Deploy Redis to GKE using Redis Enterprise

Standard

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The guide shows you how to deploy Redis Enterprise to Google Kubernetes Engine (GKE) clusters.

Redis is an open source in-memory NoSQL database primarily used for caching. It has built-in replication, Lua scripting, LRU eviction, transactions, on-disk persistence, and high availability.

Redis Enterprise is an enterprise-grade solution that extends the Redis open-source with simplified management including geo-replicated data distribution, linear scaling of operations throughput, data tiering, advanced security features, and more.

Redis Enterprise has different pricing for each deployment option, including: Software, Cloud, or Hybrid and Multi-cloud.

This guide is intended for platform administrators, cloud architects, and operations professionals interested in deploying Redis Enterprise on Google Kubernetes Engine (GKE).

Note: Redis Enterprise Software distributed under a Redis Enterprise License, and license restrictions require users to have an active subscription after a 30 day trial to keep using Redis Enterprise.

Objectives

• Plan and deploy GKE infrastructure for Redis
• Deploy the Redis Enterprise Operator
• Deploy a Redis Enterprise Cluster
• Create a Redis Enterprise Database
• Demonstrate database authentication

Benefits

Redis Enterprise offers the following benefits:

• A Kubernetes-native way to manage Redis Enterprise Cluster (REC) lifecycle and Redis Enterprise Databases (REDBs)
• Resource utilization by co-locating multiple Redis databases within a single Kubernetes Pod
• Reduced operational overhead by handling routine maintenance tasks such as patching and upgrades
• Support for Redis software images from private container registries, such as Artifact Registry, to enhance the security and availability of containers
• Support for Google Cloud Managed Service for Prometheus for database monitoring and observability
• Enhanced security features such as encryption, access controls, and integration with Kubernetes RBAC (Role-Based Access Control)
• Advanced authentication methods including LDAP and third party credential managers like Vault
• Ability to configure scheduled backups

Deployment architecture

Redis Enterprise manages the following Kubernetes resources:

• The Enterprise cluster and its configuration in a StatefulSet. The cluster consists of Redis nodes (Pods) with installed Redis packages. These nodes have running processes to ensure the node is part of a cluster. Each node provides a container to run multiple database instances (shards). Although Kubernetes best practices state that a Pod should represent one application with one container, Redis Enterprise deploys multiple Redis databases to a single container. This approach provides better resource utilization, performance, and network throughput. Each container also has a zero-latency proxy to route and manage traffic to specific Redis database processes within a container.
• The RedisEnterpriseDatabase (REDBs) custom resource that represents the Redis database instances created within the REC
• Kubernetes Services that serve REDB instances as database endpoints
• A controller Pod called Service Rigger that creates and deletes database endpoints when a database is created or deleted

In this tutorial, you create a one-to-many deployment by deploying a REC into a dedicated namespace and using separate namespaces for application deployments for better isolation.

The following diagram describes Redis Enterprise components and how they are interconnected:

Figure 1: An example Redis Enterprise architecture.

In this tutorial, you configure Redis Enterprise Cluster to be highly available. To accomplish this, the REC requires an odd number of nodes and a minimum of three nodes. You also set affinity, anti-affinity rules, and node taints that ensure that each Redis node is placed in a different Kubernetes node and the Redis nodes are spread evenly across the Kubernetes cluster.

Using multiple nodes and zones is crucial for achieving a high-available GKE cluster for the following reasons:

• Fault tolerance: Multiple nodes distribute the workload across the cluster, ensuring that if one node fails, the other nodes can take over the tasks, preventing downtime and service interruptions.
• Scalability: Having multiple nodes allows for horizontal scaling by adding or removing nodes as needed, ensuring optimal resource allocation and accommodating increased traffic or workload demands.
• High availability: Using multiple zones within a region ensures redundancy and minimizes the risk of a single point of failure. If an entire availability zone experiences an outage, the cluster can continue running in other zones, maintaining service availability.
• Geographic redundancy: By spanning nodes across regions, the cluster's data and services are geographically distributed, providing resilience against natural disasters, power outages, or other local disruptions that might impact a single zone.
• Rolling updates and maintenance: By using multiple nodes, you can perform rolling updates and maintenance on individual nodes without impacting the overall availability of the cluster. This ensures continuous service while allowing you to perform necessary updates and apply patches seamlessly.
• Service Level Agreements (SLAs): Google Cloud provides SLAs for multi-zone deployments, guaranteeing a minimum level of uptime and availability.

