Category: docker

How to Run Java in a Docker Container? ”; Previous Next Docker allows you to set up Java environments that you can use in production and development servers alike. It enhances the efficiency and the manageability of executing Java programs. Irrespective of the underlying configurations, environment, and dependencies, Docker allows you to run Java programs reliably, and consistently across all the platforms. It greatly simplifies the deployment procedure as well and the problem of “it works only on my machine” is resolved. You can run Java in Docker containers using the two main approaches discussed below − You can use the official Java Docker base images provided by Oracle or AdoptOpenJDK. You can create your own Docker images with custom dependencies tailored specifically for Java applications by using Dockerfiles. In this chapter, we will explain both of these approaches to create and run Java applications inside the Docker container with the help of step-by-step instructions, commands, and examples. Benefits of Using Docker Containers to Run Java Applications There are several benefits associated with running Java applications inside Docker containers. They enhance the development and deployment workflows and improve scalability and reliability. Here are some of the key advantages of using Docker containers for Java applications. Isolation − Ensures independent operation. Consistency − Maintains uniform runtime environments. Portability − Facilitates easy migration between environments. Resource Efficiency − Maximizes resource utilization. Scalability − Allows seamless adjustment to workload demands. How to Run Java in Docker Using Java Base Images? One of the easiest ways to run Java in Docker is by using the existing Java base images provided by trusted organizations like Oracle or AdoptOpenJDK. To do so, here are the steps and commands. Step 1: Pull the Java Base Image You can start by pulling the Java base image from Docker Hub using the Docker pull command. For example, if you want to pull the OpenJDK 11 image from AdoptOpenJDK, you can use the following command. docker pull adoptopenjdk/openjdk11 Step 2: Run the Docker Container Now that you have the base image pulled to your local, you can run the Docker container using the pulled image. You can specify the Java application JAR that you want to run inside the container and copy it to the container. To do so, you can use the following command. docker run -d –name my-java-container -v /path/to/your/jar:/usr/src/app/my-java-app.jar adoptopenjdk/openjdk11 In this command − -d − This flag helps you to detach the container and you can run it in the background. –name my-java-container − You can assign a name to the running container for your reference using this flag. -v /path/to/your/jar:/usr/src/app/my-java-app.jar − This flag helps you to mount the directory that contains your Java application JAR into the container at /usr/src/app/my-java-app.jar. adoptopenjdk/openjdk11 − This is the name of the base image that you want to pull. Step 3: Access the Container”s Bash If you want to check whether Java is installed in the container that you created using the Docker image, you can access the bash shell of the container. To do so, you can use the following command. docker exec -it my-java-container /bin/bash In this command − docker exec − It lets you execute a command inside a running container. -it − It allocates a pseudo-TTY and helps keep the stdin open. This allows you to interact with the container”s bash shell. my-java-container − This is the name of the running container. /bin/bash − This specifies the command that you want to execute inside the container. This command opens a bash shell inside the container. Step 4: Check Java Installation Now that you have access to the bash shell of the container, you can check if Java is installed by running the below command. java -version This command is used to display the version of Java and JDK that has been installed in the container. On running this command, if you see the information related to the Java version, it means Java is installed properly in the container. Now that you have verified the successful installation of Java inside the container, you can exit the bash of the container by typing “exit” and pressing the enter key. How to Use Dockerfile to Create Custom Java Images? You can define your specific environment and run configurations for your Java applications using a Dockerfile to create and build Docker images. Here are the steps that you can follow to create a custom Docker image using Dockerfile. Step 1: Create a Dockerfile First, create a Dockerfile in the directory of your Java application. In the Dockerfile, we will mention the instructions to build image layers. Here’s a Dockerfile for a Docker image that has Java pre-installed in it − # Use a base Java image FROM adoptopenjdk/openjdk11:latest # Set the working directory inside the container WORKDIR /usr/src/app # Copy the Java application JAR file into the container COPY target/my-java-app.jar . # Expose the port on which your Java application runs (if applicable) EXPOSE 8080 # Command to run the Java application CMD [“java”, “-jar”, “my-java-app.jar”] In this Dockerfile − FROM − You can use FROM to specify the base image to be used. In this case, we have used OpenJDK 11 from AdoptOpenJDK as the base image. WORKDIR − This directive helps you to set the default working directory inside the Docker container where all the subsequent commands will be run. COPY − It helps you to copy the Java application JAR file from your local directory into the container. EXPOSE − This directive helps you to expose a particular port of the Docker container.

