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Kubernetes Requests and Limits

Kubernetes Requests and Limits
Hostman Team
Technical writer
Kubernetes
29.01.2025
Reading time: 9 min

When working with the Kubernetes containerization platform, it is important to control resource usage for cluster objects such as pods. The requests and limits parameters allow you to configure resource consumption limits, such as how many resources a pod can use in a Kubernetes cluster. This article will explore the use of requests and limits in Kubernetes through practical examples.

Prerequisites

To work with requests and limits in a Kubernetes cluster, we need:

  1. A Kubernetes cluster (you can create one in the Hostman control panel). For testing purposes, a cluster with two nodes will suffice. The cluster can also be deployed manually by renting the necessary number of cloud or dedicated (physical) servers, setting up the operating system, and installing the required packages.

  2. Lens or kubectl for connecting to and managing your Kubernetes clusters.

Connecting to a Kubernetes Cluster Using Lens

  1. First, go to the cluster management page in your Hostman panel. Download the Kubernetes cluster configuration file (the kubeconfig file).

4cb03fea 995a 43c2 A035 870d6505acda.png

  1. Once Lens is installed on your system, launch the program, and from the left menu, go to the Catalog (app) section:

Image9

  1. Select Clusters and click the blue plus button at the bottom right.

Image2

  1. Choose the directory where you downloaded the Kubernetes configuration file by clicking the Sync button at the bottom right.

  2. After this, our cluster will appear in the list of available clusters. Click on the cluster's name to open its dashboard:

Image21

What are Requests and Limits in Kubernetes

First, let's understand what requests and limits are in Kubernetes.

Requests are a mechanism in Kubernetes that is responsible for allocating physical resources, such as memory and CPU cores, to the container being launched. In simple terms, requests in Kubernetes are the minimum system requirements for an application to function properly.

Limits are a mechanism in Kubernetes that limits the physical resources (memory and CPU cores) allocated to the container being launched. In other words, limits in Kubernetes are the maximum values for physical resources, ensuring that the launched application cannot consume more resources than specified in the limits. The container can only use resources up to the limit specified in the Limits.

The request and limit mechanisms apply only to objects of type pod and are defined in the pod configuration files, including deployment, StatefulSet, and ReplicaSet files.

Requests are added in the containers block using the resources parameter. In the resources section, you need to add the requests block, which consists of two values: cpu (CPU resource request) and memory (memory resource request). The syntax for requests is as follows:

containers:
  ...
  resources:
    requests:
      cpu: "1.0"
      memory: "150Mi"

In this example, for the container to be launched on a selected node in the cluster, at least one free CPU core and 150 megabytes of memory must be available.

Limits are set in the same way. For example:

containers:
  ...
  resources:
    limits:
      cpu: "2.0"
      memory: "500Mi"

In this example, the container cannot use more than two CPU cores and no more than 500 megabytes of memory.

The units of measurement for requests and limits are as follows:

  • CPU — in millicores (milli-cores)
  • RAM — in bytes

For CPU resources, cores are used. For example, if we need to allocate one physical CPU core to a container, the manifest should specify 1.0. To allocate half a core, specify 0.5. A core can be logically divided into millicores, so you can allocate, for example, 100m, which means one-thousandth of a core (1 full CPU core contains 1000 millicores).

For RAM, we specify values in bytes. You can use numbers with the suffixes E, P, T, G, M, k. For example, if a container needs to be allocated 1 gigabyte of memory, you should specify 1G. In megabytes, it would be 1024M, in kilobytes, it would be 1048576k, and so on.

The requests and limits parameters are optional; however, it is important to note that if both parameters are not set, the container will be able to run on any available node in the cluster regardless of the free resources and will consume as many resources as are physically available on each node. Essentially, the cluster will allocate excess resources. This practice can negatively affect the stability of the entire cluster, as it significantly increases the risk of errors such as OOM (Out of Memory) and OutOfCPU (lack of CPU resources). To prevent these errors, Kubernetes introduced the request and limit mechanisms.

Practical Use of Requests and Limits in Kubernetes

Let's look at the practical use of requests and limits. First, we will deploy a deployment file with an Nginx image where we will set only the requests.

