Deploy PostgreSQL to GKE using Zalando

Autopilot Standard

---

The guide shows you how to use the Zalando Postgres operator to deploy Postgres clusters to Google Kubernetes Engine (GKE).

PostgreSQL is a powerful, open source object-relational database system with several decades of active development that has earned it a strong reputation for reliability, feature robustness, and performance.

This guide is intended for platform administrators, cloud architects, and operations professionals interested in running PostgreSQL as a database application on GKE instead of using Cloud SQL for PostgreSQL.

Objectives

• Plan and deploy GKE infrastructure for Postgres
• Deploy and configure the Zalando Postgres operator
• Configure Postgres using the operator to ensure availability, security, observability and performance

Benefits

Zalando offers the following benefits:

• A declarative and Kubernetes-native way to manage and configure the PostgreSQL clusters
• High availability provided by Patroni
• Backup management support using Cloud Storage buckets
• Rolling updates on Postgres cluster changes, including quick minor version updates
• Declarative User management with password generation and rotation using custom resources
• Support for TLS, certificate rotation, and connection pools
• Cluster cloning and data replication

Deployment architecture

In this tutorial, you use the Zalando Postgres operator to deploy and configure a highly available Postgres cluster to GKE. The cluster has one leader replica and two standby (read-only) replicas managed by Patroni. Patroni is an open source solution maintained by Zalando to provide high-availability and auto-failover capabilities to Postgres. In case of leader failure, one standby replica is automatically promoted to a leader role.

You also deploy a highly available regional GKE cluster for Postgres, with multiple >Kubernetes nodes spread across several availability zones. This setup helps ensure fault tolerance, scalability, and geographic redundancy. It allows for rolling updates and maintenance while providing SLAs for uptime and availability. For more information, see Regional clusters.

The following diagram shows a Postgres cluster running on multiple nodes and zones in a GKE cluster:

In the diagram, the Postgres StatefulSet is deployed across three nodes in three different zones. You can control how GKE deploys to nodes by setting the required Pod affinity and anti-affinity rules on the postgresql custom resource specification. If one zone fails, using the recommended configuration, GKE reschedules Pods on other available nodes in your cluster. For persisting data, you use SSD disks (premium-rwo StorageClass), which are recommended in most cases for highly loaded databases due to their low latency and high IOPS.

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

Cloud Shell is preinstalled with the software you need for this tutorial, including kubectl, the gcloud CLI, Helm, and Terraform. If you don't use Cloud Shell, you must install the gcloud CLI.

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, Backup for GKE APIs:

   gcloud services enable compute.googleapis.com iam.googleapis.com container.googleapis.com gkebackup.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, Backup for GKE APIs:

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

12. Grant roles to your user account. Run the following command once for each of the following IAM roles: roles/storage.objectViewer, roles/container.admin, roles/iam.serviceAccountAdmin, roles/compute.admin, roles/gkebackup.admin, roles/monitoring.viewer

    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

To set up your environment, follow these steps

1. Set environment variables:

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

   Replace PROJECT_ID with your Google Cloud project ID.

2. Clone the GitHub repository:

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

3. Change to the working directory:

   cd kubernetes-engine-samples/databases/postgres-zalando
   

Create your cluster infrastructure

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

You can install the operator using a Standard or Autopilot cluster.

Standard

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

Deploy this infrastructure:

export GOOGLE_OAUTH_ACCESS_TOKEN=$(gcloud auth print-access-token)
terraform -chdir=terraform/gke-standard init
terraform -chdir=terraform/gke-standard apply \
  -var project_id=${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
• A node pools with auto scaling enabled (one to two nodes per zone, one node per zone minimum)
• A ServiceAccount with logging and monitoring permissions
• Backup for GKE for disaster recovery
• Google Cloud Managed Service for Prometheus for cluster monitoring

The output is similar to the following:

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

Autopilot

The following diagram shows a private regional Autopilot GKE cluster:

Deploy the infrastructure:

export GOOGLE_OAUTH_ACCESS_TOKEN=$(gcloud auth print-access-token)
terraform -chdir=terraform/gke-autopilot init
terraform -chdir=terraform/gke-autopilot apply \
  -var project_id=${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
• A ServiceAccount with logging and monitoring permission
• Google Cloud Managed Service for Prometheus for cluster monitoring

The output is similar to the following:

...
Apply complete! Resources: 12 added, 0 changed, 0 destroyed.
...

Connect to the cluster

Configure kubectl to communicate with the cluster:

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

Deploy the Zalando operator to your cluster

Deploy the Zalando operator to your Kubernetes cluster using a Helm chart.