Costs

In this document, you use the following billable components of Google Cloud:

• Compute Engine
• GKE
• Google Cloud Managed Service for Prometheus

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

When you finish the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, see Clean up.

Before you begin

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
2. Install the Google Cloud CLI.

3. To initialize the gcloud CLI, run the following command:

   gcloud init

   Note: If you installed the gcloud CLI previously, make sure you have the latest version by running gcloud components update.

4. Create or select a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   • Create a Google Cloud project:

     gcloud projects create PROJECT_ID

     Replace PROJECT_ID with a name for the Google Cloud project you are creating.

   • Select the Google Cloud project that you created:

     gcloud config set project PROJECT_ID

     Replace PROJECT_ID with your Google Cloud project name.

5. Make sure that billing is enabled for your Google Cloud project.

6. Enable the Compute Engine, IAM, GKE, and Resource Manager APIs:

   gcloud services enable compute.googleapis.com iam.googleapis.com container.googleapis.com cloudresourcemanager.googleapis.com

7. Install the Google Cloud CLI.

8. To initialize the gcloud CLI, run the following command:

   gcloud init

   Note: If you installed the gcloud CLI previously, make sure you have the latest version by running gcloud components update.

9. Create or select a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   • Create a Google Cloud project:

     gcloud projects create PROJECT_ID

     Replace PROJECT_ID with a name for the Google Cloud project you are creating.

   • Select the Google Cloud project that you created:

     gcloud config set project PROJECT_ID

     Replace PROJECT_ID with your Google Cloud project name.

10. Make sure that billing is enabled for your Google Cloud project.

11. Enable the Compute Engine, IAM, GKE, and Resource Manager APIs:

    gcloud services enable compute.googleapis.com iam.googleapis.com container.googleapis.com cloudresourcemanager.googleapis.com

12. Grant roles to your user account. Run the following command once for each of the following IAM roles: roles/compute.securityAdmin, roles/compute.viewer, roles/container.clusterAdmin, roles/container.admin, roles/iam.serviceAccountAdmin, roles/iam.serviceAccountUser

    gcloud projects add-iam-policy-binding PROJECT_ID --member="USER_IDENTIFIER" --role=ROLE

    • Replace PROJECT_ID with your project ID.

    • Replace USER_IDENTIFIER with the identifier for your user account. For example, user:[e-mail].

    • Replace ROLE with each individual role.

Set up your environment

In this tutorial, you use Cloud Shell to manage resources hosted on Google Cloud. Cloud Shell comes preinstalled with the software you need for this tutorial, including kubectl, the gcloud CLI, and Terraform.

To set up your environment with Cloud Shell, follow these steps:

1. Launch a Cloud Shell session from the Google Cloud console, by clicking Activate Cloud Shell in the Google Cloud console. This launches a session in the bottom pane of the Google Cloud console.

2. Set environment variables:

   export PROJECT_ID=PROJECT_ID
   export KUBERNETES_CLUSTER_PREFIX=redis
   export REGION=us-central1
   

   Replace PROJECT_ID: your Google Cloud with your project ID.

3. Clone the GitHub repository:

   git clone https://github.com/GoogleCloudPlatform/kubernetes-engine-samples
   

4. Change to the working directory:

   cd kubernetes-engine-samples/databases/redis-enterprise-operator
   

Create your cluster infrastructure

In this section, you run a Terraform script to create a private, highly-available, regional GKE cluster and VPC.

The following diagram shows a private regional Standard GKE cluster deployed across three different zones:

To deploy this infrastructure, run the following commands from the Cloud Shell:

  cd terraform/gke-standard
  export GOOGLE_OAUTH_ACCESS_TOKEN=$(gcloud auth print-access-token)
  terraform init
  terraform apply -var project_id=${PROJECT_ID}   \
    -var region=${REGION}  \
    -var cluster_prefix=${KUBERNETES_CLUSTER_PREFIX}

When prompted, type yes. It might take several minutes for this command to complete and for the cluster to show a ready status.

Terraform creates the following resources:

• A VPC network and private subnet for the Kubernetes nodes
• A router to access the internet through NAT
• A private GKE cluster in the us-central1 region
• One node pool with auto-scaling enabled (One to two nodes per zone, one node per zone minimum)

The output is similar to the following:

...
Apply complete! Resources: 14 added, 0 changed, 0 destroyed.
...

