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Project Description

Mongo Data Persistence

This document outlines the general capabilities of the mongopersist package. mongopersist is a Mongo storage implementation for persistent Python objects. It is not a storage for the ZODB.

The goal of mongopersist is to provide a data manager that serializes objects to Mongo at transaction boundaries. The mongo data manager is a persistent data manager, which handles events at transaction boundaries (see transaction.interfaces.IDataManager) as well as events from the persistency framework (see persistent.interfaces.IPersistentDataManager).

An instance of a data manager is supposed to have the same life time as the transaction, meaning that it is assumed that you create a new data manager when creating a new transaction:

>>> import transaction

Note: The conn object is a pymongo.connection.Connection instance. In this case our tests use the mongopersist_test database.

Let’s now define a simple persistent object:

>>> import datetime
>>> import persistent
>>> class Person(persistent.Persistent):
...
...     def __init__(self, name, phone=None, address=None, friends=None,
...                  visited=(), birthday=None):
...         self.name = name
...         self.address = address
...         self.friends = friends or {}
...         self.visited = visited
...         self.phone = phone
...         self.birthday = birthday
...         self.today = datetime.datetime.now()
...
...     def __str__(self):
...         return self.name
...
...     def __repr__(self):
...         return '<%s %s>' %(self.__class__.__name__, self)

We will fill out the other objects later. But for now, let’s create a new person and store it in Mongo:

>>> stephan = Person(u'Stephan')
>>> stephan
<Person Stephan>

The datamanager provides a root attribute in which the object tree roots can be stored. It is special in the sense that it immediately writes the data to the DB:

>>> dm.root['stephan'] = stephan
>>> dm.root['stephan']
<Person Stephan>

Custom Persistence Collections

By default, persistent objects are stored in a collection having the Python path of the class:

>>> from mongopersist import serialize
>>> person_cn = serialize.get_dotted_name(Person)
>>> person_cn
'__main__.Person'
>>> import pprint
>>> pprint.pprint(list(conn[DBNAME][person_cn].find()))
[{u'_id': ObjectId('4e7ddf12e138237403000000'),
  u'address': None,
  u'birthday': None,
  u'friends': {},
  u'name': u'Stephan',
  u'phone': None,
  u'today': datetime.datetime(2011, 10, 1, 9, 45),
  u'visited': []}]

As you can see, the stored document for the person looks very Mongo. But oh no, I forgot to specify the full name for Stephan. Let’s do that:

>>> dm.root['stephan'].name = u'Stephan Richter'

This time, the data is not automatically saved:

>>> conn[DBNAME][person_cn].find_one()['name']
u'Stephan'

So we have to commit the transaction first:

>>> transaction.commit()
>>> conn[DBNAME][person_cn].find_one()['name']
u'Stephan Richter'

Let’s now add an address for Stephan. Addresses are also persistent objects:

>>> class Address(persistent.Persistent):
...     _p_mongo_collection = 'address'
...
...     def __init__(self, city, zip):
...         self.city = city
...         self.zip = zip
...
...     def __str__(self):
...         return '%s (%s)' %(self.city, self.zip)
...
...     def __repr__(self):
...         return '<%s %s>' %(self.__class__.__name__, self)

MongoPersist supports a special attribute called _p_mongo_collection, which allows you to specify a custom collection to use.

>>> stephan = dm.root['stephan']
>>> stephan.address = Address('Maynard', '01754')
>>> stephan.address
<Address Maynard (01754)>

Note that the address is not immediately saved in the database:

>>> list(conn[DBNAME]['address'].find())
[]

But once we commit the transaction, everything is available:

>>> transaction.commit()
>>> pprint.pprint(list(conn[DBNAME]['address'].find()))
[{u'_id': ObjectId('4e7de388e1382377f4000003'),
  u'city': u'Maynard',
  u'zip': u'01754'}]
>>> pprint.pprint(list(conn[DBNAME][person_cn].find()))
[{u'_id': ObjectId('4e7ddf12e138237403000000'),
  u'address': DBRef(u'address',
                    ObjectId('4e7ddf12e138237403000000'),
                    u'mongopersist_test'),
  u'birthday': None,
  u'friends': {},
  u'name': u'Stephan Richter',
  u'phone': None,
  u'today': datetime.datetime(2011, 10, 1, 9, 45)
  u'visited': []}]
>>> dm.root['stephan'].address
<Address Maynard (01754)>

