File CSV Cloud Storage ke template BigQuery

File CSV Cloud Storage ke pipeline BigQuery adalah pipeline batch yang memungkinkan Anda membaca data dari file CSV yang disimpan di Cloud Storage dan menambahkan hasilnya ke tabel BigQuery. File CSV dapat tidak dikompresi atau dikompresi dalam format yang tercantum di halaman Compression Enum SDK.

Catatan: Jika Anda ingin menimpa data di tabel BigQuery, bukan menambahkan, di kode sumber template, perbarui WriteDisposition dari WRITE_APPEND menjadi WRITE_TRUNCATE.

Persyaratan pipeline

Untuk menggunakan template ini, pipeline Anda harus memenuhi persyaratan berikut.

File JSON skema BigQuery

Buat file JSON yang mendeskripsikan skema BigQuery Anda. Pastikan skema memiliki array JSON level atas yang berjudul BigQuery Schema dan kontennya mengikuti pola {"name": "COLUMN_NAME", "type": "DATA_TYPE"}.

File CSV Cloud Storage ke template batch BigQuery tidak mendukung impor data ke kolom STRUCT (Record) di tabel BigQuery target.

JSON berikut menjelaskan contoh skema BigQuery:

{
  "BigQuery Schema": [
    {
      "name": "location",
      "type": "STRING"
    },
    {
      "name": "name",
      "type": "STRING"
    },
    {
      "name": "age",
      "type": "STRING"
    },
    {
      "name": "color",
      "type": "STRING"
    },
    {
      "name": "coffee",
      "type": "STRING"
    }
  ]
}

Skema tabel error

Tabel BigQuery yang menyimpan data yang ditolak dari file CSV harus cocok dengan skema tabel yang ditentukan di sini.

{
  "BigQuery Schema": [
    {
      "name": "RawContent",
      "type": "STRING"
    },
    {
      "name": "ErrorMsg",
      "type": "STRING"
    }
  ]
}

Parameter template

Parameter	Deskripsi
inputFilePattern	Jalur Cloud Storage ke file CSV yang berisi teks yang akan diproses. Misalnya, gs://path/to/my/text/data.csv.
schemaJSONPath	Jalur Cloud Storage ke file JSON yang menentukan skema BigQuery Anda. Misalnya, gs://path/to/my/schema.json.
outputTable	Nama tabel BigQuery yang menyimpan data yang diproses. Jika Anda menggunakan kembali tabel BigQuery yang sudah ada, data akan ditambahkan ke tabel tujuan. Misalnya, my-project-name:my-dataset.my-table.
bigQueryLoadingTemporaryDirectory	Direktori sementara yang akan digunakan selama proses pemuatan BigQuery. Misalnya, gs://my-bucket/my-files/temp_dir.
badRecordsOutputTable	Nama tabel BigQuery yang akan digunakan untuk menyimpan data yang ditolak saat memproses file CSV. Jika Anda menggunakan kembali tabel BigQuery yang sudah ada, data akan ditambahkan ke tabel tujuan. Misalnya, my-project-name:my-dataset.my-bad-records-table. Skema tabel ini harus cocok dengan skema tabel error.
delimiter	Pemisah kolom file CSV input. Misalnya, ",".
csvFormat	Spesifikasi format CSV yang akan digunakan untuk mengurai data. Misalnya, Default. Nilai ini harus sama persis dengan nama format dalam Enum CSVFormat.Predefined.
containsHeaders	Apakah file CSV input berisi record header. Nilai defaultnya adalah false.
csvFileEncoding	Format encoding karakter file CSV. Nilai yang diizinkan adalah US-ASCII, ISO-8859-1, UTF-8, dan UTF-16. Setelan defaultnya adalah UTF-8.

Menjalankan template

Konsol

1. Buka halaman Create job from template Dataflow.
Buka Buat tugas dari template
2. Di kolom Job name, masukkan nama pekerjaan yang unik.
3. Opsional: Untuk Endpoint regional, pilih nilai dari menu drop-down. Region default-nya adalah us-central1.

