4.0.X

Getting Started with Trilio for Upstream Kubernetes (K8S)

Learn how to install, license and test Trilio for Kubernetes (T4K) in an upstream Kubernetes environment.

Table of Contents

  1. What is Trilio for Kubernetes?

  2. Prerequisites

    1. Verify Prerequisites

  3. Installation

    1. Helm Quickstart

    2. Manual Install

    3. Air-Gapped Install

    4. Proxy Enabled Environments

  4. Licensing

  5. Create a Backup Target

  6. Testing Backup and Restore

  7. Troubleshooting

What is Trilio for Kubernetes?

Trilio for Kubernetes is a cloud-native backup and restore application. Being a cloud-native application for Kubernetes, all operations are managed with CRDs (Customer Resource Definitions).

Trilio utilizes Control Plane and Data Plane controllers to carry out the backup and restore operations defined by the associated CRDs. When a CRD is created or modified the controller reconciles the definitions to the cluster.

Trilio gives you the power and flexibility to backup your entire cluster or select a specific namespace(s), label, Helm chart, or Operator as the scope for your backup operations.

In this tutorial, we'll show you how to install and test operation of Trilio for Kubernetes on your Upstream Kubernetes deployment.

Prerequisites

Before installing Trilio for Kubernetes, please review the compatibility matrix to ensure Trilio can function smoothly in your Kubernetes environment.

Trilio for Kubernetes requires a compatible Container Storage Interface (CSI) driver that provides the Snapshot feature.

Check the Kubernetes CSI Developer Documentation to select a driver appropriate for your backend storage solution. See the selected CSI driver's documentation for details on the installation of the driver in your cluster.

Trilio will assume that the selected storage driver is a supported CSI driver when the volumesnapshotclass and storageclassare utilized.

Trilio for Kubernetes requires the following Custom Resource Definitions (CRD) to be installed on your cluster:VolumeSnapshot, VolumeSnapshotContent, and VolumeSnapshotClass.

Installing the Required VolumeSnapshot CRDs

Before attempting to install the VolumeSnapshot CRDs, it is important to confirm that the CRDs are not already present on the system.

To do this, run the following command:

kubectl api-resources | grep volumesnapshot

If CRDs are already present, the output should be similar to the output displayed below. The second column displays the version of the CRD installed (v1 in this case). Ensure that it is the correct version required by the CSI driver being used.

volumesnapshotclasses                          snapshot.storage.k8s.io/v1             false        VolumeSnapshotClass
volumesnapshotcontents                         snapshot.storage.k8s.io/v1             false        VolumeSnapshotContent
volumesnapshots                                snapshot.storage.k8s.io/v1             true         VolumeSnapshot

Installing CRDs

Be sure to only install v1 version of VolumeSnapshot CRDs

  1. Read the external-snapshotter GitHub project documentation. This is compatible with v1.22+.

  2. Run the following commands to install directly, check the repo for the latest version:

RELEASE_VERSION=6.3
kubectl apply -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/release-${RELEASE_VERSION}/client/config/crd/snapshot.storage.k8s.io_volumesnapshotclasses.yaml
kubectl apply -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/release-${RELEASE_VERSION}/client/config/crd/snapshot.storage.k8s.io_volumesnapshotcontents.yaml
kubectl apply -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/release-${RELEASE_VERSION}/client/config/crd/snapshot.storage.k8s.io_volumesnapshots.yaml

For non-air-gapped environments, the following URLs must be accessed from your Kubernetes cluster.

If the Kubernetes cluster's control plane and worker nodes are separated by a firewall, then the firewall must allow traffic on the following port(s)

  • 9443

Verify Prerequisites with the Trilio Preflight Check

Make sure your cluster is ready to Install Trilio for Kubernetes by installing the Preflight Check Plugin and running the Trilio Preflight Check.

Trilio provides a preflight check tool that allows customers to validate their environment for Trilio installation.

The tool generates a report detailing all the requirements and whether they are met or not.

If you encounter any failures, please send the Preflight Check output to your Trilio Professional Services and Solutions Architect so we may assist you in satisfying any missing requirements before proceeding with the installation.

