使用客户端库执行工作流

您可以使用客户端库执行工作流和查看执行结果。

如需详细了解如何安装客户端库和设置开发环境,请参阅 Workflows 客户端库概览

准备工作

您的组织定义的安全限制条件可能会导致您无法完成以下步骤。如需了解相关问题排查信息,请参阅在受限的 Google Cloud 环境中开发应用

  1. 以下示例假定您已部署工作流, myFirstWorkflow。如果还没有部署,请立即使用 Google Cloud 控制台Google Cloud CLI
  2. 下载并 安装 Git 源代码管理工具。

获取示例代码

  1. 将示例应用代码库克隆到本地机器:

    Java

    git clone https://github.com/GoogleCloudPlatform/java-docs-samples.git

    或者,您也可以下载该示例的 zip 文件并将其解压缩。

    Node.js

    git clone https://github.com/GoogleCloudPlatform/nodejs-docs-samples.git

    或者,您也可以下载该示例的 zip 文件并将其解压缩。

    Python

    git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git

    或者,您也可以下载该示例的 zip 文件并将其解压缩。

  2. 切换到包含工作流示例代码的目录:

    Java

    cd java-docs-samples/workflows/cloud-client/

    Node.js

    cd nodejs-docs-samples/workflows/quickstart/

    Python

    cd python-docs-samples/workflows/cloud-client/

  3. 查看示例代码:

    Java

    // Imports the Google Cloud client library
    
    import com.google.cloud.workflows.executions.v1.CreateExecutionRequest;
    import com.google.cloud.workflows.executions.v1.Execution;
    import com.google.cloud.workflows.executions.v1.ExecutionsClient;
    import com.google.cloud.workflows.executions.v1.WorkflowName;
    import java.io.IOException;
    import java.util.concurrent.ExecutionException;
    
    public class WorkflowsQuickstart {
    
      private static final String PROJECT = System.getenv("GOOGLE_CLOUD_PROJECT");
      private static final String LOCATION = System.getenv().getOrDefault("LOCATION", "us-central1");
      private static final String WORKFLOW =
          System.getenv().getOrDefault("WORKFLOW", "myFirstWorkflow");
    
      public static void main(String... args)
          throws IOException, InterruptedException, ExecutionException {
        if (PROJECT == null) {
          throw new IllegalArgumentException(
              "Environment variable 'GOOGLE_CLOUD_PROJECT' is required to run this quickstart.");
        }
        workflowsQuickstart(PROJECT, LOCATION, WORKFLOW);
      }
    
      private static volatile boolean finished;
    
      public static void workflowsQuickstart(String projectId, String location, String workflow)
          throws IOException, InterruptedException, ExecutionException {
        // Initialize client that will be used to send requests. This client only needs
        // to be created once, and can be reused for multiple requests. After completing all of your
        // requests, call the "close" method on the client to safely clean up any remaining background
        // resources.
        try (ExecutionsClient executionsClient = ExecutionsClient.create()) {
          // Construct the fully qualified location path.
          WorkflowName parent = WorkflowName.of(projectId, location, workflow);
    
          // Creates the execution object.
          CreateExecutionRequest request =
              CreateExecutionRequest.newBuilder()
                  .setParent(parent.toString())
                  .setExecution(Execution.newBuilder().build())
                  .build();
          Execution response = executionsClient.createExecution(request);
    
          String executionName = response.getName();
          System.out.printf("Created execution: %s%n", executionName);
    
          long backoffTime = 0;
          long backoffDelay = 1_000; // Start wait with delay of 1,000 ms
          final long backoffTimeout = 10 * 60 * 1_000; // Time out at 10 minutes
          System.out.println("Poll for results...");
    
          // Wait for execution to finish, then print results.
          while (!finished && backoffTime < backoffTimeout) {
            Execution execution = executionsClient.getExecution(executionName);
            finished = execution.getState() != Execution.State.ACTIVE;
    
            // If we haven't seen the results yet, wait.
            if (!finished) {
              System.out.println("- Waiting for results");
              Thread.sleep(backoffDelay);
              backoffTime += backoffDelay;
              backoffDelay *= 2; // Double the delay to provide exponential backoff.
            } else {
              System.out.println("Execution finished with state: " + execution.getState().name());
              System.out.println("Execution results: " + execution.getResult());
            }
          }
        }
      }
    }

    Node.js

    const {ExecutionsClient} = require('@google-cloud/workflows');
    const client = new ExecutionsClient();
    /**
     * TODO(developer): Uncomment these variables before running the sample.
     */
    // const projectId = 'my-project';
    // const location = 'us-central1';
    // const workflow = 'myFirstWorkflow';
    // const searchTerm = '';
    
    /**
     * Executes a Workflow and waits for the results with exponential backoff.
     * @param {string} projectId The Google Cloud Project containing the workflow
     * @param {string} location The workflow location
     * @param {string} workflow The workflow name
     * @param {string} searchTerm Optional search term to pass to the Workflow as a runtime argument
     */
    async function executeWorkflow(projectId, location, workflow, searchTerm) {
      /**
       * Sleeps the process N number of milliseconds.
       * @param {Number} ms The number of milliseconds to sleep.
       */
      function sleep(ms) {
        return new Promise(resolve => {
          setTimeout(resolve, ms);
        });
      }
      const runtimeArgs = searchTerm ? {searchTerm: searchTerm} : {};
      // Execute workflow
      try {
        const createExecutionRes = await client.createExecution({
          parent: client.workflowPath(projectId, location, workflow),
          execution: {
            // Runtime arguments can be passed as a JSON string
            argument: JSON.stringify(runtimeArgs),
          },
        });
        const executionName = createExecutionRes[0].name;
        console.log(`Created execution: ${executionName}`);
    
