Kubernetes monitoring is crucial as it adds yet another level of complexity to the already complex IT infrastructures. When implementing Kubernetes, it is essential to keep track of various smaller parts, including the hosts, the Kubernetes platform, containers, and containerized applications, which all require monitoring.

The process of monitoring a tool like Kubernetes can be challenging due to the numerous variables involved, which requires the use of new techniques, tools, and methods to collect the relevant data efficiently.

In this article, we will emphasize the significance of monitoring K8S, enumerate the metrics that can be applied, talk about the best practices to follow, and do a comparison of the different tools available.

What is Kubernetes Monitoring?

Kubernetes monitoring is the practice of observing and measuring the health and performance of a Kubernetes cluster and its components, including nodes, pods, containers, and applications running on the cluster.

It involves collecting and analyzing various metrics such as CPU usage, memory consumption, network traffic, and other performance indicators to ensure that the cluster is running optimally and to detect and troubleshoot any issues that may arise.

The main goal of Kubernetes monitoring is to ensure the availability, reliability, and scalability of the cluster and the applications running on it.

Why Kubernetes Monitoring?

Monitoring is imperative when it comes to identifying bottlenecks in an environment. However, various organizations find it quite challenging.

The unprecedented growth in data volumes and the large-scale adoption of microservices have sophisticated monitoring and logging processes. Now, a plethora of different applications, which are spread out and varied in their functions, interact with each other. Thus, if there is a problem with one part of the system, it can bring the entire process to a halt.

Kubernetes (K8s) has emerged as an ideal solution for the challenges associated with distributed systems. Kubernetes is an open-source container orchestration tool. It allows developers to create containerized applications and services with ease. Additionally, Kubernetes makes it simple to scale, schedule, and keep an eye on the containers.

To manage the services that support your applications, Kubernetes acts as a middleman between your physical or virtual infrastructure and the services. This is why it is necessary to monitor the status of the Kubernetes environment.

With Kubernetes, additional layers of complexity are introduced, such as distributing services across multiple instances and the ephemeral nature of containers that move across the infrastructure as needed.

Therefore, it is essential to monitor the condition of all resources to determine whether Kubernetes is serving its purpose accordingly.

Key Kubernetes Monitoring Metrics to Measure

In Kubernetes, multiple aspects require monitoring. To ensure the system’s optimal performance, it is necessary to monitor the cluster, including all its nodes. Furthermore, monitoring pods, deployments, and services is crucial to Kubernetes monitoring. Following are the key Kubernetes Monitoring metrics you ought to measure.

Cluster Metrics

To begin with, it is important to monitor the well-being of your Kubernetes cluster comprehensively.

This involves gaining insight into various metrics, such as the utilization of disk and memory, network bandwidth, and overall resource consumption by the cluster.

By monitoring these metrics, you can:

  • Keep track of the overall health of the cluster.
  • Verify that the nodes are functioning correctly and at the appropriate capacity.
  • Estimate how many applications are running on each node.

Thus cluster monitoring process ensures that the entire Kubernetes cluster is being monitored and evaluated for optimal performance.

Node Metrics

By monitoring the CPU and memory utilization of each Kubernetes node, you can prevent them from running out of resources.

Multiple factors are at play to determine the status of a running node, including Ready, MemoryPressure, DiskPressure, OutOfDisk, and NetworkUnavailable.

These conditions are used to describe the state of the node and indicate whether it is running smoothly or experiencing issues related to memory, disk space, network connectivity, or other factors.

Therefore, regularly checking these metrics can help ensure the optimal functioning of Kubernetes nodes.

Pod Metrics

When it comes to pod-level monitoring, the focus is on analyzing the metrics associated with the containers and applications within the pod. This type of monitoring helps identify any issues that may be affecting a specific pod, including resource utilization, application metrics, and metrics related to replication or autoscaling.

By closely tracking these metrics, you can gain insight into the performance of individual pods and detect any potential issues that may require attention. This enables you to optimize the functioning of your pods and ensure that your applications are running efficiently.

Deployment metrics

Deployment monitoring involves examining the health of pods, the frequency of crash loops, and the utilization of resources.

These metrics enable you to gain insight into the performance of your cluster and track the status of your deployments, allowing you to quickly identify any issues that may be impacting your applications. By leveraging these monitoring capabilities, you can ensure that your deployments are running smoothly and efficiently.

Moreover, with Prometheus you have the ability to monitor Kubernetes deployments, which provides access to key metrics such as CPU usage, Kube state, cAdvisor, and memory usage.

