Template BigQuery Export ke Parquet (melalui Storage API)
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Template BigQuery Export ke Parquet adalah pipeline batch yang membaca data dari tabel BigQuery dan menulisnya ke bucket Cloud Storage dalam format Parquet.
Template ini menggunakan BigQuery Storage API untuk mengekspor data.
Persyaratan pipeline
Tabel BigQuery input harus ada sebelum menjalankan pipeline.
Bucket Cloud Storage output harus ada sebelum menjalankan pipeline.
Parameter template
Parameter
Deskripsi
tableRef
Lokasi tabel input BigQuery. Misalnya, <my-project>:<my-dataset>.<my-table>.
bucket
Folder Cloud Storage tempat menulis file Parquet. Misalnya, gs://mybucket/exports.
numShards
(Opsional) Jumlah shard file output. Nilai default adalah 1.
fields
(Opsional) Daftar kolom yang dipisahkan koma yang dapat dipilih dari tabel BigQuery input.
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
BIGQUERY_TABLE: nama tabel BigQuery Anda
OUTPUT_DIRECTORY: folder Cloud Storage untuk file output
NUM_SHARDS: jumlah shard file output yang diinginkan
FIELDS: daftar kolom yang dipisahkan koma untuk dipilih dari tabel BigQuery input
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
BIGQUERY_TABLE: nama tabel BigQuery Anda
OUTPUT_DIRECTORY: folder Cloud Storage untuk file output
NUM_SHARDS: jumlah shard file output yang diinginkan
FIELDS: daftar kolom yang dipisahkan koma untuk dipilih dari tabel BigQuery input
Kode sumber template
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",
helpText = "BigQuery table location to export in the format <project>:<dataset>.<table>.",
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)",
helpText = "Path and filename prefix for writing output files.",
example = "gs://your-bucket/export/")
@Required
String getBucket();
void setBucket(String bucket);
@TemplateParameter.Integer(
order = 3,
optional = true,
description = "Maximum output shards",
helpText =
"The maximum number of output shards produced when writing. A higher number of shards"
+ " means higher throughput for writing to Cloud Storage, but potentially higher"
+ " data aggregation cost across shards when processing output Cloud Storage"
+ " files.")
@Default.Integer(0)
Integer getNumShards();
void setNumShards(Integer numShards);
@TemplateParameter.Text(
order = 4,
optional = true,
description = "List of field names",
helpText = "Comma separated list of fields to select from the 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();
}
}
[[["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."],[],[]]
