Version: nightly

Common Helm Chart Configurations

For each Helm Chart, you can create a values.yaml file for configuration. When you need to apply configurations, you can use the helm upgrade command:

helm upgrade --install ${release-name} ${chart-name} --namespace ${namespace} -f values.yaml

GreptimeDB Cluster Chart

For complete configuration options, please refer to GreptimeDB Cluster Chart.

GreptimeDB Image Configuration

The top-level variable image is used to configure the global GreptimeDB image for the cluster, as shown below:

image:
  # -- The image registry
  registry: docker.io
  # -- The image repository
  repository: greptime/greptimedb
  # -- The image tag
  tag: "v0.11.3"
  # -- The image pull secrets
  pullSecrets: []

If you want to configure different images for each role in the cluster, you can use the ${role}.podTemplate.main.image field (where role can be meta, frontend, datanode and flownode). This field will override the top-level image configuration, as shown below:

image:
  # -- The image registry
  registry: docker.io
  # -- The image repository
  repository: greptime/greptimedb
  # -- The image tag
  tag: "v0.11.3"
  # -- The image pull secrets
  pullSecrets: []

frontend:
  podTemplate:
    main:
      image: "greptime/greptimedb:latest"

In this case, the frontend image will be set to greptime/greptimedb:latest, while other components will use the top-level image configuration.

Service Ports Configuration

You can configure service ports using the following fields:

httpServicePort: Configures the HTTP service port, default 4000
grpcServicePort: Configures the SQL service port, default 4001
mysqlServicePort: Configures the MySQL service port, default 4002
postgresServicePort: Configures the PostgreSQL service port, default 4003

Datanode Storage Configuration

You can configure Datanode storage through the datanode.storage field, as shown below:

datanode:
  storage:
    # -- Storage class for datanode persistent volume
    storageClassName: null
    # -- Storage size for datanode persistent volume
    storageSize: 10Gi
    # -- Storage retain policy for datanode persistent volume
    storageRetainPolicy: Retain
    # -- The dataHome directory, default is "/data/greptimedb/"
    dataHome: "/data/greptimedb"

storageClassName: Configures the StorageClass, defaults to Kubernetes current default StorageClass
storageSize: Configures the storage size, default 10Gi. You can use common capacity units, such as 10Gi, 10GB, etc.
storageRetainPolicy: Configures the storage retention policy, default Retain. If set to Delete, the storage will be deleted when the cluster is deleted
dataHome: Configures the data directory, default /data/greptimedb/

Resource Configuration

The top-level variable base.podTemplate.main.resources is used to globally configure resources for each role, as shown below:

base:
  podTemplate:
    main:
      resources:
        requests:
          memory: "1Gi"
          cpu: "1"
        limits:
          memory: "2Gi"
          cpu: "2"

If you want to configure different resources for each role in the cluster, you can use the ${role}.podTemplate.main.resources field (where role can be meta, frontend, datanode, etc.). This field will override the top-level base.podTemplate.main.resources configuration, as shown below:

base:
  podTemplate:
    main:
      resources:
        requests:
          memory: "1Gi"
          cpu: "1"
        limits:
          memory: "2Gi"
          cpu: "2"

frontend:
  podTemplate:
    main:
      resources:
        requests:
          cpu: "2"
          memory: "4Gi"
        limits:
          cpu: "4"
          memory: "8Gi"

Role Replicas Configuration

For different roles, the number of replicas can be configured through the ${role}.replicas field, as shown below:

frontend:
  replicas: 3

datanode:
  replicas: 3

You can achieve horizontal scaling by configuring the number of replicas.

Environment Variable Configuration

You can configure global environment variables through the base.podTemplate.main.env field, and configure different environment variables for each Role through the ${role}.podTemplate.main.env field, as shown below:

base:
  podTemplate:
    main:
      env:
        - name: GLOBAL_ENV
          value: "global_value"

frontend:
  podTemplate:
    main:
      env:
        - name: FRONTEND_ENV
          value: "frontend_value"

Injecting Configuration Files

For different Role services, youcan inject custom TOML configuration files through the ${role}.configData field, as shown below:

frontend:
  configData: |
    [[region_engine]]
    [region_engine.mito]
    # Number of region workers
    num_workers = 8

You can learn about GreptimeDB configuration options through config.md.

In addition to using the ${role}.configData field to inject configuration files, you can also specify corresponding files through ${role}.configFile, as shown below:

frontend:
  configFile: "configs/frontend.toml"

In this case, ensure that the configuration file path matches the directory where the helm upgrade command is executed.

note

User-injected configuration files have lower priority by default than configuration items managed by GreptimeDB Operator. Some configuration items can only be configured through GreptimeDB Operator, and these items are exposed by default in values.yaml.

The following default configurations are managed by GreptimeDB Operator:

Logging configuration;
Datanode's Node ID;

Authentication Configuration

The Helm Chart does not enable User Provider mode authentication by default. You can enable User Provider mode authentication and configure user information through the auth.enabled field, as shown below:

auth:
  enabled: true
  users:
    - name: "admin"
      password: "admin"

Logging Configuration

The top-level variable logging is used to configure global logging levels, as shown below:

# -- Global logging configuration
logging:
  # -- The log level for greptimedb, only support "debug", "info", "warn", "debug"
  level: "info"

  # -- The log format for greptimedb, only support "json" and "text"
  format: "text"

  # -- The logs directory for greptimedb
  logsDir: "/data/greptimedb/logs"

  # -- Whether to log to stdout only
  onlyLogToStdout: false

  # -- indicates whether to persist the log with the datanode data storage. It **ONLY** works for the datanode component.
  persistentWithData: false

