Template Cloud Storage Avro ke Bigtable

Template Cloud Storage Avro to Bigtable adalah pipeline yang membaca data dari file Avro di bucket Cloud Storage dan menulis data ke tabel Bigtable. Anda dapat menggunakan template untuk menyalin data dari Cloud Storage ke Bigtable.

Persyaratan pipeline

• Tabel Bigtable harus ada dan memiliki grup kolom yang sama seperti yang diekspor dalam file Avro.
• File Avro input harus ada di bucket Cloud Storage sebelum menjalankan pipeline.
• Bigtable mengharapkan skema tertentu dari file Avro input.

Parameter template

Parameter	Deskripsi
bigtableProjectId	ID Google Cloud project instance Bigtable yang ingin Anda tulis datanya.
bigtableInstanceId	ID instance Bigtable yang berisi tabel.
bigtableTableId	ID tabel Bigtable yang akan diimpor.
inputFilePattern	Pola jalur Cloud Storage tempat data berada. Misalnya, gs://mybucket/somefolder/prefix*.

Menjalankan template

Konsol

1. Buka halaman Create job from template Dataflow.
Buka Buat tugas dari template
2. Di kolom Nama tugas, masukkan nama tugas yang unik.
3. Opsional: Untuk Endpoint regional, pilih nilai dari menu drop-down. Region defaultnya adalah us-central1.

   Untuk mengetahui daftar region tempat Anda dapat menjalankan tugas Dataflow, lihat Lokasi Dataflow.

4. Dari menu drop-down Dataflow template, pilih the Avro Files on Cloud Storage to Cloud Bigtable template.
5. Di kolom parameter yang disediakan, masukkan nilai parameter Anda.
6. Klik Run job.

gcloud

Catatan: Untuk menggunakan Google Cloud CLI guna menjalankan template classic, Anda harus memiliki Google Cloud CLI versi 138.0.0, , atau yang lebih baru.

Di shell atau terminal, jalankan template:

gcloud dataflow jobs run JOB_NAME \
    --gcs-location gs://dataflow-templates-REGION_NAME/VERSION/GCS_Avro_to_Cloud_Bigtable \
    --region REGION_NAME \
    --parameters \
bigtableProjectId=BIGTABLE_PROJECT_ID,\
bigtableInstanceId=INSTANCE_ID,\
bigtableTableId=TABLE_ID,\
inputFilePattern=INPUT_FILE_PATTERN

Ganti kode berikut:

• JOB_NAME: nama tugas unik pilihan Anda
• VERSION: versi template yang ingin Anda gunakan

  Anda dapat menggunakan nilai berikut:

  • latest untuk menggunakan template versi terbaru, yang tersedia di folder induk tanpa tanggal di bucket—gs://dataflow-templates-REGION_NAME/latest/
  • nama versi, seperti 2023-09-12-00_RC00, untuk menggunakan versi template tertentu, yang dapat ditemukan bertingkat dalam folder induk bertanggal masing-masing di bucket—gs://dataflow-templates-REGION_NAME/
  Perhatian: Template versi terbaru mungkin diupdate dengan perubahan yang dapat menyebabkan gangguan. Lingkungan produksi Anda harus menggunakan template yang disimpan di folder induk terbaru yang bertanggal untuk mencegah perubahan yang menyebabkan error ini memengaruhi alur kerja produksi Anda.
• REGION_NAME: region tempat Anda ingin men-deploy tugas Dataflow—misalnya, us-central1
• BIGTABLE_PROJECT_ID: ID Google Cloud project instance Bigtable tempat Anda ingin membaca data
• INSTANCE_ID: ID instance Bigtable yang berisi tabel
• TABLE_ID: ID tabel Bigtable yang akan diekspor
• INPUT_FILE_PATTERN: pola jalur Cloud Storage tempat data berada, misalnya, gs://mybucket/somefolder/prefix*

API

Untuk menjalankan template menggunakan REST API, kirim permintaan POST HTTP. Untuk mengetahui informasi selengkapnya tentang API dan cakupan otorisasinya, lihat projects.templates.launch.

