In this post, we’ll be introducing the concept of serverless computing and how it can help you to build scalable and cost-effective applications.
What is serverless computing?
Serverless computing is a cloud-computing execution model in which the cloud provider is responsible for managing the infrastructure and servers, while the user is only responsible for writing and uploading code. This means that the user can simply run their code without having to worry about provisioning, scaling, or maintaining servers.
One of the main differences between serverless computing and traditional server-based computing is the level of control and responsibility that the user has over the underlying infrastructure. In serverless computing, the user’s focus is on writing and deploying code, while in traditional server-based computing, the user is responsible for managing the entire infrastructure that the code runs on.
Another key difference is cost. In serverless computing, users are only charged for the resources they use, such as the number of requests made to their functions and the amount of memory and CPU used. This can result in significant cost savings, as users only pay for what they need, and do not need to maintain idle resources. In traditional server-based computing, users are typically required to pay for a fixed amount of resources, regardless of whether they are being used or not.
Serverless computing is becoming increasingly popular among developers for several reasons:
- Cost-effective: you only pay for the compute time that you consume, and not for idle servers. This can lead to significant cost savings, especially for applications with variable or unpredictable workloads.
- Flexibility: focus on writing code, rather than managing servers. This means that they can be more productive, and can quickly and easily deploy new features and updates.
- Scalability: AWS automatically scales the underlying infrastructure in response to changes in demand. This means that applications built using serverless technologies can handle large amounts of traffic without the need for manual scaling.
- Event-driven computing: Serverless computing allows you to build applications that are triggered by specific events, such as changes to data in a database or the arrival of new files in an S3 bucket. This makes it an ideal solution for event-driven and real-time applications, such as IoT, chatbots, and real-time analytics.
Common use cases for serverless computing
Serverless computing is a versatile technology that can be used in a wide range of use cases. Here are a few examples of how serverless computing is commonly used:
- APIs: Serverless computing can be used to build APIs, allowing developers to focus on the business logic while the cloud provider handles the underlying infrastructure. This can include tasks such as handling user authentication, processing payments, and providing access to databases.
- Real-time data processing: Serverless computing can be used to process large amounts of data in real-time, such as analyzing log files or processing sensor data. This can include tasks such as data validation, data transformation, and data storage.
- Event-driven computing: Serverless computing is an event-driven model, which means that the code is only executed when a specific event occurs. This makes it a natural fit for tasks such as image processing, where images are uploaded to a cloud storage bucket and a serverless function is triggered to process the images.
- Automated Business Processes: Serverless computing can be used to automate business processes that require human intervention, like approvals or notifications. For example, using a serverless function, one can trigger an email or slack message to specific individuals when certain conditions are met.
- Microservices: Serverless computing can be used to build microservices, which are small, loosely-coupled services that can be composed to build a larger application. This can include tasks such as building an API Gateway, handling request routing and validation, and providing access to databases.
Main building blocks
The main building blocks of serverless computing are:
- Serverless functions: These are small, single-purpose units of code that are executed in response to specific events or triggers. Examples of serverless functions include AWS Lambda, Azure Functions and Google Cloud Functions.
- Event triggers: These are the events that invoke serverless functions. Examples of event triggers include a new file being added to an S3 bucket, an HTTP request, a message in a queue, or a change in a database. These triggers are used to invoke the serverless function and pass in any required data.
- Databases: Serverless functions often need to store and retrieve data, and this is typically done using databases. Serverless computing platforms typically provide a variety of databases that are well-suited for serverless applications, such as DynamoDB (AWS) that can be used to store and retrieve data and is automatically scaled.
Together, these building blocks provide a powerful and flexible way to build, deploy, and run applications without the need to provision and manage servers. Functions are triggered by events, and they can interact with databases, creating a powerful and flexible system that can be used to build a wide variety of applications and services.
Current state of serverless computing
Serverless computing is an increasingly popular way to build, deploy, and run applications, and it has seen significant growth in recent years. Overall, the current state of serverless computing is one of growth, with increasing adoption, more use cases, improved tooling and support, and more complex use cases being tackled with this technology.
AWS is my go to place
As a software engineer, my personal preference goes to Amazon Web Services (AWS) for serverless computing. There are several reasons why I prefer AWS over other options:
- Comprehensive and mature platform: AWS has been around for over a decade and has a wide range of services and capabilities. It is a mature platform that is constantly being updated with new features and capabilities. This makes it an ideal choice for building complex, scalable, and reliable applications.
- Large ecosystem: AWS has a large ecosystem of partners and third-party tools that can be used to extend the capabilities of the platform. This makes it easy to find solutions for specific needs, such as monitoring, logging, security, and more.
- Strong community: AWS has a large and active community of developers and users. This makes it easy to find answers to questions, share knowledge, and get help with specific problems.
- Flexibility: AWS Lambda, the serverless computing service, is highly flexible and allows for multiple languages and frameworks.
- Cost-effective: AWS Lambda allows for a pay-per-use model, which means you only pay for the compute time you consume. This makes it a cost-effective option for applications that have varying levels of traffic.
Overall, my personal preference for AWS is driven by its comprehensive and mature platform, large ecosystem, strong community, flexibility, cost-effectiveness, and easy integration with other services.