   Untuk daftar region tempat Anda dapat menjalankan tugas Dataflow, lihat Lokasi Dataflow.

4. Dari menu drop-down Dataflow template, pilih the CSV files on Cloud Storage to BigQuery (Batch) template.
5. Di kolom parameter yang disediakan, masukkan parameter value Anda.
6. Klik Run job.

gcloud

Catatan: Untuk menggunakan Google Cloud CLI guna menjalankan template classic, Anda harus memiliki Google Cloud CLI versi 138.0.0 atau yang lebih baru.

Di shell atau terminal Anda, jalankan template:

gcloud dataflow jobs run JOB_NAME \
    --gcs-location gs://dataflow-templates-REGION_NAME/VERSION/GCS_CSV_to_BigQuery \
    --region REGION_NAME \
    --parameters \
inputFilePattern=PATH_TO_CSV_DATA,\
schemaJSONPath=PATH_TO_BIGQUERY_SCHEMA_JSON,\
outputTable=BIGQUERY_DESTINATION_TABLE,\
badRecordsOutputTable=BIGQUERY_BAD_RECORDS_TABLE,\
csvFormat=CSV_FORMAT,\
delimiter=DELIMITER,\
bigQueryLoadingTemporaryDirectory=PATH_TO_TEMP_DIR_ON_GCS,\
containsHeaders=CONTAINS_HEADERS,\
csvFileEncoding=CSV_FILE_ENCODING

Ganti kode berikut:

• PROJECT_ID: ID project Google Cloud tempat Anda ingin menjalankan tugas Dataflow
• JOB_NAME: nama pekerjaan unik pilihan Anda
• VERSION: versi template yang ingin Anda gunakan

  Anda dapat menggunakan nilai berikut:

  • latest untuk menggunakan versi terbaru template, yang tersedia di folder induk tidak bertanggal di bucket— gs://dataflow-templates-REGION_NAME/latest/
  • nama versi, seperti 2023-09-12-00_RC00, untuk menggunakan versi template tertentu, yang dapat ditemukan bertingkat di folder induk bertanggal masing-masing dalam bucket— gs://dataflow-templates-REGION_NAME/
  Perhatian: Versi terbaru template mungkin diperbarui dengan perubahan yang dapat menyebabkan gangguan. Lingkungan produksi Anda harus menggunakan template yang disimpan di folder induk bertanggal terbaru agar perubahan yang dapat menyebabkan gangguan ini tidak memengaruhi alur kerja produksi Anda.
• REGION_NAME: region tempat Anda ingin men-deploy tugas Dataflow, misalnya us-central1
• PATH_TO_CSV_DATA: jalur Cloud Storage ke file CSV Anda
• PATH_TO_BIGQUERY_SCHEMA_JSON: jalur Cloud Storage ke file JSON yang berisi definisi skema
• BIGQUERY_DESTINATION_TABLE: nama tabel tujuan BigQuery
• BIGQUERY_BAD_RECORDS_TABLE: Nama tabel data buruk BigQuery
• PATH_TO_TEMP_DIR_ON_GCS: jalur Cloud Storage ke direktori sementara
• DELIMITER: Pemisah file CSV
• CSV_FORMAT: Spesifikasi format CSV untuk mengurai data
• CONTAINS_HEADERS: apakah file CSV berisi header atau tidak
• CSV_FILE_ENCODING: mengenkode file CSV

API

Untuk menjalankan template menggunakan REST API, kirim permintaan HTTP POST. Untuk informasi selengkapnya tentang API dan cakupan otorisasinya, lihat projects.templates.launch.

