Use SQLAlchemy ORMs to Access Couchbase Data in Python

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Couchbase Python Connector

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

The CData Python Connector for Couchbase enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Couchbase data.

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

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

Connecting to Couchbase Data

Connecting to Couchbase 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 using the Login method, set User, Password, and Server to the credentials for the account and the address of the server you want to connect to.

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

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model Couchbase Data in Python

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

engine = create_engine("couchbase:///?User=myuseraccount&Password=mypassword&Server=http://mycouchbaseserver")

Declare a Mapping Class for Couchbase 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"
	FirstName = Column(String,primary_key=True)
	TotalDue = Column(String)

Query Couchbase 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("couchbase:///?User=myuseraccount&Password=mypassword&Server=http://mycouchbaseserver")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Customer).filter_by(FirstName="Bob"):
	print("FirstName: ", instance.FirstName)
	print("TotalDue: ", instance.TotalDue)

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( == "Bob")):
	print("FirstName: ", instance.FirstName)
	print("TotalDue: ", instance.TotalDue)

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

Insert Couchbase Data

To insert Couchbase 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 Couchbase.

new_rec = Customer(FirstName="placeholder", FirstName="Bob")

Update Couchbase Data

To update Couchbase 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 Couchbase.

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

Delete Couchbase Data

To delete Couchbase 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 recoreds (rows).

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

Free Trial & More Information

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