Deploy with Helm

Deploying ZenML in a Kubernetes cluster with Helm.

If you wish to manually deploy and manage ZenML in a Kubernetes cluster of your choice, ZenML also includes a Helm chart among its available deployment options.

You can find the chart on this ArtifactHub repository, along with the templates, default values and instructions on how to install it. Read on to find detailed explanations on prerequisites, configuration, and deployment scenarios.

Prerequisites

You'll need the following:

A Kubernetes cluster
Optional, but recommended: a MySQL-compatible database reachable from the Kubernetes cluster (e.g. one of the managed databases offered by Google Cloud, AWS, or Azure). A MySQL server version of 8.0 or higher is required
the Kubernetes client already installed on your machine and configured to access your cluster
Helm installed on your machine
Optional: an external Secrets Manager service (e.g. one of the managed secrets management services offered by Google Cloud, AWS, Azure, or HashiCorp Vault). By default, ZenML stores secrets inside the SQL database that it's connected to, but you also have the option of using an external cloud Secrets Manager service if you already happen to use one of those cloud or service providers

ZenML Helm Configuration

You can start by taking a look at the values.yaml file and familiarize yourself with some of the configuration settings that you can customize for your ZenML deployment.

In addition to tools and infrastructure, you will also need to collect and prepare information related to your database and information related to your external secrets management service to be used for the Helm chart configuration and you may also want to install additional optional services in your cluster.

When you are ready, you can proceed to the installation section.

Collect information from your SQL database service

Using an external MySQL-compatible database service is optional, but is recommended for production deployments. If omitted, ZenML will default to using an embedded SQLite database, which has the following limitations:

the SQLite database is not persisted, meaning that it will be lost if the ZenML server pod is restarted or deleted
the SQLite database does not scale horizontally, meaning that you will not be able to use more than one replica at a time for the ZenML server pod

If you decide to use an external MySQL-compatible database service, you will need to collect and prepare the following information for the Helm chart configuration:

the hostname and port where the SQL database is reachable from the Kubernetes cluster
the username and password that will be used to connect to the database. It is recommended that you create a dedicated database user for the ZenML server and that you restrict its privileges to only access the database that will be used by ZenML. Enforcing secure SSL connections for the user/database is also recommended. See the MySQL documentation for more information on how to set up users and privileges.
the name of the database that will be used by ZenML. The database does not have to exist prior to the deployment ( ZenML will create it on the first start). However, you need to create the database if you follow the best practice of restricting database user privileges to only access it.
if you plan on using SSL to secure the client database connection, you may also need to prepare additional SSL certificates and keys:
- the TLS CA certificate that was used to sign the server TLS certificate, if you're using a self-signed certificate or signed by a custom certificate authority that is not already trusted by default by most operating systems.
- the TLS client certificate and key. This is only needed if you decide to use client certificates for your DB connection (some managed DB services support this, CloudSQL is an example).

Collect information from your secrets management service

Using an externally managed secrets management service like those offered by Google Cloud, AWS, Azure or HashiCorp Vault is optional, but is recommended if you are already using those cloud service providers. If omitted, ZenML will default to using the SQL database to store secrets.

If you decide to use an external secrets management service, you will need to collect and prepare the following information for the Helm chart configuration (for supported back-ends only):

For the AWS secrets manager:

the AWS region that you want to use to store your secrets
an AWS access key ID and secret access key that provides full access to the AWS secrets manager service. You can create a dedicated IAM user for this purpose, or use an existing user with the necessary permissions. If you deploy the ZenML server in an EKS Kubernetes cluster that is already configured to use implicit authorization with an IAM role for service accounts, you can omit this step.

For the Google Cloud secrets manager:

the Google Cloud project ID that you want to use to store your secrets
a Google Cloud service account that has access to the secrets manager service. You can create a dedicated service account for this purpose, or use an existing service account with the necessary permissions.

For the Azure Key Vault:

the name of the Azure Key Vault that you want to use to store your secrets
the Azure tenant ID, client ID, and client secret associated with the Azure service principal that will be used to access the Azure Key Vault. You can create a dedicated application service principal for this purpose, or use an existing service principal with the necessary permissions. If you deploy the ZenML server in an AKS Kubernetes cluster that is already configured to use implicit authorization through the Azure-managed identity service, you can omit this step.

For the HashiCorp Vault:

the URL of the HashiCorp Vault server
the token that will be used to access the HashiCorp Vault server.