POST https://dataflow.googleapis.com/v1b3/projects/PROJECT_ID/locations/LOCATION/templates:launch?gcsPath=gs://dataflow-templates-LOCATION/VERSION/GCS_CSV_to_BigQuery
{
   "jobName": "JOB_NAME",
   "parameters": {
       "inputFilePattern":"PATH_TO_CSV_DATA",
       "schemaJSONPath": "PATH_TO_BIGQUERY_SCHEMA_JSON",
       "outputTable":"BIGQUERY_DESTINATION_TABLE",
       "badRecordsOutputTable":"BIGQUERY_BAD_RECORDS_TABLE",
       "csvFormat":"CSV_FORMAT",
       "delimiter":"DELIMITER",
       "bigQueryLoadingTemporaryDirectory": "PATH_TO_TEMP_DIR_ON_GCS",
       "containsHeaders": "CONTAINS_HEADERS",
       "csvFileEncoding": "CSV_FILE_ENCODING"
   },
   "environment": { "zone": "us-central1-f" }
}

Ganti kode berikut:

• PROJECT_ID: ID project Google Cloud tempat Anda ingin menjalankan tugas Dataflow
• JOB_NAME: nama pekerjaan unik pilihan Anda
• VERSION: versi template yang ingin Anda gunakan

  Anda dapat menggunakan nilai berikut:

  • latest untuk menggunakan versi terbaru template, yang tersedia di folder induk tidak bertanggal di bucket— gs://dataflow-templates-REGION_NAME/latest/
  • nama versi, seperti 2023-09-12-00_RC00, untuk menggunakan versi template tertentu, yang dapat ditemukan bertingkat di folder induk bertanggal masing-masing dalam bucket— gs://dataflow-templates-REGION_NAME/
  Perhatian: Versi terbaru template mungkin diperbarui dengan perubahan yang dapat menyebabkan gangguan. Lingkungan produksi Anda harus menggunakan template yang disimpan di folder induk bertanggal terbaru agar perubahan yang dapat menyebabkan gangguan ini tidak memengaruhi alur kerja produksi Anda.
• LOCATION: region tempat Anda ingin men-deploy tugas Dataflow, misalnya us-central1
• PATH_TO_CSV_DATA: jalur Cloud Storage ke file CSV Anda
• PATH_TO_BIGQUERY_SCHEMA_JSON: jalur Cloud Storage ke file JSON yang berisi definisi skema
• BIGQUERY_DESTINATION_TABLE: nama tabel tujuan BigQuery
• BIGQUERY_BAD_RECORDS_TABLE: Nama tabel data buruk BigQuery
• PATH_TO_TEMP_DIR_ON_GCS: jalur Cloud Storage ke direktori sementara
• DELIMITER: Pemisah file CSV
• CSV_FORMAT: Spesifikasi format CSV untuk mengurai data
• CONTAINS_HEADERS: apakah file CSV berisi header atau tidak
• CSV_FILE_ENCODING: mengenkode file CSV

Kode sumber template

Java

/*
 * Copyright (C) 2024 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.templates;

