import("context""fmt""cloud.google.com/go/bigquery")// createTableExternalHivePartitioned demonstrates creating an external table with hive partitioning.funccreateTableExternalHivePartitioned(projectID,datasetID,tableIDstring)error{// projectID := "my-project-id"// datasetID := "mydatasetid"// tableID := "mytableid"ctx:=context.Background()client,err:=bigquery.NewClient(ctx,projectID)iferr!=nil{returnfmt.Errorf("bigquery.NewClient: %w",err)}deferclient.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.parquetmetadata:=&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)iferr:=tableRef.Create(ctx,metadata);err!=nil{returnfmt.Errorf("table creation failure: %w",err)}returnnil}

importcom.google.cloud.bigquery.BigQuery;importcom.google.cloud.bigquery.BigQueryException;importcom.google.cloud.bigquery.BigQueryOptions;importcom.google.cloud.bigquery.ExternalTableDefinition;importcom.google.cloud.bigquery.FormatOptions;importcom.google.cloud.bigquery.HivePartitioningOptions;importcom.google.cloud.bigquery.TableId;importcom.google.cloud.bigquery.TableInfo;// Sample to create external table using hive partitioningpublicclassCreateTableExternalHivePartitioned{publicstaticvoidmain(String[]args){// TODO(developer): Replace these variables before running the sample.StringdatasetName="MY_DATASET_NAME";StringtableName="MY_TABLE_NAME";StringsourceUri="gs://cloud-samples-data/bigquery/hive-partitioning-samples/customlayout/*";StringsourceUriPrefix="gs://cloud-samples-data/bigquery/hive-partitioning-samples/customlayout/{pkey:STRING}/";createTableExternalHivePartitioned(datasetName,tableName,sourceUriPrefix,sourceUri);}publicstaticvoidcreateTableExternalHivePartitioned(StringdatasetName,StringtableName,StringsourceUriPrefix,StringsourceUri){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.BigQuerybigquery=BigQueryOptions.getDefaultInstance().getService();// Configuring partitioning optionsHivePartitioningOptionshivePartitioningOptions=HivePartitioningOptions.newBuilder().setMode("CUSTOM").setRequirePartitionFilter(true).setSourceUriPrefix(sourceUriPrefix).build();TableIdtableId=TableId.of(datasetName,tableName);ExternalTableDefinitioncustomTable=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(BigQueryExceptione){System.out.println("External table was not created"+e.toString());}}}

# 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.parqueturi="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/")fromgoogle.cloudimportbigquery# 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=Truehive_partitioning_opts.source_uri_prefix=source_uri_prefixexternal_config.hive_partitioning=hive_partitioning_optstable=bigquery.Table(table_id)table.external_data_configuration=external_configtable=client.create_table(table)# Make an API request.print("Created table{}.{}.{}".format(table.project,table.dataset_id,table.table_id))