Inserción de transmisión con tipos de datos complejos

Inserta datos de varios tipos compatibles con BigQuery en una tabla.

Muestra de código

Java

Antes de probar este ejemplo, sigue las instrucciones de configuración para Java incluidas en la guía de inicio rápido de BigQuery sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de BigQuery para Java.

import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryError;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.Field;
import com.google.cloud.bigquery.InsertAllRequest;
import com.google.cloud.bigquery.InsertAllResponse;
import com.google.cloud.bigquery.Schema;
import com.google.cloud.bigquery.StandardSQLTypeName;
import com.google.cloud.bigquery.StandardTableDefinition;
import com.google.cloud.bigquery.TableDefinition;
import com.google.cloud.bigquery.TableId;
import com.google.cloud.bigquery.TableInfo;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

// Sample to insert data types in a table
public class InsertingDataTypes {

  public static void main(String[] args) {
    // TODO(developer): Replace these variables before running the sample.
    String datasetName = "MY_DATASET_NAME";
    String tableName = "MY_TABLE_NAME";
    insertingDataTypes(datasetName, tableName);
  }

  public static void insertingDataTypes(String datasetName, String tableName) {
    try {
      // Initialize client that will be used to send requests. This client only needs to be created
      // once, and can be reused for multiple requests.
      BigQuery bigquery = BigQueryOptions.getDefaultInstance().getService();

      // Inserting data types
      Field name = Field.of("name", StandardSQLTypeName.STRING);
      Field age = Field.of("age", StandardSQLTypeName.INT64);
      Field school =
          Field.newBuilder("school", StandardSQLTypeName.BYTES)
              .setMode(Field.Mode.REPEATED)
              .build();
      Field location = Field.of("location", StandardSQLTypeName.GEOGRAPHY);
      Field measurements =
          Field.newBuilder("measurements", StandardSQLTypeName.FLOAT64)
              .setMode(Field.Mode.REPEATED)
              .build();
      Field day = Field.of("day", StandardSQLTypeName.DATE);
      Field firstTime = Field.of("firstTime", StandardSQLTypeName.DATETIME);
      Field secondTime = Field.of("secondTime", StandardSQLTypeName.TIME);
      Field thirdTime = Field.of("thirdTime", StandardSQLTypeName.TIMESTAMP);
      Field datesTime =
          Field.of("datesTime", StandardSQLTypeName.STRUCT, day, firstTime, secondTime, thirdTime);
      Schema schema = Schema.of(name, age, school, location, measurements, datesTime);

      TableId tableId = TableId.of(datasetName, tableName);
      TableDefinition tableDefinition = StandardTableDefinition.of(schema);
      TableInfo tableInfo = TableInfo.newBuilder(tableId, tableDefinition).build();

      bigquery.create(tableInfo);

      // Inserting Sample data
      Map<String, Object> datesTimeContent = new HashMap<>();
      datesTimeContent.put("day", "2019-1-12");
      datesTimeContent.put("firstTime", "2019-02-17 11:24:00.000");
      datesTimeContent.put("secondTime", "14:00:00");
      datesTimeContent.put("thirdTime", "2020-04-27T18:07:25.356Z");

      Map<String, Object> rowContent = new HashMap<>();
      rowContent.put("name", "Tom");
      rowContent.put("age", 30);
      rowContent.put("school", "Test University".getBytes());
      rowContent.put("location", "POINT(1 2)");
      rowContent.put("measurements", new Float[] {50.05f, 100.5f});
      rowContent.put("datesTime", datesTimeContent);

      InsertAllResponse response =
          bigquery.insertAll(InsertAllRequest.newBuilder(tableId).addRow(rowContent).build());

      if (response.hasErrors()) {
        // If any of the insertions failed, this lets you inspect the errors
        for (Map.Entry<Long, List<BigQueryError>> entry : response.getInsertErrors().entrySet()) {
          System.out.println("Response error: \n" + entry.getValue());
        }
      }
      System.out.println("Rows successfully inserted into table");
    } catch (BigQueryException e) {
      System.out.println("Insert operation not performed \n" + e.toString());
    }
  }
}

Node.js

Antes de probar este ejemplo, sigue las instrucciones de configuración para Node.js incluidas en la guía de inicio rápido de BigQuery sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la Documentación de referencia de la API de Node.js de BigQuery.

// Import the Google Cloud client library
const {BigQuery} = require('@google-cloud/bigquery');
const bigquery = new BigQuery();

async function instertingDataTypes() {
  // Inserts data of various BigQuery-supported types into a table.

  /**
   * TODO(developer): Uncomment the following lines before running the sample.
   */
  // const datasetId = 'my_dataset';
  // const tableId = 'my_table';

  // Describe the schema of the table
  // For more information on supported data types, see
  // https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types
  const schema = [
    {
      name: 'name',
      type: 'STRING',
    },
    {
      name: 'age',
      type: 'INTEGER',
    },
    {
      name: 'school',
      type: 'BYTES',
    },
    {
      name: 'location',
      type: 'GEOGRAPHY',
    },
    {
      name: 'measurements',
      mode: 'REPEATED',
      type: 'FLOAT',
    },
    {
      name: 'datesTimes',
      type: 'RECORD',
      fields: [
        {
          name: 'day',
          type: 'DATE',
        },
        {
          name: 'firstTime',
          type: 'DATETIME',
        },
        {
          name: 'secondTime',
          type: 'TIME',
        },
        {
          name: 'thirdTime',
          type: 'TIMESTAMP',
        },
      ],
    },
  ];

  // For all options, see https://cloud.google.com/bigquery/docs/reference/v2/tables#resource
  const options = {
    schema: schema,
  };

  // Create a new table in the dataset
  const [table] = await bigquery
    .dataset(datasetId)
    .createTable(tableId, options);

  console.log(`Table ${table.id} created.`);

  // The DATE type represents a logical calendar date, independent of time zone.
  // A DATE value does not represent a specific 24-hour time period.
  // Rather, a given DATE value represents a different 24-hour period when
  // interpreted in different time zones, and may represent a shorter or longer
  // day during Daylight Savings Time transitions.
  const bqDate = bigquery.date('2019-1-12');
  // A DATETIME object represents a date and time, as they might be
  // displayed on a calendar or clock, independent of time zone.
  const bqDatetime = bigquery.datetime('2019-02-17 11:24:00.000');
  // A TIME object represents a time, as might be displayed on a watch,
  // independent of a specific date and timezone.
  const bqTime = bigquery.time('14:00:00');
  // A TIMESTAMP object represents an absolute point in time,
  // independent of any time zone or convention such as Daylight
  // Savings Time with microsecond precision.
  const bqTimestamp = bigquery.timestamp('2020-04-27T18:07:25.356Z');
  const bqGeography = bigquery.geography('POINT(1 2)');
  const schoolBuffer = Buffer.from('Test University');

  // Rows to be inserted into table
  const rows = [
    {
      name: 'Tom',
      age: '30',
      location: bqGeography,
      school: schoolBuffer,
      measurements: [50.05, 100.5],
      datesTimes: {
        day: bqDate,
        firstTime: bqDatetime,
        secondTime: bqTime,
        thirdTime: bqTimestamp,
      },
    },
    {
      name: 'Ada',
      age: '35',
      measurements: [30.08, 121.7],
    },
  ];

  // Insert data into table
  await bigquery
    .dataset(datasetId)
    .table(tableId)
    .insert(rows);

  console.log(`Inserted ${rows.length} rows`);
}

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