Carga datos por lotes con la API de Storage Write

En este documento, se describe cómo usar la API de BigQuery Storage Write para cargar datos por lotes en BigQuery.

En situaciones de carga por lotes, una aplicación escribe datos y los confirma como una sola transacción atómica. Cuando usas la API de Storage Write para cargar datos por lotes, crea una o más transmisiones de tipo pendiente. El tipo pendiente admite transacciones a nivel de transmisión. Los registros se almacenan en búfer en un estado pendiente hasta que confirmes la transmisión.

Para las cargas de trabajo por lotes, también considera usar la API de Storage Write a través del conector de SQL de Apache Spark para BigQuery mediante Dataproc, en lugar de escribir un código personalizado de la API de Storage Write.

La API de Storage Write es adecuada para una arquitectura de canalización de datos. Un proceso principal crea una cantidad de transmisiones. Para cada transmisión, asigna un subproceso de trabajador o un proceso separado a fin de escribir una parte de los datos por lotes. Cada trabajador crea una conexión a la transmisión, escribe datos y finaliza la transmisión cuando se completa. Una vez que todos los trabajadores indican que el proceso principal se completó con éxito, el proceso principal confirma los datos. Si un trabajador falla, su parte asignada de los datos no aparecerá en los resultados finales y se puede reintentar de forma segura todo el trabajador. En una canalización más sofisticada, los trabajadores verifican su progreso mediante el informe de la última compensación escrita en el proceso principal. Este enfoque puede dar como resultado una canalización sólida que sea resistente a las fallas.

Carga datos por lotes con el tipo pendiente

Para usar el tipo pendiente, la aplicación hace esto:

1. Llama a CreateWriteStream para crear una o más transmisiones de tipo pendiente.
2. Para cada transmisión, llama a AppendRows en un bucle a fin de escribir lotes de registros.
3. Para cada transmisión, llama a FinalizeWriteStream. Después de llamar a este método, no puedes escribir más filas en la transmisión. Si llamas a AppendRows después de llamar a FinalizeWriteStream, se muestra un StorageError con StorageErrorCode.STREAM_FINALIZED en el error google.rpc.Status. Para obtener más información sobre el modelo de error google.rpc.Status, consulta Errores.
4. Llama a BatchCommitWriteStreams para confirmar las transmisiones. Después de llamar a este método, los datos estarán disponibles para su lectura. Si se produce un error cuando se confirma alguna de las transmisiones, el error se muestra en el campo stream_errors de BatchCommitWriteStreamsResponse.

La confirmación es una operación atómica, por lo que puedes confirmar varias transmisiones a la vez. Una transmisión solo se puede confirmar una vez, por lo que si la operación de confirmación falla, es seguro volver a intentarlo. Hasta que confirmes una transmisión, los datos estarán pendientes y no serán visibles en las lecturas.

Una vez que se completa la transmisión y antes de que se confirme, los datos pueden permanecer en el búfer hasta por 4 horas. Las transmisiones pendientes deben confirmarse en un plazo de 24 horas. Existe un límite de cuota para el tamaño total del búfer de transmisión pendiente.

En el siguiente código, se muestra cómo escribir datos de tipo pendiente:

C#

Para aprender a instalar y usar la biblioteca cliente de BigQuery, consulta las bibliotecas cliente de BigQuery. Para obtener más información, consulta la documentación de referencia de la API de BigQuery para C#.

Para autenticarte en BigQuery, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para bibliotecas cliente.

using Google.Api.Gax.Grpc;
using Google.Cloud.BigQuery.Storage.V1;
using Google.Protobuf;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;
using static Google.Cloud.BigQuery.Storage.V1.AppendRowsRequest.Types;

public class AppendRowsPendingSample
{
    /// <summary>
    /// This code sample demonstrates how to write records in pending mode.
    /// Create a write stream, write some sample data, and commit the stream to append the rows.
    /// The CustomerRecord proto used in the sample can be seen in Resources folder and generated C# is placed in Data folder in
    /// https://github.com/GoogleCloudPlatform/dotnet-docs-samples/tree/main/bigquery-storage/api/BigQueryStorage.Samples
    /// </summary>
    public async Task AppendRowsPendingAsync(string projectId, string datasetId, string tableId)
    {
        BigQueryWriteClient bigQueryWriteClient = await BigQueryWriteClient.CreateAsync();
        // Initialize a write stream for the specified table.
        // When creating the stream, choose the type. Use the Pending type to wait
        // until the stream is committed before it is visible. See:
        // https://cloud.google.com/bigquery/docs/reference/storage/rpc/google.cloud.bigquery.storage.v1#google.cloud.bigquery.storage.v1.WriteStream.Type
        WriteStream stream = new WriteStream { Type = WriteStream.Types.Type.Pending };
        TableName tableName = TableName.FromProjectDatasetTable(projectId, datasetId, tableId);

        stream = await bigQueryWriteClient.CreateWriteStreamAsync(tableName, stream);

