Containers have become the norm for creating standard applications. Clients and engineers don’t like downtime in the production environment, and recently, several organizations have adopted containers as a solution. The desire for scalable, high-performing applications that don’t experience downtime has led many to orchestration technologies like Kubernetes.
Kubernetes is popular yet complex. This article will be a guide to everything you need to know about Kubernetes. We’ll explore Kubernetes architecture, major components, and regular use instances in real-life scenarios. We’ll further discuss the pros and cons of Kubernetes and how SolarWinds® Papertrail™ and Kubernetes can ease the frustration of log management.
As organizations moved away from monolithic applications, most of these large monolithic applications were decoupled into small, independently running components called microservices. These microservices can dissociate from the whole, making them easy to deploy, update, and scale independently.
With the increase in deployable components and data centers, it became more complex to manage, configure, and successfully run these containers across multiple environments using scripts and self-made tools. Consequently, this increased the demand for container orchestration technologies such as Kubernetes.
Kubernetes is an open-source container orchestration technology developed by Google to help manage containerized applications in different deployment environments.
It helps you manage applications consisting of hundreds or thousands of containers in different environments (physical, virtual, or cloud). You can use this orchestration tool to manage your scaling requirements, failover, deployment patterns, and more.
An orchestration tool like Kubernetes has several advantages for developers and the Operations team. Alongside making it easier to improve, deploy, and control software programs, here are some other services Kubernetes offers:
Kubernetes lets you select a preferred storage system to mount on. This can be a local storage system, a public cloud provider like AWS, or a network storage system.
Suppose an infrastructure experiences data loss. If Kubernetes can restore the data, it will restart, replace, or terminate containers that don’t reply to user-defined checks.
You can balance and distribute the network traffic in a container to adjust to increasing or decreasing load. Using the DNS name or IP address, Kubernetes can expose containers.
Using Secrets and ConfigMaps, Kubernetes lets you store and manage confidential information such as passwords, OAuth tokens, and SSH keys. Kubernetes gives you the privileges of deploying application configurations and updating secrets without having to rebuild your container images or expose your secrets.
For most containerized applications, managing CI/CD is a pain point because there are likely over 100 instances of the application running. By contrast, rolling out updates in centralized applications is simple: Kubernetes automatically manages the changes. You can instruct Kubernetes to handle changes like rolling out updates, scaling up, and adopting resources in new containers.
Now let’s look at the structure and core concepts of Kubernetes.
The Kubernetes cluster comprises many nodes, divided into master and worker nodes. With Kubernetes, you can run your software applications on thousands of computer nodes as though they were a single, enormous computer.
Every cluster comprises at least one master node, which hosts the Kubernetes Control Plane controlling and managing the entire Kubernetes system. The master node holds all crucial processes running within your Kubernetes application. A couple of worker nodes are connected to this master node, but they are less valuable to the cluster than the master node. Importantly, if you lose access to the master node, you’ll lose access to the cluster altogether.
Worker nodes are what run the applications you deploy. Each worker node comprises multiple containers of different applications running on it. They’re big and contain many resources, and consequently, the worker node does most of the workload on your Kubernetes application. The worker nodes have a kubelet process designed to run on them.
The controller manager keeps an overview of activities happening in the cluster and handles repairs, replicating components, and restarting containers.
ETCD is key-value storage designed to back up the storage. It contains configuration data insights and status data of each node and each container in the node. Kubernetes’ backup and repair capabilities are viable due to the ETCD snapshot.
This is the entry point to a Kubernetes cluster—the point where Kubernetes clients communicate with the cluster. This communication can happen through the user interface (UI) if you use the Kubernetes dashboard, an API using scripts, and a command-line tool.
The scheduler is an intelligent process designed to handle pod placement. It schedules containers on different nodes based on their workload-available server resources. Scheduler occasionally decides which worker node containers should be scheduled.
The node is a virtual or physical machine containing the services required to run a pod. The control plane manages nodes, and a typical Kubernetes cluster may also have numerous nodes. You can find the kubelet, a container runtime, and the Kube-proxy within a node.
The smallest deployable units in Kubernetes, pods, are abstractions over a container that create a running environment on top of the container. Pods represent a single instance of a Kubernetes application and are responsible for running the application. There’s an application container in every pod, governed through the pod, and once the container exits, the pod immediately dies. Each pod is assigned an IP address enabling it to communicate.
A service is a static IP address attached to each pod. The lifecycle of a pod and service aren’t linked, so the service may additionally live on even after the pod dies.
A cluster is simply a collective group of nodes. If one node fails, you’ll still be able to access the other nodes.
This command-line interface is used to perform every possible Kubernetes operation in your cluster.
