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Use SQLAlchemy ORMs to Access Alfresco Data in Python

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

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

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

Connecting to Alfresco Data

Connecting to Alfresco 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.

To connect to Alfresco, the following connection properties must be supplied: User, Password, and InstanceUrl. User and Password correspond to the login credentials that you use to access Alfresco in a web browser. InstanceUrl corresponds to the Alfresco instance you will be querying. For instance, if you expect your queries to hit, you should supply for InstanceUrl.

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

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

engine = create_engine("alfresco///?User=MyUsername& Password=MyPassword& Format=Solr&")

Declare a Mapping Class for Alfresco 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 Alfresco 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 Alfresco(base):
	__tablename__ = "Alfresco"
	DBID = Column(String,primary_key=True)
	Column1 = Column(String)

Query Alfresco 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("alfresco///?User=MyUsername& Password=MyPassword& Format=Solr&")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Alfresco).filter_by(Column2="MyFilter"):
	print("DBID: ", instance.DBID)
	print("Column1: ", instance.Column1)

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Alfresco_table = Alfresco.metadata.tables["Alfresco"]
for instance in session.execute( == "MyFilter")):
	print("DBID: ", instance.DBID)
	print("Column1: ", instance.Column1)

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 Alfresco Python Connector to start building Python apps and scripts with connectivity to Alfresco data. Reach out to our Support Team if you have any questions.