neomodel 0.3.5

An object mapper for the neo4j graph database.

An Object Graph Mapper (OGM) for the neo4j graph database.

Don’t need an OGM? Try the awesome py2neo (which this library is built on).

Supports: neo4j 1.8+ (1.9 recommended), python 2.7, 3.3

The basics

Set the location of neo4j via an environment variable (default is http://localhost:7474/db/data/):

export NEO4J_REST_URL=http://user:password@localhost:7474/db/data/

In the example below, there is one type of relationship present IS_FROM, we are defining two different ways for traversing it one accessible via Person objects and one via Country objects:

from neomodel import (StructuredNode, StringProperty, IntegerProperty,
    RelationshipTo, RelationshipFrom)

class Country(StructuredNode):
    code = StringProperty(unique_index=True, required=True)

    # traverse incoming IS_FROM relation, inflate to Person objects
    inhabitant = RelationshipFrom('Person', 'IS_FROM')


class Person(StructuredNode):
    name = StringProperty(unique_index=True)
    age = IntegerProperty(index=True, default=0)

    # traverse outgoing IS_FROM relations, inflate to Country objects
    country = RelationshipTo(Country, 'IS_FROM')

Create, save delete etc:

jim = Person(name='Jim', age=3).save()
jim.age = 4
jim.save() # validation happens here
jim.delete()
jim.refresh() # reload properties from neo

Using relationships:

germany = Country(code='DE').save()
jim.country.connect(germany)

if jim.country.is_connected(germany):
    print("Jim's from Germany")

for p in germany.inhabitant.all()
    print(p.name) # Jim

len(germany.inhabitant) # 1

jim.country.disconnect(germany)

You can also add properties when creating relationships, for example the previous code could be:

jim.country.connect(germany, {'arrived': '10/12/2012'})

Search related nodes. This example starts at the germany node and traverses incoming ‘IS_FROM’ relations and returns the nodes with the property name that is equal to ‘Jim’:

germany.inhabitant.search(name='Jim')

If you don’t care about the direction of the relationship:

class Person(StructuredNode):
    friends = Relationship('Person', 'FRIEND')

You may also reference classes from another module:

class Person(StructuredNode):
    car = RelationshipTo('transport.models.Car', 'CAR')

Traversals - EXPERIMENTAL

The argument for the traverse method is the name of the relationship manager on the class, in this example we traverse the friends relationship skipping the first and limit to 10 nodes:

# query executes on iteration
for friend in jim.traverse('friends').order_by_desc('age').skip(1)limit(10):
    print friend.name

You can traverse as many levels as you like, run() executes the query:

# order by country name
results = jim.traverse('friends').traverse('country').order_by('name').run()

# or friends name
jim.traverse('friends').traverse('country').order_by('friends.name')

Filtering by node properties also works:

results = jim.traverse('friends').where('age', '>', 18).run()

length and bool operations work as expected:

print "Jim has " + len(jim.traverse('friends') + " friends"

Category nodes

Access all your instances of a class via the category node:

country_category = Country.category()
for c in country_category.instance.all()

Note that connect and disconnect are not available through the instance relation.

Cardinality

It’s possible to enforce cardinality restrictions on your relationships. Remember this needs to be declared on both sides of the relationship for it to work:

class Person(StructuredNode):
    car = RelationshipTo('Car', 'CAR', cardinality=One)

class Car(StructuredNode):
    owner = RelationshipFrom('Person', cardinality=One)

The following cardinality classes are available:

ZeroOMore (default), OneOrMore, ZeroOrOne, One

If cardinality is broken by existing data a CardinalityViolation exception is raised. On attempting to break a cardinality restriction a AttemptedCardinalityViolation is raised.

Cypher queries

You may handle more complex queries via cypher. Each node provides an ‘inflate’ class method, this inflates py2neo nodes to neomodel node objects:

class Person(StructuredNode):
    def friends(self):
        results, metadata = self.cypher("START a=node({self}) MATCH a-[:FRIEND]->(b) RETURN b");
        return [self.__class__.inflate(row[0]) for row in results]

The self query parameter is prepopulated with the current node id. It’s possible to pass in your own query parameters to the cypher method.

Relating to many node types

You can define relations of a single type to different StructuredNode classes.:

class Humanbeing(StructuredNode):
    name = StringProperty()
    has_a = RelationshipTo(['Location', 'Nationality'], 'HAS_A')

class Location(StructuredNode):
    name = StringProperty()

class Nationality(StructuredNode):
    name = StringProperty()

Remember that when traversing the has_a relation you will retrieve objects of different types.

Batch create

Atomically create multiple nodes in a single operation:

people = Person.create(
    {'name': 'Tim', 'age': 83},
    {'name': 'Bob', 'age': 23},
    {'name': 'Jill', 'age': 34},
)

This is useful for creating large sets of data. It’s worth experimenting with the size of batches to find the optimum performance suggestions on size around 300 - 500.

Hooks and Signals

You may define the following hook methods on your nodes:

pre_save, post_save, pre_delete, post_delete, post_create

Signals are also supported if django is available:

from django.db.models import signals
signals.post_save.connect(your_func, sender=Person)

Indexing

Make use of indexes:

jim = Person.index.get(name='Jim')
for p in Person.index.search(age=3):
    print(p.name)

germany = Country(code='DE').save()

Use advanced Lucene queries with the lucene-querybuilder module:

from lucenequerybuilder import Q

Human(name='sarah', age=3).save()
Human(name='jim', age=4).save()
Human(name='bob', age=5).save()
Human(name='tim', age=2).save()

for h in Human.index.search(Q('age', inrange=[3, 5])):
    print(h.name) # sarah, jim, bob

Or as a lucene query string:

sarah = Human.index.search('name:sar*')

Properties

The following properties are available:

StringProperty, IntegerProperty, FloatProperty, BooleanProperty

DateProperty, DateTimeProperty, JSONProperty, AliasProperty

The DateTimeProperty accepts datetime.datetime objects of any timezone and stores them as a UTC epoch value. These epoch values are inflated to datetime.datetime objects with the UTC timezone set.

The DateProperty accepts datetime.date objects which are stored as a string property ‘YYYY-MM-DD’.

Default values you may provide a default value to any property, this can also be a function or any callable:

from uuid import uuid4
my_id = StringProperty(unique_index=True, default=uuid4)

You may provide arguments using a wrapper function or lambda:

my_datetime = DateTimeProperty(default=lambda: datetime.now(pytz.utc))

The AliasProperty a special property for aliasing other properties and providing ‘magic’ behaviour:

class Person(StructuredNode):
    full_name = StringProperty(index=True)
    name = AliasProperty(to='full_name')

Person.index.search(name='Jim') # just works