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Both the Apache DolphinScheduler Master and Worker components support multi-node deployment with a decentralized design. The Master is mainly responsible for splitting the DAG workflow and distributing tasks to Worker nodes through RPC, as well as handling task status updates from the Worker.


The worker is responsible for executing tasks and reporting the task status back to the Master, which processes this information. But what happens in case of failure? What if the Master fails? Since it’s responsible for managing workflow instances, the Worker can no longer report task statuses, and the Master can’t process them either.


What if the Worker fails? Since it’s the one executing the actual tasks, how does the Master handle this situation? Let’s dive into the fault tolerance mechanism with the help of an illustration: Fault Tolerance Here’s a breakdown of how DolphinScheduler handles failures: If the Master fails: Other Master nodes will handle the failover using a distributed lock. The workflow instance will switch from the failed Master to a new Master. In this case, the system will issue the new Master’s host address to the Worker, enabling it to report the task status to the new Master. If the Worker fails: The system retries the task. However, before retrying, it needs to kill the task that is still running on YARN. Currently, DolphinScheduler does not support this out-of-the-box, because in a non-client-separated mode, the ProcessBuilder.waitFor method waits for the client process to exit. The applicationId is parsed only after this process exits, meaning the system can only obtain the applicationId after the program has finished running. In other words, you can’t get the applicationIduntil the process is completed. Here’s the relevant code section: org.apache.dolphinscheduler.server.master.service.WorkerFailoverService#killYarnTask 
private void killYarnTask(TaskInstance taskInstance, ProcessInstance processInstance) {
    try {
        if (!masterConfig.isKillApplicationWhenTaskFailover()) {
            return;
        }
        if (StringUtils.isEmpty(taskInstance.getHost()) || StringUtils.isEmpty(taskInstance.getLogPath())) {
            return;
        }
        TaskExecutionContext taskExecutionContext = TaskExecutionContextBuilder.get()
                .buildWorkflowInstanceHost(masterConfig.getMasterAddress())
                .buildTaskInstanceRelatedInfo(taskInstance)
                .buildProcessInstanceRelatedInfo(processInstance)
                .buildProcessDefinitionRelatedInfo(processInstance.getProcessDefinition())
                .create();
        log.info("TaskInstance failover begin kill the task related yarn or k8s job");
        ILogService iLogService =
                SingletonJdkDynamicRpcClientProxyFactory.getProxyClient(taskInstance.getHost(), ILogService.class);
        GetAppIdResponse getAppIdResponse =
                iLogService.getAppId(new GetAppIdRequest(taskInstance.getId(), taskInstance.getLogPath()));
        ProcessUtils.killApplication(getAppIdResponse.getAppIds(), taskExecutionContext);
    } catch (Exception ex) {
        log.error("Kill yarn task error", ex);
    }
} What can be done? In version 1.3.3, the LoggerServer and Masterwere separated, allowing the Master node (if it had the YARN client) to kill the applicationId running on YARN. So what now? Two Possible Solutions: Master kills the task using the YARN REST API: curl -X PUT -d '{"state":"KILLED"}' \
    -H "Content-Type: application/json" \
    http://xx.xx.xx.xx:8088/ws/v1/cluster/apps/application_1694766249884_1098/state?user.name=hdfs Note: You need to specify the user. 2. Worker kills the task:In this case, the task should be marked as a failover task. During retry, the task should be scheduled on a designated Worker node. Before retrying, the runningapplicationId needs to be killed. One optimization would be to first check the YARN status before killing. If the status is abnormal, then kill it. If it's RUNNING, you can wait for a set timeout period. Both the Apache DolphinScheduler Master and Worker components support multi-node deployment with a decentralized design. The Master is mainly responsible for splitting the DAG workflow and distributing tasks to Worker nodes through RPC, as well as handling task status updates from the Worker. The worker is responsible for executing tasks and reporting the task status back to the