Below you can see an example of how to create User and Language node classes, and a relationship class of type SPEAKS. Along with that, you can see how to create a new node and relationship and how to save them in the database. After that, you can load those nodes and relationship from the database.

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

db = Memgraph()

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

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

class Speaks(Relationship, type="SPEAKS"):
    pass

user = User(id="3", username="John").save(db)
language = Language(name="en").save(db)
speaks_rel = Speaks(
    _start_node_id = user._id,
    _end_node_id = language._id
).save(db)

loaded_user = User(id="3").load(db=db)
print(loaded_user)
loaded_speaks = Speaks(
        _start_node_id=user._id,
        _end_node_id=language._id
    ).load(db)
print(loaded_speaks)

from gqlalchemy import create, match
from gqlalchemy.query_builder import Operator

query_create = create()
        .node(labels="Person", name="Leslie")
        .to(relationship_type="FRIENDS_WITH")
        .node(labels="Person", name="Ron")
        .execute()

query_match = match()
        .node(labels="Person", variable="p1")
        .to()
        .node(labels="Person", variable="p2")
        .where(item="p1.name", operator=Operator.EQUAL, literal="Leslie")
        .return_(results=["p1", ("p2", "second")])
        .execute()

You can create and start Kafka or Pulsar stream using GQLAlchemy.

Kafka stream

from gqlalchemy import MemgraphPulsarStream

stream = MemgraphPulsarStream(name="ratings_stream", topics=["ratings"], transform="movielens.rating", service_url="localhost:6650")
db.create_stream(stream)
db.start_stream(stream)

Pulsar stream

from gqlalchemy import MemgraphKafkaStream

stream = MemgraphKafkaStream(name="ratings_stream", topics=["ratings"], transform="movielens.rating", bootstrap_servers="localhost:9093")
db.create_stream(stream)
db.start_stream(stream)

Import table data to a graph database

You can translate table data from a file to graph data and import it to Memgraph. Currently, we support reading of CSV, Parquet, ORC and IPC/Feather/Arrow file formats via the PyArrow package.

Read all about it in table to graph importer how-to guide.

Make a custom file system importer

If you want to read from a file system not currently supported by GQLAlchemy, or use a file type currently not readable, you can implement your own by extending abstract classes FileSystemHandler and DataLoader, respectively.

Read all about it in custom file system importer how-to guide.

You can start, stop, connect to and monitor Memgraph instances with GQLAlchemy.

Manage Memgraph Docker instance

from gqlalchemy.instance_runner import (
    DockerImage,
    MemgraphInstanceDocker
)

memgraph_instance = MemgraphInstanceDocker(
    docker_image=DockerImage.MEMGRAPH, docker_image_tag="latest", host="0.0.0.0", port=7687
)
memgraph = memgraph_instance.start_and_connect(restart=False)

memgraph.execute_and_fetch("RETURN 'Memgraph is running' AS result"))[0]["result"]

Manage Memgraph binary instance

from gqlalchemy.instance_runner import MemgraphInstanceBinary

memgraph_instance = MemgraphInstanceBinary(
    host="0.0.0.0", port=7698, binary_path="/usr/lib/memgraph/memgraph", user="memgraph"
)
memgraph = memgraph_instance.start_and_connect(restart=False)

memgraph.execute_and_fetch("RETURN 'Memgraph is running' AS result"))[0]["result"]

Because Memgraph supports database triggers on CREATE, UPDATE and DELETE operations, GQLAlchemy also implements a simple interface for maintaining these triggers.

from gqlalchemy import Memgraph, MemgraphTrigger
from gqlalchemy.models import (
    TriggerEventType,
    TriggerEventObject,
    TriggerExecutionPhase,
)

db = Memgraph()

trigger = MemgraphTrigger(
    name="ratings_trigger",
    event_type=TriggerEventType.CREATE,
    event_object=TriggerEventObject.NODE,
    execution_phase=TriggerExecutionPhase.AFTER,
    statement="UNWIND createdVertices AS node SET node.created_at = LocalDateTime()",
)

db.create_trigger(trigger)
triggers = db.get_triggers()
print(triggers)

Since Memgraph is an in-memory graph database, the GQLAlchemy library provides an on-disk storage solution for large properties not used in graph algorithms. This is useful when nodes or relationships have metadata that doesn’t need to be used in any of the graph algorithms that need to be carried out in Memgraph, but can be fetched after. Learn all about it in the on-disk storage how-to guide.