239 lines
6.8 KiB
Markdown
239 lines
6.8 KiB
Markdown
---
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categories:
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- DevOps
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- Backend
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tags: [AWS]
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---
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# AWS SAM
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SAM stands for **serverless application model**. It is a framework developed by AWS to simplify the process of building, deploying and managing serverless applications. It provides a concise syntax for defining the components of a serverless application, such as [Lambda functions](/DevOps/AWS/AWS_Lambda/Lambda_programming_model.md), [API gateway](/DevOps/AWS/AWS_API_Gateway.md) and database tables.
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The SAM infrastructure is defined in a YAML file which is then deployed to AWS. SAM syntax gets transformed into CloudFormation during the deployment process. (CloudFormation is a broader and more robust AWS tool for large, highly scaleable infrastructures).
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## Key features of SAM
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- Single deployment configuration
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- Integration with development tools
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- Local testing and debugging
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- Built on AWS CloudFormation
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## Main technologies required
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### Docker
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Whilst SAM can be used to create a deployable file for AWS it can also be run as a container for local development with Docker.
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### AWS CLI
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This is installed using Python and allows you to interact directly with AWS via the command-line.
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### AWS SAM CLI
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See [https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/install-sam-cli.html](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/install-sam-cli.html)
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## Setting up credentials for the AWS CLI
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You require an access key for the given [IAM user](/DevOps/AWS/AWS_User_management_and_roles.md#iam). You should create an IAM account specific to the project with bounded permissions.
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```
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aws configure
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AWS Access Key ID [None]: AK*******
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AWS Secret Access Key [None]: ukp******
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Default region name [None]:
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Default output format [None]:
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```
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This information can be found in the Security Credentials section of the given [IAM](/DevOps/AWS/AWS_User_management_and_roles.md#iam) user:
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## Starting a SAM project
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First create a directory for your project which will serve as the repository:
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```sh
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mkdir aws-sam-learning
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cd aws-sam-learning
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```
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Then we can use the `sam` cli to bootstrap the project:
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```sh
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sam init --runtime nodejs16.x
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```
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We can just click through and accept the basic HelloWorld Lambda.
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This will create the Lambda as well as an API Gateway trigger URL.
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### `template.yaml`
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This is autogenerated and details the main constituents of the project. There are lots of fields but the most important are the following:
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```yaml
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HelloWorldFunction:
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Type: AWS::Serverless::Function
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Properties:
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CodeUri: hello-world/
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Handler: app.lambdaHandler
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Runtime: nodejs16.x
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Architectures:
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- x86_64
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Events:
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HelloWorld:
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Type: Api
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Properties:
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Path: /hello
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Method: get
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```
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This details the location of the [handler function](/DevOps/AWS/AWS_Lambda/Lambda_handler_function.md) which is contained at the path `hello-world/app.js`:
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```js
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exports.lambdaHandler = async (event, context) => {
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try {
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// const ret = await axios(url);
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response = {
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statusCode: 200,
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body: JSON.stringify({
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message: "hello world",
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// location: ret.data.trim()
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}),
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};
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} catch (err) {
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console.log(err);
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return err;
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}
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return response;
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};
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```
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It also lists the `get` event that we can use to call API Gateway and trigger the Lambda.
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The full template is below:
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## Adding our own code
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We will create our own function and API Gateway trigger.
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We will place our function after the existing `HelloWorldFunction`
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```yaml
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ClockFunction:
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Type: AWS::Serverless::Function
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Properties:
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CodeUri: clock/
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Handler: handler.clock
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Runtime: nodejs16.x
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Events:
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ClockApi:
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Type: Api
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Properties:
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Path: /clock
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Method: get
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```
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Just like with `HelloWorld`, we will create a directory for this function: `clock` and we will initialise it as an `npm` project.
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```sh
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mkdir clock
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cd clock
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npm init
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```
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We will use `handler.js` as our root, handler function.
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We have said in the template file that our `Handler: handler.clock`, therefore the main function in the `handler` module should be `clock`:
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```js
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const moment = require("moment");
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exports.clock = async (event) => {
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console.log("Clock function run");
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const message = moment().format();
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const response = {
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statusCode: 200,
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body: JSON.stringify(message),
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};
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return response;
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};
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```
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The directory structure is as follows:
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When we call the API Gateway path `/clock` with `GET`, our function will be triggered.
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## Deploying the project
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We will now deploy our project to AWS from the local environment.
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The process is as follows:
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1. Build
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2. Package
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3. Deploy
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### Build
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We need to install the runtime dependencies for the function. We do this by running `sam build`. This ignores test files and development dependencies and installs the project dependencies and source files to a temporary subdirectory.
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The build directory is `.aws-sam/build/`. There will be a subdirectory for each of our files.
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### Package
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As noted, CloudFront handles the deployment of the application. It can only receive one file as an input. The packaging process consists in creating that single file.
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The packaging proces will first archive all of the project artefacts into a zip file and then upload that to [S3](/DevOps/AWS/AWS_S3.md). A reference to this S3 entity is then provided to CloudFormation.
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The command is as follows:
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```sh
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sam package
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--template-file template.yaml
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--output-template-file pkg.yml
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--region eu-west-1
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```
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This will automatically create a hashed bucket name for you in S3 (I have tried to add my own naming but it doesn't comply.)
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### Local development with Docker
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In order to work with your application locally without actually sending requests to AWS and using credit, you can run a local instance.
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See [Local AWS Development with SAM](/DevOps/AWS/SAM/Local_AWS_development_with_SAM.md).
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### Deploy
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Once you have packaged the app you can deploy with `sam deploy --guided`. This will talk you through the defaults and will deploy the package to CloudFormation. In CloudFormation each individual project is called a **stack**.
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If we then go to Cloud Formation we will see the deployed application.
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## Call the endpoint
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If we now go to the Lambda console, we will see our function listed, and the API Gateway endpoint under `triggers`:
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We can then call this from Postman to check everything is working as it should:
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## Clean up and erase the stack
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We can delete the stack and remove all the resources we have created with a single CLI method:
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```sh
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aws cloudformation delete-stack --stack-name <name> --region <region>
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```
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