Ejecuta un flujo de trabajo con bibliotecas cliente

Obtén el código de muestra

1. Clona el repositorio de la app de muestra en tu máquina local:

   Java

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

   De manera opcional, puedes descargar la muestra como un archivo zip y extraerla.

   Node.js

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

   De manera opcional, puedes descargar la muestra como un archivo zip y extraerla.

   Python

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

   De manera opcional, puedes descargar la muestra como un archivo zip y extraerla.

2. Ve al directorio que contiene el código de muestra de Workflows:

   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. Ve el código de muestra:

   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
   
   

El programa hace lo siguiente:

1. Configura las bibliotecas cliente de Cloud para Workflows.
2. Ejecuta un flujo de trabajo.
3. Sondea la ejecución del flujo de trabajo (con una retirada exponencial) hasta que se complete.
4. Imprime los resultados de la ejecución.

Ejecuta la muestra

1. Para ejecutar la muestra, primero instala las dependencias:

   Java

   mvn compile

   Node.js

   npm install

   Python

   pip3 install -r requirements.txt

2. Ejecuta la secuencia de comandos:

   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

   Reemplaza lo siguiente:

   • PROJECT_ID: (obligatorio) El ID del proyecto de Google Cloud
   • CLOUD_REGION: Es la ubicación del flujo de trabajo (predeterminada: us-central1).
   • WORKFLOW_NAME: Es el ID del flujo de trabajo (predeterminado: myFirstWorkflow).

   El resultado es similar a este:

   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)"]
   

Cómo pasar datos en una solicitud de ejecución

Según el lenguaje de la biblioteca cliente, también puedes pasar un argumento del entorno de ejecución en una solicitud de ejecución.

Por ejemplo, con 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;

O bien, usa Java:

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

Para obtener más información sobre cómo pasar argumentos del entorno de ejecución, consulta Cómo pasar argumentos del entorno de ejecución en una solicitud de ejecución.