How to use object graph mapper

Info

You can also use this feature with Neo4j:

db = Neo4j(host="localhost", port="7687", username="neo4j", password="test")

Through this guide, you will learn how to use the GQLAlchemy object graph mapper to:

• Map nodes and relationships
• Save nodes and relationships
• Load nodes and relationships
  • Find node properties
  • Create relationship between existing nodes
  • Merge nodes and relationships
• Create indexes
• Create constraints

Hopefully, this guide will teach you how to properly use GQLAlchemy object graph mapper. If you have any more questions, join our community and ping us on Discord.

Info

To test the above features, you must install GQLAlchemy and have a running Memgraph instance. If you're unsure how to run Memgraph, check out the Memgraph Quick start).

Map nodes and relationships

First, we need to import all the necessary classes from GQLAlchemy:

from gqlalchemy import Memgraph, Node, Relationship

After that, instantiate Memgraph and create classes representing nodes.

db = Memgraph()

class User(Node):
    id: str
    username: str

class Streamer(User):
    id: str
    username: str
    followers: int

class Language(Node):
    name: str

Node is a Python class which maps to a graph object in Memgraph. User, Streamer and Language are classes which inherit from Node and they map to a label in a graph database. Class User maps to a single :User label with properties id and username, class Streamer maps to multiple labels :Streamer:User with properties id, username and followers, and class Language maps to a single :Language label with name property.

In a similar way, you can create relationship classes:

class ChatsWith(Relationship, type="CHATS_WITH"):
    last_chatted: str

class Speaks(Relationship):
    since: str

The code above maps to a relationship of type CHATS_WITH with the string property last_chatted and to a relationship of type SPEAKS with the string property since. There was no need to add type argument to Speaks class, since the label it maps to will automatically be set to uppercase class name in a graph database.

If you want to create a node class without any properties, use pass statement:

class User(Node):
    pass

For relationships without any properties also use pass statement:

class ChatsWith(Relationship, type="CHATS_WITH"):
    pass

Info

Objects are modeled using GQLAlchemy’s Object Graph Mapper (OGM) which provides schema validation, so you can be sure that the data inside Memgraph is accurate. If you tried saving data that is not following the defined schema, you will get a ValidationError.

To use the above classes, you need to save or load data first.

Save nodes and relationships

In order to save a node using the object graph mapper, first define node classes:

from gqlalchemy import Memgraph, Node, Relationship

db = Memgraph()

class User(Node):
    id: str 
    username: str

class Language(Node):
    name: str

The above classes map to User and Language nodes in the database. User nodes have properties id and username and Language nodes have property name.

To create and save node objects use the following code:

john = User(id="1", username="John").save(db)
jane = Streamer(id="2", username="janedoe", followers=111).save(db)
language = Language(name="en").save(db)

There is another way of creating and saving node objects:

john = User(id="1", username="John")
db.save_node(john)

jane = Streamer(id="2", username="janedoe", followers=111)
db.save_node(jane)

language = Language(name="en")
db.save_node(language)

Danger

The save() and save_node() procedures will save nodes in Memgraph even if they already exist. This means that if you run the above code twice, you will have duplicate nodes in the database. To avoid that, add constraints for properties or first load the node from the database to check if it already exists.

To save relationships using the object graph mapper, first define relationship classes:

class ChatsWith(Relationship, type="CHATS_WITH"):
    last_chatted: str

class Speaks(Relationship):
    since: str

The code above maps to a relationship of type CHATS_WITH with the string property last_chatted and to a relationship of type SPEAKS with the string property since. There was no need to add type argument to Speaks class, since the label it maps to will automatically be set to uppercase class name in a graph database.

To save relationships, create them with appropriate start and end nodes and then use the save() procedure:

ChatsWith(
    _start_node_id=john._id, _end_node_id=jane._id, last_chatted="2023-02-14"
).save(db)

Speaks(_start_node_id=john._id, _end_node_id=language._id, since="2023-02-14").save(db)

The property _id is an internal Memgraph id - an id given to each node upon saving to the database. This means that you have to first load nodes from the database or save them to variables in order to create a relationship between them.

