How to use SQLAlchemy ORM to access JD Edwards 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 JD Edwards and the SQLAlchemy toolkit, you can build JD Edwards-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to JD Edwards data to query JD Edwards data.

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

Connecting to JD Edwards Data

Connecting to JD Edwards 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.

The driver connects to JD Edwards through your Application Interface Services (AIS) Server. Set the following connection properties:

• URL: The base HTTPS URL of your AIS Server (e.g., https://jde-ais.example.com:8300).
• User: Your JD Edwards username.
• Password: Your JD Edwards password.
• Environment (optional): The JD Edwards environment to use (e.g., PD920 for production or DV920 for development). If not specified, the AIS Server's default environment is used.
• Role (optional): The JD Edwards role for the session. If not specified, the AIS Server's default role is used.
• DeviceName (optional): An identifier for the connecting device or application, used for auditing and logging on the AIS Server.
• Jasserver (optional): The specific Java Application Server (JAS) instance to route requests through, useful in clustered environments.

Choosing Which Data Is Exposed

JD Edwards organizes tables and business views by System Code, and the driver exposes each System Code as its own schema. Use these properties to control which schemas are available:

• DataModel: One or more ERP modules (comma-separated) whose System Codes are exposed as schemas, or All to expose every System Code in the connected instance. Defaults to FinancialManagement.
• SystemCodes: A comma-separated list of additional System Codes to expose alongside those from DataModel (e.g., 42,43).

When you connect, the driver sends your credentials to the AIS Server to obtain a session token and caches it. The driver requests a new token automatically before the session expires.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with JD Edwards 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("jdedwards:///?URL=https://your-jde-environment-app.example.com&User=admin&Password=myPassword")

Declare a Mapping Class for JD Edwards 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 AccountsPayable.AccountLedger 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 AccountsPayable.AccountLedger(base):
	__tablename__ = "AccountsPayable.AccountLedger"
	DocumentNumber = Column(String,primary_key=True)
	Amount = Column(String)
	...

Query JD Edwards 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("jdedwards:///?URL=https://your-jde-environment-app.example.com&User=admin&Password=myPassword")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(AccountsPayable.AccountLedger).filter_by(BusinessUnit="100"):
	print("DocumentNumber: ", instance.DocumentNumber)
	print("Amount: ", instance.Amount)
	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

AccountsPayable.AccountLedger_table = AccountsPayable.AccountLedger.metadata.tables["AccountsPayable.AccountLedger"]
for instance in session.execute(AccountsPayable.AccountLedger_table.select().where(AccountsPayable.AccountLedger_table.c.BusinessUnit == "100")):
	print("DocumentNumber: ", instance.DocumentNumber)
	print("Amount: ", instance.Amount)
	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 JD Edwards to start building Python apps and scripts with connectivity to JD Edwards data. Reach out to our Support Team if you have any questions.