Optional cluster services

It is common practice to install additional infrastructure-related services in a Kubernetes cluster to support the deployment and long-term management of applications. For example:

an Ingress service like nginx-ingress is recommended if you want to expose HTTP services to the internet. An Ingress is required if you want to use secure HTTPS for your ZenML deployment. The alternative is to use a LoadBalancer service to expose the ZenML service using plain HTTP, but this is not recommended for production.
a cert-manager is recommended if you want to generate and manage TLS certificates for your ZenML deployment. It can be used to automatically provision TLS certificates from a certificate authority (CA) of your choice, such as Let's Encrypt. As an alternative, the ZenML Helm chart can be configured to auto-generate self-signed or you can generate the certificates yourself and provide them to the Helm chart, but this makes it more difficult to manage the certificates and you need to manually renew them when they expire.

ZenML Helm Installation

Configure the Helm chart

To use the Helm chart with custom values that includes path to files like the database SSL certificates, you need to pull the chart to your local directory first. You can do this with the following command:

helm pull oci://public.ecr.aws/zenml/zenml --version <VERSION> --untar

Next, to customize the Helm chart for your deployment, you should create a copy of the values.yaml file that you can find at ./zenml/values.yaml (let’s call this custom-values.yaml). You’ll use this as a template to customize your configuration. Any values that you don’t override you should simply remove from your custom-values.yaml file to keep it clean and compatible with future Helm chart releases.

In most cases, you’ll need to change the following configuration values in custom-values.yaml:

the database configuration, if you mean to use an external database:
- the database URL, formatted as mysql://<username>:<password>@<hostname>:<port>/<database>
- CA and/or client TLS certificates, if you’re using SSL to secure the connection to the database
the Ingress configuration, if enabled:
- enabling TLS
- enabling self-signed certificates
- configuring the hostname that will be used to access the ZenML server, if different from the IP address or hostname associated with the Ingress service installed in your cluster

Note All the file paths that you use in your helm chart (e.g. for certificates like database.sslCa) must be relative to the ./zenml helm chart directory, meaning that you also have to copy these files there.

Install the Helm chart

Once everything is configured, you can run the following command in the ./zenml folder to install the Helm chart.

helm -n <namespace> install zenml-server . --create-namespace --values custom-values.yaml

Connect to the deployed ZenML server

Immediately after deployment, the ZenML server needs to be activated before it can be used. The activation process includes creating an initial admin user account and configuring some server settings. You can do this only by visiting the ZenML server URL in your browser and following the on-screen instructions. Connecting your local ZenML client to the server is not possible until the server is properly initialized.

The Helm chart should print out a message with the URL of the deployed ZenML server. You can use the URL to open the ZenML UI in your browser.

To connect your local client to the ZenML server, you can run:

zenml login https://zenml.example.com:8080 --no-verify-ssl

To disconnect from the current ZenML server and revert to using the local default database, use the following command:

zenml logout

ZenML Helm Deployment Scenarios

This section covers some common Helm deployment scenarios for ZenML.

Minimal deployment

The example below is a minimal configuration for a ZenML server deployment that uses a temporary SQLite database and a ClusterIP service that is not exposed to the internet:

zenml:

  ingress:
    enabled: false

Once deployed, you have to use port-forwarding to access the ZenML server and to connect to it from your local machine:

kubectl -n zenml-server port-forward svc/zenml-server 8080:8080
zenml login http://localhost:8080

This is just a simple example only fit for testing and evaluation purposes. For production deployments, you should use an external database and an Ingress service with TLS certificates to secure and expose the ZenML server to the internet.

Basic deployment with local database

This deployment use-case still uses a local database, but it exposes the ZenML server to the internet using an Ingress service with TLS certificates generated by the cert-manager and signed by Let's Encrypt.

First, you need to install cert-manager and nginx-ingress in your Kubernetes cluster. You can use the following commands to install them with their default configuration:

helm repo add jetstack https://charts.jetstack.io
helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
helm repo update
helm install cert-manager jetstack/cert-manager --namespace cert-manager --create-namespace --set installCRDs=true
helm install nginx-ingress ingress-nginx/ingress-nginx --namespace nginx-ingress --create-namespace

Next, you need to create a ClusterIssuer resource that will be used by cert-manager to generate TLS certificates with Let's Encrypt:

cat <<EOF | kubectl apply -f -
apiVersion: cert-manager.io/v1
kind: ClusterIssuer
metadata:
  name: letsencrypt-staging
  namespace: cert-manager
spec:
  acme:
    server: https://acme-v02.api.letsencrypt.org/directory
    email: <your email address here>
    privateKeySecretRef:
      name: letsencrypt-staging
    solvers:
    - http01:
        ingress:
          class: nginx
EOF

Finally, you can deploy the ZenML server with the following Helm values:

zenml:

  ingress:
    enabled: true
    annotations:
      cert-manager.io/cluster-issuer: "letsencrypt-staging"
    tls:
      enabled: true
      generateCerts: false

Note This use-case exposes ZenML at the root URL path of the IP address or hostname of the Ingress service. You cannot share the same Ingress hostname and URL path for multiple applications. See the next section for a solution to this problem.

