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Use SQLAlchemy ORMs to Access Jira Service Desk Data in Python

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

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The CData Python Connector for Jira Service Desk enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Jira Service Desk 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 Service Desk and the SQLAlchemy toolkit, you can build Jira Service Desk-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Jira Service Desk data to query, update, delete, and insert Jira Service Desk data.

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

Connecting to Jira Service Desk Data

Connecting to Jira Service Desk 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.

You can establish a connection to any Jira Service Desk Cloud account or Server instance.

Connecting with a Cloud Account

To connect to a Cloud account, you'll first need to retrieve an APIToken. To generate one, log in to your Atlassian account and navigate to API tokens > Create API token. The generated token will be displayed.

Supply the following to connect to data:

  • User: Set this to the username of the authenticating user.
  • APIToken: Set this to the API token found previously.

Connecting with a Service Account

To authenticate with a service account, you will need to supply the following connection properties:

  • User: Set this to the username of the authenticating user.
  • Password: Set this to the password of the authenticating user.
  • URL: Set this to the URL associated with your JIRA Service Desk endpoint. For example, https://yoursitename.atlassian.net.

Note: Password has been deprecated for connecting to a Cloud Account and is now used only to connect to a Server Instance.

Accessing Custom Fields

By default, the connector only surfaces system fields. To access the custom fields for Issues, set IncludeCustomFields.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Jira Service Desk 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("jiraservicedesk:///?ApiKey=myApiKey&User=MyUser&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Jira Service Desk 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 Requests 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 Requests(base): __tablename__ = "Requests" RequestId = Column(String,primary_key=True) ReporterName = Column(String) ...

Query Jira Service Desk 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("jiraservicedesk:///?ApiKey=myApiKey&User=MyUser&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Requests).filter_by(CurrentStatus="Open"): print("RequestId: ", instance.RequestId) print("ReporterName: ", instance.ReporterName) 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

Requests_table = Requests.metadata.tables["Requests"] for instance in session.execute(Requests_table.select().where(Requests_table.c.CurrentStatus == "Open")): print("RequestId: ", instance.RequestId) print("ReporterName: ", instance.ReporterName) print("---------")

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

Insert Jira Service Desk Data

To insert Jira Service Desk 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 Jira Service Desk.

new_rec = Requests(RequestId="placeholder", CurrentStatus="Open") session.add(new_rec) session.commit()

Update Jira Service Desk Data

To update Jira Service Desk 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 Jira Service Desk.

updated_rec = session.query(Requests).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.CurrentStatus = "Open" session.commit()

Delete Jira Service Desk Data

To delete Jira Service Desk 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(Requests).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 Jira Service Desk Python Connector to start building Python apps and scripts with connectivity to Jira Service Desk data. Reach out to our Support Team if you have any questions.