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[Improvement-12293] Update the common.properties in api-test-case and e2e-case (#12295)

3.2.0-release
rickchengx 2 years ago committed by GitHub
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2 changed files with 124 additions and 36 deletions
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  1. 83
      dolphinscheduler-api-test/dolphinscheduler-api-test-case/src/test/resources/docker/file-manage/common.properties
  2. 73
      dolphinscheduler-e2e/dolphinscheduler-e2e-case/src/test/resources/docker/file-manage/common.properties

83
dolphinscheduler-api-test/dolphinscheduler-api-test-case/src/test/resources/docker/file-manage/common.properties
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@ -14,29 +14,61 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
# #
# user data local directory path, please make sure the directory exists and have read write permissions # user data local directory path, please make sure the directory exists and have read write permissions
data.basedir.path=/tmp/dolphinscheduler data.basedir.path=/tmp/dolphinscheduler
# resource storage type: HDFS, S3, NONE
# 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, NONE
resource.storage.type=S3 resource.storage.type=S3
# resource store on HDFS/S3 path, resource file will store to this hadoop hdfs path, self configuration # 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
# please make sure the directory exists on hdfs and have read write permissions. "/dolphinscheduler" is recommended resource.storage.upload.base.path=/dolphinscheduler
resource.upload.path=/dolphinscheduler
# 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=<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
# 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 # whether to startup kerberos
hadoop.security.authentication.startup.state=false hadoop.security.authentication.startup.state=false
# java.security.krb5.conf path # java.security.krb5.conf path
java.security.krb5.conf.path=/opt/krb5.conf java.security.krb5.conf.path=/opt/krb5.conf
# login user from keytab username # login user from keytab username
login.user.keytab.username=hdfs-mycluster@ESZ.COM login.user.keytab.username=hdfs-mycluster@ESZ.COM
# login user from keytab path # login user from keytab path
login.user.keytab.path=/opt/hdfs.headless.keytab login.user.keytab.path=/opt/hdfs.headless.keytab
# kerberos expire time, the unit is hour # kerberos expire time, the unit is hour
kerberos.expire.time=2 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
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
fs.defaultFS=s3a://dolphinscheduler
# resourcemanager port, the default value is 8088 if not specified # resourcemanager port, the default value is 8088 if not specified
resource.manager.httpaddress.port=8088 resource.manager.httpaddress.port=8088
# if resourcemanager HA is enabled, please set the HA IPs; if resourcemanager is single, keep this value empty # if resourcemanager HA is enabled, please set the HA IPs; if resourcemanager is single, keep this value empty
@ -45,25 +77,48 @@ yarn.resourcemanager.ha.rm.ids=192.168.xx.xx,192.168.xx.xx
yarn.application.status.address=http://ds1:%s/ws/v1/cluster/apps/%s 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) # 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 yarn.job.history.status.address=http://ds1:19888/ws/v1/history/mapreduce/jobs/%s
# datasource encryption enable # datasource encryption enable
datasource.encryption.enable=false datasource.encryption.enable=false
# datasource encryption salt # datasource encryption salt
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 # 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 sudo.enable=true
# network interface preferred like eth0, default: empty # network interface preferred like eth0, default: empty
#dolphin.scheduler.network.interface.preferred= #dolphin.scheduler.network.interface.preferred=
# network IP gets priority, default: inner outer # network IP gets priority, default: inner outer
#dolphin.scheduler.network.priority.strategy=default #dolphin.scheduler.network.priority.strategy=default
# system env path # system env path
#dolphinscheduler.env.path=dolphinscheduler_env.sh #dolphinscheduler.env.path=dolphinscheduler_env.sh
# development state # development state
development.state=false development.state=false
# rpc port # rpc port
alert.rpc.port=50052 alert.rpc.port=50052
aws.access.key.id=accessKey123
aws.secret.access.key=secretKey123 # set path of conda.sh
aws.region=us-east-1 conda.path=/opt/anaconda3/etc/profile.d/conda.sh
aws.endpoint=http://s3:9000
# Task resource limit state # Task resource limit state
task.resource.limit.state=false 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"

73
dolphinscheduler-e2e/dolphinscheduler-e2e-case/src/test/resources/docker/file-manage/common.properties
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@ -18,12 +18,41 @@
# user data local directory path, please make sure the directory exists and have read write permissions # user data local directory path, please make sure the directory exists and have read write permissions
data.basedir.path=/tmp/dolphinscheduler data.basedir.path=/tmp/dolphinscheduler
# resource storage type: HDFS, S3, NONE # resource view suffixs
resource.storage.type=S3 #resource.view.suffixs=txt,log,sh,bat,conf,cfg,py,java,sql,xml,hql,properties,json,yml,yaml,ini,js
# resource store on HDFS/S3 path, resource file will store to this hadoop hdfs path, self configuration, please make sure the directory exists on hdfs and have read write permissions. "/dolphinscheduler" is recommended # resource storage type: HDFS, S3, OSS, 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 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=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=<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
# 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 # whether to startup kerberos
hadoop.security.authentication.startup.state=false hadoop.security.authentication.startup.state=false
@ -39,25 +68,13 @@ login.user.keytab.path=/opt/hdfs.headless.keytab
# kerberos expire time, the unit is hour # kerberos expire time, the unit is hour
kerberos.expire.time=2 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=s3a://dolphinscheduler
# resourcemanager port, the default value is 8088 if not specified # resourcemanager port, the default value is 8088 if not specified
resource.manager.httpaddress.port=8088 resource.manager.httpaddress.port=8088
# if resourcemanager HA is enabled, please set the HA IPs; if resourcemanager is single, keep this value empty # 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 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 # 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 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) # 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 yarn.job.history.status.address=http://ds1:19888/ws/v1/history/mapreduce/jobs/%s
@ -67,6 +84,16 @@ datasource.encryption.enable=false
# datasource encryption salt # datasource encryption salt
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 # 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 sudo.enable=true
@ -75,17 +102,23 @@ sudo.enable=true
# network IP gets priority, default: inner outer # network IP gets priority, default: inner outer
#dolphin.scheduler.network.priority.strategy=default #dolphin.scheduler.network.priority.strategy=default
# system env path # system env path
#dolphinscheduler.env.path=dolphinscheduler_env.sh #dolphinscheduler.env.path=dolphinscheduler_env.sh
# development state # development state
development.state=false development.state=false
# rpc port # rpc port
alert.rpc.port=50052 alert.rpc.port=50052
resource.aws.access.key.id=accessKey123
resource.aws.secret.access.key=secretKey123 # set path of conda.sh
resource.aws.region=us-east-1 conda.path=/opt/anaconda3/etc/profile.d/conda.sh
resource.aws.s3.bucket.name=dolphinscheduler
resource.aws.s3.endpoint=http://s3:9000
# Task resource limit state # Task resource limit state
task.resource.limit.state=false 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"
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