1. Add the Zalando operator Helm Chart repository:

   helm repo add postgres-operator-charts https://opensource.zalando.com/postgres-operator/charts/postgres-operator
   

2. Create a namespace for the Zalando operator and the Postgres cluster:

   kubectl create ns postgres
   kubectl create ns zalando
   

3. Deploy the Zalando operator using the Helm command-line tool:

   helm install postgres-operator postgres-operator-charts/postgres-operator -n zalando \
       --set configKubernetes.enable_pod_antiaffinity=true \
       --set configKubernetes.pod_antiaffinity_preferred_during_scheduling=true \
       --set configKubernetes.pod_antiaffinity_topology_key="topology.kubernetes.io/zone" \
       --set configKubernetes.spilo_fsgroup="103"
   

   You can't configure podAntiAffinity settings directly on the custom resource representing the Postgres cluster. Instead, set podAntiAffinity settings globally for all Postgres clusters in the operator settings.

4. Check the deployment status of the Zalando operator using Helm:

   helm ls -n zalando
   

   The output is similar to the following:

   NAME                 NAMESPACE    REVISION    UPDATED                                STATUS      CHART                       APP VERSION
   postgres-operator    zalando     1           2023-10-13 16:04:13.945614 +0200 CEST    deployed    postgres-operator-1.10.1    1.10.1
   

Deploy Postgres

The basic configuration for the Postgres cluster instance includes the following components:

• Three Postgres replicas: one leader and two standby replicas.
• CPU resource allocation of one CPU request and two CPU limits, with 4 GB memory requests and limits.
• Tolerations, nodeAffinities, and topologySpreadConstraints configured for each workload, ensuring proper distribution across Kubernetes nodes, utilizing their respective node pools and different availability zones.

This configuration represents the minimal setup required to create a production-ready Postgres cluster.

The following manifest describes a Postgres cluster:

apiVersion: "acid.zalan.do/v1"
kind: postgresql
metadata:
  name: my-cluster
spec:
  dockerImage: ghcr.io/zalando/spilo-15:3.0-p1
  teamId: "my-team"
  numberOfInstances: 3
  users:
    mydatabaseowner:
    - superuser
    - createdb
    myuser: []
  databases:
    mydatabase: mydatabaseowner
  postgresql:
    version: "15"
    parameters:
      shared_buffers: "32MB"
      max_connections: "10"
      log_statement: "all"
      password_encryption: scram-sha-256
  volume:
    size: 5Gi
    storageClass: premium-rwo
  enableShmVolume: true
  podAnnotations:
    cluster-autoscaler.kubernetes.io/safe-to-evict: "true"
  tolerations:
  - key: "app.stateful/component"
    operator: "Equal"
    value: "postgres-operator"
    effect: NoSchedule
  nodeAffinity:
    preferredDuringSchedulingIgnoredDuringExecution:
    - weight: 1
      preference:
        matchExpressions:
        - key: "app.stateful/component"
          operator: In
          values:
          - "postgres-operator"
  resources:
    requests:
      cpu: "1"
      memory: 4Gi
    limits:
      cpu: "2"
      memory: 4Gi
  sidecars:
    - name: exporter
      image: quay.io/prometheuscommunity/postgres-exporter:v0.14.0
      args:
      - --collector.stat_statements
      ports:
      - name: exporter
        containerPort: 9187
        protocol: TCP
      resources:
        limits:
          cpu: 500m
          memory: 256M
        requests:
          cpu: 100m
          memory: 256M
      env:
      - name: "DATA_SOURCE_URI"
        value: "localhost/postgres?sslmode=require"
      - name: "DATA_SOURCE_USER"
        value: "$(POSTGRES_USER)"
      - name: "DATA_SOURCE_PASS"
        value: "$(POSTGRES_PASSWORD)"

This manifest has the following fields:

• spec.teamId: a prefix for the cluster objects that you choose
• spec.numberOfInstances: the total number of instances for a cluster
• spec.users: the user list with privileges
• spec.databases: the database list in the format dbname: ownername
• spec.postgresql: postgres parameters
• spec.volume: Persistent Disk parameters
• spec.tolerations: the tolerations Pod template that allows cluster Pods to be scheduled on pool-postgres nodes
• spec.nodeAffinity: the nodeAffinity Pod template that tells GKE that cluster Pods prefer to be scheduled on pool-postgres nodes.
• spec.resources: requests and limits for cluster Pods
• spec.sidecars: a list of sidecar containers, which contains postgres-exporter

For more information, see Cluster manifest reference in the Postgres documentation.

Create a basic Postgres cluster

1. Create a new Postgres cluster using the basic configuration:

   kubectl apply -n postgres -f manifests/01-basic-cluster/my-cluster.yaml
   

   This command creates a PostgreSQL Custom Resource of the Zalando operator with:

   • CPU and memory requests and limits
   • Taints and affinities to distribute the provisioned Pod replicas across GKE nodes.
   • A database
   • Two users with database owner permissions
   • A user with no permissions

2. Wait for GKE to start the required workloads:

   kubectl wait pods -l cluster-name=my-cluster  --for condition=Ready --timeout=300s -n postgres
   

   This command might take a few minutes to complete.