Non-Persistent Objects

As you can see, even the reference looks nice and uses the standard Mongo DB reference construct. But what about arbitrary non-persistent, but picklable, objects? Well, let’s create a phone number object for that:

>>> class Phone(object):
...
...     def __init__(self, country, area, number):
...         self.country = country
...         self.area = area
...         self.number = number
...
...     def __str__(self):
...         return '%s-%s-%s' %(self.country, self.area, self.number)
...
...     def __repr__(self):
...         return '<%s %s>' %(self.__class__.__name__, self)
>>> dm.root['stephan'].phone = Phone('+1', '978', '394-5124')
>>> dm.root['stephan'].phone
<Phone +1-978-394-5124>

Let’s now commit the transaction and look at the Mongo document again:

>>> transaction.commit()
>>> dm.root['stephan'].phone
<Phone +1-978-394-5124>
>>> pprint.pprint(list(conn[DBNAME][person_cn].find()))
[{u'_id': ObjectId('4e7ddf12e138237403000000'),
  u'address': DBRef(u'address',
                    ObjectId('4e7ddf12e138237403000000'),
                    u'mongopersist_test'),
  u'birthday': None,
  u'friends': {},
  u'name': u'Stephan Richter',
  u'phone': {u'_py_type': u'__main__.Phone',
             u'area': u'978',
             u'country': u'+1',
             u'number': u'394-5124'},
  u'today': datetime.datetime(2011, 10, 1, 9, 45)
  u'visited': []}]

As you can see, for arbitrary non-persistent objects we need a small hint in the sub-document, but it is very minimal. If the __reduce__ method returns a more complex construct, more meta-data is written. We will see that next when storing a date and other arbitrary data:

>>> dm.root['stephan'].friends = {'roy': Person(u'Roy Mathew')}
>>> dm.root['stephan'].visited = (u'Germany', u'USA')
>>> dm.root['stephan'].birthday = datetime.date(1980, 1, 25)
>>> transaction.commit()
>>> dm.root['stephan'].friends
{u'roy': <Person Roy Mathew>}
>>> dm.root['stephan'].visited
[u'Germany', u'USA']
>>> dm.root['stephan'].birthday
datetime.date(1980, 1, 25)

As you can see, a dictionary key is always converted to unicode and tuples are always maintained as lists, since BSON does not have two sequence types.

>>> pprint.pprint(conn[DBNAME][person_cn].find_one(
...     {'name': 'Stephan Richter'}))
{u'_id': ObjectId('4e7df744e138230a3e000000'),
 u'address': DBRef(u'address',
                   ObjectId('4e7df744e138230a3e000003'),
                   u'mongopersist_test'),
 u'birthday': {u'_py_factory': u'datetime.date',
               u'_py_factory_args': [Binary('\x07\xbc\x01\x19', 0)]},
 u'friends': {u'roy': DBRef(u'__main__.Person',
                            ObjectId('4e7df745e138230a3e000004'),
                            u'mongopersist_test')},
 u'name': u'Stephan Richter',
 u'phone': {u'_py_type': u'__main__.Phone',
            u'area': u'978',
            u'country': u'+1',
            u'number': u'394-5124'},
 u'today': datetime.datetime(2011, 9, 24, 11, 29, 8, 930000),
 u'visited': [u'Germany', u'USA']}

Custom Serializers

As you can see, the serialization of the birthay is all but ideal. We can, however, provide a custom serializer that uses the ordinal to store the data.

>>> class DateSerializer(serialize.ObjectSerializer):
...
...     def can_read(self, state):
...         return isinstance(state, dict) and \
...                state.get('_py_type') == 'datetime.date'
...
...     def read(self, state):
...         return datetime.date.fromordinal(state['ordinal'])
...
...     def can_write(self, obj):
...         return isinstance(obj, datetime.date)
...
...     def write(self, obj):
...         return {'_py_type': 'datetime.date',
...                 'ordinal': obj.toordinal()}
>>> serialize.SERIALIZERS.append(DateSerializer())
>>> dm.root['stephan']._p_changed = True
>>> transaction.commit()

Let’s have a look again:

>>> dm.root['stephan'].birthday
datetime.date(1980, 1, 25)
>>> pprint.pprint(conn[DBNAME][person_cn].find_one(
...     {'name': 'Stephan Richter'}))
{u'_id': ObjectId('4e7df803e138230aeb000000'),
 u'address': DBRef(u'address',
                   ObjectId('4e7df803e138230aeb000003'),
                   u'mongopersist_test'),
 u'birthday': {u'_py_type': u'datetime.date', u'ordinal': 722839},
 u'friends': {u'roy': DBRef(u'__main__.Person',
                            ObjectId('4e7df803e138230aeb000004'),
                            u'mongopersist_test')},
 u'name': u'Stephan Richter',
 u'phone': {u'_py_type': u'__main__.Phone',
            u'area': u'978',
            u'country': u'+1',
            u'number': u'394-5124'},
 u'today': datetime.datetime(2011, 9, 24, 11, 32, 19, 640000),
 u'visited': [u'Germany', u'USA']}

Much better!

Persistent Objects as Sub-Documents

In order to give more control over which objects receive their own collections and which do not, the developer can provide a special flag marking a persistent class so that it becomes part of its parent object’s document:

>>> class Car(persistent.Persistent):
...     _p_mongo_sub_object = True
...
...     def __init__(self, year, make, model):
...         self.year = year
...         self.make = make
...         self.model = model
...
...     def __str__(self):
...         return '%s %s %s' %(self.year, self.make, self.model)
...
...     def __repr__(self):
...         return '<%s %s>' %(self.__class__.__name__, self)

The _p_mongo_sub_object is used to mark a type of object to be just part of another document:

>>> dm.root['stephan'].car = car = Car('2005', 'Ford', 'Explorer')
>>> transaction.commit()
>>> dm.root['stephan'].car
<Car 2005 Ford Explorer>
>>> pprint.pprint(conn[DBNAME][person_cn].find_one(
...     {'name': 'Stephan Richter'}))
{u'_id': ObjectId('4e7dfac7e138230d3d000000'),
 u'address': DBRef(u'address',
                   ObjectId('4e7dfac7e138230d3d000003'),
                   u'mongopersist_test'),
 u'birthday': {u'_py_type': u'datetime.date', u'ordinal': 722839},
 u'car': {u'_py_persistent_type': u'__main__.Car',
          u'make': u'Ford',
          u'model': u'Explorer',
          u'year': u'2005'},
 u'friends': {u'roy': DBRef(u'__main__.Person',
                            ObjectId('4e7dfac7e138230d3d000004'),
                            u'mongopersist_test')},
 u'name': u'Stephan Richter',
 u'phone': {u'_py_type': u'__main__.Phone',
            u'area': u'978',
            u'country': u'+1',
            u'number': u'394-5124'},
 u'today': datetime.datetime(2011, 9, 24, 11, 44, 7, 662000),
 u'visited': [u'Germany', u'USA']}

The reason we want objects to be persistent is so that they pick up changes automatically:

>>> dm.root['stephan'].car.year = '2004'
>>> transaction.commit()
>>> dm.root['stephan'].car
<Car 2004 Ford Explorer>

Collection Sharing

Since Mongo is so flexible, it sometimes makes sense to store multiple types of (similar) objects in the same collection. In those cases you instruct the object type to store its Python path as part of the document.

Warning: Please note though that this method is less efficient, since the document must be loaded in order to create a ghost causing more database access.

>>> class ExtendedAddress(Address):
...
...     def __init__(self, city, zip, country):
...         super(ExtendedAddress, self).__init__(city, zip)
...         self.country = country
...
...     def __str__(self):
...         return '%s (%s) in %s' %(self.city, self.zip, self.country)

In order to accomplish collection sharing, you simply create another class that has the same _p_mongo_collection string as another (sub-classing will ensure that).

So let’s give Stephan an extended address now.

>>> dm.root['stephan'].address2 = ExtendedAddress(
...     'Tettau', '01945', 'Germany')
>>> dm.root['stephan'].address2
<ExtendedAddress Tettau (01945) in Germany>
>>> transaction.commit()

When loading the addresses, they should be of the right type:

>>> dm.root['stephan'].address
<Address Maynard (01754)>
>>> dm.root['stephan'].address2
<ExtendedAddress Tettau (01945) in Germany>

Tricky Cases

Changes in Basic Mutable Type

Tricky, tricky. How do we make the framework detect changes in mutable objects, such as lists and dictionaries? Answer: We keep track of which persistent object they belong to and provide persistent implementations.

