Use SQLAlchemy ORMs to Access Jira Data in Python

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Jira Python Connector

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

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

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

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

Connecting to Jira Data

Connecting to Jira 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 JIRA, provide the User and Password. Additionally, provide the Url; for example, https://yoursitename.atlassian.net.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Jira 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("jira:///?User=admin&Password=123abc&Url=https://yoursitename.atlassian.net")

Declare a Mapping Class for Jira 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 Issues 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 Issues(base): __tablename__ = "Issues" Summary = Column(String,primary_key=True) TimeSpent = Column(String) ...

Query Jira 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("jira:///?User=admin&Password=123abc&Url=https://yoursitename.atlassian.net") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Issues).filter_by(ReporterDisplayName="Bob"): print("Summary: ", instance.Summary) print("TimeSpent: ", instance.TimeSpent) 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

Issues_table = Issues.metadata.tables["Issues"] for instance in session.execute(Issues_table.select().where(Issues_table.c.ReporterDisplayName == "Bob")): print("Summary: ", instance.Summary) print("TimeSpent: ", instance.TimeSpent) 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 Jira Python Connector to start building Python apps and scripts with connectivity to Jira data. Reach out to our Support Team if you have any questions.