Bigtable to Vertex AI Vector Search template

The template for Bigtable to Vertex AI Vector Search files on Cloud Storage creates a batch pipeline that reads data from a Bigtable table and writes it to a Cloud Storage bucket in the JSON format. Use this template for vector embeddings.

Pipeline requirements

• The Bigtable table must exist.
• The output Cloud Storage bucket must exist before you run the pipeline.

Template parameters

Parameter	Description
bigtableProjectId	The ID for the Google Cloud project that contains the Bigtable instance that you want to read data from.
bigtableInstanceId	The ID of the Bigtable instance that contains the table.
bigtableTableId	The ID of the Bigtable table to read.
filenamePrefix	The prefix of the JSON filename. For example: table1-. If no value is provided, defaults to part.
idColumn	The fully qualified column name where the ID is stored. In the format cf:col or _key.
embeddingColumn	The fully qualified column name where the embeddings are stored. In the format cf:col or _key.
outputDirectory	Optional: The Cloud Storage path where the output JSON files are stored. For example: gs://your-bucket/your-path/.
crowdingTagColumn	Optional: The fully qualified column name where the crowding tag is stored. In the format cf:col or _key.
embeddingByteSize	Optional: The byte size of each entry in the embeddings array. For float, use the value 4. For double, use the value 8. Defaults to: 4.
allowRestrictsMappings	Optional: The comma-separated, fully qualified column names for the columns to use as the allow restricts, with their aliases. In the format cf:col->alias.
denyRestrictsMappings	Optional: The comma-separated, fully qualified column names for the columns to use as the deny restricts, with their aliases. In the format cf:col->alias.
intNumericRestrictsMappings	Optional: The comma-separated, fully qualified column names of the columns to use as integer numeric_restricts, with their aliases. In the format cf:col->alias.
floatNumericRestrictsMappings	Optional: The comma-separated, fully qualified column names of the columns to use as float (4 bytes) numeric_restricts, with their aliases. In the format cf:col->alias.
doubleNumericRestrictsMappings	Optional: The comma-separated, fully qualified column names of the columns to use as double (8 bytes) numeric_restricts, with their aliases. In the format cf:col->alias.

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 Cloud Bigtable to Vector Embeddings template.
5. In the provided parameter fields, enter your parameter values.
6. Click Run job.

gcloud CLI

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/Cloud_Bigtable_to_Vector_Embeddings \
    --project=PROJECT_ID \
    --region=REGION_NAME \
    --parameters \
       bigtableProjectId=BIGTABLE_PROJECT_ID,\
       bigtableInstanceId=BIGTABLE_INSTANCE_ID,\
       bigtableTableId=BIGTABLE_TABLE_ID,\
       filenamePrefix=FILENAME_PREFIX,\
       idColumn=ID_COLUMN,\
       embeddingColumn=EMBEDDING_COLUMN,\

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 project ID
• BIGTABLE_INSTANCE_ID: the instance ID
• BIGTABLE_TABLE_ID: the table ID
• FILENAME_PREFIX: the JSON file prefix
• ID_COLUMN: the ID column
• EMBEDDING_COLUMN: the embeddings column

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/Cloud_Bigtable_to_Vector_Embeddings
{
   "jobName": "JOB_NAME",
   "parameters": {
     "bigtableProjectId": "BIGTABLE_PROJECT_ID",
     "bigtableInstanceId": "BIGTABLE_INSTANCE_ID",
     "bigtableTableId": "BIGTABLE_TABLE_ID",
     "filenamePrefix": "FILENAME_PREFIX",
     "idColumn": "ID_COLUMN",
     "embeddingColumn": "EMBEDDING_COLUMN",
   },
   "environment": { "maxWorkers": "10" }
}

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 project ID
• BIGTABLE_INSTANCE_ID: the instance ID
• BIGTABLE_TABLE_ID: the table ID
• FILENAME_PREFIX: the JSON file prefix
• ID_COLUMN: the ID column
• EMBEDDING_COLUMN: the embeddings column

