如何使用Google DataProc Java Client在相关的GS存储桶中使用jar文件和类提交spark作业？ [英] How do you use the Google DataProc Java Client to submit spark jobs using jar files and classes in associated GS bucket?

问题描述

我需要触发Spark Jobs以使用API​​调用从JSON文件聚合数据。我使用spring-boot来创建资源。因此，解决方案的步骤如下：

I need to trigger Spark Jobs to aggregate data from a JSON file using an API call. I use spring-boot to create the resources. Thus, the steps for the solution is the following:

1. 用户以json文件作为输入发出POST请求


2. JSON文件存储在与数据中心群集关联的谷歌存储桶中。


3. 在REST方法中使用指定的jar，类和参数触发聚合spark作业是json文件链接。

我希望使用Dataproc的Java Client而不是控制台或命令行来触发作业。你是怎么做到的？

I want the job to be triggered using Dataproc's Java Client instead of console or command line. How do you do it?

推荐答案

我们希望很快能在官方文档，但要开始使用，请访问以下API概述： https://developers.google.com/api-client-library/java/apis/dataproc/v1

We're hoping to have a more thorough guide shortly on the official documentation, but to get started, visit the following API overview: https://developers.google.com/api-client-library/java/apis/dataproc/v1

它包含指向 Dataproc javadocs ;如果您的服务器代表您自己的项目而不代表最终用户的Google项目拨打电话，那么您可能需要基于密钥文件的服务帐户身份验证，在此解释以创建凭据用于初始化 Dataproc 客户端存根的对象。

It includes links to the Dataproc javadocs; if your server is making calls on behalf of your own project and not on behalf of your end-users' Google projects, then you probably want the keyfile-based service-account auth explained here to create the Credential object you use to initialize the Dataproc client stub.

对于数据流特定的部分，这只是意味着如果使用Maven，则将以下依赖项添加到Maven pom文件中：

As for the dataproc-specific parts, this just means you add the following dependency to your Maven pomfile if using Maven:

<project>
  <dependencies>
    <dependency>
      <groupId>com.google.apis</groupId>
      <artifactId>google-api-services-dataproc</artifactId>
      <version>v1-rev4-1.21.0</version>
    </dependency>
  </dependencies>
</project>

然后你会得到如下代码：

And then you'll have code like:

Dataproc dataproc = new Dataproc.Builder(new NetHttpTransport(), new JacksonFactory(), credential)
    .setApplicationName("my-webabb/1.0")
    .build();
dataproc.projects().regions().jobs().submit(
    projectId, "global", new SubmitJobRequest()
        .setJob(new Job()
            .setPlacement(new JobPlacement()
                .setClusterName("my-spark-cluster"))
            .setSparkJob(new SparkJob()
                .setMainClass("FooSparkJobMain")
                .setJarFileUris(ImmutableList.of("gs://bucket/path/to/your/spark-job.jar"))
                .setArgs(ImmutableList.of(
                    "arg1", "arg2", "arg3")))))
    .execute();

由于不同的中间服务器可能会进行低级重试，或者您的请求可能会抛出IOException要知道作业提交是否成功，您可能想要的一个额外步骤是生成您自己的 jobId ;然后你知道什么jobId要进行民意调查，以确定它是否已经提交，即使你的请求超时或抛出一些未知的异常：

Since different intermediary servers may do low-level retries or your request may throw an IOException where you don't know whether the job-submission succeeded or not, an addition step you may want to take is to generate your own jobId; then you know what jobId to poll on to figure out if it got submitted even if your request times out or throws some unknown exception:

import java.util.UUID;

...

Dataproc dataproc = new Dataproc.Builder(new NetHttpTransport(), new JacksonFactory(), credential)
    .setApplicationName("my-webabb/1.0")
    .build();

String curJobId = "json-agg-job-" + UUID.randomUUID().toString();
Job jobSnapshot = null;
try {
  jobSnapshot = dataproc.projects().regions().jobs().submit(
      projectId, "global", new SubmitJobRequest()
          .setJob(new Job()
              .setReference(new JobReference()
                   .setJobId(curJobId))
              .setPlacement(new JobPlacement()
                  .setClusterName("my-spark-cluster"))
              .setSparkJob(new SparkJob()
                  .setMainClass("FooSparkJobMain")
                  .setJarFileUris(ImmutableList.of("gs://bucket/path/to/your/spark-job.jar"))
                  .setArgs(ImmutableList.of(
                      "arg1", "arg2", "arg3")))))
      .execute();
} catch (IOException ioe) {
  try {
    jobSnapshot = dataproc.projects().regions().jobs().get(
        projectId, "global", curJobId).execute();
    logger.info(ioe, "Despite exception, job was verified submitted");
  } catch (IOException ioe2) {
    // Handle differently; if it's a GoogleJsonResponseException you can inspect the error
    // code, and if it's a 404, then it means the job didn't get submitted; you can add retry
    // logic in that case.
  }
}

// We can poll on dataproc.projects().regions().jobs().get(...) until the job reports being
// completed or failed now.

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