Connect to the cluster

Using Cloud Shell, configure kubectl to communicate with the cluster:

gcloud container clusters get-credentials ${KUBERNETES_CLUSTER_PREFIX}-cluster --region ${REGION}

Deploy the Redis Enterprise operator to your cluster

In this section, you deploy the Redis Enterprise operator to your Kubernetes cluster.

1. Create namespaces for the REC and its applications:

   kubectl create namespace rec-ns
   kubectl create namespace application
   

2. Label the namespaces:

   kubectl label namespace rec-ns connection=redis
   kubectl label namespace application connection=redis
   

3. Get the latest version of the Redis Enterprise Operator bundle:

   VERSION=`curl --silent https://api.github.com/repos/RedisLabs/redis-enterprise-k8s-docs/releases/latest | grep tag_name | awk -F'"' '{print $4}'`
   

4. Install the Redis Enterprise operator:

   kubectl apply -n rec-ns -f https://raw.githubusercontent.com/RedisLabs/redis-enterprise-k8s-docs/$VERSION/bundle.yaml
   

   The output is similar to the following:

   role.rbac.authorization.k8s.io/redis-enterprise-operator created
   rolebinding.rbac.authorization.k8s.io/redis-enterprise-operator created
   serviceaccount/redis-enterprise-operator created
   service/admission created
   customresourcedefinition.apiextensions.k8s.io/redisenterpriseclusters.app.redislabs.com created
   customresourcedefinition.apiextensions.k8s.io/redisenterprisedatabases.app.redislabs.com created
   customresourcedefinition.apiextensions.k8s.io/redisenterpriseremoteclusters.app.redislabs.com created
   customresourcedefinition.apiextensions.k8s.io/redisenterpriseactiveactivedatabases.app.redislabs.com created
   deployment.apps/redis-enterprise-operator created
   

Deploy Redis Enterprise Cluster

1. Apply the manifest to your cluster:

   kubectl apply -n rec-ns -f manifests/01-basic-cluster/rec.yaml
   

   The command might take several minutes to complete.

2. Check the status of the REC deployment:

   kubectl get rec -n rec-ns
   

   The output is similar to the following:

   NAME      NODES   VERSION    STATE     SPEC STATUS   LICENSE STATE   SHARDS LIMIT   LICENSE EXPIRATION DATE   AGE
   gke-rec   3       7.2.4-52   Running   Valid         Valid           4              2023-09-29T20:15:32Z      4m7s
   

   The cluster is ready when STATE is RUNNING.

Optional: Configure the admission controller

You can optionally configure infrastructure for the database validation on deployment.

1. Setup the admission controller and check if the admission tls Secret is present:

   kubectl get secret admission-tls -n rec-ns
   

2. Get the certificate:

   export CERT=$(kubectl get secret admission-tls -n rec-ns -o jsonpath='{.data.cert}')
   

3. Copy the certificate into the webhook.yaml file:

   sed -i -e 's/CRT/'$CERT'/g' manifests/01-basic-cluster/webhook.yaml
   

4. Deploy the validation webhook:

   sed -i -e 's/CRT/'$CERT'/g' manifests/01-basic-cluster/webhook.yaml
   

   The admission controller validates database syntax on labeled namespaces.

5. Verify the admission controller by creating a non-functional database:

   kubectl apply -n rec-ns -f - << EOF
   apiVersion: app.redislabs.com/v1alpha1
   kind: RedisEnterpriseDatabase
   metadata:
     name: redis-enterprise-database
   spec:
     evictionPolicy: illegal
   EOF
   

   The output is similar to the following:

   Error from server: error when creating "STDIN": admission webhook "redisenterprise.admission.redislabs" denied the request: 'illegal' is an invalid value for 'eviction_policy'. Possible values are ['volatile-lru', 'volatile-ttl', 'volatile-random', 'allkeys-lru', 'allkeys-random', 'noeviction', 'volatile-lfu', 'allkeys-lfu']
   

Create namespaces

By default, the Redis Enterprise Operator has no privileges to perform actions outside its own namespace. To allow the Redis Enterprise Operator to create REDB and database endpoints in other namespaces, you must configure RBAC.

