# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # user data local directory path, please make sure the directory exists and have read write permissions data.basedir.path=/tmp/dolphinscheduler # resource view suffixs #resource.view.suffixs=txt,log,sh,bat,conf,cfg,py,java,sql,xml,hql,properties,json,yml,yaml,ini,js # resource storage type: HDFS, S3, OSS, GCS, ABS, NONE resource.storage.type=S3 # 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 Azure client ID (Azure Application (client) ID) resource.azure.client.id=minioadmin # The Azure client secret in the Azure application resource.azure.client.secret=minioadmin # The Azure data factory subscription ID resource.azure.subId=minioadmin # The Azure tenant id in the Azure Active Directory resource.azure.tenant.id=minioadmin # The query interval resource.query.interval=10000 # The AWS access key. if resource.storage.type=S3 or use EMR-Task, This configuration is required resource.aws.access.key.id=accessKey123 # The AWS secret access key. if resource.storage.type=S3 or use EMR-Task, This configuration is required resource.aws.secret.access.key=secretKey123 # The AWS Region to use. if resource.storage.type=S3 or use EMR-Task, This configuration is required resource.aws.region=us-east-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://s3:9000 # 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=s3a://dolphinscheduler # whether to startup kerberos hadoop.security.authentication.startup.state=false # java.security.krb5.conf path java.security.krb5.conf.path=/opt/krb5.conf # login user from keytab username login.user.keytab.username=hdfs-mycluster@ESZ.COM # login user from keytab path login.user.keytab.path=/opt/hdfs.headless.keytab # kerberos expire time, the unit is hour kerberos.expire.time=2 # 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 # datasource encryption enable datasource.encryption.enable=false # datasource encryption salt datasource.encryption.salt=!@#$%^&* # data quality option, it would auto discovery from libs directory. You can also specific the jar name in libs directory # if you re-build it alone, or auto discovery mechanism fail data-quality.jar.name= #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 # 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" # way to collect applicationId: log(original regex match), aop appId.collect: log