3. Verify that GKE created the Postgres workloads:

   kubectl get pod,svc,statefulset,deploy,pdb,secret -n postgres
   

   The output is similar to the following:

   NAME                                    READY   STATUS  RESTARTS   AGE
   pod/my-cluster-0                        1/1     Running   0         6m41s
   pod/my-cluster-1                        1/1     Running   0         5m56s
   pod/my-cluster-2                        1/1     Running   0         5m16s
   pod/postgres-operator-db9667d4d-rgcs8   1/1     Running   0         12m
   
   NAME                        TYPE        CLUSTER-IP  EXTERNAL-IP   PORT(S)   AGE
   service/my-cluster          ClusterIP   10.52.12.109   <none>       5432/TCP   6m43s
   service/my-cluster-config   ClusterIP   None        <none>      <none>  5m55s
   service/my-cluster-repl     ClusterIP   10.52.6.152 <none>      5432/TCP   6m43s
   service/postgres-operator   ClusterIP   10.52.8.176 <none>      8080/TCP   12m
   
   NAME                        READY   AGE
   statefulset.apps/my-cluster   3/3   6m43s
   
   NAME                                READY   UP-TO-DATE   AVAILABLE   AGE
   deployment.apps/postgres-operator   1/1     1           1           12m
   
   NAME                                                MIN AVAILABLE   MAX UNAVAILABLE   ALLOWED DISRUPTIONS   AGE
   poddisruptionbudget.policy/postgres-my-cluster-pdb   1              N/A             0                   6m44s
   
   NAME                                                            TYPE                DATA   AGE
   secret/my-user.my-cluster.credentials.postgresql.acid.zalan.do  Opaque              2   6m45s
   secret/postgres.my-cluster.credentials.postgresql.acid.zalan.do   Opaque            2   6m44s
   secret/sh.helm.release.v1.postgres-operator.v1                  helm.sh/release.v1   1      12m
   secret/standby.my-cluster.credentials.postgresql.acid.zalan.do  Opaque              2   6m44s
   secret/zalando.my-cluster.credentials.postgresql.acid.zalan.do  Opaque              2   6m44s
   

The operator creates the following resources:

• A Postgres StatefulSet, which controls three Pod replicas for Postgres
• A PodDisruptionBudgets, ensuring a minimum of one available replica
• The my-cluster Service, which targets the leader replica only
• The my-cluster-repl Service, which exposes the Postgres port for incoming connections and for replication between Postgres replicas
• The my-cluster-config headless Service, to get the list of running Postgres Pod replicas
• Secrets with user credentials for accessing the database and replication between Postgres nodes

Authenticate to Postgres

You can create Postgres users and assign them database permissions. For example, the following manifest describes a custom resource that assigns users and roles:

apiVersion: "acid.zalan.do/v1"
kind: postgresql
metadata:
  name: my-cluster
spec:
  ...
  users:
    mydatabaseowner:
    - superuser
    - createdb
    myuser: []
  databases:
    mydatabase: mydatabaseowner

In this manifest:

• The mydatabaseowner user has the SUPERUSER and CREATEDB roles, which permit full administrator rights (i.e. manage Postgres configuration, create new databases, tables, and users). You shouldn't share this user with clients. For example Cloud SQL doesn't allow customers to have access to users with the SUPERUSER role.
• The myuser user has no roles assigned. This follows the best practice of using the SUPERUSER to create users with least privileges. Granular rights are granted to myuser by mydatabaseowner. To maintain security, you should only share myuser credentials with client applications.

Store passwords

You should use the scram-sha-256 recommended method for storing passwords. For example, the following manifest describes a custom resource that specifies scram-sha-256 encryption using the postgresql.parameters.password_encryption field:

apiVersion: "acid.zalan.do/v1"
kind: postgresql
metadata:
  name: my-cluster
spec:
  ...
  postgresql:
    parameters:
      password_encryption: scram-sha-256

Rotate user credentials

You can rotate user credentials that are stored in Kubernetes Secrets with Zalando. For example, the following manifest describes a custom resource that defines user credential rotation using the usersWithSecretRotation field:

apiVersion: "acid.zalan.do/v1"
kind: postgresql
metadata:
  name: my-cluster
spec:
  ...
  usersWithSecretRotation:
  - myuser
  - myanotheruser
  - ...

Authentication example: connect to Postgres

This section shows you how to deploy an example Postgres client and connect to the database using the password from a Kubernetes Secret.

1. Run the client Pod to interact with your Postgres cluster:

   kubectl apply -n postgres -f manifests/02-auth/client-pod.yaml
   

   The credentials of the myuser and mydatabaseowner users are taken from the related Secrets and mounted as environment variables to the Pod.

