There are two basic ways to deploy to Kubernetes: Imperative acts as a command which is active and immediate, whereas declarative is passive, by writing manifest file and using kubectl apply.
The imperative command is the first mode of managing objects, to use CLI for CUD (Create, Update, Delete) objects on Kubernetes cluster without specifying on manifest file ahead of time. They are a blessing for Kubernetes application developers and administrators because they are very easy to remember and handy. According to K8s, it’s like a ‘Swiss Army Knife” of container orchestration and management.
Imperative commands can help in getting tasks done quickly, as well as generating definition file templates easily. It saves a considerable amount of time and prevents human errors.
Docker is a platform for developers and sysadmins to develop, deploy, and run applications with containers. And, as we all know Docker image is a read-only template that contains a set of instructions for creating a container that wraps up the software and its dependencies into a standardized unit for software development to run on the Docker platform.
In this post, we are going to step through describing some of the best practices and common pitfalls we encountered while developing our first Dockerfile for Tomcat.
Kubernetes is one of the widely used orchestration tools for container application and container management. With a variety of features and options, it helps organizations remove manual intervention at every stage. With lots of requirements & scenarios, the user or any organization deals with lots of Kubernetes resources types options which leads to having proper knowledge of every Kubernetes resources type to fit specific or combination of resources with different scenarios that organizations generally require. To know more about different Kubernetes resource types, you can visit the official documentation provided by Kubernetes.
This blog will cover one of the Kubernetes resources which are only used for a specific use-case. There are multiple resources where we can leverage those resources at specific use-case, but for now, we are only focusing on DaemonSet which is very important and has a unique functionality that we cannot cover by using another pod controller.
It was like any other day working on micro-services project, running on Docker environment. In general, we’ve had worked on making our Image Builds more efficient, secure, and faster following basic aspects that significantly affect building and working with Docker.
Understanding Docker layers and structuring the Dockerfile to maximize their efficiency.
Reducing the weight of the Docker image, by being specific about our Base Image Tags which comes up with minimal packages.
The majority of businesses now are using Docker to run applications. They devote a lot of time, energy, and resources to stabilize their success and invest heavily in a variety of advanced observation techniques. Despite this, they are experiencing poor performance and the containers are being stressed as a result of the heavy traffic flow. The motive of this blog is to help those running Java applications on docker containers in getting optimal performance.
A lot of times people complain that the application does not seem to be running as well as it did on the server. Here are some things to keep in mind when running a Java program on Docker.