diff --git a/deploy/kubernetes/dolphinscheduler/values.yaml b/deploy/kubernetes/dolphinscheduler/values.yaml index 1929a153a2..440cda1f2f 100644 --- a/deploy/kubernetes/dolphinscheduler/values.yaml +++ b/deploy/kubernetes/dolphinscheduler/values.yaml @@ -77,6 +77,42 @@ conf: # resource store on HDFS/S3 path, resource file will store to this base path, self configuration, please make sure the directory exists on hdfs and have read write permissions. "/dolphinscheduler" is recommended resource.storage.upload.base.path: /dolphinscheduler + # The AWS access key. if resource.storage.type=S3 or use EMR-Task, This configuration is required + resource.aws.access.key.id: minioadmin + + # The AWS secret access key. if resource.storage.type=S3 or use EMR-Task, This configuration is required + resource.aws.secret.access.key: minioadmin + + # The AWS Region to use. if resource.storage.type=S3 or use EMR-Task, This configuration is required + resource.aws.region: cn-north-1 + + # The name of the bucket. You need to create them by yourself. Otherwise, the system cannot start. All buckets in Amazon S3 share a single namespace; ensure the bucket is given a unique name. + resource.aws.s3.bucket.name: dolphinscheduler + + # You need to set this parameter when private cloud s3. If S3 uses public cloud, you only need to set resource.aws.region or set to the endpoint of a public cloud such as S3.cn-north-1.amazonaws.com.cn + resource.aws.s3.endpoint: http://localhost:9000 + + # alibaba cloud access key id, required if you set resource.storage.type=OSS + resource.alibaba.cloud.access.key.id: + + # alibaba cloud access key secret, required if you set resource.storage.type=OSS + resource.alibaba.cloud.access.key.secret: + + # alibaba cloud region, required if you set resource.storage.type=OSS + resource.alibaba.cloud.region: cn-hangzhou + + # oss bucket name, required if you set resource.storage.type=OSS + resource.alibaba.cloud.oss.bucket.name: dolphinscheduler + + # oss bucket endpoint, required if you set resource.storage.type=OSS + resource.alibaba.cloud.oss.endpoint: https://oss-cn-hangzhou.aliyuncs.com + + # if resource.storage.type=HDFS, the user must have the permission to create directories under the HDFS root path + resource.hdfs.root.user: hdfs + + # if resource.storage.type=S3, the value like: s3a://dolphinscheduler; if resource.storage.type=HDFS and namenode HA is enabled, you need to copy core-site.xml and hdfs-site.xml to conf dir + resource.hdfs.fs.defaultFS: hdfs://mycluster:8020 + # whether to startup kerberos hadoop.security.authentication.startup.state: false @@ -91,28 +127,16 @@ conf: # kerberos expire time, the unit is hour kerberos.expire.time: 2 - # resource view suffixs - #resource.view.suffixs: txt,log,sh,bat,conf,cfg,py,java,sql,xml,hql,properties,json,yml,yaml,ini,js - # if resource.storage.type=HDFS, the user must have the permission to create directories under the HDFS root path - resource.hdfs.root.user: hdfs - # if resource.storage.type=S3, the value like: s3a://dolphinscheduler; if resource.storage.type=HDFS and namenode HA is enabled, you need to copy core-site.xml and hdfs-site.xml to conf dir - resource.hdfs.fs.defaultFS: hdfs://mycluster:8020 - # The AWS access key. if resource.storage.type=S3 or use EMR-Task, This configuration is required - resource.aws.access.key.id: minioadmin - # The AWS secret access key. if resource.storage.type=S3 or use EMR-Task, This configuration is required - resource.aws.secret.access.key: minioadmin - # The AWS Region to use. if resource.storage.type=S3 or use EMR-Task, This configuration is required - resource.aws.region: cn-north-1 - # The name of the bucket. You need to create them by yourself. Otherwise, the system cannot start. All buckets in Amazon S3 share a single namespace; ensure the bucket is given a unique name. - resource.aws.s3.bucket.name: dolphinscheduler - # You need to set this parameter when private cloud s3. If S3 uses public cloud, you only need to set resource.aws.region or set to the endpoint of a public cloud such as S3.cn-north-1.amazonaws.com.cn - resource.aws.s3.endpoint: http://localhost:9000 + # resourcemanager port, the default value is 8088 if not specified resource.manager.httpaddress.port: 8088 + # if resourcemanager HA is enabled, please set the HA IPs; if resourcemanager is single, keep this value empty yarn.resourcemanager.ha.rm.ids: 192.168.xx.xx,192.168.xx.xx + # if resourcemanager HA is enabled or not use resourcemanager, please keep the default value; If resourcemanager is single, you only need to replace ds1 to actual resourcemanager hostname yarn.application.status.address: http://ds1:%s/ws/v1/cluster/apps/%s + # job history status url when application number threshold is reached(default 10000, maybe it was set to 1000) yarn.job.history.status.address: http://ds1:19888/ws/v1/history/mapreduce/jobs/%s @@ -125,30 +149,29 @@ conf: # data quality option data-quality.jar.name: dolphinscheduler-data-quality-dev-SNAPSHOT.jar - #data-quality.error.output.path: /tmp/data-quality-error-data - - # Network IP gets priority, default inner outer - # Whether hive SQL is executed in the same session support.hive.oneSession: false # use sudo or not, if set true, executing user is tenant user and deploy user needs sudo permissions; if set false, executing user is the deploy user and doesn't need sudo permissions sudo.enable: true - # network interface preferred like eth0, default: empty - #dolphin.scheduler.network.interface.preferred: - - # network IP gets priority, default: inner outer - #dolphin.scheduler.network.priority.strategy: default - - # system env path - #dolphinscheduler.env.path: dolphinscheduler_env.sh # development state development.state: false + # rpc port alert.rpc.port: 50052 - # Url endpoint for zeppelin RESTful API - zeppelin.rest.url: http://localhost:8080 + + # set path of conda.sh + conda.path: /opt/anaconda3/etc/profile.d/conda.sh + + # Task resource limit state + task.resource.limit.state: false + + # mlflow task plugin preset repository + ml.mlflow.preset_repository: https://github.com/apache/dolphinscheduler-mlflow + + # mlflow task plugin preset repository version + ml.mlflow.preset_repository_version: "main" common: ## Configmap