Helm Installation fails


I am following this demo https://www.dremio.com/tutorials/python-dremio-and-kubernetes/
for deploying dremio on kubernetes. But helm is giving this error
Error: release jaundiced-woodpecker failed: timed out waiting for the condition
Any recommendations will be helpful.

Muhammad Hassaan Rafique

@HassaanRafique, can you provide the following?

  1. Please run a helm status and send the output.
  2. Please send your values.yaml file.
  3. Is it the initial deployment or an update?

The result for helm status:

LAST DEPLOYED: Wed Nov 13 17:36:38 2019

NAMESPACE: default


Here is my values.yaml file:

# The image used to build the Dremio cluster. It is recommended to update the
# version tag to the version that you are using. This will ensure that all
# the pods are using the same version of the software.
image: dremio/dremio-oss
imageTag: latest

# Check out Dremio documentation for memory and cpu requirements for
# the coordinators and the executors.
# The value of memory should be in MB. CPU is in no of cores.
  memory: 1024
  cpu: 0.3
  # This count is for slave coordinators only.
  # The chart will always create one master coordinator - you are
  # not required to have more than one master coordinator.
  count: 1
  master_count: 1
    port: 9047
    port: 31010
  memory: 1024
  cpu: 0.3
  count: 1
    # Requires Dremio version 4.0.0 or later
    enabled: true
      # Percentage of the diskspace for the running Kubernetes node
      # that can be used for Cloud Cache files.
      fs_pct: 70
      # Percentage of that space that can be used for the internal
      # Cloud Cache database.
      db_pct: 70
      # Percentage of that space that can be used for cacheing
      # materialised reflections.  This is an upper-bound, not a
      # reservation.
      cache_pct: 100
  memory: 1024
  cpu: 0.5
  count: 3
  volumeSize: 10Gi

# To create a TLS secret, use the following command:
# kubectl create secret tls ${TLS_SECRET_NAME} --key ${KEY_FILE} --cert ${CERT_FILE}
    # To enable TLS for the web UI, set the enabled flag to true and provide
    # the appropriate Kubernetes TLS secret.
    enabled: false
    secret: dremio-tls-secret-ui
    # To enable TLS for the client endpoints, set the enabled flag to
    # true and provide the appropriate Kubernetes TLS secret. Client
    # endpoint encryption is available only on Dremio Enterprise
    # Edition and should not be enabled otherwise.
    enabled: false
    secret: dremio-tls-secret-client

# If your Kubernetes cluster does not support LoadBalancer,
# comment out the line below for the helm chart to succeed or add
# the correct serviceType for your cluster.
# serviceType: LoadBalancer

# If the loadBalancer supports sessionAffinity and
# you have one or more coordinators, enable it
#sessionAffinity: ClientIP

# Enable the following flag if you wish to route traffic through a shared VPC
# for the LoadBalancer's external IP.
# The chart is setup for internal IP support for AKS, EKS, GKE.
# For more information, see https://kubernetes.io/docs/concepts/services-networking/service/#internal-load-balancer
#internalLoadBalancer: true

# To use custom storage class, uncomment and provide the value here.
# Otherwise the default storage class configured for your K8S cluster is used.
#storageClass: managed-premium

# For private and protected docker image repository, you should store
# the credentials in a kubernetes secret and provide the secret name
# here.  For more information, see
# https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod
#imagePullSecrets: secretname

# Target pods to nodes based on labels set on the nodes.  For more
# information, see
# https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#nodeselector
#  key: value

# Control where uploaded files are stored.  See
# https://docs.dremio.com/deployment/distributed-storage.html for more
# information
  # Valid values are local, aws, azure or azureStorage. aws and azure
  # choice requires additional configuration data.
  type: "local"
  aws: # S3
    bucketName: "Your_AWS_bucket_name"
    path: "/"
    accessKey: "Your_AWS_Access_Key"
    secret: "Your_AWS_Secret"
  azure: # ADLS gen1
    datalakeStoreName: "Your_Azure_DataLake_Storage_name"
    path: "/"
    applicationId: "Your_Azure_Application_Id"
    secret: "Your_Azure_Secret"
    oauth2EndPoint: "Azure_OAuth2_Endpoint"
  azureStorage: # AzureStorage gen2v2
    accountName: "Azure_storage_v2_account_name"
    accessKey: "Access_key_for_the_storage_account"

    filesystem: "Filesystem_in_storage_account"
    path: "/"