2. Connect to the Pod when it is ready:

   kubectl wait pod postgres-client --for=condition=Ready --timeout=300s -n postgres
   kubectl exec -it postgres-client -n postgres -- /bin/bash
   

3. Connect to Postgres and attempt to create a new table using myuser credentials:

   PGPASSWORD=$CLIENTPASSWORD psql \
     -h my-cluster \
     -U $CLIENTUSERNAME \
     -d mydatabase \
     -c "CREATE TABLE test (id serial PRIMARY KEY, randomdata VARCHAR ( 50 ) NOT NULL);"
   

   The command should fail with an error similar to the following:

   ERROR:  permission denied for schema public
   LINE 1: CREATE TABLE test (id serial PRIMARY KEY, randomdata VARCHAR...
   

   The command fails because users without assigned privileges by default can only login to Postgres and list databases.

4. Create a table with mydatabaseowner credentials and grant all privileges on the table to myuser:

   PGPASSWORD=$OWNERPASSWORD psql \
     -h my-cluster \
     -U $OWNERUSERNAME \
     -d mydatabase \
     -c "CREATE TABLE test (id serial PRIMARY KEY, randomdata VARCHAR ( 50 ) NOT NULL);GRANT ALL ON test TO myuser;GRANT ALL ON SEQUENCE test_id_seq TO myuser;"
   

   The output is similar to the following:

   CREATE TABLE
   GRANT
   GRANT
   

5. Insert random data into the table using myuser credentials:

   for i in {1..10}; do
     DATA=$(tr -dc A-Za-z0-9 </dev/urandom | head -c 13)
     PGPASSWORD=$CLIENTPASSWORD psql \
     -h my-cluster \
     -U $CLIENTUSERNAME \
     -d mydatabase \
     -c "INSERT INTO test(randomdata) VALUES ('$DATA');"
   done
   

   The output is similar to the following:

   INSERT 0 1
   INSERT 0 1
   INSERT 0 1
   INSERT 0 1
   INSERT 0 1
   INSERT 0 1
   INSERT 0 1
   INSERT 0 1
   INSERT 0 1
   INSERT 0 1
   

6. Get the values that you inserted:

   PGPASSWORD=$CLIENTPASSWORD psql \
     -h my-cluster \
     -U $CLIENTUSERNAME \
     -d mydatabase \
     -c "SELECT * FROM test;"
   

   The output is similar to the following:

   id |  randomdata
   ----+---------------
     1 | jup9HYsAjwtW4
     2 | 9rLAyBlcpLgNT
     3 | wcXSqxb5Yz75g
     4 | KoDRSrx3muD6T
     5 | b9atC7RPai7En
     6 | 20d7kC8E6Vt1V
     7 | GmgNxaWbkevGq
     8 | BkTwFWH6hWC7r
     9 | nkLXHclkaqkqy
    10 | HEebZ9Lp71Nm3
   (10 rows)
   

7. Exit the Pod shell:

   exit
   

Understand how Prometheus collects metrics for your Postgres cluster

The following diagram shows how Prometheus metrics collecting works:

In the diagram, a GKE private cluster contains:

• A Postgres Pod that gathers metrics on path / and port 9187
• Prometheus-based collectors that process the metrics from the Postgres Pod
• A PodMonitoring resource that sends metrics to Cloud Monitoring

Google Cloud Managed Service for Prometheus supports metrics collection in the Prometheus format. Cloud Monitoring uses an integrated dashboard for Postgres metrics.

Zalando exposes cluster metrics in the Prometheus format using the postgres_exporter component as a sidecar container.

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

   kubectl apply -n postgres -f manifests/03-prometheus-metrics/pod-monitoring.yaml
   

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

   Go to GKE Clusters Dashboard

   The dashboard shows a non-zero metrics ingestion rate.

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

   Go to Dashboards

4. Open the PostgreSQL Prometheus Overview dashboard. The dashboard shows the number of fetched rows. It might take several minutes for the dashboard to auto-provision.

5. Connect to the client Pod:

   kubectl exec -it postgres-client -n postgres -- /bin/bash
   

6. Insert random data:

   for i in {1..100}; do
     DATA=$(tr -dc A-Za-z0-9 </dev/urandom | head -c 13)
     PGPASSWORD=$CLIENTPASSWORD psql \
     -h my-cluster \
     -U $CLIENTUSERNAME \
     -d mydatabase \
     -c "INSERT INTO test(randomdata) VALUES ('$DATA');"
   done
   

7. Refresh the page. The Rows and Blocks graphs update to show the actual database state.

8. 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=postgres
   export REGION=us-central1
   

2. Run the terraform destroy command:

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

   Replace FOLDER with either gke-autopilot or gke-standard.

   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.