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The Bigtable to Cloud Storage Parquet template is a pipeline that reads data from
a Bigtable table and writes it to a Cloud Storage bucket in Parquet format.
You can use the template to move data from Bigtable to Cloud Storage.
Pipeline requirements
The Bigtable table must exist.
The output Cloud Storage bucket must exist before running the pipeline.
Template parameters
Required parameters
bigtableProjectId : The ID of the Google Cloud project that contains the Cloud Bigtable instance that you want to read data from.
bigtableInstanceId : The ID of the Cloud Bigtable instance that contains the table.
bigtableTableId : The ID of the Cloud Bigtable table to export.
outputDirectory : The path and filename prefix for writing output files. Must end with a slash. DateTime formatting is used to parse the directory path for date and time formatters. For example: gs://your-bucket/your-path.
filenamePrefix : The prefix of the Parquet file name. For example, "table1-". Defaults to: part.
Optional parameters
numShards : 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. The default value is decided by Dataflow.
the version name, like 2023-09-12-00_RC00, to use a specific version of the
template, which can be found nested in the respective dated parent folder in the bucket—
gs://dataflow-templates-REGION_NAME/
REGION_NAME:
the region where you want to
deploy your Dataflow job—for example, us-central1
BIGTABLE_PROJECT_ID: the ID of the Google Cloud project of the Bigtable instance that you want to read data from
INSTANCE_ID: the ID of the Bigtable instance that contains the table
TABLE_ID: the ID of the Bigtable table to export
OUTPUT_DIRECTORY: the Cloud Storage path where data is written, for example, gs://mybucket/somefolder
FILENAME_PREFIX: the prefix of the Parquet filename, for example, output-
NUM_SHARDS: the number of Parquet files to output, for example, 1
API
To run the template using the REST API, send an HTTP POST request. For more information on the
API and its authorization scopes, see
projects.templates.launch.
the version name, like 2023-09-12-00_RC00, to use a specific version of the
template, which can be found nested in the respective dated parent folder in the bucket—
gs://dataflow-templates-REGION_NAME/
LOCATION:
the region where you want to
deploy your Dataflow job—for example, us-central1
BIGTABLE_PROJECT_ID: the ID of the Google Cloud project of the Bigtable instance that you want to read data from
INSTANCE_ID: the ID of the Bigtable instance that contains the table
TABLE_ID: the ID of the Bigtable table to export
OUTPUT_DIRECTORY: the Cloud Storage path where data is written, for example, gs://mybucket/somefolder
FILENAME_PREFIX: the prefix of the Parquet filename, for example, output-
NUM_SHARDS: the number of Parquet files to output, for example, 1
Template source code
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.BigtableToAvro.toByteArray;
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.BigtableToParquet.Options;
import com.google.cloud.teleport.metadata.Template;
import com.google.cloud.teleport.metadata.TemplateCategory;
import com.google.cloud.teleport.metadata.TemplateParameter;
import java.nio.ByteBuffer;
import java.util.ArrayList;
import java.util.List;
import org.apache.avro.generic.GenericRecord;
import org.apache.avro.generic.GenericRecordBuilder;
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.coders.AvroCoder;
import org.apache.beam.sdk.io.FileIO;
import org.apache.beam.sdk.io.gcp.bigtable.BigtableIO;
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.ValueProvider;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.transforms.SimpleFunction;
import org.apache.beam.sdk.values.PCollection;
/**
* Dataflow pipeline that exports data from a Cloud Bigtable table to Parquet 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_Parquet.md">README</a>
* for instructions on how to use or modify this template.
*/
@Template(
name = "Cloud_Bigtable_to_GCS_Parquet",
category = TemplateCategory.BATCH,
displayName = "Cloud Bigtable to Parquet Files on Cloud Storage",
description =
"The Bigtable to Cloud Storage Parquet template is a pipeline that reads data from a Bigtable table and writes it to a Cloud Storage bucket in Parquet 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-parquet",
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 BigtableToParquet {
/** Options for the export pipeline. */
public interface Options extends PipelineOptions {
@TemplateParameter.ProjectId(
order = 1,
groupName = "Source",
description = "Project ID",
helpText =
"The ID of the Google Cloud project that contains 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,
groupName = "Source",
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,
groupName = "Source",
regexes = {"[_a-zA-Z0-9][-_.a-zA-Z0-9]*"},
description = "Table ID",
helpText = "The ID of the Cloud Bigtable table to export.")
