将 BigQuery 支持的各种类型的数据插入表中。
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"
"time"
"cloud.google.com/go/bigquery"
"cloud.google.com/go/civil"
)
// ComplexType represents a complex row item
type ComplexType struct {
Name string `bigquery:"name"`
Age int `bigquery:"age"`
School []byte `bigquery:"school"`
Location bigquery.NullGeography `bigquery:"location"`
Measurements []float64 `bigquery:"measurements"`
DatesTime DatesTime `bigquery:"datesTime"`
}
// DatesTime shows different date/time representation
type DatesTime struct {
Day civil.Date `bigquery:"day"`
FirstTime civil.DateTime `bigquery:"firstTime"`
SecondTime civil.Time `bigquery:"secondTime"`
ThirdTime time.Time `bigquery:"thirdTime"`
}
// insertingDataTypes demonstrates inserting data into a table using the streaming insert mechanism.
func insertingDataTypes(projectID, datasetID, tableID string) error {
// projectID := "my-project-id"
// datasetID := "mydataset"
// tableID := "mytable"
ctx := context.Background()
client, err := bigquery.NewClient(ctx, projectID)
if err != nil {
return fmt.Errorf("bigquery.NewClient: %w", err)
}
defer client.Close()
// Manually defining schema
schema := bigquery.Schema{
{Name: "name", Type: bigquery.StringFieldType},
{Name: "age", Type: bigquery.IntegerFieldType},
{Name: "school", Type: bigquery.BytesFieldType},
{Name: "location", Type: bigquery.GeographyFieldType},
{Name: "measurements", Type: bigquery.FloatFieldType, Repeated: true},
{Name: "datesTime", Type: bigquery.RecordFieldType, Schema: bigquery.Schema{
{Name: "day", Type: bigquery.DateFieldType},
{Name: "firstTime", Type: bigquery.DateTimeFieldType},
{Name: "secondTime", Type: bigquery.TimeFieldType},
{Name: "thirdTime", Type: bigquery.TimestampFieldType},
}},
}
// Infer schema from struct
// schema, err := bigquery.InferSchema(ComplexType{})
table := client.Dataset(datasetID).Table(tableID)
err = table.Create(ctx, &bigquery.TableMetadata{
Schema: schema,
})
if err != nil {
return fmt.Errorf("table.Create: %w", err)
}
day, err := civil.ParseDate("2019-01-12")
if err != nil {
return fmt.Errorf("civil.ParseDate: %w", err)
}
firstTime, err := civil.ParseDateTime("2019-02-17T11:24:00.000")
if err != nil {
return fmt.Errorf("civil.ParseDateTime: %w", err)
}
secondTime, err := civil.ParseTime("14:00:00")
if err != nil {
return fmt.Errorf("civil.ParseTime: %w", err)
}
thirdTime, err := time.Parse(time.RFC3339Nano, "2020-04-27T18:07:25.356Z")
if err != nil {
return fmt.Errorf("time.Parse: %w", err)
}
row := &ComplexType{
Name: "Tom",
Age: 30,
School: []byte("Test University"),
Location: bigquery.NullGeography{GeographyVal: "POINT(1 2)", Valid: true},
Measurements: []float64{50.05, 100.5},
DatesTime: DatesTime{
Day: day,
FirstTime: firstTime,
SecondTime: secondTime,
ThirdTime: thirdTime,
},
}
rows := []*ComplexType{row}
// Uncomment to simulate insert errors.
// This example row is missing required fields.
// badRow := &ComplexType{
// Name: "John",
// Age: 24,
// }
// rows = append(rows, badRow)
inserter := table.Inserter()
err = inserter.Put(ctx, rows)
if err != nil {
if multiErr, ok := err.(bigquery.PutMultiError); ok {
for _, putErr := range multiErr {
fmt.Printf("failed to insert row %d with err: %v \n", putErr.RowIndex, putErr.Error())
}
}
return err
}
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.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.Base64;
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.of("school", StandardSQLTypeName.BYTES);
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", Base64.getEncoder().encodeToString("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
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
const {BigQuery} = require('@google-cloud/bigquery');
const bigquery = new BigQuery();
async function insertingDataTypes() {
// 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: 'metadata',
type: 'JSON',
},
{
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');
// a JSON field needs to be converted to a string
const metadata = JSON.stringify({
owner: 'John Doe',
contact: 'johndoe@example.com',
});
// Rows to be inserted into table
const rows = [
{
name: 'Tom',
age: '30',
location: bqGeography,
school: schoolBuffer,
metadata: metadata,
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`);
}后续步骤
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