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Monitor workload deployments

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It's important to monitor the health and performance of your Kubernetes workloads to ensure that they run optimally. Azure Kubernetes Service (AKS) has several tools that you can use to check the health and performance of your deployments, DaemonSet features, and services.

Tools

It's important to determine whether all deployments and DaemonSet features are running. This article describes how to determine whether the replicas in the ready and available states match the expected replica count by using:

  • The Azure portal.
  • The container insights feature of Azure Monitor.
  • The kubectl command-line tool.
  • Prometheus and Grafana.

The Azure portal

You can use the Azure portal to verify the health of the following components in your workloads. For more information, see Access Kubernetes resources from the Azure portal.

Deployment, ReplicaSet, StatefulSet, and DaemonSet

Verify that the number of replicas that are in a ready state matches the number of desired replicas. The portal shows:

  • The number of replicas that are currently available and ready to serve traffic. These replicas have been successfully scheduled onto worker nodes, completed their startup process, and passed their readiness checks.

  • The desired number of replicas specified for the deployment, or the number of replicas that the deployment aims to maintain. The Kubernetes deployment controller constantly monitors the state of the deployment and ensures that the actual number of replicas matches the desired number.

Services and ingresses

Ensure that the status is ok for all services and ingresses.

Storage

Ensure that the status is bound for all the persistent volume claims and persistent volumes.

Container insights

Container insights is a feature of Monitor that provides monitoring capabilities for container workloads that are deployed to AKS or managed by Azure Arc-enabled Kubernetes. This feature gathers performance and health information, like memory and processor metrics from controllers, nodes, and containers. It also captures container logs for analysis.

You can use various views and prebuilt workbooks to analyze the collected data. Examine the performance and behavior of various components within your cluster. With container insights, you can get insights about the overall state of your container workloads so you can make informed decisions to optimize their performance and troubleshoot issues.

You can use container insights to:

  • Identify resource bottlenecks by identifying containers that run on each node and their processor and memory usage.

  • Identify the processor and memory usage of container groups and their containers that are hosted in container instances.

  • View the controller's or pod's overall performance by identifying where the container resides in a controller or a pod.

  • Review the resource usage of workloads that run on the host and are unrelated to the standard processes that support the pod.

  • Understand the behavior of a cluster under average and heavy loads so you can identify the capacity needs and determine the maximum load that the cluster can sustain.

  • Access live container logs and metrics that the container engine generates so you can troubleshoot issues in real time.

  • Configure alerts to proactively notify you or record when CPU and memory usage on nodes or containers exceed your thresholds, or when a health state change occurs in the cluster at the infrastructure or nodes health rollup.

In the Azure portal, container insights provides several tools to help monitor and analyze an AKS cluster's health and performance.

  • Cluster: This feature provides an overview of your AKS cluster, including key metrics like CPU and memory usage, pod and node counts, and network traffic. You can get insights into the overall health and resource usage of the cluster.

  • Reports: This feature provides prebuilt reports that you can use to visualize and analyze various aspects of your cluster's performance, such as resource usage, pod health, and container insights. This data helps you understand the behavior and performance of your containers and workloads.

  • Nodes: This feature provides detailed information about the nodes in your cluster. It shows the metrics for CPU and memory usage, disk and network I/O, and the condition and status of each node. You can use this data to monitor individual node performance, identify potential bottlenecks, and ensure efficient resource allocation.

  • Controllers: This feature provides visibility into the Kubernetes controllers in your AKS cluster. It shows information such as the number of controller instances, the current state, and the status of controller operations. You can monitor the health and performance of controllers that manage workload deployments, services, and other resources.

  • Containers: This feature provides insights into containers that run in your AKS cluster. It provides information related to resource usage, restarts, and the lifecycle events of each container. You can use this data to help monitor and troubleshoot containers in your workloads.

  • Live logs: The live logs feature provides a live stream of log events from running containers, so you can view container logs in real time. You can use this data to effectively monitor and troubleshoot applications and quickly identify and resolve issues in your containers.

For more information, see the following resources:

Command-line tool

To check the status of your workloads, you can use the kubectl command-line tool to communicate with a Kubernetes cluster's control plane via the Kubernetes API.

Pods

To list the pods running in all namespaces, run the following command:

kubectl get pod -A

In the output from the command, the READY column provides important information about the readiness state of the pod's containers.

The first number signifies the count of containers that are currently in a ready state. These containers have passed the readiness probes and are prepared to handle incoming traffic. The second number represents the total count of containers that are defined within the pod, regardless of their readiness state. It includes containers that are ready and those that are still being initialized or experiencing issues.

