Serverless Computing: What You Must Know

The TL-DR

Serverless computing is a hosting model where your hosting provider automatically handles configuration and scaling of your application. You only pay as needed. It has benefits in ease of use and pricing at low usage levels, but drawbacks with lock-in, higher prices under consistently high usage, and can be slower under some circumstances. There are a lot of providers and use cases for serverless computing.

What is the difference between serverless computing and traditional server-based computing?

Serverless computing and traditional server-based computing are both models for delivering computing resources, but they differ in key ways.

Traditional Server Computing

Traditional server-based computing, also known as “server-full” or “server-centric” computing, involves running applications on dedicated servers that are owned, managed, and maintained by the organization using them. In this model, the organization must provision, configure, and scale the servers as needed to meet the demands of the applications running on them. This can involve significant upfront investment in hardware and ongoing costs for maintenance, power, cooling, and staffing.

Serverless Computing

Serverless computing, on the other hand, is a model in which the organization does not need to provision, configure, or manage servers at all. Instead, the organization writes code that runs in response to specific events, such as a user request or a change in data, and the cloud provider is responsible for provisioning the resources required to run that code. The cloud provider automatically scales the resources as needed and only charges the organization for the resources used while the code is running.

Comparision

One of the key advantages of serverless computing is that it can be much more cost-effective than traditional server-based computing, particularly for workloads that have variable and unpredictable resource requirements. Serverless computing can also be more scalable, since the cloud provider can automatically provision and scale resources as needed.

However, serverless computing does have some limitations. It is generally best suited for stateless workloads that can be broken down into small, discrete functions. It can also be more complex to manage and monitor serverless applications than traditional server-based applications. Additionally, serverless computing can be more restrictive in terms of the types of workloads that can be run and the resources that can be consumed.

In summary, the main difference between serverless computing and traditional server-based computing is that in serverless computing, the organization does not need to provision, configure or manage servers, and the cloud provider takes care of it, while in traditional server-based computing, the organization is responsible for the infrastructure.

What are the advantages of using serverless computing?

There are several advantages of using serverless computing, some of which include:

Cost savings

One of the biggest advantages of serverless computing is that it can be much more cost-effective than traditional server-based computing. With serverless computing, you only pay for the compute resources that you use, rather than having to pay for dedicated servers that may not be fully utilized. Additionally, with serverless computing, you don’t have to pay for the resources required to run and maintain the underlying infrastructure.

Scalability

Serverless computing allows for automatic and instantaneous scaling, which means that your application can handle a large number of requests without any manual intervention. This is because the cloud provider automatically provisions and scales resources as needed to meet the demands of the application.

Flexibility

Serverless computing allows you to build applications using small, discrete functions that can be triggered by different events. This allows you to break down your application into smaller, more manageable pieces, and also makes it easier to update and maintain.

Improved Availability

Since serverless computing uses a pay-as-you-go model and does not require any upfront investment, you don’t need to worry about overprovisioning or underprovisioning resources. This can result in improved availability and better performance for your application.

Reduced Complexity

With serverless computing, you don’t have to worry about managing and maintaining the underlying infrastructure, which can be a significant source of complexity in traditional server-based computing.

Improved Security

Many serverless computing platforms provide built-in security features such as encryption, authentication, and access control. By taking advantage of these features, it can reduce the complexity of implementing security in your application.

Better integration: Serverless computing platforms often provide native integration with other services, such as databases, storage, and analytics. This can make it easier to build and deploy applications that use these services.

What are the disadvantages of using serverless computing?

While serverless computing can provide many benefits, there are also some disadvantages to consider:

Cold Start

One of the main disadvantages of serverless computing is the so-called “cold start” problem. When a function is triggered after a period of inactivity, it can take longer to start up and process the request because the cloud provider needs to provision and initialize the resources required to run the function. This can result in increased latency and reduced performance for the initial request.

Limited Control over the Runtime Environment

With serverless computing, the cloud provider is responsible for managing and maintaining the underlying infrastructure, which can limit the level of control that you have over the runtime environment. This can make it more difficult to implement certain types of workloads and can also limit the ability to customize the environment to meet specific requirements.