        // Initialize streaming call, retrieving the stream object
        BigQueryWriteClient.AppendRowsStream rowAppender = bigQueryWriteClient.AppendRows();

        // Sending requests and retrieving responses can be arbitrarily interleaved.
        // Exact sequence will depend on client/server behavior.
        // Create task to do something with responses from server.
        Task appendResultsHandlerTask = Task.Run(async () =>
        {
            AsyncResponseStream<AppendRowsResponse> appendRowResults = rowAppender.GetResponseStream();
            while (await appendRowResults.MoveNextAsync())
            {
                AppendRowsResponse responseItem = appendRowResults.Current;
                // Do something with responses.
                Console.WriteLine($"Appending rows resulted in: {responseItem.AppendResult}");
            }
            // The response stream has completed.
        });

        // List of records to be appended in the table.
        List<CustomerRecord> records = new List<CustomerRecord>
        {
            new CustomerRecord { CustomerNumber = 1, CustomerName = "Alice" },
            new CustomerRecord { CustomerNumber = 2, CustomerName = "Bob" }
        };

        // Create a batch of row data by appending serialized bytes to the
        // SerializedRows repeated field.
        ProtoData protoData = new ProtoData
        {
            WriterSchema = new ProtoSchema { ProtoDescriptor = CustomerRecord.Descriptor.ToProto() },
            Rows = new ProtoRows { SerializedRows = { records.Select(r => r.ToByteString()) } }
        };

        // Initialize the append row request.
        AppendRowsRequest appendRowRequest = new AppendRowsRequest
        {
            WriteStreamAsWriteStreamName = stream.WriteStreamName,
            ProtoRows = protoData
        };

        // Stream a request to the server.
        await rowAppender.WriteAsync(appendRowRequest);

        // Append a second batch of data.
        protoData = new ProtoData
        {
            Rows = new ProtoRows { SerializedRows = { new CustomerRecord { CustomerNumber = 3, CustomerName = "Charles" }.ToByteString() } }
        };

        // Since this is the second request, you only need to include the row data.
        // The name of the stream and protocol buffers descriptor is only needed in
        // the first request.
        appendRowRequest = new AppendRowsRequest
        {
            // If Offset is not present, the write is performed at the current end of stream.
            ProtoRows = protoData
        };

        await rowAppender.WriteAsync(appendRowRequest);

        // Complete writing requests to the stream.
        await rowAppender.WriteCompleteAsync();

        // Await the handler. This will complete once all server responses have been processed.
        await appendResultsHandlerTask;

        // A Pending type stream must be "finalized" before being committed. No new
        // records can be written to the stream after this method has been called.
        await bigQueryWriteClient.FinalizeWriteStreamAsync(stream.Name);
        BatchCommitWriteStreamsRequest batchCommitWriteStreamsRequest = new BatchCommitWriteStreamsRequest
        {
            Parent = tableName.ToString(),
            WriteStreams = { stream.Name }
        };

        BatchCommitWriteStreamsResponse batchCommitWriteStreamsResponse =
            await bigQueryWriteClient.BatchCommitWriteStreamsAsync(batchCommitWriteStreamsRequest);
        if (batchCommitWriteStreamsResponse.StreamErrors?.Count > 0)
        {
            // Handle errors here.
            Console.WriteLine("Error committing write streams. Individual errors:");
            foreach (StorageError error in batchCommitWriteStreamsResponse.StreamErrors)
            {
                Console.WriteLine(error.ErrorMessage);
            }            
        }
        else
        {
            Console.WriteLine($"Writes to stream {stream.Name} have been committed.");
        }
    }
}

Go

Para aprender a instalar y usar la biblioteca cliente de BigQuery, consulta las bibliotecas cliente de BigQuery. Para obtener más información, consulta la documentación de referencia de la API de BigQuery para Go.

