Metadata-Version: 2.1
Name: m01.mongo
Version: 3.3.3
Summary: MongoDB connection pool and container implementation for Zope3
Home-page: http://pypi.python.org/pypi/m01.mongo
Author: Roger Ineichen, Projekt01 GmbH
Author-email: dev@projekt01.ch
License: ZPL 2.1
Description: This package provides a mongodb object mapper framework including zope
        transaction support based on some core zope component libraries. This package
        can get used with or without zope.persistent and as a full replacement for the
        ZODB. The package is not heavy based on zope itself and can get used in any
        python project which requires a bridge from mongodb to python object.
        
        
        ======
        README
        ======
        
        IMPORTANT:
        If you run the tests with the --all option a real mongodb stub server will
        start at port 45017!
        
        This package provides non persistent MongoDB object implementations. They can
        simply get mixed with persistent.Persistent and contained.Contained if you like
        to use them in a mixed MongoDB/ZODB application setup. We currently use this
        framework as ORM (object relation mapper) where we map MongoDB objects
        to python/zope schema based objects including validation etc.
        
        In our last project, we started with a mixed ZODB/MongoDB application where we
        mixed persistent.persistent into IMongoContainer objects. But later we where
        so exited about the performance and stability that we removed the ZODB
        persistence layer at all. Now we use a ZODB less setup in our application
        where we start with a non persistent item as our application root. All required
        tools where we use for such a ZODB less application setup are located in the
        p01.publisher and p01.recipe.setup package.
        
        NOTE: Some of this test use a fake mongodb located in m01/mongo/testing and some
        other tests will use our mongdb stub from the m01.stub package. You can run
        the tests with the --all option if you like to run the full tests which will
        start and stop the mongodb stub server.
        
        NOTE:
        All mongo item interfaces will not provide ILocation or IContained but the
        base mongo item implementations will implement Location which provides the
        ILocation interface directly. This makes it simpler for permission
        declaration in ZCML.
        
        
        Setup
        -----
        
          >>> import pymongo
          >>> import zope.component
          >>> from m01.mongo import interfaces
        
        
        MongoClient
        -----------
        
        Setup a mongo client:
        
          >>> client = pymongo.MongoClient('localhost', 45017)
          >>> client
          MongoClient(host=['127.0.0.1:45017'])
        
        As you can see the client is able to access the database:
        
          >>> db = client.m01MongoTesting
          >>> db
          Database(MongoClient(host=['127.0.0.1:45017']), u'm01MongoTesting')
        
        A data base can retrun a collection:
        
          >>> collection = db['m01MongoTest']
          >>> collection
          Collection(Database(MongoClient(host=['127.0.0.1:45017']), u'm01MongoTesting'), u'm01MongoTest')
        
        As you can see we can write to the collection:
        
          >>> res = collection.update_one({'_id': '123'}, {'$inc': {'counter': 1}},
          ...     upsert=True)
          >>> res
          <pymongo.results.UpdateResult object at ...>
        
          >>> res.raw_result
          {'updatedExisting': False, 'nModified': 0, 'ok': 1, 'upserted': '123', 'n': 1}
        
        And we can read from the collection:
        
          >>> collection.find_one({'_id': '123'})
          {u'_id': u'123', u'counter': 1}
        
        Remove the result from our test collection:
        
          >>> res = collection.delete_one({'_id': '123'})
          >>> res
          <pymongo.results.DeleteResult object at ...>
        
          >>> res.raw_result
          {'ok': 1, 'n': 1}
        
        
        tear down
        ---------
        
        Now tear down our MongoDB database with our current MongoDB connection:
        
          >>> import time
          >>> time.sleep(1)
          >>> client.drop_database('m01MongoTesting')
        
        
        ==============
        MongoContainer
        ==============
        
        The MongoContainer can store IMongoContainerItem objects in a MongoDB. A
        MongoContainerItem must be able to dump it's data to valid mongodb data. This
        test will show how our MongoContainer works.
        
        
        Condition
        ---------
        
        First import some components:
        
          >>> import json
          >>> import transaction
          >>> import zope.interface
          >>> import zope.schema
          >>> import m01.mongo.item
          >>> import m01.mongo.testing
          >>> from m01.mongo.fieldproperty import MongoFieldProperty
          >>> from m01.mongo import interfaces
        
        Befor we start testing, check if our thread local cache is empty or if we have
        left over some junk from previous tests:
        
          >>> from m01.mongo import LOCAL
          >>> m01.mongo.testing.pprint(LOCAL.__dict__)
          {}
        
        
        Setup
        -----
        
        And set up a database root:
        
          >>> root = {}
        
        
        MongoContainerItem
        ------------------
        
          >>> class ISampleContainerItem(interfaces.IMongoContainerItem,
          ...     zope.location.interfaces.ILocation):
          ...     """Sample item interface."""
          ...
          ...     title = zope.schema.TextLine(
          ...         title=u'Object Title',
          ...         description=u'Object Title',
          ...         required=True)
        
        
          >>> class SampleContainerItem(m01.mongo.item.MongoContainerItem):
          ...     """Sample container item"""
          ...
          ...     zope.interface.implements(ISampleContainerItem)
          ...
          ...     title = MongoFieldProperty(ISampleContainerItem['title'])
          ...
          ...     dumpNames = ['title']
        
        
        MongoContainer
        --------------
        
          >>> class ISampleContainer(interfaces.IMongoContainer):
          ...     """Sample container interface."""
        
        
          >>> class SampleContainer(m01.mongo.container.MongoContainer):
          ...     """Sample container."""
          ...
          ...     zope.interface.implements(ISampleContainer)
          ...
          ...     @property
          ...     def collection(self):
          ...         db = m01.mongo.testing.getTestDatabase()
          ...         return db['test']
          ...
          ...     def load(self, data):
          ...         """Load data into the right mongo item."""
          ...         return SampleContainerItem(data)
        
          >>> container = SampleContainer()
          >>> root['container'] = container
        
        
        Create an object tree
        ---------------------
        
        Now we can add a sample MongoContainerItem to our container using the mapping
        api:
        
          >>> data = {'title': u'Title'}
          >>> item = SampleContainerItem(data)
          >>> container = root['container']
          >>> container[u'item'] = item
        
        Transaction
        -----------
        
        Zope provides transactions for store objects in the database. We also provide
        such a transaction and a transation data manager for store our objects in the
        mongodb. This means right now nothing get stored in our test database because
        we didn't commit the transaction:
        
          >>> collection = m01.mongo.testing.getTestCollection()
          >>> collection.count()
          0
        
        Let's commit our transaction an store the container item in mongodb:
        
          >>> transaction.commit()
        
          >>> collection = m01.mongo.testing.getTestCollection()
          >>> collection.count()
          1
        
        After commit, the thread local storage is empty:
        
          >>> LOCAL.__dict__
          {}
        
        
        Mongodb data
        ------------
        
        As you can see the following data get stored in our mongodb:
        
          >>> data = collection.find_one({'__name__': 'item'})
          >>> m01.mongo.testing.pprint(data)
          {u'__name__': u'item',
           u'_id': ObjectId('...'),
           u'_pid': None,
           u'_type': u'SampleContainerItem',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'title': u'Title'}
        
        
        Object
        ------
        
        We can get from our container and mongo will load the data from mongodb:
        
          >>> obj = container[u'item']
          >>> obj
          <SampleContainerItem u'item'>
        
          >>> obj.title
          u'Title'
        
        Let's tear down our test setup:
        
          >>> transaction.commit()
          >>> from m01.mongo import clearThreadLocalCache
          >>> clearThreadLocalCache()
        
        As you can see our cache items get removed:
        
          >>> from m01.mongo import LOCAL
          >>> m01.mongo.testing.pprint(LOCAL.__dict__)
          {}
        
        
        ============
        MongoStorage
        ============
        
        The MongoStorage can store IMongoStorageItem objects in a MongoDB. A
        MongoStorageItem must be able to dump it's data to valid mongo values. This
        test will show how our MongoStorage works and also shows the limitations.
        
        Note: the mongo container also implements a container/mapping pattern like the
        storage implementation. The only difference is, the container only provides the
        mapping api using contaner[key] = obj, container[key] and del container[key].
        The storage api provides no explicit mapping key and offers add and remove
        methods instead. This means the container uses it's own naming pattern and the
        storage is using the mongodb._id as it's object name (obj.__name__).
        
        
        Condition
        ---------
        
        Befor we start testing, check if our thread local cache is empty or if we have
        let over some junk from previous tests:
        
          >>> from m01.mongo import LOCAL
          >>> from m01.mongo.testing import pprint
          >>> pprint(LOCAL.__dict__)
          {}
        
        
        Setup
        -----
        
        First import some components:
        
          >>> import datetime
          >>> import transaction
          >>> from zope.container.interfaces import IReadContainer
          >>> from m01.mongo import interfaces
          >>> from m01.mongo import testing
        
        And set up a database root:
        
          >>> root = {}
        
        
        MongoStorageItem
        ----------------
        
        The mongo item provides by default a ObjectId stored as _id. If there is none
        given during create an object, we will set one:
        
          >>> data = {}
          >>> obj = testing.SampleStorageItem(data)
          >>> obj._id
          ObjectId('...')
        
        The ObjectId is also use as our __name__  value. See the MongoContainer and
        MongoContainerItem implementation if you need to choose your own names:
        
          >>> obj.__name__
          u'...'
        
