Kubernetes as an open-source container orchestrator, has become the standard for automating the deployment of containerized applications. Many organizations turn towards Kubernetes to facilitate their digital transformations as it ensures consistency and reliability regardless of the underlying infrastructure.
Being more than six years old, Kubernetes has proven its worth not only in DevOps and agile environments but also in any environment aiming for rapid development and scaling. But as with every technology, there are benefits and shortcomings, and Kubernetes is no exception.
Along with many problems Kubernetes solves, some scenarios can be challenging to set up and maintain. This blog will discuss all of those scenarios and how they will affect the Kubernetes adoption in coming years.
Let’s get started.
Kubernetes consists of several components, services, and systems that need deployment before running any containerized applications. Many concepts such as load balancers, selectors, pods, and endpoints have to be pre-configured to spin a typical k8s system for testing and running applications.
In many scenarios, enterprises won’t require all of these concepts. But Kubernetes architecture wants these workloads up and running to properly run application code and perform configuration processes on each pod to guarantee high availability and infrastructure efficiency.
Locally running applications on Kubernetes is also complex, requiring a variety of staging environments, proxies into the local machine forcing developers to depend on third-party monitoring solutions. Third-party tools are great to get visibility into the environment and insights across the clusters to isolate service data but to set up, learn and manage these tools is another complexity on top of core components.
Kubernetes automates most of the tasks associated with the deployment and management of containerized applications. But, there are various processes in Kubernetes that favor automation by default, making it fairly difficult for businesses who want to have exercise strict control over workflows.
Kubernetes determines whether a container is performing optimally and schedule them accordingly to run on a particular server within a cluster. This makes sense for large-scale deployments where there are thousands of servers and workloads as enterprises don’t want to configure them manually. But if an organization at a small scale intends to have more control over the structuring of the workflows, Kubernetes does not make it easy.
In Kubernetes, most essential tasks natively require coding in declarative YAML files, which gets applied to the Kubernetes command-line for implementations. This can be advantageous for some production environments that favor everything-as-code architecture as it makes it possible to manage everything using a single methodology.
But there are challenges for developers who are not familiar with declarative languages. YAML is static and can be very helpful for basic setups, but as Kubernetes applications grow, YAML files become complex to write and manage. Businesses who do not want to resort to writing or tweaking or a long YAML file have to rely on open source frameworks that provide a way to define infrastructure through a wide variety of programming languages.
Choosing the right deployment model for Kubernetes can quickly become a hassle as its implementation can be done in various ways. Majorly there are three Kubernetes deployment patterns. The first is deploying Kubernetes using its upstream version, the second is using Kubernetes as a service(KaaS) platform, and the third is deploying Kubernetes on hosted cloud infrastructure.
Do-It-Yourself Upstream Kubernetes deployments make it possible to build and manage Kubernetes clusters either on-premise or off-premise using the native open-source version of Kubernetes. This is best suited for organizations that want to have full control over the infrastructure, scaling, and costs.
Kubernetes as a Service(KaaS) deployments combine the necessary components to distribute and manage Kubernetes, such as security, monitoring, and storage/networking integration, and can be deployed on-premise or off-premise to simplify container operations management and increase microservice developments experience through toolchains and application infrastructure software.
Hosted cloud Kubernetes deployments provide developers access to a managed Kubernetes service through a public cloud infrastructure. Public cloud deployments are easiest to consume and are powered by major cloud vendors like Amazon, Microsoft, Google, and others. The prominent public cloud options are Google Container Engine, Amazon Elastic Container Service(ECS), and Azure Container Service (ACS).
There are pros and cons to each of these deployment models, so it is necessary to select a solution that suits organizations’ strategies and agility. For on-premise deployments, storage has become the major challenge as organizations manage their own storage infrastructure while organizations using public cloud infrastructure face monitoring and logging challenges.
Lack of Kubernetes skills is one of the biggest challenges that require mitigation for broader adoption of Kubernetes. Kubernetes was designed to embrace a specific set of site reliability engineering (SRE) practices for DevOps. In contrast, a typical operations team in largescale enterprises is made up of administrators who have very little to no engineering experience.
