AWS Lambda Functions: Building Serverless Applications with AWS Lambda and .NET Core

5 min readApr 19, 2024

In this blog post, we’ll delve into AWS Lambda Functions, exploring their purpose, significance, and real-world applications. We’ll also provide detailed guidance on configuring Lambda Functions in .NET applications.

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Understanding AWS Lambda Functions:

1. Purpose and Significance:

AWS Lambda Functions are serverless compute services that allow you to run code without provisioning or managing servers. They offer several key benefits:

Scalability: Lambda Functions automatically scale in response to incoming requests or events, ensuring optimal performance without manual intervention.
Cost-Effectiveness: With Lambda, you only pay for the compute time consumed by your functions, making it a cost-effective solution for executing code.
Flexibility: Lambda supports multiple programming languages, including Node.js, Python, Java, and .NET Core, providing developers with flexibility in choosing their preferred language.

2. Need and Use Cases:

Lambda Functions are commonly used for various purposes, including:

Event-Driven Processing: Responding to events from other AWS services, such as S3 object uploads, SNS notifications, DynamoDB changes, etc.
Web Applications: Implementing backend APIs, microservices, or serverless web applications.
Data Processing: Performing data transformations, ETL (Extract, Transform, Load) tasks, or real-time analytics.
Scheduled Tasks: Running scheduled jobs or cron-like tasks using CloudWatch Events.

Real-World Example: Order Processing Workflow

Let’s consider an order processing scenario to illustrate the usage of Lambda Functions:

Trigger: Whenever a new order is placed in an e-commerce application, an event is generated and sent to an S3 bucket.
Lambda Function: A Lambda Function is triggered by the S3 event. It retrieves the order details, validates them, and processes the payment using a payment gateway API.
Data Processing: After successful payment processing, the Lambda Function updates the order status in a DynamoDB table and sends a confirmation email to the customer using Amazon SES (Simple Email Service).

Configuring Lambda Functions in .NET Applications:

1. Developing Lambda Functions:

Use the AWS Toolkit for Visual Studio or AWS CLI to create, deploy, and manage Lambda Functions in .NET.
Write your Lambda Function code in C# using the .NET Core runtime.

2. Deploying Lambda Functions:

Package your Lambda Function code along with any dependencies into a ZIP file.
Use the AWS Management Console, AWS CLI, or CI/CD pipelines to deploy the Lambda Function to AWS Lambda.

3. Trigger Configuration:

Configure triggers for your Lambda Function using AWS services such as S3, API Gateway, SNS, etc.
Define event sources and configure permissions to allow the Lambda Function to access the event sources.

we’ll walk through the process of creating and deploying an AWS Lambda Function using .NET Core. We’ll use a simple order processing scenario as an example to demonstrate the end-to-end implementation.

Step 1: Setup AWS Toolkit for Visual Studio

Install the AWS Toolkit for Visual Studio from the Visual Studio Marketplace.
Open Visual Studio and sign in to your AWS account using the AWS Explorer.

Step 2: Create a New Lambda Function Project

In Visual Studio, create a new AWS Lambda Project using the AWS Lambda Project (.NET Core — C#) template.
Choose a project name and solution name, and click Create.

Step 3: Write the Lambda Function Code

using Amazon.Lambda.Core;
using Amazon.Lambda.S3Events;
using Amazon.S3;
using Amazon.S3.Util;
using Newtonsoft.Json;
using System.IO;
using System.Threading.Tasks;

[assembly: LambdaSerializer(typeof(Amazon.Lambda.Serialization.SystemTextJson.DefaultLambdaJsonSerializer))]

public class Function
{
    private readonly IAmazonS3 _s3Client;

    public Function()
    {
        _s3Client = new AmazonS3Client();
    }

    public async Task FunctionHandler(S3Event evnt, ILambdaContext context)
    {
        var record = evnt.Records?[0];
        if (record == null) return;

        var s3Event = record.S3;
        if (s3Event == null) return;

        var response = await _s3Client.GetObjectAsync(s3Event.Bucket.Name, s3Event.Object.Key);
        using (var streamReader = new StreamReader(response.ResponseStream))
        {
            var content = await streamReader.ReadToEndAsync();
            var order = JsonConvert.DeserializeObject<Order>(content);

            // Validate the order
            if (!ValidateOrder(order))
            {
                context.Logger.LogLine("Invalid order. Aborting processing.");
                return;
            }

            // Update inventory
            if (!UpdateInventory(order))
            {
                context.Logger.LogLine("Failed to update inventory. Aborting processing.");
                return;
            }

            context.Logger.LogLine("Order processed successfully.");
        }
    }

    private bool ValidateOrder(Order order)
    {
        // Perform validation logic here
        // Example: Check if the order quantity is valid
        return order.Quantity > 0;
    }

    private bool UpdateInventory(Order order)
    {
        // Perform inventory update logic here
        // Example: Update inventory in the database
        try
        {
            // Update inventory in the database or external system
            // Example: Update quantity of product in inventory table
            // inventoryService.UpdateInventory(order.ProductId, order.Quantity);
            return true;
        }
        catch (Exception ex)
        {
            // Log error or handle exception
            // Example: Log exception to CloudWatch Logs
            // logger.LogError($"Failed to update inventory: {ex.Message}");
            return false;
        }
    }
}

public class Order
{
    public string OrderId { get; set; }
    public string ProductId { get; set; }
    public int Quantity { get; set; }
}

In the above code:

The ValidateOrder method performs validation logic on the order object. In this example, it checks if the order quantity is greater than zero.
The UpdateInventory method updates the inventory based on the order. In a real-world scenario, this would involve updating a database or external inventory management system.

These implementations can be customized to fit the specific requirements of your application. Remember to handle errors and exceptions appropriately to ensure the robustness of your Lambda function.

Step 4: Package and Deploy the Lambda Function

Right-click on the project and select Publish to AWS Lambda.
Choose an existing Lambda function or create a new one, and configure the deployment settings.
Click Upload to deploy the Lambda function to AWS Lambda.

Step 5: Configure the Trigger

Navigate to the Amazon S3 console and create an S3 bucket.
Upload a sample order JSON file to the S3 bucket.
Configure the S3 bucket to trigger the Lambda function whenever a new object is created.

Step 6: Testing the Lambda Function

Upload a sample order JSON file to the S3 bucket and observe the Lambda function’s execution in the AWS Lambda console.
Verify that the Lambda function processes the order and performs the required actions.

Conclusion:

AWS Lambda Functions provide a powerful and scalable platform for running code in response to events or triggers without managing servers. With their flexibility, cost-effectiveness, and ease of integration, Lambda Functions are ideal for a wide range of use cases, including event-driven processing, web applications, data processing, and scheduled tasks.

By following this step-by-step guide, you’ve learned how to create and deploy an AWS Lambda function using .NET Core. You’ve also configured an S3 trigger to automatically invoke the Lambda function in response to new file uploads. This example demonstrates the power of serverless computing and how you can leverage AWS Lambda to build scalable and cost-effective solutions for various use cases, such as order processing in e-commerce applications.