          >>> obj.__name__ == unicode(obj._id)
          True
        
        A mongo item also provides created and modified date attributes. If we
        initialize an object without a given created date, a new utc datetime instance
        get used:
        
          >>> obj.created
          datetime.datetime(..., tzinfo=UTC)
        
          >>> obj.modified is None
          True
        
        A mongo storage item knows if a state get changed. This means we can find out
        if we should write the item back to the MongoDB. The MongoItem stores the state
        in a _m_changed value like persistent objects do in _p_changed. As you can see
        the initial state is ```None``:
        
          >>> obj._m_changed is None
          True
        
        The MongoItem also has a version number which we increment each time we change
        the item. By default this version is set as _version attribute and set by
        default to 0 (zero):
        
          >>> obj._version
          0
        
        If we change a value in a MongoItem, the state get changed:
        
          >>> obj.title = u'New Title'
          >>> obj._m_changed
          True
        
        but the version get not imcremented. We only imcrement the version if we save
        the item in MongoDB:
        
          >>> obj._version
          0
        
        We also change the _m_change marker if we remove a value:
        
          >>> obj = testing.SampleStorageItem(data)
          >>> obj._m_changed is None
          True
        
          >>> obj.title
          u''
        
          >>> obj.title = u'New Title'
          >>> obj._m_changed
          True
        
          >>> obj.title
          u'New Title'
        
        Now let's set the _m_chande property set to False before we delete the attr:
        
          >>> obj._m_changed = False
          >>> obj._m_changed
          False
        
          >>> del obj.title
        
        As you can see we can delete an attribute but it only falls back to the default
        schema field value. This seems fine.
        
          >>> obj.title
          u''
        
          >>> obj._m_changed
          True
        
        
        MongoStorage
        ------------
        
        Now we can add a MongoStorage to the zope datbase:
        
          >>> storage = testing.SampleStorage()
          >>> root['storage'] = storage
          >>> transaction.commit()
        
        Now we can add a sample MongoStorageItem to our storage. Note we can only use the
        add method which will return the new generated __name__. Using own names is not
        supported by this implementation. As you can see the name is an MongoDB
        24 hex character string objectId representation.
        
          >>> data = {'title': u'Title',
          ...         'description': u'Description'}
          >>> item = testing.SampleStorageItem(data)
          >>> storage = root['storage']
        
        Our storage provides the IMongoStorage and IReadContainer interfaces:
        
          >>> interfaces.IMongoStorage.providedBy(storage)
          True
        
          >>> IReadContainer.providedBy(storage)
          True
        
        
        add
        ---
        
        We can add a mongo item to our storage by using the add method.
        
          >>> __name__ = storage.add(item)
          >>> __name__
          u'...'
          >>> len(__name__)
          24
        
          >>> transaction.commit()
        
        After adding our item, the item provides a created date:
        
          >>> item.created
          datetime.datetime(..., tzinfo=UTC)
        
        
        __len__
        -------
        
          >>> storage = root['storage']
          >>> len(storage)
          1
        
        
        __getitem__
        -----------
        
          >>> item = storage[__name__]
          >>> item
          <SampleStorageItem ...>
        
        As you can see our MongoStorageItem provides the following data. We can dump
        the item. Note, you probaly have to implement a custom dump method which will
        dump the right data for you MongoStorageItem.
        
          >>> pprint(item.dump())
          {'__name__': '...',
           '_id': ObjectId('...'),
           '_pid': None,
           '_type': 'SampleStorageItem',
           '_version': 1,
           'comments': [],
           'created': datetime.datetime(..., tzinfo=UTC),
           'date': None,
           'description': 'Description',
           'item': None,
           'modified': datetime.datetime(..., tzinfo=UTC),
           'number': None,
           'numbers': [],
           'title': 'Title'}
        
        The object provides also a name which is the name we've got during adding the
        object:
        
          >>> item.__name__ == __name__
          True
        
        
        keys
        ----
        
        The container can also return key:
        
          >>> tuple(storage.keys())
          (u'...',)
        
        
        values
        ------
        
        The container can also return values:
        
          >>> tuple(storage.values())
          (<SampleStorageItem ...>,)
        
        items
        -----
        
        The container can also return items:
        
          >>> tuple(storage.items())
          ((u'...', <SampleStorageItem ...>),)
        
        
        __delitem__
        -----------
        
        As next we will remove the item:
        
          >>> del storage[__name__]
          >>> storage.get(__name__) is None
          True
        
          >>> transaction.commit()
        
        
        Object modification
        -------------------
        
        If we get a mongo item from a storage and modify the item, the version get
        increased by one and a current modified datetime get set.
        
        Let's add a new item:
        
          >>> data = {'title': u'A Title',
          ...         'description': u'A Description'}
          >>> item = testing.SampleStorageItem(data)
          >>> __name__ = storage.add(item)
          >>> transaction.commit()
        
        Now get the item::
        
          >>> item = storage[__name__]
          >>> item.title
          u'A Title'
        
        and change the titel:
        
          >>> item.title = u'New Title'
          >>> item.title
          u'New Title'
        
        As you can see the item get marked as changed:
        
          >>> item._m_changed
          True
        
        Now get the mongo item version. This should be set to 1 (one) since we only
        added the object and didn't change since we added them:
        
          >>> item._version
          1
        
        If we now commit the transaction, the version get increased by one:
        
          >>> transaction.commit()
          >>> item._version
          2
        
        If you now load the mongo item from the MongoDB aain, you can see that the
        title get changed:
        
          >>> item = storage[__name__]
          >>> item.title
          u'New Title'
        
        And that the version get updated to 2:
        
          >>> item._version
          2
        
          >>> transaction.commit()
        
        Check our thread local cache before we leave this test:
        
          >>> pprint(LOCAL.__dict__)
          {}
        
        
        =====================
        Shared MongoContainer
        =====================
        
        The MongoContainer can store non persistent IMongoContainerItem objects in a
        MongoDB. A MongoContainerItem must be able to dump it's data to valid mongo
        values. This test will show how our MongoContainer works.
        
        
        Condition
        ---------
        
        Befor we start testing, check if our thread local cache is empty or if we have
        let over some junk from previous tests:
        
          >>> from m01.mongo.testing import pprint
          >>> from m01.mongo import LOCAL
          >>> pprint(LOCAL.__dict__)
          {}
        
        
        Setup
        -----
        
        First import some components:
        
          >>> import datetime
          >>> import transaction
          >>> from zope.container.interfaces import IContainer
        
          >>> import m01.mongo
          >>> import m01.mongo.base
          >>> import m01.mongo.container
          >>> from m01.mongo import interfaces
          >>> from m01.mongo import testing
        
        We also need a application root object. Let's define a static MongoContainer
        as our application database root item.
        
          >>> class MongoRoot(m01.mongo.container.MongoContainer):
          ...     """Mongo application root"""
          ...
          ...     _id = m01.mongo.getObjectId(0)
          ...
          ...     def __init__(self):
          ...         pass
          ...
          ...     @property
          ...     def collection(self):
          ...         return testing.getRootItems()
          ...
          ...     @property
          ...     def cacheKey(self):
          ...         return 'root'
          ...
          ...     def load(self, data):
          ...         """Load data into the right mongo item."""
          ...         return testing.Companies(data)
          ...
          ...     def __repr__(self):
          ...         return '<%s %s>' % (self.__class__.__name__, self._id)
        
        
        As you can see our MongoRoot class defines a static mongo ObjectID as _id. This
        means the same _id get use every time. This _id acts as our __parent__
        reference.
        
        The following method allows us to generate new MongoRoot item instances. This
        allows us to show that we generate different root items like we would do on a
        server restart.
        
          >>> def getRoot():
          ...     return MongoRoot()
        
        Here is our database root item:
        
          >>> root = getRoot()
          >>> root
          <MongoRoot 000000000000000000000000>
        
          >>> root._id
          ObjectId('000000000000000000000000')
        
        
        Containers
        ----------
        
        Now let's use our enhanced testing data and setup a content structure:
        
          >>> data = {'name': u'Europe'}
          >>> europe = testing.Companies(data)
          >>> root[u'europe'] = europe
        
          >>> data = {'name': u'Asia'}
          >>> asia = testing.Companies(data)
          >>> root[u'asia'] = asia
        
          >>> transaction.commit()
        
        Let's check our companies in Mongo:
        
          >>> rootCollection = testing.getRootItems()
          >>> obj = rootCollection.find_one({'name': 'Europe'})
          >>> pprint(obj)
          {u'__name__': u'europe',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'Companies',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'Europe'}
        
        Now let's add a Company, Employer and some documents:
        
          >>> data = {'name': u'Projekt01 GmbH'}
          >>> pro = testing.Company(data)
          >>> europe[u'pro'] = pro
        
          >>> data = {'name': u'Roger Ineichen'}
          >>> roger = testing.Employer(data)
          >>> pro[u'roger'] = roger
        
          >>> data = {'name': u'Manual'}
          >>> manual = testing.Document(data)
          >>> roger[u'manual'] = manual
        
          >>> transaction.commit()
        
        As you can see we added a data structure using our container, item objects:
        
          >>> root['europe']
          <Companies u'europe'>
        
          >>> root['europe']['pro']
          <Company u'pro'>
        
          >>> root['europe']['pro']['roger']
          <Employer u'roger'>
        
          >>> root['europe']['pro']['roger']['manual']
          <Document u'manual'>
        
        As you can see this structure is related to their __parent__ references. This
        means if we add another structure into the same mongodb, each item knows it's
        container.
        
