Here are three common ways to create your Lambda functions with AWS

AWS Lambda is a powerful serverless compute service that lets you run code without provisioning or managing servers. Lambda functions are event-driven, meaning they run your code in response to events such as HTTP requests, file uploads to S3, or messages from other AWS services.

The beauty of Lambda is you only pay for the compute time your code actually consumes – there is no charge when your code is not running. This makes it very cost-effective for workloads that are intermittent or unpredictable.

Some key benefits of using AWS Lambda functions:

• No servers to manage – AWS handles all the compute infrastructure
• Automatic scaling based on incoming requests or events
• Integrated security model and resource-based permissions
• Pay only for the compute time used, measured in 100ms increments

In this post, we‘ll explore three common ways to create and manage your Lambda functions. Which method you choose depends on factors like the complexity of your functions, your development workflow, and how much control vs convenience you want.

Method 1: Authoring in the AWS Lambda Console

The easiest way to get started with Lambda is to author your functions directly in the AWS Management Console. When you create a new function, you have an inline code editor for writing and testing your function.

Here‘s how to create a simple Node.js function that returns a "Hello World" message:

1. Open the Lambda console and click "Create function"
2. Choose "Author from scratch"
3. Configure the basic settings:
   • Name: hello-world
   • Runtime: Node.js 14.x
   • Role: Create a new role with basic Lambda permissions
4. Click "Create function"
5. Paste the following code in the inline editor:

exports.handler = async (event) => {
    const message = ‘Hello World!‘;
    return { 
        statusCode: 200,
        body: JSON.stringify(message)
    }
};

6. Click "Deploy" to save and publish the function
7. Click "Test" and create a new test event with default settings
8. Invoke the function and verify the "Hello World!" response

Using the console editor is great for quick prototyping and testing of simple functions. However it has some limitations:

• No way to install dependencies or use a package manager
• Lacks advanced IDE features like autocomplete and debugging
• Unable to easily test locally or integrate with source control
• Only suitable for small, self-contained functions

For anything beyond basic functions, you‘ll likely want to use a more full-featured development environment like Cloud9 or your local machine.

Method 2: Developing in the AWS Cloud9 IDE

AWS Cloud9 is a cloud-based integrated development environment (IDE) that lets you write, run, and debug code from your browser. It comes with a terminal for running commands and a visual debugger for stepping through your code.

The nice thing about Cloud9 is it provides a consistent dev environment that is always available from anywhere. Your workspace is automatically saved in the cloud and you can collaborate with others in real-time.

To use Cloud9 for Lambda development:

1. Open the Cloud9 console and create a new environment
2. Select "Create a new EC2 instance for environment"
3. Choose the default instance type (t2.micro is free tier eligible)
4. Open the environment and familiarize yourself with the IDE layout
5. Create a new Lambda function from the AWS Resources panel
6. Write your function code in the editor using your preferred language
7. Use the terminal to install any dependencies with your package manager
8. Debug your code using breakpoints in the visual debugger
9. Deploy the function by right-clicking it in the Lambda panel and selecting "Deploy"

Some advantages of Cloud9 over the console editor:

• Supports installing dependencies with npm, pip, maven, etc.
• Includes a full-featured IDE with intelligent code completion
• Built-in visual debugger for stepping through code
• Easy access to a terminal for running shell commands
• Ability to clone and push to remote Git repositories

However, Cloud9 may not be ideal if you prefer using your existing IDE or dev tools. It also still requires an active internet connection to write and deploy your code.

Method 3: Developing Locally and Deploying with CLI

For maximum flexibility and control, many developers choose to write their Lambda functions locally using their preferred tools and workflows. You can use any code editor, get full access to the file system, and leverage all your regular dev processes.