POST https://dataflow.googleapis.com/v1b3/projects/PROJECT_ID/locations/LOCATION/templates:launch?gcsPath=gs://dataflow-templates-LOCATION/VERSION/GCS_Avro_to_Cloud_Bigtable
{
   "jobName": "JOB_NAME",
   "parameters": {
       "bigtableProjectId": "BIGTABLE_PROJECT_ID",
       "bigtableInstanceId": "INSTANCE_ID",
       "bigtableTableId": "TABLE_ID",
       "inputFilePattern": "INPUT_FILE_PATTERN",
   },
   "environment": { "zone": "us-central1-f" }
}

Ganti kode berikut:

• PROJECT_ID: ID project Google Cloud tempat Anda ingin menjalankan tugas Dataflow
• JOB_NAME: nama tugas unik pilihan Anda
• VERSION: versi template yang ingin Anda gunakan

  Anda dapat menggunakan nilai berikut:

  • latest untuk menggunakan template versi terbaru, yang tersedia di folder induk tanpa tanggal di bucket—gs://dataflow-templates-REGION_NAME/latest/
  • nama versi, seperti 2023-09-12-00_RC00, untuk menggunakan versi template tertentu, yang dapat ditemukan bertingkat dalam folder induk bertanggal masing-masing di bucket—gs://dataflow-templates-REGION_NAME/
  Perhatian: Template versi terbaru mungkin diupdate dengan perubahan yang dapat menyebabkan gangguan. Lingkungan produksi Anda harus menggunakan template yang disimpan di folder induk terbaru yang bertanggal untuk mencegah perubahan yang menyebabkan error ini memengaruhi alur kerja produksi Anda.
• LOCATION: region tempat Anda ingin men-deploy tugas Dataflow—misalnya, us-central1
• BIGTABLE_PROJECT_ID: ID Google Cloud project instance Bigtable tempat Anda ingin membaca data
• INSTANCE_ID: ID instance Bigtable yang berisi tabel
• TABLE_ID: ID tabel Bigtable yang akan diekspor
• INPUT_FILE_PATTERN: pola jalur Cloud Storage tempat data berada, misalnya, gs://mybucket/somefolder/prefix*

Kode sumber template

Java

/*
 * Copyright (C) 2018 Google LLC
 *
 * Licensed under the Apache License, Version 2.0 (the "License"); 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.
 */
package com.google.cloud.teleport.bigtable;

import com.google.bigtable.v2.Mutation;
import com.google.bigtable.v2.Mutation.SetCell;
import com.google.cloud.teleport.bigtable.AvroToBigtable.Options;
import com.google.cloud.teleport.metadata.Template;
import com.google.cloud.teleport.metadata.TemplateCategory;
import com.google.cloud.teleport.metadata.TemplateParameter;
import com.google.common.base.MoreObjects;
import com.google.common.collect.ImmutableList;
import com.google.protobuf.ByteString;
import java.nio.ByteBuffer;
import org.apache.beam.runners.dataflow.options.DataflowPipelineOptions;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.PipelineResult;
import org.apache.beam.sdk.extensions.avro.io.AvroIO;
import org.apache.beam.sdk.io.gcp.bigtable.BigtableIO;
import org.apache.beam.sdk.options.PipelineOptions;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.ValueProvider;
import org.apache.beam.sdk.options.ValueProvider.StaticValueProvider;
import org.apache.beam.sdk.transforms.DoFn;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.values.KV;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * Dataflow pipeline that imports data from Avro files in GCS to a Cloud Bigtable table. The Cloud
 * Bigtable table must be created before running the pipeline and must have a compatible table
 * schema. For example, if {@link BigtableCell} from the Avro files has a 'family' of "f1", the
 * Bigtable table should have a column family of "f1".
 *
 * <p>Check out <a
 * href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v1/README_GCS_Avro_to_Cloud_Bigtable.md">README</a>
 * for instructions on how to use or modify this template.
 */
@Template(
    name = "GCS_Avro_to_Cloud_Bigtable",
    category = TemplateCategory.BATCH,
    displayName = "Avro Files on Cloud Storage to Cloud Bigtable",
    description =
        "The Cloud Storage Avro to Bigtable template is a pipeline that reads data from Avro files in a Cloud Storage bucket and writes the data to a Bigtable table. "
            + "You can use the template to copy data from Cloud Storage to Bigtable.",
    optionsClass = Options.class,
    documentation =
        "https://cloud.google.com/dataflow/docs/guides/templates/provided/avro-to-bigtable",
    contactInformation = "https://cloud.google.com/support",
    requirements = {
      "The Bigtable table must exist and have the same column families as exported in the Avro files.",
      "The input Avro files must exist in a Cloud Storage bucket before running the pipeline.",
      "Bigtable expects a specific <a href=\"https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v1/src/main/resources/schema/avro/bigtable.avsc\">schema</a> from the input Avro files.",
    })
public final class AvroToBigtable {
  private static final Logger LOG = LoggerFactory.getLogger(AvroToBigtable.class);