          >>> data = {'name': u'Credit Suisse'}
          >>> cs = testing.Company(data)
          >>> asia[u'cs'] = cs
        
          >>> data = {'name': u'Max Muster'}
          >>> max = testing.Employer(data)
          >>> cs[u'max'] = max
        
          >>> data = {'name': u'Paper'}
          >>> paper = testing.Document(data)
          >>> max[u'paper'] = paper
        
          >>> transaction.commit()
        
          >>> root['asia']
          <Companies u'asia'>
        
          >>> root['asia']['cs']
          <Company u'cs'>
        
          >>> root['asia']['cs']['max']
          <Employer u'max'>
        
          >>> root['asia']['cs']['max']['paper']
          <Document u'paper'>
        
        We can't access another item from the same type from another parent container:
        
          >>> root['europe']['cs']
          Traceback (most recent call last):
          ...
          KeyError: 'cs'
        
          >>> transaction.commit()
        
        As you can see the KeyError left items back in our thread local cache. We can
        use our thread local cache cleanup event handler which is by default registered
        as an EndRequestEvent subscriber for cleanup our thread local cache:
        
          >>> pprint(LOCAL.__dict__)
          {u'europe': {'loaded': {}, 'removed': {}}}
        
        Let's use our subscriber:
        
          >>> from m01.mongo import clearThreadLocalCache
          >>> clearThreadLocalCache()
        
        As you can see our cache items get removed:
        
          >>> from m01.mongo import LOCAL
          >>> pprint(LOCAL.__dict__)
          {}
        
        
        Shared Container
        ----------------
        
        Now let's implement a shared container which contains all IEmployer items:
        
          >>> class SharedEployers(m01.mongo.container.MongoContainer):
          ...     """Shared Employer container"""
          ...
          ...     # mark a container as shared by set the _mpid to None
          ...     _mpid = None
          ...
          ...     @property
          ...     def collection(self):
          ...         return testing.getEmployers()
          ...
          ...     def load(self, data):
          ...         return testing.Employer(data)
        
        Now let's try if the shared container can access all Employer items:
        
          >>> shared = SharedEployers()
          >>> pprint(tuple(shared.items()))
          ((u'roger', <Employer u'roger'>), (u'max', <Employer u'max'>))
        
          >>> for obj in shared.values():
          ...     pprint(obj.dump())
          {'__name__': u'roger',
           '_id': ObjectId('...'),
           '_pid': ObjectId('...'),
           '_type': u'Employer',
           '_version': 1,
           'created': datetime.datetime(..., tzinfo=UTC),
           'modified': datetime.datetime(..., tzinfo=UTC),
           'name': u'Roger Ineichen'}
          {'__name__': u'max',
           '_id': ObjectId('...'),
           '_pid': ObjectId('...'),
           '_type': u'Employer',
           '_version': 1,
           'created': datetime.datetime(..., tzinfo=UTC),
           'modified': datetime.datetime(..., tzinfo=UTC),
           'name': u'Max Muster'}
        
        Now commit our transaction which will cleanup our caches. Database cleanup is
        done in our test teardown:
        
          >>> transaction.commit()
        
        Check our thread local cache before we leave this test:
        
          >>> pprint(LOCAL.__dict__)
          {}
        
        
        ===========
        MongoObject
        ===========
        
        A MongoObject can get stored independent from anything else in a MongoDB. Such
        MongoObject can get used together with a field property called
        MongoOjectProperty. The field property is responsible for set and get such
        MongoObject to and from MongoDB. A persistent item which provides such a
        MongoObject within a MongoObjectProperty only has to provide an oid attribute
        with a unique value. You can use the m01.oid package for such a unique oid
        or implement an own pattern.
        
        The MongoObject uses the __parent__._moid and the attribute (field) name as
        it's unique MongoDB key.
        
        Note, this test uses a fake MongoDB server setup. But this fake server is far
        away from beeing complete. We will add more feature to this fake server if we
        need them in other projects. See testing.py for more information.
        
        
        Condition
        ---------
        
        Befor we start testing, check if our thread local cache is empty or if we have
        let over some junk from previous tests:
        
          >>> from m01.mongo.testing import pprint
          >>> from m01.mongo import LOCAL
          >>> pprint(LOCAL.__dict__)
          {}
        
        
        Setup
        -----
        
        First import some components:
        
          >>> import datetime
          >>> import transaction
          >>> from m01.mongo import interfaces
          >>> from m01.mongo import testing
        
        First, we need to setup a persistent object:
        
          >>> content = testing.Content(42)
          >>> content._moid
          42
        
        And add them to the ZODB:
        
          >>> root = {}
          >>> root['content'] = content
          >>> transaction.commit()
        
          >>> content = root['content']
          >>> content
          <Content 42>
        
        
        MongoObject
        -----------
        
        Now let's add a MongoObject instance to our sample content object:
        
          >>> data = {'title': u'Mongo Object Title',
          ...         'description': u'A Description',
          ...         'item': {'text':u'Item'},
          ...         'date': datetime.date(2010, 2, 28).toordinal(),
          ...         'numbers': [1,2,3],
          ...         'comments': [{'text':u'Comment 1'}, {'text':u'Comment 2'}]}
          >>> obj = testing.SampleMongoObject(data)
          >>> obj._id
          ObjectId('...')
        
          obj.title
          u'Mongo Object Title'
        
          >>> obj.description
          u'A Description'
        
          >>> obj.item
          <SampleSubItem u'...'>
        
          >>> obj.item.text
          u'Item'
        
          >>> obj.numbers
          [1, 2, 3]
        
          >>> obj.comments
          [<SampleSubItem u'...'>, <SampleSubItem u'...'>]
        
          >>> tuple(obj.comments)[0].text
          u'Comment 1'
        
          >>> tuple(obj.comments)[1].text
          u'Comment 2'
        
        Our MongoObject doesn't provide a _aprent__ or __name__ right now:
        
          >>> obj.__parent__ is None
          True
        
          >>> obj.__name__ is None
          True
        
        But after adding the mongo object to our content which uses a
        MongoObjectProperty, the mongo object get located and becomes the attribute
        name as _field value. If the object didn't provide a __name__, the same value
        will also get applied for __name__:
        
          >>> content.obj = obj
          >>> obj.__parent__
          <Content 42>
        
          >>> obj.__name__
          u'obj'
        
          >>> obj.__name__
          u'obj'
        
        After adding our mongo object, there should be a reference in our thread local
        cache:
        
          >>> pprint(LOCAL.__dict__)
          {u'42:obj': <SampleMongoObject u'obj'>,
           'MongoTransactionDataManager': <m01.mongo.tm.MongoTransactionDataManager object at ...>}
        
        A MongoObject provides a _oid attribute which is used as the MongoDB key. This
        value uses the __parent__._moid and the mongo objects attribute name:
        
          >>> obj._oid == '%s:%s' % (content._moid, obj.__name__)
          True
        
          >>> obj._oid
          u'42:obj'
        
        Now check if we can get the mongo object again and if we still get the same
        values:
        
          >>> obj = content.obj
          >>> obj.title
          u'Mongo Object Title'
        
          >>> obj.description
          u'A Description'
        
          >>> obj.item
          <SampleSubItem u'...'>
        
          >>> obj.item.text
          u'Item'
        
          >>> obj.numbers
          [1, 2, 3]
        
          >>> obj.comments
          [<SampleSubItem u'...'>, <SampleSubItem u'...'>]
        
          >>> tuple(obj.comments)[0].text
          u'Comment 1'
        
          >>> tuple(obj.comments)[1].text
          u'Comment 2'
        
        Now let's commit the transaction which will store the obj in our fake mongo DB:
        
          >>> transaction.commit()
        
        After we commited to the MongoDB, the mongo object and our transaction data
        manger reference should be gone in the thread local cache:
        
          >>> pprint(LOCAL.__dict__)
          {}
        
        Now check our mongo object values again. If your content item is stored in a
        ZODB, you would get the content item from a ZODB connection root:
        
          >>> content = root['content']
          >>> content
          <Content 42>
        
          >>> obj = content.obj
          >>> obj
          <SampleMongoObject u'obj'>
        
          >>> obj.title
          u'Mongo Object Title'
        
          >>> obj.description
          u'A Description'
        
          >>> obj.item
          <SampleSubItem u'...'>
        
          >>> obj.item.text
          u'Item'
        
          >>> obj.numbers
          [1, 2, 3]
        
          >>> obj.comments
          [<SampleSubItem u'...'>, <SampleSubItem u'...'>]
        
          >>> tuple(obj.comments)[0].text
          u'Comment 1'
        
          >>> tuple(obj.comments)[1].text
          u'Comment 2'
        
          >>> pprint(obj.dump())
          {'__name__': u'obj',
           '_field': u'obj',
           '_id': ObjectId('...'),
           '_oid': u'42:obj',
           '_type': u'SampleMongoObject',
           '_version': 1,
           'comments': [{'_id': ObjectId('...'),
                         '_type': u'SampleSubItem',
                         'created': datetime.datetime(...),
                         'modified': None,
                         'text': u'Comment 1'},
                        {'_id': ObjectId('...'),
                         '_type': u'SampleSubItem',
                         'created': datetime.datetime(...),
                         'modified': None,
                         'text': u'Comment 2'}],
           'created': datetime.datetime(...),
           'date': 733831,
           'description': u'A Description',
           'item': {'_id': ObjectId('...'),
                    '_type': u'SampleSubItem',
                    'created': datetime.datetime(...),
                    'modified': None,
                    'text': u'Item'},
           'modified': datetime.datetime(...),
           'number': None,
           'numbers': [1, 2, 3],
           'removed': False,
           'title': u'Mongo Object Title'}
        
          >>> transaction.commit()
        
          >>> pprint(LOCAL.__dict__)
          {}
        
        Now let's replace the existing item with a new one and add another item to
        the item lists. Also make sure we can use append instead of re-apply the full
        list like zope widgets do:
        
