Application Deployment is a crucial stage for any developer, and they need to ensure its success to effectively deliver their code in the production stage.
This is the most crucial stage in DevOps because that is where the code is migrated from the backend to a live ecosystem. However, a few developers might find it difficult to design and deliver a code or framework within the expected timeframe. Even the smallest mistake in the deployment can create disruptions in a live environment, which can have a significant impact on business continuity.
So while deploying any application, code, or framework, it is crucial for developers to consider the butterfly effect that one small mistake can trigger too. Hence, it is essential for the developers to have an effective deployment strategy in place to deploy a code or a framework in the live environment without impacting the other workflows.
The following are a few aspects that developers need to consider to ensure successful application deployment:
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Determine the application deployment scale
One of the most significant aspects to consider while deploying a code or a framework in the live environment is the deployment scale. It is crucial for software developers to understand if the application deployment type is small, medium, or large to make strategic changes.
Moreover, it is also crucial to understand the impact of deploying the framework on other platforms for different business processes, because there may be differences in complexity or size it needs to be deployed in. A few development professionals might witness massive application transitions, moving away from legacy and outdated systems.
Whatever be the size of the deployment, it is crucial to get all users on the same page and train multiple users to effectively handle the deployment. Developers especially need to ensure they are more vigilant while executing a large-scale deployment because they are usually riskier and more strategic planning than smaller deployments.
Segment production and non-production cluster
Keeping one single cluster for everything will develop challenges, issues, and concerns for resource utilization, consumption, and security.
Developers need to consider maintaining two clusters and keeping one for production and another one for non-production resources, while ensuring successful application deployment.
Maintaining different clusters will help businesses to prevent interaction between the different pods in each cluster. The most effective practices are those that enable developers to prevent situations where they deploy a test functionality in the namespace on the cluster housing productions. A single cluster has multi-tenancy functionality, but it needs skilled resources and expertise to ensure successful deployment.
Developers need to consider creating segmented clusters because it is usually easier. A few development teams have multiple dedicated clusters, like production, shadow, developer, tool, and other specialized clusters, to streamline their deployment processes. One of the most basic aspects that developers need to consider while deploying is keeping the production cluster separate from the others.
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Utilize limited resources for successful application deployment
Usually, there are no resource limitations while deploying an application on Kubernetes. Developers that do not set any limitations might end up consuming the entire cluster by disrupting performance in the production cluster. Software engineers should set resource limits for every application. They might not be able to set it by themselves, but they know the CPU and memory limits to set to utilize limited resources, very well.
Developers need to consider the potential traffic and load bursts that will happen post-deployment to set the appropriate limits. They also need to consider the programming language used in coding while setting resource limitations.
Utilizing Kubernetes offers resource elasticity, but it is crucial for the developers to strike the perfect balance to ensure successful deployment. Software engineers that set limits that low might result in an application crash, and if it is very high, the cluster will become inefficient.
Developers that consider these strategies will ensure a successful application deployment of the code, algorithm, or framework in the live environment.
