Modèle BigQuery Export vers Parquet (via l'API Storage)
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L'export de BigQuery vers Parquet est un pipeline par lots qui lit les données à partir d'une table BigQuery et les écrit dans un bucket Cloud Storage au format Parquet.
Ce modèle utilise l'API BigQuery Storage pour exporter les données.
Conditions requises pour ce pipeline
La table d'entrée BigQuery doit exister avant l'exécution du pipeline.
Le bucket Cloud Storage de sortie doit exister avant l'exécution du pipeline.
Paramètres de modèle
Paramètres obligatoires
tableRef : emplacement de la table d'entrée BigQuery (par exemple, your-project:your-dataset.your-table-name).
bucket : dossier Cloud Storage dans lequel écrire les fichiers Parquet (par exemple, gs://your-bucket/export/).
Paramètres facultatifs
numShards : nombre de segments de fichiers de sortie. La valeur par défaut est de 1.
fields : liste des champs à sélectionner dans la table BigQuery d'entrée, séparés par une virgule.
Le nom de la version, par exemple 2023-09-12-00_RC00, pour utiliser une version spécifique du modèle, qui est imbriqué dans le dossier parent daté respectif dans le bucket : gs://dataflow-templates-REGION_NAME/
REGION_NAME : région dans laquelle vous souhaitez déployer votre job Dataflow, par exemple us-central1
BIGQUERY_TABLE : nom de votre table BigQuery.
OUTPUT_DIRECTORY : dossier Cloud Storage pour les fichiers de sortie
NUM_SHARDS : nombre de segments de fichiers de sortie souhaité
FIELDS : liste des champs à sélectionner dans la table BigQuery d'entrée, séparés par une virgule
API
Pour exécuter le modèle à l'aide de l'API REST, envoyez une requête HTTP POST. Pour en savoir plus sur l'API, ses autorisations et leurs champs d'application, consultez la section projects.templates.launch.
Le nom de la version, par exemple 2023-09-12-00_RC00, pour utiliser une version spécifique du modèle, qui est imbriqué dans le dossier parent daté respectif dans le bucket : gs://dataflow-templates-REGION_NAME/
LOCATION : région dans laquelle vous souhaitez déployer votre job Dataflow, par exemple us-central1
BIGQUERY_TABLE : nom de votre table BigQuery.
OUTPUT_DIRECTORY : dossier Cloud Storage pour les fichiers de sortie
NUM_SHARDS : nombre de segments de fichiers de sortie souhaité
FIELDS : liste des champs à sélectionner dans la table BigQuery d'entrée, séparés par une virgule
Code source du modèle
Java
/*
* Copyright (C) 2019 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.v2.templates;
import com.google.api.gax.rpc.InvalidArgumentException;
import com.google.api.services.bigquery.model.TableReference;
import com.google.cloud.bigquery.storage.v1beta1.BigQueryStorageClient;
import com.google.cloud.bigquery.storage.v1beta1.ReadOptions.TableReadOptions;
import com.google.cloud.bigquery.storage.v1beta1.Storage.CreateReadSessionRequest;
import com.google.cloud.bigquery.storage.v1beta1.Storage.ReadSession;
import com.google.cloud.bigquery.storage.v1beta1.TableReferenceProto;
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.v2.common.UncaughtExceptionLogger;
import com.google.cloud.teleport.v2.templates.BigQueryToParquet.BigQueryToParquetOptions;
import com.google.common.base.Splitter;
import com.google.common.base.Strings;
import java.io.IOException;
import java.util.Arrays;
import java.util.List;
import org.apache.avro.Schema;
import org.apache.avro.generic.GenericRecord;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.PipelineResult;
import org.apache.beam.sdk.extensions.avro.coders.AvroCoder;
import org.apache.beam.sdk.io.FileIO;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryHelpers;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.TypedRead;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.TypedRead.Method;
import org.apache.beam.sdk.io.gcp.bigquery.SchemaAndRecord;
import org.apache.beam.sdk.io.parquet.ParquetIO;
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.Validation.Required;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* The {@link BigQueryToParquet} pipeline exports data from a BigQuery table to Parquet file(s) in a
* Google Cloud Storage bucket.
