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Template Cloud Storage Avro ke Bigtable adalah pipeline yang membaca data dari file Avro dalam bucket Cloud Storage dan menulis data ke tabel Bigtable. Anda dapat menggunakan template ini untuk menyalin data dari Cloud Storage ke Bigtable.
Persyaratan pipeline
Tabel Bigtable harus ada dan memiliki kelompok kolom yang sama dengan yang diekspor dalam file Avro.
File Avro input harus ada di bucket Cloud Storage sebelum menjalankan pipeline.
Bigtable mengharapkan
skema spesifik dari file Avro input.
Parameter template
Parameter
Deskripsi
bigtableProjectId
ID project Google Cloud dari instance Bigtable yang ingin Anda jadikan tujuan penulisan data.
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*.
nama versi, seperti 2023-09-12-00_RC00, untuk menggunakan versi template
tertentu, yang dapat ditemukan bertingkat di folder induk bertanggal masing-masing dalam bucket—
gs://dataflow-templates-REGION_NAME/
REGION_NAME: region tempat Anda ingin men-deploy tugas Dataflow, misalnya us-central1
BIGTABLE_PROJECT_ID: ID project Google Cloud dari instance Bigtable yang datanya ingin Anda baca
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 HTTP POST. Untuk informasi selengkapnya tentang API dan cakupan otorisasinya, lihat projects.templates.launch.
nama versi, seperti 2023-09-12-00_RC00, untuk menggunakan versi template
tertentu, yang dapat ditemukan bertingkat di folder induk bertanggal masing-masing dalam bucket—
gs://dataflow-templates-REGION_NAME/
LOCATION: region tempat Anda ingin men-deploy tugas Dataflow, misalnya us-central1
BIGTABLE_PROJECT_ID: ID project Google Cloud dari instance Bigtable yang datanya ingin Anda baca
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,
description = "Project ID",
helpText =
"The ID of the Google Cloud project of the Cloud Bigtable instance that you want to write data to")
ValueProvider<String> getBigtableProjectId();
@SuppressWarnings("unused")
void setBigtableProjectId(ValueProvider<String> projectId);
@TemplateParameter.Text(
order = 2,
regexes = {"[a-z][a-z0-9\\-]+[a-z0-9]"},
description = "Instance ID",
helpText = "The ID of the Cloud Bigtable instance that contains the table")
ValueProvider<String> getBigtableInstanceId();
@SuppressWarnings("unused")
void setBigtableInstanceId(ValueProvider<String> instanceId);
@TemplateParameter.Text(
order = 4,
regexes = {"[_a-zA-Z0-9][-_.a-zA-Z0-9]*"},
description = "Table ID",
helpText = "The ID of the Cloud Bigtable table to write")
ValueProvider<String> getBigtableTableId();
@SuppressWarnings("unused")
void setBigtableTableId(ValueProvider<String> tableId);
@TemplateParameter.GcsReadFile(
order = 5,
description = "Input Cloud Storage File(s)",
helpText = "The Cloud Storage location of the files you'd like to process.",
example = "gs://your-bucket/your-files/*.avro")
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());
}
}
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2024-03-29 UTC."],[],[]]
