_changed attribute is a three-state flag: it can be one of None (the object is not loaded), False (the object has not been changed since it was loaded) or True (the object has been changed). Until the object is assigned a jar, this attribute will always be False.

>>> p._p_changed
False

The _p_state attribute is an integaer, representing which of the “persistent lifecycle” states the object is in. Until the object is assigned a jar, this attribute will always be 0 (the UPTODATE constant):

>>> p._p_state == UPTODATE
True

The _p_jar attribute is the object’s data manager. Since it has not yet been assigned, its value is None:

>>> print p._p_jar
None

The _p_oid attribute is the object id, a unique value normally assigned by the object’s data manager. Since the object has not yet been associated with its jar, its value is None:

>>> print p._p_oid
None

Without a data manager, modifying a persistent object has no effect on its _p_state or _p_changed.

>>> p.inc()
>>> p.inc()
>>> p.x
2
>>> p._p_changed
False
>>> p._p_state
0

Try all sorts of different ways to change the object’s state:

>>> p._p_deactivate()
>>> p._p_state
0
>>> p._p_changed
False
>>> p._p_changed = True
>>> p._p_changed
False
>>> p._p_state
0
>>> del p._p_changed
>>> p._p_changed
False
>>> p._p_state
0
>>> p.x
2

Associating an Object with a Data Manager

Once associated with a data manager, a persistent object’s behavior changes:

>>> p = P()
>>> dm = DM()
>>> p._p_oid = "00000012"
>>> p._p_jar = dm
>>> p._p_changed
False
>>> p._p_state
0
>>> p.__dict__
{'x': 0}
>>> dm.registered
0

Modifying the object marks it as changed and registers it with the data manager. Subsequent modifications don’t have additional side-effects.

>>> p.inc()
>>> p.x
1
>>> p.__dict__
{'x': 1}
>>> p._p_changed
True
>>> p._p_state
1
>>> dm.registered
1
>>> p.inc()
>>> p._p_changed
True
>>> p._p_state
1
>>> dm.registered
1

Object which register themselves with the data manager are candidates for storage to the backing store at a later point in time.

Note that mutating a non-persistent attribute of a persistent object such as a dict or list will not cause the containing object to be changed. Instead you can either explicitly control the state as described below, or use a PersistentList or PersistentMapping.

Explicitly controlling _p_state

Persistent objects expose three methods for moving an object into and out of the “ghost” state:: persistent.Persistent._p_activate(), persistent.Persistent._p_activate_p_deactivate(), and persistent.Persistent._p_invalidate():

>>> p = P()
>>> p._p_oid = '00000012'
>>> p._p_jar = DM()

After being assigned a jar, the object is initially in the UPTODATE state:

>>> p._p_state
0

From that state, _p_deactivate rests the object to the GHOST state:

>>> p._p_deactivate()
>>> p._p_state
-1

From the GHOST state, _p_activate reloads the object’s data and moves it to the UPTODATE state:

>>> p._p_activate()
>>> p._p_state
0
>>> p.x
42

Changing the object puts it in the CHANGED state:

>>> p.inc()
>>> p.x
43
>>> p._p_state
1

Attempting to deactivate in the CHANGED state is a no-op:

>>> p._p_deactivate()
>>> p.__dict__
{'x': 43}
>>> p._p_changed
True
>>> p._p_state
1

_p_invalidate forces objects into the GHOST state; it works even on objects in the CHANGED state, which is the key difference between deactivation and invalidation:

>>> p._p_invalidate()
>>> p.__dict__
{}
>>> p._p_state
-1

You can manually reset the _p_changed field to False: in this case, the object changes to the UPTODATE state but retains its modifications:

>>> p.inc()
>>> p.x
43
>>> p._p_changed = False
>>> p._p_state
0
>>> p._p_changed
False
>>> p.x
43

For an object in the “ghost” state, assigning True (or any value which is coercible to True) to its _p_changed attributes activates the object, which is exactly the same as calling _p_activate:

>>> p._p_invalidate()
>>> p._p_state
-1
>>> p._p_changed = True
>>> p._p_changed
True
>>> p._p_state
1
>>> p.x
42

The pickling protocol

Because persistent objects need to control how they are pickled and unpickled, the persistent.Persistent base class overrides the implementations of __getstate__() and __setstate__():

>>> p = P()
>>> dm = DM()
>>> p._p_oid = "00000012"
>>> p._p_jar = dm
>>> p.__getstate__()
{'x': 0}
>>> p._p_state
0

Calling __setstate__ always leaves the object in the uptodate state.

>>> p.__setstate__({'x': 5})
>>> p._p_state
0

A volatile attribute is an attribute those whose name begins with a special prefix (_v__). Unlike normal attributes, volatile attributes do not get stored in the object’s pickled data.

>>> p._v_foo = 2
>>> p.__getstate__()
{'x': 5}

Assigning to volatile attributes doesn’t cause the object to be marked as changed:

>>> p._p_state
0

The _p_serial attribute is not affected by calling setstate.

>>> p._p_serial = "00000012"
>>> p.__setstate__(p.__getstate__())
>>> p._p_serial
'00000012'

Estimated Object Size

We can store a size estimation in _p_estimated_size. Its default is 0. The size estimation can be used by a cache associated with the data manager to help in the implementation of its replacement strategy or its size bounds.

>>> p._p_estimated_size
0
>>> p._p_estimated_size = 1000
>>> p._p_estimated_size
1024

Huh? Why is the estimated size coming out different than what we put in? The reason is that the size isn’t stored exactly. For backward compatibility reasons, the size needs to fit in 24 bits, so, internally, it is adjusted somewhat.

Of course, the estimated size must not be negative.

>>> p._p_estimated_size = -1
Traceback (most recent call last):
....
ValueError: _p_estimated_size must not be negative

Overriding the attribute protocol

Subclasses which override the attribute-management methods provided by persistent.Persistent, but must obey some constraints:

__getattribute__()
When overriding __getattribute__, the derived class implementation must first call persistent.IPersistent._p_getattr(), passing the name being accessed. This method ensures that the object is activated, if needed, and handles the “special” attributes which do not require activation (e.g., _p_oid, __class__, __dict__, etc.) If _p_getattr returns True, the derived class implementation must delegate to the base class implementation for the attribute.
__setattr__()
When overriding __setattr__, the derived class implementation must first call persistent.IPersistent._p_setattr(), passing the name being accessed and the value. This method ensures that the object is activated, if needed, and handles the “special” attributes which do not require activation (_p_*). If _p_setattr returns True, the derived implementation must assume that the attribute value has been set by the base class.
__detattr__()
When overriding __detattr__, the derived class implementation must first call persistent.IPersistent._p_detattr(), passing the name being accessed. This method ensures that the object is activated, if needed, and handles the “special” attributes which do not require activation (_p_*). If _p_delattr returns True, the derived implementation must assume that the attribute has been deleted base class.
__getattr__()
For the __getattr__ method, the behavior is like that for regular Python classes and for earlier versions of ZODB 3.