2.1.1.2. Lambda Execution Environment & Runtime

First Principle: Lambda's execution environment provides a secure, isolated, and managed runtime for your code, abstracting server operating systems and simplifying dependency management.

When your Lambda function is invoked, AWS launches an execution environment. This environment includes the operating system, the runtime (e.g., Node.js, Python), and any configured Lambda Layers or container images.

Key Aspects of Lambda Execution Environment & Runtime:

• Runtime: The language environment in which your code runs (e.g., Python 3.9, Node.js 16.x). AWS manages the updates and security of these runtimes.
• Execution Context: Provides information about the invocation, function, and execution environment through the context object or environment variables.
• Temporary Disk Space (/tmp): Each execution environment gets a small, temporary disk space accessible at /tmp for processing files. This is ephemeral and cleared after execution.
• Memory & CPU: You configure the memory allocated to your Lambda function. CPU power is automatically scaled proportionally to the memory allocated.
• Lambda Layers: Packages common dependencies (libraries, runtimes) separately from your function code, promoting reusability and reducing deployment package size.
• Container Images: Lambda now supports packaging functions as container images, offering more control over the runtime environment and dependencies.

Scenario: You're developing a Python Lambda function that needs to process a large image file (several MBs), requiring temporary local storage during processing. You also want to include a common Python library without bundling it directly in your function's deployment package.

Reflection Question: How does Lambda's execution environment provide managed resources like the /tmp directory for temporary storage and support Lambda Layers for shared dependencies, abstracting server management and simplifying your development workflow?