  /** Maximum number of mutations allowed per row by Cloud bigtable. */
  private static final int MAX_MUTATIONS_PER_ROW = 100000;

  private static final Boolean DEFAULT_SPLIT_LARGE_ROWS = false;

  /** Options for the import pipeline. */
  public interface Options extends PipelineOptions {
    @TemplateParameter.ProjectId(
        order = 1,
        groupName = "Target",
        description = "Project ID",
        helpText =
            "The ID of the Google Cloud project that contains the Bigtable instance that you want to write data to.")
    ValueProvider<String> getBigtableProjectId();

    @SuppressWarnings("unused")
    void setBigtableProjectId(ValueProvider<String> projectId);

    @TemplateParameter.Text(
        order = 2,
        groupName = "Target",
        regexes = {"[a-z][a-z0-9\\-]+[a-z0-9]"},
        description = "Instance ID",
        helpText = "The ID of the Bigtable instance that contains the table.")
    ValueProvider<String> getBigtableInstanceId();

    @SuppressWarnings("unused")
    void setBigtableInstanceId(ValueProvider<String> instanceId);

    @TemplateParameter.Text(
        order = 4,
        groupName = "Target",
        regexes = {"[_a-zA-Z0-9][-_.a-zA-Z0-9]*"},
        description = "Table ID",
        helpText = "The ID of the Bigtable table to import.")
    ValueProvider<String> getBigtableTableId();

    @SuppressWarnings("unused")
    void setBigtableTableId(ValueProvider<String> tableId);

    @TemplateParameter.GcsReadFile(
        order = 5,
        groupName = "Source",
        description = "Input Cloud Storage File(s)",
        helpText = "The Cloud Storage path pattern where data is located.",
        example = "gs://<BUCKET_NAME>/FOLDER/PREFIX*")
    ValueProvider<String> getInputFilePattern();

    @SuppressWarnings("unused")
    void setInputFilePattern(ValueProvider<String> inputFilePattern);

    @TemplateParameter.Boolean(
        order = 6,
        optional = true,
        description = "If true, large rows will be split into multiple MutateRows requests",
        helpText =
            "The flag for enabling splitting of large rows into multiple MutateRows requests. Note that when a large row is split between multiple API calls, the updates to the row are not atomic.")
    ValueProvider<Boolean> getSplitLargeRows();

    void setSplitLargeRows(ValueProvider<Boolean> splitLargeRows);
  }

  /**
   * Runs a pipeline to import Avro files in GCS to a Cloud Bigtable table.
   *
   * @param args arguments to the pipeline
   */
  public static void main(String[] args) {
    Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
    PipelineResult result = run(options);

    // Wait for pipeline to finish only if it is not constructing a template.
    if (options.as(DataflowPipelineOptions.class).getTemplateLocation() == null) {
      result.waitUntilFinish();
    }
  }

  public static PipelineResult run(Options options) {
    Pipeline pipeline = Pipeline.create(PipelineUtils.tweakPipelineOptions(options));