Installation

Follow the instructions in this section to Install Trilio for Kubernetes in an upstream Kubernetes environment. This section assumes that you have installed kubectl and helm installed and correctly configured to work with desired Kubernetes cluster. T4K supports v3 version of helm.

There are multiple methods of installing:

Helm Quickstart Installation

In this installation method for upstream operator, a cluster scope TVM custom resource triliovault-manager is created. Perform the following steps to install:

  1. To add the repository where the triliovault-operator helm chart is located, use the command:

helm repo add triliovault-operator https://charts.k8strilio.net/trilio-stable/k8s-triliovault-operator

2. Install the chart from the added repository:

To install the chart from the added repository using default configurations, use the following command:

helm install tvm triliovault-operator/k8s-triliovault-operator

Installation Configuration Options

ParameterDescriptionDefaultExample

installTVK.enabled

T4K-Quickstart install feature is enabled

true

installTVK.applicationScope

scope of T4K application created

Cluster

installTVK.tvkInstanceName

tvk instance name

"

"tvk-instance"

installTVK.ingressConfig.host

host of the ingress resource created

""

installTVK.ingressConfig.tlsSecretName

tls secret name which contains ingress certs

""

installTVK.ingressConfig.annotations

annotations to be added on ingress resource

""

installTVK.ingressConfig.ingressClass

ingress class name for the ingress resource

""

installTVK.ComponentConfiguration.ingressController.enabled

T4K ingress controller should be deployed

true

installTVK.ComponentConfiguration.ingressController.service.type

T4K ingress controller service type

"NodePort"

preflight.enabled

enables preflight check for tvk

false

preflight.storageClass

Name of storage class to use for preflight checks (Required)

""

preflight.cleanupOnFailure

Cleanup the resources on cluster if preflight checks fail (Optional)

false

preflight.imagePullSecret

Name of the secret for authentication while pulling the images from the local registry (Optional)

""

preflight.limits

Pod memory and cpu resource limits for DNS and volume snapshot preflight check (Optional)

""

"cpu=600m,memory=256Mi"

preflight.localRegistry

Name of the local registry from where the images will be pulled (Optional)

""

preflight.nodeSelector

Node selector labels for pods to schedule on a specific nodes of cluster (Optional)

""

"key=value"

preflight.pvcStorageRequest

PVC storage request for volume snapshot preflight check (Optional)

""

"2Gi"

preflight.requests

Pod memory and cpu resource requests for DNS and volume snapshot preflight check (Optional)

""

"cpu=300m,memory=128Mi"

preflight.volumeSnapshotClass

Name of volume snapshot class to use for preflight checks (Optional)

""

priorityClassName

Name of the Priority Class which is used by all the triliovault deployment (Optional)

""

"high-priority"

Check out T4K Integration with Observability Stack for additional options to enable observability stack for T4K.

If using an external ingress controller, you must use the following command:

--set installTVK.ComponentConfiguration.ingressController.enabled=false --set installTVK.ingressConfig.ingressClass="" --set installTVK.ingressConfig.host="" --set installTVK.ingressConfig.tlsSecretName=""

4. Check the output from the previous command and ensure that the installation was successful.

5. Check the TVM CR configuration using the following command:

kubectl get triliovaultmanagers.triliovault.trilio.io triliovault-manager -o yaml

7. Once the operator pod is in a running state, confirm that the T4K pods are up and running:

Check T4K Install

  • Firstly, check that the pods were created:

kubectl get pods

The readout should be similar to this:

NAME                                                         READY   STATUS    RESTARTS   AGE
k8s-triliovault-admission-webhook-6ff5f98c8-qwmfc            1/1     Running   0          81s
k8s-triliovault-web-backend-6f66b6b8d5-gxtmz                 1/1     Running   0          81s
k8s-triliovault-control-plane-6c464c5d78-ftk6g               1/1     Running   0          81s
k8s-triliovault-exporter-59566f97dd-gs4xc                    1/1     Running   0          81s
k8s-triliovault-ingress-nginx-controller-867c764cd5-qhpx6    1/1     Running   0          18s
k8s-triliovault-web-967c8475-m7pc6                           1/1     Running   0          81s
k8s-triliovault-operator-66bd7d86d5-dvhzb                    1/1     Running   0          6m48s

  • Secondly, check that ingress controller service is of type nodePort.