          >>> content = root['content']
          >>> obj = content.obj
        
          >>> obj.item = testing.SampleSubItem({'text': u'New Item'})
        
          >>> newItem = testing.SampleSubItem({'text': u'New List Item'})
          >>> obj.comments.append(newItem)
        
          >>> obj.numbers.append(4)
        
          >>> transaction.commit()
        
        check again:
        
          >>> content = root['content']
          >>> obj = content.obj
        
          >>> obj.title
          u'Mongo Object Title'
        
          >>> obj.description
          u'A Description'
        
          >>> obj.item
          <SampleSubItem u'...'>
        
          >>> obj.item.text
          u'New Item'
        
          >>> obj.numbers
          [1, 2, 3, 4]
        
          >>> obj.comments
          [<SampleSubItem u'...'>, <SampleSubItem u'...'>]
        
          >>> tuple(obj.comments)[0].text
          u'Comment 1'
        
          >>> tuple(obj.comments)[1].text
          u'Comment 2'
        
        And now re-apply a full list of values to the list field:
        
          >>> comOne = testing.SampleSubItem({'text': u'First List Item'})
          >>> comTwo = testing.SampleSubItem({'text': u'Second List Item'})
          >>> comments = [comOne, comTwo]
          >>> obj.comments = comments
          >>> obj.numbers = [1,2,3,4,5]
          >>> transaction.commit()
        
        check again:
        
          >>> content = root['content']
          >>> obj = content.obj
        
          >>> len(obj.comments)
          2
        
          >>> obj.comments
          [<SampleSubItem u'...'>, <SampleSubItem u'...'>]
        
          >>> len(obj.numbers)
          5
        
          >>> obj.numbers
          [1, 2, 3, 4, 5]
        
        Also check if we can remove list items:
        
          >>> obj.numbers.remove(1)
          >>> obj.numbers.remove(2)
        
          >>> obj.comments.remove(comTwo)
        
          >>> transaction.commit()
        
        check again:
        
          >>> content = root['content']
          >>> obj = content.obj
        
          >>> len(obj.comments)
          1
        
          >>> obj.comments
          [<SampleSubItem u'...'>]
        
          >>> len(obj.numbers)
          3
        
          >>> obj.numbers
          [3, 4, 5]
        
          >>> transaction.commit()
        
        We can also remove items from the item list by it's __name__:
        
          >>> content = root['content']
          >>> obj = content.obj
        
          >>> del obj.comments[comOne.__name__]
        
          >>> transaction.commit()
        
        check again:
        
          >>> content = root['content']
          >>> obj = content.obj
        
          >>> len(obj.comments)
          0
        
          >>> obj.comments
          []
        
          >>> transaction.commit()
        
        Or we can add items to the item list by name:
        
          >>> content = root['content']
          >>> obj = content.obj
        
          >>> obj.comments[comOne.__name__] = comOne
        
          >>> transaction.commit()
        
        check again:
        
          >>> content = root['content']
          >>> obj = content.obj
        
          >>> len(obj.comments)
          1
        
          >>> obj.comments
          [<SampleSubItem u'...'>]
        
          >>> transaction.commit()
        
        
        Coverage
        --------
        
        Our items list also provides the following methods:
        
          >>> obj.comments.__contains__(comOne.__name__)
          True
        
          >>> comOne.__name__ in obj.comments
          True
        
          >>> obj.comments.get(comOne.__name__)
          <SampleSubItem u'...'>
        
          >>> obj.comments.keys() == [comOne.__name__]
          True
        
          >>> obj.comments.values()
          <generator object ...>
        
          >>> tuple(obj.comments.values())
          (<SampleSubItem u'...'>,)
        
          >>> obj.comments.items()
          <generator object ...>
        
          >>> tuple(obj.comments.items())
          ((u'...', <SampleSubItem u'...'>),)
        
          >>> obj.comments == obj.comments
          True
        
        Let's test some internals for increase coverage:
        
          >>> obj.comments._m_changed
          Traceback (most recent call last):
          ...
          AttributeError: _m_changed is a write only property
        
          >>> obj.comments._m_changed = False
          Traceback (most recent call last):
          ...
          ValueError: Can only dispatch True to __parent__
        
          >>> obj.comments.locate(42)
        
        Our simple value typ list also provides the following methods:
        
          >>> obj.numbers.__contains__(3)
          True
        
          >>> 3 in obj.numbers
          True
        
          >>> obj.numbers == obj.numbers
          True
        
          >>> obj.numbers.pop()
          5
        
          >>> del obj.numbers[0]
        
          >>> obj.numbers[0] = 42
        
          >>> obj.numbers._m_changed
          Traceback (most recent call last):
          ...
          AttributeError: _m_changed is a write only property
        
          >>> obj.numbers._m_changed = False
          Traceback (most recent call last):
          ...
          ValueError: Can only dispatch True to __parent__
        
        Check our thread local cache before we leave this test:
        
          >>> pprint(LOCAL.__dict__)
          {}
        
        
        ===========
        GeoLocation
        ===========
        
        The GeoLocation item can store a geo location and is used in an item as
        a kind of sub item providing longitude and latitude. Additional to this
        fields a GeoLocation provides the _m_changed dispatching concept and is able
        to notify the __parent__ item if lon/lat get changed. The item also provides
        ILocation for security lookup support. The field property is responsible for
        apply a __parent__ and __name__.
        
        The GeoLocation item supports the order longitude, latitude and preserves them.
        
        
        Condition
        ---------
        
        Befor we start testing, check if our thread local cache is empty or if we have
        let over some junk from previous tests:
        
          >>> from m01.mongo.testing import pprint
          >>> from m01.mongo import LOCAL
          >>> from m01.mongo.testing import reNormalizer
          >>> pprint(LOCAL.__dict__)
          {}
        
        
        Setup
        -----
        
        First import some components:
        
          >>> import datetime
          >>> import transaction
        
          >>> import m01.mongo
          >>> import m01.mongo.base
          >>> import m01.mongo.geo
          >>> import m01.mongo.container
          >>> from m01.mongo import interfaces
          >>> from m01.mongo import testing
        
        We also need a application root object. Let's define a static MongoContainer
        as our application database root item.
        
          >>> class MongoRoot(m01.mongo.container.MongoContainer):
          ...     """Mongo application root"""
          ...
          ...     _id = m01.mongo.getObjectId(0)
          ...
          ...     def __init__(self):
          ...         pass
          ...
          ...     @property
          ...     def collection(self):
          ...         return testing.getRootItems()
          ...
          ...     @property
          ...     def cacheKey(self):
          ...         return 'root'
          ...
          ...     def load(self, data):
          ...         """Load data into the right mongo item."""
          ...         return testing.GeoSample(data)
          ...
          ...     def __repr__(self):
          ...         return '<%s %s>' % (self.__class__.__name__, self._id)
        
        
        The following method allows us to generate new MongoRoot item instances. This
        allows us to show that we generate different root items like we would do on a
        server restart.
        
          >>> def getRoot():
          ...     return MongoRoot()
        
        Here is our database root item:
        
          >>> root = getRoot()
          >>> root
          <MongoRoot 000000000000000000000000>
        
          >>> root._id
          ObjectId('000000000000000000000000')
        
        
        indexing
        --------
        
        First setup an index:
        
          >>> collection = testing.getRootItems()
        
          >>> from pymongo import GEO2D
          >>> collection.create_index([('lonlat', GEO2D)])
          u'lonlat_2d'
        
        
        GeoSample
        ---------
        
        As you can see, we can initialize a GeoLocation within a list of lon/lat values
        or within a lon/lat dict:
        
          >>> data = {'name': u'sample', 'lonlat': {'lon': 1, 'lat': 3}}
          >>> sample = testing.GeoSample(data)
          >>> sample.lonlat
          <GeoLocation lon:1.0, lat:3.0>
        
          >>> data = {'name': u'sample', 'lonlat': [1, 3]}
          >>> sample = testing.GeoSample(data)
          >>> sample.lonlat
          <GeoLocation lon:1.0, lat:3.0>
        
          >>> root[u'sample'] = sample
        
          >>> transaction.commit()
        
        Let's check our item in Mongo:
        
          >>> data = collection.find_one({'name': 'sample'})
          >>> reNormalizer.pprint(data)
          {u'__name__': u'sample',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': [1.0, 3.0],
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
        
        We can also use a GeoLocation as lonlat data:
        
          >>> geo = m01.mongo.geo.GeoLocation({u'lat': 4, u'lon': 2})
          >>> data = {'name': u'sample2', 'lonlat': geo}
          >>> sample2 = testing.GeoSample(data)
          >>> root[u'sample2'] = sample2
        
          >>> transaction.commit()
        
          >>> data = collection.find_one({'name': 'sample2'})
          >>> reNormalizer.pprint(data)
          {u'__name__': u'sample2',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'lat': 4.0, u'lon': 2.0},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample2'}
        
        
        We can also set a GeoLocation as lonlat value:
        
          >>> sample2 = root[u'sample2']
          >>> geo = m01.mongo.geo.GeoLocation({'lon': 4, 'lat': 6})
          >>> sample2.lonlat = geo
        
          >>> transaction.commit()
        
          >>> data = collection.find_one({'name': 'sample2'})
          >>> reNormalizer.pprint(data)
          {u'__name__': u'sample2',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoSample',
           u'_version': 2,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'lat': 6.0, u'lon': 4.0},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample2'}
        
        
        search
        ------
        
        Let's test some geo location search query and make sure our lon/lat order
        will fit and get preserved during the mongodb roundtrip.
        