In the configuration below, to launch a pod with a container, the node must have at least 100 millicores of CPU (1/1000 of a CPU core) and 150 megabytes of free memory:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-test-deployment
  namespace: ns-for-nginx
  labels:
    app: nginx-test
spec:
  selector:
    matchLabels:
      app: nginx-test
  template:
    metadata:
      labels:
        app: nginx-test
    spec:
      containers:
        - name: nginx-test
          image: nginx:1.25
          resources:
            requests:
              cpu: "100m"
              memory: "150Mi"

Before deploying the deployment, let's create a new namespace named ns-for-nginx:

kubectl create ns ns-for-nginx

After creating the namespace, we will deploy the deployment file using the following command:

kubectl apply -f nginx-test-deployment.yml

Now, let's check if the deployment was successfully created:

kubectl get deployments -A

Image22

Also, check the status of the pod:

kubectl get po -n ns-for-nginx

Image5

The deployment file and the pod have been successfully launched. To ensure that the minimum resource request was set for the Nginx pod, we will use the kubectl describe pod command (where nginx-test-deployment-786d6fcb57-7kddf is the name of the running pod):

kubectl describe pod nginx-test-deployment-786d6fcb57-7kddf -n ns-for-nginx

In the output of this command, you can find the requests block, which contains the previously set minimum requirements for our container to run:

Image10

In the example above, we created a deployment that sets only the minimum required resources for deployment. Now, let's add limits for the container to run with 1 full CPU core and 1 gigabyte of RAM by creating a new deployment file:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-test-deployment-2
  namespace: ns-for-nginx
  labels:
    app: nginx-test2
spec:
  selector:
    matchLabels:
      app: nginx-test2
  template:
    metadata:
      labels:
        app: nginx-test2
    spec:
      containers:
        - name: nginx-test2
          image: nginx:1.25
          resources:
            requests:
              cpu: "100m"
              memory: "150Mi"
            limits:
              cpu: "1.0"
              memory: "1G"

Let's create the deployment in the cluster:

kubectl apply -f nginx-test-deployment2.yml

Using the kubectl describe command, let's verify that both requests and limits have been applied (where nginx-test-deployment-2-6d5df6c95c-brw8n is the name of the pod):

kubectl describe pod nginx-test-deployment-2-6d5df6c95c-brw8n -n ns-for-nginx

Image24

In the screenshot above, both requests and limits have been set for the container. With these quotas, the container will be scheduled on a node with at least 150 megabytes of RAM and 100 milli-CPU. At the same time, the container will not be allowed to consume more than 1 gigabyte of RAM and 1 CPU core.

Using ResourceQuota

In addition to manually assigning resources for each container, Kubernetes provides a way to allocate quotas to specific namespaces in the cluster. The ResourceQuota mechanism allows setting resource usage limits within a particular namespace. ResourceQuota is intended to limit resources such as CPU and memory.

The practical use of ResourceQuota looks like this:

  1. Create a new namespace with quota settings:

kubectl create ns ns-for-resource-quota
  1. Create a ResourceQuota object:

apiVersion: v1
kind: ResourceQuota
metadata:
  name: resource-quota-test
  namespace: ns-for-resource-quota
spec:
  hard:
    pods: "2"
    requests.cpu: "0.5"
    requests.memory: "800Mi"
    limits.cpu: "1"
    limits.memory: "1G"

In this example, for all objects created in the ns-for-resource-quota namespace, the following limits will apply:

    • A maximum of 2 pods can be created.
    • The minimum CPU resources required for starting the pods is 0.5 milliCPU.
    • The minimum memory required for starting the pods is 800MB.
    • CPU limits are set to 1 core (no more can be allocated).
    • Memory limits are set to 1GB (no more can be allocated).
  1. Apply the configuration file:

kubectl apply -f test-resource-quota.yaml
  1. Check the properties of the ResourceQuota object:

kubectl get resourcequota resource-quota-test -n ns-for-resource-quota

Image18

As you can see, resource quotas have been set.

Also, verify the output of the kubectl describe ns command:

kubectl describe ns ns-for-resource-quota

Image25

The previously created namespace ns-for-resource-quota will have the corresponding resource quotas.

  1. Example of an Nginx pod with the following configuration:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-with-quota
  namespace: ns-for-resource-quota
  labels:
    app: nginx-with-quota
spec:
  selector:
    matchLabels:
      app: nginx-with-quota
  replicas: 3
  template:
    metadata:
      labels:
        app: nginx-with-quota
    spec:
      containers:
      - name: nginx
        image: nginx:1.22.1
        resources:
          requests:
            cpu: 100m
            memory: 100Mi
          limits:
            cpu: 100m
            memory: 100Mi

Here we define 3 replicas of the Nginx pod to test the quota mechanism. We also set minimum resource requests for the containers and apply limits to ensure the containers don't exceed the defined resources.

