Wondering about Cloud-Native Development? We explain what cloud-native development is and how it can help build fast and reliable applications.
Cloud-native development is the concept of building and running applications in distributed computing systems.
Developing on the cloud aims to reduce the time to bring new ideas to the market and respond faster to customer needs. Cloud-native apps are designed to be more scalable, resilient, flexible, and elastic. The most important characteristic of cloud-native apps is their ability to offer on-demand computing power and services to developers rather than being constrained by developers needing to provide their own servers.
Cloud-native technologies can empower all cloud computing models, including private, public, and hybrid clouds. Cloud computing delivery models are not restricted to the public cloud. It focuses on how applications are developed and deployed, not where. The aim is to build and operate applications with dynamic resource usage, and bring new ideas to market and meet the customer demands faster.
Cloud-native is a relatively recent term coined by companies like Netflix. Netflix leveraged cloud technologies to transition from a mail-order company to one of the biggest on-demand content delivery networks in the world. In 2016, Yury Izrailevsky, VP of Cloud and Platform Engineering for Netflix commented on Netflix’s cloud migration journey:
“We chose the cloud-native approach, rebuilding virtually all of our technology and fundamentally changing the way we operate the company. Architecturally, we migrated from a monolithic app to hundreds of microservices, and denormalized our data model, using NoSQL databases.”
It all began with the shift from monolithic architecture towards a microservice-based architecture. Adhering to the promise of delivering new features faster, Netflix acknowledged the limitations of a monolithic architecture. Microservices allows for more frequent deployment of features.
Container technology plays a critical role in cloud-native development, and in 2000, Jalis (an early implementation of container technology) was added to FreeBSD (the popular open source operating system). Later, the open-source release of Docker in 2013 and Kubernetes in 2014 led to widespread adoption of container technology.
In 2015, tech giants such as Google, Intel, IBM, and VMWare created the Cloud Native Computing Foundation (CNCF), which organizes and supports open source communities for cloud native technology such as Kubernetes and Prometheus. Its creation helped to establish cloud-native as the standard model for future applications.
At its core, cloud-native is simply a way to accelerate innovation and take advantage of the automation and scalability offered by cloud-native technologies like Kubernetes.
Cloud-native incorporates various concepts such as continuous delivery, DevOps, microservices, and containers. We will discuss each concept below and how it relates to cloud-native development.
Building cloud-native applications brings together the development and operations teams with a shared goal of improving via steady feedback through DevOps. DevOps is a practice that helps organizations improve communication and collaboration between the development and operations teams. It promotes a culture and environment where the process of building, testing, and releasing software updates happen more frequently and consistently. This is achieved through processes being built into a complete DevOps lifecycle that ensures code progresses efficiently and reliably.
By nature, cloud-native allows engineers to take an agile approach to software development. Basically, engineers can develop in small and more frequent waves. Agile also enables continuous integration and delivery (CI/CD), which promotes software quality and reliability.
Containers also have an important role in cloud-native development. A container is a package of a piece of code along with everything that the code needs to run. For example, executable files, system tools, libraries, etc. Compared to the standard VMs (virtual machines), containers offer speed and efficiency. Considering the low overhead of building and destroying containers, they’re ideal for deploying microservices.
Nearly all cloud-based architectures are based on a microservices architecture. Microservices are an architectural approach to developing software applications as a collection of services. Each service in a microservice architecture implements a unique business capability, runs its own processes, and communicates with other services by sending things like HTTP requests.
Cloud-native development relies on the modularity of architecture, but not necessarily microservices. Organizations with a legacy application can still optimize their apps using a service-based architecture. If you use containers to run a system of microservices in production, then you will need to make use of a container orchestration solution to manage the system.
Cloud-native development allows companies to deliver features faster to the customer. That way, businesses can respond quickly to user demands. Since they deliver faster, they usually gain a competitive advantage over other businesses delivering similar products.
Cloud-native architecture is based on microservices, where each service is independent of the other. New services can be managed and deployed individually and teams can build a resilient architecture. Even if a microservice fails, the rest of the service can remain functional while that microservice is repaired.
Cloud-native apps take advantage of the scalability that the cloud offers. On-demand elasticity allows nearly limitless computing, storage, and other resources. Businesses can expand on-demand without having to buy physical resources. Rather than having to spin up and deploy new servers themselves, scaling up on the cloud is handled by the cloud provider allocating more resources.
By utilizing DevOps best practices, developers can automate continuous integration and continuous delivery (CI/CD) pipelines to test and push code to production. It allows companies to bring ideas to production within hours instead of days or weeks.
Additionally, the use of methodologies such as blue-green (a technique involving two identical production environments and gradually transferring user traffic from one environment to another) and canary deployment (rolling out releases to a certain set of users or servers) to reduce downtime and make improvements without compromising reliability. All of this is much easier when developing cloud-natively, as it allows for dynamic allocation of resources.
Due to the scale and geographical spread of data centers, the cloud offers greater redundancy. It’s easier to deal with outages by simply directing traffic to another region. Also, thanks to container technology, companies can deploy software updates with zero downtime and higher uptime by deploying modular updates and not requiring a total overhaul of the production environment.
Making the transition from a traditional to cloud-native architecture comes with its own challenges. The challenges range from adapting to a cloud-centric security model to hiring top talent equipped to make the transition. A few major challenges in cloud-native development are:
Cloud-native is about keeping a balance between agility and resilience. The goal is to build applications that are responsive, reliable, and scalable. Distributed systems are complex, customer expectations are sky-high, and the cost of downtime can be catastrophic. According to a study by Gartner, the average cost of downtime is $300K per hour.
SRE (site reliability engineering) is a concept introduced by Google. In their documentation, Google provides this apt description:
“SRE is what you get when you treat operations as if it’s a software problem.”
SRE relates development and operations teams, and meanwhile derives its key objective from the business side of the organization, i.e. customer happiness. According to site reliability engineering, what matters most to customers is reliability. Reliability is a qualitative measure that embodies quantitative data such as availability and latency. It’s the job of an SRE to protect and manage reliability. How do they do that? First, they use something called the service level indicator (SLI) to quantify service health. An example could be [# good events / # total events]. Then the company agrees on a service level agreement (SLA) with the customer, which is basically a minimum threshold that the company agrees to never fall below. Beyond that, SRE best practice calls for a service level objective (SLO) which is basically an even higher standard than the SLA. By following the SLO, you keep yourself from coming too close to falling below the SLA. If you’d like to delve deeper, here’s another blog on SLI, SLO, and SLA.
SLOs are internal metrics and are defined by both the development and SRE teams. On the other hand, SLA is external and relates directly to the customer. Businesses that are building on cloud-native technologies can strive for a higher standard of reliability. However, change translates to innovation, which is inevitable, and always leads to some instability. Therefore, even cloud-native service providers cannot guarantee 100% uptime. SREs help companies set reasonable expectations, innovate without compromising reliability, and mitigate and resolve inevitable failure.
Cloud-native has introduced an innovative way to deploy complex, scalable systems. In complex systems, some failure is inevitable. Only by balancing the pace of innovation with reliability can your company keep up with evolving customer demands.
Blameless offers the best end-to-end SRE platform that helps you optimize your service without compromising innovation. Request a demo today, or sign up for our newsletter below to learn more about SRE and the Blameless culture.