Esegui un flusso di lavoro utilizzando le librerie client di Cloud

Questa guida rapida mostra come eseguire un flusso di lavoro e visualizzare i risultati dell'esecuzione utilizzando le librerie client di Cloud.

Per ulteriori informazioni sull'installazione delle librerie client di Cloud e sulla configurazione dell'ambiente di sviluppo, consulta la panoramica delle librerie client di Workflows.

Puoi completare i passaggi che seguono utilizzando Google Cloud CLI nel terminale o in Cloud Shell.

Prima di iniziare

I vincoli di sicurezza definiti dalla tua organizzazione potrebbero impedirti di completare i passaggi seguenti. Per informazioni sulla risoluzione dei problemi, vedi Sviluppare applicazioni in un ambiente Google Cloud vincolato.

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
  2. Install the Google Cloud CLI.
  3. To initialize the gcloud CLI, run the following command:

    gcloud init
  4. Create or select a Google Cloud project.

    • Create a Google Cloud project:

      gcloud projects create PROJECT_ID

      Replace PROJECT_ID with a name for the Google Cloud project you are creating.

    • Select the Google Cloud project that you created:

      gcloud config set project PROJECT_ID

      Replace PROJECT_ID with your Google Cloud project name.

  5. Make sure that billing is enabled for your Google Cloud project.

  6. Enable the Workflows API:

    gcloud services enable workflows.googleapis.com
  7. Set up authentication:

    1. Create the service account:

      gcloud iam service-accounts create SERVICE_ACCOUNT_NAME

      Replace SERVICE_ACCOUNT_NAME with a name for the service account.

    2. Grant the roles/owner IAM role to the service account:

      gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com" --role=roles/owner

      Replace the following:

      • SERVICE_ACCOUNT_NAME: the name of the service account
      • PROJECT_ID: the project ID where you created the service account
  8. Install the Google Cloud CLI.
  9. To initialize the gcloud CLI, run the following command:

    gcloud init
  10. Create or select a Google Cloud project.

    • Create a Google Cloud project:

      gcloud projects create PROJECT_ID

      Replace PROJECT_ID with a name for the Google Cloud project you are creating.

    • Select the Google Cloud project that you created:

      gcloud config set project PROJECT_ID

      Replace PROJECT_ID with your Google Cloud project name.

  11. Make sure that billing is enabled for your Google Cloud project.

  12. Enable the Workflows API:

    gcloud services enable workflows.googleapis.com
  13. Set up authentication:

    1. Create the service account:

      gcloud iam service-accounts create SERVICE_ACCOUNT_NAME

      Replace SERVICE_ACCOUNT_NAME with a name for the service account.

    2. Grant the roles/owner IAM role to the service account:

      gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com" --role=roles/owner

      Replace the following:

      • SERVICE_ACCOUNT_NAME: the name of the service account
      • PROJECT_ID: the project ID where you created the service account
  14. (Facoltativo) Per inviare i log a Cloud Logging, concedi il ruolo roles/logging.logWriter all'account di servizio.

    gcloud projects add-iam-policy-binding PROJECT_ID \
        --member "serviceAccount:SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com" \
        --role "roles/logging.logWriter"

    Per scoprire di più sui ruoli e sulle autorizzazioni degli account di servizio, consulta Concedere un'autorizzazione di flusso di lavoro per accedere alle Google Cloud risorse.

  15. Se necessario, scarica e installa lo strumento di gestione del codice sorgente Git.

Esegui il deployment di un flusso di lavoro di esempio

Dopo aver definito un flusso di lavoro, esegui il deployment per renderlo disponibile per l'esecuzione. Il passaggio di deployment convalida anche che il file di origine possa essere eseguito.

Il seguente flusso di lavoro invia una richiesta a un'API pubblica e poi restituisce la risposta dell'API.

  1. Crea un file di testo con il nome file myFirstWorkflow.yaml con i seguenti contenuti:

    - getCurrentTime:
        call: http.get
        args:
          url: https://timeapi.io/api/Time/current/zone?timeZone=Europe/Amsterdam
        result: currentTime
    - readWikipedia:
        call: http.get
        args:
          url: https://en.wikipedia.org/w/api.php
          query:
            action: opensearch
            search: ${currentTime.body.dayOfWeek}
        result: wikiResult
    - returnResult:
        return: ${wikiResult.body[1]}
  2. Dopo aver creato il flusso di lavoro, puoi eseguirlo, ma non eseguire il flusso di lavoro:

    gcloud workflows deploy myFirstWorkflow \
        --source=myFirstWorkflow.yaml \
        --service-account=SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com \
        --location=CLOUD_REGION

    Sostituisci CLOUD_REGION con una posizione supportata per il flusso di lavoro. La regione predefinita utilizzata negli esempi di codice è us-central1.

recupera il codice campione

Puoi clonare il codice di esempio da GitHub.

  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 di esempio. Ogni app di esempio esegue le seguenti operazioni:

    1. Configura le librerie client Cloud per i flussi di lavoro.
    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.

    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 os
    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
    
    PROJECT = os.getenv("GOOGLE_CLOUD_PROJECT")
    LOCATION = os.getenv("LOCATION", "us-central1")
    WORKFLOW_ID = os.getenv("WORKFLOW", "myFirstWorkflow")
    
    
    def execute_workflow(project: str, location: str, workflow: str) -> 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
    
    
    if __name__ == "__main__":
        assert PROJECT, "'GOOGLE_CLOUD_PROJECT' environment variable not set."
        execute_workflow(PROJECT, LOCATION, WORKFLOW_ID)

Esegui il codice campione

Puoi eseguire il codice di esempio ed eseguire il flusso di lavoro. L'esecuzione di un flusso di lavoro esegue la definizione del flusso di lavoro di cui è stato eseguito il deployment associata al flusso di lavoro.

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

    Java

    mvn compile

    Node.js

    npm install -D tsx

    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

    npx tsx index.js

    Python

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

    Sostituisci quanto segue:

    • PROJECT_ID: il nome del tuo Google Cloud progetto
    • CLOUD_REGION: la posizione del flusso di lavoro (valore predefinito: us-central1)
    • WORKFLOW_NAME: il nome del flusso di lavoro (valore predefinito: myFirstWorkflow)

    L'output è simile al seguente:

    Execution finished with state: SUCCEEDED
    Execution results: ["Thursday","Thursday Night Football","Thursday (band)","Thursday Island","Thursday (album)","Thursday Next","Thursday at the Square","Thursday's Child (David Bowie song)","Thursday Afternoon","Thursday (film)"]
    

Passare i 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:

Java

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

Node.js

// 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;

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

Esegui la pulizia

Per evitare che al tuo account Google Cloud vengano addebitati costi relativi alle risorse utilizzate in questa pagina, elimina il progetto Google Cloud con le risorse.

  1. Elimina il flusso di lavoro creato:

    gcloud workflows delete myFirstWorkflow
    
  2. Quando ti viene chiesto se vuoi continuare, digita y.

Il flusso di lavoro viene eliminato.

Passaggi successivi