Serverless computing has changed the technological landscape — and for good reason. The benefit is clear: serverless architectures make it so developers just worry about the code, business logic and user experience free from caring for servers of any kind (maintenance, scaling or availability). This is where a serverless service like AWS Lambda comes in the picture. In this article, we overview the main principles of the serverless architecture, go into more detail on AWS Lambda implementation and walkthrough a step-by-step guide to build serverless applications.

Serverless Architecture Explained A Step by Step Guide

Serverless architecture is a cloud-computing execution model in which the cloud provider allocates machine resources on demand. Prior to serverless computing, traditional server-based models required developers to manage infrastructure resources, design for scaling and maintain uptime. Serverless abstracts these concerns, charging only for compute time and disk storage when the code is running.

Although the term “serverless” can be misleading since servers are still required (and always existed on some level), they still exist and are used simply abstracted away, allowing developers to never think about a server throughout their development process. Here are some common characteristics of a serverless architecture:

• Hybrid Processing Model Event-driven processing: Code is triggered in response to events (e.g. an HTTP request, a database update or an file upload).
• Auto-scaling: Applications scale out and back in based on load without manual intervention.
• Pay-as-you-go: Instead of typical models where you pay for running time, here you only need to pay every second your code executes.
• Streamlined infrastructure management — developers are only writing code and the cloud provider is responsible for server provisioning, patching, and scaling.

AWS Lambda: The Core of Serverless on AWS

AWS Lambda is a serverless computing service provided by Amazon Web Services Lambda, introduced in 2014, is a way to run code without provisioning or managing servers in response to different events. When you upload a code, it will be run in isolated environments known as “Lambda functions” by AWS.

Key Features of AWS Lambda:

• Architecture Pattern: Event Sources anti-patterns → Amazon S3, DynamoDB, SNS to trigger lambda functions from empty sqs whenever a new item is created they can add emit an event as well -> API Gateway -> Lambda Functions.
• Freedom from server management: No more problem provisioning, configuring and managing the underlying infrastructure.
• Built in fault tolerance: Lambda scale out automatically and is replicated across multiple availability zones.
• Automated scaling: Lambda automatically scales out horizontally in response to incoming events, so that you can approach any spike in traffic without pre-warming.
• Pay-per-use pricing: Billing is based on the number of requests and the 100ms duration your function’s execution.

AWS Lambda Architecture

AWS Lambda is an event-driven based framework. Architecture Overview

Trigger: An object, such as an HTTP request (A), file upload or a modification in a database, that triggers the function.

• Lambda execution environment: Provides an isolated environment for executing the function. It takes care of managing, maintenance of runtime and security context.
• Function execution: Lambda invokes your function with the provided event data.
• Scaling: Here AWS Lambda offers out of box scaling, which is quite nice and you do not have to worry about the number of instances getting created at the same time for handling incoming events.
• Integration with Other AWS Services: Lambda offers deep integration with other services, such as Amazon S3, DynamoDB, Kinesis, CloudWatch and API Gateway which makes it proficient enough to run in various serverless applications.

Setting Up Your First AWS Lambda Function

Step 1: Create lambda Function

1. Sign in to the AWS Management Console
2. Go to AWS Lambda and select “Create function.”
3. Choose “Author from Scratch”, then give a name to your Function.
4. A runtime (a Node one in this case) js, Python, or Java.
5. Name a new or existing IAM role & give it the desired permissions for your function

Step 2:Write the Function CodeFirst

In this example, we will create an empty Lambda function which just returns a hardcoded greeting:

def lambda_handler(event, context):
return {
'statusCode': 200,
'body': 'Hello from Lambda!'
}

You can write the code in AWS Lambda console and then can test it as well or you can also upload a zip file which contains all your function code along with dependencies.

Step 3: Configuring a Trigger

The next step is to then set up a trigger, which initiates your Lambda function. Popular triggers include:

• API Gateway: To take care of HTTP requests.
• S3: To upload files.
• CloudWatch : for scheduling jobs

One might be an API Gateway trigger, for example to call your Lambda via a HTTP request.

Step 4: Test Lambda Function

Once the function and trigger are setup, AWS Lambda provides native testing environment. You can trigger your function with test events, adjust the input payloads, and check what happens. You can also use CloudWatch Logs to monitor function executions and errors when working with AWS Lambda.

Deploy Serverless Application with AWS Lambda and API Gateway

As a way of understanding what is so kick-ass about AWS Lambda, let’s do something simpler; deploying a simple serverless application we can trigger responding to HTTP requests via an API Gateway.

Step 1: Create API Gateway

In the API Gateway dashboard:

1. Click on “Create API”, and then click on “HTTP API”.
2. Routes: Define your Lambda function for particular vs general HTTP requests (GET, POST etc)

Step 2: Deploy the API

Deploy the API once you have finished creating it. You can have the api deployed in different stages (like dev, prod.). For this, API Gateway will offer you an endpoint to trigger your Lambda function.

Step 3: Monitor and Scale

CloudWatch — to retain logs and monitor API calls, Lambda function execution time and potential errors. With AWS Lambda, there is no need to worry about the increased titles in traffic as it automatically scales itself.

Use Cases for AWS Lambda

AWS Howze Lambda is suitable for myriad use cases;

• Lambda can be triggered on step-up based events like S3 file upload, DynamoDB changes or Kinesis streams to process real-time data with out long running server.
• Microservices: You can decompose your application into manageable services in lambda by separating functions that handle specific parts of the app. The API Gateway from which these Microservices can be all exposed via HTTP.
• Web Apps — Mostly Full-stack apps can be made using a combination of Lambda as backend, for Static hosting S3 and communicate between frontend and backend using API Gateway.
• Scheduled Jobs: Lambda can be scheduled using CloudWatch Events to execute tasks like cleaning databases, generating reports or sending notifications.

Benefits of AWS Lambda

1. Cheap : you only pay for your stuff running rather than entire server time as is case with regular servers. As a result, it helps reduce costs drastically — especially for low-traffic or on-demand triggered applications.
2. Auto Scaling: No Auto-Scaling Groups, No Capacity Planning Moreover, AWS Lambda automatically scales up or down based on traffic.
3. Focus on writing good code without bothering about infrastructure (greater agility). This makes development more agile and faster time to market for new features.
4. Less operational overhead: AWS manages server maintenance tasks such as patching, updating, and monitoring for you. This means your teams can focus on more valuable work.

Challenges and Considerations

Basically, this means AWS Lambda has its own unique features with a few downsides:

• Cold starts when an AWS Lambda function has been idle for a period of time, there may be short delays while AWS provisions resources.
• State management: As Lambda functions are stateless, it is hard to maintain shared state across invocations. It is often solved by utilizing services like DynamoDB, or Redis.
• Execution Time: Lambda functions have an upper execution time limit (currently 15 minutes). Long running tasks might need to be divided into smaller jobs.

Conclusion

One of the big things that is going to be happening in 2016 is serverless architectures and AWS Lambda offers an answer to a lot of those problems. With AWS Lambda, you simply upload your code and let AWS Lambda take care of everything required to run and scale your application with high availability at an extremely low-cost. AWS Lambda is a powerful tool in your serverless toolbox for building real-time data processing pipeline, scalable web or mobile applications, and automating scheduled tasks.

Serverless architectures with AWS Lambda are here to stay and companies that embrace it will be more flexible, cost-effective, and scalable as the industry continues to evolve. And no matter if you are a startup that wants to minimize the infrastructure overhead, or an enterprise level company that is looking for operational efficiency, AWS Lambda will provide your modern cloud-native application with the perfect foundation.