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

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How to use SQLAlchemy ORM to access Confluence Data in Python


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

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

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

Connecting to Confluence Data

Connecting to Confluence 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.

Obtaining an API Token

An API token is necessary for account authentication. To generate one, login to your Atlassian account and navigate to API tokens > Create API token. The generated token will be displayed.

Connect Using a Confluence Cloud Account

To connect to a Cloud account, provide the following (Note: Password has been deprecated for connecting to a Cloud Account and is now used only to connect to a Server Instance.):

  • User: The user which will be used to authenticate with the Confluence server.
  • APIToken: The API Token associated with the currently authenticated user.
  • Url: The URL associated with your JIRA endpoint. For example, https://yoursitename.atlassian.net.

Connect Using a Confluence Server Instance

To connect to a Server instance, provide the following:

  • User: The user which will be used to authenticate with the Confluence instance.
  • Password: The password which will be used to authenticate with the Confluence server.
  • Url: The URL associated with your JIRA endpoint. For example, https://yoursitename.atlassian.net.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Confluence 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("confluence:///?User=admin&APIToken=myApiToken&Url=https://yoursitename.atlassian.net&Timezone=America/New_York")

Declare a Mapping Class for Confluence 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 Pages 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 Pages(base): __tablename__ = "Pages" Key = Column(String,primary_key=True) Name = Column(String) ...

Query Confluence 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("confluence:///?User=admin&APIToken=myApiToken&Url=https://yoursitename.atlassian.net&Timezone=America/New_York") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Pages).filter_by(Id="10000"): print("Key: ", instance.Key) print("Name: ", instance.Name) 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

Pages_table = Pages.metadata.tables["Pages"] for instance in session.execute(Pages_table.select().where(Pages_table.c.Id == "10000")): print("Key: ", instance.Key) print("Name: ", instance.Name) 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 Confluence to start building Python apps and scripts with connectivity to Confluence data. Reach out to our Support Team if you have any questions.