Without doubt, presently Kubernetes is the strongest container orchestration system. After Docker, Kubernetes redefined the scope of containerization. Docker has a hype part, it is really a moderate set of wrapper capabilities around the Linux kernel. Kubernetes is used for automating application deployment, scaling, and management. Kubernetes works with a range of container tools, including Docker. Many cloud computing services offer a Kubernetes-based platform or infrastructure as a service on which Kubernetes can be deployed as a platform-providing service. Kubernetes was out of an effort led by Google to take their internal operations and delivery process mixed with the new containerized trends of software development. Presently, Kubernetes is a project of The Linux Foundation (The Cloud Native Computing Foundation) with a massive community behind it. Founding members of Cloud Native Computing Foundation includes CoreOS, Mesosphere, Red Hat, Twitter, Huawei, Intel, Cisco, IBM, Docker, VMware. Docker achieved process isolation in GNU/Linux. Images are binary stored and hashed representations of a recipe. They have a name and we can build an image based on others’ image like forking a repository which provides productivity, and collaboration.
Containers have many resource assignment related challenges. How to assign volumes to store data, how much CPU and RAM will be allocated, how to scale up, how to easily manage networking policies are some of the well-known challenges with containerization. Kubernetes delivers a resource-oriented abstraction to define the desired state of the cluster.
Kubernetes is incompatible with existing Docker CLI. Container deployment can be only done by Kubernetes deployment tools. Networking in Kubernetes is and complicated. Despite a good amount of documentation, Kubernetes is not user-friendly.
Kubernetes is really difficult to install and configure. Kubernetes invariably invites experiments with the configuration from the beginning and it takes a good time to get used. It is not really simple to manage the services. Load balancing in Kubernetes is a hectic task. Kubernetes definitely has a steep learning curve which to lead to resistance in self-learning. Also, the cost to run Kubernetes to work, in terms of computing resource is about 20%. Kubernetes demands significant effort to bring up a production capable cluster. It is not easy to get an expert at cheaper cost or on-demand. It is not astonishing to watch the startups and smaller companies not making a shift to the container and Kubernetes way of doing works.