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Python Connector Libraries for Neo4J Data Connectivity. Integrate Neo4J with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

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How to use SQLAlchemy ORM to access Neo4J Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Neo4J data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Neo4J and the SQLAlchemy toolkit, you can build Neo4J-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Neo4J data to query Neo4J data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Neo4J data in Python. When you issue complex SQL queries from Neo4J, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Neo4J and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Neo4J Data

Connecting to Neo4J data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

To connect to Neo4j, set the following connection properties:

  • Server: The server hosting the Neo4j instance.
  • Port: The port on which the Neo4j service is running. The provider connects to port 7474 by default.
  • User: The username of the user using the Neo4j instance.
  • Password: The password of the user using the Neo4j instance.

Follow the procedure below to install SQLAlchemy and start accessing Neo4J through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker

Model Neo4J Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Neo4J data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("neo4j:///?Server=localhost&Port=7474&User=my_user&Password=my_password")

Declare a Mapping Class for Neo4J Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the ProductCategory table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base() class ProductCategory(base): __tablename__ = "ProductCategory" CategoryId = Column(String,primary_key=True) CategoryName = Column(String) ...

Query Neo4J Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("neo4j:///?Server=localhost&Port=7474&User=my_user&Password=my_password") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(ProductCategory).filter_by(CategoryOwner="CData Software"): print("CategoryId: ", instance.CategoryId) print("CategoryName: ", instance.CategoryName) print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

ProductCategory_table = ProductCategory.metadata.tables["ProductCategory"] for instance in session.execute(ProductCategory_table.select().where(ProductCategory_table.c.CategoryOwner == "CData Software")): print("CategoryId: ", instance.CategoryId) print("CategoryName: ", instance.CategoryName) print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Neo4J to start building Python apps and scripts with connectivity to Neo4J data. Reach out to our Support Team if you have any questions.