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Wherever Kubernetes is part of a complex architecture, the excess of the cluster remains a huge challenge. K8s users often became overwhelmed with cluster management. Even though cluster management is a tough nut to crack, it’s not wise to ignore it. K8s monitoring is one of many viable ways to maintain the clusters efficiently. Let’s explore this concept in detail.
Kubernetes Monitoring: A Quick Overview
K8s clusters are complex to handle, and this complexity is what keeps K8s users hesitant to use the famous platform in full swing.
However, cluster management will no longer be a hassle for the DevOps team if they adopt to track/monitor their K8s setup that involves revealing cluster issues quickly and looking after the clusters proactively. A lot of activities are involved in the process. For instance, keeping a track of cluster uptime, CPU usage, storage consumed, and the communication happening between multiple cluster entities.
The key component that monitors the cluster is cluster operators. Cluster operators watch out for everything a cluster is doing. If any abnormality is spotted, the cluster operator alerts the system. In general, cluster abnormalities involve any configuration failure, excess running of several pods, usage of resources beyond the threshold, or any pod error. A lot is monitored by the cluster operator. However, it’s not sufficient for extensive K8s monitoring.
For extensive monitoring, experts recommend using specific cloud-based tools and applications for monitoring/surveillance.
Why is it important?
Those performing K8s continuous deployment put in the required effort because they know that using K8s means easy movement between multiple computing ecosystems. The deployed applications can switch the computing environment with the least possible effort.
Also, Kubernetes deployment YAML is preferred as it lets the DevOps team operate a wide range of application containers on a single system. Hence, developing a shared cluster system is easy with K8s. As applications are fully isolated from each other, they remain disconnected from the fundamental host ecosystem. Hence, scaling in multiple clouds of such applications becomes an easy job.
Before Kubernetes came into being, the world had to compromise with non-portable applications as no way for portability was offered. The launch of K8s not only supported application portability but also allowed the DevOps team to build multiple apps over a single OS. That’s the reason why K8s has become very famous.
One successful Kubernetes deployment example is Google. This tech giant is running billions of containers per week due to this in-house platform only.
However, deployment Kubernetes is not always fruitful. There are always certain downsides involved when technology is used. In the case of K8s, the problem is the lack of one-to-one interaction between the application and the related server. An application will keep on changing the server, and the DevOps team won’t be able to predict its health as they won’t be able to track the server.
In K8s, the health of application also heavily depends on clusters and containers. Basically, there is not one component that will impact the health of the K8s application. A lot of factors play their role.
K8s monitoring is crucial because it breaks down extensive app health monitoring into different categories and provides the DevOps team with an easy way to spot errors and eliminate vulnerabilities.
If monitoring of metrics like CPU usage, storage, node capacities, and many more are not done, the K8s application might face serious operational hassles and fail to operate in an expected manner.
Key Metrics for K8s Monitoring
As mentioned above, keeping an eye on K8s is the only way to reduce the risks and improve the application performance. However, the expected results are only delivered when you rightly monitor your ecosystem.
It’s important to comprehend the metrics that actually impact the health and operations of an application. As you start the monitoring, your focus should remain on those metrics only. IT has to be done on two primary levels in Kubernetes’ case.
Cluster monitoring - It is done to make sure the K8s clusters are fine. At this level, a wide range of metrics is tracked to make sure that the concerned nodes are perfectly fine and are used in an ideal capacity.
Pod monitoring - It’s the level where metrics related to pod operations and performance are concerned. The pod is the basic unit of K8s, and if it’s not working properly, we can’t expect the application to be decent and functional.
Let’s see which metrics are involved at both these levels:
There are three categories of metrics that are tracked at this particular level.
#1 - Cluster nodes - This metric help you find out the number of nodes open for your use so that you can decide the total cloud resources you will require to run that specific cluster.
#2 - Cluster pods - With this metric, the DevOps team will remain updated about functional pods and replace the failed pod immediately. If you’re not aware of the number of pods you have, you will have a tough time ensuring seamless cluster operation.
#3 - Resource utilization - It allows you to track the entire resource utilization by the nodes and decide the future resource needs. This avoids shortage and excessive utilization of resources.
At this level also, the metrics are divided into three categories that are:
#1 - Container metrics - These include CPU, network, and memory usage by the container. Metrics-server is required to access all these metrics.
#2 - Application metrics - They are associated with the business logic of that app directly. There is no certain set that define app metrics. For instance, some applications will keep several users under consideration, while others might keep the user experience a priority.
#3 - K8s availability and scaling metrics - These metrics are helpful in determining how the orchestrator is handling a particular pod. In general, the tracked instances are the real number of pods per moment v/s expected number, network data, on-progress implementation, and health checks.
Using these metrics, it’s easy for a DevOps team to perform extensive and end-to-end K8s monitoring, and thereby, boosting the app’s performance.
Types of K8s Monitoring
As you continue the deployment of K8s, you must first figure out the way of monitoring that you must adopt. In general, it’s a highly extensive process with multiple components under consideration.
K8s Cluster monitoring
This type of surveillance/monitoring is only related to the cluster’s health and processing. To maintain the seamless operations of K8s clusters, the DevOps team will have to keep an eye on the functionality of all the concerned nodes in the cluster, nodes' efficiency, per-cluster consumption of resources, and the count of apps functional over every node.
As one tries to start monitoring clusters for K8s, metrics like disk utilization, CPU consumption, network bandwidth, memory usage, cluster usage, overheads, and running pods should be tracked for sure.
K8s pod monitoring
As the name suggests, this procedure concerns real-time pod monitoring. As we all know, the pod is the basic executable K8s entity. Each pod is only used once and if the original pod fails, the platform will create a duplicate of the failed pod so that the operation remains undisturbed.
