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Use SQLAlchemy ORMs to Access Salesforce Chatter Data in Python

The CData Python Connector for Salesforce Chatter enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Salesforce Chatter data.

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

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

Connecting to Salesforce Chatter Data

Connecting to Salesforce Chatter 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.

Salesforce Chatter uses OAuth 2.0 authentication. To authenticate to Salesforce Chatter via OAuth 2.0, you will need to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL by registering an app with Salesforce Chatter.

Follow the procedure below to install SQLAlchemy and start accessing Salesforce Chatter through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model Salesforce Chatter Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Salesforce Chatter data.

engine = create_engine("salesforcechatter///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:343343&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Salesforce Chatter 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 Users 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 Users(base):
	__tablename__ = "Users"
	Name = Column(String,primary_key=True)
	PostCount = Column(String)
	...

Query Salesforce Chatter 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("salesforcechatter///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:343343&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Users).filter_by(SearchTerms="Smoked*BBQ"):
	print("Name: ", instance.Name)
	print("PostCount: ", instance.PostCount)
	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

Users_table = Users.metadata.tables["Users"]
for instance in session.execute(Users_table.select().where(Users_table.c.SearchTerms == "Smoked*BBQ")):
	print("Name: ", instance.Name)
	print("PostCount: ", instance.PostCount)
	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 Salesforce Chatter Python Connector to start building Python apps and scripts with connectivity to Salesforce Chatter data. Reach out to our Support Team if you have any questions.