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

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How to use SQLAlchemy ORM to access Cassandra Data in Python


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

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

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

Connecting to Cassandra Data

Connecting to Cassandra 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.

Set the Server, Port, and Database connection properties to connect to Cassandra. Additionally, to use internal authentication set the User and Password connection properties.

Follow the procedure below to install SQLAlchemy and start accessing Cassandra 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 Cassandra Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Cassandra 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("cassandra:///?Database=MyCassandraDB&Port=7000&Server=127.0.0.1")

Declare a Mapping Class for Cassandra 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 Customer 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 Customer(base): __tablename__ = "Customer" City = Column(String,primary_key=True) TotalDue = Column(String) ...

Query Cassandra 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("cassandra:///?Database=MyCassandraDB&Port=7000&Server=127.0.0.1") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Customer).filter_by(FirstName="Bob"): print("City: ", instance.City) print("TotalDue: ", instance.TotalDue) 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

Customer_table = Customer.metadata.tables["Customer"] for instance in session.execute(Customer_table.select().where(Customer_table.c.FirstName == "Bob")): print("City: ", instance.City) print("TotalDue: ", instance.TotalDue) print("---------")

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

Insert Cassandra Data

To insert Cassandra data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Cassandra.

new_rec = Customer(City="placeholder", FirstName="Bob") session.add(new_rec) session.commit()

Update Cassandra Data

To update Cassandra data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Cassandra.

updated_rec = session.query(Customer).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.FirstName = "Bob" session.commit()

Delete Cassandra Data

To delete Cassandra data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Customer).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

Free Trial & More Information

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