Merge branch 'main' of https://github.com/aws-samples/serverless-patterns

Author: julianwood

terraform-lambda-aurora-serverless/.gitignore

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+builds/*
+.terraform
+.terraform.*
+function.zip
+src/function/pymysql
+src/function/pymysql/*
+src/function/PyMySQL-1.1.0.dist-info
+src/function/PyMySQL-1.1.0.dist-info/*

terraform-lambda-aurora-serverless/README.md

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+# AWS Lambda function to Amazon Aurora Serverless
+
+The pattern creates an AWS Lambda function and an Amazon Aurora Serverless cluster, a Log group and the IAM resources required to run the application. The database credentials are stored in AWS Secrets Manager secret.
+
+The Lambda function is written in Python that uses pymysql client to establish connectivity with the serverless database.
+
+## Getting started with Terraform Serverless Patterns
+
+Read more about general requirements and deployment instructions for Terraform Serverless Patterns [here](https://github.com/aws-samples/serverless-patterns/blob/main/terraform-fixtures/docs/README.md).
+
+## Steps
+
+First of all, you will need to install the 'pymysql' client depedency which is used in the Lambda function code.
+```shell
+cd src/function
+pip3 install -r requirements.txt -t .
+cd ../..
+```
+Then perform the following terraform commands to deploy the stack
+```shell
+terraform init
+terrform apply
+```
+
+Naming constraints with Amazon Aurora for Master password:
+- The password for the database master user can include any printable ASCII character except /, ', ", @, or a space.
+- The password can contain the following number of printable ASCII characters depending on the DB engine: Aurora MySQL: 8–41 and Aurora PostgreSQL: 8–99.
+For more information, please refer to [this](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Limits.html#RDS_Limits.Constraints)
+
+
+
+## Testing
+
+After deployment, invoke Lambda function with multiple inputs, and go to the Step Function Console and view the different invocations to note the different behavior with the different inputs.
+
+To do this, you can run these commands in the terminal (replace `<function-name>` with the value returned in `lambda_function_name`):
+
+```shell
+aws lambda invoke --function-name <function-name> --payload '{"key": "value"}' response.json
+```
+## Output
+
+Upon successful invocation, the function returns the following response -
+
+```json
+{
+  "statusCode": 200,
+  "body": "{\"message\": \"Successfully connected to the database\", \"database\": \"mydb\", \"host\": \"aurora-serverless-cluster.cluster-cna4c0mg426r.us-east-1.rds.amazonaws.com\"}"
+}
+```
+
+## Providers
+
+| Name | Version |
+|------|---------|
+| <a name="provider_aws"></a> [aws](#provider\_aws) | >= "5.84.0" |

terraform-lambda-aurora-serverless/example-pattern.json

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+{
+  "title": "AWS Lambda function to Amazon Aurora Serverless",
+  "description": "The pattern creates a Lambda function and an Amazon Aurora Cluster with Serverless instance. The Database credentials are stored and accessed from AWS Secrets Manager.",
+  "language": "Python",
+  "level": "300",
+  "framework": "Terraform",
+  "introBox": {
+    "headline": "How it works",
+    "text": [
+      "The pattern creates a Lambda function and an Amazon Aurora Cluster with Serverless instance.",
+      "The Database credentials are stored and accessed from AWS Secrets Manager.",
+      "It also guides how to invoke the function to access the database."
+    ]
+  },
+  "gitHub": {
+    "template": {
+      "repoURL": "https://github.com/aws-samples/serverless-patterns/tree/main/terraform-lambda-aurora-serverless",
+      "templateURL": "serverless-patterns/terraform-lambda-aurora-serverless",
+      "projectFolder": "terraform-lambda-aurora-serverless",
+      "templateFile": "terraform-lambda-aurora-serverless/main.tf"
+    }
+  },
+  "resources": {
+    "bullets": [
+      {
+        "test": "Terraform AWS Lambda examples",
+        "link": "https://github.com/terraform-aws-modules/terraform-aws-lambda/tree/master/examples"
+      },
+      {
+        "test": "Terraform Registry - RDS (Relational Database)",
+        "link": "https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/rds_cluster.html"
+      },
+      {
+        "text": "Amazon Aurora User Guide",
+        "link": "https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_AuroraOverview.html"
+      },
+      {
+        "test": "AWS Lambda Developer Guide",
+        "link": "https://docs.aws.amazon.com/lambda/latest/dg/welcome.html"
+      }
+    ]
+  },
+  "deploy": {
+    "text": [
+      "terraform init && terraform apply"
+    ]
+  },
+  "testing": {
+    "text": [
+      "See the GitHub repo for detailed testing instructions."
+    ]
+  },
+  "cleanup": {
+    "text": [
+      "terraform destroy"
+    ]
+  },
+  "authors": [
+    {
+      "name": "Saborni Bhattacharya",
+      "image": "https://drive.google.com/file/d/1AZFquOkafEQRUlrT4hKOtIbt4Cq66SHd/view?usp=sharing",
+      "bio": "AWS SA, Cloud Enthusiast",
+      "linkedin": "https://www.linkedin.com/in/saborni-bhattacharya-5b523812a/"
+    }
+  ]
+}