嵌套的重复架构

在架构中指定嵌套和重复的列。

深入探索

如需查看包含此代码示例的详细文档，请参阅以下内容：

• 在表架构中指定嵌套和重复的列

代码示例

Go

试用此示例之前，请按照 BigQuery 快速入门：使用客户端库中的 Go 设置说明进行操作。 如需了解详情，请参阅 BigQuery Go API 参考文档。

如需向 BigQuery 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为客户端库设置身份验证。

import (
	"context"
	"fmt"
	"io"

	"cloud.google.com/go/bigquery"
)

// createTableComplexSchema demonstrates creating a BigQuery table and specifying a complex schema that includes
// an array of Struct types.
func createTableComplexSchema(w io.Writer, projectID, datasetID, tableID string) error {
	// projectID := "my-project-id"
	// datasetID := "mydatasetid"
	// tableID := "mytableid"
	ctx := context.Background()

	client, err := bigquery.NewClient(ctx, projectID)
	if err != nil {
		return fmt.Errorf("bigquery.NewClient: %w", err)
	}
	defer client.Close()

	sampleSchema := bigquery.Schema{
		{Name: "id", Type: bigquery.StringFieldType},
		{Name: "first_name", Type: bigquery.StringFieldType},
		{Name: "last_name", Type: bigquery.StringFieldType},
		{Name: "dob", Type: bigquery.DateFieldType},
		{Name: "addresses",
			Type:     bigquery.RecordFieldType,
			Repeated: true,
			Schema: bigquery.Schema{
				{Name: "status", Type: bigquery.StringFieldType},
				{Name: "address", Type: bigquery.StringFieldType},
				{Name: "city", Type: bigquery.StringFieldType},
				{Name: "state", Type: bigquery.StringFieldType},
				{Name: "zip", Type: bigquery.StringFieldType},
				{Name: "numberOfYears", Type: bigquery.StringFieldType},
			}},
	}

	metaData := &bigquery.TableMetadata{
		Schema: sampleSchema,
	}
	tableRef := client.Dataset(datasetID).Table(tableID)
	if err := tableRef.Create(ctx, metaData); err != nil {
		return err
	}
	fmt.Fprintf(w, "created table %s\n", tableRef.FullyQualifiedName())
	return nil
}

Java

试用此示例之前，请按照 BigQuery 快速入门：使用客户端库中的 Java 设置说明进行操作。 如需了解详情，请参阅 BigQuery Java API 参考文档。

如需向 BigQuery 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为客户端库设置身份验证。

import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.Field;
import com.google.cloud.bigquery.Field.Mode;
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;

public class NestedRepeatedSchema {

  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";
    createTableWithNestedRepeatedSchema(datasetName, tableName);
  }

  public static void createTableWithNestedRepeatedSchema(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();

      TableId tableId = TableId.of(datasetName, tableName);

      Schema schema =
          Schema.of(
              Field.of("id", StandardSQLTypeName.STRING),
              Field.of("first_name", StandardSQLTypeName.STRING),
              Field.of("last_name", StandardSQLTypeName.STRING),
              Field.of("dob", StandardSQLTypeName.DATE),
              // create the nested and repeated field
              Field.newBuilder(
                      "addresses",
                      StandardSQLTypeName.STRUCT,
                      Field.of("status", StandardSQLTypeName.STRING),
                      Field.of("address", StandardSQLTypeName.STRING),
                      Field.of("city", StandardSQLTypeName.STRING),
                      Field.of("state", StandardSQLTypeName.STRING),
                      Field.of("zip", StandardSQLTypeName.STRING),
                      Field.of("numberOfYears", StandardSQLTypeName.STRING))
                  .setMode(Mode.REPEATED)
                  .build());

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

      bigquery.create(tableInfo);
      System.out.println("Table with nested and repeated schema created successfully");
    } catch (BigQueryException e) {
      System.out.println("Table was not created. \n" + e.toString());
    }
  }
}

Node.js

试用此示例之前，请按照 BigQuery 快速入门：使用客户端库中的 Node.js 设置说明进行操作。 如需了解详情，请参阅 BigQuery Node.js API 参考文档。

如需向 BigQuery 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为客户端库设置身份验证。

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

async function nestedRepeatedSchema() {
  // Creates a new table named "my_table" in "my_dataset"
  // with nested and repeated columns in schema.

  /**
   * TODO(developer): Uncomment the following lines before running the sample.
   */
  // const datasetId = "my_dataset";
  // const tableId = "my_table";
  // const schema = [
  //   {name: 'Name', type: 'STRING', mode: 'REQUIRED'},
  //   {
  //     name: 'Addresses',
  //     type: 'RECORD',
  //     mode: 'REPEATED',
  //     fields: [
  //       {name: 'Address', type: 'STRING'},
  //       {name: 'City', type: 'STRING'},
  //       {name: 'State', type: 'STRING'},
  //       {name: 'Zip', type: 'STRING'},
  //     ],
  //   },
  // ];

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

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

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

Python

试用此示例之前，请按照 BigQuery 快速入门：使用客户端库中的 Python 设置说明进行操作。 如需了解详情，请参阅 BigQuery Python API 参考文档。

如需向 BigQuery 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为客户端库设置身份验证。

from google.cloud import bigquery

client = bigquery.Client()

# TODO(dev): Change table_id to the full name of the table you want to create.
table_id = "your-project.your_dataset.your_table_name"

schema = [
    bigquery.SchemaField("id", "STRING", mode="NULLABLE"),
    bigquery.SchemaField("first_name", "STRING", mode="NULLABLE"),
    bigquery.SchemaField("last_name", "STRING", mode="NULLABLE"),
    bigquery.SchemaField("dob", "DATE", mode="NULLABLE"),
    bigquery.SchemaField(
        "addresses",
        "RECORD",
        mode="REPEATED",
        fields=[
            bigquery.SchemaField("status", "STRING", mode="NULLABLE"),
            bigquery.SchemaField("address", "STRING", mode="NULLABLE"),
            bigquery.SchemaField("city", "STRING", mode="NULLABLE"),
            bigquery.SchemaField("state", "STRING", mode="NULLABLE"),
            bigquery.SchemaField("zip", "STRING", mode="NULLABLE"),
            bigquery.SchemaField("numberOfYears", "STRING", mode="NULLABLE"),
        ],
    ),
]
table = bigquery.Table(table_id, schema=schema)
table = client.create_table(table)  # API request

print(f"Created table {table.project}.{table.dataset_id}.{table.table_id}.")

后续步骤

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