Create a clustered table

  import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "time" 
  
 "cloud.google.com/go/bigquery" 
 ) 
 // createTableClustered demonstrates creating a BigQuery table with advanced properties like 
 // partitioning and clustering features. 
 func 
  
 createTableClustered 
 ( 
 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 
 () 
  
 sampleSchema 
  
 := 
  
 bigquery 
 . 
  Schema 
 
 { 
  
 { 
 Name 
 : 
  
 "timestamp" 
 , 
  
 Type 
 : 
  
 bigquery 
 . 
  TimestampFieldType 
 
 }, 
  
 { 
 Name 
 : 
  
 "origin" 
 , 
  
 Type 
 : 
  
 bigquery 
 . 
  StringFieldType 
 
 }, 
  
 { 
 Name 
 : 
  
 "destination" 
 , 
  
 Type 
 : 
  
 bigquery 
 . 
  StringFieldType 
 
 }, 
  
 { 
 Name 
 : 
  
 "amount" 
 , 
  
 Type 
 : 
  
 bigquery 
 . 
  NumericFieldType 
 
 }, 
  
 } 
  
 metaData 
  
 := 
  
& bigquery 
 . 
  TableMetadata 
 
 { 
  
 Schema 
 : 
  
 sampleSchema 
 , 
  
 TimePartitioning 
 : 
  
& bigquery 
 . 
  TimePartitioning 
 
 { 
  
 Field 
 : 
  
 "timestamp" 
 , 
  
 Expiration 
 : 
  
 90 
  
 * 
  
 24 
  
 * 
  
 time 
 . 
 Hour 
 , 
  
 }, 
  
 Clustering 
 : 
  
& bigquery 
 . 
  Clustering 
 
 { 
  
 Fields 
 : 
  
 [] 
 string 
 { 
 "origin" 
 , 
  
 "destination" 
 }, 
  
 }, 
  
 } 
  
 tableRef 
  
 := 
  
 client 
 . 
 Dataset 
 ( 
 datasetID 
 ). 
 Table 
 ( 
 tableID 
 ) 
  
 if 
  
 err 
  
 := 
  
 tableRef 
 . 
 Create 
 ( 
 ctx 
 , 
  
 metaData 
 ); 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 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. Clustering 
 
 ; 
 import 
  
 com.google.cloud.bigquery. Field 
 
 ; 
 import 
  
 com.google.cloud.bigquery. Schema 
 
 ; 
 import 
  
 com.google.cloud.bigquery. StandardSQLTypeName 
 
 ; 
 import 
  
 com.google.cloud.bigquery. StandardTableDefinition 
 
 ; 
 import 
  
 com.google.cloud.bigquery. TableId 
 
 ; 
 import 
  
 com.google.cloud.bigquery. TableInfo 
 
 ; 
 import 
  
 com.google.cloud.bigquery. TimePartitioning 
 
 ; 
 import 
  
 com.google.common.collect.ImmutableList 
 ; 
 import 
  
 java.util.List 
 ; 
 public 
  
 class 
 CreateClusteredTable 
  
 { 
  
 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" 
 ; 
  
  Schema 
 
  
 schema 
  
 = 
  
  Schema 
 
 . 
 of 
 ( 
  
  Field 
 
 . 
 of 
 ( 
 "name" 
 , 
  
  StandardSQLTypeName 
 
 . 
 STRING 
 ), 
  
  Field 
 
 . 
 of 
 ( 
 "post_abbr" 
 , 
  
  StandardSQLTypeName 
 
 . 
 STRING 
 ), 
  
  Field 
 
 . 
 of 
 ( 
 "date" 
 , 
  
  StandardSQLTypeName 
 
 . 
 DATE 
 )); 
  
 createClusteredTable 
 ( 
 datasetName 
 , 
  
 tableName 
 , 
  
 schema 
 , 
  
 ImmutableList 
 . 
 of 
 ( 
 "name" 
 , 
  
 "post_abbr" 
 )); 
  
 } 
  
 public 
  
 static 
  
 void 
  
 createClusteredTable 
 ( 
  
 String 
  
 datasetName 
 , 
  
 String 
  
 tableName 
 , 
  
  Schema 
 
  
 schema 
 , 
  
 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. 
  
  BigQuery 
 
  
 bigquery 
  
 = 
  
  BigQueryOptions 
 
 . 
 getDefaultInstance 
 (). 
 getService 
 (); 
  
  TableId 
 
  
 tableId 
  
 = 
  
  TableId 
 
 . 
 of 
 ( 
 datasetName 
 , 
  
 tableName 
 ); 
  
  TimePartitioning 
 
  
 partitioning 
  
 = 
  
  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. 
  