Info

Objects are modeled using GQLAlchemy’s Object Graph Mapper (OGM) which provides schema validation, so you can be sure that the data inside Memgraph is accurate. If you tried saving data that is not following the defined schema, you will get ValidationError.

Another way of saving relationships is by using the save_relationship() procedure:

db.save_relationship(
    ChatsWith(_start_node_id=john._id, _end_node_id=jane._id, last_chatted="2023-02-14")
)

db.save_relationship(
    Speaks(_start_node_id=user._id, _end_node_id=language._id, since="2023-02-14")
)

Danger

The save() and save_relationship() procedures will save relationships in Memgraph even if they already exist. This means that if you run the above code twice, you will have duplicate relationships in the database. To avoid that, first load the relationship from the database to check if it already exists.

Load nodes and relationships

Let's continue with the previously defined classes:

class User(Node):
    id: str
    username: str


class Streamer(User):
    id: str
    username: str
    followers: int


class Language(Node):
    name: str


class ChatsWith(Relationship, type="CHATS_WITH"):
    last_chatted: str


class Speaks(Relationship, type="SPEAKS"):
    since: str

For this example, we will also use previously saved nodes:

jane = Streamer(id="2", username="janedoe", followers=111).save(db)
language = Language(name="en").save(db)

There are many examples of when loading a node from the database may come in handy, but let's cover the two most common.

Find node properties

Suppose you just have the id of the streamer and you want to know the streamer's name. You have to load that node from the database to check its name property. If you try running the following code:

loaded_streamer = Streamer(id="2").load(db=db)

you will get a ValidationError. This happens because the schema you defined expects username and followers properties for the Streamer instance. To avoid that, define Streamer class like this:

class Streamer(User):
    id: str
    username: Optional[str]
    followers: Optional[str]

The above class definition is not ideal, since it is not enforcing schema as before. To do that, add constraints.

If you try loading the node again, the following code:

loaded_streamer = Streamer(id="2").load(db=db)

will print out the username of the streamer whose id equals "2", that is, "janedoe".

Create relationship between existing nodes

To create a new relationship of type SPEAKS, between already saved streamer and language you need to first load those nodes:

loaded_streamer = Streamer(id="2").load(db=db)
loaded_language = Language(name="en").load(db=db)

The load() method returns one result above, since it matches unique database objects. When the matching object is not unique, the load() method will return a list of matching results.

To create a relationship between loaded_streamer and loaded_language nodes run:

Speaks(
    _start_node_id=loaded_streamer._id,
    _end_node_id=loaded_language._id,
    since="2023-02-15",
).save(db)

In the above example, the relationship will be created even if it existed before. To avoid that, check merging nodes and relationships section.

To load a relationship from the database based on its start and end node, first mark its property as optional:

class Speaks(Relationship, type="SPEAKS"):
    since: Optional[str]

The above class definition is not ideal, since it is not enforcing schema as before. To do that, add constraints.

To load the relationship, run the following:

loaded_speaks = Speaks(
        _start_node_id=streamer._id,
        _end_node_id=language._id
    ).load(db)

It's easy to get its since property:

print(loaded_speaks.since)

The output of the above print is 2023-02-15.

Merge nodes and relationships

To merge nodes, first try loading them from the database to see if they exist, and if not, save them:

try:
    streamer = Streamer(id="3").load(db=db)
except:
    print("Creating new Streamer node in the database.")
    streamer = Streamer(id="3", username="anne", followers=222).save(db=db)

To merge relationships first try loading them from the database to see if they exist, and if not, save them:

try:
    speaks = Speaks(_start_node_id=streamer._id, _end_node_id=language._id).load(db)
except:
    print("Creating new Speaks relationship in the database.")
    speaks = Speaks(
        _start_node_id=streamer._id,
        _end_node_id=language._id,
        since="2023-02-20",
    ).save(db)