Master, which processes this information. The Master is mainly responsible for splitting the DAG workflow and distributing tasks to Worker nodes through RPC, as well as handling task status updates from the Worker. The Master is mainly responsible for splitting the DAG workflow and distributing tasks to Worker nodes through RPC, as well as handling task status updates from the Worker. The worker is responsible for executing tasks and reporting the task status back to the Master, which processes this information. The worker is responsible for executing tasks and reporting the task status back to the Master, which processes this information. But what happens in case of failure? But what happens in case of failure? What if the Master fails? Since it’s responsible for managing workflow instances, the Worker can no longer report task statuses, and the Master can’t process them either. What if the Worker fails? Since it’s the one executing the actual tasks, how does the Master handle this situation? What if the Master fails? Since it’s responsible for managing workflow instances, the Worker can no longer report task statuses, and the Master can’t process them either. What if the Master fails? Since it’s responsible for managing workflow instances, the Worker can no longer report task statuses, and the Master can’t process them either. What if the Worker fails? Since it’s the one executing the actual tasks, how does the Master handle this situation? What if the Worker fails? Since it’s the one executing the actual tasks, how does the Master handle this situation? Let’s dive into the fault tolerance mechanism with the help of an illustration: Let’s dive into the fault tolerance mechanism with the help of an illustration: Fault Tolerance Here’s a breakdown of how DolphinScheduler handles failures: If the Master fails: Other Master nodes will handle the failover using a distributed lock. The workflow instance will switch from the failed Master to a new Master. In this case, the system will issue the new Master’s host address to the Worker, enabling it to report the task status to the new Master. If the Master fails: Other Master nodes will handle the failover using a distributed lock. The workflow instance will switch from the failed Master to a new Master. In this case, the system will issue the new Master’s host address to the Worker, enabling it to report the task status to the new Master. If the Worker fails: The system retries the task. However, before retrying, it needs to kill the task that is still running on YARN. Currently, DolphinScheduler does not support this out-of-the-box, because in a non-client-separated mode, the ProcessBuilder.waitFor method waits for the client process to exit. The applicationId is parsed only after this process exits, meaning the system can only obtain the applicationId after the program has finished running. If the Worker fails: The system retries the task. However, before retrying, it needs to kill the task that is still running on YARN. Currently, DolphinScheduler does not support this out-of-the-box, because in a non-client-separated mode, the ProcessBuilder.waitFor method waits for the client process to exit. The applicationId is parsed only after this process exits, meaning the system can only obtain the applicationId after the program has finished running. If the Worker fails: The system retries the task. However, before retrying, it needs to kill the task that is still running on YARN. Currently, DolphinScheduler does not support this out-of-the-box, because in a non-client-separated mode, the ProcessBuilder.waitFor method waits for the client process to exit. The applicationId is parsed only after this process exits, meaning the system can only obtain the applicationId after the program has finished running. ProcessBuilder.waitFor applicationId applicationId In other words, you can’t get the applicationId until the process is completed. applicationId Here’s the relevant code section: org.apache.dolphinscheduler.server.master.service.WorkerFailoverService#killYarnTask 
private void killYarnTask(TaskInstance taskInstance, ProcessInstance processInstance) {
    try {
        if (!masterConfig.isKillApplicationWhenTaskFailover()) {
            return;
        }
        if (StringUtils.isEmpty(taskInstance.getHost()) || StringUtils.isEmpty(taskInstance.getLogPath())) {
            return;
        }
        TaskExecutionContext taskExecutionContext = TaskExecutionContextBuilder.get()
