Package: pjpersist
PostGreSQL/JSONB Data PersistenceThis document outlines the general capabilities of thepjpersistpackage.pjpersistis a PostGreSQL/JSONB storage implementation for
persistent Python objects. It isnota storage for the ZODB.The goal ofpjpersistis to provide a data manager that serializes
objects to JSONB blobs at transaction boundaries. The PJ data manager is a
persistent data manager, which handles events at transaction boundaries (seetransaction.interfaces.IDataManager) as well as events from the
persistency framework (seepersistent.interfaces.IPersistentDataManager).An instance of a data manager is supposed to have the same life time as the
transaction, meaning that it is assumed that you create a new data manager
when creating a new transaction:>>> import transactionNote: Theconnobject is apsycopg.Connectioninstance. In this case
our tests use thepjpersist_testdatabase.Let’s now define a simple persistent object:>>> import datetime
>>> import persistent>>> class Person(persistent.Persistent):
...
...     def __init__(self, name, phone=None, address=None, friends=None,
...                  visited=(), birthday=None):
...         self.name = name
...         self.address = address
...         self.friends = friends or {}
...         self.visited = visited
...         self.phone = phone
...         self.birthday = birthday
...         self.today = datetime.datetime(2014, 5, 14, 12, 30)
...
...     def __str__(self):
...         return self.name
...
...     def __repr__(self):
...         return '<%s %s>' %(self.__class__.__name__, self)We will fill out the other objects later. But for now, let’s create a new
person and store it in PJ:>>> stephan = Person('Stephan')
>>> stephan
<Person Stephan>The datamanager provides arootattribute in which the object tree roots
can be stored. It is special in the sense that it immediately writes the data
to the DB:>>> dm.root['stephan'] = stephan
>>> dm.root['stephan']
<Person Stephan>Custom Persistence TablesBy default, persistent objects are stored in a table having the escaped
Python path of the class:>>> from pjpersist import serialize
>>> person_cn = serialize.get_dotted_name(Person, True)
>>> person_cn
'u__main___dot_Person'>>> transaction.commit()
>>> dumpTable(person_cn)
[{'data': {'_py_persistent_type': '__main__.Person',
           'address': None,
           'birthday': None,
           'friends': {},
           'name': 'Stephan',
           'phone': None,
           'today': {'_py_type': 'datetime.datetime',
                      'value': '2014-05-14T12:30:00.000000'},
           'visited': []},
  'id': '0001020304050607080a0b0c0'}]As you can see, the stored document for the person looks very much like a
natural JSON document. But oh no, I forgot to specify the full name for
Stephan. Let’s do that:>>> dm.root['stephan'].name = 'Stephan Richter'
>>> dm.root['stephan']._p_changed
TrueThis time, the data is not automatically saved:>>> fetchone(person_cn)['data']['name']
'Stephan'So we have to commit the transaction first:>>> dm.root['stephan']._p_changed
True
>>> transaction.commit()
>>> dm.root['stephan']._p_changed
>>> fetchone(person_cn)['data']['name']
'Stephan Richter'Let’s now add an address for Stephan. Addresses are also persistent objects:>>> class Address(persistent.Persistent):
...     _p_pj_table = 'address'
...
...     def __init__(self, city, zip):
...         self.city = city
...         self.zip = zip
...
...     def __str__(self):
...         return '%s (%s)' %(self.city, self.zip)
...
...     def __repr__(self):
...         return '<%s %s>' %(self.__class__.__name__, self)pjpersist supports a special attribute called_p_pj_table,
which allows you to specify a custom table to use.>>> stephan = dm.root['stephan']
>>> stephan.address = Address('Maynard', '01754')
>>> stephan.address
<Address Maynard (01754)>Note that the address is not immediately saved in the database:>>> dumpTable('address', isolate=True)
relation "address" does not exist
...But once we commit the transaction, everything is available:>>> transaction.commit()
>>> dumpTable('address')
[{'data': {'_py_persistent_type': '__main__.Address',
           'city': 'Maynard',
           'zip': '01754'},
  'id': '0001020304050607080a0b0c0'}]>>> dumpTable(person_cn)
[{'data': {'_py_persistent_type': '__main__.Person',
           'address': {'_py_type': 'DBREF',
                        'database': 'pjpersist_test',
                        'id': '0001020304050607080a0b0c0',
                        'table': 'address'},
           'birthday': None,
           'friends': {},
           'name': 'Stephan Richter',
           'phone': None,
           'today': {'_py_type': 'datetime.datetime',
                      'value': '2014-05-14T12:30:00.000000'},
           'visited': []},
  'id': '0001020304050607080a0b0c0'}]>>> dm.root['stephan'].address
<Address Maynard (01754)>Non-Persistent ObjectsAs you can see, even the reference looks nice and all components are easily
visible. But what about arbitrary non-persistent, but picklable,
objects? Well, let’s create a phone number object for that:>>> class Phone(object):
...