        Now seearch for a geo location:
        
          >>> def printFind(collection, query):
          ...     for data in collection.find(query):
          ...         reNormalizer.pprint(data)
        
        Using the geospatial index we can find documents near another point:
        
          >>> printFind(collection, {'lonlat': {'$near': [0, 2]}})
          {u'__name__': u'sample',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': [1.0, 3.0],
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
          {u'__name__': u'sample2',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoSample',
           u'_version': 2,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'lat': 6.0, u'lon': 4.0},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample2'}
        
        It's also possible to query for all items within a given rectangle
        (specified by lower-left and upper-right coordinates):
        
          >>> printFind(collection, {'lonlat': {'$within': {'$box': [[1,2], [2,3]]}}})
          {u'__name__': u'sample',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': [1.0, 3.0],
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
        
        As you can see if we use the wrong order for lon/lat (lat/lon), we will not
        get a value:
        
          >>> printFind(collection, {'lonlat': {'$within': {'$box': [[10,20], [20,30]]}}})
        
        We can also search for a circle (specified by center point and radius):
        
          >>> printFind(collection, {'lonlat': {'$within': {'$center': [[0, 0], 2]}}})
        
          >>> printFind(collection, {'lonlat': {'$within': {'$center': [[0, 0], 4]}}})
          {u'__name__': u'sample',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': [1.0, 3.0],
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
        
          >>> printFind(collection, {'lonlat': {'$within': {'$center': [[0, 0], 10]}}})
          {u'__name__': u'sample',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': [1.0, 3.0],
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
          {u'__name__': u'sample2',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoSample',
           u'_version': 2,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'lat': 6.0, u'lon': 4.0},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample2'}
        
        Also check if the lat/lon order matters:
        
          >>> printFind(collection, {'lonlat': {'$within': {'$center': [[1, 2], 1]}}})
          {u'__name__': u'sample',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': [1.0, 3.0],
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
        
          >>> printFind(collection, {'lonlat': {'$within': {'$center': [[2, 1], 1]}}})
        
        
        And check if we can store real lon/lat values by using a float:
        
          >>> data = {'name': u'sample', 'lonlat': {'lon': 20.123, 'lat': 29.123}}
          >>> sample3 = testing.GeoSample(data)
          >>> root[u'sample3'] = sample3
        
          >>> transaction.commit()
        
          >>> printFind(collection, {'lonlat': {'$within': {'$center': [[25, 25], 4]}}})
        
          >>> printFind(collection, {'lonlat': {'$within': {'$center': [[25, 25], 10]}}})
          {u'__name__': u'sample3',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'lat': 29.123, u'lon': 20.123},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
        
        
        tear down
        ---------
        
          >>> from m01.mongo import clearThreadLocalCache
          >>> clearThreadLocalCache()
        
        As you can see our cache items get removed:
        
          >>> from m01.mongo import LOCAL
          >>> pprint(LOCAL.__dict__)
          {}
        
        
        ========
        GeoPoint
        ========
        
        The GeoPoint item can store a geo location and is used in an item as
        a kind of sub item providing longitude and latitude and type. Additional to this
        fields a GeoPoint provides the _m_changed dispatching concept and is able
        to notify the __parent__ item if lon/lat get changed. The item also provides
        ILocation for security lookup support. The MongoGeoPointProperty field property
        is responsible for apply a __parent__ and __name__ and use the right class
        factory.
        
        The GeoPoint item supports the order longitude, latitude and preserves them.
        
        
        Condition
        ---------
        
        Befor we start testing, check if our thread local cache is empty or if we have
        let over some junk from previous tests:
        
          >>> from m01.mongo.testing import pprint
          >>> from m01.mongo import LOCAL
          >>> from m01.mongo.testing import reNormalizer
          >>> pprint(LOCAL.__dict__)
          {}
        
        
        Setup
        -----
        
        First import some components:
        
          >>> import datetime
          >>> import transaction
        
          >>> import m01.mongo
          >>> import m01.mongo.base
          >>> import m01.mongo.geo
          >>> import m01.mongo.container
          >>> from m01.mongo import interfaces
          >>> from m01.mongo import testing
        
        We also need a application root object. Let's define a static MongoContainer
        as our application database root item.
        
          >>> class MongoRoot(m01.mongo.container.MongoContainer):
          ...     """Mongo application root"""
          ...
          ...     _id = m01.mongo.getObjectId(0)
          ...
          ...     def __init__(self):
          ...         pass
          ...
          ...     @property
          ...     def collection(self):
          ...         return testing.getRootItems()
          ...
          ...     @property
          ...     def cacheKey(self):
          ...         return 'root'
          ...
          ...     def load(self, data):
          ...         """Load data into the right mongo item."""
          ...         return testing.GeoPointSample(data)
          ...
          ...     def __repr__(self):
          ...         return '<%s %s>' % (self.__class__.__name__, self._id)
        
        
        The following method allows us to generate new MongoRoot item instances. This
        allows us to show that we generate different root items like we would do on a
        server restart.
        
          >>> def getRoot():
          ...     return MongoRoot()
        
        Here is our database root item:
        
          >>> root = getRoot()
          >>> root
          <MongoRoot 000000000000000000000000>
        
          >>> root._id
          ObjectId('000000000000000000000000')
        
        
        indexing
        --------
        
        First setup an index:
        
          >>> collection = testing.getRootItems()
        
          >>> from pymongo import GEOSPHERE
          >>> collection.create_index([('lonlat', GEOSPHERE)])
          u'lonlat_2dsphere'
        
        
        GeoPointSample
        --------------
        
        As you can see, we can initialize a GeoPoint within a list of lon/lat values
        or within a lon/lat dict:
        
          >>> data = {'name': u'sample', 'lonlat': {'lon': 1, 'lat': 3}}
          >>> sample = testing.GeoPointSample(data)
          >>> sample.lonlat
          <GeoPoint lon:1.0, lat:3.0>
        
          >>> data = {'name': u'sample', 'lonlat': [1, 3]}
          >>> sample = testing.GeoPointSample(data)
          >>> sample.lonlat
          <GeoPoint lon:1.0, lat:3.0>
        
          >>> root[u'sample'] = sample
        
          >>> transaction.commit()
        
        Let's check our item in Mongo:
        
          >>> data = collection.find_one({'name': 'sample'})
          >>> reNormalizer.pprint(data)
          {u'__name__': u'sample',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoPointSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'coordinates': [1.0, 3.0], u'type': u'Point'},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
        
        We can also use a GeoPoint as lonlat data:
        
          >>> geo = m01.mongo.geo.GeoPoint({u'lat': 4, u'lon': 2})
          >>> data = {'name': u'sample2', 'lonlat': geo}
          >>> sample2 = testing.GeoPointSample(data)
          >>> root[u'sample2'] = sample2
        
          >>> transaction.commit()
        
          >>> data = collection.find_one({'name': 'sample2'})
          >>> reNormalizer.pprint(data)
          {u'__name__': u'sample2',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoPointSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'coordinates': [2.0, 4.0], u'type': u'Point'},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample2'}
        
        
        We can also set a GeoPoint as lonlat value:
        
          >>> sample2 = root[u'sample2']
          >>> geo = m01.mongo.geo.GeoPoint({'lon': 4, 'lat': 6})
          >>> sample2.lonlat = geo
        
          >>> transaction.commit()
        
          >>> data = collection.find_one({'name': 'sample2'})
          >>> reNormalizer.pprint(data)
          {u'__name__': u'sample2',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoPointSample',
           u'_version': 2,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'coordinates': [4.0, 6.0], u'type': u'Point'},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample2'}
        
        
        index
        -----
        
          >>> pprint(collection.index_information())
          {'_id_': {'key': [('_id', 1)], 'ns': 'm01_mongo_testing.items', 'v': 1},
           'lonlat_2dsphere': {'2dsphereIndexVersion': 2,
                                'key': [('lonlat', '2dsphere')],
                                'ns': 'm01_mongo_testing.items',
                                'v': 1}}
        
        
        search
        ------
        
        Let's test some geo location search query and make sure our lon/lat order
        will fit and get preserved during the mongodb roundtrip.
        
        Now seearch for a geo location:
        
          >>> def printFind(collection, query):
          ...     for data in collection.find(query):
          ...         reNormalizer.pprint(data)
        
        Using the geospatial index we can find documents within another point:
        
          >>> point = {"type": "Polygon",
          ...          "coordinates": [[[0,0], [0,6], [2,6], [2,0], [0,0]]]}
          >>> query = {"lonlat": {"$within": {"$geometry": point}}}
          >>> printFind(collection, query)
          {u'__name__': u'sample',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoPointSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'coordinates': [1.0, 3.0], u'type': u'Point'},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
        
        Using the geospatial index we can find documents near another point:
        
          >>> point = {'type': 'Point', 'coordinates': [0, 2]}
          >>> printFind(collection, {'lonlat': {'$near': {'$geometry': point}}})
          {u'__name__': u'sample',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoPointSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'coordinates': [1.0, 3.0], u'type': u'Point'},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
          {u'__name__': u'sample2',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoPointSample',
           u'_version': 2,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'coordinates': [4.0, 6.0], u'type': u'Point'},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample2'}
        
        It's also possible to query for all items within a given rectangle
        (specified by lower-left and upper-right coordinates):
        
          >>> printFind(collection, {'lonlat': {'$within': {'$box': [[1,2], [2,3]]}}})
          {u'__name__': u'sample',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoPointSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'coordinates': [1.0, 3.0], u'type': u'Point'},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
        
        As you can see if we use the wrong order for lon/lat (lat/lon), we will not
        get a value:
        