        // Wait for execution to finish, then print results.
        let executionFinished = false;
        let backoffDelay = 1000; // Start wait with delay of 1,000 ms
        console.log('Poll every second for result...');
        while (!executionFinished) {
          const [execution] = await client.getExecution({
            name: executionName,
          });
          executionFinished = execution.state !== 'ACTIVE';
    
          // If we haven't seen the result yet, wait a second.
          if (!executionFinished) {
            console.log('- Waiting for results...');
            await sleep(backoffDelay);
            backoffDelay *= 2; // Double the delay to provide exponential backoff.
          } else {
            console.log(`Execution finished with state: ${execution.state}`);
            console.log(execution.result);
            return execution.result;
          }
        }
      } catch (e) {
        console.error(`Error executing workflow: ${e}`);
      }
    }
    
    executeWorkflow(projectId, location, workflowName, searchTerm).catch(err => {
      console.error(err.message);
      process.exitCode = 1;
    });
    

    Python

    import time
    
    from google.cloud import workflows_v1
    from google.cloud.workflows import executions_v1
    from google.cloud.workflows.executions_v1 import Execution
    from google.cloud.workflows.executions_v1.types import executions
    
    
    def execute_workflow(
        project: str, location: str = "us-central1", workflow: str = "myFirstWorkflow"
    ) -> Execution:
        """Execute a workflow and print the execution results.
    
        A workflow consists of a series of steps described using the Workflows syntax, and can be written in either YAML or JSON.
    
        Args:
            project: The Google Cloud project id which contains the workflow to execute.
            location: The location for the workflow
            workflow: The ID of the workflow to execute.
    
        Returns:
            The execution response.
        """
        # Set up API clients.
        execution_client = executions_v1.ExecutionsClient()
        workflows_client = workflows_v1.WorkflowsClient()
        # Construct the fully qualified location path.
        parent = workflows_client.workflow_path(project, location, workflow)
    
        # Execute the workflow.
        response = execution_client.create_execution(request={"parent": parent})
        print(f"Created execution: {response.name}")
    
        # Wait for execution to finish, then print results.
        execution_finished = False
        backoff_delay = 1  # Start wait with delay of 1 second
        print("Poll for result...")
        while not execution_finished:
            execution = execution_client.get_execution(request={"name": response.name})
            execution_finished = execution.state != executions.Execution.State.ACTIVE
    
            # If we haven't seen the result yet, wait a second.
            if not execution_finished:
                print("- Waiting for results...")
                time.sleep(backoff_delay)
                # Double the delay to provide exponential backoff.
                backoff_delay *= 2
            else:
                print(f"Execution finished with state: {execution.state.name}")
                print(f"Execution results: {execution.result}")
                return execution
    
    

该程序将执行以下操作:

  1. 为工作流设置 Cloud 客户端库。
  2. 执行工作流。
  3. 轮询工作流的执行(使用指数退避算法),直到执行终止为止。
  4. 打印执行结果。

运行示例

  1. 要运行示例,请先安装依赖项:

    Java

    mvn compile

    Node.js

    npm install

    Python

    pip3 install -r requirements.txt

  2. 运行脚本:

    Java

    GOOGLE_CLOUD_PROJECT=PROJECT_ID LOCATION=CLOUD_REGION WORKFLOW=WORKFLOW_NAME mvn compile exec:java -Dexec.mainClass=com.example.workflows.WorkflowsQuickstart

    Node.js

    node . PROJECT_ID CLOUD_REGION WORKFLOW_NAME

    Python

    GOOGLE_CLOUD_PROJECT=PROJECT_ID LOCATION=CLOUD_REGION WORKFLOW=WORKFLOW_NAME python3 main.py

    替换以下内容:

    • PROJECT_ID:(必需)Google Cloud 项目的 ID
    • CLOUD_REGION:工作流的位置(默认值:us-central1
    • WORKFLOW_NAME:替换为工作流的 ID(默认值:myFirstWorkflow

    输出类似于以下内容:

    Execution finished with state: SUCCEEDED
    ["Sunday","Sunday in the Park with George","Sunday shopping","Sunday Bloody Sunday","Sunday Times Golden Globe Race","Sunday All Stars","Sunday Night (South Korean TV series)","Sunday Silence","Sunday Without God","Sunday Independent (Ireland)"]
    

在执行请求中传递数据

根据客户端库语言,您还可以传递运行时参数 执行请求中包含的命令

例如,使用 JavaScript:

// Execute workflow
try {
  const createExecutionRes = await client.createExecution({
    parent: client.workflowPath(projectId, location, workflow),
    execution: {
      argument: JSON.stringify({"searchTerm": "Friday"})
    }
});
const executionName = createExecutionRes[0].name;

或者,使用 Java:

// Creates the execution object.
CreateExecutionRequest request =
    CreateExecutionRequest.newBuilder()
        .setParent(parent.toString())
        .setExecution(Execution.newBuilder().setArgument("{\"searchTerm\":\"Friday\"}").build())
        .build();

如需详细了解如何传递运行时参数,请参阅 在执行请求中传递运行时参数

清理

为避免因本页中使用的资源导致您的 Google Cloud 账号产生费用,请按照以下步骤操作。

  1. 在 Google Cloud 控制台中,进入 Workflows 页面:

    进入 Workflows

  2. 在工作流列表中,点击某个工作流以转到其 工作流详细信息页面。

  3. 点击 删除

  4. 输入工作流的名称,然后点击确认

后续步骤