                .buildWorkflowInstanceHost(masterConfig.getMasterAddress())
                .buildTaskInstanceRelatedInfo(taskInstance)
                .buildProcessInstanceRelatedInfo(processInstance)
                .buildProcessDefinitionRelatedInfo(processInstance.getProcessDefinition())
                .create();
        log.info("TaskInstance failover begin kill the task related yarn or k8s job");
        ILogService iLogService =
                SingletonJdkDynamicRpcClientProxyFactory.getProxyClient(taskInstance.getHost(), ILogService.class);
        GetAppIdResponse getAppIdResponse =
                iLogService.getAppId(new GetAppIdRequest(taskInstance.getId(), taskInstance.getLogPath()));
        ProcessUtils.killApplication(getAppIdResponse.getAppIds(), taskExecutionContext);
    } catch (Exception ex) {
        log.error("Kill yarn task error", ex);
    }
} org.apache.dolphinscheduler.server.master.service.WorkerFailoverService#killYarnTask 
private void killYarnTask(TaskInstance taskInstance, ProcessInstance processInstance) {
    try {
        if (!masterConfig.isKillApplicationWhenTaskFailover()) {
            return;
        }
        if (StringUtils.isEmpty(taskInstance.getHost()) || StringUtils.isEmpty(taskInstance.getLogPath())) {
            return;
        }
        TaskExecutionContext taskExecutionContext = TaskExecutionContextBuilder.get()
                .buildWorkflowInstanceHost(masterConfig.getMasterAddress())
                .buildTaskInstanceRelatedInfo(taskInstance)
                .buildProcessInstanceRelatedInfo(processInstance)
                .buildProcessDefinitionRelatedInfo(processInstance.getProcessDefinition())
                .create();
        log.info("TaskInstance failover begin kill the task related yarn or k8s job");
        ILogService iLogService =
                SingletonJdkDynamicRpcClientProxyFactory.getProxyClient(taskInstance.getHost(), ILogService.class);
        GetAppIdResponse getAppIdResponse =
                iLogService.getAppId(new GetAppIdRequest(taskInstance.getId(), taskInstance.getLogPath()));
        ProcessUtils.killApplication(getAppIdResponse.getAppIds(), taskExecutionContext);
    } catch (Exception ex) {
        log.error("Kill yarn task error", ex);
    }
} What can be done? In version 1.3.3, the LoggerServer and Master were separated, allowing the Master node (if it had the YARN client) to kill the applicationId running on YARN. So what now? LoggerServer Master applicationId Two Possible Solutions: Master kills the task using the YARN REST API: Master kills the task using the YARN REST API: curl -X PUT -d '{"state":"KILLED"}' \
    -H "Content-Type: application/json" \
    http://xx.xx.xx.xx:8088/ws/v1/cluster/apps/application_1694766249884_1098/state?user.name=hdfs curl -X PUT -d '{"state":"KILLED"}' \
    -H "Content-Type: application/json" \
    http://xx.xx.xx.xx:8088/ws/v1/cluster/apps/application_1694766249884_1098/state?user.name=hdfs Note: You need to specify the user. Note: You need to specify the user. 2. Worker kills the task: In this case, the task should be marked as a failover task. During retry, the task should be scheduled on a designated Worker node. Before retrying, the running applicationId needs to be killed. One optimization would be to first check the YARN status before killing. If the status is abnormal, then kill it. If it's RUNNING, you can wait for a set timeout period. applicationId

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DolphinScheduler is a powerful open-source distributed task scheduling system widely used in the big data field for managing complex workflows. This article will provide a detailed guide on how to install and configure DolphinScheduler using Docker Compose, allowing you to quickly set up and start using the system. 1. Environment Preparation First, ensure that Docker and Docker Compose are installed on your system. Docker is an open-source containerization platform that allows developers to package applications and their dependencies into containers, providing high portability and consistency. Docker Compose is a tool for defining and managing multi-container applications. It uses a YAML file to configure the services and provides a single command to start or stop those services. 1.1 Verifying Docker and Docker Compose Installation You can verify if Docker and Docker Compose are installed correctly using the following commands: docker --versiondocker-compose --version If you see the version information, the installation was successful. 