So, after this myth buster, you may be thinking what the logic behind the name “serverless” is. Let me give you a hint: Instead of “no server,” it is HOW the servers are managed and implemented is what “Serverless” entails. Sounds confusing? Well, we will learn all about serverless and other terms related to it to clear your doubts. For starters, serverless is becoming famous as we speak. In fact, the serverless market is likely to reach $7.7 billion by 2021 from $1.9 billion in 2016. So, let’s discuss serverless and try figuring out the reason behind its popularity.
What is Serverless Computing?
Serverless or serverless computing is a cloud-based execution model in which cloud service providers provision on-demand machine resources and manage the servers by themselves instead of customers or developers. It is a way that combines services, strategies, and practices to help developers build cloud-based apps by letting them focus on their code rather than server management. From resource allocation, capacity planning, management, configurations, and scaling to patches, updates, scheduling, and maintenance, the cloud service provider (like AWS or Google Cloud Platform) takes all the responsibility of managing common infrastructure tasks. As a result, developers can concentrate their effort and time on business logic for their processes and applications. This serverless computing architecture never holds compute resources in volatile memory; instead, computing takes place in short parts. Suppose you are not using an application, no resources will be allocated to it. Therefore, you are paying for what resource you actually consume on apps. The main aim behind creating the serverless model is to simplify the code deployment process into production. Many a time, it also works with traditional styles like microservices. Once serverless is deployed, the applications it powers start responding to demands quickly and scale up or down automatically as required. Serverless computing uses an event-driven model to determine scaling requirements. Hence, developers no longer need to anticipate an application’s usage to decide how many servers or bandwidth they require. You can demand more servers and bandwidth based on your increasing needs without prior booking or scale down at any time without hassles.
How did Serverless Evolve?
The traditional system had challenges associated with scalability and agility in the app development process and deployment. As demands for high-quality apps increased with quick time-to-market, the need for a better system that can offer more scalability and agility started surfacing. It resulted in the evolution of cloud computing and serverless models. The Serverless model evolved in various stages, from monolithic to microservices to serverless architecture or Function-as-a-Service (FaaS).
Monolithic architecture is a traditional unified approach for software development. It is a tightly coupled model where each component and its sub-components compile or execute code. If a service is defective, the entire application server and running services on it can go down. Microservice architecture is a collection of smaller services inside a large, single application deployed independently to perform a specific function. It enables rapid large-scale app delivery, providing developers with flexibility using Infrastructure-as-a-Service (IaaS) and Platform as a Service (PaaS). However, choosing between PaaS and IaaS is challenging in this model. Serverless architecture evolved with cloud computing and offers more scalability and business agility. Instead of IaaS and PaaS, it utilizes FaaS and Backend-as-a-Service (BaaS). Here, apps are deployed as needed, along with the resources for it. You don’t have to manage the server and can stop paying if the code execution finishes.
Attributes of Serverless Computing
Some of the attributes of serverless computing are as follows:
Most applications using serverless comprise single functions and small units of code. It runs code only on demand, generally in a stateless software container, and scales seamlessly based on demand. No server management is needed from customers. Features event-based execution where the computer environment gets created once a function is triggered or an event is received to execute the request. Flexible scalability so you can scale up or down easily. Once a code is executed, the infrastructure stops running, and the cost is saved. Similarly, when the function continues to execute, you can scale up infinitely as needed. You can use managed cloud services to handle complex tasks like file storage, queuing, databases, and more.
How Does Serverless Work?
The serverless architecture combines two main ideas – Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS). It is based more on FaaS, which allows cloud services for code execution without needing completely provisioned instances. FaaS consists of stateless, event-driven, scalable, and server-side functions that cloud services manage fully. The model enables DevOps teams to write code focusing on their business logic. Next, they define an event that can trigger the function, like HTTP requests, for execution. Consequently, the cloud provider executes the function and sends results to apps that users can view. This way, the serverless model offers cost-efficiency and convenience with auto-scaling, on-demand, and pay-as-you-go facilities. Hence, many businesses and DevOps teams are going serverless these days.
Who Uses Serverless and Why?
Serverless is amongst the most emerging technologies in software development. It could eliminate infrastructure management and provisioning needs in the future. It is useful for:
Organizations desiring more scalability and flexibility with better app testability can go serverless. Developers who want to reduce time-to-market by building agile and high-performing apps Companies that don’t need their servers running all the time. They can call module-based functions using applications, when required, to save cost. Organizations wanting to build efficient cloud-based apps and simplify cloud migration Developer’s looking for ways to reduce latency can offer users access to some functions or apps. A company that doesn’t have sufficient resources to handle IT infrastructure maintenance and complexity can go for serverless computing to resolve issues automatically and need no maintenance from their end.
