Use SQLAlchemy ORMs to Access Slack Data in Python

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Slack Python Connector

Python Connector Libraries for Slack Data Connectivity. Integrate Slack with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



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

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

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

Connecting to Slack Data

Connecting to Slack 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.

Slack uses the OAuth authentication standard. To authenticate using OAuth, you will need to create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the Getting Started section of the help documentation for an authentication guide.

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

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

engine = create_engine("slack:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Slack 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 Channels 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 Channels(base):
	__tablename__ = "Channels"
	Id = Column(String,primary_key=True)
	Name = Column(String)
	...

Query Slack 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("slack:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Channels).filter_by(IsPublic="True"):
	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

Channels_table = Channels.metadata.tables["Channels"]
for instance in session.execute(Channels_table.select().where(Channels_table.c.IsPublic == "True")):
	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 Slack Data

To insert Slack 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 Slack.

new_rec = Channels(Id="placeholder", IsPublic="True")
session.add(new_rec)
session.commit()

Update Slack Data

To update Slack 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 Slack.

updated_rec = session.query(Channels).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.IsPublic = "True"
session.commit()

Delete Slack Data

To delete Slack 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(Channels).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()

Free Trial & More Information

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