  Clustering 
 
  
 clustering 
  
 = 
  
  Clustering 
 
 . 
 newBuilder 
 (). 
 setFields 
 ( 
 clusteringFields 
 ). 
 build 
 (); 
  
  StandardTableDefinition 
 
  
 tableDefinition 
  
 = 
  
  StandardTableDefinition 
 
 . 
 newBuilder 
 () 
  
 . 
 setSchema 
 ( 
 schema 
 ) 
  
 . 
 setTimePartitioning 
 ( 
 partitioning 
 ) 
  
 . 
 setClustering 
 ( 
 clustering 
 ) 
  
 . 
 build 
 (); 
  
  TableInfo 
 
  
 tableInfo 
  
 = 
  
  TableInfo 
 
 . 
 newBuilder 
 ( 
 tableId 
 , 
  
 tableDefinition 
 ). 
 build 
 (); 
  
 bigquery 
 . 
  create 
 
 ( 
 tableInfo 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Clustered table created successfully" 
 ); 
  
 } 
  
 catch 
  
 ( 
  BigQueryException 
 
  
 e 
 ) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Clustered table was not created. \n" 
  
 + 
  
 e 
 . 
 toString 
 ()); 
  
 } 
  
 } 
 } 
 

  // Import the Google Cloud client library 
 const 
  
 { 
 BigQuery 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/bigquery 
' 
 ); 
 const 
  
 bigquery 
  
 = 
  
 new 
  
  BigQuery 
 
 (); 
 async 
  
 function 
  
 createTableClustered 
 () 
  
 { 
  
 // Creates 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"; 
  
 const 
  
 schema 
  
 = 
  
 'name:string, city:string, zipcode:integer' 
 ; 
  
 // For all options, see https://cloud.google.com/bigquery/docs/reference/v2/tables#resource 
  
 const 
  
 options 
  
 = 
  
 { 
  
 schema 
 : 
  
 schema 
 , 
  
 clustering 
 : 
  
 { 
  
 fields 
 : 
  
 [ 
 'city' 
 , 
  
 'zipcode' 
 ], 
  
 }, 
  
 }; 
  
 // Create a new table in the dataset 
  
 const 
  
 [ 
 table 
 ] 
  
 = 
  
 await 
  
 bigquery 
  
 . 
 dataset 
 ( 
 datasetId 
 ) 
  
 . 
  createTable 
 
 ( 
 tableId 
 , 
  
 options 
 ); 
  
 console 
 . 
 log 
 ( 
 `Table 
 ${ 
 table 
 . 
 id 
 } 
 created with clustering:` 
 ); 
  
 console 
 . 
 log 
 ( 
 table 
 . 
 metadata 
 . 
 clustering 
 ); 
 } 
 

  from 
  
 google.cloud 
  
 import 
  bigquery 
 
 # 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" 
 schema 
 = 
 [ 
  bigquery 
 
 . 
  SchemaField 
 
 ( 
 "full_name" 
 , 
 "STRING" 
 ), 
  bigquery 
 
 . 
  SchemaField 
 
 ( 
 "city" 
 , 
 "STRING" 
 ), 
  bigquery 
 
 . 
  SchemaField 
 
 ( 
 "zipcode" 
 , 
 "INTEGER" 
 ), 
 ] 
 table 
 = 
  bigquery 
 
 . 
  Table 
 
 ( 
 table_id 
 , 
 schema 
 = 
 schema 
 ) 
 table 
 . 
 clustering_fields 
 = 
 [ 
 "city" 
 , 
 "zipcode" 
 ] 
 table 
 = 
 client 
 . 
  create_table 
 
 ( 
 table 
 ) 
 # Make an API request. 
 print 
 ( 
 "Created clustered table 
 {} 
 . 
 {} 
 . 
 {} 
 " 
 . 
 format 
 ( 
 table 
 . 
 project 
 , 
 table 
 . 
 dataset_id 
 , 
 table 
 . 
 table_id 
 ) 
 ) 
 

  resource 
  
 "google_bigquery_dataset" 
  
 "default" 
  
 { 
  
 dataset_id 
  
 = 
  
 "mydataset" 
  
 default_partition_expiration_ms 
  
 = 
  
 2592000000 
 # 30 days 
  
 default_table_expiration_ms 
  
 = 
  
 31536000000 
 # 365 days 
  
 description 
  
 = 
  
 "dataset description" 
  
 location 
  
 = 
  
 "US" 
  
 max_time_travel_hours 
  
 = 
  
 96 
 # 4 days 
  
 labels 
  
 = 
  
 { 
  
 billing_group 
  
 = 
  
 "accounting" 
 , 
  
 pii 
  
 = 
  
 "sensitive" 
  
 } 
 } 
 resource 
  
 "google_bigquery_table" 
  
 "default" 
  
 { 
  
 dataset_id 
  
 = 
  
 google_bigquery_dataset.default.dataset_id 
  
 table_id 
  
 = 
  
 "mytable" 
  
 deletion_protection 
  
 = 
  
 false 
 # set to "true" in production 
  
 clustering 
  
 = 
  
 [ 
 "ID", "Created" 
 ] 
  
 schema 
  
 = 
  
<< EOF 
 [ 
  
 { 
  
 "name" 
 : 
  
 "ID" 
 , 
  
 "type" 
 : 
  
 "INT64" 
 , 
  
 "description" 
 : 
  
 "Item ID" 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "Item" 
 , 
  
 "type" 
 : 
  
 "STRING" 
 , 
  
 "mode" 
 : 
  
 "NULLABLE" 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "Created" 
 , 
  
 "type" 
 : 
  
 "TIMESTAMP" 
  
 } 
 ] 
 EOF 
 }