202 lines
9.4 KiB
Properties
202 lines
9.4 KiB
Properties
#
|
|
# 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.
|
|
#
|
|
|
|
string.property=string
|
|
int.property=1
|
|
long.property=1
|
|
double.property=1.1
|
|
boolean.property=true
|
|
|
|
# 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: LOCAL, HDFS, S3, OSS, GCS, ABS, OBS. LOCAL type is default type, and it's a specific type of HDFS with "resource.hdfs.fs.defaultFS = file:///" configuration
|
|
# please notice that LOCAL mode does not support reading and writing in distributed mode, which mean you can only use your resource in one machine, unless
|
|
# use shared file mount point
|
|
resource.storage.type=LOCAL
|
|
# 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=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=<your-access-key-id>
|
|
# alibaba cloud access key secret, required if you set resource.storage.type=OSS
|
|
resource.alibaba.cloud.access.key.secret=<your-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
|
|
|
|
# the location of the google cloud credential, required if you set resource.storage.type=GCS
|
|
resource.google.cloud.storage.credential=/path/to/credential
|
|
# gcs bucket name, required if you set resource.storage.type=GCS
|
|
resource.google.cloud.storage.bucket.name=<your-bucket>
|
|
|
|
# abs container name, required if you set resource.storage.type=ABS
|
|
resource.azure.blob.storage.container.name=<your-container>
|
|
# abs account name, required if you set resource.storage.type=ABS
|
|
resource.azure.blob.storage.account.name=<your-account-name>
|
|
# abs connection string, required if you set resource.storage.type=ABS
|
|
resource.azure.blob.storage.connection.string=<your-connection-string>
|
|
|
|
# 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
|
|
|
|
# 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 jar directory path, it would auto discovery data quality jar from this given dir. You should keep it empty if you do not change anything in
|
|
# data-quality, it will auto discovery by dolphinscheduler itself. Change it only if you want to use your own data-quality jar and it is not in worker-server
|
|
# libs directory(but may sure your jar name start with `dolphinscheduler-data-quality`).
|
|
data-quality.jar.dir=
|
|
|
|
#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 interface restrict like docker0,docker1 , default: docker0
|
|
dolphin.scheduler.network.interface.restrict=docker0
|
|
|
|
# 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
|
|
|
|
# The default env list will be load by Shell task, e.g. /etc/profile,~/.bash_profile
|
|
shell.env_source_list=
|
|
# The interceptor type of Shell task, e.g. bash, sh, cmd
|
|
shell.interceptor.type=bash
|
|
|
|
# Whether to enable remote logging
|
|
remote.logging.enable=false
|
|
# if remote.logging.enable = true, set the target of remote logging
|
|
remote.logging.target=OSS
|
|
# if remote.logging.enable = true, set the log base directory
|
|
remote.logging.base.dir=logs
|
|
# if remote.logging.enable = true, set the number of threads to send logs to remote storage
|
|
remote.logging.thread.pool.size=10
|
|
# oss access key id, required if you set remote.logging.target=OSS
|
|
remote.logging.oss.access.key.id=<access.key.id>
|
|
# oss access key secret, required if you set remote.logging.target=OSS
|
|
remote.logging.oss.access.key.secret=<access.key.secret>
|
|
# oss bucket name, required if you set remote.logging.target=OSS
|
|
remote.logging.oss.bucket.name=<bucket.name>
|
|
# oss endpoint, required if you set remote.logging.target=OSS
|
|
remote.logging.oss.endpoint=<endpoint>
|
|
# s3 access key id, required if you set remote.logging.target=S3
|
|
remote.logging.s3.access.key.id=<access.key.id>
|
|
# s3 access key secret, required if you set remote.logging.target=S3
|
|
remote.logging.s3.access.key.secret=<access.key.secret>
|
|
# s3 bucket name, required if you set remote.logging.target=S3
|
|
remote.logging.s3.bucket.name=<bucket.name>
|
|
# s3 endpoint, required if you set remote.logging.target=S3
|
|
remote.logging.s3.endpoint=<endpoint>
|
|
# s3 region, required if you set remote.logging.target=S3
|
|
remote.logging.s3.region=<region>
|
|
# the location of the google cloud credential, required if you set remote.logging.target=GCS
|
|
remote.logging.google.cloud.storage.credential=/path/to/credential
|
|
# gcs bucket name, required if you set remote.logging.target=GCS
|
|
remote.logging.google.cloud.storage.bucket.name=<your-bucket>
|
|
|
|
|