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AWS Lambda Deep Dive

AWS Lambda Deep Dive

Learn the fundamentals of AWS Lambda, including function structure, event triggers, invocation methods, versioning, and operational limits.

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In this lesson, we explore AWS Lambda service that abstracts infrastructure management and enables event-driven application development. We learn how Lambda functions are structured, triggered, invoked, versioned, and integrated with other AWS services. Building this foundational understanding prepares us to create scalable, cost-efficient, and resilient serverless applications.

Introduction to AWS Lambda

AWS Lambda is a serverless compute service that allows us to run code without provisioning or managing servers. It operates under the Function-as-a-Service (FaaS) model, where we provide the code, and AWS manages the underlying infrastructure.

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In a serverless architecture, Lambda functions typically contain business logic and serve as the glue connecting multiple AWS-managed services. Users are charged based on the number of function invocations and the time it takes the code to execute, making Lambda a cost-efficient solution for scalable workloads.

How Lambda functions work

To interact consistently with different AWS services, Lambda functions follow a structured format. Each Lambda function operates using three key elements:

  • Invocation event: The structured data that triggers the function. Its format varies based on the source service, such as API Gateway, S3, or DynamoDB Streams. For instance, an API Gateway event may include the HTTP method, query string parameters, headers, and body, while an S3 event contains the bucket name and object key.

  • Function handler: The entry point within the function code that processes incoming events. This is a standard method written in the chosen programming language.

  • Context object: Metadata about the function invocation, including function name, memory configuration, request ID, and the remaining execution time.

The code snippet below shows the default function handler in Python.

def lambda_handler(event, context):
    # The function logic goes here
    return {
        'statusCode': 200,
        'body': 'Hello from Lambda!'
    }
lambda_handler.py

This handler receives two arguments, event and context, and returns a JSON-formatted HTTP response. When invoked, Lambda launches a container runtime that initializes the function’s environment, loads the code, and executes the handler. This environment may be reused across multiple invocations to optimize performance.

Understanding the function structure is the first step. Next, we examine how a Lambda function interacts securely with other AWS services through an execution role.

Lambda execution role

When a Lambda function needs to access other AWS services, it requires permissions to perform those actions securely. AWS Lambda achieves this by assuming an execution role—an AWS Identity and Access Management (IAM) role assigned to the function when it is created.

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Lambda function accessing other AWS resources
Lambda function accessing other AWS resources

The execution role defines what resources the function can interact with during runtime. For example, if a function needs to read an object from an S3 bucket or write a record to a DynamoDB table, the permissions must be explicitly granted in its execution role policy. Without proper permissions, Lambda invocations will fail with AccessDenied errors.

Managing execution roles carefully is critical for security and operational success. We should follow the principle of least privilege, granting only ...