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

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

Getting Started

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Connecting to Salesforce Data

Connecting to Salesforce 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.

There are several authentication methods available for connecting to Salesforce: OAuth, Login (or basic), and SSO. The Login method requires you to have the username, password, and security token of the user.

OAuth Authentication (default)

The default authentication mechanism (and the one preferred by Salesforce) is OAuth. To use OAuth with CData's embedded OAuth application, leave the connection properties blank. If you have configured your own custom OAuth application with Salesforce (see the Help documentation for more information), set OAuthClientId, OAuthClientSecret, and CallbackURL to the properties for you application. Set InitiateOAuth to the desired OAuth flow ("GETANDREFRESH" will have the connector manage the entire OAuth flow).

Login (or Basic) Authentication

If you do not wish do not wish to use OAuth authentication, you can use Login (or basic) authentication. Set AuthScheme to Basic, and set the User, Password, and SecurityToken properties. You can configure your security token in Salesforce.

SSO (single sign-on) Authentication

SSO (single sign-on) can be used by setting the SSOProperties, SSOLoginUrl, and SSOExchangeURL connection properties, which allow you to authenticate to an identity provider. See the "Getting Started" chapter in the Help documentation for more information.

Multi-Factor Authentication (MFA)

If your Salesforce org has MFA enforcement enabled, set MFACode to the time-based one-time passcode (TOTP) generated by your authenticator app (such as Salesforce Authenticator or Google Authenticator). MFACode applies to both OAuth and Login authentication flows.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Salesforce 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("salesforce:///?InitiateOAuth=GETANDREFRESH&MFACode=YourMFACode")

Declare a Mapping Class for Salesforce 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 Account 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 Account(base):
	__tablename__ = "Account"
	Industry = Column(String,primary_key=True)
	AnnualRevenue = Column(String)
	...

Query Salesforce 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("salesforce:///?InitiateOAuth=GETANDREFRESH&MFACode=YourMFACode")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Account).filter_by(Name="GenePoint"):
	print("Industry: ", instance.Industry)
	print("AnnualRevenue: ", instance.AnnualRevenue)
	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

Account_table = Account.metadata.tables["Account"]
for instance in session.execute(Account_table.select().where(Account_table.c.Name == "GenePoint")):
	print("Industry: ", instance.Industry)
	print("AnnualRevenue: ", instance.AnnualRevenue)
	print("---------")

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

Insert Salesforce Data

To insert Salesforce 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 Salesforce.

new_rec = Account(Industry="placeholder", Name="GenePoint")
session.add(new_rec)
session.commit()

Update Salesforce Data

To update Salesforce 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 Salesforce.

updated_rec = session.query(Account).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Name = "GenePoint"
session.commit()

Delete Salesforce Data

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