Cloud Storage Parquet to Bigtable 템플릿은 Cloud Storage 버킷의 Parquet 파일에서 데이터를 읽고 Bigtable 테이블에 데이터를 쓰는 파이프라인입니다. 템플릿을 사용하여 Cloud Storage에서 Bigtable로 데이터를 복사할 수 있습니다.
파이프라인 요구사항
Bigtable 테이블이 있어야 하며 Parquet 파일에서 내보낸 것과 같은 column family가 있어야 합니다.
파이프라인을 실행하기 전에 Cloud Storage 버킷에 입력 Parquet 파일이 있어야 합니다.
BIGTABLE_PROJECT_ID: 데이터를 읽으려는 Bigtable 인스턴스의 Google Cloud 프로젝트 ID
INSTANCE_ID: 테이블을 포함하는 Bigtable 인스턴스의 ID입니다.
TABLE_ID: 내보낼 Bigtable 테이블의 ID입니다.
INPUT_FILE_PATTERN: 데이터가 있는 Cloud Storage 경로 패턴입니다(예: gs://mybucket/somefolder/prefix*).
템플릿 소스 코드
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.bigtable;
import static com.google.cloud.teleport.bigtable.AvroToBigtable.toByteString;
import com.google.bigtable.v2.Mutation;
import com.google.cloud.teleport.bigtable.ParquetToBigtable.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.protobuf.ByteString;
import java.nio.ByteBuffer;
import java.util.List;
import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericRecord;
import org.apache.beam.runners.direct.DirectRunner;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.PipelineResult;
import org.apache.beam.sdk.io.gcp.bigtable.BigtableIO;
import org.apache.beam.sdk.io.parquet.ParquetIO;
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.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.MoreObjects;
import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* The {@link ParquetToBigtable} pipeline imports data from Parquet 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 Parquet 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_Parquet_to_Cloud_Bigtable.md">README</a>
* for instructions on how to use or modify this template.
*/
@Template(
name = "GCS_Parquet_to_Cloud_Bigtable",
category = TemplateCategory.BATCH,
displayName = "Parquet Files on Cloud Storage to Cloud Bigtable",
description =
"The Cloud Storage Parquet to Bigtable template is a pipeline that reads data from Parquet 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/parquet-to-bigtable",
contactInformation = "https://cloud.google.com/support",
requirements = {
"The Bigtable table must exist and have the same column families as exported in the Parquet files.",
"The input Parquet 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 Parquet files."
})
public class ParquetToBigtable {
private static final Logger LOG = LoggerFactory.getLogger(ParquetToBigtable.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 Google Cloud project ID associated with the Bigtable instance.")
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 Bigtable table to import.")
ValueProvider<String> getBigtableTableId();
@SuppressWarnings("unused")
void setBigtableTableId(ValueProvider<String> tableId);
@TemplateParameter.GcsReadFile(
order = 4,
description = "Input Cloud Storage File(s)",
helpText = "The Cloud Storage path with the files that contain the data.",
example = "gs://your-bucket/your-files/*.parquet")
ValueProvider<String> getInputFilePattern();
@SuppressWarnings("unused")
void setInputFilePattern(ValueProvider<String> inputFilePattern);
@TemplateParameter.Boolean(
order = 5,
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 Parquet 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);
}
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());
/**
* Steps: 1) Read records from Parquet File. 2) Convert a GenericRecord to a
* KV<ByteString,Iterable<Mutation>>. 3) Write KV to Bigtable's table.
*/
pipeline
.apply(
"Read from Parquet",
ParquetIO.read(BigtableRow.getClassSchema()).from(options.getInputFilePattern()))
.apply(
"Transform to Bigtable",
ParDo.of(
ParquetToBigtableFn.createWithSplitLargeRows(
options.getSplitLargeRows(), MAX_MUTATIONS_PER_ROW)))
.apply("Write to Bigtable", write);
return pipeline.run();
}
static class ParquetToBigtableFn extends DoFn<GenericRecord, KV<ByteString, Iterable<Mutation>>> {
private final ValueProvider<Boolean> splitLargeRowsFlag;
private Boolean splitLargeRows;
private final int maxMutationsPerRow;
public static ParquetToBigtableFn create() {
return new ParquetToBigtableFn(StaticValueProvider.of(false), MAX_MUTATIONS_PER_ROW);
}
public static ParquetToBigtableFn createWithSplitLargeRows(
ValueProvider<Boolean> splitLargeRowsFlag, int maxMutationsPerRequest) {
return new ParquetToBigtableFn(splitLargeRowsFlag, 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);
}
private ParquetToBigtableFn(
ValueProvider<Boolean> splitLargeRowsFlag, int maxMutationsPerRequest) {
this.splitLargeRowsFlag = splitLargeRowsFlag;
this.maxMutationsPerRow = maxMutationsPerRequest;
}
@ProcessElement
public void processElement(ProcessContext ctx) {
Class runner = ctx.getPipelineOptions().getRunner();
ByteString key = toByteString((ByteBuffer) ctx.element().get(0));
// 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();
List<Object> cells = (List) ctx.element().get(1);
int cellsProcessed = 0;
for (Object element : cells) {
Mutation.SetCell setCell = null;
if (runner.isAssignableFrom(DirectRunner.class)) {
setCell =
Mutation.SetCell.newBuilder()
.setFamilyName(((GenericData.Record) element).get(0).toString())
.setColumnQualifier(
toByteString((ByteBuffer) ((GenericData.Record) element).get(1)))
.setTimestampMicros((Long) ((GenericData.Record) element).get(2))
.setValue(toByteString((ByteBuffer) ((GenericData.Record) element).get(3)))
.build();
} else {
BigtableCell bigtableCell = (BigtableCell) element;
setCell =
Mutation.SetCell.newBuilder()
.setFamilyName(bigtableCell.getFamily().toString())
.setColumnQualifier(toByteString(bigtableCell.getQualifier()))
.setTimestampMicros(bigtableCell.getTimestamp())
.setValue(toByteString(bigtableCell.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.
ctx.output(KV.of(key, mutations.build()));
mutations = ImmutableList.builder();
}
}
// Flush any remaining mutations.
ImmutableList remainingMutations = mutations.build();
if (!remainingMutations.isEmpty()) {
ctx.output(KV.of(key, remainingMutations));
}
}
}
}
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","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"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2024-05-21(UTC)"],[],[]]
