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139 lines
6.2 KiB
139 lines
6.2 KiB
# |
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# Licensed to the Apache Software Foundation (ASF) under one or more |
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# contributor license agreements. See the NOTICE file distributed with |
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# this work for additional information regarding copyright ownership. |
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# The ASF licenses this file to You under the Apache License, Version 2.0 |
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# (the "License"); you may not use this file except in compliance with |
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# the License. You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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# |
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# user data local directory path, please make sure the directory exists and have read write permissions |
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data.basedir.path=/tmp/dolphinscheduler |
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# resource view suffixs |
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#resource.view.suffixs=txt,log,sh,bat,conf,cfg,py,java,sql,xml,hql,properties,json,yml,yaml,ini,js |
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# resource storage type: HDFS, S3, OSS, GCS, ABS, NONE |
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resource.storage.type=S3 |
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# 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 |
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resource.storage.upload.base.path=/dolphinscheduler |
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# The Azure client ID (Azure Application (client) ID) |
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resource.azure.client.id=minioadmin |
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# The Azure client secret in the Azure application |
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resource.azure.client.secret=minioadmin |
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# The Azure data factory subscription ID |
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resource.azure.subId=minioadmin |
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# The Azure tenant id in the Azure Active Directory |
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resource.azure.tenant.id=minioadmin |
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# The query interval |
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resource.query.interval=10000 |
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# The AWS access key. if resource.storage.type=S3 or use EMR-Task, This configuration is required |
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resource.aws.access.key.id=accessKey123 |
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# The AWS secret access key. if resource.storage.type=S3 or use EMR-Task, This configuration is required |
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resource.aws.secret.access.key=secretKey123 |
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# The AWS Region to use. if resource.storage.type=S3 or use EMR-Task, This configuration is required |
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resource.aws.region=us-east-1 |
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# 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. |
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resource.aws.s3.bucket.name=dolphinscheduler |
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# 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 |
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resource.aws.s3.endpoint=http://s3:9000 |
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# alibaba cloud access key id, required if you set resource.storage.type=OSS |
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resource.alibaba.cloud.access.key.id=<your-access-key-id> |
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# alibaba cloud access key secret, required if you set resource.storage.type=OSS |
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resource.alibaba.cloud.access.key.secret=<your-access-key-secret> |
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# alibaba cloud region, required if you set resource.storage.type=OSS |
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resource.alibaba.cloud.region=cn-hangzhou |
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# oss bucket name, required if you set resource.storage.type=OSS |
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resource.alibaba.cloud.oss.bucket.name=dolphinscheduler |
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# oss bucket endpoint, required if you set resource.storage.type=OSS |
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resource.alibaba.cloud.oss.endpoint=https://oss-cn-hangzhou.aliyuncs.com |
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# if resource.storage.type=HDFS, the user must have the permission to create directories under the HDFS root path |
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resource.hdfs.root.user=hdfs |
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# 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 |
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resource.hdfs.fs.defaultFS=s3a://dolphinscheduler |
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# whether to startup kerberos |
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hadoop.security.authentication.startup.state=false |
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# java.security.krb5.conf path |
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java.security.krb5.conf.path=/opt/krb5.conf |
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# login user from keytab username |
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login.user.keytab.username=hdfs-mycluster@ESZ.COM |
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# login user from keytab path |
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login.user.keytab.path=/opt/hdfs.headless.keytab |
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# kerberos expire time, the unit is hour |
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kerberos.expire.time=2 |
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# resourcemanager port, the default value is 8088 if not specified |
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resource.manager.httpaddress.port=8088 |
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# if resourcemanager HA is enabled, please set the HA IPs; if resourcemanager is single, keep this value empty |
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yarn.resourcemanager.ha.rm.ids=192.168.xx.xx,192.168.xx.xx |
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# 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 |
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yarn.application.status.address=http://ds1:%s/ws/v1/cluster/apps/%s |
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# job history status url when application number threshold is reached(default 10000, maybe it was set to 1000) |
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yarn.job.history.status.address=http://ds1:19888/ws/v1/history/mapreduce/jobs/%s |
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# datasource encryption enable |
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datasource.encryption.enable=false |
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# datasource encryption salt |
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datasource.encryption.salt=!@#$%^&* |
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# data quality option, it would auto discovery from libs directory. You can also specific the jar name in libs directory |
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# if you re-build it alone, or auto discovery mechanism fail |
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data-quality.jar.name= |
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#data-quality.error.output.path=/tmp/data-quality-error-data |
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# Network IP gets priority, default inner outer |
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# Whether hive SQL is executed in the same session |
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support.hive.oneSession=false |
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# 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 |
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sudo.enable=true |
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# network interface preferred like eth0, default: empty |
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#dolphin.scheduler.network.interface.preferred= |
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# network IP gets priority, default: inner outer |
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#dolphin.scheduler.network.priority.strategy=default |
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# system env path |
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#dolphinscheduler.env.path=dolphinscheduler_env.sh |
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# development state |
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development.state=false |
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# rpc port |
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alert.rpc.port=50052 |
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# set path of conda.sh |
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conda.path=/opt/anaconda3/etc/profile.d/conda.sh |
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# Task resource limit state |
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task.resource.limit.state=false |
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# mlflow task plugin preset repository |
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ml.mlflow.preset_repository=https://github.com/apache/dolphinscheduler-mlflow |
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# mlflow task plugin preset repository version |
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ml.mlflow.preset_repository_version="main" |
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# way to collect applicationId: log(original regex match), aop |
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appId.collect: log |