Savan Kharod is a growth marketer at Middleware. He is an engineer turned marketer and a tech enthusiast. He likes to read novels when not solving dev marketing issues at middleware. Say hello to him on LinkedIn.
If you’ve been following Kubernetes and its ecosystem, you know that it is the hottest technology in the Docker community. If not, Kubernetes is a platform for managing containerized applications across multiple hosts, providing basic constructs such as deploying containers and managing their life cycle.
In this ultimate guide to Kubernetes monitoring, we'll go in-depth to understand all the nuanced tools and best industry practices, the importance of observability, and much more.
But first, let's start with what Kubernetes monitoring exactly is!
Kubernetes monitoring is the process of collecting, storing, and analyzing data about the health, performance, and security of Kubernetes clusters. It’s important to monitor your cluster because it helps you know how it is performing and if it needs improvement.
Chances are you will also want to understand which applications or services are using which resources in your Kubernetes clusters so that you can optimize performance and cost savings based on actual usage patterns.
Kubernetes monitoring is important because it helps you gain observability in your clusters so that you can make adjustments to improve their health and stability.
You should also monitor the performance of individual applications running on your clusters. This will help you identify potential issues with these applications before they have a chance to impact other users or services.
Kubernetes is a powerful tool for container orchestration. It’s the most popular tool in the space, used by companies like Google, Netflix, and Uber.
With Kubernetes, you can manage your containers across multiple clusters. A cluster is a group of machines that are geographically distributed to ensure high availability in case one of them fails.
When running multiple clusters at scale, it becomes essential to monitor their health and behavior to identify any issues before they become critical problems affecting your business continuity.
Kubernetes has an extensive set of built-in metrics that can help you monitor the health of your cluster. You can use these metrics to understand your applications' performance and see if they are using resources efficiently.
But, with so many metrics available, it’s hard to know where to start. So let's cover everything you need to know about Kubernetes monitoring metrics.
Kubernetes metrics are key to understanding the health of your cluster and pods.
There are many types of metrics you can collect, but the two main metrics that are useful for Kubernetes monitoring are Cluster Metrics and Pod Metrics -
The cluster metrics are useful for monitoring the health of your Kubernetes cluster. You can use them to monitor the number of pods, nodes, and other resources that have been created in a cluster.
You can also monitor the status of your cluster and its network. Some examples include:
A pod is a group of one or more containers on the same host and with the same network namespace.
Pods have names, which are unique within a namespace, that correspond to their DNS name. Pods must have at least one container running in them. They can also have multiple containers running in them and even other components like volumes or services.
A key metric that you’ll want to monitor when it comes to pods is CPU usage because, as we saw above, CPU limits can be used to restrict how much CPU resources are available for each pod.
This helps ensure fair sharing between pods within a cluster because if one particular pod is using too many resources, then other pods will not be able to complete their work efficiently due to limited resources being shared across all containers within an individual pod.
Let’s face it, Kubernetes monitoring is challenging.
A lot of the tools out there are still being developed and haven’t reached maturity yet. If a new version of Kubernetes comes out, it will be hard for these tools to keep up with all the changes that happen.
Some of the features in these tools are not fully implemented yet, making them less than ideal for production use cases.
There is no “silver bullet” tool that does everything you need at once - instead you will likely have to pick and choose which component you want to monitor based on your needs (e.g. monitoring CPU usage vs memory usage).
There are so many different tools to choose from, and a lot of them are still being developed, making it hard for you to pick one that will do everything you need.
To add to this mix, constant changes are happening in Kubernetes itself, which means that even if you do find a tool that works for now, it might be replaced or outdated in a few months as things evolve.
So how do we overcome these challenges? Well, firstly, by understanding why those challenges exist:
The amount of data you collect can grow exponentially when you have a large cluster and/or many applications within it.
The more metrics you have, the more difficult it becomes to find the important ones. In this case, Prometheus can help by aggregating metrics from multiple Kubernetes clusters.
For example, say your company has three Kubernetes clusters: one in the US East region, one in Europe West, and another in Australia East. You can configure Prometheus to collect metrics from all three of these clusters at once with just one configuration file.
