Query a clustered table

  import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "io" 
  
 "cloud.google.com/go/bigquery" 
  
 "google.golang.org/api/iterator" 
 ) 
 // queryClusteredTable demonstrates querying a table that has a clustering specification. 
 func 
  
 queryClusteredTable 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 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 
 () 
  
 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 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "query.Read(): %w" 
 , 
  
 err 
 ) 
  
 } 
  
 for 
  
 { 
  
 var 
  
 row 
  
 [] 
 bigquery 
 . 
  Value 
 
  
 err 
  
 := 
  
 it 
 . 
 Next 
 ( 
& row 
 ) 
  
 if 
  
 err 
  
 == 
  
 iterator 
 . 
 Done 
  
 { 
  
 break 
  
 } 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 row 
 ) 
  
 } 
  
 return 
  
 nil 
 } 
 

  import 
  
 com.google.cloud.bigquery. BigQuery 
 
 ; 
 import 
  
 com.google.cloud.bigquery. BigQueryException 
 
 ; 
 import 
  
 com.google.cloud.bigquery. BigQueryOptions 
 
 ; 
 import 
  
 com.google.cloud.bigquery. QueryJobConfiguration 
 
 ; 
 import 
  
 com.google.cloud.bigquery. TableResult 
 
 ; 
 public 
  
 class 
 QueryClusteredTable 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 Exception 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 projectId 
  
 = 
  
 "MY_PROJECT_ID" 
 ; 
  
 String 
  
 datasetName 
  
 = 
  
 "MY_DATASET_NAME" 
 ; 
  
 String 
  
 tableName 
  
 = 
  
 "MY_TABLE_NAME" 
 ; 
  
 queryClusteredTable 
 ( 
 projectId 
 , 
  
 datasetName 
 , 
  
 tableName 
 ); 
  
 } 
  
 public 
  
 static 
  
 void 
  
 queryClusteredTable 
 ( 
 String 
  
 projectId 
 , 
  
 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 
 (); 
  
 String 
  
 sourceTable 
  
 = 
  
 "`" 
  
 + 
  
 projectId 
  
 + 
  
 "." 
  
 + 
  
 datasetName 
  
 + 
  
 "." 
  
 + 
  
 tableName 
  
 + 
  
 "`" 
 ; 
  
 String 
  
 query 
  
 = 
  
 "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" 
 ; 
  
  QueryJobConfiguration 
 
  
 queryConfig 
  
 = 
  
  QueryJobConfiguration 
 
 . 
 newBuilder 
 ( 
 query 
 ). 
 build 
 (); 
  
  TableResult 
 
  
 results 
  
 = 
  
 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 
 
  
 | 
  
 InterruptedException 
  
 e 
 ) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Query not performed \n" 
  
 + 
  
 e 
 . 
 toString 
 ()); 
  
 } 
  
 } 
 } 
 

  // Import the Google Cloud client library 
 const 
  
 { 
 BigQuery 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/bigquery 
' 
 ); 
 const 
  
 bigquery 
  
 = 
  
 new 
  
  BigQuery 
 
 (); 
 async 
  
 function 
  
 queryClusteredTable 
 () 
  
 { 
  
 // Queries a table that has a clustering specification. 
  
 // Create destination table reference 
  
 const 
  
 dataset 
  
 = 
  
 bigquery 
 . 
 dataset 
 ( 
 datasetId 
 ); 
  
 const 
  
 destinationTableId 
  
 = 
  
 dataset 
 . 
 table 
 ( 
 tableId 
 ); 
  
 const 
  
 query 
  
 = 
  
 'SELECT * FROM `bigquery-public-data.samples.shakespeare`' 
 ; 
  
 const 
  
 fields 
  
 = 
  
 [ 
 'corpus' 
 ]; 
  
 // For all options, see https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs/query 
  
 const 
  
 options 
  
 = 
  
 { 
  
 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 job 
  
 const 
  
 [ 
 job 
 ] 
  
 = 
  
 await 
  
 bigquery 
 . 
 createQueryJob 
 ( 
 options 
 ); 
  
 console 
 . 
 log 
 ( 
 `Job 
 ${ 
  job 
 
 . 
 id 
 } 
 started.` 
 ); 
  
 // Print the status and statistics 
  
 console 
 . 
 log 
 ( 
 'Status:' 
 ); 
  
 console 
 . 
 log 
 ( 
  job 
 
 . 
 metadata 
 . 
 status 
 ); 
  
 console 
 . 
 log 
 ( 
 '\nJob Statistics:' 
 ); 
  
 console 
 . 
 log 
 ( 
  job 
 
 . 
 metadata 
 . 
 statistics 
 ); 
 } 
 

  from 
  
 google.cloud 
  
 import 
  bigquery 
 
 # 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. 
 if 
 table 
 . 
 clustering_fields 
 == 
 cluster_fields 
 : 
 print 
 ( 
 "The destination table is written using the cluster_fields configuration." 
 )