Learn more
 

How to use SQLAlchemy ORM to access Redis Data in Python


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

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

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

Connecting to Redis Data

Connecting to Redis 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.

Set the following connection properties to connect to a Redis instance:

  • Server: Set this to the name or address of the server your Redis instance is running on. You can specify the port in Port.
  • Password: Set this to the password used to authenticate with a password-protected Redis instance , using the Redis AUTH command.

Set UseSSL to negotiate SSL/TLS encryption when you connect.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Redis 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("redis:///?Server=127.0.0.1&Port=6379&Password=myPassword")

Declare a Mapping Class for Redis 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 Customers 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 Customers(base): __tablename__ = "Customers" City = Column(String,primary_key=True) CompanyName = Column(String) ...

Query Redis 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("redis:///?Server=127.0.0.1&Port=6379&Password=myPassword") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Customers).filter_by(Country="US"): print("City: ", instance.City) print("CompanyName: ", instance.CompanyName) 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

Customers_table = Customers.metadata.tables["Customers"] for instance in session.execute(Customers_table.select().where(Customers_table.c.Country == "US")): print("City: ", instance.City) print("CompanyName: ", instance.CompanyName) print("---------")

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

Insert Redis Data

To insert Redis 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 Redis.

new_rec = Customers(City="placeholder", Country="US") session.add(new_rec) session.commit()

Update Redis Data

To update Redis 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 Redis.

updated_rec = session.query(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Country = "US" session.commit()

Delete Redis Data

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

Ready to get started?

Download a free trial of the Redis Connector to get started:

 Download Now

Learn more:

Redis Icon Redis Python Connector

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

In this article

Related articles