>>> type(dm.root['stephan'].friends)
 <class 'mongopersist.serialize.PersistentDict'>
>>> dm.root['stephan'].friends[u'roger'] = Person(u'Roger')
>>> transaction.commit()
>>> sorted(dm.root['stephan'].friends.keys())
[u'roger', u'roy']

The same is true for lists:

>>> type(dm.root['stephan'].visited)
 <class 'mongopersist.serialize.PersistentList'>
>>> dm.root['stephan'].visited.append('France')
>>> transaction.commit()
>>> dm.root['stephan'].visited
[u'Germany', u'USA', u'France']

Circular Non-Persistent References

Any mutable object that is stored in a sub-document, cannot have multiple references in the object tree, since there is no global referencing. These circular references are detected and reported:

>>> class Top(persistent.Persistent):
...     foo = None
>>> class Foo(object):
...     bar = None
>>> class Bar(object):
...     foo = None
>>> top = Top()
>>> foo = Foo()
>>> bar = Bar()
>>> top.foo = foo
>>> foo.bar = bar
>>> bar.foo = foo
>>> dm.root['top'] = top
Traceback (most recent call last):
...
CircularReferenceError: <__main__.Foo object at 0x7fec75731890>

Circular Persistent References

In general, circular references among persistent objects are not a problem, since we always only store a link to the object. However, there is a case when the circular dependencies become a problem.

If you set up an object tree with circular references and then add the tree to the storage at once, it must insert objects during serialization, so that references can be created. However, care needs to be taken to only create a minimal reference object, so that the system does not try to recursively reduce the state.

>>> class PFoo(persistent.Persistent):
...     bar = None
>>> class PBar(persistent.Persistent):
...     foo = None
>>> top = Top()
>>> foo = PFoo()
>>> bar = PBar()
>>> top.foo = foo
>>> foo.bar = bar
>>> bar.foo = foo
>>> dm.root['ptop'] = top

Containers and Collections

Now that we have talked so much about the gory details on storing one object, what about mappings that reflect an entire collection, for example a collection of people.

There are many approaches that can be taken. The folowing implementation defines an attribute in the document as the mapping key and names a collection:

>>> from mongopersist import mapping
>>> class People(mapping.MongoCollectionMapping):
...     __mongo_collection__ = person_cn
...     __mongo_mapping_key__ = 'short_name'

The mapping takes the data manager as an argument. One can easily create a sub-class that assigns the data manager automatically. Let’s have a look:

>>> People(dm).keys()
[]

The reason no person is in the list yet, is because no document has the key yet or the key is null. Let’s change that:

>>> People(dm)['stephan'] = dm.root['stephan']
>>> transaction.commit()
>>> People(dm).keys()
[u'stephan']
>>> People(dm)['stephan']
<Person Stephan Richter>

Also note that setting the “short-name” attribute on any other person will add it to the mapping:

>>> dm.root['stephan'].friends['roy'].short_name = 'roy'
>>> transaction.commit()
>>> sorted(People(dm).keys())
[u'roy', u'stephan']

Write-Conflict Detection

Since Mongo has no support for MVCC, it does not provide a concept of write conflict detection. However, a simple write-conflict detection can be easily implemented using a serial number on the document.

Let’s reset the database and create a data manager with enabled conflict detection:

>>> from mongopersist import conflict, datamanager
>>> conn.drop_database(DBNAME)
>>> dm2 = datamanager.MongoDataManager(
...     conn,
...     default_database=DBNAME,
...     root_database=DBNAME,
...     conflict_handler_factory=conflict.SimpleSerialConflictHandler)

Now we add a person and see that the serial got stored.

>>> dm2.root['stephan'] = Person(u'Stephan')
>>> dm2.root['stephan']._p_serial
'\x00\x00\x00\x00\x00\x00\x00\x01'
>>> pprint.pprint(dm2._conn[DBNAME][person_cn].find_one())
{u'_id': ObjectId('4e7fe18de138233a5b000009'),
 u'_py_serial': 1,
 u'address': None,
 u'birthday': None,
 u'friends': {},
 u'name': u'Stephan',
 u'phone': None,
 u'today': datetime.datetime(2011, 9, 25, 22, 21, 1, 656000),
 u'visited': []}

Next we change the person and commit it again:

>>> dm2.root['stephan'].name = u'Stephan <Unknown>'
>>> transaction.commit()
>>> pprint.pprint(dm2._conn[DBNAME][person_cn].find_one())
{u'_id': ObjectId('4e7fe18de138233a5b000009'),
 u'_py_serial': 2,
 u'address': None,
 u'birthday': None,
 u'friends': {},
 u'name': u'Stephan <Unknown>',
 u'phone': None,
 u'today': datetime.datetime(2011, 9, 25, 22, 21, 1, 656000),
 u'visited': []}

Let’s now start a new transaction with some modifications:

>>> dm2.root['stephan'].name = u'Stephan Richter'

However, in the mean time another transaction modifies the object. (We will do this here directly via Mongo for simplicity.)