Template source code

Java

/*
 * Copyright (C) 2023 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 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.bigtable.v2.RowFilter;
import com.google.cloud.teleport.bigtable.BigtableToVectorEmbeddings.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.gson.stream.JsonWriter;
import com.google.protobuf.ByteString;
import java.io.IOException;
import java.io.StringWriter;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Optional;
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.io.TextIO;
import org.apache.beam.sdk.io.gcp.bigtable.BigtableIO;
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.SerializableFunction;
import org.apache.beam.sdk.transforms.SimpleFunction;
import org.apache.commons.lang3.StringUtils;
import org.apache.hadoop.hbase.util.Bytes;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * Dataflow pipeline that exports data from a Cloud Bigtable table to JSON files in GCS,
 * specifically for Vector Embedding purposes. 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_Vector_Embeddings.md">README</a>
 * for instructions on how to use or modify this template.
 */
@Template(
    name = "Cloud_Bigtable_to_Vector_Embeddings",
    category = TemplateCategory.BATCH,
    displayName = "Cloud Bigtable to Vector Embeddings",
    description =
        "The Bigtable to Vector Embedding template is a pipeline that reads data from a Bigtable table and writes it to a Cloud Storage bucket in JSON format, for vector embeddings",
    optionsClass = Options.class,
    documentation =
        "https://cloud.google.com/dataflow/docs/guides/templates/provided/bigtable-to-vector-embeddings",
    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 BigtableToVectorEmbeddings {
  private static final Logger LOG = LoggerFactory.getLogger(BigtableToVectorEmbeddings.class);

  /** Options for the export pipeline. */
  public interface Options extends PipelineOptions {
    @TemplateParameter.ProjectId(
        order = 1,
        description = "Project ID",
        helpText =
            "The ID of the Google Cloud project of 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,
        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 Cloud Bigtable table to read")
    ValueProvider<String> getBigtableTableId();

    @SuppressWarnings("unused")
    void setBigtableTableId(ValueProvider<String> tableId);

    @TemplateParameter.GcsWriteFolder(
        order = 4,
        optional = true,
        description = "Cloud Storage directory for storing JSON files",
        helpText = "The Cloud Storage path where the output JSON files can be stored.",
        example = "gs://your-bucket/your-path/")
    ValueProvider<String> getOutputDirectory();

    @SuppressWarnings("unused")
    void setOutputDirectory(ValueProvider<String> outputDirectory);

    @TemplateParameter.Text(
        order = 5,
        description = "JSON file prefix",
        helpText = "The prefix of the JSON file name. For example, \"table1-\"")
    @Default.String("part")
    ValueProvider<String> getFilenamePrefix();

    @SuppressWarnings("unused")
    void setFilenamePrefix(ValueProvider<String> filenamePrefix);

    @TemplateParameter.Text(
        order = 6,
        description = "ID column",
        helpText =
            "The fully qualified column name where the ID is stored. In the format cf:col or _key.")
    ValueProvider<String> getIdColumn();

    @SuppressWarnings("unused")
    void setIdColumn(ValueProvider<String> value);

    @TemplateParameter.Text(
        order = 7,
        description = "Embedding column",
        helpText =
            "The fully qualified column name where the embeddings are stored. In the format cf:col or _key.")
    ValueProvider<String> getEmbeddingColumn();

    @SuppressWarnings("unused")
    void setEmbeddingColumn(ValueProvider<String> value);

    @TemplateParameter.Text(
        order = 8,
        optional = true,
        description = "Crowding tag column",
        helpText =
            "The fully qualified column name where the crowding tag is stored. In the format cf:col or _key.")
    ValueProvider<String> getCrowdingTagColumn();

    @SuppressWarnings("unused")
    void setCrowdingTagColumn(ValueProvider<String> value);