Limited Resource Allocation

With serverless computing, there are often limitations on the amount of resources that can be allocated to a single function, such as memory and CPU. This can make it more difficult to run certain types of workloads, particularly those that require a large amount of resources.

Vendor Lock-In

Serverless computing platforms are provided by specific vendors, and each vendor has its own set of services and limitations. This can make it more difficult to switch between different vendors or to move an application from one vendor to another.

Limited Debugging and Monitoring

Serverless computing can make it more difficult to debug and monitor applications because the underlying infrastructure is abstracted away and the cloud provider is responsible for scaling and managing the resources.

Network Latency

If your serverless application is communicating with another service or data storage that is not in the same region or availability zone, it could increase the latency and affect the performance of your application.

Increased Complexity

While serverless computing can reduce the complexity of managing and maintaining the underlying infrastructure, it can also introduce new complexities in terms of designing, developing, and deploying serverless applications, particularly if you are not familiar with the platform and its limitations.

It’s worth noting that while serverless computing may not be the best fit for all workloads and use cases, it can be a great fit for many use cases and can provide many benefits such as cost savings, scalability and flexibility. However, it’s important to carefully evaluate the specific requirements of your application and to consider the potential disadvantages of serverless computing before making a decision.

What Companies Provide Serverless Computing Services?

There are several companies that provide serverless hosting services, including:

Amazon Web Services (AWS)

AWS offers a serverless computing platform called AWS Lambda, which allows you to write and deploy code that runs in response to specific events, such as changes in data or user requests. AWS Lambda is one of the most widely used serverless computing platforms and is used by companies of all sizes and across a wide range of industries.

Google Cloud Platform (GCP)

GCP offers a serverless computing platform called Google Cloud Functions, which allows you to write and deploy code that runs in response to specific events, such as changes in data or user requests. Google Cloud Functions is a powerful and flexible serverless computing platform and is used by many companies.

Microsoft Azure

Azure offers a serverless computing platform called Azure Functions, which allows you to write and deploy code that runs in response to specific events, such as changes in data or user requests. Azure Functions is a powerful and flexible serverless computing platform and is used by many companies.

IBM Cloud

IBM Cloud offers a serverless computing platform called IBM Cloud Functions, which allows you to write and deploy code that runs in response to specific events, such as changes in data or user requests. IBM Cloud Functions is a powerful and flexible serverless computing platform and is used by many companies.

Alibaba Cloud

Alibaba Cloud offers a serverless computing platform called Function Compute, which allows you to write and deploy code that runs in response to specific events, such as changes in data or user requests. Function Compute is a powerful and flexible serverless computing platform and is used by many companies.

OpenFaaS

OpenFaaS is an open-source serverless platform that allows you to write and deploy functions using any programming language. It can be deployed on any Kubernetes cluster and can be used by any company that wants to use a open-source alternative.

Cloudflare Workers

Cloudflare Workers is a serverless computing platform offered by Cloudflare that allows you to write and deploy code that runs on Cloudflare’s edge servers, which are located in data centers around the world. It allows you to run your code closer to your users, which can improve performance and reduce latency.

How does serverless computing compare to containers and microservices?

Serverless computing and containers/microservices are both popular approaches for delivering computing resources, but they differ in key ways.

What are Containers?

Containers are a lightweight and portable way of packaging software that allows you to run an application and its dependencies in an isolated environment. Containers provide a consistent runtime environment, which allows you to easily move an application from one environment to another, and also make it easier to scale and manage the application. Containers can be deployed on-premises, in a private cloud, or in a public cloud.

What are Microservices?

Microservices are a software architectural style that structures an application as a collection of small, independent services that communicate with each other over a network. Microservices allow you to build and deploy applications as a set of small, loosely-coupled services that can be developed, deployed, and scaled independently. Microservices can be deployed on any infrastructure, including containers.

