In this article, I will explain Server and Serverless Computing.
Server and serverless computing are two different approaches to managing and deploying applications and services in the cloud. Amazon Web Services (AWS) offers both options, each with its own benefits and use cases. The following sections show a comparison between server and serverless computing in the context of AWS.
- Managed Virtual Servers (Instances). In server computing, you provision and manage virtual servers (EC2 instances) on AWS. Therefore, you select the instance type, operating system, and other configurations.
- Infrastructure Management. Also, with server computing, you’re responsible for managing the underlying infrastructure, including patching, scaling, and capacity planning. This provides more control but also requires more operational effort.
- Scaling. You need to manually scale your instances up or down based on demand. This involves configuring auto-scaling groups or manually adjusting instance counts.
- Flexibility and Customization. Server computing offers high customization and flexibility, as you have direct control over the instance’s configuration and software stack.
- Persistent State. Similarly, if your application requires state persistence, you need to manage databases and data storage separately.
- Use Cases. Basically, Server computing is suitable for applications with predictable and consistent workloads, legacy systems, applications requiring specific OS configurations, and when fine-tuned performance control is essential.
Serverless Computing (AWS Lambda)
- Event-Driven Execution. In serverless computing, you write code (functions) that execute in response to events, such as HTTP requests, database changes, or scheduled triggers.
- Managed Service. AWS Lambda manages the infrastructure for you. So, you don’t need to worry about provisioning servers, scaling, or patching. In fact, you only pay for the actual compute time used by your functions.
- Automatic Scaling. Similarly, AWS Lambda automatically scales your functions based on the incoming workload. It handles the scaling seamlessly, allowing you to focus solely on your code.
- Stateless Execution. Also, AWS Lambda functions are stateless, meaning they don’t maintain a persistent state between invocations. So, any stateful data needs to be stored externally, typically in AWS services like DynamoDB or S3.
- Reduced Overhead. Additionally, Serverless computing reduces operational overhead, as you don’t have to manage servers, infrastructure, or scaling settings.
- Rapid Development. Moreover, Serverless computing allows you to quickly develop and deploy code in response to specific events, promoting agile development practices.
- Use Cases. Therefore, Serverless computing is ideal for event-driven applications, microservices, APIs, data processing tasks, and scenarios where rapid development and automatic scaling are crucial.
In short, server computing involves provisioning and managing virtual servers, offering high customization but requiring more operational overhead. Furthermore, Serverless computing, exemplified by AWS Lambda, focuses on event-driven execution, abstracting away infrastructure management and scaling complexities, allowing developers to focus solely on writing code. So, the choice between the two depends on your application’s requirements, development speed, operational preferences, and scalability needs.
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