하이브 파티션 나누기로 외부 테이블 만들기

하이브 파티션 나누기를 사용하여 외부 테이블을 만듭니다.

코드 샘플

Go

이 샘플을 사용해 보기 전에 BigQuery 빠른 시작: 클라이언트 라이브러리 사용의 Go 설정 안내를 따르세요. 자세한 내용은 BigQuery Go API 참조 문서를 확인하세요.

BigQuery에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

import (
	"context"
	"fmt"

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

// createTableExternalHivePartitioned demonstrates creating an external table with hive partitioning.
func createTableExternalHivePartitioned(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()

	// First, we'll define table metadata to represent a table that's backed by parquet files held in
	// Cloud Storage.
	//
	// Example file:
	// gs://cloud-samples-data/bigquery/hive-partitioning-samples/autolayout/dt=2020-11-15/file1.parquet
	metadata := &bigquery.TableMetadata{
		Description: "An example table that demonstrates hive partitioning against external parquet files",
		ExternalDataConfig: &bigquery.ExternalDataConfig{
			SourceFormat: bigquery.Parquet,
			SourceURIs:   []string{"gs://cloud-samples-data/bigquery/hive-partitioning-samples/autolayout/*"},
			AutoDetect:   true,
		},
	}

	// The layout of the files in here is compatible with the layout requirements for hive partitioning,
	// so we can add an optional Hive partitioning configuration to leverage the object paths for deriving
	// partitioning column information.
	//
	// For more information on how partitions are extracted, see:
	// https://cloud.google.com/bigquery/docs/hive-partitioned-queries-gcs
	//
	// We have a "/dt=YYYY-MM-DD/" path component in our example files as documented above.  Autolayout will
	// expose this as a column named "dt" of type DATE.
	metadata.ExternalDataConfig.HivePartitioningOptions = &bigquery.HivePartitioningOptions{
		Mode:                   bigquery.AutoHivePartitioningMode,
		SourceURIPrefix:        "gs://cloud-samples-data/bigquery/hive-partitioning-samples/autolayout/",
		RequirePartitionFilter: true,
	}

	// Create the external table.
	tableRef := client.Dataset(datasetID).Table(tableID)
	if err := tableRef.Create(ctx, metadata); err != nil {
		return fmt.Errorf("table creation failure: %w", err)
	}
	return nil
}

Java

이 샘플을 사용해 보기 전에 BigQuery 빠른 시작: 클라이언트 라이브러리 사용의 Java 설정 안내를 따르세요. 자세한 내용은 BigQuery Java API 참조 문서를 확인하세요.

BigQuery에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

GitHub에서 보기 의견 보내기

import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.ExternalTableDefinition;
import com.google.cloud.bigquery.FormatOptions;
import com.google.cloud.bigquery.HivePartitioningOptions;
import com.google.cloud.bigquery.TableId;
import com.google.cloud.bigquery.TableInfo;

// Sample to create external table using hive partitioning
public class CreateTableExternalHivePartitioned {

  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";
    String sourceUri = "gs://cloud-samples-data/bigquery/hive-partitioning-samples/customlayout/*";
    String sourceUriPrefix =
        "gs://cloud-samples-data/bigquery/hive-partitioning-samples/customlayout/{pkey:STRING}/";
    createTableExternalHivePartitioned(datasetName, tableName, sourceUriPrefix, sourceUri);
  }

  public static void createTableExternalHivePartitioned(
      String datasetName, String tableName, String sourceUriPrefix, String sourceUri) {
    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();

      // Configuring partitioning options
      HivePartitioningOptions hivePartitioningOptions =
          HivePartitioningOptions.newBuilder()
              .setMode("CUSTOM")
              .setRequirePartitionFilter(true)
              .setSourceUriPrefix(sourceUriPrefix)
              .build();

      TableId tableId = TableId.of(datasetName, tableName);
      ExternalTableDefinition customTable =
          ExternalTableDefinition.newBuilder(sourceUri, FormatOptions.parquet())
              .setAutodetect(true)
              .setHivePartitioningOptions(hivePartitioningOptions)
              .build();
      bigquery.create(TableInfo.of(tableId, customTable));
      System.out.println("External table created using hivepartitioningoptions");
    } catch (BigQueryException e) {
      System.out.println("External table was not created" + e.toString());
    }
  }
}

Python

이 샘플을 사용해 보기 전에 BigQuery 빠른 시작: 클라이언트 라이브러리 사용의 Python 설정 안내를 따르세요. 자세한 내용은 BigQuery Python API 참조 문서를 확인하세요.

BigQuery에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

GitHub에서 보기 의견 보내기

# Demonstrates creating an external table with hive partitioning.

# TODO(developer): Set table_id to the ID of the table to create.
table_id = "your-project.your_dataset.your_table_name"

# TODO(developer): Set source uri.
# Example file:
# gs://cloud-samples-data/bigquery/hive-partitioning-samples/autolayout/dt=2020-11-15/file1.parquet
uri = "gs://cloud-samples-data/bigquery/hive-partitioning-samples/autolayout/*"

# TODO(developer): Set source uri prefix.
source_uri_prefix = (
    "gs://cloud-samples-data/bigquery/hive-partitioning-samples/autolayout/"
)

from google.cloud import bigquery

# Construct a BigQuery client object.
client = bigquery.Client()

# Configure the external data source.
external_config = bigquery.ExternalConfig("PARQUET")
external_config.source_uris = [uri]
external_config.autodetect = True

# Configure partitioning options.
hive_partitioning_opts = bigquery.HivePartitioningOptions()

# The layout of the files in here is compatible with the layout requirements for hive partitioning,
# so we can add an optional Hive partitioning configuration to leverage the object paths for deriving
# partitioning column information.

# For more information on how partitions are extracted, see:
# https://cloud.google.com/bigquery/docs/hive-partitioned-queries-gcs

# We have a "/dt=YYYY-MM-DD/" path component in our example files as documented above.
# Autolayout will expose this as a column named "dt" of type DATE.
hive_partitioning_opts.mode = "AUTO"
hive_partitioning_opts.require_partition_filter = True
hive_partitioning_opts.source_uri_prefix = source_uri_prefix

external_config.hive_partitioning = hive_partitioning_opts

table = bigquery.Table(table_id)
table.external_data_configuration = external_config

table = client.create_table(table)  # Make an API request.
print(
    "Created table {}.{}.{}".format(table.project, table.dataset_id, table.table_id)
)

다음 단계

다른 Google Cloud 제품의 코드 샘플을 검색하고 필터링하려면 Google Cloud 샘플 브라우저를 참조하세요.