DolphinScheduler/dolphinscheduler-common/src/main/resources/common.properties

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# 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, NONE. LOCAL type is 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>

# 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 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

# 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=

# 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>