  1. Apply the configuration file:
kubectl apply -f nginx-deployment-with-quota.yaml
kubectl get all -n ns-for-resource-quota

Image23

As a result, only two of the three replicas of the pod will be successfully created. The deployment will show an error message indicating that the resource quota for pod creation has been exceeded (in this case, we're trying to create more pods than allowed):

Image4

However, the remaining two Nginx pods were successfully started:

Image6

Conclusion

Requests and limits are critical mechanisms in Kubernetes that allow for flexible resource allocation and control within the cluster, preventing unexpected errors in running applications and ensuring the stability of the cluster itself.

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Kubernetes
29.01.2025
Reading time: 9 min

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The Kubernetes containerization platform processes and stores large volumes of data from various cluster components, including persistent storage blocks (Persistent Volumes), various manifests, and configuration files such as Deployments, ConfigMaps, and Secrets. It is important to organize backups to protect this data. There are various solutions for simplifying the Kubernetes backup process. One of them is Velero, specifically designed to create Kubernetes cluster backups. Today, we will take a detailed look at the process of creating backups using Velero. Prerequisites A deployed and running Kubernetes cluster. It can be a self-hosted cluster deployed or a Kubernetes cluster in the Hostman cloud. Object storage for backup files. In this guide, we will use Hostman S3 object storage. A server or a computer from which we will manage the cluster and install Velero. We'll use a machine with Ubuntu 24.04. kubectl utility installed. The major version of kubectl should not differ from that of the cluster. For instance, if the cluster version is 1.31, you can use versions from 1.30 to 1.32. To download a specific version of kubectl, specify it in the URL, for example: curl -LO https://dl.k8s.io/release/v1.32.0/bin/linux/amd64/kubectl After installation, check the version: kubectl version --client Helm package manager installed. Helm simplifies installing, upgrading, and managing applications within a Kubernetes cluster. Helm organizes complex Kubernetes configurations into manageable packages called charts. Creating S3 Storage S3 is an object storage service for reliable storage of large datasets. Since Velero requires object storage, let's create one in the S3 Storage section of the Hostman management panel. Click the Create button: For this guide, we'll select the minimum storage size of 10 GB. In practice, you should choose a size that meets your needs. Set the storage type to Public. You can also rename the bucket if needed. Velero Overview Velero is an open-source client-server utility for creating backups and restoring Kubernetes cluster resources. It works with Kubernetes objects (such as Pods, Deployments, and Services) and saves them as snapshots. Additionally, it can back up data from Persistent Volume (PV) objects. Velero Key Features: Backup Creation: Save the state of the Kubernetes cluster, including manifests and Persistent Volumes. Data Restoration: Restore the entire cluster or individual resources from a backup. Data Migration: Move resources between Kubernetes clusters. Velero Architecture The Velero architecture consists of the following key components: Velero Server (deployed inside the Kubernetes cluster): The server component runs as a Deployment object within the Kubernetes cluster. It handles backup and recovery tasks. 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04 February 2025 · 10 min to read
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For example, Starting indicates that an object in the cluster was started, and Pulling means that an image for a container was pulled. OBJECT — The cluster object that triggered the event, including nodes in the cluster (e.g., during initialization). MESSAGE — Displays the detailed message of the event, which can be useful for troubleshooting. To narrow down the list of events, you can use a specific namespace: kubectl get events -n kube-system For more detailed event output, use the wide option: kubectl get events -o wide The wide option adds additional columns of information, including: SUBOBJECT — Displays the subobject related to the event (e.g., container, volume, secret). SOURCE — The source of the event, which could be components like kubelet, node-controller, etc. FIRST SEEN — The timestamp when the event was first recorded in the cluster. COUNT — The number of times the event has been repeated since it was first seen in the cluster. NAME — The name of the object (e.g., pod, secret) associated with the event. To view events in real-time, use the -w flag: kubectl get events -w The get events command also supports filtering via the --field-selector option, where you specify a field from the get events output. For example, to display all events with a Warning type in the cluster: kubectl get events --field-selector type=Warning -A Additionally, filtering by timestamps is supported. To display events in the order they first occurred, use the .metadata.creationTimestamp parameter: kubectl get events --sort-by='.metadata.creationTimestamp' Monitoring