Serverless Computing

Serverless computing, on the other hand, is a model in which the organization does not need to provision, configure, or manage servers at all. Instead, the organization writes code that runs in response to specific events, such as a user request or a change in data, and the cloud provider is responsible for provisioning the resources required to run that code. The cloud provider automatically scales the resources as needed and only charges the organization for the resources used while the code is running.

What are the Differences?

While containers and microservices are focused on delivering and managing the underlying infrastructure, serverless computing focuses on delivering and managing the code. With containers, the organization is responsible for managing and scaling the underlying infrastructure, and with microservices, the organization is responsible for managing and scaling the individual services. With serverless computing, the organization only needs to write and deploy code and the cloud provider takes care of the underlying infrastructure and scaling.

In summary, containers and microservices are focused on delivering and managing the underlying infrastructure, while serverless computing is focused on delivering and managing the code. Both can be used to build and deploy scalable and resilient applications, but serverless computing can provide additional benefits such as cost savings, automatic scaling, and a reduced operational complexity.

Can you give some real-world examples of companies using serverless computing in production?

Yes, there are many companies that are using serverless computing in production across a wide range of industries. Here are a few examples:

Google Cloud Functions & Spotify

Google Cloud Functions is a serverless computing platform offered by Google Cloud. It allows you to write and deploy code that runs in response to specific events, such as changes in data or user requests. Google Cloud Functions is used by companies such as Spotify, which uses it to handle tasks such as image processing and user authentication.

Azure Functions & Bosch

Azure Functions: Azure Functions is a serverless computing platform offered by Microsoft Azure. It allows you to write and deploy code that runs in response to specific events, such as changes in data or user requests. Azure Functions is used by companies such as Bosch, which uses it to handle tasks such as image processing and data analysis.

OpenFaas & GitHub

OpenFaaS is an open-source serverless computing platform that allows you to write and deploy functions using any programming language. OpenFaaS is used by companies such as GitHub, which uses it to handle tasks such as user authentication and sending notifications.

Cloudflare Workers & Algolia

Cloudflare Workers is a serverless computing platform offered by Cloudflare. It allows you to write and deploy code that runs on Cloudflare’s edge servers, which are located in data centers around the world. Cloudflare Workers is used by companies such as Algolia, which uses it to handle tasks such as user authentication and data processing.

Knative & Salesforce

Knative: Knative is an open-source serverless computing platform that allows you to build, deploy, and scale containerized applications on Kubernetes. It’s used by companies such as Salesforce, which uses it to handle tasks such as data processing and analytics.

Netflix

Netflix uses serverless computing to power its streaming service. They use AWS Lambda to process and analyze the data generated by the service, and also to handle the backend logic for their mobile and web applications. Netflix’s serverless architecture has allowed them to scale their service to handle millions of concurrent users with minimal operational overhead.

Capital One

Capital One uses serverless computing to power its banking services. They use AWS Lambda to handle the backend logic for their mobile and web applications, as well as to process and analyze large amounts of financial data. Capital One’s serverless architecture has allowed them to scale their services to handle millions of concurrent users and transactions with minimal operational overhead.

The New York Times

The New York Times uses serverless computing to handle the backend logic for their digital subscriptions service. They use AWS Lambda to process and analyze large amounts of data and also to handle the backend logic for their mobile and web applications. The New York Times’s serverless architecture has allowed them to scale their service to handle millions of concurrent users with minimal operational overhead.

AirBnB

Airbnb uses serverless computing to power its online marketplace. They use AWS Lambda to process and analyze large amounts of data, as well as to handle the backend logic for their mobile and web applications. Airbnb’s serverless architecture has allowed them to scale their service to handle millions of concurrent users with minimal operational overhead.

Uber

Uber uses serverless computing to power its ride-hailing service. They use AWS Lambda to handle the backend logic for their mobile and web applications, as well as to process and analyze large amounts of data. Uber’s serverless architecture has allowed them to scale their service to handle millions of concurrent users and transactions with minimal operational overhead.

Twitch

Twitch, a live-streaming platform for gaming and other content, uses AWS Lambda to handle a variety of tasks, including image and video processing, analytics, and moderation. The platform handles a huge number of concurrent users and streamers, and serverless computing allows them to scale the service up and down as needed, while paying only for the resources used.

