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Template Bigtable ke Cloud Storage Avro adalah pipeline yang membaca data dari tabel Bigtable dan menulisnya ke bucket Cloud Storage dalam format Avro.
Anda dapat menggunakan template untuk memindahkan data dari Bigtable ke Cloud Storage.
Persyaratan pipeline
Tabel Bigtable harus ada.
Bucket Cloud Storage output harus ada sebelum menjalankan pipeline.
Parameter template
Parameter
Deskripsi
bigtableProjectId
ID project Google Cloud dari instance Bigtable yang datanya ingin Anda baca.
bigtableInstanceId
ID instance Bigtable yang berisi tabel.
bigtableTableId
ID tabel Bigtable yang akan diekspor.
outputDirectory
Jalur Cloud Storage tempat data ditulis. Misalnya, gs://mybucket/somefolder.
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
OUTPUT_DIRECTORY: jalur Cloud Storage tempat data ditulis, misalnya, gs://mybucket/somefolder
FILENAME_PREFIX: awalan nama file Avro, misalnya, output-
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
OUTPUT_DIRECTORY: jalur Cloud Storage tempat data ditulis, misalnya, gs://mybucket/somefolder
FILENAME_PREFIX: awalan nama file Avro, misalnya, output-
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.Cell;
import com.google.bigtable.v2.Column;
import com.google.bigtable.v2.Family;
import com.google.bigtable.v2.Row;
import com.google.cloud.teleport.bigtable.BigtableToAvro.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.cloud.teleport.util.DualInputNestedValueProvider;
import com.google.cloud.teleport.util.DualInputNestedValueProvider.TranslatorInput;
import com.google.protobuf.ByteOutput;
import com.google.protobuf.ByteString;
import com.google.protobuf.UnsafeByteOperations;
import java.io.IOException;
import java.nio.ByteBuffer;
import java.util.ArrayList;
import java.util.List;
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.FileSystems;
import org.apache.beam.sdk.io.fs.ResolveOptions.StandardResolveOptions;
import org.apache.beam.sdk.io.gcp.bigtable.BigtableIO;
import org.apache.beam.sdk.options.Default;
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.transforms.MapElements;
import org.apache.beam.sdk.transforms.SerializableFunction;
import org.apache.beam.sdk.transforms.SimpleFunction;
/**
* Dataflow pipeline that exports data from a Cloud Bigtable table to Avro files in GCS. Currently,
* filtering on Cloud Bigtable table is not supported.
*
* <p>Check out <a
* href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v1/README_Cloud_Bigtable_to_GCS_Avro.md">README</a>
* for instructions on how to use or modify this template.
*/
@Template(
name = "Cloud_Bigtable_to_GCS_Avro",
category = TemplateCategory.BATCH,
displayName = "Cloud Bigtable to Avro Files in Cloud Storage",
description =
"The Bigtable to Cloud Storage Avro template is a pipeline that reads data from a Bigtable table and writes it to a Cloud Storage bucket in Avro format. "
+ "You can use the template to move data from Bigtable to Cloud Storage.",
optionsClass = Options.class,
documentation =
"https://cloud.google.com/dataflow/docs/guides/templates/provided/bigtable-to-avro",
contactInformation = "https://cloud.google.com/support",
requirements = {
"The Bigtable table must exist.",
"The output Cloud Storage bucket must exist before running the pipeline."