...     def __init__(self, country, area, number):
...         self.country = country
...         self.area = area
...         self.number = number
...
...     def __str__(self):
...         return '%s-%s-%s' %(self.country, self.area, self.number)
...
...     def __repr__(self):
...         return '<%s %s>' %(self.__class__.__name__, self)>>> dm.root['stephan'].phone = Phone('+1', '978', '394-5124')
>>> dm.root['stephan'].phone
<Phone +1-978-394-5124>Let’s now commit the transaction and look at the JSONB document again:>>> transaction.commit()
>>> dm.root['stephan'].phone
<Phone +1-978-394-5124>>>> dumpTable(person_cn)
[{'data': {'_py_persistent_type': '__main__.Person',
           'address': {'_py_type': 'DBREF',
                        'database': 'pjpersist_test',
                        'id': '0001020304050607080a0b0c0',
                        'table': 'address'},
           'birthday': None,
           'friends': {},
           'name': 'Stephan Richter',
           'phone': {'_py_type': '__main__.Phone',
                      'area': '978',
                      'country': '+1',
                      'number': '394-5124'},
           'today': {'_py_type': 'datetime.datetime',
                      'value': '2014-05-14T12:30:00.000000'},
           'visited': []},
  'id': '0001020304050607080a0b0c0'}]As you can see, for arbitrary non-persistent objects we need a small hint in
the sub-document, but it is very minimal. If the__reduce__method returns
a more complex construct, more meta-data is written. We will see that next
when storing a date and other arbitrary data:>>> dm.root['stephan'].friends = {'roy': Person('Roy Mathew')}
>>> dm.root['stephan'].visited = ('Germany', 'USA')
>>> dm.root['stephan'].birthday = datetime.date(1980, 1, 25)>>> transaction.commit()
>>> dm.root['stephan'].friends
{'roy': <Person Roy Mathew>}
>>> dm.root['stephan'].visited
['Germany', 'USA']
>>> dm.root['stephan'].birthday
datetime.date(1980, 1, 25)As you can see, a dictionary key is always converted to unicode and tuples are
always maintained as lists, since JSON does not have two sequence types.>>> import pprint
>>> pprint.pprint(dict(
...     fetchone(person_cn, """data @> '{"name": "Stephan Richter"}'""")))
{'data': {'_py_persistent_type': '__main__.Person',
          'address': {'_py_type': 'DBREF',
                       'database': 'pjpersist_test',
                       'id': '0001020304050607080a0b0c0',
                       'table': 'address'},
          'birthday': {'_py_type': 'datetime.date',
                        'value': '1980-01-25'},
          'friends': {'roy': {'_py_type': 'DBREF',
                                'database': 'pjpersist_test',
                                'id': '0001020304050607080a0b0c0',
                                'table': 'u__main___dot_Person'}},
          'name': 'Stephan Richter',
          'phone': {'_py_type': '__main__.Phone',
                     'area': '978',
                     'country': '+1',
                     'number': '394-5124'},
          'today': {'_py_type': 'datetime.datetime',
                     'value': '2014-05-14T12:30:00.000000'},
          'visited': ['Germany', 'USA']},
 'id': '0001020304050607080a0b0c0'}Custom Serializers(A patch to demonstrate)>>> dm.root['stephan'].birthday = datetime.date(1981, 1, 25)
>>> transaction.commit()>>> pprint.pprint(
...     fetchone(person_cn,
...         """data @> '{"name": "Stephan Richter"}'""")['data']['birthday'])
{'_py_type': 'datetime.date', 'value': '1981-01-25'}As you can see, the serialization of the birthay is an ISO string. We can,
however, provide a custom serializer that uses the ordinal to store the data.>>> class DateSerializer(serialize.ObjectSerializer):
...
