relationship-manager 2.0.0

Lightweight Object Database, manages relationships between classes

Relationship Manager - a lightweight Object Database class

A central mediating class which records all the one-to-one, one-to-many and many-to-many relationships between a group of classes.

What is it?

Classes that use a Relationship Manager to implement their relationship properties and methods have a consistent metaphor and trivial implementation code (one line calls). In contrast - traditional "pointer" and "arraylist" techniques of implementing relationships are fully flexible but often require a reasonable amount of non-trivial code which can be tricky to get working correctly and are almost always a pain to maintain due to the detailed coding and coupling between classes involved, especially when back-pointers are involved.

Using a Relationship Manager object to manage the relationships can mitigate these problems and make managing relationships straightforward. It also opens up the possibility of powerful querying of relationships, a very simple version of something like LINQ.

In a sense, an Object Database is an elaborate implementation of the Relationship Manager pattern. However the intent of the Relationship Manager pattern is lighter weight, to replace the wirings between objects rather than acting as a huge central database on disk - though persistence is built into Relationship Manager too.

Here are various implementations of the Relationship Manager Pattern in this GitHub repository:

• Python: Uses Python 3, there are no dependencies.
• Java
• C#: Visual Studio 2005 project with unit test. Very fast implementation used in at least one commercial product.

Python

Installation

pip install relationship-manager

Usage

from relmgr import RelationshipManager

rm = RelationshipManager()
rm.enforce("xtoy", "onetoone", "directional")
x = object()
y = object()
rm.add_rel(x, y, "xtoy")
assert rm.find_target(x, "xtoy") == y

• Read the unit tests to see all functionality being exercised, incl. backpointer queries.
• See the examples below and in the relmgr/examples/ directory of this repository.
• See full API documentation.
• See the Relationship Manager pattern referred to above for lots more documentation.

Python API

Quick summary:

def add_rel(self, source, target, rel_id: Union[int,str]=1) -> None:
def remove_rel(self, source, target, rel_id=1) -> None:
def enforce(self, rel_id, cardinality, directionality="directional"):
def clear(self) -> None:

# query API
def find_targets(self, source, rel_id) -> List:
def find_target(self, source, rel_id) -> object:
def find_sources(self, target, rel_id) -> List: # Back pointer query
def find_source(self, target, rel_id) -> object: # Back pointer query
def find_rels(self, source, target) -> List:
def is_rel(self, source, target, rel_id=1) -> bool:

# persistence related
objects: Namespace
relationships = property(_get_relationships, _set_relationships)  # flat list of rel. tuples
def dumps(self) -> bytes:
def loads(asbytes: bytes) -> RelationshipManager:  # @staticmethod

See full API documentation.

Hiding the use of Relationship Manager

Its probably best practice to hide the use of Relationship Manager and simply use it as an implementation underneath traditional wiring methods like .add() and setY() or properties like .subject etc.

For example, to implement:

         ______________        ______________
        |       X      |      |       Y      |
        |______________|      |______________|
        |              |      |              |
        |void  setY(y) |1    1|              |
        |Y     getY()  |----->|              |
        |void  clearY()|      |              |
        |______________|      |______________|

write the Python code like this:

from relmgr import RelMgr

RM = RelMgr()

class X:
    def __init__(self):        rm.enforce("xtoy", "onetoone", "directional")
    def setY(self, y):         rm.add_rel(self, y, "xtoy")
    def getY(self):     return rm.find_target(source=self, rel_id="xtoy")
    def clearY(self):          rm.remove_rel(self, self.getY(), "xtoy")

class Y:
    pass

Note the use of the abbreviated Relationship Manager API in this example.

Another example

Here is another example of hiding the use of Relationship Manager, found in the examples folder as relmgr/examples/observer.py - the classic Subject/Observer pattern:

from relmgr import RelationshipManager


rm = RelationshipManager()


class Observer:

    @property
    def subject(self):
        return rm.find_target(self)

    @subject.setter
    def subject(self, _subject):
        rm.add_rel(self, _subject)

    def notify(self, subject, notification_type):
        pass  # implementations override this and do something


class Subject:

    def notify_all(self, notification_type: str):
        observers = rm.find_sources(self)  # all things pointing at me
        for o in observers:
            o.Notify(self, notification_type)

    def add_observer(self, observer):
        rm.add_rel(observer, self)

    def remove_observer(self, observer):
        rm.remove_rel(source=observer, target=self)

Persistence

The easiest approach to persistence is to use the built in dumps and loads methods of RelationshipManager. Relationship Manager also provides an attribute object called .objects where you should keep all the objects involved in relationships e.g.

rm.objects.obj1 = Entity(strength=1, wise=True, experience=80)

Then when you persist the Relationship Manager both the objects and relations are pickled and later restored. This means your objects are accessible by attribute name e.g. rm.objects.obj1 at all times. You can assign these references to local variables for convenience e.g. obj1 = rm.objects.obj1.