2. Downloading the DolphinScheduler Docker Compose Configuration File Before installing and running DolphinScheduler, you need to obtain its Docker Compose configuration file. This file defines the runtime environment for DolphinScheduler and its dependent services. Follow these steps to get the configuration file: 2.1 Clone the DolphinScheduler Project First, use Git to clone the official DolphinScheduler repository: git clone https://github.com/apache/dolphinscheduler.git This will download the DolphinScheduler project to your local machine. Next, navigate to the project directory: cd dolphinscheduler/docker In this directory, you will find a file named docker-compose.yml, which is the core configuration file for Docker Compose. 3. Configuring the Docker Compose File The docker-compose.yml file defines the services needed to run DolphinScheduler, including a MySQL database, ZooKeeper cluster, and DolphinScheduler's Master and Worker nodes. You can modify this file as needed to adjust the configuration of each service. 3.1 Overview of the Docker Compose File The docker-compose.yml file has the following basic structure: version: '3.1'
services:
  zookeeper:
    image: zookeeper:3.5.6
    ports:
      - "2181:2181"
  mysql:
    image: mysql:5.7
    environment:
      MYSQL_ROOT_PASSWORD: root
      MYSQL_DATABASE: dolphinscheduler
    ports:
      - "3306:3306"
  dolphinscheduler-master:
    image: apache/dolphinscheduler:latest
    depends_on:
      - mysql
      - zookeeper
    ports:
      - "12345:12345"
    environment:
      - DOLPHINSCHEDULER_OPTS="-Xms512m -Xmx512m"
  dolphinscheduler-worker:
    image: apache/dolphinscheduler:latest
    depends_on:
      - dolphinscheduler-master
    environment:
      - DOLPHINSCHEDULER_OPTS="-Xms512m -Xmx512m" In this configuration file: zookeeper: Responsible for cluster coordination and service discovery.
mysql: Stores the metadata for DolphinScheduler.
dolphinscheduler-master: The master node responsible for scheduling and managing tasks.
dolphinscheduler-worker: The worker node responsible for executing tasks. 4.Starting DolphinScheduler Once the docker-compose.yml file is configured correctly, you can start DolphinScheduler using Docker Compose: docker-compose up -d This command will start all the services defined in the docker-compose.yml file in the background. You can check the status of the services with the following command: docker-compose ps If all services are listed as Up, DolphinScheduler has been successfully started. 5.Configuring DolphinScheduler 5.1 Initial Setup Once started, you can access DolphinScheduler’s web UI through a browser. By default, the access URL is: http://localhost:12345 At the login screen, use the default admin credentials (username: admin, password: admin). After logging in, you may want to change the default password to enhance system security. 5.2 Creating Projects and Tasks In the web UI, you can create projects and define tasks. DolphinScheduler supports various task types such as Shell, Python, and SQL. You can create workflows by dragging and dropping tasks and setting dependencies between them. 5.3 System Monitoring and Log Management DolphinScheduler offers rich monitoring and logging features. Users can view task execution statuses, monitor cluster health in real-time, and access detailed execution logs, which help debug and optimize workflows. 6.Common Issues and Solutions During usage, you may encounter some issues. Below are common problems and their solutions. 6.1 Service Startup Failures If a service fails to start, you can check the logs to diagnose the issue using the following command: docker-compose logs <service_name> For example: docker-compose logs dolphinscheduler-master The log information can help identify errors such as database connection failures or port conflicts. 6.2 Database Connection Issues If there are database connection failures during startup, it may be due to the MySQL service not starting in time. In this case, try restarting DolphinScheduler manually: docker-compose restart dolphinscheduler-master dolphinscheduler-worker 7.Advantages and Use Cases of DolphinScheduler DolphinScheduler excels in big data processing and ETL task scheduling. Some key advantages include: User-Friendly Interface: Its graphical interface makes task management and monitoring easy, lowering the barrier to entry.