NAME                                                 TYPE           CLUSTER-IP     EXTERNAL-IP      PORT(S)                      AGE
k8s-triliovault-admission-webhook                    ClusterIP      10.7.243.24    <none>           443/TCP                      129m
k8s-triliovault-ingress-nginx-controller             nodePort       10.7.246.193   35.203.155.148   80:30362/TCP,443:32327/TCP   129m
k8s-triliovault-ingress-nginx-controller-admission   ClusterIP      10.7.250.31    <none>           443/TCP                      129m
k8s-triliovault-web                                  ClusterIP      10.7.254.41    <none>           80/TCP                       129m
k8s-triliovault-web-backend                          ClusterIP      10.7.252.146   <none>           80/TCP                       129m
k8s-triliovault-operator-webhook-service             ClusterIP      10.7.248.163   <none>           443/TCP                      130m

  • Thirdly, check that ingress resources have the host defined by the user:

NAME              CLASS                           HOSTS   ADDRESS          PORTS   AGE
k8s-triliovault   k8s-triliovault-default-nginx   *       35.203.155.148   80      129m

  • Lastly, check that you can access the T4K UI by typing this address in your browser: https://35.203.155.148 Trilio is now successfully installed on your cluster.

8. If the install was not successful or the T4K pods were not spawned as expected:

Preflight jobs are not cleaned up immediately following failure. If your cluster version is 1.21 or above, the job is cleaned up after one hour, so you should collect any failure logs within one hour of a job failure.

Additionally, there is a bug on the helm side affecting the auto-deletion of resources following failure. Until this Helm bug is fixed, to run preflight again, users must clean the following resources left behind after the first failed attempt. Once this bug is fixed, the cleanup will be handled automatically. Run the following commands to clean up the temporary resources:

  • Cleanup Service Account:

kubectl delete sa k8s-triliovault-operator-preflight-service-account -n <helm-release-namespace>

  • Cleanup Cluster Role Binding:

kubectl delete clusterrolebinding <helm-release-name>-<helm-release-namespace>-preflight-rolebinding

  • Cleanup Cluster Role:

kubectl delete clusterrole <helm-release-name>-<helm-release-namespace>-preflight-role

Manual Installation

To install the operator manually, run the latest helm charts from the following repository:

  1. To add the repository where the triliovault-operator helm chart is located, use the command:

helm repo add triliovault-operator https://charts.k8strilio.net/trilio-stable/k8s-triliovault-operator

2. Install the chart from the added repository, but with the quick install method flag set to false, so that users can have more control over the installation:

helm install tvm triliovault-operator/k8s-triliovault-operator --set installTVK.enabled=false

Note that in step 2, you can also set additional parameters as set out inInstallation Configuration Options above.

3. Copy the sample TrilioVaultManager CR contents below and paste them into a new YAML file.

apiVersion: triliovault.trilio.io/v1
kind: TrilioVaultManager
metadata:
  labels:
    triliovault: k8s
  name: tvk
spec:
  trilioVaultAppVersion: latest
  applicationScope: Cluster
  # User can configure tvk instance name
  tvkInstanceName: tvk-instance
  # User can configure the ingress hosts, annotations and TLS secret through the ingressConfig section
  ingressConfig:
    host: ""
    tlsSecretName: "secret-name"
  # T4K components configuration, currently supports control-plane, web, exporter, web-backend, ingress-controller, admission-webhook.
  # User can configure resources for all components and can configure service type and host for the ingress-controller
  componentConfiguration:
    web-backend:
      resources:
        requests:
          memory: "400Mi"
          cpu: "200m"
        limits:
          memory: "2584Mi"
          cpu: "1000m"
    ingress-controller:
      enabled: true
      service:
        type: LoadBalancer

5. Customize the T4K resources configuration in the YAML file and then save it.

If using an external ingress controller, you must set these parameters in the yaml:

ingress-controller: enabled: false

6. Now apply the CR YAML file using the command:

kubectl create -f TVM.yaml

7. Once the operator pod is in a running state, confirm that the T4K pods are up.

8. If the install was not successful or the T4K pods were not spawned as expected:

Preflight jobs are not cleaned up immediately following failure. If your cluster version is 1.21 or above, the job is cleaned up after one hour, so you should collect any failure logs within one hour of a job failure.