>>> _ = dm2._conn[DBNAME][person_cn].update(
...     {'name': u'Stephan <Unknown>'},
...     {'$set': {'name': u'Stephan R.', '_py_serial': 3}})
>>> pprint.pprint(dm2._conn[DBNAME][person_cn].find_one())
{u'_id': ObjectId('4e7fe1f4e138233ac4000009'),
 u'_py_serial': 3,
 u'address': None,
 u'birthday': None,
 u'friends': {},
 u'name': u'Stephan R.',
 u'phone': None,
 u'today': datetime.datetime(2011, 9, 25, 22, 22, 44, 343000),
 u'visited': []}

Now our changing transaction tries to commit:

>>> transaction.commit()
Traceback (most recent call last):
...
ConflictError: database conflict error
    (oid DBRef(u'__main__.Person',
               ObjectId('4e7ddf12e138237403000000'),
               u'mongopersist_test'),
     class Person,
     orig serial 2, cur serial 3, new serial 3)
>>> transaction.abort()

CHANGES

0.8.4 (2013-06-13)

  • Fix insert followed by remove in the same transaction. The document was not removed from mongo.
  • Fix transaction.abort() behaviour for complex objects. _py_type information is not lost after transaction abort().

0.8.3 (2013-04-09)

  • Fixed MongoContainer vs IdNamesMongoContainer add and __setitem__ behavour on None keys. ObjectAddedEvent or ObjectMovedEvent were not fired because zope.container.contained.setitem got the just inserted object back.

    MongoContainer always requires _m_mapping_key and uses that attribute of the object to determine the new key.

    IdNamesMongoContainer requires _m_mapping_key None and uses _id to determine the new key.

0.8.2 (2013-04-03)

  • Fixed check_conflict: make sure we use the same db and collection as in the object

0.8.1 (2013-03-19)

  • Fixed _p_changed setting on object loading which was caused by assigning directly to __name__. That caused all objects read from containers to be marked changed on load. That wrecked cache performance too.

0.8.0 (2013-02-09)

  • Feature: Added find_objects() and find_one_object() to the collection wrapper, so that whenever you get a collection from the data manager, you can load objects directly through the find API.
  • Feature: Added the ability for MongoContained objects to fully reference and load their parents. This allows one to query mongo directly and create the object from the doc without going through the right container, which you might not know easily.

0.7.7 (2013-02-08)

  • Bug: Do not fail if we cannot delete the parent and name attributes.

0.7.6 (2013-02-08)

  • Feature: Switch to pymongo.MongoClient, set default write concern values, allow override of write concern values.

0.7.5 (2013-02-06)

  • Tests: Added, cleaned tests
  • Bug: Re-release after missing files in 0.7.4

0.7.4 (2013-02-05)

  • Bug: Due to UserDict implementing dict comparison semantics, any empty MongoContainer would equate to another empty one. This behavior would cause object changes to not be properly recognzed by the mongo data manager. The implemented solution is to implement default object comparison behavior for mongo containers.

0.7.3 (2013-01-29)

  • Feature: Update to latest package versions, specifically pymongo 2.4.x. In this release, pymongo does not reexport objectid and dbref.

0.7.2 (2012-04-19)

  • Bug: avoid caching MongoDataManager instances in mongo container to avoid multiple MongoDataManagers in the single transaction in multithreaded environment. Cache IMongoDataManagerProvider instead.