  # -- The log filters, use the syntax of `target[span\{field=value\}]=level` to filter the logs.
  filters: []

  # -- The slow query log configuration.
  slowQuery:
    # -- Enable slow query log.
    enabled: false

    # -- The threshold of slow query log in seconds.
    threshold: "10s"

    # -- Sample ratio of slow query log.
    sampleRatio: "1.0"

Where:

logging.level: Configures the global log level, supports debug, info, warn, error.
logging.format: Configures the global log format, supports json and text.
logging.logsDir: Configures the global log directory, default /data/greptimedb/logs.
logging.onlyLogToStdout: Configures whether to output only to stdout, disabled by default.
logging.persistentWithData: Configures whether to persist logs with data storage, only applies to the datanode component, disabled by default.
logging.filters: Configures global log filters, supports the syntax target[span\{field=value\}]=level. For example, if you want to enable debug level logging for certain components:
```
logging:
  level: "info"
  format: "json"
  filters:
  - mito2=debug
```

You can also enable slow query logging through the logging.slowQuery field configuration, as shown below:

logging:
  slowQuery:
    enabled: true
    threshold: "100ms"
    sampleRatio: "1.0"

Where:

logging.slowQuery.enabled: Configures whether to enable slow query logging, disabled by default.
logging.slowQuery.threshold: Configures the threshold for slow query logging.
logging.slowQuery.sampleRatio: Configures the sampling ratio for slow query logging, default 1.0 (full sampling).

If the output directory logging.logsDir is configured, slow query logs will be output to that directory.

Each role's logging configuration can be configured through the ${role}.logging field, with fields consistent with the top-level logging and will override the top-level logging configuration, for example:

frontend:
  logging:
    level: "debug"

Enabling Flownode

The Helm Chart does not enable Flownode by default. You can enable Flownode through the flownode.enabled field, as shown below:

flownode:
  enabled: true

Other fields of flownode are configured similarly to other Roles, for example:

flownode:
  enabled: true
  replicas: 1
  podTemplate:
    main:
      resources:
        requests:
          memory: "1Gi"
          cpu: "1"
        limits:
          memory: "2Gi"
          cpu: "2"

Object Storage Configuration

The objectStorage field is used to configure cloud object storage (such as AWS S3 and Azure Blob Storage, etc.) as the GreptimeDB storage layer.

AWS S3

objectStorage:
  credentials:
    # AWS access key ID
    accessKeyId: ""
    # AWS secret access key
    secretAccessKey: ""
  s3:
    # AWS S3 bucket name
    bucket: ""
    # AWS S3 region
    region: ""
    # The root path in bucket is 's3://<bucket>/<root>/data/...'
    root: ""
    # The AWS S3 endpoint, see more detail: https://docs.aws.amazon.com/general/latest/gr/s3.html
    endpoint: ""

Google Cloud Storage

objectStorage:
  credentials:
    # GCP serviceAccountKey JSON-formatted base64 value 
    serviceAccountKey: ""
  gcs:
    # Google Cloud Storage bucket name
    bucket: ""
    # Google Cloud OAuth 2.0 Scopes, example: "https://www.googleapis.com/auth/devstorage.read_write"
    scope: ""
    # The root path in bucket is 'gcs://<bucket>/<root>/data/...'
    root: ""
    # Google Cloud Storage endpoint, example: "https://storage.googleapis.com"
    endpoint: ""

Azure Blob

objectStorage:
  credentials:
    # Azure account name
    accountName: ""
    # Azure account key
    accountKey: ""
  azblob:
    # Azure Blob container name
    container: ""
    # The root path in container is 'blob://<bucket>/<root>/data/...'
    root: ""
    # Azure Blob endpoint, see: "https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blob-query-endpoint-srp?tabs=dotnet#query-for-the-blob-storage-endpoint"
    endpoint: ""

AliCloud OSS

objectStorage:
  credentials:
    # AliCloud access key ID
    accessKeyId: ""
    # AliCloud access key secret
    secretAccessKey: ""
  oss:
    # AliCloud OSS bucket name
    bucket: ""
    # AliCloud OSS region
    region: ""
    # The root path in bucket is 'oss://<bucket>/<root>/data/...'
    root: ""
    # The AliCloud OSS endpoint
    endpoint: ""

Prometheus Monitor Configuration

If you have prometheus-operator installed, you can create Prometheus PodMonitor to monitor GreptimeDB through the prometheusMonitor.enabled field as follows:

prometheusMonitor:
  # -- Create PodMonitor resource for scraping metrics using PrometheusOperator
  enabled: false
  # -- Interval at which metrics should be scraped
  interval: "30s"
  # -- Add labels to the PodMonitor
  labels:
    release: prometheus

Debug Pod Configuration

The debug pod has various tools installed (such as mysql-client, psql-client, etc.). You can exec into the debug pod for debugging. Create it with the debugPod.enabled field as follows:

debugPod:
  # -- Enable debug pod
  enabled: false

  # -- The debug pod image
  image:
    registry: docker.io
    repository: greptime/greptime-tool
    tag: "20241107-9c210d18"

  # -- The debug pod resource
  resources:
    requests:
      memory: 64Mi
      cpu: 50m
    limits:
      memory: 256Mi
      cpu: 200m

Common Helm Chart Configurations

GreptimeDB Cluster Chart​

GreptimeDB Image Configuration​

Service Ports Configuration​

Datanode Storage Configuration​

Resource Configuration​

Role Replicas Configuration​

Environment Variable Configuration​

Injecting Configuration Files​

Authentication Configuration​

Logging Configuration​

Enabling Flownode​

Object Storage Configuration​

AWS S3​

Google Cloud Storage​

Azure Blob​

AliCloud OSS​

Prometheus Monitor Configuration​

Debug Pod Configuration​