Hem's Blog: Lambda Function

Serverless computing is a cloud-based computing execution model in which the cloud provider dynamically manages the allocation of machine resources. With serverless computing, the cloud provider is responsible for provisioning, scaling, and managing the servers that run the code, rather than the user. This allows developers to focus on writing code and deploying their applications, without the need to worry about the underlying infrastructure.

In serverless computing, the code is run in stateless compute containers that are triggered by events and automatically scaled to match the rate of incoming requests. This eliminates the need for provisioning, scaling, and maintaining servers, resulting in lower costs and increased scalability.

Examples of serverless computing include AWS Lambda, Azure Functions, and Google Cloud Functions. These services allow developers to create and deploy their code as small, single-purpose functions, which are automatically triggered by events such as an HTTP request or a database update.

Serverless computing is commonly used for building web and mobile backends, real-time data processing, and event-driven architectures.

It's important to note that despite the name, there are servers still running behind the scene, the difference is that the provider manages the servers and the user only pays for the resources used (compute, storage, etc) and not for the servers.

Here are a few common use cases for serverless computing:

Event-driven computing: Serverless architectures are well-suited for processing events, such as changes to a database or new files being uploaded to a storage service. This allows for real-time data processing and efficient scaling.
APIs and Microservices: Serverless computing is often used to build and deploy APIs, as well as to run microservices. This allows for better scalability and cost management, as resources are only allocated when an API request is made or a microservice is invoked.
Background tasks and cron jobs: Serverless computing can be used to run background tasks, such as image processing or data analysis, which can be triggered by a schedule or a specific event.
Web and mobile apps: Serverless architectures can be used to build and deploy web and mobile applications, allowing for faster development, lower costs, and better scalability.
IoT and edge computing: Serverless computing can be used to build and deploy applications for Internet of Things (IoT) devices and for edge computing, where compute resources are located at or near the edge of a network.
Chatbot and voice assistants: Serverless function can be used to handle the logic of chatbot and voice assistants, this way only the necessary compute power is used when a user interacts with the chatbot or assistant.

Advantages of using serverless applications in .NET Core :

Cost-effective: serverless architecture eliminates the need for provisioning and maintaining servers, resulting in lower costs.
Scalability: serverless applications can automatically scale in response to increased traffic, without the need for manual intervention.
Flexibility: serverless architecture allows for the deployment of small, single-purpose functions, making it easier to build and maintain a microservices-based architecture.
Reduced operational complexity: serverless applications are abstracted away from the underlying infrastructure, reducing the operational complexity of deploying and managing applications.
Improved availability: serverless applications can be designed to automatically failover to other instances in the case of a failure, improving the overall availability of the application.

Disadvantages of using serverless applications:

Cold start: serverless applications may experience a delay in response time when they first receive a request after a period of inactivity, known as a "cold start."
Limited control over the underlying infrastructure: serverless applications do not provide the same level of control over the underlying infrastructure as traditional server-based applications.
Concurrency limitations: serverless applications may be subject to concurrency limitations, depending on the platform and the number of instances available.
Limited support for long-running tasks: serverless architecture is best suited for short-lived, stateless tasks, and may not be the best choice for long-running, stateful tasks.
Higher latency: serverless applications may experience higher latency because of the need to spin up new instances to handle incoming requests.

Here are the general steps to build a Serverless application:

Choose a cloud provider: There are several popular cloud providers that offer serverless computing services, such as AWS Lambda, Azure Functions, and Google Cloud Functions. Choose the one that best fits your needs and has good Java support.
Set up the development environment: Before you start building your serverless application, you will need to set up your development environment. This typically involves installing the necessary software and configuring your development environment.
Create a new function: Once your development environment is set up, you can create a new function. This typically involves specifying the function's name, the trigger that will invoke the function, and the code that will be executed when the function is invoked.
Write the code: Write the code for your function using Java. The code should handle the input and output of the function. Test the function:
Test the function locally before deploying it to the cloud. This can be done using the cloud provider's command-line tools or SDK.
Deploy the function: Once your function is tested and working, deploy it to the cloud provider's serverless computing service.
Monitor and maintain: After deploying your function, monitor and maintain it. This includes monitoring the function's performance and error logs, and making updates and fixes as necessary.

Steps to build serverless application in .NET Core

Install the .NET Core SDK and the AWS SDK for .NET on your local machine.
Create a new .NET Core project using the "dotnet new" command.
Add the AWS Lambda NuGet package to the project.
Create a new class that will serve as the entry point for the Lambda function. This class should implement Amazon.Lambda.Core.ILambda function interface.
Add the necessary code to handle the input and output of the Lambda function in the class created in the previous step.
Create an AWS profile and configure the AWS SDK for .NET with the appropriate credentials.
Use the "dotnet lambda deploy-function" command to deploy the Lambda function to AWS.
Test the deployed Lambda function using the AWS Lambda console or the AWS CLI.
Add any other functionality, such as connecting to a database or invoking other AWS services, as needed for your application.
Continuously monitor and update your serverless application to ensure optimal performance and stability.

Happy Coding and Keep Sharing !!

When you have a very important and bulk publishing is going on and you want to monitor each state then AWS service is a great way of doing it. Recently, I had this opportunity to implement AWS services to monitor SDL Tridion publishing and Broker Database spike. We need to monitor the publishing state and Broker DB connections limit and for that I used the following AWS services.

All these activity is required and becomes almost mandatory when yon have Huge INFRA to manage.

AWS CloudWatch
AWS SNS (Simple Notification Service)
AWS lambda Function

To monitor the publishing I used the AWS Lambda function which is in python and some inline SQL script. Yes, just few lines of code gives you all the info.

This matrix is then used in the dashboard to generate the realtime GRAPH and Similarly, we have script for other publishing state which helps us in monitoring the progress of publishing. These scripts are very helpful when you have thousands of the items in queue and waiting for publishing.

Failed items

Published items

Next, is we need to implement the notification service to send the notification whenever the Broker Database DBconnectin limit reaches the higher side or more than expected so that we can take action pro-actively. To Implement this we used the AWS default Matrix and with the help of AWS SNS we are sending the notification, for notification you can use (EMAIL,SMS,HTTP,Notification etc) depending upon your requirement.

You need to go to the CloudWatch--> Alarm and create a New Alarm. By Default in SQL Server the default DB connection is set to 0 which mean Unlimited, but using AWS CloudWatch you can monitor and can take pro.active steps when its starts increasing.

Next is send the notification if the limit is crossed and for that we can use AWS SNS.

Configure SNS to send Notification.

Where SNS is your notification service. We first need to create a Topics and based on Publisher and Subscriber model we can send the notification. Protocol supported by the AWS SNS to subscribe.

Protocol Available

Or, you can configure the Auto Scaling of you EC2 instance, we only have notifications service configured but yes, we also have this options as well. It all depends on your requirements.