How to use SQLAlchemy ORM to access Jira Assets Data in Python

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

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

Connecting to Jira Assets Data

Connecting to Jira Assets 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.

Jira Assets supports connecting and authenticating via the APIToken.

To generate an API token:

1. Log in to your Atlassian account.
2. Navigate to Security < Create and manage API Token < Create API Token.

Atlassian generates and then displays the API token.

After you have generated the API token, set these parameters:

• AuthScheme: APIToken.
• User: The login of the authenticating user.
• APIToken: The API token you just generated.

You are now ready to connect and authenticate to Jira Assets.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Jira Assets 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("jiraassets:///?User=MyUser&APIToken=myApiToken&Url=https://yoursitename.atlassian.net")

Declare a Mapping Class for Jira Assets 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 Objects 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 Objects(base):
	__tablename__ = "Objects"
	ID = Column(String,primary_key=True)
	Name = Column(String)
	...

Query Jira Assets 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("jiraassets:///?User=MyUser&APIToken=myApiToken&Url=https://yoursitename.atlassian.net")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Objects).filter_by(Label="SYD-1"):
	print("ID: ", instance.ID)
	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

Objects_table = Objects.metadata.tables["Objects"]
for instance in session.execute(Objects_table.select().where(Objects_table.c.Label == "SYD-1")):
	print("ID: ", instance.ID)
	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.

Insert Jira Assets Data

To insert Jira Assets 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 Assets.

new_rec = Objects(ID="placeholder", Label="SYD-1")
session.add(new_rec)
session.commit()

Update Jira Assets Data

To update Jira Assets 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 Assets.

updated_rec = session.query(Objects).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Label = "SYD-1"
session.commit()

Delete Jira Assets Data

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