Some notable users of the serverless model are Slack, Coca-Cola, NetFlix, etc. Due to its unique attributes, the serverless model is suitable for many use cases, such as:
Web applications: You can build fast and scalable web applications using this model that responds quickly to user demands. It is ideal for building stateless apps that you can start instantly and apps that can cater to unpredictable, infrequent surges in user demands. API back-ends: In serverless platforms, any function can easily be turned into HTTP endpoints ready for use by clients. These functions or actions are known as web actions when enabled on the web. And once these are enabled, assembling the functions into a fully-fledged API becomes easy. You can also use a decent API gateway to bring more security, domain support, rate limiting, and OAuth support. Microservices: Serverless is widely used in the microservices model that focuses on building small services capable of performing a single function and communicating with each other using APIs. Although microservices are possible to create using software containers and PaaS, serverless is more efficient. It facilitates smaller lines of code that perform one thing and offer rapid provisioning, automatic scaling, and flexible pricing that doesn’t charge customers when the resources are not in use. Data processing: Serverless is great to work with data containing videos, audio, images, and structured text. It is also favorable for various tasks like data validation, transformation, enrichment, cleansing, audio normalization, and PDF processing. You can leverage it for image processing that includes sharpening, rotation, thumbnail generation, noise reduction. Other uses of serverless in data processing can be video transcoding and optical character recognition (OCR). Stream/batch processing: You can create powerful streaming apps and data pipelines using FaaS and a database with Apache Kafka. The serverless model suits different stream ingestions, including data for app logs, IoT sensors, business logic, and the financial market. Parallel computation: Serverless is excellent for tasks related to parallel computation, where each task runs in parallel to execute a specific task. It may include data search, processing, map operations, web scraping, genome processing, hyperparameter tuning, etc. Other uses: Serverless is also used for various applications, such as customer relationship management (CRM), finance, chatbots, and business intelligence and analytics, to name a few.
Note: Serverless may not be ideal for some cases. For example, large apps with predictable and almost constant workloads might benefit more from a traditional system architecture. They can go for dedicated servers, either managed or self-managed. Also, if your organization has complete traditional setups with legacy systems and applications, it may be expensive and challenging to move to an entirely new and different architecture.
Advantages and Disadvantages of Serverless Computing
Every coin has two sides, and so does the serverless architecture. It also has some advantages and disadvantages based on different parameters. So, before you go ahead, it’s important to know both sides to decide whether it would be better for your organization or not.
Advantages 👍
Here are some of the advantages of serverless architecture:
Cost-efficient
Serverless can offer more cost-efficiency than buying or renting servers where you pay for resources even if you don’t use them. Serverless employs a pay-as-you-go model where you will pay only for the resources you consume. The serverless provider will charge you only for the memory allocated and time to run the code without incurring costs for idle time. As a result, you will save on operating costs for tasks such as installation, licenses, maintenance, patching, support, etc. With no server hardware, you save on labor costs.
Scalability
Serverless systems offer a high level of scalability as you can scale up or down whenever you want based on the demands. They are also called “elastic” for this reason. Here, developers don’t need dedicated time for setting the auto-scaling systems or policies or tuning them. The cloud provider you chose is responsible for managing all that. In addition, developers from small teams can also run their code by themselves without requiring support engineers or infrastructure.
Reduced latency
As apps are not hosted over a single origin server, you can run the code from anywhere. If the cloud provider you have chosen supports it, you can run app functions on a server close to end-users. Hence, it incurs less latency due to reduced distance between the user requests and server.
Productivity
The serverless model helps improve your developers’ productivity as they don’t have to handle server management. Also, they don’t have to think about managing HTTP requests or multithreading in their code directly. As a result, it simplifies backend development, all thanks to FaaS, where exposed code are event-driven functions. All of these save the time that they can dedicate to improving the code and application.
Faster app deployment
With serverless, developers don’t perform backend configuration or upload code to the server in order to deploy an app version. They can also quickly upload the code in bits to release new products. They also have the flexibility to deploy code at once or function one after the other as it’s not a monolithic architecture. Additionally, you can patch, update, add features, or fix errors from an app quickly. Other benefits include green computing due to reduced energy consumption with on-demand servers, building an app that becomes easier with built-in integrations, faster time-to-market, and more.
Disadvantages 👎
Now, let’s look at the disadvantages of serverless computing:
Performance
Sometimes, serverless code used less frequently may exhibit more response latency than those running continuously on dedicated servers, software containers, or virtual machines (VM). It’s because it may need more time to start over and create extra latency.
Difficult to debug and test
You need to know how your code performs once you deploy it. For this, you need to test it, which is challenging in a serverless environment. Also, as developers lack visibility into each backend process and apps are divided into smaller functions, debugging gets complicated.
Security issues
New and advanced cybersecurity concerns are growing. But, it’s not possible to completely know or measure the cloud provider’s security. So, when they handle your entire backend with sensitive data stored on applications, it’s risky.
Not suitable for long-running application processes
Serverless is cost-efficient, but not for all types of applications. If you have an application having long-running processes, the cost of running it based on the time and allocated resources can be very high. At this time, you might want to go ahead with a dedicated server hosting. Other drawbacks of serverless are difficulty in switching from one vendor to another and privacy issues.
