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Menjalankan alur kerja menggunakan Library Klien Cloud

Panduan memulai ini menunjukkan cara menjalankan alur kerja dan melihat hasil eksekusi menggunakan Library Klien Cloud.

Untuk informasi selengkapnya tentang cara menginstal Library Klien Cloud dan menyiapkan lingkungan pengembangan, lihat Ringkasan library klien Alur Kerja.

Anda dapat menyelesaikan langkah-langkah berikut menggunakan Google Cloud CLI di terminal atau Cloud Shell.

Sebelum memulai

Batasan keamanan yang ditentukan oleh organisasi mungkin mencegah Anda menyelesaikan langkah-langkah berikut. Untuk mengetahui informasi pemecahan masalah, lihat Mengembangkan aplikasi di lingkungan Google Cloud yang terbatas.

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.

Install the Google Cloud CLI.

To initialize the gcloud CLI, run the following command:

gcloud init

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.

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

Enable the Workflows API:

gcloud services enable workflows.googleapis.com

Set up authentication:

Create the service account:
```
gcloud iam service-accounts create SERVICE_ACCOUNT_NAME
```
Replace SERVICE_ACCOUNT_NAME with a name for the service account.
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
Note: The --role flag affects which resources the service account can access in your project. You can revoke these roles or grant additional roles later. In production environments, do not grant the Owner, Editor, or Viewer roles. Instead, grant a predefined role or custom role that meets your needs.

Install the Google Cloud CLI.

To initialize the gcloud CLI, run the following command:

gcloud init

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.

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

Enable the Workflows API:

gcloud services enable workflows.googleapis.com

Set up authentication:

Create the service account:
```
gcloud iam service-accounts create SERVICE_ACCOUNT_NAME
```
Replace SERVICE_ACCOUNT_NAME with a name for the service account.
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
Note: The --role flag affects which resources the service account can access in your project. You can revoke these roles or grant additional roles later. In production environments, do not grant the Owner, Editor, or Viewer roles. Instead, grant a predefined role or custom role that meets your needs.

(Opsional) Untuk mengirim log ke Cloud Logging, berikan peran roles/logging.logWriter ke akun layanan.
```
gcloud projects add-iam-policy-binding PROJECT_ID \
    --member "serviceAccount:SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com" \
    --role "roles/logging.logWriter"
```
Untuk mempelajari peran dan izin akun layanan lebih lanjut, lihat Memberikan izin alur kerja untuk mengakses Google Cloud resource.
Jika diperlukan, download dan instal alat pengelolaan kode sumber Git.

Men-deploy alur kerja contoh

Setelah menentukan alur kerja, Anda men-deploy alur kerja tersebut agar tersedia untuk dieksekusi. Langkah deploy juga memvalidasi bahwa file sumber dapat dieksekusi.

Alur kerja berikut mengirim permintaan ke API publik, lalu menampilkan respons API.

Buat file teks dengan nama file myFirstWorkflow.yaml dengan konten berikut:

- 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]}

Setelah membuat alur kerja, Anda dapat men-deploynya, tetapi jangan jalankan alur kerja:
```
gcloud workflows deploy myFirstWorkflow \
    --source=myFirstWorkflow.yaml \
    --service-account=SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com \
    --location=CLOUD_REGION
```
Ganti CLOUD_REGION dengan lokasi yang didukung untuk alur kerja. Wilayah default yang digunakan dalam contoh kode adalah us-central1.

Mendapatkan kode contoh

Anda dapat meng-clone kode contoh dari GitHub.

Clone repositori aplikasi contoh ke komputer lokal Anda:
Java
```
git clone https://github.com/GoogleCloudPlatform/java-docs-samples.git
```
Atau, Anda dapat mendownload contoh dalam file ZIP dan mengekstraknya.
Node.js
```
git clone https://github.com/GoogleCloudPlatform/nodejs-docs-samples.git
```
Atau, Anda dapat mendownload contoh dalam file ZIP dan mengekstraknya.
Python
```
git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
```
Atau, Anda dapat mendownload contoh dalam file ZIP dan mengekstraknya.

Ubah ke direktori yang berisi kode contoh 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/

Lihat kode contoh: Setiap aplikasi contoh melakukan hal berikut:

Menyiapkan Library Klien Cloud untuk Alur Kerja.
Menjalankan alur kerja.
Melakukan polling terhadap eksekusi alur kerja (menggunakan backoff eksponensial) hingga eksekusi dihentikan.
Mencetak hasil eksekusi.

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)

Menjalankan kode contoh

Anda dapat menjalankan kode contoh dan menjalankan alur kerja. Menjalankan alur kerja akan menjalankan definisi alur kerja yang di-deploy yang terkait dengan alur kerja.

Untuk menjalankan contoh, instal dependensi terlebih dahulu:
Java
```
mvn compile
```
Node.js
```
npm install -D tsx
```
Python
```
pip3 install -r requirements.txt
```

Jalankan skrip:

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

Ganti kode berikut:

PROJECT_ID: Google Cloud nama project Anda
CLOUD_REGION: lokasi alur kerja Anda (default: us-central1)
WORKFLOW_NAME: nama alur kerja Anda (default: myFirstWorkflow)

Outputnya mirip dengan hal berikut ini:

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)"]

Meneruskan data dalam permintaan eksekusi

Bergantung pada bahasa library klien, Anda juga dapat meneruskan argumen runtime dalam permintaan eksekusi. Contoh:

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;

Untuk informasi selengkapnya tentang cara meneruskan argumen runtime, lihat Teruskan argumen runtime dalam permintaan eksekusi.

Pembersihan

Agar tidak menimbulkan biaya pada akun Google Cloud Anda untuk resource yang digunakan di halaman ini, hapus project Google Cloud yang berisi resource tersebut.

Hapus alur kerja yang Anda buat:

gcloud workflows delete myFirstWorkflow

Ketika ditanya apakah Anda ingin melanjutkan, tekan y.

Alur kerja akan dihapus.

Menjalankan alur kerja menggunakan Library Klien Cloud

Sebelum memulai

Men-deploy alur kerja contoh

Mendapatkan kode contoh

Java

Node.js

Python

Java

Node.js

Python

Java

Node.js

Python

Menjalankan kode contoh

Java

Node.js

Python

Java

Node.js

Python

Meneruskan data dalam permintaan eksekusi

Java

Node.js

Pembersihan

Langkah selanjutnya