aurora-data-api - A Python DB-API 2.0 client for the AWS Aurora Serverless Data API

Installation

pip install aurora-data-api

Prerequisites

• Set up an AWS Aurora Serverless cluster and enable Data API access for it. If you have previously set up an Aurora Serverless cluster, you can enable Data API with the following AWS CLI command:

  aws rds modify-db-cluster --db-cluster-identifier DB_CLUSTER_NAME --enable-http-endpoint --apply-immediately
  

• Save the database credentials in AWS Secrets Manager using a format expected by the Data API (a JSON object with the keys username and password):

  aws secretsmanager put-secret-value --secret-id MY_DB_CREDENTIALS --secret-string "$(jq -n '.username=env.PGUSER | .password=env.PGPASSWORD')"
  

• Configure your AWS command line credentials using standard AWS conventions. You can verify that everything works correctly by running a test query via the AWS CLI:

  aws rds-data execute-statement --resource-arn RESOURCE_ARN --secret-arn SECRET_ARN --sql "select * from pg_catalog.pg_tables"
  

Usage

Use this module as you would use any DB-API compatible driver module. The aurora_data_api.connect() method is the standard main entry point, and accepts two implementation-specific keyword arguments:

• aurora_cluster_arn (also referred to as resourceArn in the Data API documentation) * If not given as a keyword argument, this can also be specified using the AURORA_CLUSTER_ARN environment variable
• secret_arn (the database credentials secret) * If not given as a keyword argument, this can also be specified using the AURORA_SECRET_ARN environment variable

import aurora_data_api

cluster_arn = "arn:aws:rds:us-east-1:123456789012:cluster:my-aurora-serverless-cluster"
secret_arn = "arn:aws:secretsmanager:us-east-1:123456789012:secret:MY_DB_CREDENTIALS"
with aurora_data_api.connect(aurora_cluster_arn=cluster_arn, secret_arn=secret_arn, database="my_db") as conn:
    with conn.cursor() as cursor:
        cursor.execute("select * from pg_catalog.pg_tables")
        print(cursor.fetchall())

The cursor supports iteration (and automatically wraps the query in a server-side cursor and paginates it if required):

with conn.cursor() as cursor:
    for row in cursor.execute("select * from pg_catalog.pg_tables"):
        print(row)

Motivation

The RDS Data API is the link between the AWS Lambda serverless environment and the sophisticated features provided by PostgreSQL and MySQL. The Data API tunnels SQL over HTTP, which has advantages in the context of AWS Lambda:

• It eliminates the need to open database ports to the AWS Lambda public IP address pool
• It uses stateless HTTP connections instead of stateful internal TCP connection pools used by most database drivers (the stateful pools become invalid after going through AWS Lambda freeze-thaw cycles, causing connection errors and burdening the database server with abandoned invalid connections)
• It uses AWS role-based authentication, eliminating the need for the Lambda to handle database credentials directly

Links

• Project home page (GitHub)
• Documentation (Read the Docs)
• Package distribution (PyPI)
• Change log
• sqlalchemy-aurora-data-api, a SQLAlchemy dialect that uses aurora-data-api

Bugs

Please report bugs, issues, feature requests, etc. on GitHub.

License

Licensed under the terms of the Apache License, Version 2.0.