Instancie un modèle de workflow intégré à l'aide des bibliothèques clientes Cloud.
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Exemple de code
Go
Avant d'essayer l'exemple ci-dessous, suivez la procédure de configuration pour Go décrite dans le guide de démarrage rapide de Dataproc avec les bibliothèques clientes. Pour en savoir plus, consultez la documentation de référence de l'API Dataproc en langage Go.
import (
"context"
"fmt"
"io"
dataproc "cloud.google.com/go/dataproc/apiv1"
"google.golang.org/api/option"
dataprocpb "google.golang.org/genproto/googleapis/cloud/dataproc/v1"
)
func instantiateInlineWorkflowTemplate(w io.Writer, projectID, region string) error {
// projectID := "your-project-id"
// region := "us-central1"
ctx := context.Background()
// Create the cluster client.
endpoint := region + "-dataproc.googleapis.com:443"
workflowTemplateClient, err := dataproc.NewWorkflowTemplateClient(ctx, option.WithEndpoint(endpoint))
if err != nil {
return fmt.Errorf("dataproc.NewWorkflowTemplateClient: %v", err)
}
defer workflowTemplateClient.Close()
// Create jobs for the workflow.
teragenJob := &dataprocpb.OrderedJob{
JobType: &dataprocpb.OrderedJob_HadoopJob{
HadoopJob: &dataprocpb.HadoopJob{
Driver: &dataprocpb.HadoopJob_MainJarFileUri{
MainJarFileUri: "file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar",
},
Args: []string{
"teragen",
"1000",
"hdfs:///gen/",
},
},
},
StepId: "teragen",
}
terasortJob := &dataprocpb.OrderedJob{
JobType: &dataprocpb.OrderedJob_HadoopJob{
HadoopJob: &dataprocpb.HadoopJob{
Driver: &dataprocpb.HadoopJob_MainJarFileUri{
MainJarFileUri: "file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar",
},
Args: []string{
"terasort",
"hdfs:///gen/",
"hdfs:///sort/",
},
},
},
StepId: "terasort",
PrerequisiteStepIds: []string{
"teragen",
},
}
// Create the cluster placement.
clusterPlacement := &dataprocpb.WorkflowTemplatePlacement{
Placement: &dataprocpb.WorkflowTemplatePlacement_ManagedCluster{
ManagedCluster: &dataprocpb.ManagedCluster{
ClusterName: "my-managed-cluster",
Config: &dataprocpb.ClusterConfig{
GceClusterConfig: &dataprocpb.GceClusterConfig{
// Leave "ZoneUri" empty for "Auto Zone Placement"
// ZoneUri: ""
ZoneUri: "us-central1-a",
},
},
},
},
}
// Create the Instantiate Inline Workflow Template Request.
req := &dataprocpb.InstantiateInlineWorkflowTemplateRequest{
Parent: fmt.Sprintf("projects/%s/regions/%s", projectID, region),
Template: &dataprocpb.WorkflowTemplate{
Jobs: []*dataprocpb.OrderedJob{
teragenJob,
terasortJob,
},
Placement: clusterPlacement,
},
}
// Create the cluster.
op, err := workflowTemplateClient.InstantiateInlineWorkflowTemplate(ctx, req)
if err != nil {
return fmt.Errorf("InstantiateInlineWorkflowTemplate: %v", err)
}
if err := op.Wait(ctx); err != nil {
return fmt.Errorf("InstantiateInlineWorkflowTemplate.Wait: %v", err)
}
// Output a success message.
fmt.Fprintf(w, "Workflow created successfully.")
return nil
}
Java
Avant d'essayer l'exemple ci-dessous, suivez la procédure de configuration pour Java décrite dans le guide de démarrage rapide de Dataproc avec les bibliothèques clientes. Pour en savoir plus, consultez la documentation de référence de l'API Dataproc en langage Java.
