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

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How to use SQLAlchemy ORM to access Act-On Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Act-On data.

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

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

Connecting to Act-On Data

Connecting to Act-On 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.

ActOn 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 guide in the CData driver documentation for more information.

Follow the procedure below to install SQLAlchemy and start accessing Act-On 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 Act-On Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Act-On 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("acton:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Act-On 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 Images 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 Images(base): __tablename__ = "Images" Id = Column(String,primary_key=True) Name = Column(String) ...

Query Act-On 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("acton:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Images).filter_by(FolderName="New Folder"): 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

Images_table = Images.metadata.tables["Images"] for instance in session.execute(Images_table.select().where(Images_table.c.FolderName == "New Folder")): 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 Act-On Data

To insert Act-On 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 Act-On.

new_rec = Images(Id="placeholder", FolderName="New Folder") session.add(new_rec) session.commit()

Update Act-On Data

To update Act-On 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 Act-On.

updated_rec = session.query(Images).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.FolderName = "New Folder" session.commit()

Delete Act-On Data

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