Shared Ingress controller

If the root URL path of your Ingress controller is already in use by another application, you cannot use it for ZenML. This section presents three possible solutions to this problem.

Use a dedicated Ingress hostname for ZenML

If you know the IP address of the load balancer in use by your Ingress controller, you can use a service like https://nip.io/ to create a new DNS name associated with it and expose ZenML at this new root URL path. For example, if your Ingress controller has the IP address 192.168.10.20, you can use a DNS name like zenml.192.168.10.20.nip.io to expose ZenML at the root URL path https://zenml.192.168.10.20.nip.io.

To find the IP address of your Ingress controller, you can use a command like the following:

kubectl -n nginx-ingress get svc nginx-ingress-ingress-nginx-controller -o jsonpath='{.status.loadBalancer.ingress[0].ip}'

You can deploy the ZenML server with the following Helm values:

zenml:

  ingress:
    enabled: true
    annotations:
      cert-manager.io/cluster-issuer: "letsencrypt-staging"
    host: zenml.<nginx ingress IP address>.nip.io
    tls:
      enabled: true
      generateCerts: false

Note This method does not work if your Ingress controller is behind a load balancer that uses a hostname mapped to several IP addresses instead of an IP address.

Use a dedicated Ingress URL path for ZenML

If you cannot use a dedicated Ingress hostname for ZenML, you can use a dedicated Ingress URL path instead. For example, you can expose ZenML at the URL path https://<your ingress hostname>/zenml.

To deploy the ZenML server with a dedicated Ingress URL path, you can use the following Helm values:

zenml:

  ingress:
    enabled: true
    annotations:
      cert-manager.io/cluster-issuer: "letsencrypt-staging"
      nginx.ingress.kubernetes.io/rewrite-target: /$1
    path: /zenml/?(.*)
    tls:
      enabled: true
      generateCerts: false

Note This method has one current limitation: the ZenML UI does not support URL rewriting and will not work properly if you use a dedicated Ingress URL path. You can still connect your client to the ZenML server and use it to run pipelines as usual, but you will not be able to use the ZenML UI.

Use a DNS service to map a different hostname to the Ingress controller

This method requires you to configure a DNS service like AWS Route 53 or Google Cloud DNS to map a different hostname to the Ingress controller. For example, you can map the hostname zenml.<subdomain> to the Ingress controller's IP address or hostname. Then, simply use the new hostname to expose ZenML at the root URL path.

Secret Store configuration

Unless explicitly disabled or configured otherwise, the ZenML server will use the SQL database as a secrets store backend where secret values are stored. If you want to use an external secrets management service like the AWS Secrets Manager, GCP Secrets Manager, Azure Key Vault, HashiCorp Vault or even your custom Secrets Store back-end implementation instead, you need to configure it in the Helm values. Depending on where you deploy your ZenML server and how your Kubernetes cluster is configured, you will also need to provide the credentials needed to access the secrets management service API.

Important: If you are updating the configuration of your ZenML Server deployment to use a different secrets store back-end or location, you should follow the documented secrets migration strategy to minimize downtime and to ensure that existing secrets are also properly migrated.

Using the SQL database as a secrets store backend (default)

The SQL database is used as the default location where the ZenML secrets store keeps the secret values. You only need to configure these options if you want to change the default behavior.

It is particularly recommended to enable encryption at rest for the SQL database if you plan on using it as a secrets store backend. You'll have to configure the secret key used to encrypt the secret values. If not set, encryption will not be used and passwords will be stored unencrypted in the database. This value should be set to a random string with a recommended length of at least 32 characters, e.g.:

generate a random string with Python:

from secrets import token_hex
token_hex(32)

or with OpenSSL:

openssl rand -hex 32

then configure it in the Helm values:

 zenml:

   # ...

   # Secrets store settings. This is used to store centralized secrets.
   secretsStore:

     # The type of the secrets store
     type: sql

     # Configuration for the SQL secrets store
     sql:
       encryptionKey: 0f00e4282a3181be32c108819e8a860a429b613e470ad58531f0730afff64545

Important: If you configure encryption for your SQL database secrets store, you should keep the encryptionKey value somewhere safe and secure, as it will always be required by the ZenML Server to decrypt the secrets in the database. If you lose the encryption key, you will not be able to decrypt the secrets anymore and will have to reset them.

