Are you struggling to ensure your applications perform optimally? Effective Kubernetes application monitoring is a key challenge in application performance monitoring. This article provides valuable insights into Kubernetes monitoring tools and best practices to help you succeed.
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Kubernetes (K8s) is an open-source container orchestration system for:
Initially developed by Google in 2014, the project is now maintained by Cloud Native Computing Foundation (CNCF). Kubernetes eliminates manual processes involved in managing containerized applications. Container orchestration combines individual containers into a cohesive, coordinated whole.
Kubernetes can be deployed on various cloud platforms and self-managed systems to improve the efficiency of IT and DevOps teams. Many cloud service providers, such as AWS, Google, and Azure, offer their users managed Kubernetes services.
By orchestrating containers across multiple hosts, Kubernetes allows you to:
Kubernetes monitoring helps you identify issues and proactively manage Kubernetes clusters.
Kubernetes monitoring allows cluster administrators and users to monitor the cluster and identify issues such as:
Many organizations use specialized cloud-native monitoring tools to gain complete visibility over cluster activity.
Several things are essential to monitor in Kubernetes, including:
This keeps track of the health of an entire Kubernetes cluster. It helps you verify if nodes function correctly and at a suitable capacity, how many applications run on a node, and how the cluster utilizes resources.
It keeps track of issues affecting individual pods, such as resource utilization, application metrics, and metrics related to replication or autoscaling.
Prometheus allows you to monitor Kubernetes deployments. This metric shows cluster CPU, Kube state, cAdvisor, and memory metrics.
Monitoring ingress traffic can help identify and manage various issues. Using controller-specific mechanisms to track workload health and network traffic statistics, you can configure ingress controllers.
Setting up monitoring for volume health enables Kubernetes to implement CSI. You can also use the external health monitor controller to monitor node failures.
You should monitor schedulers, API servers, and controllers to track and visualize cluster performance for troubleshooting purposes.
Monitoring CPU and memory for each Kubernetes node can help ensure they never run out. Several conditions describe the status of a running node, such as:
Understanding resource utilization metrics reveals the strengths and weaknesses of cluster nodes, helping determine adequacy and excess. Key monitoring resources include:
Understanding Kubernetes' scaling and availability capabilities helps configure auto-scaling tools for clusters. The number of containers or pods in a cluster determines node requirements.
The load average indicates the number of programs running or waiting to run on the CPU. Ensure it doesn’t exceed the number of CPU cores. Monitor load average with sys CPU usage and I/O wait for effective troubleshooting.
Alerty is a cloud monitoring service for developers and early-stage startups, offering:
It supports technologies like NextJS, React, Vue, and Node.js, helping developers identify and fix issues.
Alerty uses AI to simplify setup, providing a cost-effective solution compared to competitors. It is designed for ease of use, allowing quick setup, and integrates with tools like Sentry, making it ideal for developers and small teams needing efficient, affordable monitoring.
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Here are several reasons your organization needs a monitoring strategy for Kubernetes:
Kubernetes applications can be particularly complex, especially those that use cloud-native or microservices architectures. If issues occur, pinpointing the problem's cause can be challenging. With proper Kubernetes monitoring, you can see where problems arise or are about to occur and access data that can help you take action to prevent or fix the problem.
Understanding what’s happening in your Kubernetes cluster can help you optimize hardware without compromising application performance.
When running Kubernetes on a public cloud infrastructure, it is crucial to track how many nodes (compute instances) you are running because this number will determine your hourly cost. Even if you are not running on a public cloud, knowing if your resources are exhausted or underutilized is essential.
Sometimes, you may want to know which groups are using which resources. Kubernetes monitoring can provide insights into usage statistics, which you can leverage to analyze chargebacks and showbacks or perform a general Kubernetes cost analysis.
In today’s threat environment, knowing what is running and where is critical, discovering pods, containers, or jobs that should not exist, and looking for malicious ingress and egress traffic is vital. Kubernetes monitoring is an essential part of a container security strategy.
Integrating tools for Application Performance Management in Kubernetes offers integrated and advanced monitoring capabilities. A tool like Alerty provides comprehensive monitoring, including:
To utilize an APM tool, deploy its agents on your Kubernetes cluster and configure them to collect your applications' metrics, logs, and traces. With Alerty's dashboards, you can visualize performance metrics and set up alerts for critical issues, ensuring efficient and proactive application management.