Additionally, there is a bug on the helm side affecting auto-deletion of resources following failure. Until this Helm bug is fixed, to run preflight again, users must clean the following resources left behind after the first failed attempt. Once this bug is fixed, the cleanup will be handled automatically. Run the following commands to clean up the temporary resources:

  • Cleanup Service Account:

kubectl delete sa k8s-triliovault-operator-preflight-service-account -n <helm-release-namespace>

  • Cleanup Cluster Role Binding:

kubectl delete clusterrolebinding <helm-release-name>-<helm-release-namespace>-preflight-rolebinding

  • Cleanup Cluster Role:

kubectl delete clusterrole <helm-release-name>-<helm-release-namespace>-preflight-role

9. Finally, check the T4K install:

Check T4K Install

  • Firstly, check that the pods were created:

kubectl get pods

The readout should be similar to this:

NAME                                                         READY   STATUS    RESTARTS   AGE
k8s-triliovault-admission-webhook-6ff5f98c8-qwmfc            1/1     Running   0          81s
k8s-triliovault-web-backend-6f66b6b8d5-gxtmz                 1/1     Running   0          81s
k8s-triliovault-control-plane-6c464c5d78-ftk6g               1/1     Running   0          81s
k8s-triliovault-exporter-59566f97dd-gs4xc                    1/1     Running   0          81s
k8s-triliovault-ingress-nginx-controller-867c764cd5-qhpx6    1/1     Running   0          18s
k8s-triliovault-web-967c8475-m7pc6                           1/1     Running   0          81s
k8s-triliovault-operator-66bd7d86d5-dvhzb                    1/1     Running   0          6m48s

  • Secondly, check that the ingress controller service is of type LoadBalancer.

NAME                                                 TYPE           CLUSTER-IP     EXTERNAL-IP      PORT(S)                      AGE
k8s-triliovault-admission-webhook                    ClusterIP      10.7.243.24    <none>           443/TCP                      129m
k8s-triliovault-ingress-nginx-controller             LoadBalancer   10.7.246.193   35.203.155.148   80:30362/TCP,443:32327/TCP   129m
k8s-triliovault-ingress-nginx-controller-admission   ClusterIP      10.7.250.31    <none>           443/TCP                      129m
k8s-triliovault-web                                  ClusterIP      10.7.254.41    <none>           80/TCP                       129m
k8s-triliovault-web-backend                          ClusterIP      10.7.252.146   <none>           80/TCP                       129m
k8s-triliovault-operator-webhook-service             ClusterIP      10.7.248.163   <none>           443/TCP                      130m

  • Thirdly, check that ingress resources have the host defined by the user:

NAME              CLASS                           HOSTS   ADDRESS          PORTS   AGE
k8s-triliovault   k8s-triliovault-default-nginx   *       35.203.155.148   80      129m

  • Lastly, check that you can access the T4K UI by typing this address in your browser: https://35.203.155.148 Trilio is now successfully installed on your cluster.

Proxy Enabled Environments

As a Prerequisite, configure a Proxy Server. For example - Squid Proxy

In order to install Trilio for Kubernetes in proxy-enabled environments. Install the operator (step 2 above) by providing the proxy settings:

Environment VariablePurpose

HTTP_PROXY

Proxy address to use when initiating HTTP connection(s)

HTTPS_PROXY

Proxy address to use when initiating HTTPS connection(s)

NO_PROXY

Network address(es), network address range(s) and domains to exclude from using the proxy when initiating connection(s)

Note NO_PROXY must be in uppercase to use network range (CIDR) notation.

