sagemaker-studio-aurora_postgresql

Aurora PostgreSQL Access with SageMaker Studio in a private VPC

This is a sample project for Python development with CDK.

The cdk.json file tells the CDK Toolkit how to execute your app.

This project is set up like a standard Python project. The initialization process also creates a virtualenv within this project, stored under the .venv directory. To create the virtualenv it assumes that there is a python3 (or python for Windows) executable in your path with access to the venv package. If for any reason the automatic creation of the virtualenv fails, you can create the virtualenv manually.

To manually create a virtualenv on MacOS and Linux:

$ python3 -m venv .venv

After the init process completes and the virtualenv is created, you can use the following step to activate your virtualenv.

$ source .venv/bin/activate

If you are a Windows platform, you would activate the virtualenv like this:

% .venv\Scripts\activate.bat

Once the virtualenv is activated, you can install the required dependencies.

$ pip install -r requirements.txt

To add additional dependencies, for example other CDK libraries, just add them to your setup.py file and rerun the pip install -r requirements.txt command.

At this point you can now synthesize the CloudFormation template for this code.

(.venv) $ cdk -c db_cluster_name='db-cluster-name' synth

Use cdk deploy command to create the stack shown above.

(.venv) $ cdk -c db_cluster_name='db-cluster-name' deploy

Also, you can check all CDK Stacks with cdk list command.

(.venv) $ cdk list
StudioAuroraPgSQLVpcStack
StudioAuroraPgSQLStack
SageMakerStudioForAuroraPgSQLStack

Clean Up

Delete the CloudFormation stacks by running the below command.

(.venv) $ cdk destroy --all

Useful commands

• cdk ls list all stacks in the app
• cdk synth emits the synthesized CloudFormation template
• cdk deploy deploy this stack to your default AWS account/region
• cdk diff compare deployed stack with current state
• cdk docs open CDK documentation

Enjoy!

References

• PostgreSQL Tutorial
• psycopg2-binary - Python-PostgreSQL Database Adapter that is a stand-alone package, not requiring a compiler or external libraries
• Supported DB engines for DB instance classes
• Extension versions for Amazon Aurora PostgreSQL
• Amazon Aurora PostgreSQL now supports pgvector for vector storage and similarity search (2023-07-13)
• Amazon RDS for PostgreSQL now supports pgvector for simplified ML model integration (2023-05-03)