Para autenticarte en BigQuery, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para bibliotecas cliente.

import (
	"context"
	"fmt"
	"io"
	"math/rand"
	"time"

	"cloud.google.com/go/bigquery/storage/managedwriter"
	"cloud.google.com/go/bigquery/storage/managedwriter/adapt"
	"github.com/GoogleCloudPlatform/golang-samples/bigquery/snippets/managedwriter/exampleproto"
	storagepb "google.golang.org/genproto/googleapis/cloud/bigquery/storage/v1"
	"google.golang.org/protobuf/proto"
)

// generateExampleMessages generates a slice of serialized protobuf messages using a statically defined
// and compiled protocol buffer file, and returns the binary serialized representation.
func generateExampleMessages(numMessages int) ([][]byte, error) {
	msgs := make([][]byte, numMessages)
	for i := 0; i < numMessages; i++ {

		random := rand.New(rand.NewSource(time.Now().UnixNano()))

		// Our example data embeds an array of structs, so we'll construct that first.
		sList := make([]*exampleproto.SampleStruct, 5)
		for i := 0; i < int(random.Int63n(5)+1); i++ {
			sList[i] = &exampleproto.SampleStruct{
				SubIntCol: proto.Int64(random.Int63()),
			}
		}

		m := &exampleproto.SampleData{
			BoolCol:    proto.Bool(true),
			BytesCol:   []byte("some bytes"),
			Float64Col: proto.Float64(3.14),
			Int64Col:   proto.Int64(123),
			StringCol:  proto.String("example string value"),

			// These types require special encoding/formatting to transmit.

			// DATE values are number of days since the Unix epoch.

			DateCol: proto.Int32(int32(time.Now().UnixNano() / 86400000000000)),

			// DATETIME uses the literal format.
			DatetimeCol: proto.String("2022-01-01 12:13:14.000000"),

			// GEOGRAPHY uses Well-Known-Text (WKT) format.
			GeographyCol: proto.String("POINT(-122.350220 47.649154)"),

			// NUMERIC and BIGNUMERIC can be passed as string, or more efficiently
			// using a packed byte representation.
			NumericCol:    proto.String("99999999999999999999999999999.999999999"),
			BignumericCol: proto.String("578960446186580977117854925043439539266.34992332820282019728792003956564819967"),

			// TIME also uses literal format.
			TimeCol: proto.String("12:13:14.000000"),

			// TIMESTAMP uses microseconds since Unix epoch.
			TimestampCol: proto.Int64(time.Now().UnixNano() / 1000),

			// Int64List is an array of INT64 types.
			Int64List: []int64{2, 4, 6, 8},

			// This is a required field, and thus must be present.
			RowNum: proto.Int64(23),

			// StructCol is a single nested message.
			StructCol: &exampleproto.SampleStruct{
				SubIntCol: proto.Int64(random.Int63()),
			},

			// StructList is a repeated array of a nested message.
			StructList: sList,
		}

		b, err := proto.Marshal(m)
		if err != nil {
			return nil, fmt.Errorf("error generating message %d: %w", i, err)
		}
		msgs[i] = b
	}
	return msgs, nil
}

// appendToPendingStream demonstrates using the managedwriter package to write some example data
// to a pending stream, and then committing it to a table.
func appendToPendingStream(w io.Writer, projectID, datasetID, tableID string) error {
	// projectID := "myproject"
	// datasetID := "mydataset"
	// tableID := "mytable"

	ctx := context.Background()
	// Instantiate a managedwriter client to handle interactions with the service.
	client, err := managedwriter.NewClient(ctx, projectID)
	if err != nil {
		return fmt.Errorf("managedwriter.NewClient: %w", err)
	}
	// Close the client when we exit the function.
	defer client.Close()

	// Create a new pending stream.  We'll use the stream name to construct a writer.
	pendingStream, err := client.CreateWriteStream(ctx, &storagepb.CreateWriteStreamRequest{
		Parent: fmt.Sprintf("projects/%s/datasets/%s/tables/%s", projectID, datasetID, tableID),
		WriteStream: &storagepb.WriteStream{
			Type: storagepb.WriteStream_PENDING,
		},
	})
	if err != nil {
		return fmt.Errorf("CreateWriteStream: %w", err)
	}

	// We need to communicate the descriptor of the protocol buffer message we're using, which
	// is analagous to the "schema" for the message.  Both SampleData and SampleStruct are
	// two distinct messages in the compiled proto file, so we'll use adapt.NormalizeDescriptor
	// to unify them into a single self-contained descriptor representation.
	m := &exampleproto.SampleData{}
	descriptorProto, err := adapt.NormalizeDescriptor(m.ProtoReflect().Descriptor())
	if err != nil {
		return fmt.Errorf("NormalizeDescriptor: %w", err)
	}