  • proxySettings.PROXY_ENABLED=true

  • proxySettings.HTTP_PROXY=http://<uname>:<password>@<IP>:<Port>

  • proxySettings.HTTPS_PROXY=https://<uname>:<password>@<IP>:<Port>

  • proxySettings.NO_PROXY="<according to user>"

  • proxySettings.CA_BUNDLE_CONFIGMAP="<configmap>"

    • For HTTPS proxy, create CA Bundle Proxy configMap in Install Namespace

    • Proxy CA certificate file key should be ca-bundle.crt

helm install tvm trilio-vault-operator/k8s-triliovault-operator \
--set proxySettings.PROXY_ENABLED=true \
--set proxySettings.NO_PROXY="localhost\,127.0.0.1\,10.239.112.0\/20\,10.240.0.0\/14" \ 
--set proxySettings.HTTP_PROXY=http://<uname>:<password>@<IP>:<Port> \
--set proxySettings.HTTPS_PROXY=https://<uname>:<password>@<IP>:<Port> \
--set proxySettings.CA_BUNDLE_CONFIGMAP="<proxy-configmap>"

After the operator is created by specifying proxy settings, the TVM will pick up these settings and leverage them directly for operations. No other configuration is required.

Licensing Trilio for Kubernetes

To generate and apply the Trilio license, perform the following steps:

Although a cluster license enables Trilio features across all namespaces in a cluster, the license only needs to be applied in the namespace where Trilio is installed. For example, trilio-system namespace.

1. Obtain a license by getting in touch with us here. The license file will contain the license key.

2. Apply the license file to a Trilio instance using the command line or UI:

  1. Execute the following command:

kubectl apply -f <licensefile> -n trilio-system

2. If the previous step is successful, check that the output generated is similar to the following:

NAMESPACE            NAME         STATUS   MESSAGE                                   CURRENT CPU COUNT   GRACE PERIOD END TIME   EDITION     CAPACITY   EXPIRATION TIME        MAX CPUS
trilio-system     license-sample   Active   Cluster License Activated successfully.   4                                           FreeTrial   10         2025-07-08T00:00:00Z   8

Additional license details can be obtained using the following:

kubectl get license -o json -m trilio-system

Upgrading a license

A license upgrade is required when moving from one license type to another.

Trilio maintains only one instance of a license for every installation of Trilio for Kubernetes.

To upgrade a license, run kubectl apply -f <licensefile> -n <install-namespace> against a new license file to activate it. The previous license will be replaced automatically.

Learn more about Licensing Trilio for Kubernetes

Create a Backup Target

The Target CR (Customer Resource) is defined from the Trilio Management Console or from your own self-prepared YAML.

The Target object references the NFS or S3 backup storage share you provide as a target for your backups. Trilio will create a validation pod in the namespace where Trilio is installed and attempt to validate the NFS or S3 settings you have defined in the Target CR.

Trilio makes it easy to automatically create your backup Target CRD from the Management Console.

Learn how to Create a Target from the Management Console

Take control of Trilio and define your own self-prepared YAML and apply it to the cluster using the kubectl tool.

Example S3 Target

kubectl apply -f sample-secret.yaml
kubectl apply -f demo-s3-target.yaml
apiVersion: v1
kind: Secret
metadata:
  name: sample-secret
type: Opaque
stringData:
  accessKey: AKIAS5B35DGFSTY7T55D
  secretKey: xWBupfGvkgkhaH8ansJU1wRhFoGoWFPmhXD6/vVDcode
apiVersion: triliovault.trilio.io/v1
kind: Target
metadata:
  name: demo-s3-target
spec:
  type: ObjectStore
  vendor: AWS
  objectStoreCredentials:
    region: us-east-1
    bucketName: trilio-browser-test
    credentialSecret:
      name: sample-secret
      namespace: TARGET_NAMESPACE
  thresholdCapacity: 5Gi

Testing Backup and Restore Operation

Trilio is a cloud-native application for Kubernetes, therefore all operations are managed with CRDs (Custom Resource Definitions). We will discuss the purpose of each Trilio CRD and provide examples of how to create these objects Automatically in the Trilio Management Console or from the kubectl tool.