0.7.1 (2012-04-13)

  • Performance: Improved the profiler a bit by allowing to disable modification of records as well.
  • Performance: Added caching of _m_jar lookups in Mongo Containers, since the computation turned out to be significantly expensive.
  • Performance: Use lazy hash computation for DBRef. Also, disable support for arbitrary keyword arguments. This makes roughly a 2-4% difference in object loading time.
  • Bug: An error occurred when _py_serial was missing. This was possible due to a bug in version 0.6. It also protects against third party software which is not aware of our meta-data.
  • Performance: Switched to repoze.lru (from lru), which is much faster.
  • Performance: To avoid excessive hash computations, we now use the hash of the DBRef references as cache keys.
  • Bug: ObjectId ids are not guaranteed to be unique across collections. Thus they are a bad key for global caches. So we use full DBRef references instead.

0.7.0 (2012-04-02)

  • Feature: A new IConflictHandler interface now controls all aspects of conflict resolution. The following implementations are provided:

    • NoCheckConflictHandler: This handler does nothing and when used, the system behaves as before when the detect_conflicts flag was set to False.
    • SimpleSerialConflictHandler: This handler uses serial numbers on each document to keep track of versions and then to detect conflicts. When a conflict is detected, a ConflictError is raised. This handler is identical to detect_conflicts being set to True.
    • ResolvingSerialConflictHandler: Another serial handler, but it has the ability to resolve a conflict. For this to happen, a persistent object must implement _p_resolveConflict(orig_state, cur_state, new_state), which returns the new, merged state. (Experimental)

    As a result, the detect_conflicts flag of the data manager was removed and replaced with the conflict_handler attribute. One can pass in the conflict_handler_factory to the data manager constructor. The factory needs to expect on argument, the data manager.

  • Feature: The new IdNamesMongoContainer class uses the natural Mongo ObjectId as the name/key for the items in the container. No more messing around with coming up or generating a name. Of course, if you specified None as a key in the past, it already used the object id, but it was sotred again in the mapping key field. Now the object id is used directly everywhere.

  • Feature: Whenever setattr() is called on a persistent object, it is marked as changed even if the new value equals the old one. To minimize writes to MongoDB, the latest database state is compared to the new state and the new state is only written when changes are detected. A flag called serialize.IGNORE_IDENTICAL_DOCUMENTS (default: True) is used to control the feature. (Experimental)

  • Feature: ConflictError has now a much more meaningful API. Instead of just referencing the object and different serials, it now actual has the original, current and new state documents.

  • Feature: Conflicts are now detected while aborting a transaction. The implemented policy will not reset the document state, if a conflict is detected.

  • Feature: Provide a flag to turn on MongoDB access logging. The flag is false by default, since access logging is very expensive.

  • Feature: Added transaction ID to LoggingDecorator.

  • Feature: Added a little script to test performance. It is not very sophisticated, but it is sufficient for a first round of optimizations.

  • Feature: Massively improved performance on all levels. This was mainly accomplished by removing unnecessary database accesses, better caching and more efficient algorithms. This results in speedups between 4-25 times.

    • When resolving the path to a class, the result is now cached. More importantly, lookup failures are also cached mapping path -> None. This is important, since an optimization the resolve() method causes a lot of failing lookups.
    • When resolving the dbref to a type, we try to resolve the dbref early using the document, if we know that the documents within the collection store their type path. This avoids frequent queries of the name map collection when it is not needed.
    • When getting the object document to read the class path, it will now read the entire document and store it in the _latest_states dictionary, so that other code may pick it up and use it. This should avoid superflous reads from MongoDB.
    • Drastically improved performance for collections that store only one type of object and where the documents do not store the type (i.e. it is stored in the name map collection).
    • The Mongo Container fast load via find() did not work correctly, since setstate() did not change the state from ghost to active and thus the state was loaded again from MongoDB and set on the object. Now we use the new _latest_states cache to lookup a document when setstate() is called through the proper channels. Now this “fast load” method truly causes O(1) database lookups.
    • Implemented several more mapping methods for the Mongo Container, so that all methods getting the full list of items are fast now.
    • Whenever the Mongo Object Id is used as a hash key, use the hash of the id instead. The __cmp__() method of the ObjectId class is way too slow.
    • Cache collection name lookup from objects in the ObjectWriter class.
  • Bug: We have seen several occasions in production where we suddenly lost some state in some documents, which prohibited the objects from being loadable again. The cause was that the _original_states attribute did not store the raw MongoDB document, but a modified one. Since those states are used during abort to reset the state, however, the modified document got stored making the affected objects inaccessible.

