AWS Lambda Tutorials - MSDN Documentation

What is AWS Lambda?

AWS Lambda is a serverless, event-driven compute service that lets you run code without provisioning or managing servers. You pay only for the compute time you consume – there is no charge when your code is not running. Simply upload your code and Lambda takes care of everything required to run and scale your code with high availability. You can use AWS Lambda to run code in response to events, such as changes to an Amazon S3 bucket, updates to an Amazon DynamoDB table, or an incoming API Gateway request.

This section will guide you through understanding the fundamental concepts of AWS Lambda and how to leverage its power for your applications.

Getting Started with AWS Lambda

Before you can start writing your first Lambda function, you'll need an AWS account. If you don't have one, you can create one for free. Once you're logged in, navigate to the AWS Lambda console.

Steps:

Sign in to the AWS Management Console.
Navigate to the Lambda service.
Click "Create function".
Choose "Author from scratch".
Configure your function: provide a name, select a runtime (e.g., Node.js, Python, Java), and choose an execution role.

For a more detailed walkthrough, refer to the official AWS Lambda Getting Started guide.

Writing Your First Lambda Function (Python Example)

Let's create a simple "Hello World" function in Python. This function will simply return a JSON response.

Lambda Function Code:


import json

def lambda_handler(event, context):
    """
    A simple Lambda function that returns a greeting.
    """
    name = event.get('name', 'World')
    message = f"Hello, {name}!"
    return {
        'statusCode': 200,
        'body': json.dumps({'message': message})
    }

Explanation:

lambda_handler(event, context): This is the entry point for your Lambda function. The event object contains the input data, and the context object provides runtime information.
event.get('name', 'World'): Retrieves the value associated with the 'name' key from the event. If 'name' is not present, it defaults to 'World'.
json.dumps({'message': message}): Converts the Python dictionary to a JSON string, which is a common format for Lambda responses.
{'statusCode': 200, 'body': ...}: This is the standard structure for HTTP responses when using Lambda with services like API Gateway.

Deploy This Function

Understanding Event Triggers

AWS Lambda functions are executed in response to events. These events can originate from various AWS services or custom applications.

Common Event Sources:

Amazon S3: Triggered by object creation, deletion, or modification in an S3 bucket.
Amazon API Gateway: Allows you to create RESTful APIs that invoke Lambda functions.
Amazon SQS: Processes messages from a queue.
Amazon SNS: Responds to messages published to a topic.
Amazon DynamoDB Streams: Captures changes to items in a DynamoDB table.
CloudWatch Events/EventBridge: Scheduled events or events from other AWS services.

Configuring the right trigger is crucial for your function to be invoked automatically. You can set up triggers directly within the Lambda console or using infrastructure-as-code tools like AWS CloudFormation or Terraform.

Core Lambda Concepts

1. Functions:

The code you upload to Lambda. Functions are stateless by default. You can write functions in various programming languages.

2. Runtimes:

The environment in which your function code executes. Lambda supports runtimes like Node.js, Python, Java, C#, Go, and Ruby, as well as custom runtimes.

3. Events:

The data that triggers your function. Events are JSON objects passed to your function's handler.

4. Handlers:

The specific method within your function code that Lambda invokes when triggered.

5. Execution Role:

An IAM role that grants your Lambda function permission to access other AWS services (e.g., read from S3, write to CloudWatch Logs).

6. Concurrency:

The number of requests your function can handle simultaneously. Lambda automatically scales your function to meet demand.

Managing Lambda Functions

The AWS Lambda console provides a comprehensive interface for managing your functions. You can:

Create, update, and delete functions.
Configure function settings like memory, timeout, and environment variables.
Test your functions directly in the console.
Monitor function performance and errors.
Manage versions and aliases for your functions.
Set up event source mappings and triggers.

For programmatic management, you can use the AWS SDKs, AWS CLI, or infrastructure-as-code tools.

Monitoring and Logging

Effective monitoring is key to ensuring your Lambda functions are performing as expected. AWS Lambda integrates seamlessly with Amazon CloudWatch.

Key Metrics:

Invocations: The number of times your function code was invoked.
Errors: The number of invocations that resulted in an error.
Duration: The time it took for your function to execute.
Throttles: The number of invocations that were throttled due to concurrency limits.
Concurrent Executions: The number of function instances processing events simultaneously.

Logging:

Lambda automatically logs the output of your function's execution (stdout and stderr) to CloudWatch Logs. You can add custom log statements using your language's standard logging mechanisms.


import json
import logging

logger = logging.getLogger()
logger.setLevel(logging.INFO)

def lambda_handler(event, context):
    logger.info(f"Received event: {json.dumps(event)}")
    return {
        'statusCode': 200,
        'body': json.dumps('Log message sent!')
    }

Best Practices for AWS Lambda

Keep Functions Small and Single-Purpose: Each function should do one thing well.
Manage Dependencies Efficiently: Package only necessary libraries.
Configure Appropriate Timeouts and Memory: Tune these based on your function's needs.
Use Environment Variables for Configuration: Avoid hardcoding sensitive information or configuration values.
Implement Robust Error Handling and Logging: Make it easy to diagnose issues.
Follow the Principle of Least Privilege for IAM Roles: Grant only the necessary permissions.
Consider Cold Starts: Understand how to mitigate latency for infrequently invoked functions.

Advanced Topics

Once you're comfortable with the basics, explore these advanced features:

Lambda Layers: Share code and dependencies across multiple functions.
Lambda Destinations: Send function results to other AWS services for further processing.
VPC Integration: Connect your Lambda functions to resources within a Virtual Private Cloud (VPC).
Provisioned Concurrency: Pre-initialize function instances to eliminate cold starts for latency-sensitive applications.
Lambda Extensions: Integrate Lambda with your favorite monitoring, observability, security, and governance tools.
Step Functions: Orchestrate multiple Lambda functions into complex workflows.

Explore Lambda Pricing

AWS Lambda: Serverless Compute Tutorials