    @TemplateParameter.Integer(
        order = 9,
        optional = true,
        description = "The byte size of the embeddings array. Can be 4 or 8.",
        helpText =
            "The byte size of each entry in the embeddings array. Use 4 for Float, and 8 for Double.")
    @Default.Integer(4)
    ValueProvider<Integer> getEmbeddingByteSize();

    @SuppressWarnings("unused")
    void setEmbeddingByteSize(ValueProvider<Integer> value);

    @TemplateParameter.Text(
        order = 10,
        optional = true,
        description = "Allow restricts mappings",
        helpText =
            "The comma separated fully qualified column names of the columns that should be used as the `allow` restricts, with their alias. In the format cf:col->alias.")
    ValueProvider<String> getAllowRestrictsMappings();

    @SuppressWarnings("unused")
    void setAllowRestrictsMappings(ValueProvider<String> value);

    @TemplateParameter.Text(
        order = 11,
        optional = true,
        description = "Deny restricts mappings",
        helpText =
            "The comma separated fully qualified column names of the columns that should be used as the `deny` restricts, with their alias. In the format cf:col->alias.")
    ValueProvider<String> getDenyRestrictsMappings();

    @SuppressWarnings("unused")
    void setDenyRestrictsMappings(ValueProvider<String> value);

    @TemplateParameter.Text(
        order = 12,
        optional = true,
        description = "Integer numeric restricts mappings",
        helpText =
            "The comma separated fully qualified column names of the columns that should be used as integer `numeric_restricts`, with their alias. In the format cf:col->alias.")
    ValueProvider<String> getIntNumericRestrictsMappings();

    @SuppressWarnings("unused")
    void setIntNumericRestrictsMappings(ValueProvider<String> value);

    @TemplateParameter.Text(
        order = 13,
        optional = true,
        description = "Float numeric restricts mappings",
        helpText =
            "The comma separated fully qualified column names of the columns that should be used as float (4 bytes) `numeric_restricts`, with their alias. In the format cf:col->alias.")
    ValueProvider<String> getFloatNumericRestrictsMappings();

    @SuppressWarnings("unused")
    void setFloatNumericRestrictsMappings(ValueProvider<String> value);

    @TemplateParameter.Text(
        order = 14,
        optional = true,
        description = "Double numeric restricts mappings",
        helpText =
            "The comma separated fully qualified column names of the columns that should be used as double (8 bytes) `numeric_restricts`, with their alias. In the format cf:col->alias.")
    ValueProvider<String> getDoubleNumericRestrictsMappings();

    @SuppressWarnings("unused")
    void setDoubleNumericRestrictsMappings(ValueProvider<String> value);
  }

  /**
   * Runs a pipeline to export data from a Cloud Bigtable table to JSON files in GCS in JSON format,
   * for use of Vertex Vector Search.
   *
   * @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);

    // Wait for pipeline to finish only if it is not constructing a template.
    if (options.as(DataflowPipelineOptions.class).getTemplateLocation() == null) {
      result.waitUntilFinish();
    }
    LOG.info("Completed pipeline setup");
  }

  public static PipelineResult run(Options options) {
    Pipeline pipeline = Pipeline.create(PipelineUtils.tweakPipelineOptions(options));

    BigtableIO.Read read =
        BigtableIO.read()
            .withProjectId(options.getBigtableProjectId())
            .withInstanceId(options.getBigtableInstanceId())
            .withTableId(options.getBigtableTableId())
            .withRowFilter(RowFilter.newBuilder().setCellsPerColumnLimitFilter(1).build());

    // Do not validate input fields if it is running as a template.
    if (options.as(DataflowPipelineOptions.class).getTemplateLocation() != null) {
      read = read.withoutValidation();
    }

    // Concatenating cloud storage folder with file prefix to get complete path
    ValueProvider<String> outputFilePrefix = options.getFilenamePrefix();