*
* <p>Check out <a
* href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v2/bigquery-to-parquet/README_BigQuery_to_Parquet.md">README</a>
* for instructions on how to use or modify this template.
*/
@Template(
name = "BigQuery_to_Parquet",
category = TemplateCategory.BATCH,
displayName = "BigQuery export to Parquet (via Storage API)",
description =
"The BigQuery export to Parquet template is a batch pipeline that reads data from a BigQuery table and writes it to a Cloud Storage bucket in Parquet format. "
+ "This template utilizes the <a href=\"https://cloud.google.com/bigquery/docs/reference/storage\">BigQuery Storage API</a> to export the data.",
optionsClass = BigQueryToParquetOptions.class,
flexContainerName = "bigquery-to-parquet",
documentation =
"https://cloud.google.com/dataflow/docs/guides/templates/provided/bigquery-to-parquet",
contactInformation = "https://cloud.google.com/support",
requirements = {
"The input BigQuery table must exist before running the pipeline.",
"The output Cloud Storage bucket must exist before running the pipeline."
})
public class BigQueryToParquet {
/* Logger for class. */
private static final Logger LOG = LoggerFactory.getLogger(BigQueryToParquet.class);
/** File suffix for file to be written. */
private static final String FILE_SUFFIX = ".parquet";
/** Factory to create BigQueryStorageClients. */
static class BigQueryStorageClientFactory {
/**
* Creates BigQueryStorage client for use in extracting table schema.
*
* @return BigQueryStorageClient
*/
static BigQueryStorageClient create() {
try {
return BigQueryStorageClient.create();
} catch (IOException e) {
LOG.error("Error connecting to BigQueryStorage API: " + e.getMessage());
throw new RuntimeException(e);
}
}
}
/** Factory to create ReadSessions. */
static class ReadSessionFactory {
/**
* Creates ReadSession for schema extraction.
*
* @param client BigQueryStorage client used to create ReadSession.
* @param tableString String that represents table to export from.
* @param tableReadOptions TableReadOptions that specify any fields in the table to filter on.
* @return session ReadSession object that contains the schema for the export.
*/
static ReadSession create(
BigQueryStorageClient client, String tableString, TableReadOptions tableReadOptions) {
TableReference tableReference = BigQueryHelpers.parseTableSpec(tableString);
String parentProjectId = "projects/" + tableReference.getProjectId();
TableReferenceProto.TableReference storageTableRef =
TableReferenceProto.TableReference.newBuilder()
.setProjectId(tableReference.getProjectId())
.setDatasetId(tableReference.getDatasetId())
.setTableId(tableReference.getTableId())
.build();
CreateReadSessionRequest.Builder builder =
CreateReadSessionRequest.newBuilder()
.setParent(parentProjectId)
.setReadOptions(tableReadOptions)
.setTableReference(storageTableRef);
try {
return client.createReadSession(builder.build());
} catch (InvalidArgumentException iae) {
LOG.error("Error creating ReadSession: " + iae.getMessage());
throw new RuntimeException(iae);
}
}
}
/**
* The {@link BigQueryToParquetOptions} class provides the custom execution options passed by the
* executor at the command-line.
*/
public interface BigQueryToParquetOptions extends PipelineOptions {
@TemplateParameter.BigQueryTable(
order = 1,
description = "BigQuery table to export",
groupName = "Source",
helpText = "The BigQuery input table location.",
example = "your-project:your-dataset.your-table-name")
@Required
String getTableRef();
void setTableRef(String tableRef);
@TemplateParameter.GcsWriteFile(
order = 2,
description = "Output Cloud Storage file(s)",
groupName = "Target",
helpText = "The Cloud Storage folder to write the Parquet files to.",
example = "gs://your-bucket/export/")
@Required
String getBucket();
void setBucket(String bucket);
@TemplateParameter.Integer(
order = 3,
optional = true,
description = "Maximum output shards",
helpText = "The number of output file shards. The default value is 1.")
@Default.Integer(0)
Integer getNumShards();
void setNumShards(Integer numShards);
@TemplateParameter.Text(
order = 4,
optional = true,
description = "List of field names",
helpText = "A comma-separated list of fields to select from the input BigQuery table.")
