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Use SQLAlchemy ORMs to Access Redshift Data in Python

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

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

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

Connecting to Redshift Data

Connecting to Redshift 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 Redshift, set the following:

  • Server: Set this to the host name or IP address of the cluster hosting the Database you want to connect to.
  • Port: Set this to the port of the cluster.
  • Database: Set this to the name of the database. Or, leave this blank to use the default database of the authenticated user.
  • User: Set this to the username you want to use to authenticate to the Server.
  • Password: Set this to the password you want to use to authenticate to the Server.

You can obtain the Server and Port values in the AWS Management Console:

  1. Open the Amazon Redshift console (http://console.aws.amazon.com/redshift).
  2. On the Clusters page, click the name of the cluster.
  3. On the Configuration tab for the cluster, copy the cluster URL from the connection strings displayed.

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

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

engine = create_engine("redshift///?User=admin&Password=admin&Database=dev&Server=examplecluster.my.us-west-2.redshift.amazonaws.com&Port=5439")

Declare a Mapping Class for Redshift 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 Orders 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 Orders(base):
	__tablename__ = "Orders"
	ShipName = Column(String,primary_key=True)
	ShipCity = Column(String)
	...

Query Redshift 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("redshift///?User=admin&Password=admin&Database=dev&Server=examplecluster.my.us-west-2.redshift.amazonaws.com&Port=5439")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Orders).filter_by(ShipCountry="USA"):
	print("ShipName: ", instance.ShipName)
	print("ShipCity: ", instance.ShipCity)
	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

Orders_table = Orders.metadata.tables["Orders"]
for instance in session.execute(Orders_table.select().where(Orders_table.c.ShipCountry == "USA")):
	print("ShipName: ", instance.ShipName)
	print("ShipCity: ", instance.ShipCity)
	print("---------")

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

Insert Redshift Data

To insert Redshift 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 Redshift.

new_rec = Orders(ShipName="placeholder", ShipCountry="USA")
session.add(new_rec)
session.commit()

Update Redshift Data

To update Redshift 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 Redshift.

updated_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.ShipCountry = "USA"
session.commit()

Delete Redshift Data

To delete Redshift 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 recoreds (rows).

deleted_rec = session.query(Orders).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 Redshift Python Connector to start building Python apps and scripts with connectivity to Redshift data. Reach out to our Support Team if you have any questions.