    ValueProvider<String> outputFilePathWithPrefix =
        ValueProvider.NestedValueProvider.of(
            options.getOutputDirectory(),
            (SerializableFunction<String, String>)
                folder -> {
                  if (!folder.endsWith("/")) {
                    // Appending the slash if not provided by user
                    folder = folder + "/";
                  }
                  return folder + outputFilePrefix.get();
                });
    pipeline
        .apply("Read from Bigtable", read)
        .apply(
            "Transform to JSON",
            MapElements.via(
                new BigtableToVectorEmbeddingsFn(
                    options.getIdColumn(),
                    options.getEmbeddingColumn(),
                    options.getEmbeddingByteSize(),
                    options.getCrowdingTagColumn(),
                    options.getAllowRestrictsMappings(),
                    options.getDenyRestrictsMappings(),
                    options.getIntNumericRestrictsMappings(),
                    options.getFloatNumericRestrictsMappings(),
                    options.getDoubleNumericRestrictsMappings())))
        .apply("Write to storage", TextIO.write().to(outputFilePathWithPrefix).withSuffix(".json"));

    return pipeline.run();
  }

  /** Translates Bigtable {@link Row} to Vector Embeddings JSON. */
  static class BigtableToVectorEmbeddingsFn extends SimpleFunction<Row, String> {
    private static final String ID_KEY = "id";
    private static final String EMBEDDING_KEY = "embedding";
    private static final String RESTRICTS_KEY = "restricts";
    private static final String NUMERIC_RESTRICTS_KEY = "numeric_restricts";
    private static final String CROWDING_TAG_KEY = "crowding_tag";

    private static final String NAMESPACE_KEY = "namespace";

    private static final String ALLOW_KEY = "allow";
    private static final String DENY_KEY = "deny";

    private static final String VALUE_INT_KEY = "value_int";
    private static final String VALUE_FLOAT_KEY = "value_float";
    private static final String VALUE_DOUBLE_KEY = "value_double";

    private String idColumn;
    private String embeddingsColumn;
    private Integer embeddingByteSize;
    private String crowdingTagColumn;
    private Map<String, String> allowRestricts;
    private Map<String, String> denyRestricts;
    private Map<String, String> intNumericRestricts;
    private Map<String, String> floatNumericRestricts;
    private Map<String, String> doubleNumericRestricts;

    private ValueProvider<Integer> embeddingByteSizeProvider;
    private ValueProvider<String> idColumnProvider;
    private ValueProvider<String> embeddingsColumnProvider;
    private ValueProvider<String> crowdingTagColumnProvider;
    private ValueProvider<String> allowRestrictsMappingsProvider;
    private ValueProvider<String> denyRestrictsMappingsProvider;
    private ValueProvider<String> intNumericRestrictsMappingsProvider;
    private ValueProvider<String> floatNumericRestrictsMappingsProvider;
    private ValueProvider<String> doubleNumericRestrictsMappingsProvider;

    public BigtableToVectorEmbeddingsFn(
        ValueProvider<String> idColumnProvider,
        ValueProvider<String> embeddingsColumnProvider,
        ValueProvider<Integer> embeddingByteSizeProvider,
        ValueProvider<String> crowdingTagColumnProvider,
        ValueProvider<String> allowRestrictsMappingsProvider,
        ValueProvider<String> denyRestrictsMappingsProvider,
        ValueProvider<String> intNumericRestrictsMappingsProvider,
        ValueProvider<String> floatNumericRestrictsMappingsProvider,
        ValueProvider<String> doubleNumericRestrictsMappingsProvider) {
      this.idColumnProvider = idColumnProvider;
      this.embeddingsColumnProvider = embeddingsColumnProvider;
      this.embeddingByteSizeProvider = embeddingByteSizeProvider;
      this.crowdingTagColumnProvider = crowdingTagColumnProvider;
      this.allowRestrictsMappingsProvider = allowRestrictsMappingsProvider;
      this.denyRestrictsMappingsProvider = denyRestrictsMappingsProvider;
      this.intNumericRestrictsMappingsProvider = intNumericRestrictsMappingsProvider;
      this.floatNumericRestrictsMappingsProvider = floatNumericRestrictsMappingsProvider;
      this.doubleNumericRestrictsMappingsProvider = doubleNumericRestrictsMappingsProvider;
    }

