Kubernetes (K8s) is an open-source system for managing containerized applications’ deployment, maintenance, and scaling. These applications run in an isolated runtime environment called “containers.”
Containers confine applications with all their dependencies, including system libraries, configuration files, and binaries. This encapsulated packaging enables an application to operate consistently across many hosts.
Google engineers developed K8s hosted by the Cloud Native Computing Foundation (CNCF) before it was open-sourced in 2014. It is a successor of a container orchestration platform, Borg, utilized internally by Google.
A broader container system and K8s mature into an all-purpose computing platform and ecosystem that emulates virtual machines (VMs) as the fundamental block of the latest cloud infrastructure and applications.
Container leverages a form of operating system (OS) virtualization that allows multiple applications to share a single instance of an OS by taping processes and controlling the CPU amount, memory, and disc processes. Here are a few attributes and challenges of K8s and robust logging practices.
Companies of different sizes that utilize K8s services save costs on their ecosystem management and manual, automated processes. Kubernetes fits containers into nodes for the best use of resources automatically. Some public cloud platforms charge a fee for managing every cluster; hence, running fewer clusters means minimal API servers and redundancies, leading to lower costs.
Moreover, applications can run with reduced downtime and process well once K8s clusters are configured. This requires minimal support when a node or pod breaks down, which would otherwise need manual repairing. The container orchestration offers a robust workflow with minimal need to repeat the same processes. This restrains the need for an inefficient administration and fewer servers.
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Container integration and storage resource access with multiple cloud providers make testing, deployment, and development seamless. Container image creation that contains processes that run the application is simpler and more efficient than creating virtual machine (VM) images leading to rapid growth, release, and deployment. This allows developers to deploy K8s during the development lifecycle while enabling them to test the code early and prevent costly mistakes in the long run.
Applications that house on microservices architecture contain different functional units that transit with each other via APIs. This means the development teams can focus on single features and operate effectively. Interestingly, namespaces, a process of stationing numerous virtual sub-clusters within an identical physical K8s cluster, enables businesses to offer access control for enhanced efficiency.
Businesses used to deploy applications on virtual machines and station a domain name system (DNS) server. One of the vital attributes of K8s is that workload can exist in a single cloud or be spread across multiple cloud servers easily.
These K8s clusters allow a straightforward and accelerated containerized applications migration from on-premises infrastructure to hybrid deployment across any cloud provider’s private or public cloud infrastructure without draining the application’s functions or performances.
This feature allows businesses to shift their workloads to an enclosed or proprietary system without lock-in. Here are three robust strategies to migrate the applications to the cloud
K8s automates and schedules container deployment across numerous computer nodes, whether on-site VMs, public cloud, or physical on-premises machines. By leveraging automatic scaling, businesses can scale up and down to fulfill the demands faster. The auto-scaling starts new containers for heavy loads or spikes as needed due to CPU usage, memory thresholds, or custom metrics.
Additionally, it auto-scales the resources again to minimize waste and allows straightforward scaling horizontally and vertically. Interestingly, K8s can actively roll back an application change if something goes wrong.
Numerous organizations were encouraged to adopt container orchestration and to introduce K8s. On the contrary, with accelerating adoption, every development team spun up its application clusters. At the same time, having each developer team manage the life cycle for each group requires a significant effort. Developing large multi-tenant clusters is difficult without proper safeguards to prevent isolation between different sections.
Businesses must design strategies keeping isolation in mind and provide more control to platform engineers to build smaller and single-tenant clusters. Hence, when deploying K8s, companies must prepare for new cluster management ramifications.
Engineering teams need transparency into the application and infrastructure’s health to determine issues early. At the same time, monitoring is highly achievable when dealing with a single cluster. However, maintaining observability becomes complex and challenging when businesses deal with many sets.
Moreover, cloud-native observability tools offer a vast number of data making it challenging to prioritize the alerts of importance. Businesses can utilize robust tools to gather all the relevant metrics in a centralized space. These tools actively track metrics and health across hundreds of applications and clusters.
Having numerous node pool configuration types could strain the K8s platform. Computing workloads perform well on slightly indistinct infrastructure and hardware. A workload operates well on a specific NVIDIA GPU chip or an ARM-based computer processor.
However, while configuring a specific workload environment accelerates performance optimizations, businesses might also run into capacity problems during container scheduling. Hence, generalizing the schedule of where the actual workloads go is an essential measure business can take to overcome this pitfall. Furthermore, companies must prevent a direct one-to-one relationship and ensure the node pools support multiple types.
Furthermore, another pitfall is a lack of governance that tooling and integration developers bring into K8s. These tools support logging, authentication, CI/CD, and database management. However, getting more layers in can cause difficulties for platform engineering teams responsible for maintaining the availability and security of the integrations.
Businesses must roll back on offering developers complete control and treat the platform like how they would treat a product to overcome the governance challenges. Call-on is a request-based model allowing the engineering teams to accept developers’ input. This way, the developers can decide which integration to encourage as the new “as-a-service.”
Every deployed container produces unique log types which the businesses track and monitor. And since K8s is actively utilized for large systems, a multitude of clusters and notes suppresses the end-to-end transparency of the architecture.
At the same time, a virtual machine dies when an application is hosted, but its logs persist until they are diminished. Moreover, in K8, the records disappear when pods expire, making it challenging for businesses to investigate the root cause of this issue. Logging helps companies to secure sensitive data in secure databases.
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Reaching the K8s log is pretty straightforward. However, the challenge is where to send and store them to be accessible for future use. Irrespective of the use cases, the logging pipeline businesses select, internal, or service must ship logs to a central location in an isolated environment.
Logs will incur much space if businesses conduct logging internally or use a third-party service. Therefore, it is essential to ensure that businesses have a transparent retention policy that stores records for the long term. Higher retention policies are expensive. Hence, companies must estimate the need for disk space based on the K8s environment. A well-managed logging service will minimize the infrastructure costs analogous to K8s logs and offer volume-based concessions.
Writing logs is standard practice when transitioning to a containerized environment. However, few businesses write apps that log to files. They should direct logs to stdout and stderr. This allows K8’s centralized logging framework to stream wherever they want automatically. Distinguishing these errors in the error stream helps with log gathering enabling businesses to filter logs seamlessly.
By building separate clusters for development and production, businesses can prevent accidents like pod deletion a critical aspect of production.
Developers can seamlessly switch between the two with a “kubectl config use-context” command. They must use the same approach for retaining development and production in different locations.
Kubernetes’ best practice indicates that the user must try to have one container instance per pod. This allows businesses to replicate the container within the pod and have numerous containers in the pod. Simultaneously, a sidecar is a second container within the same pod that apprehends the output from the first container taking up resources from a per pod level.
If businesses, for example, have five pods running on their node with sidecars, they work with ten logging containers. More importantly, there are several instances when sidecar containers are unavoidable when businesses cannot control the app, prompting it to write logs to files.
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Apoorva Kasam is a Global News Correspondent with OnDot Media. She has done her master's in Bioinformatics and has 18 months of experience in clinical and preclinical data management. She is a content-writing enthusiast, and this is her first stint writing articles on business technology. She specializes in Blockchain, data governance, and supply chain management. Her ideal and digestible writing style displays the current trends, efficiencies, challenges, and relevant mitigation strategies businesses can look forward to. She is looking forward to exploring more technology insights in-depth.