import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.dataproc.v1.ClusterConfig;
import com.google.cloud.dataproc.v1.GceClusterConfig;
import com.google.cloud.dataproc.v1.HadoopJob;
import com.google.cloud.dataproc.v1.ManagedCluster;
import com.google.cloud.dataproc.v1.OrderedJob;
import com.google.cloud.dataproc.v1.RegionName;
import com.google.cloud.dataproc.v1.WorkflowMetadata;
import com.google.cloud.dataproc.v1.WorkflowTemplate;
import com.google.cloud.dataproc.v1.WorkflowTemplatePlacement;
import com.google.cloud.dataproc.v1.WorkflowTemplateServiceClient;
import com.google.cloud.dataproc.v1.WorkflowTemplateServiceSettings;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
public class InstantiateInlineWorkflowTemplate {
public static void instantiateInlineWorkflowTemplate() throws IOException, InterruptedException {
// TODO(developer): Replace these variables before running the sample.
String projectId = "your-project-id";
String region = "your-project-region";
instantiateInlineWorkflowTemplate(projectId, region);
}
public static void instantiateInlineWorkflowTemplate(String projectId, String region)
throws IOException, InterruptedException {
String myEndpoint = String.format("%s-dataproc.googleapis.com:443", region);
// Configure the settings for the workflow template service client.
WorkflowTemplateServiceSettings workflowTemplateServiceSettings =
WorkflowTemplateServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
// Create a workflow template service client with the configured settings. The client only
// needs to be created once and can be reused for multiple requests. Using a try-with-resources
// closes the client, but this can also be done manually with the .close() method.
try (WorkflowTemplateServiceClient workflowTemplateServiceClient =
WorkflowTemplateServiceClient.create(workflowTemplateServiceSettings)) {
// Configure the jobs within the workflow.
HadoopJob teragenHadoopJob =
HadoopJob.newBuilder()
.setMainJarFileUri("file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar")
.addArgs("teragen")
.addArgs("1000")
.addArgs("hdfs:///gen/")
.build();
OrderedJob teragen =
OrderedJob.newBuilder().setHadoopJob(teragenHadoopJob).setStepId("teragen").build();
HadoopJob terasortHadoopJob =
HadoopJob.newBuilder()
.setMainJarFileUri("file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar")
.addArgs("terasort")
.addArgs("hdfs:///gen/")
.addArgs("hdfs:///sort/")
.build();
OrderedJob terasort =
OrderedJob.newBuilder()
.setHadoopJob(terasortHadoopJob)
.addPrerequisiteStepIds("teragen")
.setStepId("terasort")
.build();
// Configure the cluster placement for the workflow.
// Leave "ZoneUri" empty for "Auto Zone Placement".
// GceClusterConfig gceClusterConfig =
// GceClusterConfig.newBuilder().setZoneUri("").build();
GceClusterConfig gceClusterConfig =
GceClusterConfig.newBuilder().setZoneUri("us-central1-a").build();
ClusterConfig clusterConfig =
ClusterConfig.newBuilder().setGceClusterConfig(gceClusterConfig).build();
ManagedCluster managedCluster =
ManagedCluster.newBuilder()
.setClusterName("my-managed-cluster")
.setConfig(clusterConfig)
.build();
WorkflowTemplatePlacement workflowTemplatePlacement =
WorkflowTemplatePlacement.newBuilder().setManagedCluster(managedCluster).build();
// Create the inline workflow template.
WorkflowTemplate workflowTemplate =
WorkflowTemplate.newBuilder()
.addJobs(teragen)
.addJobs(terasort)
.setPlacement(workflowTemplatePlacement)
.build();
// Submit the instantiated inline workflow template request.
String parent = RegionName.format(projectId, region);
OperationFuture<Empty, WorkflowMetadata> instantiateInlineWorkflowTemplateAsync =
workflowTemplateServiceClient.instantiateInlineWorkflowTemplateAsync(
parent, workflowTemplate);
instantiateInlineWorkflowTemplateAsync.get();
// Print out a success message.