Pinterest

Pinterest uses AWS Lambda to handle image processing and analysis, as well as to handle the backend logic for their mobile and web applications. They also use serverless computing to handle real-time data processing and analysis to improve the user experience and help users discover new content.

A Cloud Guru

A Cloud Guru, an online learning platform for cloud computing, uses AWS Lambda to handle a variety of tasks, including user authentication, payment processing, and sending emails and notifications. The platform is built on a serverless architecture, which allows them to scale their service up and down as needed while only paying for the resources used.

Adobe

Adobe uses AWS Lambda to power its Creative Cloud service, which allows users to collaborate on and share creative projects. The service uses serverless computing to handle image and video processing, real-time collaboration, and other tasks, allowing the service to scale up and down as needed to meet the demands of its users.

Eventbrite

Eventbrite, an event management platform, uses serverless computing to handle a variety of tasks, including handling payments, sending notifications, and processing images and videos. The platform uses a serverless architecture, which allows it to handle a huge number of events and attendees while only paying for the resources used.

The Guardian

The Guardian, a British daily newspaper, uses serverless computing to power its online news platform. They use AWS Lambda to handle image and video processing, real-time data processing, and other tasks, which allows them to scale the platform up and down as needed to meet the demands of their readers.

Amazon Web Services

Amazon Web Services (AWS) uses serverless computing extensively in its own operations. AWS Lambda, their serverless computing platform, is used by many of their own internal services such as AWS Budgets, AWS Cost Explorer, and AWS Personal Health Dashboard.

Samsung

Samsung, a multinational electronics company, uses serverless computing to power its smart home devices. They use AWS Lambda to handle the backend logic for their smart devices, including processing sensor data, sending notifications, and controlling devices remotely.

Twilio

Twilio, a cloud communications platform, uses serverless computing to handle a variety of tasks, including sending text messages, making phone calls, and handling user authentication. They use AWS Lambda to handle the backend logic for their services and automatically scale the platform up and down as needed to meet the demands of their customers.

iRobot

iRobot, a consumer robot company, uses serverless computing to power its smart home devices such as Roomba vacuum cleaners. They use AWS Lambda to handle the backend logic for their devices, including processing sensor data, sending notifications, and controlling devices remotely. The serverless architecture allows iRobot to handle a large number of devices with minimal operational overhead and quickly add new features and capabilities to their products.

The Financial Times

The Financial Times, a British multinational newspaper, uses serverless computing to power its online news platform. They use AWS Lambda to handle image and video processing, real-time data processing, and other tasks, which allows them to scale the platform up and down as needed to meet the demands of their readers.

Cerner

Cerner, a healthcare technology company, uses serverless computing to handle a variety of tasks related to patient care and data management. They use AWS Lambda to process and analyze patient data, handle user authentication, and send notifications to healthcare providers.

Kong

Kong, a company that provides API management solutions, uses serverless computing to handle a variety of tasks related to API management and security. They use AWS Lambda to handle user authentication, rate limiting, and other security tasks, which allows them to scale their platform up and down as needed to meet the demands of their customers.

Zillow

Zillow, an online real estate marketplace, uses serverless computing to handle a variety of tasks related to property listings and search. They use AWS Lambda to handle image processing, data analysis, and other tasks, which allows them to scale their platform up and down as needed to meet the demands of their users.

T-Mobile

T-Mobile, a telecommunications company, uses serverless computing to handle a variety of tasks related to customer service and data management. They use AWS Lambda to handle user authentication, process customer data, and send notifications to customers, which allows them to scale their services up and down as needed to meet the demands of their customers.

What are the key serverless architecture patterns to know?

There are several key serverless architecture patterns that are important to know when building serverless applications. Some of the most important patterns include:

Event-Driven Architecture

This pattern is at the core of serverless computing, as it allows you to build applications that respond to specific events in real-time. This pattern is typically implemented using a combination of event sources, such as message queues or event streams, and event handlers, such as Lambda functions or Cloud Functions, that process the events.