    @Override
    public String apply(Row row) {
      this.embeddingByteSize = this.embeddingByteSizeProvider.get();
      if (this.embeddingByteSize != 4 && this.embeddingByteSize != 8) {
        throw new RuntimeException("embeddingByteSize can be either 4 or 8");
      }
      this.idColumn = this.idColumnProvider.get();
      this.embeddingsColumn = this.embeddingsColumnProvider.get();
      this.crowdingTagColumn = this.crowdingTagColumnProvider.get();
      this.allowRestricts =
          Optional.ofNullable(this.allowRestricts)
              .orElse(extractColumnsAliases(this.allowRestrictsMappingsProvider));
      this.denyRestricts =
          Optional.ofNullable(this.denyRestricts)
              .orElse(extractColumnsAliases(this.denyRestrictsMappingsProvider));
      this.intNumericRestricts =
          Optional.ofNullable(this.intNumericRestricts)
              .orElse(extractColumnsAliases(this.intNumericRestrictsMappingsProvider));
      this.floatNumericRestricts =
          Optional.ofNullable(this.floatNumericRestricts)
              .orElse(extractColumnsAliases(this.floatNumericRestrictsMappingsProvider));
      this.doubleNumericRestricts =
          Optional.ofNullable(this.doubleNumericRestricts)
              .orElse(extractColumnsAliases(this.doubleNumericRestrictsMappingsProvider));

      StringWriter stringWriter = new StringWriter();
      JsonWriter jsonWriter = new JsonWriter(stringWriter);
      VectorEmbeddings vectorEmbeddings = buildObject(row);
      try {
        serialize(jsonWriter, vectorEmbeddings);
      } catch (IOException e) {
        throw new RuntimeException(e);
      }
      return stringWriter.toString();
    }

    private void serialize(JsonWriter jsonWriter, VectorEmbeddings vectorEmbeddings)
        throws IOException {
      jsonWriter.beginObject();

      // Required fields.
      jsonWriter.name(ID_KEY).value(vectorEmbeddings.id);
      jsonWriter.name(EMBEDDING_KEY);
      jsonWriter.beginArray();
      if (this.embeddingByteSize == 4) {
        for (Float f : vectorEmbeddings.floatEmbeddings) {
          jsonWriter.value(f);
        }
      } else if (this.embeddingByteSize == 8) {
        for (Double d : vectorEmbeddings.doubleEmbeddings) {
          jsonWriter.value(d);
        }
      }
      jsonWriter.endArray();