Using the AWS Secrets Manager as a secrets store backend

The AWS Secrets Store uses the ZenML AWS Service Connector under the hood to authenticate with the AWS Secrets Manager API. This means that you can use any of the authentication methods supported by the AWS Service Connector to authenticate with the AWS Secrets Manager API.

The minimum set of permissions that must be attached to the implicit or configured AWS credentials are: secretsmanager:CreateSecret, secretsmanager:GetSecretValue, secretsmanager:DescribeSecret, secretsmanager:PutSecretValue, secretsmanager:TagResource and secretsmanager:DeleteSecret and they must be associated with secrets that have a name starting with zenml/ in the target region and account. The following IAM policy example can be used as a starting point:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "ZenMLSecretsStore",
            "Effect": "Allow",
            "Action": [
                "secretsmanager:CreateSecret",
                "secretsmanager:GetSecretValue",
                "secretsmanager:DescribeSecret",
                "secretsmanager:PutSecretValue",
                "secretsmanager:TagResource",
                "secretsmanager:DeleteSecret"
            ],
            "Resource": "arn:aws:secretsmanager:<AWS-region>:<AWS-account-id>:secret:zenml/*"
        }
    ]
}

Example configuration for the AWS Secrets Store:

 zenml:

   # ...

   # Secrets store settings. This is used to store centralized secrets.
   secretsStore:

     # Set to false to disable the secrets store.
     enabled: true

     # The type of the secrets store
     type: aws

     # Configuration for the AWS Secrets Manager secrets store
     aws:

       # The AWS Service Connector authentication method to use.
       authMethod: secret-key

       # The AWS Service Connector configuration.
       authConfig:
        # The AWS region to use. This must be set to the region where the AWS
        # Secrets Manager service that you want to use is located.
        region: us-east-1

        # The AWS credentials to use to authenticate with the AWS Secrets
        aws_access_key_id: <your AWS access key ID>
        aws_secret_access_key: <your AWS secret access key>

Using the GCP Secrets Manager as a secrets store backend

The GCP Secrets Store uses the ZenML GCP Service Connector under the hood to authenticate with the GCP Secrets Manager API. This means that you can use any of the authentication methods supported by the GCP Service Connector to authenticate with the GCP Secrets Manager API.

The minimum set of permissions that must be attached to the implicit or configured GCP credentials are as follows:

secretmanager.secrets.create for the target GCP project (i.e. no condition on the name prefix)
secretmanager.secrets.get, secretmanager.secrets.update, secretmanager.versions.access, secretmanager.versions.add and secretmanager.secrets.delete for the target GCP project and for secrets that have a name starting with zenml-

This can be achieved by creating two custom IAM roles and attaching them to the principal (e.g. user or service account) that will be used to access the GCP Secrets Manager API with a condition configured when attaching the second role to limit access to secrets with a name prefix of zenml-. The following gcloud CLI command examples can be used as a starting point:

gcloud iam roles create ZenMLServerSecretsStoreCreator \
  --project <your GCP project ID> \
  --title "ZenML Server Secrets Store Creator" \
  --description "Allow the ZenML Server to create new secrets" \
  --stage GA \
  --permissions "secretmanager.secrets.create"

gcloud iam roles create ZenMLServerSecretsStoreEditor \
  --project <your GCP project ID> \
  --title "ZenML Server Secrets Store Editor" \
  --description "Allow the ZenML Server to manage its secrets" \
  --stage GA \
  --permissions "secretmanager.secrets.get,secretmanager.secrets.update,secretmanager.versions.access,secretmanager.versions.add,secretmanager.secrets.delete"

gcloud projects add-iam-policy-binding <your GCP project ID> \
  --member serviceAccount:<your GCP service account email> \
  --role projects/<your GCP project ID>/roles/ZenMLServerSecretsStoreCreator \
  --condition None