	// Instantiate a ManagedStream, which manages low level details like connection state and provides
	// additional features like a future-like callback for appends, etc.  NewManagedStream can also create
	// the stream on your behalf, but in this example we're being explicit about stream creation.
	managedStream, err := client.NewManagedStream(ctx, managedwriter.WithStreamName(pendingStream.GetName()),
		managedwriter.WithSchemaDescriptor(descriptorProto))
	if err != nil {
		return fmt.Errorf("NewManagedStream: %w", err)
	}
	defer managedStream.Close()

	// First, we'll append a single row.
	rows, err := generateExampleMessages(1)
	if err != nil {
		return fmt.Errorf("generateExampleMessages: %w", err)
	}

	// We'll keep track of the current offset in the stream with curOffset.
	var curOffset int64
	// We can append data asyncronously, so we'll check our appends at the end.
	var results []*managedwriter.AppendResult

	result, err := managedStream.AppendRows(ctx, rows, managedwriter.WithOffset(0))
	if err != nil {
		return fmt.Errorf("AppendRows first call error: %w", err)
	}
	results = append(results, result)

	// Advance our current offset.
	curOffset = curOffset + 1

	// This time, we'll append three more rows in a single request.
	rows, err = generateExampleMessages(3)
	if err != nil {
		return fmt.Errorf("generateExampleMessages: %w", err)
	}
	result, err = managedStream.AppendRows(ctx, rows, managedwriter.WithOffset(curOffset))
	if err != nil {
		return fmt.Errorf("AppendRows second call error: %w", err)
	}
	results = append(results, result)

	// Advance our offset again.
	curOffset = curOffset + 3

	// Finally, we'll append two more rows.
	rows, err = generateExampleMessages(2)
	if err != nil {
		return fmt.Errorf("generateExampleMessages: %w", err)
	}
	result, err = managedStream.AppendRows(ctx, rows, managedwriter.WithOffset(curOffset))
	if err != nil {
		return fmt.Errorf("AppendRows third call error: %w", err)
	}
	results = append(results, result)

	// Now, we'll check that our batch of three appends all completed successfully.
	// Monitoring the results could also be done out of band via a goroutine.
	for k, v := range results {
		// GetResult blocks until we receive a response from the API.
		recvOffset, err := v.GetResult(ctx)
		if err != nil {
			return fmt.Errorf("append %d returned error: %w", k, err)
		}
		fmt.Fprintf(w, "Successfully appended data at offset %d.\n", recvOffset)
	}

	// We're now done appending to this stream.  We now mark pending stream finalized, which blocks
	// further appends.
	rowCount, err := managedStream.Finalize(ctx)
	if err != nil {
		return fmt.Errorf("error during Finalize: %w", err)
	}

	fmt.Fprintf(w, "Stream %s finalized with %d rows.\n", managedStream.StreamName(), rowCount)

	// To commit the data to the table, we need to run a batch commit.  You can commit several streams
	// atomically as a group, but in this instance we'll only commit the single stream.
	req := &storagepb.BatchCommitWriteStreamsRequest{
		Parent:       managedwriter.TableParentFromStreamName(managedStream.StreamName()),
		WriteStreams: []string{managedStream.StreamName()},
	}

	resp, err := client.BatchCommitWriteStreams(ctx, req)
	if err != nil {
		return fmt.Errorf("client.BatchCommit: %w", err)
	}
	if len(resp.GetStreamErrors()) > 0 {
		return fmt.Errorf("stream errors present: %v", resp.GetStreamErrors())
	}

	fmt.Fprintf(w, "Table data committed at %s\n", resp.GetCommitTime().AsTime().Format(time.RFC3339Nano))

	return nil
}

Java

Para aprender a instalar y usar la biblioteca cliente de BigQuery, consulta las bibliotecas cliente de BigQuery. Para obtener más información, consulta la documentación de referencia de la API de BigQuery para Java.