Docker – Cloud ”; Previous Next The Docker Cloud is a service provided by Docker in which you can carry out the following operations − Nodes − You can connect the Docker Cloud to your existing cloud providers such as Azure and AWS to spin up containers on these environments. Cloud Repository − Provides a place where you can store your own repositories. Continuous Integration − Connect with Github and build a continuous integration pipeline. Application Deployment − Deploy and scale infrastructure and containers. Continuous Deployment − Can automate deployments. Getting started You can go to the following link to getting started with Docker Cloud − https://cloud.docker.com/ Once logged in, you will be provided with the following basic interface − Connecting to the Cloud Provider The first step is to connect to an existing cloud provider. The following steps will show you how to connect with an Amazon Cloud provider. Step 1 − The first step is to ensure that you have the right AWS keys. This can be taken from the aws console. Log into your aws account using the following link − https://aws.amazon.com/console/ Step 2 − Once logged in, go to the Security Credentials section. Make a note of the access keys which will be used from Docker Hub. Step 3 − Next, you need to create a policy in aws that will allow Docker to view EC2 instances. Go to the profiles section in aws. Click the Create Policy button. Step 4 − Click on ‘Create Your Own Policy’ and give the policy name as dockercloudpolicy and the policy definition as shown below. { “Version”: “2012-10-17”, “Statement”: [ { “Action”: [ “ec2:*”, “iam:ListInstanceProfiles” ], “Effect”: “Allow”, “Resource”: “*” } ] } Next, click the Create Policy button Step 5 − Next, you need to create a role which will be used by Docker to spin up nodes on AWS. For this, go to the Roles section in AWS and click the Create New Role option. Step 6 − Give the name for the role as dockercloud-role. Step 7 − On the next screen, go to ‘Role for Cross Account Access’ and select “Provide access between your account and a 3rd party AWS account”. Step 8 − On the next screen, enter the following details − In the Account ID field, enter the ID for the Docker Cloud service: 689684103426. In the External ID field, enter your Docker Cloud username. Step 9 − Then, click the Next Step button and on the next screen, attach the policy which was created in the earlier step. Step 10 − Finally, on the last screen when the role is created, make sure to copy the arn role which is created. arn:aws:iam::085363624145:role/dockercloud-role Step 11 − Now go back to Docker Cloud, select Cloud Providers, and click the plug symbol next to Amazon Web Services. Enter the arn role and click the Save button. Once saved, the integration with AWS would be complete. Setting Up Nodes Once the integration with AWS is complete, the next step is to setup a node. Go to the Nodes section in Docker Cloud. Note that the setting up of nodes will automatically setup a node cluster first. Step 1 − Go to the Nodes section in Docker Cloud. Step 2 − Next, you can give the details of the nodes which will be setup in AWS. You can then click the Launch Node cluster which will be present at the bottom of the screen. Once the node is deployed, you will get the notification in the Node Cluster screen. Deploying a Service The next step after deploying a node is to deploy a service. To do this, we need to perform the following steps. Step 1 − Go to the Services Section in Docker Cloud. Click the Create button Step 2 − Choose the Service which is required. In our case, let’s choose mongo. Step 3 − On the next screen, choose the Create & Deploy option. This will start deploying the Mongo container on your node cluster. Once deployed, you will be able to see the container in a running state. Print Page Previous Next Advertisements ”;

Docker – Overview ”; Previous Next Currently, Docker accounts for over 32 percent market share of the containerization technologies market, and this number is only expected to grow. In general, any containerization software allows you to run without launching an entire virtual machine. Docker makes repetitive and time-consuming configuration tasks redundant. This allows for quick and efficient development of applications both on desktop and cloud environments. However, to get comfortable with Docker, it’s important to get a clear understanding of its underlying architecture and other underpinnings. In this chapter, let’s explore the overview of Docker and understand how various components of Docker work and interact with each other. What is Docker? Docker is an open-source platform for developing, delivering, and running applications. It makes it easier to detach applications from infrastructure, which guarantees quick software delivery. Docker shortens the time between code creation and deployment by coordinating infrastructure management with application processing. Applications are packaged and run inside what are known as containers which are loosely isolated environments in the Docker ecosystem. Because of this isolation, more containers can run concurrently on a single