1. Apply the corresponding role and role binding in the application namespace:

   kubectl apply -f manifests/01-basic-cluster/role.yaml -n application
   kubectl apply -f manifests/01-basic-cluster/role-binding.yaml -n application
   

2. Create cluster role and cluster role binding in the rec-ns namespace:

   kubectl apply -n rec-ns -f manifests/01-basic-cluster/cluster_role.yaml 
   kubectl apply -n rec-ns -f manifests/01-basic-cluster/cluster_role_binding.yaml
   

3. Edit the REC ConfigMap to add control over the application namespace:

   kubectl patch ConfigMap/operator-environment-config --type merge -p '{"data": {"REDB_NAMESPACES_LABEL": "connection=redis"}}' -n rec-ns
   

   Each namespace labeled as ConfigMap is patched.

4. Check the status of the resources in your Redis infrastructure in the rec-ns namespace:.

   kubectl get pod,deploy,svc,rec,statefulset,cm,secrets -n rec-ns
   

   The output is similar to the following:

   NAME                                             READY   STATUS    RESTARTS        AGE
   pod/gke-rec-0                                    2/2     Running   0               172m
   pod/gke-rec-1                                    2/2     Running   0               171m
   pod/gke-rec-2                                    2/2     Running   0               168m
   pod/gke-rec-services-rigger-5f885f59dc-gc79g     1/1     Running   0               172m
   pod/redis-enterprise-operator-6668ccd8dc-kx29z   2/2     Running   2 (5m58s ago)   5h
   
   NAME                                        READY   UP-TO-DATE   AVAILABLE   AGE
   deployment.apps/gke-rec-services-rigger     1/1     1            1           172m
   deployment.apps/redis-enterprise-operator   1/1     1            1           5h
   
   NAME                   TYPE        CLUSTER-IP    EXTERNAL-IP   PORT(S)             AGE
   service/admission      ClusterIP   10.52.11.13   <none>        443/TCP             5h
   service/gke-rec        ClusterIP   10.52.5.44    <none>        9443/TCP,8001/TCP   172m
   service/gke-rec-prom   ClusterIP   None          <none>        8070/TCP            172m
   service/gke-rec-ui     ClusterIP   10.52.3.29    <none>        8443/TCP            172m
   
   NAME                                               NODES   VERSION    STATE     SPEC STATUS   LICENSE STATE   SHARDS LIMIT   LICENSE EXPIRATION DATE   AGE
   redisenterprisecluster.app.redislabs.com/gke-rec   3       7.2.4-52   Running   Valid         Valid           4              2023-10-05T11:07:20Z      172m
   
   NAME                       READY   AGE
   statefulset.apps/gke-rec   3/3     172m
   
   NAME                                    DATA   AGE
   configmap/gke-rec-bulletin-board        1      172m
   configmap/gke-rec-health-check          5      172m
   configmap/kube-root-ca.crt              1      5h2m
   configmap/operator-environment-config   1      5h
   
   NAME                   TYPE     DATA   AGE
   secret/admission-tls   Opaque   2      5h
   secret/gke-rec         Opaque   2      172m
   

Deploy Redis Enterprise Databases

1. Create Redis Enterprise Databases in the application namespaces:

   kubectl apply -f manifests/01-basic-cluster/a-rdb.yaml -n application
   

2. Check the REDB status:

   kubectl get redb --all-namespaces
   

   The output is similar to the following:

   NAMESPACE       NAME       VERSION   PORT    CLUSTER   SHARDS   STATUS   SPEC STATUS   AGE
   application   app-db   7.2.0     12999   gke-rec   1        active   Valid         15s
   

3. Verify that the Services for each REDB are running:

   kubectl get svc --all-namespaces
   

   The output is similar to the following:

   NAMESPACE      NAME      TYPE          CLUSTER-IP   EXTERNAL-IP                           PORT(S)    AGE
   application  app-db  ExternalName  <none>       redis-12999.rec-ns.svc.cluster.local  12999/TCP  72m
   

4. Verify that the Secret was created:

   kubectl get secrets -n application
   

   The output is similar to the following:

   NAME            TYPE     DATA   AGE
   redb-app-db   Opaque   3      96m
   

Authenticate using passwords

You can connect to REDB using a Pod with redis-cli in the application namespace. The client Pod uses the secrets available in the application namespace (REDB) to establish a connection.

Databases created with the Custom Resource REDB only support password authentication without ACL.