Ensure that the first number (ready containers) matches the second number (total containers) for the pod. If they differ, some containers might not be ready or there might be issues preventing them from reaching the ready state.

Deployment, StatefulSet, DaemonSet, and StatefulSet

Run the following command to retrieve the deployments in all namespaces:

kubectl get deploy -A

In the output of the kubectl get deploy command, the numbers in the READY column indicate the current readiness state of the replicas in a deployment.

The first number represents the number of replicas that are ready and available to serve traffic. These replicas have successfully started and passed their readiness checks. The second number represents the desired number of replicas specified in the deployment configuration. It's the target number of replicas that the deployment aims to maintain.

It's important to ensure that the first number matches the second number. It indicates that the desired number of replicas are running and ready. Any discrepancy between the two numbers might indicate scaling or readiness issues that you must address.

Run the following command to retrieve the StatefulSet features in all namespaces:

kubectl get statefulset -A

Run the following command to retrieve the DaemonSet features in all namespaces:

kubectl get ds -A

You can run the kubectl get ds command to verify that a DaemonSet is running as expected. For example, you can run the following command to verify that the container insights agent is deployed successfully:

kubectl get ds ama-logs --namespace=kube-system

Likewise, if you configure your AKS cluster to collect Prometheus metrics in Monitor for managed Prometheus, you can run the following command to verify that the DaemonSet is deployed properly on the Linux node pools:

kubectl get ds ama-metrics-node --namespace=kube-system

This output provides information about the DaemonSet features in your cluster. Examine the output to ensure that the number of pods in the ready, current, and desired states are the same. If they're the same, the desired number of pods specified in the DaemonSet configuration is equal to the number of pods that are currently running and ready.

We recommended that you perform the same check for ReplicaSet features. You can use the following command to retrieve the ReplicaSet features in all namespaces:

kubectl get rs -A

Ensure that the numbers in this output are the same for each state so that the intended number of pods or replicas are running as expected. Discrepancies might indicate a need for further investigation or troubleshooting by using one of the following commands.

kubectl describe: You can use the kubectl describe command to get detailed information about Kubernetes resources, such as pods, deployments, and services. You can get a comprehensive overview of the specified resource, including its current state, events, conditions, and related metadata. The information is retrieved from the Kubernetes API server. This command is useful for troubleshooting and understanding the status of a resource.

You can run kubectl describe pod <pod-name> to get detailed information about a specific pod, including its current state, events, labels, and the containers that are associated with it. The output shows information like pod status, events, volumes, and conditions.

kubectl logs: You can use the kubectl logs command to retrieve logs that are generated by a container within a pod. This command is helpful for debugging and troubleshooting. You can view the logs in real time, or retrieve historical logs from a container.

To view container logs, you can use the command kubectl logs <pod-name> -c <container-name>. Replace <pod-name> with the name of the pod. Replace <container-name> with the name of the container from which you want to fetch the logs. If there's only one container in the pod, you don't need to specify the container name. You can also use the -f flag with kubectl logs to follow the logs in real time. This flag is similar to the tail -f Linux command.

kubectl events: You can use the kubectl events command for troubleshooting when a deployment, DaemonSet, ReplicaSet, or pod doesn't start or encounters an issue during startup. This command provides a chronological list of events that are associated with the specified resource. You can get insights into what might have caused the problem.

To use kubectl events, you can run the command kubectl events followed by a specific resource name. Or you can use selectors to filter events based on labels, namespaces, or other criteria.

For example, to retrieve events related to a specific pod, you can run kubectl events --field-selector involvedObject.name=<pod-name> --field-selector involvedObject.kind=Pod. Replace <pod-name> with the name of the pod that you want to investigate. The output of the kubectl events command displays information such as the event type (normal or warning), the event message, the reason for the event, and the time stamp when the event occurred. You can use this information to help determine what caused the failure or issue during startup.

If you suspect that a specific resource like a deployment, DaemonSet, or ReplicaSet is experiencing problems, you can filter events by using selectors. For example, kubectl events --field-selector involvedObject.name=<deployment-name> --field-selector involvedObject.kind=Deployment shows events related to a specific deployment. Examine events so you can gather important details about potential errors, failures, or other events that might have prevented the resource from starting properly. Use this data to help troubleshoot and resolve issues that affect the resource.

