import("context""fmt""cloud.google.com/go/bigquery")// importClusteredTable demonstrates creating a table from a load job and defining partitioning and clustering// properties.funcimportClusteredTable(projectID,destDatasetID,destTableIDstring)error{// projectID := "my-project-id"// datasetID := "mydataset"// tableID := "mytable"ctx:=context.Background()client,err:=bigquery.NewClient(ctx,projectID)iferr!=nil{returnfmt.Errorf("bigquery.NewClient: %w",err)}deferclient.Close()gcsRef:=bigquery.NewGCSReference("gs://cloud-samples-data/bigquery/sample-transactions/transactions.csv")gcsRef.SkipLeadingRows=1gcsRef.Schema=bigquery.Schema{{Name:"timestamp",Type:bigquery.TimestampFieldType},{Name:"origin",Type:bigquery.StringFieldType},{Name:"destination",Type:bigquery.StringFieldType},{Name:"amount",Type:bigquery.NumericFieldType},}loader:=client.Dataset(destDatasetID).Table(destTableID).LoaderFrom(gcsRef)loader.TimePartitioning=&bigquery.TimePartitioning{Field:"timestamp",}loader.Clustering=&bigquery.Clustering{Fields:[]string{"origin","destination"},}loader.WriteDisposition=bigquery.WriteEmptyjob,err:=loader.Run(ctx)iferr!=nil{returnerr}status,err:=job.Wait(ctx)iferr!=nil{returnerr}ifstatus.Err()!=nil{returnfmt.Errorf("job completed with error: %w",status.Err())}returnnil}

importcom.google.cloud.bigquery.BigQuery;importcom.google.cloud.bigquery.BigQueryException;importcom.google.cloud.bigquery.BigQueryOptions;importcom.google.cloud.bigquery.Clustering;importcom.google.cloud.bigquery.Field;importcom.google.cloud.bigquery.FormatOptions;importcom.google.cloud.bigquery.Job;importcom.google.cloud.bigquery.JobInfo;importcom.google.cloud.bigquery.LoadJobConfiguration;importcom.google.cloud.bigquery.Schema;importcom.google.cloud.bigquery.StandardSQLTypeName;importcom.google.cloud.bigquery.TableId;importcom.google.cloud.bigquery.TimePartitioning;importcom.google.common.collect.ImmutableList;importjava.util.List;// Sample to load clustered table.publicclassLoadTableClustered{publicstaticvoidmain(String[]args){// TODO(developer): Replace these variables before running the sample.StringdatasetName="MY_DATASET_NAME";StringtableName="MY_TABLE_NAME";StringsourceUri="/path/to/file.csv";Schemaschema=Schema.of(Field.of("name",StandardSQLTypeName.STRING),Field.of("post_abbr",StandardSQLTypeName.STRING),Field.of("date",StandardSQLTypeName.DATE));loadTableClustered(datasetName,tableName,sourceUri,schema,ImmutableList.of("name","post_abbr"));}publicstaticvoidloadTableClustered(StringdatasetName,StringtableName,StringsourceUri,Schemaschema,List<String>clusteringFields){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();TableIdtableId=TableId.of(datasetName,tableName);TimePartitioningpartitioning=TimePartitioning.of(TimePartitioning.Type.DAY);// Clustering fields will be consisted of fields mentioned in the schema.// BigQuery supports clustering for both partitioned and non-partitioned tables.Clusteringclustering=Clustering.newBuilder().setFields(clusteringFields).build();LoadJobConfigurationloadJobConfig=LoadJobConfiguration.builder(tableId,sourceUri).setFormatOptions(FormatOptions.csv()).setSchema(schema).setTimePartitioning(partitioning).setClustering(clustering).build();JobloadJob=bigquery.create(JobInfo.newBuilder(loadJobConfig).build());// Load data from a GCS parquet file into the table// Blocks until this load table job completes its execution, either failing or succeeding.Jobjob=loadJob.waitFor();// Check for errorsif(job.isDone() &&job.getStatus().getError()==null){System.out.println("Data successfully loaded into clustered table during load job");}else{System.out.println("BigQuery was unable to load into the table due to an error:"+job.getStatus().getError());}}catch(BigQueryException|InterruptedExceptione){System.out.println("Data not loaded into clustered table during load job \n"+e.toString());}}}

// Import the Google Cloud client libraryconst{BigQuery}=require('@google-cloud/bigquery');const{Storage}=require('@google-cloud/storage');// Instantiate clientsconstbigquery=newBigQuery();conststorage=newStorage();/** * This sample loads the CSV file at * https://storage.googleapis.com/cloud-samples-data/sample-transactions/transactions.csv * * TODO(developer): Replace the following lines with the path to your file. */constbucketName='cloud-samples-data';constfilename='bigquery/sample-transactions/transactions.csv';asyncfunctionloadTableClustered(){// Loads a new clustered table named "my_table" in "my_dataset"./**   * TODO(developer): Uncomment the following lines before running the sample.   */// const datasetId = "my_dataset";// const tableId = "my_table";constmetadata={sourceFormat:'CSV',skipLeadingRows:1,schema:{fields:[{name:'timestamp',type:'TIMESTAMP'},{name:'origin',type:'STRING'},{name:'destination',type:'STRING'},{name:'amount',type:'NUMERIC'},],},clustering:{fields:['origin','destination'],},};// Load data from a Google Cloud Storage file into the tableconst[job]=awaitbigquery.dataset(datasetId).table(tableId).load(storage.bucket(bucketName).file(filename),metadata);// load() waits for the job to finishconsole.log(`Job${job.id} completed.`);}

fromgoogle.cloudimportbigquery# Construct a BigQuery client object.client=bigquery.Client()# TODO(developer): Set table_id to the ID of the table to create.# table_id = "your-project.your_dataset.your_table_name"job_config=bigquery.LoadJobConfig(skip_leading_rows=1,source_format=bigquery.SourceFormat.CSV,schema=[bigquery.SchemaField("timestamp",bigquery.SqlTypeNames.TIMESTAMP),bigquery.SchemaField("origin",bigquery.SqlTypeNames.STRING),bigquery.SchemaField("destination",bigquery.SqlTypeNames.STRING),bigquery.SchemaField("amount",bigquery.SqlTypeNames.NUMERIC),],time_partitioning=bigquery.TimePartitioning(field="timestamp"),clustering_fields=["origin","destination"],)job=client.load_table_from_uri(["gs://cloud-samples-data/bigquery/sample-transactions/transactions.csv"],table_id,job_config=job_config,)job.result()# Waits for the job to complete.table=client.get_table(table_id)# Make an API request.print("Loaded{} rows and{} columns to{}".format(table.num_rows,len(table.schema),table_id))