Container Metrics

The metrics associated with containers provide valuable information that enables you to assess the proximity to the configured resource limits. These metrics include container CPU usage, container memory utilization, and network usage, among others. By monitoring these metrics, you can maintain the health of your containers and ensure that they are functioning efficiently. Additionally, the container metrics can help you identify any potential issues before they become critical, allowing you to take proactive measures to optimize the performance of your containers.

Best Practices in Kubernetes Monitoring

While managing a multi-layered intricate system, it’s essential to maintain a uniform monitoring approach throughout the whole cluster.

It’s crucial to keep in mind that monitoring Kubernetes containers doesn’t demand numerous binaries, libraries, or components. Even a bare minimum operating system can support Kubernetes monitoring. Nonetheless, it’s also vital to be aware of and implement some of the most effective Kubernetes monitoring practices, which don’t demand significant effort.

Following are a few recommended techniques that can assist you in efficiently monitoring and resolving issues in Kubernetes environments:

  1. Examine the Details:
    When it comes to Kubernetes monitoring, it’s crucial to delve into the specifics rather than simply scratching the surface of the operating system. It’s necessary to get to the granular level of data to determine how processes operate and interact with elements like ports, files, memory, network, etc.
  2. Historical System Data Beyond Metrics:
    Containers are ephemeral, making it challenging to pinpoint the root cause of issues. This is where Kubernetes monitoring comes in handy. It captures all the host activities surrounding each event, providing historical data beyond metrics and logs data.
  3. Understand Your Kubernetes Control Plane Monitoring:
    The Kubernetes cluster is reliant on the Kubernetes control plane. It handles critical functions, and cluster resources, and reads all the data and secrets within the cluster. Not monitoring the Kubernetes control plane is akin to driving a car without paying attention to the road you’re on.
  4. Instrumentation Strategy with Kubernetes Alerting:
    There are various strategies for collecting system metrics. Injecting an instrumentation library into your containers is one of the recommended strategies for monitoring Kubernetes applications. However, sometimes, libraries provide only limited data, making it difficult to debug. Hence, open-source tools like Prometheus can be combined with informational probes to obtain better data.
  5. Use a Platform and Run Containers on Physical or Virtual Machine Clusters:
    Kubernetes provides a platform to schedule and run containers on physical or virtual machine clusters. It automates operational tasks and optimizes application development for the cloud. Kubernetes monitoring best practices are often utilized when running containers on physical or virtual machine clusters.
  6. Kubernetes Monitoring with Prometheus:
    Prometheus is one of the open-source instrumentation frameworks utilized by Kubernetes. It can absorb vast amounts of data every second, making it popular with complex workloads. It analyzes application performance and infrastructure by obtaining machine-level metrics and application information.
  7. Kubernetes Monitoring Tools Provide a Comprehensive Kubernetes Solution:
    Currently, numerous Kubernetes monitoring tools are available. These tools provide comprehensive solutions, with features like log aggregation, high availability, and data integration, making them essential in managing countless and unstable software entities. Some of these tools are cAdvisor, Grafana, Fluentd, and the ELK Stack.

What are the best Kubernetes monitoring tools?

There are many Kubernetes monitoring tools available, but some of the top-tiered tools include Prometheus, Grafana, Fluentd, Apica, and the ELK (Elasticsearch, Logstash, and Kibana) Stack. These tools provide comprehensive solutions for monitoring, logging, and debugging Kubernetes environments, with features such as data aggregation, visualization, alerting, and high availability.

Some of the top Kubernetes monitoring platforms include:

Prometheus

Prometheus is the go-to open-source time-series database for Kubernetes users. It was developed by SoundCloud and is now managed by CNCF.

Over the years, Prometheus has become the preferred open-source standard for Kubernetes monitoring due to its multi-dimensional data model, built-in alerting features, PromQL querying language, and pull model, as well as its growing community.

Moreover, Prometheus and Kubernetes are closely linked, and users can quickly run Prometheus using the Prometheus Operator.

Pros: Prometheus is built for Kubernetes, is easy to operate, and has a large user community.

Cons: When used on a large scale, Prometheus may present some difficulties, particularly regarding storage.

Grafana

Grafana is an excellent tool for monitoring Kubernetes metrics and creating visually appealing dashboards. Typically, Grafana is used in conjunction with Prometheus, although some people use it with InfluxDB or Graphite as well.

