Daten im Batch mit der Storage Write API laden

In diesem Dokument wird beschrieben, wie Sie mit der BigQuery Storage Write API Daten im Batch in BigQuery laden.

In Szenarien mit Batchladevorgängen schreibt eine Anwendung Daten und übergibt sie per Commit in einer einzigen unteilbaren Transaktion. Wenn Sie die Storage Write API für das Laden von Daten im Batch verwenden, erstellen Sie einen oder mehrere Streams vom Typ „Ausstehend“. Der Typ „Ausstehend“ unterstützt Transaktionen auf Streamebene. Datensätze werden im Status "Ausstehend" zwischengespeichert, bis Sie den Stream per Commit übergeben.

Prüfen Sie bei Batcharbeitslasten auch die Verwendung der Storage Write API über den Apache Spark SQL Connector für BigQuery mithilfe von Dataproc, anstatt benutzerdefinierten Storage Write API-Code zu schreiben.

Die Storage Write API eignet sich gut für eine Datenpipeline-Architektur. Ein Hauptprozess erzeugt eine Reihe von Streams. Jedem Stream wird ein Worker-Thread oder ein separater Prozess zugewiesen, um einen Teil der Batch-Daten zu schreiben. Jeder Worker erstellt eine Verbindung zu seinem Stream, schreibt Daten und finalisiert seinen Stream, wenn er abgeschlossen ist. Nachdem alle Worker den erfolgreichen Abschluss zum Hauptprozess signalisiert haben, übergibt der Hauptprozess die Daten per Commit. Wenn ein Worker fehlschlägt, wird der zugewiesene Teil der Daten nicht in den Endergebnissen angezeigt und der gesamte Worker kann wiederholt werden. In einer komplexeren Pipeline prüfen Worker ihren Fortschritt, indem sie den letzten an den Hauptprozess geschriebenen Offset melden. Dieser Ansatz kann zu einer robusten Pipeline führen, die ausfallsicher ist.

Daten im Batch unter Verwendung des Typs „Ausstehend“ laden

Die Anwendung geht so vor, um den Typ „Ausstehend“ zu verwenden:

1. Rufen Sie CreateWriteStream auf, um einen oder mehrere Streams vom Typ „Ausstehend“ zu erstellen.
2. Rufen Sie für jeden Stream AppendRows in einer Schleife auf, um Datensätze in Batches zu schreiben.
3. Rufen Sie für jeden Stream FinalizeWriteStream auf. Nach dem Aufrufen dieser Methode können Sie keine weiteren Zeilen in den Stream schreiben. Wenn Sie AppendRows nach dem Aufruf von FinalizeWriteStream aufrufen, wird ein StorageError mit StorageErrorCode.STREAM_FINALIZED im Fehler google.rpc.Status zurückgegeben. Weitere Informationen zum Fehlermodell google.rpc.Status finden Sie unter Fehler.
4. Rufen Sie BatchCommitWriteStreams auf, um die Streams per Commit zu übergeben. Nach dem Aufrufen dieser Methode stehen die Daten zum Lesen zur Verfügung. Wenn beim Commit eines der Streams ein Fehler auftritt, wird der Fehler im Feld stream_errors der BatchCommitWriteStreamsResponse zurückgegeben.

Das Commit ist ein unteilbarer Vorgang und Sie können mehrere Streams gleichzeitig per Commit übergeben. Für einen Stream kann nur einmal ein Commit durchgeführt werden. Wenn der Commit-Vorgang fehlschlägt, kann der Vorgang sicher wiederholt werden. Bis zum Commit eines Streams stehen die Daten aus und sind für Lesevorgänge nicht sichtbar.

Nachdem der Stream abgeschlossen wurde und bevor er übergeben wird, können die Daten bis zu 4 Stunden im Puffer verbleiben. Ausstehende Streams müssen innerhalb von 24 Stunden per Commit bestätigt werden. Für die Gesamtgröße des Zwischenspeichers für ausstehende Streams gilt ein Kontingentlimit.

Der folgende Code zeigt, wie Daten vom Typ „Ausstehend“ geschrieben werden.

C#

Informationen zum Installieren und Verwenden der Clientbibliothek für BigQuery finden Sie unter BigQuery-Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur BigQuery C# API.

Richten Sie zur Authentifizierung bei BigQuery die Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für Clientbibliotheken einrichten.

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.");
        }
    }
}

Einfach loslegen (Go)

Informationen zum Installieren und Verwenden der Clientbibliothek für BigQuery finden Sie unter BigQuery-Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur BigQuery Go API.

Richten Sie zur Authentifizierung bei BigQuery die Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für Clientbibliotheken einrichten.

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

Informationen zum Installieren und Verwenden der Clientbibliothek für BigQuery finden Sie unter BigQuery-Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur BigQuery Java API.

Richten Sie zur Authentifizierung bei BigQuery die Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für Clientbibliotheken einrichten.

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

Informationen zum Installieren und Verwenden der Clientbibliothek für BigQuery finden Sie unter BigQuery-Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur BigQuery Node.js API.

Richten Sie zur Authentifizierung bei BigQuery die Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für Clientbibliotheken einrichten.

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 streamId = await writeClient.createWriteStream({
      streamType,
      destinationTable,
    });
    console.log(`Stream created: ${streamId}`);

    // Append data to the given stream.
    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

Dieses Beispiel zeigt einen einfachen Datensatz mit zwei Feldern. Ein längeres Beispiel, das zeigt, wie Sie verschiedene Datentypen einschließlich STRUCT-Typen senden, finden Sie im append_rows_proto2-Beispiel auf GitHub.

Informationen zum Installieren und Verwenden der Clientbibliothek für BigQuery finden Sie unter BigQuery-Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur BigQuery Python API.

Richten Sie zur Authentifizierung bei BigQuery die Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für Clientbibliotheken einrichten.

"""
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.")

Dieses Codebeispiel hängt vom kompilierten Protokollmodul customer_record_pb2.py ab. Führen Sie zum Erstellen des kompilierten Moduls protoc --python_out=. customer_record.proto aus, wobei protoc der Protokollzwischenspeicher-Compiler ist. Die Datei customer_record.proto definiert das Format der im Python-Beispiel verwendeten Nachrichten.

// 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;
}