In-cluster monitoring with Prometheus and Grafana

If you deploy Prometheus and Grafana in your AKS cluster, you can use the K8 Cluster Detail Dashboard to get insights. This dashboard presents information that's gathered from the Prometheus cluster metrics, such as CPU and memory usage, network activity, and file system usage. It also shows detailed statistics for individual pods, containers, and systemd services.

To ensure the health and performance of your deployments, jobs, pods, and containers, you can use the features in the dashboard. Select Deployments to view the number of replicas for each deployment and the total number of replicas. Select Containers to view a chart that shows the running, pending, failed, and succeeded containers.

Monitor managed service for Prometheus and Azure Managed Grafana

You can use prebuilt dashboards to visualize and analyze Prometheus metrics. To do so, you must set up your AKS cluster to collect Prometheus metrics in Monitor managed service for Prometheus, and connect your Monitor workspace to an Azure Managed Grafana workspace.

Install the prebuilt dashboards to get a comprehensive view of your Kubernetes cluster's performance and health. For detailed installation instructions, see Prometheus monitoring mixin for Kubernetes. The dashboards are provisioned in the specified Azure Managed Grafana instance in the Managed Prometheus folder. Some dashboards include:

  • Kubernetes / Compute Resources / Cluster
  • Kubernetes / Compute Resources / Namespace (Pods)
  • Kubernetes / Compute Resources / Node (Pods)
  • Kubernetes / Compute Resources / Pod
  • Kubernetes / Compute Resources / Namespace (Workloads)
  • Kubernetes / Compute Resources / Workload
  • Kubernetes / Kubelet
  • Node Exporter / USE Method / Node
  • Node Exporter / Nodes
  • Kubernetes / Compute Resources / Cluster (Windows)
  • Kubernetes / Compute Resources / Namespace (Windows)
  • Kubernetes / Compute Resources / Pod (Windows)
  • Kubernetes / USE Method / Cluster (Windows)
  • Kubernetes / USE Method / Node (Windows)

These built-in dashboards are widely used in the open-source community for monitoring Kubernetes clusters with Prometheus and Grafana. Use these dashboards to see metrics, such as resource usage, pod health, and network activity. You can also create custom dashboards that are tailored to your monitoring needs. Dashboards help you to effectively monitor and analyze Prometheus metrics in your AKS cluster, which enables you to optimize performance, troubleshoot issues, and ensure smooth operation of your Kubernetes workloads.

You can use the Kubernetes / Compute Resources / Node (Pods) dashboard to see metrics for your Linux agent nodes. You can visualize the CPU usage, CPU quota, memory usage, and memory quota for each pod.

The Kubernetes / Compute Resources / Pod Grafana dashboard provides insights about the resource consumption and performance metrics of a selected cluster, namespace, and pod. You can use this dashboard to get metrics related to CPU usage, CPU throttling, the CPU quota, memory usage, the memory quota, networking metrics, and storage metrics. In the dashboard, select an AKS cluster, namespace, and pod within the chosen namespace to see the following details:

  • CPU usage: This chart displays the CPU usage over time for the selected pod. You can review the CPU consumption pattern and identify potential spikes or abnormalities.

  • CPU throttling: This chart provides insights into CPU throttling, which occurs when a pod exceeds its CPU resource limits. Monitor this metric to help identify areas where the pod's performance is restricted due to CPU throttling.

  • CPU quota: This chart shows the allocated CPU quota for the selected pod. If the pod exceeds its assigned CPU quota, it might require resource adjustments.

  • Memory usage: This chart presents the memory usage of the selected pod. Monitor the memory consumption pattern and identify any memory-related issues.

  • Memory quota: This chart displays the allocated memory quota for the pod. If the pod exceeds its assigned memory quota, it might indicate a need for resource optimization.

  • Networking metrics: These charts show the received and transmitted bandwidth, and the rate of received and transmitted packets. These metrics help you monitor network usage and detect any potential networking bottlenecks or anomalies.

  • Storage metrics: This section provides information about storage-related metrics, such as I/O operations per second (IOPS) and throughput. Monitor these metrics to help gauge the performance and efficiency of the pod storage.

You can use the Kubernetes / Compute Resources / Pod Grafana dashboard to get insights about the resource usage, performance, and behavior of pods in your Kubernetes cluster. Use this information to optimize resource allocation, troubleshoot performance issues, and make informed decisions to ensure the smooth operation of your containerized workloads.

Contributors

This article is maintained by Microsoft. It was originally written by the following contributors.

Principal author:

Other contributors:

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Next steps

Validate the admission controllers