Microservices Architecture

This pattern is becoming increasingly popular with serverless computing, as it allows you to break down a monolithic application into smaller, independent services that can be developed, deployed, and scaled independently. This pattern is typically implemented using a combination of serverless functions and APIs, such as AWS Lambda and API Gateway.

Backend for Front-end (BFF)

This pattern is used to build and deploy a separate backend for each front-end application, such as mobile and web. This pattern allows for better scalability and security, also allows for better separation of concerns and improve the development process.

Fan-Out/Fan-in

This pattern is used when an event that occurs in one service needs to trigger multiple other services, in parallel, in order to process the event. This pattern can be implemented using a combination of message queues, such as SQS or Kinesis, and serverless functions, such as AWS Lambda or Cloud Functions.

Stateful Serverless

This pattern is used to build serverless applications that need to maintain state, such as user sessions or shopping carts. This pattern is typically implemented using a combination of serverless functions, such as AWS Lambda or Cloud Functions, and stateful storage services, such as DynamoDB or Cloud Firestore.

The Pay-per-Execution

This pattern is used when a service is invoked only when a specific event occurs, and it is charged based on the number of invocations. This pattern can reduce costs, especially if the service is only used occasionally.

How does serverless computing integrate with multi-cloud environments?

Serverless computing can integrate with multi-cloud environments in a few different ways. Some of the key ways include:

Multi-cloud Function as a Service (FaaS)

This approach allows you to write and deploy serverless functions to multiple cloud providers, such as AWS Lambda, Google Cloud Functions, and Azure Functions, using a single codebase. This approach allows you to take advantage of the unique features and services offered by each cloud provider, and also provides a level of redundancy and failover in case one cloud provider experiences an outage.

Cloud-native Interoperability

This approach allows you to use cloud-native services and APIs across different cloud providers. For example, you can use AWS S3, Google Cloud Storage, and Azure Blob Storage interchangeably, regardless of where your application is running.

Multi-cloud Management Platforms

This approach allows you to use a multi-cloud management platform, such as Kubernetes or Terraform, to deploy and manage your serverless applications across multiple cloud providers. This approach provides a consistent way to deploy and manage your applications, regardless of the cloud provider, and allows you to take advantage of the unique features and services offered by each cloud provider.

Hybrid Cloud

This approach allows you to run some parts of your serverless application on-premises and other parts in the cloud. This approach can be useful if your application needs to process sensitive data that cannot be stored in the cloud or if you need to meet specific compliance requirements.

Cloud-agnostic Serverless Frameworks

This approach allows you to use serverless frameworks, such as OpenFaaS or Fission, that can be deployed on any infrastructure, including multiple cloud providers. This approach provides a consistent way to deploy and manage your serverless applications, regardless of the cloud provider.

These are just a few examples of how serverless computing can integrate with multi-cloud environments. The best approach will depend on the specific requirements of your application and the cloud providers that you are using.

It’s important to note that, while multi-cloud environments offer many benefits, such as cost savings, redundancy and failover, they also introduce additional complexity in terms of management, security, and compliance.

What is the relationship between serverless computing and edge computing?

Serverless computing and edge computing are closely related, but distinct technologies.

Edge Computing

Edge computing refers to the practice of processing data at the edge of the network, as close to the source of the data as possible. This is achieved by using edge devices, such as gateways, routers, and IoT devices, to perform compute and storage functions. Edge computing allows for faster and more efficient processing of data, as well as reduced latency and bandwidth usage.

Serverless Computing & Edge Computing

The relationship between serverless computing and edge computing is that serverless computing can be used to run code on edge devices. This allows for the implementation of edge computing as a function of serverless computing, by deploying serverless functions at the edge of the network, close to the source of the data, to process data in real-time. This approach allows for the efficient processing of data with low latency, and can be useful for use cases such as IoT, real-time analytics and image and video processing.

Therefore, serverless computing can be used to build and run edge computing applications, which can help to leverage the benefits of both technologies. As a result, the combination of serverless computing and edge computing can enable new use cases, such as real-time data processing and analytics, that would be difficult or impossible to implement using traditional server-based architectures.

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