Para autenticarte en BigQuery, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para bibliotecas cliente.

import com.google.api.core.ApiFuture;
import com.google.api.core.ApiFutureCallback;
import com.google.api.core.ApiFutures;
import com.google.api.gax.retrying.RetrySettings;
import com.google.cloud.bigquery.storage.v1.AppendRowsResponse;
import com.google.cloud.bigquery.storage.v1.BatchCommitWriteStreamsRequest;
import com.google.cloud.bigquery.storage.v1.BatchCommitWriteStreamsResponse;
import com.google.cloud.bigquery.storage.v1.BigQueryWriteClient;
import com.google.cloud.bigquery.storage.v1.CreateWriteStreamRequest;
import com.google.cloud.bigquery.storage.v1.Exceptions;
import com.google.cloud.bigquery.storage.v1.Exceptions.StorageException;
import com.google.cloud.bigquery.storage.v1.FinalizeWriteStreamResponse;
import com.google.cloud.bigquery.storage.v1.JsonStreamWriter;
import com.google.cloud.bigquery.storage.v1.StorageError;
import com.google.cloud.bigquery.storage.v1.TableName;
import com.google.cloud.bigquery.storage.v1.WriteStream;
import com.google.common.util.concurrent.MoreExecutors;
import com.google.protobuf.Descriptors.DescriptorValidationException;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.Phaser;
import javax.annotation.concurrent.GuardedBy;
import org.json.JSONArray;
import org.json.JSONObject;
import org.threeten.bp.Duration;

public class WritePendingStream {

  public static void runWritePendingStream()
      throws DescriptorValidationException, InterruptedException, IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "MY_PROJECT_ID";
    String datasetName = "MY_DATASET_NAME";
    String tableName = "MY_TABLE_NAME";

    writePendingStream(projectId, datasetName, tableName);
  }

  public static void writePendingStream(String projectId, String datasetName, String tableName)
      throws DescriptorValidationException, InterruptedException, IOException {
    BigQueryWriteClient client = BigQueryWriteClient.create();
    TableName parentTable = TableName.of(projectId, datasetName, tableName);

    DataWriter writer = new DataWriter();
    // One time initialization.
    writer.initialize(parentTable, client);

    try {
      // Write two batches of fake data to the stream, each with 10 JSON records.  Data may be
      // batched up to the maximum request size:
      // https://cloud.google.com/bigquery/quotas#write-api-limits
      long offset = 0;
      for (int i = 0; i < 2; i++) {
        // Create a JSON object that is compatible with the table schema.
        JSONArray jsonArr = new JSONArray();
        for (int j = 0; j < 10; j++) {
          JSONObject record = new JSONObject();
          record.put("col1", String.format("batch-record %03d-%03d", i, j));
          jsonArr.put(record);
        }
        writer.append(jsonArr, offset);
        offset += jsonArr.length();
      }
    } catch (ExecutionException e) {
      // If the wrapped exception is a StatusRuntimeException, check the state of the operation.
      // If the state is INTERNAL, CANCELLED, or ABORTED, you can retry. For more information, see:
      // https://grpc.github.io/grpc-java/javadoc/io/grpc/StatusRuntimeException.html
      System.out.println("Failed to append records. \n" + e);
    }

    // Final cleanup for the stream.
    writer.cleanup(client);
    System.out.println("Appended records successfully.");

    // Once all streams are done, if all writes were successful, commit all of them in one request.
    // This example only has the one stream. If any streams failed, their workload may be
    // retried on a new stream, and then only the successful stream should be included in the
    // commit.
    BatchCommitWriteStreamsRequest commitRequest =
        BatchCommitWriteStreamsRequest.newBuilder()
            .setParent(parentTable.toString())
            .addWriteStreams(writer.getStreamName())
            .build();
    BatchCommitWriteStreamsResponse commitResponse = client.batchCommitWriteStreams(commitRequest);
    // If the response does not have a commit time, it means the commit operation failed.
    if (commitResponse.hasCommitTime() == false) {
      for (StorageError err : commitResponse.getStreamErrorsList()) {
        System.out.println(err.getErrorMessage());
      }
      throw new RuntimeException("Error committing the streams");
    }
    System.out.println("Appended and committed records successfully.");
  }

  // A simple wrapper object showing how the stateful stream writer should be used.
  private static class DataWriter {

    private JsonStreamWriter streamWriter;
    // Track the number of in-flight requests to wait for all responses before shutting down.
    private final Phaser inflightRequestCount = new Phaser(1);

    private final Object lock = new Object();

