Menggunakan library klien untuk menjalankan alur kerja. Melakukan polling pada eksekusi alur kerja menggunakan backoff eksponensial hingga eksekusi dihentikan, lalu mencetak hasilnya.
Jelajahi lebih lanjut
Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat artikel berikut:
Contoh kode
Java
Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Java di Panduan memulai alur kerja menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat dokumentasi referensi API alur kerja Java.
Untuk mengautentikasi ke Workflows, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.
// 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;
});
Node.js
import {ExecutionsClient} from '@google-cloud/workflows';
const client: ExecutionsClient = 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: string,
location: string,
workflow: string,
searchTerm: string
) {
/**
* Sleeps the process N number of milliseconds.
* @param {Number} ms The number of milliseconds to sleep.
*/
function sleep(ms: number): Promise<unknown> {
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: Error) => {
console.error(err.message);
process.exitCode = 1;
}
);Python
Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai alur kerja menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat dokumentasi referensi API alur kerja Python.
Untuk mengautentikasi ke Workflows, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.
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
Langkah selanjutnya
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