GQLAlchemy is a fully open-source Python library and Object Graph Mapper (OGM) - a link between graph database objects and Python objects.
An Object Graph Mapper or OGM provides a developer-friendly workflow that allows for writing object-oriented notation to communicate with graph databases. Instead of writing Cypher queries, you will be able to write object-oriented code, which the OGM will automatically translate into Cypher queries.
Before you install
gqlalchemy, make sure that you have
cmake installed by running:
You can install
cmake by following the official instructions.
gqlalchemy, simply run the following command:
pip install gqlalchemy
If you are using Conda for Python environment management, you can install GQLAlchemy through pip.
Build & Test
The project uses Poetry to build the GQLAlchemy Python library. To build and run tests, execute the following command:
Before starting the tests, make sure you have an active Memgraph instance running. Execute the following command:
poetry run pytest .
🗺️ Object graph mapper
Below you can see an example of how to create
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)
🔨 Query builder
When building a Cypher query, you can use a set of methods that are wrappers around Cypher clauses.
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()
🚰 Manage streams
You can create and start Kafka or Pulsar stream using GQLAlchemy.
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)
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 from different sources
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
Read all about it in custom file system importer how-to guide.
⚙️ Manage Memgraph instances
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"))["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"))["result"]
🔫 Manage database triggers
Because Memgraph supports database triggers on
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)
💽 On-disk storage
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.
If you want to learn more about OGM, query builder, managing streams, importing data from different source, managing Memgraph instances, managing database triggers and using on-disk storage, check out the GQLAlchemy how-to guides.
Development (how to build)
poetry run flake8 . poetry run black . poetry run pytest . -k "not slow"
The GQLAlchemy documentation is available on memgraph.com/docs/gqlalchemy.
The documentation can be generated by executing:
pip3 install python-markdown python-markdown
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