Heapster is a monitoring pod that collects and interprets signals and Kubernetes metrics. It serves as a link between the cluster and backend storage and also collects logs. Heapster enables performance analysis and visualization.
Heapster queries the nodes about the containers running on them. It works with cAdvisor to expose container metrics such as:
To efficiently monitor your containers using the Heapster method, you must use the pod with a time-series database (TSDB) like InfluxDB and a visualization tool like Grafana. This requires deploying InfluxDB and Grafana and editing the Heapster deployment to send data to InfluxDB. These prerequisites make the Heapster monitoring method more complex than the DaemonSet method.
Think of this second method as a variation of the first. Instead of implementing the instrumentation directly in the code of the application or service to be monitored, this method deploys a sidecar container that runs alongside the container that hosts the application. The sidecar container executes the instrumentation code or logging agent and exports the data to the corresponding observability platform.
This strategy is possible because containers of the same pod share resources, such as storage volumes and network interfaces, in Kubernetes. In other words, the sidecar container can easily access the logs and other metadata that reside in the main container’s filesystem. Among the advantages of sidecars is how easy it is to deploy them in Kubernetes.
Kubernetes DaemonSets monitor individual pods and ensure that each one runs on every node in the cluster. They report a node’s ability to run the pods allocated to it.
Since a DaemonSet is a pod, you can create your specialized monitoring DaemonSet, which functions as a sister pod to your nodes. You can then configure Kubernetes to attach this DaemonSet to each node you create automatically. This allows the DaemonSet to monitor the node and its components. When a node is terminated manually (by you) or automatically (by Kubernetes self-healers), its designated monitoring DaemonSet is also terminated.
Kubernetes monitoring best practices are consistent whether your nodes are deployed in the exact location, across clouds, or in a hybrid cloud. Metric collection should happen the same way across the entire cluster. Here are a few helpful best practices to consider in Kubernetes monitoring:
Organizing your Kubernetes cluster with namespaces provides a logical barrier that separates resources. They help simplify container management and minimize the risk of resource conflicts between different teams or projects. Namespace objects reduce errors in complex environments by allowing you to divide cluster resources between multiple users or teams. This practice simplifies access control when multiple teams utilize a single Kubernetes cluster.
Updating to the latest version of Kubernetes is a best practice to ensure you can access new features and security patches. It is important to keep your Kubernetes security up to date to prevent any vulnerabilities. You want to ensure you run the latest software and have the most secure and stable software possible.
Alerts and monitoring are critical components of maintaining a healthy Kubernetes cluster. Kubernetes clusters are complex and distributed systems, and monitoring can help identify issues as they arise. Creating alerts and monitoring are vital to system health and performance. Automated monitoring is crucial to ensuring the ongoing health and performance of your Kubernetes clusters.
RBAC allows you to define which users or groups can access different resources in your Kubernetes cluster. It is essential for managing access to users and applications, enabling you to control and delegate privileges without sharing credentials. With roles and role bindings, you can determine what actions can be performed and which resources can help administer users and service accounts.
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Monitor everything - application performance, databases (Supabase, PostgreSQL, RDS), and even external services (Stripe API, OpenAI, Vercel).
We support popular frameworks like NextJS, React, Vue.js, and Node.js, so you can pinpoint issues before they crash your app.
See your app's performance from your users' perspective and optimize for a flawless experience.
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Alerty simplifies everything. An AI-powered setup lets you monitor in minutes, not hours. Plus, it integrates seamlessly with tools like Sentry.
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Alerty is a cloud monitoring service for developers and early-stage startups, offering:
It supports technologies like NextJS, React, Vue, and Node.js, helping developers identify and fix issues.
Alerty uses AI to simplify setup, providing a cost-effective solution compared to competitors. It is designed for ease of use, allowing quick setup, and integrates with tools like Sentry, making it ideal for developers and small teams needing efficient, affordable monitoring.