})
public class BigtableToAvro {
/** Options for the export 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"
+ " read data from")
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 = 3,
regexes = {"[_a-zA-Z0-9][-_.a-zA-Z0-9]*"},
description = "Table ID",
helpText = "The ID of the Cloud Bigtable table to read")
ValueProvider<String> getBigtableTableId();
@SuppressWarnings("unused")
void setBigtableTableId(ValueProvider<String> tableId);
@TemplateParameter.GcsWriteFolder(
order = 4,
description = "Output file directory in Cloud Storage",
helpText =
"The path and filename prefix for writing output files. Must end with a slash. DateTime"
+ " formatting is used to parse directory path for date & time formatters.",
example = "gs://your-bucket/your-path")
ValueProvider<String> getOutputDirectory();
@SuppressWarnings("unused")
void setOutputDirectory(ValueProvider<String> outputDirectory);
@TemplateParameter.Text(
order = 5,
description = "Avro file prefix",
helpText = "The prefix of the Avro file name. For example, \"table1-\"")
@Default.String("part")
ValueProvider<String> getFilenamePrefix();
@SuppressWarnings("unused")
void setFilenamePrefix(ValueProvider<String> filenamePrefix);
}
/**
* Runs a pipeline to export data from a Cloud Bigtable table to Avro files in GCS.
*
* @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.Read read =
BigtableIO.read()
.withProjectId(options.getBigtableProjectId())
.withInstanceId(options.getBigtableInstanceId())
.withTableId(options.getBigtableTableId());
// Do not validate input fields if it is running as a template.
if (options.as(DataflowPipelineOptions.class).getTemplateLocation() != null) {
read = read.withoutValidation();
}
ValueProvider<String> filePathPrefix =
DualInputNestedValueProvider.of(
options.getOutputDirectory(),
options.getFilenamePrefix(),
new SerializableFunction<TranslatorInput<String, String>, String>() {
@Override
public String apply(TranslatorInput<String, String> input) {
return FileSystems.matchNewResource(input.getX(), true)
.resolve(input.getY(), StandardResolveOptions.RESOLVE_FILE)
.toString();
}
});
pipeline
.apply("Read from Bigtable", read)
.apply("Transform to Avro", MapElements.via(new BigtableToAvroFn()))
.apply(
"Write to Avro in GCS",
AvroIO.write(BigtableRow.class).to(filePathPrefix).withSuffix(".avro"));
return pipeline.run();
}
/** Translates Bigtable {@link Row} to Avro {@link BigtableRow}. */
static class BigtableToAvroFn extends SimpleFunction<Row, BigtableRow> {
@Override
public BigtableRow apply(Row row) {
ByteBuffer key = ByteBuffer.wrap(toByteArray(row.getKey()));
List<BigtableCell> cells = new ArrayList<>();
for (Family family : row.getFamiliesList()) {
String familyName = family.getName();
for (Column column : family.getColumnsList()) {
ByteBuffer qualifier = ByteBuffer.wrap(toByteArray(column.getQualifier()));
for (Cell cell : column.getCellsList()) {
long timestamp = cell.getTimestampMicros();
ByteBuffer value = ByteBuffer.wrap(toByteArray(cell.getValue()));
cells.add(new BigtableCell(familyName, qualifier, timestamp, value));
}
}
}
return new BigtableRow(key, cells);
}
}
/**
* Extracts the byte array from the given {@link ByteString} without copy.
*
* @param byteString A {@link ByteString} from which to extract the array.
* @return an array of byte.
*/
protected static byte[] toByteArray(final ByteString byteString) {
try {
ZeroCopyByteOutput byteOutput = new ZeroCopyByteOutput();
UnsafeByteOperations.unsafeWriteTo(byteString, byteOutput);
return byteOutput.bytes;
} catch (IOException e) {
return byteString.toByteArray();
}
}
private static final class ZeroCopyByteOutput extends ByteOutput {
private byte[] bytes;
@Override
public void writeLazy(byte[] value, int offset, int length) {
if (offset != 0 || length != value.length) {
throw new UnsupportedOperationException();
}
bytes = value;
}
@Override
public void write(byte value) {
throw new UnsupportedOperationException();
}
@Override
public void write(byte[] value, int offset, int length) {
throw new UnsupportedOperationException();
}
@Override
public void write(ByteBuffer value) {
throw new UnsupportedOperationException();
}
@Override
public void writeLazy(ByteBuffer value) {
throw new UnsupportedOperationException();
}
}
}
[[["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."],[],[]]
