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File CSV Cloud Storage ke template BigQuery

Pipeline Cloud Storage CSV files to 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 berupa file yang tidak dikompresi atau dikompresi dalam format yang tercantum di halaman SDK Enum Compression.

Catatan: Jika Anda ingin mengganti data di tabel BigQuery dan 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 tingkat teratas yang berjudul BigQuery Schema dan isinya mengikuti pola {"name": "COLUMN_NAME", "type": "DATA_TYPE"}.

Template batch file CSV Cloud Storage ke BigQuery tidak mendukung impor data ke kolom STRUCT (Kumpulan data) 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 yang diperlukan

inputFilePattern: Jalur Cloud Storage ke file CSV yang berisi teks yang akan diproses. Contoh, gs://your-bucket/path/*.csv.
schemaJSONPath: Jalur Cloud Storage ke file JSON yang menentukan skema BigQuery Anda.
outputTable: Nama tabel BigQuery yang menyimpan data yang diproses. Jika Anda menggunakan kembali tabel BigQuery yang sudah ada, data akan ditambahkan ke tabel tujuan.
bigQueryLoadingTemporaryDirectory: Direktori sementara yang akan digunakan selama proses pemuatan BigQuery. Contoh, gs://your-bucket/your-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. Skema tabel ini harus cocok dengan skema tabel error (https://cloud.google.com/dataflow/docs/guides/templates/provided/cloud-storage-csv-to-bigquery#GcsCSVToBigQueryBadRecordsSchema).
delimiter: Pembatas kolom yang digunakan file CSV. Contoh, ,.
csvFormat: Format CSV sesuai dengan format Apache Commons CSV. Default: Default.

Parameter opsional

containsHeaders: Apakah header disertakan dalam file CSV. Default: false.
csvFileEncoding: Format encoding karakter file CSV. Nilai yang Diizinkan adalah US-ASCII, ISO-8859-1, UTF-8, dan UTF-16. Nilai defaultnya: UTF-8.

Menjalankan template

Konsol

Buka halaman Dataflow Membuat tugas dari template.

Buka Membuat tugas dari template

Di kolom Nama tugas, masukkan nama tugas yang unik.
Opsional: Untuk Endpoint regional, pilih nilai dari menu drop-down. Region default-nya adalah us-central1.
Untuk mengetahui daftar region tempat Anda dapat menjalankan tugas Dataflow, lihat Lokasi Dataflow.
Dari menu drop-down Template Dataflow, pilih the CSV files on Cloud Storage to BigQuery (Batch) template.
Di kolom parameter yang disediakan, masukkan nilai parameter Anda.
Klik Jalankan tugas.

gcloud

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: Google Cloud Project ID tempat Anda ingin menjalankan tugas Dataflow
JOB_NAME: nama tugas unik pilihan Anda
VERSION: versi template yang ingin Anda gunakan
Anda dapat menggunakan nilai berikut:
- latest untuk menggunakan template versi terbaru, yang tersedia di folder induk tanpa tanggal di bucket—gs://dataflow-templates-REGION_NAME/latest/
- nama versi, seperti 2023-09-12-00_RC00, untuk menggunakan template versi tertentu, yang dapat ditemukan bertingkat di masing-masing folder induk yang diberi tanggal dalam bucket—gs://dataflow-templates-REGION_NAME/
Perhatian: Template versi terbaru dapat diupdate dengan perubahan yang dapat menyebabkan gangguan. Lingkungan produksi Anda harus menggunakan template yang disimpan di folder induk berisi tanggal terbaru untuk mencegah perubahan yang dapat menyebabkan gangguan ini 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 rekaman buruk BigQuery
PATH_TO_TEMP_DIR_ON_GCS: jalur Cloud Storage ke direktori sementara
DELIMITER: Pembatas file CSV
CSV_FORMAT: Spesifikasi format CSV untuk mengurai data
CONTAINS_HEADERS: apakah file CSV berisi header
CSV_FILE_ENCODING: encoding dalam file CSV

