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코드 샘플
Go
이 샘플을 사용해 보기 전에 클라이언트 라이브러리 사용한 Dataproc 빠른 시작의 Go 설정 안내를 따르세요. 자세한 내용은 Dataproc Go API 참고 문서를 참조하세요.
Dataproc에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
// This quickstart shows how you can use the Dataproc Client library to create a
// Dataproc cluster, submit a PySpark job to the cluster, wait for the job to finish
// and finally delete the cluster.
//
// Usage:
//
// go build
// ./quickstart --project_id <PROJECT_ID> --region <REGION> \
// --cluster_name <CLUSTER_NAME> --job_file_path <GCS_JOB_FILE_PATH>
package main
import (
"context"
"flag"
"fmt"
"io"
"log"
"regexp"
dataproc "cloud.google.com/go/dataproc/apiv1"
"cloud.google.com/go/dataproc/apiv1/dataprocpb"
"cloud.google.com/go/storage"
"google.golang.org/api/option"
)
func main() {
var projectID, clusterName, region, jobFilePath string
flag.StringVar(&projectID, "project_id", "", "Cloud Project ID, used for creating resources.")
flag.StringVar(®ion, "region", "", "Region that resources should be created in.")
flag.StringVar(&clusterName, "cluster_name", "", "Name of Cloud Dataproc cluster to create.")
flag.StringVar(&jobFilePath, "job_file_path", "", "Path to job file in GCS.")
flag.Parse()
ctx := context.Background()
// Create the cluster client.
endpoint := fmt.Sprintf("%s-dataproc.googleapis.com:443", region)
clusterClient, err := dataproc.NewClusterControllerClient(ctx, option.WithEndpoint(endpoint))
if err != nil {
log.Fatalf("error creating the cluster client: %s\n", err)
}
// Create the cluster config.
createReq := &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.
createOp, err := clusterClient.CreateCluster(ctx, createReq)
if err != nil {
log.Fatalf("error submitting the cluster creation request: %v\n", err)
}
createResp, err := createOp.Wait(ctx)
if err != nil {
log.Fatalf("error creating the cluster: %v\n", err)
}
// Defer cluster deletion.
defer func() {
dReq := &dataprocpb.DeleteClusterRequest{
ProjectId: projectID,
Region: region,
ClusterName: clusterName,
}
deleteOp, err := clusterClient.DeleteCluster(ctx, dReq)
deleteOp.Wait(ctx)
if err != nil {
fmt.Printf("error deleting cluster %q: %v\n", clusterName, err)
return
}
fmt.Printf("Cluster %q successfully deleted\n", clusterName)
}()
// Output a success message.
fmt.Printf("Cluster created successfully: %q\n", createResp.ClusterName)
// Create the job client.
jobClient, err := dataproc.NewJobControllerClient(ctx, option.WithEndpoint(endpoint))
// Create the job config.
submitJobReq := &dataprocpb.SubmitJobRequest{
ProjectId: projectID,
Region: region,
Job: &dataprocpb.Job{
Placement: &dataprocpb.JobPlacement{
ClusterName: clusterName,
},
TypeJob: &dataprocpb.Job_PysparkJob{
PysparkJob: &dataprocpb.PySparkJob{
MainPythonFileUri: jobFilePath,
},
},
},
}
submitJobOp, err := jobClient.SubmitJobAsOperation(ctx, submitJobReq)
if err != nil {
fmt.Printf("error with request to submitting job: %v\n", err)
return
}
submitJobResp, err := submitJobOp.Wait(ctx)
if err != nil {
fmt.Printf("error submitting job: %v\n", err)
return
}
re := regexp.MustCompile("gs://(.+?)/(.+)")
matches := re.FindStringSubmatch(submitJobResp.DriverOutputResourceUri)
if len(matches) < 3 {
fmt.Printf("regex error: %s\n", submitJobResp.DriverOutputResourceUri)
return
}
// Dataproc job outget gets saved to a GCS bucket allocated to it.
storageClient, err := storage.NewClient(ctx)
if err != nil {
fmt.Printf("error creating storage client: %v\n", err)
return
}
obj := fmt.Sprintf("%s.000000000", matches[2])
reader, err := storageClient.Bucket(matches[1]).Object(obj).NewReader(ctx)
if err != nil {
fmt.Printf("error reading job output: %v\n", err)
return
}
defer reader.Close()
body, err := io.ReadAll(reader)
if err != nil {
fmt.Printf("could not read output from Dataproc Job: %v\n", err)
return
}
fmt.Printf("Job finished successfully: %s", body)
}
Java
이 샘플을 사용해 보기 전에 클라이언트 라이브러리 사용한 Dataproc 빠른 시작의 Java 설정 안내를 따르세요. 자세한 내용은 Dataproc Java API 참고 문서를 참조하세요.