...     def can_read(self, state):
...         return isinstance(state, dict) and \
...                state.get('_py_type') == 'custom_date'
...
...     def read(self, state):
...         return datetime.date.fromordinal(state['ordinal'])
...
...     def can_write(self, obj):
...         return isinstance(obj, datetime.date)
...
...     def write(self, obj):
...         return {'_py_type': 'custom_date',
...                 'ordinal': obj.toordinal()}>>> serialize.SERIALIZERS.append(DateSerializer())
>>> dm.root['stephan']._p_changed = True
>>> transaction.commit()Let’s have a look again:>>> dm.root['stephan'].birthday
datetime.date(1981, 1, 25)>>> pprint.pprint(dict(
...     fetchone(person_cn, """data @> '{"name": "Stephan Richter"}'""")))
{'data': {'_py_persistent_type': '__main__.Person',
          'address': {'_py_type': 'DBREF',
                       'database': 'pjpersist_test',
                       'id': '0001020304050607080a0b0c0',
                       'table': 'address'},
          'birthday': {'_py_type': 'custom_date', 'ordinal': 723205},
          'friends': {'roy': {'_py_type': 'DBREF',
                                'database': 'pjpersist_test',
                                'id': '0001020304050607080a0b0c0',
                                'table': 'u__main___dot_Person'}},
          'name': 'Stephan Richter',
          'phone': {'_py_type': '__main__.Phone',
                     'area': '978',
                     'country': '+1',
                     'number': '394-5124'},
          'today': {'_py_type': 'custom_date', 'ordinal': 735367},
          'visited': ['Germany', 'USA']},
 'id': '0001020304050607080a0b0c0'}Much better!>>> del serialize.SERIALIZERS[:]Persistent Objects as Sub-DocumentsIn order to give more control over which objects receive their own tables
and which do not, the developer can provide a special flag marking a
persistent class so that it becomes part of its parent object’s document:>>> class Car(persistent.Persistent):
...     _p_pj_sub_object = True
...
...     def __init__(self, year, make, model):
...         self.year = year
...         self.make = make
...         self.model = model
...
...     def __str__(self):
...         return '%s %s %s' %(self.year, self.make, self.model)
...
...     def __repr__(self):
...         return '<%s %s>' %(self.__class__.__name__, self)The_p_pj_sub_objectis used to mark a type of object to be just part
of another document:>>> dm.root['stephan'].car = car = Car('2005', 'Ford', 'Explorer')
>>> transaction.commit()>>> dm.root['stephan'].car
<Car 2005 Ford Explorer>>>> pprint.pprint(dict(
...     fetchone(person_cn, """data @> '{"name": "Stephan Richter"}'""")))
{'data': {'_py_persistent_type': '__main__.Person',
          'address': {'_py_type': 'DBREF',
                       'database': 'pjpersist_test',
                       'id': '0001020304050607080a0b0c0',
                       'table': 'address'},
          'birthday': {'_py_type': 'datetime.date',
                        'value': '1981-01-25'},
          'car': {'_py_persistent_type': '__main__.Car',
                   'make': 'Ford',
                   'model': 'Explorer',
                   'year': '2005'},
          'friends': {'roy': {'_py_type': 'DBREF',
                                'database': 'pjpersist_test',
                                'id': '0001020304050607080a0b0c0',
                                'table': 'u__main___dot_Person'}},
          'name': 'Stephan Richter',
          'phone': {'_py_type': '__main__.Phone',
                     'area': '978',
                     'country': '+1',
                     'number': '394-5124'},
          'today': {'_py_type': 'datetime.date', 'value': '2014-05-14'},
          'visited': ['Germany', 'USA']},
 'id': '0001020304050607080a0b0c0'}The reason we want objects to be persistent is so that they pick up changes
automatically:>>> dm.root['stephan'].car.year = '2004'
>>> transaction.commit()
>>> dm.root['stephan'].car
<Car 2004 Ford Explorer>Table SharingSince PostGreSQL/JSONB is so flexible, it sometimes makes sense to store
multiple types of (similar) objects in the same table. In those cases you
instruct the object type to store its Python path as part of the document.Warning: Please note though that this method is less efficient, since the
document must be loaded in order to create a ghost causing more database
access.>>> class ExtendedAddress(Address):
...