The Kubernetes dashboard is the primary web-based user interface for monitoring Kubernetes. It provides a reliable way to visualize important information from the containers and pods running in your clusters. It is an integral part of the Kubernetes environment, allowing you to view and handle all aspects of monitoring a Kubernetes cluster.
Prometheus is a popular open-source monitoring tool for Kubernetes. Originally a SoundCloud venture, it is now one of only a few Graduated Projects managed by the Cloud Native Computing Foundation (CNCF). The tool has evolved into a standard for monitoring Kubernetes. Prometheus retrieves resource metrics using time series from specific endpoints.
Prometheus is divided into three components that perform different tasks:
The Prometheus server handles service deployment, extracts metrics from exporters, and stores them in a database for monitoring. AlertManager sets up alerts and sends notifications when specific triggers are activated. Exporters are independent containers that use APIs to create and export metrics.
Jaeger is an end-to-end distributed tracing solution that Uber Engineering open-sourced and is currently an incubating project with the CNCF. It lets you monitor and troubleshoot transactions in complex distributed systems. In modern microservices architectures, most operational issues are within the scope of networking and observability.
If there is a service failure, you cannot determine how requests are passed from one service to another over the network when completing a business transaction. This makes debugging very difficult. Jaeger uses tracing to analyze root causes, optimize performance and latency, and monitor distributed transactions. Jaeger works with Istio, a widespread service mesh implementation open-sourced by Google.
This open-source platform for visualizing metrics and analytics provides four built-in dashboards for Kubernetes: Cluster, Node, Pod/Container, and Deployment. Kubernetes administrators can create data-rich dashboards in Grafana using information sourced from Prometheus.
Migrating traditional, monolithic applications to Kubernetes can be time-consuming and error-prone. Nevertheless, enterprises are willing to take this risk to achieve the following:
Still, companies that migrate monolithic applications to microservices lack visibility into the Kubernetes environment, making it impossible to see every microservice's interactions in real-time.
Another reason Kubernetes is complicated to monitor is that a Kubernetes cluster is considerably more complex, with multiple servers and private and public cloud services. When a problem starts, many logs and other data and components must be investigated. A legacy monolithic environment might require a few log searches, but a Kubernetes environment may contain one or more logs for multiple microservices involved in the issue you're investigating.
Kubernetes monitoring with an application performance monitoring solution gives organizations visibility into application and business performance, including:
This visibility allows enterprises to enhance container-level metrics and gain visibility into:
Organizations can then baseline these metrics, associated health rules, and resource usage statistics on their APM-monitored container applications.
By comparing APM metrics with the underlying container and server metrics, companies quickly gain insight into the performance of their containerized applications and learn of potential impediments in the infrastructure stack. Specific metrics, for instance, can help identify bandwidth-hogging applications and container-level network errors.
If you are a developer or part of an early-stage startup looking for a reliable and efficient cloud monitoring service, Alerty might just be your solution. It offers a comprehensive set of monitoring tools that can help you monitor the performance of your applications, databases, and server resources. Whether you are working with NextJS, React, Vue, or Node.js, Alerty has got you covered.
Alerty's database monitoring capabilities extend to popular technologies like Supabase, PostgreSQL, and RDS. With Alerty, you can keep track of key performance metrics like CPU usage and memory consumption, enabling you to identify and resolve issues before they escalate.
Alerty also offers features like Real User Monitoring (RUM) to help you optimize the user experience of your applications. By tracking user interactions in real time, you can gain valuable insights into how users interact with your app and identify potential bottlenecks or areas for improvement.
The Universal Service Monitoring feature allows you to monitor dependencies like the Stripe API, OpenAI, and Vercel. By monitoring these external services closely, you can ensure that your application's performance remains top-notch even when relying on third-party services.
One of Alerty's key advantages is its ease of use. Thanks to its AI-powered setup process, you can start monitoring your applications within minutes without requiring extensive configuration or technical expertise. Alerty also integrates seamlessly with tools like Sentry, making incorporating monitoring into your existing development workflows easy.
Compared to other application performance monitoring tools, Alerty offers a cost-effective solution ideal for developers and small teams looking to optimize their monitoring efforts without breaking the bank. With its focus on efficiency and affordability, Alerty is an excellent choice for startups and early-stage companies looking to maximize their monitoring budget.
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