About Backup Plans and Backups

  • The Backup Plan CR is defined from the Trilio Management Console or from your own self-prepared YAML.

The Backup Plan CR must reference the following:

  1. Your Application Data (label/helm/operator)

  2. Backup Target CR

  3. Scheduling Policy CR

  4. Retention Policy CR

  • A Target CR is defined from the Trilio Management Console or from your own self-prepared YAML. Trilio will test the backup target to ensure it is reachable and writable. Look at Trilio validation pod logs to troubleshoot any backup target creation issues.

  • Retention and Schedule Policy CRs are defined from the Trilio Management Console or from your own self-prepared YAML.

    • Scheduling Policies allow users to automate the backup of Kubernetes applications on a periodic basis. With this feature, users can create a scheduling policy that includes multiple cron strings to specify the frequency of backups.

    • Retention Policies make it easy for users to define the number of backups they want to retain and the rate at which old backups should be deleted. With the retention policy CR, users can use a simple YAML specification to define the number of backups to retain in terms of days, weeks, months, years, or the latest backup. This provides a flexible and customizable way to manage your backup retention policy and ensure you meet your compliance requirements.

  • The Backup CR is defined from the Trilio Management Console or from your own self-prepared YAML.

    The backup object references the actual backup Trilio creates on the Target. The backup is taken as either a Full or Incremental backup as defined by the user in the Backup CR.

Creating a Backup Plan and Backup

Trilio makes it easy to automatically create your backup plans and all required target and policy CRDs from the Management Console.

Learn more about Creating Backups from the Management Console

Take control of Trilio, define your self-prepared YAML, and apply it to the cluster using the kubectl tool.

Example Namespace Scope BackupPlan:

# kubectl apply -f ns-backupplan.yaml
apiVersion: triliovault.trilio.io/v1
kind: BackupPlan
metadata:
  name: ns-backupplan
spec:
  backupConfig:
    target:
      namespace: default
      name: demo-s3-target
kubectl apply -f sample-schedule.yaml
kind: "Policy"
apiVersion: "triliovault.trilio.io/v1"
metadata:
  name: "sample-schedule"
spec:
  type: "Schedule"
  scheduleConfig:
    schedule:
      - "0 0 * * *"
      - "0 */1 * * *"
      - "0 0 * * 0"
      - "0 0 1 * *"
      - "0 0 1 1 *"
kubectl apply -f sample-retention.yaml
apiVersion: triliovault.trilio.io/v1
kind: Policy
metadata:
  name: sample-retention
spec:
  type: Retention
  default: false
  retentionConfig:
    latest: 2
    weekly: 1
    dayOfWeek: Wednesday
    monthly: 1
    dateOfMonth: 15
    monthOfYear: March
    yearly: 1
kubectl apply -f sample-backup.yaml
apiVersion: triliovault.trilio.io/v1
kind: Backup
metadata:
  name: sample-backup
spec:
  type: Full
  backupPlan:
    name: sample-application
    namespace: default

See more Examples of Backup Plan YAML

About Restores

A Restore CR (Custom Resource) is defined from the Trilio Management Console or from your own self-prepared YAML. The Restore CR references a backup object that has been created previously from a Backup CR.

In a Migration scenario, the location of the backup should be specified within the desired target as there will be no Backup CR defining the location.

Trilio restores the backup into a specified namespace and upon completion of the restore operation, the application is ready to be used on the cluster.

Creating a Restore

Trilio makes it easy to automatically create your Restore CRDs from the Management Console.

Learn more about Creating Restores from the Management Console

Take control of Trilio, define your self-prepared YAML, and apply it to the cluster using the kubectl tool.

kubectl apply -f sample-restore.yaml
apiVersion: triliovault.trilio.io/v1
kind: Restore
metadata:
  name: sample-restore
spec:
  source:
    type: Backup
    backup:
      name: sample-backup
      namespace: default

See more Examples of Restore YAML

Troubleshooting

Problems? Learn about Troubleshooting Trilio for Kubernetes

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