Kubernetes API Server The API server is a critical component and the "brain" of Kubernetes, that processes all requests to the cluster. The API server should always be available to respond to requests. To check its status, you can use special API endpoints: livez and readyz. To check the live status of the API server, use the following command: kubectl get --raw '/livez?verbose' To check the readiness status of the API server, use the following command: kubectl get --raw '/readyz?verbose' If both the livez and readyz requests return an ok status, it means the API server is running and ready to handle requests. Kubernetes Cluster Components To quickly display the status of all cluster components, use the following command: kubectl get componentstatuses If the STATUS and MESSAGE columns show Healthy and ok, it means the components are running successfully. If any component encounters an error or failure, the STATUS column will display Unhealthy, and the MESSAGE column will provide an error message. Container Runtime As is known, Kubernetes itself does not run pods with containers. Instead, it uses an external component called the Container Runtime Interface (CRI), or simply the container runtime. It’s important to ensure that the container runtime environment is functioning correctly. At the time of writing, Kubernetes supports the following container runtimes: containerd CRI-O It’s worth mentioning that Docker's container runtime is no longer supported starting from Kubernetes version 1.24. CRI-O First, check the status of the container runtime. To do this, on the node where the error appears, run the following command: systemctl status crio In the Active line, the status should show active (running). If it shows failed, further investigation is needed in the crio information messages and log files. To display the basic information about crio, including the latest error messages, use the command: crictl info If an error occurs while using crio, the message parameter will show a detailed description. crio also logs all its activities to log files, typically found in the /var/log/crio/pods directory. Additionally, you can use the journalctl logs. To display all logs for the crio unit, run: journalctl -u crio Containerd As with crio, start by checking the status of the container runtime. On the node where the error appears, run: systemctl status containerd In the Active line, the status should show active (running). If the status shows failed, you can get more detailed information by using the built-in status command, which will display all events, including errors: containerd status Alternatively, you can view the logs using journalctl. To display logs for the containerd unit, run: journalctl -u containerd You can also check the configuration file parameters for containerd using two commands (the output is usually quite large): containerd config default — Displays the default configuration file. Use this if no changes have been made to the file. If errors occur, this file can be used for rollback. containerd config dump — Displays the current configuration file, which may have been modified. Pods Health Check Kubernetes operates with pods, the smallest software units in the cluster, where containers with applications are run. The status of pods should always be READY. To display a list of all pods in the cluster and their statuses, use the following command: kubectl get po -A To display pods in a specific namespace, use the -n flag followed by the namespace name: kubectl get po -n kube-system For more detailed information about a pod, including any possible errors, use the kubectl describe pod command, which provides the most detailed information about the pod: kubectl describe pod coredns-6997b8f8bd-b5dq6 -n kube-system All events related to the pod, including errors, are displayed in the Events section. Getting Information About Objects with kubectl describe The kubectl describe command is a powerful tool for finding detailed information about an object, including searching for and viewing various errors. You can apply this command to all Kubernetes objects that are listed in the output of the kubectl api-resources command. Deployment files are widely used when deploying applications in a Kubernetes cluster. They allow you to control the state of service deployments, including scaling application replicas. To display the statuses of all available deployments in the Kubernetes cluster, use the command: kubectl get deployments -A It is important that the columns READY, UP-TO-DATE, and AVAILABLE display the same number of pods as specified in the deployment file. If the READY column shows 0 or fewer pods than specified, the pod with the application will not be started. To find the error's cause, use the describe command with the type of object, in this case, deployment: kubectl describe deployment coredns -n kube-system Just like when using describe for pods, all events, including errors, are displayed in the Conditions section. Conclusion Checking the health of a Kubernetes cluster is an important step in troubleshooting and resolving issues. Kubernetes consists of many different components, each with its own verification algorithm. It is important to know what and how to check to identify and fix errors quickly.
28 January 2025 · 11 min to read
Kubernetes