The typical local development workflow looks like:

1. Set up your local development environment
   • Install Node.js, Python, Java or other required runtimes
   • Configure your IDE or code editor as desired
2. Create a new directory for your Lambda function
3. Initialize the project and install any dependencies
   • e.g. npm init for Node or pip install for Python
4. Write your function code in one or more files
5. Write unit tests for your code
6. Test the function locally
   • Either manually invoke the handler or use a test framework
7. Package the function and dependencies into a zip file
8. Deploy the function zip using the AWS CLI or a tool like Serverless Framework

Here‘s how you can deploy a function using the AWS CLI:

# Zip up the function code
zip function.zip index.js

# Create a new Lambda function 
aws lambda create-function \
  --function-name my-function \
  --runtime nodejs14.x \
  --zip-file fileb://function.zip \
  --handler index.handler \
  --role arn:aws:iam::123456789012:role/lambda-ex

# Or update the code for an existing function
aws lambda update-function-code \
  --function-name my-function \
  --zip-file fileb://function.zip

Some benefits of developing locally:

• Use your existing IDE, plugins, and workflows
• Easily integrate with version control systems like Git
• Write and run unit tests to ensure code quality
• No need for an internet connection while developing
• Can automate deployments using CI/CD pipelines

The downside is there is a bit more setup and configuration required compared to the other methods. You also have to be comfortable using command line tools.

Advanced Lambda Development Techniques

Regardless of which development method you choose, there are some advanced techniques that can help you build and manage Lambda functions more effectively.

Versioning and Aliases

As you make changes to your functions over time, it‘s a good idea to publish new versions. This allows you to adopt an immutable infrastructure approach where function deployments are versioned and irreversible.

You can also create aliases, which are pointers to specific function versions. Aliases enable blue/green deployments by routing a portion of traffic to a new version. You can also use them for canary testing of new changes.

CI/CD Pipelines

To enable frequent and reliable releases, you can use a Continuous Integration and Continuous Delivery (CI/CD) pipeline to build, test, and deploy your functions automatically.

Here‘s an example of a simple pipeline using AWS CodePipeline:

The pipeline is triggered whenever code is pushed to the Git repository. CodeBuild runs the tests and creates the function package. If the tests pass, the function is deployed to the Beta alias for testing. After manual approval, CodeDeploy shifts production traffic from the Live alias to the new version.

Observability

Monitoring and debugging serverless applications can be challenging since you don‘t have access to the underlying infrastructure. However, AWS provides several tools to help you observe your Lambda functions:

• CloudWatch Logs – Stores the output and error logs from function invocations. Be sure to include relevant output statements in your function code.

• CloudWatch Metrics – Tracks function metrics like invocation count, duration, errors, and throttles. You can create alarms to notify you of issues.

• X-Ray – Helps analyze and debug performance issues by tracing requests as they travel through your application. Requires instrumenting your function code.

Security Best Practices

AWS Lambda follows the shared responsibility model where AWS secures the underlying compute infrastructure and you secure your function code and configuration. Some best practices:

• Follow the principle of least privilege by limiting the permissions of the function‘s execution role to only what is needed
• Don‘t store sensitive config values like database passwords in your function code. Use AWS Systems Manager Parameter Store or Secrets Manager instead.
• For functions exposed via API Gateway, configure usage plans and API keys to protect against abuse
• Validate and sanitize any input from event sources to prevent injection attacks

Performance Optimization

Some ways to optimize the performance and cost of your functions:

• Choose the appropriate memory size for your function. More memory also allocates more CPU which can improve performance.
• Minimize the size of your deployment package to reduce cold start times. Use layers for common dependencies.
• Use provisioned concurrency to keep functions initialized and ready to respond immediately to an invoke.
• Avoid running long-running tasks in synchronous invocations. Use async invocations or step functions for long workflows.

Summary

AWS provides multiple ways to create and manage Lambda functions, including:

1. Authoring directly in the AWS console
2. Using the Cloud9 online IDE
3. Developing locally and deploying with the AWS CLI or frameworks

The method you choose depends on the complexity of the function, your familiarity with development tools, and need for control vs convenience.

Regardless of approach, be sure to utilize versioning, CI/CD pipelines, observability tooling, and security best practices. Optimize function performance by allocating appropriate memory, minimizing deployment packages, leveraging concurrency, and avoiding long-running synchronous invocations.

By following these techniques, you can build robust, scalable, and cost-effective applications using AWS Lambda and serverless computing. The flexibility and power of Lambda makes it a great fit for a wide variety of use cases from data processing to web APIs to chatbots and more.