      // Optional fields.
      if (vectorEmbeddings.crowdingTag != "") {
        jsonWriter.name(CROWDING_TAG_KEY).value(vectorEmbeddings.crowdingTag);
      }
      if (vectorEmbeddings.restricts != null && !vectorEmbeddings.restricts.isEmpty()) {
        jsonWriter.name(RESTRICTS_KEY);
        jsonWriter.beginArray();
        for (Restrict r : vectorEmbeddings.restricts) {
          jsonWriter.beginObject();
          jsonWriter.name(NAMESPACE_KEY).value(r.namespace);
          if (r.allow != null && !r.allow.isEmpty()) {
            jsonWriter.name(ALLOW_KEY);
            jsonWriter.beginArray();
            for (String a : r.allow) {
              jsonWriter.value(a);
            }
            jsonWriter.endArray();
          } else if (r.deny != null && !r.deny.isEmpty()) {
            jsonWriter.name(DENY_KEY);
            jsonWriter.beginArray();
            for (String d : r.deny) {
              jsonWriter.value(d);
            }
            jsonWriter.endArray();
          }
          jsonWriter.endObject();
        }
        jsonWriter.endArray();
      }
      if (vectorEmbeddings.numericRestricts != null
          && !vectorEmbeddings.numericRestricts.isEmpty()) {
        jsonWriter.name(NUMERIC_RESTRICTS_KEY);
        jsonWriter.beginArray();
        for (NumericRestrict numericRestrict : vectorEmbeddings.numericRestricts) {
          jsonWriter.beginObject();
          jsonWriter.name(NAMESPACE_KEY).value(numericRestrict.namespace);
          switch (numericRestrict.type) {
            case INT:
              jsonWriter.name(VALUE_INT_KEY).value(numericRestrict.valueInt);
              break;
            case FLOAT:
              jsonWriter.name(VALUE_FLOAT_KEY).value(numericRestrict.valueFloat);
              break;
            case DOUBLE:
              jsonWriter.name(VALUE_DOUBLE_KEY).value(numericRestrict.valueDouble);
              break;
          }
          jsonWriter.endObject();
        }
        jsonWriter.endArray();
      }
      jsonWriter.endObject();
    }

    private VectorEmbeddings buildObject(Row row) {
      VectorEmbeddings vectorEmbeddings = new VectorEmbeddings();

      maybeAddToObject(vectorEmbeddings, "_key", row.getKey());
      for (Family family : row.getFamiliesList()) {
        String familyName = family.getName();
        for (Column column : family.getColumnsList()) {
          for (Cell cell : column.getCellsList()) {
            maybeAddToObject(
                vectorEmbeddings,
                familyName + ":" + column.getQualifier().toStringUtf8(),
                cell.getValue());
          }
        }
      }

      // Assert fields
      if (StringUtils.isEmpty(vectorEmbeddings.id)) {
        throw new RuntimeException(
            String.format(
                "'%s' value is missing for row '%s'", ID_KEY, row.getKey().toStringUtf8()));
      }
      if (this.embeddingByteSize == 4
          && (vectorEmbeddings.floatEmbeddings == null
              || vectorEmbeddings.floatEmbeddings.isEmpty())) {
        throw new RuntimeException(
            String.format(
                "'%s' value is missing for row '%s'", EMBEDDING_KEY, row.getKey().toStringUtf8()));
      }
      if (this.embeddingByteSize == 8
          && (vectorEmbeddings.doubleEmbeddings == null
              || vectorEmbeddings.doubleEmbeddings.isEmpty())) {
        throw new RuntimeException(
            String.format(
                "'%s' value is missing for row '%s'", EMBEDDING_KEY, row.getKey().toStringUtf8()));
      }
      return vectorEmbeddings;
    }

    private void maybeAddToObject(
        VectorEmbeddings vectorEmbeddings, String columnQualifier, ByteString value) {
      if (columnQualifier.equals(this.idColumn)) {
        vectorEmbeddings.id = value.toStringUtf8();
      } else if (columnQualifier.equals(this.crowdingTagColumn)) {
        vectorEmbeddings.crowdingTag = value.toStringUtf8();
      } else if (columnQualifier.equals(this.embeddingsColumn)) {
        vectorEmbeddings.floatEmbeddings = new ArrayList<Float>();
        vectorEmbeddings.doubleEmbeddings = new ArrayList<Double>();

