BigQuery to Bigtable template

The BigQuery to Bigtable template is a batch pipeline that copies data from a BigQuery table into an existing Bigtable table. The template can either read the entire table or read specific records using a supplied query.

Pipeline requirements

• The source BigQuery table must exist.
• The Bigtable table must exist.
• The worker service account needs the roles/bigquery.datasets.create permission. For more information, see Introduction to IAM.

Template parameters

Required parameters

• readIdColumn : The name of the BigQuery column storing the unique identifier of the row.
• bigtableWriteInstanceId : The ID of the Bigtable instance that contains the table.
• bigtableWriteTableId : The ID of the Bigtable table to write to.
• bigtableWriteColumnFamily : The name of the column family of the Bigtable table to write data into.

Optional parameters

• inputTableSpec : The BigQuery table to read from. Format: projectId:datasetId.tablename. If you specify inputTableSpec, the template reads the data directly from BigQuery storage by using the BigQuery Storage Read API (https://cloud.google.com/bigquery/docs/reference/storage). For information about limitations in the Storage Read API, see https://cloud.google.com/bigquery/docs/reference/storage#limitations. You must specify either inputTableSpec or query. If you set both parameters, the template uses the query parameter. (Example: bigquery-project:dataset.input_table).
• outputDeadletterTable : The BigQuery table for messages that failed to reach the output table, in the format <PROJECT_ID>:<DATASET_NAME>.<DEADLETTER_TABLE>. If a table doesn't exist, is is created during pipeline execution. If not specified, <outputTableSpec>_error_records is used. (Example: your-project-id:your-dataset.your-table-name).
• query : The SQL query to use to read data from BigQuery. If the BigQuery dataset is in a different project than the Dataflow job, specify the full dataset name in the SQL query, for example: <PROJECT_ID>.<DATASET_NAME>.<TABLE_NAME>. By default, the query parameter uses GoogleSQL (https://cloud.google.com/bigquery/docs/introduction-sql), unless useLegacySql is true. You must specify either inputTableSpec or query. If you set both parameters, the template uses the query parameter. (Example: select * from sampledb.sample_table).
• useLegacySql : Set to true to use legacy SQL. This parameter only applies when using the query parameter. Defaults to: false.
• queryLocation : Needed when reading from an authorized view without underlying table's permission. (Example: US).
• queryTempDataset : With this option, you can set an existing dataset to create the temporary table to store the results of the query. (Example: temp_dataset).
• bigtableRpcAttemptTimeoutMs : The timeout for each Bigtable RPC attempt in milliseconds.
• bigtableRpcTimeoutMs : The total timeout for a Bigtable RPC operation in milliseconds.
• bigtableAdditionalRetryCodes : The additional retry codes. (Example: RESOURCE_EXHAUSTED,DEADLINE_EXCEEDED).
• bigtableWriteAppProfile : The ID of the Bigtable application profile to use for the export. If you do not specify an app profile, Bigtable uses the default app profile (https://cloud.google.com/bigtable/docs/app-profiles#default-app-profile) of the instance.
• bigtableWriteProjectId : The ID of the Google Cloud project that contains the Bigtable instanceto write data to.
• bigtableBulkWriteLatencyTargetMs : The latency target of Bigtable in milliseconds for latency-based throttling.
• bigtableBulkWriteMaxRowKeyCount : The maximum number of row keys in a Bigtable batch write operation.
• bigtableBulkWriteMaxRequestSizeBytes : The maximum bytes to include per Bigtable batch write operation.

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 BigQuery to 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 flex templates, you must have Google Cloud CLI version 284.0.0 or later.

In your shell or terminal, run the template:

gcloud dataflow flex-template run JOB_NAME \
    --project=PROJECT_ID \
    --region=REGION_NAME \
    --template-file-gcs-location=gs://dataflow-templates-REGION_NAME/VERSION/flex/BigQuery_to_Bigtable \
    --parameters \
readIdColumn=READ_COLUMN_ID,\
inputTableSpec=INPUT_TABLE_SPEC,\
bigtableWriteInstanceId=BIGTABLE_INSTANCE_ID,\
bigtableWriteTableId=BIGTABLE_TABLE_ID,\
bigtableWriteColumnFamily=BIGTABLE_COLUMN_FAMILY

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
• REGION_NAME: the region where you want to deploy your Dataflow job—for example, us-central1
• 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.
• READ_COLUMN_ID: your BigQuery unique id column.
• INPUT_TABLE_SPEC: your BigQuery table name.
• BIGTABLE_INSTANCE_ID: your Bigtable instance id.
• BIGTABLE_TABLE_ID: your Bigtable table id.
• BIGTABLE_COLUMN_FAMILY: your Bigtable table column family.

