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Amazon Aurora serverless REST API lending library example

Purpose

Shows how to use the AWS SDK for Python (Boto3) with the Amazon Relational Database Service (Amazon RDS) API and AWS Chalice to create a REST API backed by an Amazon Aurora database. The web service is fully serverless and represents a simple lending library where patrons can borrow and return books. Learn how to:

  • Create and manage a serverless Amazon Aurora database cluster.
  • Use AWS Secrets Manager to manage database credentials.
  • Implement a data storage layer that uses Amazon RDS Data Service to move data into and out of the database.
  • Use AWS Chalice to deploy a serverless REST API to Amazon API Gateway and AWS Lambda.
  • Use the Requests package to send requests to the web service.

Prerequisites

Cautions

  • As an AWS best practice, grant this code least privilege, or only the permissions required to perform a task. For more information, see Grant Least Privilege in the AWS Identity and Access Management User Guide.
  • This code has not been tested in all AWS Regions. Some AWS services are available only in specific Regions. For more information, see the AWS Region Table on the AWS website.
  • Running this code might result in charges to your AWS account.

Running the code

Install prerequisites by running the following at a command prompt.

python -m pip install -r requirements.txt

This example contains two deployment steps, a REST demo, and a cleanup step that all must be run separately.

  1. Database deployment

    Creates an Amazon Aurora serverless data cluster and an AWS Secrets Manager secret to hold the database user credentials. It fills the database with example data pulled from the Internet Archive's Open Library.

    Deploy and fill the database by running the following command at a command prompt.

    py library_demo.py deploy_database

    The lendinglibrary database is now ready and can be accessed through the AWS Console Query Editor or the Boto3 rds-data client. Or, run the next step to deploy the REST API.

  2. REST API deployment

    Uses Chalice and AWS CLI commands to deploy routing and data-handling layers to AWS Lambda, set up API Gateway to handle HTTP requests, and establish AWS Identity and Access Management (IAM) roles and profiles to manage permissions.

    Deploy the REST API by running the following command at a command prompt.

    py library_demo.py deploy_rest

    The REST API is now deployed and can received HTTP requests. Try it yourself using your favorite HTTP client or run the next step to see a demonstration of how to use the Requests package to call the web service.

  3. REST API demonstration

    Uses the Requests package to send a series of HTTP requests to the web service to perform the following tasks.

    1. List the books in the library.
    2. Add a library patron.
    3. Lend a book to the new patron.
    4. Return the book from the patron.

    See the demo by running the following command at a command prompt.

    py library_demo.py demo_rest

  4. Cleanup

    Remove all resources created during the demonstration by running the following command at a command prompt.

    py library_demo.py cleanup

    Be sure to run cleanup after you're done to avoid additional charges to your account.

Example structure

The example is subdivided into two main sections.

  • The library_demo.py script and rds_tools folder are used to deploy resources and manage the demo.
  • The library_api folder contains the REST API code and resource definitions that are deployed to AWS by Chalice.

The example contains the following files.

library_demo.py

Deploys database and REST API resources, fills the database with example books, runs a REST request demonstration, and cleans up resources.

rds_tools/aurora_tools.py

Wraps parts of the Boto3 RDS and Secrets Manager API to show how to create database clusters and secrets.

library_api/app.py

Contains REST API routes that receive HTTP requests. This file and supporting chalicelib files are deployed to AWS Lambda as part of the Chalice deployment.

library_api/requirements.txt

Lists packages that are required in the AWS Lambda environment.

library_api/resources.json

Defines an IAM role and policy to grant AWS Lambda permission to perform specific actions on RDS, Secrets Manager, RDS Data Service, and Amazon CloudWatch Logs.

.chalice/config.json

Defines environment variables that define the cluster, secret, and database used by the data-handling layer.

chalicelib/library_data.py

Handles calls from the REST routing layer. Runs MySQL statements through RDS Data Service to move data into and out of the database.

chalicelib/mysql_helper.py

A simplified object-relational mapping (ORM) layer that translates between Python structures and SQL statements.

Running the tests

The unit tests in this module use the botocore Stubber. This captures requests before they are sent to AWS, and returns a mocked response. To run all of the tests, run the following in your [GitHub root]/python/example_code/rds/lending_library/rds_tools and [GitHub root]/python/example_code/rds/lending_library/library_api folders.

py -m pytest

The tests in the test_library_api_app.py script use the chalice.test.Client object to help with route testing. For details, see Chalice Testing.

Additional information

Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

SPDX-License-Identifier: Apache-2.0