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values.yaml
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# Default values for jupyter-with-spark helm chart.
# Declare variables to be passed into your templates.
image:
repository: snappydatainc/jupyter-notebook
tag: 5.2.2-spark-v2.2.0-kubernetes-0.5.1
pullPolicy: IfNotPresent
jupyterService:
type: LoadBalancer
jupyterPort: 8888
sparkUIPort: 4040
# Set your password to access the notebook server. A default ('abc123') has been set for you.
# Setting the password to empty string will disable the authentication (not recommended).
password: 'abc123'
sparkWebUI:
type: LoadBalancer
port: 4040
serviceAccount: default
mountSecrets: false
sparkEventLog:
enableHistoryEvents: false
# eventsLogDir should point to a URI of GCS bucket where history events will be dumped
eventLogDir: "gs://spark-history-server-store/"
persistence:
enabled: false
# A manually managed Persistent Volume and Claim
# Requires persistence.enabled: true
# If defined, PVC must be created manually before the volume can be bound.
# existingClaim:
# If defined, storageClassName: <storageClass>
# If set to "-", storageClassName: "", which disables dynamic provisioning
# If undefined (the default) or set to null, no storageClassName spec is
# set, choosing the default provisioner. (gp2 on AWS, standard on
# GKE, Azure & OpenStack)
#
# storageClass: "-"
accessMode: ReadWriteOnce
size: 6Gi
# Whether to keep the PVC when chart is deleted, if PV is dynamically provisioned
keepResource: true
resources: {}
# We usually recommend not to specify default resources and to leave this as a conscious
# choice for the user. This also increases chances charts run on environments with little
# resources, such as Minikube. If you do want to specify resources, uncomment the following
# lines, adjust them as necessary, and remove the curly braces after 'resources:'.
# limits:
# cpu: 100m
# memory: 128Mi
# requests:
# cpu: 100m
# memory: 128Mi
nodeSelector: {}
tolerations: []
affinity: {}
#internal attribute, do not change
global:
umbrellaChart: false