        byte[] bytes = value.toByteArray();
        for (int i = 0; i < bytes.length; i += embeddingByteSize) {
          if (embeddingByteSize == 4) {
            vectorEmbeddings.floatEmbeddings.add(Bytes.toFloat(bytes, i));
          } else if (embeddingByteSize == 8) {
            vectorEmbeddings.doubleEmbeddings.add(Bytes.toDouble(bytes, i));
          }
        }
      } else if (this.allowRestricts.containsKey(columnQualifier)) {
        vectorEmbeddings.addRestrict(
            Restrict.allowRestrict(allowRestricts.get(columnQualifier), value));
      } else if (this.denyRestricts.containsKey(columnQualifier)) {
        vectorEmbeddings.addRestrict(
            Restrict.denyRestrict(denyRestricts.get(columnQualifier), value));
      } else if (this.intNumericRestricts.containsKey(columnQualifier)) {
        vectorEmbeddings.addNumericRestrict(
            NumericRestrict.intValue(intNumericRestricts.get(columnQualifier), value));
      } else if (this.floatNumericRestricts.containsKey(columnQualifier)) {
        vectorEmbeddings.addNumericRestrict(
            NumericRestrict.floatValue(floatNumericRestricts.get(columnQualifier), value));
      } else if (this.doubleNumericRestricts.containsKey(columnQualifier)) {
        vectorEmbeddings.addNumericRestrict(
            NumericRestrict.doubleValue(doubleNumericRestricts.get(columnQualifier), value));
      }
    }

    private Map<String, String> extractColumnsAliases(ValueProvider<String> restricts) {
      Map<String, String> columnsWithAliases = new HashMap<>();
      if (StringUtils.isBlank(restricts.get())) {
        return columnsWithAliases;
      }
      String[] columnsList = restricts.get().split(",");

      for (String columnsWithAlias : columnsList) {
        String[] columnWithAlias = columnsWithAlias.split("->");
        if (columnWithAlias.length == 2) {
          columnsWithAliases.put(columnWithAlias[0], columnWithAlias[1]);
        }
      }
      return columnsWithAliases;
    }
  }
}

// Data model classes.
class Restrict {
  String namespace;
  List<String> allow;
  List<String> deny;

  static Restrict allowRestrict(String namespace, ByteString value) {
    Restrict restrict = new Restrict();
    restrict.namespace = namespace;
    restrict.allow = new ArrayList<String>();
    restrict.allow.add(value.toStringUtf8());
    return restrict;
  }

  static Restrict denyRestrict(String namespace, ByteString value) {
    Restrict restrict = new Restrict();
    restrict.namespace = namespace;
    restrict.deny = new ArrayList<String>();
    restrict.deny.add(value.toStringUtf8());
    return restrict;
  }
}

class NumericRestrict {
  enum Type {
    INT,
    FLOAT,
    DOUBLE
  };

  String namespace;
  Type type;
  Integer valueInt;
  Float valueFloat;
  Double valueDouble;

  static NumericRestrict intValue(String namespace, ByteString value) {
    NumericRestrict restrict = new NumericRestrict();
    restrict.namespace = namespace;
    restrict.valueInt = Bytes.toInt(value.toByteArray());
    restrict.type = Type.INT;
    return restrict;
  }

  static NumericRestrict floatValue(String namespace, ByteString value) {
    NumericRestrict restrict = new NumericRestrict();
    restrict.namespace = namespace;
    restrict.valueFloat = Bytes.toFloat(value.toByteArray());
    restrict.type = Type.FLOAT;
    return restrict;
  }

  static NumericRestrict doubleValue(String namespace, ByteString value) {
    NumericRestrict restrict = new NumericRestrict();
    restrict.namespace = namespace;
    restrict.valueDouble = Bytes.toDouble(value.toByteArray());
    restrict.type = Type.DOUBLE;
    return restrict;
  }
}

class VectorEmbeddings {
  String id;
  String crowdingTag;
  List<Float> floatEmbeddings;
  List<Double> doubleEmbeddings;
  List<Restrict> restricts;
  List<NumericRestrict> numericRestricts;

  void addRestrict(Restrict restrict) {
    if (this.restricts == null) {
      this.restricts = new ArrayList<Restrict>();
    }
    restricts.add(restrict);
  }

  void addNumericRestrict(NumericRestrict numericRestrict) {
    if (this.numericRestricts == null) {
      this.numericRestricts = new ArrayList<NumericRestrict>();
    }
    numericRestricts.add(numericRestrict);
  }
}

What's next

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