To help you get started, we've collected the most common ways that @google-cloud/dataproc is being used within popular public projects.

googleapis / nodejs-dataproc / samples / quickstart.js View on Github

// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

'use strict';

// [START dataproc_quickstart]
const dataproc = require('@google-cloud/dataproc');
const client = new dataproc.v1.ClusterControllerClient();

async function quickstart() {
  const projectId = await client.getProjectId();
  const request = {
    region: 'global',
    projectId,
  };
  const [resources] = await client.listClusters(request);
  console.log('Total resources:', resources.length);
  for (const resource of resources) {
    console.log(resource);
  }

  let nextRequest = request;
  // Or obtain the paged response.
  const options = {autoPaginate: false};

View more ways to use @google-cloud/dataproc

Google Cloud Dataproc: Node.js Client

Google Cloud Dataproc API client for Node.js

A comprehensive list of changes in each version may be found in the CHANGELOG.

• Google Cloud Dataproc Node.js Client API Reference
• Google Cloud Dataproc Documentation
• github.com/googleapis/google-cloud-node/packages/google-cloud-dataproc

Read more about the client libraries for Cloud APIs, including the older Google APIs Client Libraries, in Client Libraries Explained.

Table of contents:

• Quickstart
  • Before you begin
  • Installing the client library
  • Using the client library
• Samples
• Versioning
• Contributing
• License

Quickstart

Before you begin

1. Select or create a Cloud Platform project.
2. Enable billing for your project.
3. Enable the Google Cloud Dataproc API.
4. Set up authentication with a service account so you can access the API from your local workstation.

Installing the client library

npm install @google-cloud/dataproc

Using the client library

// 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);

Samples

Samples are in the samples/ directory. Each sample's README.md has instructions for running its sample.

Sample	Source Code	Try it
Autoscaling_policy_service.create_autoscaling_policy	source code
Autoscaling_policy_service.delete_autoscaling_policy	source code
Autoscaling_policy_service.get_autoscaling_policy	source code
Autoscaling_policy_service.list_autoscaling_policies	source code
Autoscaling_policy_service.update_autoscaling_policy	source code
Batch_controller.create_batch	source code
Batch_controller.delete_batch	source code
Batch_controller.get_batch	source code
Batch_controller.list_batches	source code
Cluster_controller.create_cluster	source code
Cluster_controller.delete_cluster	source code
Cluster_controller.diagnose_cluster	source code
Cluster_controller.get_cluster	source code
Cluster_controller.list_clusters	source code
Cluster_controller.start_cluster	source code
Cluster_controller.stop_cluster	source code
Cluster_controller.update_cluster	source code
Job_controller.cancel_job	source code
Job_controller.delete_job	source code
Job_controller.get_job	source code
Job_controller.list_jobs	source code
Job_controller.submit_job	source code
Job_controller.submit_job_as_operation	source code
Job_controller.update_job	source code
Node_group_controller.create_node_group	source code
Node_group_controller.get_node_group	source code
Node_group_controller.resize_node_group	source code
Session_controller.create_session	source code
Session_controller.delete_session	source code
Session_controller.get_session	source code
Session_controller.list_sessions	source code
Session_controller.terminate_session	source code
Session_template_controller.create_session_template	source code
Session_template_controller.delete_session_template	source code
Session_template_controller.get_session_template	source code
Session_template_controller.list_session_templates	source code
Session_template_controller.update_session_template	source code
Workflow_template_service.create_workflow_template	source code
Workflow_template_service.delete_workflow_template	source code
Workflow_template_service.get_workflow_template	source code
Workflow_template_service.instantiate_inline_workflow_template	source code
Workflow_template_service.instantiate_workflow_template	source code
Workflow_template_service.list_workflow_templates	source code
Workflow_template_service.update_workflow_template	source code
Quickstart	source code

The Google Cloud Dataproc Node.js Client API Reference documentation also contains samples.

Supported Node.js Versions

Our client libraries follow the Node.js release schedule. Libraries are compatible with all current active and maintenance versions of Node.js. If you are using an end-of-life version of Node.js, we recommend that you update as soon as possible to an actively supported LTS version.

Google's client libraries support legacy versions of Node.js runtimes on a best-efforts basis with the following warnings:

• Legacy versions are not tested in continuous integration.
• Some security patches and features cannot be backported.
• Dependencies cannot be kept up-to-date.

Client libraries targeting some end-of-life versions of Node.js are available, and can be installed through npm dist-tags. The dist-tags follow the naming convention legacy-(version). For example, npm install @google-cloud/dataproc@legacy-8 installs client libraries for versions compatible with Node.js 8.

Versioning

This library follows Semantic Versioning.

This library is considered to be stable. The code surface will not change in backwards-incompatible ways unless absolutely necessary (e.g. because of critical security issues) or with an extensive deprecation period. Issues and requests against stable libraries are addressed with the highest priority.

More Information: Google Cloud Platform Launch Stages

Contributing

Contributions welcome! See the Contributing Guide.

Please note that this README.md, the samples/README.md, and a variety of configuration files in this repository (including .nycrc and tsconfig.json) are generated from a central template. To edit one of these files, make an edit to its templates in directory.

License

Apache Version 2.0

See LICENSE