import("context""fmt""io""cloud.google.com/go/bigquery""google.golang.org/api/iterator")// queryClusteredTable demonstrates querying a table that has a clustering specification.funcqueryClusteredTable(wio.Writer,projectID,datasetID,tableIDstring)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()q:=client.Query(fmt.Sprintf(`SELECT  COUNT(1) as transactions,  SUM(amount) as total_paid,  COUNT(DISTINCT destination) as distinct_recipients    FROM  `+"`%s.%s`"+` WHERE    timestamp > TIMESTAMP('2015-01-01')AND origin = @wallet`,datasetID,tableID))q.Parameters=[]bigquery.QueryParameter{{Name:"wallet",Value:"wallet00001866cb7e0f09a890",},}// Run the query and process the returned row iterator.it,err:=q.Read(ctx)iferr!=nil{returnfmt.Errorf("query.Read(): %w",err)}for{varrow[]bigquery.Valueerr:=it.Next(&row)iferr==iterator.Done{break}iferr!=nil{returnerr}fmt.Fprintln(w,row)}returnnil}

importcom.google.cloud.bigquery.BigQuery;importcom.google.cloud.bigquery.BigQueryException;importcom.google.cloud.bigquery.BigQueryOptions;importcom.google.cloud.bigquery.QueryJobConfiguration;importcom.google.cloud.bigquery.TableResult;publicclassQueryClusteredTable{publicstaticvoidmain(String[]args)throwsException{// TODO(developer): Replace these variables before running the sample.StringprojectId="MY_PROJECT_ID";StringdatasetName="MY_DATASET_NAME";StringtableName="MY_TABLE_NAME";queryClusteredTable(projectId,datasetName,tableName);}publicstaticvoidqueryClusteredTable(StringprojectId,StringdatasetName,StringtableName){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();StringsourceTable="`"+projectId+"."+datasetName+"."+tableName+"`";Stringquery="SELECT word, word_count\n"+"FROM "+sourceTable+"\n"// Optimize query performance by filtering the clustered columns in sort order+"WHERE corpus = 'romeoandjuliet'\n"+"AND word_count >= 1";QueryJobConfigurationqueryConfig=QueryJobConfiguration.newBuilder(query).build();TableResultresults=bigquery.query(queryConfig);results.iterateAll().forEach(row->row.forEach(val->System.out.printf("%s,",val.toString())));System.out.println("Query clustered table performed successfully.");}catch(BigQueryException|InterruptedExceptione){System.out.println("Query not performed \n"+e.toString());}}}

// Import the Google Cloud client libraryconst{BigQuery}=require('@google-cloud/bigquery');constbigquery=newBigQuery();asyncfunctionqueryClusteredTable(){// Queries a table that has a clustering specification.// Create destination table referenceconstdataset=bigquery.dataset(datasetId);constdestinationTableId=dataset.table(tableId);constquery='SELECT * FROM `bigquery-public-data.samples.shakespeare`';constfields=['corpus'];// For all options, see https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs/queryconstoptions={query:query,// Location must match that of the dataset(s) referenced in the query.location:'US',destination:destinationTableId,clusterFields:fields,};// Run the query as a jobconst[job]=awaitbigquery.createQueryJob(options);console.log(`Job${job.id} started.`);// Print the status and statisticsconsole.log('Status:');console.log(job.metadata.status);console.log('\nJob Statistics:');console.log(job.metadata.statistics);}

fromgoogle.cloudimportbigquery# Construct a BigQuery client object.client=bigquery.Client()# TODO(developer): Set table_id to the ID of the destination table.# table_id = "your-project.your_dataset.your_table_name"sql="SELECT * FROM `bigquery-public-data.samples.shakespeare`"cluster_fields=["corpus"]job_config=bigquery.QueryJobConfig(clustering_fields=cluster_fields,destination=table_id)# Start the query, passing in the extra configuration.client.query_and_wait(sql,job_config=job_config)# Make an API request and wait for job to complete.table=client.get_table(table_id)# Make an API request.iftable.clustering_fields==cluster_fields:print("The destination table is written using the cluster_fields configuration.")