...     def __init__(self, city, zip, country):
...         super(ExtendedAddress, self).__init__(city, zip)
...         self.country = country
...
...     def __str__(self):
...         return '%s (%s) in %s' %(self.city, self.zip, self.country)In order to accomplish table sharing, you simply create another class
that has the same_p_pj_tablestring as another (sub-classing will
ensure that).So let’s give Stephan two extended addresses now.>>> dm.root['stephan'].address2 = ExtendedAddress(
...     'Tettau', '01945', 'Germany')
>>> dm.root['stephan'].address2
<ExtendedAddress Tettau (01945) in Germany>>>> dm.root['stephan'].address3 = ExtendedAddress(
...     'Arnsdorf', '01945', 'Germany')
>>> dm.root['stephan'].address3
<ExtendedAddress Arnsdorf (01945) in Germany>>>> transaction.commit()When loading the addresses, they should be of the right type:>>> dm.root['stephan'].address
<Address Maynard (01754)>
>>> dm.root['stephan'].address2
<ExtendedAddress Tettau (01945) in Germany>
>>> dm.root['stephan'].address3
<ExtendedAddress Arnsdorf (01945) in Germany>Persistent Serialization HooksWhen persistent components implement theIPersistentSerializationHooks, it
is possible for the object to conduct some custom storage function.>>> from pjpersist.persistent import PersistentSerializationHooks
>>> class Usernames(PersistentSerializationHooks):
...     _p_pj_table = 'usernames'
...     format = 'email'
...
...     def _pj_after_store_hook(self, conn):
...         print('After Store Hook')
...
...     def _pj_after_load_hook(self, conn):
...         print('After Load Hook')When we store the object, the hook is called:
(actually twice, because this is a new object)>>> dm.root['stephan'].usernames = Usernames()
>>> transaction.commit()
After Store Hook
After Store HookWhen loading, the same happens:>>> dm.root['stephan'].usernames.format
After Load Hook
'email'The store hook fires just once if the object is not new:>>> dm.root['stephan'].usernames.format = 'snailmail'
>>> transaction.commit()
After Store HookColumn Serializationpjpersist also allows for the object to specify values, usually attributes or
properties, to be stored as columns on the object’s storage table.Note that we support only a one-way transformation, because object state
will be always deserialized from thedatajsonb field.>>> import zope.schema
>>> class IPerson(zope.interface.Interface):
...
...     name = zope.schema.TextLine(title='Name')
...     address = zope.schema.TextLine(title='Address')
...     visited = zope.schema.Datetime(title='Visited')
...     phone = zope.schema.TextLine(title='Phone')Initially, we are storing only the name in a column:>>> from pjpersist.persistent import SimpleColumnSerialization, select_fields
>>> @zope.interface.implementer(IPerson)
... class ColumnPerson(SimpleColumnSerialization, Person):
...     _p_pj_table = 'cperson'
...     _pj_column_fields = select_fields(IPerson, 'name')So once I create such a person and commit the transaction, the person table is
extended to store the attribute and the person is added to the table:>>> dm.root['anton'] = anton = ColumnPerson('Anton')
>>> transaction.commit()>>> dumpTable('cperson')
[{'data': {'_py_persistent_type': '__main__.ColumnPerson',
           'address': None,
           'birthday': None,
           'friends': {},
           'name': 'Anton',
           'phone': None,
           'today': {'_py_type': 'datetime.datetime',
                      'value': '2014-05-14T12:30:00.000000'},
           'visited': []},
  'id': '0001020304050607080a0b0c0',
  'name': 'Anton'}]Tricky CasesChanges in Basic Mutable TypeTricky, tricky. How do we make the framework detect changes in mutable
objects, such as lists and dictionaries? Answer: We keep track of which
persistent object they belong to and provide persistent implementations.>>> type(dm.root['stephan'].friends)
 <class 'pjpersist.serialize.PersistentDict'>>>> dm.root['stephan'].friends['roger'] = Person('Roger')
>>> transaction.commit()
>>> sorted(dm.root['stephan'].friends.keys())
['roger', 'roy']The same is true for lists:>>> type(dm.root['stephan'].visited)
 <class 'pjpersist.serialize.PersistentList'>>>> dm.root['stephan'].visited.append('France')
>>> transaction.commit()
>>> dm.root['stephan'].visited
['Germany', 'USA', 'France']Circular Non-Persistent ReferencesAny mutable object that is stored in a sub-document, cannot have multiple
references in the object tree, since there is no global referencing. These
circular references are detected and reported:>>> class Top(persistent.Persistent):
...     foo = None>>> class Foo(object):
...     bar = None>>> class Bar(object):
...     foo = None>>> top = Top()
>>> foo = Foo()
>>> bar = Bar()
>>> top.foo = foo
>>> foo.bar = bar
>>> bar.foo = foo>>> dm.root['top'] = top
Traceback (most recent call last):
...