1. Create the client Pod:

   kubectl apply -n application -f manifests/03-auth/client_pod.yaml
   

2. Connect to the client Pod:

   kubectl exec -n application -i -t redis-client -c redis-client -- /bin/sh
   

3. Connect to the database:

   redis-cli -h $SERVICE -p $PORT --pass $PASS
   

4. Create a key:

   SET mykey "Hello World"
   

   The output is similar to the following:

   OK
   

5. Get the key:

   GET mykey
   

   The output is similar to the following:

   "Hello World"
   

6. Exit the Pod shell

   exit
   

Understand how Prometheus collects metrics for your Redis cluster

The following diagram shows how Prometheus metrics collecting works:

In the diagram, a GKE private cluster contains:

• A Redis Pod that gathers metrics on path / and port 8070
• Prometheus-based collectors that process the metrics from the Redis Pod
• A PodMonitoring resource that sends metrics to Cloud Monitoring

The Redis Enterprise operator exposes cluster metrics in Prometheus format.

1. Create the metrics-proxy Deployment:

   kubectl apply -n rec-ns -f manifests/02-prometheus-metrics/metrics-proxy.yaml
   

   Because the operator only provides an HTTPS endpoint with the self-signed certificate and the PodMonitoring resource doesn't support disabling TLS certificate verification, you use the metrics-proxy Pod as a reverse proxy for this endpoint to expose the metrics on the HTTP port.

2. Create the PodMonitoring resource to scrape metrics by labelSelector:

   kubectl apply -n rec-ns -f manifests/02-prometheus-metrics/pod-monitoring.yaml
   

3. In the Google Cloud console, go to the GKE Clusters Dashboard page.

   Go to GKE Clusters Dashboard

   The dashboard shows non-zero metrics ingestion rate.

Create a Dashboard

You can view the metrics by creating a dashboard.

1. Create the dashboard:

   gcloud --project "${PROJECT_ID}" monitoring dashboards create --config-from-file monitoring/dashboard.json
   

   The output is similar to the following:

   Created [f4efbe4e-2605-46b4-9910-54b13d29b3be].
   

2. In the Google Cloud console, go to the Dashboards page.

   Go to Dashboards

3. Open the Redis Enterprise Cluster dashboard. It might take several minutes for the dashboard to auto-provision.

Verify the exported metrics

To verify the metrics, create new database and examine the metrics.

1. Open the Redis Enterprise Cluster dashboard.

2. Create an additional Redis database:

   kubectl apply -n rec-ns -f manifests/02-prometheus-metrics/c-rdb.yaml
   

   The Database Count on the dashboard should update.

3. Create a client Pod to connect to the new database:

   kubectl apply -n rec-ns -f manifests/02-prometheus-metrics/client_pod.yaml
   

4. Connect to the client Pod and prepare variables:

   kubectl exec -it redis-client-c -n rec-ns -- /bin/bash
   

5. Use the redis-cli tool to create new keys:

   for i in {1..50}; do \
     redis-cli -h $SERVICE -p $PORT -a $PASS \
     --no-auth-warning SET mykey-$i "myvalue-$i"; \
   done
   

6. Refresh the page and observe that graphs have been updated to show the actual database state.

7. Exit the Pod shell

   exit
   

Clean up

Delete the project

Caution: Deleting a project has the following effects:

• Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

Delete a Google Cloud project:

gcloud projects delete PROJECT_ID

Delete individual resources

1. Set environment variables.

   export PROJECT_ID=${PROJECT_ID}
   export KUBERNETES_CLUSTER_PREFIX=redis
   export REGION=us-central1
   

2. Run the terraform destroy command:

   export GOOGLE_OAUTH_ACCESS_TOKEN=$(gcloud auth print-access-token)
   cd terraform/gke-standard
   terraform destroy -var project_id=${PROJECT_ID}   \
     -var region=${REGION}  \
     -var cluster_prefix=${KUBERNETES_CLUSTER_PREFIX}
   

   When prompted, type yes.

3. Find all unattached disks:

   export disk_list=$(gcloud compute disks list --filter="-users:* AND labels.name=${KUBERNETES_CLUSTER_PREFIX}-cluster" --format "value[separator=|](name,zone)")
   

4. Delete the disks:

   for i in $disk_list; do
     disk_name=$(echo $i| cut -d'|' -f1)
     disk_zone=$(echo $i| cut -d'|' -f2|sed 's|.*/||')
     echo "Deleting $disk_name"
     gcloud compute disks delete $disk_name --zone $disk_zone --quiet
   done
   

5. Delete the GitHub repository:

   rm -r ~/kubernetes-engine-samples/
   

What's next

• Explore reference architectures, diagrams, and best practices about Google Cloud. Take a look at our Cloud Architecture Center.