How to Optimize Java Apps on Kubernetes
Categories: Java 9 Java 8(JDK1.8) Kubernetes
How to Optimize Java Apps on Kubernetes?
We know that Java is a high-level object-oriented programming language designed to make application development easier for developers. Also, it is continuously updated with new developments that help the language to stay up to date along with cross-platform capabilities. Java apps are programs written in the Java language that executes on the client or server side. Even though modern world technologies and tools like Kubernetes came to light, Java is still a developer's choice.
Having so many benefits of using Java also includes some challenges related to resource management. The issues come like resource conflicts and Kubernetes on the other hand helps to identify the resource conflict issues and solves the resource management issues.
Kubernetes is an open-source portable platform that manages containerized workloads and services. It allows us to place containers in the system and limit the resources used for them. Thus, Kubernetes will get more control over the resources and their usage in a cluster. Even though there will be some critical issues in executing Java apps on Kubernetes.
Here in this article, we will see some best practices that help to optimize Java apps on Kubernetes.
Optimize Java applications on Kubernetes
Let us know the best possible way to run Java apps on Kubernetes to achieve the best performance at the lowest cost. Here we do an experiment in a non-operational environment.
But we need to install some applications that support the experiment through a demo.
- We need a Kubernetes cluster where we use the minikube 1.26 version that helps to install local Kubernetes cluster version 1.21.
- Then we will install Kubectl version 1.21.
- Later we need Git CLI and Stormforge CLI tools. Here, to continue the process we are using a Pro account of the StormForge app.
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Cluster Creation and Configuration of Tools
Here, we need to run a command to build Kubernetes Cluster through version 1.21. The command will be-
$ minikube begin --kubernetes-version v1.21.0
Then we need to place the environment variables by running the following three commands individually-
$ mkdir -p $HOME/.kube
$ sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
$ sudo chown $(id -u):$(id -g) $HOME/.kube/config
Now, we need to verify the configuration that we processed using the below command.
$ kubectl get nodes
Then, we should log in to the StormForge account that we installed before the experiment.
The command to log in to the account here is-
$ storm forge login --url
Check the API connection by using the following command.
$ stormforge ping
Now you can start StormForge by running the below command:
$ stormforge init
Check the configuration of the StormForge Controller whether it is done properly:
$ stormforge verify controller
Creating and Running the task
Now we need to download the demo project that we use to build deployment and create and run an experiment that includes many trials. Upon completion of the experiment, we will check the approved configurations and will implement one of them.
Moreover, the primary configurations will exist in the demo.yaml file.
To start this experiment, we need to do a final check here.
Just go to the StormForge app to confirm that we have appended a cluster. And the same should emerge on the cluster page.
If you found the cluster there, then you are ready to start the experiment. But if you can’t see the cluster, then you need to refresh the web page once.
Now replicate the example experiments recovery through the below command:
$ git clone (for cloning the examples)
$ cd examples
Let us design and start the Java-tuning demo with this command:
$ kubectl apply -f jvm/demo.yaml
Need to examine the existing status of the demo using this command:
$ kubectl get trials -o wide -w
Now you can see the status of the demo easily.
After finishing it, just return to the StormForge app where the account exists. Then you can navigate to the demo by just clicking on the jvm-reactors app. You will see an outline or a scenario will open there. Click on the renaissance-associated outline there.
Now click on the jvm. reactors demo run. You will see the experiment results screen here. Then select the featured suggested trial from the screen.
To get the trial’s pod name, you need to run the below command.
$ kubectl get pods
After this, just run the following to view the configurations of the pod-
$ kubectl define pod <pod name>
Now you can find the suggested configs on the screen that will appear there.
Finally, click on Export the configuration appearing on the screen. This will help to download the YAML file having the optimal layout. Thus, the demo experiment completes here. Now you can see the configuration/layout found by the ML-based trial for the Java application optimization.
Why is difficult to optimize Java Applications?
If you work on any Java apps then you must come across the optimization concept. Moreover, it is very important to check that the code we write should not only be clean, and without defects but also optimized. This means the time taken for code execution should be within planned limits. For this, you must refer to Java coding standards.
Further, you can regulate Java apps for cost or performance. Here, optimizing is about giving the best arrangement among these objectives provided for your business goals. We can also say it to provide high-level performance to meet SLAs at the lowest possible cost.
But there is no perfect solution that can fit one-in-all. So, it’s necessary to know the basic priorities for your app to develop the exact mix of cost & performance.
Moreover, you also need to consider another important factor here. This means a Java application runs on a JVM. Further, the deployment distributes memory heap size to this JVM only.
When we place the Java apps as a microservice without mentioning any requests and limits. Kubernetes here decides the resources need for allocation. Here the challenge is it is generally very plentiful with the allocated resources. And it will make our cluster much more expensive to run. In case we assign the resources manually. Then we need to choose one of the following options:
- Oversupply of resources to make sure that we are not regularly facing out-of-memory issues
- Diminish the resources supplied and accept the risks like out-of-memory errors and CPU control
- Moreover, spend crucial time and effort to decide on the perfect choice of resources using a trial and error play manually.
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Thus, we have seen how to run and optimize Java apps on Kubernetes. Java coding language has been used to develop many powerful apps for many years. But we can see there are some unique difficulties in running a Java app on Kubernetes. This is due to the stack of layout (Config) settings that need to examine for each app.
If the same is not perfectly configured, the app may fail to run as per need. Hence, it increases the expenditure.
Here, for this experiment, the StormForge app is used as it offers ML optimization solutions. But you can use any other application for this. Thus, all these factors should be kept in mind while running Java apps.
Author Bio : My name is Sai Thirumal, and I work for HKR Training’s as a content writer. I have a lot of experience writing technical stuff, and I want to keep learning new things to advance my career. I am skilled at presenting content on the most in-demand technologies, like AlterYX Training, PTC Windchill Course, Arcsight Training, Blockchain Training, Java Training, Kubernetes Training, Looker Training, etc.