    BigtableIO.Write write =
        BigtableIO.write()
            .withProjectId(options.getBigtableProjectId())
            .withInstanceId(options.getBigtableInstanceId())
            .withTableId(options.getBigtableTableId());

    pipeline
        .apply("Read from Avro", AvroIO.read(BigtableRow.class).from(options.getInputFilePattern()))
        .apply(
            "Transform to Bigtable",
            ParDo.of(
                AvroToBigtableFn.createWithSplitLargeRows(
                    options.getSplitLargeRows(), MAX_MUTATIONS_PER_ROW)))
        .apply("Write to Bigtable", write);

    return pipeline.run();
  }

  /**
   * Translates {@link BigtableRow} to {@link Mutation}s along with a row key. The mutations are
   * {@link SetCell}s that set the value for specified cells with family name, column qualifier and
   * timestamp.
   */
  static class AvroToBigtableFn extends DoFn<BigtableRow, KV<ByteString, Iterable<Mutation>>> {
    private final ValueProvider<Boolean> splitLargeRowsFlag;
    private Boolean splitLargeRows;
    private final int maxMutationsPerRow;

    public static AvroToBigtableFn create() {
      return new AvroToBigtableFn(StaticValueProvider.of(false), MAX_MUTATIONS_PER_ROW);
    }

    public static AvroToBigtableFn createWithSplitLargeRows(
        ValueProvider<Boolean> splitLargeRowsFlag, int maxMutationsPerRequest) {
      return new AvroToBigtableFn(splitLargeRowsFlag, maxMutationsPerRequest);
    }

    private AvroToBigtableFn(
        ValueProvider<Boolean> splitLargeRowsFlag, int maxMutationsPerRequest) {
      this.splitLargeRowsFlag = splitLargeRowsFlag;
      this.maxMutationsPerRow = maxMutationsPerRequest;
    }

    @Setup
    public void setup() {
      if (splitLargeRowsFlag != null) {
        splitLargeRows = splitLargeRowsFlag.get();
      }
      splitLargeRows = MoreObjects.firstNonNull(splitLargeRows, DEFAULT_SPLIT_LARGE_ROWS);
      LOG.info("splitLargeRows set to: " + splitLargeRows);
    }

    @ProcessElement
    public void processElement(
        @Element BigtableRow row, OutputReceiver<KV<ByteString, Iterable<Mutation>>> out) {
      ByteString key = toByteString(row.getKey());
      // BulkMutation doesn't split rows. Currently, if a single row contains more than 100,000
      // mutations, the service will fail the request.
      ImmutableList.Builder<Mutation> mutations = ImmutableList.builder();
      int cellsProcessed = 0;
      for (BigtableCell cell : row.getCells()) {
        SetCell setCell =
            SetCell.newBuilder()
                .setFamilyName(cell.getFamily().toString())
                .setColumnQualifier(toByteString(cell.getQualifier()))
                .setTimestampMicros(cell.getTimestamp())
                .setValue(toByteString(cell.getValue()))
                .build();

        mutations.add(Mutation.newBuilder().setSetCell(setCell).build());
        cellsProcessed++;

        if (this.splitLargeRows && cellsProcessed % maxMutationsPerRow == 0) {
          // Send a MutateRow request when we have accumulated max mutations per row.
          out.output(KV.of(key, mutations.build()));
          mutations = ImmutableList.builder();
        }
      }

      // Flush any remaining mutations.
      ImmutableList remainingMutations = mutations.build();
      if (!remainingMutations.isEmpty()) {
        out.output(KV.of(key, remainingMutations));
      }
    }
  }

  /** Copies the content in {@code byteBuffer} into a {@link ByteString}. */
  protected static ByteString toByteString(ByteBuffer byteBuffer) {
    return ByteString.copyFrom(byteBuffer.array());
  }
}

Langkah berikutnya

• Pelajari template Dataflow.
• Lihat daftar template yang disediakan Google.