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코드 샘플
Go
이 샘플을 사용해 보기 전에 BigQuery 빠른 시작: 클라이언트 라이브러리 사용의 Go 설정 안내를 따르세요. 자세한 내용은 Dataproc Go API 참조 문서를 참조하세요.
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 createCluster(w io.Writer, projectID, region, clusterName string) error {
// projectID := "your-project-id"
// region := "us-central1"
// clusterName := "your-cluster"
ctx := context.Background()
// Create the cluster client.
endpoint := region + "-dataproc.googleapis.com:443"
clusterClient, err := dataproc.NewClusterControllerClient(ctx, option.WithEndpoint(endpoint))
if err != nil {
return fmt.Errorf("dataproc.NewClusterControllerClient: %v", err)
}
defer clusterClient.Close()
// Create the cluster config.
req := &dataprocpb.CreateClusterRequest{
ProjectId: projectID,
Region: region,
Cluster: &dataprocpb.Cluster{
ProjectId: projectID,
ClusterName: clusterName,
Config: &dataprocpb.ClusterConfig{
MasterConfig: &dataprocpb.InstanceGroupConfig{
NumInstances: 1,
MachineTypeUri: "n1-standard-2",
},
WorkerConfig: &dataprocpb.InstanceGroupConfig{
NumInstances: 2,
MachineTypeUri: "n1-standard-2",
},
},
},
}
// Create the cluster.
op, err := clusterClient.CreateCluster(ctx, req)
if err != nil {
return fmt.Errorf("CreateCluster: %v", err)
}
resp, err := op.Wait(ctx)
if err != nil {
return fmt.Errorf("CreateCluster.Wait: %v", err)
}
// Output a success message.
fmt.Fprintf(w, "Cluster created successfully: %s", resp.ClusterName)
return nil
}
자바
이 샘플을 사용해 보기 전에 Dataproc 빠른 시작: 클라이언트 라이브러리 사용의 자바 설정 안내를 따르세요. 자세한 내용은 Dataproc 자바 API 참조 문서를 확인하세요.
import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.dataproc.v1.Cluster;
import com.google.cloud.dataproc.v1.ClusterConfig;
import com.google.cloud.dataproc.v1.ClusterControllerClient;
import com.google.cloud.dataproc.v1.ClusterControllerSettings;
import com.google.cloud.dataproc.v1.ClusterOperationMetadata;
import com.google.cloud.dataproc.v1.InstanceGroupConfig;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
public class CreateCluster {
public static void createCluster() throws IOException, InterruptedException {
// TODO(developer): Replace these variables before running the sample.
String projectId = "your-project-id";
String region = "your-project-region";
String clusterName = "your-cluster-name";
createCluster(projectId, region, clusterName);
}
public static void createCluster(String projectId, String region, String clusterName)
throws IOException, InterruptedException {
String myEndpoint = String.format("%s-dataproc.googleapis.com:443", region);
// Configure the settings for the cluster controller client.
ClusterControllerSettings clusterControllerSettings =
ClusterControllerSettings.newBuilder().setEndpoint(myEndpoint).build();
// Create a cluster controller 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 (ClusterControllerClient clusterControllerClient =
ClusterControllerClient.create(clusterControllerSettings)) {
// Configure the settings for our cluster.
InstanceGroupConfig masterConfig =
InstanceGroupConfig.newBuilder()
.setMachineTypeUri("n1-standard-2")
.setNumInstances(1)
.build();
InstanceGroupConfig workerConfig =
InstanceGroupConfig.newBuilder()
.setMachineTypeUri("n1-standard-2")
.setNumInstances(2)
.build();
ClusterConfig clusterConfig =
ClusterConfig.newBuilder()
.setMasterConfig(masterConfig)
.setWorkerConfig(workerConfig)
.build();
// Create the cluster object with the desired cluster config.
Cluster cluster =
Cluster.newBuilder().setClusterName(clusterName).setConfig(clusterConfig).build();
// Create the Cloud Dataproc cluster.
OperationFuture<Cluster, ClusterOperationMetadata> createClusterAsyncRequest =
clusterControllerClient.createClusterAsync(projectId, region, cluster);
Cluster response = createClusterAsyncRequest.get();
// Print out a success message.
System.out.printf("Cluster created successfully: %s", response.getClusterName());
} catch (ExecutionException e) {
System.err.println(String.format("Error executing createCluster: %s ", e.getMessage()));
}
}
}Node.js
이 샘플을 사용해 보기 전에 Dataproc 빠른 시작: 클라이언트 라이브러리 사용의 Node.js 설정 안내를 따르세요. 자세한 내용은 Dataproc Node.js API 참조 문서를 참조하세요.
const dataproc = require('@google-cloud/dataproc');
// TODO(developer): Uncomment and set the following variables
// projectId = 'YOUR_PROJECT_ID'
// region = 'YOUR_CLUSTER_REGION'
// clusterName = 'YOUR_CLUSTER_NAME'
// Create a client with the endpoint set to the desired cluster region
const client = new dataproc.v1.ClusterControllerClient({
apiEndpoint: `${region}-dataproc.googleapis.com`,
projectId: projectId,
});
async function createCluster() {
// Create the cluster config
const request = {
projectId: projectId,
region: region,
cluster: {
clusterName: clusterName,
config: {
masterConfig: {
numInstances: 1,
machineTypeUri: 'n1-standard-2',
},
workerConfig: {
numInstances: 2,
machineTypeUri: 'n1-standard-2',
},
},
},
};
// Create the cluster
const [operation] = await client.createCluster(request);
const [response] = await operation.promise();
// Output a success message
console.log(`Cluster created successfully: ${response.clusterName}`);Python
이 샘플을 사용해 보기 전에 IAM 빠른 시작: 클라이언트 라이브러리 사용의 Python 설정 안내를 따르세요. 자세한 내용은 Dataproc Python API 참조 문서를 확인하세요.
from google.cloud import dataproc_v1 as dataproc
def create_cluster(project_id, region, cluster_name):
"""This sample walks a user through creating a Cloud Dataproc cluster
using the Python client library.
Args:
project_id (string): Project to use for creating resources.
region (string): Region where the resources should live.
cluster_name (string): Name to use for creating a cluster.
"""
# Create a client with the endpoint set to the desired cluster region.
cluster_client = dataproc.ClusterControllerClient(
client_options={"api_endpoint": f"{region}-dataproc.googleapis.com:443"}
)
# Create the cluster config.
cluster = {
"project_id": project_id,
"cluster_name": cluster_name,
"config": {
"master_config": {"num_instances": 1, "machine_type_uri": "n1-standard-2"},
"worker_config": {"num_instances": 2, "machine_type_uri": "n1-standard-2"},
},
}
# Create the cluster.
operation = cluster_client.create_cluster(
request={"project_id": project_id, "region": region, "cluster": cluster}
)
result = operation.result()
# Output a success message.
print(f"Cluster created successfully: {result.cluster_name}")다음 단계
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