String getFields();
void setFields(String fields);
@TemplateParameter.Text(
order = 5,
optional = true,
description = "Row restrictions/filter.",
helpText =
"Read only rows which match the specified filter, which must be a SQL expression"
+ " compatible with Google standard SQL"
+ " (https://cloud.google.com/bigquery/docs/reference/standard-sql). If no value is"
+ " specified, then all rows are returned.")
String getRowRestriction();
void setRowRestriction(String restriction);
}
/**
* The {@link BigQueryToParquet#getTableSchema(ReadSession)} method gets Avro schema for table
* using from the {@link ReadSession} object.
*
* @param session ReadSession that contains schema for table, filtered by fields if any.
* @return avroSchema Avro schema for table. If fields are provided then schema will only contain
* those fields.
*/
private static Schema getTableSchema(ReadSession session) {
Schema avroSchema;
avroSchema = new Schema.Parser().parse(session.getAvroSchema().getSchema());
LOG.info("Schema for export is: " + avroSchema.toString());
return avroSchema;
}
/**
* Main entry point for pipeline execution.
*
* @param args Command line arguments to the pipeline.
*/
public static void main(String[] args) {
UncaughtExceptionLogger.register();
BigQueryToParquetOptions options =
PipelineOptionsFactory.fromArgs(args).withValidation().as(BigQueryToParquetOptions.class);
run(options);
}
/**
* Runs the pipeline with the supplied options.
*
* @param options The execution parameters to the pipeline.
* @return The result of the pipeline execution.
*/
private static PipelineResult run(BigQueryToParquetOptions options) {
// Create the pipeline.
Pipeline pipeline = Pipeline.create(options);
TableReadOptions.Builder builder = TableReadOptions.newBuilder();
/* Add fields to filter export on, if any. */
if (options.getFields() != null) {
builder.addAllSelectedFields(Arrays.asList(options.getFields().split(",\\s*")));
}
TableReadOptions tableReadOptions = builder.build();
BigQueryStorageClient client = BigQueryStorageClientFactory.create();
ReadSession session =
ReadSessionFactory.create(client, options.getTableRef(), tableReadOptions);
// Extract schema from ReadSession
Schema schema = getTableSchema(session);
client.close();
TypedRead<GenericRecord> readFromBQ =
BigQueryIO.read(SchemaAndRecord::getRecord)
.from(options.getTableRef())
.withTemplateCompatibility()
.withMethod(Method.DIRECT_READ)
.withCoder(AvroCoder.of(schema));
if (options.getFields() != null) {
List<String> selectedFields = Splitter.on(",").splitToList(options.getFields());
readFromBQ =
selectedFields.isEmpty() ? readFromBQ : readFromBQ.withSelectedFields(selectedFields);
}
// Add row restrictions/filter if any.
if (!Strings.isNullOrEmpty(options.getRowRestriction())) {
readFromBQ = readFromBQ.withRowRestriction(options.getRowRestriction());
}
/*
* Steps: 1) Read records from BigQuery via BigQueryIO.
* 2) Write records to Google Cloud Storage in Parquet format.
*/
pipeline
/*
* Step 1: Read records via BigQueryIO using supplied schema as a PCollection of
* {@link GenericRecord}.
*/
.apply("ReadFromBigQuery", readFromBQ)
/*
* Step 2: Write records to Google Cloud Storage as one or more Parquet files
* via {@link ParquetIO}.
*/
.apply(
"WriteToParquet",
FileIO.<GenericRecord>write()
.via(ParquetIO.sink(schema))
.to(options.getBucket())
.withNumShards(options.getNumShards())
.withSuffix(FILE_SUFFIX));
// Execute the pipeline and return the result.
return pipeline.run();
}
}
Sauf indication contraire, le contenu de cette page est régi par une licence Creative Commons Attribution 4.0, et les échantillons de code sont régis par une licence Apache 2.0. Pour en savoir plus, consultez les Règles du site Google Developers. Java est une marque déposée d'Oracle et/ou de ses sociétés affiliées.
Dernière mise à jour le 2024/08/01 (UTC).
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