    @GuardedBy("lock")
    private RuntimeException error = null;

    void initialize(TableName parentTable, BigQueryWriteClient client)
        throws IOException, DescriptorValidationException, InterruptedException {
      // Initialize a write stream for the specified table.
      // For more information on WriteStream.Type, see:
      // https://googleapis.dev/java/google-cloud-bigquerystorage/latest/com/google/cloud/bigquery/storage/v1/WriteStream.Type.html
      WriteStream stream = WriteStream.newBuilder().setType(WriteStream.Type.PENDING).build();

      // Configure in-stream automatic retry settings.
      // Error codes that are immediately retried:
      // * ABORTED, UNAVAILABLE, CANCELLED, INTERNAL, DEADLINE_EXCEEDED
      // Error codes that are retried with exponential backoff:
      // * RESOURCE_EXHAUSTED
      RetrySettings retrySettings =
          RetrySettings.newBuilder()
              .setInitialRetryDelay(Duration.ofMillis(500))
              .setRetryDelayMultiplier(1.1)
              .setMaxAttempts(5)
              .setMaxRetryDelay(Duration.ofMinutes(1))
              .build();

      CreateWriteStreamRequest createWriteStreamRequest =
          CreateWriteStreamRequest.newBuilder()
              .setParent(parentTable.toString())
              .setWriteStream(stream)
              .build();
      WriteStream writeStream = client.createWriteStream(createWriteStreamRequest);

      // Use the JSON stream writer to send records in JSON format.
      // For more information about JsonStreamWriter, see:
      // https://googleapis.dev/java/google-cloud-bigquerystorage/latest/com/google/cloud/bigquery/storage/v1beta2/JsonStreamWriter.html
      streamWriter =
          JsonStreamWriter.newBuilder(writeStream.getName(), writeStream.getTableSchema())
              .setRetrySettings(retrySettings)
              .build();
    }

    public void append(JSONArray data, long offset)
        throws DescriptorValidationException, IOException, ExecutionException {
      synchronized (this.lock) {
        // If earlier appends have failed, we need to reset before continuing.
        if (this.error != null) {
          throw this.error;
        }
      }
      // Append asynchronously for increased throughput.
      ApiFuture<AppendRowsResponse> future = streamWriter.append(data, offset);
      ApiFutures.addCallback(
          future, new AppendCompleteCallback(this), MoreExecutors.directExecutor());
      // Increase the count of in-flight requests.
      inflightRequestCount.register();
    }

    public void cleanup(BigQueryWriteClient client) {
      // Wait for all in-flight requests to complete.
      inflightRequestCount.arriveAndAwaitAdvance();

      // Close the connection to the server.
      streamWriter.close();

      // Verify that no error occurred in the stream.
      synchronized (this.lock) {
        if (this.error != null) {
          throw this.error;
        }
      }

      // Finalize the stream.
      FinalizeWriteStreamResponse finalizeResponse =
          client.finalizeWriteStream(streamWriter.getStreamName());
      System.out.println("Rows written: " + finalizeResponse.getRowCount());
    }

    public String getStreamName() {
      return streamWriter.getStreamName();
    }

    static class AppendCompleteCallback implements ApiFutureCallback<AppendRowsResponse> {

      private final DataWriter parent;

      public AppendCompleteCallback(DataWriter parent) {
        this.parent = parent;
      }

      public void onSuccess(AppendRowsResponse response) {
        System.out.format("Append %d success\n", response.getAppendResult().getOffset().getValue());
        done();
      }

      public void onFailure(Throwable throwable) {
        synchronized (this.parent.lock) {
          if (this.parent.error == null) {
            StorageException storageException = Exceptions.toStorageException(throwable);
            this.parent.error =
                (storageException != null) ? storageException : new RuntimeException(throwable);
          }
        }
        System.out.format("Error: %s\n", throwable.toString());
        done();
      }

      private void done() {
        // Reduce the count of in-flight requests.
        this.parent.inflightRequestCount.arriveAndDeregister();
      }
    }
  }
}

Node.js

Para aprender a instalar y usar la biblioteca cliente de BigQuery, consulta las bibliotecas cliente de BigQuery. Para obtener más información, consulta la documentación de referencia de la API de BigQuery para Node.js.