This issue creates a high demand for Kubernetes certified professionals that outruns the available supply. Due to the shortage, organizations are forced to train their existing IT staff and relying on managed Kubernetes services provided by third-party cloud providers for performing day two operations like upgrades, patches, and more.
The degree to which organizations leverage the Kubernetes training or managed Kubernetes service is clearly dependent upon their financial stability and architecture. Many organizations only favor trained on-premises IT operations teams to perform backup, recovery, and application upgrades as they cannot risk sharing their data with cloud vendors.
On the other hand, some organizations mandate all developers to learn cloud vendors’ architecture technicality. They want developers to become more aware of in-depth workings and operational tasks that are not relevant to their day-to-day development regime.
It is true that Kubernetes saw a significant rise in the adoption of modernized data centers by enterprises that want to leverage digital transformation. But along with that growth, platform security has become one of the biggest concerns for organizations forcing administrators and developers to rethink whether to adopt a single Kubernetes framework for managing all the workloads.
According to the survey conducted for D2iQ, one of the most challenging aspects that were hindering the broader adoption for Kubernetes was security (47%), lack of resources (34%), and scaling (37%).
The Kubernetes ecosystem and CNCF have tried to address these concerns by rolling out critical updates. Still, the complex and large codebase of Kubernetes can quickly become prone to security holes, according to the CNCF open-source security audit.
The audit mentioned that Kubernetes requires more straightforward security controls; not well-defined security settings can easily expose Kubernetes to the security holes. The documentation for setting up Kubernetes security assets should be detailed to guide administrators and developers properly.
The codebase should be centralized to reduce re-implementations and complexity while understanding how the infrastructure and workloads are built and whether they can be easily configured to operate and scale.
Another challenge with the Kubernetes ecosystem is that it can execute many different tools, implementations, and distributions that create a large learning deficit between the platforms.
Vendors have implemented Kubernetes offerings. If enterprises, which are already adopted the Kubernetes environment and are looking forward to shifting to other Kubernetes-based offerings, will experience a steep training curve.
Vendors majorly make these differences to differentiate their offering, but These platforms end up being all-controlling and not syncable with other offerings according to the user perspective.
Let’s take an example; if you are an organization using Mirantis Kubernetes Engine and wanted to shift to Rancher, there are many monitoring tools and mechanisms which have to be taught to system admins before migration. However, both the Kubernetes distribution utilizes the same container orchestration platform.
This is the same for public cloud Kubernetes deployments. Migration between public cloud Kubernetes offerings is very efficient if the organization’s previous workloads leverage their technologies. But what if there are workflows that have to be run as part of different offerings. There is very little native functionality for these scenarios, and Enterprises faces a challenge with user experience and management tools provided by both vendors.
Launched in 2014, its been more than six years for Kubernetes, and it has been steadily adopted in the enterprise infrastructure.
Yes, there are various shortcomings if you have to manage legacy workloads or small-scale deployments. Platform security has also been identified as a significant bottleneck. But overall, Kubernetes has proven its worth and is an excellent tool for developers managing containers at scale.
There are various use cases for which Kubernetes is well-suited and is driving interest of enterprise IT. According to the CNCF Cloud Native Survey for 2020, 83% of respondents used Kubernetes in production, up from 78% the previous year and 58% in 2018. The 2020 Red Hat Enterprise Open Source Report predicts that 70% of organizations by 2023 will be running at least two or more containerized production applications, which is 50% up from 2019.
So, as the number of containerized applications starts to rise, Kubernetes applications will become a norm in the coming months increasing the need to use a Kubernetes solution that avoids wastage of time and cost.
Enterprise leaders need to understand that a well-organized strategy plan will go a long way to manage operations and properly using Kubernetes solutions. There are various misconceptions within enterprises that using the trendiest technology like Kubernetes guarantees success; one of the most challenging situations for companies is when they have adopted Kubernetes. They have to make the company successful now.
This is not a great way to find business value. Kubernetes is indeed a smart platform that automates various decisions, but it is not a solution for every situation. Just because Kubernetes is widely adopted in recent years doesn’t mean every organization can adopt it through solutions and best practices. Solutions and best practices are also for a particular situation unless the situation is properly understood Kubernetes can be a curse or a blessing.