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/flexTemplates:launch
{
   "launch_parameter": {
      "jobName": "JOB_NAME",
      "parameters": {
          "readIdColumn": "READ_COLUMN_ID",
          "inputTableSpec": "INPUT_TABLE_SPEC",
          "bigtableWriteInstanceId": "BIGTABLE_INSTANCE_ID",
          "bigtableWriteTableId": "BIGTABLE_TABLE_ID",
          "bigtableWriteColumnFamily": "BIGTABLE_COLUMN_FAMILY"
      },
      "containerSpecGcsPath": "gs://dataflow-templates-LOCATION/VERSION/flex/BigQuery_to_Bigtable",
   }
}

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
• LOCATION: the region where you want to deploy your Dataflow job—for example, us-central1
• 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.
• READ_COLUMN_ID: your BigQuery unique id column.
• INPUT_TABLE_SPEC: your BigQuery table name.
• BIGTABLE_INSTANCE_ID: your Bigtable instance id.
• BIGTABLE_TABLE_ID: your Bigtable table id.
• BIGTABLE_COLUMN_FAMILY: your Bigtable table column family.

Template source code

Java

/*
 * Copyright (C) 2021 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 static com.google.cloud.teleport.v2.bigtable.utils.BigtableConfig.generateCloudBigtableWriteConfiguration;

import com.google.cloud.bigtable.beam.CloudBigtableIO;
import com.google.cloud.bigtable.beam.CloudBigtableTableConfiguration;
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.bigtable.options.BigtableCommonOptions;
import com.google.cloud.teleport.v2.bigtable.transforms.BigtableConverters;
import com.google.cloud.teleport.v2.common.UncaughtExceptionLogger;
import com.google.cloud.teleport.v2.templates.BigQueryToBigtable.BigQueryToBigtableOptions;
import com.google.cloud.teleport.v2.transforms.BigQueryConverters;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.extensions.gcp.options.GcpOptions;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.Validation.Required;
import org.apache.hadoop.hbase.client.Mutation;

/**
 * Dataflow template which reads BigQuery data and writes it to Bigtable. The source data can be
 * either a BigQuery table or an SQL query.
 *
 * <p>Check out <a
 * href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v2/bigquery-to-bigtable/README_BigQuery_to_Bigtable.md">README</a>
 * for instructions on how to use or modify this template.
 */
@Template(
    name = "BigQuery_to_Bigtable",
    category = TemplateCategory.BATCH,
    displayName = "BigQuery to Bigtable",
    description = "A pipeline to export a BigQuery table into Bigtable.",
    optionsClass = BigQueryToBigtableOptions.class,
    optionsOrder = {
      BigQueryToBigtableOptions.class,
      BigQueryConverters.BigQueryReadOptions.class,
      BigtableCommonOptions.class,
      BigtableCommonOptions.WriteOptions.class
    },
    optionalOptions = {"inputTableSpec"},
    flexContainerName = "bigquery-to-bigtable",
    documentation =
        "https://cloud.google.com/dataflow/docs/guides/templates/provided/bigquery-to-bigtable",
    contactInformation = "https://cloud.google.com/support",
    requirements = {
      "The source BigQuery table must exist.",
      "The Bigtable table must exist.",
      "The <a href=\"https://cloud.google.com/dataflow/docs/concepts/security-and-permissions#worker-service-account\">worker service account</a>"
          + " needs the <code>roles/bigquery.datasets.create</code> permission. For"
          + " more information, see <a href=\"https://cloud.google.com/bigquery/docs/access-control\">Introduction to IAM</a>."
    })
public class BigQueryToBigtable {

  /**
   * The {@link BigQueryToBigtableOptions} class provides the custom execution options passed by the
   * executor at the command-line.
   */
  public interface BigQueryToBigtableOptions
      extends BigQueryConverters.BigQueryReadOptions,
          BigtableCommonOptions.WriteOptions,
          GcpOptions {

    @TemplateParameter.Text(
        order = 1,
        regexes = {"[A-Za-z_][A-Za-z_0-9]*"},
        description = "Unique identifier column",
        helpText = "The name of the BigQuery column storing the unique identifier of the row.")
    @Required
    String getReadIdColumn();

    void setReadIdColumn(String value);
  }

  /**
   * Runs a pipeline which reads data from BigQuery and writes it to Bigtable.
   *
   * @param args arguments to the pipeline
   */
  public static void main(String[] args) {
    UncaughtExceptionLogger.register();

    BigQueryToBigtableOptions options =
        PipelineOptionsFactory.fromArgs(args).withValidation().as(BigQueryToBigtableOptions.class);

    CloudBigtableTableConfiguration bigtableTableConfig =
        generateCloudBigtableWriteConfiguration(options);

    Pipeline pipeline = Pipeline.create(options);

    pipeline
        .apply(
            "AvroToMutation",
            BigQueryConverters.ReadBigQuery.<Mutation>newBuilder()
                .setOptions(options.as(BigQueryToBigtableOptions.class))
                .setReadFunction(
                    BigQueryIO.read(
                        BigtableConverters.AvroToMutation.newBuilder()
                            .setColumnFamily(options.getBigtableWriteColumnFamily())
                            .setRowkey(options.getReadIdColumn())
                            .build()))
                .build())
        .apply("WriteToTable", CloudBigtableIO.writeToTable(bigtableTableConfig));

    pipeline.run();
  }
}

What's next

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