Containerization has deeply changed the way applications are built, deployed, and managed. Containers include both an application and its dependencies, enabling consistent and efficient deployment across diverse environments. However, as applications scale and become more complex, managing numerous containers manually becomes increasingly challenging.
Container orchestration streamlines the deployment, scaling, and management of containerized applications. By leveraging container orchestration, organizations can achieve greater agility, scalability, and efficiency in their software delivery processes.
This post covers details about different container orchestration platforms, like Kubernetes and what container orchestration means. Next, you’ll learn why observability plays a crucial role, how to implement container orchestration, and how to build a Kubernetes-native application. Finally, the post concludes with the development lifecycle of Kubernetes-native microservices and best practices you should consider whenever developing applications with containers.
Container orchestration automates the deployment, scaling, and management of containerized applications. By providing a centralized platform for managing the life cycle of containers, container orchestration ensures efficient resource utilization, load balancing, and high availability. Container orchestration platforms abstract away the complexities of managing containerized workloads, enabling developers to focus on building and delivering applications.
Several container orchestration solutions exist, such as Docker Swarm, Apache Mesos, and Nomad. However, Kubernetes has emerged as the industry-leading platform. An open-source platform developed by Google, Kubernetes offers a reliable and scalable solution for managing containerized applications across on-premises, cloud, and hybrid environments.
Kubernetes is the standard for container orchestration, offering a comprehensive set and capabilities. The platform automates various tasks, including container deployment, scaling, load balancing, self-healing, and rolling updates. Kubernetes achieves this functionality by using a set of abstractions and resources, such as pods (groups of containers), deployments (managing pods), services (load balancing and service discovery), and ingress (routing external traffic).
For example, consider a microservices-based e-commerce application consisting of several services, such as a product catalog, shopping cart, and payment processing. With Kubernetes, each service can be packaged as a container and deployed as a pod. Kubernetes can then manage the life cycle of these pods, ensuring they are running and available across multiple nodes in the cluster.
Microservices architecture has gained widespread adoption, offering developers the ability to break down monolithic applications into smaller, independently deployable services. However, managing and monitoring these distributed services can be challenging. Observability plays a crucial role in Kubernetes environments, in particular, for microservices.
Observability enables you to understand the internal state and behavior of a system based on external outputs. In the context of microservices, observability involves monitoring, logging, tracing, and analyzing the interactions and dependencies between services. Enabling observability from the beginning ensures effective troubleshooting, performance optimization, reliability and overall health of your applications.
Kubernetes provides built-in open-source monitoring and logging capabilities (such as Prometheus bundled with Kubernetes distributions), allowing developers to gain insights into the health and performance of their applications. For instance, Kubernetes incorporates metrics servers that collect and expose resource usage data. Metrics servers allow developers to monitor CPU, memory, and network utilization of microservices. Additionally, Kubernetes supports integrations with both open-source and commercial logging, monitoring, and observability platforms (such as BMC Helix), providing a comprehensive observability stack. But if you’d like to reduce complexity, avoid having to worry about another component, and get visibility into microservices performance, use Stackify Retrace to smooth your journey to microservices visibility and Kubernetes adoption. Further, by integrating Stackify Retrace data with the BMC Helix platform, you get open full-stack observability.
In addition, microservices that run on Kubernetes can be easily instrumented with distributed tracing – OpenTelemetry (the most popular, which supports metrics and logs as well), Jaeger or Zipkin. With distributed tracing, developers track and visualize the flow of requests across multiple services. Visualizations simplify identifying bottlenecks, latencies, and potential issues in the overall system.
By enabling observability from the outset, organizations can proactively identify and address issues before they escalate and ensure the smooth operation and performance of microservices-based applications.
Implementing container orchestration with Kubernetes involves several steps, including setting up a Kubernetes cluster, defining application resources (such as deployments and services), and managing the application life cycle. Kubernetes supports various deployment strategies, ensuring seamless application updates with minimal downtime.
To illustrate, let’s consider deploying a microservices-based web application on Kubernetes. First, you would need to set up a Kubernetes cluster, either locally using tools like Minikube or on a cloud provider like Google Cloud Platform (GCP) or Amazon Web Services (AWS). Next, you would define the application resources using Kubernetes manifests (YAML or JSON files). Manifests describe the desired state of the application, including the number of replicas, container images, environment variables, and resource requests/limits.
Once the manifests are defined, you can deploy the application using the kubectl command-line tool or through a continuous integration/continuous deployment (CI/CD) pipeline. Kubernetes will then schedule the pods across the available nodes in the cluster, ensuring high availability and load balancing.
Building microservices that are tailored for Kubernetes requires developers to consider several factors. First, developers must adopt a cloud-native mindset, embracing principles such as containerization, service-oriented architecture, and declarative configuration management. When developing Kubernetes-native microservices, developers should strive to create stateless, lightweight, and independently deployable services. Creating such services aligns with the principles of microservices architecture and facilitates seamless scaling and deployment on Kubernetes.
Additionally, developers should design their microservices to be observable from the outset. Incorporating logging, metrics, and distributed tracing capabilities into the application code enables comprehensive observability, improves application reliability, and expedites troubleshooting within the Kubernetes environment.
The development lifecycle of a Kubernetes-native microservice typically involves iterative cycles of coding, building, testing, and deploying. However, the traditional approach of developing locally and then deploying to a remote Kubernetes cluster can introduce latency issues and slow down the feedback loop.
To address this challenge, developers can leverage remote development environments, such as Okteto and Telepresence. These tools allow developers to develop and test their microservices directly within the Kubernetes cluster, providing a seamless and efficient development experience.
Okteto, for instance, enables developers to spin up development environments within the Kubernetes cluster, complete with code synchronization, port forwarding, and access to cluster resources. Eliminating the need for local development environments streamlines the development workflow. Similarly, Telepresence creates a secure network connection between the developer’s local machine and the Kubernetes cluster, allowing the developer to run and test their microservices as if they were running locally, while still interacting with the rest of the cluster.
By using these remote development environments, developers can significantly speed up the feedback loop, enable faster iterations, and more efficient debugging and testing within the Kubernetes environment.
To maximize the benefits of container orchestration, follow these best practices:
Container orchestration, particularly with Kubernetes, has become an indispensable tool for managing and scaling containerized applications, including microservices-based architectures. By automating deployment, scaling, and management tasks, organizations can achieve greater efficiency, reliability, and agility in their software delivery processes.
As microservices architectures continue to gain traction, container orchestration will play a pivotal role in ensuring observability, scalability, and resilience. Embracing container orchestration best practices will enable organizations to unlock the full potential of containerized applications, as well as stay ahead in the rapidly evolving software development landscape.
With its robust feature set, extensibility, and vibrant community, Kubernetes has solidified its position as the leading container orchestration platform. Leveraging Kubernetes, organizations streamline the deployment and management of microservices-based applications, enabling faster time to market, improved scalability, and enhanced observability.
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