You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
201 lines
9.4 KiB
201 lines
9.4 KiB
# |
|
# 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> |
|
|
|
|
|
|