How to use 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. In this how-to guide, you'll learn how to use an SQL database to store node properties seamlessly as if they were being stored in Memgraph.

Info

You can also use this feature with Neo4j:

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

Connect to Memgraph and an SQL database

First you need to do all necessary imports and connect to the running Memgraph and SQL database instance:

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

graphdb = Memgraph()
SQLitePropertyDatabase('path-to-my-db.db', graphdb)

The graphdb creates a connection to an in-memory graph database and SQLitePropertyDatabase attaches to graphdb in its constructor.

Define schema

For example, you can create the class User which maps to a node object in the graph database.

class User(Node):
    id: int = Field(unique=True, exists=True, index=True, db=graphdb)
    huge_string: Optional[str] = Field(on_disk=True)

Here the property id is a required int that creates uniqueness and existence constraints inside Memgraph. You can notice that the property id is also indexed on label User. The huge_string property is optional, and because the on_disk argument is set to True, it will be saved into the SQLite database.

Create data

Next, you can create some huge string, which won't be saved into the graph database, but rather into the SQLite databse.

my_secret = "I LOVE DUCKS" * 1000
john = User(id=5, huge_string=my_secret).save(db)
john2 = User(id=5).load(db)
print(john2.huge_string)  # prints I LOVE DUCKS, a 1000 times

Hopefully this guide has taught you how to use on-disk storage along with the in-memory graph database. If you have any more questions, join our community and ping us on Discord.