API

Untuk menjalankan template menggunakan REST API, kirim permintaan POST HTTP. Untuk mengetahui 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: Google Cloud Project ID tempat Anda ingin menjalankan tugas Dataflow
JOB_NAME: nama tugas unik pilihan Anda
VERSION: versi template yang ingin Anda gunakan
Anda dapat menggunakan nilai berikut:
- latest untuk menggunakan template versi terbaru, yang tersedia di folder induk tanpa tanggal di bucket—gs://dataflow-templates-REGION_NAME/latest/
- nama versi, seperti 2023-09-12-00_RC00, untuk menggunakan template versi tertentu, yang dapat ditemukan bertingkat di masing-masing folder induk yang diberi tanggal dalam bucket—gs://dataflow-templates-REGION_NAME/
Perhatian: Template versi terbaru dapat diupdate dengan perubahan yang dapat menyebabkan gangguan. Lingkungan produksi Anda harus menggunakan template yang disimpan di folder induk berisi tanggal terbaru untuk mencegah perubahan yang dapat menyebabkan gangguan ini 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 rekaman buruk BigQuery
PATH_TO_TEMP_DIR_ON_GCS: jalur Cloud Storage ke direktori sementara
DELIMITER: Pembatas file CSV
CSV_FORMAT: Spesifikasi format CSV untuk mengurai data
CONTAINS_HEADERS: apakah file CSV berisi header
CSV_FILE_ENCODING: encoding dalam 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.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,
        groupName = "Source",
        description = "Cloud Storage Input File(s)",
        helpText = "The Cloud Storage path to the CSV file that contains the text to process.",
        regexes = {"^gs:\\/\\/[^\\n\\r]+$"},
        example = "gs://your-bucket/path/*.csv")
    ValueProvider<String> getInputFilePattern();

    void setInputFilePattern(ValueProvider<String> value);

    @TemplateParameter.GcsReadFile(
        order = 2,
        groupName = "Target",
        description = "Cloud Storage location of your BigQuery schema file, described as a JSON",
        helpText = "The Cloud Storage path to the JSON file that defines your BigQuery schema.")
    ValueProvider<String> getSchemaJSONPath();

    void setSchemaJSONPath(ValueProvider<String> value);

    @TemplateParameter.BigQueryTable(
        order = 3,
        groupName = "Target",
        description = "BigQuery output table",
        helpText =
            "The name of the BigQuery table that stores your processed data. If you reuse an existing "
                + "BigQuery table, the data is appended to the destination table.")
    ValueProvider<String> getOutputTable();

    void setOutputTable(ValueProvider<String> value);

    @TemplateParameter.GcsWriteFolder(
        order = 4,
        description = "Temporary directory for BigQuery loading process",
        helpText = "The temporary directory to use during the 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 =
            "The name of the BigQuery table to use to store the rejected data when processing the"
                + " CSV files. If you reuse an existing BigQuery table, the data is appended to the"
                + " destination table. The schema of this table must match the"
                + " error table schema (https://cloud.google.com/dataflow/docs/guides/templates/provided/cloud-storage-csv-to-bigquery#GcsCSVToBigQueryBadRecordsSchema).")
    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<Iterable<String>> CSV_HEADERS = new TupleTag<Iterable<String>>() {};

  /** The tag for the lines of the CSV. */
  private static final TupleTag<Iterable<String>> CSV_LINES = new TupleTag<Iterable<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 StringListToTableRowFn extends DoFn<Iterable<String>, TableRow> {
    private final ValueProvider<String> delimiter;
    private final NestedValueProvider<List<String>, String> fields;

    public StringListToTableRowFn(
        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 = ImmutableList.copyOf(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 StringListToTableRowFn(
                            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 berikutnya

Pelajari template Dataflow.
Lihat daftar template yang disediakan Google.

File CSV Cloud Storage ke template BigQuery Tetap teratur dengan koleksi Simpan dan kategorikan konten berdasarkan preferensi Anda.

Persyaratan pipeline

File JSON skema BigQuery

Skema tabel error

Parameter template

Parameter yang diperlukan

Parameter opsional

Menjalankan template

Konsol

gcloud

API

Kode sumber template

Java

Langkah berikutnya

File CSV Cloud Storage ke template BigQuery