Flexible Task Dependency Management: It allows defining complex task dependencies, making scheduling more efficient.
High Scalability and Availability: Its distributed architecture is suitable for large-scale data processing. 8.Conclusion By following the above steps, you have successfully installed and configured DolphinScheduler using Docker Compose. Its powerful features and flexible configuration make it an ideal choice for distributed task scheduling. Whether for enterprise-level big data processing or small-to-medium-sized data integration projects, DolphinScheduler is a reliable solution. If you encounter issues during usage, you can refer to DolphinScheduler’s official documentation or community resources for more detailed technical support. With continued learning and exploration, you will be able to fully leverage DolphinScheduler’s potential, significantly improving your workflow management. DolphinScheduler is a powerful open-source distributed task scheduling system widely used in the big data field for managing complex workflows. This article will provide a detailed guide on how to install and configure DolphinScheduler using Docker Compose, allowing you to quickly set up and start using the system. 1. Environment Preparation First, ensure that Docker and Docker Compose are installed on your system. Docker is an open-source containerization platform that allows developers to package applications and their dependencies into containers, providing high portability and consistency. Docker Compose is a tool for defining and managing multi-container applications. It uses a YAML file to configure the services and provides a single command to start or stop those services. Docker 1.1 Verifying Docker and Docker Compose Installation You can verify if Docker and Docker Compose are installed correctly using the following commands: docker --versiondocker-compose --version docker --versiondocker-compose --version If you see the version information, the installation was successful. 2. Downloading the DolphinScheduler Docker Compose Configuration File Before installing and running DolphinScheduler, you need to obtain its Docker Compose configuration file. This file defines the runtime environment for DolphinScheduler and its dependent services. Follow these steps to get the configuration file: 2.1 Clone the DolphinScheduler Project First, use Git to clone the official DolphinScheduler repository: git clone https://github.com/apache/dolphinscheduler.git git clone https://github.com/apache/dolphinscheduler.git This will download the DolphinScheduler project to your local machine. Next, navigate to the project directory: cd dolphinscheduler/docker cd dolphinscheduler/docker In this directory, you will find a file named docker-compose.yml , which is the core configuration file for Docker Compose. docker-compose.yml 3. Configuring the Docker Compose File The docker-compose.yml file defines the services needed to run DolphinScheduler, including a MySQL database, ZooKeeper cluster, and DolphinScheduler's Master and Worker nodes. You can modify this file as needed to adjust the configuration of each service. docker-compose.yml 3.1 Overview of the Docker Compose File The docker-compose.yml file has the following basic structure: docker-compose.yml version: '3.1'
services:
  zookeeper:
    image: zookeeper:3.5.6
    ports:
      - "2181:2181"
  mysql:
    image: mysql:5.7
    environment:
      MYSQL_ROOT_PASSWORD: root
      MYSQL_DATABASE: dolphinscheduler
    ports:
      - "3306:3306"
  dolphinscheduler-master:
    image: apache/dolphinscheduler:latest
    depends_on:
      - mysql
      - zookeeper
    ports:
      - "12345:12345"
    environment:
      - DOLPHINSCHEDULER_OPTS="-Xms512m -Xmx512m"
  dolphinscheduler-worker:
    image: apache/dolphinscheduler:latest
    depends_on:
      - dolphinscheduler-master
    environment:
      - DOLPHINSCHEDULER_OPTS="-Xms512m -Xmx512m" version: '3.1'
services:
  zookeeper:
    image: zookeeper:3.5.6
    ports:
      - "2181:2181"
  mysql:
    image: mysql:5.7
    environment:
      MYSQL_ROOT_PASSWORD: root
      MYSQL_DATABASE: dolphinscheduler
    ports:
      - "3306:3306"
  dolphinscheduler-master:
    image: apache/dolphinscheduler:latest
    depends_on:
      - mysql
      - zookeeper
    ports:
      - "12345:12345"
    environment:
      - DOLPHINSCHEDULER_OPTS="-Xms512m -Xmx512m"
  dolphinscheduler-worker:
    image: apache/dolphinscheduler:latest
    depends_on:
      - dolphinscheduler-master
    environment:
