Kubernetes Challenges for Developers - Part II

in Kubernetes

Kubernetes Challenges for Developers

In part I of Kubernetes Challenges for Developers, we discussed five of the most common challenges that developers face when working with the Kubernetes framework. These challenges included: Manifest Management, Application Lifecycle Update, Volume Management, Dynamic Parameters and Kubernetes Cluster Bootstrapping.

This article further explores five more limitations that are posed by the Kubernetes framework.


As Kubernetes enjoys widespread adoption and increased usage, understanding these new complexities and limitations becomes increasingly important. Only when one understands the crucial challenges of this framework can they truly utilize its complete potential. Let us have a detailed and comprehensive view of these five challenges and understand them better.

1. When you want to manage your cluster permissions

The RBAC or role-based access control mechanism enables developers to configure very specific sets of permissions. How users interact with the Kubernetes object is defined by this RBAC mechanism. This mechanism is also found built-in in the Kubernetes framework.

Three important elements need to be created when specifying permissions using the in-built RBAC mechanism. These elements include service accounts, roles, and roles binding. Further, specific RBAC configurations need to be made for all individual Docker containers.

When it comes to Docker containers, the developer needs to create bindings between the roles and service accounts to specify RBAC configurations. This entire process is incredibly time-consuming and effort-intensive. The configuration is highly susceptible to human errors and can lead to massive blunders if not taken care of properly.

This is another crucial challenge that developers face when working with Kubernetes. Errors in configuring the Kubernetes RBAC can also lead to a completely nonfunctional Kubernetes framework thus rendering the entire system purpose-less.

2. When you manage network and traffic

Kubernetes is used to develop numerous business-critical applications. In such applications, network and traffic management is not just critical but also a challenge.

One cannot take this lightly since weak and poorly-managed networks and traffic on any business-critical application can lead to unusual and undesirable faults. If the fault is too severe, the entire application can crash down as well. Such crashes can result in a lot of financial and data loss for the end-users using the application.

Now, to avoid such blunders, a Kubernetes-based application needs to be thoroughly assessed for all network and traffic parameters. All configurations need to be carried out for all pods, nodes, and services manually. There are also numerous service mesh resources such as destination rule, virtual service, etc, where service information needs to be added. Now the challenge that this form of network and traffic management poses is that all of these configurations are made manually. This manual work eventually makes the management immensely long-drawn and effort-intensive.

3. When you need your applications to autoscale

Autoscaling is one of the biggest reasons why Kubernetes has become very popular in the developer community. Autoscaling is made possible using containerization. It enables developers to scale an individual microservice in the Kubernetes application up or down depending upon the demand of the end-user.

Now, even though Kubernetes enables very easy autoscaling, this feature doesn’t come built-in with the framework. One needs to integrate the Metrics Server to make autoscaling of microservices easy.

Integrating Metrics Server with Kubernetes isn’t a challenge in itself. However, limitations arise when developers need to configure the Metrics Server in Kubernetes to enable autoscaling. Configuring Metrics Server isn’t exactly straightforward requires one to understand a very specific set of parameters.

These parameters also change depending on the choice of your public cloud provider. Plus, these challenges further complicate when it comes to autoscaling nodes. Once you have configured the nodes with Metrics Server, you then need to join them back to the cluster manually.

4. When you need to associate Pods to Nodes

If a pod is placed on a node that doesn’t have sufficient free resources, the cluster will run out of resources and cease to operate functionally. This is a critical challenge that the Kubernetes application needs to take care of in order to avoid any nonfunctional applications.

To prevent this from happening, the Kubernetes scheduler is made use of. However, this Kubernetes scheduler does not provide freedom of usage and one cannot associate pods and nodes in any way they’d like. For customized purposes, a developer needs to deploy specific pods to specific nodes.

Carrying out this task doesn’t seem very complicated, however, if a developer is working with an intensive and heterogeneous pool of nodes, the process can become very tricky. This non-friendly scheme of configurations prevents developers from carrying out the association easily. Customized features become difficult to configure and the entire task becomes incredibly laborious. Thus, even though the Kubernetes scheduler is a powerful tool, it isn’t exactly very friendly to work with and can pose numerous limitations.

5. When you need to integrate with legacy services and VMs

Migration of the workloads to the cloud can be done by several strategies - Refactoring, Rehosting, Replatforming, and more. At this stage, developers choose to keep some workloads on the Kubernetes containers and some on the VMs. Now, for seamless and proper functioning, it is critically important to secure effective communication between all of these services. Any faults in the communication channel will lead to the complete application becoming non-functional.

This integration of the legacy services with Kubernetes Docker containers can be extremely complex. Here, each service requires a manual configuration, and carrying that out can take a lot of time. This is especially complex when some VM services need to be run behind a firewall.

Conclusion

Kubernetes offers immense functionality and power to developers, however, it isn’t very friendly to use. Thousands of manual configurations and the need for deeper understanding can make working with Kubernetes very tough. This is where Cloudplex can offer numerous benefits.

Cloudplex enables:

  • Configuration of services in a single interface
  • Application-Centric Collaboration
  • Policy-Based Access Control
  • Policy-based Autoscaling
  • Seamless integration with Istio and Knative
  • Full integration with container registries
  • Integration with Legacy Applications
  • Flexibility of running limitation-free clusters
  • CI/CD Integration, and more

Whether you wish to address Volume Management or Kubernetes Autoscaling, making use of robust and functional tools is incredibly beneficial. Doing so can enable you to not just understand the challenges you are facing but also resolve them completely for high-performing business-critical applications.



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