CircularReferenceError: <...>Circular Persistent ReferencesIn general, circular references among persistent objects are not a problem,
since we always only store a link to the object. However, there is a case when
the circular dependencies become a problem.If you set up an object tree with circular references and then add the tree to
the storage at once, it must insert objects during serialization, so that
references can be created. However, care needs to be taken to only create a
minimal reference object, so that the system does not try to recursively
reduce the state.>>> class PFoo(persistent.Persistent):
...     bar = None>>> class PBar(persistent.Persistent):
...     foo = None>>> top = Top()
>>> foo = PFoo()
>>> bar = PBar()
>>> top.foo = foo
>>> foo.bar = bar
>>> bar.foo = foo>>> dm.root['ptop'] = topContainers and TablesNow that we have talked so much about the gory details on storing one object,
what about mappings that reflect an entire table, for example a
table of people.There are many approaches that can be taken. The following implementation
defines an attribute in the document as the mapping key and names a
table:>>> from pjpersist import mapping
>>> class People(mapping.PJTableMapping):
...     __pj_table__ = person_cn
...     __pj_mapping_key__ = 'short_name'The mapping takes the data manager as an argument. One can easily create a
sub-class that assigns the data manager automatically. Let’s have a look:>>> People(dm).keys()
[]The reason no person is in the list yet, is because no document has the key
yet or the key is null. Let’s change that:>>> People(dm)['stephan'] = dm.root['stephan']
>>> transaction.commit()>>> People(dm).keys()
['stephan']
>>> People(dm)['stephan']
<Person Stephan Richter>Also note that setting the “short-name” attribute on any other person will add
it to the mapping:>>> dm.root['stephan'].friends['roy'].short_name = 'roy'
>>> transaction.commit()
>>> sorted(People(dm).keys())
['roy', 'stephan']CHANGES3.1.3 (2022-11-23)Faster PJDataManager flush() when it contains a lot of objects.3.1.2 (2022-07-15)Replace collections with collections.abc for python 3.10 compatibility3.1.1 (2022-06-06)FixMappingViewfix, do not emithintin the log message, hint object is the one
being loaded, this just causes pain.3.1.0 (2022-06-03)Fixcollections.abc.MappingViewsubclass persistence. It was very bad, did not
store the underlying mapping at all,caused silent data loss.
Urgent update advised!
Also failed on loading the state of such.3.0.2 (2022-05-03)FixDBRefcomparisons to return valid results instead
of failing hard  when comparing toNoneand notDBRefinstances.