import com.google.api.services.bigquery.model.TableFieldSchema;
import com.google.api.services.bigquery.model.TableRow;
import com.google.api.services.bigquery.model.TableSchema;
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.templates.CSVToBigQuery.Options;
import com.google.cloud.teleport.templates.common.CsvConverters;
import com.google.cloud.teleport.templates.common.CsvConverters.CsvPipelineOptions;
import com.google.common.collect.ImmutableList;
import java.util.ArrayList;
import java.util.List;
import org.apache.beam.runners.dataflow.options.DataflowPipelineOptions;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.Write.CreateDisposition;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.Write.WriteDisposition;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.Validation;
import org.apache.beam.sdk.options.ValueProvider;
import org.apache.beam.sdk.options.ValueProvider.NestedValueProvider;
import org.apache.beam.sdk.transforms.DoFn;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.values.PCollectionTuple;
import org.apache.beam.sdk.values.TupleTag;
import org.apache.beam.sdk.values.TupleTagList;
import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Splitter;
import org.json.JSONArray;
import org.json.JSONObject;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * Templated pipeline to read CSV files from Cloud Storage, and write it to BigQuery.
 *
 * <p>Check out <a
 * href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v1/README_GCS_CSV_to_BigQuery.md">README</a>
 * for instructions on how to use or modify this template.
 */
@Template(
    name = "GCS_CSV_to_BigQuery",
    category = TemplateCategory.BATCH,
    displayName = "CSV Files on Cloud Storage to BigQuery",
    description =
        "The Cloud Storage CSV to BigQuery pipeline is a batch pipeline that allows you to read CSV files stored in "
            + "Cloud Storage, and append the result to a BigQuery table. The CSV files can be uncompressed or compressed in formats listed in https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/io/Compression.html.",
    optionsClass = Options.class,
    contactInformation = "https://cloud.google.com/support",
    requirements = {
      "Create a JSON file that describes your {{bigquery_name_short}} schema.\n"
          + "    <p>Ensure that there is a top-level JSON array titled <code>BigQuery Schema</code> and that its\n"
          + "      contents follow the pattern <code>{\"name\": \"COLUMN_NAME\", \"type\": \"DATA_TYPE\"}</code>.</p>\n"
          + "    <p>The following JSON describes an example BigQuery schema:</p>\n"
          + "<pre class=\"prettyprint lang-json\">\n"
          + "{\n"
          + "  \"BigQuery Schema\": [\n"
          + "    {\n"
          + "      \"name\": \"location\",\n"
          + "      \"type\": \"STRING\"\n"
          + "    },\n"
          + "    {\n"
          + "      \"name\": \"name\",\n"
          + "      \"type\": \"STRING\"\n"
          + "    },\n"
          + "    {\n"
          + "      \"name\": \"age\",\n"
          + "      \"type\": \"STRING\"\n"
          + "    },\n"
          + "    {\n"
          + "      \"name\": \"color\",\n"
          + "      \"type\": \"STRING\"\n"
          + "    },\n"
          + "    {\n"
          + "      \"name\": \"coffee\",\n"
          + "      \"type\": \"STRING\"\n"
          + "    }\n"
          + "  ]\n"
          + "}\n"
    })
public class CSVToBigQuery {

  /** Options supported by {@link CSVToBigQuery}. */
  public interface Options extends DataflowPipelineOptions, CsvPipelineOptions {

    @TemplateParameter.Text(
        order = 1,
        description = "Cloud Storage Input File(s)",
        helpText = "Path of the file pattern glob to read from.",
        regexes = {"^gs:\\/\\/[^\\n\\r]+$"},
        example = "gs://your-bucket/path/*.csv")
    ValueProvider<String> getInputFilePattern();

    void setInputFilePattern(ValueProvider<String> value);

    @TemplateParameter.GcsReadFile(
        order = 2,
        description = "Cloud Storage location of your BigQuery schema file, described as a JSON",
        helpText =
            "JSON file with BigQuery Schema description. JSON Example: {\n"
                + "\t\"BigQuery Schema\": [\n"
                + "\t\t{\n"
                + "\t\t\t\"name\": \"location\",\n"
                + "\t\t\t\"type\": \"STRING\"\n"
                + "\t\t},\n"
                + "\t\t{\n"
                + "\t\t\t\"name\": \"name\",\n"
                + "\t\t\t\"type\": \"STRING\"\n"
                + "\t\t},\n"
                + "\t\t{\n"
                + "\t\t\t\"name\": \"age\",\n"
                + "\t\t\t\"type\": \"STRING\"\n"
                + "\t\t},\n"
                + "\t\t{\n"
                + "\t\t\t\"name\": \"color\",\n"
                + "\t\t\t\"type\": \"STRING\"\n"
                + "\t\t},\n"
                + "\t\t{\n"
                + "\t\t\t\"name\": \"coffee\",\n"
                + "\t\t\t\"type\": \"STRING\"\n"
                + "\t\t}\n"
                + "\t]\n"
                + "}")
    ValueProvider<String> getSchemaJSONPath();

    void setSchemaJSONPath(ValueProvider<String> value);

    @TemplateParameter.BigQueryTable(
        order = 3,
        description = "BigQuery output table",
        helpText =
            "BigQuery table location to write the output to. The table's schema must match the "
                + "input objects.")
    ValueProvider<String> getOutputTable();

    void setOutputTable(ValueProvider<String> value);

