เลเยอร์และตัวแปรสภาพแวดล้อมของ Lambda

AWS Lambda Layers help you manage common dependencies and custom runtimes more efficiently. Think of them as shared libraries for your functions.

Instead of bundling all your code and dependencies with every function deployment, you can package reusable components into a Layer.

Layers offer several benefits for your serverless applications:

• Smaller Deployment Packages: Keep your function code lean by separating dependencies.
• Code Reusability: Share common code (e.g., utility functions, database connectors) across multiple Lambda functions.
• Faster Deployments: Smaller packages upload quicker.
• Easier Updates: Update a dependency once in a layer, and all functions using it benefit.

A Lambda Layer is essentially a .zip file archive containing library code, custom runtimes, or other dependencies.

When you attach a layer to a function, Lambda extracts its content to the /opt directory in the function's execution environment. Your function code can then import modules from /opt.

Let's say you have a common logging utility or a specific SDK version you want to use. Instead of including it in every Lambda function's deployment package, you'd put it in a layer.

The layer's ZIP file structure might look like this for Python:

Your Lambda function would then simply import it as import mylogger.

You can attach up to 5 layers to a single Lambda function. This is typically done through the AWS Management Console, AWS CLI, or Infrastructure as Code tools like SAM or CloudFormation.

When attaching, you specify the Layer ARN (Amazon Resource Name) which uniquely identifies the layer and its specific version.

Imagine we have a layer containing a utils.py with a greet function. Here's how a Lambda function would use it. (We simulate the layer's presence for this runnable demo):

Environment variables allow you to pass configuration settings to your Lambda function without changing its underlying code.

They are key-value pairs that are available to your function's code at runtime. This is ideal for settings that vary between environments (e.g., development, test, production) or for non-sensitive configuration data.

You can define environment variables when you create or update your Lambda function. This is typically done via:

• AWS Management Console: Under the function's "Configuration" tab, then "Environment variables".
• AWS CLI: Using the --environment "Variables={KEY=VALUE}" parameter.
• Infrastructure as Code: For example, in a SAM template, under the function's Environment: Variables: section.

Important: For highly sensitive data like database passwords or API keys, always use AWS Secrets Manager instead of environment variables.

Let's say you have an environment variable named GREETING_PREFIX set to "Welcome". Your Lambda function can easily access its value using the os module in Python:

Which of the following are valid reasons to use AWS Lambda Layers?

Today, you learned about two powerful tools for managing your Lambda functions more effectively:

• Lambda Layers: Used for packaging shared code, libraries, and dependencies, which reduces deployment package size and promotes code reusability across functions.
• Environment Variables: Used for passing configuration settings to your function at runtime, making your code more flexible and easier to manage across different deployment environments.

Mastering these features will help you build more organized, efficient, and maintainable serverless applications!