# NOTE: use the GCP project NUMBER, not the project ID in the condition
gcloud projects add-iam-policy-binding <your GCP project ID> \
  --member serviceAccount:<your GCP service account email> \
  --role projects/<your GCP project ID>/roles/ZenMLServerSecretsStoreEditor \
  --condition 'title=limit_access_zenml,description="Limit access to secrets with prefix zenml-",expression=resource.name.startsWith("projects/<your GCP project NUMBER>/secrets/zenml-")'

Example configuration for the GCP Secrets Store:

 zenml:

   # ...

   # Secrets store settings. This is used to store centralized secrets.
   secretsStore:

     # Set to false to disable the secrets store.
     enabled: true

     # The type of the secrets store
     type: gcp

     # Configuration for the GCP Secrets Manager secrets store
     gcp:

       # The GCP Service Connector authentication method to use.
       authMethod: service-account

       # The GCP Service Connector configuration.
       authConfig:

          # The GCP project ID to use. This must be set to the project ID where the
          # GCP Secrets Manager service that you want to use is located.
          project_id: my-gcp-project

          # GCP credentials JSON to use to authenticate with the GCP Secrets
          # Manager instance. 
          google_application_credentials: |
            {
              "type": "service_account",
              "project_id": "my-project",
              "private_key_id": "...",
              "private_key": "-----BEGIN PRIVATE KEY-----\n...=\n-----END PRIVATE KEY-----\n",
              "client_email": "...",
              "client_id": "...",
              "auth_uri": "https://accounts.google.com/o/oauth2/auth",
              "token_uri": "https://oauth2.googleapis.com/token",
              "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
              "client_x509_cert_url": "..."
            }

 serviceAccount:

   # If you're using workload identity, you need to annotate the service
   # account with the GCP service account name (see https://cloud.google.com/kubernetes-engine/docs/how-to/workload-identity)
   annotations:
     iam.gke.io/gcp-service-account: <SERVICE_ACCOUNT_NAME>@<PROJECT_NAME>.iam.gserviceaccount.com

Using the Azure Key Vault as a secrets store backend

The Azure Secrets Store uses the ZenML Azure Service Connector under the hood to authenticate with the Azure Key Vault API. This means that you can use any of the authentication methods supported by the Azure Service Connector to authenticate with the Azure Key Vault API.

Example configuration for the Azure Key Vault Secrets Store:

 zenml:

   # ...

   # Secrets store settings. This is used to store centralized secrets.
   secretsStore:

     # Set to false to disable the secrets store.
     enabled: true

     # The type of the secrets store
     type: azure

     # Configuration for the Azure Key Vault secrets store
     azure:

       # The name of the Azure Key Vault. This must be set to point to the Azure
       # Key Vault instance that you want to use.
       key_vault_name:

       # The Azure Service Connector authentication method to use.
       authMethod: service-principal

       # The Azure Service Connector configuration.
       authConfig:

          # The Azure application service principal credentials to use to
          # authenticate with the Azure Key Vault API.
          client_id: <your Azure client ID>
          client_secret: <your Azure client secret>
          tenant_id: <your Azure tenant ID>

Using the HashiCorp Vault as a secrets store backend

To use the HashiCorp Vault service as a Secrets Store back-end, it must be configured in the Helm values:

 zenml:

   # ...

   # Secrets store settings. This is used to store centralized secrets.
   secretsStore:

     # Set to false to disable the secrets store.
     enabled: true

     # The type of the secrets store
     type: hashicorp

     # Configuration for the HashiCorp Vault secrets store
     hashicorp:

       # The url of the HashiCorp Vault server to use
       vault_addr: https://vault.example.com
       # The token used to authenticate with the Vault server
       vault_token: <your Vault token>
       # The Vault Enterprise namespace. Not required for Vault OSS.
       vault_namespace: <your Vault namespace>

Using a custom secrets store backend implementation

You have the option of using a custom implementation of the secrets store API as your secrets store back-end. This must come in the form of a class derived from zenml.zen_stores.secrets_stores.base_secrets_store.BaseSecretsStore. This class must be importable from within the ZenML server container, which means you most likely need to build a custom container image that contains the class. Then, you can configure the Helm values to use your custom secrets store as follows:

 zenml:

   # ...

   # Secrets store settings. This is used to store centralized secrets.
   secretsStore:

     # Set to false to disable the secrets store.
     enabled: true

     # The type of the secrets store
     type: custom

     # Configuration for the HashiCorp Vault secrets store
     custom:

       # The class path of the custom secrets store implementation. This should
       # point to a full Python class that extends the
       # `zenml.zen_stores.secrets_stores.base_secrets_store.BaseSecretsStore`
       # base class. The class should be importable from the container image
       # that you are using for the ZenML server.
       class_path: my.custom.secrets.store.MyCustomSecretsStore

