Serverless computing, also known as Function as a Service (FaaS), is a cloud computing execution model where the cloud provider dynamically manages the allocation and provisioning of servers. A serverless vendor allows users to write and deploy code without the hassle of worrying about the underlying infrastructure. A company that gets serverless services is charged based on their computation and do not have to reserve and pay for a fixed amount of bandwidth or number of servers, as the service is auto-scaling.
Serverless computing can simplify the process of deploying code into production. Scaling, capacity planning and maintenance operations may be hidden from the developer or operator. Serverless code can be used in conjunction with code deployed in traditional styles, such as microservices. Alternatively, applications can be written to be purely serverless and use no provisioned servers at all.
Definition and Explanation
Serverless computing is a method of providing backend services on an as-used basis. A serverless provider allows users to write and deploy code without the worry of the underlying infrastructure. With the serverless model, the cloud provider is responsible for executing a piece of code by dynamically allocating the resources. And charging based on the amount of resources consumed by the code, not on pre-purchased units of capacity or bandwidth.
The term 'serverless' can be misleading as servers are still involved in serving applications. The name 'serverless' comes from the idea that the tasks associated with infrastructure are invisible to the developer. This approach can be more cost-effective and result in more scalable and responsive applications.
Function as a Service (FaaS)
Function as a Service (FaaS) is another term often used to describe serverless and its capabilities. FaaS is a category of cloud computing services that provides a platform allowing customers to develop, run, and manage application functionalities without the complexity of building and maintaining the infrastructure typically associated with developing and launching an app. Building an application following this model is one way of achieving a serverless architecture, and is typically used when building microservices applications.
With FaaS, applications are broken down into separate functions that can be invoked and scaled individually. This means you can scale the most resource intensive functions without scaling the entire application, which can lead to cost reductions.
History of Serverless Computing
The concept of serverless computing has been around in one form or another for more than a decade, but it wasn't until 2014, with the launch of AWS Lambda, that it started to gain traction in the industry. AWS Lambda was designed to use an event-driven model for cloud computing, allowing developers to execute code in AWS without provisioning or managing servers.
Following the success of AWS Lambda, other major cloud providers launched their own serverless platforms. Google introduced Google Cloud Functions in 2016, and Microsoft introduced Azure Functions in the same year. These platforms, along with others like IBM's OpenWhisk, have helped popularize the serverless model and have led to a significant growth in the use of serverless computing over the past few years.
Evolution of Serverless Computing
Serverless computing has evolved significantly since its inception. Early versions of serverless computing focused on performing small tasks in response to event triggers. However, as the technology matured, it has become increasingly capable of running complex applications.
Modern serverless platforms offer a wide range of capabilities, including real-time file and data processing, machine learning, and more. They also offer a variety of tools and services to help manage, deploy, and monitor serverless applications. This has led to an increase in the adoption of serverless computing across a wide range of industries.
Use Cases of Serverless Computing
Serverless computing can be used in a variety of scenarios, ranging from traditional web applications to data analysis and machine learning. It's particularly well-suited to applications that need to be able to scale quickly and easily in response to changes in demand.
Common use cases for serverless computing include microservices, real-time file processing, data transformation (ETL), real-time analytics, chatbots, and IoT applications. In each of these cases, serverless computing allows developers to focus on writing their application code, rather than managing and operating infrastructure.
Microservices
Serverless computing is often used in microservices architectures, where an application is broken down into smaller, independent services. Each of these services can be developed, deployed, and scaled independently. This allows for greater agility and scalability, and can also help to improve fault isolation.
With serverless computing, each microservice can be packaged as a function, and the serverless platform takes care of routing requests, scaling, and failover. This can greatly simplify the development and operation of microservices architectures.
Data Transformation
Data transformation, or Extract, Transform, Load (ETL) tasks, are another common use case for serverless computing. These tasks involve extracting data from one source, transforming it in some way, and then loading it into another source.
With serverless computing, each step in the ETL process can be packaged as a function, and the serverless platform can handle the execution and scaling of these functions. This can make it easier to process large volumes of data, and can also help to reduce costs as you only pay for the compute resources you actually use.
AWS Lambda and Azure Functions
AWS Lambda and Azure Functions are two of the most popular serverless computing platforms. Both platforms allow developers to write and deploy code without having to worry about the underlying infrastructure, and both offer a wide range of features and capabilities.
AWS Lambda supports a variety of programming languages, including Node.js, Python, Java, and C#. It also integrates with other AWS services, making it easy to build complex applications. Azure Functions also supports a variety of programming languages, and integrates with other Azure services.
AWS Lambda
AWS Lambda is a serverless compute service that lets you run your code without provisioning or managing servers. AWS Lambda executes your code only when needed and scales automatically, from a few requests per day to thousands per second. You pay only for the compute time you consume - there is no charge when your code is not running.
With AWS Lambda, you can run code for virtually any type of application or backend service - all with zero administration. AWS Lambda runs your code on a high-availability compute infrastructure and performs all the administration of the compute resources, including server and operating system maintenance, capacity provisioning and automatic scaling, code monitoring and logging.
Azure Functions
Azure Functions is a serverless solution that allows you to write less code, maintain less infrastructure, and save on costs. Instead of worrying about deploying and maintaining servers, the cloud infrastructure provides all the up-to-date resources needed to keep your applications running.
You focus on the pieces of code that matter most to you, and Azure Functions handles the rest. Functions can be written in a variety of programming languages, and can be managed via Azure Portal, Azure CLI, Azure PowerShell, and other popular tools.
Benefits of Serverless Computing
Serverless computing offers a number of benefits over traditional cloud computing models. One of the main benefits is the reduction in operational costs. Since you only pay for the compute time you consume, and because the cloud provider manages the servers, you can often save money over running your own servers.
Another major benefit of serverless computing is the ability to scale automatically. The serverless provider is responsible for scaling the application in response to the demand. This means that the application can handle sudden spikes in traffic without any intervention from the developer.
Reduced Operational Costs
One of the key benefits of serverless computing is the potential for reduced operational costs. In a traditional cloud computing model, you would need to provision and maintain servers to run your applications. This requires a significant amount of time and resources, especially for larger applications.
In a serverless model, the cloud provider takes care of all the infrastructure management tasks, freeing up your resources to focus on other areas. Additionally, you only pay for the compute time you consume, which can result in significant cost savings if your application has variable or unpredictable workloads.
Automatic Scaling
Another key benefit of serverless computing is the ability to scale automatically. In a traditional cloud computing model, you would need to manually scale your application in response to changes in demand. This can be a complex and time-consuming process, and it can be difficult to get right.
In a serverless model, the cloud provider takes care of scaling for you. This means that your application can handle sudden spikes in traffic without any intervention from you. This can be particularly beneficial for applications with variable or unpredictable workloads.
Conclusion
Serverless computing represents a significant shift in the way that applications are developed and deployed. By removing the need to manage servers, it allows developers to focus on writing code, rather than managing and operating infrastructure. This can lead to faster development cycles, lower costs, and more scalable applications.
With the rise of serverless platforms like AWS Lambda and Azure Functions, serverless computing is becoming increasingly accessible to developers of all sizes and skill levels. As the technology continues to mature, it's likely that we'll see even more innovative uses of serverless computing in the future.