Terminologies Important in Serverless Architecture
Serverless is never complete without talking about some key terminologies related to it. FaaS and BaaS are two of the most prominent ideas that led to the evolution of serverless we know today. And to build a serverless system, you need a database, storage system, technology stack, framework, and so on. So, let’s discuss a bit about them.
Function as a Service (FaaS)
FaaS is a central idea in serverless and works like its subset. This event-driven code execution model (apps running in response to a request) lets you write logic deployed in software containers, executed on-demand, and a cloud platform manages it. If you compare it to BaaS, FaaS offers more control to developers in creating custom apps instead of depending on libraries containing pre-made code. The software containers where the code is deployed are stateless to simplify data integration, and the code runs for a shorter time. Moreover, developers can invoke serverless applications via APIs using FaaS that the cloud providers manage via an API Gateway.
Backend-as-a-Service (BaaS)
BaaS is similar to FaaS because both of them need a 3rd-party service provider. In this model, a cloud provider provides backend services like data storage to help developers focus on writing their frontend code. However, BaaS applications may not be event-driven or run on the edge as with serverless apps. A good example for BaaS is AWS Lambda. Developers use serverless code in containers with Lambda that provides guidelines to follow while submitting the code. It also automates processes in entering the code into software containers and offers a managed service.
Serverless stack
As with other software technologies, serverless architecture also comes with a technology stack. It brings together various components essential to creating a serverless system or application. The serverless stack includes:
A programming language: The programming language in which the developers will write the code. Depending upon the vendor, you can choose from Java, JavaScript, Python, C#, Go, Node.js, F#, etc. A Serverless framework: A framework provides the skeleton or structure to the code. There are plenty of Serverless frameworks for you to get it started. It enables building, packaging, and compiling code and finally to cloud deployment. Serverless frameworks expedite the coding process and simplify scaling with reduced configuration time. Examples of server frameworks are Apex, AWS Serverless Application Model, etc. Serverless databases: They are used to store data that the code requires to access. They are also needed to interact with functions for triggers. These databases behave like serverless functions but store data indefinitely. Examples of serverless databases are DynamoDB, Azure Cosmos DB, Aurora Serverless, and Cloud Firestore. A set of triggers: They help start the code execution like HTTP requests Software containers: They empower the serverless model and offer containerized microservices with no complexity. They also work as a repository for your code and ease developers while writing the code for multiple platforms such as desktop or iOS. API gateways: They work as a proxy to web actions. They offer HTTP routing, rate limits, viewing API usage and response logs, client ID, etc.
How to Implement Serverless Model and Optimize It?
Going serverless will incur significant changes in terms of your applications, technology, costs, security, and benefits. Suppose you are a start-up or small business. In that case, it will accelerate your time-to-market and help you push updates quickly with simplified testing, debugging, collecting feedback, working on issues, and more to offer a polished application to users. If you are a larger organization, you will experience benefits like more scalability to satisfy your user demands, but it will require significant cost investment. Hence, it’s best to gauge the pros and cons of serverless specifically for your business type and demands and then proceed. And if you are serious about it, start by:
Understanding your needs and identify a suitable serverless technology stack Choose a serverless vendor such as Google Cloud Functions, Azure Functions, AWS Lambda, etc. Empower your team with powerful tools to monitor system performance and functions. Watch out for the total number of requests, throttles, error counts, success rates, request duration, and latency.
Serverless Vendors
There are many serverless vendors or cloud providers out there in the market for you to choose from. Some of the top ones are:
AWS Lambda: It is perfect for organizations already leveraging AWS services. It integrates with a wide range of services for storage, streaming, and databases. Microsoft Azure Functions: If you use Visual Studio Code, go for it. It works smoothly with DevOps and Azure Pipelines for CI/CD. It also supports Durable Functions for stateful functions and offers integrated monitoring. Google Cloud Functions: If you’re using Google services, it’s good. It supports JS, Go, and Python apps, enables functions to be triggered from Google assistant or GCP, and offers built-in scaling. IBM Cloud Functions: If you want to go for a serverless model based on Apache OpenWhisk, IBM Cloud Functions is for you. It includes excellent performance monitoring, event triggering from a REST API or IBM cloud services and integrates with IBM’s API Gateway to manage endpoints. Knative: If you are running services on Kubernetes, go for it. It is backed by Google, Red Hat, IBM, etc. Cloudflare Workers: It’s good for apps requiring high responsiveness, especially JavaScript apps. It supports Workers KV for data storage and WebAssembly to help you compile and deliver multiple languages. Also, its high distribution network with 193 datacenters improves latency and responsiveness.
Conclusion: The Future of Serverless
Serverless computing is evolving with the increasing demand for highly scalable applications. It also provides many benefits that cloud computing offers, like more convenience, cost-efficiency, higher productivity, and more. According to an O’Reilly survey, 40% of the respondents work at companies that have embraced serverless architecture. Although serverless still has certain concerns, such as latency due to cold starts, testing, debugging, etc., cloud providers are working on them. Soon, a more refined form of serverless could surface with more benefits and resolved issues. Hence, the popularity and usage of the serverless model are expected to increase in the future. You may also be interested in: 7 Ways Serverless Computing is a Rising Technology
