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Exportar recursos para o BigQuery

Exporte um inventário de recursos para o BigQuery.

Mais informações

Para ver a documentação detalhada que inclui este exemplo de código, consulte:

Bibliotecas de cliente do Inventário de recursos do Cloud

Exemplo de código

Go

Para saber como instalar e usar a biblioteca de cliente do Cloud Asset Inventory, consulte Bibliotecas de cliente do Cloud Asset Inventory.

Para autenticar no Inventário de recursos do Cloud, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.


// Sample asset-quickstart exports assets to given bigquery table.
package main

import (
	"context"
	"fmt"
	"log"
	"os"
	"strings"

	asset "cloud.google.com/go/asset/apiv1"
	"cloud.google.com/go/asset/apiv1/assetpb"
)

func main() {
	ctx := context.Background()
	projectID := os.Getenv("GOOGLE_CLOUD_PROJECT")
	client, err := asset.NewClient(ctx)
	if err != nil {
		log.Fatalf("asset.NewClient: %v", err)
	}
	defer client.Close()
	datasetID := strings.Replace(fmt.Sprintf("%s-for-assets", projectID), "-", "_", -1)
	dataset := fmt.Sprintf("projects/%s/datasets/%s", projectID, datasetID)
	req := &assetpb.ExportAssetsRequest{
		Parent: fmt.Sprintf("projects/%s", projectID),
		OutputConfig: &assetpb.OutputConfig{
			Destination: &assetpb.OutputConfig_BigqueryDestination{
				BigqueryDestination: &assetpb.BigQueryDestination{
					Dataset: dataset,
					Table:   "test",
					Force:   true,
				},
			},
		},
	}
	op, err := client.ExportAssets(ctx, req)
	if err != nil {
		log.Fatalf("ExportAssets: %v", err)
	}
	resp, err := op.Wait(ctx)
	if err != nil {
		log.Fatalf("Wait: %v", err)
	}
	fmt.Print(resp)
}

Java

Para saber como instalar e usar a biblioteca de cliente do Cloud Asset Inventory, consulte Bibliotecas de cliente do Cloud Asset Inventory.

Para autenticar no Cloud Asset Inventory, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

// Imports the Google Cloud client library

import com.google.cloud.ServiceOptions;
import com.google.cloud.asset.v1.AssetServiceClient;
import com.google.cloud.asset.v1.BigQueryDestination;
import com.google.cloud.asset.v1.ContentType;
import com.google.cloud.asset.v1.ExportAssetsRequest;
import com.google.cloud.asset.v1.ExportAssetsRequest.Builder;
import com.google.cloud.asset.v1.ExportAssetsResponse;
import com.google.cloud.asset.v1.OutputConfig;
import com.google.cloud.asset.v1.PartitionSpec;
import com.google.cloud.asset.v1.ProjectName;
import java.io.IOException;
import java.util.Arrays;
import java.util.concurrent.ExecutionException;

public class ExportAssetsBigqueryExample {

  // Use the default project Id.
  private static final String projectId = ServiceOptions.getDefaultProjectId();

  /** 
   * Export assets to BigQuery for a project.

   * @param bigqueryDataset which dataset the results will be exported to
   * @param bigqueryTable which table the results will be exported to
   * @param contentType determines the schema for the table
   * @param assetTypes a list of asset types to export. if empty, export all.
   * @param isPerType separate BigQuery tables for each resource type
   */
  public static void exportBigQuery(String bigqueryDataset, String bigqueryTable,
      ContentType contentType, String[] assetTypes, boolean isPerType)
      throws IOException, IllegalArgumentException, InterruptedException, ExecutionException {
    try (AssetServiceClient client = AssetServiceClient.create()) {
      ProjectName parent = ProjectName.of(projectId);
      OutputConfig outputConfig;
      // Outputs to per-type BigQuery table.
      if (isPerType) {
        outputConfig =
            OutputConfig.newBuilder()
                .setBigqueryDestination(
                    BigQueryDestination.newBuilder()
                        .setDataset(bigqueryDataset)
                        .setTable(bigqueryTable)
                        .setForce(true)
                        .setSeparateTablesPerAssetType(true)
                        .setPartitionSpec(
                            PartitionSpec.newBuilder()
                                .setPartitionKey(PartitionSpec.PartitionKey.READ_TIME)
                                .build())
                        .build())
                .build();
      } else {
        outputConfig =
            OutputConfig.newBuilder()
                .setBigqueryDestination(
                    BigQueryDestination.newBuilder()
                        .setDataset(bigqueryDataset)
                        .setTable(bigqueryTable)
                        .setForce(true)
                        .build())
                .build();
      }
      Builder exportAssetsRequestBuilder = ExportAssetsRequest.newBuilder()
          .setParent(parent.toString()).setContentType(contentType).setOutputConfig(outputConfig);
      if (assetTypes.length > 0) {
        exportAssetsRequestBuilder.addAllAssetTypes(Arrays.asList(assetTypes));
      }
      ExportAssetsRequest request = exportAssetsRequestBuilder.build();
      ExportAssetsResponse response = client.exportAssetsAsync(request).get();
      System.out.println(response);
    }
  }
}

Node.js

Para saber como instalar e usar a biblioteca de cliente do Cloud Asset Inventory, consulte Bibliotecas de cliente do Cloud Asset Inventory.

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const dataSet = 'projects/project_id/datasets/dataset_id';
// const table = 'mytable';

const {AssetServiceClient} = require('@google-cloud/asset');
const client = new AssetServiceClient();

async function exportAssetsBigquery() {
  const projectId = await client.getProjectId();
  const projectResource = client.projectPath(projectId);
  const dataset = dataSet;

  const request = {
    parent: projectResource,
    outputConfig: {
      bigqueryDestination: {
        dataset: `projects/${projectId}/${dataset}`,
        table: table,
        force: true,
      },
    },
  };

  // Handle the operation using the promise pattern.
  const [operation] = await client.exportAssets(request);

  // Operation#promise starts polling for the completion of the operation.
  const [result] = await operation.promise();

  // Do things with with the response.
  console.log(result);
}

exportAssetsBigquery();

Python

Para saber como instalar e usar a biblioteca de cliente do Cloud Asset Inventory, consulte Bibliotecas de cliente do Cloud Asset Inventory.

from google.cloud import asset_v1

# TODO project_id = 'Your Google Cloud Project ID'
# TODO dataset = 'Your BigQuery dataset path'
# TODO table = 'Your BigQuery table name'
# TODO content_type ="Content type to export"

client = asset_v1.AssetServiceClient()
parent = f"projects/{project_id}"
output_config = asset_v1.OutputConfig()
output_config.bigquery_destination.dataset = dataset
output_config.bigquery_destination.table = table
output_config.bigquery_destination.force = True
response = client.export_assets(
    request={
        "parent": parent,
        "content_type": content_type,
        "output_config": output_config,
    }
)
print(response.result())

A seguir

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