          >>> printFind(collection, {'lonlat': {'$within': {'$box': [[2,1], [3,2]]}}})
        
        We can also search for a circle (specified by center point and radius):
        
          >>> printFind(collection, {'lonlat': {'$within': {'$center': [[0, 0], 2]}}})
        
          >>> printFind(collection, {'lonlat': {'$within': {'$center': [[0, 0], 4]}}})
          {u'__name__': u'sample',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoPointSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'coordinates': [1.0, 3.0], u'type': u'Point'},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
        
          >>> printFind(collection, {'lonlat': {'$within': {'$center': [[0, 0], 10]}}})
          {u'__name__': u'sample',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoPointSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'coordinates': [1.0, 3.0], u'type': u'Point'},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
          {u'__name__': u'sample2',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoPointSample',
           u'_version': 2,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'coordinates': [4.0, 6.0], u'type': u'Point'},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample2'}
        
        Also check if the lat/lon order matters:
        
          >>> printFind(collection, {'lonlat': {'$within': {'$center': [[1, 2], 1]}}})
          {u'__name__': u'sample',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoPointSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'coordinates': [1.0, 3.0], u'type': u'Point'},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
        
          >>> printFind(collection, {'lonlat': {'$within': {'$center': [[2, 1], 1]}}})
        
        
        And check if we can store real lon/lat values by using a float:
        
          >>> data = {'name': u'sample', 'lonlat': {'lon': 20.123, 'lat': 29.123}}
          >>> sample3 = testing.GeoPointSample(data)
          >>> root[u'sample3'] = sample3
        
          >>> transaction.commit()
        
          >>> printFind(collection, {'lonlat': {'$within': {'$center': [[25, 25], 4]}}})
        
          >>> printFind(collection, {'lonlat': {'$within': {'$center': [[25, 25], 10]}}})
          {u'__name__': u'sample3',
           u'_id': ObjectId('...'),
           u'_pid': ObjectId('...'),
           u'_type': u'GeoPointSample',
           u'_version': 1,
           u'created': datetime.datetime(..., tzinfo=UTC),
           u'lonlat': {u'coordinates': [20.123, 29.123], u'type': u'Point'},
           u'modified': datetime.datetime(..., tzinfo=UTC),
           u'name': u'sample'}
        
        
        tear down
        ---------
        
          >>> from m01.mongo import clearThreadLocalCache
          >>> clearThreadLocalCache()
        
        As you can see our cache items get removed:
        
          >>> from m01.mongo import LOCAL
          >>> pprint(LOCAL.__dict__)
          {}
        
        
        ========
        Batching
        ========
        
        The MongoMappingBase base class used by MongoStorage and MongoContainer can
        return batched data or items and batch information.
        
        Note; this test runs in level 2 because it uses a working MongoDB. This is
        needed because we like to test the real sort and limit functions in a MongoDB.
        
        
        Condition
        ---------
        
        Befor we start testing, check if our thread local cache is empty or if we have
        left over some junk from previous tests:
        
          >>> from m01.mongo.testing import pprint
          >>> from m01.mongo import LOCAL
          >>> pprint(LOCAL.__dict__)
          {}
        
        Setup
        -----
        
        First import some components:
        
          >>> import datetime
          >>> import transaction
          >>> from m01.mongo import testing
        
        
        setup
        -----
        
        Now we can add a MongoStorage to the database. Let's just use a simple
        dict as database root:
        
          >>> root = {}
          >>> storage = testing.SampleStorage()
          >>> root['storage'] = storage
          >>> transaction.commit()
        
        Now let's add 1000 MongoItems:
        
          >>> storage = root['storage']
          >>> for i in range(1000):
          ...     data = {u'title': u'Title %i' % i,
          ...             u'description': u'Description %i' % i,
          ...             u'number': i}
          ...     item = testing.SampleStorageItem(data)
          ...     __name__ = storage.add(item)
        
          >>> transaction.commit()
        
        After we commited to the MongoDB, the mongo object and our transaction data
        manger reference should be gone in the thread local cache:
        
          >>> from m01.mongo import LOCAL
          >>> pprint(LOCAL.__dict__)
          {}
        
        As you can see, our collection contains 1000 items:
        
          >>> storage = root['storage']
          >>> len(storage)
          1000
        
        
        batching
        --------
        
        Note, this method does not return items, it only returns the MongoDB data. This
        is what you should use. If this doesn't fit because you need a list of the real
        MongoItem this would be complicated beause we could have removed marked items
        in our LOCAL cache which the MongoDB doesn't know about.
        
        Let's get the batch information:
        
          >>> storage.getBatchData()
          (<...Cursor object at ...>, 1, 40, 1000)
        
        As you an see, we've got a curser with mongo data, the start index, the total
        amount of items and the page counter. Note, the first page starts at 1 (one)
        and not zero. Let's show another ample with different values:
        
          >>> storage.getBatchData(page=5, size=10)
          (<...Cursor object at ...>, 5, 100, 1000)
        
        As you can see we can iterate our cursor:
        
          >>> cursor, page, total, pages = storage.getBatchData(page=1, size=3)
        
          >>> pprint(tuple(cursor))
          ({'__name__': '...',
            '_id': ObjectId('...'),
            '_pid': None,
            '_type': 'SampleStorageItem',
            '_version': 1,
            'comments': [],
            'created': datetime.datetime(..., tzinfo=UTC),
            'date': None,
            'description': 'Description ...',
            'item': None,
            'modified': datetime.datetime(..., tzinfo=UTC),
            'number': ...,
            'numbers': [],
            'title': 'Title ...'},
           {'__name__': '...',
            '_id': ObjectId('...'),
            '_pid': None,
            '_type': 'SampleStorageItem',
            '_version': 1,
            'comments': [],
            'created': datetime.datetime(..., tzinfo=UTC),
            'date': None,
            'description': 'Description ...',
            'item': None,
            'modified': datetime.datetime(..., tzinfo=UTC),
            'number': ...,
            'numbers': [],
            'title': 'Title ...'},
           {'__name__': '...',
            '_id': ObjectId('...'),
            '_pid': None,
            '_type': 'SampleStorageItem',
            '_version': 1,
            'comments': [],
            'created': datetime.datetime(..., tzinfo=UTC),
            'date': None,
            'description': 'Description ...',
            'item': None,
            'modified': datetime.datetime(..., tzinfo=UTC),
            'number': ...,
            'numbers': [],
            'title': 'Title ...'})
        
        As you can see, the cursor counts the total amount of items:
        
          >>> cursor.count()
          1000
        
        But we can force to count the result based on limit and skip arguments by use
        True as argument:
        
          >>> cursor.count(True)
          3
        
        As you can see batching or any other object lookup will left items back in our
        thread local cache. We can use our thread local cache cleanup event handler
        which is normal registered as an EndRequestEvent subscriber:
        
          >>> from m01.mongo import LOCAL
          >>> pprint(LOCAL.__dict__)
          {u'm01_mongo_testing.test...': {'added': {}, 'removed': {}}}
        
        Let's use our subscriber:
        
          >>> from m01.mongo import clearThreadLocalCache
          >>> clearThreadLocalCache()
        
        As you can see our cache items get removed:
        
          >>> from m01.mongo import LOCAL
          >>> pprint(LOCAL.__dict__)
          {}
        
        
        order
        -----
        
        An important part in batching is ordering. As you can see, we can limit the
        batch size and get a slice of data from a sequence. It is very important that
        the data get ordered at the MongoDB before we slice the data into a batch.
        Let's test if this works based on our ordable number value and a sort order
        where lowest value comes first. Start with page=0:
        
          >>> cursor, page, pages, total = storage.getBatchData(page=1, size=3,
          ...     sortName='number', sortOrder=1)
        
          >>> cursor
          <pymongo.cursor.Cursor object at ...>
        
          >>> page
          1
        
          >>> pages
          334
        
          >>> total
          1000
        
        When ordering is done right, the first item should have a number value 0 (zero):
        
          >>> pprint(tuple(cursor))
          ({u'__name__': u'...',
            u'_id': ObjectId('...'),
            '_pid': None,
            u'_type': u'SampleStorageItem',
            u'_version': 1,
            u'comments': [],
            u'created': datetime.datetime(..., tzinfo=UTC),
            'date': None,
            u'description': u'Description 0',
            'item': None,
            u'modified': datetime.datetime(..., tzinfo=UTC),
            u'number': 0,
            u'numbers': [],
            u'title': u'Title 0'},
           {u'__name__': u'...',
            u'_id': ObjectId('...'),
            '_pid': None,
            u'_type': u'SampleStorageItem',
            u'_version': 1,
            u'comments': [],
            u'created': datetime.datetime(..., tzinfo=UTC),
            'date': None,
            u'description': u'Description 1',
            'item': None,
            u'modified': datetime.datetime(..., tzinfo=UTC),
            u'number': 1,
            u'numbers': [],
            u'title': u'Title 1'},
           {u'__name__': u'...',
            u'_id': ObjectId('...'),
            '_pid': None,
            u'_type': u'SampleStorageItem',
            u'_version': 1,
            u'comments': [],
            u'created': datetime.datetime(..., tzinfo=UTC),
            'date': None,
            u'description': u'Description 2',
            'item': None,
            u'modified': datetime.datetime(..., tzinfo=UTC),
            u'number': 2,
            u'numbers': [],
            u'title': u'Title 2'})
        
        The second page (page=1) should start with number == 3:
        