Dataproc에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
/* This quickstart sample walks a user through creating a Cloud Dataproc
* cluster, submitting a PySpark job from Google Cloud Storage to the
* cluster, reading the output of the job and deleting the cluster, all
* using the Java client library.
*
* Usage:
* mvn clean package -DskipTests
*
* mvn exec:java -Dexec.args="<PROJECT_ID> <REGION> <CLUSTER_NAME> <GCS_JOB_FILE_PATH>"
*
* You can also set these arguments in the main function instead of providing them via the CLI.
*/
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 com.google.cloud.dataproc.v1.Job;
import com.google.cloud.dataproc.v1.JobControllerClient;
import com.google.cloud.dataproc.v1.JobControllerSettings;
import com.google.cloud.dataproc.v1.JobMetadata;
import com.google.cloud.dataproc.v1.JobPlacement;
import com.google.cloud.dataproc.v1.PySparkJob;
import com.google.cloud.storage.Blob;
import com.google.cloud.storage.Storage;
import com.google.cloud.storage.StorageOptions;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
public class Quickstart {
public static void quickstart(
String projectId, String region, String clusterName, String jobFilePath)
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();
// Configure the settings for the job controller client.
JobControllerSettings jobControllerSettings =
JobControllerSettings.newBuilder().setEndpoint(myEndpoint).build();
// Create both a cluster controller client and job 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);
JobControllerClient jobControllerClient =
JobControllerClient.create(jobControllerSettings)) {
// 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 clusterResponse = createClusterAsyncRequest.get();
System.out.println(
String.format("Cluster created successfully: %s", clusterResponse.getClusterName()));
// Configure the settings for our job.
JobPlacement jobPlacement = JobPlacement.newBuilder().setClusterName(clusterName).build();
PySparkJob pySparkJob = PySparkJob.newBuilder().setMainPythonFileUri(jobFilePath).build();
Job job = Job.newBuilder().setPlacement(jobPlacement).setPysparkJob(pySparkJob).build();
// Submit an asynchronous request to execute the job.
OperationFuture<Job, JobMetadata> submitJobAsOperationAsyncRequest =
jobControllerClient.submitJobAsOperationAsync(projectId, region, job);
Job jobResponse = submitJobAsOperationAsyncRequest.get();
// Print output from Google Cloud Storage.
Matcher matches =
Pattern.compile("gs://(.*?)/(.*)").matcher(jobResponse.getDriverOutputResourceUri());
matches.matches();
Storage storage = StorageOptions.getDefaultInstance().getService();
Blob blob = storage.get(matches.group(1), String.format("%s.000000000", matches.group(2)));
System.out.println(
String.format("Job finished successfully: %s", new String(blob.getContent())));
// Delete the cluster.
OperationFuture<Empty, ClusterOperationMetadata> deleteClusterAsyncRequest =
clusterControllerClient.deleteClusterAsync(projectId, region, clusterName);
deleteClusterAsyncRequest.get();
System.out.println(String.format("Cluster \"%s\" successfully deleted.", clusterName));
} catch (ExecutionException e) {
System.err.println(String.format("quickstart: %s ", e.getMessage()));
}
}
public static void main(String... args) throws IOException, InterruptedException {
if (args.length != 4) {
System.err.println(
"Insufficient number of parameters provided. Please make sure a "
+ "PROJECT_ID, REGION, CLUSTER_NAME and JOB_FILE_PATH are provided, in this order.");
return;
}
String projectId = args[0]; // project-id of project to create the cluster in
String region = args[1]; // region to create the cluster
String clusterName = args[2]; // name of the cluster
String jobFilePath = args[3]; // location in GCS of the PySpark job
quickstart(projectId, region, clusterName, jobFilePath);
}
}Node.js
이 샘플을 사용해 보기 전에 클라이언트 라이브러리 사용한 Dataproc 빠른 시작의 Node.js 설정 안내를 따르세요. 자세한 내용은 Dataproc Node.js API 참고 문서를 참조하세요.
Dataproc에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
// This quickstart sample walks a user through creating a Dataproc
// cluster, submitting a PySpark job from Google Cloud Storage to the
// cluster, reading the output of the job and deleting the cluster, all
// using the Node.js client library.
