WHAT IS KUBERNETES?

Kubernetes is a powerful container orchestration system for managing scalable, distributed systems.

Kubernetes can be used to manage containers. Containers allow developers to build applications on a host machine without worrying about the underlying operating system and hardware. This makes application development much more efficient and secure. By managing containerized applications with Kubernetes, teams can scale their applications quickly and easily by making it easy to deploy and scale new instances of the containers across multiple hosts. The Kubernetes API will enable developers and administrators to create and manage various applications that run on top of Kubernetes. Users can handle thousands of applications all from one console. The developers can then create a template that they can use to deploy all of their apps seamlessly across different clusters as needed. Unlike some container management systems, Kubernetes does not require you to rebuild your applications to scale them. With Kubernetes, you can modify your application and add additional resources without rebuilding your application from scratch. This allows you to make changes more quickly and get back up and running quickly in the event of an issue. Many different tools are available for monitoring Kubernetes, each with its unique set of features and capabilities. This article will discuss why we should monitor Kubernetes, the key elements to consider when choosing a monitoring tool for your Kubernetes cluster and introduce some of the most popular options available.

Why should we monitor Kubernetes?

There are several reasons why we should monitor Kubernetes:

  • First, to ensure the availability and performance of the applications and services running on Kubernetes. Monitoring helps to identify and troubleshoot issues that may affect the availability and performance of these applications and services.
  • To ensure the health and stability of the Kubernetes cluster itself. Monitoring helps identify and resolve issues that may affect the cluster’s strength, such as resource contention or network issues.
  • To monitor resource usage and optimize the allocation of resources within the cluster. This can help to reduce costs and improve efficiency.
  • To identify and resolve security issues within the cluster. Monitoring can help identify and address the cluster’s vulnerabilities or malicious activity.
  • To provide insight and visibility into the cluster’s operations, which can help with debugging, troubleshooting, and capacity planning.

In general, Kubernetes metrics are collected in one of two ways:

1. Using DaemonSets

DaemonSets are features in Kubernetes that run copies of required pods on all nodes. This runs a monitoring agent on all desired pods to collect health and performance metrics. Many tools use this approach since DaemonSets are easy to provision.

2. Using Metrics Server

Engineers install a Metrics Server as a regular pod inside Kubernetes to collect data and events from pods and containers within a cluster. The metrics Server used to be Heapster before the deprecation. Metrics Servers are an excellent choice if you work with large workloads because they can monitor clusters with up to 5,000 nodes.

Now, the following tools simplify monitoring Kubernetes for engineers and team leaders.

Monitoring is an essential part of managing a Kubernetes cluster, as it helps to ensure the reliability, availability, and performance of the applications running on the cluster. Several components of a Kubernetes cluster need to be monitored to ensure the smooth operation of applications. These include:

  • Pods: Pods are the minor deployable units in a Kubernetes cluster and contain one or more containers. Monitoring the status and resource usage of pods can help identify issues with individual containers.
  • Nodes: These are the physical or virtual machines that host the pods in a Kubernetes cluster. Monitoring nodes’ health and resource utilization can help identify issues with the underlying infrastructure.
  • Services: These are logical sets of pods exposed to external clients. Monitoring the performance and availability of services can help identify issues with the application.
  • Deployments: These are a way to manage the lifecycle of pods and ensure that a specified number of replicas are running at any given time. Monitoring the status and resource usage of deployments can help identify issues with the application.When choosing a monitoring tool, one key feature is the ability to collect and visualize metrics from your cluster and applications. This includes CPU and memory usage, network traffic, and application-specific metrics like request latencies and error rates. A reliable monitoring solution should be able to collect these metrics in real time and provide easy-to-understand visualizations, such as graphs and charts, to help you understand how your cluster and applications are performing.Another vital feature to consider is alerting. When particular thresholds are surpassed, or specific events occur, a proper monitoring system should be able to send notifications through email or other messaging platforms. This helps you identify and resolve problems before they become serious difficulties. In addition to collecting and visualizing metrics and providing alerting capabilities, a good monitoring tool should also be able to handle the scale and complexity of a Kubernetes cluster. This includes managing the dynamic nature of a cluster, where pods and nodes are constantly being created and destroyed. It should also handle the wide range of applications and workloads that can be deployed on a Kubernetes cluster, such as stateful and stateless applications, batch and streaming workloads, and more.

