Terraform AWS ARC Lambda-Function Module Usage Guide¶

Introduction¶

Purpose of the Document¶

This document provides guidelines and instructions for users looking to create lambda-function using the module.

Module Overview¶

The ARC Terraform-aws-arc-lambda module provides a comprehensive and unified solution for deploying AWS Lambda serverless computing infrastructure on AWS. This versatile module supports multiple deployment methods including local source code, S3-based deployments, and container images, allowing you to choose the deployment approach that best fits your application requirements and operational needs.

Prerequisites¶

Before using this module, ensure you have the following:

AWS credentials configured.
Terraform installed.
A working knowledge of AWS VPC, Docker and Terraform concepts.

Getting Started¶

Module Source¶

To use the module in your Terraform configuration, include the following source block:

module "lambda-function" {
  source                 = "sourcefuse/arc-lambda-function/aws"
  version                = "0.0.1"

  # Basic configuration
  function_name = var.function_name
  description   = "Basic Lambda function example"
  runtime       = "python3.11"
  handler       = "lambda_function.lambda_handler"
  memory_size   = 128
  timeout       = 10

  # Deployment package
  filename         = data.archive_file.lambda_zip.output_path
  source_code_hash = data.archive_file.lambda_zip.output_base64sha256

  # Environment variables
  environment_variables = {
    ENVIRONMENT = var.environment
    LOG_LEVEL   = var.log_level
  }

  # CloudWatch Logs
  create_log_group      = true
  log_retention_in_days = 7

 tags = module.tags.tags
}

Refer to the Terraform Registry for the latest version.

Integration with Existing Terraform Configurations¶

Refer to the Terraform Registry for the latest version.

Integration with Existing Terraform Configurations¶

Integrate the module with your existing Terraform mono repo configuration, follow the steps below:

Create a new folder in terraform/lambda-function named .
Create the required files, see the examples to base off of.
Configure with your backend:
- Create the environment backend configuration file: config..hcl
- region: Where the backend resides
- key: /terraform.tfstate
- bucket: Bucket name where the terraform state will reside
- dynamodb_table: Lock table so there are not duplicate tfplans in the mix
- encrypt: Encrypt all traffic to and from the backend

Required AWS Permissions¶

Ensure that the AWS credentials used to execute Terraform have the necessary permissions to create, list and modify:

All lambda-function services
VPC and networking configuration

Module Configuration¶

Input Variables¶

For a list of input variables, see the README Inputs section.

Output Values¶

For a list of outputs, see the README Outputs section.

Module Usage¶

Basic Usage¶

For basic usage, see the examples folder.

This example will create:

lambda-function Studio Domain: Complete

Tips and Recommendations¶

The module focuses on provisioning lambda-function. The convention-based approach enables downstream services to easily attach to the lambda-function. Adjust the configuration parameters as needed for your specific use case.

Troubleshooting¶

Reporting Issues¶

If you encounter a bug or issue, please report it on the GitHub repository.

Security Considerations¶

AWS VPC¶

Understand the security considerations related to lambda-function on AWS when using this module.

Best Practices for AWS lambda-function¶

Follow best practices to ensure secure lambda-function configurations:

AWS lambda-function Security Best Practices

Contributing and Community Support¶

Contributing Guidelines¶

Contribute to the module by following the guidelines outlined in the CONTRIBUTING.md file.

Reporting Bugs and Issues¶

If you find a bug or issue, report it on the GitHub repository.

License¶

License Information¶

This module is licensed under the Apache 2.0 license. Refer to the LICENSE file for more details.

Open Source Contribution¶

Contribute to open source by using and enhancing this module. Your contributions are welcome!