Google Dataproc是Google Cloud Platform(GCP)提供的一项托管式的Apache Hadoop和Apache Spark服务。它允许用户轻松地在云上创建、配置、管理和使用Hadoop和Spark集群,以进行大数据处理和分析。
在Google Dataproc Java API中使用beta特性,可以通过以下步骤完成:
<dependency>
<groupId>com.google.cloud</groupId>
<artifactId>google-cloud-dataproc</artifactId>
<version>1.113.0</version>
</dependency>
import com.google.api.gax.rpc.ApiException;
import com.google.cloud.dataproc.v1.*;
import com.google.protobuf.Empty;
import java.io.IOException;
public class DataprocClientExample {
private static final String PROJECT_ID = "your-project-id";
private static final String REGION = "your-region";
public static void main(String[] args) throws IOException {
try (JobControllerClient jobControllerClient = JobControllerClient.create()) {
// 创建Dataproc客户端
JobControllerSettings jobControllerSettings =
JobControllerSettings.newBuilder().setRegionLocation(RegionLocation.newBuilder().setRegion(REGION).build()).build();
JobControllerClient jobControllerClient = JobControllerClient.create(jobControllerSettings);
// 执行您的操作
// ...
} catch (ApiException e) {
System.err.println("Dataproc API调用失败:" + e.toString());
}
}
}
import com.google.cloud.dataproc.v1.*;
import com.google.protobuf.Empty;
public class DataprocClientExample {
// ...
public static void main(String[] args) throws IOException {
// ...
// 创建一个使用自定义脚本的作业
JobPlacement jobPlacement = JobPlacement.newBuilder().setClusterName("your-cluster-name").build();
Job job = Job.newBuilder()
.setPlacement(jobPlacement)
.setHadoopJob(HadoopJob.newBuilder()
.setMainClass("your-main-class")
.addJarFileUris("gs://your-bucket/your-jar-file.jar")
.addArgs("arg1")
.addArgs("arg2")
.build())
.build();
JobControllerClient.SubmitJobRequest request =
JobControllerClient.SubmitJobRequest.newBuilder()
.setProjectId(PROJECT_ID)
.setRegion(REGION)
.setJob(job)
.build();
JobControllerClient.SubmitJobResponse response = jobControllerClient.submitJob(request);
String jobId = response.getReference().getJobId();
System.out.println("作业已提交,作业ID:" + jobId);
}
}
领取专属 10元无门槛券
手把手带您无忧上云