Esegui un flusso di lavoro utilizzando le librerie client

recupera il codice campione

1. Clona il repository dell'app di esempio sulla tua macchina locale:

   Java

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

   In alternativa, puoi scaricare l'esempio come file ZIP ed estrarlo.

   Node.js

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

   In alternativa, puoi scaricare l'esempio come file ZIP ed estrarlo.

   Python

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

   In alternativa, puoi scaricare l'esempio come file ZIP ed estrarlo.

2. Passa alla directory che contiene il codice di esempio di 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. Dai un'occhiata al codice campione:

   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
   
   

Il programma esegue le seguenti operazioni:

1. Configura le librerie client Cloud per Workflows.
2. Esegue un flusso di lavoro.
3. Esegue il polling dell'esecuzione del flusso di lavoro (utilizzando il backoff esponenziale) fino al termine dell'esecuzione.
4. Stampa i risultati dell'esecuzione.

Esegui il sample

1. Per eseguire il sample, installa prima le dipendenze:

   Java

   mvn compile

   Node.js

   npm install

   Python

   pip3 install -r requirements.txt

2. Esegui lo script:

   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

   Sostituisci quanto segue:

   • PROJECT_ID: (obbligatorio) l'ID progetto del progetto Google Cloud
   • CLOUD_REGION: la posizione del flusso di lavoro (valore predefinito: us-central1)
   • WORKFLOW_NAME: l'ID del flusso di lavoro (valore predefinito: myFirstWorkflow)

   L'output è simile al seguente:

   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)"]
   

Passare dati in una richiesta di esecuzione

A seconda del linguaggio della libreria client, puoi anche passare un argomento di runtime in una richiesta di esecuzione.

Ad esempio, utilizzando 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;

In alternativa, utilizza Java:

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

Per ulteriori informazioni sul passaggio degli argomenti di runtime, consulta Passare gli argomenti di runtime in una richiesta di esecuzione.