System.out.printf("Workflow ran successfully.");
} catch (ExecutionException e) {
System.err.println(String.format("Error running workflow: %s ", e.getMessage()));
}
}
}Node.js
Avant d'essayer l'exemple ci-dessous, suivez la procédure de configuration pour Node.js décrite dans le guide de démarrage rapide de Dataproc avec les bibliothèques clientes. Pour en savoir plus, consultez la documentation de référence de l'API Dataproc pour Node.js.
const dataproc = require('@google-cloud/dataproc');
// TODO(developer): Uncomment and set the following variables
// projectId = 'YOUR_PROJECT_ID'
// region = 'YOUR_REGION'
// Create a client with the endpoint set to the desired region
const client = new dataproc.v1.WorkflowTemplateServiceClient({
apiEndpoint: `${region}-dataproc.googleapis.com`,
projectId: projectId,
});
async function instantiateInlineWorkflowTemplate() {
// Create the formatted parent.
const parent = client.regionPath(projectId, region);
// Create the template
const template = {
jobs: [
{
hadoopJob: {
mainJarFileUri:
'file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar',
args: ['teragen', '1000', 'hdfs:///gen/'],
},
stepId: 'teragen',
},
{
hadoopJob: {
mainJarFileUri:
'file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar',
args: ['terasort', 'hdfs:///gen/', 'hdfs:///sort/'],
},
stepId: 'terasort',
prerequisiteStepIds: ['teragen'],
},
],
placement: {
managedCluster: {
clusterName: 'my-managed-cluster',
config: {
gceClusterConfig: {
// Leave 'zoneUri' empty for 'Auto Zone Placement'
// zoneUri: ''
zoneUri: 'us-central1-a',
},
},
},
},
};
const request = {
parent: parent,
template: template,
};
// Submit the request to instantiate the workflow from an inline template.
const [operation] = await client.instantiateInlineWorkflowTemplate(request);
await operation.promise();
// Output a success message
console.log('Workflow ran successfully.');Python
Avant d'essayer l'exemple ci-dessous, suivez la procédure de configuration pour Python décrite dans le guide de démarrage rapide de Dataproc avec les bibliothèques clientes. Pour en savoir plus, consultez la documentation de référence de l'API Dataproc en langage Python.
from google.cloud import dataproc_v1 as dataproc
def instantiate_inline_workflow_template(project_id, region):
"""This sample walks a user through submitting a workflow
for a Cloud Dataproc using the Python client library.
Args:
project_id (string): Project to use for running the workflow.
region (string): Region where the workflow resources should live.
"""
# Create a client with the endpoint set to the desired region.
workflow_template_client = dataproc.WorkflowTemplateServiceClient(
client_options={"api_endpoint": f"{region}-dataproc.googleapis.com:443"}
)
parent = "projects/{}/regions/{}".format(project_id, region)
template = {
"jobs": [
{
"hadoop_job": {
"main_jar_file_uri": "file:///usr/lib/hadoop-mapreduce/"
"hadoop-mapreduce-examples.jar",
"args": ["teragen", "1000", "hdfs:///gen/"],
},
"step_id": "teragen",
},
{
"hadoop_job": {
"main_jar_file_uri": "file:///usr/lib/hadoop-mapreduce/"
"hadoop-mapreduce-examples.jar",
"args": ["terasort", "hdfs:///gen/", "hdfs:///sort/"],
},
"step_id": "terasort",
"prerequisite_step_ids": ["teragen"],
},
],
"placement": {
"managed_cluster": {
"cluster_name": "my-managed-cluster",
"config": {
"gce_cluster_config": {
# Leave 'zone_uri' empty for 'Auto Zone Placement'
# 'zone_uri': ''
"zone_uri": "us-central1-a"
}
},
}
},
}
# Submit the request to instantiate the workflow from an inline template.
operation = workflow_template_client.instantiate_inline_workflow_template(
request={"parent": parent, "template": template}
)
operation.result()
# Output a success message.
print("Workflow ran successfully.")Étape suivante
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