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Il modello Avro da Bigtable a Cloud Storage è una pipeline che legge i dati da una tabella Bigtable e li scrive in un bucket Cloud Storage in formato Avro.
Puoi utilizzare il modello per spostare i dati da Bigtable a Cloud Storage.
Requisiti della pipeline
La tabella Bigtable deve esistere.
Il bucket Cloud Storage di output deve esistere prima di eseguire la pipeline.
Parametri del modello
Parametro
Descrizione
bigtableProjectId
L'ID del progetto Google Cloud dell'istanza Bigtable da cui vuoi leggere i dati.
bigtableInstanceId
L'ID dell'istanza Bigtable che contiene la tabella.
bigtableTableId
L'ID della tabella Bigtable da esportare.
outputDirectory
Il percorso di Cloud Storage in cui vengono scritti i dati. Ad esempio, gs://mybucket/somefolder.
filenamePrefix
Il prefisso del nome file Avro. Ad esempio, output-.
il nome della versione, come 2023-09-12-00_RC00, per utilizzare una versione specifica del modello, che si trova nidificata nella rispettiva cartella padre con data all'interno del bucket:
gs://dataflow-templates-REGION_NAME/
REGION_NAME: la regione in cui vuoi eseguire il deployment del job Dataflow, ad esempio us-central1
BIGTABLE_PROJECT_ID: l'ID del progetto Google Cloud dell'istanza Bigtable da cui vuoi leggere i dati
INSTANCE_ID: l'ID dell'istanza Bigtable che contiene la tabella
TABLE_ID: l'ID della tabella Bigtable da esportare
OUTPUT_DIRECTORY: il percorso di Cloud Storage in cui vengono scritti i dati, ad esempio gs://mybucket/somefolder
FILENAME_PREFIX: il prefisso del nome file Avro, ad esempio output-
API
Per eseguire il modello utilizzando l'API REST, invia una richiesta POST HTTP. Per maggiori informazioni sull'API e sui relativi ambiti di autorizzazione, consulta projects.templates.launch.
il nome della versione, come 2023-09-12-00_RC00, per utilizzare una versione specifica del modello, che si trova nidificata nella rispettiva cartella padre con data all'interno del bucket:
gs://dataflow-templates-REGION_NAME/
LOCATION: la regione in cui vuoi eseguire il deployment del job Dataflow, ad esempio us-central1
BIGTABLE_PROJECT_ID: l'ID del progetto Google Cloud dell'istanza Bigtable da cui vuoi leggere i dati
INSTANCE_ID: l'ID dell'istanza Bigtable che contiene la tabella
TABLE_ID: l'ID della tabella Bigtable da esportare
OUTPUT_DIRECTORY: il percorso di Cloud Storage in cui vengono scritti i dati, ad esempio gs://mybucket/somefolder
FILENAME_PREFIX: il prefisso del nome file Avro, ad esempio output-
Codice sorgente del modello
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();
}
}
}