      - DOLPHINSCHEDULER_OPTS="-Xms512m -Xmx512m" In this configuration file: In this configuration file: zookeeper: Responsible for cluster coordination and service discovery. mysql: Stores the metadata for DolphinScheduler. dolphinscheduler-master: The master node responsible for scheduling and managing tasks. dolphinscheduler-worker: The worker node responsible for executing tasks. zookeeper: Responsible for cluster coordination and service discovery. mysql: Stores the metadata for DolphinScheduler. dolphinscheduler-master: The master node responsible for scheduling and managing tasks. dolphinscheduler-worker: The worker node responsible for executing tasks. 4.Starting DolphinScheduler Once the docker-compose.yml file is configured correctly, you can start DolphinScheduler using Docker Compose: docker-compose.yml docker-compose up -d docker-compose up -d This command will start all the services defined in the docker-compose.yml file in the background. You can check the status of the services with the following command: docker-compose.yml docker-compose ps docker-compose ps If all services are listed as Up, DolphinScheduler has been successfully started. 5.Configuring DolphinScheduler 5.1 Initial Setup Once started, you can access DolphinScheduler’s web UI through a browser. By default, the access URL is: http://localhost:12345 http://localhost:12345 At the login screen, use the default admin credentials (username: admin , password: admin ). After logging in, you may want to change the default password to enhance system security. admin admin 5.2 Creating Projects and Tasks In the web UI, you can create projects and define tasks. DolphinScheduler supports various task types such as Shell, Python, and SQL. You can create workflows by dragging and dropping tasks and setting dependencies between them. 5.3 System Monitoring and Log Management DolphinScheduler offers rich monitoring and logging features. Users can view task execution statuses, monitor cluster health in real-time, and access detailed execution logs, which help debug and optimize workflows. 6.Common Issues and Solutions During usage, you may encounter some issues. Below are common problems and their solutions. 6.1 Service Startup Failures If a service fails to start, you can check the logs to diagnose the issue using the following command: docker-compose logs <service_name> docker-compose logs <service_name> For example: docker-compose logs dolphinscheduler-master docker-compose logs dolphinscheduler-master The log information can help identify errors such as database connection failures or port conflicts. 6.2 Database Connection Issues If there are database connection failures during startup, it may be due to the MySQL service not starting in time. In this case, try restarting DolphinScheduler manually: docker-compose restart dolphinscheduler-master dolphinscheduler-worker docker-compose restart dolphinscheduler-master dolphinscheduler-worker 7.Advantages and Use Cases of DolphinScheduler DolphinScheduler excels in big data processing and ETL task scheduling. Some key advantages include: User-Friendly Interface: Its graphical interface makes task management and monitoring easy, lowering the barrier to entry. Flexible Task Dependency Management: It allows defining complex task dependencies, making scheduling more efficient. High Scalability and Availability: Its distributed architecture is suitable for large-scale data processing. User-Friendly Interface: Its graphical interface makes task management and monitoring easy, lowering the barrier to entry. Flexible Task Dependency Management: It allows defining complex task dependencies, making scheduling more efficient. High Scalability and Availability: Its distributed architecture is suitable for large-scale data processing. 8.Conclusion By following the above steps, you have successfully installed and configured DolphinScheduler using Docker Compose. Its powerful features and flexible configuration make it an ideal choice for distributed task scheduling. Whether for enterprise-level big data processing or small-to-medium-sized data integration projects, DolphinScheduler is a reliable solution. If you encounter issues during usage, you can refer to DolphinScheduler’s official documentation or community resources for more detailed technical support. With continued learning and exploration, you will be able to fully leverage DolphinScheduler’s potential, significantly improving your workflow management.

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