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.
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)
graphdb creates a connection to an in-memory graph database and
SQLitePropertyDatabase attaches to
graphdb in its constructor.
For example, you can create the class
User which maps to a node object in the
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
huge_string property is optional, and because the
on_disk argument is set to
True, it will be saved into the SQLite database.
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.