          >>> cursor, page, pages, total = storage.getBatchData(page=2, size=3,
          ...     sortName='number', sortOrder=1)
          >>> pprint(tuple(cursor))
          ({u'__name__': u'...',
            u'_id': ObjectId('...'),
            '_pid': None,
            u'_type': u'SampleStorageItem',
            u'_version': 1,
            u'comments': [],
            u'created': datetime.datetime(..., tzinfo=UTC),
            'date': None,
            u'description': u'Description 3',
            'item': None,
            u'modified': datetime.datetime(..., tzinfo=UTC),
            u'number': 3,
            u'numbers': [],
            u'title': u'Title 3'},
           {u'__name__': u'...',
            u'_id': ObjectId('...'),
            '_pid': None,
            u'_type': u'SampleStorageItem',
            u'_version': 1,
            u'comments': [],
            u'created': datetime.datetime(..., tzinfo=UTC),
            'date': None,
            u'description': u'Description 4',
            'item': None,
            u'modified': datetime.datetime(..., tzinfo=UTC),
            u'number': 4,
            u'numbers': [],
            u'title': u'Title 4'},
           {u'__name__': u'...',
            u'_id': ObjectId('...'),
            '_pid': None,
            u'_type': u'SampleStorageItem',
            u'_version': 1,
            u'comments': [],
            u'created': datetime.datetime(..., tzinfo=UTC),
            'date': None,
            u'description': u'Description 5',
            'item': None,
            u'modified': datetime.datetime(..., tzinfo=UTC),
            u'number': 5,
            u'numbers': [],
            u'title': u'Title 5'})
        
        As you can see your page size is 334. Let's show this batch slice. The
        item in this batch should have a number == 999. but note:
        
          >>> pages
          334
        
          >>> cursor, page, total, pages = storage.getBatchData(page=334, size=3,
          ...     sortName='number', sortOrder=1)
          >>> pprint(tuple(cursor))
          ({u'__name__': u'...',
            u'_id': ObjectId('...'),
            '_pid': None,
            u'_type': u'SampleStorageItem',
            u'_version': 1,
            u'comments': [],
            u'created': datetime.datetime(..., tzinfo=UTC),
            'date': None,
            u'description': u'Description 999',
            'item': None,
            u'modified': datetime.datetime(..., tzinfo=UTC),
            u'number': 999,
            u'numbers': [],
            u'title': u'Title 999'},)
        
        
        teardown
        --------
        
        Call transaction commit which will cleanup our LOCAL caches:
        
          >>> transaction.commit()
        
        Again, clear thread local cache:
        
          >>> clearThreadLocalCache()
        
        Check our thread local cache before we leave this test:
        
          >>> pprint(LOCAL.__dict__)
          {}
        
        
        =======
        Testing
        =======
        
        Let's test some testing methods.
        
          >>> import re
          >>> import datetime
          >>> import bson.tz_util
          >>> import m01.mongo
          >>> import m01.mongo.testing
          >>> from m01.mongo.testing import pprint
        
        RENormalizer
        ------------
        
        The RENormalizer is able to normalize text and produce comparable output. You
        can setup the RENormalizer with a list of input, output expressions. This is
        usefull if you dump mongodb data which contains dates or other not so simple
        reproducable data. Such a dump result can get normalized before the unit test
        will compare the output. Also see zope.testing.renormalizing for the same
        pattern which is useable as a doctest checker.
        
          >>> normalizer = m01.mongo.testing.RENormalizer([
          ...    (re.compile('[0-9]*[.][0-9]* seconds'), '... seconds'),
          ...    (re.compile('at 0x[0-9a-f]+'), 'at ...'),
          ...    ])
        
          >>> text = """
          ... <object object at 0xb7f14438>
          ... completed in 1.234 seconds.
          ... ...
          ... <object object at 0xb7f14450>
          ... completed in 1.234 seconds.
          ... """
        
          >>> print normalizer(text)
          <BLANKLINE>
          <object object at ...>
          completed in ... seconds.
          ...
          <object object at ...>
          completed in ... seconds.
          <BLANKLINE>
        
        Now let's test some mongodb relevant stuff:
        
          >>> from bson.dbref import DBRef
          >>> from bson.min_key import MinKey
          >>> from bson.max_key import MaxKey
          >>> from bson.objectid import ObjectId
          >>> from bson.timestamp import Timestamp
        
          >>> oid = m01.mongo.getObjectId(42)
          >>> oid
          ObjectId('0000002a0000000000000000')
        
          >>> data = {'oid': oid,
          ...         'dbref': DBRef("foo", 5, "db"),
          ...         'date': datetime.datetime(2011, 5, 7, 1, 12),
          ...         'utc': datetime.datetime(2011, 5, 7, 1, 12, tzinfo=bson.tz_util.utc),
          ...         'min': MinKey(),
          ...         'max': MaxKey(),
          ...         'timestamp': Timestamp(4, 13),
          ...         're': re.compile("a*b", re.IGNORECASE),
          ...         'string': 'string',
          ...         'unicode': u'unicode',
          ...         'int': 42}
        
        Now let's pretty print the data:
        
          >>> pprint(data)
          {'date': datetime.datetime(...),
           'dbref': DBRef('foo', 5, 'db'),
           'int': 42,
           'max': MaxKey(),
           'min': MinKey(),
           'oid': ObjectId('...'),
           're': <_sre.SRE_Pattern object at ...>,
           'string': 'string',
           'timestamp': Timestamp('...'),
           'unicode': 'unicode',
           'utc': datetime.datetime(..., tzinfo=UTC)}
        
        
        reNormalizer
        ~~~~~~~~~~~~
        
        As you can see our predefined reNormalizer will convert the values using our
        given patterns:
        
          >>> import m01.mongo.testing
          >>> res = m01.mongo.testing.reNormalizer(data)
          >>> print res
          {'date': datetime.datetime(...),
           'dbref': DBRef('foo', 5, 'db'),
           'int': 42,
           'max': MaxKey(),
           'min': MinKey(),
           'oid': ObjectId('...'),
           're': <_sre.SRE_Pattern object at ...>,
           'string': 'string',
           'timestamp': Timestamp('...'),
           'unicode': u'unicode',
           'utc': datetime.datetime(..., tzinfo=UTC)}
        
        
        pprint
        ~~~~~~
        
          >>> m01.mongo.testing.reNormalizer.pprint(data)
          {'date': datetime.datetime(...),
           'dbref': DBRef('foo', 5, 'db'),
           'int': 42,
           'max': MaxKey(),
           'min': MinKey(),
           'oid': ObjectId('...'),
           're': <_sre.SRE_Pattern object at ...>,
           'string': 'string',
           'timestamp': Timestamp('...'),
           'unicode': u'unicode',
           'utc': datetime.datetime(..., tzinfo=UTC)}
        
        
        UTC
        ---
        
        The pymongo library offers a custom UTC implementation including pickle support
        used by deepcopy. Let's test if this implementation works and replace our custom
        timezone with the bson.tz_info.utc:
        
          >>> dt = data['utc']
          >>> dt
          datetime.datetime(2011, 5, 7, 1, 12, tzinfo=UTC)
        
          >>> import copy
          >>> copy.deepcopy(dt)
          datetime.datetime(2011, 5, 7, 1, 12, tzinfo=UTC)
        
        ===========================
        Speedup your implementation
        ===========================
        
        Since not every strategy is the best for every applications and we can't
        implement all concepts in this package, we will list here some imporvements.
        
        
        values and items
        ----------------
        
        The MongoContainers and MongoStorage implementation will load all data within
        the values and items methods. Even if we already cached them in our thread
        local cache. Here is an optimized method which could get used if you need to
        load a large set of data.
        
        The original implementation of MongoMappingBase.values looks like::
        
            def values(self):
                # join transaction handling
                self.ensureTransaction()
                for data in self.doFind(self.collection):
                    __name__ = data['__name__']
                    if __name__ in self._cache_removed:
                        # skip removed items
                        continue
                    obj = self._cache_loaded.get(__name__)
                    if obj is None:
                        try:
                            # load, locate and cache if not cached
                            obj = self.doLoad(data)
                        except (KeyError, TypeError):
                            continue
                    yield obj
                # also return items not stored in MongoDB yet
                for k, v in self._cache_added.items():
                    yield v
        
        If you like to prevent loading all data, you could probably only load
        keys and lookup data for items which didn't get cached yet. This would
        reduce network traffic and could look like::
        
            def values(self):
                # join transaction handling
                self.ensureTransaction()
                # only get __name__ and _id
                for data in self.doFind(self.collection, {}, ['__name__', '_id']):
                    __name__ = data['__name__']
                    if __name__ in self._cache_removed:
                        # skip removed items
                        continue
                    obj = self._cache_loaded.get(__name__)
                    if obj is None:
                        try:
                            # now we can load data from mongo
                            d = self.doFindOne(self.collection, data)
                            # load, locate and cache if not cached
                            obj = self.doLoad(d)
                        except (KeyError, TypeError):
                            continue
                    yield obj
                # also return items not stored in MongoDB yet
                for k, v in self._cache_added.items():
                    yield v
        
        Note: the same concept can get used for the items method.
        
        Note: I don't recommend to call keys, values or items for large collections
        at any time. Take a look at the batching concept we implemented. The
        getBatchData method is probably what you need to use with a large set of data.
        
        
        AdvancedConverter
        -----------------
        
        The class below shows an advanced implementation which is able to convert a
        nested data structure.
        
        Normaly a converter can convert attribute values. If the attribute
        value is a list of items which contains another list of items, then you need to
        use another converter which is able to convert this nested structure. But
        normaly this is the responsibility of the first level item to convert it's
        values. This is the reason why we didn't implement this concept by default.
        