    @TemplateParameter.GcsWriteFolder(
        order = 4,
        description = "Temporary directory for BigQuery loading process",
        helpText = "Temporary directory for BigQuery loading process",
        example = "gs://your-bucket/your-files/temp_dir")
    @Validation.Required
    ValueProvider<String> getBigQueryLoadingTemporaryDirectory();

    void setBigQueryLoadingTemporaryDirectory(ValueProvider<String> directory);

    @TemplateParameter.BigQueryTable(
        order = 5,
        description = "BigQuery output table for bad records",
        helpText =
            "BigQuery table location to write the bad record. The table's schema must match the {RawContent: STRING, ErrorMsg:STRING}")
    ValueProvider<String> getBadRecordsOutputTable();

    void setBadRecordsOutputTable(ValueProvider<String> value);
  }

  private static final Logger LOG = LoggerFactory.getLogger(CSVToBigQuery.class);

  private static final String BIGQUERY_SCHEMA = "BigQuery Schema";
  private static final String NAME = "name";
  private static final String TYPE = "type";
  private static final String MODE = "mode";
  private static final String RECORD_TYPE = "RECORD";
  private static final String FIELDS_ENTRY = "fields";

  /** The tag for the headers of the CSV if required. */
  private static final TupleTag<String> CSV_HEADERS = new TupleTag<String>() {};

  /** The tag for the lines of the CSV. */
  private static final TupleTag<String> CSV_LINES = new TupleTag<String>() {};

  /** The tag for the line of the CSV that matches destination table schema. */
  private static final TupleTag<TableRow> GOOD_RECORDS = new TupleTag<TableRow>() {};

  /** The tag for the lines of the CSV that does not match destination table schema. */
  private static final TupleTag<TableRow> BAD_RECORDS = new TupleTag<TableRow>() {};

  /** The schema of the BigQuery table for the bad records. */
  private static final TableSchema errorTableSchema =
      new TableSchema()
          .setFields(
              ImmutableList.of(
                  new TableFieldSchema().setName("RawContent").setType("STRING"),
                  new TableFieldSchema().setName("ErrorMsg").setType("STRING")));

  private static class StringToTableRowFn extends DoFn<String, TableRow> {
    private final ValueProvider<String> delimiter;
    private final NestedValueProvider<List<String>, String> fields;

    public StringToTableRowFn(
        NestedValueProvider<List<String>, String> schemaFields, ValueProvider<String> delimiter) {
      this.delimiter = delimiter;
      this.fields = schemaFields;
    }

    @ProcessElement
    public void processElement(ProcessContext context) throws IllegalArgumentException {
      TableRow outputTableRow = new TableRow();
      String[] rowValue =
          Splitter.on(delimiter.get()).splitToList(context.element()).toArray(new String[0]);
      if (rowValue.length != fields.get().size()) {
        LOG.error("Number of fields in the schema and number of Csv headers do not match.");
        outputTableRow.set("RawContent", String.join(delimiter.get(), rowValue));
        outputTableRow.set(
            "ErrorMsg", "Number of fields in the schema and number of Csv headers do not match.");
        context.output(BAD_RECORDS, outputTableRow);
      } else {
        for (int i = 0; i < fields.get().size(); ++i) {
          outputTableRow.set(fields.get().get(i), rowValue[i]);
        }
        context.output(GOOD_RECORDS, outputTableRow);
      }
    }
  }

  public static void main(String[] args) {
    Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
    Pipeline pipeline = Pipeline.create(options);

    PCollectionTuple tableRows =
        pipeline
            .apply(
                "ReadCsvFile",
                CsvConverters.ReadCsv.newBuilder()
                    .setInputFileSpec(options.getInputFilePattern())
                    .setHasHeaders(options.getContainsHeaders())
                    .setHeaderTag(CSV_HEADERS)
                    .setLineTag(CSV_LINES)
                    .setCsvFormat(options.getCsvFormat())
                    .setDelimiter(options.getDelimiter())
                    .setFileEncoding(options.getCsvFileEncoding())
                    .build())
            .get(CSV_LINES)
            .apply(
                "ConvertToTableRow",
                ParDo.of(
                        new StringToTableRowFn(
                            NestedValueProvider.of(
                                options.getSchemaJSONPath(),
                                jsonPath -> {
                                  List<String> fields = new ArrayList<>();
                                  SchemaParser schemaParser = new SchemaParser();

