How to use SQLAlchemy ORM to access DB2 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 IBM DB2 and the SQLAlchemy toolkit, you can build DB2-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to DB2 data to query, update, delete, and insert DB2 data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live DB2 data in Python. When you issue complex SQL queries from DB2, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to DB2 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to DB2 Data

Connecting to DB2 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.

Set the following properties to connect to DB2:

• Server: Set this to the name of the server running DB2.
• Port: Set this to the port the DB2 server is listening on.
• Database: Set this to the name of the DB2 database.
• User: Set this to the username of a user allowed to access the database.
• Password: Set this to the password of a user allowed to access the database.

You will also need to install the corresponding DB2 driver:

• Windows: Install the IBM Data Server Provider for .NET.

  On Windows, installing the IBM Data Server Provider is sufficient, as the installation registers it in the machine.config.

• Java: Install the IBM Data Server Driver for JDBC.

  In the Java version, place the IBM Data Server Driver JAR in the www\WEB-INF\lib\ folder for this application.

Follow the procedure below to install SQLAlchemy and start accessing DB2 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 DB2 Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with DB2 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("db2:///?Server=10.0.1.2&Port=50000&User=admin&Password=admin&Database=test")

Declare a Mapping Class for DB2 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 Orders 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 Orders(base):
	__tablename__ = "Orders"
	OrderName = Column(String,primary_key=True)
	Freight = Column(String)
	...

Query DB2 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("db2:///?Server=10.0.1.2&Port=50000&User=admin&Password=admin&Database=test")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Orders).filter_by(ShipCity="New York"):
	print("OrderName: ", instance.OrderName)
	print("Freight: ", instance.Freight)
	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

Orders_table = Orders.metadata.tables["Orders"]
for instance in session.execute(Orders_table.select().where(Orders_table.c.ShipCity == "New York")):
	print("OrderName: ", instance.OrderName)
	print("Freight: ", instance.Freight)
	print("---------")

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

Insert DB2 Data

To insert DB2 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 DB2.

new_rec = Orders(OrderName="placeholder", ShipCity="New York")
session.add(new_rec)
session.commit()

Update DB2 Data

To update DB2 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 DB2.

updated_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.ShipCity = "New York"
session.commit()

Delete DB2 Data

To delete DB2 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(Orders).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 IBM DB2 to start building Python apps and scripts with connectivity to DB2 data. Reach out to our Support Team if you have any questions.