ValueProvider<String> getBigtableTableId();
@SuppressWarnings("unused")
void setBigtableTableId(ValueProvider<String> tableId);
@TemplateParameter.GcsWriteFolder(
order = 4,
groupName = "Target",
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 the directory path for date and time formatters. For example: gs://your-bucket/your-path.")
ValueProvider<String> getOutputDirectory();
@SuppressWarnings("unused")
void setOutputDirectory(ValueProvider<String> outputDirectory);
@TemplateParameter.Text(
order = 5,
groupName = "Target",
description = "Parquet file prefix",
helpText =
"The prefix of the Parquet file name. For example, \"table1-\". Defaults to: part.")
@Default.String("part")
ValueProvider<String> getFilenamePrefix();
@SuppressWarnings("unused")
void setFilenamePrefix(ValueProvider<String> filenamePrefix);
@TemplateParameter.Integer(
order = 6,
groupName = "Target",
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. The default value is decided by Dataflow.")
@Default.Integer(0)
ValueProvider<Integer> getNumShards();
@SuppressWarnings("unused")
void setNumShards(ValueProvider<Integer> numShards);
@TemplateParameter.Text(
order = 7,
groupName = "Source",
optional = true,
regexes = {"[_a-zA-Z0-9][-_.a-zA-Z0-9]*"},
description = "Application profile ID",
helpText =
"The ID of the Bigtable application profile to use for the export. If you don't specify an app profile, Bigtable uses the instance's default app profile: https://cloud.google.com/bigtable/docs/app-profiles#default-app-profile.")
@Default.String("default")
ValueProvider<String> getBigtableAppProfileId();
@SuppressWarnings("unused")
void setBigtableAppProfileId(ValueProvider<String> appProfileId);
}
/**
* Main entry point for pipeline execution.
*
* @param args Command line 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();
}
}
/**
* Runs a pipeline to export data from a Cloud Bigtable table to Parquet file(s) in GCS.
*
* @param options arguments to the pipeline
*/
public static PipelineResult run(Options options) {
Pipeline pipeline = Pipeline.create(PipelineUtils.tweakPipelineOptions(options));
BigtableIO.Read read =
BigtableIO.read()
.withProjectId(options.getBigtableProjectId())
.withInstanceId(options.getBigtableInstanceId())
.withAppProfileId(options.getBigtableAppProfileId())
.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();
}
/**
* Steps: 1) Read records from Bigtable. 2) Convert a Bigtable Row to a GenericRecord. 3) Write
* GenericRecord(s) to GCS in parquet format.
*/
FileIO.Write<Void, GenericRecord> write =
FileIO.<GenericRecord>write()
.via(ParquetIO.sink(BigtableRow.getClassSchema()))
.to(options.getOutputDirectory())
.withPrefix(options.getFilenamePrefix())
.withSuffix(".parquet");
ValueProvider<Integer> numShardsOpt = options.getNumShards();
if (numShardsOpt.isAccessible()) {
Integer numShards = numShardsOpt.get();
if (numShards != null && numShards > 0) {
write = write.withNumShards(options.getNumShards());
}
}
pipeline
.apply("Read from Bigtable", read)
.apply("Transform to Parquet", MapElements.via(new BigtableToParquetFn()))
.setCoder(AvroCoder.of(GenericRecord.class, BigtableRow.getClassSchema()))
.apply("Write to Parquet in GCS", write);
return pipeline.run();
}
/**
* Translates a {@link PCollection} of Bigtable {@link Row} to a {@link PCollection} of {@link
* GenericRecord}.
*/
static class BigtableToParquetFn extends SimpleFunction<Row, GenericRecord> {
@Override
public GenericRecord 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 GenericRecordBuilder(BigtableRow.getClassSchema())
.set("key", key)
.set("cells", cells)
.build();
}
}
}