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O modelo Avro do Cloud Storage para Bigtable é um pipeline que lê dados de arquivos Avro em um bucket do Cloud Storage e grava os dados em uma tabela do Bigtable. É possível usar o modelo para copiar dados do Cloud Storage para o Bigtable.
Requisitos de pipeline
A tabela do Bigtable precisa existir e ter as mesmas famílias de colunas que foram exportadas nos arquivos Avro.
Os arquivos Avro de entrada precisam existir em um bucket do Cloud Storage antes de o pipeline ser executado.
O Bigtable espera um
esquema específico dos arquivos Avro de entrada.
Parâmetros do modelo
Parâmetro
Descrição
bigtableProjectId
O ID do projeto do Google Cloud da instância do Bigtable em que você quer gravar os dados.
bigtableInstanceId
O ID da instância do Bigtable que contém a tabela.
bigtableTableId
O ID da tabela do Bigtable a ser importada.
inputFilePattern
O padrão de caminho do Cloud Storage em que os dados estão localizados. Por exemplo, gs://mybucket/somefolder/prefix*.
Executar o modelo
Console
Acesse a página Criar job usando um modelo do Dataflow.
o nome da versão, como 2023-09-12-00_RC00, para usar uma versão específica do
modelo, que pode ser aninhada na respectiva pasta mãe datada no bucket:
gs://dataflow-templates-REGION_NAME/
REGION_NAME:
a região em que você quer
implantar o job do Dataflow, por exemplo, us-central1
BIGTABLE_PROJECT_ID: o ID do projeto do Google Cloud da instância do Bigtable da qual você quer ler os dados.
INSTANCE_ID: o ID da instância do Bigtable que contém a tabela.
TABLE_ID: o ID da tabela do Cloud Bigtable a ser exportada.
INPUT_FILE_PATTERN: o padrão de caminho do Cloud Storage em que os dados estão localizados, por exemplo, gs://mybucket/somefolder/prefix*
API
Para executar o modelo usando a API REST, envie uma solicitação HTTP POST. Para mais informações sobre a
API e os respectivos escopos de autorização, consulte
projects.templates.launch.
o nome da versão, como 2023-09-12-00_RC00, para usar uma versão específica do
modelo, que pode ser aninhada na respectiva pasta mãe datada no bucket:
gs://dataflow-templates-REGION_NAME/
LOCATION:
a região em que você quer
implantar o job do Dataflow, por exemplo, us-central1
BIGTABLE_PROJECT_ID: o ID do projeto do Google Cloud da instância do Bigtable da qual você quer ler os dados.
INSTANCE_ID: o ID da instância do Bigtable que contém a tabela.
TABLE_ID: o ID da tabela do Cloud Bigtable a ser exportada.
INPUT_FILE_PATTERN: o padrão de caminho do Cloud Storage em que os dados estão localizados, por exemplo, gs://mybucket/somefolder/prefix*
Código-fonte do modelo
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 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,
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,
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,
description = "Input Cloud Storage File(s)",
helpText = "The Cloud Storage path pattern where data is located.",
example = "gs://<BUCKET>/<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());
}
}