'use strict';
function main(projectId, region, clusterName, jobFilePath) {
const dataproc = require('@google-cloud/dataproc');
const {Storage} = require('@google-cloud/storage');
// Create a cluster client with the endpoint set to the desired cluster region
const clusterClient = new dataproc.v1.ClusterControllerClient({
apiEndpoint: `${region}-dataproc.googleapis.com`,
projectId: projectId,
});
// Create a job client with the endpoint set to the desired cluster region
const jobClient = new dataproc.v1.JobControllerClient({
apiEndpoint: `${region}-dataproc.googleapis.com`,
projectId: projectId,
});
async function quickstart() {
// Create the cluster config
const cluster = {
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 clusterClient.createCluster(cluster);
const [response] = await operation.promise();
// Output a success message
console.log(`Cluster created successfully: ${response.clusterName}`);
const job = {
projectId: projectId,
region: region,
job: {
placement: {
clusterName: clusterName,
},
pysparkJob: {
mainPythonFileUri: jobFilePath,
},
},
};
const [jobOperation] = await jobClient.submitJobAsOperation(job);
const [jobResponse] = await jobOperation.promise();
const matches =
jobResponse.driverOutputResourceUri.match('gs://(.*?)/(.*)');
const storage = new Storage();
const output = await storage
.bucket(matches[1])
.file(`${matches[2]}.000000000`)
.download();
// Output a success message.
console.log(`Job finished successfully: ${output}`);
// Delete the cluster once the job has terminated.
const deleteClusterReq = {
projectId: projectId,
region: region,
clusterName: clusterName,
};
const [deleteOperation] =
await clusterClient.deleteCluster(deleteClusterReq);
await deleteOperation.promise();
// Output a success message
console.log(`Cluster ${clusterName} successfully deleted.`);
}
quickstart();
}
const args = process.argv.slice(2);
if (args.length !== 4) {
console.log(
'Insufficient number of parameters provided. Please make sure a ' +
'PROJECT_ID, REGION, CLUSTER_NAME and JOB_FILE_PATH are provided, in this order.'
);
}
main(...args);Python
이 샘플을 사용해 보기 전에 클라이언트 라이브러리 사용한 Dataproc 빠른 시작의 Python 설정 안내를 따르세요. 자세한 내용은 Dataproc Python API 참고 문서를 참조하세요.
Dataproc에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
"""
This quickstart sample walks a user through creating a Cloud Dataproc
cluster, submitting a PySpark job from Google Cloud Storage to the
cluster, reading the output of the job and deleting the cluster, all
using the Python client library.
Usage:
python quickstart.py --project_id <PROJECT_ID> --region <REGION> \
--cluster_name <CLUSTER_NAME> --job_file_path <GCS_JOB_FILE_PATH>
"""
import argparse
import re
from google.cloud import dataproc_v1 as dataproc
from google.cloud import storage
def quickstart(project_id, region, cluster_name, job_file_path):
# Create the cluster client.
cluster_client = dataproc.ClusterControllerClient(
client_options={"api_endpoint": "{}-dataproc.googleapis.com:443".format(region)}
)
# 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",
"disk_config": {"boot_disk_size_gb": 100},
},
"worker_config": {
"num_instances": 2,
"machine_type_uri": "n1-standard-2",
"disk_config": {"boot_disk_size_gb": 100},
},
},
}
# Create the cluster.
operation = cluster_client.create_cluster(
request={"project_id": project_id, "region": region, "cluster": cluster}
)
result = operation.result()
print("Cluster created successfully: {}".format(result.cluster_name))
# Create the job client.
job_client = dataproc.JobControllerClient(
client_options={"api_endpoint": "{}-dataproc.googleapis.com:443".format(region)}
)
# Create the job config.
job = {
"placement": {"cluster_name": cluster_name},
"pyspark_job": {"main_python_file_uri": job_file_path},
}
operation = job_client.submit_job_as_operation(
request={"project_id": project_id, "region": region, "job": job}
)
response = operation.result()
# Dataproc job output gets saved to the Google Cloud Storage bucket
# allocated to the job. Use a regex to obtain the bucket and blob info.
matches = re.match("gs://(.*?)/(.*)", response.driver_output_resource_uri)
output = (
storage.Client()
.get_bucket(matches.group(1))
.blob(f"{matches.group(2)}.000000000")
.download_as_bytes()
.decode("utf-8")
)
print(f"Job finished successfully: {output}")
# Delete the cluster once the job has terminated.
operation = cluster_client.delete_cluster(
request={
"project_id": project_id,
"region": region,
"cluster_name": cluster_name,
}
)
operation.result()
print("Cluster {} successfully deleted.".format(cluster_name))
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter,
)
parser.add_argument(
"--project_id",
type=str,
required=True,
help="Project to use for creating resources.",
)
parser.add_argument(
"--region",
type=str,
required=True,
help="Region where the resources should live.",
)
parser.add_argument(
"--cluster_name",
type=str,
required=True,
help="Name to use for creating a cluster.",
)
parser.add_argument(
"--job_file_path",
type=str,
required=True,
help="Job in GCS to execute against the cluster.",
)
args = parser.parse_args()
quickstart(args.project_id, args.region, args.cluster_name, args.job_file_path)다음 단계
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