(__neq__was unused because__ne__is the right method and__ne__anyway
delegates to__eq__)3.0.1 (2022-02-03)Fix imports intesting.pyDeclare Python 3.9 compatibilitySmall improvement inpjpersist.zope.container.PJContainer._load_oneGet the local cache just once, since the_cacheproperty became
a bit more expensive.Addedsqlbuilder.ILIKE– case-insensitive pair of LIKE3.0.0 (2021-02-22)Backwards incompatible change: PJDataManager now accepts a pool instead
of connection object. PJDataManager will get the connection from the pool
when joining the transaction, and return it back when transaction
completes (aborts or commits). This allows for more flexible connection
management. The connection pool must implement IPJConnectionPool interface
(it is compatible with psycopg2.pool).IPJDataManager.begin()is renamed tosetTransactionOptions()Errors executing SQL statements now doom the entire transaction,
causingtransaction.interfaces.DoomedTransactionexception on
any attempts to commit it.  A failed transaction must be aborted.2.0.1 (2020-10-13)Fixed persisting tuple keyed dicts. Persisting such objects worked,
but reading failed.2.0.0 (2020-06-02)Drop Python 2.7 and 3.6 support, add 3.8.Remove buildout support.Support for nested flushing. In complex use cases it can happen that during
serialization of an object, a query is made to look up another object. That
in turn causes a flush, resulting in a flush inside a flush. Theflush()method did not expect that behavior and failed if the inner flush would
flush objects that the outer flush had already handled.1.7.2 (2020-02-10)Optimization: do not dig indatawhen we have a native field for_pj_mapping_key, should allow creating indexes for lookup1.7.1 (2019-06-19)Fixed an edge case when the serializer gets a mapping with a keydict_data.
Reading such object failed.Fixed an edge case with the serializer, when an object’s state living
in a persistent object became ‘empty’. Basically the state was just{‘_py_persistent_type’: ‘SomeClass’}SomeClass.__setstate__was not called, thus the object could miss
attributes. Like a subclass ofUserDictwould miss thedataattribute.Removed checking for 0x00 chars in dict keys. Turns out PostGreSQL just
can not store 0x00.1.7.0 (2019-05-29)Support for sub-second datetime and time resolution during serialization.Adduse_cacheargument toPJContainer._load_one()to support ignoring
the cache. (This became handy if a container keeps track of multiple
versions of an item and you try to load all old revisions.)1.6.0 (2019-05-29)Makeidanddatacolumn name configurable via_pj_id_columnand_pj_data_columnattributes inPJContainer, respectively.Auto-assign a name to objects when usingPJContainer, not justIdNamesPJContainer.1.5.0 (2018-10-10)Support for Python 3.7. Removed Python 3.5 testing from tox.1.4.1 (2018-09-13)No need to log in tpc_finish.1.4.0 (2018-09-13)Implemented skipping tpc_prepare when DM has no writes.
We found out that AWS Aurora is dog slow at the moment on tpc_prepare.
When the DataManager has no writes, there’s no need to call tpc_prepare.
SeeCALL_TPC_PREPARE_ON_NO_WRITE_TRANSACTION, by default True for backwards
compatibility.Added ability to log whether the transaction had writes.
SeeLOG_READ_WRITE_TRANSACTION, by default False1.3.2 (2018-04-19)More precise flushing of datamanager to avoid unnecessary database
writes.1.3.1 (2018-04-11)Enabled concurrent adds to IdNamesPJContainer by eliminating a query
that was causing transaction conflicts.1.3.0 (2018-03-22)Python 3 compatibility fixesMore efficient PJContainer.values() implementation1.2.2 (2017-12-12)Need to protect all DB calls againstDatabaseDisconnected1.2.1 (2017-12-12)psycopg2.OperationalErrorandpsycopg2.InterfaceErrorwill be caught
on SQL command execution and reraised asDatabaseDisconnected1.2.0 (2017-10-24)Added a new helper function to link subobject to main doc object. This is
needed when a custom__getstate__()and__setstate__()is implemented. A
detailed example is provided.Implementedflush_hintargument forIDataManager.execute()to allow
flushing only some objects during query.flush_hintsis a list table names
that need to be flushed for the query to return a correct result.The Zope-specific containers use theflush_hintto only flush objects they
manage when a query is run on the container.While flushing objects, every main document object is now only flushed
once. Before that fix, any subobject would cause its doc object to be dumped
again.Note: These optimizations provide a 15% performance improvements in real-world
applications.1.1.2 (2017-09-14)Make sure changed objects aren’t_p_changedanymore after commit.1.1.1 (2017-07-03)Nothing changed yet.1.0.0 (2017-03-18)Initial Public ReleaseProject forked from mongopersist to work with PostGreSQL and JSONB data
type. The main motiviation is the ability to utilize PostGreSQL’s great
transactional support.