Now that we have discussed some of the key features to look for in a monitoring tool for Kubernetes, let’s take a look at some of the most popular options available:

  • Prometheus: Prometheus is an open-source monitoring tool widely used in the Kubernetes community. It has a powerful query language and a wide range of exporters for collecting metrics from different sources. As a result, Prometheus is particularly well-suited for monitoring applications and infrastructure, and it can be easily integrated with other tools, such as Grafana, for visualization and alerting.
  • Datadog: Datadog is a cloud-based monitoring and analytics platform widespread among Kubernetes users. It provides various features, including real-time metrics and logs, alerting, and integration with multiple tools and platforms. One of the critical benefits of Datadog is its ability to handle large-scale environments and provide a unified view of all your monitoring data.
  • New Relic: New Relic is a cloud-based monitoring and performance management platform well-suited for monitoring applications running on Kubernetes. It provides detailed insights into the performance of your applications, including request latencies, error rates, and more. New Relic also provides alerting capabilities and integration with various tools and platforms.
  • Grafana: Grafana is an open-source data visualization, and monitoring platform often used with other monitoring tools, such as Prometheus, to provide visualizations and alerts. It has a wide range of plugins and integrations, making it easy to customize and extend.
  • Jaeger : Jaeger is a distributed tracing and monitoring tool for complex distributed systems such as a Kubernetes environment. It also acts as a troubleshooting tool, allowing your team to perform distributed transaction monitoring and context propagations, root cause and service dependency analyses, latency, and performance optimizations. It supports several data sources, including Cassandra, memory, Kafka, and Elasticsearch, and has been open-source since 2016. Like Grafana and cAdvisor, you can use a DaemonSet configuration to deploy it. Or you can use the Jaeger Operator.
  • The ELK Stack – Open-source Kubernetes observability platform: The ELK Stack is an open-source monitoring tool for logging Kubernetes. The acronym stands for Elasticsearch, Logstash, and Kibana, which form the basis of a small logging pipeline. But the stack usually includes Beats for data collection and Kafka for buffering when dealing with massive amounts of data. The stack combines Elasticsearch’s scalability, Logstash’s log aggregation, and analysis capabilities, and Kibana’s rich analysis capabilities. Despite being challenging to maintain at scale, the ELK Stack deploys quickly and has a robust developer community to support it.
  • Sematext – Full-Stack Kubernetes monitoring: Monitoring Kubernetes with Sematext provides full-stack visibility for container orchestrators and containers if you want a comprehensive logging and monitoring solution. Yet, it is also compatible with traditional systems. You can collect all events, logs, and metrics in a Kubernetes cluster, structure them, and visualize them in custom monitoring dashboards. This all happens in real time. With Sematext, you can detect real-time anomalies and receive alerts regarding pod-level issues. Besides monitoring resource usage, it also captures network throughput. In addition, it is also easy to install as a Sematext Operator, DaemonSet, Helm chart, or Kubernetes Operator.
  • Weave Scope – Automatically detect containers, hosts, and processes: Weave Scope is a robust improvement over Kubernetes-native Kube-state-metrics because it allows engineers to run diagnostic commands on and manage containers within the interface. You’ll have access to a drill-down view of their app, the infrastructure you deploy it on, and the connections between components through the user interface. Weave Scope also displays contextual logs, metrics, and metadata for Docker and Kubernetes containers.
  • Dynatrace – K8s observability, AIOps, and security monitoring: Dynatrace is also a full-stack monitoring solution for Kubernetes infrastructure. You can use it to monitor the availability and health of applications and processes, dependencies, and connections among hosts, containers, and cloud instances. Dynatrace enables you to unify and harness insights from over 500 tools you probably already use, including AWS, Azure, OpenShift, Google Cloud, and Kubernetes. Even better, it uses events, traces, metrics, and behavioral information to reveal the inner workings of Kubernetes applications. Dynatrace, like DataDog, provides superior APM integration and is best suited for complex, distributed systems. However, it does require substantial investment, too.
  • ¬†Kubewatch – Open-source events and trigger handlers watcher: Kubewatch is a K8s watcher, tracking specific Kubernetes events. As soon as those events/changes occur, it pushes notifications about them to multiple endpoints, like Slack and PagerDuty. Some Kubernetes resources you set it up to watch include pods, daemon sets, services, deployments, secrets, replica sets, replication controllers, and configuration maps. You can also configure and deploy Kubewatch via a Helm or a custom deployment. Note that VMware’s Bitnami no longer maintains the project on GitHub. However, the project is actively being supported as a Robusta fork.

FINAL THOUGHTS

In conclusion, monitoring is an essential part of managing a Kubernetes cluster, and many different tools are available to help you do so. When choosing a monitoring tool, consider the key features discussed in this article, such as the ability to collect and visualize metrics, provide alerting capabilities, and handle the scale and complexity of large deployments. Based on what we learned above, we determined that Prometheus is the monitoring tool of choice for most Kubernetes users. It is robust, adaptable, and packed with capabilities that make it simple to gather and analyze data from applications and infrastructure components. It also integrates with many other tools and platforms so that you can deploy it alongside additional monitoring and logging solutions.

About The Author

Rejith Krishnan

Rejith Krishnan is the co-founder and CEO of CloudControl, a startup that provides SRE-as-a-Service. He’s also a thought leader and Kubernetes evangelist who loves to code in Python. When he’s not working or spending time with his two boys, Rejith enjoys hiking in the New England outdoors, biking, kayaking, and playing tennis.

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