How to Deploy PostgreSQL on Kubernetes

PostgreSQL is a popular relational database management system (RDBMS) that provides high-availability features like streaming replication, logical replication, and failover solutions. Deploying PostgreSQL on Kubernetes allows organizations to build resilient systems that ensure minimal downtime and data availability. With Kubernetes StatefulSets, you can scale PostgreSQL deployment in response to demand. Kubernetes Environment Setup To get started, make sure you have the following: Kubernetes Cluster (Cloud or Local):  You can set up a Kubernetes cluster on Hostman within no time. To follow this tutorial with a local Kubernetes cluster, you can use one of these tools: k3s, minikube, microk8s, kind. Kubectl: Kubectl allows users to interact with a Kubernetes cluster. The kubectl needs a configuration YAML file which contains cluster details and is usually provided by your cloud provider.  From the Hostman control panel, you can simply download this configuration file with a click of a button as indicated in the below screenshot. To connect, you need to set KUBECONFIG environment variable accordingly. export KUBECONFIG=/absolute/path/to/file/k8s-cluster-config.yaml Helm: You need Helm CLI to install Helm charts. Helm version 3 is required. Deploy PostgreSQL Using a Helm Chart Helm is a package manager for Kubernetes just like apt for Ubuntu and Debian. Instead of manually creating multiple YAML files for Pods, Services, Persistent Volumes, Secrets, etc., the Helm chart simplifies this to a single command (e.g., helm install), streamlining the deployment process. Step 1: Add helm repository To add the Bitnami PostgreSQL Helm repo, run this command: helm repo add bitnami https://charts.bitnami.com/bitnami To sync your local Helm repository with the remote one: helm repo update Step 2: Manage Data Persistence PostgreSQL requires persistent storage to ensure that data is preserved even if a pod crashes or is rescheduled. When a Persistent Volume Claim (PVC) is combined with a Persistent Volume (PV), Kubernetes can allocate a desired chunk of storage either in disk or cloud storage. PVC requests the Kubernetes cluster for storage space. Kubernetes then looks at the available PVs and assigns one to it. Create a file named postgres-local-pv.yaml with the YAML manifest: apiVersion: v1 kind: PersistentVolume metadata: name: postgresql-local-pv spec: capacity: storage: 5Gi accessModes: - ReadWriteOnce persistentVolumeReclaimPolicy: Retain storageClassName: manual hostPath: path: /mnt/data/postgresql This manifest creates a PersistentVolume backed by a local directory (/mnt/data/postgresql) on a specific node. This means if the node goes down or becomes unavailable, the data stored in that PV will be inaccessible, which is a critical risk in production. Therefore, it’s highly recommended to use cloud-native storage solutions instead of hostPath to ensure reliability, scalability and data protection. This PV has a reclaim policy of Retain, ensuring that it is not deleted when no longer in use by a PVC. You can set storageClassName to ceph-storage, glusterfs, portworx-sc, or openebs-standard based on your needs. Create a file named postgres-local-pvc.yaml with this text: apiVersion: v1 kind: PersistentVolumeClaim metadata: name: postgresql-local-pvc spec: accessModes: - ReadWriteOnce resources: requests: storage: 5Gi storageClassName: manual The ReadWriteOnce config means the volume can be read-write by a single node at a time. You might think, replacing it with ReadWriteMany will make your application highly available. This isn’t the case. ReadWriteMany (RWX) access mode allows multiple pods to access the same PersistentVolume simultaneously, this can indeed create serious issues leading to potential race conditions, data corruption, or inconsistent state. Apply these manifests using kubectl and create new resources. kubectl apply -f postgres-local-pv.yamlkubectl apply -f postgres-local-pvc.yaml Step 3: Install PostgreSQL Helm Chart Run the following command to install the Helm chart. helm install tutorial-db bitnami/postgresql --set auth.username=bhuwan \ --set auth.password=”AeSeigh2gieshe” \ --set auth.database=k8s-tutorial \ --set auth.postgresPassword=”Ze4hahshez6dop9vaing” \ --set primary.persistence.existingClaim=postgresql-local-pvc \ --set volumePermissions.enabled=true After a couple of minutes, verify if things have worked successfully with this command: kubectl get all Step 4: Test and Connect The following command runs a temporary PostgreSQL client pod. The pod connects to the database named k8s-tutorial, using the username bhuwan and the password from the environment variable $POSTGRES_PASSWORD. export POSTGRES_PASSWORD=$(kubectl get secret --namespace default tutorial-db-postgresql -o jsonpath="{.data.password}" | base64 -d) kubectl run tutorial-db-postgresql-client --rm --tty -i --restart='Never' \ --image docker.io/bitnami/postgresql:17.2.0-debian-12-r6 \ --env="PGPASSWORD=$POSTGRES_PASSWORD" \ --command -- psql --host tutorial-db-postgresql \ -U bhuwan -d k8s-tutorial -p 5432 After the session ends, the pod will be deleted automatically due to the --rm flag. A quick reminder, if you have changed the Helm chart release name, users, or database name, adjust the above commands accordingly. Deploy Postgres on Kubernetes from scratch A StatefulSet is the best Kubernetes resource for deploying stateful applications like PostgreSQL. This way, every PostgreSQL pod gets its own stable network identities and persistent volumes. Note: you’ll be using a previously created Persistent Volume Claim (PVC) and Persistent Volume(PV). So, do some cleanup and recreate those resources. helm delete tutorial-db kubectl delete pvc postgresql-local-pvc kubectl delete pv postgresql-local-pv kubectl apply -f postgres-local-pv.yaml -f postgres-local-pvc.yaml Create a file named postgres-statefulset.yaml with the following text: apiVersion: apps/v1 kind: StatefulSet metadata: name: postgres-statefulset labels: app: postgres spec: serviceName: "postgresql-headless-svc" replicas: 1 selector: matchLabels: app: postgres template: metadata: labels: app: postgres spec: containers: - name: postgres image: postgres:17.2 envFrom: - secretRef: name: postgresql-secret ports: - containerPort: 5432 name: postgresdb volumeMounts: - name: pv-data mountPath: /var/lib/postgresql/db volumes: - name: pv-data persistentVolumeClaim: claimName: postgresql-local-pvc Before you can apply these changes, create a new Secret for handling sensitive details like passwords with kubectl. kubectl create secret generic postgresql-secret --from-literal=POSTGRES_PASSWORD=Ze4hahshez6dop9vaing kubectl apply -f postgres-statefulset.yaml If the pod gets stuck with Pending state, you can try creating a StorageClass with the following manifest. kind: StorageClass apiVersion: storage.k8s.io/v1 metadata: name: manual provisioner: kubernetes.io/no-provisioner volumeBindingMode: WaitForFirstConsumer To investigate any further issues with the pod, you can use the command: kubectl describe pod postgres-statefulset-0 This command will report any issues related to scheduling the pod to a node, mounting volumes, or resource constraints. Databases like PostgreSQL are typically accessed internally by other services or applications within the cluster, so it's better to create a Headless service for it. Create a file called postgres-service.yaml and include the following YAML manifest: apiVersion: v1 kind: Service metadata: name: postgresql-headless-svc spec: type: ClusterIP selector: app: postgres ports: - port: 5432 targetPort: 5432 clusterIP: None Finally, you can test the connection with kubectl run. kubectl run tutorial-db-postgresql-client --rm --tty -i --restart='Never' \ --image docker.io/bitnami/postgresql:17.2.0-debian-12-r6 \ --env="PGPASSWORD=Ze4hahshez6dop9vaing" \ --command -- psql --host postgres-statefulset-0.postgresql-headless-svc \ -U postgres -p 5432 Scale, Replication, and Backup To scale up a Statefulset, simply pass the number of replicas with --replicas flag.  kubectl scale statefulset postgres-statefulset --replicas=3  To reach replicas, you can make use of headless service. For instance, with hostname postgres-statefulset-1.postgresql-headless-svc you can send requests to pod 1. For handling backups, you can use CronJob with the pg_dump utility provided by PostgreSQL. Best Practices Throughout the tutorial, the decision to handle passwords via Kubernetes Secret, using StatefulSet instead of Deployment was a good move. To make this deployment even more secure, reliable, and highly available, here are some ideas: Set Resource Requests and Limits: Set appropriate CPU and memory requests and limits to avoid over-provisioning and under-provisioning. Backups: Use Kubernetes CronJobs to regularly back up your PostgreSQL data. Consider implementing Volume Snapshots as well. Monitoring and Log Postgresql: You can use tools like Prometheus and Grafana to collect and visualize PostgreSQL metrics, such as query performance, disk usage, and replication status. Use Pod Disruption Budgets (PDBs): If too many PostgreSQL pods are disrupted at once (e.g., during a rolling update), it can lead to database unavailability or replication issues. Conclusion Helm chart is the recommended way of complex and production deployment. Helm provides an automated version manager alongside hiding the complexities of configuring individual Kubernetes components. Using the Helm template command, you can even render the Helm chart locally and make necessary adjustments with its YAML Kubernetes manifests. Kubernetes provides scalability, flexibility, and ease of automation for PostgreSQL databases. By leveraging Kubernetes features like StatefulSets, PVCs, PDBs, and secrets management, you can ensure that your PostgreSQL database is tuned for the production environment.
24 January 2025 · 8 min to read

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