Logs are not always available, not always structured, not descriptive, and often not even available in real-time.
Logs can be stored in many different locations or even multiple locations on the same machine. They can be unstructured and lack metadata.
Logs can also be messy depending on how well they are maintained. They don’t always come with any standardized structure or format and can sometimes just be plain text files that you might need to parse yourself when analyzing them for key metrics like errors or latency spikes.
Kubernetes is a container orchestrator that also happens to be a distributed system, event-driven, dynamic, and distributed database. This means that it has some properties that make monitoring difficult.
The most important component in Kubernetes is its API server. The API server exposes an interface for managing resources (such as pods and deployments) in the cluster through REST API calls or gRPC APIs.
Here are some of the challenges associated with monitoring Kubernetes:
Kubernetes has a lot of moving parts. It's not just the containers but also the Pods, Replication Controllers, Namespaces, and more. A lot of these things can be configured to do more than one thing at once (e.g., Replication Controllers can both run pods and maintain consistency in your state), and all of these lead to a lack of observability.
It's important to have tools that show you not only what is happening on the cluster now but also what was happening before this moment as well. Without this visibility into your cluster's behavior, it becomes very hard to troubleshoot issues.
This is especially true when they arise because everything is so tightly coupled together in Kubernetes that it becomes increasingly difficult to tell exactly where things went wrong or why something might have gone wrong in the first place.
There are many tools available for monitoring Kubernetes, but there is a lot of overlap between them.
For example, Middleware, has a built-in builder that makes it possible to creat custom dashboards. Similarly Prometheus and Grafana both have an API that makes it possible to create custom dashboards that can be added to any existing monitoring solution.
So what’s the difference?
When you're not just starting with Kubernetes monitoring and are ready to take the next step, there are a few best practices you should consider following.
The next step is to choose a monitoring tool. All of the three monitoring tools mentioned below can be used to monitor Kubernetes. However, they differ in features and user interface configuration, so let's go over them individually in detail.
Kubernetes monitoring using Prometheus collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.
It has an intuitive user interface that provides an overview of metrics, as well as more detailed information about specific metrics and their values over time which helps you reduce MTTR.
In addition to showing data that can be collected via Prometheus itself, it also supports pulling data from other systems like OpenTracing or StatsD/Graphite if you want to add custom monitoring for your application components or third-party services like AWS CloudWatch Logs or Google Stackdriver Logging.
Middleware provides a bridge between data from your Kubernetes API server and your application endpoints. It is responsible for handling multiple data requests and their visualization.
Monitoring Kubernetes becomes easy with Middleware because it gets you end-to-end visibility into the health and performance of containerized environments and applications.
Middleware provides a single point of entry for all of your application logs - no matter where they come from (e.g., containers, pods). This enables you to view all of the data related directly back into one place rather than having multiple applications generate their separate log files.
This may then need to be manually aggregated together by hand each day/week/month, depending upon how frequently those artifacts were generated before being archived somewhere else entirely.
To get a quick overview of the cluster, you can look at the Kubernetes Dashboard.
The dashboard provides an overview of pods, services, replication controllers, and other metadata about your cluster. It shows which nodes are running tubeless, the number of available CPUs, and memory utilization on each node in real time.
The Kubernetes Dashboard is built with Prometheus and Grafana. As previously mentioned, Prometheus is a monitoring system that collects metrics from various sources (including Docker) and writes them to its database: either Elasticsearch or InfluxDB.
Grafana is then used to visualize this data using graphs and dashboards.
Kubernetes monitoring is no longer a luxury. It’s becoming a necessity in this fast-paced world. An increasing number of businesses are adopting Kubernetes to automate their DevOps and achieve continuous delivery for their products.
In this ultimate guide, we not only defined Kubernetes monitoring but also outlined why it is important, shared out best practices for Kubernetes monitoring, and gave you our top 3 Kubernetes monitoring tools.
Get a beautiful status page that's free forever.
With unlimited team members & unlimited subscribers!