Daten aus verschiedenen von BigQuery unterstützten Typen in eine Tabelle einfügen
Codebeispiel
Go
Bevor Sie dieses Beispiel anwenden, folgen Sie den Schritten zur Einrichtung von Go in der BigQuery-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Angaben finden Sie in der Referenzdokumentation zur BigQuery Go API.
Richten Sie zur Authentifizierung bei BigQuery die Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für Clientbibliotheken einrichten.
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
Bevor Sie dieses Beispiel ausprobieren, folgen Sie der Java-Einrichtungsanleitung in der BigQuery-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Angaben finden Sie in der Referenzdokumentation zur BigQuery Java API.
Richten Sie zur Authentifizierung bei BigQuery die Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für Clientbibliotheken einrichten.
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
Bevor Sie dieses Beispiel ausprobieren, folgen Sie der Node.js-Einrichtungsanleitung in der BigQuery-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Angaben finden Sie in der Referenzdokumentation zur BigQuery Node.js API.
Richten Sie zur Authentifizierung bei BigQuery die Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für Clientbibliotheken einrichten.
// 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`);
}Nächste Schritte
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