During an active session, Kubernetes will use a huge amount of pods. Hence, its effective management is crucial. The ideal pod monitoring practice is to limit per-process pod quality. When each process has only limited pods running, health monitoring becomes easy.
You may also try kubectl to easily monitor the K8s pod. Just use the “get pod” command in kubectl and pay attention to the value of the STATUS column. There you will find details of every running pod. For better understanding, tracking metrics like health checks, on-progress development, instances per pod, expected instance per pod, and network/data usage is recommended.
Of course, we won’t keep an eye on these metrics/parameters manually, as it’s impossible for Kubernetes deployment environment variables and clusters. Hence, an automated tool like Sumo Logic is of great help.
K8s application performance monitoring
Whatever apps run utilizing K8s should be monitored continuously to avoid any failure. There are multiple tools that can help you in doing the same effectively. The consider-worthy metrics in this type are traces, log details, performance data, and K8s system events.
It is important so that K8s workloads are used effectively, and cluster cost remains on budget. It’s easy to do so by monitoring load-balancing, CPU usage, storage, cluster management expenses, and subscription cost of common services used.
As you plan to do K8s cost monitoring, you need to make sure that the right-sized nodes and correct-sized pods are used. You can also avail of AWS cloud discounts to save some costs. Using spot instances to run Kubernetes workloads also saves a huge deal of cost.
Even though tough, one must never overlook it as ignored security hassles will make the entire Kubernetes scale deployment collapse in no time. In general, Kubernetes components such as API, network layer, containers, OS of the hot, container runtimes, and Kubectl have higher security risks. Hence, the scope of K8s security monitoring covers all of them.
K8s network monitoring
Lastly, we have Kubernetes networking monitoring to recommend. If you leave the network that your cluster uses to stay connected unmonitored, multiple complexities and errors will show up. Hence, its monitoring is imperative. In this type of monitoring, the concerned metrics are endpoint transactions, response time, error rate, and service map.
Kubernetes Monitoring Tools
Manual K8s monitoring is impossible and too tedious. Practically, this is not even possible. Hence, it’s suggested you use feature-rich and fully automated K8s monitoring tools. Gladly, there is a wide range of Kubernetes tools offered, for example –
It definitely is a consider-worthy K8s monitoring tool that comes with a powerful web-based interface. The tool is useful for a wide range of processes, e.g., for monitoring the deployed containerized apps alongside the cluster and fully managing the cluster resources.
In addition, it’s a great way to troubleshoot a containerized application, develop individual K8s resources, and get an overview of the applications operating on the cluster.
We will recommend using Grafana as this open-source platform is great for visualizing tracked metrics. The 4 main in-built metrics that it can track are node, pod, deployment, and cluster.
It’s easy for administrators to create a real-time dashboard for the information they want to track with this tool.
Developed by SoundCloud, Prometheus is a highly feature-rich K8s monitoring tool that was donated to CNCF. The tool works great with Docker and K8s and can track a wide range of metrics. It is often combined with Grafana to introduce data visualization features, which are absent by default. It can track containerized applications and microservices.
Jaeger is a globally appreciated tracing system. This open-source tool was developed using Uber and is highly viable for fixing and real-time monitoring of distributed transactions. The tool is highly capable of figuring out software-related issues such as latency optimization and context propagation.
Crafted using Go Lang, Kubewatch is a powerful open-source K8s monitoring tool that is loved because of its unmatched user-friendliness. Its interface is highly simplified so that any beginner can use it without any difficulties. It allows collaboration tools and clusters to communicate in real-time.
Kubernetes Monitoring Best Practices
Even though K8s monitoring is crucial, it will only bring desired results when it’s done in the right manner. Here are some of the best K8s practices for tracking/monitoring to adopt to enjoy the best possible results.
When microservices are concerned, you must not track granular resource metrics as things become too cumbersome. These metrics are mainly load, memory, and CPU, and their tracking is very much difficult for any DevOps. The ideal KPIs here to track are API metrics like latency, request rate, and call errors.
The real-time tracking of the above metrics is easy and provides an immediate update about the errors/vulnerabilities existing in the microservices. Metrics are a great way to discover vulnerabilities existing in REST API, Nginx, and Istio. The best part of preferring these service-level metrics is that they provide consistent tracking in all the K8s services.
Don’t ignore high disk-utilization
As long as you can spot this error, don’t make the mistake of ignoring it. In fact, you should take immediate action to resolve it. We understand that this problem is hard to resolve. Yet, you should respond immediately to high disk usage. The alter notification should be set to 75 to 85%.
To accurately gauge the consumption of the disk, we recommend monitoring all the disk volumes.
Even the root file system should be a part of the consideration.
Keep end-user experience under consideration
When you’re using or running Kubernetes, end-user experience management should be under consideration, even though it’s not built over K8s. The kind of experience end-users get with K8s should be a part of your K8s monitoring experience.
When we talk about the end-user experience, synthetic and real-user monitoring data can help greatly. These metrics will help you understand greatly how an end-user will be able to communicate with Kubernetes workloads. In addition, you will have clarity on app response and user-friendliness.
Keep the cloud under consideration as well
If the concerned Kubernetes is deployed on the cloud, you need to make your K8s monitoring based on certain cloud-based aspects such as IAM events, Cloud API, network performance, and the cost involved.
There are multiple components involved in this category. For instance, IAM events involve failed logins and permission changes. You need to plan your network monitoring strategy based on these factors when your cloud sits on Kubernetes, especially when you have enabled kubernetes continuous deployment.