                                  try {
                                    JSONObject jsonSchema = schemaParser.parseSchema(jsonPath);
                                    JSONArray bqSchemaJsonArray =
                                        jsonSchema.getJSONArray(BIGQUERY_SCHEMA);

                                    for (int i = 0; i < bqSchemaJsonArray.length(); i++) {
                                      JSONObject inputField = bqSchemaJsonArray.getJSONObject(i);
                                      fields.add(inputField.getString(NAME));
                                    }

                                  } catch (Exception e) {
                                    throw new RuntimeException(
                                        "Error parsing schema " + jsonPath, e);
                                  }
                                  return fields;
                                }),
                            options.getDelimiter()))
                    .withOutputTags(GOOD_RECORDS, TupleTagList.of(BAD_RECORDS)));

    tableRows
        .get(GOOD_RECORDS)
        .apply(
            "Insert good records into Bigquery",
            BigQueryIO.writeTableRows()
                .withSchema(
                    NestedValueProvider.of(
                        options.getSchemaJSONPath(),
                        schemaPath -> {
                          TableSchema tableSchema = new TableSchema();
                          List<TableFieldSchema> fields = new ArrayList<>();
                          SchemaParser schemaParser = new SchemaParser();

                          try {
                            JSONObject jsonSchema = schemaParser.parseSchema(schemaPath);
                            JSONArray bqSchemaJsonArray = jsonSchema.getJSONArray(BIGQUERY_SCHEMA);

                            for (int i = 0; i < bqSchemaJsonArray.length(); i++) {
                              JSONObject inputField = bqSchemaJsonArray.getJSONObject(i);
                              fields.add(convertToTableFieldSchema(inputField));
                            }
                            tableSchema.setFields(fields);

                          } catch (Exception e) {
                            throw new RuntimeException("Error parsing schema " + schemaPath, e);
                          }
                          return tableSchema;
                        }))
                .to(options.getOutputTable())
                .withCreateDisposition(CreateDisposition.CREATE_IF_NEEDED)
                .withWriteDisposition(WriteDisposition.WRITE_APPEND)
                .withCustomGcsTempLocation(options.getBigQueryLoadingTemporaryDirectory()));

    tableRows
        .get(BAD_RECORDS)
        .apply(
            "Insert bad records into Bigquery",
            BigQueryIO.writeTableRows()
                .withSchema(errorTableSchema)
                .to(options.getBadRecordsOutputTable())
                .withCreateDisposition(CreateDisposition.CREATE_IF_NEEDED)
                .withWriteDisposition(WriteDisposition.WRITE_APPEND)
                .withCustomGcsTempLocation(options.getBigQueryLoadingTemporaryDirectory()));

    pipeline.run();
  }

  /**
   * Convert a JSONObject from the Schema JSON to a TableFieldSchema. In case of RECORD, it handles
   * the conversion recursively.
   *
   * @param inputField Input field to convert.
   * @return TableFieldSchema instance to populate the schema.
   */
  private static TableFieldSchema convertToTableFieldSchema(JSONObject inputField) {
    TableFieldSchema field =
        new TableFieldSchema()
            .setName(inputField.getString(NAME))
            .setType(inputField.getString(TYPE));

    if (inputField.has(MODE)) {
      field.setMode(inputField.getString(MODE));
    }

    if (inputField.getString(TYPE) != null && inputField.getString(TYPE).equals(RECORD_TYPE)) {
      List<TableFieldSchema> nestedFields = new ArrayList<>();
      JSONArray fieldsArr = inputField.getJSONArray(FIELDS_ENTRY);
      for (int i = 0; i < fieldsArr.length(); i++) {
        JSONObject nestedJSON = fieldsArr.getJSONObject(i);
        nestedFields.add(convertToTableFieldSchema(nestedJSON));
      }
      field.setFields(nestedFields);
    }

    return field;
  }
}

Langkah selanjutnya

• Pelajari Template Dataflow.
• Lihat daftar template yang disediakan Google.