Imagine that a critical application in your company is having performance issues. This application is available to customers 24-7, and during business hours, the CPU and memory utilization reaches 100%. This is resulting in an increased response time for customers.

Around 10 years ago, a good migration plan to solve this issue would involve the procurement and deployment of new hardware resources for both the application and its databases, the installation of all required software and application code, performing all functional and performance quality analysis work, and finally, migrating the application. The cost of this would run into millions. However, nowadays, this issue can be resolved with new technologies that offer different approaches to customers – going Serverless is definitely one of them.

In this chapter, we'll start with an explanation of the serverless model, and get started with AWS and Lambda, the building blocks of a serverless applications on AWS. Finally, you'll learn how to create and run Lambda functions.

To understand the serverless model, let's first understand how we build traditional applications such as mobile applications and web applications. Figure 1.1 shows a traditional on-premises architecture, where you would take care of every layer of application development and the deployment process, starting with setting up hardware, software installation, setting up a database, networking, middleware configuration, and storage setup. Moreover, you would need a staff of engineers to set up and maintain this kind of on-premises setup, making it very time-consuming and costly. Moreover, the life cycle of these servers was no longer than 5-6 years, which meant that you would end up upgrading your infrastructure every few years.

The work wouldn't end there, as you would have to perform regular server maintenance, including setting up server reboot cycles and running regular patch updates. And despite doing all the groundwork and making sure that the system ran fine, the system would actually fail and cause application downtime. The following diagram shows a traditional on-premises architecture:

Figure 1.1 : Diagram of traditional on-premises architecture

The serverless model changes this paradigm completely, as it abstracts all the complexity attached with provisioning and managing data centers, servers, and software. Let's understand it in more detail.

The serverless model refers to applications in which server management and operational tasks are completely hidden from end users, such as developers. In the serverless model, developers are dedicated specifically to business code and the application itself, and they do not need to care about the servers where the application will be executed or run from, or about the performance of those servers, or any restrictions on them. The serverless model is scalable and is actually very flexible. With the serverless model, you focus on things that are more important to you, which is most probably solving business problems. The serverless model allows you to focus on your application architecture without you needing to think about servers.

Sometimes, the term "serverless" can be confusing. Serverless does not mean that you don't need any servers at all, but that you are not doing the work of provisioning servers, managing software, and installing patches. The term "the serverless model" just means that it is someone else's servers. Serverless architectures, if implemented properly, can provide great advantages in terms of lowering costs and providing operational excellence, thus improving overall productivity. However, you have to be careful when dealing with the challenges imposed by serverless frameworks. You need to make sure that your application doesn't have issues with performance, resource bottlenecks, or security.

Figure 1.2 shows the different services that are part of the serverless model. Here, we have different services for doing different kinds of work. We have the API Gateway service, a fully managed REST interface, which helps to create, publish, maintain, monitor, and secure APIs. Then, we have the AWS Lambda service that executes the application code and does all the computation work. Once computation is done, data gets stored in the DynamoDB database, which is again a fully managed service that provides a fast and scalable database management system. We also have the S3 storage service, where you can store all your data in raw formats that can be used later for data analytics. The following diagram talks about the serverless model:

Figure 1.2 : The serverless model (using AWS services)

Serverless models have become quite popular in recent times, and many big organizations have moved their complete infrastructure to serverless architectures and have been running them successfully, getting better performance a much a lower cost. Many serverless frameworks have been designed, making it easier to build, test, and deploy serverless applications. However, our focus in this book will be on serverless solutions built on Amazon Web Services (AWS). Amazon Web Services is a subsidiary of Amazon.com that provides on-demand cloud service platforms.

Benefits of the Serverless Model

There are a number of benefits to using a serverless model:

• No server management: Provisioning and managing servers is a complex task and it can take anything from days to months to provision and test new servers before you can start using them. If not done properly, and with a specific timeline, it can become a potential obstacle for the release of your software onto the market. Serverless models provide great relief here by masking all the system engineering work from the project development team.
• High availability and fault tolerance: Serverless applications have built-in architecture that supports high availability (HA). So, you don't need to worry about the implementation of these capabilities. For example, AWS uses the concept of regions and availability zones to maintain the high availability of all AWS services. An availability zone is an isolated location inside a region and you can develop your application in such a way that, if one of the availability zones goes down, your application will continue to run from another availability zone.
• Scalability: We all want our applications to be successful, but we need to make sure that we are ready when there is an absolute need for scaling. Obviously, we don't want to spawn very big servers in the beginning (since this can escalate costs quickly), but we want to do it as and when the need occurs. With serverless models, you can scale your applications very easily. Serverless models run under the limits defined by you, so you can easily expand those limits in the future. You can adjust the computing power, the memory, or IO needs of your application with just a few clicks, and you can do that within minutes. This will help you to control costs as well.
• Developer productivity: In a serverless model, your serverless vendor takes all the pain of setting up hardware, networking, and installing and managing software. Developers need to focus only on implementing the business logic and don't need to worry about underlying system engineering work, resulting in higher developer productivity.
• No idle capacity: With serverless models, you don't need to provision computing and storage capacity in advance. And you can scale up and down based on your application requirements. For example, if you have an e-commerce site, then you might need higher capacity during festive seasons than other days. So, you can just scale up resources for that period only.

