TheWindIsRising opened a new issue, #15120: URL: https://github.com/apache/dolphinscheduler/issues/15120
### Search before asking - [X] I had searched in the [issues](https://github.com/apache/dolphinscheduler/issues?q=is%3Aissue) and found no similar issues. ### What happened dolphinscheduler版本：3.1.2 value.xml已配置持久化，worker-Pod内配置了spark-2.4.7-bin-hadoop2.7与hadoop-2.7.0.tar 环境 并拷贝了外部的hadoop中的hadoop、yarn、hdfs等site.xml文件 使用helm部署的dolphinscheduler3.1.2， 提交任务到外部的hadoop集群上进行调度，在工作流实例页面点停止任务后， 外部的yarn上任务还在继续执行，workerPod报错： ``` Caused by: java.io.IOException: error=2, No such file or directory at java.lang.UNIXProcess.forkAndExec(Native Method) at java.lang.UNIXProcess.<init>(UNIXProcess.java:247) at java.lang.ProcessImpl.start(ProcessImpl.java:134) at java.lang.ProcessBuilder.start(ProcessBuilder.java:1029) ... 17 common frames omitted [INFO] 2023-11-03 15:52:14.679 +0800 org.apache.dolphinscheduler.server.worker.processor.TaskKillProcessor:[238] - [WorkflowInstance-0][TaskInstance-111] - Get appIds from worker dolphinscheduler-worker-0.dolphinscheduler-worker-headless:1234 taskLogPath: /opt/dolphinscheduler/logs/20231103/10676729589025_23-107-111.log [INFO] 2023-11-03 15:52:14.679 +0800 org.apache.dolphinscheduler.service.log.LogClient:[208] - [WorkflowInstance-0][TaskInstance-111] - Begin to get appIds from worker: dolphinscheduler-worker-0.dolphinscheduler-worker-headless:1234 taskLogPath: /opt/dolphinscheduler/logs/20231103/10676729589025_23-107-111.log [INFO] 2023-11-03 15:52:14.680 +0800 org.apache.dolphinscheduler.plugin.task.api.utils.LogUtils:[66] - [WorkflowInstance-0][TaskInstance-111] - Find appId: application_1693365157704_0040 from /opt/dolphinscheduler/logs/20231103/10676729589025_23-107-111.log [INFO] 2023-11-03 15:52:14.680 +0800 org.apache.dolphinscheduler.service.log.LogClient:[222] - [WorkflowInstance-0][TaskInstance-111] - Get appIds: [application_1693365157704_0040] from worker: dolphinscheduler-worker-0.dolphinscheduler-worker-headless:1234 taskLogPath: /opt/dolphinscheduler/logs/20231103/10676729589025_23-107-111.log [INFO] 2023-11-03 15:52:14.686 +0800 org.apache.dolphinscheduler.service.utils.ProcessUtils:[96] - [WorkflowInstance-0][TaskInstance-111] - get kerberos init command [INFO] 2023-11-03 15:52:14.687 +0800 org.apache.dolphinscheduler.server.worker.processor.TaskKillProcessor:[144] - [WorkflowInstance-0][TaskInstance-111] - kill cmd:sudo -u hdfs sh /tmp/dolphinscheduler/exec/process/hdfs/10667691377184/10676729589025_23/107/111/application_1693365157704_0040.kill [ERROR] 2023-11-03 15:52:14.696 +0800 org.apache.dolphinscheduler.server.worker.processor.TaskKillProcessor:[147] - [WorkflowInstance-0][TaskInstance-111] - Kill yarn application app id [application_1693365157704_0040] failed: [/tmp/dolphinscheduler/exec/process/hdfs/10667691377184/10676729589025_23/107/111/application_1693365157704_0040.kill: 4: source: not found /tmp/dolphinscheduler/exec/process/hdfs/10667691377184/10676729589025_23/107/111/application_1693365157704_0040.kill: 7: yarn: not `found` ```   下面是关于value.xml的部分配置 ip与密码已涂抹 conf: common: # user data local directory path, please make sure the directory exists and have read write permissions data.basedir.path: /tmp/dolphinscheduler # resource storage type: HDFS, S3, 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 # 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: [email protected] # login user from keytab path login.user.keytab.path: /opt/hdfs.headless.keytab # kerberos expire time, the unit is hour 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 # The AWS access key. if resource.storage.type=S3 or use EMR-Task, This configuration is required resource.aws.access.key.id: admin # The AWS secret access key. if resource.storage.type=S3 or use EMR-Task, This configuration is required resource.aws.secret.access.key: xxxxxxx # 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://10.200.x.xxx:9000 # 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://ndsc03.slave.com:%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://ndsc03.slave.com: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 # Url endpoint for zeppelin RESTful API zeppelin.rest.url: http://localhost:8080 ### What you expected to happen 我认为是后端接口的问题 ### How to reproduce 使用helm部署dolphinscheduler， value.xml已配置持久化，worker-Pod内配置了spark-2.4.7-bin-hadoop2.7与hadoop-2.7.0.tar 环境 并拷贝了外部的hadoop中的hadoop、yarn、hdfs等site.xml文件，在ui页面进行工作流定义，定义一个spark任务，当任务提交到外部的yarn上时，在工作流实例中进行停止任务，去yarn的ui页面上观察任务是否真的被kill，然后在worker的pod内查看日志 kubectl logs -f dolphinscheduler-worker-0 --tail 500 -n namespace ### Anything else _No response_ ### Version 3.1.x ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://www.apache.org/foundation/policies/conduct) -- This is an automated message from the Apache Git Service. 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