Para autenticarte en BigQuery, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para bibliotecas cliente.

const {adapt, managedwriter} = require('@google-cloud/bigquery-storage');
const {WriterClient, Writer} = managedwriter;

const customer_record_pb = require('./customer_record_pb.js');
const {CustomerRecord} = customer_record_pb;

const protobufjs = require('protobufjs');
require('protobufjs/ext/descriptor');

async function appendRowsPending() {
  /**
   * If you make updates to the customer_record.proto protocol buffers definition,
   * run:
   *   pbjs customer_record.proto -t static-module -w commonjs -o customer_record.js
   *   pbjs customer_record.proto -t json --keep-case -o customer_record.json
   * from the /samples directory to generate the customer_record module.
   */

  // So that BigQuery knows how to parse the serialized_rows, create a
  // protocol buffer representation of your message descriptor.
  const root = protobufjs.loadSync('./customer_record.json');
  const descriptor = root.lookupType('CustomerRecord').toDescriptor('proto2');
  const protoDescriptor = adapt.normalizeDescriptor(descriptor).toJSON();

  /**
   * TODO(developer): Uncomment the following lines before running the sample.
   */
  // projectId = 'my_project';
  // datasetId = 'my_dataset';
  // tableId = 'my_table';

  const destinationTable = `projects/${projectId}/datasets/${datasetId}/tables/${tableId}`;
  const streamType = managedwriter.PendingStream;
  const writeClient = new WriterClient({projectId});
  try {
    const writeStream = await writeClient.createWriteStreamFullResponse({
      streamType,
      destinationTable,
    });
    const streamId = writeStream.name;
    console.log(`Stream created: ${streamId}`);

    const connection = await writeClient.createStreamConnection({
      streamId,
    });
    const writer = new Writer({
      connection,
      protoDescriptor,
    });

    let serializedRows = [];
    const pendingWrites = [];

    // Row 1
    let row = {
      rowNum: 1,
      customerName: 'Octavia',
    };
    serializedRows.push(CustomerRecord.encode(row).finish());

    // Row 2
    row = {
      rowNum: 2,
      customerName: 'Turing',
    };
    serializedRows.push(CustomerRecord.encode(row).finish());

    // Set an offset to allow resuming this stream if the connection breaks.
    // Keep track of which requests the server has acknowledged and resume the
    // stream at the first non-acknowledged message. If the server has already
    // processed a message with that offset, it will return an ALREADY_EXISTS
    // error, which can be safely ignored.

    // The first request must always have an offset of 0.
    let offsetValue = 0;

    // Send batch.
    let pw = writer.appendRows({serializedRows}, offsetValue);
    pendingWrites.push(pw);

    serializedRows = [];

    // Row 3
    row = {
      rowNum: 3,
      customerName: 'Bell',
    };
    serializedRows.push(CustomerRecord.encode(row).finish());

    // Offset must equal the number of rows that were previously sent.
    offsetValue = 2;

    // Send batch.
    pw = writer.appendRows({serializedRows}, offsetValue);
    pendingWrites.push(pw);

    const results = await Promise.all(
      pendingWrites.map(pw => pw.getResult())
    );
    console.log('Write results:', results);

    const {rowCount} = await connection.finalize();
    console.log(`Row count: ${rowCount}`);

    const response = await writeClient.batchCommitWriteStream({
      parent: destinationTable,
      writeStreams: [streamId],
    });

    console.log(response);
  } catch (err) {
    console.log(err);
  } finally {
    writeClient.close();
  }
}

Python

En este ejemplo, se muestra un registro simple con dos campos. Para ver un ejemplo más largo en el que se muestre cómo enviar diferentes tipos de datos, incluidos los tipos de STRUCT, consulta el ejemplo de append_rows_proto2 en GitHub.

Para aprender a instalar y usar la biblioteca cliente de BigQuery, consulta las bibliotecas cliente de BigQuery. Para obtener más información, consulta la documentación de referencia de la API de BigQuery para Python.

Para autenticarte en BigQuery, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para bibliotecas cliente.

"""
This code sample demonstrates how to write records in pending mode
using the low-level generated client for Python.
"""

from google.protobuf import descriptor_pb2

from google.cloud import bigquery_storage_v1
from google.cloud.bigquery_storage_v1 import types, writer

# If you update the customer_record.proto protocol buffer definition, run:
#
#   protoc --python_out=. customer_record.proto
#
# from the samples/snippets directory to generate the customer_record_pb2.py module.
from . import customer_record_pb2


def create_row_data(row_num: int, name: str):
    row = customer_record_pb2.CustomerRecord()
    row.row_num = row_num
    row.customer_name = name
    return row.SerializeToString()


def append_rows_pending(project_id: str, dataset_id: str, table_id: str):
    """Create a write stream, write some sample data, and commit the stream."""
    write_client = bigquery_storage_v1.BigQueryWriteClient()
    parent = write_client.table_path(project_id, dataset_id, table_id)
    write_stream = types.WriteStream()