        Remember, a default converter definition looks like::
        
          def itemConverter(value):
              _type = value.get('_type')
              if _type == 'Car':
                  return Car
              if _type == 'House':
                  return House
              else:
                  return value
        
        And the class defines something like::
        
          converters = {'myItems': itemConverter}
        
        Our advanced converter sample can convert a nested data structure and looks
        like::
        
          def toCar(value):
              return Car(value)
        
          converters = {'myItems': {'House': toHouse, 'Car': toCar}}
        
          class AdvancedConverter(object):
        
              converters = {} # attr-name/converter or {_type:converter}
              def convert(self, key, value):
                  """This convert method knows how to handle nested converters."""
                  converter = self.converters.get(key)
                  if converter is not None:
                      if isinstance(converter, dict):
                          if isinstance(value, (list, tuple)):
                              res = []
                              for o in value:
                                  if isinstance(o, dict):
                                      _type = o.get('_type')
                                      if _type is not None:
                                          converter = converter.get(_type)
                                          value = converter(o)
                                  res.append(value)
                              value = res
                          elif isinstance(value, dict):
                              _type = o.get('_type')
                              if _type is not None:
                                  converter = converter.get(_type)
                                  value = converter(value)
                          else:
                              value = converter(value)
                      else:
                          if isinstance(value, (list, tuple)):
                              # convert list values
                              value = [converter(d) for d in value]
                          else:
                              # convert simple values
                              value = converter(value)
                  return value
        
        I'm sure if you understand what we implemented, you will find a lot of space
        to improve and write your own special methods which can do the right thing for
        your use cases.
        
        
        =======
        CHANGES
        =======
        
        3.3.3 (2021-0-23)
        ------------------
        
        - feature: implemented MongoSubObject object and a MongoSubObjectProperty. The
          MongoSubObjectProperty provides a converter and factory and can get used for
          apply an object as attribute. This allows to traverse the object within the attriute name. Compared to the MongoObject whcih stores the data in an own
          collection, the MongoSubobject implementation stores it's data in the
          parent object.
        
        
        3.3.2 (2021-01-14)
        ------------------
        
        - added TLS options for pymongo client setup
        
        - bugfix: fix order method in MongoItemsData compare with set. The existing
          implementation was using pop with values instead of indexes for validate
          the new order names.
        
        - added tests for bugfix
        
        
        3.3.1 (2020-04-22)
        ------------------
        
        - bugfix: register MongoListData class and allow interface IMongoListData.
          This allows to access the internal implementation like a simply built in
          type. Note: the object property using this implementation is still protected.
          We just let our instance act like a buit in simply python type.
        
        
        3.3.0 (2018-02-04)
        ------------------
        
        - use new p01.env package for pymongo client environment setup
        
        
        3.2.3 (2018-02-04)
        ------------------
        
        - bugfix: removed FakeMongoConnectionPool from mongo client testing setup
        
        - set MONGODB_CONNECT to False as default because client setup takes too long
          for testing setup. Add MONGODB_CONNECT to your os environment if you need
          to connect on application startup.
        
        
        3.2.2 (2018-01-29)
        ------------------
        
        - bugfix: fix timeout milli seconds and MONGODB_REVOCATION_LIST attr usage
        
        
        3.2.1 (2018-01-29)
        ------------------
        
        - bugfix: multiply MONGODB_SERVER_SELECTION_TIMEOUT with 1000because it's used
          as milli seconds
        
        
        3.2.0 (2018-01-29)
        ------------------
        
        - feature: implemented pymongo client setup based on enviroment variables and
          default settings.py file
        
        
        3.1.0 (2017-01-22)
        ------------------
        
        - bugfix: make sure we override existing mongodb values with None if None is
          given as value in python object. Previous versions didn't override existing values with None. The new implementation will use the default schema value
          as mongodb value even if default is None. Note, this will break existing
          test output.
        
        - bugfix: fix performance test setup, conditional include ZODB for performance
          tests. Supported with extras_require in setup.py.
        
        
        3.0.0 (2015-11-11)
        ------------------
        
        - Use 3.0.0 as package version and reflect pymongo > 3.0.0 compatibility.
        
        - feature: change internal doFind, doInsert and doRemove methods, remove old
          method arguments like safe etc..
        
        - feature: reflect changes in pymongo > 3.0.0. Replace disconnect with close
          method like the MongoClient does.
        
        - removed MongoConnectionPool, replace them with MongoClient in your code. There
          is no need for a thread safe connection pool since pymongo is thread safe.
          Also replace MongoConnection with MongoClient in your test code.
        
        - switch from m01.mongofake to m01.fake including pymongo >= 3.0.0 support
        
        - remove write_concern options in mapping base class. The MongoClient should
          define the right write concern.
        
        
        1.0.0 (2015-03-17)
        ------------------
        
        - improve AttributeError handling on object setup. Additional catch ValueError
          and zope.interface.Invalid and raise AttributeError with detailed attribute
          and value information
        
        
        0.11.1 (2014-04-10)
        -------------------
        
        - feature: changed mongo client max_pool_size value from 10MB to 100MB which
          reflects changes in pymongo >= 2.6.
        
        
        0.11.0 (2013-1-23)
        -------------------
        
        - implement GeoPoint used for 2dsphere geo location indexes. Also provide a
          MongoGeoPointProperty which is able to create such GeoPoint items.
        
        
        0.10.2 (2013-01-04)
        -------------------
        
        - support _m_insert_write_concern, _m_update_write_concern,
          _m_remove_write_concern in MongoObject
        
        
        0.10.1 (2012-12-19)
        -------------------
        
        - feature: implemented MongoDatetime schema field supporting timezone info
          attribute (tzinfo=UTC).
        
        
        0.10.0 (2012-12-16)
        -------------------
        
        - switch from Connection to MongoClient recommended since pymongo 2.4. Replaced
          safe with write concern options. By default pymongo will now use safe writes.
        
        - use MongoClient as factory in MongoConnectionPool. We didn't rename the class
          MongoConnectionPool, we will keep them as is. We also don't rename the
          IMongoConnectionPool interface.
        
        - replaced _m_safe_insert, _m_safe_update, _m_safe_remove with
          _m_insert_write_concern, _m_update_write_concern, _m_remove_write_concern.
          This new mapping base class options are an empty dict and can get replaced
          with the new write concern settings. The default empty dict will force to
          use the write concern defined in the connection.
        
        
        0.9.0 (2012-12-10)
        ------------------
        
        - use m01.mongofake for fake mongodb, collection and friends
        
        
        0.8.0 (2012-11-18)
        ------------------
        
        - bugfix: add missing security declaration for dump data
        
        - switch to bson import
        
        - reflect changes in test output based on pymongo 2.3
        
        - remove p01.i18n package dependency
        
        - improve, prevent mark items as changed for same values
        
        - improve sort, support key or list as sortName and allow to skip sortOrder if
          sortName is given
        
        - added MANIFEST.in file
        
        
        0.7.0 (2012-05-22)
        ------------------
        
        - bugfix: FakeCollection.remove: use find to find documents
        
        - preserve order by using SON for query filter and dump methods
        
        - implemented m01.mongo.dictify which can recoursive replace all bson.son.SON
          with plain dict instances.
        
        
        0.6.2 (2012-03-12)
        ------------------
        
        - bugfix: left out a method
        
        
        0.6.1 (2012-03-12)
        ------------------
        
        - bugfix: return self in FakeMongoConnection __call__method. This let's an
          instance act similar then the original pymongo Connection class __init__
          method.
        
        - feature: Add `sort` parameter for FakeMongoConnection.find()
        
        0.6.0 (2012-01-17)
        ------------------
        
        - bugfix: During a query, if a spec key is missing from the doc, the doc is
          always ignored.
        
        - bugfix: correctly generate an object id in UTC. It was relying on GMT+1
          (i.e. Roger's timezone).
        
        - bugfix: allow to use None as MongoDateProperty value
        
        - bugfix: set __parent__ in MongoSubItem __init__ method if given
        
        - implemented _m_initialized as a marker for find out when we need to trace
          changed attributes
        
        - implemented clear method in MongoListData and MongoItemsData which allows to
          remove sequence items at once wihout to pop each item from the sequence
        
        - improve MongoObject implementation, implemented _field which stores the
          parent field name which the MongoObject is stored at. Also adjsut the
          MongoObjectProperty and support backward compatibility by apply the previous
          stored __name__ as _field if not given. This new _field and __name__
          separation allos us to use explicit names e.g. the _id or custom names which
          we can use for traversing to a MongoObject via traverser or other container
          like implementations.
        
        - Implemented __getattr__ in FakeCollection. This allows to get a sub
          collection like in pymongo which is a part of the gridfs concept.
        
        
        0.5.5 (2011-10-14)
        ------------------
        
        - Implement filtering with dot notation
        
        
        0.5.4 (2011-09-27)
        ------------------
        
        - Fix: a real mongo DB accepts tuple as the `fields` parameter of `find`.
        
        
        0.5.3 (2011-09-20)
        ------------------
        
        - Fix minimum filtering expressions (Albertas)
        
        
        0.5.2 (2011-09-19)
        ------------------
        
        - Added minimum filtering expressions (Albertas)
        
        - moved created and modified to an own interface called ICreatedModified
        
        - implemented simple and generic initial geo location support
        
        
        0.5.1 (2011-09-09)
        ------------------
        
        - fix performance test
        - Added database_names and collection_names
        
        
        0.5.0 (2011-08-19)
        ------------------
        
        - initial release
        
Keywords: Zope3 z3c p01 m01 mongo connection pool container
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Zope Public License
Classifier: Programming Language :: Python
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Topic :: Internet :: WWW/HTTP
Classifier: Framework :: Zope3
Provides-Extra: test
Provides-Extra: performance