Ingress is an API object that allows external access to services with the cluster. You need an ingress controller to read the ingress resource information, process that data, and get traffic into your Kubernetes cluster.
This object stores sensitive data like passwords and API keys. Secrets are similar to ConfigMaps, except they hold confidential data, not in plain text format. Due to this confidentiality, secret components must be encrypted using third-party encryption tools.
A system is considered highly available if it’s responsive and available at all times. Not only does Kubernetes make containers much easier to manage, but it replicates fundamental components across multiple master clusters, thereby avoiding downtime. Therefore, even if the masters fail, the other master clusters keep the cluster fully operational.
Kubernetes makes your application more flexible and adaptable to increasing or decreasing loads. So, you can scale up rapidly while there’s traffic inside the load and users are seeking to enter your application. Conversely, you can scale the application down when the load decreases.
If an infrastructure’s server center experiences an accident, Kubernetes makes it possible to retrieve the lost data with the help of ETCD. This mechanism restores lost data in your containerized application to the latest state after the recovery.
Many cutting-edge applications have shifted from self-hosted data centers to major cloud platform providers like AWS, GCP, and Azure. Because Kubernetes is fully compatible with every major cloud platform and provides ready-to-use starters for most, it’s generally easy to set up.
Becoming familiar with using Kubernetes is the scariest part of adopting this tool. It’s challenging to learn due to how vast the technology is and learning Kubernetes calls for an immense understanding of fundamental DevOps concepts. Consequently, many developers and DevOps engineers consider this process time-consuming and tiring.
When thinking about the cost to migrate to Kubernetes, you have to consider the resource costs for maintaining Kubernetes engines, which can become complex and time-consuming to manage. In small-scale applications, migrating to Kubernetes might not have the same impact on the development and deployment processes in large-scale applications. This means your team may spend more time managing the Kubernetes environment than developing new commercial enterprise capabilities.
Transitioning from a non-containerized to a containerized application isn’t easy. Learning Kubernetes is complex, and as a project owner, you must invest significant time and money in training your engineers or hiring educated engineers. In addition, the infrastructure fee of running Kubernetes is high, which can be overkill for smaller applications. Therefore, you may spend more than you would on non-containerized software.
We’ve seen the benefits of Kubernetes and how it makes it easier to manage containerized applications. However, these benefits brought new challenges due to Kubernetes’ ephemeral and dynamic nature. One main challenge is how to centralize Kubernetes logs.
Logging provides valuable insight into how Kubernetes, its containers, and its nodes perform. You can trace problems in the cluster back to their source.
Because of Kubernetes’ highly distributed and dynamic nature, clusters in Kubernetes develop several layers of complexity and abstraction, which impacts the type of logs generated. These logs, however, are transient. So, when a pod is evicted, crashed, deleted, or scheduled on a different node, the logs from the containers are lost. The system cleans up after itself. Therefore, unlike in traditional servers or virtual machines, you lose all information about why or how the anomaly occurred.
Although Kubernetes has logging and monitoring functionality, effective log management is inherently complicated. That’s why you need log management tools external to Kubernetes like Papertrail to help you capture and aggregate logs for your cluster.
Papertrail is a log management tool that offers simple, powerful log management designed for engineers by engineers to help you troubleshoot quickly and get the most from your log data. Papertrail works with almost every log type, including Kubernetes. It aggregates log data from applications, devices, and platforms to a central location for easy searching. With Papertrail, you can view, pause, search, and tail events in real time from a single UI.
Papertrail helps you get quick insights from your logs with live tail and interactive search features. Sending Kubernetes logs to Papertrail in Kubernetes lets you investigate logs from every one of your containers simultaneously and monitor your logs in real time. Papertrail is fast to set up, easy to use, and helps you spend less time troubleshooting.
With big companies adopting Kubernetes, it’s becoming the standard way to run distributed apps in the cloud and on-premises infrastructure. Kubernetes lets you run your software applications on thousands of computer nodes as if all those nodes were a single, enormous computer.
Logs are a vital part of managing containerized applications. Papertrail supports Kubernetes logs and simplifies troubleshooting error messages, app requests, slow database queries, config modifications, and more. You can also analyze logs from your containers simultaneously and monitor your logs in real time. For more information on Papertrail, check out the documentation.
This post was written by Anita Ihuman. Anita is a software developer with experience working with React (Next.js, Gatsby) and in the web development industry. She is proficient in technical blogging and public speaking, and she enjoys exchanging information. Anita loves contributing to open-source projects. Anita is the community manager at layer5. She creates blog posts for the community blog and is a content creator for the Gnome Africa Blog.