    # When creating the stream, choose the type. Use the PENDING type to wait
    # until the stream is committed before it is visible. See:
    # https://cloud.google.com/bigquery/docs/reference/storage/rpc/google.cloud.bigquery.storage.v1#google.cloud.bigquery.storage.v1.WriteStream.Type
    write_stream.type_ = types.WriteStream.Type.PENDING
    write_stream = write_client.create_write_stream(
        parent=parent, write_stream=write_stream
    )
    stream_name = write_stream.name

    # Create a template with fields needed for the first request.
    request_template = types.AppendRowsRequest()

    # The initial request must contain the stream name.
    request_template.write_stream = stream_name

    # So that BigQuery knows how to parse the serialized_rows, generate a
    # protocol buffer representation of your message descriptor.
    proto_schema = types.ProtoSchema()
    proto_descriptor = descriptor_pb2.DescriptorProto()
    customer_record_pb2.CustomerRecord.DESCRIPTOR.CopyToProto(proto_descriptor)
    proto_schema.proto_descriptor = proto_descriptor
    proto_data = types.AppendRowsRequest.ProtoData()
    proto_data.writer_schema = proto_schema
    request_template.proto_rows = proto_data

    # Some stream types support an unbounded number of requests. Construct an
    # AppendRowsStream to send an arbitrary number of requests to a stream.
    append_rows_stream = writer.AppendRowsStream(write_client, request_template)

    # Create a batch of row data by appending proto2 serialized bytes to the
    # serialized_rows repeated field.
    proto_rows = types.ProtoRows()
    proto_rows.serialized_rows.append(create_row_data(1, "Alice"))
    proto_rows.serialized_rows.append(create_row_data(2, "Bob"))

    # Set an offset to allow resuming this stream if the connection breaks.
    # Keep track of which requests the server has acknowledged and resume the
    # stream at the first non-acknowledged message. If the server has already
    # processed a message with that offset, it will return an ALREADY_EXISTS
    # error, which can be safely ignored.
    #
    # The first request must always have an offset of 0.
    request = types.AppendRowsRequest()
    request.offset = 0
    proto_data = types.AppendRowsRequest.ProtoData()
    proto_data.rows = proto_rows
    request.proto_rows = proto_data

    response_future_1 = append_rows_stream.send(request)

    # Send another batch.
    proto_rows = types.ProtoRows()
    proto_rows.serialized_rows.append(create_row_data(3, "Charles"))

    # Since this is the second request, you only need to include the row data.
    # The name of the stream and protocol buffers DESCRIPTOR is only needed in
    # the first request.
    request = types.AppendRowsRequest()
    proto_data = types.AppendRowsRequest.ProtoData()
    proto_data.rows = proto_rows
    request.proto_rows = proto_data

    # Offset must equal the number of rows that were previously sent.
    request.offset = 2

    response_future_2 = append_rows_stream.send(request)

    print(response_future_1.result())
    print(response_future_2.result())

    # Shutdown background threads and close the streaming connection.
    append_rows_stream.close()

    # A PENDING type stream must be "finalized" before being committed. No new
    # records can be written to the stream after this method has been called.
    write_client.finalize_write_stream(name=write_stream.name)

    # Commit the stream you created earlier.
    batch_commit_write_streams_request = types.BatchCommitWriteStreamsRequest()
    batch_commit_write_streams_request.parent = parent
    batch_commit_write_streams_request.write_streams = [write_stream.name]
    write_client.batch_commit_write_streams(batch_commit_write_streams_request)

    print(f"Writes to stream: '{write_stream.name}' have been committed.")

Este ejemplo de código depende de un módulo de protocolo compilado, customer_record_pb2.py. Para crear el módulo compilado, ejecuta protoc --python_out=. customer_record.proto, en el que protoc es el compilador del búfer de protocolo. El archivo customer_record.proto define el formato de los mensajes que se usan en el ejemplo de Python.

// The BigQuery Storage API expects protocol buffer data to be encoded in the
// proto2 wire format. This allows it to disambiguate missing optional fields
// from default values without the need for wrapper types.
syntax = "proto2";

// Define a message type representing the rows in your table. The message
// cannot contain fields which are not present in the table.
message CustomerRecord {

  optional string customer_name = 1;

  // Use the required keyword for client-side validation of required fields.
  required int64 row_num = 2;
}