Cloud Storage Parquet to Bigtable template

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.

Pipeline 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 schema from the input Parquet files.

Template parameters

Required parameters

• bigtableProjectId: The Google Cloud project ID associated with the Bigtable instance.
• bigtableInstanceId: The ID of the Cloud Bigtable instance that contains the table.
• bigtableTableId: The ID of the Bigtable table to import.
• inputFilePattern: The Cloud Storage path with the files that contain the data. For example, gs://your-bucket/your-files/*.parquet.

Optional parameters

• splitLargeRows: 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.

Run the template

Console

1. Go to the Dataflow Create job from template page.
Go to Create job from template
2. In the Job name field, enter a unique job name.
3. Optional: For Regional endpoint, select a value from the drop-down menu. The default region is us-central1.

   For a list of regions where you can run a Dataflow job, see Dataflow locations.

4. From the Dataflow template drop-down menu, select the Parquet Files on Cloud Storage to Cloud Bigtable template.
5. In the provided parameter fields, enter your parameter values.
6. Click Run job.

gcloud

Note: To use the Google Cloud CLI to run classic templates, you must have Google Cloud CLI version 138.0.0 or later.

In your shell or terminal, run the template:

gcloud dataflow jobs run JOB_NAME \
    --gcs-location gs://dataflow-templates-REGION_NAME/VERSION/GCS_Parquet_to_Cloud_Bigtable \
    --region REGION_NAME \
    --parameters \
bigtableProjectId=BIGTABLE_PROJECT_ID,\
bigtableInstanceId=INSTANCE_ID,\
bigtableTableId=TABLE_ID,\
inputFilePattern=INPUT_FILE_PATTERN

Replace the following:

• JOB_NAME: a unique job name of your choice
• VERSION: the version of the template that you want to use

  You can use the following values:

  • latest to use the latest version of the template, which is available in the non-dated parent folder in the bucket— gs://dataflow-templates-REGION_NAME/latest/
  • 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/
  Caution: The latest version of templates might update with breaking changes. Your production environments should use templates kept in the most recent dated parent folder to prevent these breaking changes from affecting your production workflows.
• 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
• INPUT_FILE_PATTERN: the Cloud Storage path pattern where data is located, for example, gs://mybucket/somefolder/prefix*

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.

POST https://dataflow.googleapis.com/v1b3/projects/PROJECT_ID/locations/LOCATION/templates:launch?gcsPath=gs://dataflow-templates-LOCATION/VERSION/GCS_Parquet_to_Cloud_Bigtable
{
   "jobName": "JOB_NAME",
   "parameters": {
       "bigtableProjectId": "BIGTABLE_PROJECT_ID",
       "bigtableInstanceId": "INSTANCE_ID",
       "bigtableTableId": "TABLE_ID",
       "inputFilePattern": "INPUT_FILE_PATTERN",
   },
   "environment": { "zone": "us-central1-f" }
}

Replace the following:

• PROJECT_ID: the Google Cloud project ID where you want to run the Dataflow job
• JOB_NAME: a unique job name of your choice
• VERSION: the version of the template that you want to use

  You can use the following values:

  • latest to use the latest version of the template, which is available in the non-dated parent folder in the bucket— gs://dataflow-templates-REGION_NAME/latest/
  • 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/
  Caution: The latest version of templates might update with breaking changes. Your production environments should use templates kept in the most recent dated parent folder to prevent these breaking changes from affecting your production workflows.
• 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
• INPUT_FILE_PATTERN: the Cloud Storage path pattern where data is located, for example, gs://mybucket/somefolder/prefix*

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.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,
        groupName = "Target",
        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,
        groupName = "Target",
        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 = "Target",
        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,
        groupName = "Source",
        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,
        groupName = "Target",
        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));
      }
    }
  }
}

What's next

• Learn about Dataflow templates.
• See the list of Google-provided templates.