What are Cloud Native Applications?
A cloud-native application is composed of independent, loosely coupled microservices. The goal of cloud-native development is to help you quickly build, release, and update applications, without compromising code quality and security.
Cloud-native applications are designed to work consistently across a wide range of environments, including private, public clouds, and hybrid clouds. Because these applications are usually designed with a microservices architecture, the apps provide more scalability.
A cloud-native development environment usually strives to quickly deliver business values by incorporating continuous feedback loops. Additionally, a cloud-native pipeline leverages automation tools to speed up the process.
The result of cloud-native development is, ideally, achieving continuous improvement while quickly meeting business requirements and customer demands.
In this article, you will learn:
- What are Cloud Native Applications?
- Cloud Native Infrastructure Components
- 5 Tips for Cloud Native Development
- Top Tools for Cloud Application Development
Cloud Native Infrastructure Components
Containers let you package software along with its dependencies. Unlike virtual machines (VMs), containers share the underlying operating system (OS) kernel. Additionally, containers are immutable and can be easily scaled. This makes containers more lightweight, and easy to configure, deploy, and manage.
When deploying applications in production, you need to manage a massive amount of containers. Container orchestration platforms can help you manage containers in an efficient manner. Kubernetes is currently the most popular open source option for managing clusters of containers, but there are other alternatives.
The majority of container orchestration platforms provide a robust set of capabilities to help you manage the entire container lifecycle. Notable features include resource provisioning, networking, storage management, access control, auto scaling, failover and healing, and more.
Serverless is a computing model that eliminates the need to manage the underlying server infrastructure of your applications. The cloud vendor or service provider manages the infrastructure, while you handle the rest of the responsibilities. Serverless models often come with automation capabilities that let you use functions to trigger events.
A cloud-based compute instance is a virtual server, also known as a virtual machine (VM), which is hosted by a cloud service. Amazon Elastic Compute Cloud (Amazon EC2) and Google Compute Engine, for example, are two popular services that let you configure, create, and manage VMs using your own settings or pre-defined images.
Platform as a Service (PaaS)
PaaS is a cloud computing model that provides cloud-based development services, including data pipelines, DevOps and CI/CD infrastructure, analytics and artificial intelligence (AI) capabilities, and more.
Infrastructure as Code (IaC)
The IaC model provides services that help you use simple config files to automate cloud infrastructure. You can check your files into source control for easy management. The goal of IaC is to help you consistently manage complex systems in the cloud.
The majority of cloud-native applications run on a microservices architecture. To ensure efficiency, large scale applications need to run not only one pipeline, but multiple CI/CD pipelines—one for each micro-service. This type of distributed CI/CD infrastructure ensures that teams can release independent microservices to production without depending on other parts of the larger application.
Auto scaling processes use config files to automatically maintain demand and capacity. It is a built-in feature in the majority of cloud-native tools, including Kubernetes. Once you enable auto scaling, the system automatically provisions or pares down resources as needed, growing and shrinking the application according to predefined requirements and actual loads.
A load balancer is a reverse proxy. In a microservices architecture, this mechanism is responsible for routing application requests to the relevant microservice. The load balancer attempts to balance instances. For example, a balancer can trigger a scaling event when the current capacity does not meet the incoming requests.
Cloud-native applications are composed of many small elements. To ensure the application runs smoothly and securely, you need to implement monitoring measures. The minimum requirement is running health checks to identify any failures and let the system automatically replace these failed components.
Ideally, you should get visibility across all components, including security aspects. For example, Amazon provides a range of monitoring tools that can help you gain visibility over cloud native workloads.
5 Tips for Cloud Native Development
Here are five tips to consider when developing your own cloud-native application:
- Fast startup and graceful shutdown—containers are immutable. When one fails, you can easily replace it with another one. When loads increase, the application can dynamically increase the number of instances. To achieve this, you need to build components that can start quickly and gracefully shut down without disrupting other operations. You can start by removing as many redundant components as possible.
- Make application dependencies explicit—to ensure consistency across environments. You can do this by using a manifest file, which ensures dependencies remain visible and lets you manage dependencies across various environments.
- Maintain statelessness whenever possible—to ensure your services can be easily scaled and partitioned. You can do this by getting environment-specific data from your environments, and by treating logs as event streams.
- Use process thinking—and design applications composed of processes. Each process is a stateless application instance. When handling requests, you should only use the memory or file system briefly for processing the same request. You can save session information on external resources like external databases, but you should not rely on data stored in local memory or a local persistent disk.
- Treat APIs as contracts—the microservices in a cloud-native application communicate via APIs. To ensure efficiency, consider establishing API practices that define the terms of communication. You can use throttling to manage excessive use of APIs, and circuit breakers to handle services that do not receive an API response. To improve response times and reduce the load on services, consider using caching.
- Shift security left—new security models such as DevSecOps are emerging, allowing organizations to integrate security into all stages of the development lifecycle. This is essential in a cloud native environment, because of the large number of components, the dynamic nature of the infrastructure and rapid release cycles. Technology solutions like eXtended Detection and Response (XDR) are evolving to help manage security at large scale for cloud native environments.
Top Tools for Cloud Application Development
Here are several popular tools you can use to develop cloud-native applications:
- AWS CodeDeploy—can help you automate application deployments. The service lets you manage deployments using the AWS CLI and AWS Management Console. Additionally, you can use AWS CodeDeploy APIs to integrate deployments with external tools.
- Azure Visual Studio (VS)—can help you build, deploy, debug, and monitor applications across environments, including cloud and on-premises. There are several versions of Azure VS, including a free community edition.
- Azure App Service—lets you develop applications for mobile and web clients, using a fully-managed infrastructure. With App Service, you can deploy apps directly as code or in containers. The solution is offered as a Platform as a Service (PaaS), billed according to subscriptions.
- Google Cloud Deployment Manager—an IaC tool that lets you use code to define and deploy infrastructure resources, as well as use templates. You can use Google Cloud Deployment Manager to build a design record, which audits the substance of your document and conveys settings according to predefined layouts and requirements.
Application Development in the Cloud with Thundra
Developing cloud-native applications is painless if you benefit from solid supportive tooling. Although cloud vendors have great native tools, third party ISVs serve the community with very useful products.
Thundra focuses on easing the “issue resolving” pains of developers by providing end-to-end observability throughout the DevOps lifecycle. From coding to monitoring, Thundra provides handy tools for developers to understand application behavior easily and fastly.
There are two main products of Thundra generally used at pre and post production environments.
Thundra Sidekick is an application debugger for remote environments. It is built for end-to-end debugging and observability and removes the debugging burdens and enables developers to put tracepoints to the code which don’t break the execution but take snapshots. Then takes it to the next level by displaying the distributed trace visually. Lastly, developers can apply hotfixes and reload without having to redeploy.
Thundra APM is an application observability tool for cloud-native microservices. DevOps, SRE, and software developers use APM to active and passive monitor their applications to optimize performance. APM's record and replay feature lets you travel back in time and debug your code line-by-line. The real-time AWS Lambda debugger lets you stop your function execution at the breakpoints you set and debug from your IDE in your own AWS environment.