Here is complete example of creating three entitys, wiring them up, persisting them then restoring them:

import pprint
import random
from dataclasses import dataclass
from relmgr import RelationshipManager

@dataclass
class Entity:
    strength: int = 0
    wise: bool = False
    experience: int = 0

    def __hash__(self):
        hash_value = hash(self.strength) ^ hash(
            self.wise) ^ hash(self.experience)
        return hash_value


rm = RelationshipManager()
obj1 = rm.objects.obj1 = Entity(strength=1, wise=True, experience=80)
obj2 = rm.objects.obj2 = Entity(strength=2, wise=False, experience=20)
obj3 = rm.objects.obj3 = Entity(strength=3, wise=True, experience=100)

rm.add_rel(obj1, obj2)
rm.add_rel(obj1, obj3)
assert rm.find_targets(obj1) == [obj2, obj3]

# persist
asbytes = rm.dumps()

# resurrect
rm2 = RelationshipManager.loads(asbytes)

# check things worked
newobj1 = rm2.objects.obj1
newobj2 = rm2.objects.obj2
newobj3 = rm2.objects.obj3
assert rm2.find_targets(newobj1) == [newobj2, newobj3]
assert rm2.find_target(newobj1) is newobj2

print('done, all OK')

Persistence API

As a reminder, the persistence API of RelationshipManager is:

objects: Namespace  

def dumps(self) -> bytes:

@staticmethod
def loads(asbytes: bytes) -> RelationshipManager:

Please create attributes on the objects property of the relationship manager, pointing to those objects involved in relationships. It is however optional, and only provides a guaranteed way of pickling and persisting the objects involved in the relationships along with the relationships themselves, when persisting the relationship manager.

You could attach your other application state to this objects property of the relationship manager and thus save your entire application state into the same file. Alternively save the pickeled bytes into your own persistence file solution.

There are currently no dump() or load() methods implemented, which would pickle to and from a file. These can easily be added in a subclass or just write and read the results of the existing dumps() and loads() methods to a file yourself, as bytes.

Manual Control of Persistence

Persistence can be a bit tricky because you need to persist both objects and relationships between those objects.

Other libraries that implement models, schemas, serializers/deserializers, ODM's/ORM's, Active Records or similar patterns will require you to define your classes in a particular way. Relationship Manager works with any Python objects like dataclass objects etc. without any special decoration or structure required.

Whilst it is possible to simply pickle a Relationship Manager instance and restore it, you won't have easy access to the objects involved. Sure, Relationship Manager will return objects which have been resurrected from persistence correctly but how, in such a unpickled situation, will you pass object instances to the Relationship Manager API? Thus its better to prepare your persitence properly and store all your objects in a dictionary or object and pickle that together with the Relationship Manager. E.g.

For code examples of custom persistence, see research/python persistence research/persist_pickle_simple.py as well as other persistence approaches in that directory, including an approach which stores objects in dictionaries with ids and uses the Relationship Manager to store relationships between those ids, rather than relationships between object references.

Running the tests

Check our this project from GitHub, open the project directory in vscode and there is a local settings.json and launch.json already populated which means you can choose the launch profile Run all tests: using -m unittest or use the vscode built in GUI test runner (hit the Discover Tests button then the Run all tests button).

Or simply:

python -m unittest discover -p 'test*' -v tests

Appendix: Installing into a new virtual environment

Either use pipenv to manage a new virtual environment or use Python's built in venv:

mkdir proj1
cd proj1
python -m venv env

env/bin/pip install relationship-manager
env/bin/python
> from relmgr import RelationshipManager

You can activate the virtual environment after you create it, which makes running pip and python etc. easier

mkdir proj1
cd proj1
python -m venv env

source env/bin/activate
pip install relationship-manager
python
> from relmgr import RelationshipManager

Final Thoughts on the Python Implementation

References and memory

Be careful - the Relationship Manager will have references to your objects so garbage collection may not be able to kick in. If you remove all relationships for an object it should be removed from the Relationship Manager, but this needs to be verified in these implementations.

Performance

Be mindful that normal object to object wiring using references and lists of references is going to be much faster than a Relationship Manager.

You can have multiple relationship manager instances to manage different areas of your programming domain, which increases efficiency and comprehensibility.

You may want to google for other more professional Object Databases. For example, in the Python space we have:

• https://github.com/grundic/awesome-python-models - A curated list of awesome Python libraries, which implement models, schemas, serializers/deserializers, ODM's/ORM's, Active Records or similar patterns.
• https://www.opensourceforu.com/2017/05/three-python-databases-pickledb-tinydb-zodb/ - A peek at three Python databases: PickleDB, TinyDB and ZODB
• https://tinydb.readthedocs.io/en/stable/usage.html#queries - Welcome to TinyDB, your tiny, document oriented database optimized for your happiness
• https://divmod.readthedocs.io/en/latest/products/axiom/index.html - Axiom is an object database whose primary goal is to provide an object-oriented layer to an SQL database
• http://www.newtdb.org/en/latest/getting-started.html - Newt DB - You’ll need a Postgres Database server.
• http://www.zodb.org/en/latest/tutorial.html#tutorial-label - This tutorial is intended to guide developers with a step-by-step introduction of how to develop an application which stores its data in the ZODB.

However most of these need a backing SQL database - Relationship Manager does not, which may be a benefit - no databases to set up - just get on with coding.

Other implementations of Relationship Manager

In this Github repository there are several other implementations of Relationship Manager. Their APIs are not the latest however - the methods names have evolved - the Python implementation is the gold standard API and implementation.

C#

Very fast implementation for .NET - has been used in a commercial project. Note that the Visual Studio 2005 projects/solutions need updating to a more recent version of Visual Studio.

Boo

The boo language for .NET is now dead, however this implementation created a .net .dll that was usable by other .NET languages. This dll is still in the project and perfectly usable, however the C# implementation is much faster.

Java

A Java implementation. Needs a bit of dusting off, but should run.

Resources

• Full API documentation.

• Official Relationship Manager Pattern page incl. academic paper by Andy Bulka.

• Python Implementation README (this page) and GitHub project.