  Moreover, today's serverless models, such as AWS, work on the "pay as you go" model, meaning that you don't pay for any capacity that you don't use. This way, you don't pay anything when your servers are idle, which helps to control costs.

• Faster time to market: With serverless models, you can start building software applications in minutes, as the infrastructure is ready to be used at any time. You can scale up or down underlying hardware in a few clicks. This saves you time with system engineering work and helps to launch applications much more quickly. This is one of the key factors for companies adopting the serverless model.
• Deploy in minutes: Today's serverless models simplify deployment by doing all the heavy lifting work and eliminating the need for managing any underlying infrastructure. These services follow DevOps practices.

AWS is a highly available, reliable, and scalable cloud service platform offered by Amazon that provides a broad set of infrastructure services. These services are delivered on an "on-demand" basis and are available in seconds. AWS was one of the first platforms to offer the "pay-as-you-go" pricing model, where there is no upfront expense. Rather, payment is made based on the usage of different AWS services. The AWS model provides users with compute, storage, and throughput as needed.

The AWS platform was first conceptualized in 2002 and Simple Queue Service (SQS) was the first AWS service, which was launched in 2004. However, the AWS concept has been reformulated over the years, and the AWS platform was officially relaunched in 2006, combining the three initial service offerings of Amazon S3 (Simple Storage Service): cloud storage, SQS, and EC2 (Elastic Compute Cloud). Over the years, AWS has become a platform for virtually every use case. From databases to deployment tools, from directories to content delivery, from networking to compute services, there are currently more than 100 different services available with AWS. More advanced features, such as machine learning, encryption, and big data are being developed at a rapid pace. Over the years, the AWS platform of products and services has become very popular with top enterprise customers. As per current estimates, over 1 million customers trust AWS for their IT infrastructure needs.

AWS Serverless Ecosystem

We will take a quick tour of the AWS serverless ecosystem and briefly talk about the different services that are available. These services will be discussed in detail in future chapters.

Figure 1.4 shows the AWS serverless ecosystem, which is comprised of eight different AWS services:

• Lambda: AWS Lambda is a compute service that runs code in response to different events, such as in-app activity, website clicks, or outputs from connected devices, and automatically manages the compute resources required by the code. Lambda is a core component of the serverless environment and integrates with different AWS services to do the work that's required.
• Simple Storage Service (S3): Amazon S3 is a storage service that you can use to store and retrieve any amount of information, at any time, from anywhere on the web. AWS S3 is a highly available and fault-tolerant storage service.
• Simple Queue Service (SQS): Amazon SQS is a distributed message queuing service that supports message communication between computers over the internet. SQS enables an application to submit a message to a queue, which another application can then pick up at a later time.
• Simple Notification Service (SNS): Amazon SNS is a notification service that coordinates the delivery of messages to subscribers. It works as a publish/subscribe (pub/sub) form of asynchronous communication.
• DynamoDB: Amazon DynamoDB is a NoSQL database service.
• Amazon Kinesis: Amazon Kinesis is a real-time, fully managed, and scalable service.
• Step Functions: AWS Step Functions make it easy to coordinate components of distributed applications. Suppose you want to start running one component of your application after another one has completed successfully, or you want to run two components in parallel. You can easily coordinate these workflows using Step Functions. This saves you the time and effort required to build such workflows yourself and helps you to focus on business logic more.
• Athena: Amazon Athena is an interactive serverless query service that makes it easy to use standard SQL to analyze data in Amazon S3. It allows you to quickly query structured, unstructured, and semi-structured data that's stored in S3. With Athena, you don't need to load any datasets locally or write any complex ETL (extract, transform, and load), as it provides the capability to read data directly from S3. We will learn more about AWS Athena in Chapter 4, Serverless Amazon Athena and the AWS Glue Data Catalog.

  Here's a diagram of the AWS serverless ecosystem:

Figure 1.3 : The AWS serverless ecosystem Ecosystem