host, improving security. As they are lightweight, containers eliminate the need for host setups by encapsulating all requirements for application execution. Since containers are constant across shared environments, collaboration is smooth. Docker provides us with comprehensive tooling and a platform for managing the container lifecycle − You can develop applications and support their components using containers. You can use containers as the distribution and testing unit for all your applications. Docker allows you to deploy applications into all environments seamlessly and consistently, whether on local data centers, cloud platforms, or hybrid infrastructures. Why is Docker Used? Rapid Application Development and Delivery Docker speeds up application development cycles by providing standardized environments in the form of local containers. These containers are integral to CI/CD workflows and they ensure fast and consistent application delivery. Consider the following example scenario − The developers in your team write programs in their local system. They can share their work with their teammates using Docker containers. Then, they can use Docker to deploy their applications into a test environment where they can run automated or manual tests. If a bug is found, they can fix it in the development environment, verify the build, and redeploy it to the test environment for further testing. After the testing is done, deploying the application to the production environment and getting the feature to the customer is as simple as pushing the updated image to the production environment. Responsive Deployment and Scaling Since Docker is a container-based platform, it facilitates highly portable workloads. This allows you to run applications seamlessly across various environments. Its portability and lightweight nature allow for dynamic workload management. Subsequently, businesses can scale applications in real time as per demand. Maximizing Hardware Utilization Docker is a cost-effective alternative to traditional virtual machines. This enables higher server capacity utilization. It allows you to create high-density environments and perform smaller deployments. This allows businesses to achieve more with limited resources. Docker Containers vs Virtual Machines Virtual machines (VMs) and Docker containers are two widely used technologies in modern computing environments, although they have different uses and benefits. Making an informed choice on which technology to choose for a given use case requires an understanding of their differences. Architecture Docker Containers − Docker containers are lightweight and portable, and they share the host OS kernel. They run on top of the host OS and encapsulate the application and its dependencies. Virtual Machines − On the other hand, Virtual Machines imitate full-fledged hardware, including the guest OS, on top of a hypervisor. Each VM runs its own OS instance which is independent of the host OS. Resource Efficiency Docker Containers − In terms of resource utilization, Docker Containers are highly efficient since they share the host OS kernel and require fewer resources compared to VMs. Virtual Machines − VMs consume more resources since they need to imitate an entire operating system, including memory, disk space, and CPU. Isolation Docker Containers − Containers provide process-level isolation. This means that they share the same OS kernel but have separate filesystems and networking. This is achieved through namespaces and control groups. Virtual Machines − Comparatively, VMs offer stronger isolation since each VM runs its kernel and has its dedicated resources. Hence, VMs are more secure but also heavier. Portability Docker Containers − As long as Docker is installed in an environment, Containers can run consistently across different environments, development or production. This makes them highly portable. Virtual Machines − VMs are less flexible compared to containers due to differences in underlying hardware and hypervisor configurations. However, they can be portable to some extent through disk images. Startup Time Docker Containers − Containers spin up almost instantly since they utilize the host OS kernel. Hence, they are best suitable for microservices architectures and rapid scaling. Virtual Machines − VMs typically take longer to start because they need to boot an entire OS. This results in slower startup times compared to containers. Use Cases Docker Containers − Docker Containers are best suited for microservices architectures, CI/CD pipelines, and applications that require rapid deployment and scaling. Virtual Machines − VMs are preferred for running legacy applications that have strict security requirements where strong isolation is necessary. Docker Architecture Docker uses a client-server architecture. The Docker client communicates with the Docker daemon, which builds, manages, and distributes your Docker containers. The Docker daemon does all the heavy lifting. A Docker client can also be connected to a remote Docker daemon, or the daemon and client can operate on the same machine. They communicate over a REST API,