Create indexes

To create indexes you need to do one additional import:

from gqlalchemy import Field

The Field class originates from pydantic, a Python library data validation and settings management. Here is the example of how Field class helps in creating label and label-property indexes:

class User(Node):
    id: str = Field(index=True, db=db)
    username: str

class Language(Node, index=True, db=db):
    name: str

The indexes will be set on class definition, before instantiation. This ensures that the index creation is run only once for each index type. To check which indexes were created, run:

print(db.get_indexes())

The other way to create indexes is by creating an instance of MemgraphIndex class. For example, to create label index NodeOne and label-property index NodeOne(name), run the following code:

from gqlalchemy import Memgraph
from gqlalchemy.models import MemgraphIndex

db = Memgraph()

index1 = MemgraphIndex("NodeOne")
index2 = MemgraphIndex("NodeOne", "name")

db.create_index(index1)
db.create_index(index2)

To learn more about indexes, head over to the indexing reference guide.

Create constraints

Uniqueness constraint enforces that each label, property_set pair is unique. Here is how you can enforce uniqueness constraint with GQLAlchemy's OGM:

class Language(Node):
    name: str = Field(unique=True, db=db)

The above is the same as running the Cypher query:

CREATE CONSTRAINT ON (n:Language) ASSERT n.name IS UNIQUE;

Read more about it at uniqueness constraint how-to guide.

Existence constraint enforces that each vertex that has a specific label also must have the specified property. Here is how you can enforce existence constraint with GQLAlchemy's OGM:

class Streamer(User):
    id: str
    username: Optional[str] = Field(exists=True, db=db)
    followers: Optional[str]

The above is the same as running the Cypher query:

CREATE CONSTRAINT ON (n:Streamer) ASSERT EXISTS (n.username);

Read more about it at existence constraint how-to guide.

To check which constraints have been created, run:

print(db.get_constraints())

Full code example

The above mentioned examples can be merged into a working code example which you can run. Here is the code:

from gqlalchemy import Memgraph, Node, Relationship, Field
from typing import Optional

db = Memgraph()

class User(Node):
    id: str = Field(index=True, db=db)
    username: str = Field(exists=True, db=db)

class Streamer(User):
    id: str
    username: Optional[str] = Field(exists=True, db=db)
    followers: Optional[str]

class Language(Node, index=True, db=db):
    name: str = Field(unique=True, db=db)

class ChatsWith(Relationship, type="CHATS_WITH"):
    last_chatted: str

class Speaks(Relationship, type="SPEAKS"):
    since: Optional[str]

john = User(id="1", username="John").save(db)
jane = Streamer(id="2", username="janedoe", followers=111).save(db)
language = Language(name="en").save(db)

ChatsWith(
    _start_node_id=john._id, _end_node_id=jane._id, last_chatted="2023-02-14"
).save(db)

Speaks(_start_node_id=john._id, _end_node_id=language._id, since="2023-02-14").save(db)

streamer = Streamer(id="2").load(db=db)
language = Language(name="en").load(db=db)

speaks = Speaks(
    _start_node_id=streamer._id,
    _end_node_id=language._id,
    since="2023-02-20",
).save(db)

speaks = Speaks(_start_node_id=streamer._id, _end_node_id=language._id).load(db)
print(speaks.since)

try:
    streamer = Streamer(id="3").load(db=db)
except:
    print("Creating new Streamer node in the database.")
    streamer = Streamer(id="3", username="anne", followers=222).save(db=db)

try:
    speaks = Speaks(_start_node_id=streamer._id, _end_node_id=language._id).load(db)
except:
    print("Creating new Speaks relationship in the database.")
    speaks = Speaks(
        _start_node_id=streamer._id,
        _end_node_id=language._id,
        since="2023-02-20",
    ).save(db)

print(db.get_indexes())
print(db.get_constraints())

Hopefully, this guide has taught you how to properly use GQLAlchemy object graph mapper. If you have any more questions, join our community and ping us on Discord.