   # Extra environment variables used to configure the custom secrets store.
   environment:
     ZENML_SECRETS_STORE_OPTION_1: value1
     ZENML_SECRETS_STORE_OPTION_2: value2

   # Extra environment variables to set in the ZenML server container that
   # should be kept secret and are used to configure the custom secrets store.
   secretEnvironment:
     ZENML_SECRETS_STORE_SECRET_OPTION_3: value3
     ZENML_SECRETS_STORE_SECRET_OPTION_4: value4

Backup secrets store

A backup secrets store back-end may be configured for high-availability and backup purposes. or as an intermediate step in the process of migrating secrets to a different external location or secrets manager provider.

To configure a backup secrets store in the Helm chart, use the same approach and instructions documented for the primary secrets store, but using the backupSecretsStore configuration section instead of secretsStore, e.g.:

 zenml:

   # ...

   # Backup secrets store settings. This is used as a backup for the primary
   # secrets store.
   backupSecretsStore:

     # Set to true to enable the backup secrets store.
     enabled: true

     # The type of the backup secrets store
     type: aws

     # Configuration for the AWS Secrets Manager backup secrets store
     aws:

       # The AWS Service Connector authentication method to use.
       authMethod: secret-key

       # The AWS Service Connector configuration.
       authConfig:
        # The AWS region to use. This must be set to the region where the AWS
        # Secrets Manager service that you want to use is located.
        region: us-east-1

        # The AWS credentials to use to authenticate with the AWS Secrets
        aws_access_key_id: <your AWS access key ID>
        aws_secret_access_key: <your AWS secret access key>

Database backup and recovery

An automated database backup and recovery feature is enabled by default for all Helm deployments. The ZenML server will automatically back up the database before every upgrade and restore it if the upgrade fails in a way that affects the database.

The database backup automatically created by the ZenML server is only temporary and only used as an immediate recovery in case of database migration failures. It is not meant to be used as a long-term backup solution. If you need to back up your database for long-term storage, you should use a dedicated backup solution.

Several database backup strategies are supported, depending on where and how the backup is stored. The strategy can be configured by means of the zenml.database.backupStrategy Helm value:

disabled - no backup is performed
in-memory - the database schema and data are stored in memory. This is the fastest backup strategy, but the backup is not persisted across pod restarts, so no manual intervention is possible in case the automatic DB recovery fails after a failed DB migration. Adequate memory resources should be allocated to the ZenML server pod when using this backup strategy with larger databases. This is the default backup strategy.
database - the database is copied to a backup database in the same database server. This requires the backupDatabase option to be set to the name of the backup database. This backup strategy is only supported for MySQL compatible databases and the user specified in the database URL must have permissions to manage (create, drop, and modify) the backup database in addition to the main database.
dump-file - the database schema and data are dumped to a file local to the database initialization and upgrade job. Users may optionally configure a persistent volume where the dump file will be stored by setting the backupPVStorageSize and optionally the backupPVStorageClass options. If a persistent volume is not configured, the dump file will be stored in an emptyDir volume, which is not persisted. If configured, the user is responsible for deleting the resulting PVC when uninstalling the Helm release.

NOTE: You should also set the podSecurityContext.fsGroup option if you are using a persistent volume to store the dump file.

The following additional rules are applied concerning the creation and lifetime of the backup:

a backup is not attempted if the database doesn't need to undergo a migration (e.g. when the ZenML server is upgraded to a new version that doesn't require a database schema change or if the ZenML version doesn't change at all).
a backup file or database is created before every database migration attempt (i.e. during every Helm upgrade). If a backup already exists (i.e. persisted in a persistent volume or backup database), it is overwritten.
the persistent backup file or database is cleaned up after the migration is completed successfully or if the database doesn't need to undergo a migration. This includes backups created by previous failed migration attempts.
the persistent backup file or database is NOT cleaned up after a failed migration. This allows the user to manually inspect and/or apply the backup if the automatic recovery fails.

The following example shows how to configure the ZenML server to use a persistent volume to store the database dump file:

 zenml:

   # ...

  database:
    url: "mysql://admin:password@my.database.org:3306/zenml"

    # Configure the database backup strategy
    backupStrategy: dump-file
    backupPVStorageSize: 1Gi

podSecurityContext:
  fsGroup: 1000 # if you're using a PVC for backup, this should necessarily be set.

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