One reason for its widespread popularity is its ability to integrate with various data sources. Additionally, Grafana is highly reliable and offers a wide range of features, such as alerts, annotations, filtering, data source-specific querying, visualization, dashboarding, authentication/authorization, and cross-organizational collaboration.

Setting up Grafana on Kubernetes is simple since numerous deployment specifications contain a Grafana container by default. Furthermore, there are several Kubernetes monitoring dashboards available for Grafana that can be easily used.

Pros: Comprehensive ecosystem, advanced visualization features, alert functionality.

Cons: Not specifically designed for managing logs in Kubernetes.

ELK

One of the widely-used Kubernetes monitoring tools for logging is the ELK Stack. However, the ELK Stack is no longer completely open source since Elastic replaced the open-source Apache 2.0 license with dual proprietary licenses.

Elasticsearch was created to be scalable and can handle storing and searching millions of documents with high performance.

Pros: Large community, easy deployment and use within Kubernetes, and advanced analysis capabilities.

Cons: Challenging to maintain at scale.

Kubernetes Monitoring with Apica

Apica provides a Kubernetes-native platform for monitoring and observability. It integrates with Kubernetes to collect and analyze metrics, logs, and events from various sources within the Kubernetes environment.

Apica provides rich visualization capabilities with its built-in dashboarding system and supports a wide range of visualizations, including graphs, charts, tables, and maps. It also features a robust alerting system that supports various notification channels, such as Slack and email, and allows users to create custom alert rules based on specific conditions.

In addition, Apica provides advanced capabilities for log analysis, including log parsing, search, filtering, and correlation, which makes it easier for users to troubleshoot and debug issues within their Kubernetes environment.

If you are running a K8S cluster, you can use fluent-bit to send data to the Apica. You can find the instructions here.

Apica has provided its own fluent-bit daemon for deploying on K8S clusters. It is available at https://bitbucket.org/Apicacloud/client-integrations/src/master/fluent-bit/

It allows the administrator to pass a human-readable CLUSTER_ID or cluster identifier with all the log data.

Following are the key features that set Apica’s Kubernetes monitoring apart:

  • Apica provides real-time, scalable monitoring of Kubernetes clusters with a high level of granularity.
  • It offers advanced analytics and machine learning capabilities for predicting and detecting anomalies in logs and metrics, allowing for proactive issue resolution.
  • Apica has a built-in log aggregation and management system that supports multiple data sources, including Fluentd and Filebeat, and integrates with popular log analysis tools.
  • Our platform has a user-friendly interface and customizable dashboards that make it easy to visualize and analyze metrics and logs from different sources.
  • Apica is designed to be cloud-agnostic and can be deployed in any cloud environment, including on-premises or hybrid cloud environments.

Bottomline

Kubernetes monitoring is a critical practice that ensures the health and performance of a Kubernetes cluster and its components. With the growth in data volumes and the adoption of microservices, monitoring, and logging processes have become more complex. Kubernetes has emerged as an ideal solution for the challenges associated with distributed systems. The practice of Kubernetes monitoring involves collecting and analyzing various metrics such as CPU usage, memory consumption, network traffic, and other performance indicators to ensure optimal performance and detect and troubleshoot any issues. It is essential to keep track of various smaller parts, including hosts, the Kubernetes platform, containers, and containerized applications that require monitoring. By implementing Kubernetes monitoring best practices and leveraging available tools, organizations can ensure the reliability, availability, and scalability of their Kubernetes environment. In case you have complex environments with large application clusters, you can try Apica for assistance. We will assist you in finding innovative approaches to enhance your monitoring outcomes while minimizing downtime. Protecting your business is crucial, and we can assist you in taking your technology to the next level smoothly. Reach out now to get started.

In a Glimpse

  • Kubernetes monitoring is the practice of observing and measuring the health and performance of a Kubernetes cluster and its components.
  • The main goal is to ensure the availability, reliability, and scalability of the cluster and the applications running on it.
  • Key metrics to measure include those related to clusters, nodes, pods, deployments, services, containers, and applications.
  • Best practices for monitoring K8S involve examining details in granular level data; gathering historical system data beyond metrics; understanding control plane monitoring; creating an instrumentation strategy with alerting; using a platform to run containers on physical/virtual machine clusters; utilizing Prometheus; selecting comprehensive Kubernetes monitoring tools.
  • Popular Kubernetes monitoring tools include Prometheus, Grafana, Fluentd, Apica, and ELK Stack.