  • Bug: When a transaction was aborted, the states of all loaded objects were reset. Now, only modified object states are reset. This should drastically lower problems (by the ratio of read over modified objects) due to lack of full MVCC.

  • Bug: When looking for an item by key/name (find_*() methods) , you would never get the right object back, but the first one found in the database. This was due to clobbering the search filter with more general parameters.

0.6.1 (2012-03-28)

  • Feature: Added quite detailed debug logging around collection methods

0.6.0 (2012-03-12)

  • Feature: Switched to optimisitc data dumping, which approaches transactions by dumping early and as the data comes. All changes are undone when the transaction fails/aborts. See optimistic-data-dumping.txt for details. Here are some of the new features:
    • Data manager keeps track of all original docs before their objects are modified, so any change can be done.
    • Added an API to data manager (DataManager.insert(obj)) to insert an object in the database.
    • Added an API to data manager (DataManager.remove(obj)) to remove an object from the database.
    • Data can be flushed to Mongo (DataManager.flush()) at any point of the transaction retaining the ability to completely undo all changes. Flushing features the following characteristics:
      • During a given transaction, we guarantee that the user will always receive the same Python object. This requires that flush does not reset the object cache.
      • The _p_serial is increased by one. (Automatically done in object writer.)
      • The object is removed from the registered objects and the _p_changed flag is set to False.
      • Before flushing, potential conflicts are detected.
    • Implemented a flushing policy: Changes are always flushed before any query is made. A simple wrapper for the pymongo collection (CollectionWrapper) ensures that flush is called before the correct method calls. Two new API methods DataManager.get_collection(db_name, coll_name) and DataManager.get_collection_from_object(obj) allows one to quickly get a wrapped collection.
  • Feature: Renamed processSpec() to process_spec() to adhere to package nameing convention.
  • Feature: Created a ProcessSpecDecorator that is used in the CollectionWrapper class to process the specs of the find(), find_one() and find_and_modify() collection methods.
  • Feature: The MongoContainer class now removes objects from the database upon container removal is _m_remove_documents is True. The default is True.
  • Feature: When adding an item to MongoContainer and the key is None, then the OID is chosen as the key. Ids are perfect keys, because they are guaranteed to be unique within the collection.
  • Feature: Since people did not like the setitem with None key implementation, I also added the MongoContainer.add(value, key=None) method, which makes specifying the key optional. The default implementation is to use the OID, if the key is None.
  • Feature: Removed fields argument from the MongoContainer.find(...) and MongoContainer.find_one(...) methods, since it was not used.
  • Feature: If a container has N items, it took N+1 queries to load the list of items completely. This was due to one query returning all DBRefs and then using one query to load the state for each. Now, the first query loads all full states and uses an extension to DataManager.setstate(obj, doc=None) to load the state of the object with the previously queried data.
  • Feature: Changed MongoContainer.get_collection() to return a CollectionWrapper instance.

0.5.5 (2012-03-09)

  • Feature: Moved ZODB dependency to test dependency
  • Bug: When an object has a SimpleContainer as attribute, then simply loading this object would cause it to written at the end of the transaction. The culprit was a persistent dictionary containing the SimpleContainer state. This dictionary got modified during state load and caused it to be registered as a changed object and it was marked as a _p_mongo_sub_object and had the original object as _p_mongo_doc_object.

0.5.4 (2012-03-05)

  • Feature: Added a hook via the IMongoSpecProcessor adapter that gets called before each find to process/log spec.

0.5.3 (2012/01/16)

  • Bug: MongoContainer did not emit any Zope container or lifecycle events. This has been fixed by using the zope.container.contained helper functions.

0.5.2 (2012-01-13)

  • Feature: Added an interface for the MongoContainer class describing the additional attributes and methods.

0.5.1 (2011-12-22)

  • Bug: The MongoContainer class did not implement the IContainer interface.

0.5.0 (2011-11-04)

  • Initial Release
Release History

Release History

0.8.4

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0.8.3

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0.8.2

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0.8.1

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0.8.0

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0.7.7

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0.7.6

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0.7.5

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0.7.4

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0.7.3

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0.7.2

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0.7.1

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0.7.0

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0.6.1

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0.6.0

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0.5.5

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0.5.4

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0.5.3

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0.5.2

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0.5.1

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0.5.0

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Download Files

Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
mongopersist-0.8.4.zip (102.2 kB) Copy SHA256 Checksum SHA256 Source Jun 13, 2013

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